From ca19b32e4d1574ad29e36dbc164c320aeca80d47 Mon Sep 17 00:00:00 2001
From: Guozhong Zhuang <guozhong.zhuang@intel.com>
Date: Wed, 14 Feb 2018 00:13:00 -0800
Subject: [PATCH 001/575] cifar 10 divergance fix and batchnorm unit test fix

---
 .../core/kernels/mkl_fused_batch_norm_op.cc   | 96 +++++++++++++------
 tensorflow/core/kernels/mkl_relu_op.cc        | 20 +++-
 2 files changed, 81 insertions(+), 35 deletions(-)

diff --git a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
index 8313224d7f..b7dee3fb3e 100644
--- a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
+++ b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
@@ -1110,19 +1110,12 @@ class MklFusedBatchNormGradOp : public OpKernel {
         return;
       }
 
-      if (dnn_shape_src.IsMklTensor())
-        depth_ = dnn_shape_src.DimSize(MklDnnDims::Dim_C);
-      else
-        ExtractParams(context);
-
-      memory::format format_m;
       if (dnn_shape_src.IsMklTensor()) {
-        if (dnn_shape_src.IsTensorInNCHWFormat())
-          format_m = memory::format::nchw;
-        else
-          format_m = memory::format::nhwc;
+        depth_ = dnn_shape_src.DimSize(MklDnnDims::Dim_C);
+      } else if (dnn_shape_diff_dst.IsMklTensor()) {
+        depth_ = dnn_shape_diff_dst.DimSize(MklDnnDims::Dim_C);
       } else {
-        format_m = TFDataFormatToMklDnnDataFormat(tensor_format_);
+        ExtractParams(context);
       }
 
       MklDnnData<T> src(&cpu_engine);
@@ -1146,20 +1139,20 @@ class MklFusedBatchNormGradOp : public OpKernel {
         diff_dst_dims =
             TFShapeToMklDnnDimsInNCHW(diff_dst_tensor.shape(), tensor_format_);
 
-      // set src and diff_dst primitives
+      // set src and diff_dst primitives according to input layout
       memory::desc src_md({}, memory::data_undef, memory::format_undef);
       memory::desc diff_dst_md({}, memory::data_undef, memory::format_undef);
-      if (dnn_shape_src.IsMklTensor() || dnn_shape_diff_dst.IsMklTensor()) {
-        if (dnn_shape_src.IsMklTensor()) {
-          src_md = dnn_shape_src.GetMklLayout();
-          diff_dst_md = src_md;
-        } else {
-          diff_dst_md = dnn_shape_diff_dst.GetMklLayout();
-          src_md = diff_dst_md;
-        }
+      if (dnn_shape_src.IsMklTensor()) {
+        src_md = dnn_shape_src.GetMklLayout();
       } else {
-        src_md = memory::desc(src_dims, MklDnnType<T>(), format_m);
-        diff_dst_md = src_md;
+        src_md =  memory::desc(src_dims, MklDnnType<T>(),
+                TFDataFormatToMklDnnDataFormat(tensor_format_));
+      }
+      if (dnn_shape_diff_dst.IsMklTensor()) {
+        diff_dst_md = dnn_shape_diff_dst.GetMklLayout();
+      } else {
+        diff_dst_md = memory::desc(diff_dst_dims, MklDnnType<T>(),
+                TFDataFormatToMklDnnDataFormat(tensor_format_));
       }
       src.SetUsrMem(src_md, &src_tensor);
       diff_dst.SetUsrMem(diff_dst_md, &diff_dst_tensor);
@@ -1211,28 +1204,64 @@ class MklFusedBatchNormGradOp : public OpKernel {
       // allocate diff_src tensor
       MklDnnShape dnn_shape_diff_src;
       TensorShape tf_shape_diff_src;
-      if (dnn_shape_src.IsMklTensor()) {
+
+      // MKL-DNN's BN primitive not provide API to fetch internal format
+      // set common_md as OpMem
+      // src and diff_dst will reorder to common_md
+      // diff_src will set as common_md
+      memory::desc common_md({}, memory::data_undef, memory::format_undef);
+      if (dnn_shape_src.IsMklTensor() || dnn_shape_diff_dst.IsMklTensor()) {
+        if (dnn_shape_src.IsMklTensor()) {
+          common_md = dnn_shape_src.GetMklLayout();
+        } else {
+          common_md = dnn_shape_diff_dst.GetMklLayout();
+        }
+      } else {
+        common_md = memory::desc(src_dims, MklDnnType<T>(),
+                TFDataFormatToMklDnnDataFormat(tensor_format_));
+      }
+      // if any of src and diff_dst as mkl layout,
+      // then we set diff_src as mkl layout
+      if (dnn_shape_src.IsMklTensor() ||
+              dnn_shape_diff_dst.IsMklTensor()) {
         dnn_shape_diff_src.SetMklTensor(true);
-        auto diff_src_pd = bnrm_fwd_pd.dst_primitive_desc();
+        // set diff_src's mkl layout as common_md
+        auto diff_src_pd = memory::primitive_desc(common_md, cpu_engine);
         dnn_shape_diff_src.SetMklLayout(&diff_src_pd);
         dnn_shape_diff_src.SetElemType(MklDnnType<T>());
-        dnn_shape_diff_src.SetTfLayout(dnn_shape_src.GetDimension(), src_dims,
-                                       format_m);
-        dnn_shape_diff_src.SetTfDimOrder(dnn_shape_src.GetDimension(),
-                                         tensor_format_);
+        if (dnn_shape_src.IsMklTensor()) {
+          dnn_shape_diff_src.SetTfLayout(
+                  dnn_shape_src.GetDimension(),
+                  src_dims,
+                  dnn_shape_src.GetTfDataFormat());
+          dnn_shape_diff_src.SetTfDimOrder(
+                  dnn_shape_src.GetDimension(),
+                  tensor_format_);
+        } else {
+          dnn_shape_diff_src.SetTfLayout(
+                  dnn_shape_diff_dst.GetDimension(),
+                  src_dims,
+                  dnn_shape_diff_dst.GetTfDataFormat());
+          dnn_shape_diff_src.SetTfDimOrder(
+                  dnn_shape_diff_dst.GetDimension(),
+                  tensor_format_);
+        }
         tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T));
       } else {
         dnn_shape_diff_src.SetMklTensor(false);
+        // both src and diff_dst are tf layout,
+        // so get tf shape from anyont should be ok
         tf_shape_diff_src = src_tensor.shape();
       }
       AllocateOutputSetMklShape(context, kDiffSrcIndex, &diff_src_tensor,
                                 tf_shape_diff_src, dnn_shape_diff_src);
 
-      diff_src.SetUsrMem(src_md, diff_src_tensor);
+      // set diff_src
+      diff_src.SetUsrMem(common_md, diff_src_tensor);
 
       prop_kind pk = prop_kind::backward;
       auto bnrm_bwd_desc = batch_normalization_backward::desc(
-          pk, diff_src.GetUsrMemDesc(), src.GetUsrMemDesc(), epsilon_,
+          pk, common_md, common_md, epsilon_,
           /* for inference, specify use_global_stats
              1. on fwd prop, use mean and variance
                 provided as inputs
@@ -1245,11 +1274,16 @@ class MklFusedBatchNormGradOp : public OpKernel {
       auto bnrm_bwd_pd = batch_normalization_backward::primitive_desc(
           bnrm_bwd_desc, cpu_engine, bnrm_fwd_pd);
 
+      std::vector<primitive> net;
+      src.CheckReorderToOpMem(memory::primitive_desc(common_md,
+                                   cpu_engine), &net);
+      diff_dst.CheckReorderToOpMem(memory::primitive_desc(common_md,
+                                   cpu_engine), &net);
+
       auto bnrm_bwd_op = batch_normalization_backward(
           bnrm_bwd_pd, src.GetOpMem(), mean.GetOpMem(), variance.GetOpMem(),
           diff_dst.GetOpMem(), weights_m, diff_src.GetOpMem(), diff_weights_m);
 
-      std::vector<primitive> net;
       net.push_back(bnrm_bwd_op);
       stream(stream::kind::eager).submit(net).wait();
 
diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc
index 51db3991e2..924b9da7e0 100644
--- a/tensorflow/core/kernels/mkl_relu_op.cc
+++ b/tensorflow/core/kernels/mkl_relu_op.cc
@@ -368,8 +368,11 @@ void MklReluGradOp<Device, T>::Compute(OpKernelContext* context) {
   mkl_context.MklCleanup();
 }
 
+
+
 #else  // INTEL_MKL_ML
 
+
 template <typename Device, typename T, algorithm alg_kind>
 class MklReluOpBase : public OpKernel {
  public:
@@ -579,17 +582,26 @@ class MklReluGradOpBase : public OpKernel {
       // allocate diff_src tensor
       MklDnnShape dnn_shape_diff_src;
       TensorShape tf_shape_diff_src;
-      if (dnn_shape_src.IsMklTensor()) {
+      if (dnn_shape_src.IsMklTensor() ||
+              dnn_shape_diff_dst.IsMklTensor()) {
         dnn_shape_diff_src.SetMklTensor(true);
         auto diff_src_pd = relu_bwd_pd.diff_src_primitive_desc();
         dnn_shape_diff_src.SetMklLayout(&diff_src_pd);
         dnn_shape_diff_src.SetElemType(MklDnnType<T>());
-        dnn_shape_diff_src.SetTfLayout(dnn_shape_src.GetDimension(),
-                                       dnn_shape_src.GetSizesAsMklDnnDims(),
-                                       dnn_shape_src.GetTfDataFormat());
+        if (dnn_shape_src.IsMklTensor()) {
+          dnn_shape_diff_src.SetTfLayout(dnn_shape_src.GetDimension(),
+                                         dnn_shape_src.GetSizesAsMklDnnDims(),
+                                         dnn_shape_src.GetTfDataFormat());
+        } else {
+          dnn_shape_diff_src.SetTfLayout(dnn_shape_diff_dst.GetDimension(),
+                                 dnn_shape_diff_dst.GetSizesAsMklDnnDims(),
+                                 dnn_shape_diff_dst.GetTfDataFormat());
+        }
         tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T));
       } else {
         dnn_shape_diff_src.SetMklTensor(false);
+        // both src and diff_dst are tf layout,
+        // so get tf shape from anyone should be ok
         tf_shape_diff_src = src_tensor.shape();
       }
       AllocateOutputSetMklShape(context, diff_src_index, &diff_src_tensor,
-- 
GitLab


From 203caffbee9470109e3f750ba847e0aa4894a1e6 Mon Sep 17 00:00:00 2001
From: Rajendra arora <rajendraarora16@yahoo.com>
Date: Fri, 16 Feb 2018 10:56:25 +0530
Subject: [PATCH 002/575] Documentation api reference badge added in Readme.md

---
 README.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 916e5200b2..efacf063e3 100644
--- a/README.md
+++ b/README.md
@@ -4,9 +4,10 @@
 
 -----------------
 
-| **`Linux CPU`** | **`Linux GPU`** | **`Mac OS CPU`** | **`Windows CPU`** | **`Android`** |
-|-----------------|---------------------|------------------|-------------------|---------------|
-| [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-cpu)](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-linux-gpu)](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-mac)](https://ci.tensorflow.org/job/tensorflow-master-mac) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) [ ![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg) ](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) |
+
+| **`Documentation`** | **`Linux CPU`** | **`Linux GPU`** | **`Mac OS CPU`** | **`Windows CPU`** | **`Android`** |
+|-----------------|---------------------|------------------|-------------------|---------------|---------------|
+| [![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-cpu)](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-linux-gpu)](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-mac)](https://ci.tensorflow.org/job/tensorflow-master-mac) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-win-cmake-py)](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [![Build Status](https://ci.tensorflow.org/buildStatus/icon?job=tensorflow-master-android)](https://ci.tensorflow.org/job/tensorflow-master-android) [ ![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg) ](https://bintray.com/google/tensorflow/tensorflow/_latestVersion)
 
 **TensorFlow** is an open source software library for numerical computation using
 data flow graphs.  The graph nodes represent mathematical operations, while
-- 
GitLab


From 4e7772e0c74a663809f9fcf39545032eb8277e6a Mon Sep 17 00:00:00 2001
From: Rajendra arora <rajendraarora16@yahoo.com>
Date: Fri, 16 Feb 2018 11:48:10 +0530
Subject: [PATCH 003/575] Added a contribution guideline header in readme.md

---
 README.md | 30 ++++++++++++++++--------------
 1 file changed, 16 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index efacf063e3..ef5bdc66ef 100644
--- a/README.md
+++ b/README.md
@@ -22,20 +22,6 @@ organization for the purposes of conducting machine learning and deep neural
 networks research.  The system is general enough to be applicable in a wide
 variety of other domains, as well.
 
-**If you want to contribute to TensorFlow, be sure to review the [contribution
-guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's
-[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to
-uphold this code.**
-
-**We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for
-tracking requests and bugs. So please see
-[TensorFlow Discuss](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) for general questions
-and discussion, and please direct specific questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).**
-
-The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
-
-[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/1486/badge)](https://bestpractices.coreinfrastructure.org/projects/1486)
-
 ## Installation
 *See [Installing TensorFlow](https://www.tensorflow.org/get_started/os_setup.html) for instructions on how to install our release binaries or how to build from source.*
 
@@ -76,6 +62,22 @@ $ python
 >>> sess.close()
 ```
 
+## Contribution guidelines
+
+**If you want to contribute to TensorFlow, be sure to review the [contribution
+guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's
+[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to
+uphold this code.**
+
+**We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for
+tracking requests and bugs. So please see
+[TensorFlow Discuss](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) for general questions
+and discussion, and please direct specific questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).**
+
+The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
+
+[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/1486/badge)](https://bestpractices.coreinfrastructure.org/projects/1486)
+
 ## For more information
 
 * [TensorFlow Website](https://www.tensorflow.org)
-- 
GitLab


From f4d95b4abc45645ff5ed1670abc73fe0ffe49a82 Mon Sep 17 00:00:00 2001
From: kdavis-mozilla <kdavis@mozilla.com>
Date: Fri, 16 Feb 2018 11:41:19 +0100
Subject: [PATCH 004/575] Added Deep Speech use

---
 tensorflow/docs_src/about/uses.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/tensorflow/docs_src/about/uses.md b/tensorflow/docs_src/about/uses.md
index 8818177a28..d646880bd3 100644
--- a/tensorflow/docs_src/about/uses.md
+++ b/tensorflow/docs_src/about/uses.md
@@ -22,6 +22,14 @@ This section describes some of the current uses of the TensorFlow system.
 > TensorFlow, or even better, send us a pull request to add an entry to this
 > file.
 
+* **Deep Speech**
+<ul>
+   <li>**Organization**: Mozilla</li>
+   <li> **Domain**: Speech Recognition</li>
+   <li> **Description**:  A TensorFlow implementation motivated by Baidu's Deep Speech architecture.</li>
+   <li> **More info**: [GitHub Repo](https://github.com/mozilla/deepspeech)</li>
+</ul>
+
 * **RankBrain**
 <ul>
    <li>**Organization**: Google</li>
-- 
GitLab


From 0e8e3dd85428ad334a270ca3d8c990d0886c8d64 Mon Sep 17 00:00:00 2001
From: Reed Wanderman-Milne <reedwm@google.com>
Date: Fri, 16 Feb 2018 11:02:33 -0800
Subject: [PATCH 005/575] Automated g4 rollback of changelist 185927310

PiperOrigin-RevId: 186018787
---
 tensorflow/python/framework/tensor_util.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py
index cbba112841..0e5f696111 100644
--- a/tensorflow/python/framework/tensor_util.py
+++ b/tensorflow/python/framework/tensor_util.py
@@ -557,7 +557,7 @@ def MakeNdarray(tensor):
   dtype = tensor_dtype.as_numpy_dtype
 
   if tensor.tensor_content:
-    return np.frombuffer(tensor.tensor_content, dtype=dtype).reshape(shape)
+    return np.fromstring(tensor.tensor_content, dtype=dtype).reshape(shape)
   elif tensor_dtype == dtypes.float16:
     # the half_val field of the TensorProto stores the binary representation
     # of the fp16: we need to reinterpret this as a proper float16
-- 
GitLab


From 8eadd8f10a5035a945cab68d31cb08c17011369d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 11:05:02 -0800
Subject: [PATCH 006/575] Internal change

PiperOrigin-RevId: 186019263
---
 tensorflow/contrib/learn/BUILD | 1 +
 tensorflow/python/BUILD        | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index abf6e393bb..6ebd0b0d39 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -7,6 +7,7 @@ exports_files(["LICENSE"])
 
 package(default_visibility = [
     "//engedu/ml/tf_from_scratch:__pkg__",
+    "//quality/sixface/sequel/experiments/deep_learning/entity_cloud:__pkg__",
     "//tensorflow:internal",
 ])
 
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index cee7c47e00..3334a08eac 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -7,6 +7,7 @@
 package(
     default_visibility = [
         "//engedu/ml/tf_from_scratch:__pkg__",
+        "//quality/sixface/sequel/experiments/deep_learning/entity_cloud:__pkg__",
         "//tensorflow:internal",
         "//tensorflow/contrib/lite/toco/python:__pkg__",
         "//tensorflow_models:__subpackages__",
-- 
GitLab


From 6cc4640cc5df1aaa349e5e26c17094351eafb240 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 11:19:15 -0800
Subject: [PATCH 007/575] Add getmodule to tf_inspect.

PiperOrigin-RevId: 186021386
---
 tensorflow/python/util/tf_inspect.py      |  5 +++++
 tensorflow/python/util/tf_inspect_test.py | 11 +++++++++++
 2 files changed, 16 insertions(+)

diff --git a/tensorflow/python/util/tf_inspect.py b/tensorflow/python/util/tf_inspect.py
index c4168f7b1a..c2fe6fc449 100644
--- a/tensorflow/python/util/tf_inspect.py
+++ b/tensorflow/python/util/tf_inspect.py
@@ -134,6 +134,11 @@ def getmembers(object, predicate=None):  # pylint: disable=redefined-builtin
   return _inspect.getmembers(object, predicate)
 
 
+def getmodule(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getmodule."""
+  return _inspect.getmodule(object)
+
+
 def getmro(cls):
   """TFDecorator-aware replacement for inspect.getmro."""
   return _inspect.getmro(cls)
diff --git a/tensorflow/python/util/tf_inspect_test.py b/tensorflow/python/util/tf_inspect_test.py
index a9e8ffb30c..8903e1156b 100644
--- a/tensorflow/python/util/tf_inspect_test.py
+++ b/tensorflow/python/util/tf_inspect_test.py
@@ -124,6 +124,17 @@ class TfInspectTest(test.TestCase):
         inspect.getmembers(TestDecoratedClass),
         tf_inspect.getmembers(TestDecoratedClass))
 
+  def testGetModule(self):
+    self.assertEqual(
+        inspect.getmodule(TestDecoratedClass),
+        tf_inspect.getmodule(TestDecoratedClass))
+    self.assertEqual(
+        inspect.getmodule(test_decorated_function),
+        tf_inspect.getmodule(test_decorated_function))
+    self.assertEqual(
+        inspect.getmodule(test_undecorated_function),
+        tf_inspect.getmodule(test_undecorated_function))
+
   def testGetSource(self):
     expected = '''@test_decorator('decorator')
 def test_decorated_function_with_defaults(a, b=2, c='Hello'):
-- 
GitLab


From df3d67109e40bd740caad05552ce030f56e55a42 Mon Sep 17 00:00:00 2001
From: Akshay Modi <nareshmodi@google.com>
Date: Fri, 16 Feb 2018 11:21:08 -0800
Subject: [PATCH 008/575] Cache a variable scope context manager in
 EagerTemplate as a minor optimization

PiperOrigin-RevId: 186021666
---
 tensorflow/python/ops/template.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py
index 806fdd3da7..424582b348 100644
--- a/tensorflow/python/ops/template.py
+++ b/tensorflow/python/ops/template.py
@@ -557,6 +557,7 @@ class EagerTemplate(Template):
       # is created in __call__.
       variable_scope_name = None
     self._template_store = _EagerTemplateVariableStore(variable_scope_name)
+    self._variable_scope_context_manager = None
 
   def _call_func(self, args, kwargs):
     try:
@@ -611,8 +612,12 @@ class EagerTemplate(Template):
     # the variable scope is opened in order to ensure that templates nested at
     # the same level correctly uniquify lower variable scope names.
     if self._variable_scope:
-      with variable_scope.variable_scope(
-          self._variable_scope, reuse=variable_scope.AUTO_REUSE):
+      # Create a cache for the variable scope context manager the first time
+      # around so that we don't have to keep recreating it.
+      if not self._variable_scope_context_manager:
+        self._variable_scope_context_manager = variable_scope.variable_scope(
+            self._variable_scope, reuse=variable_scope.AUTO_REUSE)
+      with self._variable_scope_context_manager:
         with self._template_store.as_default():
           result = self._call_func(args, kwargs)
       return result
-- 
GitLab


From 8dfaa05d2824290b33eb922a5269f0772f53478e Mon Sep 17 00:00:00 2001
From: David Majnemer <majnemer@google.com>
Date: Fri, 16 Feb 2018 11:37:49 -0800
Subject: [PATCH 009/575] [XLA] Factor out the code which adds operands to a
 fusion node

This makes it easier for Hlo passes to do interesting rewrites with new,
additional parameters which were not operands to the original fusion node.

PiperOrigin-RevId: 186024182
---
 .../compiler/xla/service/hlo_instruction.cc   | 24 +++++++++++++------
 .../compiler/xla/service/hlo_instruction.h    |  3 +++
 2 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 0981f1f4fe..0d9912d07d 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -801,6 +801,22 @@ static string FusionNodeName(HloInstruction::FusionKind fusion_kind) {
   return instruction;
 }
 
+HloInstruction* HloInstruction::AddFusionOperand(HloInstruction* new_operand) {
+  CHECK_EQ(opcode(), HloOpcode::kFusion);
+  CHECK_EQ(operand_count(),
+           fused_instructions_computation()->parameter_instructions().size());
+  const int64 param_no = operand_count();
+  // Name the parameter after the instruction it represents in the outer
+  // (non-fusion) computation.
+  string param_name = StrCat(new_operand->name(), ".param_", param_no);
+  HloInstruction* fused_parameter =
+      fused_instructions_computation()->AddParameter(
+          HloInstruction::CreateParameter(param_no, new_operand->shape(),
+                                          param_name));
+  AppendOperand(new_operand);
+  return fused_parameter;
+}
+
 void HloInstruction::MergeFusionInstruction(
     HloInstruction* instruction_to_merge) {
   CHECK_EQ(opcode_, HloOpcode::kFusion);
@@ -993,13 +1009,7 @@ HloInstruction* HloInstruction::CloneAndFuseInternal(
       // Clone's operand was not already an operand of the fusion
       // instruction. Add it as an operand and add a corresponding fused
       // parameter instruction.
-      int64 param_no = fused_parameters.size();
-      // Name the parameter after the instruction it represents in the outer
-      // (non-fusion) computation.
-      string param_name = StrCat(operand->name(), ".param_", param_no);
-      fused_param = fused_instructions_computation()->AddParameter(
-          CreateParameter(param_no, operand->shape(), param_name));
-      AppendOperand(operand);
+      fused_param = AddFusionOperand(operand);
     }
     TF_CHECK_OK(clone->ReplaceOperandWith(operand_num, fused_param));
   }
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 50931c563a..a4c41c9de8 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -917,6 +917,9 @@ class HloInstruction {
   // Return true if this operator has a sharding assigned.
   bool has_sharding() const { return sharding_ != nullptr; }
 
+  // Adds a new operand the fusion instruction.
+  HloInstruction* AddFusionOperand(HloInstruction* new_operand);
+
   // Merges the fused instructions from 'instruction_to_merge' into the
   // fused instruction set of 'this', updating operands as necessary.
   //
-- 
GitLab


From 0f063cab519f582e2c84560fe1bd82f58f5095c8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 12:29:00 -0800
Subject: [PATCH 010/575] Optimized quantized LSTM cell runtime NEON
 implementation.

Notice: unlike many NEON paths that we have in this optimized_ops.h file,
which are enabled also on x86 by means of arm_neon_sse.h (#ifdef USE_NEON),
this one is only enabled on real NEON (#ifdef GEMMLOWP_NEON). The reason
for that is that gemmlowp's FixedPoint class is templatized in the
underlying raw integer/register type, e.g. here int16x8_t, and on SSE
there is only a single __m128i type for all integer types (both int16x8_t
and int32x4_t), making it non-trivial to support this on SSE without
contriving this code on NEON.
PiperOrigin-RevId: 186031054
---
 .../internal/optimized/optimized_ops.h        | 159 +++++++++++++-----
 .../internal/reference/reference_ops.h        |   5 +-
 tensorflow/workspace.bzl                      |   8 +-
 3 files changed, 123 insertions(+), 49 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index dec58fea4f..57965724db 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -2095,7 +2095,8 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
               const Dims<4>& output_activ_dims, uint8* concat_temp_data_uint8,
               const Dims<4>& concat_temp_dims, int16* activ_temp_data_int16,
               const Dims<4>& activ_temp_dims, int32 weights_zero_point,
-              int32 accum_multiplier, int accum_shift) {
+              int32 accum_multiplier, int accum_shift,
+              gemmlowp::GemmContext* gemm_context) {
   gemmlowp::ScopedProfilingLabel label(
       "LstmCell/quantized (8bit external, 16bit internal)");
   // Gather dimensions information, and perform consistency checks.
@@ -2144,42 +2145,112 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
   // integers, and the output is 16-bit fixed-point with 3 integer bits so
   // the output range is [-2^3, 2^3] == [-8, 8]. The rationale for that
   // is explained in the function comment above.
-  for (int b = 0; b < fc_batches; ++b) {
-    for (int out_c = 0; out_c < fc_output_depth; ++out_c) {
-      // Internal accumulation.
-      // Initialize accumulator with the bias-value.
-      int32 accum = bias_data_int32[out_c];
-      // Accumulation loop.
-      for (int d = 0; d < fc_accum_depth; ++d) {
-        int16 input_val = concat_temp_data_uint8[b * fc_accum_depth + d] - 128;
-        int16 weights_val =
-            weights_data_uint8[out_c * fc_accum_depth + d] - weights_zero_point;
-        accum += input_val * weights_val;
-      }
-      // Down-scale the final int32 accumulator to the scale used by our
-      // (16-bit, using 3 integer bits) fixed-point format. The quantized
-      // multiplier and shift here have been pre-computed offline
-      // (e.g. by toco).
-      // Note that the implicit assumption here, that this multiplier is smaller
-      // than one, is equivalent to the assumption that the fully-connected
-      // weights min-max is enclosed within [-4, 4] (it may be narrower).
-      // If that eventually fails, offline tools (e.g. toco) will fail early
-      // and that will be easy to support as needed. For now, assuming that
-      // this multiplier is less than one allows us to use a simpler, more
-      // accurate implementation.
-      accum =
-          MultiplyByQuantizedMultiplier(accum, accum_multiplier, accum_shift);
-      // Saturate, cast to int16, and store to the temporary activations array.
-      accum = std::max(-32768, std::min(32767, accum));
-      activ_temp_data_int16[out_c + fc_output_depth * b] = accum;
-    }
-  }
+
+  gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::RowMajor> weights_matrix(
+      weights_data_uint8, fc_output_depth, fc_accum_depth);
+  gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::ColMajor> input_matrix(
+      concat_temp_data_uint8, fc_accum_depth, fc_batches);
+  gemmlowp::MatrixMap<int16, gemmlowp::MapOrder::ColMajor> output_matrix(
+      activ_temp_data_int16, fc_output_depth, fc_batches);
+  typedef gemmlowp::VectorMap<const int32, gemmlowp::VectorShape::Col>
+      ColVectorMap;
+  ColVectorMap bias_vector(bias_data_int32, fc_output_depth);
+  gemmlowp::OutputStageBiasAddition<ColVectorMap> bias_addition_stage;
+  bias_addition_stage.bias_vector = bias_vector;
+  gemmlowp::OutputStageScaleInt32ByFixedPointAndExponent scale_stage;
+  scale_stage.result_offset_after_shift = 0;
+  scale_stage.result_fixedpoint_multiplier = accum_multiplier;
+  scale_stage.result_exponent = accum_shift;
+  gemmlowp::OutputStageSaturatingCastToInt16 saturating_cast_int16_stage;
+  auto output_pipeline = std::make_tuple(bias_addition_stage, scale_stage,
+                                         saturating_cast_int16_stage);
+  gemmlowp::GemmWithOutputPipeline<uint8, int16,
+                                   gemmlowp::L8R8WithLhsNonzeroBitDepthParams>(
+      gemm_context, weights_matrix, input_matrix, &output_matrix,
+      -weights_zero_point, -128, output_pipeline);
 
   // Rest of the LSTM cell: tanh and logistic math functions, and some adds
   // and muls, all done in 16-bit fixed-point.
   const int outer_size = batches * width * height;
+  const int16* input_gate_input_ptr = activ_temp_data_int16;
+  const int16* input_modulation_gate_input_ptr =
+      activ_temp_data_int16 + output_depth;
+  const int16* forget_gate_input_ptr = activ_temp_data_int16 + 2 * output_depth;
+  const int16* output_gate_input_ptr = activ_temp_data_int16 + 3 * output_depth;
+  const int16* prev_state_ptr = prev_state_data_int16;
+  int16* output_state_data_ptr = output_state_data_int16;
+  uint8* output_activ_data_ptr = output_activ_data_uint8;
+
   for (int b = 0; b < outer_size; ++b) {
-    for (int c = 0; c < output_depth; ++c) {
+    int c = 0;
+#ifdef GEMMLOWP_NEON
+    for (; c <= output_depth - 8; c += 8) {
+      // Define the fixed-point data types that we will use here. All use
+      // int16 as the underlying integer type i.e. all are 16-bit fixed-point.
+      // They only differ by the number of integral vs. fractional bits,
+      // determining the range of values that they can represent.
+      //
+      // F0 uses 0 integer bits, range [-1, 1].
+      // This is the return type of math functions such as tanh, logistic,
+      // whose range is in [-1, 1].
+      using F0 = gemmlowp::FixedPoint<int16x8_t, 0>;
+      // F3 uses 3 integer bits, range [-8, 8].
+      // This is the range of the previous fully-connected node's output,
+      // which is our input here.
+      using F3 = gemmlowp::FixedPoint<int16x8_t, 3>;
+      // FS uses StateIntegerBits integer bits, range [-2^StateIntegerBits,
+      // 2^StateIntegerBits]. It's used to represent the internal state, whose
+      // number of integer bits is currently dictated by the model. See comment
+      // on the StateIntegerBits template parameter above.
+      using FS = gemmlowp::FixedPoint<int16x8_t, StateIntegerBits>;
+      // Implementation of input gate, using fixed-point logistic function.
+      F3 input_gate_input = F3::FromRaw(vld1q_s16(input_gate_input_ptr));
+      input_gate_input_ptr += 8;
+      F0 input_gate_output = gemmlowp::logistic(input_gate_input);
+      // Implementation of input modulation gate, using fixed-point tanh
+      // function.
+      F3 input_modulation_gate_input =
+          F3::FromRaw(vld1q_s16(input_modulation_gate_input_ptr));
+      input_modulation_gate_input_ptr += 8;
+      F0 input_modulation_gate_output =
+          gemmlowp::tanh(input_modulation_gate_input);
+      // Implementation of forget gate, using fixed-point logistic function.
+      F3 forget_gate_input = F3::FromRaw(vld1q_s16(forget_gate_input_ptr));
+      forget_gate_input_ptr += 8;
+      F0 forget_gate_output = gemmlowp::logistic(forget_gate_input);
+      // Implementation of output gate, using fixed-point logistic function.
+      F3 output_gate_input = F3::FromRaw(vld1q_s16(output_gate_input_ptr));
+      output_gate_input_ptr += 8;
+      F0 output_gate_output = gemmlowp::logistic(output_gate_input);
+      // Implementation of internal multiplication nodes, still in fixed-point.
+      F0 input_times_input_modulation =
+          input_gate_output * input_modulation_gate_output;
+      FS prev_state = FS::FromRaw(vld1q_s16(prev_state_ptr));
+      prev_state_ptr += 8;
+      FS prev_state_times_forget_state = forget_gate_output * prev_state;
+      // Implementation of internal addition node, saturating.
+      FS new_state = gemmlowp::SaturatingAdd(
+          gemmlowp::Rescale<StateIntegerBits>(input_times_input_modulation),
+          prev_state_times_forget_state);
+      // Implementation of last internal tanh node, still in fixed-point.
+      F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state);
+      // Store the new internal state back to memory, as 16-bit integers.
+      vst1q_s16(output_state_data_ptr, new_state.raw());
+      output_state_data_ptr += 8;
+      // Down-scale the output activations to 8-bit integers, saturating,
+      // and store back to memory.
+      int16 buf16[8];
+      vst1q_s16(buf16, output_activ_int16.raw());
+      for (int i = 0; i < 8; i++) {
+        int16 rescaled_output_activ =
+            gemmlowp::RoundingDivideByPOT(buf16[i], 8);
+        int16 clamped_output_activ =
+            std::max<int16>(-128, std::min<int16>(127, rescaled_output_activ));
+        *output_activ_data_ptr++ = 128 + clamped_output_activ;
+      }
+    }
+#endif
+    for (; c < output_depth; ++c) {
       // Define the fixed-point data types that we will use here. All use
       // int16 as the underlying integer type i.e. all are 16-bit fixed-point.
       // They only differ by the number of integral vs. fractional bits,
@@ -2199,27 +2270,24 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
       // on the StateIntegerBits template parameter above.
       using FS = gemmlowp::FixedPoint<std::int16_t, StateIntegerBits>;
       // Implementation of input gate, using fixed-point logistic function.
-      F3 input_gate_input = F3::FromRaw(
-          activ_temp_data_int16[b * fc_output_depth + 0 * output_depth + c]);
+      F3 input_gate_input = F3::FromRaw(*input_gate_input_ptr++);
       F0 input_gate_output = gemmlowp::logistic(input_gate_input);
       // Implementation of input modulation gate, using fixed-point tanh
       // function.
-      F3 input_modulation_gate_input = F3::FromRaw(
-          activ_temp_data_int16[b * fc_output_depth + 1 * output_depth + c]);
+      F3 input_modulation_gate_input =
+          F3::FromRaw(*input_modulation_gate_input_ptr++);
       F0 input_modulation_gate_output =
           gemmlowp::tanh(input_modulation_gate_input);
       // Implementation of forget gate, using fixed-point logistic function.
-      F3 forget_gate_input = F3::FromRaw(
-          activ_temp_data_int16[b * fc_output_depth + 2 * output_depth + c]);
+      F3 forget_gate_input = F3::FromRaw(*forget_gate_input_ptr++);
       F0 forget_gate_output = gemmlowp::logistic(forget_gate_input);
       // Implementation of output gate, using fixed-point logistic function.
-      F3 output_gate_input = F3::FromRaw(
-          activ_temp_data_int16[b * fc_output_depth + 3 * output_depth + c]);
+      F3 output_gate_input = F3::FromRaw(*output_gate_input_ptr++);
       F0 output_gate_output = gemmlowp::logistic(output_gate_input);
       // Implementation of internal multiplication nodes, still in fixed-point.
       F0 input_times_input_modulation =
           input_gate_output * input_modulation_gate_output;
-      FS prev_state = FS::FromRaw(prev_state_data_int16[b * output_depth + c]);
+      FS prev_state = FS::FromRaw(*prev_state_ptr++);
       FS prev_state_times_forget_state = forget_gate_output * prev_state;
       // Implementation of internal addition node, saturating.
       FS new_state = gemmlowp::SaturatingAdd(
@@ -2228,16 +2296,19 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
       // Implementation of last internal tanh node, still in fixed-point.
       F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state);
       // Store the new internal state back to memory, as 16-bit integers.
-      output_state_data_int16[b * output_depth + c] = new_state.raw();
+      *output_state_data_ptr++ = new_state.raw();
       // Down-scale the output activations to 8-bit integers, saturating,
       // and store back to memory.
       int16 rescaled_output_activ =
           gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8);
       int16 clamped_output_activ =
           std::max<int16>(-128, std::min<int16>(127, rescaled_output_activ));
-      output_activ_data_uint8[b * output_depth + c] =
-          128 + clamped_output_activ;
+      *output_activ_data_ptr++ = 128 + clamped_output_activ;
     }
+    input_gate_input_ptr += 3 * output_depth;
+    input_modulation_gate_input_ptr += 3 * output_depth;
+    forget_gate_input_ptr += 3 * output_depth;
+    output_gate_input_ptr += 3 * output_depth;
   }
 }
 
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 5f4d5be323..55618ea971 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -1453,7 +1453,10 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
               const Dims<4>& output_activ_dims, uint8* concat_temp_data_uint8,
               const Dims<4>& concat_temp_dims, int16* activ_temp_data_int16,
               const Dims<4>& activ_temp_dims, int32 weights_zero_point,
-              int32 accum_multiplier, int accum_shift) {
+              int32 accum_multiplier, int accum_shift,
+              gemmlowp::GemmContext* gemm_context) {
+  (void)gemm_context;  // only used in optimized code.
+
   // Gather dimensions information, and perform consistency checks.
   const int batches =
       MatchingArraySize(input_dims, 3, prev_activ_dims, 3, prev_state_dims, 3,
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 96bd2d5326..2e84d83fe4 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -179,11 +179,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "gemmlowp",
       urls = [
-          "https://mirror.bazel.build/github.com/google/gemmlowp/archive/d4d1e29a62192d8defdc057b913ef36ca582ac98.zip",
-          "https://github.com/google/gemmlowp/archive/d4d1e29a62192d8defdc057b913ef36ca582ac98.zip",
+          "https://mirror.bazel.build/github.com/google/gemmlowp/archive/7c7c744640ddc3d0af18fb245b4d23228813a71b.zip",
+          "https://github.com/google/gemmlowp/archive/7c7c744640ddc3d0af18fb245b4d23228813a71b.zip",
       ],
-      sha256 = "e2bee7afd3c43028f23dd0d7f85ddd8b21aaf79c572b658e56164ef502b2b9c7",
-      strip_prefix = "gemmlowp-d4d1e29a62192d8defdc057b913ef36ca582ac98",
+      sha256 = "b852cc90259a7357c8a323f108f2cec6e85979fc3b18b5590b99e0130044b2cf",
+      strip_prefix = "gemmlowp-7c7c744640ddc3d0af18fb245b4d23228813a71b",
   )
 
   tf_http_archive(
-- 
GitLab


From 96c2a846609d3a68f9a88c60c4c68a243f74ee44 Mon Sep 17 00:00:00 2001
From: Bjarke Hammersholt Roune <broune@google.com>
Date: Fri, 16 Feb 2018 12:41:27 -0800
Subject: [PATCH 011/575] Add TODOs.

PiperOrigin-RevId: 186032527
---
 tensorflow/compiler/xla/service/hlo_instruction.h | 4 ++++
 tensorflow/compiler/xla/service/shape_inference.h | 5 +++++
 2 files changed, 9 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index a4c41c9de8..3cf43f0adf 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -770,6 +770,10 @@ class HloInstruction {
   //
   // (We express the default options using an overload rather than a default
   // param because gdb ignores default params, but does resolve overloads.)
+  //
+  // TODO(b/73348663): Make ToString() adaptive to the size of the string by
+  // default, backing off on providing full information for very large strings,
+  // or provide a different name for a ToString-like function that does that.
   string ToString() const { return ToString(HloPrintOptions()); }
   string ToString(const HloPrintOptions& options) const;
 
diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h
index b39151ebbc..c4a1da28f3 100644
--- a/tensorflow/compiler/xla/service/shape_inference.h
+++ b/tensorflow/compiler/xla/service/shape_inference.h
@@ -37,6 +37,11 @@ namespace xla {
 // the expected result type for computations that are built up via the API --
 // the shape that results from an operation is inferred. Some methods have
 // overloads for inferring shape at the HLO level.
+//
+// TODO(b/73352135): Shape inference does not issue very good error messages, in
+// part because HloInstruction::ToString() is not available since shape
+// inference runs before the HloInstruction object is created. We need a
+// solution for this.
 class ShapeInference {
  public:
   // Infers the shape produced by applying the given unary operation to the
-- 
GitLab


From 428d034227c9e7b637de0194d80cac3976a37eef Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 13:20:13 -0800
Subject: [PATCH 012/575] Fix pontential issue with number of blocks launched
 for depthwise kernels: the number of work_elements was too small, which could
 return a block_count that is too small to cover all elements.

We also have been ignoring the suggested thread_per_block, so were potentially launching more blocks than necessary to fill the GPU (which is inefficient, but functionally correct).

Changing 'assert(false && ...' to LOG(FATAL) because it shouldn't be debug only.

PiperOrigin-RevId: 186037306
---
 .../core/kernels/depthwise_conv_op_gpu.cu.cc  | 159 +++++++++++-------
 tensorflow/core/util/cuda_launch_config.h     |  46 +++--
 2 files changed, 130 insertions(+), 75 deletions(-)

diff --git a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
index e9d43f6496..2b3b7184dc 100644
--- a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
@@ -186,6 +186,8 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNHWCSmall(
   const int pad_height = args.pad_rows;
   const int pad_width = args.pad_cols;
 
+  assert(blockDim.x == kBlockDepth);
+  assert(blockDim.y == args.in_cols);
   const int block_height = blockDim.z;
 
   // These values are the same for all threads and could
@@ -465,6 +467,8 @@ __global__ __launch_bounds__(1024, 2) void DepthwiseConv2dGPUKernelNCHWSmall(
   const int pad_width = args.pad_cols;
 
   // Fixed blockDim.z, tailored for maximum grid size for images of size 16x16.
+  assert(blockDim.x == args.in_cols);
+  assert(blockDim.z == kBlockDepth);
   const int block_height = blockDim.y;
 
   // These values are the same for all threads and could
@@ -588,20 +592,30 @@ void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& device,
                                    TensorFormat data_format) {
   const int block_height = (args.in_rows + 1) / 2;
   dim3 block_dim;
+  int block_count;
   void (*kernel)(const DepthwiseArgs, const T*, const T*, T*);
-  if (data_format == FORMAT_NHWC) {
-    block_dim = dim3(kBlockDepth, args.in_cols, block_height);
-    kernel = DepthwiseConv2dGPUKernelNHWCSmall<T, kDirection, kKnownFilterWidth,
-                                               kKnownFilterHeight, kBlockDepth,
-                                               kKnownEvenHeight>;
-  } else if (data_format == FORMAT_NCHW) {
-    block_dim = dim3(args.in_cols, block_height, kBlockDepth);
-    kernel = DepthwiseConv2dGPUKernelNCHWSmall<T, kDirection, kKnownFilterWidth,
-                                               kKnownFilterHeight, kBlockDepth,
-                                               kKnownEvenHeight>;
-  } else {
-    assert(false && "Incorrect data format");
-    return;
+  switch (data_format) {
+    case FORMAT_NHWC:
+      block_dim = dim3(kBlockDepth, args.in_cols, block_height);
+      block_count =
+          args.batch * DivUp(args.out_depth, kBlockDepth) * kBlockDepth;
+      kernel =
+          DepthwiseConv2dGPUKernelNHWCSmall<T, kDirection, kKnownFilterWidth,
+                                            kKnownFilterHeight, kBlockDepth,
+                                            kKnownEvenHeight>;
+      break;
+    case FORMAT_NCHW:
+      block_dim = dim3(args.in_cols, block_height, kBlockDepth);
+      block_count =
+          DivUp(args.batch * args.out_depth, kBlockDepth) * kBlockDepth;
+      kernel =
+          DepthwiseConv2dGPUKernelNCHWSmall<T, kDirection, kKnownFilterWidth,
+                                            kKnownFilterHeight, kBlockDepth,
+                                            kKnownEvenHeight>;
+      break;
+    case FORMAT_NCHW_VECT_C:
+      LOG(ERROR) << "FORMAT_NCHW_VECT_C is not supported";
+      return;
   }
   const int tile_width = args.in_cols + args.filter_cols - 1;
   const int tile_height = block_height * 2 + args.filter_rows - 1;
@@ -609,11 +623,10 @@ void LaunchDepthwiseConv2dGPUSmall(const GpuDevice& device,
   const int filter_pixels = args.filter_rows * args.filter_cols;
   const int shared_memory_size =
       kBlockDepth * (tile_pixels + filter_pixels) * sizeof(T);
-  const int num_outputs =
-      args.batch * args.out_rows * args.out_cols * args.out_depth;
-  CudaLaunchConfig config =
-      GetCudaLaunchConfig(num_outputs, device, kernel, shared_memory_size,
-                          block_dim.x * block_dim.y * block_dim.z);
+  const int num_outputs = args.out_rows * args.out_cols * block_count;
+  CudaLaunchConfig config = GetCudaLaunchConfigFixedBlockSize(
+      num_outputs, device, kernel, shared_memory_size,
+      block_dim.x * block_dim.y * block_dim.z);
   kernel<<<config.block_count, block_dim, shared_memory_size,
            device.stream()>>>(args, input, filter, output);
 }
@@ -666,17 +679,20 @@ void LaunchDepthwiseConv2dGPU(const GpuDevice& device,
                               const T* filter, T* output,
                               TensorFormat data_format) {
   void (*kernel)(const DepthwiseArgs, const T*, const T*, T*, int);
-  if (data_format == FORMAT_NHWC) {
-    kernel =
-        DepthwiseConv2dGPUKernelNHWC<T, kKnownFilterWidth, kKnownFilterHeight,
-                                     kKnownDepthMultiplier>;
-  } else if (data_format == FORMAT_NCHW) {
-    kernel =
-        DepthwiseConv2dGPUKernelNCHW<T, kKnownFilterWidth, kKnownFilterHeight,
-                                     kKnownDepthMultiplier>;
-  } else {
-    assert(false && "Incorrect data format");
-    return;
+  switch (data_format) {
+    case FORMAT_NHWC:
+      kernel =
+          DepthwiseConv2dGPUKernelNHWC<T, kKnownFilterWidth, kKnownFilterHeight,
+                                       kKnownDepthMultiplier>;
+      break;
+    case FORMAT_NCHW:
+      kernel =
+          DepthwiseConv2dGPUKernelNCHW<T, kKnownFilterWidth, kKnownFilterHeight,
+                                       kKnownDepthMultiplier>;
+      break;
+    case FORMAT_NCHW_VECT_C:
+      LOG(ERROR) << "FORMAT_NCHW_VECT_C is not supported";
+      return;
   }
   const int num_outputs =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
@@ -894,15 +910,18 @@ void LaunchDepthwiseConv2dBackpropInputGPU(const GpuDevice& device,
                                            const T* filter, T* in_backprop,
                                            TensorFormat data_format) {
   void (*kernel)(const DepthwiseArgs, const T*, const T*, T*, int);
-  if (data_format == FORMAT_NHWC) {
-    kernel = DepthwiseConv2dBackpropInputGPUKernelNHWC<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
-  } else if (data_format == FORMAT_NCHW) {
-    kernel = DepthwiseConv2dBackpropInputGPUKernelNCHW<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
-  } else {
-    assert(false && "Incorrect data format");
-    return;
+  switch (data_format) {
+    case FORMAT_NHWC:
+      kernel = DepthwiseConv2dBackpropInputGPUKernelNHWC<
+          T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
+      break;
+    case FORMAT_NCHW:
+      kernel = DepthwiseConv2dBackpropInputGPUKernelNCHW<
+          T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
+      break;
+    case FORMAT_NCHW_VECT_C:
+      LOG(ERROR) << "FORMAT_NCHW_VECT_C is not supported";
+      return;
   }
   const int num_in_backprop =
       args.batch * args.in_rows * args.in_cols * args.in_depth;
@@ -1113,6 +1132,8 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall(
   const int pad_height = args.pad_rows;
   const int pad_width = args.pad_cols;
 
+  assert(blockDim.x == kBlockDepth);
+  assert(blockDim.y == args.in_cols);
   const int block_height = blockDim.z;
 
   // These values are the same for all threads and could
@@ -1381,6 +1402,8 @@ __launch_bounds__(1024, 2) void DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall(
   const int pad_height = args.pad_rows;
   const int pad_width = args.pad_cols;
 
+  assert(blockDim.x == args.in_cols);
+  assert(blockDim.z == kBlockDepth);
   const int block_height = blockDim.y;
 
   // These values are the same for all threads and could
@@ -1519,24 +1542,31 @@ bool TryLaunchDepthwiseConv2dBackpropFilterGPUSmall(
   }
 
   dim3 block_dim;
+  int block_count;
   void (*kernel)(const DepthwiseArgs, const T*, const T*, T*);
-  if (data_format == FORMAT_NHWC) {
-    block_dim = dim3(kBlockDepth, args.in_cols, block_height);
-    kernel = DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockDepth, kAccumPixels>;
-  } else if (data_format == FORMAT_NCHW) {
-    block_dim = dim3(args.in_cols, block_height, kBlockDepth);
-    kernel = DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
-        T, kKnownFilterWidth, kKnownFilterHeight, kBlockDepth, kAccumPixels>;
-  } else {
-    assert(false && "Incorrect data format");
-    return false;
+  switch (data_format) {
+    case FORMAT_NHWC:
+      block_dim = dim3(kBlockDepth, args.in_cols, block_height);
+      block_count =
+          args.batch * DivUp(args.out_depth, kBlockDepth) * kBlockDepth;
+      kernel = DepthwiseConv2dBackpropFilterGPUKernelNHWCSmall<
+          T, kKnownFilterWidth, kKnownFilterHeight, kBlockDepth, kAccumPixels>;
+      break;
+    case FORMAT_NCHW:
+      block_dim = dim3(args.in_cols, block_height, kBlockDepth);
+      block_count =
+          DivUp(args.batch * args.out_depth, kBlockDepth) * kBlockDepth;
+      kernel = DepthwiseConv2dBackpropFilterGPUKernelNCHWSmall<
+          T, kKnownFilterWidth, kKnownFilterHeight, kBlockDepth, kAccumPixels>;
+      break;
+    case FORMAT_NCHW_VECT_C:
+      LOG(ERROR) << "FORMAT_NCHW_VECT_C is not supported";
+      return false;
   }
-  const int num_out_backprop =
-      args.batch * args.out_rows * args.out_cols * args.out_depth;
-  CudaLaunchConfig config =
-      GetCudaLaunchConfig(num_out_backprop, device, kernel, shared_memory_size,
-                          block_dim.x * block_dim.y * block_dim.z);
+  const int num_out_backprop = args.out_rows * args.out_cols * block_count;
+  CudaLaunchConfig config = GetCudaLaunchConfigFixedBlockSize(
+      num_out_backprop, device, kernel, shared_memory_size,
+      block_dim.x * block_dim.y * block_dim.z);
   kernel<<<config.block_count, block_dim, shared_memory_size,
            device.stream()>>>(args, out_backprop, input, filter_backprop);
   return true;
@@ -1623,15 +1653,18 @@ void LaunchDepthwiseConv2dBackpropFilterGPU(const GpuDevice& device,
                                             const T* input, T* filter_backprop,
                                             TensorFormat data_format) {
   void (*kernel)(const DepthwiseArgs, const T*, const T*, T*, int);
-  if (data_format == FORMAT_NHWC) {
-    kernel = DepthwiseConv2dBackpropFilterGPUKernelNHWC<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
-  } else if (data_format == FORMAT_NCHW) {
-    kernel = DepthwiseConv2dBackpropFilterGPUKernelNCHW<
-        T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
-  } else {
-    assert(false && "Incorrect data format");
-    return;
+  switch (data_format) {
+    case FORMAT_NHWC:
+      kernel = DepthwiseConv2dBackpropFilterGPUKernelNHWC<
+          T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
+      break;
+    case FORMAT_NCHW:
+      kernel = DepthwiseConv2dBackpropFilterGPUKernelNCHW<
+          T, kKnownFilterWidth, kKnownFilterHeight, kKnownDepthMultiplier>;
+      break;
+    case FORMAT_NCHW_VECT_C:
+      LOG(ERROR) << "FORMAT_NCHW_VECT_C is not supported";
+      return;
   }
   const int num_out_backprop =
       args.batch * args.out_rows * args.out_cols * args.out_depth;
diff --git a/tensorflow/core/util/cuda_launch_config.h b/tensorflow/core/util/cuda_launch_config.h
index 3ea33ee6cf..81df7a51d7 100644
--- a/tensorflow/core/util/cuda_launch_config.h
+++ b/tensorflow/core/util/cuda_launch_config.h
@@ -169,6 +169,30 @@ inline CudaLaunchConfig GetCudaLaunchConfig(int work_element_count,
   return config;
 }
 
+// Calculate the Cuda launch config we should use for a kernel launch. This
+// variant takes the resource limits of func into account to maximize occupancy.
+// The returned launch config has thread_per_block set to fixed_block_size.
+// REQUIRES: work_element_count > 0.
+template <typename DeviceFunc>
+inline CudaLaunchConfig GetCudaLaunchConfigFixedBlockSize(
+    int work_element_count, const Eigen::GpuDevice& d, DeviceFunc func,
+    size_t dynamic_shared_memory_size, int fixed_block_size) {
+  CHECK_GT(work_element_count, 0);
+  CudaLaunchConfig config;
+  int block_count = 0;
+
+  cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
+      &block_count, func, fixed_block_size, dynamic_shared_memory_size);
+  CHECK_EQ(err, cudaSuccess);
+  block_count = std::min(block_count * d.getNumCudaMultiProcessors(),
+                         DivUp(work_element_count, fixed_block_size));
+
+  config.virtual_thread_count = work_element_count;
+  config.thread_per_block = fixed_block_size;
+  config.block_count = block_count;
+  return config;
+}
+
 struct Cuda2DLaunchConfig {
   dim3 virtual_thread_count = dim3(0, 0, 0);
   dim3 thread_per_block = dim3(0, 0, 0);
@@ -236,20 +260,18 @@ inline Cuda3DLaunchConfig GetCuda3DLaunchConfig(
       block_size_limit);
   CHECK_EQ(err, cudaSuccess);
 
-  auto min3 = [](int a, int b, int c) { return std::min(a, std::min(b, c)); };
-
-  int threadsx = min3(xdim, thread_per_block, xthreadlimit);
+  int threadsx = std::min({xdim, thread_per_block, xthreadlimit});
   int threadsy =
-      min3(ydim, std::max(thread_per_block / threadsx, 1), ythreadlimit);
+      std::min({ydim, std::max(thread_per_block / threadsx, 1), ythreadlimit});
   int threadsz =
-      min3(zdim, std::max(thread_per_block / (threadsx * threadsy), 1),
-           zthreadlimit);
-
-  int blocksx = min3(block_count, DivUp(xdim, threadsx), xgridlimit);
-  int blocksy =
-      min3(DivUp(block_count, blocksx), DivUp(ydim, threadsy), ygridlimit);
-  int blocksz = min3(DivUp(block_count, (blocksx * blocksy)),
-                     DivUp(zdim, threadsz), zgridlimit);
+      std::min({zdim, std::max(thread_per_block / (threadsx * threadsy), 1),
+                zthreadlimit});
+
+  int blocksx = std::min({block_count, DivUp(xdim, threadsx), xgridlimit});
+  int blocksy = std::min(
+      {DivUp(block_count, blocksx), DivUp(ydim, threadsy), ygridlimit});
+  int blocksz = std::min({DivUp(block_count, (blocksx * blocksy)),
+                          DivUp(zdim, threadsz), zgridlimit});
 
   config.virtual_thread_count = dim3(xdim, ydim, zdim);
   config.thread_per_block = dim3(threadsx, threadsy, threadsz);
-- 
GitLab


From 6e5ca37827d95b12c4712cf237ec2f8124ed885c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 13:21:17 -0800
Subject: [PATCH 013/575] Clarifying the docstring for how gradients are
 reduced across towers in replicate_model_fn

PiperOrigin-RevId: 186037416
---
 .../contrib/estimator/python/estimator/replicate_model_fn.py   | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
index 7134cd3f5a..e0fae2c992 100644
--- a/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
+++ b/tensorflow/contrib/estimator/python/estimator/replicate_model_fn.py
@@ -110,7 +110,8 @@ def replicate_model_fn(model_fn,
   Certain algorithms were chosen for aggregating results of computations on
   multiple towers:
     - Losses from all towers are reduced according to `loss_reduction`.
-    - Gradients are reduced using sum for each trainable variable.
+    - Gradients from all towers are reduced according to `loss_reduction`
+      for each trainable variable.
     - `eval_metrics_ops` are reduced per metric using `reduce_mean`.
     - `EstimatorSpec.predictions` and `EstimatorSpec.export_outputs` are
       reduced using concatenation.
-- 
GitLab


From 1bde8c61e14b1c42db20a1b07684ea2534cfdf01 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 13:31:04 -0800
Subject: [PATCH 014/575] Automated g4 rollback of changelist 185623948

PiperOrigin-RevId: 186038783
---
 .../compiler/xla/service/copy_insertion.cc    |   3 +-
 .../compiler/xla/service/heap_simulator.cc    |   1 +
 .../compiler/xla/service/hlo_computation.cc   |   7 +-
 .../compiler/xla/service/hlo_computation.h    |   8 +
 .../compiler/xla/service/hlo_instruction.cc   |   4 +-
 tensorflow/compiler/xla/service/hlo_module.cc |  27 +++
 tensorflow/compiler/xla/service/hlo_module.h  |   4 +
 .../compiler/xla/service/hlo_ordering.cc      |  16 ++
 .../xla/service/hlo_rematerialization.cc      |   1 +
 .../compiler/xla/service/layout_assignment.cc | 212 ++++++++++++------
 .../xla/service/layout_assignment_test.cc     |  63 ++++++
 11 files changed, 270 insertions(+), 76 deletions(-)

diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc
index cd983bc03e..c812df4235 100644
--- a/tensorflow/compiler/xla/service/copy_insertion.cc
+++ b/tensorflow/compiler/xla/service/copy_insertion.cc
@@ -729,7 +729,8 @@ class CopyRemover {
       // has a different operand (the operand of the elided copy).
       for (const HloUse* copy_use : copy_value_node->uses) {
         operand_node->uses.push_back(copy_use);
-        if (copy_use->instruction->opcode() == HloOpcode::kCopy) {
+        if (copy_use->instruction->opcode() == HloOpcode::kCopy &&
+            ContainsKey(copy_map_, copy_use->instruction)) {
           copy_map_.at(copy_use->instruction).src = operand_node;
         }
       }
diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc
index cde5877e29..a2d13c013c 100644
--- a/tensorflow/compiler/xla/service/heap_simulator.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator.cc
@@ -225,6 +225,7 @@ Status HeapSimulator::RunComputation(
     // sub-computations will never be run concurrently.
     if (module_sequence_ != nullptr) {
       if (instruction->opcode() == HloOpcode::kCall ||
+          instruction->opcode() == HloOpcode::kConditional ||
           instruction->opcode() == HloOpcode::kWhile) {
         for (const HloComputation* called_computation :
              instruction->called_computations()) {
diff --git a/tensorflow/compiler/xla/service/hlo_computation.cc b/tensorflow/compiler/xla/service/hlo_computation.cc
index 5432419e4a..21e6b2ca73 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.cc
+++ b/tensorflow/compiler/xla/service/hlo_computation.cc
@@ -509,13 +509,14 @@ StatusOr<HloInstruction*> HloComputation::DeepCopyInstruction(
         "Can't deep copy instruction %s: instruction is not in computation %s",
         instruction->name().c_str(), name().c_str());
   }
-
   if (indices_to_copy != nullptr &&
       !ShapeUtil::Compatible(instruction->shape(), indices_to_copy->shape())) {
     return FailedPrecondition(
         "Can't deep copy instruction %s: given shape tree of indices to copy "
-        "has incompatible shape",
-        instruction->name().c_str());
+        "has incompatible shapes: %s vs. %s",
+        instruction->name().c_str(),
+        ShapeUtil::HumanString(instruction->shape()).c_str(),
+        ShapeUtil::HumanString(indices_to_copy->shape()).c_str());
   }
 
   ShapeIndex index;
diff --git a/tensorflow/compiler/xla/service/hlo_computation.h b/tensorflow/compiler/xla/service/hlo_computation.h
index 061c59abe5..39d864efcb 100644
--- a/tensorflow/compiler/xla/service/hlo_computation.h
+++ b/tensorflow/compiler/xla/service/hlo_computation.h
@@ -77,6 +77,14 @@ class HloComputation {
       return last_added_instruction_;
     }
 
+    Status ForEachInstruction(
+        const std::function<Status(const HloInstruction*)>& func) const {
+      for (const auto& instruction : instructions_) {
+        TF_RETURN_IF_ERROR(func(instruction.get()));
+      }
+      return Status::OK();
+    }
+
    private:
     const string name_;
     HloInstruction* last_added_instruction_;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 0d9912d07d..d719ff857d 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1371,7 +1371,9 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       break;
     case HloOpcode::kRecv:
       CHECK_EQ(new_operands.size(), 0);
-      clone = CreateRecv(shape, channel_id());
+      // The shape is a tuple, but CreateRecv() wants the raw data shape.
+      clone =
+          CreateRecv(ShapeUtil::GetTupleElementShape(shape, 0), channel_id());
       break;
     case HloOpcode::kRecvDone:
       CHECK_EQ(new_operands.size(), 1);
diff --git a/tensorflow/compiler/xla/service/hlo_module.cc b/tensorflow/compiler/xla/service/hlo_module.cc
index 60270b0595..cb2fe9f874 100644
--- a/tensorflow/compiler/xla/service/hlo_module.cc
+++ b/tensorflow/compiler/xla/service/hlo_module.cc
@@ -145,6 +145,21 @@ void HloModule::ReplaceComputations(
           }
           break;
         }
+        case HloOpcode::kConditional: {
+          HloComputation* new_true_computation =
+              tensorflow::gtl::FindWithDefault(
+                  replacements, instruction->true_computation(), nullptr);
+          if (new_true_computation != nullptr) {
+            instruction->set_true_computation(new_true_computation);
+          }
+          HloComputation* new_false_computation =
+              tensorflow::gtl::FindWithDefault(
+                  replacements, instruction->false_computation(), nullptr);
+          if (new_false_computation != nullptr) {
+            instruction->set_false_computation(new_false_computation);
+          }
+          break;
+        }
         case HloOpcode::kSelectAndScatter: {
           HloComputation* new_select = tensorflow::gtl::FindWithDefault(
               replacements, instruction->select(), nullptr);
@@ -563,6 +578,18 @@ std::unique_ptr<HloModule> HloModule::Clone(const string& suffix) const {
   return module;
 }
 
+HloComputation* HloModule::DeepCloneComputation(HloComputation* computation) {
+  HloComputation* clone = AddEmbeddedComputation(computation->Clone("", this));
+  TF_CHECK_OK(
+      clone->root_instruction()->Accept([this](HloInstruction* instruction) {
+        instruction->ReplaceCalledComputations([this](HloComputation* callee) {
+          return DeepCloneComputation(callee);
+        });
+        return Status::OK();
+      }));
+  return clone;
+}
+
 uint64 HloModule::RandomNew64() const {
   tensorflow::mutex_lock l(rng_mutex_);
   return rng_();
diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index 4bfe8d89ce..06d92f94fd 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -85,6 +85,10 @@ class HloModule {
   // Returns a deep copy of this module including all computations.
   std::unique_ptr<HloModule> Clone(const string& suffix = "clone") const;
 
+  // Performs a deep clone of the computation, by recursively cloning all
+  // the called computations as well.
+  HloComputation* DeepCloneComputation(HloComputation* computation);
+
   // Return a pointer to the entry computation of the module..
   const HloComputation* entry_computation() const {
     CHECK_NE(nullptr, entry_computation_);
diff --git a/tensorflow/compiler/xla/service/hlo_ordering.cc b/tensorflow/compiler/xla/service/hlo_ordering.cc
index 68e3c9618c..1b24d8da9e 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering.cc
@@ -186,6 +186,22 @@ bool HloOrdering::UseIsBeforeValueDefinition(
     }
   }
 
+  if (use.instruction->opcode() == HloOpcode::kConditional) {
+    const HloInstruction* conditional = use.instruction;
+    if (call_graph_->InstructionIsNestedIn(value.defining_instruction(),
+                                           conditional->true_computation())) {
+      VLOG(4) << "  use is conditional " << use.instruction->name()
+              << " and def is in TRUE computation";
+      return true;
+    }
+    if (call_graph_->InstructionIsNestedIn(value.defining_instruction(),
+                                           conditional->false_computation())) {
+      VLOG(4) << "  use is conditional " << use.instruction->name()
+              << " and def is in FALSE computation";
+      return true;
+    }
+  }
+
   VLOG(4) << "  use is not before value";
   return false;
 }
diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
index c6b4dc0368..98b8d34be1 100644
--- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc
+++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc
@@ -60,6 +60,7 @@ bool IsRematerializable(const HloInstruction* instruction) {
   switch (instruction->opcode()) {
     case HloOpcode::kCall:
     case HloOpcode::kConstant:
+    case HloOpcode::kConditional:
     case HloOpcode::kCrossReplicaSum:
     case HloOpcode::kCustomCall:
     case HloOpcode::kParameter:
diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index fce135ef61..0668f66051 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -53,6 +53,83 @@ limitations under the License.
 
 namespace xla {
 
+// For now moving only one API here, but we should have a single top level
+// anonymous namespace, instead of three or four spread all over this file.
+namespace {
+
+// Creates and returns a copy of the given instruction with a different
+// layout. Tuple-shaped instructions will be deep-copied, and the last Tuple
+// instruction producing the copy is returned.
+StatusOr<HloInstruction*> CreateCopyWithNewLayout(
+    const Shape& shape_with_layout, HloInstruction* instruction) {
+  TF_RET_CHECK(LayoutUtil::HasLayout(shape_with_layout));
+  DCHECK(ShapeUtil::Compatible(shape_with_layout, instruction->shape()))
+      << ShapeUtil::HumanString(shape_with_layout) << " "
+      << ShapeUtil::HumanString(instruction->shape())
+      << " instruction: " << instruction->ToString();
+
+  if (ShapeUtil::IsTuple(instruction->shape())) {
+    // Deep-copy tuples.
+    std::vector<HloInstruction*> element_copies;
+    for (int64 i = 0; i < ShapeUtil::TupleElementCount(instruction->shape());
+         ++i) {
+      HloInstruction* gte = instruction->parent()->AddInstruction(
+          HloInstruction::CreateGetTupleElement(
+              ShapeUtil::GetSubshape(instruction->shape(), {i}), instruction,
+              i));
+
+      // Recurse to copy each elements.
+      TF_ASSIGN_OR_RETURN(
+          HloInstruction * element_copy,
+          CreateCopyWithNewLayout(
+              ShapeUtil::GetSubshape(shape_with_layout, {i}), gte));
+      element_copies.push_back(element_copy);
+    }
+    // Gather element copies into a tuple with a new Tuple instruction.
+    HloInstruction* tuple_copy = instruction->parent()->AddInstruction(
+        HloInstruction::CreateTuple(element_copies));
+    LayoutUtil::ClearLayout(tuple_copy->mutable_shape());
+    TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes(
+        shape_with_layout, tuple_copy->mutable_shape()));
+    return tuple_copy;
+  } else if (ShapeUtil::IsArray(instruction->shape())) {
+    HloInstruction* copy =
+        instruction->parent()->AddInstruction(HloInstruction::CreateUnary(
+            instruction->shape(), HloOpcode::kCopy, instruction));
+    LayoutUtil::ClearLayout(copy->mutable_shape());
+    TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes(
+        shape_with_layout, copy->mutable_shape()));
+
+    return copy;
+  } else {
+    return FailedPrecondition(
+        "Can only copy array and tuple shaped instructions");
+  }
+}
+
+// Creates a copy of the given operand if the operand's layout does not match
+// the given layout. This copy replaces the use in the given instruction. Tuple
+// operands will be deep-copied.
+Status CopyOperandIfLayoutsDiffer(const ShapeLayout& operand_layout,
+                                  HloInstruction* instruction,
+                                  int64 operand_no) {
+  HloInstruction* operand = instruction->mutable_operand(operand_no);
+  TF_RET_CHECK(operand_layout.LayoutIsSet());
+  TF_RET_CHECK(LayoutUtil::HasLayout(operand->shape()));
+
+  if (ShapeUtil::Equal(operand_layout.shape(), operand->shape())) {
+    // Operand layout already matches our constraint. Nothing to do.
+    return Status::OK();
+  }
+
+  TF_ASSIGN_OR_RETURN(HloInstruction * operand_copy,
+                      CreateCopyWithNewLayout(operand_layout.shape(), operand));
+
+  return instruction->ReplaceOperandWith(operand_no, operand_copy);
+}
+
+}  // namespace
+
 std::ostream& operator<<(std::ostream& out,
                          const LayoutConstraint& constraint) {
   out << constraint.ToString();
@@ -512,6 +589,36 @@ Status LayoutAssignment::AddMandatoryConstraints(
           body_layout.result_shape(), instruction));
       TF_RETURN_IF_ERROR(constraints->SetOperandLayout(
           body_layout.result_shape(), instruction, 0));
+    } else if (instruction->opcode() == HloOpcode::kConditional) {
+      // The layout of the true and false computations must match, and must
+      // be the layout of the kConditional instruction.
+      TF_RET_CHECK(instruction->operand_count() == 3);
+
+      HloComputation* true_computation = instruction->true_computation();
+      HloComputation* false_computation = instruction->false_computation();
+      const HloInstruction* true_operand = instruction->operand(1);
+      const HloInstruction* false_operand = instruction->operand(2);
+
+      TF_RET_CHECK(true_computation->num_parameters() == 1);
+      TF_RET_CHECK(false_computation->num_parameters() == 1);
+      ComputationLayout& true_computation_layout =
+          FindOrDie(computation_layouts_, true_computation);
+      ComputationLayout& false_computation_layout =
+          FindOrDie(computation_layouts_, false_computation);
+
+      DCHECK(ShapeUtil::Compatible(true_operand->shape(),
+                                   true_computation_layout.parameter_shape(0)));
+      DCHECK(ShapeUtil::Compatible(
+          false_operand->shape(), false_computation_layout.parameter_shape(0)));
+
+      TF_RETURN_IF_ERROR(constraints->SetInstructionLayout(
+          true_computation_layout.result_shape(), instruction));
+      TF_RETURN_IF_ERROR(constraints->SetOperandLayout(
+          true_computation_layout.parameter_shape(0), instruction, 1,
+          /*mandatory=*/true));
+      TF_RETURN_IF_ERROR(constraints->SetOperandLayout(
+          false_computation_layout.parameter_shape(0), instruction, 2,
+          /*mandatory=*/true));
     } else if (instruction->opcode() == HloOpcode::kCustomCall) {
       if (!CustomCallRequiresMajorFirstLayout(instruction)) {
         continue;
@@ -598,6 +705,33 @@ Status CheckWhileLayout(HloInstruction* while_inst,
   return Status::OK();
 }
 
+Status CheckConditionalLayout(
+    HloInstruction* instruction,
+    const ComputationLayout& true_computation_layout,
+    const ComputationLayout& false_computation_layout) {
+  HloComputation* true_computation = instruction->true_computation();
+  HloComputation* false_computation = instruction->false_computation();
+  const HloInstruction* true_operand = instruction->operand(1);
+  const HloInstruction* false_operand = instruction->operand(2);
+
+  TF_RET_CHECK(true_computation_layout.result_layout() ==
+               false_computation_layout.result_layout());
+  TF_RET_CHECK(true_computation_layout.result_layout().MatchesLayoutInShape(
+      instruction->shape()));
+  TF_RET_CHECK(true_computation_layout.result_layout().MatchesLayoutInShape(
+      true_computation->root_instruction()->shape()));
+  TF_RET_CHECK(false_computation_layout.result_layout().MatchesLayoutInShape(
+      instruction->shape()));
+  TF_RET_CHECK(false_computation_layout.result_layout().MatchesLayoutInShape(
+      false_computation->root_instruction()->shape()));
+  TF_RET_CHECK(true_computation_layout.parameter_layout(0).MatchesLayoutInShape(
+      true_operand->shape()));
+  TF_RET_CHECK(
+      false_computation_layout.parameter_layout(0).MatchesLayoutInShape(
+          false_operand->shape()));
+  return Status::OK();
+}
+
 // Fusion parameters must match the layout of the fusion instructions operands,
 // and the root of the fusion expression must match the layout of the fusion
 // instruction.
@@ -710,6 +844,13 @@ Status LayoutAssignment::CheckLayouts(HloModule* module) {
               FindOrDie(computation_layouts_, instruction->while_condition()),
               FindOrDie(computation_layouts_, instruction->while_body())));
           break;
+        case HloOpcode::kConditional:
+          TF_RETURN_IF_ERROR(CheckConditionalLayout(
+              instruction,
+              FindOrDie(computation_layouts_, instruction->true_computation()),
+              FindOrDie(computation_layouts_,
+                        instruction->false_computation())));
+          break;
         default:
           break;
       }
@@ -1165,77 +1306,6 @@ StatusOr<Layout> InferArrayLayout(
   return *first_buffer_layout;
 }
 
-// Creates and returns a copy of the given instruction with a different
-// layout. Tuple-shaped instructions will be deep-copied, and the last Tuple
-// instruction producing the copy is returned.
-StatusOr<HloInstruction*> CreateCopyWithNewLayout(
-    const Shape& shape_with_layout, HloInstruction* instruction) {
-  TF_RET_CHECK(LayoutUtil::HasLayout(shape_with_layout));
-  DCHECK(ShapeUtil::Compatible(shape_with_layout, instruction->shape()))
-      << ShapeUtil::HumanString(shape_with_layout) << " "
-      << ShapeUtil::HumanString(instruction->shape())
-      << " instruction: " << instruction->ToString();
-
-  if (ShapeUtil::IsTuple(instruction->shape())) {
-    // Deep-copy tuples.
-    std::vector<HloInstruction*> element_copies;
-    for (int64 i = 0; i < ShapeUtil::TupleElementCount(instruction->shape());
-         ++i) {
-      HloInstruction* gte = instruction->parent()->AddInstruction(
-          HloInstruction::CreateGetTupleElement(
-              ShapeUtil::GetSubshape(instruction->shape(), {i}), instruction,
-              i));
-
-      // Recurse to copy each elements.
-      TF_ASSIGN_OR_RETURN(
-          HloInstruction * element_copy,
-          CreateCopyWithNewLayout(
-              ShapeUtil::GetSubshape(shape_with_layout, {i}), gte));
-      element_copies.push_back(element_copy);
-    }
-    // Gather element copies into a tuple with a new Tuple instruction.
-    HloInstruction* tuple_copy = instruction->parent()->AddInstruction(
-        HloInstruction::CreateTuple(element_copies));
-    LayoutUtil::ClearLayout(tuple_copy->mutable_shape());
-    TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes(
-        shape_with_layout, tuple_copy->mutable_shape()));
-    return tuple_copy;
-  } else if (ShapeUtil::IsArray(instruction->shape())) {
-    HloInstruction* copy =
-        instruction->parent()->AddInstruction(HloInstruction::CreateUnary(
-            instruction->shape(), HloOpcode::kCopy, instruction));
-    LayoutUtil::ClearLayout(copy->mutable_shape());
-    TF_RETURN_IF_ERROR(LayoutUtil::CopyLayoutBetweenShapes(
-        shape_with_layout, copy->mutable_shape()));
-
-    return copy;
-  } else {
-    return FailedPrecondition(
-        "Can only copy array and tuple shaped instructions");
-  }
-}
-
-// Creates a copy of the given operand if the operand's layout does not match
-// the given layout. This copy replaces the use in the given instruction. Tuple
-// operands will be deep-copied.
-Status CopyOperandIfLayoutsDiffer(const ShapeLayout& operand_layout,
-                                  HloInstruction* instruction,
-                                  int64 operand_no) {
-  HloInstruction* operand = instruction->mutable_operand(operand_no);
-  TF_RET_CHECK(operand_layout.LayoutIsSet());
-  TF_RET_CHECK(LayoutUtil::HasLayout(operand->shape()));
-
-  if (ShapeUtil::Equal(operand_layout.shape(), operand->shape())) {
-    // Operand layout already matches our constraint. Nothing to do.
-    return Status::OK();
-  }
-
-  TF_ASSIGN_OR_RETURN(HloInstruction * operand_copy,
-                      CreateCopyWithNewLayout(operand_layout.shape(), operand));
-
-  return instruction->ReplaceOperandWith(operand_no, operand_copy);
-}
-
 // For fusion instructions, set the layout of each fused parameter instruction
 // to match the layout of its corresponding fusion instruction operand. Also,
 // set the layout of the fused root to match the layout of the fusion
diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index e269a13459..dd0fba2758 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -658,5 +658,68 @@ TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) {
               ElementsAre(2, 1, 0));
 }
 
+TEST_F(LayoutAssignmentTest, ConditionalAsymmetricLayout) {
+  auto builder = HloComputation::Builder(TestName());
+  auto module = CreateNewModule();
+  Shape shape = ShapeUtil::MakeShape(F32, {128, 8});
+  Shape tshape = ShapeUtil::MakeTupleShape({shape, shape});
+  Shape result_tshape = ShapeUtil::MakeTupleShape({shape});
+
+  auto param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, shape, "param0"));
+  auto param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, shape, "param1"));
+  auto pred = builder.AddInstruction(HloInstruction::CreateParameter(
+      2, ShapeUtil::MakeShape(PRED, {}), "param2"));
+  auto tuple =
+      builder.AddInstruction(HloInstruction::CreateTuple({param0, param1}));
+
+  auto true_builder = HloComputation::Builder(TestName() + "_TrueBranch");
+  {
+    auto param = true_builder.AddInstruction(
+        HloInstruction::CreateParameter(0, tshape, "param"));
+    auto gte0 = true_builder.AddInstruction(
+        HloInstruction::CreateGetTupleElement(shape, param, 0));
+    auto gte1 = true_builder.AddInstruction(
+        HloInstruction::CreateGetTupleElement(shape, param, 1));
+    auto add = true_builder.AddInstruction(
+        HloInstruction::CreateBinary(shape, HloOpcode::kAdd, gte0, gte1));
+    true_builder.AddInstruction(HloInstruction::CreateTuple({add}));
+  }
+  HloComputation* true_computation =
+      module->AddEmbeddedComputation(true_builder.Build());
+
+  auto false_builder = HloComputation::Builder(TestName() + "_FalseBranch");
+  {
+    Shape xshape = ShapeUtil::MakeShapeWithLayout(F32, {128, 8}, {0, 1});
+    false_builder.AddInstruction(
+        HloInstruction::CreateParameter(0, tshape, "param"));
+    // Using infeed as layout assignment does not mess up with it.
+    auto infeed =
+        false_builder.AddInstruction(HloInstruction::CreateInfeed(xshape, ""));
+    false_builder.AddInstruction(HloInstruction::CreateTuple({infeed}));
+  }
+  HloComputation* false_computation =
+      module->AddEmbeddedComputation(false_builder.Build());
+  builder.AddInstruction(HloInstruction::CreateConditional(
+      result_tshape, pred, tuple, true_computation, tuple, false_computation));
+
+  HloComputation* computation = module->AddEntryComputation(builder.Build());
+  ComputationLayout computation_layout(computation->ComputeProgramShape());
+
+  AssignLayouts(module.get(), &computation_layout);
+
+  const HloInstruction* true_root = true_computation->root_instruction();
+  const HloInstruction* false_root = false_computation->root_instruction();
+  EXPECT_THAT(true_root->opcode(), HloOpcode::kTuple);
+  EXPECT_THAT(false_root->opcode(), HloOpcode::kTuple);
+
+  const HloInstruction* true_result = true_root->operand(0);
+  const HloInstruction* false_result = false_root->operand(0);
+  EXPECT_TRUE(LayoutUtil::Equal(true_result->shape().layout(),
+                                false_result->shape().layout()));
+  EXPECT_THAT(false_result->opcode(), HloOpcode::kCopy);
+}
+
 }  // namespace
 }  // namespace xla
-- 
GitLab


From f83bb6db5bd17df215994bde7adacef50ece0192 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Fri, 16 Feb 2018 13:33:55 -0800
Subject: [PATCH 015/575] [XLA:CPU] Minor cleanup to simple_orc_jit

SimpleResolver became unused after an LLVM upstream merge, and we never needed
the name mangling logic in what is now FindCompiledSymbol.

PiperOrigin-RevId: 186039307
---
 .../xla/service/cpu/cpu_executable.cc         |  2 +-
 .../service/cpu/parallel_cpu_executable.cc    |  2 +-
 .../xla/service/cpu/simple_orc_jit.cc         | 73 +++++--------------
 .../compiler/xla/service/cpu/simple_orc_jit.h |  4 +-
 4 files changed, 25 insertions(+), 56 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
index 802d0a6fb4..c053703c35 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc
@@ -63,7 +63,7 @@ CpuExecutable::CpuExecutable(
       assignment_(std::move(assignment)) {
   // Resolve symbols in the constructor rather than at execution time to avoid
   // races because FindSymbol is not thread safe.
-  llvm::JITSymbol sym = jit_->FindSymbol(entry_function_name);
+  llvm::JITSymbol sym = jit_->FindCompiledSymbol(entry_function_name);
   // We expect to find the symbol provided with entry_function_name; otherwise
   // this is an internal error.
   CHECK(sym) << "Symbol " << entry_function_name << " not found.";
diff --git a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
index cd997f0789..07a9f0efcb 100644
--- a/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
+++ b/tensorflow/compiler/xla/service/cpu/parallel_cpu_executable.cc
@@ -394,7 +394,7 @@ Status ParallelCpuExecutable::ExecuteComputeFunctions(
   for (auto& entry : *function_names_) {
     tensorflow::mutex_lock lock(jit_mutex_);
     HloInstruction* instruction = entry.first;
-    llvm::JITSymbol sym = jit_->FindSymbol(entry.second);
+    llvm::JITSymbol sym = jit_->FindCompiledSymbol(entry.second);
     TF_RET_CHECK(sym);
     InsertOrDie(
         &functions, instruction,
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index cfed551eed..aa8d4ad9dc 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -44,36 +44,6 @@ namespace xla {
 namespace cpu {
 namespace {
 
-// A simple SymbolResolver that delegates to the host dynamic linker.
-class SimpleResolver : public llvm::LegacyJITSymbolResolver {
- public:
-  explicit SimpleResolver(ExternalConstantPool* external_constant_pool)
-      : external_constant_pool_(external_constant_pool) {}
-
-  llvm::JITSymbol findSymbol(const std::string& name) override {
-    if (const uint8* from_constant_pool =
-            external_constant_pool_->Find(string(name))) {
-      return llvm::JITEvaluatedSymbol(
-          reinterpret_cast<uint64_t>(from_constant_pool),
-          llvm::JITSymbolFlags::None);
-    }
-
-    void* func_addr = CustomCallTargetRegistry::Global()->Lookup(name);
-    if (func_addr == nullptr) {
-      return nullptr;
-    }
-    llvm::JITEvaluatedSymbol symbol_info(reinterpret_cast<uint64_t>(func_addr),
-                                         llvm::JITSymbolFlags::None);
-    return symbol_info;
-  }
-  llvm::JITSymbol findSymbolInLogicalDylib(const std::string& name) override {
-    return nullptr;
-  }
-
- private:
-  ExternalConstantPool* external_constant_pool_;
-};
-
 llvm::SmallVector<std::string, 0> DetectMachineAttributes() {
   llvm::SmallVector<std::string, 0> result;
   llvm::StringMap<bool> host_features;
@@ -119,21 +89,7 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions& target_options,
       execution_session_(string_pool_),
       symbol_resolver_(llvm::orc::createLegacyLookupResolver(
           [this](const std::string& name) -> llvm::JITSymbol {
-            if (const uint8* from_constant_pool =
-                    external_constant_pool_.Find(string(name))) {
-              return llvm::JITEvaluatedSymbol(
-                  reinterpret_cast<uint64_t>(from_constant_pool),
-                  llvm::JITSymbolFlags::None);
-            }
-
-            void* func_addr = CustomCallTargetRegistry::Global()->Lookup(name);
-            if (func_addr == nullptr) {
-              return nullptr;
-            }
-            llvm::JITEvaluatedSymbol symbol_info(
-                reinterpret_cast<uint64_t>(func_addr),
-                llvm::JITSymbolFlags::None);
-            return symbol_info;
+            return this->ResolveRuntimeSymbol(name);
           },
           [](llvm::Error Err) {
             cantFail(std::move(Err), "lookupFlags failed");
@@ -157,6 +113,23 @@ SimpleOrcJIT::SimpleOrcJIT(const llvm::TargetOptions& target_options,
           << " features: " << target_machine_->getTargetFeatureString().str();
 }
 
+llvm::JITSymbol SimpleOrcJIT::ResolveRuntimeSymbol(const std::string& name) {
+  if (const uint8* from_constant_pool =
+          external_constant_pool_.Find(string(name))) {
+    return llvm::JITEvaluatedSymbol(
+        reinterpret_cast<uint64_t>(from_constant_pool),
+        llvm::JITSymbolFlags::None);
+  }
+
+  void* func_addr = CustomCallTargetRegistry::Global()->Lookup(name);
+  if (func_addr == nullptr) {
+    return nullptr;
+  }
+  llvm::JITEvaluatedSymbol symbol_info(reinterpret_cast<uint64_t>(func_addr),
+                                       llvm::JITSymbolFlags::None);
+  return symbol_info;
+}
+
 SimpleOrcJIT::VModuleKeyT SimpleOrcJIT::AddModule(
     std::unique_ptr<llvm::Module> module) {
   auto key = execution_session_.allocateVModule();
@@ -171,19 +144,13 @@ void SimpleOrcJIT::RemoveModule(SimpleOrcJIT::VModuleKeyT key) {
   cantFail(compile_layer_.removeModule(key));
 }
 
-llvm::JITSymbol SimpleOrcJIT::FindSymbol(const std::string& name) {
-  std::string mangled_name;
-  {
-    llvm::raw_string_ostream mangled_name_stream(mangled_name);
-    llvm::Mangler::getNameWithPrefix(mangled_name_stream, name, data_layout_);
-  }
-
+llvm::JITSymbol SimpleOrcJIT::FindCompiledSymbol(const std::string& name) {
   // Resolve symbol from last module to first, allowing later redefinitions of
   // symbols shadow earlier ones.
   for (auto& key :
        llvm::make_range(module_keys_.rbegin(), module_keys_.rend())) {
     if (auto symbol =
-            compile_layer_.findSymbolIn(key, mangled_name,
+            compile_layer_.findSymbolIn(key, name,
                                         /*ExportedSymbolsOnly=*/true)) {
       return symbol;
     }
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index 50993afc8f..d0011e0a18 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -89,7 +89,7 @@ class SimpleOrcJIT {
 
   // Get the runtime address of the compiled symbol whose name is given. Returns
   // nullptr if the symbol cannot be found.
-  llvm::JITSymbol FindSymbol(const std::string& name);
+  llvm::JITSymbol FindCompiledSymbol(const std::string& name);
 
   llvm::TargetMachine* target_machine() const { return target_machine_.get(); }
 
@@ -98,6 +98,8 @@ class SimpleOrcJIT {
   }
 
  private:
+  llvm::JITSymbol ResolveRuntimeSymbol(const std::string& name);
+
   std::vector<VModuleKeyT> module_keys_;
   std::unique_ptr<llvm::TargetMachine> target_machine_;
   const Disassembler disassembler_;
-- 
GitLab


From 00ff4f56d54bff3cc6f078ceb64da23b45021d42 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 16 Feb 2018 13:38:11 -0800
Subject: [PATCH 016/575] TFTS: Support tf.Example input

PiperOrigin-RevId: 186039949
---
 .../python/timeseries/input_pipeline.py       | 35 +++++++++++
 .../python/timeseries/input_pipeline_test.py  | 60 +++++++++++++++++++
 2 files changed, 95 insertions(+)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py
index d4ee590366..04225333b9 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py
@@ -500,6 +500,41 @@ class CSVReader(ReaderBaseTimeSeriesParser):
     return features
 
 
+class TFExampleReader(ReaderBaseTimeSeriesParser):
+  """Reads and parses `tf.Example`s from a TFRecords file."""
+
+  def __init__(self,
+               filenames,
+               features):
+    """Configure `tf.Example` parsing.
+
+    Args:
+      filenames: A filename or list of filenames to read the time series
+          from. Each line must have columns corresponding to `column_names`.
+      features: A dictionary mapping from feature keys to `tf.FixedLenFeature`
+          objects. Must include `TrainEvalFeatures.TIMES` (scalar integer) and
+          `TrainEvalFeatures.VALUES` (floating point vector) features.
+    Raises:
+      ValueError: If required times/values features are not present.
+    """
+    if feature_keys.TrainEvalFeatures.TIMES not in features:
+      raise ValueError("'{}' is a required column.".format(
+          feature_keys.TrainEvalFeatures.TIMES))
+    if feature_keys.TrainEvalFeatures.VALUES not in features:
+      raise ValueError("'{}' is a required column.".format(
+          feature_keys.TrainEvalFeatures.VALUES))
+    self._features = features
+    super(TFExampleReader, self).__init__(filenames=filenames)
+
+  def _get_reader(self):
+    return io_ops.TFRecordReader()
+
+  def _process_records(self, examples):
+    """Parse `tf.Example`s into `Tensors`."""
+    return parsing_ops.parse_example(
+        serialized=examples, features=self._features)
+
+
 class TimeSeriesInputFn(object):
   """Base for classes which create batches of windows from a time series."""
 
diff --git a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py
index ed78a835a4..703537abf0 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/input_pipeline_test.py
@@ -27,7 +27,11 @@ from tensorflow.contrib.timeseries.python.timeseries import input_pipeline
 from tensorflow.contrib.timeseries.python.timeseries import test_utils
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
 
+from tensorflow.core.example import example_pb2
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
+from tensorflow.python.lib.io import tf_record
+from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.training import coordinator as coordinator_lib
@@ -52,6 +56,21 @@ def _make_csv_time_series(num_features, num_samples, test_tmpdir):
   return filename
 
 
+def _make_tfexample_series(num_features, num_samples, test_tmpdir):
+  _, data_file = tempfile.mkstemp(dir=test_tmpdir)
+  with tf_record.TFRecordWriter(data_file) as writer:
+    for i in range(num_samples):
+      example = example_pb2.Example()
+      times = example.features.feature[TrainEvalFeatures.TIMES]
+      times.int64_list.value.append(i)
+      values = example.features.feature[TrainEvalFeatures.VALUES]
+      values.float_list.value.extend(
+          [float(i) * 2. + feature_number
+           for feature_number in range(num_features)])
+      writer.write(example.SerializeToString())
+  return data_file
+
+
 def _make_numpy_time_series(num_features, num_samples):
   times = numpy.arange(num_samples)
   values = times[:, None] * 2. + numpy.arange(num_features)[None, :]
@@ -107,6 +126,19 @@ class RandomWindowInputFnTests(test.TestCase):
     time_series_reader = input_pipeline.CSVReader([filename])
     self._test_out_of_order(time_series_reader, discard_out_of_order=False)
 
+  def test_tfexample_sort_out_of_order(self):
+    filename = _make_tfexample_series(
+        num_features=1, num_samples=50,
+        test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.TFExampleReader(
+        [filename],
+        features={
+            TrainEvalFeatures.TIMES: parsing_ops.FixedLenFeature(
+                shape=[], dtype=dtypes.int64),
+            TrainEvalFeatures.VALUES: parsing_ops.FixedLenFeature(
+                shape=[1], dtype=dtypes.float32)})
+    self._test_out_of_order(time_series_reader, discard_out_of_order=False)
+
   def test_numpy_sort_out_of_order(self):
     data = _make_numpy_time_series(num_features=1, num_samples=50)
     time_series_reader = input_pipeline.NumpyReader(data)
@@ -183,6 +215,20 @@ class RandomWindowInputFnTests(test.TestCase):
     self._test_multivariate(time_series_reader=time_series_reader,
                             num_features=2)
 
+  def test_tfexample_multivariate(self):
+    filename = _make_tfexample_series(
+        num_features=2, num_samples=50,
+        test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.TFExampleReader(
+        [filename],
+        features={
+            TrainEvalFeatures.TIMES: parsing_ops.FixedLenFeature(
+                shape=[], dtype=dtypes.int64),
+            TrainEvalFeatures.VALUES: parsing_ops.FixedLenFeature(
+                shape=[2], dtype=dtypes.float32)})
+    self._test_multivariate(time_series_reader=time_series_reader,
+                            num_features=2)
+
   def test_numpy_multivariate(self):
     data = _make_numpy_time_series(num_features=3, num_samples=50)
     time_series_reader = input_pipeline.NumpyReader(data)
@@ -248,6 +294,20 @@ class WholeDatasetInputFnTests(test.TestCase):
       self._whole_dataset_input_fn_test_template(
           time_series_reader=time_series_reader, num_features=1, num_samples=50)
 
+  def test_tfexample(self):
+    filename = _make_tfexample_series(
+        num_features=4, num_samples=100,
+        test_tmpdir=self.get_temp_dir())
+    time_series_reader = input_pipeline.TFExampleReader(
+        [filename],
+        features={
+            TrainEvalFeatures.TIMES: parsing_ops.FixedLenFeature(
+                shape=[], dtype=dtypes.int64),
+            TrainEvalFeatures.VALUES: parsing_ops.FixedLenFeature(
+                shape=[4], dtype=dtypes.float32)})
+    self._whole_dataset_input_fn_test_template(
+        time_series_reader=time_series_reader, num_features=4, num_samples=100)
+
   def test_numpy(self):
     data = _make_numpy_time_series(num_features=4, num_samples=100)
     time_series_reader = input_pipeline.NumpyReader(data)
-- 
GitLab


From 2d6a550ab4e54de12f03dd04a892562dd85425db Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 16 Feb 2018 13:46:47 -0800
Subject: [PATCH 017/575] Remove the __setattr__ override for Variables

Was slowing down the creation of _UnreadVariable objects. Adds CheckpointableBase without the __setattr__ override.

It's tempting to just override __setattr__ in variables to try making it faster, but it's already just doing an isinstance check. Removing the override entirely seems to be the cleanest option.

PiperOrigin-RevId: 186041147
---
 tensorflow/python/ops/variables.py            |  2 +-
 tensorflow/python/training/checkpointable.py  | 94 ++++++++++---------
 .../api/golden/tensorflow.-variable.pbtxt     |  2 +-
 ...tensorflow.train.-adadelta-optimizer.pbtxt |  1 +
 ...sorflow.train.-adagrad-d-a-optimizer.pbtxt |  1 +
 .../tensorflow.train.-adagrad-optimizer.pbtxt |  1 +
 .../tensorflow.train.-adam-optimizer.pbtxt    |  1 +
 .../tensorflow.train.-ftrl-optimizer.pbtxt    |  1 +
 ...ow.train.-gradient-descent-optimizer.pbtxt |  1 +
 ...tensorflow.train.-momentum-optimizer.pbtxt |  1 +
 .../golden/tensorflow.train.-optimizer.pbtxt  |  1 +
 ...ow.train.-proximal-adagrad-optimizer.pbtxt |  1 +
 ...-proximal-gradient-descent-optimizer.pbtxt |  1 +
 ...nsorflow.train.-r-m-s-prop-optimizer.pbtxt |  1 +
 ...rflow.train.-sync-replicas-optimizer.pbtxt |  1 +
 15 files changed, 66 insertions(+), 44 deletions(-)

diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 125922e296..b785d0ede7 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -37,7 +37,7 @@ from tensorflow.python.util.tf_export import tf_export
 
 
 @tf_export("Variable")
-class Variable(checkpointable.Checkpointable):
+class Variable(checkpointable.CheckpointableBase):
   """See the @{$variables$Variables How To} for a high level overview.
 
   A variable maintains state in the graph across calls to `run()`. You add a
diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py
index c2fea0f40d..aaca67755e 100644
--- a/tensorflow/python/training/checkpointable.py
+++ b/tensorflow/python/training/checkpointable.py
@@ -280,33 +280,13 @@ class _Checkpoint(object):
     self.session = session
 
 
-class Checkpointable(object):
-  """Manages dependencies on other objects.
+class CheckpointableBase(object):
+  """Base class for `Checkpointable` objects without automatic dependencies.
 
-  `Checkpointable` objects may have dependencies: other `Checkpointable` objects
-  which should be saved if the object declaring the dependency is saved. A
-  correctly saveable program has a dependency graph such that if changing a
-  global variable affects an object (e.g. changes the behavior of any of its
-  methods) then there is a chain of dependencies from the influenced object to
-  the variable.
-
-  Dependency edges have names, and are created implicitly when a
-  `Checkpointable` object is assigned to an attribute of another
-  `Checkpointable` object. For example:
-
-  ```
-  obj = Checkpointable()
-  obj.v = ResourceVariable(0.)
-  ```
-
-  The `Checkpointable` object `obj` now has a dependency named "v" on a
-  variable.
-
-  `Checkpointable` objects may specify `Tensor`s to be saved and restored
-  directly (e.g. a `Variable` indicating how to save itself) rather than through
-  dependencies on other objects. See
-  `Checkpointable._scatter_tensors_from_checkpoint` and
-  `Checkpointable._gather_tensors_for_checkpoint` for details.
+  This class has no __setattr__ override for performance reasons. Dependencies
+  must be added explicitly. Unless attribute assignment is performance-critical,
+  use `Checkpointable` instead. Use `CheckpointableBase` for `isinstance`
+  checks.
   """
 
   def _maybe_initialize_checkpointable(self):
@@ -333,21 +313,6 @@ class Checkpointable(object):
           "initialization code was run.")
     self._update_uid = -1
 
-  def __setattr__(self, name, value):
-    """Support self.foo = checkpointable syntax."""
-    # Perform the attribute assignment, and potentially call other __setattr__
-    # overrides such as that for tf.keras.Model.
-    super(Checkpointable, self).__setattr__(name, value)
-    if isinstance(value, Checkpointable):
-      self._track_checkpointable(
-          value, name=name,
-          # Allow the user to switch the Checkpointable which is tracked by this
-          # name, since assigning a new variable to an attribute has
-          # historically been fine (e.g. Adam did this).
-          # TODO(allenl): Should this be a warning once Checkpointable save/load
-          # is usable?
-          overwrite=True)
-
   def _add_variable_with_custom_getter(
       self, name, shape=None, dtype=dtypes.float32,
       initializer=None, getter=None, **kwargs_for_getter):
@@ -487,7 +452,7 @@ class Checkpointable(object):
       ValueError: If another object is already tracked by this name.
     """
     self._maybe_initialize_checkpointable()
-    if not isinstance(checkpointable, Checkpointable):
+    if not isinstance(checkpointable, CheckpointableBase):
       raise TypeError(
           ("Checkpointable._track_checkpointable() passed type %s, not a "
            "Checkpointable.") % (type(checkpointable),))
@@ -582,3 +547,48 @@ class Checkpointable(object):
   def _gather_tensors_for_checkpoint(self):
     """Returns a dictionary of Tensors to save with this object."""
     return {}
+
+
+class Checkpointable(CheckpointableBase):
+  """Manages dependencies on other objects.
+
+  `Checkpointable` objects may have dependencies: other `Checkpointable` objects
+  which should be saved if the object declaring the dependency is saved. A
+  correctly saveable program has a dependency graph such that if changing a
+  global variable affects an object (e.g. changes the behavior of any of its
+  methods) then there is a chain of dependencies from the influenced object to
+  the variable.
+
+  Dependency edges have names, and are created implicitly when a
+  `Checkpointable` object is assigned to an attribute of another
+  `Checkpointable` object. For example:
+
+  ```
+  obj = Checkpointable()
+  obj.v = ResourceVariable(0.)
+  ```
+
+  The `Checkpointable` object `obj` now has a dependency named "v" on a
+  variable.
+
+  `Checkpointable` objects may specify `Tensor`s to be saved and restored
+  directly (e.g. a `Variable` indicating how to save itself) rather than through
+  dependencies on other objects. See
+  `Checkpointable._scatter_tensors_from_checkpoint` and
+  `Checkpointable._gather_tensors_for_checkpoint` for details.
+  """
+
+  def __setattr__(self, name, value):
+    """Support self.foo = checkpointable syntax."""
+    # Perform the attribute assignment, and potentially call other __setattr__
+    # overrides such as that for tf.keras.Model.
+    super(Checkpointable, self).__setattr__(name, value)
+    if isinstance(value, CheckpointableBase):
+      self._track_checkpointable(
+          value, name=name,
+          # Allow the user to switch the Checkpointable which is tracked by this
+          # name, since assigning a new variable to an attribute has
+          # historically been fine (e.g. Adam did this).
+          # TODO(allenl): Should this be a warning once Checkpointable save/load
+          # is usable?
+          overwrite=True)
diff --git a/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt b/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt
index 069200065a..5a02bb2175 100644
--- a/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-variable.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.Variable"
 tf_class {
   is_instance: "<class \'tensorflow.python.ops.variables.Variable\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "SaveSliceInfo"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt
index 4eea52596a..c02e54adfb 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.adadelta.AdadeltaOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt
index 5aaaf0e20b..2b619908fc 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.adagrad_da.AdagradDAOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt
index 7f1201879c..2005cf4677 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.adagrad.AdagradOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt
index 503c439d83..0a2bae1d90 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.adam.AdamOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
index 39c071748c..847f9ad759 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.ftrl.FtrlOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt
index 6b441786ca..13a58e0608 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.gradient_descent.GradientDescentOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt
index 80f3963bac..bfbc2357a3 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.momentum.MomentumOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt
index c880ba328a..437efa0a2b 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt
@@ -2,6 +2,7 @@ path: "tensorflow.train.Optimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt
index 6acdf35f78..72f224605f 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.proximal_adagrad.ProximalAdagradOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt
index 00b1e309e3..316275b1fb 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.proximal_gradient_descent.ProximalGradientDescentOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt
index 05dc391cab..af50a19861 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.rmsprop.RMSPropOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt
index 4be2819261..6edc516c93 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.training.sync_replicas_optimizer.SyncReplicasOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "GATE_GRAPH"
-- 
GitLab


From 785ee91c0d4f9a0e8eafa082f725c25ae134c9b3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 14:06:26 -0800
Subject: [PATCH 018/575] Optimization of quantized LSTM cell for the common
 case of batch size 1, where it needs efficient matrix*vector ("GEMV") code,
 but it's not exactly the same as the case of stand-alone fully-connected
 layers as here the output activations are 16bit-quantized.

PiperOrigin-RevId: 186044068
---
 .../internal/optimized/optimized_ops.h        | 265 +++++++++++++++---
 1 file changed, 233 insertions(+), 32 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index 57965724db..7e8db95760 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -2081,6 +2081,198 @@ inline void LstmCell(const float* input_data, const Dims<4>& input_dims,
       output_state_map.tanh();
 }
 
+#ifdef GEMMLOWP_NEON
+// In the common case of batch size 1, a fully-connected node degenerates
+// to a matrix*vector product. LSTM cells contain a fully-connected node;
+// when quantized, this becomes a special type of GEMV operation where
+// the output is 16bit-quantized, thus needs its own special path.
+inline void GEMVForLstmCell(const uint8* input_data, const Dims<4>& input_dims,
+                            const uint8* weights_data,
+                            const Dims<4>& weights_dims,
+                            uint8 weights_zero_point, const int32* bias_data,
+                            const Dims<4>& bias_dims, int32 accum_multiplier,
+                            int accum_shift, int16* output_data,
+                            const Dims<4>& output_dims) {
+  gemmlowp::ScopedProfilingLabel label("GEMVForLstmCell");
+  TFLITE_DCHECK(IsPackedWithoutStrides(input_dims));
+  TFLITE_DCHECK(IsPackedWithoutStrides(weights_dims));
+  TFLITE_DCHECK(IsPackedWithoutStrides(bias_dims));
+  TFLITE_DCHECK(IsPackedWithoutStrides(output_dims));
+  TFLITE_DCHECK_EQ(ArraySize(output_dims, 1) * ArraySize(output_dims, 2) *
+                       ArraySize(output_dims, 3),
+                   1);
+  const int input_size = input_dims.strides[3];
+  const int output_size = MatchingArraySize(weights_dims, 1, output_dims, 0);
+  // This special fast path for quantized LSTM cells does not try to support
+  // odd sizes that we haven't encountered in any LSTM cell, that would
+  // require special code (that would go untested until any LSTM cell
+  // exercises it). We just guard our assumptions about size evenness with
+  // the following assertions.
+  TFLITE_DCHECK(!(output_size % 4));
+  TFLITE_DCHECK(!(input_size % 8));
+  const int32* bias_ptr = bias_data;
+  int16* output_ptr = output_data;
+  for (int out = 0; out < output_size; out += 4) {
+    int32x4_t acc_0 = vdupq_n_s32(0);
+    int32x4_t acc_1 = vdupq_n_s32(0);
+    int32x4_t acc_2 = vdupq_n_s32(0);
+    int32x4_t acc_3 = vdupq_n_s32(0);
+    const int16x8_t input_offset_vec = vdupq_n_s16(-128);
+    const int16x8_t weights_offset_vec = vdupq_n_s16(-weights_zero_point);
+    int in = 0;
+    // Handle 16 levels of depth at a time.
+    for (; in <= input_size - 16; in += 16) {
+      const uint8x16_t input_val_u8 = vld1q_u8(input_data + in);
+      const uint8* weights_ptr = weights_data + in + out * input_size;
+      uint8x16_t weights_val_u8_0 = vld1q_u8(weights_ptr + 0 * input_size);
+      uint8x16_t weights_val_u8_1 = vld1q_u8(weights_ptr + 1 * input_size);
+      uint8x16_t weights_val_u8_2 = vld1q_u8(weights_ptr + 2 * input_size);
+      uint8x16_t weights_val_u8_3 = vld1q_u8(weights_ptr + 3 * input_size);
+      int16x8_t input_val_0, input_val_1;
+      const uint8x8_t low = vget_low_u8(input_val_u8);
+      const uint8x8_t high = vget_high_u8(input_val_u8);
+      input_val_0 = vreinterpretq_s16_u16(vmovl_u8(low));
+      input_val_1 = vreinterpretq_s16_u16(vmovl_u8(high));
+      input_val_0 = vaddq_s16(input_val_0, input_offset_vec);
+      input_val_1 = vaddq_s16(input_val_1, input_offset_vec);
+      int16x8_t weights_val_0_0, weights_val_1_0, weights_val_2_0,
+          weights_val_3_0;
+      int16x8_t weights_val_0_1, weights_val_1_1, weights_val_2_1,
+          weights_val_3_1;
+      weights_val_0_0 = vaddq_s16(
+          vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_0))),
+          weights_offset_vec);
+      weights_val_0_1 = vaddq_s16(
+          vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_0))),
+          weights_offset_vec);
+      weights_val_1_0 = vaddq_s16(
+          vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_1))),
+          weights_offset_vec);
+      weights_val_1_1 = vaddq_s16(
+          vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_1))),
+          weights_offset_vec);
+      weights_val_2_0 = vaddq_s16(
+          vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_2))),
+          weights_offset_vec);
+      weights_val_2_1 = vaddq_s16(
+          vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_2))),
+          weights_offset_vec);
+      weights_val_3_0 = vaddq_s16(
+          vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(weights_val_u8_3))),
+          weights_offset_vec);
+      weights_val_3_1 = vaddq_s16(
+          vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(weights_val_u8_3))),
+          weights_offset_vec);
+      acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0_0),
+                        vget_low_s16(input_val_0));
+      acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1_0),
+                        vget_low_s16(input_val_0));
+      acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2_0),
+                        vget_low_s16(input_val_0));
+      acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3_0),
+                        vget_low_s16(input_val_0));
+      acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0_0),
+                        vget_high_s16(input_val_0));
+      acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1_0),
+                        vget_high_s16(input_val_0));
+      acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2_0),
+                        vget_high_s16(input_val_0));
+      acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3_0),
+                        vget_high_s16(input_val_0));
+      acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0_1),
+                        vget_low_s16(input_val_1));
+      acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1_1),
+                        vget_low_s16(input_val_1));
+      acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2_1),
+                        vget_low_s16(input_val_1));
+      acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3_1),
+                        vget_low_s16(input_val_1));
+      acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0_1),
+                        vget_high_s16(input_val_1));
+      acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1_1),
+                        vget_high_s16(input_val_1));
+      acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2_1),
+                        vget_high_s16(input_val_1));
+      acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3_1),
+                        vget_high_s16(input_val_1));
+    }
+    // Handle 8 levels of depth at a time.
+    for (; in < input_size; in += 8) {
+      const uint8x8_t input_val_u8 = vld1_u8(input_data + in);
+      const uint8* weights_ptr = weights_data + in + out * input_size;
+      uint8x8_t weights_val_u8_0 = vld1_u8(weights_ptr + 0 * input_size);
+      uint8x8_t weights_val_u8_1 = vld1_u8(weights_ptr + 1 * input_size);
+      uint8x8_t weights_val_u8_2 = vld1_u8(weights_ptr + 2 * input_size);
+      uint8x8_t weights_val_u8_3 = vld1_u8(weights_ptr + 3 * input_size);
+      int16x8_t input_val;
+      input_val = vreinterpretq_s16_u16(vmovl_u8(input_val_u8));
+      input_val = vaddq_s16(input_val, input_offset_vec);
+      int16x8_t weights_val_0, weights_val_1, weights_val_2, weights_val_3;
+      weights_val_0 =
+          vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_0)),
+                    weights_offset_vec);
+      weights_val_1 =
+          vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_1)),
+                    weights_offset_vec);
+      weights_val_2 =
+          vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_2)),
+                    weights_offset_vec);
+      weights_val_3 =
+          vaddq_s16(vreinterpretq_s16_u16(vmovl_u8(weights_val_u8_3)),
+                    weights_offset_vec);
+      acc_0 = vmlal_s16(acc_0, vget_low_s16(weights_val_0),
+                        vget_low_s16(input_val));
+      acc_1 = vmlal_s16(acc_1, vget_low_s16(weights_val_1),
+                        vget_low_s16(input_val));
+      acc_2 = vmlal_s16(acc_2, vget_low_s16(weights_val_2),
+                        vget_low_s16(input_val));
+      acc_3 = vmlal_s16(acc_3, vget_low_s16(weights_val_3),
+                        vget_low_s16(input_val));
+      acc_0 = vmlal_s16(acc_0, vget_high_s16(weights_val_0),
+                        vget_high_s16(input_val));
+      acc_1 = vmlal_s16(acc_1, vget_high_s16(weights_val_1),
+                        vget_high_s16(input_val));
+      acc_2 = vmlal_s16(acc_2, vget_high_s16(weights_val_2),
+                        vget_high_s16(input_val));
+      acc_3 = vmlal_s16(acc_3, vget_high_s16(weights_val_3),
+                        vget_high_s16(input_val));
+    }
+    // Horizontally reduce accumulators
+    int32x2_t pairwise_reduced_acc_0, pairwise_reduced_acc_1,
+        pairwise_reduced_acc_2, pairwise_reduced_acc_3;
+    pairwise_reduced_acc_0 =
+        vpadd_s32(vget_low_s32(acc_0), vget_high_s32(acc_0));
+    pairwise_reduced_acc_1 =
+        vpadd_s32(vget_low_s32(acc_1), vget_high_s32(acc_1));
+    pairwise_reduced_acc_2 =
+        vpadd_s32(vget_low_s32(acc_2), vget_high_s32(acc_2));
+    pairwise_reduced_acc_3 =
+        vpadd_s32(vget_low_s32(acc_3), vget_high_s32(acc_3));
+    const int32x2_t reduced_lo =
+        vpadd_s32(pairwise_reduced_acc_0, pairwise_reduced_acc_1);
+    const int32x2_t reduced_hi =
+        vpadd_s32(pairwise_reduced_acc_2, pairwise_reduced_acc_3);
+    int32x4_t reduced = vcombine_s32(reduced_lo, reduced_hi);
+    // Add bias values.
+    int32x4_t bias_vec = vld1q_s32(bias_ptr);
+    bias_ptr += 4;
+    reduced = vaddq_s32(reduced, bias_vec);
+    int left_shift = accum_shift > 0 ? accum_shift : 0;
+    int right_shift = accum_shift > 0 ? 0 : -accum_shift;
+    reduced = vshlq_s32(reduced, vdupq_n_s32(left_shift));
+    // Multiply by the fixed-point multiplier.
+    reduced = vqrdmulhq_n_s32(reduced, accum_multiplier);
+    // Rounding-shift-right.
+    using gemmlowp::RoundingDivideByPOT;
+    reduced = RoundingDivideByPOT(reduced, right_shift);
+    // Narrow values down to 16 bit signed.
+    const int16x4_t res16 = vqmovn_s32(reduced);
+    vst1_s16(output_ptr, res16);
+    output_ptr += 4;
+  }
+}
+#endif
+
 // Quantized LSTM cell. Currently just a copy of the reference impl in
 // reference_ops.h. See the big function comment there, not replicating it
 // here.
@@ -2145,29 +2337,40 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
   // integers, and the output is 16-bit fixed-point with 3 integer bits so
   // the output range is [-2^3, 2^3] == [-8, 8]. The rationale for that
   // is explained in the function comment above.
-
-  gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::RowMajor> weights_matrix(
-      weights_data_uint8, fc_output_depth, fc_accum_depth);
-  gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::ColMajor> input_matrix(
-      concat_temp_data_uint8, fc_accum_depth, fc_batches);
-  gemmlowp::MatrixMap<int16, gemmlowp::MapOrder::ColMajor> output_matrix(
-      activ_temp_data_int16, fc_output_depth, fc_batches);
-  typedef gemmlowp::VectorMap<const int32, gemmlowp::VectorShape::Col>
-      ColVectorMap;
-  ColVectorMap bias_vector(bias_data_int32, fc_output_depth);
-  gemmlowp::OutputStageBiasAddition<ColVectorMap> bias_addition_stage;
-  bias_addition_stage.bias_vector = bias_vector;
-  gemmlowp::OutputStageScaleInt32ByFixedPointAndExponent scale_stage;
-  scale_stage.result_offset_after_shift = 0;
-  scale_stage.result_fixedpoint_multiplier = accum_multiplier;
-  scale_stage.result_exponent = accum_shift;
-  gemmlowp::OutputStageSaturatingCastToInt16 saturating_cast_int16_stage;
-  auto output_pipeline = std::make_tuple(bias_addition_stage, scale_stage,
-                                         saturating_cast_int16_stage);
-  gemmlowp::GemmWithOutputPipeline<uint8, int16,
-                                   gemmlowp::L8R8WithLhsNonzeroBitDepthParams>(
-      gemm_context, weights_matrix, input_matrix, &output_matrix,
-      -weights_zero_point, -128, output_pipeline);
+  bool gemm_already_performed = false;
+#ifdef GEMMLOWP_NEON
+  if (fc_batches == 1 && !(fc_output_depth % 4) && !(fc_accum_depth % 8)) {
+    GEMVForLstmCell(concat_temp_data_uint8, concat_temp_dims,
+                    weights_data_uint8, weights_dims, weights_zero_point,
+                    bias_data_int32, bias_dims, accum_multiplier, accum_shift,
+                    activ_temp_data_int16, activ_temp_dims);
+    gemm_already_performed = true;
+  }
+#endif
+  if (!gemm_already_performed) {
+    gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::RowMajor>
+        weights_matrix(weights_data_uint8, fc_output_depth, fc_accum_depth);
+    gemmlowp::MatrixMap<const uint8, gemmlowp::MapOrder::ColMajor> input_matrix(
+        concat_temp_data_uint8, fc_accum_depth, fc_batches);
+    gemmlowp::MatrixMap<int16, gemmlowp::MapOrder::ColMajor> output_matrix(
+        activ_temp_data_int16, fc_output_depth, fc_batches);
+    typedef gemmlowp::VectorMap<const int32, gemmlowp::VectorShape::Col>
+        ColVectorMap;
+    ColVectorMap bias_vector(bias_data_int32, fc_output_depth);
+    gemmlowp::OutputStageBiasAddition<ColVectorMap> bias_addition_stage;
+    bias_addition_stage.bias_vector = bias_vector;
+    gemmlowp::OutputStageScaleInt32ByFixedPointAndExponent scale_stage;
+    scale_stage.result_offset_after_shift = 0;
+    scale_stage.result_fixedpoint_multiplier = accum_multiplier;
+    scale_stage.result_exponent = accum_shift;
+    gemmlowp::OutputStageSaturatingCastToInt16 saturating_cast_int16_stage;
+    auto output_pipeline = std::make_tuple(bias_addition_stage, scale_stage,
+                                           saturating_cast_int16_stage);
+    gemmlowp::GemmWithOutputPipeline<
+        uint8, int16, gemmlowp::L8R8WithLhsNonzeroBitDepthParams>(
+        gemm_context, weights_matrix, input_matrix, &output_matrix,
+        -weights_zero_point, -128, output_pipeline);
+  }
 
   // Rest of the LSTM cell: tanh and logistic math functions, and some adds
   // and muls, all done in 16-bit fixed-point.
@@ -2239,15 +2442,13 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
       output_state_data_ptr += 8;
       // Down-scale the output activations to 8-bit integers, saturating,
       // and store back to memory.
-      int16 buf16[8];
-      vst1q_s16(buf16, output_activ_int16.raw());
-      for (int i = 0; i < 8; i++) {
-        int16 rescaled_output_activ =
-            gemmlowp::RoundingDivideByPOT(buf16[i], 8);
-        int16 clamped_output_activ =
-            std::max<int16>(-128, std::min<int16>(127, rescaled_output_activ));
-        *output_activ_data_ptr++ = 128 + clamped_output_activ;
-      }
+      int16x8_t rescaled_output_activ =
+          gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8);
+      int8x8_t int8_output_activ = vqmovn_s16(rescaled_output_activ);
+      uint8x8_t uint8_output_activ =
+          vadd_u8(vdup_n_u8(128), vreinterpret_u8_s8(int8_output_activ));
+      vst1_u8(output_activ_data_ptr, uint8_output_activ);
+      output_activ_data_ptr += 8;
     }
 #endif
     for (; c < output_depth; ++c) {
-- 
GitLab


From ea70fb58f923a2c86ccc14cd38618afdc0dfa1bc Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu <yuanzx@google.com>
Date: Fri, 16 Feb 2018 14:17:13 -0800
Subject: [PATCH 019/575] [XLA] HLO scheduling: update entries in ready queue
 when priority changes.

PiperOrigin-RevId: 186045619
---
 .../compiler/xla/service/hlo_scheduling.cc    | 77 ++++++++++---------
 1 file changed, 41 insertions(+), 36 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_scheduling.cc b/tensorflow/compiler/xla/service/hlo_scheduling.cc
index 8dc4d4f7ba..f6e33403f5 100644
--- a/tensorflow/compiler/xla/service/hlo_scheduling.cc
+++ b/tensorflow/compiler/xla/service/hlo_scheduling.cc
@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/hlo_scheduling.h"
 
-#include <queue>
+#include <map>
 #include <utility>
 #include <vector>
 
@@ -151,8 +151,10 @@ class ListScheduler {
     int64 bytes_defined;
 
     // For each buffer B used by this instruction, we keep a pair (B, U), where
-    // U is the number of uses of B that have not yet been scheduled.
-    std::vector<std::pair<const LogicalBuffer* const, int64>>
+    // U is the number of uses of B that have not yet been scheduled. This pair
+    // is a pointer into the unscheduled_use_count_ map, so it gets updated for
+    // free when we update counts in the map.
+    std::vector<const std::pair<const LogicalBuffer* const, int64>*>
         used_buffer_unscheduled_use_counts;
   };
 
@@ -175,8 +177,8 @@ class ListScheduler {
       }
       auto unscheduled_use_count_it = unscheduled_use_count_.find(buffer);
       CHECK(unscheduled_use_count_it != unscheduled_use_count_.end());
-      entry.used_buffer_unscheduled_use_counts.emplace_back(
-          unscheduled_use_count_it->first, unscheduled_use_count_it->second);
+      entry.used_buffer_unscheduled_use_counts.push_back(
+          &*unscheduled_use_count_it);
     }
     return entry;
   }
@@ -185,8 +187,8 @@ class ListScheduler {
   int64 BytesFreedIfScheduled(const ReadyListEntry& entry) {
     int64 freed_bytes = 0;
     for (const auto& kv : entry.used_buffer_unscheduled_use_counts) {
-      auto buffer = kv.first;
-      auto use_count = kv.second;
+      auto buffer = kv->first;
+      auto use_count = kv->second;
       if (use_count == 1) {
         freed_bytes += size_function_(*buffer);
       }
@@ -217,23 +219,18 @@ class ListScheduler {
       }
     }
 
-    auto priority_comparator =
-        [this](const std::pair<Priority, ReadyListEntry>& lhs,
-               const std::pair<Priority, ReadyListEntry>& rhs) {
-          return lhs.first < rhs.first;
-        };
-    std::priority_queue<std::pair<Priority, ReadyListEntry>,
-                        std::vector<std::pair<Priority, ReadyListEntry>>,
-                        decltype(priority_comparator)>
-        ready_queue(priority_comparator);
+    // Use a multimap to sort ReadyListEntry according to their priority.
+    std::multimap<Priority, ReadyListEntry> ready_queue;
 
-    // Set of instructions in the ready list.
-    tensorflow::gtl::FlatSet<const HloInstruction*> ready_instructions;
+    // Map of ready instructions to their iterators in ready_queue.
+    tensorflow::gtl::FlatMap<const HloInstruction*,
+                             std::multimap<Priority, ReadyListEntry>::iterator>
+        ready_instructions;
 
     auto add_to_ready_queue = [&](HloInstruction* inst) {
       auto entry = MakeReadyListEntry(inst);
-      ready_queue.emplace(GetPriority(entry), std::move(entry));
-      ready_instructions.insert(inst);
+      auto it = ready_queue.emplace(GetPriority(entry), std::move(entry));
+      ready_instructions[inst] = it;
     };
 
     for (auto* instruction : computation_.instructions()) {
@@ -247,14 +244,10 @@ class ListScheduler {
     while (!ready_queue.empty()) {
       // Remove the selected instruction from the ready list and add it to the
       // schedule.
-      const HloInstruction* best = ready_queue.top().second.instruction;
-      ready_queue.pop();
-      // We may have duplicates in the priority queue, because when a ready
-      // instruction's priority goes up, we reinsert it to the priority queue.
-      // Skip the duplicate.
-      if (scheduled_instructions_.find(best) != scheduled_instructions_.end()) {
-        continue;
-      }
+      auto best_it = ready_queue.end();
+      --best_it;
+      const HloInstruction* best = best_it->second.instruction;
+      ready_queue.erase(best_it);
       ready_instructions.erase(best);
       schedule.push_back(best);
       scheduled_instructions_.insert(best);
@@ -287,16 +280,27 @@ class ListScheduler {
         update_pred_count(succ);
       }
       // The unscheduled use count for a buffer has changed to 1, so the
-      // priorities of some ready instructions may go up. We reinsert them to
-      // the priority queue, so that they can appear earlier. The old entries
-      // will become duplicates and will be skipped.
+      // priorities of some ready instructions may go up. We update them in the
+      // ready queue, so that they can appear earlier.
       if (adjust_ready_queue) {
         for (HloInstruction* operand : best->operands()) {
           for (HloInstruction* operand_user : operand->users()) {
-            if (ready_instructions.find(operand_user) !=
-                ready_instructions.end()) {
-              add_to_ready_queue(operand_user);
+            auto ready_instructions_it = ready_instructions.find(operand_user);
+            if (ready_instructions_it == ready_instructions.end()) {
+              continue;
+            }
+            auto ready_queue_it = ready_instructions_it->second;
+            auto& entry = ready_queue_it->second;
+            Priority new_priority = GetPriority(entry);
+            if (new_priority == ready_queue_it->first) {
+              continue;
             }
+            // Create a new entry in ready_queue, then update
+            // ready_instructions[operand_user] to refer to the new entry.
+            ready_instructions_it->second =
+                ready_queue.emplace(new_priority, std::move(entry));
+            // Remove the old entry in ready_queue.
+            ready_queue.erase(ready_queue_it);
           }
         }
       }
@@ -317,8 +321,9 @@ class ListScheduler {
       buffer_uses_;
 
   // A map containing the count of unscheduled HLOs which using a particular
-  // LogicalBuffer.  We rely on iterator stability in this map.
-  tensorflow::gtl::FlatMap<const LogicalBuffer*, int64> unscheduled_use_count_;
+  // LogicalBuffer.  We rely on iterator stability in this map, and that the map
+  // entries are std::pair's.
+  std::unordered_map<const LogicalBuffer*, int64> unscheduled_use_count_;
 
   // Set of instructions which have been scheduled.
   tensorflow::gtl::FlatSet<const HloInstruction*> scheduled_instructions_;
-- 
GitLab


From dd2efdf8cf0346e49592d7a339f09b1862a256c6 Mon Sep 17 00:00:00 2001
From: Reed Wanderman-Milne <reedwm@google.com>
Date: Fri, 16 Feb 2018 14:20:36 -0800
Subject: [PATCH 020/575] Automated g4 rollback of changelist 186018787

PiperOrigin-RevId: 186046129
---
 tensorflow/python/framework/tensor_util.py    |  3 ++-
 .../python/framework/tensor_util_test.py      | 19 +++++++++++++++++++
 2 files changed, 21 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py
index 0e5f696111..27afaa074a 100644
--- a/tensorflow/python/framework/tensor_util.py
+++ b/tensorflow/python/framework/tensor_util.py
@@ -557,7 +557,8 @@ def MakeNdarray(tensor):
   dtype = tensor_dtype.as_numpy_dtype
 
   if tensor.tensor_content:
-    return np.fromstring(tensor.tensor_content, dtype=dtype).reshape(shape)
+    return (np.frombuffer(tensor.tensor_content, dtype=dtype).copy()
+            .reshape(shape))
   elif tensor_dtype == dtypes.float16:
     # the half_val field of the TensorProto stores the binary representation
     # of the fp16: we need to reinterpret this as a proper float16
diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py
index f2de69e159..bea0ee34fd 100644
--- a/tensorflow/python/framework/tensor_util_test.py
+++ b/tensorflow/python/framework/tensor_util_test.py
@@ -199,6 +199,25 @@ class TensorUtilTest(test.TestCase):
               dtype=nptype),
           a)
 
+  def testFloatMutateArray(self):
+    t = tensor_util.make_tensor_proto([10.0, 20.0, 30.0], dtype=dtypes.float32)
+    a = tensor_util.MakeNdarray(t)
+    a[0] = 5.0
+    self.assertEquals(np.float32, a.dtype)
+    self.assertAllClose(np.array([5.0, 20.0, 30.0], dtype=np.float32), a)
+    if sys.byteorder == "big":
+      self.assertProtoEquals("""
+        dtype: DT_FLOAT
+        tensor_shape { dim { size: 3 } }
+        tensor_content: "A \000\000A\240\000\000A\360\000\000"
+        """, t)
+    else:
+      self.assertProtoEquals("""
+        dtype: DT_FLOAT
+        tensor_shape { dim { size: 3 } }
+        tensor_content: "\000\000 A\000\000\240A\000\000\360A"
+        """, t)
+
   def testHalf(self):
     t = tensor_util.make_tensor_proto(np.array([10.0, 20.0], dtype=np.float16))
     self.assertProtoEquals("""
-- 
GitLab


From 13df417665f216bfb527440f1fd8f04958000ec5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 14:34:16 -0800
Subject: [PATCH 021/575] [TF:XLA] Adds HostCompute HLO - a pseudo-op to
 represent host-side computation.

PiperOrigin-RevId: 186047964
---
 .../xla/client/computation_builder.cc         | 14 +++++
 .../compiler/xla/client/computation_builder.h | 10 ++++
 .../compiler/xla/service/dfs_hlo_visitor.h    |  1 +
 .../service/dfs_hlo_visitor_with_default.h    |  3 ++
 .../compiler/xla/service/hlo_cost_analysis.cc |  4 ++
 .../compiler/xla/service/hlo_cost_analysis.h  |  1 +
 .../compiler/xla/service/hlo_graph_dumper.cc  |  1 +
 .../compiler/xla/service/hlo_instruction.cc   | 21 ++++++++
 .../compiler/xla/service/hlo_instruction.h    | 12 +++++
 tensorflow/compiler/xla/service/hlo_opcode.h  |  1 +
 .../compiler/xla/service/hlo_verifier.cc      |  4 ++
 .../compiler/xla/service/hlo_verifier.h       |  1 +
 .../xla/service/instruction_fusion.cc         |  1 +
 tensorflow/compiler/xla/service/service.cc    |  5 ++
 .../compiler/xla/service/user_computation.cc  | 52 +++++++++++++++++++
 .../compiler/xla/service/user_computation.h   |  4 ++
 .../compiler/xla/tools/parser/hlo_parser.cc   | 14 +++++
 tensorflow/compiler/xla/xla_data.proto        | 17 +++++-
 18 files changed, 165 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index b1dcad6a49..e6dfe0aefb 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -789,6 +789,20 @@ ComputationDataHandle ComputationBuilder::CustomCall(
   return RunOpAndParseResponse(&op_request);
 }
 
+ComputationDataHandle ComputationBuilder::HostCompute(
+    tensorflow::gtl::ArraySlice<ComputationDataHandle> operands,
+    const string& channel_name, int64 cost_estimate_ns, const Shape& shape) {
+  OpRequest op_request;
+  HostComputeRequest* request = op_request.mutable_host_compute_request();
+  for (const ComputationDataHandle& operand : operands) {
+    *request->add_operands() = operand;
+  }
+  *request->mutable_shape() = shape;
+  request->set_channel_name(channel_name);
+  request->set_cost_estimate_ns(cost_estimate_ns);
+  return RunOpAndParseResponse(&op_request);
+}
+
 ComputationDataHandle ComputationBuilder::Complex(
     const ComputationDataHandle& real, const ComputationDataHandle& imag,
     tensorflow::gtl::ArraySlice<int64> broadcast_dimensions) {
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 7cae91e9e0..aa2622174d 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -446,6 +446,16 @@ class ComputationBuilder {
       tensorflow::gtl::ArraySlice<ComputationDataHandle> operands,
       const Shape& shape);
 
+  // Enqueues a pseudo-op to represent host-side computation data-dependencies.
+  // During code generation, host send and receive operations will be generated
+  // to transfer |operands| to the host and a single result of |shape| back to
+  // the device.  Host send/recv operations are emitted using |channel_name|.
+  // Dataflow dependencies and the |cost_estimate_ns| field may be used in HLO
+  // instruction scheduling.
+  ComputationDataHandle HostCompute(
+      tensorflow::gtl::ArraySlice<ComputationDataHandle> operands,
+      const string& channel_name, int64 cost_estimate_ns, const Shape& shape);
+
   // The following methods enqueue element-wise binary arithmetic operations
   // onto the computation. The shapes of the operands have to match unless one
   // of the operands is a scalar, or an explicit broadcast dimension is given
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index a803b3171f..5b09e4931e 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -190,6 +190,7 @@ class DfsHloVisitorBase {
 
   virtual Status HandleInfeed(HloInstructionPtr hlo) = 0;
   virtual Status HandleOutfeed(HloInstructionPtr hlo) = 0;
+  virtual Status HandleHostCompute(HloInstructionPtr hlo) = 0;
   virtual Status HandleRng(HloInstructionPtr hlo) = 0;
   virtual Status HandleReverse(HloInstructionPtr hlo) = 0;
   virtual Status HandleSort(HloInstructionPtr hlo) = 0;
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
index 170adb3d24..ffc4f3bb79 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
@@ -103,6 +103,9 @@ class DfsHloVisitorWithDefaultBase
   Status HandleOutfeed(HloInstructionPtr outfeed) override {
     return DefaultAction(outfeed);
   }
+  Status HandleHostCompute(HloInstructionPtr host_compute) override {
+    return DefaultAction(host_compute);
+  }
   Status HandleReverse(HloInstructionPtr reverse) override {
     return DefaultAction(reverse);
   }
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index 9cd5a1e2b7..6a4651d83f 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -229,6 +229,10 @@ Status HloCostAnalysis::HandleOutfeed(const HloInstruction*) {
   return Status::OK();
 }
 
+Status HloCostAnalysis::HandleHostCompute(const HloInstruction*) {
+  return Status::OK();
+}
+
 Status HloCostAnalysis::HandleMap(const HloInstruction* map) {
   // Compute properties of the mapped function.
   TF_ASSIGN_OR_RETURN(const Properties sub_properties,
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index e5783539e5..af52ea06ca 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -71,6 +71,7 @@ class HloCostAnalysis : public ConstDfsHloVisitor {
   Status HandleCrossReplicaSum(const HloInstruction* crs) override;
   Status HandleInfeed(const HloInstruction* infeed) override;
   Status HandleOutfeed(const HloInstruction* outfeed) override;
+  Status HandleHostCompute(const HloInstruction* host_compute) override;
   Status HandleRng(const HloInstruction* random) override;
   Status HandleReverse(const HloInstruction* reverse) override;
   Status HandleSort(const HloInstruction* sort) override;
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 44fcd36370..9b0e2fd7d6 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -988,6 +988,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) {
     case HloOpcode::kCall:
     case HloOpcode::kConditional:
     case HloOpcode::kCustomCall:
+    case HloOpcode::kHostCompute:
     case HloOpcode::kWhile:
       return kDarkGreen;
     case HloOpcode::kConstant:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index d719ff857d..0d925ad00d 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1094,6 +1094,7 @@ bool HloInstruction::HasSideEffect() const {
     case HloOpcode::kInfeed:
     case HloOpcode::kOutfeed:
     case HloOpcode::kTrace:
+    case HloOpcode::kHostCompute:
       return true;
     default: {
       // Check if any of the called computations has a side effect.
@@ -1131,6 +1132,19 @@ bool HloInstruction::HasSideEffect() const {
   return instruction;
 }
 
+/* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateHostCompute(
+    const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
+    tensorflow::StringPiece channel_name, const int64 cost_estimate_ns) {
+  std::unique_ptr<HloInstruction> instruction =
+      WrapUnique(new HloInstruction(HloOpcode::kHostCompute, shape));
+  for (auto operand : operands) {
+    instruction->AppendOperand(operand);
+  }
+  instruction->channel_name_ = channel_name.ToString();
+  instruction->cost_estimate_ns_ = cost_estimate_ns;
+  return instruction;
+}
+
 /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateTuple(
     tensorflow::gtl::ArraySlice<HloInstruction*> elements) {
   std::vector<Shape> element_shapes;
@@ -1222,6 +1236,10 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
     case HloOpcode::kCustomCall:
       clone = CreateCustomCall(shape, new_operands, custom_call_target_);
       break;
+    case HloOpcode::kHostCompute:
+      clone = CreateHostCompute(shape, new_operands, channel_name_,
+                                cost_estimate_ns_);
+      break;
     case HloOpcode::kConcatenate:
       clone = CreateConcatenate(shape, new_operands, dimensions(0));
       break;
@@ -1792,6 +1810,7 @@ bool HloInstruction::IdenticalSlowPath(
     case HloOpcode::kRecvDone:
     case HloOpcode::kSend:
     case HloOpcode::kSendDone:
+    case HloOpcode::kHostCompute:
       return false;
   }
 }
@@ -2577,6 +2596,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase<HloInstructionPtr>* visitor) {
       return visitor->HandleInfeed(this);
     case HloOpcode::kOutfeed:
       return visitor->HandleOutfeed(this);
+    case HloOpcode::kHostCompute:
+      return visitor->HandleHostCompute(this);
     case HloOpcode::kRng:
       return visitor->HandleRng(this);
     case HloOpcode::kWhile:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 3cf43f0adf..e898a83739 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -475,6 +475,12 @@ class HloInstruction {
       const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
       tensorflow::StringPiece custom_call_target);
 
+  // Creates a HostCompute instruction, which records host-side control and
+  // data dependencies for use in instruction scheduling.
+  static std::unique_ptr<HloInstruction> CreateHostCompute(
+      const Shape& shape, tensorflow::gtl::ArraySlice<HloInstruction*> operands,
+      tensorflow::StringPiece channel_name, const int64 cost_estimate_ns);
+
   // Creates a tuple instruction with the given elements. This is a convenience
   // wrapper around CreateVariadic.
   static std::unique_ptr<HloInstruction> CreateTuple(
@@ -1398,6 +1404,12 @@ class HloInstruction {
   // Name of a global symbol to call, only present for kCustomCall.
   string custom_call_target_;
 
+  // Name to use for host send/recv channels, only present for kHostCompute.
+  string channel_name_;
+
+  // Estimate of the duration of a host computation in nanoseconds.
+  int64 cost_estimate_ns_;
+
   // Computations called by this instruction.
   std::vector<HloComputation*> called_computations_;
 
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index 3d64523a79..088dd15dbf 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -79,6 +79,7 @@ namespace xla {
   V(kGe, "greater-than-or-equal-to", kHloOpcodeIsComparison) \
   V(kGetTupleElement, "get-tuple-element")                   \
   V(kGt, "greater-than", kHloOpcodeIsComparison)             \
+  V(kHostCompute, "host-compute")                            \
   V(kImag, "imag")                                           \
   V(kInfeed, "infeed")                                       \
   V(kIsFinite, "is-finite")                                  \
diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc
index e2b3bb9d71..f3378309c2 100644
--- a/tensorflow/compiler/xla/service/hlo_verifier.cc
+++ b/tensorflow/compiler/xla/service/hlo_verifier.cc
@@ -125,6 +125,10 @@ Status ShapeVerifier::HandleOutfeed(HloInstruction* outfeed) {
   return CheckShape(outfeed, ShapeUtil::MakeNil());
 }
 
+Status ShapeVerifier::HandleHostCompute(HloInstruction*) {
+  return tensorflow::Status::OK();
+}
+
 Status ShapeVerifier::HandleRng(HloInstruction*) {
   return tensorflow::Status::OK();
 }
diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h
index 7eccf834bb..f9f898c236 100644
--- a/tensorflow/compiler/xla/service/hlo_verifier.h
+++ b/tensorflow/compiler/xla/service/hlo_verifier.h
@@ -60,6 +60,7 @@ class ShapeVerifier : public DfsHloVisitor {
   Status HandleFusion(HloInstruction*) override;
   Status HandleCall(HloInstruction* call) override;
   Status HandleCustomCall(HloInstruction*) override;
+  Status HandleHostCompute(HloInstruction*) override;
   Status HandleSlice(HloInstruction* slice) override;
   Status HandleDynamicSlice(HloInstruction* dynamic_slice) override;
   Status HandleDynamicUpdateSlice(
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index 90e1f0acdc..f08d809d79 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -102,6 +102,7 @@ namespace xla {
     case HloOpcode::kExp:
     case HloOpcode::kFft:
     case HloOpcode::kFusion:
+    case HloOpcode::kHostCompute:
     case HloOpcode::kLog:
     case HloOpcode::kMap:
     case HloOpcode::kParameter:
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 98dfc89867..95c853b5c4 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -44,6 +44,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
@@ -1456,6 +1457,10 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) {
       handle_status =
           computation->AddOutfeedInstruction(arg->outfeed_request());
       break;
+    case OpRequest::kHostComputeRequest:
+      handle_status =
+          computation->AddHostComputeInstruction(arg->host_compute_request());
+      break;
     case OpRequest::kMapRequest: {
       TF_ASSIGN_OR_RETURN(
           UserComputation * to_apply,
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index fead9b9236..d42cb6cdf3 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -1276,6 +1276,28 @@ StatusOr<ComputationDataHandle> UserComputation::AddCustomCallInstruction(
   return handle;
 }
 
+StatusOr<ComputationDataHandle> UserComputation::AddHostComputeInstruction(
+    const HostComputeRequest& host_compute_request) {
+  tensorflow::mutex_lock lock(mutex_);
+
+  for (const ComputationDataHandle& handle : host_compute_request.operands()) {
+    TF_RETURN_IF_ERROR(LookUpRequest(handle).status());
+  }
+
+  ComputationDataHandle handle = CreateComputationDataHandle();
+  OperationRequest& request =
+      (*session_computation_.mutable_requests())[handle.handle()];
+  *request.mutable_output_handle() = handle;
+  *request.mutable_output_shape() = host_compute_request.shape();
+  *request.mutable_request()->mutable_host_compute_request() =
+      host_compute_request;
+
+  VLOG(1) << "AddHostComputeInstruction (" << GetVersionedHandleInternal()
+          << "), data handle " << handle.handle() << ": "
+          << host_compute_request.ShortDebugString();
+  return handle;
+}
+
 StatusOr<ComputationDataHandle> UserComputation::AddDotInstruction(
     const DotRequest& dot_request) {
   tensorflow::mutex_lock lock(mutex_);
@@ -1713,6 +1735,11 @@ void PureFunctionalVisitor(const SessionComputation& session_computation,
       break;
     }
 
+    case OpRequest::kHostComputeRequest: {
+      *is_functional = false;
+      break;
+    }
+
     case OpRequest::kCallRequest: {
       const CallRequest& call_request = request.request().call_request();
       for (const ComputationDataHandle& handle : call_request.operands()) {
@@ -2643,6 +2670,15 @@ static void ForEachOperand(
       break;
     }
 
+    case OpRequest::kHostComputeRequest: {
+      const HostComputeRequest& hc_request =
+          request.request().host_compute_request();
+      for (const ComputationDataHandle& operand : hc_request.operands()) {
+        apply(operand);
+      }
+      break;
+    }
+
     case OpRequest::kDotRequest: {
       const DotRequest& dot_request = request.request().dot_request();
       apply(dot_request.rhs());
@@ -3299,6 +3335,22 @@ void ComputationLowerer::Visit(
       break;
     }
 
+    case OpRequest::kHostComputeRequest: {
+      const HostComputeRequest& host_compute_request =
+          request.request().host_compute_request();
+      std::vector<HloInstruction*> operands;
+      for (const ComputationDataHandle& operand :
+           host_compute_request.operands()) {
+        operands.push_back(lookup_instruction(operand));
+      }
+      auto output_shape = host_compute_request.shape();
+      auto channel_name = host_compute_request.channel_name();
+      auto cost_estimate_ns = host_compute_request.cost_estimate_ns();
+      hlo_instruction = add_instruction(HloInstruction::CreateHostCompute(
+          output_shape, operands, channel_name, cost_estimate_ns));
+      break;
+    }
+
     case OpRequest::kUnaryOpRequest: {
       const UnaryOpRequest& unary_op_request =
           request.request().unary_op_request();
diff --git a/tensorflow/compiler/xla/service/user_computation.h b/tensorflow/compiler/xla/service/user_computation.h
index 54bb24d6d7..81a72583f7 100644
--- a/tensorflow/compiler/xla/service/user_computation.h
+++ b/tensorflow/compiler/xla/service/user_computation.h
@@ -149,6 +149,10 @@ class UserComputation {
   StatusOr<ComputationDataHandle> AddOutfeedInstruction(
       const OutfeedRequest& outfeed_request);
 
+  // Enqueues a host compute instruction onto this user computation.
+  StatusOr<ComputationDataHandle> AddHostComputeInstruction(
+      const HostComputeRequest& host_compute_request);
+
   // Enqueues a call instruction onto this user computation.
   StatusOr<ComputationDataHandle> AddCallInstruction(
       const CallRequest& call_request,
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index 89def5d561..5dd5780835 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -994,6 +994,20 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder,
           shape, operands, *custom_call_target));
       break;
     }
+    case HloOpcode::kHostCompute: {
+      optional<string> channel_name;
+      optional<int64> cost_estimate_ns;
+      attrs["channel_name"] = {/*required=*/true, AttrTy::kString,
+                               &channel_name};
+      attrs["cost_estimate_ns"] = {/*required=*/true, AttrTy::kInt64,
+                                   &cost_estimate_ns};
+      if (!ParseOperands(&operands) || !ParseAttributes(attrs)) {
+        return false;
+      }
+      instruction = builder->AddInstruction(HloInstruction::CreateHostCompute(
+          shape, operands, *channel_name, *cost_estimate_ns));
+      break;
+    }
     case HloOpcode::kDot: {
       optional<std::vector<int64>> lhs_contracting_dims;
       attrs["lhs_contracting_dims"] = {
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 3aea021753..4fa5d28211 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -519,6 +519,20 @@ message CustomCallRequest {
   Shape shape = 4;
 }
 
+message HostComputeRequest {
+  // Operand to the HostCompute. Supports tuple.
+  repeated ComputationDataHandle operands = 1;
+
+  // Name used to identify HostSend/Recv channels.
+  string channel_name = 2;
+
+  // Cost estimate in nanoseconds.
+  int64 cost_estimate_ns = 3;
+
+  // The shape of any data returned by host.
+  Shape shape = 4;
+}
+
 message DotDimensionNumbers {
   // The dimension numbers that represent the 'lhs' contracting dimensions.
   repeated int64 lhs_contracting_dimensions = 1;
@@ -957,7 +971,8 @@ message OpRequest {
     FftRequest fft_request = 41;
     ConvertRequest bitcast_convert_request = 42;
     ConditionalRequest conditional_request = 44;
-    // Next: 45
+    HostComputeRequest host_compute_request = 45;
+    // Next: 46
   }
 }
 
-- 
GitLab


From 0b3b795b521a487159fd51c075a777d43efc9c1d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 14:39:04 -0800
Subject: [PATCH 022/575] Changed FTRL formula for scalars to match vector
 version better.

PiperOrigin-RevId: 186048665
---
 tensorflow/core/kernels/training_ops.cc | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/kernels/training_ops.cc b/tensorflow/core/kernels/training_ops.cc
index 07befa27bc..233aa03c32 100644
--- a/tensorflow/core/kernels/training_ops.cc
+++ b/tensorflow/core/kernels/training_ops.cc
@@ -1228,11 +1228,8 @@ inline T FtrlCompute(const T& accum, const T& linear, const T& lr, const T& l1,
     quadratic =
         Eigen::numext::pow(accum, -lr_power) / lr + static_cast<T>(2) * l2;
   }
-  if (Eigen::numext::abs(linear) > l1) {
-    return (l1 * sgn(linear) - linear) / quadratic;
-  } else {
-    return static_cast<T>(0.0);
-  }
+  auto l1_reg_adjust = std::max(std::min(linear, l1), -l1);
+  return (l1_reg_adjust - linear) / quadratic;
 }
 }  // namespace
 
-- 
GitLab


From 670b6b4b177566faf3df967b8ce009d726238549 Mon Sep 17 00:00:00 2001
From: Billy Lamberta <blamb@google.com>
Date: Fri, 16 Feb 2018 14:42:19 -0800
Subject: [PATCH 023/575] Fix crop on images in datasets_performance guide.

PiperOrigin-RevId: 186049156
---
 tensorflow/docs_src/performance/datasets_performance.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/docs_src/performance/datasets_performance.md b/tensorflow/docs_src/performance/datasets_performance.md
index 4f95e17c35..46b43b7673 100644
--- a/tensorflow/docs_src/performance/datasets_performance.md
+++ b/tensorflow/docs_src/performance/datasets_performance.md
@@ -92,11 +92,11 @@ transform the data.
 
 Without pipelining, the CPU and the GPU/TPU sit idle much of the time:
 
-![without pipelining](https://www.tensorflow.org/images/datasets_without_pipelining.png)
+![without pipelining](/images/datasets_without_pipelining.png)
 
 With pipelining, idle time diminishes significantly:
 
-![with pipelining](https://www.tensorflow.org/images/datasets_with_pipelining.png)
+![with pipelining](/images/datasets_with_pipelining.png)
 
 The `tf.data` API provides a software pipelining mechanism through the
 @{tf.data.Dataset.prefetch} transformation, which can be used to decouple the
@@ -139,7 +139,7 @@ multiple CPU cores. To make this possible, the `map` transformation provides the
 the following diagram illustrates the effect of setting `num_parallel_calls=2`
 to the `map` transformation:
 
-![parallel map](https://www.tensorflow.org/images/datasets_parallel_map.png)
+![parallel map](/images/datasets_parallel_map.png)
 
 Choosing the best value for the `num_parallel_calls` argument depends on your
 hardware, characteristics of your training data (such as its size and shape),
@@ -213,7 +213,7 @@ number of datasets to overlap can be specified by the `cycle_length` argument.
 The following diagram illustrates the effect of supplying `cycle_length=2` to
 the `parallel_interleave` transformation:
 
-![parallel io](https://www.tensorflow.org/images/datasets_parallel_io.png)
+![parallel io](/images/datasets_parallel_io.png)
 
 To apply this change to our running example, change:
 
-- 
GitLab


From 970d2e2d9bcfabe66c8eb8e96f5a3a4972d05ef1 Mon Sep 17 00:00:00 2001
From: Billy Lamberta <blamb@google.com>
Date: Fri, 16 Feb 2018 14:52:36 -0800
Subject: [PATCH 024/575] Fix sentence in Getting Started for ML Beginners
 guide.

PiperOrigin-RevId: 186050529
---
 .../docs_src/get_started/get_started_for_beginners.md    | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/tensorflow/docs_src/get_started/get_started_for_beginners.md b/tensorflow/docs_src/get_started/get_started_for_beginners.md
index 069f5c13dd..e680234d4d 100644
--- a/tensorflow/docs_src/get_started/get_started_for_beginners.md
+++ b/tensorflow/docs_src/get_started/get_started_for_beginners.md
@@ -91,11 +91,10 @@ a number.  Here's the representation scheme:
 
 A **model** is the relationship between features
 and the label.  For the Iris problem, the model defines the relationship
-between the sepal and petal measurements and the Iris species.
-Some simple models can be described with a few lines of algebra;
-more complex machine learning models
-contain such a large number of interlacing mathematical functions and
-parameters that they become hard to summarize mathematically.
+between the sepal and petal measurements and the predicted Iris species. Some
+simple models can be described with a few lines of algebra, but complex machine
+learning models have a large number of parameters that are difficult to
+summarize.
 
 Could you determine the relationship between the four features and the
 Iris species *without* using machine learning?  That is, could you use
-- 
GitLab


From f89350986422c3ae754738660ff555e5e102e3d4 Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Fri, 16 Feb 2018 14:55:43 -0800
Subject: [PATCH 025/575] Add a `hash_keys` argument to the sparse hash column
 to enable it hash a single input to multiple hash ids. This column can be
 then used by one_hot_column to create a multi-hot column.

PiperOrigin-RevId: 186050928
---
 .../layers/python/layers/feature_column.py    | 59 +++++++++++++++++--
 .../python/layers/feature_column_test.py      | 49 +++++++++++++++
 2 files changed, 102 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index b7d34d6435..9ccb589d69 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -154,6 +154,7 @@ from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import deprecation
+from tensorflow.python.util import nest
 
 
 # Imports the core `InputLayer` symbol in contrib during development.
@@ -554,28 +555,70 @@ def sparse_column_with_integerized_feature(column_name,
 class _SparseColumnHashed(_SparseColumn):
   """See `sparse_column_with_hash_bucket`."""
 
+  def __new__(cls,
+              column_name,
+              is_integerized=False,
+              bucket_size=None,
+              lookup_config=None,
+              combiner="sum",
+              dtype=dtypes.string,
+              hash_keys=None):
+    if hash_keys is not None:
+      if not isinstance(hash_keys, list) or not hash_keys:
+        raise ValueError("hash_keys must be a non-empty list.")
+      if (any([not isinstance(key_pair, list) for key_pair in hash_keys]) or
+          any([len(key_pair) != 2 for key_pair in hash_keys]) or
+          any([not isinstance(key, int) for key in nest.flatten(hash_keys)])):
+        raise ValueError(
+            "Each element of hash_keys must be a pair of integers.")
+    obj = super(_SparseColumnHashed, cls).__new__(
+        cls,
+        column_name,
+        is_integerized=is_integerized,
+        bucket_size=bucket_size,
+        lookup_config=lookup_config,
+        combiner=combiner,
+        dtype=dtype)
+    obj.hash_keys = hash_keys
+    return obj
+
   def _do_transform(self, input_tensor):
     if self.dtype.is_integer:
       sparse_values = string_ops.as_string(input_tensor.values)
     else:
       sparse_values = input_tensor.values
 
-    sparse_id_values = string_ops.string_to_hash_bucket_fast(
-        sparse_values, self.bucket_size, name="lookup")
-    return sparse_tensor_py.SparseTensor(input_tensor.indices, sparse_id_values,
-                                         input_tensor.dense_shape)
+    if self.hash_keys:
+      result = []
+      for key in self.hash_keys:
+        sparse_id_values = string_ops.string_to_hash_bucket_strong(
+            sparse_values, self.bucket_size, key)
+        result.append(
+            sparse_tensor_py.SparseTensor(input_tensor.indices,
+                                          sparse_id_values,
+                                          input_tensor.dense_shape))
+      return sparse_ops.sparse_concat(axis=1, sp_inputs=result, name="lookup")
+    else:
+      sparse_id_values = string_ops.string_to_hash_bucket_fast(
+          sparse_values, self.bucket_size, name="lookup")
+      return sparse_tensor_py.SparseTensor(
+          input_tensor.indices, sparse_id_values, input_tensor.dense_shape)
 
 
 def sparse_column_with_hash_bucket(column_name,
                                    hash_bucket_size,
                                    combiner="sum",
-                                   dtype=dtypes.string):
+                                   dtype=dtypes.string,
+                                   hash_keys=None):
   """Creates a _SparseColumn with hashed bucket configuration.
 
   Use this when your sparse features are in string or integer format, but you
   don't have a vocab file that maps each value to an integer ID.
   output_id = Hash(input_feature_string) % bucket_size
 
+  When hash_keys is set, multiple integer IDs would be created with each key
+  pair in the `hash_keys`. This is useful to reduce the collision of hashed ids.
+
   Args:
     column_name: A string defining sparse column name.
     hash_bucket_size: An int that is > 1. The number of buckets.
@@ -588,6 +631,9 @@ def sparse_column_with_hash_bucket(column_name,
         * "sqrtn": do l2 normalization on features in the column
       For more information: `tf.embedding_lookup_sparse`.
     dtype: The type of features. Only string and integer types are supported.
+    hash_keys: The hash keys to use. It is a list of lists of two uint64s. If
+      None, simple and fast hashing algorithm is used. Otherwise, multiple
+      strong hash ids would be produced with each two unit64s in this argument.
 
   Returns:
     A _SparseColumn with hashed bucket configuration
@@ -600,7 +646,8 @@ def sparse_column_with_hash_bucket(column_name,
       column_name,
       bucket_size=hash_bucket_size,
       combiner=combiner,
-      dtype=dtype)
+      dtype=dtype,
+      hash_keys=hash_keys)
 
 
 class _SparseColumnKeys(_SparseColumn):
diff --git a/tensorflow/contrib/layers/python/layers/feature_column_test.py b/tensorflow/contrib/layers/python/layers/feature_column_test.py
index fc8f153fe3..1de9ab7056 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_test.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_test.py
@@ -329,6 +329,55 @@ class FeatureColumnTest(test.TestCase):
     self.assertEqual(one_hot.sparse_id_column.name, "ids_weighted_by_weights")
     self.assertEqual(one_hot.length, 3)
 
+  def testOneHotColumnWithSparseColumnWithHashKeys(self):
+    input_values = ["marlo", "unknown", "omar"]
+    inputs = constant_op.constant(input_values)
+    hash_keys = [[10, 20], [20, 30]]
+    hash_column = fc.sparse_column_with_hash_bucket(
+        column_name="ids", hash_bucket_size=10, hash_keys=hash_keys)
+    columns_to_tensors = {}
+    columns_to_tensors["ids"] = inputs
+    hash_column.insert_transformed_feature(columns_to_tensors)
+    self.assertEqual(len(columns_to_tensors), 2)
+    self.assertTrue(hash_column in columns_to_tensors)
+
+    one_hot_column = fc.one_hot_column(hash_column)
+    one_hot_output = one_hot_column._to_dnn_input_layer(
+        columns_to_tensors[hash_column])
+
+    expected = np.array([[0., 1., 0., 0., 0., 0., 0., 1., 0.,
+                          0.], [0., 1., 0., 0., 0., 0., 0., 0., 0., 1.],
+                         [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
+    with self.test_session() as sess:
+      one_hot_value = sess.run(one_hot_output)
+    self.assertTrue(np.array_equal(one_hot_value, expected))
+
+  def testSparseColumnWithHashKeysWithUnexpectedHashKeys(self):
+    with self.assertRaisesRegexp(ValueError,
+                                 "hash_keys must be a non-empty list."):
+      fc.sparse_column_with_hash_bucket(
+          column_name="ids", hash_bucket_size=100, hash_keys=[])
+
+    with self.assertRaisesRegexp(ValueError,
+                                 "hash_keys must be a non-empty list."):
+      fc.sparse_column_with_hash_bucket(
+          column_name="ids", hash_bucket_size=100, hash_keys=1)
+
+    with self.assertRaisesRegexp(
+        ValueError, "Each element of hash_keys must be a pair of integers."):
+      fc.sparse_column_with_hash_bucket(
+          column_name="ids", hash_bucket_size=100, hash_keys=[1, 2])
+
+    with self.assertRaisesRegexp(
+        ValueError, "Each element of hash_keys must be a pair of integers."):
+      fc.sparse_column_with_hash_bucket(
+          column_name="ids", hash_bucket_size=100, hash_keys=["key"])
+
+    with self.assertRaisesRegexp(
+        ValueError, "Each element of hash_keys must be a pair of integers."):
+      fc.sparse_column_with_hash_bucket(
+          column_name="ids", hash_bucket_size=100, hash_keys=[[1, 2.0]])
+
   def testMissingValueInOneHotColumnForWeightedSparseColumn(self):
     # Github issue 12583
     ids = fc.sparse_column_with_keys("ids", ["marlo", "omar", "stringer"])
-- 
GitLab


From 17f9c60bc422d2db30ba440bcc632ea9cad9826a Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Fri, 16 Feb 2018 15:02:22 -0800
Subject: [PATCH 026/575] Add support for explicit `training` argument in
 subclassed models.

PiperOrigin-RevId: 186051752
---
 .../keras/_impl/keras/engine/topology.py      | 10 ++-
 .../keras/_impl/keras/engine/training.py      | 82 +++++++++++++++++--
 .../_impl/keras/engine/training_eager.py      | 36 +++++---
 .../_impl/keras/model_subclassing_test.py     | 37 ++++++++-
 4 files changed, 145 insertions(+), 20 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/engine/topology.py b/tensorflow/python/keras/_impl/keras/engine/topology.py
index dd7436e3d0..7de5af41c5 100644
--- a/tensorflow/python/keras/_impl/keras/engine/topology.py
+++ b/tensorflow/python/keras/_impl/keras/engine/topology.py
@@ -39,6 +39,7 @@ from tensorflow.python.layers import base as tf_base_layers
 from tensorflow.python.layers import network as tf_network
 from tensorflow.python.layers import utils as tf_layers_util
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -263,7 +264,7 @@ class Layer(tf_base_layers.Layer):
 
     # Un-built subclassed network: build it
     if isinstance(self, Network) and not self.inputs:
-      self._set_inputs(inputs)
+      self._set_inputs(inputs, training=kwargs.get('training'))
 
     # Update learning phase info.
     output_tensors = _to_list(output)
@@ -702,6 +703,8 @@ class Network(tf_network.GraphNetwork, Layer):
     super(Network, self).__init__(inputs, outputs, name=name)
 
     self._is_compiled = False
+    self._expects_training_arg = False
+
     self.supports_masking = False
     self.optimizer = None
 
@@ -744,6 +747,11 @@ class Network(tf_network.GraphNetwork, Layer):
     self._layers = []
     self._is_graph_network = False
     self._is_compiled = False
+    if 'training' in tf_inspect.getargspec(self.call).args:
+      self._expects_training_arg = True
+    else:
+      self._expects_training_arg = False
+
     self.outputs = None
     self.inputs = None
     self.trainable = True
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index fd14bf3d05..d8ea2fe3db 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -515,7 +515,65 @@ def _standardize_weights(y,
 
 @tf_export('keras.models.Model', 'keras.Model')
 class Model(Network):
-  """The `Model` class adds training & evaluation routines to a `Network`.
+  """`Model` groups layers into an object with training and inference features.
+
+  There are two ways to instantiate a `Model`:
+
+  1 - With the "functional API", where you start from `Input`,
+  you chain layer calls to specify the model's forward pass,
+  and finally you create your model from inputs and outputs:
+
+  ```python
+  import tensorflow as tf
+
+  inputs = tf.keras.Input(shape=(3,))
+  x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
+  outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
+  model = tf.keras.Model(inputs=inputs, outputs=outputs)
+  ```
+
+  2 - By subclassing the `Model` class: in that case, you should define your
+  layers in `__init__` and you should implement the model's forward pass
+  in `call`.
+
+  ```python
+  import tensorflow as tf
+
+  class MyModel(tf.keras.Model):
+
+    def __init__(self):
+      self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
+      self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
+
+    def call(self, inputs):
+      x = self.dense1(inputs)
+      return self.dense2(x)
+
+  model = MyModel()
+  ```
+
+  If you subclass `Model`, you can optionally have
+  a `training` argument (boolean) in `call`, which you can use to specify
+  a different behavior in training and inference:
+
+  ```python
+  import tensorflow as tf
+
+  class MyModel(tf.keras.Model):
+
+    def __init__(self):
+      self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
+      self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
+      self.dropout = tf.keras.layers.Dropout(0.5)
+
+    def call(self, inputs, training=False):
+      x = self.dense1(inputs)
+      if training:
+        x = self.dropout(x, training=training)
+      return self.dense2(x)
+
+  model = MyModel()
+  ```
   """
 
   def compile(self,
@@ -1709,7 +1767,7 @@ class Model(Network):
                          str(x[0].shape[0]) + ' samples')
     return x, y, sample_weights
 
-  def _set_inputs(self, inputs):
+  def _set_inputs(self, inputs, training=None):
     """Set model's input and output specs based on the input data received.
 
     This is to be used for Model subclasses, which do not know at instantiation
@@ -1725,11 +1783,14 @@ class Model(Network):
           when calling `fit`/etc.
         - if data tensors: the model is built on top of these tensors.
           We do not expect any Numpy data to be provided when calling `fit`/etc.
+      training: Boolean or None. Only relevant in symbolic mode. Specifies
+        whether to build the model's graph in inference mode (False), training
+        mode (True), or using the Keras learning phase (None).
     """
     if context.in_eager_mode():
       self._eager_set_inputs(inputs)
     else:
-      self._symbolic_set_inputs(inputs)
+      self._symbolic_set_inputs(inputs, training=training)
 
   def _eager_set_inputs(self, inputs):
     """Set model's input and output specs based on the input data received.
@@ -1775,7 +1836,7 @@ class Model(Network):
         'output_%d' % (i + 1) for i in range(len(dummy_output_values))]
     self.built = True
 
-  def _symbolic_set_inputs(self, inputs):
+  def _symbolic_set_inputs(self, inputs, training=None):
     """Set model's inputs based on the input data received from the user.
 
     This is to be used for Model subclasses, which do not know at instantiation
@@ -1783,6 +1844,9 @@ class Model(Network):
 
     Args:
       inputs: Argument `x` (input data) passed by the user upon first model use.
+      training: Boolean or None. Only relevant in symbolic mode. Specifies
+        whether to build the model's graph in inference mode (False), training
+        mode (True), or using the Keras learning phase (None).
 
     Raises:
       ValueError: If the model's inputs are already set.
@@ -1831,9 +1895,15 @@ class Model(Network):
 
     # Obtain symbolic outputs by calling the model.
     if len(self.inputs) == 1:
-      outputs = self.call(self.inputs[0])
+      if self._expects_training_arg:
+        outputs = self.call(self.inputs[0], training=training)
+      else:
+        outputs = self.call(self.inputs[0])
     else:
-      outputs = self.call(self.inputs)
+      if self._expects_training_arg:
+        outputs = self.call(self.inputs, training=training)
+      else:
+        outputs = self.call(self.inputs)
     if isinstance(outputs, (list, tuple)):
       outputs = list(outputs)
     else:
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 477bb2fe7a..3507f36e14 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -98,7 +98,7 @@ def _eager_metrics_fn(model, outputs, targets):
   return metric_names, metric_results
 
 
-def _model_loss(model, inputs, targets):
+def _model_loss(model, inputs, targets, training=False):
   """Calculates the loss for a given model.
 
   Arguments:
@@ -106,6 +106,7 @@ def _model_loss(model, inputs, targets):
      inputs: The inputs of the given model. This is typically the mini batch of
               data that is fed to the model.
      targets: The predictions or targets of the given model.
+     training: Whether the model should be run in inference or training mode.
 
   Returns:
      Returns the model output, total loss and loss value calculated using the
@@ -114,9 +115,15 @@ def _model_loss(model, inputs, targets):
   """
   total_loss = 0
   if len(inputs) == 1:
-    outs = model.call(inputs[0])
+    if model._expects_training_arg:
+      outs = model.call(inputs[0], training=training)
+    else:
+      outs = model.call(inputs[0])
   else:
-    outs = model.call(inputs)
+    if model._expects_training_arg:
+      outs = model.call(inputs, training=training)
+    else:
+      outs = model.call(inputs)
   if not isinstance(outs, list):
     outs = [outs]
 
@@ -172,7 +179,7 @@ def _model_loss(model, inputs, targets):
 
 
 def _process_single_batch(eager_model_inputs, eager_model_outputs, model,
-                          training=True):
+                          training=False):
   """Calculate the loss and gradient for one input batch.
 
      The model weights are updated if training is set to True.
@@ -195,7 +202,8 @@ def _process_single_batch(eager_model_inputs, eager_model_outputs, model,
   K.set_learning_phase(training)
   with GradientTape() as tape:
     outs, loss, loss_metrics = _model_loss(model, eager_model_inputs,
-                                           eager_model_outputs)
+                                           eager_model_outputs,
+                                           training=training)
     if loss is None:
       raise ValueError('The model cannot be run '
                        'because it has no loss to optimize.')
@@ -230,7 +238,7 @@ def train_on_batch(model, ins):
   for i in range(len(model.inputs), len(ins_batch_converted)):
     eager_model_outputs.append(ins_batch_converted[i])
   outs, loss, _ = _process_single_batch(
-      eager_model_inputs, eager_model_outputs, model)
+      eager_model_inputs, eager_model_outputs, model, training=True)
   if not isinstance(outs, list):
     outs = [outs]
   _, metrics_results = _eager_metrics_fn(
@@ -415,7 +423,8 @@ def fit_loop(
 
       outs, loss, loss_metrics = _process_single_batch(eager_model_inputs,
                                                        eager_model_outputs,
-                                                       model)
+                                                       model,
+                                                       training=True)
 
       if not isinstance(outs, list):
         outs = [outs]
@@ -517,7 +526,8 @@ def test_loop(model, ins, batch_size=None, verbose=0, steps=None):
       eager_model_outputs.append(ins_batch_converted[i])
 
     loss_outs, loss, loss_metrics = _model_loss(model, eager_model_inputs,
-                                                eager_model_outputs)
+                                                eager_model_outputs,
+                                                training=False)
     _, metrics_results = _eager_metrics_fn(model, loss_outs,
                                            eager_model_outputs)
     batch_outs = []
@@ -590,9 +600,15 @@ def predict_loop(model, ins, batch_size=32, verbose=0, steps=None):
       eager_model_inputs.append(ins_batch_converted[i])
 
     if len(eager_model_inputs) == 1:
-      batch_outs = model.call(eager_model_inputs[0])
+      if model._expects_training_arg:
+        batch_outs = model.call(eager_model_inputs[0], training=False)
+      else:
+        batch_outs = model.call(eager_model_inputs[0])
     else:
-      batch_outs = model.call(eager_model_inputs)
+      if model._expects_training_arg:
+        batch_outs = model.call(eager_model_inputs, training=False)
+      else:
+        batch_outs = model.call(eager_model_inputs)
 
     if not isinstance(batch_outs, list):
       batch_outs = [batch_outs]
diff --git a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
index 275985aa36..3d71a620fc 100644
--- a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
+++ b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
@@ -376,11 +376,11 @@ class ModelSubclassingTest(test.TestCase):
     with self.test_session():
       model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
       model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32)
+      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
       model.fit({'input_1': x1, 'input_2': x2},
                 {'output_1': y1, 'output_2': y2},
                 epochs=2, batch_size=32)
-      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32,
+      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0,
                 validation_data=([x1, x2], [y1, y2]))
 
       model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
@@ -438,7 +438,7 @@ class ModelSubclassingTest(test.TestCase):
     with self.test_session():
       model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
       model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32)
+      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
       y_ref_1, y_ref_2 = model.predict([x1, x2])
 
       fd, fname = tempfile.mkstemp('.h5')
@@ -553,6 +553,37 @@ class ModelSubclassingTest(test.TestCase):
           len(model.non_trainable_weights), 4)
       self.assertEqual(len(model.trainable_weights), 12)
 
+  @test_util.run_in_graph_and_eager_modes()
+  def test_support_for_manual_training_arg(self):
+    # In most cases, the `training` argument is left unspecified, in which
+    # case it defaults to value corresponding to the Model method being used
+    # (fit -> True, predict -> False, etc).
+    # If the user writes their model `call` method to take
+    # an explicit `training` argument, we must check that the correct value
+    # is being passed to the model for each method call.
+
+    class DPNet(keras.Model):
+
+      def __init__(self):
+        super(DPNet, self).__init__()
+        self.dp = keras.layers.Dropout(0.5)
+        self.dense = keras.layers.Dense(1,
+                                        use_bias=False,
+                                        kernel_initializer='ones')
+
+      def call(self, inputs, training=False):
+        x = self.dp(inputs, training=training)
+        return self.dense(x)
+
+    with self.test_session():
+      model = DPNet()
+      x = np.ones((10, 10))
+      y = model.predict(x)
+      self.assertEqual(np.sum(y), np.sum(x))
+      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+      loss = model.train_on_batch(x, y)
+      self.assertGreater(loss, 0.1)
+
 
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From 047c7cbc1c95f7423ee44c841eba6b0b3ec691bb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 15:10:48 -0800
Subject: [PATCH 027/575] Add qint8 to list of types supported by the GPU
 ConstOp.

PiperOrigin-RevId: 186053061
---
 tensorflow/core/kernels/constant_op.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/core/kernels/constant_op.cc b/tensorflow/core/kernels/constant_op.cc
index 4ab6fdbca1..fdb03a5aae 100644
--- a/tensorflow/core/kernels/constant_op.cc
+++ b/tensorflow/core/kernels/constant_op.cc
@@ -102,6 +102,7 @@ REGISTER_KERNEL(GPU, float);
 REGISTER_KERNEL(GPU, double);
 REGISTER_KERNEL(GPU, uint8);
 REGISTER_KERNEL(GPU, int8);
+REGISTER_KERNEL(GPU, qint8);
 REGISTER_KERNEL(GPU, uint16);
 REGISTER_KERNEL(GPU, int16);
 REGISTER_KERNEL(GPU, int64);
-- 
GitLab


From f4fd28d275887da0918f411dff2bced18a18c785 Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Fri, 16 Feb 2018 15:16:38 -0800
Subject: [PATCH 028/575] TFLite Conv2D: Create temporary tensors in Prepare
 phase.

PiperOrigin-RevId: 186053793
---
 tensorflow/contrib/lite/arena_planner.cc |  5 +++++
 tensorflow/contrib/lite/kernels/conv.cc  | 14 ++++++++++----
 2 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/lite/arena_planner.cc b/tensorflow/contrib/lite/arena_planner.cc
index 87b17c338e..8e47e2375e 100644
--- a/tensorflow/contrib/lite/arena_planner.cc
+++ b/tensorflow/contrib/lite/arena_planner.cc
@@ -128,6 +128,11 @@ TfLiteStatus ArenaPlanner::PlanAllocations() {
 }
 
 TfLiteStatus ArenaPlanner::ExecuteAllocations(int first_node, int last_node) {
+  // Grow the size of `allocs_` if necessary. This allows allocating temporary
+  // tensors in op's `prepare` function.
+  TF_LITE_ENSURE(context_, graph_info_->num_tensors() >= allocs_.size());
+  allocs_.resize(graph_info_->num_tensors());
+
   TF_LITE_ENSURE_STATUS(CalculateAllocations(first_node, last_node));
   TF_LITE_ENSURE_STATUS(Commit());
 
diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc
index 66d2c04bba..495910aab6 100644
--- a/tensorflow/contrib/lite/kernels/conv.cc
+++ b/tensorflow/contrib/lite/kernels/conv.cc
@@ -51,11 +51,13 @@ enum KernelType {
   kCblasOptimized,
 };
 
+const int kTensorNotAllocated = -1;
+
 struct OpData {
   // IDs are the arbitrary identifiers used by TF Lite to identify and access
   // memory buffers.
-  int im2col_id;
-  int hwcn_weights_id;
+  int im2col_id = kTensorNotAllocated;
+  int hwcn_weights_id = kTensorNotAllocated;
 
   TfLitePaddingValues padding;
   // The scaling factor from input to output (aka the 'real multiplier') can
@@ -80,8 +82,6 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) {
   // Instead, we allocate a new object to use as scratch space for im2col, and
   // to carry information from Prepare() to Eval().
   auto* data = new OpData;
-  context->AddTensors(context, 1, &data->im2col_id);
-  context->AddTensors(context, 1, &data->hwcn_weights_id);
   gemm_support::IncrementUsageCounter(context);
   return data;
 }
@@ -219,10 +219,16 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   int temporaries_count = 0;
   if (data->need_im2col) {
     data->im2col_index = temporaries_count;
+    if (data->im2col_id == kTensorNotAllocated) {
+      context->AddTensors(context, 1, &data->im2col_id);
+    }
     ++temporaries_count;
   }
   if (data->need_hwcn_weights) {
     data->hwcn_weights_index = temporaries_count;
+    if (data->hwcn_weights_id == kTensorNotAllocated) {
+      context->AddTensors(context, 1, &data->hwcn_weights_id);
+    }
     ++temporaries_count;
   }
 
-- 
GitLab


From 1873ed4faab980ad239c06e8b92b8f4a85154fe3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 15:17:04 -0800
Subject: [PATCH 029/575] Expose the main API to the generated code as well.
 This allows recursive runtime conversion, and is a prerequisite to supporting
 dynamic non-recursive functions.

PiperOrigin-RevId: 186053846
---
 tensorflow/contrib/py2tf/impl/api.py          |  6 ++--
 tensorflow/contrib/py2tf/impl/config.py       |  3 +-
 tensorflow/contrib/py2tf/impl/conversion.py   | 31 ++++++++++++++-----
 .../contrib/py2tf/impl/conversion_test.py     |  6 ++--
 4 files changed, 33 insertions(+), 13 deletions(-)

diff --git a/tensorflow/contrib/py2tf/impl/api.py b/tensorflow/contrib/py2tf/impl/api.py
index 8ae1c70169..29d2e038a7 100644
--- a/tensorflow/contrib/py2tf/impl/api.py
+++ b/tensorflow/contrib/py2tf/impl/api.py
@@ -175,7 +175,8 @@ def to_graph(e,
   conversion_map = conversion.ConversionMap(
       recursive=recursive,
       nocompile_decorators=(convert, graph_ready, convert_inline),
-      partial_types=partial_types)
+      partial_types=partial_types,
+      api_module=tf_inspect.getmodule(to_graph))
   _, name = conversion.entity_to_graph(e, conversion_map, arg_values, arg_types)
 
   module = gast.Module([])
@@ -221,7 +222,8 @@ def to_code(e,
   conversion_map = conversion.ConversionMap(
       recursive=recursive,
       nocompile_decorators=(convert, graph_ready, convert_inline),
-      partial_types=partial_types)
+      partial_types=partial_types,
+      api_module=tf_inspect.getmodule(to_graph))
   conversion.entity_to_graph(e, conversion_map, arg_values, arg_types)
 
   imports = '\n'.join(config.COMPILED_IMPORT_STATEMENTS)
diff --git a/tensorflow/contrib/py2tf/impl/config.py b/tensorflow/contrib/py2tf/impl/config.py
index 7c3ecefff0..c90e85c96b 100644
--- a/tensorflow/contrib/py2tf/impl/config.py
+++ b/tensorflow/contrib/py2tf/impl/config.py
@@ -36,10 +36,11 @@ DEFAULT_UNCOMPILED_MODULES = set((
 NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',))
 
 # TODO(mdan): Also allow controlling the generated names (for testability).
-# TODO(mdan): Verify that these names are not hidden by generated code.
 # TODO(mdan): Make sure copybara renames the reference below.
 COMPILED_IMPORT_STATEMENTS = (
     'from __future__ import print_function',
     'import tensorflow as tf',
+    'from tensorflow.contrib.py2tf.impl import api as '
+    'py2tf_api',
     'from tensorflow.contrib.py2tf import utils as '
     'py2tf_utils')
diff --git a/tensorflow/contrib/py2tf/impl/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py
index 3d5624b187..7610f0427b 100644
--- a/tensorflow/contrib/py2tf/impl/conversion.py
+++ b/tensorflow/contrib/py2tf/impl/conversion.py
@@ -58,16 +58,20 @@ class ConversionMap(object):
         converted AST
     name_map: dict[string]: string; maps original entities to the name of
         their converted counterparts
+    api_module: A reference to the api module. The reference needs to be passed
+        to avoid circular dependencies.
   """
 
   # TODO(mdan): Rename to ConversionContext, and pull in additional flags.
 
-  def __init__(self, recursive, nocompile_decorators, partial_types):
+  def __init__(self, recursive, nocompile_decorators, partial_types,
+               api_module):
     self.recursive = recursive
     self.nocompile_decorators = nocompile_decorators
     self.partial_types = partial_types if partial_types else ()
     self.dependency_cache = {}
     self.name_map = {}
+    self.api_module = api_module
 
   def new_namer(self, namespace):
     return naming.Namer(namespace, self.recursive, self.name_map,
@@ -170,6 +174,24 @@ def class_to_graph(c, conversion_map):
   return node, class_name
 
 
+def _add_self_references(namespace, api_module):
+  """Self refs are only required for analysis and are not used directly."""
+  # Manually add the utils namespace which may be used from generated code.
+  if 'py2tf_util' not in namespace:
+    namespace['py2tf_utils'] = utils
+  elif namespace['py2tf_utils'] != utils:
+    raise ValueError(
+        'The module name "py2tf_utils" is reserved and may not be used.')
+
+  # We also make reference to the api module for dynamic conversion, but
+  # to avoid circular references we don't import it here.
+  if 'py2tf_api' not in namespace:
+    namespace['py2tf_api'] = api_module
+  elif namespace['py2tf_api'] != api_module:
+    raise ValueError(
+        'The module name "py2tf_api" is reserved and may not be used.')
+
+
 def function_to_graph(f, conversion_map, arg_values, arg_types,
                       owner_type=None):
   """Specialization of `entity_to_graph` for callable functions."""
@@ -185,12 +207,7 @@ def function_to_graph(f, conversion_map, arg_values, arg_types,
         fn = e.cell_contents
         namespace[fn.__name__] = fn
 
-  # Manually add the utils namespace which may be used from generated code.
-  if 'py2tf_util' not in namespace:
-    namespace['py2tf_utils'] = utils
-  elif namespace['py2tf_utils'] != utils:
-    raise ValueError(
-        'The module name py2tf_utils is reserved and may not be used.')
+  _add_self_references(namespace, conversion_map.api_module)
 
   namer = conversion_map.new_namer(namespace)
   ctx = context.EntityContext(
diff --git a/tensorflow/contrib/py2tf/impl/conversion_test.py b/tensorflow/contrib/py2tf/impl/conversion_test.py
index 3888958f19..75e95ed888 100644
--- a/tensorflow/contrib/py2tf/impl/conversion_test.py
+++ b/tensorflow/contrib/py2tf/impl/conversion_test.py
@@ -28,7 +28,7 @@ class ConversionTest(test.TestCase):
 
   def test_entity_to_graph_unsupported_types(self):
     with self.assertRaises(ValueError):
-      conversion_map = conversion.ConversionMap(True, (), ())
+      conversion_map = conversion.ConversionMap(True, (), (), None)
       conversion.entity_to_graph('dummy', conversion_map, None, None)
 
   def test_entity_to_graph_callable(self):
@@ -36,7 +36,7 @@ class ConversionTest(test.TestCase):
     def f(a):
       return a
 
-    conversion_map = conversion.ConversionMap(True, (), ())
+    conversion_map = conversion.ConversionMap(True, (), (), None)
     ast, new_name = conversion.entity_to_graph(f, conversion_map, None, None)
     self.assertTrue(isinstance(ast, gast.FunctionDef), ast)
     self.assertEqual('tf__f', new_name)
@@ -49,7 +49,7 @@ class ConversionTest(test.TestCase):
     def f(a):
       return g(a)
 
-    conversion_map = conversion.ConversionMap(True, (), ())
+    conversion_map = conversion.ConversionMap(True, (), (), None)
     conversion.entity_to_graph(f, conversion_map, None, None)
 
     self.assertTrue(f in conversion_map.dependency_cache)
-- 
GitLab


From ba019dc689d6393d8dba04ca57e8b01b374db14f Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Fri, 16 Feb 2018 15:29:35 -0800
Subject: [PATCH 030/575] [XLA] Add some plumbing, documentation, verification
 and shape inference for Gather

Pretty much everything other than HLO verification and shape inference will fail
for Gather with Unimplemented.

Note that this CL is intentionally incomplete -- I figured it would be nicer to
get some of the boiler-platey stuff out of the way early.  Let me know if you
want me to send in a larger but more complete CL instead.

PiperOrigin-RevId: 186055521
---
 .../xla/client/computation_builder.cc         |  16 +
 .../compiler/xla/client/computation_builder.h |   7 +
 tensorflow/compiler/xla/service/BUILD         |   3 +-
 .../compiler/xla/service/dfs_hlo_visitor.h    |   1 +
 .../service/dfs_hlo_visitor_with_default.h    |   3 +
 .../xla/service/gpu/ir_emitter_unnested.cc    |   5 +
 .../xla/service/gpu/ir_emitter_unnested.h     |   1 +
 tensorflow/compiler/xla/service/hlo.proto     |   4 +
 .../compiler/xla/service/hlo_cost_analysis.cc |   5 +
 .../compiler/xla/service/hlo_cost_analysis.h  |   1 +
 .../compiler/xla/service/hlo_graph_dumper.cc  |   1 +
 .../compiler/xla/service/hlo_instruction.cc   |  74 ++++
 .../compiler/xla/service/hlo_instruction.h    |  28 ++
 .../xla/service/hlo_instruction_test.cc       |  35 ++
 tensorflow/compiler/xla/service/hlo_opcode.h  |   1 +
 .../compiler/xla/service/hlo_verifier.cc      |   8 +
 .../compiler/xla/service/hlo_verifier.h       |   1 +
 .../xla/service/instruction_fusion.cc         |   1 +
 tensorflow/compiler/xla/service/service.cc    |   3 +
 .../compiler/xla/service/shape_inference.cc   | 193 ++++++++++
 .../compiler/xla/service/shape_inference.h    |   8 +
 .../xla/service/shape_inference_test.cc       | 341 +++++++++++++++++-
 .../compiler/xla/service/user_computation.cc  |  61 ++++
 .../compiler/xla/service/user_computation.h   |   4 +
 .../compiler/xla/tools/parser/hlo_parser.cc   |   3 +
 tensorflow/compiler/xla/util.h                |  29 +-
 tensorflow/compiler/xla/xla_data.proto        |  37 +-
 .../performance/xla/operation_semantics.md    | 188 ++++++++++
 28 files changed, 1057 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index e6dfe0aefb..2a6e02649d 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -1234,6 +1234,22 @@ ComputationDataHandle ComputationBuilder::While(
   return RunOpAndParseResponse(&op_request);
 }
 
+ComputationDataHandle ComputationBuilder::Gather(
+    const ComputationDataHandle& input,
+    const ComputationDataHandle& gather_indices,
+    const GatherDimensionNumbers& dimension_numbers,
+    tensorflow::gtl::ArraySlice<int64> window_bounds) {
+  OpRequest op_request;
+  GatherRequest* gather_request = op_request.mutable_gather_request();
+  *gather_request->mutable_input() = input;
+  *gather_request->mutable_gather_indices() = gather_indices;
+  *gather_request->mutable_dimension_numbers() = dimension_numbers;
+  for (int64 window_bound : window_bounds) {
+    gather_request->add_window_bounds(window_bound);
+  }
+  return RunOpAndParseResponse(&op_request);
+}
+
 ComputationDataHandle ComputationBuilder::Conditional(
     const ComputationDataHandle& predicate,
     const ComputationDataHandle& true_operand,
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index aa2622174d..e3facb3f25 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -718,6 +718,13 @@ class ComputationBuilder {
                                         const int exponent_bits,
                                         const int mantissa_bits);
 
+  // Enqueues a Gather node onto the computation.
+  ComputationDataHandle Gather(
+      const ComputationDataHandle& input,
+      const ComputationDataHandle& gather_indices,
+      const GatherDimensionNumbers& dimension_numbers,
+      tensorflow::gtl::ArraySlice<int64> window_bounds);
+
   // Enqueues a Send node onto the computation, to send the given operand to
   // a Recv instruction that shares the same channel handle.
   void Send(const ComputationDataHandle& operand, const ChannelHandle& handle);
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 9f5f2f96b7..0ceb9ca9e0 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -145,7 +145,8 @@ tf_cc_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
-        "//tensorflow/compiler/xla/tests:xla_internal_test_main",
+        "//tensorflow/compiler/xla/tests:xla_internal_test_main",  # fixdeps: keep
+        "//tensorflow/core:lib",
     ],
 )
 
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
index 5b09e4931e..56723e7650 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h
@@ -214,6 +214,7 @@ class DfsHloVisitorBase {
   virtual Status HandleSelectAndScatter(HloInstructionPtr hlo) = 0;
   virtual Status HandleWhile(HloInstructionPtr hlo) = 0;
   virtual Status HandleConditional(HloInstructionPtr hlo) = 0;
+  virtual Status HandleGather(HloInstructionPtr hlo) = 0;
 
   virtual Status HandlePad(HloInstructionPtr hlo) = 0;
 
diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
index ffc4f3bb79..ecda5288ee 100644
--- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
+++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h
@@ -188,6 +188,9 @@ class DfsHloVisitorWithDefaultBase
   Status HandleSendDone(HloInstructionPtr send_done) override {
     return DefaultAction(send_done);
   }
+  Status HandleGather(HloInstructionPtr gather) override {
+    return DefaultAction(gather);
+  }
 
   // Invoked to inform the visitor that the traversal has completed, and that
   // the root was "root".
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
index aa2a0a9800..30c88c0a5d 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
@@ -2064,6 +2064,11 @@ GetHloBufferSlices(const HloInstruction* hlo,
   return slices;
 }
 
+Status IrEmitterUnnested::HandleGather(HloInstruction* gather) {
+  // TODO(b/72710576): Gather is not implemented on GPUs
+  return Unimplemented("Gather is not implemented on GPUs.");
+}
+
 std::unique_ptr<Thunk> IrEmitterUnnested::BuildKernelThunk(
     const HloInstruction* inst) {
   const BufferAssignment& buffer_assn =
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h
index 688760efbd..b83a2337e2 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h
@@ -67,6 +67,7 @@ class IrEmitterUnnested : public IrEmitter {
   Status HandleDot(HloInstruction* dot) override;
   Status HandleFft(HloInstruction* fft) override;
   Status HandleFusion(HloInstruction* fusion) override;
+  Status HandleGather(HloInstruction* gather) override;
   Status HandleGetTupleElement(HloInstruction* get_tuple_element) override;
   Status HandleReduce(HloInstruction* reduce) override;
   Status HandleSelectAndScatter(HloInstruction* instruction) override;
diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto
index 36db711c6c..a43785b4a9 100644
--- a/tensorflow/compiler/xla/service/hlo.proto
+++ b/tensorflow/compiler/xla/service/hlo.proto
@@ -129,6 +129,10 @@ message HloInstructionProto {
 
   // FFT length.
   repeated int64 fft_length = 32;
+
+  // Gather dimension numbers.
+  xla.GatherDimensionNumbers gather_dimension_numbers = 33;
+  repeated int64 gather_window_bounds = 34;
 }
 
 // Serialization of HloComputation.
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
index 6a4651d83f..4ec2ef27bf 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc
@@ -533,6 +533,11 @@ Status HloCostAnalysis::HandleConditional(const HloInstruction* conditional) {
   return Status::OK();
 }
 
+Status HloCostAnalysis::HandleGather(const HloInstruction* gather) {
+  // Gather does not issue any flops.
+  return Status::OK();
+}
+
 Status HloCostAnalysis::FinishVisit(const HloInstruction*) {
   return Status::OK();
 }
diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
index af52ea06ca..d17678d20f 100644
--- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h
@@ -100,6 +100,7 @@ class HloCostAnalysis : public ConstDfsHloVisitor {
   Status HandleTranspose(const HloInstruction* transpose) override;
   Status HandleWhile(const HloInstruction* xla_while) override;
   Status HandleConditional(const HloInstruction* conditional) override;
+  Status HandleGather(const HloInstruction* gather) override;
   Status FinishVisit(const HloInstruction* root) override;
 
   Status Preprocess(const HloInstruction* hlo) override;
diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 9b0e2fd7d6..2861fec39e 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -940,6 +940,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) {
     case HloOpcode::kConcatenate:
     case HloOpcode::kCopy:
     case HloOpcode::kDynamicSlice:
+    case HloOpcode::kGather:
     case HloOpcode::kPad:
     case HloOpcode::kReshape:
     case HloOpcode::kReverse:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index 0d925ad00d..b7dd055d7c 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1155,6 +1155,38 @@ bool HloInstruction::HasSideEffect() const {
   return CreateVariadic(tuple_shape, HloOpcode::kTuple, elements);
 }
 
+/* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateGather(
+    const Shape& shape, HloInstruction* operand, HloInstruction* gather_indices,
+    const GatherDimensionNumbers& gather_dim_numbers,
+    tensorflow::gtl::ArraySlice<int64> window_bounds) {
+  std::unique_ptr<HloInstruction> instruction =
+      WrapUnique(new HloInstruction(HloOpcode::kGather, shape));
+  instruction->AppendOperand(operand);
+  instruction->AppendOperand(gather_indices);
+  instruction->gather_dimension_numbers_ =
+      MakeUnique<GatherDimensionNumbers>(gather_dim_numbers);
+  c_copy(window_bounds, std::back_inserter(instruction->gather_window_bounds_));
+  return instruction;
+}
+
+/* static */ GatherDimensionNumbers HloInstruction::MakeGatherDimNumbers(
+    tensorflow::gtl::ArraySlice<int64> output_window_dims,
+    tensorflow::gtl::ArraySlice<int64> elided_window_dims,
+    tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims) {
+  GatherDimensionNumbers gather_dim_numbers;
+  for (int64 output_window_dim : output_window_dims) {
+    gather_dim_numbers.add_output_window_dims(output_window_dim);
+  }
+  for (int64 elided_window_dim : elided_window_dims) {
+    gather_dim_numbers.add_elided_window_dims(elided_window_dim);
+  }
+  for (int64 gather_dim_to_input_dim : gather_dims_to_operand_dims) {
+    gather_dim_numbers.add_gather_dims_to_operand_dims(gather_dim_to_input_dim);
+  }
+
+  return gather_dim_numbers;
+}
+
 std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
     const Shape& shape,
     tensorflow::gtl::ArraySlice<HloInstruction*> new_operands,
@@ -1397,6 +1429,11 @@ std::unique_ptr<HloInstruction> HloInstruction::CloneWithNewOperands(
       CHECK_EQ(new_operands.size(), 1);
       clone = CreateRecvDone(new_operands[0]);
       break;
+    case HloOpcode::kGather:
+      CHECK_EQ(new_operands.size(), 2);
+      clone = CreateGather(shape, new_operands[0], new_operands[1],
+                           *gather_dimension_numbers_, gather_window_bounds_);
+      break;
     case HloOpcode::kTrace:
       LOG(FATAL) << "Not yet implemented, clone: " << HloOpcodeString(opcode_);
   }
@@ -1740,6 +1777,11 @@ bool HloInstruction::IdenticalSlowPath(
       return protobuf_util::ProtobufEquals(dot_dimension_numbers(),
                                            other.dot_dimension_numbers());
 
+    case HloOpcode::kGather:
+      return protobuf_util::ProtobufEquals(gather_dimension_numbers(),
+                                           other.gather_dimension_numbers()) &&
+             gather_window_bounds() == other.gather_window_bounds();
+
     // FFT has various types & lengths.
     case HloOpcode::kFft:
       return fft_type() == other.fft_type() &&
@@ -2171,6 +2213,11 @@ std::vector<string> HloInstruction::ExtraAttributesToString(
   if (dot_dimension_numbers_ != nullptr) {
     extra.push_back(DotDimensionNumbersToString());
   }
+  if (gather_dimension_numbers_ != nullptr) {
+    extra.push_back(GatherDimensionNumbersToString());
+    extra.push_back(
+        StrCat("window_bounds={", Join(gather_window_bounds(), ","), "}"));
+  }
   if (opcode() == HloOpcode::kFft) {
     extra.push_back(StrCat("fft_type=", FftType_Name(fft_type())));
     extra.push_back(StrCat("fft_length={", Join(fft_length(), ","), "}"));
@@ -2302,6 +2349,14 @@ HloInstructionProto HloInstruction::ToProto() const {
   if (dot_dimension_numbers_ != nullptr) {
     *proto.mutable_dot_dimension_numbers() = *dot_dimension_numbers_;
   }
+  if (gather_dimension_numbers_ != nullptr) {
+    *proto.mutable_gather_dimension_numbers() = *gather_dimension_numbers_;
+  }
+  if (opcode() == HloOpcode::kGather) {
+    for (int64 bound : gather_window_bounds()) {
+      proto.add_gather_window_bounds(bound);
+    }
+  }
   for (int i = 0; i < slice_starts_.size(); ++i) {
     auto* slice_dimension = proto.add_slice_dimensions();
     slice_dimension->set_start(slice_starts_[i]);
@@ -2618,6 +2673,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase<HloInstructionPtr>* visitor) {
       return visitor->HandleSend(this);
     case HloOpcode::kSendDone:
       return visitor->HandleSendDone(this);
+    case HloOpcode::kGather:
+      return visitor->HandleGather(this);
 
     // These opcodes are not handled here.
     case HloOpcode::kTrace:
@@ -3301,6 +3358,23 @@ string HloInstruction::DotDimensionNumbersToString() const {
   return Join(result, ", ");
 }
 
+string HloInstruction::GatherDimensionNumbersToString() const {
+  CHECK_NE(gather_dimension_numbers_.get(), nullptr);
+  string output_window_dims =
+      StrCat("output_window_dims={",
+             Join(gather_dimension_numbers_->output_window_dims(), ","), "}");
+  string elided_window_dims =
+      StrCat("elided_window_dims={",
+             Join(gather_dimension_numbers_->elided_window_dims(), ","), "}");
+  string gather_dims_to_operand_dims = StrCat(
+      "gather_dims_to_operand_dims={",
+      Join(gather_dimension_numbers_->gather_dims_to_operand_dims(), ","), "}");
+
+  return Join<std::initializer_list<string>>(
+      {output_window_dims, elided_window_dims, gather_dims_to_operand_dims},
+      ", ");
+}
+
 bool HloInstruction::CouldBeBitcast() const {
   switch (opcode_) {
     case HloOpcode::kTranspose:
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index e898a83739..1762d227be 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -451,6 +451,12 @@ class HloInstruction {
       HloInstruction* true_computation_arg, HloComputation* true_computation,
       HloInstruction* false_computation_arg, HloComputation* false_computation);
 
+  static std::unique_ptr<HloInstruction> CreateGather(
+      const Shape& shape, HloInstruction* operand,
+      HloInstruction* gather_indices,
+      const GatherDimensionNumbers& gather_dim_numbers,
+      tensorflow::gtl::ArraySlice<int64> window_bounds);
+
   // Creates a fusion instruction. A fusion instruction contains one or more
   // fused instructions forming an expression with a single root
   // "fused_root". Additional instructions can be added to the fusion
@@ -492,6 +498,12 @@ class HloInstruction {
       const Shape& shape, HloInstruction* operand,
       tensorflow::gtl::ArraySlice<int64> dimensions);
 
+  // Creates an instance of GatherDimensionNumbers.
+  static GatherDimensionNumbers MakeGatherDimNumbers(
+      tensorflow::gtl::ArraySlice<int64> output_window_dims,
+      tensorflow::gtl::ArraySlice<int64> elided_window_dims,
+      tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims);
+
   // Returns the opcode for this instruction.
   HloOpcode opcode() const { return opcode_; }
 
@@ -1102,6 +1114,19 @@ class HloInstruction {
   // Returns the dump string of the dot dimension numbers.
   string DotDimensionNumbersToString() const;
 
+  const GatherDimensionNumbers& gather_dimension_numbers() const {
+    CHECK(gather_dimension_numbers_ != nullptr);
+    return *gather_dimension_numbers_;
+  }
+
+  tensorflow::gtl::ArraySlice<int64> gather_window_bounds() const {
+    CHECK_EQ(opcode(), HloOpcode::kGather);
+    return gather_window_bounds_;
+  }
+
+  // Returns the dump string of the gather dimension numbers.
+  string GatherDimensionNumbersToString() const;
+
   // Returns the random distribution for this rng node.
   //
   // Precondition: opcode() == HloOpcode::kRng
@@ -1366,6 +1391,9 @@ class HloInstruction {
   // Describes the dimension numbers used for a dot.
   std::unique_ptr<DotDimensionNumbers> dot_dimension_numbers_;
 
+  std::unique_ptr<GatherDimensionNumbers> gather_dimension_numbers_;
+  std::vector<int64> gather_window_bounds_;
+
   // Describes FFT type for an FFT instruction.
   FftType fft_type_ = FftType::FFT;
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
index 94e9bfe56e..32d3ed272b 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
@@ -1271,5 +1271,40 @@ TEST_F(HloInstructionTest, Stringification) {
             "true_computation=%TransposeDot, false_computation=%TransposeDot");
 }
 
+TEST_F(HloInstructionTest, StringifyGather) {
+  Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46});
+  Shape gather_indices_tensor_shape =
+      ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5});
+  Shape gather_result_shape =
+      ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 30, 29, 28, 27, 26});
+
+  HloComputation::Builder builder("Gather");
+  HloInstruction* input = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_tensor_shape, "input_tensor"));
+  HloInstruction* gather_indices =
+      builder.AddInstruction(HloInstruction::CreateParameter(
+          1, gather_indices_tensor_shape, "gather_indices"));
+
+  HloInstruction* gather_instruction =
+      builder.AddInstruction(HloInstruction::CreateGather(
+          gather_result_shape, input, gather_indices,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{4, 5, 6, 7, 8},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  HloModule module(TestName());
+  module.AddEntryComputation(builder.Build());
+
+  EXPECT_EQ(gather_instruction->ToString(),
+            "%gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} "
+            "gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, "
+            "s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), "
+            "output_window_dims={4,5,6,7,8}, elided_window_dims={}, "
+            "gather_dims_to_operand_dims={0,1,2,3,4}, "
+            "window_bounds={30,29,28,27,26}");
+}
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h
index 088dd15dbf..af24604c39 100644
--- a/tensorflow/compiler/xla/service/hlo_opcode.h
+++ b/tensorflow/compiler/xla/service/hlo_opcode.h
@@ -76,6 +76,7 @@ namespace xla {
   V(kFft, "fft")                                             \
   V(kFloor, "floor")                                         \
   V(kFusion, "fusion", kHloOpcodeIsVariadic)                 \
+  V(kGather, "gather")                                       \
   V(kGe, "greater-than-or-equal-to", kHloOpcodeIsComparison) \
   V(kGetTupleElement, "get-tuple-element")                   \
   V(kGt, "greater-than", kHloOpcodeIsComparison)             \
diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc
index f3378309c2..b1fd068115 100644
--- a/tensorflow/compiler/xla/service/hlo_verifier.cc
+++ b/tensorflow/compiler/xla/service/hlo_verifier.cc
@@ -424,6 +424,14 @@ Status CheckMixedPrecisionOperands(const HloInstruction* instruction) {
 
 }  // namespace
 
+Status ShapeVerifier::HandleGather(HloInstruction* gather) {
+  return CheckShape(
+      gather,
+      ShapeInference::InferGatherShape(
+          gather->operand(0)->shape(), gather->operand(1)->shape(),
+          gather->gather_dimension_numbers(), gather->gather_window_bounds()));
+}
+
 Status ShapeVerifier::CheckShape(const HloInstruction* instruction,
                                  const Shape& inferred_shape) {
   // If allow_mixed_precision_ is false, check if there are operands with
diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h
index f9f898c236..1dd7ec3c51 100644
--- a/tensorflow/compiler/xla/service/hlo_verifier.h
+++ b/tensorflow/compiler/xla/service/hlo_verifier.h
@@ -80,6 +80,7 @@ class ShapeVerifier : public DfsHloVisitor {
   Status HandleBatchNormInference(
       HloInstruction* batch_norm_inference) override;
   Status HandleBatchNormGrad(HloInstruction* batch_norm_grad) override;
+  Status HandleGather(HloInstruction* gather) override;
 
   Status FinishVisit(HloInstruction*) override {
     return tensorflow::Status::OK();
diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc
index f08d809d79..f494748e17 100644
--- a/tensorflow/compiler/xla/service/instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/instruction_fusion.cc
@@ -102,6 +102,7 @@ namespace xla {
     case HloOpcode::kExp:
     case HloOpcode::kFft:
     case HloOpcode::kFusion:
+    case HloOpcode::kGather:
     case HloOpcode::kHostCompute:
     case HloOpcode::kLog:
     case HloOpcode::kMap:
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index 95c853b5c4..e278eab690 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -1446,6 +1446,9 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) {
     case OpRequest::kFftRequest:
       handle_status = computation->AddFftInstruction(arg->fft_request());
       break;
+    case OpRequest::kGatherRequest:
+      handle_status = computation->AddGatherInstruction(arg->gather_request());
+      break;
     case OpRequest::kGetTupleElementRequest:
       handle_status = computation->AddGetTupleElementInstruction(
           arg->get_tuple_element_request());
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 004889b5f2..c9692757b2 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -2448,4 +2448,197 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   return to_apply.result();
 }
 
+static Status ValidateGatherDimensionNumbers(
+    const Shape& input_shape,
+    tensorflow::gtl::ArraySlice<int64> gather_indices_shape,
+    const GatherDimensionNumbers& dim_numbers) {
+  if (!c_is_sorted(dim_numbers.output_window_dims())) {
+    return InvalidArgument(
+        "Output window dimensions in gather op must be ascending; got: %s",
+        Join(dim_numbers.output_window_dims(), ", ").c_str());
+  }
+
+  if (c_adjacent_find(dim_numbers.output_window_dims()) !=
+      dim_numbers.output_window_dims().end()) {
+    return InvalidArgument(
+        "Output window dimensions in gather op must not repeat; got: %s",
+        Join(dim_numbers.output_window_dims(), ", ").c_str());
+  }
+
+  const int64 output_window_dim_count = dim_numbers.output_window_dims_size();
+  const int64 output_shape_rank =
+      output_window_dim_count + gather_indices_shape.size();
+
+  for (int i = 0; i < dim_numbers.output_window_dims_size(); ++i) {
+    int64 window_index = dim_numbers.output_window_dims(i);
+    if (window_index < 0 || window_index >= output_shape_rank) {
+      return InvalidArgument(
+          "Window index %d in gather op is out of bounds; got %lld, but should "
+          "have been in"
+          "[0,%lld)",
+          i, window_index, output_shape_rank);
+    }
+  }
+
+  if (dim_numbers.gather_dims_to_operand_dims_size() !=
+      gather_indices_shape.back()) {
+    return InvalidArgument(
+        "There must be exactly as many elements in gather_dims_to_operand_dims "
+        "as there are elements in the last dimension of %%gather_indices; got: "
+        "%d, expected %lld",
+        dim_numbers.gather_dims_to_operand_dims_size(),
+        gather_indices_shape.back());
+  }
+
+  for (int i = 0; i < dim_numbers.gather_dims_to_operand_dims_size(); i++) {
+    int64 gather_dim_to_input_dim = dim_numbers.gather_dims_to_operand_dims(i);
+    if (gather_dim_to_input_dim < 0 ||
+        gather_dim_to_input_dim >= input_shape.dimensions_size()) {
+      return InvalidArgument(
+          "Invalid gather_dims_to_operand_dims mapping; domain is [0, %d), "
+          "got: %d->%lld",
+          input_shape.dimensions_size(), i, gather_dim_to_input_dim);
+    }
+  }
+
+  std::vector<int64> sorted_gather_dims_to_operand_dims(
+      dim_numbers.gather_dims_to_operand_dims().begin(),
+      dim_numbers.gather_dims_to_operand_dims().end());
+
+  c_sort(sorted_gather_dims_to_operand_dims);
+
+  if (c_adjacent_find(sorted_gather_dims_to_operand_dims) !=
+      sorted_gather_dims_to_operand_dims.end()) {
+    return InvalidArgument(
+        "Repeated dimensions are not allowed in gather_dims_to_operand_dims; "
+        "got: %s",
+        Join(dim_numbers.gather_dims_to_operand_dims(), ", ").c_str());
+  }
+
+  for (int64 elided_dim : dim_numbers.elided_window_dims()) {
+    if (elided_dim < 0 || elided_dim >= input_shape.dimensions_size()) {
+      return InvalidArgument(
+          "Invalid elided_window_dims set in gather op; valid range is [0, "
+          "%d), got: %lld",
+          input_shape.dimensions_size(), elided_dim);
+    }
+  }
+
+  if (!c_is_sorted(dim_numbers.elided_window_dims())) {
+    return InvalidArgument(
+        "elided_window_dims in gather op must be sorted; got: %s",
+        Join(dim_numbers.elided_window_dims(), ", ").c_str());
+  }
+
+  if (c_adjacent_find(dim_numbers.elided_window_dims()) !=
+      dim_numbers.elided_window_dims().end()) {
+    return InvalidArgument(
+        "Repeated dimensions not allowed in elided_window_dims in gather op; "
+        "got: %s",
+        Join(dim_numbers.elided_window_dims(), ", ").c_str());
+  }
+
+  return Status::OK();
+}
+
+/*static*/ StatusOr<Shape> ShapeInference::InferGatherShape(
+    const Shape& input_shape, const Shape& gather_indices_shape,
+    const GatherDimensionNumbers& gather_dim_numbers,
+    tensorflow::gtl::ArraySlice<int64> window_bounds) {
+  TF_RETURN_IF_ERROR(
+      ExpectNotTupleOrOpaque(input_shape, "input tensor operand gather op"));
+  TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(
+      gather_indices_shape, "gather indices operand of gather op"));
+
+  if (gather_indices_shape.dimensions_size() < 1) {
+    return InvalidArgument(
+        "Gather indices parameter must at least of rank 1; got %s",
+        ShapeUtil::HumanString(gather_indices_shape).c_str());
+  }
+
+  if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) {
+    return InvalidArgument(
+        "Gather indices parameter must be an integral tensor; got %s",
+        ShapeUtil::HumanString(gather_indices_shape).c_str());
+  }
+
+  std::vector<int64> expanded_gather_indices_shape;
+  // We implicitly reshape gather indices of shape P[N] to P[N,1].
+  expanded_gather_indices_shape.reserve(gather_indices_shape.dimensions_size());
+  c_copy(gather_indices_shape.dimensions(),
+         std::back_inserter(expanded_gather_indices_shape));
+  if (expanded_gather_indices_shape.size() == 1) {
+    expanded_gather_indices_shape.push_back(1);
+  }
+
+  TF_RETURN_IF_ERROR(ValidateGatherDimensionNumbers(
+      input_shape, expanded_gather_indices_shape, gather_dim_numbers));
+
+  if (window_bounds.size() != input_shape.dimensions_size()) {
+    return InvalidArgument(
+        "Gather op must have one window bound for every input dimension; got: "
+        "len(window_bounds)=%lu, input_shape.rank=%d",
+        window_bounds.size(), input_shape.dimensions_size());
+  }
+
+  if (window_bounds.size() !=
+      gather_dim_numbers.output_window_dims_size() +
+          gather_dim_numbers.elided_window_dims_size()) {
+    return InvalidArgument(
+        "All components of the window index in a gather op must either be a "
+        "output window index or explicitly elided; got len(window_bounds)=%lu, "
+        "output_window_bounds=%s, elided_window_bounds=%s",
+        window_bounds.size(),
+        Join(gather_dim_numbers.output_window_dims(), ",").c_str(),
+        Join(gather_dim_numbers.elided_window_dims(), ",").c_str());
+  }
+
+  for (int i = 0; i < window_bounds.size(); i++) {
+    int64 window_bound = window_bounds[i];
+    int64 corresponding_input_bound = input_shape.dimensions(i);
+    if (window_bound < 0 || window_bound > corresponding_input_bound) {
+      return InvalidArgument(
+          "Window bound at index %d in gather op is out of range, must be "
+          "within "
+          "[0, %lld), got %lld",
+          i, corresponding_input_bound + 1, window_bound);
+    }
+  }
+
+  for (int i = 0; i < gather_dim_numbers.elided_window_dims_size(); i++) {
+    if (window_bounds[gather_dim_numbers.elided_window_dims(i)] != 1) {
+      return InvalidArgument(
+          "Gather op can only elide window indices with bound 1, but bound is "
+          "%lld for index %lld at position %d",
+          window_bounds[gather_dim_numbers.elided_window_dims(i)],
+          gather_dim_numbers.elided_window_dims(i), i);
+    }
+  }
+
+  int64 result_rank = gather_dim_numbers.output_window_dims_size() +
+                      (expanded_gather_indices_shape.size() - 1);
+  int64 window_dims_seen = 0;
+  int64 gather_dims_seen = 0;
+  std::vector<int64> output_dim_bounds;
+  output_dim_bounds.reserve(result_rank);
+  for (int64 i = 0; i < result_rank; i++) {
+    int64 current_bound;
+    bool is_window_index =
+        c_binary_search(gather_dim_numbers.output_window_dims(), i);
+    if (is_window_index) {
+      while (c_binary_search(gather_dim_numbers.elided_window_dims(),
+                             window_dims_seen)) {
+        window_dims_seen++;
+      }
+      current_bound = window_bounds[window_dims_seen++];
+    } else {
+      current_bound = expanded_gather_indices_shape[gather_dims_seen++];
+    }
+
+    output_dim_bounds.push_back(current_bound);
+  }
+
+  return ShapeUtil::MakeShape(input_shape.element_type(), output_dim_bounds);
+}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h
index c4a1da28f3..0d3045213d 100644
--- a/tensorflow/compiler/xla/service/shape_inference.h
+++ b/tensorflow/compiler/xla/service/shape_inference.h
@@ -253,6 +253,14 @@ class ShapeInference {
       const Shape& lhs, const Shape& rhs,
       const DotDimensionNumbers& dimension_numbers);
 
+  // Helper that infers the shape of the tensor produced by a gather operation
+  // with the given input shape, gather indices shape and gather dimension
+  // numbers.
+  static StatusOr<Shape> InferGatherShape(
+      const Shape& input_shape, const Shape& gather_indices_shape,
+      const GatherDimensionNumbers& gather_dim_numbers,
+      tensorflow::gtl::ArraySlice<int64> window_bounds);
+
  private:
   // Helper that infers the shape produced by performing an element-wise binary
   // operation with the given LHS and RHS shapes.
diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc
index 026c021165..7eb120843f 100644
--- a/tensorflow/compiler/xla/service/shape_inference_test.cc
+++ b/tensorflow/compiler/xla/service/shape_inference_test.cc
@@ -18,15 +18,16 @@ limitations under the License.
 #include <string>
 
 #include "tensorflow/compiler/xla/shape_util.h"
-#include "tensorflow/compiler/xla/xla_data.pb.h"
-
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/test_helpers.h"
 #include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
 
 namespace xla {
 namespace {
 
+using ::tensorflow::gtl::ArraySlice;
 using ::testing::ContainsRegex;
 using ::testing::HasSubstr;
 
@@ -1527,5 +1528,341 @@ TEST_F(ShapeInferenceTest, BadSlice) {
       << statusor.status();
 }
 
+class GatherShapeInferenceTest : public ShapeInferenceTest {
+ protected:
+  const Shape s64_vector_32_ = ShapeUtil::MakeShape(S64, {32});
+  const Shape s64_4d_tensor_10_9_8_7_1_ =
+      ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 1});
+  const Shape s64_4d_tensor_10_9_8_7_5_ =
+      ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5});
+  const Shape f32_5d_tensor_50_49_48_47_46_ =
+      ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46});
+  const Shape tuple_shape_ = ShapeUtil::MakeTupleShape(
+      {s64_4d_tensor_10_9_8_7_1_, s64_4d_tensor_10_9_8_7_1_});
+};
+
+TEST_F(GatherShapeInferenceTest, TensorFlowGather) {
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(matrix_64_48_, s64_vector_32_,
+                                       HloInstruction::MakeGatherDimNumbers(
+                                           /*output_window_dims=*/{0},
+                                           /*elided_window_dims=*/{1},
+                                           /*gather_dims_to_operand_dims=*/{1}),
+                                       /*window_bounds=*/{64, 1}));
+  EXPECT_TRUE(
+      ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {64, 32})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, TensorFlowGatherV2) {
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(matrix_64_48_, s64_vector_32_,
+                                       HloInstruction::MakeGatherDimNumbers(
+                                           /*output_window_dims=*/{1},
+                                           /*elided_window_dims=*/{0},
+                                           /*gather_dims_to_operand_dims=*/{0}),
+                                       /*window_bounds=*/{1, 48}));
+  EXPECT_TRUE(
+      ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {32, 48})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, TensorFlowGatherNd) {
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(matrix_64_48_, s64_4d_tensor_10_9_8_7_1_,
+                                       HloInstruction::MakeGatherDimNumbers(
+                                           /*output_window_dims=*/{4},
+                                           /*elided_window_dims=*/{0},
+                                           /*gather_dims_to_operand_dims=*/{0}),
+                                       /*window_bounds=*/{1, 48}));
+  EXPECT_TRUE(ShapeUtil::Equal(gather_shape,
+                               ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 48})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) {
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(
+          f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{4, 5, 6, 7, 8},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+  EXPECT_TRUE(ShapeUtil::Equal(
+      gather_shape,
+      ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 30, 29, 28, 27, 26})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      tuple_shape_, s64_vector_32_,
+      HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
+                                           /*elided_window_dims=*/{1},
+                                           /*gather_dims_to_operand_dims=*/{1}),
+      /*window_bounds=*/{64, 1});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Expected non-tuple argument for input"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      s64_vector_32_, tuple_shape_,
+      HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
+                                           /*elided_window_dims=*/{1},
+                                           /*gather_dims_to_operand_dims=*/{1}),
+      /*window_bounds=*/{64, 1});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Expected non-tuple argument for gather indices"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest, ScalarGatherIndicesInput) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      s64_vector_32_, s32_,
+      HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
+                                           /*elided_window_dims=*/{1},
+                                           /*gather_dims_to_operand_dims=*/{1}),
+      /*window_bounds=*/{64, 1});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Gather indices parameter must at least of rank 1"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest, FloatingPointGatherIndicesInput) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      s64_vector_32_, vector_32_,
+      HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
+                                           /*elided_window_dims=*/{1},
+                                           /*gather_dims_to_operand_dims=*/{1}),
+      /*window_bounds=*/{64, 1});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Gather indices parameter must be an integral tensor"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_NonAscendingWindowIndices) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 8, 7},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(
+      statusor.status().error_message(),
+      HasSubstr("Output window dimensions in gather op must be ascending"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_RepeatedWindowIndices) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 7},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(
+      statusor.status().error_message(),
+      HasSubstr("Output window dimensions in gather op must not repeat"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_WindowIndexOutOfBounds) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 99, 100, 101},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Window index 2 in gather op is out of bounds"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_MismatchingElidedWindowDims) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{4},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(
+      statusor.status().error_message(),
+      HasSubstr("All components of the window index in a gather op must either "
+                "be a output window index or explicitly elided"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_OutOfBoundsWindowToInputMapping) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{0, 1, 2, 3, 19},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Invalid elided_window_dims set in gather op; valid "
+                        "range is [0, 5), got: 19"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_RepeatedWindowToInputMapping) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{0, 1, 2, 3, 3},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(
+      statusor.status().error_message(),
+      HasSubstr(
+          "Repeated dimensions not allowed in elided_window_dims in gather op"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_MismatchingGatherToInputMapping) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(
+      statusor.status().error_message(),
+      HasSubstr(
+          "There must be exactly as many elements in "
+          "gather_dims_to_operand_dims "
+          "as there are elements in the last dimension of %gather_indices"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_OutOfBoundsGatherToInputMapping) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(
+      statusor.status().error_message(),
+      HasSubstr("Invalid gather_dims_to_operand_dims mapping; domain is "
+                "[0, 5), got: 4->7"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_RepeatedGatherToInputMapping) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3}),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(
+      statusor.status().error_message(),
+      HasSubstr(
+          "Repeated dimensions are not allowed in gather_dims_to_operand_dims"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_NonAscendingElidedWindowDims) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{2, 1},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{1, 1, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("elided_window_dims in gather op must be sorted"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowBoundsTooLarge) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7},
+          /*elided_window_dims=*/{2},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 1, 300, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Window bound at index 3 in gather op is out of range, "
+                        "must be within [0, 48), got 300"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_MismatchingNumberOfWindowBounds) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 28, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(
+      statusor.status().error_message(),
+      HasSubstr(
+          "Gather op must have one window bound for every input dimension"))
+      << statusor.status();
+}
+
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_WindowBoundsNot1ForElidedDim) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7},
+          /*elided_window_dims=*/{1},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+      /*window_bounds=*/{30, 29, 28, 26, 20});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Gather op can only elide window indices with bound 1, "
+                        "but bound is 29 for index 1 at position 0"))
+      << statusor.status();
+}
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index d42cb6cdf3..4a55e4095a 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -315,6 +315,36 @@ StatusOr<ComputationDataHandle> UserComputation::AddConstantInstruction(
   return handle;
 }
 
+StatusOr<ComputationDataHandle> UserComputation::AddGatherInstruction(
+    const GatherRequest& gather_request) {
+  tensorflow::mutex_lock lock(mutex_);
+
+  TF_ASSIGN_OR_RETURN(const OperationRequest* input_request,
+                      LookUpRequest(gather_request.input()));
+  TF_ASSIGN_OR_RETURN(const OperationRequest* gather_indices_request,
+                      LookUpRequest(gather_request.gather_indices()));
+
+  TF_ASSIGN_OR_RETURN(
+      Shape shape,
+      ShapeInference::InferGatherShape(
+          input_request->output_shape(), gather_indices_request->output_shape(),
+          gather_request.dimension_numbers(),
+          AsInt64Slice(gather_request.window_bounds())));
+
+  const ComputationDataHandle handle = CreateComputationDataHandle();
+
+  OperationRequest& request =
+      (*session_computation_.mutable_requests())[handle.handle()];
+  *request.mutable_output_handle() = handle;
+  *request.mutable_output_shape() = shape;
+  *request.mutable_request()->mutable_gather_request() = gather_request;
+
+  VLOG(1) << "AddGatherInstruction (" << GetVersionedHandleInternal()
+          << "), data handle " << handle.handle() << ": "
+          << gather_request.ShortDebugString();
+  return handle;
+}
+
 StatusOr<ComputationDataHandle> UserComputation::AddGetTupleElementInstruction(
     const GetTupleElementRequest& get_tuple_element_request) {
   tensorflow::mutex_lock lock(mutex_);
@@ -2018,6 +2048,16 @@ void PureFunctionalVisitor(const SessionComputation& session_computation,
       break;
     }
 
+    case OpRequest::kGatherRequest: {
+      PureFunctionalVisitor(session_computation,
+                            request.request().gather_request().input(),
+                            num_parameters, visited, is_functional);
+      PureFunctionalVisitor(session_computation,
+                            request.request().gather_request().gather_indices(),
+                            num_parameters, visited, is_functional);
+      break;
+    }
+
     case OpRequest::OP_NOT_SET:
       LOG(FATAL) << "OperationRequest doesn't contain a request";
 
@@ -2720,6 +2760,13 @@ static void ForEachOperand(
       break;
     }
 
+    case OpRequest::kGatherRequest: {
+      const GatherRequest& gather_request = request.request().gather_request();
+      apply(gather_request.input());
+      apply(gather_request.gather_indices());
+      break;
+    }
+
     case OpRequest::OP_NOT_SET:
       LOG(FATAL) << "OperationRequest doesn't contain a request";
 
@@ -3453,6 +3500,20 @@ void ComputationLowerer::Visit(
       break;
     }
 
+    case OpRequest::kGatherRequest: {
+      const GatherRequest& gather_request = request.request().gather_request();
+      HloInstruction* input_operand =
+          lookup_instruction(gather_request.input());
+      HloInstruction* gather_indices_operand =
+          lookup_instruction(gather_request.gather_indices());
+      std::vector<int64> window_bounds;
+      c_copy(gather_request.window_bounds(), std::back_inserter(window_bounds));
+      hlo_instruction = add_instruction(HloInstruction::CreateGather(
+          request.output_shape(), input_operand, gather_indices_operand,
+          gather_request.dimension_numbers(), window_bounds));
+      break;
+    }
+
     case OpRequest::OP_NOT_SET:
       LOG(FATAL) << "OperationRequest doesn't contain a request";
 
diff --git a/tensorflow/compiler/xla/service/user_computation.h b/tensorflow/compiler/xla/service/user_computation.h
index 81a72583f7..fd5a2ace9b 100644
--- a/tensorflow/compiler/xla/service/user_computation.h
+++ b/tensorflow/compiler/xla/service/user_computation.h
@@ -242,6 +242,10 @@ class UserComputation {
   StatusOr<ComputationDataHandle> AddRecvInstruction(
       const RecvRequest& recv_request);
 
+  // Enqueues a Gather instruction onto this user computation.
+  StatusOr<ComputationDataHandle> AddGatherInstruction(
+      const GatherRequest& gather_request);
+
   // Returns the user-provided name of this user computation, which is provided
   // via the XLA computation-building API.
   const string& name() const { return name_; }
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index 5dd5780835..cd2b843ad3 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -1049,6 +1049,9 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder,
           HloInstruction::CreateDot(shape, operands[0], operands[1], dnum));
       break;
     }
+    case HloOpcode::kGather:
+      // TODO(b/72710576): HLO parsing is not implemented for Gather.
+      return TokenError("HLO parsing is not implemented for Gather");
     case HloOpcode::kTrace:
       return TokenError(StrCat("parsing not yet implemented for op: ",
                                HloOpcodeString(opcode)));
diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index 08df5b12b3..46ec7af542 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -448,11 +448,38 @@ OutputIterator c_copy_if(InputContainer input_container,
                       output_iterator, predicate);
 }
 
+template <class InputContainer, class OutputIterator>
+OutputIterator c_copy(InputContainer input_container,
+                      OutputIterator output_iterator) {
+  return std::copy(std::begin(input_container), std::end(input_container),
+                   output_iterator);
+}
+
+template <class InputContainer>
+void c_sort(InputContainer& input_container) {
+  std::sort(std::begin(input_container), std::end(input_container));
+}
+
 template <class InputContainer, class Comparator>
 void c_sort(InputContainer& input_container, Comparator comparator) {
-  std::sort(input_container.begin(), input_container.end(), comparator);
+  std::sort(std::begin(input_container), std::end(input_container), comparator);
 }
 
+template <typename Sequence, typename T>
+bool c_binary_search(Sequence& sequence, T&& value) {
+  return std::binary_search(std::begin(sequence), std::end(sequence),
+                            std::forward<T>(value));
+}
+
+template <typename C>
+bool c_is_sorted(const C& c) {
+  return std::is_sorted(std::begin(c), std::end(c));
+}
+
+template <typename C>
+auto c_adjacent_find(const C& c) -> decltype(std::begin(c)) {
+  return std::adjacent_find(std::begin(c), std::end(c));
+}
 }  // namespace xla
 
 #define XLA_LOG_LINES(SEV, STRING) \
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 4fa5d28211..28620c3b86 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -393,6 +393,33 @@ message Window {
   repeated WindowDimension dimensions = 1;
 }
 
+// Describes the dimension numbers for a gather operation.
+//
+// See https://www.tensorflow.org/performance/xla/operation_semantics#gather for
+// more details.
+message GatherDimensionNumbers {
+  // "Window indices" is a term for a set of indices that index into the
+  // interior of a dynamic-slice from the input tensor, the starting indices for
+  // which were computed from output_gather_dims (see the operation semantic for
+  // how this is defined) and the gather_indices tensor.
+  //
+  // The window indices for a specific output index Out is computed as:
+  //
+  //  i = 0
+  //  for (k : [0, input_tensor_shape.rank))
+  //    window_indices[k] =
+  //      if k in elided_window_dims
+  //      then 0
+  //      else Out[output_window_dims[i++]]
+  repeated int64 output_window_dims = 1;
+  repeated int64 elided_window_dims = 2;
+
+  // This is interpreted as a map from i to gather_dims_to_operand_dims[i]. It
+  // transforms the gather index looked up from the gather_indices tensor into
+  // the starting index in the input space.
+  repeated int64 gather_dims_to_operand_dims = 3;
+}
+
 // Operation requests that are all collected as a tagged union with a oneof
 // field in OpRequest.
 
@@ -894,6 +921,13 @@ message RecvRequest {
   ChannelHandle channel_handle = 2;
 }
 
+message GatherRequest {
+  ComputationDataHandle input = 1;
+  ComputationDataHandle gather_indices = 2;
+  GatherDimensionNumbers dimension_numbers = 3;
+  repeated int64 window_bounds = 4;
+}
+
 message OpSharding {
   enum Type {
     // This sharding is replicated across all devices (implies maximal,
@@ -972,7 +1006,8 @@ message OpRequest {
     ConvertRequest bitcast_convert_request = 42;
     ConditionalRequest conditional_request = 44;
     HostComputeRequest host_compute_request = 45;
-    // Next: 46
+    GatherRequest gather_request = 46;
+    // Next: 47
   }
 }
 
diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index 5431572db8..daa2d4767c 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -1027,6 +1027,194 @@ Arguments | Type                    | Semantics
 The function is applied to each element in the `operand` array, resulting in an
 array with the same shape. It is allowed for `operand` to be a scalar (rank 0).
 
+## Gather
+
+The XLA gather operation stitches together several slices (each slice at a
+potentially different runtime offset) of an input tensor into an output tensor.
+
+### General Semantics
+
+See also
+[`ComputationBuilder::Gather`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/computation_builder.h).
+For a more intuitive description, see the "Informal Description" section below.
+
+<b> `gather(operand, gather_indices, output_window_dims, elided_window_dims, window_bounds, gather_dims_to_operand_dims)` </b>
+
+|Arguments         | Type                    | Semantics                       |
+|----------------- | ----------------------- | --------------------------------|
+|`operand`         | `ComputationDataHandle` | The tensor we’re gathering      |
+:                  :                         : from.                           :
+|`gather_indices`  | `ComputationDataHandle` | Tensor containing the starting  |
+:                  :                         : indices of the slices we're     :
+:                  :                         : we're stitching together into   :
+:                  :                         : the output tensor.              :
+|`output_window_dims` | `ArraySlice<int64>`  | The set of dimensions in the    |
+:                  :                         : output shape that are _window   :
+:                  :                         : dimensions_ (defined below).    :
+:                  :                         : Not all window dimensions may   :
+:                  :                         : be present in the output shape. :
+|`elided_window_dims` | `ArraySlice<int64>`  | The set of _window dimensions_  |
+:                  :            : that are not present in the output shape.    :
+:                  :            : `window_bounds[i]` must be `1` for all `i`   :
+:                  :            : in `elided_window_dims`.                     :
+|`window_bounds`   | `ArraySlice<int64>`    | `window_bounds[i]` is the bounds |
+:                  :            : for  window dimension `i`. This includes     :
+:                  :            : both the window dimensions that are          :
+:                  :            : explicitly part of the output shape (via     :
+:                  :            : `output_window_dims`) and the window         :
+:                  :            : dimensions that are elided (via              :
+:                  :            : `elided_window_dims`).                       :
+|`gather_dims_to_operand_dims` | `ArraySlice<int64>` | A dimension map (the  |
+:                  :            : array is interpreted as mapping `i` to       :
+:                  :            : `gather_dims_to_operand_dims[i]`)  from      :
+:                  :            : the gather indices in `gather_indices` to    :
+:                  :            : the operand index space.  It has to be       :
+:                  :            : one-to-one and total.                        :
+
+If `gather_indices` is a vector with `N` elements then we implicitly reshape it
+to a tensor of shape `[N,1]` before proceeding.
+
+For every index `Out` in the output tensor, we compute two things (more
+precisely described later):
+
+  - An index into the first `gather_indices.rank` - `1` dimensions of
+    `gather_indices`, which gives us a starting index of a slice, _operand
+    slice_, in the operand tensor.
+
+  - A _window index_ that has the same rank as the operand.  This index is
+    composed of the values in `Out` at dimensions `output_window_dims`, embedded
+    with zeroes according to `elided_window_dims`.
+
+The _window index_ is the relative index of the element in _operand slice_ that
+should be present in the output at index `Out`.
+
+The output is a tensor of rank `output_window_dims.size` + `gather_indices.rank`
+- `1`.  Additionally, as a shorthand, we define `output_gather_dims` of type
+`ArraySlice<int64>` as the set of dimensions in the output shape but not in
+`output_window_dims`, in ascending order.  E.g. if the output tensor has rank 5,
+`output_window_dims` is {`2`, `4`} then `output_gather_dims` is {`0`, `1`, `3`}
+
+The bounds for the output tensor along dimension `i` is computed as follows:
+
+  1. If `i` is present in `output_gather_dims` (i.e. is equal to
+    `output_gather_dims[k]` for some `k`) then we pick the corresponding
+    dimension bounds out of `gather_indices.shape` (i.e. pick
+    `gather_indices.shape.dims[k]`).
+  2. If `i` is present in `output_window_dims` (i.e. equal to
+     `output_window_dims[k]` for some `k`) then we pick the corresponding bound
+     out of `window_bounds` after accounting for `elided_window_dims` (i.e. we
+     pick `adjusted_window_bounds[k]` where `adjusted_window_bounds` is
+     `window_bounds` with the bounds at indices `elided_window_dims` removed).
+
+The operand index `In` corresponding to an output index `Out` is computed as
+follows:
+
+  1. Let `G` = { `Out`[`k`] for `k` in `output_gather_dims` }.  Use `G` to slice
+     out vector `S` such that `S`[`i`] = `gather_indices`[`G`, `i`].
+  2. Create an index, `S`<sub>`in`</sub>, into `operand` using `S` by scattering
+     `S` using the `gather_dims_to_operand_dims` map (`S`<sub>`in`</sub> is the
+     starting indices for _operand slice_ mentioned above.).  More precisely:
+       1. `S`<sub>`in`</sub>[`gather_dims_to_operand_dims`[`k`]] = `S`[`k`] if `k` <
+          `gather_dims_to_operand_dims.size`.
+       2. `S`<sub>`in`</sub>[`_`] = `0` otherwise.
+  3. Create an index `W`<sub>`in`</sub> into `operand` by scattering the indices
+     at the output window dimensions in `Out` according to
+     the `elided_window_dims` set (`W`<sub>`in`</sub> is the _window index_
+     mentioned above).  More precisely:
+       1. `W`<sub>`in`</sub>[`window_dims_to_operand_dims`(`k`)] = `Out`[`k`] if
+          `k` < `output_window_dims.size` (`window_dims_to_operand_dims` is
+          defined below).
+       2. `W`<sub>`in`</sub>[`_`] = `0` otherwise.
+  4. `In` is `W`<sub>`in`</sub> + `S`<sub>`in`</sub> where + is element-wise
+     addition.
+
+`window_dims_to_operand_dims` is the monotonic function with domain [`0`,
+`output_window_dims.size`) and range [`0`, `operand.rank`) \
+`elided_window_dims`.  So if, e.g., `output_window_dims.size` is `4`,
+`operand.rank` is `6` and `elided_window_dims` is {`0`, `2`} then
+`window_dims_to_operand_dims` is {`0`→`1`, `1`→`3`, `2`→`4`, `3`→`5`}.
+
+### Informal Description
+
+To get an intuition on how all of the above fits together, let's look at an
+example that gathers 5 slices of shape `[8,6]` from a `[16,11]` tensor.  The
+position of a slice into the `[16,11]` tensor can be represented as an index
+vector of shape `S64[2]`, so the set of 5 positions can be represented as a
+`S64[5,2]` tensor.
+
+The behavior of the gather operation can then be depicted as an index
+transformation that takes [`G`,`W`<sub>`0`</sub>,`W`<sub>`1`</sub>], an index in
+the output shape, and maps it to an element in the input tensor in the following
+way:
+
+<div style="width:95%; margin:auto; margin-bottom:10px; margin-top:20px;">
+  <img style="width:100%" src="../../images/ops_xla_gather_0.svg">
+</div>
+
+We first select an (`X`,`Y`) vector from the gather indices tensor using `G`.
+The element in the output tensor at index
+[`G`,`W`<sub>`0`</sub>,`W`<sub>`1`</sub>] is then the element in the input
+tensor at index [`X`+`W`<sub>`0`</sub>,`Y`+`W`<sub>`1`</sub>].
+
+`window_bounds` is `[8,6]`, which decides the range of W<sub>`0`</sub> and
+W<sub>`1`</sub>, and this in turn decides the bounds of the slice.
+
+This gather operation acts as a batch dynamic slice with `G` as the batch
+dimension.
+
+The gather indices may be multidimensional.  For instance, a more general
+version of the example above using a "gather indices" tensor of shape `[4,5,2]`
+would translate indices like this:
+
+<div style="width:95%; margin:auto; margin-bottom:10px; margin-top:20px;">
+  <img style="width:100%" src="../../images/ops_xla_gather_1.svg">
+</div>
+
+Again, this acts as a batch dynamic slice `G`<sub>`0`</sub> and
+`G`<sub>`1`</sub> as the batch dimensions.  The window bounds are still `[8,6]`.
+
+The gather operation in XLA generalizes the informal semantics outlined above in
+the following ways:
+
+ 1. We can configure which dimensions in the output shape are the window
+    dimensions (dimensions containing `W`<sub>`0`</sub>, `W`<sub>`1`</sub> in
+    the last example).  The output gather dimensions (dimensions containing
+    `G`<sub>`0`</sub>, `G`<sub>`1`</sub> in the last example) are defined to be
+    the output dimensions that are not window dimensions.
+
+ 2. The number of output window dimensions explicitly present in the output
+    shape may be smaller than the input rank.  These "missing" dimensions, which
+    are listed explicitly as `elided_window_dims`, must have a window bound of
+    `1`.  Since they have a window bound of `1` the only valid index for them is
+    `0` and eliding them does not introduce ambiguity.
+
+ 3. The slice extracted from the "Gather Indices" tensor ((`X`, `Y`) in the last
+    example) may have fewer elements than the input tensor rank, and an explicit
+    mapping dictates how the index should be expanded to have the same rank as
+    the input.
+
+As a final example, we use (2) and (3) to implement `tf.gather_nd`:
+
+<div style="width:95%; margin:auto; margin-bottom:10px; margin-top:20px;">
+  <img style="width:100%" src="../../images/ops_xla_gather_2.svg">
+</div>
+
+`G`<sub>`0`</sub> and `G`<sub>`1`</sub> are used to slice out a starting index
+from the gather indices tensor as usual, except the starting index has only one
+element, `X`.  Similarly, there is only one output window index with the value
+`W`<sub>`0`</sub>.  However, before being used as indices into the input tensor,
+these are expanded in accordance to "Gather Index Mapping"
+(`gather_dims_to_operand_dims` in the formal description) and "Window Mapping"
+(`window_dims_to_operand_dims` in the formal description) into
+[`0`,`W`<sub>`0`</sub>] and [`X`,`0`] respectively, adding up to
+[`X`,`W`<sub>`0`</sub>].  In other words, the output index
+[`G`<sub>`0`</sub>,`G`<sub>`1`</sub>,`W`<sub>`0`</sub>] maps to the input index
+[`GatherIndices`[`G`<sub>`0`</sub>,`G`<sub>`1`</sub>,`0`],`X`] which gives us
+the semantics for `tf.gather_nd`.
+
+`window_bounds` for this case is `[1,11]`.  Intuitively this means that every
+index `X` in the gather indices tensor picks an entire row and the result is the
+concatenation of all these rows.
 
 ## GetTupleElement
 
-- 
GitLab


From 9739b75fe965ad497b37d209e6a33965010bdc61 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Fri, 16 Feb 2018 15:30:46 -0800
Subject: [PATCH 031/575] Default eager tensor device name should match default
 device name.

PiperOrigin-RevId: 186055679
---
 tensorflow/c/eager/c_api.cc | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index 8e834eb99c..98ef6f0d0a 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -161,10 +161,11 @@ int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index) {
 }
 
 const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h) {
-  // This might be a bit confusing as a tensor on CPU can sometimes return
-  // "CPU:0" and sometimes "/job:localhost/replica:0/task:0/cpu:0".
-  // TODO(ashankar): Figure out which one would be nicer.
-  return (h->d == nullptr) ? "CPU:0" : h->d->name().c_str();
+  // TODO(apassos) this will be potentially incorrect in the distributed case as
+  // our local device will have a name which depends on the ClusterSpec and
+  // hence will require the context to resolve.
+  return (h->d == nullptr) ? "/job:localhost/replica:0/task:0/device:CPU:0"
+                           : h->d->name().c_str();
 }
 
 TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, TF_Status* status) {
-- 
GitLab


From f5e2ef8efd7f3f204d071a48200d9c2b548e2e3e Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 16 Feb 2018 16:01:54 -0800
Subject: [PATCH 032/575] Checkpointable: Don't run ops automatically when
 graph building.

This is a prerequisite to moving toward a Saver-like model when graph building. We no longer mess with initializers (when graph building; eager needs it), and restore ops just get queued up and returned.

Since initializers are left alone when graph building, there is a new special case for slot variables which needs to be handled. This is the third(!) queue for deferred slot restorations ((1) variable -> slot, (2) optimizer -> slot, (3) (optimizer, variable) -> slot), and should be the last one I need (it's a hypergraph with 3-tuple edges).

The plan after this is to switch over to tf.train.Saver's existing restore op creation infrastructure, which will handle any SaveableObjects. There will also be a few CLs for making graph usage prettier, and eventually allowing eager/graph agnostic save/restore.

PiperOrigin-RevId: 186059387
---
 .../eager/python/checkpointable_utils.py      |  44 ++++++--
 .../eager/python/checkpointable_utils_test.py |  99 +++++++++++------
 tensorflow/python/training/checkpointable.py  |  48 +++-----
 tensorflow/python/training/optimizer.py       | 105 +++++++++++++-----
 4 files changed, 191 insertions(+), 105 deletions(-)

diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py
index d3c57bc606..0506af391c 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils.py
@@ -329,6 +329,7 @@ def save(file_prefix, root_checkpointable, checkpoint_number=None,
 
 
 class CheckpointLoadStatus(object):
+  """Checks the status of checkpoint loading."""
 
   def __init__(self, checkpoint):
     self._checkpoint = checkpoint
@@ -338,12 +339,22 @@ class CheckpointLoadStatus(object):
     for node_id, node in enumerate(self._checkpoint.object_graph_proto.nodes):
       checkpointable = self._checkpoint.object_by_proto_id.get(node_id, None)
       if checkpointable is None:
-        raise AssertionError("Unresolved object in checkpoint: %s" % (node))
+        raise AssertionError("Unresolved object in checkpoint: %s" % (node,))
       if checkpointable._update_uid < self._checkpoint.restore_uid:  # pylint: disable=protected-access
         raise AssertionError(
-            "Object not assigned a value from checkpoint: %s" % (node))
+            "Object not assigned a value from checkpoint: %s" % (node,))
+    if self._checkpoint.slot_restorations:
+      # Sanity check; this collection should be clear if everything has been
+      # restored.
+      raise AssertionError("Unresolved slot restorations: %s" % (
+          self._checkpoint.slot_restorations,))
     return self
 
+  @property
+  def restore_ops(self):
+    """Operations to restore objects in the dependency graph."""
+    return self._checkpoint.restore_ops
+
 
 def restore(save_path, root_checkpointable, session=None):
   """Restore a training checkpoint.
@@ -355,8 +366,8 @@ def restore(save_path, root_checkpointable, session=None):
   `root_checkpointable` after this call will be matched if they have a
   corresponding object in the checkpoint.
 
-  When building a graph, restorations are executed in the default session if
-  `session` is `None`. Variable initializers read checkpointed values.
+  When building a graph, restorations are added to the graph but not run. A
+  session is required to retrieve checkpoint metadata.
 
   To disallow deferred loading, assert immediately that all checkpointed
   variables have been matched to variable objects:
@@ -368,21 +379,32 @@ def restore(save_path, root_checkpointable, session=None):
   An exception will be raised unless every object was matched and its variables
   already exist.
 
+  When graph building, `assert_consumed()` indicates that all of the restore ops
+  which will be created for this checkpoint have been created. They are
+  available in the `restore_ops` property of the status object:
+
+  ```python
+  session.run(restore(path, root).assert_consumed().restore_ops)
+  ```
+
+  If the checkpoint has not been consumed completely, then the list of
+  `restore_ops` will grow as more objects are added to the dependency graph.
+
   Args:
     save_path: The path to the checkpoint, as returned by `save` or
       `tf.train.latest_checkpoint`. If None (as when there is no latest
       checkpoint for `tf.train.latest_checkpoint` to return), does nothing.
     root_checkpointable: The root of the object graph to restore. Variables to
       restore need not have been created yet, but all dependencies on other
-      Checkpointable objects should already be declared. Objects in the
+      `Checkpointable` objects should already be declared. Objects in the
       dependency graph are matched to objects in the checkpointed graph, and
       matching objects have their variables restored (or the checkpointed values
       saved for eventual restoration when the variable is created).
-    session: The session to evaluate assignment ops in. Ignored when executing
+    session: The session to retrieve metadata with. Ignored when executing
       eagerly. If not provided when graph building, the default session is used.
   Returns:
-    A CheckpointLoadStatus object, which can be used to make assertions about
-    the status of checkpoint restoration.
+    A `CheckpointLoadStatus` object, which can be used to make assertions about
+    the status of checkpoint restoration and fetch restore ops.
   """
   if save_path is None:
     return
@@ -406,8 +428,8 @@ def restore(save_path, root_checkpointable, session=None):
   object_graph_proto.ParseFromString(object_graph_string)
   checkpoint = core_checkpointable._Checkpoint(  # pylint: disable=protected-access
       object_graph_proto=object_graph_proto,
-      save_path=save_path,
-      session=session)
+      save_path=save_path)
   core_checkpointable._CheckpointPosition(  # pylint: disable=protected-access
       checkpoint=checkpoint, proto_id=0).restore(root_checkpointable)
-  return CheckpointLoadStatus(checkpoint)
+  load_status = CheckpointLoadStatus(checkpoint)
+  return load_status
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index 1394f0cf0f..21ba6adc6a 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -398,36 +398,37 @@ class CheckpointingTests(test.TestCase):
     optimizer_variables = self.evaluate(optimizer.variables())
     self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
     # Immediate restoration
-    root_checkpointable.restore(save_path=save_path).assert_consumed()
+    status = root_checkpointable.restore(save_path=save_path).assert_consumed()
+    self.evaluate(status.restore_ops)
     self.assertAllEqual([42.], self.evaluate(network._named_dense.variables[1]))
     self.assertAllEqual(1, self.evaluate(root_checkpointable.save_counter))
     self.assertAllEqual([1.5], self.evaluate(m_bias_slot))
-    with ops.Graph().as_default():
-      on_create_network = MyNetwork()
-      on_create_optimizer = CheckpointableAdam(0.001)
-      on_create_root = Checkpoint(
-          optimizer=on_create_optimizer, network=on_create_network)
-      with self.test_session(graph=ops.get_default_graph()):
-        # Deferred restoration
-        status = on_create_root.restore(save_path=save_path)
-        on_create_network(constant_op.constant([[3.]]))  # create variables
-        self.assertAllEqual(1, self.evaluate(on_create_root.save_counter))
-        self.assertAllEqual([42.],
-                            self.evaluate(
-                                on_create_network._named_dense.variables[1]))
-        on_create_m_bias_slot = on_create_optimizer.get_slot(
-            on_create_network._named_dense.variables[1], "m")
-        # Optimizer slot variables are created when the original variable is
-        # restored.
-        self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
-        self.assertAllEqual(optimizer_variables[2:],
-                            self.evaluate(on_create_optimizer.variables()))
-        on_create_optimizer._create_slots(
-            [resource_variable_ops.ResourceVariable([1.])])
-        status.assert_consumed()
-        beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators()
-        self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power))
-        self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power))
+    if context.in_graph_mode():
+      return  # Restore-on-create is only supported when executing eagerly
+    on_create_network = MyNetwork()
+    on_create_optimizer = CheckpointableAdam(0.001)
+    on_create_root = Checkpoint(
+        optimizer=on_create_optimizer, network=on_create_network)
+    # Deferred restoration
+    status = on_create_root.restore(save_path=save_path)
+    on_create_network(constant_op.constant([[3.]]))  # create variables
+    self.assertAllEqual(1, self.evaluate(on_create_root.save_counter))
+    self.assertAllEqual([42.],
+                        self.evaluate(
+                            on_create_network._named_dense.variables[1]))
+    on_create_m_bias_slot = on_create_optimizer.get_slot(
+        on_create_network._named_dense.variables[1], "m")
+    # Optimizer slot variables are created when the original variable is
+    # restored.
+    self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
+    self.assertAllEqual(optimizer_variables[2:],
+                        self.evaluate(on_create_optimizer.variables()))
+    on_create_optimizer._create_slots(
+        [resource_variable_ops.ResourceVariable([1.])])
+    status.assert_consumed()
+    beta1_power, beta2_power = on_create_optimizer._get_beta_accumulators()
+    self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power))
+    self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power))
 
   def testDeferredRestorationUsageEager(self):
     """An idiomatic eager execution example."""
@@ -479,10 +480,11 @@ class CheckpointingTests(test.TestCase):
               # if no checkpoint is being loaded. This would make deferred
               # loading a bit more useful with graph execution.
             else:
-              checkpointable_utils.restore(
+              status = checkpointable_utils.restore(
                   save_path=checkpoint_path,
                   root_checkpointable=root,
-                  session=session)
+                  session=session).assert_consumed()
+              session.run(status.restore_ops)
             for _ in range(num_training_steps):
               session.run(train_op)
             root.save(file_prefix=checkpoint_prefix,
@@ -560,6 +562,7 @@ class CheckpointingTests(test.TestCase):
       status.assert_consumed()
     load_into.add_dep()
     status.assert_consumed()
+    self.evaluate(status.restore_ops)
     self.assertEqual(123., self.evaluate(load_into.dep.var))
 
   @test_util.run_in_graph_and_eager_modes()
@@ -591,6 +594,7 @@ class CheckpointingTests(test.TestCase):
         save_path, loaded_dep_after_var)
     loaded_dep_after_var.add_dep()
     status.assert_consumed()
+    self.evaluate(status.restore_ops)
     self.assertEqual(-14., self.evaluate(loaded_dep_after_var.dep.var))
 
   @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
@@ -627,16 +631,28 @@ class CheckpointingTests(test.TestCase):
       no_slot_status.assert_consumed()
     new_root.var = checkpointable_utils.add_variable(
         new_root, name="var", shape=[])
-    self.assertEqual(12., self.evaluate(new_root.var))
     no_slot_status.assert_consumed()
+    self.evaluate(no_slot_status.restore_ops)
+    self.assertEqual(12., self.evaluate(new_root.var))
     new_root.optimizer = CheckpointableAdam(0.1)
     with self.assertRaisesRegexp(AssertionError, "beta1_power"):
       slot_status.assert_consumed()
     self.assertEqual(12., self.evaluate(new_root.var))
-    self.assertEqual(14., self.evaluate(
-        new_root.optimizer.get_slot(name="m", var=new_root.var)))
+    if context.in_eager_mode():
+      # Slot variables are only created with restoring initializers when
+      # executing eagerly.
+      self.assertEqual(14., self.evaluate(
+          new_root.optimizer.get_slot(name="m", var=new_root.var)))
+    else:
+      self.assertIs(new_root.optimizer.get_slot(name="m", var=new_root.var),
+                    None)
     if context.in_graph_mode():
       train_op = new_root.optimizer.minimize(new_root.var)
+      # The slot variable now exists; restore() didn't create it, but we should
+      # now have a restore op for it.
+      self.evaluate(slot_status.restore_ops)
+      self.assertEqual(14., self.evaluate(
+          new_root.optimizer.get_slot(name="m", var=new_root.var)))
       self.evaluate(train_op)
     else:
       new_root.optimizer.minimize(new_root.var.read_value)
@@ -667,9 +683,12 @@ class CheckpointingTests(test.TestCase):
         load_dep, name="var", shape=[])
     first_root.dep = load_dep
     first_status.assert_consumed()
+    self.evaluate(first_status.restore_ops)
+    self.assertEqual([], second_status.restore_ops)
     self.assertEqual(12., self.evaluate(load_dep.var))
     second_root.dep = load_dep
     second_status.assert_consumed()
+    self.evaluate(second_status.restore_ops)
     self.assertEqual(13., self.evaluate(load_dep.var))
 
     # Try again with the order of the restore() reversed. The last restore
@@ -685,9 +704,12 @@ class CheckpointingTests(test.TestCase):
         load_dep, name="var", shape=[])
     first_root.dep = load_dep
     first_status.assert_consumed()
+    self.assertEqual([], second_status.restore_ops)
+    self.evaluate(first_status.restore_ops)
     self.assertEqual(12., self.evaluate(load_dep.var))
     second_root.dep = load_dep
     second_status.assert_consumed()
+    self.evaluate(second_status.restore_ops)
     self.assertEqual(12., self.evaluate(load_dep.var))
 
   @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
@@ -734,7 +756,9 @@ class CheckpointingTests(test.TestCase):
         load_root.dep_one, name="var1", shape=[], dtype=dtypes.float64)
     v2 = checkpointable_utils.add_variable(
         load_root.dep_one, name="var2", shape=[], dtype=dtypes.float64)
-    checkpointable_utils.restore(save_path, load_root).assert_consumed()
+    status = checkpointable_utils.restore(
+        save_path, load_root).assert_consumed()
+    self.evaluate(status.restore_ops)
     self.assertEqual(32., self.evaluate(v1))
     self.assertEqual(64., self.evaluate(v2))
 
@@ -768,6 +792,7 @@ class CheckpointingTests(test.TestCase):
     second_load.v = checkpointable_utils.add_variable(
         second_load, "v2", shape=[4])
     status.assert_consumed()
+    self.evaluate(status.restore_ops)
     self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v))
     self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v))
 
@@ -776,8 +801,9 @@ class CheckpointingTests(test.TestCase):
     self.assertAllEqual([2., 7., 1.], self.evaluate(first_load.v))
     self.evaluate(second_load.v.assign([2., 7., 1., 8.]))
     self.assertAllEqual([2., 7., 1., 8.], self.evaluate(second_load.v))
-    checkpointable_utils.restore(
+    status = checkpointable_utils.restore(
         save_path, first_load).assert_consumed()
+    self.evaluate(status.restore_ops)
     self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v))
     self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v))
 
@@ -801,13 +827,16 @@ class CheckpointingTests(test.TestCase):
       second = checkpointable.Checkpointable()
       second.var2 = variable_scope.get_variable(
           name="blah", initializer=0.)
-      checkpointable_utils.restore(save_path, root_checkpointable=second)
+      status = checkpointable_utils.restore(
+          save_path, root_checkpointable=second)
       recreated_var1 = variable_scope.get_variable(
           name="outside_var", initializer=0.)
+      self.evaluate(status.restore_ops)
       self.assertEqual(8., self.evaluate(second.var2))
       self.evaluate(recreated_var1.assign(-2.))
       self.assertEqual(-2., self.evaluate(recreated_var1))
       second.var1 = recreated_var1
+      self.evaluate(status.restore_ops)
       self.assertEqual(4., self.evaluate(recreated_var1))
 
   # TODO(allenl): Saver class that doesn't pollute the graph with constants.
diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py
index aaca67755e..9d62c5ff91 100644
--- a/tensorflow/python/training/checkpointable.py
+++ b/tensorflow/python/training/checkpointable.py
@@ -99,9 +99,8 @@ class _CheckpointPosition(object):
       # This object's correspondence with a checkpointed object is new, so
       # process deferred restorations for it and its dependencies.
       restore_ops = checkpointable._restore_from_checkpoint_position(self)  # pylint: disable=protected-access
-      session = self._checkpoint.session
-      if session:
-        session.run(restore_ops)
+      if restore_ops:
+        self._checkpoint.restore_ops.extend(restore_ops)
 
   def bind_object(self, checkpointable):
     """Set a checkpoint<->object correspondence and process slot variables.
@@ -120,13 +119,13 @@ class _CheckpointPosition(object):
       checkpoint.object_by_proto_id[self._proto_id] = checkpointable
       for deferred_slot_restoration in (
           checkpoint.deferred_slot_restorations.pop(self._proto_id, ())):
-        checkpointable._process_slot_restoration(  # pylint: disable=protected-access
+        checkpointable._create_or_restore_slot_variable(  # pylint: disable=protected-access
             slot_variable_position=_CheckpointPosition(
                 checkpoint=checkpoint,
                 proto_id=deferred_slot_restoration.slot_variable_id),
             variable=deferred_slot_restoration.original_variable,
             slot_name=deferred_slot_restoration.slot_name)
-      for slot_restoration in checkpoint.slot_restorations.get(
+      for slot_restoration in checkpoint.slot_restorations.pop(
           self._proto_id, ()):
         optimizer_object = checkpoint.object_by_proto_id.get(
             slot_restoration.optimizer_id, None)
@@ -140,7 +139,7 @@ class _CheckpointPosition(object):
                       slot_variable_id=slot_restoration.slot_variable_id,
                       slot_name=slot_restoration.slot_name))
         else:
-          optimizer_object._process_slot_restoration(  # pylint: disable=protected-access
+          optimizer_object._create_or_restore_slot_variable(  # pylint: disable=protected-access
               slot_variable_position=_CheckpointPosition(
                   checkpoint=checkpoint,
                   proto_id=slot_restoration.slot_variable_id),
@@ -229,7 +228,7 @@ _SlotVariableRestoration = collections.namedtuple(
 class _Checkpoint(object):
   """Holds the status of an object-based checkpoint load."""
 
-  def __init__(self, object_graph_proto, save_path, session):
+  def __init__(self, object_graph_proto, save_path):
     """Specify the checkpoint being loaded.
 
     Args:
@@ -237,11 +236,6 @@ class _Checkpoint(object):
         associated with this checkpoint.
       save_path: The path to the checkpoint, as returned by
         `tf.train.latest_checkpoint`.
-      session: The session to evaluate assignment ops in. Should be None if
-        executing eagerly.
-
-    Raises:
-      ValueError: If `session` is not None and eager execution is enabled.
     """
     self.object_graph_proto = object_graph_proto
     self.restore_uid = ops.uid()
@@ -255,6 +249,9 @@ class _Checkpoint(object):
     self.save_path = save_path
     reader = pywrap_tensorflow.NewCheckpointReader(save_path)
     self.dtype_map = reader.get_variable_to_dtype_map()
+    # When graph building, contains a list of ops to run to restore objects from
+    # this checkpoint.
+    self.restore_ops = []
     # A mapping from optimizer proto ids to lists of slot variables to be
     # restored when the optimizer is tracked. Only includes slot variables whose
     # regular variables have already been created, and only for optimizer
@@ -274,10 +271,6 @@ class _Checkpoint(object):
                     optimizer_id=node_index,
                     slot_variable_id=slot_reference.slot_variable_node_id,
                     slot_name=slot_reference.slot_name))
-    if session is not None and context.in_eager_mode():
-      raise ValueError(
-          "Passed a session %s when executing eagerly." % (session,))
-    self.session = session
 
 
 class CheckpointableBase(object):
@@ -348,11 +341,15 @@ class CheckpointableBase(object):
            "Checkpointable._add_variable called to create another with "
            "that name. Variable names must be unique within a Checkpointable "
            "object.") % (name,))
-    # If this is a variable with a single Tensor stored in the checkpoint, we
-    # can set that value as an initializer rather than initializing and then
-    # assigning (when executing eagerly).
-    checkpoint_initializer = self._preload_simple_restoration(
-        name=name, shape=shape)
+    if context.in_eager_mode():
+      # If this is a variable with a single Tensor stored in the checkpoint, we
+      # can set that value as an initializer rather than initializing and then
+      # assigning (when executing eagerly). This call returns None if there is
+      # nothing to restore.
+      checkpoint_initializer = self._preload_simple_restoration(
+          name=name, shape=shape)
+    else:
+      checkpoint_initializer = None
     if (checkpoint_initializer is not None
         and not (
             isinstance(initializer, CheckpointInitialValue)
@@ -365,20 +362,11 @@ class CheckpointableBase(object):
       # effort" to set the initializer with the highest restore UID.
       initializer = checkpoint_initializer
       shape = None
-      checkpoint_position = checkpoint_initializer.checkpoint_position
-    else:
-      checkpoint_position = None
 
     new_variable = getter(
         name=name, shape=shape, dtype=dtype, initializer=initializer,
         **kwargs_for_getter)
 
-    if (checkpoint_position is not None
-        and hasattr(new_variable, "_update_uid")
-        and new_variable._update_uid == checkpoint_position.restore_uid):  # pylint: disable=protected-access
-      session = checkpoint_position.checkpoint.session
-      if session:
-        session.run(new_variable.initializer)
     # If we set an initializer and the variable processed it, tracking will not
     # assign again. It will add this variable to our dependencies, and if there
     # is a non-trivial restoration queued, it will handle that. This also
diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py
index 762658175a..678d6322aa 100644
--- a/tensorflow/python/training/optimizer.py
+++ b/tensorflow/python/training/optimizer.py
@@ -324,9 +324,18 @@ class Optimizer(checkpointable.Checkpointable):
     self._use_locking = use_locking
     self._name = name
     # Dictionary of slots.
-    #  {slot_name : { variable_to_train: slot_for_the_variable, ...}, ... }
+    #  {slot_name :
+    #      {_var_key(variable_to_train): slot_for_the_variable, ... },
+    #   ... }
     self._slots = {}
     self._non_slot_dict = {}
+    # For implementing Checkpointable. Stores information about how to restore
+    # slot variables which have not yet been created
+    # (checkpointable._CheckpointPosition objects).
+    #  {slot_name :
+    #      {_var_key(variable_to_train): [checkpoint_position, ... ], ... },
+    #   ... }
+    self._deferred_slot_restorations = {}
 
   def get_name(self):
     return self._name
@@ -884,7 +893,11 @@ class Optimizer(checkpointable.Checkpointable):
     """
     named_slots = self._slot_dict(slot_name)
     if _var_key(var) not in named_slots:
-      named_slots[_var_key(var)] = slot_creator.create_slot(var, val, op_name)
+      new_slot_variable = slot_creator.create_slot(var, val, op_name)
+      self._restore_slot_variable(
+          slot_name=slot_name, variable=var,
+          slot_variable=new_slot_variable)
+      named_slots[_var_key(var)] = new_slot_variable
     return named_slots[_var_key(var)]
 
   def _get_or_make_slot_with_initializer(self, var, initializer, shape, dtype,
@@ -905,8 +918,12 @@ class Optimizer(checkpointable.Checkpointable):
     """
     named_slots = self._slot_dict(slot_name)
     if _var_key(var) not in named_slots:
-      named_slots[_var_key(var)] = slot_creator.create_slot_with_initializer(
+      new_slot_variable = slot_creator.create_slot_with_initializer(
           var, initializer, shape, dtype, op_name)
+      self._restore_slot_variable(
+          slot_name=slot_name, variable=var,
+          slot_variable=new_slot_variable)
+      named_slots[_var_key(var)] = new_slot_variable
     return named_slots[_var_key(var)]
 
   def _zeros_slot(self, var, slot_name, op_name):
@@ -923,12 +940,43 @@ class Optimizer(checkpointable.Checkpointable):
     """
     named_slots = self._slot_dict(slot_name)
     if _var_key(var) not in named_slots:
-      named_slots[_var_key(var)] = slot_creator.create_zeros_slot(var, op_name)
+      new_slot_variable = slot_creator.create_zeros_slot(var, op_name)
+      self._restore_slot_variable(
+          slot_name=slot_name, variable=var,
+          slot_variable=new_slot_variable)
+      named_slots[_var_key(var)] = new_slot_variable
     return named_slots[_var_key(var)]
 
-  def _process_slot_restoration(
+  # --------------
+  # For implementing the Checkpointable interface.
+  # --------------
+
+  def _restore_slot_variable(self, slot_name, variable, slot_variable):
+    """Restore a newly created slot variable's value."""
+    variable_key = _var_key(variable)
+    deferred_restorations = self._deferred_slot_restorations.get(
+        slot_name, {}).pop(variable_key, [])
+    # Iterate over restores, highest restore UID first to minimize the number
+    # of assignments.
+    deferred_restorations.sort(key=lambda position: position.restore_uid,
+                               reverse=True)
+    for checkpoint_position in deferred_restorations:
+      checkpoint_position.restore(slot_variable)
+
+  def _create_or_restore_slot_variable(
       self, slot_variable_position, slot_name, variable):
-    """Restore a slot variable's value (creating it if necessary).
+    """Restore a slot variable's value, possibly creating it.
+
+    Called when a variable which has an associated slot variable is created or
+    restored. When executing eagerly, we create the slot variable with a
+    restoring initializer.
+
+    No new variables are created when graph building. Instead,
+    _restore_slot_variable catches these after normal creation and adds restore
+    ops to the graph. This method is nonetheless important when graph building
+    for the case when a slot variable has already been created but `variable`
+    has just been added to a dependency graph (causing us to realize that the
+    slot variable needs to be restored).
 
     Args:
       slot_variable_position: A `checkpointable._CheckpointPosition` object
@@ -939,28 +987,16 @@ class Optimizer(checkpointable.Checkpointable):
     named_slots = self._slot_dict(slot_name)
     variable_key = _var_key(variable)
     slot_variable = named_slots.get(variable_key, None)
-    if slot_variable is None:
-      if slot_variable_position.is_simple_variable():
-        initializer = checkpointable.CheckpointInitialValue(
-            checkpoint_position=slot_variable_position)
-        slot_variable = self._get_or_make_slot(
-            var=variable,
-            val=initializer,
-            slot_name=slot_name,
-            op_name=self._name)
-        if slot_variable._update_uid == slot_variable_position.restore_uid:  # pylint: disable=protected-access
-          # If our restoration was set (not given with custom getters), run
-          # it. Otherwise wait for the restore() call below to restore if
-          # necessary.
-          session = slot_variable_position.checkpoint.session
-          if session:
-            session.run(slot_variable.initializer)
-
-      else:
-        raise NotImplementedError(
-            "Currently only variables with no dependencies can be loaded as "
-            "slot variables. File a feature request if this limitation bothers "
-            "you. (Got %s)" % (slot_variable_position,))
+    if (slot_variable is None
+        and context.in_eager_mode()
+        and slot_variable_position.is_simple_variable()):
+      initializer = checkpointable.CheckpointInitialValue(
+          checkpoint_position=slot_variable_position)
+      slot_variable = self._get_or_make_slot(
+          var=variable,
+          val=initializer,
+          slot_name=slot_name,
+          op_name=self._name)
       # Slot variables are not owned by any one object (because we don't want to
       # save the slot variable if the optimizer is saved without the non-slot
       # variable, or if the non-slot variable is saved without the optimizer;
@@ -968,4 +1004,15 @@ class Optimizer(checkpointable.Checkpointable):
       # variable, variable)). So we don't _track_ slot variables anywhere, and
       # instead special-case this dependency and otherwise pretend it's a normal
       # graph.
-    slot_variable_position.restore(slot_variable)
+    if slot_variable is not None:
+      # If we've either made this slot variable, or if we've pulled out an
+      # existing slot variable, we should restore it.
+      slot_variable_position.restore(slot_variable)
+    else:
+      # We didn't make the slot variable. Defer restoring until it gets created
+      # normally. We keep a list rather than the one with the highest restore
+      # UID in case slot variables have their own dependencies, in which case
+      # those could differ between restores.
+      self._deferred_slot_restorations.setdefault(
+          slot_name, {}).setdefault(variable_key, []).append(
+              slot_variable_position)
-- 
GitLab


From dc484fa17d7693a563abf32138ad3d65d9bfc8aa Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Fri, 16 Feb 2018 16:15:21 -0800
Subject: [PATCH 033/575] Reset the DAZ bit when entering the XLA CPU/GPU
 compiler

In an ideal world this won't make a difference since the compiler should be
disciplined about not leaking host-level optimization artifacts into generated
code.  However, I think this provides some defense-in-depth in preventing
non-obvious denormal behavior on the host side from messing up floating point
constants etc. we want to embed into generated code.

PiperOrigin-RevId: 186061140
---
 tensorflow/compiler/xla/service/BUILD         |  1 +
 .../compiler/xla/service/llvm_compiler.cc     | 13 +++++
 tensorflow/core/platform/denormal.cc          | 49 ++++++++++++-------
 tensorflow/core/platform/denormal.h           | 28 +++++++++--
 4 files changed, 71 insertions(+), 20 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 0ceb9ca9e0..22d1c5a9bd 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -721,6 +721,7 @@ cc_library(
     hdrs = ["llvm_compiler.h"],
     deps = [
         ":compiler",
+        "//tensorflow/core:lib_internal",
         "@llvm//:core",
     ],
 )
diff --git a/tensorflow/compiler/xla/service/llvm_compiler.cc b/tensorflow/compiler/xla/service/llvm_compiler.cc
index 68c35c0c1f..911b243fe2 100644
--- a/tensorflow/compiler/xla/service/llvm_compiler.cc
+++ b/tensorflow/compiler/xla/service/llvm_compiler.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/llvm_compiler.h"
+#include "tensorflow/core/platform/denormal.h"
 
 #ifdef __FAST_MATH__
 #error "Don't build XLA with -ffast-math"
@@ -24,6 +25,18 @@ StatusOr<std::vector<std::unique_ptr<Executable>>> LLVMCompiler::Compile(
     std::vector<std::unique_ptr<HloModule>> modules,
     std::vector<std::vector<perftools::gputools::StreamExecutor*>> stream_execs,
     DeviceMemoryAllocator* device_allocator) {
+  // Tensorflow tries to enable the following behaviors in all its threads:
+  //
+  //  - Denormals are zero (DAZ): roughly, operations treat denormal floats as
+  //    zero.
+  //  - Flush denormals to zero (FTZ): roughly, operations produce zero instead
+  //    of denormal floats.
+  //
+  // In theory enabling these shouldn't matter since the compiler should ideally
+  // not leak its environment into generated code, but we turn off DAZ and FTZ
+  // to get some defense-in-depth.
+  tensorflow::port::ScopedDontFlushDenormal dont_flush_denormals;
+
   std::vector<std::unique_ptr<Executable>> result;
   for (size_t i = 0; i < modules.size(); i++) {
     if (stream_execs[i].size() != 1) {
diff --git a/tensorflow/core/platform/denormal.cc b/tensorflow/core/platform/denormal.cc
index e00dbdb4ae..3631d9ddf9 100644
--- a/tensorflow/core/platform/denormal.cc
+++ b/tensorflow/core/platform/denormal.cc
@@ -40,36 +40,51 @@ limitations under the License.
 namespace tensorflow {
 namespace port {
 
-ScopedFlushDenormal::ScopedFlushDenormal() {
+static void SetDenormalState(bool flush_zero_mode, bool denormals_zero_mode) {
   // For now, we flush denormals only on SSE 3.  Other architectures such as ARM
   // can be added as needed.
 
 #ifdef DENORM_USE_INTRINSICS
   if (TestCPUFeature(SSE3)) {
-    // Save existing flags
-    flush_zero_mode_ = _MM_GET_FLUSH_ZERO_MODE() == _MM_FLUSH_ZERO_ON;
-    denormals_zero_mode_ =
-        _MM_GET_DENORMALS_ZERO_MODE() == _MM_DENORMALS_ZERO_ON;
-
-    // Flush denormals to zero (the FTZ flag).
-    _MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON);
-
-    // Interpret denormal inputs as zero (the DAZ flag).
-    _MM_SET_DENORMALS_ZERO_MODE(_MM_DENORMALS_ZERO_ON);
+    // Restore flags
+    _MM_SET_FLUSH_ZERO_MODE(flush_zero_mode ? _MM_FLUSH_ZERO_ON
+                                            : _MM_FLUSH_ZERO_OFF);
+    _MM_SET_DENORMALS_ZERO_MODE(denormals_zero_mode ? _MM_DENORMALS_ZERO_ON
+                                                    : _MM_DENORMALS_ZERO_OFF);
   }
 #endif
 }
 
-ScopedFlushDenormal::~ScopedFlushDenormal() {
+static std::pair<bool, bool> GetDernormalState() {
+  // For now, we flush denormals only on SSE 3.  Other architectures such as ARM
+  // can be added as needed.
+
 #ifdef DENORM_USE_INTRINSICS
   if (TestCPUFeature(SSE3)) {
-    // Restore flags
-    _MM_SET_FLUSH_ZERO_MODE(flush_zero_mode_ ? _MM_FLUSH_ZERO_ON
-                                             : _MM_FLUSH_ZERO_OFF);
-    _MM_SET_DENORMALS_ZERO_MODE(denormals_zero_mode_ ? _MM_DENORMALS_ZERO_ON
-                                                     : _MM_DENORMALS_ZERO_OFF);
+    // Save existing flags
+    bool flush_zero_mode = _MM_GET_FLUSH_ZERO_MODE() == _MM_FLUSH_ZERO_ON;
+    bool denormals_zero_mode =
+        _MM_GET_DENORMALS_ZERO_MODE() == _MM_DENORMALS_ZERO_ON;
+    return {flush_zero_mode, denormals_zero_mode};
   }
 #endif
+  return {false, false};
+}
+
+ScopedRestoreFlushDenormalState::ScopedRestoreFlushDenormalState() {
+  std::tie(flush_zero_mode_, denormals_zero_mode_) = GetDernormalState();
+}
+
+ScopedRestoreFlushDenormalState::~ScopedRestoreFlushDenormalState() {
+  SetDenormalState(flush_zero_mode_, denormals_zero_mode_);
+}
+
+ScopedFlushDenormal::ScopedFlushDenormal() {
+  SetDenormalState(/*flush_zero_mode=*/true, /*denormals_zero_mode=*/true);
+}
+
+ScopedDontFlushDenormal::ScopedDontFlushDenormal() {
+  SetDenormalState(/*flush_zero_mode=*/false, /*denormals_zero_mode=*/false);
 }
 
 }  // namespace port
diff --git a/tensorflow/core/platform/denormal.h b/tensorflow/core/platform/denormal.h
index 5e34131a3b..09bb0352a2 100644
--- a/tensorflow/core/platform/denormal.h
+++ b/tensorflow/core/platform/denormal.h
@@ -21,19 +21,41 @@ limitations under the License.
 namespace tensorflow {
 namespace port {
 
+// Remembers the flush denormal state on construction and restores that same
+// state on destruction.
+class ScopedRestoreFlushDenormalState {
+ public:
+  ScopedRestoreFlushDenormalState();
+  ~ScopedRestoreFlushDenormalState();
+
+ private:
+  bool flush_zero_mode_;
+  bool denormals_zero_mode_;
+  TF_DISALLOW_COPY_AND_ASSIGN(ScopedRestoreFlushDenormalState);
+};
+
 // While this class is active, denormal floating point numbers are flushed
 // to zero.  The destructor restores the original flags.
 class ScopedFlushDenormal {
  public:
   ScopedFlushDenormal();
-  ~ScopedFlushDenormal();
 
  private:
-  bool flush_zero_mode_;
-  bool denormals_zero_mode_;
+  ScopedRestoreFlushDenormalState restore_;
   TF_DISALLOW_COPY_AND_ASSIGN(ScopedFlushDenormal);
 };
 
+// While this class is active, denormal floating point numbers are not flushed
+// to zero.  The destructor restores the original flags.
+class ScopedDontFlushDenormal {
+ public:
+  ScopedDontFlushDenormal();
+
+ private:
+  ScopedRestoreFlushDenormalState restore_;
+  TF_DISALLOW_COPY_AND_ASSIGN(ScopedDontFlushDenormal);
+};
+
 }  // namespace port
 }  // namespace tensorflow
 
-- 
GitLab


From 2095372775b800f3b99d4f5cedd847c54d9f43fd Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Fri, 16 Feb 2018 16:30:17 -0800
Subject: [PATCH 034/575] Initializing the thread-local device to the right
 value.

PiperOrigin-RevId: 186062850
---
 tensorflow/python/eager/context.py   | 3 ++-
 tensorflow/python/eager/core_test.py | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py
index 0e9c21b221..07652d3e02 100644
--- a/tensorflow/python/eager/context.py
+++ b/tensorflow/python/eager/context.py
@@ -60,7 +60,8 @@ class _EagerContext(threading.local):
 
   def __init__(self):
     super(_EagerContext, self).__init__()
-    self.device_spec = pydev.DeviceSpec.from_string("")
+    self.device_spec = pydev.DeviceSpec.from_string(
+        "/job:localhost/replica:0/task:0/device:CPU:0")
     self.device_name = self.device_spec.to_string()
     self.mode = _default_mode
     self.scope_name = ""
diff --git a/tensorflow/python/eager/core_test.py b/tensorflow/python/eager/core_test.py
index ee3c10633e..c68e2f422e 100644
--- a/tensorflow/python/eager/core_test.py
+++ b/tensorflow/python/eager/core_test.py
@@ -65,7 +65,8 @@ class TFETest(test_util.TensorFlowTestCase):
     ctx.summary_writer_resource = 'mock'
     self.assertEqual('mock', ctx.summary_writer_resource)
 
-    self.assertEqual('', ctx.device_name)
+    self.assertEqual('/job:localhost/replica:0/task:0/device:CPU:0',
+                     ctx.device_name)
     self.assertEqual(ctx.device_name, ctx.device_spec.to_string())
     with ctx.device('GPU:0'):
       self.assertEqual('/job:localhost/replica:0/task:0/device:GPU:0',
-- 
GitLab


From bde6b79a2df3cfc4542c8e9883429e3970ebb5d6 Mon Sep 17 00:00:00 2001
From: Akshay Modi <nareshmodi@google.com>
Date: Fri, 16 Feb 2018 16:40:02 -0800
Subject: [PATCH 035/575] Make tf.py_func and tf.smart_cond play better with
 eager mode.

PiperOrigin-RevId: 186063941
---
 tensorflow/python/layers/utils.py   | 7 +++++++
 tensorflow/python/ops/script_ops.py | 7 +++++++
 2 files changed, 14 insertions(+)

diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py
index 1bbf4e6dff..1195284024 100644
--- a/tensorflow/python/layers/utils.py
+++ b/tensorflow/python/layers/utils.py
@@ -20,6 +20,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.eager import context
 from tensorflow.python.ops import variables
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.framework import ops
@@ -201,6 +202,12 @@ def smart_cond(pred, fn1, fn2, name=None):
   if not callable(fn2):
     raise TypeError('`fn2` must be callable.')
 
+  if context.in_eager_mode():
+    if pred:
+      return fn1()
+    else:
+      return fn2()
+
   pred_value = constant_value(pred)
   if pred_value is not None:
     if pred_value:
diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py
index 0ba29cbf32..c7e8c28efd 100644
--- a/tensorflow/python/ops/script_ops.py
+++ b/tensorflow/python/ops/script_ops.py
@@ -33,6 +33,7 @@ from tensorflow.python.eager import context
 from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import gen_script_ops
+from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -318,6 +319,12 @@ def py_func(func, inp, Tout, stateful=True, name=None):
   Returns:
     A list of `Tensor` or a single `Tensor` which `func` computes.
   """
+  if context.in_eager_mode():
+    result = func(*[x.numpy() for x in inp])
+    result = nest.flatten(result)
+
+    return [x if x is None else ops.convert_to_tensor(x) for x in result]
+
   return _internal_py_func(
       func=func, inp=inp, Tout=Tout, stateful=stateful, eager=False, name=name)
 
-- 
GitLab


From 0c14cf398c4c805d53cfce661f96c4869fa375af Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 17:55:07 -0800
Subject: [PATCH 036/575] Changes keep_dims to keepdims to remove deprecation
 warning. #labeledtensor

PiperOrigin-RevId: 186071210
---
 tensorflow/contrib/labeled_tensor/python/ops/ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/labeled_tensor/python/ops/ops.py b/tensorflow/contrib/labeled_tensor/python/ops/ops.py
index c957b41a49..3ba1026383 100644
--- a/tensorflow/contrib/labeled_tensor/python/ops/ops.py
+++ b/tensorflow/contrib/labeled_tensor/python/ops/ops.py
@@ -951,7 +951,7 @@ def define_reduce_op(op_name, reduce_fn):
           intermediate_axes.append(axis)
 
       reduce_op = reduce_fn(
-          labeled_tensor.tensor, reduction_dimensions, keep_dims=True)
+          labeled_tensor.tensor, reduction_dimensions, keepdims=True)
       reduce_lt = core.LabeledTensor(reduce_op, intermediate_axes)
 
       return squeeze(reduce_lt, axes_to_squeeze, name=scope)
-- 
GitLab


From a189502cc3032f0bc8f3294b0e39062e89fe9181 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 17:56:36 -0800
Subject: [PATCH 037/575] Activates Eigen path for CPU implementation of
 atrous/dilated convolution (only forward path).

PiperOrigin-RevId: 186071285
---
 tensorflow/core/kernels/conv_2d.h             |  28 +++--
 .../core/kernels/conv_grad_filter_ops.cc      |   3 +-
 .../core/kernels/conv_grad_input_ops.cc       |   3 +-
 tensorflow/core/kernels/conv_ops.cc           |  22 ++--
 .../python/kernel_tests/conv_ops_test.py      | 113 +++++++-----------
 5 files changed, 75 insertions(+), 94 deletions(-)

diff --git a/tensorflow/core/kernels/conv_2d.h b/tensorflow/core/kernels/conv_2d.h
index 2142207b0d..6949e5b5fd 100644
--- a/tensorflow/core/kernels/conv_2d.h
+++ b/tensorflow/core/kernels/conv_2d.h
@@ -54,10 +54,12 @@ struct InflatePadAndShuffle {
 template <typename Device, typename Input, typename Filter, typename Output>
 void SpatialConvolutionFunc(const Device& d, Output output, Input input,
                             Filter filter, int row_stride, int col_stride,
+                            int row_dilation, int col_dilation,
                             const Eigen::PaddingType& padding) {
   // Need to swap row/col when calling Eigen.
   output.device(d) =
-      Eigen::SpatialConvolution(input, filter, col_stride, row_stride, padding);
+      Eigen::SpatialConvolution(input, filter, col_stride, row_stride, padding,
+                                col_dilation, row_dilation);
 }
 
 template <typename Device, typename T>
@@ -65,9 +67,10 @@ struct SpatialConvolution {
   void operator()(const Device& d, typename TTypes<T, 4>::Tensor output,
                   typename TTypes<T, 4>::ConstTensor input,
                   typename TTypes<T, 4>::ConstTensor filter, int row_stride,
-                  int col_stride, const Eigen::PaddingType& padding) {
+                  int col_stride, int row_dilation, int col_dilation,
+                  const Eigen::PaddingType& padding) {
     SpatialConvolutionFunc(d, output, input, filter, row_stride, col_stride,
-                           padding);
+                           row_dilation, col_dilation, padding);
   }
 };
 
@@ -77,11 +80,12 @@ struct SpatialConvolution<Device, Eigen::half> {
                   typename TTypes<Eigen::half, 4>::Tensor output,
                   typename TTypes<Eigen::half, 4>::ConstTensor input,
                   typename TTypes<Eigen::half, 4>::ConstTensor filter,
-                  int row_stride, int col_stride,
-                  const Eigen::PaddingType& padding) {
+                  int row_stride, int col_stride, int row_dilation,
+                  int col_dilation, const Eigen::PaddingType& padding) {
     output.device(d) =
         Eigen::SpatialConvolution(input.cast<float>(), filter.cast<float>(),
-                                  col_stride, row_stride, padding)
+                                  col_stride, row_stride, padding, col_dilation,
+                                  row_dilation)
             .cast<Eigen::half>();
   }
 };
@@ -91,11 +95,13 @@ struct SpatialConvolutionBackwardInput {
   void operator()(const Device& d, typename TTypes<T, 4>::Tensor input_backward,
                   typename TTypes<T, 4>::ConstTensor kernel,
                   typename TTypes<T, 4>::ConstTensor output_backward,
-                  int row_stride, int col_stride) {
+                  int row_stride, int col_stride, int row_dilation,
+                  int col_dilation) {
     // Need to swap row/col when calling Eigen.
     input_backward.device(d) = Eigen::SpatialConvolutionBackwardInput(
         kernel, output_backward, input_backward.dimension(2),
-        input_backward.dimension(1), col_stride, row_stride);
+        input_backward.dimension(1), col_stride, row_stride, col_dilation,
+        row_dilation);
   }
 };
 
@@ -105,11 +111,13 @@ struct SpatialConvolutionBackwardFilter {
                   typename TTypes<T, 4>::Tensor kernel_backward,
                   typename TTypes<T, 4>::ConstTensor input,
                   typename TTypes<T, 4>::ConstTensor output_backward,
-                  int row_stride, int col_stride) {
+                  int row_stride, int col_stride, int row_dilation,
+                  int col_dilation) {
     // Need to swap row/col when calling Eigen.
     kernel_backward.device(d) = Eigen::SpatialConvolutionBackwardKernel(
         input, output_backward, kernel_backward.dimension(1),
-        kernel_backward.dimension(0), col_stride, row_stride);
+        kernel_backward.dimension(0), col_stride, row_stride, col_dilation,
+        row_dilation);
   }
 };
 
diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc
index 512bcc6c01..b8a5ae6a08 100644
--- a/tensorflow/core/kernels/conv_grad_filter_ops.cc
+++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc
@@ -101,7 +101,8 @@ struct LaunchConv2DBackpropFilterOp<CPUDevice, T> {
     const CPUDevice& d = ctx->eigen_device<CPUDevice>();
     functor::SpatialConvolutionBackwardFilter<CPUDevice, T>()(
         d, filter_backprop->tensor<T, 4>(), input.tensor<T, 4>(),
-        out_backprop.tensor<T, 4>(), row_stride, col_stride);
+        out_backprop.tensor<T, 4>(), row_stride, col_stride,
+        /*row_dilation=*/1, /*col_dilation=*/1);
   }
 };
 
diff --git a/tensorflow/core/kernels/conv_grad_input_ops.cc b/tensorflow/core/kernels/conv_grad_input_ops.cc
index 0356ff4c0f..b87c7899c0 100644
--- a/tensorflow/core/kernels/conv_grad_input_ops.cc
+++ b/tensorflow/core/kernels/conv_grad_input_ops.cc
@@ -106,7 +106,8 @@ struct LaunchConv2DBackpropInputOp<CPUDevice, T> {
     const CPUDevice& d = ctx->eigen_device<CPUDevice>();
     functor::SpatialConvolutionBackwardInput<CPUDevice, T>()(
         d, in_backprop->tensor<T, 4>(), filter.tensor<T, 4>(),
-        out_backprop.tensor<T, 4>(), row_stride, col_stride);
+        out_backprop.tensor<T, 4>(), row_stride, col_stride,
+        /*row_dilation=*/1, /*col_dilation=*/1);
   }
 };
 
diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc
index dbddaf3dc6..2b81e14f95 100644
--- a/tensorflow/core/kernels/conv_ops.cc
+++ b/tensorflow/core/kernels/conv_ops.cc
@@ -60,8 +60,8 @@ template <typename Device, typename T>
 struct LaunchGeneric {
   void operator()(OpKernelContext* ctx, const Tensor& input,
                   const Tensor& filter, int row_stride, int col_stride,
-                  const Padding& padding, Tensor* output,
-                  TensorFormat data_format) {
+                  int row_dilation, int col_dilation, const Padding& padding,
+                  Tensor* output, TensorFormat data_format) {
     CHECK(data_format == FORMAT_NHWC) << "Generic conv implementation only "
                                          "supports NHWC tensor format for now.";
     if (filter.dim_size(0) == 1 && filter.dim_size(1) == 1 && row_stride == 1 &&
@@ -86,7 +86,8 @@ struct LaunchGeneric {
           filter.shaped<T, 2>({filter.dim_size(2), filter.dim_size(3)}),
           dim_pair);
     } else if (filter.dim_size(0) == input.dim_size(1) &&
-               filter.dim_size(1) == input.dim_size(2) && padding == VALID) {
+               filter.dim_size(1) == input.dim_size(2) && row_dilation == 1 &&
+               col_dilation == 1 && padding == VALID) {
       // If the input data and filter have the same height/width,
       // the 2D convolution is reduced to matrix multiplication.
       const int k =  // Length of reduction dimension.
@@ -103,7 +104,7 @@ struct LaunchGeneric {
       functor::SpatialConvolution<Device, T>()(
           ctx->eigen_device<Device>(), output->tensor<T, 4>(),
           input.tensor<T, 4>(), filter.tensor<T, 4>(), row_stride, col_stride,
-          BrainPadding2EigenPadding(padding));
+          row_dilation, col_dilation, BrainPadding2EigenPadding(padding));
     }
   }
 };
@@ -122,15 +123,9 @@ struct LaunchConv2DOp<CPUDevice, T> {
                                 "NHWC tensor format for now."));
       return;
     }
-    // TODO(yangzihao): Add the CPU implementation of dilated conv 2D.
-    if (row_dilation > 1 || col_dilation > 1) {
-      ctx->SetStatus(
-          errors::Unimplemented("Generic conv implementation only supports "
-                                "dilated rate of 1 for now."));
-      return;
-    }
     LaunchGeneric<CPUDevice, T>()(ctx, input, filter, row_stride, col_stride,
-                                  padding, output, data_format);
+                                  row_dilation, col_dilation, padding, output,
+                                  data_format);
   }
 };
 
@@ -792,7 +787,8 @@ namespace functor {
       const GPUDevice& d, typename TTypes<T, 4>::Tensor output,              \
       typename TTypes<T, 4>::ConstTensor input,                              \
       typename TTypes<T, 4>::ConstTensor filter, int row_stride,             \
-      int col_stride, const Eigen::PaddingType& padding);                    \
+      int col_stride, int row_dilation, int col_dilation,                    \
+      const Eigen::PaddingType& padding);                                    \
   extern template struct SpatialConvolution<GPUDevice, T>;                   \
   template <>                                                                \
   void MatMulConvFunctor<GPUDevice, T>::operator()(                          \
diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py
index edfb20d6a2..2785798916 100644
--- a/tensorflow/python/kernel_tests/conv_ops_test.py
+++ b/tensorflow/python/kernel_tests/conv_ops_test.py
@@ -302,25 +302,20 @@ class Conv2DTest(test.TestCase):
                                padding, dilations):
     expected_results = []
     computed_results = []
-    default_dilations = (dilations[0] == 1 and dilations[1] == 1)
     for data_format, use_gpu in GetTestConfigs():
-      # If any dilation rate is larger than 1, only do test on the GPU
-      # because we currently do not have a CPU implementation for arbitrary
-      # dilation rates.
-      if default_dilations or use_gpu:
-        expected, computed = self._ComputeReferenceDilatedConv(
-            tensor_in_sizes, filter_in_sizes, strides, dilations, padding,
-            data_format, use_gpu)
-        expected_results.append(expected)
-        computed_results.append(computed)
-        tolerance = 1e-2 if use_gpu else 1e-5
-        expected_values = self.evaluate(expected_results)
-        computed_values = self.evaluate(computed_results)
-        for e_value, c_value in zip(expected_values, computed_values):
-          print("expected = ", e_value)
-          print("actual = ", c_value)
-          self.assertAllClose(
-              e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-4)
+      expected, computed = self._ComputeReferenceDilatedConv(
+          tensor_in_sizes, filter_in_sizes, strides, dilations, padding,
+          data_format, use_gpu)
+      expected_results.append(expected)
+      computed_results.append(computed)
+      tolerance = 1e-2 if use_gpu else 1e-5
+      expected_values = self.evaluate(expected_results)
+      computed_values = self.evaluate(computed_results)
+      for e_value, c_value in zip(expected_values, computed_values):
+        print("expected = ", e_value)
+        print("actual = ", c_value)
+        self.assertAllClose(
+            e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-4)
 
   def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, strides, padding,
                     expected):
@@ -365,13 +360,12 @@ class Conv2DTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2D2x2Filter2x1Dilation(self):
-    if test.is_gpu_available(cuda_only=True):
-      self._VerifyDilatedConvValues(
-          tensor_in_sizes=[1, 4, 4, 1],
-          filter_in_sizes=[2, 2, 1, 1],
-          strides=[1, 1],
-          dilations=[2, 1],
-          padding="VALID")
+    self._VerifyDilatedConvValues(
+        tensor_in_sizes=[1, 4, 4, 1],
+        filter_in_sizes=[2, 2, 1, 1],
+        strides=[1, 1],
+        dilations=[2, 1],
+        padding="VALID")
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2DEmpty(self):
@@ -385,13 +379,12 @@ class Conv2DTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2DEmptyDilation(self):
-    if test.is_gpu_available(cuda_only=True):
-      self._VerifyDilatedConvValues(
-          tensor_in_sizes=[0, 2, 3, 3],
-          filter_in_sizes=[1, 1, 3, 3],
-          strides=[1, 1],
-          dilations=[2, 1],
-          padding="VALID")
+    self._VerifyDilatedConvValues(
+        tensor_in_sizes=[0, 2, 3, 3],
+        filter_in_sizes=[1, 1, 3, 3],
+        strides=[1, 1],
+        dilations=[2, 1],
+        padding="VALID")
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2D2x2Filter(self):
@@ -406,13 +399,12 @@ class Conv2DTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2D2x2FilterDilation(self):
-    if test.is_gpu_available(cuda_only=True):
-      self._VerifyDilatedConvValues(
-          tensor_in_sizes=[1, 2, 3, 3],
-          filter_in_sizes=[2, 2, 3, 3],
-          strides=[1, 1],
-          dilations=[1, 2],
-          padding="VALID")
+    self._VerifyDilatedConvValues(
+        tensor_in_sizes=[1, 2, 3, 3],
+        filter_in_sizes=[2, 2, 3, 3],
+        strides=[1, 1],
+        dilations=[1, 2],
+        padding="VALID")
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2D1x2Filter(self):
@@ -430,13 +422,12 @@ class Conv2DTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2D1x2FilterDilation(self):
-    if test.is_gpu_available(cuda_only=True):
-      self._VerifyDilatedConvValues(
-          tensor_in_sizes=[1, 2, 3, 3],
-          filter_in_sizes=[1, 2, 3, 3],
-          strides=[1, 1],
-          dilations=[2, 1],
-          padding="VALID")
+    self._VerifyDilatedConvValues(
+        tensor_in_sizes=[1, 2, 3, 3],
+        filter_in_sizes=[1, 2, 3, 3],
+        strides=[1, 1],
+        dilations=[2, 1],
+        padding="VALID")
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2D2x2FilterStride2(self):
@@ -512,13 +503,12 @@ class Conv2DTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testConv2DKernelSizeMatchesInputSizeDilation(self):
-    if test.is_gpu_available(cuda_only=True):
-      self._VerifyDilatedConvValues(
-          tensor_in_sizes=[1, 3, 3, 1],
-          filter_in_sizes=[2, 2, 1, 2],
-          strides=[1, 1],
-          dilations=[2, 2],
-          padding="VALID")
+    self._VerifyDilatedConvValues(
+        tensor_in_sizes=[1, 3, 3, 1],
+        filter_in_sizes=[2, 2, 1, 2],
+        strides=[1, 1],
+        dilations=[2, 2],
+        padding="VALID")
 
   # TODO(yzhwang): this currently fails.
   # self._VerifyValues(tensor_in_sizes=[1, 8, 8, 1],
@@ -1538,21 +1528,6 @@ class Conv2DTest(test.TestCase):
             use_gpu=False)
         self.evaluate(conv)
 
-  def testCPUConv2DDilatedUnimplemented(self):
-    with self.test_session(use_gpu=False):
-      with self.assertRaisesRegexp(errors_impl.UnimplementedError,
-                                   "dilated rate of 1 for now"):
-        conv = self._SetupValuesForDevice(
-            tensor_in_sizes=[1, 4, 4, 1],
-            filter_in_sizes=[2, 2, 1, 1],
-            dilations=[2, 1],
-            strides=[1, 1],
-            padding="VALID",
-            data_format="NHWC",
-            dtype=dtypes.float32,
-            use_gpu=False)
-        self.evaluate(conv)
-
 
 class DepthwiseConv2DTest(test.TestCase):
 
@@ -1887,7 +1862,7 @@ def GetInceptionFwdTest(input_size, filter_size, stride, padding,
 def GetInceptionFwdDilatedConvTest(input_size, filter_size, stride, padding):
 
   def Test(self):
-    if test.is_gpu_available(cuda_only=True) and stride == 1:
+    if stride == 1:
       tf_logging.info("Testing InceptionFwd with dilations %s",
                       (input_size, filter_size, stride, padding))
       self._VerifyDilatedConvValues(
-- 
GitLab


From 090bb9168cbcb5bbb3d7fb8e0b64f7d00013d188 Mon Sep 17 00:00:00 2001
From: Michael Kuperstein <mkuper@google.com>
Date: Fri, 16 Feb 2018 18:13:53 -0800
Subject: [PATCH 038/575] [XLA] Pass the module to HloDataflowAnalysis by const
 reference.

PiperOrigin-RevId: 186072673
---
 .../compiler/xla/service/copy_insertion.cc    |  2 +-
 .../xla/service/gpu/gpu_copy_insertion.cc     |  2 +-
 .../xla/service/hlo_alias_analysis.cc         |  2 +-
 .../xla/service/hlo_dataflow_analysis.cc      | 22 +++++++++----------
 .../xla/service/hlo_dataflow_analysis.h       |  6 ++---
 .../xla/service/hlo_dataflow_analysis_test.cc |  2 +-
 .../compiler/xla/service/hlo_ordering_test.cc |  4 ++--
 .../xla/service/liveness_util_test.cc         |  3 +--
 tensorflow/compiler/xla/tests/test_utils.cc   |  2 +-
 9 files changed, 22 insertions(+), 23 deletions(-)

diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc
index c812df4235..cc195879a6 100644
--- a/tensorflow/compiler/xla/service/copy_insertion.cc
+++ b/tensorflow/compiler/xla/service/copy_insertion.cc
@@ -1156,7 +1156,7 @@ bool IsWhileBody(const HloComputation* computation,
     HloModule* module) {
   std::unique_ptr<CallGraph> call_graph = CallGraph::Build(module);
   TF_ASSIGN_OR_RETURN(std::unique_ptr<HloDataflowAnalysis> dataflow,
-                      HloDataflowAnalysis::Run(module));
+                      HloDataflowAnalysis::Run(*module));
 
   bool changed = false;
 
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc
index 916b556fd4..9db85bc788 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc
@@ -49,7 +49,7 @@ StatusOr<bool> GpuCopyInsertion::Run(HloModule* module) {
   TF_ASSIGN_OR_RETURN(bool changed, generic_copy_insertion.Run(module));
 
   TF_ASSIGN_OR_RETURN(std::unique_ptr<HloDataflowAnalysis> dataflow,
-                      HloDataflowAnalysis::Run(module));
+                      HloDataflowAnalysis::Run(*module));
 
   // Make sure all operands of a library call are in memory instead of constants
   // in IR.
diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
index 6d2a3aa5b5..30e32a46d7 100644
--- a/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.cc
@@ -419,7 +419,7 @@ StatusOr<std::unique_ptr<HloAliasAnalysis>> HloAliasAnalysis::Run(
   auto alias_analysis = WrapUnique(new HloAliasAnalysis(module));
   TF_ASSIGN_OR_RETURN(
       alias_analysis->dataflow_analysis_,
-      HloDataflowAnalysis::Run(module, /*ssa_form=*/true,
+      HloDataflowAnalysis::Run(*module, /*ssa_form=*/true,
                                /*bitcast_defines_value=*/false));
 
   BufferValueMap buffer_map(alias_analysis->dataflow_analysis());
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
index ccbbe8f196..934e43ba48 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc
@@ -38,12 +38,12 @@ namespace xla {
 using ::tensorflow::strings::StrAppend;
 using ::tensorflow::strings::StrCat;
 
-HloDataflowAnalysis::HloDataflowAnalysis(HloModule* module, bool ssa_form,
+HloDataflowAnalysis::HloDataflowAnalysis(const HloModule& module, bool ssa_form,
                                          bool bitcast_defines_value)
     : module_(module),
       ssa_form_(ssa_form),
       bitcast_defines_value_(bitcast_defines_value),
-      call_graph_(CallGraph::Build(module)) {}
+      call_graph_(CallGraph::Build(&module)) {}
 
 bool HloDataflowAnalysis::ValueIsDefinedAt(const HloInstruction* instruction,
                                            const ShapeIndex& index) const {
@@ -115,9 +115,9 @@ void HloDataflowAnalysis::DeleteMarkedValues() {
 }
 
 string HloDataflowAnalysis::ToString() const {
-  string out = StrCat("HloDataflowAnalysis, module ", module_->name(), "\n");
+  string out = StrCat("HloDataflowAnalysis, module ", module_.name(), "\n");
   StrAppend(&out, "  Instruction value sets:\n");
-  for (const HloComputation* computation : module_->computations()) {
+  for (const HloComputation* computation : module_.computations()) {
     for (const HloInstruction* instruction : computation->instructions()) {
       StrAppend(&out, "    ", instruction->name(), ":\n");
       if (ShapeUtil::IsTuple(instruction->shape())) {
@@ -592,7 +592,7 @@ void HloDataflowAnalysis::Propagate() {
     }
   };
 
-  for (HloComputation* computation : module_->computations()) {
+  for (HloComputation* computation : module_.computations()) {
     for (HloInstruction* instruction : computation->instructions()) {
       add_to_worklist(instruction);
     }
@@ -686,7 +686,7 @@ InstructionValueSet& HloDataflowAnalysis::GetInstructionValueSet(
 }
 
 Status HloDataflowAnalysis::InitializeInstructionValueSets() {
-  for (const HloComputation* computation : module_->computations()) {
+  for (const HloComputation* computation : module_.computations()) {
     const CallGraphNode& call_graph_node = call_graph_->GetNode(computation);
     for (HloInstruction* instruction : computation->instructions()) {
       // Create an empty shape tree.
@@ -787,9 +787,9 @@ Status HloDataflowAnalysis::InitializeInstructionValueSets() {
 
 /* static */
 StatusOr<std::unique_ptr<HloDataflowAnalysis>> HloDataflowAnalysis::Run(
-    HloModule* module, bool ssa_form, bool bitcast_defines_value) {
-  VLOG(1) << "HloDataflowAnalysis::Run on module " << module->name();
-  XLA_VLOG_LINES(2, module->ToString());
+    const HloModule& module, bool ssa_form, bool bitcast_defines_value) {
+  VLOG(1) << "HloDataflowAnalysis::Run on module " << module.name();
+  XLA_VLOG_LINES(2, module.ToString());
 
   auto dataflow_analysis = WrapUnique(
       new HloDataflowAnalysis(module, ssa_form, bitcast_defines_value));
@@ -806,7 +806,7 @@ StatusOr<std::unique_ptr<HloDataflowAnalysis>> HloDataflowAnalysis::Run(
   // lookup is faster.
   std::vector<std::vector<HloPosition>> value_positions(
       dataflow_analysis->next_value_id_);
-  for (const HloComputation* computation : module->computations()) {
+  for (const HloComputation* computation : module.computations()) {
     for (HloInstruction* instruction : computation->instructions()) {
       for (const auto& pair :
            dataflow_analysis->GetInstructionValueSet(instruction)) {
@@ -858,7 +858,7 @@ Status HloDataflowAnalysis::Verify() const {
 
   // For each value in each value set, verify that the value set's position
   // appears in the value's positions().
-  for (const auto& computation : module_->computations()) {
+  for (const auto& computation : module_.computations()) {
     for (const auto& instruction : computation->instructions()) {
       for (const auto& pair : GetInstructionValueSet(instruction)) {
         const ShapeIndex& index = pair.first;
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
index 89d318188f..7b8a74b096 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.h
@@ -60,7 +60,7 @@ class HloDataflowAnalysis {
   //     a new HLO value in the analysis. If false then Bitcast forwards the
   //     value of its operand.
   static StatusOr<std::unique_ptr<HloDataflowAnalysis>> Run(
-      HloModule* module, bool ssa_form = false,
+      const HloModule& module, bool ssa_form = false,
       bool bitcast_defines_value = false);
 
   // Returns true if 'instruction' defines an HLO value at the given shape index
@@ -119,7 +119,7 @@ class HloDataflowAnalysis {
   string ToString() const;
 
  protected:
-  HloDataflowAnalysis(HloModule* module, bool ssa_form,
+  HloDataflowAnalysis(const HloModule& module, bool ssa_form,
                       bool bitcast_defines_value = false);
 
   // Returns a new HloValue defined at the given instruction and shape index.
@@ -180,7 +180,7 @@ class HloDataflowAnalysis {
   // Verify various invariants of the dataflow analysis.
   Status Verify() const;
 
-  HloModule* const module_;
+  const HloModule& module_;
   const bool ssa_form_;
   const bool bitcast_defines_value_;
 
diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
index e714b2567f..7bf3a1a060 100644
--- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc
@@ -50,7 +50,7 @@ class HloDataflowAnalysisTest : public HloTestBase,
                                          bool bitcast_defines_value = false) {
     hlo_graph_dumper::MaybeDumpHloModule(*module_, "Before dataflow analysis");
     analysis_ =
-        HloDataflowAnalysis::Run(module_.get(), ssa_form, bitcast_defines_value)
+        HloDataflowAnalysis::Run(*module_, ssa_form, bitcast_defines_value)
             .ConsumeValueOrDie();
     return *analysis_;
   }
diff --git a/tensorflow/compiler/xla/service/hlo_ordering_test.cc b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
index aba66114de..a989fce632 100644
--- a/tensorflow/compiler/xla/service/hlo_ordering_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_ordering_test.cc
@@ -262,8 +262,8 @@ TEST_F(HloOrderingTest, ValuesInWhileComputations) {
       scalar_shape, HloOpcode::kAdd, constant, xla_while));
   module->AddEntryComputation(builder.Build());
 
-  TF_ASSERT_OK_AND_ASSIGN(
-      auto dataflow, HloDataflowAnalysis::Run(module.get(), /*ssa_form=*/true));
+  TF_ASSERT_OK_AND_ASSIGN(auto dataflow,
+                          HloDataflowAnalysis::Run(*module, /*ssa_form=*/true));
   DependencyHloOrdering ordering(module.get());
 
   // Init value is defined before the while, but live range is not before the
diff --git a/tensorflow/compiler/xla/service/liveness_util_test.cc b/tensorflow/compiler/xla/service/liveness_util_test.cc
index 2c2a02f637..f8b309488e 100644
--- a/tensorflow/compiler/xla/service/liveness_util_test.cc
+++ b/tensorflow/compiler/xla/service/liveness_util_test.cc
@@ -35,8 +35,7 @@ class PointsToAnalysisTestBase : public HloTestBase {
     CHECK_NOTNULL(module_.get());
     points_to_analysis_ =
         TuplePointsToAnalysis::Run(module_.get()).ConsumeValueOrDie();
-    dataflow_analysis_ =
-        HloDataflowAnalysis::Run(module_.get()).ConsumeValueOrDie();
+    dataflow_analysis_ = HloDataflowAnalysis::Run(*module_).ConsumeValueOrDie();
   }
 
   void BuildModuleAndRunAnalysis(std::unique_ptr<HloComputation> computation) {
diff --git a/tensorflow/compiler/xla/tests/test_utils.cc b/tensorflow/compiler/xla/tests/test_utils.cc
index b060fb13b1..0bc7df2a65 100644
--- a/tensorflow/compiler/xla/tests/test_utils.cc
+++ b/tensorflow/compiler/xla/tests/test_utils.cc
@@ -287,7 +287,7 @@ StatusOr<std::unique_ptr<Literal>> MakeFakeLiteral(const Shape& shape) {
 
 StatusOr<std::vector<std::unique_ptr<Literal>>> MakeFakeArguments(
     HloModule* const module) {
-  TF_ASSIGN_OR_RETURN(auto dataflow, HloDataflowAnalysis::Run(module));
+  TF_ASSIGN_OR_RETURN(auto dataflow, HloDataflowAnalysis::Run(*module));
   const auto params = module->entry_computation()->parameter_instructions();
   std::minstd_rand0 engine;
   std::vector<std::unique_ptr<Literal>> arguments(params.size());
-- 
GitLab


From 128572c316e6f2eb6346f920314ef98e88e75069 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 18:18:35 -0800
Subject: [PATCH 039/575] Adds a `shape` property to LabeledTensor.
 #labeledtensor

PiperOrigin-RevId: 186073035
---
 tensorflow/contrib/labeled_tensor/python/ops/core.py      | 4 ++++
 tensorflow/contrib/labeled_tensor/python/ops/core_test.py | 3 +++
 2 files changed, 7 insertions(+)

diff --git a/tensorflow/contrib/labeled_tensor/python/ops/core.py b/tensorflow/contrib/labeled_tensor/python/ops/core.py
index abc18aa123..0c6bba758b 100644
--- a/tensorflow/contrib/labeled_tensor/python/ops/core.py
+++ b/tensorflow/contrib/labeled_tensor/python/ops/core.py
@@ -361,6 +361,10 @@ class LabeledTensor(object):
   def dtype(self):
     return self._tensor.dtype
 
+  @property
+  def shape(self):
+    return self._tensor.shape
+
   @property
   def name(self):
     return self._tensor.name
diff --git a/tensorflow/contrib/labeled_tensor/python/ops/core_test.py b/tensorflow/contrib/labeled_tensor/python/ops/core_test.py
index e70b492374..e378db56af 100644
--- a/tensorflow/contrib/labeled_tensor/python/ops/core_test.py
+++ b/tensorflow/contrib/labeled_tensor/python/ops/core_test.py
@@ -244,6 +244,9 @@ class LabeledTensorTest(test_util.Base):
   def test_dtype(self):
     self.assertEqual(self.lt.dtype, self.lt.tensor.dtype)
 
+  def test_shape(self):
+    self.assertEqual(self.lt.shape, self.lt.tensor.shape)
+
   def test_get_shape(self):
     self.assertEqual(self.lt.get_shape(), self.lt.tensor.get_shape())
 
-- 
GitLab


From 0e6f39d1bd7fe8daa86944f6ab0dd94fbeb4962a Mon Sep 17 00:00:00 2001
From: Ankur Taly <ataly@google.com>
Date: Fri, 16 Feb 2018 18:22:55 -0800
Subject: [PATCH 040/575] Merge changes from github.

PiperOrigin-RevId: 186073337
---
 CONTRIBUTING.md                               |  10 +-
 RELEASE.md                                    | 114 ++-
 configure.py                                  |  28 +
 tensorflow/compiler/tf2xla/graph_compiler.h   |   2 +-
 tensorflow/compiler/xla/index_util.h          |   2 +-
 tensorflow/compiler/xla/python/xla_client.py  |   2 +-
 tensorflow/compiler/xla/service/BUILD         |   2 -
 .../xla/service/buffer_assignment_test.cc     |   2 +-
 .../compiler/xla/service/cpu/ir_emitter.cc    |   2 +-
 .../compiler/xla/service/cpu/ir_function.cc   |   4 +-
 .../compiler/xla/service/hlo_evaluator.cc     |   2 -
 .../contrib/all_reduce/python/all_reduce.py   |   2 +-
 tensorflow/contrib/cmake/CMakeLists.txt       |  16 +-
 .../contrib/cmake/external/boringssl.cmake    |   1 +
 .../contrib/cmake/external/farmhash.cmake     |   1 +
 tensorflow/contrib/cmake/external/fft2d.cmake |   1 +
 tensorflow/contrib/cmake/external/gif.cmake   |   1 +
 .../contrib/cmake/external/googletest.cmake   |  10 +-
 tensorflow/contrib/cmake/external/grpc.cmake  |  16 +-
 .../contrib/cmake/external/highwayhash.cmake  |   1 +
 .../contrib/cmake/external/jemalloc.cmake     |  15 +-
 tensorflow/contrib/cmake/external/jpeg.cmake  |   1 +
 .../contrib/cmake/external/jsoncpp.cmake      |   7 +-
 tensorflow/contrib/cmake/external/lmdb.cmake  |  13 +-
 tensorflow/contrib/cmake/external/nsync.cmake |   1 +
 tensorflow/contrib/cmake/external/png.cmake   |  17 +-
 .../contrib/cmake/external/protobuf.cmake     |  44 +-
 tensorflow/contrib/cmake/external/re2.cmake   |   7 +-
 .../contrib/cmake/external/snappy.cmake       |   7 +-
 .../contrib/cmake/external/sqlite.cmake       |   1 +
 tensorflow/contrib/cmake/external/zlib.cmake  |  17 +-
 tensorflow/contrib/cmake/python_modules.txt   |   6 +
 .../cmake/tests/cuda/compatibility_test.c     |  22 +
 .../cmake/tests/cuda/compatibility_test.cc    |  20 +
 tensorflow/contrib/cmake/tf_cc_ops.cmake      |   6 +-
 tensorflow/contrib/cmake/tf_python.cmake      |  25 +-
 tensorflow/contrib/cmake/tf_shared_lib.cmake  |   6 +-
 tensorflow/contrib/cmake/tf_tests.cmake       |   2 +
 tensorflow/contrib/cmake/tf_tools.cmake       |   3 -
 .../contrib/data/python/ops/dataset_ops.py    | 691 ++++++++++++++++++
 .../contrib/data/python/ops/grouping.py       |   2 +-
 tensorflow/contrib/distributions/__init__.py  |   1 -
 .../eager/python/examples/gan/README.md       |   2 +-
 .../eager/python/examples/mnist/mnist.py      |   2 +-
 .../framework/python/ops/accumulate_n_v2.py   |   2 +-
 .../gan/python/eval/python/summaries_impl.py  |   7 +-
 .../gan/python/eval/python/summaries_test.py  |  14 +-
 tensorflow/contrib/gdr/README.md              |   2 +-
 tensorflow/contrib/layers/__init__.py         |   2 +
 .../contrib/layers/python/layers/layers.py    |  69 +-
 .../layers/python/layers/layers_test.py       |  50 +-
 .../contrib/learn/python/learn/monitors.py    |   2 +-
 tensorflow/contrib/lite/README.md             |   2 +-
 .../label_image/bitmap_helpers_impl.h         |   6 +
 .../contrib/lite/g3doc/custom_operators.md    |   2 +-
 tensorflow/contrib/lite/graph_info.cc         |   2 +-
 .../internal/optimized/tensor_utils_impl.h    |   2 +-
 .../reference/portable_tensor_utils.h         |   2 +-
 tensorflow/contrib/lite/simple_memory_arena.h |   4 +-
 .../contrib/lite/testing/generate_examples.py |   2 +-
 .../resolve_constant_concatenation.cc         |   2 +-
 .../resolve_svdf_test.cc                      |  12 +-
 tensorflow/contrib/makefile/README.md         |   2 +-
 .../contrib/makefile/build_all_android.sh     |   2 +-
 .../contrib/metrics/python/ops/metric_ops.py  |   4 +-
 .../contrib/nearest_neighbor/kernels/heap.h   |   2 +-
 .../training/elastic_average_optimizer.py     |   2 +-
 .../training/model_average_optimizer_test.py  |   1 -
 tensorflow/contrib/py2tf/converters/BUILD     |   1 +
 .../rnn/python/kernel_tests/rnn_cell_test.py  |   1 +
 .../seq2seq/python/ops/attention_wrapper.py   |   2 +-
 tensorflow/contrib/slim/README.md             |   2 +-
 .../solvers/python/ops/linear_equations.py    |   1 +
 .../contrib/tpu/profiler/pip_package/setup.py |   2 +-
 tensorflow/contrib/tpu/tpu_estimator.md       |   2 +-
 tensorflow/contrib/verbs/README.md            |  12 +-
 .../rpc/grpc_serialization_traits.h           |   2 +-
 tensorflow/core/framework/op_def_util.h       |   2 +-
 tensorflow/core/framework/op_kernel.cc        |   2 +-
 tensorflow/core/kernels/constant_op_test.cc   |   2 +-
 .../core/kernels/cwise_op_gpu_invert.cu.cc    |   2 +-
 tensorflow/core/kernels/cwise_op_invert.cc    |  10 +-
 .../core/kernels/cwise_ops_gpu_common.cu.h    |   3 +
 tensorflow/core/lib/gif/gif_io.cc             |  42 +-
 tensorflow/core/lib/gtl/optional.h            |   2 +-
 tensorflow/core/lib/io/record_reader.cc       |   2 +-
 tensorflow/core/ops/image_ops.cc              |  24 +
 tensorflow/core/platform/env.cc               |  37 +
 tensorflow/core/platform/env.h                |   8 +
 tensorflow/core/platform/file_system.cc       |   4 +
 tensorflow/core/platform/file_system.h        |   3 +
 .../core/platform/posix/posix_file_system.cc  |  72 ++
 .../core/platform/posix/posix_file_system.h   |   2 +
 tensorflow/core/public/version.h              |   4 +-
 .../python/contrib.distributions.md           |   1 -
 .../api_guides/python/regression_examples.md  |   2 +-
 tensorflow/docs_src/community/welcome.md      |   2 +-
 .../get_started/get_started_for_beginners.md  |   2 +-
 tensorflow/docs_src/install/install_c.md      |   2 +-
 tensorflow/docs_src/install/install_go.md     |   2 +-
 tensorflow/docs_src/install/install_java.md   |  22 +-
 tensorflow/docs_src/install/install_linux.md  |  22 +-
 tensorflow/docs_src/install/install_mac.md    |  10 +-
 .../docs_src/install/install_sources.md       |  12 +-
 .../docs_src/programmers_guide/debugger.md    |   2 +-
 .../docs_src/programmers_guide/index.md       |   2 +-
 .../examples/image_retraining/retrain.py      |   4 +-
 .../java/org/tensorflow/NativeLibrary.java    |   2 +-
 tensorflow/python/client/session.py           |  18 +-
 tensorflow/python/data/ops/dataset_ops.py     |   2 +-
 tensorflow/python/debug/cli/cli_shared.py     |   2 +-
 tensorflow/python/debug/cli/tensor_format.py  |   2 +-
 tensorflow/python/framework/test_util.py      |  96 ++-
 .../python/keras/_impl/keras/backend.py       |   8 +
 .../python/keras/_impl/keras/backend_test.py  |   9 +
 .../keras/_impl/keras/layers/lstm_test.py     |  16 +
 .../keras/_impl/keras/layers/recurrent.py     |   3 +-
 .../kernel_tests/conv2d_transpose_test.py     |   6 +-
 .../distributions/bernoulli_test.py           |   7 -
 .../kernel_tests/distributions/beta_test.py   |   4 +-
 .../neon_depthwise_conv_op_test.py            |   4 +-
 .../python/kernel_tests/py_func_test.py       |  11 +
 tensorflow/python/layers/maxout.py            |   2 +-
 tensorflow/python/lib/io/file_io.i            |  16 +-
 tensorflow/python/ops/bitwise_ops_test.py     |   6 +-
 .../python/ops/distributions/bernoulli.py     |  20 -
 tensorflow/python/ops/distributions/beta.py   |   9 +-
 tensorflow/python/ops/image_ops_test.py       |  42 +-
 .../python/ops/linalg/linear_operator.py      |   1 -
 tensorflow/python/ops/losses/losses_impl.py   |   8 +-
 tensorflow/python/ops/script_ops.py           |   4 +-
 tensorflow/python/ops/standard_ops.py         |   1 +
 tensorflow/python/ops/state_ops.py            |   2 +-
 tensorflow/python/util/compat_internal.py     |   2 +
 tensorflow/stream_executor/dnn.h              |   2 +-
 tensorflow/tools/ci_build/ci_sanity.sh        |   2 +-
 tensorflow/tools/docker/Dockerfile.devel      |   2 +-
 .../tools/docker/Dockerfile.devel-cpu-mkl     |   2 +-
 tensorflow/tools/docker/Dockerfile.devel-gpu  |   2 +-
 tensorflow/tools/graph_transforms/BUILD       |  11 +-
 tensorflow/tools/pip_package/setup.py         |   2 +-
 third_party/repo.bzl                          |   2 +-
 142 files changed, 1673 insertions(+), 320 deletions(-)
 create mode 100644 tensorflow/contrib/cmake/tests/cuda/compatibility_test.c
 create mode 100644 tensorflow/contrib/cmake/tests/cuda/compatibility_test.cc
 create mode 100644 tensorflow/contrib/data/python/ops/dataset_ops.py

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index de4fded6ae..3dad41a88c 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -41,7 +41,7 @@ TensorFlow coding style.
 #### General guidelines and philosophy for contribution
 
 * Include unit tests when you contribute new features, as they help to
-  a) prove that your code works correctly, b) guard against future breaking
+  a) prove that your code works correctly, and b) guard against future breaking
   changes to lower the maintenance cost.
 * Bug fixes also generally require unit tests, because the presence of bugs
   usually indicates insufficient test coverage.
@@ -51,7 +51,7 @@ TensorFlow coding style.
   non-backward-compatible API changes without a major release. Reviewers of your
   pull request will comment on any API compatibility issues.
 * When you contribute a new feature to TensorFlow, the maintenance burden is (by
-  default) transferred to the TensorFlow team. This means that benefit of
+  default) transferred to the TensorFlow team. This means that benefit of the
   contribution must be compared against the cost of maintaining the feature.
 * Full new features (e.g., a new op implementing a cutting-edge algorithm)
   typically will live in
@@ -68,8 +68,8 @@ Include a license at the top of new files.
 * [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)
 * [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1)
 * [Bash license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/ci_build/ci_sanity.sh#L2)
-* [HTML license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/dist/index.html#L2)
-* [JavaScript/TypeScript license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/components/tf_backend/backend.ts#L1)
+* [HTML license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/tf-backend.html#L2)
+* [JavaScript/TypeScript license example](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/components/tf_backend/backend.ts#L1)
 
 Bazel BUILD files also need to include a license section, e.g.,
 [BUILD example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/BUILD#L61).
@@ -163,7 +163,7 @@ There are two ways to run TensorFlow unit tests.
    bazel test ${flags} //tensorflow/python/...
    ```
 
-2. Using [Docker](www.docker.com) and TensorFlow's CI scripts.
+2. Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.
 
    ```bash
    # Install Docker first, then this will build and run cpu tests
diff --git a/RELEASE.md b/RELEASE.md
index b11b1e40db..0720a8c639 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -1,9 +1,98 @@
+# Release 1.6.0
+
+## Breaking Changes
+* Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
+* Prebuilt binaries will use AVX instructions. This may break TF on older CPUs.
+
+## Major Features And Improvements
+* New Optimizer internal API for non-slot variables. Descendants of AdamOptimizer that access _beta[12]_power will need to be updated.
+* `tf.estimator.{FinalExporter,LatestExporter}` now export stripped SavedModels. This improves forward compatibility of the SavedModel.
+* FFT support added to XLA CPU/GPU.
+
+## Bug Fixes and Other Changes
+* Documentation updates:
+  * Added a second version of Getting Started, which is aimed at ML
+newcomers.
+  * Clarified documentation on `resize_images.align_corners` parameter.
+  * Additional documentation for TPUs.
+* Google Cloud Storage (GCS):
+  * Add client-side throttle.
+  * Add a `FlushCaches()` method to the FileSystem interface, with an implementation for GcsFileSystem.
+* Other:
+  * Add `tf.contrib.distributions.Kumaraswamy`.
+  * `RetryingFileSystem::FlushCaches()` calls the base FileSystem's `FlushCaches()`.
+  * Add auto_correlation to distributions.
+  * Add `tf.contrib.distributions.Autoregressive`.
+  * Add SeparableConv1D layer.
+  * Add convolutional Flipout layers.
+  * When both inputs of `tf.matmul` are bfloat16, it returns bfloat16, instead of float32.
+  * Added `tf.contrib.image.connected_components`.
+  * Add `tf.contrib.framework.CriticalSection` that allows atomic variable access.
+  * Output variance over trees predictions for classifications tasks.
+  * For `pt` and `eval` commands, allow writing tensor values to filesystem as numpy files.
+  * gRPC: Propagate truncated errors (instead of returning gRPC internal error).
+  * Augment parallel_interleave to support 2 kinds of prefetching.
+  * Improved XLA support for C64-related ops log, pow, atan2, tanh.
+  * Add probabilistic convolutional layers.
+
+## API Changes
+* Introducing prepare_variance boolean with default setting to False for backward compatibility.
+* Move `layers_dense_variational_impl.py` to `layers_dense_variational.py`.
+
+## Known Bugs
+* Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
+  `CUDA_ILLEGAL_ADDRESS` failures.
+
+  Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
+  and CUDA 9.1 sometimes does not properly compute the carry bit when
+  decomposing 64-bit address calculations with large offsets (e.g. `load [x +
+  large_constant]`) into 32-bit arithmetic in SASS.
+
+  As a result, these versions of `ptxas` miscompile most XLA programs which use
+  more than 4GB of temp memory.  This results in garbage results and/or
+  `CUDA_ERROR_ILLEGAL_ADDRESS` failures.
+
+  A fix in CUDA 9.1.121 is expected in late February 2018.  We do not expect a
+  fix for CUDA 9.0.x.  Until the fix is available, the only workaround is to
+  [downgrade](https://developer.nvidia.com/cuda-toolkit-archive) to CUDA 8.0.x
+  or disable XLA:GPU.
+
+  TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
+  CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.
+
+## Thanks to our Contributors
+
+This release contains contributions from many people at Google, as well as:
+
+4d55397500, Ag Ramesh, Aiden Scandella, Akimasa Kimura, Alex Rothberg, Allen Goodman,
+amilioto, Andrei Costinescu, Andrei Nigmatulin, Anjum Sayed, Anthony Platanios,
+Anush Elangovan, Armando Fandango, Ashish Kumar Ram, Ashwini Shukla, Ben, Bhavani Subramanian,
+Brett Koonce, Carl Thomé, cclauss, Cesc, Changming Sun, Christoph Boeddeker, Clayne Robison,
+Clemens Schulz, Clint (Woonhyuk Baek), codrut3, Cole Gerdemann, Colin Raffel, Daniel Trebbien,
+Daniel Ylitalo, Daniel Zhang, Daniyar, Darjan Salaj, Dave Maclachlan, David Norman, Dong--Jian,
+dongsamb, dssgsra, Edward H, eladweiss, elilienstein, Eric Lilienstein, error.d, Eunji Jeong, fanlu,
+Florian Courtial, fo40225, Fred, Gregg Helt, Guozhong Zhuang, Hanchen Li, hsm207, hyunyoung2,
+ImSheridan, Ishant Mrinal Haloi, Jacky Ko, Jay Young, Jean Flaherty, Jerome, JerrikEph, Jesse
+Kinkead, jfaath, Jian Lin, jinghuangintel, Jiongyan Zhang, Joel Hestness, Joel Shor, Johnny Chan,
+Julian Niedermeier, Julian Wolff, JxKing, K-W-W, Karl Lessard, Kasper Marstal, Keiji Ariyama,
+Koan-Sin Tan, Loki Der Quaeler, Loo Rong Jie, Luke Schaefer, Lynn Jackson, ManHyuk, Matt Basta,
+Matt Smith, Matthew Schulkind, Michael, michaelkhan3, Miguel Piedrafita, Mikalai Drabovich,
+Mike Knapp, mjwen, mktozk, Mohamed Aly, Mohammad Ashraf Bhuiyan, Myungjoo Ham, Naman Bhalla,
+Namrata-Ibm, Nathan Luehr, nathansilberman, Netzeband, Niranjan Hasabnis, Omar Aflak, Ozge
+Yalcinkaya, Parth P Panchal, patrickzzy, Patryk Chrabaszcz, Paul Van Eck, Paweł Kapica, Peng Yu,
+Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu Wang, Rajendra Arora, Rasmus, Renat
+Idrisov, resec, Robin Richtsfeld, Ronald Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri,
+Santiago Castro, Sayed Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash
+Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental, starsblinking, Stephen Lumenta,
+Steven Hickson, Su Tang, Taehoon Lee, Takuya Wakisaka, Ted Chang, Ted Ying, Tijmen Verhulsdonck,
+Timofey Kondrashov, vade, vaibhav, Valentin Khrulkov, vchigrin, Victor Costan, Viraj Navkal,
+Vivek Rane, wagonhelm, Yan Facai (颜发才), Yanbo Liang, Yaroslav Bulatov, yegord, Yong Tang,
+Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田传武
+
 # Release 1.5.0
 
 ## Breaking Changes
 * Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
-* Our Linux binaries are built using ubuntu 16 containers, potentially
-  introducing glibc incompatibility issues with ubuntu 14.
 * Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
   This may break TF on older CPUs.
 
@@ -146,6 +235,27 @@
 * Minor refactor: move stats files from `stochastic` to `common` and remove
   `stochastic`.
 
+## Known Bugs
+* Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
+  `CUDA_ILLEGAL_ADDRESS` failures.
+
+  Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
+  and CUDA 9.1 sometimes does not properly compute the carry bit when
+  decomposing 64-bit address calculations with large offsets (e.g. `load [x +
+  large_constant]`) into 32-bit arithmetic in SASS.
+
+  As a result, these versions of `ptxas` miscompile most XLA programs which use
+  more than 4GB of temp memory.  This results in garbage results and/or
+  `CUDA_ERROR_ILLEGAL_ADDRESS` failures.
+
+  A fix in CUDA 9.1.121 is expected in late February 2018.  We do not expect a
+  fix for CUDA 9.0.x.  Until the fix is available, the only workaround is to
+  [downgrade](https://developer.nvidia.com/cuda-toolkit-archive) to CUDA 8.0.x
+  or disable XLA:GPU.
+
+  TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
+  CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.
+
 ## Thanks to our Contributors
 
 This release contains contributions from many people at Google, as well as:
diff --git a/configure.py b/configure.py
index 27519b4aba..6b1fa7f1a8 100644
--- a/configure.py
+++ b/configure.py
@@ -827,6 +827,28 @@ def set_gcc_host_compiler_path(environ_cp):
   write_action_env_to_bazelrc('GCC_HOST_COMPILER_PATH', gcc_host_compiler_path)
 
 
+def reformat_version_sequence(version_str, sequence_count):
+  """Reformat the version string to have the given number of sequences.
+
+  For example:
+  Given (7, 2) -> 7.0
+        (7.0.1, 2) -> 7.0
+        (5, 1) -> 5
+        (5.0.3.2, 1) -> 5
+
+  Args:
+      version_str: String, the version string.
+      sequence_count: int, an integer.
+  Returns:
+      string, reformatted version string.
+  """
+  v = version_str.split('.')
+  if len(v) < sequence_count:
+    v = v + (['0'] * (sequence_count - len(v)))
+
+  return '.'.join(v[:sequence_count])
+
+
 def set_tf_cuda_version(environ_cp):
   """Set CUDA_TOOLKIT_PATH and TF_CUDA_VERSION."""
   ask_cuda_version = (
@@ -837,6 +859,7 @@ def set_tf_cuda_version(environ_cp):
     # Configure the Cuda SDK version to use.
     tf_cuda_version = get_from_env_or_user_or_default(
         environ_cp, 'TF_CUDA_VERSION', ask_cuda_version, _DEFAULT_CUDA_VERSION)
+    tf_cuda_version = reformat_version_sequence(str(tf_cuda_version), 2)
 
     # Find out where the CUDA toolkit is installed
     default_cuda_path = _DEFAULT_CUDA_PATH
@@ -893,6 +916,7 @@ def set_tf_cudnn_version(environ_cp):
     tf_cudnn_version = get_from_env_or_user_or_default(
         environ_cp, 'TF_CUDNN_VERSION', ask_cudnn_version,
         _DEFAULT_CUDNN_VERSION)
+    tf_cudnn_version = reformat_version_sequence(str(tf_cudnn_version), 1)
 
     default_cudnn_path = environ_cp.get('CUDA_TOOLKIT_PATH')
     ask_cudnn_path = (r'Please specify the location where cuDNN %s library is '
@@ -1400,6 +1424,10 @@ def main():
     if is_linux():
       set_tf_tensorrt_install_path(environ_cp)
     set_tf_cuda_compute_capabilities(environ_cp)
+    if 'LD_LIBRARY_PATH' in environ_cp and environ_cp.get(
+        'LD_LIBRARY_PATH') != '1':
+      write_action_env_to_bazelrc('LD_LIBRARY_PATH',
+                                  environ_cp.get('LD_LIBRARY_PATH'))
 
     set_tf_cuda_clang(environ_cp)
     if environ_cp.get('TF_CUDA_CLANG') == '1':
diff --git a/tensorflow/compiler/tf2xla/graph_compiler.h b/tensorflow/compiler/tf2xla/graph_compiler.h
index ba00160b6d..127562eb23 100644
--- a/tensorflow/compiler/tf2xla/graph_compiler.h
+++ b/tensorflow/compiler/tf2xla/graph_compiler.h
@@ -70,7 +70,7 @@ class GraphCompiler {
 
  private:
   // Partially sets params. This partially set params can be reused
-  // across multple nodes visit.
+  // across multiple nodes visit.
   void PartiallySetupParams(OpKernelContext::Params* params);
 
   // Tests if a node is a functional node. A functional node represents a
diff --git a/tensorflow/compiler/xla/index_util.h b/tensorflow/compiler/xla/index_util.h
index 0b9188e852..142006f262 100644
--- a/tensorflow/compiler/xla/index_util.h
+++ b/tensorflow/compiler/xla/index_util.h
@@ -37,7 +37,7 @@ class IndexUtil {
   static int64 MultidimensionalIndexToLinearIndex(
       const Shape& shape, tensorflow::gtl::ArraySlice<int64> multi_index);
 
-  // Coverts a linear index into multidimensional index (eg {x, y, z}) based on
+  // Converts a linear index into multidimensional index (eg {x, y, z}) based on
   // the shape and its layout. The first index in the returned multidimensional
   // index is dimension 0.
   static std::vector<int64> LinearIndexToMultidimensionalIndex(
diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py
index bcff9508fb..9bda9d0929 100644
--- a/tensorflow/compiler/xla/python/xla_client.py
+++ b/tensorflow/compiler/xla/python/xla_client.py
@@ -1128,7 +1128,7 @@ def initialize_replica_count(replica_count):
 
   Args:
     replica_count: number of replicas that are desired for set up during XLA
-      initalization.
+      initialization.
 
   Raises:
     A runtime exception if the XLA service has already been initialized.
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 22d1c5a9bd..4a076ac090 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -171,8 +171,6 @@ cc_library(
         ":shape_inference",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla:status",
-        "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
index ef067cc31f..cd73654b8f 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc
@@ -614,7 +614,7 @@ TEST_F(BufferAssignmentTest, TrivialMap) {
   BufferAllocation map_buffer = GetAssignedOutputAllocation(*buffers, map);
   EXPECT_NE(param0_buffer.index(), map_buffer.index());
 
-  // The final computation node of the map is an add of an f32 parm and a
+  // The final computation node of the map is an add of an f32 param and a
   // constant.
   EXPECT_EQ(HloOpcode::kAdd, inner_last->opcode());
   const BufferAllocation& inner_add_buffer =
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 496aea051c..4dffaee87f 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -1337,7 +1337,7 @@ IrEmitter::ReductionGenerator IrEmitter::MatchReductionGenerator(
   if (ShapeUtil::ElementIsComplex(root_shape)) {
     // TODO(b/65408531): Complex add could by done via bitcast to <float x [2N]>
     // Complex multiply would be more challenging. We could perhaps use a
-    // strided load to get all reals in a vector, all imags in a vector, or use
+    // strided load to get all reals in a vector, all images in a vector, or use
     // CreateShuffleVector on a bitcast to float x [2N].
     *failure_reason = "complex values not supported";
     return nullptr;
diff --git a/tensorflow/compiler/xla/service/cpu/ir_function.cc b/tensorflow/compiler/xla/service/cpu/ir_function.cc
index ca8c290dd1..2d6f2f3818 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_function.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_function.cc
@@ -209,9 +209,9 @@ std::vector<llvm::Value*> GetArrayFunctionCallArguments(
         parameter_addresses[i], ir_builder->getInt8PtrTy(),
         AsStringRef(tensorflow::strings::StrCat(name, "_parameter_", i,
                                                 "_address_as_i8ptr")));
-    llvm::Value* slot_in_param_adresses = ir_builder->CreateInBoundsGEP(
+    llvm::Value* slot_in_param_addresses = ir_builder->CreateInBoundsGEP(
         parameter_addresses_buffer, {ir_builder->getInt64(i)});
-    ir_builder->CreateStore(parameter_as_i8ptr, slot_in_param_adresses);
+    ir_builder->CreateStore(parameter_as_i8ptr, slot_in_param_addresses);
   }
 
   const auto to_int8_ptr = [=](llvm::Value* ptr) {
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 8016b38d15..296f010a92 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -34,8 +34,6 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_query.h"
 #include "tensorflow/compiler/xla/service/shape_inference.h"
 #include "tensorflow/compiler/xla/shape_util.h"
-#include "tensorflow/compiler/xla/status.h"
-#include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/util.h"
 #include "tensorflow/compiler/xla/window_util.h"
diff --git a/tensorflow/contrib/all_reduce/python/all_reduce.py b/tensorflow/contrib/all_reduce/python/all_reduce.py
index 16617b7266..6658f0d9c1 100644
--- a/tensorflow/contrib/all_reduce/python/all_reduce.py
+++ b/tensorflow/contrib/all_reduce/python/all_reduce.py
@@ -758,7 +758,7 @@ def _build_nccl_hybrid(input_tensors, red_op, upper_level_f):
 
 
 def _reduce_non_singleton(input_tensors, red_f, un_op):
-  """If input_tenors has more than one element apply red_f, else apply un_op."""
+  """If input_tensors has more than one element apply red_f, else apply un_op."""
   if len(input_tensors) > 1:
     return red_f(input_tensors)
   else:
diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index c9ba1f4cc0..524946a9a5 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -286,7 +286,21 @@ if (tensorflow_ENABLE_GPU)
     list(APPEND CMAKE_LIBRARY_PATH "${tensorflow_CUDA_LIBRARY_PATH}/stubs")
   endif (NOT WIN32)
 
-  find_package(CUDA ${tensorflow_CUDA_VERSION} REQUIRED)
+  # later command will make use of the value in tensorflow_CUDA_VERSION
+  find_package(CUDA ${tensorflow_CUDA_VERSION} REQUIRED EXACT)
+
+  # Test compatibility of compiler on CUDA
+  try_compile(CUDA_TEST_COMPILE_C
+    ${CMAKE_CURRENT_BINARY_DIR}/tests/cuda
+    ${CMAKE_CURRENT_SOURCE_DIR}/tests/cuda/compatibility_test.c
+    CMAKE_FLAGS -DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS})
+  try_compile(CUDA_TEST_COMPILE_CXX
+    ${CMAKE_CURRENT_BINARY_DIR}/tests/cuda
+    ${CMAKE_CURRENT_SOURCE_DIR}/tests/cuda/compatibility_test.cc
+    CMAKE_FLAGS -DINCLUDE_DIRECTORIES=${CUDA_INCLUDE_DIRS})
+  if(NOT (CUDA_TEST_COMPILE_C AND CUDA_TEST_COMPILE_CXX))
+    message(FATAL_ERROR "Selected compiler (or version) is not supported for CUDA")
+  endif()
 
   # by default we assume compute cabability 3.5 and 5.2. If you change this change it in
   # CUDA_NVCC_FLAGS and cuda_config.h below
diff --git a/tensorflow/contrib/cmake/external/boringssl.cmake b/tensorflow/contrib/cmake/external/boringssl.cmake
index 5ad477fdff..3c4bb01e24 100644
--- a/tensorflow/contrib/cmake/external/boringssl.cmake
+++ b/tensorflow/contrib/cmake/external/boringssl.cmake
@@ -37,6 +37,7 @@ ExternalProject_Add(boringssl
     GIT_TAG ${boringssl_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     # BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${boringssl_STATIC_LIBRARIES}
     INSTALL_COMMAND ""
     CMAKE_CACHE_ARGS
         -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE}
diff --git a/tensorflow/contrib/cmake/external/farmhash.cmake b/tensorflow/contrib/cmake/external/farmhash.cmake
index 0cd0c1030c..d51569bc21 100644
--- a/tensorflow/contrib/cmake/external/farmhash.cmake
+++ b/tensorflow/contrib/cmake/external/farmhash.cmake
@@ -33,6 +33,7 @@ if(WIN32)
       URL_HASH ${farmhash_HASH}
       DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
       BUILD_IN_SOURCE 1
+      BUILD_BYPRODUCTS ${farmhash_STATIC_LIBRARIES}
       PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/farmhash/CMakeLists.txt ${farmhash_BUILD}
       INSTALL_DIR ${farmhash_INSTALL}
       CMAKE_CACHE_ARGS
diff --git a/tensorflow/contrib/cmake/external/fft2d.cmake b/tensorflow/contrib/cmake/external/fft2d.cmake
index d3af2a4676..a7bc50d5bc 100644
--- a/tensorflow/contrib/cmake/external/fft2d.cmake
+++ b/tensorflow/contrib/cmake/external/fft2d.cmake
@@ -29,6 +29,7 @@ if(WIN32)
       URL_HASH ${fft2d_HASH}
       DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
       BUILD_IN_SOURCE 1
+      BUILD_BYPRODUCTS ${fft2d_STATIC_LIBRARIES}
       PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/fft2d/CMakeLists.txt ${fft2d_BUILD}/src/fft2d/CMakeLists.txt
       INSTALL_DIR ${fft2d_INSTALL}
       CMAKE_CACHE_ARGS
diff --git a/tensorflow/contrib/cmake/external/gif.cmake b/tensorflow/contrib/cmake/external/gif.cmake
index 3d53c51fff..e1f8d13f8e 100644
--- a/tensorflow/contrib/cmake/external/gif.cmake
+++ b/tensorflow/contrib/cmake/external/gif.cmake
@@ -33,6 +33,7 @@ if(WIN32)
       PREFIX gif
       URL ${gif_URL}
       URL_HASH ${gif_HASH}
+      BUILD_BYPRODUCTS ${gif_STATIC_LIBRARIES}
       PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_SOURCE_DIR}/patches/gif/CMakeLists.txt ${gif_BUILD}
       INSTALL_DIR ${gif_INSTALL}
       DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
diff --git a/tensorflow/contrib/cmake/external/googletest.cmake b/tensorflow/contrib/cmake/external/googletest.cmake
index d09bb02890..7cc5ae6390 100644
--- a/tensorflow/contrib/cmake/external/googletest.cmake
+++ b/tensorflow/contrib/cmake/external/googletest.cmake
@@ -20,8 +20,13 @@ set(googletest_BUILD ${CMAKE_CURRENT_BINARY_DIR}/googletest/)
 set(googletest_TAG ec44c6c1675c25b9827aacd08c02433cccde7780)
 
 if(WIN32)
-  set(googletest_STATIC_LIBRARIES
-      ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/$(Configuration)/gtest.lib)
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set(googletest_STATIC_LIBRARIES
+        ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/$(Configuration)/gtest.lib)
+  else()
+    set(googletest_STATIC_LIBRARIES
+        ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/gtest.lib)
+  endif()
 else()
   set(googletest_STATIC_LIBRARIES
       ${CMAKE_CURRENT_BINARY_DIR}/googletest/src/googletest/googletest/${CMAKE_BUILD_TYPE}/gtest.a)
@@ -33,6 +38,7 @@ ExternalProject_Add(googletest
     GIT_TAG ${googletest_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${googletest_STATIC_LIBRARIES}
     #PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_SOURCE_DIR}/patches/grpc/CMakeLists.txt ${GRPC_BUILD}
     INSTALL_COMMAND ""
     CMAKE_CACHE_ARGS
diff --git a/tensorflow/contrib/cmake/external/grpc.cmake b/tensorflow/contrib/cmake/external/grpc.cmake
index 28adb4fe84..a9f43a3ecb 100644
--- a/tensorflow/contrib/cmake/external/grpc.cmake
+++ b/tensorflow/contrib/cmake/external/grpc.cmake
@@ -20,10 +20,17 @@ set(GRPC_BUILD ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc)
 set(GRPC_TAG 730b778632e79cc3c96ad237f282d687ee325ce7)
 
 if(WIN32)
-  set(grpc_STATIC_LIBRARIES
-      ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc++_unsecure.lib
-      ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc_unsecure.lib
-      ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/gpr.lib)
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set(grpc_STATIC_LIBRARIES
+        ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc++_unsecure.lib
+        ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/grpc_unsecure.lib
+        ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/Release/gpr.lib)
+  else()
+    set(grpc_STATIC_LIBRARIES
+        ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/grpc++_unsecure.lib
+        ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/grpc_unsecure.lib
+        ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/gpr.lib)
+  endif()
 else()
   set(grpc_STATIC_LIBRARIES
       ${CMAKE_CURRENT_BINARY_DIR}/grpc/src/grpc/libgrpc++_unsecure.a
@@ -40,6 +47,7 @@ ExternalProject_Add(grpc
     GIT_TAG ${GRPC_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${grpc_STATIC_LIBRARIES}
     BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target grpc++_unsecure
     COMMAND ${CMAKE_COMMAND} --build . --config Release --target grpc_cpp_plugin
     INSTALL_COMMAND ""
diff --git a/tensorflow/contrib/cmake/external/highwayhash.cmake b/tensorflow/contrib/cmake/external/highwayhash.cmake
index 2c23bef8a3..a6e8a38d8c 100644
--- a/tensorflow/contrib/cmake/external/highwayhash.cmake
+++ b/tensorflow/contrib/cmake/external/highwayhash.cmake
@@ -42,6 +42,7 @@ ExternalProject_Add(highwayhash
     GIT_TAG ${highwayhash_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${highwayhash_STATIC_LIBRARIES}
     PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/highwayhash/CMakeLists.txt ${highwayhash_BUILD}
     INSTALL_DIR ${highwayhash_INSTALL}
     CMAKE_CACHE_ARGS
diff --git a/tensorflow/contrib/cmake/external/jemalloc.cmake b/tensorflow/contrib/cmake/external/jemalloc.cmake
index 198ba13e64..afadcc007d 100644
--- a/tensorflow/contrib/cmake/external/jemalloc.cmake
+++ b/tensorflow/contrib/cmake/external/jemalloc.cmake
@@ -24,8 +24,11 @@ if (WIN32)
         ${jemalloc_INCLUDE_DIRS} 
         ${CMAKE_CURRENT_BINARY_DIR}/jemalloc/src/jemalloc/include/msvc_compat
     )
-    set(jemalloc_ADDITIONAL_CMAKE_OPTIONS -A x64)
-    set(jemalloc_STATIC_LIBRARIES ${jemalloc_BUILD}/Release/jemalloc.lib)
+    if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+        set(jemalloc_STATIC_LIBRARIES ${jemalloc_BUILD}/Release/jemalloc.lib)
+    else()
+        set(jemalloc_STATIC_LIBRARIES ${jemalloc_BUILD}/jemalloc.lib)
+    endif()
 else()
     set(jemalloc_STATIC_LIBRARIES ${jemalloc_BUILD}/Release/jemalloc.a)
 endif()
@@ -36,12 +39,12 @@ ExternalProject_Add(jemalloc
     URL_HASH ${jemalloc_HASH}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
-    CONFIGURE_COMMAND ${CMAKE_COMMAND}
+    BUILD_BYPRODUCTS ${jemalloc_STATIC_LIBRARIES}
+    BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target jemalloc
+    INSTALL_COMMAND ${CMAKE_COMMAND} -E echo "Skipping install step."
+    CMAKE_CACHE_ARGS
         -DCMAKE_BUILD_TYPE:STRING=Release
         -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
         -Dwith-jemalloc-prefix:STRING=jemalloc_
         -Dwithout-export:BOOL=ON
-        ${jemalloc_ADDITIONAL_CMAKE_OPTIONS}
-    BUILD_COMMAND ${CMAKE_COMMAND} --build . --config Release --target jemalloc
-    INSTALL_COMMAND ${CMAKE_COMMAND} -E echo "Skipping install step."
 )
diff --git a/tensorflow/contrib/cmake/external/jpeg.cmake b/tensorflow/contrib/cmake/external/jpeg.cmake
index d9a165e856..c1c5842aa4 100644
--- a/tensorflow/contrib/cmake/external/jpeg.cmake
+++ b/tensorflow/contrib/cmake/external/jpeg.cmake
@@ -46,6 +46,7 @@ if (WIN32)
         PREFIX jpeg
         URL ${jpeg_URL}
         URL_HASH ${jpeg_HASH}
+        BUILD_BYPRODUCTS ${jpeg_STATIC_LIBRARIES}
         PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/jpeg/CMakeLists.txt ${jpeg_BUILD}
         INSTALL_DIR ${jpeg_INSTALL}
         DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
diff --git a/tensorflow/contrib/cmake/external/jsoncpp.cmake b/tensorflow/contrib/cmake/external/jsoncpp.cmake
index 861201f97e..84c52e3652 100644
--- a/tensorflow/contrib/cmake/external/jsoncpp.cmake
+++ b/tensorflow/contrib/cmake/external/jsoncpp.cmake
@@ -23,7 +23,11 @@ set(jsoncpp_LIBRARIES ${jsoncpp_BUILD}/obj/so/libjsoncpp.so)
 set(jsoncpp_INCLUDES ${jsoncpp_BUILD})
 
 if(WIN32)
-  set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/$(Configuration)/jsoncpp.lib)
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/$(Configuration)/jsoncpp.lib)
+  else()
+    set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/jsoncpp.lib)
+  endif()
 else()
   set(jsoncpp_STATIC_LIBRARIES ${jsoncpp_BUILD}/libjsoncpp.a)
 endif()
@@ -40,6 +44,7 @@ ExternalProject_Add(jsoncpp
     GIT_TAG ${jsoncpp_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${jsoncpp_STATIC_LIBRARIES}
     INSTALL_COMMAND ""
     CMAKE_CACHE_ARGS
         -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE}
diff --git a/tensorflow/contrib/cmake/external/lmdb.cmake b/tensorflow/contrib/cmake/external/lmdb.cmake
index 41b314e285..ed5ab788ac 100644
--- a/tensorflow/contrib/cmake/external/lmdb.cmake
+++ b/tensorflow/contrib/cmake/external/lmdb.cmake
@@ -20,10 +20,17 @@ set(lmdb_HASH SHA256=108532fb94c6f227558d45be3f3347b52539f0f58290a7bb31ec06c462d
 set(lmdb_BUILD ${CMAKE_BINARY_DIR}/lmdb/src/lmdb)
 set(lmdb_INSTALL ${CMAKE_BINARY_DIR}/lmdb/install)
 
+if(WIN32)
+    set(lmdb_STATIC_LIBRARIES ${lmdb_INSTALL}/lib/lmdb.lib)
+else()
+    set(lmdb_STATIC_LIBRARIES ${lmdb_INSTALL}/lib/liblmdb.a)
+endif()
+
 ExternalProject_Add(lmdb
     PREFIX lmdb
     URL ${lmdb_URL}
     URL_HASH ${lmdb_HASH}
+    BUILD_BYPRODUCTS ${lmdb_STATIC_LIBRARIES}
     PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different
         ${CMAKE_CURRENT_SOURCE_DIR}/patches/lmdb/CMakeLists.txt ${lmdb_BUILD}
     INSTALL_DIR ${lmdb_INSTALL}
@@ -35,12 +42,6 @@ ExternalProject_Add(lmdb
         -DCMAKE_INSTALL_PREFIX:STRING=${lmdb_INSTALL}
 )
 
-if(WIN32)
-    set(lmdb_STATIC_LIBRARIES ${lmdb_INSTALL}/lib/lmdb.lib)
-else()
-    set(lmdb_STATIC_LIBRARIES ${lmdb_INSTALL}/lib/liblmdb.a)
-endif()
-
 set(lmdb_HEADERS
     "${lmdb_INSTALL}/include/lmdb.h"
     "${lmdb_INSTALL}/include/midl.h"
diff --git a/tensorflow/contrib/cmake/external/nsync.cmake b/tensorflow/contrib/cmake/external/nsync.cmake
index 0508006047..f3a37ff508 100644
--- a/tensorflow/contrib/cmake/external/nsync.cmake
+++ b/tensorflow/contrib/cmake/external/nsync.cmake
@@ -42,6 +42,7 @@ ExternalProject_Add(nsync
     GIT_TAG ${nsync_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${nsync_STATIC_LIBRARIES}
     PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/nsync/CMakeLists.txt ${nsync_BUILD}
     INSTALL_DIR ${nsync_INSTALL}
     CMAKE_CACHE_ARGS
diff --git a/tensorflow/contrib/cmake/external/png.cmake b/tensorflow/contrib/cmake/external/png.cmake
index b277be5690..6cd66a6599 100644
--- a/tensorflow/contrib/cmake/external/png.cmake
+++ b/tensorflow/contrib/cmake/external/png.cmake
@@ -21,9 +21,19 @@ set(png_BUILD ${CMAKE_BINARY_DIR}/png/src/png)
 set(png_INSTALL ${CMAKE_BINARY_DIR}/png/install)
 
 if(WIN32)
-  set(png_STATIC_LIBRARIES 
-    debug ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_staticd.lib
-    optimized ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_static.lib)
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set(png_STATIC_LIBRARIES 
+      debug ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_staticd.lib
+      optimized ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_static.lib)
+  else()
+    if(CMAKE_BUILD_TYPE EQUAL Debug)
+      set(png_STATIC_LIBRARIES 
+        ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_staticd.lib)
+    else()
+      set(png_STATIC_LIBRARIES 
+        ${CMAKE_BINARY_DIR}/png/install/lib/libpng12_static.lib)
+    endif()
+  endif()
 else()
   set(png_STATIC_LIBRARIES ${CMAKE_BINARY_DIR}/png/install/lib/libpng12.a)
 endif()
@@ -38,6 +48,7 @@ ExternalProject_Add(png
     DEPENDS zlib
     URL ${png_URL}
     URL_HASH ${png_HASH}
+    BUILD_BYPRODUCTS ${png_STATIC_LIBRARIES}
     INSTALL_DIR ${png_INSTALL}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     CMAKE_CACHE_ARGS
diff --git a/tensorflow/contrib/cmake/external/protobuf.cmake b/tensorflow/contrib/cmake/external/protobuf.cmake
index fd05fa6d47..aba8a5244e 100644
--- a/tensorflow/contrib/cmake/external/protobuf.cmake
+++ b/tensorflow/contrib/cmake/external/protobuf.cmake
@@ -19,11 +19,34 @@ set(PROTOBUF_URL https://github.com/google/protobuf.git)
 set(PROTOBUF_TAG 396336eb961b75f03b25824fe86cf6490fb75e3a)
 
 if(WIN32)
-  set(protobuf_STATIC_LIBRARIES 
-    debug ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/libprotobufd.lib
-    optimized ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/libprotobuf.lib)
-  set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/protoc.exe)
-  set(PROTOBUF_ADDITIONAL_CMAKE_OPTIONS	-Dprotobuf_MSVC_STATIC_RUNTIME:BOOL=OFF -A x64)
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set(protobuf_STATIC_LIBRARIES 
+      debug ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/libprotobufd.lib
+      optimized ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/libprotobuf.lib)
+    set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/$(Configuration)/protoc.exe)
+  else()
+    if(CMAKE_BUILD_TYPE EQUAL Debug)
+      set(protobuf_STATIC_LIBRARIES
+        ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/libprotobufd.lib)
+    else()
+      set(protobuf_STATIC_LIBRARIES
+        ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/libprotobuf.lib)
+    endif()
+    set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/protoc.exe)
+  endif()
+
+  # This section is to make sure CONFIGURE_COMMAND use the same generator settings
+  set(PROTOBUF_GENERATOR_PLATFORM)
+  if (CMAKE_GENERATOR_PLATFORM)
+    set(PROTOBUF_GENERATOR_PLATFORM -A ${CMAKE_GENERATOR_PLATFORM})
+  endif()
+  set(PROTOBUF_GENERATOR_TOOLSET)
+  if (CMAKE_GENERATOR_TOOLSET)
+  set(PROTOBUF_GENERATOR_TOOLSET -T ${CMAKE_GENERATOR_TOOLSET})
+  endif()
+  set(PROTOBUF_ADDITIONAL_CMAKE_OPTIONS	-Dprotobuf_MSVC_STATIC_RUNTIME:BOOL=OFF
+    -G${CMAKE_GENERATOR} ${PROTOBUF_GENERATOR_PLATFORM} ${PROTOBUF_GENERATOR_TOOLSET})
+  # End of section
 else()
   set(protobuf_STATIC_LIBRARIES ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/libprotobuf.a)
   set(PROTOBUF_PROTOC_EXECUTABLE ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf/protoc)
@@ -36,10 +59,15 @@ ExternalProject_Add(protobuf
     GIT_TAG ${PROTOBUF_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${PROTOBUF_PROTOC_EXECUTABLE} ${protobuf_STATIC_LIBRARIES}
     SOURCE_DIR ${CMAKE_CURRENT_BINARY_DIR}/protobuf/src/protobuf
+    # SOURCE_SUBDIR cmake/ # Requires CMake 3.7, this will allow removal of CONFIGURE_COMMAND
+    # CONFIGURE_COMMAND resets some settings made in CMAKE_CACHE_ARGS and the generator used
     CONFIGURE_COMMAND ${CMAKE_COMMAND} cmake/
-        -Dprotobuf_BUILD_TESTS=OFF
-        -DCMAKE_POSITION_INDEPENDENT_CODE=ON
+        -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE}
+        -DCMAKE_BUILD_TYPE:STRING=Release
+        -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
+        -Dprotobuf_BUILD_TESTS:BOOL=OFF
         -DZLIB_ROOT=${ZLIB_INSTALL}
         ${PROTOBUF_ADDITIONAL_CMAKE_OPTIONS}
     INSTALL_COMMAND ""
@@ -47,5 +75,7 @@ ExternalProject_Add(protobuf
         -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE}
         -DCMAKE_BUILD_TYPE:STRING=Release
         -DCMAKE_VERBOSE_MAKEFILE:BOOL=OFF
+        -Dprotobuf_BUILD_TESTS:BOOL=OFF
+        -Dprotobuf_MSVC_STATIC_RUNTIME:BOOL=OFF
         -DZLIB_ROOT:STRING=${ZLIB_INSTALL}
 )
diff --git a/tensorflow/contrib/cmake/external/re2.cmake b/tensorflow/contrib/cmake/external/re2.cmake
index 371d8447f9..c4bc0b1707 100644
--- a/tensorflow/contrib/cmake/external/re2.cmake
+++ b/tensorflow/contrib/cmake/external/re2.cmake
@@ -21,7 +21,11 @@ set(re2_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/re2/install)
 set(re2_TAG e7efc48)
 
 if(WIN32)
-  set(re2_STATIC_LIBRARIES ${re2_BUILD}/$(Configuration)/re2.lib)
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set(re2_STATIC_LIBRARIES ${re2_BUILD}/$(Configuration)/re2.lib)
+  else()
+    set(re2_STATIC_LIBRARIES ${re2_BUILD}/re2.lib)
+  endif()
 else()
   set(re2_STATIC_LIBRARIES ${re2_BUILD}/libre2.a)
 endif()
@@ -36,6 +40,7 @@ ExternalProject_Add(re2
     GIT_TAG ${re2_TAG}
     INSTALL_DIR ${re2_INSTALL}
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${re2_STATIC_LIBRARIES}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     CMAKE_CACHE_ARGS
         -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE}
diff --git a/tensorflow/contrib/cmake/external/snappy.cmake b/tensorflow/contrib/cmake/external/snappy.cmake
index fd57734298..f54197643b 100644
--- a/tensorflow/contrib/cmake/external/snappy.cmake
+++ b/tensorflow/contrib/cmake/external/snappy.cmake
@@ -20,7 +20,11 @@ set(snappy_BUILD ${CMAKE_CURRENT_BINARY_DIR}/snappy/src/snappy)
 set(snappy_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/snappy/src/snappy)
 
 if(WIN32)
-    set(snappy_STATIC_LIBRARIES ${snappy_BUILD}/$(Configuration)/snappy.lib)
+    if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+        set(snappy_STATIC_LIBRARIES ${snappy_BUILD}/$(Configuration)/snappy.lib)
+    else()
+        set(snappy_STATIC_LIBRARIES ${snappy_BUILD}/snappy.lib)
+    endif()
 else()
     set(snappy_STATIC_LIBRARIES ${snappy_BUILD}/libsnappy.a)
 endif()
@@ -35,6 +39,7 @@ ExternalProject_Add(snappy
     GIT_TAG ${snappy_TAG}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${snappy_STATIC_LIBRARIES}
     INSTALL_COMMAND ""
     LOG_DOWNLOAD ON
     LOG_CONFIGURE ON
diff --git a/tensorflow/contrib/cmake/external/sqlite.cmake b/tensorflow/contrib/cmake/external/sqlite.cmake
index 8297c60712..57c4ae7651 100644
--- a/tensorflow/contrib/cmake/external/sqlite.cmake
+++ b/tensorflow/contrib/cmake/external/sqlite.cmake
@@ -36,6 +36,7 @@ if (WIN32)
         PREFIX sqlite
         URL ${sqlite_URL}
         URL_HASH ${sqlite_HASH}
+        BUILD_BYPRODUCTS ${sqlite_STATIC_LIBRARIES}
         PATCH_COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/patches/sqlite/CMakeLists.txt ${sqlite_BUILD}
         INSTALL_DIR ${sqlite_INSTALL}
         DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
diff --git a/tensorflow/contrib/cmake/external/zlib.cmake b/tensorflow/contrib/cmake/external/zlib.cmake
index 5bec14fb00..c5eb0cbcc7 100644
--- a/tensorflow/contrib/cmake/external/zlib.cmake
+++ b/tensorflow/contrib/cmake/external/zlib.cmake
@@ -21,9 +21,19 @@ set(ZLIB_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/zlib/install)
 set(ZLIB_TAG 50893291621658f355bc5b4d450a8d06a563053d)
 
 if(WIN32)
-  set(zlib_STATIC_LIBRARIES
-      debug ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib
-      optimized ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib)
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set(zlib_STATIC_LIBRARIES
+        debug ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib
+        optimized ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib)
+  else()
+    if(CMAKE_BUILD_TYPE EQUAL Debug)
+      set(zlib_STATIC_LIBRARIES
+          ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib)
+    else()
+      set(zlib_STATIC_LIBRARIES
+          ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib)
+    endif()
+  endif()
 else()
   set(zlib_STATIC_LIBRARIES
       ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/libz.a)
@@ -40,6 +50,7 @@ ExternalProject_Add(zlib
     GIT_TAG ${ZLIB_TAG}
     INSTALL_DIR ${ZLIB_INSTALL}
     BUILD_IN_SOURCE 1
+    BUILD_BYPRODUCTS ${zlib_STATIC_LIBRARIES}
     DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
     CMAKE_CACHE_ARGS
         -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE}
diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt
index 2720c43b78..f55043c93d 100644
--- a/tensorflow/contrib/cmake/python_modules.txt
+++ b/tensorflow/contrib/cmake/python_modules.txt
@@ -299,7 +299,9 @@ tensorflow/contrib/linear_optimizer/kernels/g3doc
 tensorflow/contrib/linear_optimizer/python
 tensorflow/contrib/linear_optimizer/python/ops
 # TODO(drpngx): Fix failing imports
+# tensorflow/contrib/lite
 # tensorflow/contrib/lite/python
+# tensorflow/contrib/lite/toco
 # tensorflow/contrib/lite/toco/python
 tensorflow/contrib/lookup
 tensorflow/contrib/losses
@@ -360,6 +362,7 @@ tensorflow/contrib/reduce_slice_ops/kernels
 tensorflow/contrib/reduce_slice_ops/ops
 tensorflow/contrib/reduce_slice_ops/python
 tensorflow/contrib/reduce_slice_ops/python/ops
+tensorflow/contrib/remote_fused_graph
 tensorflow/contrib/remote_fused_graph/pylib
 tensorflow/contrib/remote_fused_graph/pylib/python
 tensorflow/contrib/remote_fused_graph/pylib/python/ops
@@ -409,6 +412,7 @@ tensorflow/contrib/summary
 tensorflow/contrib/tensorboard
 tensorflow/contrib/tensorboard/plugins
 tensorflow/contrib/tensorboard/plugins/projector
+tensorflow/contrib/tensorboard/plugins/trace
 tensorflow/contrib/tensor_forest
 tensorflow/contrib/tensor_forest/client
 tensorflow/contrib/tensor_forest/hybrid
@@ -419,6 +423,7 @@ tensorflow/contrib/tensor_forest/hybrid/python/layers
 tensorflow/contrib/tensor_forest/hybrid/python/models
 tensorflow/contrib/tensor_forest/hybrid/python/ops
 tensorflow/contrib/tensor_forest/kernels
+tensorflow/contrib/tensor_forest/proto
 tensorflow/contrib/tensor_forest/python
 tensorflow/contrib/tensor_forest/python/ops
 tensorflow/contrib/testing
@@ -439,6 +444,7 @@ tensorflow/contrib/timeseries/python/timeseries/state_space_models
 tensorflow/contrib/tpu
 tensorflow/contrib/tpu/ops
 tensorflow/contrib/tpu/profiler
+tensorflow/contrib/tpu/proto
 tensorflow/contrib/tpu/python
 tensorflow/contrib/tpu/python/ops
 tensorflow/contrib/tpu/python/profiler
diff --git a/tensorflow/contrib/cmake/tests/cuda/compatibility_test.c b/tensorflow/contrib/cmake/tests/cuda/compatibility_test.c
new file mode 100644
index 0000000000..9e355da33a
--- /dev/null
+++ b/tensorflow/contrib/cmake/tests/cuda/compatibility_test.c
@@ -0,0 +1,22 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This is a program to test if compiler is compatible with CUDA.
+#define __CUDACC__
+#include "crt/host_config.h"
+
+int main(void) {
+  return 0;
+}
diff --git a/tensorflow/contrib/cmake/tests/cuda/compatibility_test.cc b/tensorflow/contrib/cmake/tests/cuda/compatibility_test.cc
new file mode 100644
index 0000000000..a50461cafd
--- /dev/null
+++ b/tensorflow/contrib/cmake/tests/cuda/compatibility_test.cc
@@ -0,0 +1,20 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+============================================================================*/
+
+// This is a program to test if compiler is compatible with CUDA.
+#define __CUDACC__
+#include "crt/host_config.h"
+
+int main(void) { return 0; }
diff --git a/tensorflow/contrib/cmake/tf_cc_ops.cmake b/tensorflow/contrib/cmake/tf_cc_ops.cmake
index f3cf3e7044..f73da0b8ab 100644
--- a/tensorflow/contrib/cmake/tf_cc_ops.cmake
+++ b/tensorflow/contrib/cmake/tf_cc_ops.cmake
@@ -149,7 +149,11 @@ add_library(tf_cc OBJECT ${tf_cc_srcs})
 add_dependencies(tf_cc tf_cc_framework tf_cc_ops)
 
 if (WIN32)
-  set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow_internal.lib")
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow_internal.lib")
+  else()
+    set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.lib")
+  endif()
 else (WIN32)
   set (pywrap_tensorflow_lib "${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so")
 endif (WIN32)
diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake
index b3c6663b8b..b730ebd3ba 100755
--- a/tensorflow/contrib/cmake/tf_python.cmake
+++ b/tensorflow/contrib/cmake/tf_python.cmake
@@ -541,7 +541,11 @@ if(WIN32)
 	${nsync_STATIC_LIBRARIES}
     )
 
-    set(pywrap_tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow.def")
+    if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+        set(pywrap_tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/pywrap_tensorflow.def")
+    else()
+        set(pywrap_tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow.def")
+    endif()
     set_source_files_properties(${pywrap_tensorflow_deffile} PROPERTIES GENERATED TRUE)
 
     add_custom_command(TARGET pywrap_tensorflow_internal_static POST_BUILD
@@ -549,6 +553,7 @@ if(WIN32)
             --input "${pywrap_tensorflow_internal_static_dependencies}"
             --output "${pywrap_tensorflow_deffile}"
             --target _pywrap_tensorflow_internal.pyd
+        BYPRODUCTS ${pywrap_tensorflow_deffile} # Required for Ninja
     )
 endif(WIN32)
 
@@ -702,11 +707,19 @@ add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD
                                    ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/testing/python/framework/)
 
 if(WIN32)
-  add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
-    COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/$(Configuration)/pywrap_tensorflow_internal.dll
-                                     ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.pyd
-    COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/$(Configuration)/pywrap_tensorflow_internal.lib
-                                     ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/)
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
+      COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/$(Configuration)/pywrap_tensorflow_internal.dll
+                                       ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.pyd
+      COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/$(Configuration)/pywrap_tensorflow_internal.lib
+                                       ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/)
+  else()
+    add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
+      COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.dll
+                                       ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/_pywrap_tensorflow_internal.pyd
+      COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/pywrap_tensorflow_internal.lib
+                                       ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/)
+  endif()
 else()
   add_custom_command(TARGET tf_python_build_pip_package POST_BUILD
     COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_BINARY_DIR}/libpywrap_tensorflow_internal.so
diff --git a/tensorflow/contrib/cmake/tf_shared_lib.cmake b/tensorflow/contrib/cmake/tf_shared_lib.cmake
index 571d2b0dec..6d36d5fc5c 100644
--- a/tensorflow/contrib/cmake/tf_shared_lib.cmake
+++ b/tensorflow/contrib/cmake/tf_shared_lib.cmake
@@ -46,7 +46,11 @@ if(WIN32)
       $<TARGET_FILE:tf_protos_cc>
   )
 
-  set(tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/tensorflow.def")
+  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
+    set(tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/tensorflow.def")
+  else()
+    set(tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/tensorflow.def")
+  endif()
   set_source_files_properties(${tensorflow_deffile} PROPERTIES GENERATED TRUE)
 
   add_custom_command(TARGET tensorflow_static POST_BUILD
diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake
index 0a8d691f32..1c4ebd7f0c 100644
--- a/tensorflow/contrib/cmake/tf_tests.cmake
+++ b/tensorflow/contrib/cmake/tf_tests.cmake
@@ -310,6 +310,8 @@ if (tensorflow_BUILD_PYTHON_TESTS)
       "${tensorflow_source_dir}/tensorflow/python/kernel_tests/control_flow_util_test.py"
       # Flaky replicate_model_fn_test
       "${tensorflow_source_dir}/tensorflow/contrib/estimator/python/estimator/replicate_model_fn_test.py"  # b/71901810
+      # Broken io_utils_test
+      "${tensorflow_source_dir}/tensorflow/python/keras/_impl/keras/utils/io_utils_test.py"  # b/72894325
   )
   endif()
   list(REMOVE_ITEM tf_test_src_py ${tf_test_src_py_exclude})
diff --git a/tensorflow/contrib/cmake/tf_tools.cmake b/tensorflow/contrib/cmake/tf_tools.cmake
index cb58a2e7df..58c7df95c8 100644
--- a/tensorflow/contrib/cmake/tf_tools.cmake
+++ b/tensorflow/contrib/cmake/tf_tools.cmake
@@ -48,9 +48,6 @@ file(GLOB_RECURSE tf_tools_transform_graph_lib_exclude_srcs
     "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/compare_graphs.cc"
     "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/summarize_graph_main.cc"
     "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/transform_graph_main.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/quantize_nodes.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/quantize_weights.cc"
-    "${tensorflow_source_dir}/tensorflow/tools/graph_transforms/round_weights.cc"
 )
 list(REMOVE_ITEM tf_tools_transform_graph_lib_srcs ${tf_tools_transform_graph_lib_exclude_srcs})
 
diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
new file mode 100644
index 0000000000..bb6b049694
--- /dev/null
+++ b/tensorflow/contrib/data/python/ops/dataset_ops.py
@@ -0,0 +1,691 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Python wrappers for Datasets and Iterators."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.data.python.ops import batching
+from tensorflow.contrib.data.python.ops import enumerate_ops
+from tensorflow.contrib.data.python.ops import error_ops
+from tensorflow.contrib.data.python.ops import grouping
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.util import nest
+from tensorflow.python.ops import gen_dataset_ops
+from tensorflow.python.ops import gen_io_ops
+from tensorflow.python.util import deprecation
+
+
+class Dataset(dataset_ops.Dataset):
+  """Represents a potentially large set of elements.
+
+  A `Dataset` can be used to represent an input pipeline as a
+  collection of elements (nested structures of tensors) and a "logical
+  plan" of transformations that act on those elements.
+  """
+
+  def __init__(self, dataset):
+    super(Dataset, self).__init__()
+    self._dataset = dataset
+
+  @deprecation.deprecated(None, "Use `ds._as_variant_tensor()`.")
+  def make_dataset_resource(self):
+    return self._as_variant_tensor()
+
+  def _as_variant_tensor(self):
+    return self._dataset._as_variant_tensor()  # pylint: disable=protected-access
+
+  @property
+  def output_classes(self):
+    return self._dataset.output_classes
+
+  @property
+  def output_shapes(self):
+    return self._dataset.output_shapes
+
+  @property
+  def output_types(self):
+    return self._dataset.output_types
+
+  @staticmethod
+  @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensors()`.")
+  def from_tensors(tensors):
+    """Creates a `Dataset` with a single element, comprising the given tensors.
+
+    Args:
+      tensors: A nested structure of tensors.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.TensorDataset(tensors))
+
+  @staticmethod
+  @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensor_slices()`.")
+  def from_tensor_slices(tensors):
+    """Creates a `Dataset` whose elements are slices of the given tensors.
+
+    Args:
+      tensors: A nested structure of tensors, each having the same size in the
+        0th dimension.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.TensorSliceDataset(tensors))
+
+  @staticmethod
+  @deprecation.deprecated(None,
+                          "Use `tf.data.Dataset.from_sparse_tensor_slices()`.")
+  def from_sparse_tensor_slices(sparse_tensor):
+    """Splits each rank-N `tf.SparseTensor` in this dataset row-wise.
+
+    Args:
+      sparse_tensor: A `tf.SparseTensor`.
+
+    Returns:
+      A `Dataset` of rank-(N-1) sparse tensors.
+    """
+    return Dataset(dataset_ops.SparseTensorSliceDataset(sparse_tensor))
+
+  @staticmethod
+  @deprecation.deprecated(None, "Use `tf.data.Dataset.from_generator()`.")
+  def from_generator(generator, output_types, output_shapes=None):
+    """Creates a `Dataset` whose elements are generated by `generator`.
+
+    The `generator` argument must be a callable object that returns
+    an object that support the `iter()` protocol (e.g. a generator function).
+    The elements generated by `generator` must be compatible with the given
+    `output_types` and (optional) `output_shapes` arguments.
+
+    For example:
+
+    ```python
+    import itertools
+
+    def gen():
+      for i in itertools.count(1):
+        yield (i, [1] * i)
+
+    ds = Dataset.from_generator(
+        gen, (tf.int64, tf.int64), (tf.TensorShape([]), tf.TensorShape([None])))
+    value = ds.make_one_shot_iterator().get_next()
+
+    sess.run(value)  # (1, array([1]))
+    sess.run(value)  # (2, array([1, 1]))
+    ```
+
+    Args:
+      generator: A callable object that takes no arguments and returns an
+        object that supports the `iter()` protocol.
+      output_types: A nested structure of `tf.DType` objects corresponding to
+        each component of an element yielded by `generator`.
+      output_shapes: (Optional.) A nested structure of `tf.TensorShape`
+        objects corresponding to each component of an element yielded by
+        `generator`.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(
+        dataset_ops.Dataset.from_generator(generator, output_types,
+                                           output_shapes))
+
+  @staticmethod
+  @deprecation.deprecated(None, "Use `tf.data.Dataset.range()`.")
+  def range(*args):
+    """Creates a `Dataset` of a step-separated range of values.
+
+    For example:
+
+    ```python
+    Dataset.range(5) == [0, 1, 2, 3, 4]
+    Dataset.range(2, 5) == [2, 3, 4]
+    Dataset.range(1, 5, 2) == [1, 3]
+    Dataset.range(1, 5, -2) == []
+    Dataset.range(5, 1) == []
+    Dataset.range(5, 1, -2) == [5, 3]
+    ```
+
+    Args:
+      *args: follow same semantics as python's xrange.
+        len(args) == 1 -> start = 0, stop = args[0], step = 1
+        len(args) == 2 -> start = args[0], stop = args[1], step = 1
+        len(args) == 3 -> start = args[0], stop = args[1, stop = args[2]
+
+    Returns:
+      A `RangeDataset`.
+
+    Raises:
+      ValueError: if len(args) == 0.
+    """
+    return Dataset(dataset_ops.RangeDataset(*args))
+
+  @staticmethod
+  @deprecation.deprecated(None, "Use `tf.data.Dataset.zip()`.")
+  def zip(datasets):
+    """Creates a `Dataset` by zipping together the given datasets.
+
+    This method has similar semantics to the built-in `zip()` function
+    in Python, with the main difference being that the `datasets`
+    argument can be an arbitrary nested structure of `Dataset` objects.
+    For example:
+
+    ```python
+    # NOTE: The following examples use `{ ... }` to represent the
+    # contents of a dataset.
+    a = { 1, 2, 3 }
+    b = { 4, 5, 6 }
+    c = { (7, 8), (9, 10), (11, 12) }
+    d = { 13, 14 }
+
+    # The nested structure of the `datasets` argument determines the
+    # structure of elements in the resulting dataset.
+    Dataset.zip((a, b)) == { (1, 4), (2, 5), (3, 6) }
+    Dataset.zip((b, a)) == { (4, 1), (5, 2), (6, 3) }
+
+    # The `datasets` argument may contain an arbitrary number of
+    # datasets.
+    Dataset.zip((a, b, c)) == { (1, 4, (7, 8)),
+                                (2, 5, (9, 10)),
+                                (3, 6, (11, 12)) }
+
+    # The number of elements in the resulting dataset is the same as
+    # the size of the smallest dataset in `datasets`.
+    Dataset.zip((a, d)) == { (1, 13), (2, 14) }
+    ```
+
+    Args:
+      datasets: A nested structure of datasets.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.ZipDataset(datasets))
+
+  def concatenate(self, dataset):
+    """Creates a `Dataset` by concatenating given dataset with this dataset.
+
+    ```python
+    # NOTE: The following examples use `{ ... }` to represent the
+    # contents of a dataset.
+    a = { 1, 2, 3 }
+    b = { 4, 5, 6, 7 }
+
+    # Input dataset and dataset to be concatenated should have same
+    # nested structures and output types.
+    # c = { (8, 9), (10, 11), (12, 13) }
+    # d = { 14.0, 15.0, 16.0 }
+    # a.concatenate(c) and a.concatenate(d) would result in error.
+
+    a.concatenate(b) == { 1, 2, 3, 4, 5, 6, 7 }
+    ```
+
+    Args:
+      dataset: `Dataset` to be concatenated.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.ConcatenateDataset(self._dataset, dataset))
+
+  def prefetch(self, buffer_size):
+    """Creates a `Dataset` that prefetches elements from this dataset.
+
+    Args:
+      buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the
+        maximum number elements that will be buffered when prefetching.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.PrefetchDataset(self._dataset, buffer_size))
+
+  @staticmethod
+  @deprecation.deprecated(None, "Use `tf.data.Dataset.list_files()`.")
+  def list_files(file_pattern):
+    """A dataset of all files matching a pattern.
+
+    Example:
+      If we had the following files on our filesystem:
+        - /path/to/dir/a.txt
+        - /path/to/dir/b.py
+        - /path/to/dir/c.py
+      If we pass "/path/to/dir/*.py" as the directory, the dataset would
+      produce:
+        - /path/to/dir/b.py
+        - /path/to/dir/c.py
+
+    Args:
+      file_pattern: A string or scalar string `tf.Tensor`, representing
+        the filename pattern that will be matched.
+
+    Returns:
+     A `Dataset` of strings corresponding to file names.
+    """
+    return Dataset.from_tensor_slices(gen_io_ops.matching_files(file_pattern))
+
+  def repeat(self, count=None):
+    """Repeats this dataset `count` times.
+
+    Args:
+      count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
+        number of times the elements of this dataset should be repeated. The
+        default behavior (if `count` is `None` or `-1`) is for the elements to
+        be repeated indefinitely.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.RepeatDataset(self._dataset, count))
+
+  @deprecation.deprecated(
+      None, "Use `ds.apply(tf.contrib.data.enumerate_dataset())`.")
+  def enumerate(self, start=0):
+    """Deprecated: Use `Dataset.apply(tf.contrib.data.enumerate_dataset(..)`."""
+
+    return self.apply(enumerate_ops.enumerate_dataset(start))
+
+  def shuffle(self, buffer_size, seed=None):
+    """Randomly shuffles the elements of this dataset.
+
+    Args:
+      buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the
+        number of elements from this dataset from which the new
+        dataset will sample.
+      seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
+        random seed that will be used to create the distribution. See
+        @{tf.set_random_seed} for behavior.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.ShuffleDataset(self._dataset, buffer_size, seed))
+
+  def cache(self, filename=""):
+    """Caches the elements in this dataset.
+
+    Args:
+      filename: A `tf.string` scalar `tf.Tensor`, representing the name of a
+        directory on the filesystem to use for caching tensors in this Dataset.
+        If a filename is not provided, the dataset will be cached in memory.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.CacheDataset(self._dataset, filename))
+
+  def take(self, count):
+    """Creates a `Dataset` with at most `count` elements from this dataset.
+
+    Args:
+      count: A `tf.int64` scalar `tf.Tensor`, representing the number of
+        elements of this dataset that should be taken to form the new dataset.
+        If `count` is -1, or if `count` is greater than the size of this
+        dataset, the new dataset will contain all elements of this dataset.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.TakeDataset(self._dataset, count))
+
+  def skip(self, count):
+    """Creates a `Dataset` that skips `count` elements from this dataset.
+
+    Args:
+      count: A `tf.int64` scalar `tf.Tensor`, representing the number
+        of elements of this dataset that should be skipped to form the
+        new dataset.  If `count` is greater than the size of this
+        dataset, the new dataset will contain no elements.  If `count`
+        is -1, skips the entire dataset.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.SkipDataset(self._dataset, count))
+
+  def shard(self, num_shards, index):
+    """Creates a `Dataset` that includes only 1/`num_shards` of this dataset.
+
+    This dataset operator is very useful when running distributed training, as
+    it allows each worker to read a unique subset.
+
+    When reading a single input file, you can skip elements as follows:
+
+    ```python
+    d = tf.data.TFRecordDataset(FLAGS.input_file)
+    d = d.shard(FLAGS.num_workers, FLAGS.worker_index)
+    d = d.repeat(FLAGS.num_epochs)
+    d = d.shuffle(FLAGS.shuffle_buffer_size)
+    d = d.map(parser_fn, num_parallel_calls=FLAGS.num_map_threads)
+    ```
+
+    Important caveats:
+
+    - Be sure to shard before you use any randomizing operator (such as
+      shuffle).
+    - Generally it is best if the shard operator is used early in the dataset
+      pipeline. For example, when reading from a set of TFRecord files, shard
+      before converting the dataset to input samples. This avoids reading every
+      file on every worker. The following is an example of an efficient
+      sharding strategy within a complete pipeline:
+
+    ```python
+    d = tf.data.Dataset.list_files(FLAGS.pattern)
+    d = d.shard(FLAGS.num_workers, FLAGS.worker_index)
+    d = d.repeat(FLAGS.num_epochs)
+    d = d.shuffle(FLAGS.shuffle_buffer_size)
+    d = d.interleave(tf.data.TFRecordDataset,
+                     cycle_length=FLAGS.num_readers, block_length=1)
+    d = d.map(parser_fn, num_parallel_calls=FLAGS.num_map_threads)
+    ```
+
+    Args:
+      num_shards: A `tf.int64` scalar `tf.Tensor`, representing the number of
+        shards operating in parallel.
+      index: A `tf.int64` scalar `tf.Tensor`, representing the worker index.
+
+    Returns:
+      A `Dataset`.
+
+    Raises:
+      ValueError: if `num_shards` or `index` are illegal values. Note: error
+        checking is done on a best-effort basis, and aren't guaranteed to be
+        caught upon dataset creation. (e.g. providing in a placeholder tensor
+        bypasses the early checking, and will instead result in an error during
+        a session.run call.)
+    """
+    return Dataset(self._dataset.shard(num_shards, index))
+
+  @deprecation.deprecated(None,
+                          "Use `ds.apply(tf.contrib.data.ignore_errors())`.")
+  def ignore_errors(self):
+    """Deprecated: Use `Dataset.apply(tf.contrib.data.ignore_errors())`."""
+
+    return self.apply(error_ops.ignore_errors())
+
+  def batch(self, batch_size):
+    """Combines consecutive elements of this dataset into batches.
+
+    Args:
+      batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
+        consecutive elements of this dataset to combine in a single batch.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.BatchDataset(self._dataset, batch_size))
+
+  def padded_batch(self, batch_size, padded_shapes, padding_values=None):
+    """Combines consecutive elements of this dataset into padded batches.
+
+    Like `Dataset.dense_to_sparse_batch()`, this method combines
+    multiple consecutive elements of this dataset, which might have
+    different shapes, into a single element. The tensors in the
+    resulting element have an additional outer dimension, and are
+    padded to the respective shape in `padded_shapes`.
+
+    Args:
+      batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
+        consecutive elements of this dataset to combine in a single batch.
+      padded_shapes: A nested structure of `tf.TensorShape` or
+        `tf.int64` vector tensor-like objects representing the shape
+        to which the respective component of each input element should
+        be padded prior to batching. Any unknown dimensions
+        (e.g. `tf.Dimension(None)` in a `tf.TensorShape` or `-1` in a
+        tensor-like object) will be padded to the maximum size of that
+        dimension in each batch.
+      padding_values: (Optional.) A nested structure of scalar-shaped
+        `tf.Tensor`, representing the padding values to use for the
+        respective components.  Defaults are `0` for numeric types and
+        the empty string for string types.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(
+        dataset_ops.PaddedBatchDataset(self._dataset, batch_size, padded_shapes,
+                                       padding_values))
+
+  @deprecation.deprecated(
+      None, "Use `ds.apply(tf.contrib.data.dense_to_sparse_batch())`.")
+  def dense_to_sparse_batch(self, batch_size, row_shape):
+    """Use: `Dataset.apply(tf.contrib.data.dense_to_sparse_batch(...))`."""
+
+    return self.apply(batching.dense_to_sparse_batch(batch_size, row_shape))
+
+  @deprecation.deprecated(None,
+                          "Use `ds.apply(tf.contrib.data.group_by_window())`.")
+  def group_by_window(self, key_func, reduce_func, window_size):
+    """Deprecated: Use `Dataset.apply(tf.contrib.data.group_by_window(...))`."""
+
+    return self.apply(
+        grouping.group_by_window(key_func, reduce_func, window_size))
+
+  @deprecation.deprecated_args(
+      None, "Replace `num_threads=T` with `num_parallel_calls=T`. Replace "
+      "`output_buffer_size=N` with `ds.prefetch(N)` on the returned dataset.",
+      "num_threads", "output_buffer_size")
+  def map(self,
+          map_func,
+          num_threads=None,
+          output_buffer_size=None,
+          num_parallel_calls=None):
+    """Maps `map_func` across this dataset.
+
+    Args:
+      map_func: A function mapping a nested structure of tensors (having
+        shapes and types defined by `self.output_shapes` and
+       `self.output_types`) to another nested structure of tensors.
+      num_threads: (Optional.) Deprecated, use `num_parallel_calls` instead.
+      output_buffer_size: (Optional.) A `tf.int64` scalar `tf.Tensor`,
+        representing the maximum number of processed elements that will be
+        buffered.
+      num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
+        representing the number elements to process in parallel. If not
+        specified, elements will be processed sequentially.
+
+    Returns:
+      A `Dataset`.
+    """
+    if num_threads is None and num_parallel_calls is None:
+      ret = Dataset(dataset_ops.MapDataset(self._dataset, map_func))
+    else:
+      if num_threads is None:
+        ret = Dataset(
+            dataset_ops.ParallelMapDataset(self._dataset, map_func,
+                                           num_parallel_calls))
+      else:
+        ret = Dataset(
+            dataset_ops.ParallelMapDataset(self._dataset, map_func,
+                                           num_threads))
+    if output_buffer_size is not None:
+      ret = ret.prefetch(output_buffer_size)
+    return ret
+
+  def flat_map(self, map_func):
+    """Maps `map_func` across this dataset and flattens the result.
+
+    Args:
+      map_func: A function mapping a nested structure of tensors (having shapes
+        and types defined by `self.output_shapes` and `self.output_types`) to a
+        `Dataset`.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.FlatMapDataset(self._dataset, map_func))
+
+  def interleave(self, map_func, cycle_length, block_length=1):
+    """Maps `map_func` across this dataset, and interleaves the results.
+
+    For example, you can use `Dataset.interleave()` to process many input files
+    concurrently:
+
+    ```python
+    # Preprocess 4 files concurrently, and interleave blocks of 16 records from
+    # each file.
+    filenames = ["/var/data/file1.txt", "/var/data/file2.txt", ...]
+    dataset = (Dataset.from_tensor_slices(filenames)
+               .interleave(lambda x:
+                   TextLineDataset(x).map(parse_fn, num_parallel_calls=1),
+                   cycle_length=4, block_length=16))
+    ```
+
+    The `cycle_length` and `block_length` arguments control the order in which
+    elements are produced. `cycle_length` controls the number of input elements
+    that are processed concurrently. If you set `cycle_length` to 1, this
+    transformation will handle one input element at a time, and will produce
+    identical results = to @{tf.data.Dataset.flat_map}. In general,
+    this transformation will apply `map_func` to `cycle_length` input elements,
+    open iterators on the returned `Dataset` objects, and cycle through them
+    producing `block_length` consecutive elements from each iterator, and
+    consuming the next input element each time it reaches the end of an
+    iterator.
+
+    For example:
+
+    ```python
+    # NOTE: The following examples use `{ ... }` to represent the
+    # contents of a dataset.
+    a = { 1, 2, 3, 4, 5 }
+
+    # NOTE: New lines indicate "block" boundaries.
+    a.interleave(lambda x: Dataset.from_tensors(x).repeat(6),
+                 cycle_length=2, block_length=4) == {
+        1, 1, 1, 1,
+        2, 2, 2, 2,
+        1, 1,
+        2, 2,
+        3, 3, 3, 3,
+        4, 4, 4, 4,
+        3, 3,
+        4, 4,
+        5, 5, 5, 5,
+        5, 5,
+    }
+    ```
+
+    NOTE: The order of elements yielded by this transformation is
+    deterministic, as long as `map_func` is a pure function. If
+    `map_func` contains any stateful operations, the order in which
+    that state is accessed is undefined.
+
+    Args:
+      map_func: A function mapping a nested structure of tensors (having shapes
+        and types defined by `self.output_shapes` and `self.output_types`) to a
+        `Dataset`.
+      cycle_length: The number of elements from this dataset that will be
+        processed concurrently.
+      block_length: The number of consecutive elements to produce from each
+        input element before cycling to another input element.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(
+        dataset_ops.InterleaveDataset(self._dataset, map_func, cycle_length,
+                                      block_length))
+
+  @deprecation.deprecated(None, "Use `ds.apply(tf.contrib.data.unbatch())`.")
+  def unbatch(self):
+    """Deprecated: Use `Dataset.apply(tf.contrib.data.unbatch()`."""
+
+    return self.apply(batching.unbatch())
+
+  def filter(self, predicate):
+    """Filters this dataset according to `predicate`.
+
+    Args:
+      predicate: A function mapping a nested structure of tensors (having shapes
+        and types defined by `self.output_shapes` and `self.output_types`) to a
+        scalar `tf.bool` tensor.
+
+    Returns:
+      A `Dataset`.
+    """
+    return Dataset(dataset_ops.FilterDataset(self._dataset, predicate))
+
+  def apply(self, transformation_func):
+    """Apply a transformation function to this dataset.
+
+    `apply` enables chaining of custom `Dataset` transformations, which are
+    represented as functions that take one `Dataset` argument and return a
+    transformed `Dataset`.
+
+    For example:
+
+    ```
+    dataset = (dataset.map(lambda x: x ** 2)
+               .(group_by_window(key_func, reduce_func, window_size))
+               .map(lambda x: x ** 3))
+    ```
+
+    Args:
+      transformation_func: A function that takes one `Dataset` argument and
+        returns a `Dataset`.
+
+    Returns:
+      The `Dataset` returned by applying `transformation_func` to this dataset.
+    """
+    dataset = transformation_func(self)
+    if not isinstance(dataset, dataset_ops.Dataset):
+      raise TypeError("`transformation_func` must return a Dataset.")
+    return Dataset(dataset)
+
+
+def get_single_element(dataset):
+  """Returns the single element in `dataset` as a nested structure of tensors.
+
+  This function enables you to use a @{tf.data.Dataset} in a stateless
+  "tensor-in tensor-out" expression, without creating a @{tf.data.Iterator}.
+  This can be useful when your preprocessing transformations are expressed
+  as a `Dataset`, and you want to use the transformation at serving time.
+  For example:
+
+  ```python
+  input_batch = tf.placeholder(tf.string, shape=[BATCH_SIZE])
+
+  def preprocessing_fn(input_str):
+    # ...
+    return image, label
+
+  dataset = (tf.data.Dataset.from_tensor_slices(input_batch)
+             .map(preprocessing_fn, num_parallel_calls=BATCH_SIZE)
+             .batch(BATCH_SIZE))
+
+  image_batch, label_batch = tf.contrib.data.get_single_element(dataset)
+  ```
+
+  Args:
+    dataset: A @{tf.data.Dataset} object containing a single element.
+
+  Returns:
+    A nested structure of @{tf.Tensor} objects, corresponding to the single
+    element of `dataset`.
+
+  Raises:
+    TypeError: if `dataset` is not a `tf.data.Dataset` object.
+    InvalidArgumentError (at runtime): if `dataset` does not contain exactly
+      one element.
+  """
+  if not isinstance(dataset, dataset_ops.Dataset):
+    raise TypeError("`dataset` must be a `tf.data.Dataset` object.")
+  return nest.pack_sequence_as(
+      dataset.output_types,
+      gen_dataset_ops.dataset_to_single_element(
+          dataset._as_variant_tensor(),  # pylint: disable=protected-access
+          output_types=nest.flatten(dataset.output_types),
+          output_shapes=nest.flatten(dataset.output_shapes)))
diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py
index ef91c56726..67b085002a 100644
--- a/tensorflow/contrib/data/python/ops/grouping.py
+++ b/tensorflow/contrib/data/python/ops/grouping.py
@@ -45,7 +45,7 @@ def group_by_window(key_func,
     key_func: A function mapping a nested structure of tensors
       (having shapes and types defined by `self.output_shapes` and
       `self.output_types`) to a scalar `tf.int64` tensor.
-    reduce_func: A function mapping a key and a dataset of up to `batch_size`
+    reduce_func: A function mapping a key and a dataset of up to `window_size`
       consecutive elements matching that key to another dataset.
     window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
       consecutive elements matching the same key to combine in a single
diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py
index f0517142ab..61c411271d 100644
--- a/tensorflow/contrib/distributions/__init__.py
+++ b/tensorflow/contrib/distributions/__init__.py
@@ -98,7 +98,6 @@ _allowed_symbols = [
     'Autoregressive',
     'Binomial',
     'Bernoulli',
-    'BernoulliWithSigmoidProbs',
     'Beta',
     'BetaWithSoftplusConcentration',
     'Categorical',
diff --git a/tensorflow/contrib/eager/python/examples/gan/README.md b/tensorflow/contrib/eager/python/examples/gan/README.md
index e8c9db1a1e..208a64b05d 100644
--- a/tensorflow/contrib/eager/python/examples/gan/README.md
+++ b/tensorflow/contrib/eager/python/examples/gan/README.md
@@ -11,7 +11,7 @@ Other eager execution examples can be found under the parent directory.
 - `mnist.py`: Model definitions and training routines.
 - `mnist_test.py`: Benchmarks for training and using the models using eager
 execution.
-- `mnist_graph_test.py`: Benchmarks for trainig and using the models using
+- `mnist_graph_test.py`: Benchmarks for training and using the models using
 graph execution. The same model definitions and loss functions are used in
 all benchmarks.
 
diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist.py b/tensorflow/contrib/eager/python/examples/mnist/mnist.py
index 241eb23ce9..58b1e89d15 100644
--- a/tensorflow/contrib/eager/python/examples/mnist/mnist.py
+++ b/tensorflow/contrib/eager/python/examples/mnist/mnist.py
@@ -39,7 +39,7 @@ class MNISTModel(tf.keras.Model):
   """MNIST Network.
 
   Network structure is equivalent to:
-  https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py
+  https://github.com/tensorflow/tensorflow/blob/r1.6/tensorflow/examples/tutorials/mnist/mnist_deep.py
   and
   https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
 
diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py b/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
index 2375ee4f55..476528b0dd 100644
--- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
+++ b/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
@@ -22,6 +22,7 @@ from __future__ import print_function
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_math_ops
 from tensorflow.python.ops import math_ops
 
@@ -108,4 +109,3 @@ def _AddNGrad(op, grad):
   """Same as gradient for AddN. Copies the gradient to all inputs."""
   # Not broadcasting.
   return [grad] * len(op.inputs)
-
diff --git a/tensorflow/contrib/gan/python/eval/python/summaries_impl.py b/tensorflow/contrib/gan/python/eval/python/summaries_impl.py
index 74811ff409..0d1afad72d 100644
--- a/tensorflow/contrib/gan/python/eval/python/summaries_impl.py
+++ b/tensorflow/contrib/gan/python/eval/python/summaries_impl.py
@@ -39,12 +39,13 @@ def _assert_is_image(data):
   data.shape[1:].assert_is_fully_defined()
 
 
-def add_gan_model_image_summaries(gan_model, grid_size=4):
+def add_gan_model_image_summaries(gan_model, grid_size=4, model_summaries=True):
   """Adds image summaries for real and fake images.
 
   Args:
     gan_model: A GANModel tuple.
     grid_size: The size of an image grid.
+    model_summaries: Also add summaries of the model.
 
   Raises:
     ValueError: If real and generated data aren't images.
@@ -83,7 +84,9 @@ def add_gan_model_image_summaries(gan_model, grid_size=4):
           image_shape=generated_image_shape,
           num_channels=generated_channels),
       max_outputs=1)
-  add_gan_model_summaries(gan_model)
+
+  if model_summaries:
+    add_gan_model_summaries(gan_model)
 
 
 def add_image_comparison_summaries(gan_model, num_comparisons=2,
diff --git a/tensorflow/contrib/gan/python/eval/python/summaries_test.py b/tensorflow/contrib/gan/python/eval/python/summaries_test.py
index a02d8772e1..5549df971d 100644
--- a/tensorflow/contrib/gan/python/eval/python/summaries_test.py
+++ b/tensorflow/contrib/gan/python/eval/python/summaries_test.py
@@ -71,9 +71,10 @@ def get_cyclegan_model():
 
 class SummariesTest(test.TestCase):
 
-  def _test_add_gan_model_image_summaries_impl(self, get_model_fn,
-                                               expected_num_summary_ops):
-    summaries.add_gan_model_image_summaries(get_model_fn(), grid_size=2)
+  def _test_add_gan_model_image_summaries_impl(
+      self, get_model_fn, expected_num_summary_ops, model_summaries):
+    summaries.add_gan_model_image_summaries(
+        get_model_fn(), grid_size=2, model_summaries=model_summaries)
 
     self.assertEquals(expected_num_summary_ops,
                       len(ops.get_collection(ops.GraphKeys.SUMMARIES)))
@@ -82,10 +83,13 @@ class SummariesTest(test.TestCase):
       summary.merge_all().eval()
 
   def test_add_gan_model_image_summaries(self):
-    self._test_add_gan_model_image_summaries_impl(get_gan_model, 5)
+    self._test_add_gan_model_image_summaries_impl(get_gan_model, 5, True)
+
+  def test_add_gan_model_image_summaries_no_model(self):
+    self._test_add_gan_model_image_summaries_impl(get_gan_model, 2, False)
 
   def test_add_gan_model_image_summaries_for_cyclegan(self):
-    self._test_add_gan_model_image_summaries_impl(get_cyclegan_model, 10)
+    self._test_add_gan_model_image_summaries_impl(get_cyclegan_model, 10, True)
 
   def _test_add_gan_model_summaries_impl(self, get_model_fn,
                                          expected_num_summary_ops):
diff --git a/tensorflow/contrib/gdr/README.md b/tensorflow/contrib/gdr/README.md
index 34ce60b360..8242d93f12 100644
--- a/tensorflow/contrib/gdr/README.md
+++ b/tensorflow/contrib/gdr/README.md
@@ -119,4 +119,4 @@ In the original design (as in the reference), tensor buffers are only registered
 Reference
 ===
 
-Bairen Yi, Jiacheng Xia, Li Chen, and Kai Chen. 2017. Towards Zero Copy Dataflows using RDMA. In Proceedings of SIGCOMM Posters and Demos'17, Los Angeles, CA, USA, August 22-24, 2017, 3 pages. https://doi.org/10.1145/3123878.3123907
+Bairen Yi, Jiacheng Xia, Li Chen, and Kai Chen. 2017. Towards Zero Copy Dataflows using RDMA. In Proceedings of SIGCOMM Posters and Demos'17, Los Angeles, CA, USA, August 22-24, 2017, 3 pages. https://doi.org/10.1145/3123878.3131975
diff --git a/tensorflow/contrib/layers/__init__.py b/tensorflow/contrib/layers/__init__.py
index ef419862b4..337c9e06b8 100644
--- a/tensorflow/contrib/layers/__init__.py
+++ b/tensorflow/contrib/layers/__init__.py
@@ -35,6 +35,7 @@ See the @{$python/contrib.layers} guide.
 @@fully_connected
 @@GDN
 @@gdn
+@@images_to_sequence
 @@layer_norm
 @@linear
 @@max_pool2d
@@ -50,6 +51,7 @@ See the @{$python/contrib.layers} guide.
 @@scale_gradient
 @@separable_conv2d
 @@separable_convolution2d
+@@sequence_to_images
 @@softmax
 @@spatial_softmax
 @@stack
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 1c3af19a6c..dcee775337 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -60,9 +60,10 @@ __all__ = [
     'conv2d_in_plane', 'conv2d_transpose', 'conv3d_transpose', 'convolution',
     'convolution2d', 'convolution2d_in_plane', 'convolution2d_transpose',
     'convolution3d', 'convolution3d_transpose', 'dense_to_sparse', 'dropout',
-    'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'layer_norm', 'linear',
-    'pool', 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', 'relu6',
-    'repeat', 'scale_gradient', 'separable_conv2d', 'separable_convolution2d',
+    'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'images_to_sequence',
+    'layer_norm', 'linear', 'pool', 'max_pool2d', 'max_pool3d',
+    'one_hot_encoding', 'relu', 'relu6', 'repeat', 'scale_gradient',
+    'separable_conv2d', 'separable_convolution2d', 'sequence_to_images',
     'softmax', 'spatial_softmax', 'stack', 'unit_norm',
     'legacy_fully_connected', 'legacy_linear', 'legacy_relu', 'maxout'
 ]
@@ -2185,6 +2186,36 @@ def layer_norm(inputs,
     return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
 
 
+@add_arg_scope
+def images_to_sequence(inputs,
+                       data_format=DATA_FORMAT_NHWC,
+                       outputs_collections=None,
+                       scope=None):
+  """Convert a batch of images into a batch of sequences.
+  Args:
+    inputs: a (num_images, height, width, depth) tensor
+    data_format: A string. `NHWC` (default) and `NCHW` are supported.
+    outputs_collections: The collections to which the outputs are added.
+    scope: Optional scope for name_scope.
+  Returns:
+    (width, num_images*height, depth) sequence tensor
+  """
+  if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC):
+    raise ValueError('data_format has to be either NCHW or NHWC.')
+  with ops.name_scope(scope, 'ImagesToSequence', [inputs]) as sc:
+    inputs = ops.convert_to_tensor(inputs)
+    df = ('channels_first'
+          if data_format and data_format.startswith('NC') else 'channels_last')
+    if df == 'channels_first':
+      inputs = array_ops.transpose(inputs, [0, 2, 3, 1])
+    _, _, width, depth = inputs.get_shape().as_list()
+    s = array_ops.shape(inputs)
+    batch_size, height = s[0], s[1]
+    transposed = array_ops.transpose(inputs, [2, 0, 1, 3])
+    outputs = array_ops.reshape(transposed, [width, batch_size * height, depth])
+    return utils.collect_named_outputs(outputs_collections, sc, outputs)
+
+
 @add_arg_scope
 def max_pool2d(inputs,
                kernel_size,
@@ -2664,6 +2695,38 @@ def separable_convolution2d(
     return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
 
 
+@add_arg_scope
+def sequence_to_images(inputs,
+                       height,
+                       output_data_format='channels_last',
+                       outputs_collections=None,
+                       scope=None):
+  """Convert a batch of sequences into a batch of images.
+  Args:
+    inputs: (num_steps, num_batches, depth) sequence tensor
+    height: the height of the images
+    output_data_format: Format of output tensor.
+      Currently supports `'channels_first'` and `'channels_last'`.
+    outputs_collections: The collections to which the outputs are added.
+    scope: Optional scope for name_scope.
+  Returns:
+    A tensor representing the output of the operation.
+  """
+  with ops.name_scope(scope, 'SequenceToImages', [inputs]) as sc:
+    inputs = ops.convert_to_tensor(inputs)
+    width, num_batches, depth = inputs.get_shape().as_list()
+    if num_batches is None:
+      num_batches = -1
+    else:
+      num_batches = num_batches // height
+    reshaped = array_ops.reshape(inputs, [width, num_batches, height, depth])
+    if output_data_format == 'channels_first':
+      outputs = array_ops.transpose(reshaped, [1, 3, 2, 0])
+    else:
+      outputs = array_ops.transpose(reshaped, [1, 2, 0, 3])
+    return utils.collect_named_outputs(outputs_collections, sc, outputs)
+
+
 @add_arg_scope
 def softmax(logits, scope=None):
   """Performs softmax on Nth dimension of N-dimensional logit tensor.
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 972ff10bf9..5c0ae9a3f1 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -708,7 +708,7 @@ class Convolution2dTransposeTests(test.TestCase):
         _layers.convolution2d_transpose(images, 32, 3, data_format='CHWN')
 
   def testOutputSizeWithStrideOneSamePaddingNCHW(self):
-    # `NCHW` data fomat is only supported for `GPU` device.
+    # `NCHW` data format is only supported for `GPU` device.
     if test.is_gpu_available(cuda_only=True):
       with self.test_session(use_gpu=True) as sess:
         num_filters = 32
@@ -2196,7 +2196,7 @@ class BatchNormTest(test.TestCase):
       # After initialization moving_mean == 0 and moving_variance == 1.
       self.assertAllClose(mean, [0] * 3)
       self.assertAllClose(variance, [1] * 3)
-      # Simulate assigment from saver restore.
+      # Simulate assignment from saver restore.
       init_assigns = [
           state_ops.assign(moving_mean, expected_mean),
           state_ops.assign(moving_variance, expected_var)
@@ -2950,6 +2950,28 @@ class GDNTest(test.TestCase):
     self.assertAllClose(y, x * np.sqrt(1 + .1 * (x**2)), rtol=0, atol=1e-6)
 
 
+class ImagesToSequenceTest(test.TestCase):
+
+  def testInvalidDataFormat(self):
+    height, width = 7, 11
+    images = np.random.uniform(size=(5, height, width, 2))
+    with self.assertRaisesRegexp(ValueError,
+                                 'data_format has to be either NCHW or NHWC.'):
+      _layers.images_to_sequence(images, data_format='CHWN')
+
+  def testImagesToSequenceDims(self):
+    height, width = 7, 11
+    images = np.random.uniform(size=(2, height, width, 5)).astype(np.float32)
+    output = _layers.images_to_sequence(images)
+    self.assertListEqual(output.get_shape().as_list(), [11, 14, 5])
+
+  def testImagesToSequenceNCHW(self):
+    height, width = 7, 11
+    images = np.random.uniform(size=(2, 5, height, width)).astype(np.float32)
+    output = _layers.images_to_sequence(images, data_format='NCHW')
+    self.assertListEqual(output.get_shape().as_list(), [11, 14, 5])
+
+
 class MaxPool2DTest(test.TestCase):
 
   def testInvalidDataFormat(self):
@@ -3418,6 +3440,30 @@ class ScaleGradientTests(test.TestCase):
       np.testing.assert_array_equal([3 * 2], g_x.eval())
 
 
+class SequenceToImagesTest(test.TestCase):
+
+  def testImagesToSequenceDims(self):
+    num_batches = 14
+    num_time_steps = 11
+    num_channels = 5
+    desired_height = 7
+    sequence = np.random.uniform(
+        size=(num_time_steps, num_batches, num_channels)).astype(np.float32)
+    output = _layers.sequence_to_images(sequence, desired_height)
+    self.assertListEqual(output.get_shape().as_list(), [2, 7, 11, 5])
+
+  def testImagesToSequenceNCHW(self):
+    num_batches = 14
+    num_time_steps = 11
+    num_channels = 5
+    desired_height = 7
+    sequence = np.random.uniform(
+        size=(num_time_steps, num_batches, num_channels)).astype(np.float32)
+    output = _layers.sequence_to_images(
+        sequence, desired_height, output_data_format='channels_first')
+    self.assertListEqual(output.get_shape().as_list(), [2, 5, 7, 11])
+
+
 class SoftmaxTests(test.TestCase):
 
   def setUp(self):
diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py
index 0948dee7e2..51381a7427 100644
--- a/tensorflow/contrib/learn/python/learn/monitors.py
+++ b/tensorflow/contrib/learn/python/learn/monitors.py
@@ -879,7 +879,7 @@ class GraphDump(BaseMonitor):
     this_output = self.data[step] if step in self.data else {}
     other_output = other_dump.data[step] if step in other_dump.data else {}
     for key in this_output:
-      if not isinstance(key, str) and not isinstance(key, unicode):
+      if not isinstance(key, six.string_types):
         continue
       if key not in other_output:
         raise ValueError("%s missing at step %s.", (key, step))
diff --git a/tensorflow/contrib/lite/README.md b/tensorflow/contrib/lite/README.md
index 7cdf91da88..3e55d2a496 100644
--- a/tensorflow/contrib/lite/README.md
+++ b/tensorflow/contrib/lite/README.md
@@ -172,7 +172,7 @@ Here is a sample command line to convert the frozen Graphdef to '.tflite' format
 ```
 bazel build tensorflow/contrib/lite/toco:toco
 
-bazel-bin/tensorflow/contrib/lite/toco/toco -- \
+bazel-bin/tensorflow/contrib/lite/toco/toco \
   --input_file=$(pwd)/mobilenet_v1_1.0_224/frozen_graph.pb \
   --input_format=TENSORFLOW_GRAPHDEF  --output_format=TFLITE \
   --output_file=/tmp/mobilenet_v1_1.0_224.tflite --inference_type=FLOAT \
diff --git a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h
index a908e0c5ee..2a64c1de72 100644
--- a/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h
+++ b/tensorflow/contrib/lite/examples/label_image/bitmap_helpers_impl.h
@@ -22,6 +22,12 @@ limitations under the License.
 #include "tensorflow/contrib/lite/string_util.h"
 #include "tensorflow/contrib/lite/version.h"
 
+#include "tensorflow/contrib/lite/builtin_op_data.h"
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/string_util.h"
+#include "tensorflow/contrib/lite/version.h"
+
 #include "tensorflow/contrib/lite/examples/label_image/label_image.h"
 
 namespace tflite {
diff --git a/tensorflow/contrib/lite/g3doc/custom_operators.md b/tensorflow/contrib/lite/g3doc/custom_operators.md
index 204a489a93..d7cc854eba 100644
--- a/tensorflow/contrib/lite/g3doc/custom_operators.md
+++ b/tensorflow/contrib/lite/g3doc/custom_operators.md
@@ -73,7 +73,7 @@ TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteRegistration* Register_SIN() {
-  static TfLiteRegistration r = {nullptr, nullptr, SinResize, SinEval};
+  static TfLiteRegistration r = {nullptr, nullptr, SinPrepare, SinEval};
   return &r;
 }
 ```
diff --git a/tensorflow/contrib/lite/graph_info.cc b/tensorflow/contrib/lite/graph_info.cc
index 186b80fe82..e60ed2c246 100644
--- a/tensorflow/contrib/lite/graph_info.cc
+++ b/tensorflow/contrib/lite/graph_info.cc
@@ -1,4 +1,4 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h
index f8be99e82f..4e324a5e10 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h
@@ -15,7 +15,7 @@ limitations under the License.
 #ifndef TF_LITE_KERNELS_INTERNAL_OPTIMIZED_TENSOR_UTILS_IMPL_H_
 #define TF_LITE_KERNELS_INTERNAL_OPTIMIZED_TENSOR_UTILS_IMPL_H_
 
-// TDOD(ghodrat): Remove this header file and the dependency to internal data
+// TODO(ghodrat): Remove this header file and the dependency to internal data
 // structure.
 #include "tensorflow/contrib/lite/builtin_op_data.h"
 
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h
index afc3e26e79..c05c21b472 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h
@@ -15,7 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_
 #define TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_
 
-// TDOD(ghodrat): Remove this header file and the dependency to internal data
+// TODO(ghodrat): Remove this header file and the dependency to internal data
 // structure.
 #include "tensorflow/contrib/lite/builtin_op_data.h"
 
diff --git a/tensorflow/contrib/lite/simple_memory_arena.h b/tensorflow/contrib/lite/simple_memory_arena.h
index 0c5e00a1f2..0535522374 100644
--- a/tensorflow/contrib/lite/simple_memory_arena.h
+++ b/tensorflow/contrib/lite/simple_memory_arena.h
@@ -36,9 +36,9 @@ struct ArenaAlloc {
   }
 };
 
-// This small class is responsible for allocating, dealocating and reusing
+// This small class is responsible for allocating, deallocating and reusing
 // dynamic memory from a common underlying buffer. The arena can be used in
-// scenarios when the pattern of memory allocations and dealocations is
+// scenarios when the pattern of memory allocations and deallocations is
 // repetitive, e.g. running NN inference in multiple iterations.
 class SimpleMemoryArena {
  public:
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 944031da24..b6c09306d6 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -629,7 +629,7 @@ def make_constant_tests(zip_path):
 
   def build_graph(parameters):
     # Since Toco & Tflite can't have a single constant op in the entire graph,
-    # this test adds a zero tesnor with a constant op tensor.
+    # this test adds a zero tensor with a constant op tensor.
     input1 = tf.placeholder(dtype=parameters["dtype"], name="input1",
                             shape=parameters["input_shape"])
     out = tf.ones(parameters["input_shape"], dtype=parameters["dtype"]) + input1
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc
index e79e2a32fc..064810b53e 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc
@@ -73,7 +73,7 @@ void CopyTensorSegments(const std::vector<Array*>& input_arrays,
 
 // Receives a series of input arrays of type Array and an integer showing the
 // axis on which those arrays will be concatenated. It returns the concatenated
-// arrray.
+// array.
 template <ArrayDataType A>
 void ConcatenateTensorBuffers(const std::vector<Array*>& input_arrays,
                               int concatenation_axis,
diff --git a/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_svdf_test.cc b/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_svdf_test.cc
index 664e828c19..646d048496 100644
--- a/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_svdf_test.cc
+++ b/tensorflow/contrib/lite/toco/tensorflow_graph_matching/resolve_svdf_test.cc
@@ -103,11 +103,11 @@ class ResolveSvdfTest : public ::testing::Test {
     // Add the float vector as an attribute to the node.
     (*node->mutable_attr())["dtype"].set_type(tensorflow::DT_FLOAT);
     tensorflow::TensorProto* allocated_tensor = new tensorflow::TensorProto;
-    tensorflow::TensorShapeProto* allocated_tesnor_shape =
+    tensorflow::TensorShapeProto* allocated_tensor_shape =
         new tensorflow::TensorShapeProto;
-    auto tensor_shape_dim0 = allocated_tesnor_shape->add_dim();
+    auto tensor_shape_dim0 = allocated_tensor_shape->add_dim();
     tensor_shape_dim0->set_size(values.size());
-    allocated_tensor->set_allocated_tensor_shape(allocated_tesnor_shape);
+    allocated_tensor->set_allocated_tensor_shape(allocated_tensor_shape);
     allocated_tensor->set_tensor_content(
         string(reinterpret_cast<const char*>(values.data()),
                values.size() * sizeof(float)));
@@ -122,11 +122,11 @@ class ResolveSvdfTest : public ::testing::Test {
     // Add the float vector as an attribute to the node.
     (*node->mutable_attr())["dtype"].set_type(tensorflow::DT_INT32);
     tensorflow::TensorProto* allocated_tensor = new tensorflow::TensorProto;
-    tensorflow::TensorShapeProto* allocated_tesnor_shape =
+    tensorflow::TensorShapeProto* allocated_tensor_shape =
         new tensorflow::TensorShapeProto;
-    auto tensor_shape_dim0 = allocated_tesnor_shape->add_dim();
+    auto tensor_shape_dim0 = allocated_tensor_shape->add_dim();
     tensor_shape_dim0->set_size(values.size());
-    allocated_tensor->set_allocated_tensor_shape(allocated_tesnor_shape);
+    allocated_tensor->set_allocated_tensor_shape(allocated_tensor_shape);
     allocated_tensor->set_tensor_content(
         string(reinterpret_cast<const char*>(values.data()),
                values.size() * sizeof(int)));
diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md
index 0613de2cab..6959ca344f 100644
--- a/tensorflow/contrib/makefile/README.md
+++ b/tensorflow/contrib/makefile/README.md
@@ -268,7 +268,7 @@ selectively register only for the operators used in your graph.
 ```bash
 tensorflow/contrib/makefile/build_all_ios.sh -a arm64 -g $HOME/graphs/inception/tensorflow_inception_graph.pb
 ```
-Please note this is an aggresive optimization of the operators and the resulting library may not work with other graphs but will reduce the size of the final library.
+Please note this is an aggressive optimization of the operators and the resulting library may not work with other graphs but will reduce the size of the final library.
 
 The `compile_ios_tensorflow.sh` script can take optional command-line arguments.
 The first argument will be passed as a C++ optimization flag and defaults to
diff --git a/tensorflow/contrib/makefile/build_all_android.sh b/tensorflow/contrib/makefile/build_all_android.sh
index f67c516186..fc88f59e09 100755
--- a/tensorflow/contrib/makefile/build_all_android.sh
+++ b/tensorflow/contrib/makefile/build_all_android.sh
@@ -52,7 +52,7 @@ shift $((OPTIND - 1))
 
 if [ "$ARCH" == "tegra" ]; then
     if [[ -z "${JETPACK}" ]]; then
-        export JETPACK="$HOME/JetPack_Android_3.0"
+        export JETPACK="$HOME/JetPack_Android_3.2"
     fi
     if [ ! -d ${JETPACK} ]; then
         echo "Can't find Jetpack at ${JETPACK}"
diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py
index fc12bfd2b7..31e274c5fd 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py
@@ -740,7 +740,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
   else:
     for include in includes:
       if include not in all_includes:
-        raise ValueError('Invaild key: %s.' % include)
+        raise ValueError('Invalid key: %s.' % include)
 
   predictions, labels, weights = metrics_impl._remove_squeezable_dimensions(  # pylint: disable=protected-access
       predictions, labels, weights)
@@ -1516,7 +1516,7 @@ def precision_recall_at_equal_thresholds(labels,
     predictions: A floating point `Tensor` of arbitrary shape and whose values
       are in the range `[0, 1]`.
     weights: Optional; If provided, a `Tensor` that has the same dtype as,
-      and broadcastable to, `predictions`. This tensor is multplied by counts.
+      and broadcastable to, `predictions`. This tensor is multiplied by counts.
     num_thresholds: Optional; Number of thresholds, evenly distributed in
       `[0, 1]`. Should be `>= 2`. Defaults to 201. Note that the number of bins
       is 1 less than `num_thresholds`. Using an even `num_thresholds` value
diff --git a/tensorflow/contrib/nearest_neighbor/kernels/heap.h b/tensorflow/contrib/nearest_neighbor/kernels/heap.h
index 32925569a8..a2dbb8052b 100644
--- a/tensorflow/contrib/nearest_neighbor/kernels/heap.h
+++ b/tensorflow/contrib/nearest_neighbor/kernels/heap.h
@@ -56,7 +56,7 @@ class HeapBase {
 
   // This method adds an element at the end of the internal array without
   // "heapifying" the array afterwards. This is useful for setting up a heap
-  // where a single call to heapify at the end of the inital insertion
+  // where a single call to heapify at the end of the initial insertion
   // operations suffices.
   void InsertUnsorted(const KeyType& key, const DataType& data) {
     if (v_.size() == static_cast<size_t>(num_elements_)) {
diff --git a/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py b/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py
index 716ee9cdf7..5763593b81 100644
--- a/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py
+++ b/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py
@@ -150,7 +150,7 @@ class ElasticAverageOptimizer(optimizer.Optimizer):
     self._global_map = ea_custom_getter._global_map
 
     if moving_rate is None:
-      self._moving_rate = BETA / communication_period / num_worker
+      self._moving_rate = self.BETA / communication_period / num_worker
     else:
       self._moving_rate = moving_rate
     if rho is None:
diff --git a/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py b/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
index 29ecd22839..6cca0a8a00 100644
--- a/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/model_average_optimizer_test.py
@@ -110,7 +110,6 @@ def _get_workers(num_workers, steps, workers):
 
 
 class ModelAverageOptimizerTest(test.TestCase):
-
   def _run(self, train_op, sess):
     sess.run(train_op)
 
diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD
index 93c751b28d..e9a96ec8d1 100644
--- a/tensorflow/contrib/py2tf/converters/BUILD
+++ b/tensorflow/contrib/py2tf/converters/BUILD
@@ -132,6 +132,7 @@ py_test(
 py_test(
     name = "for_loops_test",
     srcs = ["for_loops_test.py"],
+    srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
         "//tensorflow/contrib/py2tf/pyct",
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
index 51933be29d..eef1ae25e9 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
@@ -1605,6 +1605,7 @@ class WeightNormLSTMCellTest(test.TestCase):
     self.assertAllClose(expected_c, actual_c, 1e-5)
     self.assertAllClose(expected_h, actual_h, 1e-5)
 
+
   def testBasicCellWithNorm(self):
     """Tests cell w/o peepholes and with normalisation"""
 
diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
index d6b5eceb47..0a53fd66db 100644
--- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
@@ -331,7 +331,7 @@ def _luong_score(query, keys, scale):
   # batched matmul on:
   #   [batch_size, 1, depth] . [batch_size, depth, max_time]
   # resulting in an output shape of:
-  #   [batch_time, 1, max_time].
+  #   [batch_size, 1, max_time].
   # we then squeeze out the center singleton dimension.
   score = math_ops.matmul(query, keys, transpose_b=True)
   score = array_ops.squeeze(score, [1])
diff --git a/tensorflow/contrib/slim/README.md b/tensorflow/contrib/slim/README.md
index c7a54cb9a2..2d9df8f27e 100644
--- a/tensorflow/contrib/slim/README.md
+++ b/tensorflow/contrib/slim/README.md
@@ -145,7 +145,7 @@ regular_variables_and_model_variables = slim.get_variables()
 
 How does this work? When you create a model variable via TF-Slim's layers or
 directly via the `slim.model_variable` function, TF-Slim adds the variable to
-a the `tf.GraphKeys.MODEL_VARIABLES` collection. What if you have your own
+the `tf.GraphKeys.MODEL_VARIABLES` collection. What if you have your own
 custom layers or variable creation routine but still want TF-Slim to manage or
 be aware of your model variables? TF-Slim provides a convenience function for
 adding the model variable to its collection:
diff --git a/tensorflow/contrib/solvers/python/ops/linear_equations.py b/tensorflow/contrib/solvers/python/ops/linear_equations.py
index 2395707257..d791d46763 100644
--- a/tensorflow/contrib/solvers/python/ops/linear_equations.py
+++ b/tensorflow/contrib/solvers/python/ops/linear_equations.py
@@ -28,6 +28,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import linalg_ops
 
 
 def conjugate_gradient(operator,
diff --git a/tensorflow/contrib/tpu/profiler/pip_package/setup.py b/tensorflow/contrib/tpu/profiler/pip_package/setup.py
index 33ade16003..76f1dd2a56 100644
--- a/tensorflow/contrib/tpu/profiler/pip_package/setup.py
+++ b/tensorflow/contrib/tpu/profiler/pip_package/setup.py
@@ -20,7 +20,7 @@ from __future__ import print_function
 
 from setuptools import setup
 
-_VERSION = '1.5.0-rc1'
+_VERSION = '1.6.0-rc0'
 
 CONSOLE_SCRIPTS = [
     'capture_tpu_profile=cloud_tpu_profiler.main:run_main',
diff --git a/tensorflow/contrib/tpu/tpu_estimator.md b/tensorflow/contrib/tpu/tpu_estimator.md
index ca1255b16b..4ef8f9eebd 100644
--- a/tensorflow/contrib/tpu/tpu_estimator.md
+++ b/tensorflow/contrib/tpu/tpu_estimator.md
@@ -231,7 +231,7 @@ Refer to this link for all [Cloud TPU documentation](https://cloud.google.com/tp
 
 
 ### Profiling
-You can profile the `worker` by using instructions as spcified in the [Cloud TPU Tools](https://cloud.google.com/tpu/docs/cloud-tpu-tools).
+You can profile the `worker` by using instructions as specified in the [Cloud TPU Tools](https://cloud.google.com/tpu/docs/cloud-tpu-tools).
 
 
 ### Is `int64` supported?
diff --git a/tensorflow/contrib/verbs/README.md b/tensorflow/contrib/verbs/README.md
index 1b99f4ce4f..58fed4e5cb 100644
--- a/tensorflow/contrib/verbs/README.md
+++ b/tensorflow/contrib/verbs/README.md
@@ -25,9 +25,9 @@ The design is based on TensorFlow r1.0. An RDMA path is added between servers fo
 During the server setup, an RDMA manager is created to manage low-level RDMA components such as RDMA channel and RDMA adapter, an RDMA rendezvous manager is created to oversee send/recv operations between servers. Following the distributed TensorFlow design philosophy, the send operation is passive, i.e. merely placing a tensor in the local out-going table. It is the receive operation that actually initiates the tensor transfer.
 
 TensorFlow dynamically allocates memory for tensors that are to be sent or received. This causes difficulty for RDMA operations where pinned memory is required. Few remedies are possible:
-1. The memory is pinned, transfered, then unpinned for each and every tensor to be transferred. This incurs significant operation overhead since pinning and unpinning memory for each dynamically generated tensor is slow. 
+1. The memory is pinned, transferred, then unpinned for each and every tensor to be transferred. This incurs significant operation overhead since pinning and unpinning memory for each dynamically generated tensor is slow. 
 2. Buffer is pre-allocated and pinned for each tensor. This incurs large memory overhead and extra copying from the tensor to its pinned buffer, but may still be faster than the former.
-3. Following HKUST research on the use of GPU direct, and their [GDR implementation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/gdr/README.md), there is a smart way to benefit from the TensorFlow allocation theme which is mostly pool based, i.e allocators pre-allocate a large memory block, and allocate the tensors from there. By attaching a custom Visitor to relevant alloactors, we can do a single registration of the entire memory block, which zeros the registration overhead. Once the block is registered, each new tensor allocated will be at a registred address, which will allow us to do direct RDMA writes to it.
+3. Following HKUST research on the use of GPU direct, and their [GDR implementation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/gdr/README.md), there is a smart way to benefit from the TensorFlow allocation theme which is mostly pool based, i.e allocators pre-allocate a large memory block, and allocate the tensors from there. By attaching a custom Visitor to relevant allocators, we can do a single registration of the entire memory block, which zeros the registration overhead. Once the block is registered, each new tensor allocated will be at a registered address, which will allow us to do direct RDMA writes to it.
 
 For best performance, we will adopt HKUST 0 copies approach in our solution. This means:
 
@@ -77,7 +77,7 @@ When the receiver receives the **RDMA_MESSAGE_META_DATA_RESPONSE**, it will loca
 
 1. Update the local meta-data cache.
 2. Reallocate the result/proxy tensors. 
-3. Re-send the tensor request. For tracability, the new message has a different name: **RDMA_MESSAGE_TENSOR_RE_REQUEST**.
+3. Re-send the tensor request. For traceability, the new message has a different name: **RDMA_MESSAGE_TENSOR_RE_REQUEST**.
 
 When the sender receives a **RDMA_MESSAGE_TENSOR_RE_REQUEST**, it will locate the relevant **RdmaTensorResponse** using the request index specified in the message, and invoke its **Resume()** method, which will RDMA write the contents of the tensor that was cloned earlier, to the new remote address specified in the re-request.
 
@@ -93,7 +93,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen
 
 1. When the sender receives a tensor request, the source tensor may or may not be ready yet. The situation is handled through a process of tag matching:
 	* If the request arrives before the tensor is ready, then a callback is put in a local table, and will be invoked once the tensor arrives.
-	* If the tensor is ready before the request arives, than the tensor is put in a local table. When the request arrives, it will invoke the callback immediatly.
+	* If the tensor is ready before the request arives, than the tensor is put in a local table. When the request arrives, it will invoke the callback immediately.
    In code it is done by calling **RecvLocalAsync()**, which receives the tensor's key, step-id, and the callback.
 2. When the callback is invoked, the relevant tensor is removed from the tag matching table. In the case where we need to send the tensor's meta-data, the **RdmaTensorResponse** will store a copy of the tensor until the re-request arrives.
 3. The sending of protocol messages (**RDMA_MESSAGE_TENSOR_REQUEST**, **RDMA_MESSAGE_META_DATA_RESPONSE** and **RDMA_MESSAGE_TENSOR_RE_REQUEST**) is done by the class **RdmaMessageBuffer**. All messages are sent using RDMA writes from/to fixed messages buffers. This implies that we cannot send on a specific channel more than one message at a time. In order to synchronize the messages, the **RdmaMessageBuffer** holds the a local and remote buffer statuses which can be either busy or idle. When a write is issued, both statuses will be changed to busy. When the write-complete event is received, the local status is changed to idle. When the write is received on the remote side, the remote side will parse the message, and return an ACK back to the sending side on which the sending side will update the remote status to idle. When both the local and remote statuses are idle, the next message can be sent.
@@ -115,7 +115,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen
 		* Reallocate the result tensor (and proxy tensor if required).
 		* Re-send the request to the remote side.
 	* **RecvTensorContent()**    - Receive tensor content from the remote side (RDMA write was completed).
-		* Decode proto if required and/or move to GPU if the content was not written to it directly (GPU direct is not avaliable).
+		* Decode proto if required and/or move to GPU if the content was not written to it directly (GPU direct is not available).
 		* Invoke the done callback.
 * **class RdmaTensorResponse**   - Holds and manages information for a single tensor response throughout the entire send cycle. API:
 	* **Start()**                - Start the response sequence. 
@@ -153,7 +153,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen
 	* request_index - Request index.
 	* is_dead/data_type/tensor_shape/tensor_bytes - The up-to-date meta-data.
 	* checksum - In data validation mode, this will hold the checksum of the source tensor.
-* **RDMA_MESSAGE_TENSOR_RE_REQUEST** - (receiver ==> sender) Tensor re-requset after meta-data update and reallocation of result/proxy tensors.
+* **RDMA_MESSAGE_TENSOR_RE_REQUEST** - (receiver ==> sender) Tensor re-request after meta-data update and reallocation of result/proxy tensors.
 	* type - The message type.
 	* name (name_size) - Name of the requested tensor.
 	* step_id - Step ID.
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h b/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
index dd114d39c6..730124c25e 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_serialization_traits.h
@@ -66,7 +66,7 @@ class GrpcBufferWriter final
     }
     // It's dangerous to keep an inlined grpc_slice as the backup slice, since
     // on a following Next() call, a reference will be returned to this slice
-    // via GRPC_SLICE_START_PTR, which will not be an adddress held by
+    // via GRPC_SLICE_START_PTR, which will not be an address held by
     // slice_buffer_.
     have_backup_ = backup_slice_.refcount != NULL;
     byte_count_ -= count;
diff --git a/tensorflow/core/framework/op_def_util.h b/tensorflow/core/framework/op_def_util.h
index d1613ee89b..0ba1325a03 100644
--- a/tensorflow/core/framework/op_def_util.h
+++ b/tensorflow/core/framework/op_def_util.h
@@ -82,7 +82,7 @@ bool AttrDefEqual(const OpDef::AttrDef& a1, const OpDef::AttrDef& a2);
 uint64 AttrDefHash(const OpDef::AttrDef& a);
 
 // Returns true if all AttrDefs in `a1` equal corresponding AttrDefs in
-// `a2`. Corrspondence is established by name.
+// `a2`. Correspondence is established by name.
 bool RepeatedAttrDefEqual(const protobuf::RepeatedPtrField<OpDef::AttrDef>& a1,
                           const protobuf::RepeatedPtrField<OpDef::AttrDef>& a2);
 
diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc
index 5d58d3e78e..8654437059 100644
--- a/tensorflow/core/framework/op_kernel.cc
+++ b/tensorflow/core/framework/op_kernel.cc
@@ -474,7 +474,7 @@ std::unique_ptr<Tensor> OpKernelContext::forward_input(
     return nullptr;
   }
   // Check that input and output memory types match, i.e.
-  // that they either both live in host or both live in device memmory.
+  // that they either both live in host or both live in device memory.
   if (input_memory_type(input_index) != output_memory_type) {
     return nullptr;
   }
diff --git a/tensorflow/core/kernels/constant_op_test.cc b/tensorflow/core/kernels/constant_op_test.cc
index 7a05d9371d..a6baae73d8 100644
--- a/tensorflow/core/kernels/constant_op_test.cc
+++ b/tensorflow/core/kernels/constant_op_test.cc
@@ -77,7 +77,7 @@ void ConstantOpTest::PersistentMemoryTrackingTest(bool on_gpu) {
     EXPECT_EQ(ctx.persistent_memory_allocated(), 480);
   }
 
-  // Remove memry leak errors.
+  // Remove memory leak errors.
   for (auto allocator_pair : ctx.wrapped_allocators()) {
     allocator_pair.second->GetRecordsAndUnRef();
   }
diff --git a/tensorflow/core/kernels/cwise_op_gpu_invert.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_invert.cu.cc
index 62f33612db..1072ef3aa6 100644
--- a/tensorflow/core/kernels/cwise_op_gpu_invert.cu.cc
+++ b/tensorflow/core/kernels/cwise_op_gpu_invert.cu.cc
@@ -19,7 +19,7 @@ limitations under the License.
 
 namespace tensorflow {
 namespace functor {
-DEFINE_UNARY6(invert, int8, int16, int32, int64, uint8, uint16);
+DEFINE_UNARY8(invert, int8, int16, int32, int64, uint8, uint16, uint32, uint64);
 }  // namespace functor
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/kernels/cwise_op_invert.cc b/tensorflow/core/kernels/cwise_op_invert.cc
index f5cafcc780..98c8d7e9b2 100644
--- a/tensorflow/core/kernels/cwise_op_invert.cc
+++ b/tensorflow/core/kernels/cwise_op_invert.cc
@@ -16,17 +16,17 @@ limitations under the License.
 #include "tensorflow/core/kernels/cwise_ops_common.h"
 
 namespace tensorflow {
-REGISTER6(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64,
-          uint8, uint16);
+REGISTER8(UnaryOp, CPU, "Invert", functor::invert, int8, int16, int32, int64,
+          uint8, uint16, uint32, uint64);
 
 #ifdef TENSORFLOW_USE_SYCL
 REGISTER6(UnaryOp, SYCL, "Invert", functor::invert, int8, int16, int32, int64,
-          uint8, uint16);
+          uint8, uint16, uint32, uint64);
 #endif  // TENSORFLOW_USE_SYCL
 
 #if GOOGLE_CUDA
-REGISTER6(UnaryOp, GPU, "Invert", functor::invert, int8, int16, int32, int64,
-          uint8, uint16);
+REGISTER8(UnaryOp, GPU, "Invert", functor::invert, int8, int16, int32, int64,
+          uint8, uint16, uint32, uint64);
 #endif  // GOOGLE_CUDA
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cwise_ops_gpu_common.cu.h b/tensorflow/core/kernels/cwise_ops_gpu_common.cu.h
index 6dd108f722..965e42dcce 100644
--- a/tensorflow/core/kernels/cwise_ops_gpu_common.cu.h
+++ b/tensorflow/core/kernels/cwise_ops_gpu_common.cu.h
@@ -136,6 +136,9 @@ struct ApproximateEqual<GPUDevice, T> {
 #define DEFINE_UNARY7(F, T0, T1, T2, T3, T4, T5, T6) \
   DEFINE_UNARY2(F, T0, T1);                          \
   DEFINE_UNARY5(F, T2, T3, T4, T5, T6)
+#define DEFINE_UNARY8(F, T0, T1, T2, T3, T4, T5, T6, T7) \
+  DEFINE_UNARY4(F, T0, T1, T2, T3);                      \
+  DEFINE_UNARY4(F, T4, T5, T6, T7)
 
 // Macros to explicitly instantiate kernels on GPU for multiple types
 // (T0, T1, etc.) for BinaryFunctor.
diff --git a/tensorflow/core/lib/gif/gif_io.cc b/tensorflow/core/lib/gif/gif_io.cc
index e5deb2b873..9a5215320f 100644
--- a/tensorflow/core/lib/gif/gif_io.cc
+++ b/tensorflow/core/lib/gif/gif_io.cc
@@ -16,6 +16,7 @@ limitations under the License.
 // Functions to read images in GIF format.
 
 #include "tensorflow/core/lib/gif/gif_io.h"
+#include <algorithm>
 #include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/gif.h"
@@ -89,23 +90,52 @@ uint8* Decode(const void* srcdata, int datasize,
   uint8* const dstdata = allocate_output(num_frames, width, height, channel);
   if (!dstdata) return nullptr;
   for (int k = 0; k < num_frames; k++) {
+    uint8* this_dst = dstdata + k * width * channel * height;
+
     SavedImage* this_image = &gif_file->SavedImages[k];
     GifImageDesc* img_desc = &this_image->ImageDesc;
+
+    int imgLeft = img_desc->Left;
+    int imgTop = img_desc->Top;
+    int imgRight = img_desc->Left + img_desc->Width;
+    int imgBottom = img_desc->Top + img_desc->Height;
+
     if (img_desc->Left != 0 || img_desc->Top != 0 || img_desc->Width != width ||
         img_desc->Height != height) {
-      *error_string = strings::StrCat("can't process optimized gif");
-      return nullptr;
+      // If the first frame does not fill the entire canvas then return error.
+      if (k == 0) {
+        *error_string =
+            strings::StrCat("the first frame does not fill the canvas");
+        return nullptr;
+      }
+      // Otherwise previous frame will be reused to fill the unoccupied canvas.
+      imgLeft = std::max(imgLeft, 0);
+      imgTop = std::max(imgTop, 0);
+      imgRight = std::min(imgRight, width);
+      imgBottom = std::min(imgBottom, height);
+
+      uint8* last_dst = dstdata + (k - 1) * width * channel * height;
+      for (int i = 0; i < height; ++i) {
+        uint8* p_dst = this_dst + i * width * channel;
+        uint8* l_dst = last_dst + i * width * channel;
+        for (int j = 0; j < width; ++j) {
+          p_dst[j * channel + 0] = l_dst[j * channel + 0];
+          p_dst[j * channel + 1] = l_dst[j * channel + 1];
+          p_dst[j * channel + 2] = l_dst[j * channel + 2];
+        }
+      }
     }
 
     ColorMapObject* color_map = this_image->ImageDesc.ColorMap
                                     ? this_image->ImageDesc.ColorMap
                                     : gif_file->SColorMap;
 
-    uint8* this_dst = dstdata + k * width * channel * height;
-    for (int i = 0; i < height; ++i) {
+    for (int i = imgTop; i < imgBottom; ++i) {
       uint8* p_dst = this_dst + i * width * channel;
-      for (int j = 0; j < width; ++j) {
-        GifByteType color_index = this_image->RasterBits[i * width + j];
+      for (int j = imgLeft; j < imgRight; ++j) {
+        GifByteType color_index =
+            this_image->RasterBits[(i - img_desc->Top) * (img_desc->Width) +
+                                   (j - img_desc->Left)];
         const GifColorType& gif_color = color_map->Colors[color_index];
         p_dst[j * channel + 0] = gif_color.Red;
         p_dst[j * channel + 1] = gif_color.Green;
diff --git a/tensorflow/core/lib/gtl/optional.h b/tensorflow/core/lib/gtl/optional.h
index fa33c24c0c..4ee3f88d18 100644
--- a/tensorflow/core/lib/gtl/optional.h
+++ b/tensorflow/core/lib/gtl/optional.h
@@ -478,7 +478,7 @@ class optional : private internal_optional::optional_data<T>,
     return *this;
   }
 
-  // Copy assigment, standard semantics.
+  // Copy assignment, standard semantics.
   optional& operator=(const optional& src) = default;
 
   // Move assignment, standard semantics.
diff --git a/tensorflow/core/lib/io/record_reader.cc b/tensorflow/core/lib/io/record_reader.cc
index 9cc6c4034f..254fdf115d 100644
--- a/tensorflow/core/lib/io/record_reader.cc
+++ b/tensorflow/core/lib/io/record_reader.cc
@@ -49,7 +49,7 @@ RecordReaderOptions RecordReaderOptions::CreateRecordReaderOptions(
 #endif  // IS_SLIM_BUILD
   } else if (compression_type != compression::kNone) {
     LOG(ERROR) << "Unsupported compression_type:" << compression_type
-               << ". No comprression will be used.";
+               << ". No compression will be used.";
   }
   return options;
 }
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index a62e2d782b..4c05b274fe 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -455,6 +455,17 @@ REGISTER_OP("SampleDistortedBoundingBox")
     .Attr("use_image_if_no_bounding_boxes: bool = false")
     .SetIsStateful()
     .SetShapeFn([](InferenceContext* c) {
+      // Get inputs and validate ranks.
+      ShapeHandle image_size;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &image_size));
+      ShapeHandle bounding_boxes;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 3, &bounding_boxes));
+      // image_size: 1-D with [height, width, channels]
+      // bounding_boxes: 3-D with shape [batch, N, 4]
+      DimensionHandle unused;
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(image_size, 0), 3, &unused));
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(bounding_boxes, 2), 4, &unused));
+
       c->set_output(0, c->Vector(3));
       c->set_output(1, c->Vector(3));
       c->set_output(2, c->MakeShape({1, 1, 4}));
@@ -477,6 +488,19 @@ REGISTER_OP("SampleDistortedBoundingBoxV2")
     .Attr("use_image_if_no_bounding_boxes: bool = false")
     .SetIsStateful()
     .SetShapeFn([](InferenceContext* c) {
+      // Get inputs and validate ranks.
+      ShapeHandle image_size;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &image_size));
+      ShapeHandle bounding_boxes;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 3, &bounding_boxes));
+      ShapeHandle min_object_covered;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &min_object_covered));
+      // image_size: 1-D with [height, width, channels]
+      // bounding_boxes: 3-D with shape [batch, N, 4]
+      DimensionHandle unused;
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(image_size, 0), 3, &unused));
+      TF_RETURN_IF_ERROR(c->WithValue(c->Dim(bounding_boxes, 2), 4, &unused));
+
       c->set_output(0, c->Vector(3));
       c->set_output(1, c->Vector(3));
       c->set_output(2, c->MakeShape({1, 1, 4}));
diff --git a/tensorflow/core/platform/env.cc b/tensorflow/core/platform/env.cc
index 1bcca1243f..12509c250e 100644
--- a/tensorflow/core/platform/env.cc
+++ b/tensorflow/core/platform/env.cc
@@ -44,6 +44,9 @@ limitations under the License.
 
 namespace tensorflow {
 
+// 128KB copy buffer
+constexpr size_t kCopyFileBufferSize = 128 * 1024;
+
 class FileSystemRegistryImpl : public FileSystemRegistry {
  public:
   Status Register(const string& scheme, Factory factory) override;
@@ -278,6 +281,17 @@ Status Env::RenameFile(const string& src, const string& target) {
   return src_fs->RenameFile(src, target);
 }
 
+Status Env::CopyFile(const string& src, const string& target) {
+  FileSystem* src_fs;
+  FileSystem* target_fs;
+  TF_RETURN_IF_ERROR(GetFileSystemForFile(src, &src_fs));
+  TF_RETURN_IF_ERROR(GetFileSystemForFile(target, &target_fs));
+  if (src_fs == target_fs) {
+    return src_fs->CopyFile(src, target);
+  }
+  return FileSystemCopyFile(src_fs, src, target_fs, target);
+}
+
 string Env::GetExecutablePath() {
   char exe_path[PATH_MAX] = {0};
 #ifdef __APPLE__
@@ -406,6 +420,29 @@ Status WriteStringToFile(Env* env, const string& fname,
   return s;
 }
 
+Status FileSystemCopyFile(FileSystem* src_fs, const string& src,
+                          FileSystem* target_fs, const string& target) {
+  std::unique_ptr<RandomAccessFile> src_file;
+  TF_RETURN_IF_ERROR(src_fs->NewRandomAccessFile(src, &src_file));
+
+  std::unique_ptr<WritableFile> target_file;
+  TF_RETURN_IF_ERROR(target_fs->NewWritableFile(target, &target_file));
+
+  uint64 offset = 0;
+  std::unique_ptr<char[]> scratch(new char[kCopyFileBufferSize]);
+  Status s = Status::OK();
+  while (s.ok()) {
+    StringPiece result;
+    s = src_file->Read(offset, kCopyFileBufferSize, &result, scratch.get());
+    if (!(s.ok() || s.code() == error::OUT_OF_RANGE)) {
+      return s;
+    }
+    TF_RETURN_IF_ERROR(target_file->Append(result));
+    offset += result.size();
+  }
+  return target_file->Close();
+}
+
 // A ZeroCopyInputStream on a RandomAccessFile.
 namespace {
 class FileStream : public ::tensorflow::protobuf::io::ZeroCopyInputStream {
diff --git a/tensorflow/core/platform/env.h b/tensorflow/core/platform/env.h
index 34aaf3f78b..4ce4e0b4e0 100644
--- a/tensorflow/core/platform/env.h
+++ b/tensorflow/core/platform/env.h
@@ -214,6 +214,9 @@ class Env {
   /// replaced.
   Status RenameFile(const string& src, const string& target);
 
+  /// \brief Copy the src to target.
+  Status CopyFile(const string& src, const string& target);
+
   /// \brief Returns the absolute path of the current executable. It resolves
   /// symlinks if there is any.
   string GetExecutablePath();
@@ -381,6 +384,11 @@ struct ThreadOptions {
   size_t guard_size = 0;  // 0: use system default value
 };
 
+/// A utility routine: copy contents of `src` in file system `src_fs`
+/// to `target` in file system `target_fs`.
+Status FileSystemCopyFile(FileSystem* src_fs, const string& src,
+                          FileSystem* target_fs, const string& target);
+
 /// A utility routine: reads contents of named file into `*data`
 Status ReadFileToString(Env* env, const string& fname, string* data);
 
diff --git a/tensorflow/core/platform/file_system.cc b/tensorflow/core/platform/file_system.cc
index b9866cf641..271d73f5f1 100644
--- a/tensorflow/core/platform/file_system.cc
+++ b/tensorflow/core/platform/file_system.cc
@@ -265,4 +265,8 @@ Status FileSystem::RecursivelyCreateDir(const string& dirname) {
   return Status::OK();
 }
 
+Status FileSystem::CopyFile(const string& src, const string& target) {
+  return FileSystemCopyFile(this, src, this, target);
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/file_system.h b/tensorflow/core/platform/file_system.h
index d32efcea09..3085b6958f 100644
--- a/tensorflow/core/platform/file_system.h
+++ b/tensorflow/core/platform/file_system.h
@@ -189,6 +189,9 @@ class FileSystem {
   /// \brief Overwrites the target if it exists.
   virtual Status RenameFile(const string& src, const string& target) = 0;
 
+  /// \brief Copy the src to target.
+  virtual Status CopyFile(const string& src, const string& target);
+
   /// \brief Translate an URI to a filename for the FileSystem implementation.
   ///
   /// The implementation in this class cleans up the path, removing
diff --git a/tensorflow/core/platform/posix/posix_file_system.cc b/tensorflow/core/platform/posix/posix_file_system.cc
index fb7a5a9995..9a8021565c 100644
--- a/tensorflow/core/platform/posix/posix_file_system.cc
+++ b/tensorflow/core/platform/posix/posix_file_system.cc
@@ -18,6 +18,9 @@ limitations under the License.
 #include <fcntl.h>
 #include <stdio.h>
 #include <sys/mman.h>
+#if !defined(__APPLE__)
+#include <sys/sendfile.h>
+#endif
 #include <sys/stat.h>
 #include <sys/time.h>
 #include <sys/types.h>
@@ -34,6 +37,9 @@ limitations under the License.
 
 namespace tensorflow {
 
+// 128KB of copy buffer
+constexpr size_t kPosixCopyFileBufferSize = 128 * 1024;
+
 // pread() based random-access
 class PosixRandomAccessFile : public RandomAccessFile {
  private:
@@ -276,4 +282,70 @@ Status PosixFileSystem::RenameFile(const string& src, const string& target) {
   return result;
 }
 
+Status PosixFileSystem::CopyFile(const string& src, const string& target) {
+  string translated_src = TranslateName(src);
+  struct stat sbuf;
+  if (stat(translated_src.c_str(), &sbuf) != 0) {
+    return IOError(src, errno);
+  }
+  int src_fd = open(translated_src.c_str(), O_RDONLY);
+  if (src_fd < 0) {
+    return IOError(src, errno);
+  }
+  string translated_target = TranslateName(target);
+  // O_WRONLY | O_CREAT:
+  //   Open file for write and if file does not exist, create the file.
+  // S_IRUSR | S_IWUSR | S_IRGRP | S_IROTH:
+  //   Create the file with permission of 0644
+  int target_fd = open(translated_target.c_str(), O_WRONLY | O_CREAT,
+                       S_IRUSR | S_IWUSR | S_IRGRP | S_IROTH);
+  if (target_fd < 0) {
+    close(src_fd);
+    return IOError(target, errno);
+  }
+  int rc = 0;
+  off_t offset = 0;
+  std::unique_ptr<char[]> buffer(new char[kPosixCopyFileBufferSize]);
+  while (offset < sbuf.st_size) {
+    // Use uint64 for safe compare SSIZE_MAX
+    uint64 chunk = sbuf.st_size - offset;
+    if (chunk > SSIZE_MAX) {
+      chunk = SSIZE_MAX;
+    }
+#if defined(__linux__) && !defined(__ANDROID__)
+    rc = sendfile(target_fd, src_fd, &offset, static_cast<size_t>(chunk));
+#else
+    if (chunk > kPosixCopyFileBufferSize) {
+      chunk = kPosixCopyFileBufferSize;
+    }
+    rc = read(src_fd, buffer.get(), static_cast<size_t>(chunk));
+    if (rc <= 0) {
+      break;
+    }
+    rc = write(target_fd, buffer.get(), static_cast<size_t>(chunk));
+    offset += chunk;
+#endif
+    if (rc <= 0) {
+      break;
+    }
+  }
+
+  Status result = Status::OK();
+  if (rc < 0) {
+    result = IOError(target, errno);
+  }
+
+  // Keep the error code
+  rc = close(target_fd);
+  if (rc < 0 && result == Status::OK()) {
+    result = IOError(target, errno);
+  }
+  rc = close(src_fd);
+  if (rc < 0 && result == Status::OK()) {
+    result = IOError(target, errno);
+  }
+
+  return result;
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/posix/posix_file_system.h b/tensorflow/core/platform/posix/posix_file_system.h
index fe050fd5a0..98ffa43b8a 100644
--- a/tensorflow/core/platform/posix/posix_file_system.h
+++ b/tensorflow/core/platform/posix/posix_file_system.h
@@ -56,6 +56,8 @@ class PosixFileSystem : public FileSystem {
   Status GetFileSize(const string& fname, uint64* size) override;
 
   Status RenameFile(const string& src, const string& target) override;
+
+  Status CopyFile(const string& src, const string& target) override;
 };
 
 Status IOError(const string& context, int err_number);
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index b02f899b87..50bfa91267 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -19,12 +19,12 @@ limitations under the License.
 // TensorFlow uses semantic versioning, see http://semver.org/.
 
 #define TF_MAJOR_VERSION 1
-#define TF_MINOR_VERSION 5
+#define TF_MINOR_VERSION 6
 #define TF_PATCH_VERSION 0
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX ""
+#define TF_VERSION_SUFFIX "-rc0"
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/docs_src/api_guides/python/contrib.distributions.md b/tensorflow/docs_src/api_guides/python/contrib.distributions.md
index 7a3d509b75..533d7dac13 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.distributions.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.distributions.md
@@ -17,7 +17,6 @@ initialized with parameters that define the distributions.
 
 *   @{tf.contrib.distributions.Binomial}
 *   @{tf.contrib.distributions.Bernoulli}
-*   @{tf.contrib.distributions.BernoulliWithSigmoidProbs}
 *   @{tf.contrib.distributions.Beta}
 *   @{tf.contrib.distributions.Categorical}
 *   @{tf.contrib.distributions.Chi2}
diff --git a/tensorflow/docs_src/api_guides/python/regression_examples.md b/tensorflow/docs_src/api_guides/python/regression_examples.md
index dae50a8f03..7de2be0552 100644
--- a/tensorflow/docs_src/api_guides/python/regression_examples.md
+++ b/tensorflow/docs_src/api_guides/python/regression_examples.md
@@ -38,7 +38,7 @@ The preceding examples rely on the following data set utility:
   <tr> <th>Utility</th> <th>Description</th></tr>
 
   <tr>
-    <td><a href="../../examples/get_started/regression/imports85.py">imports85.py</a></td>
+    <td><a href="https://www.tensorflow.org/code/tensorflow/examples/get_started/regression/imports85.py">imports85.py</a></td>
     <td>This program provides utility functions that load the
         <tt>imports85</tt> data set into formats that other TensorFlow
         programs (for example, <tt>linear_regression.py</tt> and
diff --git a/tensorflow/docs_src/community/welcome.md b/tensorflow/docs_src/community/welcome.md
index d2d3f9edae..9f6fe91b14 100644
--- a/tensorflow/docs_src/community/welcome.md
+++ b/tensorflow/docs_src/community/welcome.md
@@ -65,5 +65,5 @@ please read the following list carefully:
     on GitHub.  For example, use the issue tracker to request a
     new operation in TensorFlow.
   * To report vulnerabilities, please follow our
-    [vulnerability disclosure guidelines](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).
+    [vulnerability disclosure guidelines](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/SECURITY.md).
 
diff --git a/tensorflow/docs_src/get_started/get_started_for_beginners.md b/tensorflow/docs_src/get_started/get_started_for_beginners.md
index e680234d4d..b88483be69 100644
--- a/tensorflow/docs_src/get_started/get_started_for_beginners.md
+++ b/tensorflow/docs_src/get_started/get_started_for_beginners.md
@@ -705,7 +705,7 @@ for pred_dict, expec in zip(predictions, expected):
 
     class_id = pred_dict['class_ids'][0]
     probability = pred_dict['probabilities'][class_id]
-    print(template.format(SPECIES[class_id], 100 * probability, expec))
+    print(template.format(iris_data.SPECIES[class_id], 100 * probability, expec))
 ```
 
 Running the program yields the following output:
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 14add7c77e..a783205b4a 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -38,7 +38,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for macOS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index d2af9d9843..5249e04615 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.5.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0-rc0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index e5388c4b1e..0c6c773e62 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.5.0</version>
+  <version>1.6.0-rc0</version>
 </dependency>
 ```
 
@@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.5.0</version>
+                 <version>1.6.0-rc0</version>
                </dependency>
              </dependencies>
          </project>
@@ -123,12 +123,12 @@ instead:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow</artifactId>
-  <version>1.5.0</version>
+  <version>1.6.0-rc0</version>
 </dependency>
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow_jni_gpu</artifactId>
-  <version>1.5.0</version>
+  <version>1.6.0-rc0</version>
 </dependency>
 ```
 
@@ -147,7 +147,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or macOS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.5.0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.5.0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.5.0.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0-rc0.zip).
   3. Extract this .zip file.
 
 
@@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.5.0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.6.0-rc0.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -239,11 +239,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and macOS X:
 
-<pre><b>java -cp libtensorflow-1.5.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.6.0-rc0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.5.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.6.0-rc0.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index cd8c14599f..105b225177 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -188,7 +188,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      Virtualenv environment:
 
      <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -293,7 +293,7 @@ take the following steps:
 
      <pre>
      $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -480,7 +480,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -648,14 +648,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -667,14 +667,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -686,14 +686,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -705,14 +705,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.5.0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index f49d3a2f08..a6ea548cfb 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -115,7 +115,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -238,7 +238,7 @@ take the following steps:
      issue the following command:
 
      <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl</b> </pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -347,7 +347,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (<i>targetDirectory</i>)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -520,7 +520,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl
 </pre>
 
 
@@ -528,5 +528,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py2-none-any.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.5.0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl
 </pre>
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 175b5c4402..90031b4b5e 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -221,7 +221,7 @@ problem, do either of the following:
   * Download Xcode 7.2 and select it as your default by issuing the following
     command:
 
-    <pre> $ <b>sudo xcode-select -s /Application/Xcode-7.2/Xcode.app</b></pre>
+    <pre> $ <b>sudo xcode-select -s /Applications/Xcode-7.2/Xcode.app</b></pre>
 
 **NOTE:** Your system must fulfill the NVIDIA software requirements described
 in one of the following documents:
@@ -359,10 +359,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.5.0 on Linux:
+for TensorFlow 1.6.0rc0 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.5.0-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc0-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
@@ -460,7 +460,8 @@ Stack Overflow and specify the `tensorflow` tag.
 **Linux**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
-
+<tr><td>tensorflow-1.6.0rc0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow_gpu-1.6.0rc0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr>
@@ -478,6 +479,7 @@ Stack Overflow and specify the `tensorflow` tag.
 **Mac**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
+<tr><td>tensorflow-1.6.0rc0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.3.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr>
@@ -491,6 +493,8 @@ Stack Overflow and specify the `tensorflow` tag.
 **Windows**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
+<tr><td>tensorflow-1.6.0rc0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow_gpu-1.6.0rc0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.5.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/programmers_guide/debugger.md
index dbc4517087..c8fdae6f60 100644
--- a/tensorflow/docs_src/programmers_guide/debugger.md
+++ b/tensorflow/docs_src/programmers_guide/debugger.md
@@ -214,7 +214,7 @@ navigate between these screens by clicking the `<--` and
 ### Other Features of the tfdbg CLI
 
 In addition to the commands listed above, the tfdbg CLI provides the following
-addditional features:
+additional features:
 
 *   To navigate through previous tfdbg commands, type in a few characters
     followed by the Up or Down arrow keys. tfdbg will show you the history of
diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md
index d45e666ce7..7a5e90081d 100644
--- a/tensorflow/docs_src/programmers_guide/index.md
+++ b/tensorflow/docs_src/programmers_guide/index.md
@@ -13,7 +13,7 @@ works. The units are as follows:
 ## Low Level APIs
 
   * @{$programmers_guide/low_level_intro}, which introduces the
-    basics of how you can to use TensorFlow outside of the high Level APIs.
+    basics of how you can use TensorFlow outside of the high Level APIs.
   * @{$programmers_guide/tensors}, which explains how to create,
     manipulate, and access Tensors--the fundamental object in TensorFlow.
   * @{$programmers_guide/variables}, which details how
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index ec22684eaf..868310cbc0 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -344,8 +344,8 @@ def maybe_download_and_extract(data_url):
     filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress)
     print()
     statinfo = os.stat(filepath)
-    tf.logging.info('Successfully downloaded', filename, statinfo.st_size,
-                    'bytes.')
+    tf.logging.info('Successfully downloaded %s %d bytes.', filename,
+                    statinfo.st_size)
     print('Extracting file from ', filepath)
     tarfile.open(filepath, 'r:gz').extractall(dest_directory)
   else:
diff --git a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
index 499757e8cf..cf773e1686 100644
--- a/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
+++ b/tensorflow/java/src/main/java/org/tensorflow/NativeLibrary.java
@@ -88,7 +88,7 @@ final class NativeLibrary {
       // Deletions are in the reverse order of requests, so we need to request that the directory be
       // deleted first, so that it is empty when the request is fulfilled.
       tempPath.deleteOnExit();
-      final String tempDirectory = tempPath.toString();
+      final String tempDirectory = tempPath.getCanonicalPath();
       if (frameworkResource != null) {
         extractResource(frameworkResource, frameworkLibName, tempDirectory);
       } else {
diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index 6befeb846d..f3c4fecdc0 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -1539,8 +1539,22 @@ class Session(BaseSession):
   def __exit__(self, exec_type, exec_value, exec_tb):
     if exec_type is errors.OpError:
       logging.error('Session closing due to OpError: %s', (exec_value,))
-    self._default_session_context_manager.__exit__(exec_type, exec_value,
-                                                   exec_tb)
+    try:
+      self._default_session_context_manager.__exit__(exec_type, exec_value,
+                                                     exec_tb)
+    except RuntimeError as error:
+      if error == exec_value:
+        # NOTE(skyewm): for some reason, in Python3,
+        # _default_session_context_manager.__exit__ will re-raise the "not
+        # re-entrant" exception raised in __enter__ above (note that if we're
+        # here, we're in the outer session context manager, since __exit__ is
+        # not called when __enter__ raises an exception). We still want to
+        # continue cleaning up this context manager before the exception is
+        # further propagated, so we ignore it here (note that it'll continue
+        # being propagated after this method completes).
+        pass
+      else:
+        raise
     self._default_graph_context_manager.__exit__(exec_type, exec_value, exec_tb)
 
     self._default_session_context_manager = None
diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py
index c4b7e4919b..b665443b7a 100644
--- a/tensorflow/python/data/ops/dataset_ops.py
+++ b/tensorflow/python/data/ops/dataset_ops.py
@@ -773,7 +773,7 @@ class Dataset(object):
     return PaddedBatchDataset(self, batch_size, padded_shapes, padding_values)
 
   def map(self, map_func, num_parallel_calls=None):
-    """Maps `map_func` across this datset.
+    """Maps `map_func` across this dataset.
 
     Args:
       map_func: A function mapping a nested structure of tensors (having
diff --git a/tensorflow/python/debug/cli/cli_shared.py b/tensorflow/python/debug/cli/cli_shared.py
index a0fe6066ac..dea019fef5 100644
--- a/tensorflow/python/debug/cli/cli_shared.py
+++ b/tensorflow/python/debug/cli/cli_shared.py
@@ -175,7 +175,7 @@ def format_tensor(tensor,
     include_numeric_summary: Whether a text summary of the numeric values (if
       applicable) will be included.
     write_path: A path to save the tensor value (after any slicing) to
-      (optinal). `numpy.save()` is used to save the value.
+      (optional). `numpy.save()` is used to save the value.
 
   Returns:
     An instance of `debugger_cli_common.RichTextLines` representing the
diff --git a/tensorflow/python/debug/cli/tensor_format.py b/tensorflow/python/debug/cli/tensor_format.py
index e0759a8bc1..9ba84e3f22 100644
--- a/tensorflow/python/debug/cli/tensor_format.py
+++ b/tensorflow/python/debug/cli/tensor_format.py
@@ -134,7 +134,7 @@ def format_tensor(tensor,
 
   if include_metadata:
     lines.append("  dtype: %s" % str(tensor.dtype))
-    lines.append("  shape: %s" % str(tensor.shape))
+    lines.append("  shape: %s" % str(tensor.shape).replace("L", ""))
 
   if lines:
     lines.append("")
diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index c09e2d8084..1560766fc9 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -691,7 +691,7 @@ class TensorFlowTestCase(googletest.TestCase):
       self._tempdir = tempfile.mkdtemp(dir=googletest.GetTempDir())
     return self._tempdir
 
-  def _AssertProtoEquals(self, a, b):
+  def _AssertProtoEquals(self, a, b, msg=None):
     """Asserts that a and b are the same proto.
 
     Uses ProtoEq() first, as it returns correct results
@@ -701,11 +701,12 @@ class TensorFlowTestCase(googletest.TestCase):
     Args:
       a: a proto.
       b: another proto.
+      msg: Optional message to report on failure.
     """
     if not compare.ProtoEq(a, b):
-      compare.assertProtoEqual(self, a, b, normalize_numbers=True)
+      compare.assertProtoEqual(self, a, b, normalize_numbers=True, msg=msg)
 
-  def assertProtoEquals(self, expected_message_maybe_ascii, message):
+  def assertProtoEquals(self, expected_message_maybe_ascii, message, msg=None):
     """Asserts that message is same as parsed expected_message_ascii.
 
     Creates another prototype of message, reads the ascii message into it and
@@ -714,8 +715,9 @@ class TensorFlowTestCase(googletest.TestCase):
     Args:
       expected_message_maybe_ascii: proto message in original or ascii form.
       message: the message to validate.
+      msg: Optional message to report on failure.
     """
-
+    msg = msg if msg else ""
     if isinstance(expected_message_maybe_ascii, type(message)):
       expected_message = expected_message_maybe_ascii
       self._AssertProtoEquals(expected_message, message)
@@ -725,20 +727,21 @@ class TensorFlowTestCase(googletest.TestCase):
           expected_message_maybe_ascii,
           expected_message,
           descriptor_pool=descriptor_pool.Default())
-      self._AssertProtoEquals(expected_message, message)
+      self._AssertProtoEquals(expected_message, message, msg=msg)
     else:
-      assert False, ("Can't compare protos of type %s and %s" %
-                     (type(expected_message_maybe_ascii), type(message)))
+      assert False, ("Can't compare protos of type %s and %s. %s" %
+                     (type(expected_message_maybe_ascii), type(message), msg))
 
   def assertProtoEqualsVersion(
       self,
       expected,
       actual,
       producer=versions.GRAPH_DEF_VERSION,
-      min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER):
+      min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER,
+      msg=None):
     expected = "versions { producer: %d min_consumer: %d };\n%s" % (
         producer, min_consumer, expected)
-    self.assertProtoEquals(expected, actual)
+    self.assertProtoEquals(expected, actual, msg=msg)
 
   def assertStartsWith(self, actual, expected_start, msg=None):
     """Assert that actual.startswith(expected_start) is True.
@@ -1028,7 +1031,7 @@ class TensorFlowTestCase(googletest.TestCase):
                     "%f != %f +/- %f%s" % (f1, f2, err, " (%s)" % msg
                                            if msg is not None else ""))
 
-  def assertArrayNear(self, farray1, farray2, err):
+  def assertArrayNear(self, farray1, farray2, err, msg=None):
     """Asserts that two float arrays are near each other.
 
     Checks that for all elements of farray1 and farray2
@@ -1038,23 +1041,25 @@ class TensorFlowTestCase(googletest.TestCase):
       farray1: a list of float values.
       farray2: a list of float values.
       err: a float value.
+      msg: Optional message to report on failure.
     """
-    self.assertEqual(len(farray1), len(farray2))
+    self.assertEqual(len(farray1), len(farray2), msg=msg)
     for f1, f2 in zip(farray1, farray2):
-      self.assertNear(float(f1), float(f2), err)
+      self.assertNear(float(f1), float(f2), err, msg=msg)
 
   def _NDArrayNear(self, ndarray1, ndarray2, err):
     return np.linalg.norm(ndarray1 - ndarray2) < err
 
-  def assertNDArrayNear(self, ndarray1, ndarray2, err):
+  def assertNDArrayNear(self, ndarray1, ndarray2, err, msg=None):
     """Asserts that two numpy arrays have near values.
 
     Args:
       ndarray1: a numpy ndarray.
       ndarray2: a numpy ndarray.
       err: a float. The maximum absolute difference allowed.
+      msg: Optional message to report on failure.
     """
-    self.assertTrue(self._NDArrayNear(ndarray1, ndarray2, err))
+    self.assertTrue(self._NDArrayNear(ndarray1, ndarray2, err), msg=msg)
 
   def _GetNdArray(self, a):
     if not isinstance(a, np.ndarray):
@@ -1096,9 +1101,16 @@ class TensorFlowTestCase(googletest.TestCase):
       np.testing.assert_allclose(
           a, b, rtol=rtol, atol=atol, err_msg=msg, equal_nan=True)
 
-  def _assertAllCloseRecursive(self, a, b, rtol=1e-6, atol=1e-6, path=None):
+  def _assertAllCloseRecursive(self,
+                               a,
+                               b,
+                               rtol=1e-6,
+                               atol=1e-6,
+                               path=None,
+                               msg=None):
     path = path or []
     path_str = (("[" + "][".join([str(p) for p in path]) + "]") if path else "")
+    msg = msg if msg else ""
 
     # Check if a and/or b are namedtuples.
     if hasattr(a, "_asdict"):
@@ -1107,18 +1119,18 @@ class TensorFlowTestCase(googletest.TestCase):
       b = b._asdict()
     a_is_dict = isinstance(a, dict)
     if a_is_dict != isinstance(b, dict):
-      raise ValueError("Can't compare dict to non-dict, a%s vs b%s." %
-                       (path_str, path_str))
+      raise ValueError("Can't compare dict to non-dict, a%s vs b%s. %s" %
+                       (path_str, path_str, msg))
     if a_is_dict:
       self.assertItemsEqual(
           a.keys(),
           b.keys(),
-          msg="mismatched keys: a%s has keys %s, but b%s has keys %s" %
-          (path_str, a.keys(), path_str, b.keys()))
+          msg="mismatched keys: a%s has keys %s, but b%s has keys %s. %s" %
+          (path_str, a.keys(), path_str, b.keys(), msg))
       for k in a:
         path.append(k)
         self._assertAllCloseRecursive(
-            a[k], b[k], rtol=rtol, atol=atol, path=path)
+            a[k], b[k], rtol=rtol, atol=atol, path=path, msg=msg)
         del path[-1]
     elif isinstance(a, (list, tuple)):
       # Try to directly compare a, b as ndarrays; if not work, then traverse
@@ -1131,17 +1143,17 @@ class TensorFlowTestCase(googletest.TestCase):
             b_as_ndarray,
             rtol=rtol,
             atol=atol,
-            msg="Mismatched value: a%s is different from b%s." % (path_str,
-                                                                  path_str))
+            msg="Mismatched value: a%s is different from b%s. %s" %
+            (path_str, path_str, msg))
       except (ValueError, TypeError) as e:
         if len(a) != len(b):
           raise ValueError(
-              "Mismatched length: a%s has %d items, but b%s has %d items" %
-              (path_str, len(a), path_str, len(b)))
+              "Mismatched length: a%s has %d items, but b%s has %d items. %s" %
+              (path_str, len(a), path_str, len(b), msg))
         for idx, (a_ele, b_ele) in enumerate(zip(a, b)):
           path.append(str(idx))
           self._assertAllCloseRecursive(
-              a_ele, b_ele, rtol=rtol, atol=atol, path=path)
+              a_ele, b_ele, rtol=rtol, atol=atol, path=path, msg=msg)
           del path[-1]
     # a and b are ndarray like objects
     else:
@@ -1159,7 +1171,7 @@ class TensorFlowTestCase(googletest.TestCase):
         e.args = ((e.args[0] + ' : ' + msg,) + e.args[1:])
         raise
 
-  def assertAllClose(self, a, b, rtol=1e-6, atol=1e-6):
+  def assertAllClose(self, a, b, rtol=1e-6, atol=1e-6, msg=None):
     """Asserts that two structures of numpy arrays, have near values.
 
     `a` and `b` can be arbitrarily nested structures. A layer of a nested
@@ -1172,6 +1184,7 @@ class TensorFlowTestCase(googletest.TestCase):
           numpy `ndarray`, or any arbitrarily nested of structure of these.
       rtol: relative tolerance.
       atol: absolute tolerance.
+      msg: Optional message to report on failure.
 
     Raises:
       ValueError: if only one of `a[p]` and `b[p]` is a dict or
@@ -1179,7 +1192,7 @@ class TensorFlowTestCase(googletest.TestCase):
           to the nested structure, e.g. given `a = [(1, 1), {'d': (6, 7)}]` and
           `[p] = [1]['d']`, then `a[p] = (6, 7)`.
     """
-    self._assertAllCloseRecursive(a, b, rtol=rtol, atol=atol)
+    self._assertAllCloseRecursive(a, b, rtol=rtol, atol=atol, msg=msg)
 
   def assertAllCloseAccordingToType(self,
                                     a,
@@ -1191,7 +1204,8 @@ class TensorFlowTestCase(googletest.TestCase):
                                     half_rtol=1e-3,
                                     half_atol=1e-3,
                                     bfloat16_rtol=1e-2,
-                                    bfloat16_atol=1e-2):
+                                    bfloat16_atol=1e-2,
+                                    msg=None):
     """Like assertAllClose, but also suitable for comparing fp16 arrays.
 
     In particular, the tolerance is reduced to 1e-3 if at least
@@ -1208,6 +1222,7 @@ class TensorFlowTestCase(googletest.TestCase):
       half_atol: absolute tolerance for float16.
       bfloat16_rtol: relative tolerance for bfloat16.
       bfloat16_atol: absolute tolerance for bfloat16.
+      msg: Optional message to report on failure.
     """
     a = self._GetNdArray(a)
     b = self._GetNdArray(b)
@@ -1224,19 +1239,21 @@ class TensorFlowTestCase(googletest.TestCase):
       rtol = max(rtol, bfloat16_rtol)
       atol = max(atol, bfloat16_atol)
 
-    self.assertAllClose(a, b, rtol=rtol, atol=atol)
+    self.assertAllClose(a, b, rtol=rtol, atol=atol, msg=msg)
 
-  def assertAllEqual(self, a, b):
+  def assertAllEqual(self, a, b, msg=None):
     """Asserts that two numpy arrays have the same values.
 
     Args:
       a: the expected numpy ndarray or anything can be converted to one.
       b: the actual numpy ndarray or anything can be converted to one.
+      msg: Optional message to report on failure.
     """
+    msg = msg if msg else ""
     a = self._GetNdArray(a)
     b = self._GetNdArray(b)
-    self.assertEqual(a.shape, b.shape, "Shape mismatch: expected %s, got %s." %
-                     (a.shape, b.shape))
+    self.assertEqual(a.shape, b.shape, "Shape mismatch: expected %s, got %s."
+                     " %s" % (a.shape, b.shape, msg))
     same = (a == b)
 
     if a.dtype == np.float32 or a.dtype == np.float64:
@@ -1253,7 +1270,7 @@ class TensorFlowTestCase(googletest.TestCase):
         x, y = a, b
       print("not equal lhs = ", x)
       print("not equal rhs = ", y)
-      np.testing.assert_array_equal(a, b)
+      np.testing.assert_array_equal(a, b, err_msg=msg)
 
   # pylint: disable=g-doc-return-or-yield
   @contextlib.contextmanager
@@ -1303,12 +1320,13 @@ class TensorFlowTestCase(googletest.TestCase):
     return self.assertRaisesWithPredicateMatch(errors.OpError,
                                                expected_err_re_or_predicate)
 
-  def assertShapeEqual(self, np_array, tf_tensor):
+  def assertShapeEqual(self, np_array, tf_tensor, msg=None):
     """Asserts that a Numpy ndarray and a TensorFlow tensor have the same shape.
 
     Args:
       np_array: A Numpy ndarray or Numpy scalar.
       tf_tensor: A Tensor.
+      msg: Optional message to report on failure.
 
     Raises:
       TypeError: If the arguments have the wrong type.
@@ -1317,19 +1335,21 @@ class TensorFlowTestCase(googletest.TestCase):
       raise TypeError("np_array must be a Numpy ndarray or Numpy scalar")
     if not isinstance(tf_tensor, ops.Tensor):
       raise TypeError("tf_tensor must be a Tensor")
-    self.assertAllEqual(np_array.shape, tf_tensor.get_shape().as_list())
+    self.assertAllEqual(
+        np_array.shape, tf_tensor.get_shape().as_list(), msg=msg)
 
-  def assertDeviceEqual(self, device1, device2):
+  def assertDeviceEqual(self, device1, device2, msg=None):
     """Asserts that the two given devices are the same.
 
     Args:
       device1: A string device name or TensorFlow `DeviceSpec` object.
       device2: A string device name or TensorFlow `DeviceSpec` object.
+      msg: Optional message to report on failure.
     """
     device1 = pydev.canonical_name(device1)
     device2 = pydev.canonical_name(device2)
-    self.assertEqual(device1, device2,
-                     "Devices %s and %s are not equal" % (device1, device2))
+    self.assertEqual(device1, device2, "Devices %s and %s are not equal. %s" %
+                     (device1, device2, msg))
 
   # Fix Python 3 compatibility issues
   if six.PY3:
diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py
index 1fa264660d..a238a3f748 100644
--- a/tensorflow/python/keras/_impl/keras/backend.py
+++ b/tensorflow/python/keras/_impl/keras/backend.py
@@ -2895,6 +2895,7 @@ def rnn(step_function,
   ndim = len(inputs.get_shape())
   if ndim < 3:
     raise ValueError('Input should be at least 3D.')
+  inputs_shape = inputs.get_shape()
   axes = [1, 0] + list(range(2, ndim))
   inputs = array_ops.transpose(inputs, (axes))
 
@@ -3079,6 +3080,13 @@ def rnn(step_function,
 
   axes = [1, 0] + list(range(2, len(outputs.get_shape())))
   outputs = array_ops.transpose(outputs, axes)
+
+  # Static shape inference: (samples, time, ...)
+  outputs_shape = outputs.get_shape().as_list()
+  outputs_shape[0] = inputs_shape[0]
+  outputs_shape[1] = inputs_shape[1]
+  outputs.set_shape(outputs_shape)
+
   last_output._uses_learning_phase = uses_learning_phase
   return last_output, outputs, new_states
 
diff --git a/tensorflow/python/keras/_impl/keras/backend_test.py b/tensorflow/python/keras/_impl/keras/backend_test.py
index 27833e368d..f29ca49378 100644
--- a/tensorflow/python/keras/_impl/keras/backend_test.py
+++ b/tensorflow/python/keras/_impl/keras/backend_test.py
@@ -915,6 +915,15 @@ class BackendNNOpsTest(test.TestCase):
         last_output, outputs, new_states = keras.backend.rnn(rnn_fn, inputs,
                                                              initial_states,
                                                              **kwargs)
+        # check static shape inference
+        self.assertEquals(last_output.get_shape().as_list(),
+                          [num_samples, output_dim])
+        self.assertEquals(outputs.get_shape().as_list(),
+                          [num_samples, timesteps, output_dim])
+        for state in new_states:
+          self.assertEquals(state.get_shape().as_list(),
+                            [num_samples, output_dim])
+
         last_output_list[i].append(keras.backend.eval(last_output))
         outputs_list[i].append(keras.backend.eval(outputs))
         self.assertEqual(len(new_states), 1)
diff --git a/tensorflow/python/keras/_impl/keras/layers/lstm_test.py b/tensorflow/python/keras/_impl/keras/layers/lstm_test.py
index 8d359bf17c..1de5485179 100644
--- a/tensorflow/python/keras/_impl/keras/layers/lstm_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/lstm_test.py
@@ -39,6 +39,22 @@ class LSTMLayerTest(test.TestCase):
                   'return_sequences': True},
           input_shape=(num_samples, timesteps, embedding_dim))
 
+  def test_static_shape_inference_LSTM(self):
+    # Github issue: 15165
+    num_samples = 2
+    timesteps = 3
+    embedding_dim = 4
+    units = 2
+
+    model = keras.models.Sequential()
+    inputs = keras.layers.Dense(
+        embedding_dim, input_shape=(timesteps, embedding_dim))
+    model.add(inputs)
+    layer = keras.layers.LSTM(units, return_sequences=True)
+    model.add(layer)
+    outputs = model.layers[-1].output
+    self.assertEquals(outputs.get_shape().as_list(), [None, timesteps, units])
+
   def test_dynamic_behavior_LSTM(self):
     num_samples = 2
     timesteps = 3
diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent.py b/tensorflow/python/keras/_impl/keras/layers/recurrent.py
index b34b92c763..2e9003f52d 100644
--- a/tensorflow/python/keras/_impl/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/_impl/keras/layers/recurrent.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numbers
 import numpy as np
 
 from tensorflow.python.framework import tensor_shape
@@ -413,7 +414,7 @@ class RNN(Layer):
   @property
   def states(self):
     if self._states is None:
-      if isinstance(self.cell.state_size, int):
+      if isinstance(self.cell.state_size, numbers.Integral):
         num_states = 1
       else:
         num_states = len(self.cell.state_size)
diff --git a/tensorflow/python/kernel_tests/conv2d_transpose_test.py b/tensorflow/python/kernel_tests/conv2d_transpose_test.py
index 1a65c3f429..b692d3da60 100644
--- a/tensorflow/python/kernel_tests/conv2d_transpose_test.py
+++ b/tensorflow/python/kernel_tests/conv2d_transpose_test.py
@@ -174,7 +174,7 @@ class Conv2DTransposeTest(test.TestCase):
     self.assertLess(err, err_tolerance)
 
   def testConv2DTransposeSingleStrideNCHW(self):
-    # `NCHW` data fomat is only supported for CUDA device.
+    # `NCHW` data format is only supported for CUDA device.
     if test.is_gpu_available(cuda_only=True):
       with self.test_session(use_gpu=True):
         strides = [1, 1, 1, 1]
@@ -209,7 +209,7 @@ class Conv2DTransposeTest(test.TestCase):
                 self.assertAllClose(target, value[n, k, h, w])
 
   def testConv2DTransposeSameNCHW(self):
-    # `NCHW` data fomat is only supported for CUDA device.
+    # `NCHW` data format is only supported for CUDA device.
     if test.is_gpu_available(cuda_only=True):
       with self.test_session(use_gpu=True):
         strides = [1, 1, 2, 2]
@@ -245,7 +245,7 @@ class Conv2DTransposeTest(test.TestCase):
                 self.assertAllClose(target, value[n, k, h, w])
 
   def testConv2DTransposeValidNCHW(self):
-    # `NCHW` data fomat is only supported for CUDA device.
+    # `NCHW` data format is only supported for CUDA device.
     if test.is_gpu_available(cuda_only=True):
       with self.test_session(use_gpu=True):
         strides = [1, 1, 2, 2]
diff --git a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
index a269d72273..09812db816 100644
--- a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
+++ b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py
@@ -25,7 +25,6 @@ import numpy as np
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.distributions import bernoulli
 from tensorflow.python.ops.distributions import kullback_leibler
 from tensorflow.python.platform import test
@@ -291,12 +290,6 @@ class BernoulliTest(test.TestCase):
                [np.sqrt(var(0.5)), np.sqrt(var(0.4))]],
               dtype=np.float32))
 
-  def testBernoulliWithSigmoidProbs(self):
-    p = np.array([8.3, 4.2])
-    dist = bernoulli.BernoulliWithSigmoidProbs(logits=p)
-    with self.test_session():
-      self.assertAllClose(math_ops.sigmoid(p).eval(), dist.probs.eval())
-
   def testBernoulliBernoulliKL(self):
     with self.test_session() as sess:
       batch_size = 6
diff --git a/tensorflow/python/kernel_tests/distributions/beta_test.py b/tensorflow/python/kernel_tests/distributions/beta_test.py
index 91a451f033..ab5041a6eb 100644
--- a/tensorflow/python/kernel_tests/distributions/beta_test.py
+++ b/tensorflow/python/kernel_tests/distributions/beta_test.py
@@ -107,8 +107,10 @@ class BetaTest(test.TestCase):
         dist.prob([-1., 0.1, 0.5]).eval()
       with self.assertRaisesOpError("sample must be positive"):
         dist.prob([0., 0.1, 0.5]).eval()
-      with self.assertRaisesOpError("sample must be no larger than `1`"):
+      with self.assertRaisesOpError("sample must be less than `1`"):
         dist.prob([.1, .2, 1.2]).eval()
+      with self.assertRaisesOpError("sample must be less than `1`"):
+        dist.prob([.1, .2, 1.0]).eval()
 
   def testPdfTwoBatches(self):
     with self.test_session():
diff --git a/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py
index 30795eed8a..d8ce9fffbd 100644
--- a/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py
+++ b/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py
@@ -148,7 +148,7 @@ class DepthwiseConv2DTest(test.TestCase):
     print("depthwise conv_2d: ", tensor_in_sizes, "*", filter_in_sizes,
           ", stride:", stride, ", padding: ", padding, ", max diff: ",
           np.amax(np.absolute(native_result - interface_result)))
-    self.assertArrayNear(
+    self.assertAllClose(
         np.ravel(native_result), np.ravel(interface_result), 1e-5)
     self.assertShapeEqual(native_result, conv_native)
     self.assertShapeEqual(native_result, conv_interface)
@@ -213,7 +213,7 @@ class DepthwiseConv2DTest(test.TestCase):
             t1, t2, strides=[1, stride, stride, 1], padding=padding)
         value = sess.run(conv)
     print("value = ", value)
-    self.assertArrayNear(expected, np.ravel(value), 1e-5)
+    self.assertAllClose(expected, np.ravel(value), 1e-5)
     self.assertShapeEqual(value, conv)
 
   def testConv2D2x2Filter(self):
diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py
index c7181497d8..61fb3f12e4 100644
--- a/tensorflow/python/kernel_tests/py_func_test.py
+++ b/tensorflow/python/kernel_tests/py_func_test.py
@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -212,6 +213,16 @@ class PyFuncTest(test.TestCase):
       value.op.run()
       self.assertAllEqual(np_array, [1.0, 2.0])
 
+  def testReturnUnicodeString(self):
+    with self.test_session():
+      correct = u"你好 世界"
+
+      def unicode_string():
+        return correct
+
+      z, = script_ops.py_func(unicode_string, [], [dtypes.string])
+      self.assertEqual(z.eval(), correct.encode("utf8"))
+
   def testBadNumpyReturnType(self):
     with self.test_session():
 
diff --git a/tensorflow/python/layers/maxout.py b/tensorflow/python/layers/maxout.py
index 20ce6c9770..765a1c4fda 100644
--- a/tensorflow/python/layers/maxout.py
+++ b/tensorflow/python/layers/maxout.py
@@ -106,6 +106,6 @@ class MaxOut(base.Layer):
       if shape[i] is None:
         shape[i] = gen_array_ops.shape(inputs)[i]
     outputs = math_ops.reduce_max(
-        gen_array_ops.reshape(inputs, shape), -1, keep_dims=False)
+        gen_array_ops.reshape(inputs, shape), -1, keepdims=False)
 
     return outputs
diff --git a/tensorflow/python/lib/io/file_io.i b/tensorflow/python/lib/io/file_io.i
index c0c4e035fc..891a7b0fd0 100644
--- a/tensorflow/python/lib/io/file_io.i
+++ b/tensorflow/python/lib/io/file_io.i
@@ -110,21 +110,15 @@ void RecursivelyCreateDir(const string& dirname, TF_Status* out_status) {
   }
 }
 
-void CopyFile(const string& oldpath, const string& newpath, bool overwrite,
+void CopyFile(const string& src, const string& target, bool overwrite,
               TF_Status* out_status) {
-  // If overwrite is false and the newpath file exists then it's an error.
-  if (!overwrite && tensorflow::Env::Default()->FileExists(newpath).ok()) {
+  // If overwrite is false and the target file exists then its an error.
+  if (!overwrite && tensorflow::Env::Default()->FileExists(target).ok()) {
     TF_SetStatus(out_status, TF_ALREADY_EXISTS, "file already exists");
     return;
   }
-  string file_content;
-  tensorflow::Status status = ReadFileToString(tensorflow::Env::Default(),
-      oldpath, &file_content);
-  if (!status.ok()) {
-    Set_TF_Status_from_Status(out_status, status);
-    return;
-  }
-  status = WriteStringToFile(tensorflow::Env::Default(), newpath, file_content);
+  tensorflow::Status status =
+      tensorflow::Env::Default()->CopyFile(src, target);
   if (!status.ok()) {
     Set_TF_Status_from_Status(out_status, status);
   }
diff --git a/tensorflow/python/ops/bitwise_ops_test.py b/tensorflow/python/ops/bitwise_ops_test.py
index f9b025b787..0a2af3716b 100644
--- a/tensorflow/python/ops/bitwise_ops_test.py
+++ b/tensorflow/python/ops/bitwise_ops_test.py
@@ -70,8 +70,10 @@ class BitwiseOpTest(test_util.TensorFlowTestCase):
         self.assertAllEqual(truth, popcnt_result)
 
   def testInvertOp(self):
-    dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
-                  dtypes.uint8, dtypes.uint16]
+    dtype_list = [
+        dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8,
+        dtypes.uint16, dtypes.uint32, dtypes.uint64
+    ]
     inputs = [0, 5, 3, 14]
     with self.test_session(use_gpu=True) as sess:
       for dtype in dtype_list:
diff --git a/tensorflow/python/ops/distributions/bernoulli.py b/tensorflow/python/ops/distributions/bernoulli.py
index 4c16d62e9a..68aaf3815e 100644
--- a/tensorflow/python/ops/distributions/bernoulli.py
+++ b/tensorflow/python/ops/distributions/bernoulli.py
@@ -157,26 +157,6 @@ class Bernoulli(distribution.Distribution):
     return math_ops.cast(self.probs > 0.5, self.dtype)
 
 
-class BernoulliWithSigmoidProbs(Bernoulli):
-  """Bernoulli with `probs = nn.sigmoid(logits)`."""
-
-  def __init__(self,
-               logits=None,
-               dtype=dtypes.int32,
-               validate_args=False,
-               allow_nan_stats=True,
-               name="BernoulliWithSigmoidProbs"):
-    parameters = locals()
-    with ops.name_scope(name):
-      super(BernoulliWithSigmoidProbs, self).__init__(
-          probs=nn.sigmoid(logits, name="sigmoid_probs"),
-          dtype=dtype,
-          validate_args=validate_args,
-          allow_nan_stats=allow_nan_stats,
-          name=name)
-    self._parameters = parameters
-
-
 @kullback_leibler.RegisterKL(Bernoulli, Bernoulli)
 def _kl_bernoulli_bernoulli(a, b, name=None):
   """Calculate the batched KL divergence KL(a || b) with a and b Bernoulli.
diff --git a/tensorflow/python/ops/distributions/beta.py b/tensorflow/python/ops/distributions/beta.py
index 6d6b40b045..469bcadb8e 100644
--- a/tensorflow/python/ops/distributions/beta.py
+++ b/tensorflow/python/ops/distributions/beta.py
@@ -304,12 +304,11 @@ class Beta(distribution.Distribution):
     if not self.validate_args:
       return x
     return control_flow_ops.with_dependencies([
-        check_ops.assert_positive(
-            x,
-            message="sample must be positive"),
+        check_ops.assert_positive(x, message="sample must be positive"),
         check_ops.assert_less(
-            x, array_ops.ones([], self.dtype),
-            message="sample must be no larger than `1`."),
+            x,
+            array_ops.ones([], self.dtype),
+            message="sample must be less than `1`."),
     ], x)
 
 
diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py
index b12bd3d5b0..18625293e0 100644
--- a/tensorflow/python/ops/image_ops_test.py
+++ b/tensorflow/python/ops/image_ops_test.py
@@ -1869,8 +1869,8 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
       image_size = constant_op.constant(
           [40, 50, 1], shape=[3], dtype=dtypes.int32)
       bounding_box = constant_op.constant(
-          [0.0, 0.0, 1.0, 1.0],
-          shape=[4],
+          [[[0.0, 0.0, 1.0, 1.0]]],
+          shape=[1, 1, 4],
           dtype=dtypes.float32,
       )
       begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box(
@@ -1884,6 +1884,10 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
       self.assertAllEqual([3], begin.get_shape().as_list())
       self.assertAllEqual([3], end.get_shape().as_list())
       self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list())
+      # Actual run to make sure shape is correct inside Compute().
+      begin = begin.eval()
+      end = end.eval()
+      bbox_for_drawing = bbox_for_drawing.eval()
 
       begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box(
           image_size=image_size,
@@ -1903,8 +1907,8 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
       image_size = constant_op.constant(
           [40, 50, 1], shape=[3], dtype=dtypes.int32)
       bounding_box = constant_op.constant(
-          [0.0, 0.0, 1.0, 1.0],
-          shape=[4],
+          [[[0.0, 0.0, 1.0, 1.0]]],
+          shape=[1, 1, 4],
           dtype=dtypes.float32,
       )
       begin, end, bbox_for_drawing = image_ops.sample_distorted_bounding_box(
@@ -1916,6 +1920,10 @@ class SelectDistortedCropBoxTest(test_util.TensorFlowTestCase):
       self.assertAllEqual([3], begin.get_shape().as_list())
       self.assertAllEqual([3], end.get_shape().as_list())
       self.assertAllEqual([1, 1, 4], bbox_for_drawing.get_shape().as_list())
+      # Actual run to make sure shape is correct inside Compute().
+      begin = begin.eval()
+      end = end.eval()
+      bbox_for_drawing = bbox_for_drawing.eval()
 
 
 class ResizeImagesTest(test_util.TensorFlowTestCase):
@@ -2822,20 +2830,9 @@ class PngTest(test_util.TensorFlowTestCase):
 
 class GifTest(test_util.TensorFlowTestCase):
 
-  def testOptimizedGifErrorString(self):
-    filename = "tensorflow/core/lib/gif/testdata/optimized.gif"
-
-    with self.test_session(use_gpu=True) as sess:
-      gif = io_ops.read_file(filename)
-      image = image_ops.decode_gif(gif)
-      with self.assertRaisesRegexp(errors.InvalidArgumentError,
-                                   "can't process optimized gif"):
-        gif, image = sess.run([gif, image])
-
-  def testValid(self):
+  def _testValid(self, filename):
     # Read some real GIFs
     prefix = "tensorflow/core/lib/gif/testdata/"
-    filename = "scan.gif"
     WIDTH = 20
     HEIGHT = 40
     STRIDE = 5
@@ -2862,16 +2859,9 @@ class GifTest(test_util.TensorFlowTestCase):
 
         self.assertAllClose(frame, gt)
 
-  def testInValid(self):
-    # Read some real GIFs
-    prefix = "tensorflow/core/lib/gif/testdata/"
-    filename = "optimized.gif"
-
-    with self.test_session(use_gpu=True) as sess:
-      gif0 = io_ops.read_file(prefix + filename)
-      image0 = image_ops.decode_gif(gif0)
-      with self.assertRaises(errors.InvalidArgumentError):
-        gif0, image0 = sess.run([gif0, image0])
+  def testValid(self):
+    self._testValid("scan.gif")
+    self._testValid("optimized.gif")
 
   def testShape(self):
     with self.test_session(use_gpu=True) as sess:
diff --git a/tensorflow/python/ops/linalg/linear_operator.py b/tensorflow/python/ops/linalg/linear_operator.py
index 2c24a29567..957a795918 100644
--- a/tensorflow/python/ops/linalg/linear_operator.py
+++ b/tensorflow/python/ops/linalg/linear_operator.py
@@ -480,7 +480,6 @@ class LinearOperator(object):
           cond,
           self._max_condition_number_to_be_non_singular(),
           message="Singular matrix up to precision epsilon.")
-    raise NotImplementedError("assert_non_singular is not implemented.")
 
   def _max_condition_number_to_be_non_singular(self):
     """Return the maximum condition number that we consider nonsingular."""
diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index 5222333d7e..ca408988dd 100644
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -726,9 +726,11 @@ def softmax_cross_entropy(
       smooth_negatives = label_smoothing / num_classes
       onehot_labels = onehot_labels * smooth_positives + smooth_negatives
 
-    losses = nn.softmax_cross_entropy_with_logits(labels=onehot_labels,
-                                                  logits=logits,
-                                                  name="xentropy")
+    onehot_labels = array_ops.stop_gradient(
+        onehot_labels, name="labels_stop_gradient")
+    losses = nn.softmax_cross_entropy_with_logits_v2(
+        labels=onehot_labels, logits=logits, name="xentropy")
+
     return compute_weighted_loss(
         losses, weights, scope, loss_collection, reduction=reduction)
 
diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py
index c7e8c28efd..6fe2f61016 100644
--- a/tensorflow/python/ops/script_ops.py
+++ b/tensorflow/python/ops/script_ops.py
@@ -98,7 +98,7 @@ class FuncRegistry(object):
     components of a tensor have different lengths.  This is bad: ignoring the
     padding is wrong for text data, and removing the padding is wrong for binary
     data.  To avoid this bug, we redo the conversion using an object dtype.
-    Additionally, we convert unicode strings to (byte-)strings for Python3
+    Additionally, we convert unicode strings to (byte-)strings for
     compatibility.
 
     Args:
@@ -112,7 +112,7 @@ class FuncRegistry(object):
     if result.dtype.char == "S" and result is not value:
       return np.asarray(value, order="C", dtype=object)
     elif result.dtype.char == "U" and result is not value:
-      value = np.vectorize(lambda x: x.encode())(value)
+      value = np.vectorize(lambda x: x.encode("utf8"))(value)
       return np.asarray(value, order="C", dtype=object)
     elif result.dtype.char == "U":
       return result.astype(np.bytes_)
diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py
index a2164f78b8..f6d9111009 100644
--- a/tensorflow/python/ops/standard_ops.py
+++ b/tensorflow/python/ops/standard_ops.py
@@ -27,6 +27,7 @@ from tensorflow.python.ops import array_grad
 from tensorflow.python.ops import data_flow_grad
 from tensorflow.python.ops import manip_grad
 from tensorflow.python.ops import math_grad
+from tensorflow.python.ops import manip_grad
 from tensorflow.python.ops import sparse_grad
 from tensorflow.python.ops import spectral_grad
 from tensorflow.python.ops import state_grad
diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py
index 1323df5a17..6c0a090d16 100644
--- a/tensorflow/python/ops/state_ops.py
+++ b/tensorflow/python/ops/state_ops.py
@@ -278,7 +278,7 @@ def assign(ref, value, validate_shape=None, use_locking=None, name=None):
     return gen_state_ops.assign(
         ref, value, use_locking=use_locking, name=name,
         validate_shape=validate_shape)
-  return ref.assign(value)
+  return ref.assign(value, name=name)
 
 
 @tf_export("count_up_to")
diff --git a/tensorflow/python/util/compat_internal.py b/tensorflow/python/util/compat_internal.py
index fee1d6fab7..1905c3e383 100644
--- a/tensorflow/python/util/compat_internal.py
+++ b/tensorflow/python/util/compat_internal.py
@@ -18,6 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.util.compat import as_str_any
+
 
 def path_to_str(path):
   """Returns the file system path representation of a `PathLike` object,
diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h
index f4162b0962..aa88fe770f 100644
--- a/tensorflow/stream_executor/dnn.h
+++ b/tensorflow/stream_executor/dnn.h
@@ -896,7 +896,7 @@ class DnnSupport {
   //  offset: offset parameters.
   //  estimated_mean: population mean estimated during training.
   //    Used for inference only; empty for training.
-  //  estimated_variance: population variance estimated during traning,
+  //  estimated_variance: population variance estimated during training,
   //    used for inference only; empty for training.
   //  x_desc: dimensions of the input data, which is the same as the dimensions
   //    of the output.
diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh
index f980ced2e4..aeac085d30 100755
--- a/tensorflow/tools/ci_build/ci_sanity.sh
+++ b/tensorflow/tools/ci_build/ci_sanity.sh
@@ -353,7 +353,7 @@ do_external_licenses_check(){
 
   # Whitelist
   echo ${EXTRA_LICENSE_FILE}
-  grep -e "@bazel_tools//src/" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -v ${EXTRA_LICENSES_FILE} > temp.txt
+  grep -e "@bazel_tools//src" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -v ${EXTRA_LICENSES_FILE} > temp.txt
   mv temp.txt ${EXTRA_LICENSES_FILE}
 
 
diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel
index 5dc4a053fd..d16761c367 100644
--- a/tensorflow/tools/docker/Dockerfile.devel
+++ b/tensorflow/tools/docker/Dockerfile.devel
@@ -70,7 +70,7 @@ RUN mkdir /bazel && \
 
 # Download and build TensorFlow.
 WORKDIR /tensorflow
-RUN git clone --branch=r1.5 --depth=1 https://github.com/tensorflow/tensorflow.git .
+RUN git clone --branch=r1.6 --depth=1 https://github.com/tensorflow/tensorflow.git .
 
 # TODO(craigcitro): Don't install the pip package, since it makes it
 # more difficult to experiment with local changes. Instead, just add
diff --git a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
index 96b260ad3a..3690e7dfe5 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
+++ b/tensorflow/tools/docker/Dockerfile.devel-cpu-mkl
@@ -3,7 +3,7 @@ FROM tensorflow/tensorflow:latest-devel
 LABEL maintainer="Clayne Robison<clayne.b.robison@intel.com>"
 
 # These arguments are parameterized. Use --build-args to override.
-ARG TF_BRANCH=r1.5
+ARG TF_BRANCH=r1.6
 ARG WHL_DIR=/whl
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index 07ffd3839a..4ef37881bc 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -79,7 +79,7 @@ RUN mkdir /bazel && \
 
 # Download and build TensorFlow.
 WORKDIR /tensorflow
-RUN git clone --branch=r1.5 --depth=1 https://github.com/tensorflow/tensorflow.git .
+RUN git clone --branch=r1.6 --depth=1 https://github.com/tensorflow/tensorflow.git .
 
 # Configure the build for our CUDA configuration.
 ENV CI_BUILD_PYTHON python
diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD
index b5465b7fb3..8601b3d0f1 100644
--- a/tensorflow/tools/graph_transforms/BUILD
+++ b/tensorflow/tools/graph_transforms/BUILD
@@ -99,22 +99,21 @@ cc_library(
         "freeze_requantization_ranges.cc",
         "fuse_convolutions.cc",
         "insert_logging.cc",
-        "remove_ema.cc",
         "obfuscate_names.cc",
+        "quantize_nodes.cc",
+        "quantize_weights.cc",
         "remove_attribute.cc",
         "remove_device.cc",
+        "remove_ema.cc",
         "remove_nodes.cc",
         "rename_attribute.cc",
         "rename_op.cc",
+        "round_weights.cc",
         "set_device.cc",
         "sort_by_execution_order.cc",
         "sparsify_gather.cc",
         "strip_unused_nodes.cc",
-    ] + if_not_windows([
-        "quantize_nodes.cc",
-        "quantize_weights.cc",
-        "round_weights.cc",
-    ]),
+    ],
     hdrs = [
         "fold_constants_lib.h",
     ],
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index bc4315c600..0e6b32bb49 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,7 +29,7 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.5.0'
+_VERSION = '1.6.0-rc0'
 
 REQUIRED_PACKAGES = [
     'absl-py >= 0.1.6',
diff --git a/third_party/repo.bzl b/third_party/repo.bzl
index 11e9c842d2..aa178fa8ca 100644
--- a/third_party/repo.bzl
+++ b/third_party/repo.bzl
@@ -27,7 +27,7 @@ def _wrap_bash_cmd(ctx, cmd):
     bazel_sh = _get_env_var(ctx, "BAZEL_SH")
     if not bazel_sh:
       fail("BAZEL_SH environment variable is not set")
-    cmd = [bazel_sh, "-c", " ".join(cmd)]
+    cmd = [bazel_sh, "-l", "-c", " ".join(cmd)]
   return cmd
 
 def _get_env_var(ctx, name):
-- 
GitLab


From bd9224f5a066be8ec591bb2ac79c8bd87a9a395b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 16 Feb 2018 19:01:28 -0800
Subject: [PATCH 041/575] Modify reference quantized LSTM implementation so
 that it only needs one instantiation of fixed-point Tanh, for 3 integer bits,
 regardless of the value of StateIntegerBits

PiperOrigin-RevId: 186075161
---
 .../kernels/internal/reference/reference_ops.h     | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 55618ea971..d8907d5d48 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -1577,9 +1577,19 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
       FS new_state = gemmlowp::SaturatingAdd(
           gemmlowp::Rescale<StateIntegerBits>(input_times_input_modulation),
           prev_state_times_forget_state);
-      // Implementation of last internal tanh node, still in fixed-point.
-      F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state);
+      // Implementation of last internal Tanh node, still in fixed-point.
+      // Since a Tanh fixed-point implementation is specialized for a given
+      // number or integer bits, and each specialization can have a substantial
+      // code size, and we already used above a Tanh on an input with 3 integer
+      // bits, and per the table in the above function comment there is no
+      // significant accuracy to be lost by clamping to [-8, +8] for a
+      // 3-integer-bits representation, let us just do that. This helps people
+      // porting this to targets where code footprint must be minimized.
+      F3 new_state_f3 = gemmlowp::Rescale<3>(new_state);
+      F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3);
       // Store the new internal state back to memory, as 16-bit integers.
+      // Note: here we store the original value with StateIntegerBits, not
+      // the rescaled 3-integer-bits value fed to tanh.
       output_state_data_int16[b * output_depth + c] = new_state.raw();
       // Down-scale the output activations to 8-bit integers, saturating,
       // and store back to memory.
-- 
GitLab


From 02bbb131b78fb0924675809ed5b549e594a51ac1 Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Fri, 16 Feb 2018 19:02:58 -0800
Subject: [PATCH 042/575] Automated g4 rollback of changelist 186053793

PiperOrigin-RevId: 186075274
---
 tensorflow/contrib/lite/arena_planner.cc |  5 -----
 tensorflow/contrib/lite/kernels/conv.cc  | 14 ++++----------
 2 files changed, 4 insertions(+), 15 deletions(-)

diff --git a/tensorflow/contrib/lite/arena_planner.cc b/tensorflow/contrib/lite/arena_planner.cc
index 8e47e2375e..87b17c338e 100644
--- a/tensorflow/contrib/lite/arena_planner.cc
+++ b/tensorflow/contrib/lite/arena_planner.cc
@@ -128,11 +128,6 @@ TfLiteStatus ArenaPlanner::PlanAllocations() {
 }
 
 TfLiteStatus ArenaPlanner::ExecuteAllocations(int first_node, int last_node) {
-  // Grow the size of `allocs_` if necessary. This allows allocating temporary
-  // tensors in op's `prepare` function.
-  TF_LITE_ENSURE(context_, graph_info_->num_tensors() >= allocs_.size());
-  allocs_.resize(graph_info_->num_tensors());
-
   TF_LITE_ENSURE_STATUS(CalculateAllocations(first_node, last_node));
   TF_LITE_ENSURE_STATUS(Commit());
 
diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc
index 495910aab6..66d2c04bba 100644
--- a/tensorflow/contrib/lite/kernels/conv.cc
+++ b/tensorflow/contrib/lite/kernels/conv.cc
@@ -51,13 +51,11 @@ enum KernelType {
   kCblasOptimized,
 };
 
-const int kTensorNotAllocated = -1;
-
 struct OpData {
   // IDs are the arbitrary identifiers used by TF Lite to identify and access
   // memory buffers.
-  int im2col_id = kTensorNotAllocated;
-  int hwcn_weights_id = kTensorNotAllocated;
+  int im2col_id;
+  int hwcn_weights_id;
 
   TfLitePaddingValues padding;
   // The scaling factor from input to output (aka the 'real multiplier') can
@@ -82,6 +80,8 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) {
   // Instead, we allocate a new object to use as scratch space for im2col, and
   // to carry information from Prepare() to Eval().
   auto* data = new OpData;
+  context->AddTensors(context, 1, &data->im2col_id);
+  context->AddTensors(context, 1, &data->hwcn_weights_id);
   gemm_support::IncrementUsageCounter(context);
   return data;
 }
@@ -219,16 +219,10 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   int temporaries_count = 0;
   if (data->need_im2col) {
     data->im2col_index = temporaries_count;
-    if (data->im2col_id == kTensorNotAllocated) {
-      context->AddTensors(context, 1, &data->im2col_id);
-    }
     ++temporaries_count;
   }
   if (data->need_hwcn_weights) {
     data->hwcn_weights_index = temporaries_count;
-    if (data->hwcn_weights_id == kTensorNotAllocated) {
-      context->AddTensors(context, 1, &data->hwcn_weights_id);
-    }
     ++temporaries_count;
   }
 
-- 
GitLab


From fa8c4d16288e3bee4a014b4d51d22dd361721ff4 Mon Sep 17 00:00:00 2001
From: Mingsheng Hong <hongm@google.com>
Date: Fri, 16 Feb 2018 22:05:07 -0800
Subject: [PATCH 043/575] Added an experimental C API TF_EnableXLACompilation()
 to enable XLA compilation.

Also ran "buildozer warn //third_party/tensorflow/c/BUILD" and removed an unused symbol.

PiperOrigin-RevId: 186081948
---
 tensorflow/c/BUILD                 | 26 ++++++++----
 tensorflow/c/c_api_experimental.cc | 39 ++++++++++++++++++
 tensorflow/c/c_api_experimental.h  | 66 ++++++++++++++++++++++++++++++
 tensorflow/c/c_test_util.cc        | 16 +-------
 4 files changed, 126 insertions(+), 21 deletions(-)
 create mode 100644 tensorflow/c/c_api_experimental.cc
 create mode 100644 tensorflow/c/c_api_experimental.h

diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD
index 9060c58c13..85cfa98908 100644
--- a/tensorflow/c/BUILD
+++ b/tensorflow/c/BUILD
@@ -12,12 +12,6 @@ load(
     "tf_custom_op_library",
 )
 
-# For platform specific build config
-load(
-    "//tensorflow/core:platform/default/build_config.bzl",
-    "tf_kernel_tests_linkstatic",
-)
-
 # -----------------------------------------------------------------------------
 # Public targets
 
@@ -101,6 +95,24 @@ tf_cuda_library(
     }),
 )
 
+tf_cuda_library(
+    name = "c_api_experimental",
+    srcs = [
+        "c_api_experimental.cc",
+    ],
+    hdrs = [
+        "c_api_experimental.h",
+    ],
+    copts = tf_copts(),
+    visibility = ["//visibility:public"],
+    deps = [
+        ":c_api",
+        ":c_api_internal",
+        "//tensorflow/compiler/jit/legacy_flags:mark_for_compilation_pass_flags",
+        "//tensorflow/core:protos_all_cc",
+    ],
+)
+
 exports_files(
     [
         "version_script.lds",
@@ -148,7 +160,7 @@ tf_cuda_library(
     ],
     deps = [
         ":c_api",
-        "//tensorflow/compiler/jit/legacy_flags:mark_for_compilation_pass_flags",
+        ":c_api_experimental",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:session_options",
diff --git a/tensorflow/c/c_api_experimental.cc b/tensorflow/c/c_api_experimental.cc
new file mode 100644
index 0000000000..be7f85a5bb
--- /dev/null
+++ b/tensorflow/c/c_api_experimental.cc
@@ -0,0 +1,39 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/c/c_api_experimental.h"
+
+#include "tensorflow/c/c_api_internal.h"
+#include "tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h"
+#include "tensorflow/core/protobuf/config.pb.h"
+
+void TF_EnableXLACompilation(TF_SessionOptions* options, unsigned char enable) {
+  tensorflow::ConfigProto& config = options->options.config;
+  auto* optimizer_options =
+      config.mutable_graph_options()->mutable_optimizer_options();
+  if (enable) {
+    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);
+
+    // These XLA flags are needed to trigger XLA properly from C (more generally
+    // non-Python) clients. If this API is called again with `enable` set to
+    // false, it is safe to keep these flag values as is.
+    tensorflow::legacy_flags::MarkForCompilationPassFlags* flags =
+        tensorflow::legacy_flags::GetMarkForCompilationPassFlags();
+    flags->tf_xla_cpu_global_jit = true;
+    flags->tf_xla_min_cluster_size = 1;
+  } else {
+    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF);
+  }
+}
diff --git a/tensorflow/c/c_api_experimental.h b/tensorflow/c/c_api_experimental.h
new file mode 100644
index 0000000000..5a7b007e40
--- /dev/null
+++ b/tensorflow/c/c_api_experimental.h
@@ -0,0 +1,66 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_C_C_API_EXPERIMENTAL_H_
+#define TENSORFLOW_C_C_API_EXPERIMENTAL_H_
+
+#include <stddef.h>
+#include <stdint.h>
+
+#include "tensorflow/c/c_api.h"
+
+// --------------------------------------------------------------------------
+// Experimental C API for TensorFlow.
+//
+// The API here is subject to changes in the future.
+
+// Macro to control visibility of exported symbols in the shared library (.so,
+// .dylib, .dll).
+// This duplicates the TF_EXPORT macro definition in
+// tensorflow/core/platform/macros.h in order to keep this .h file independent
+// of any other includes.$a
+#ifdef SWIG
+#define TF_CAPI_EXPORT
+#else
+#if defined(COMPILER_MSVC)
+#ifdef TF_COMPILE_LIBRARY
+#define TF_CAPI_EXPORT __declspec(dllexport)
+#else
+#define TF_CAPI_EXPORT __declspec(dllimport)
+#endif  // TF_COMPILE_LIBRARY
+#else
+#define TF_CAPI_EXPORT __attribute__((visibility("default")))
+#endif  // COMPILER_MSVC
+#endif  // SWIG
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// When `enable` is true, set
+// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
+// set XLA flag values to prepare for XLA compilation. Otherwise set
+// global_jit_level to OFF.
+//
+// This API is syntax sugar over TF_SetConfig(), and is used by clients that
+// cannot read/write the tensorflow.ConfigProto proto.
+TF_CAPI_EXPORT extern void TF_EnableXLACompilation(TF_SessionOptions* options,
+                                                   unsigned char enable);
+
+#ifdef __cplusplus
+} /* end extern "C" */
+#endif
+
+#endif  // TENSORFLOW_C_C_API_EXPERIMENTAL_H_
diff --git a/tensorflow/c/c_test_util.cc b/tensorflow/c/c_test_util.cc
index a55af46ae2..3db2852ce6 100644
--- a/tensorflow/c/c_test_util.cc
+++ b/tensorflow/c/c_test_util.cc
@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/c/c_test_util.h"
 
-#include "tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h"
+#include "tensorflow/c/c_api_experimental.h"
 #include "tensorflow/core/framework/function.pb.h"
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/tensor.pb.h"
@@ -404,19 +404,7 @@ std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {
 
 CSession::CSession(TF_Graph* graph, TF_Status* s, bool use_XLA) {
   TF_SessionOptions* opts = TF_NewSessionOptions();
-  tensorflow::legacy_flags::MarkForCompilationPassFlags* flags =
-      tensorflow::legacy_flags::GetMarkForCompilationPassFlags();
-  flags->tf_xla_cpu_global_jit = use_XLA;
-  if (use_XLA) {
-    tensorflow::ConfigProto config;
-    config.mutable_graph_options()
-        ->mutable_optimizer_options()
-        ->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);
-    std::string contents;
-    contents.resize(config.ByteSizeLong());
-    config.SerializeToArray(&contents[0], contents.size());
-    TF_SetConfig(opts, contents.data(), contents.size(), s);
-  }
+  TF_EnableXLACompilation(opts, use_XLA);
   session_ = TF_NewSession(graph, opts, s);
   TF_DeleteSessionOptions(opts);
 }
-- 
GitLab


From d61535528dc7581c56ea0de6ba92bc1f1410c6a9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 17 Feb 2018 04:46:24 -0800
Subject: [PATCH 044/575] Automated g4 rollback of changelist 186019263

PiperOrigin-RevId: 186098155
---
 tensorflow/contrib/learn/BUILD | 1 -
 tensorflow/python/BUILD        | 1 -
 2 files changed, 2 deletions(-)

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 6ebd0b0d39..abf6e393bb 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -7,7 +7,6 @@ exports_files(["LICENSE"])
 
 package(default_visibility = [
     "//engedu/ml/tf_from_scratch:__pkg__",
-    "//quality/sixface/sequel/experiments/deep_learning/entity_cloud:__pkg__",
     "//tensorflow:internal",
 ])
 
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 3334a08eac..cee7c47e00 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -7,7 +7,6 @@
 package(
     default_visibility = [
         "//engedu/ml/tf_from_scratch:__pkg__",
-        "//quality/sixface/sequel/experiments/deep_learning/entity_cloud:__pkg__",
         "//tensorflow:internal",
         "//tensorflow/contrib/lite/toco/python:__pkg__",
         "//tensorflow_models:__subpackages__",
-- 
GitLab


From eb29cf2895a7347ee293bbca38891dde5f0bd701 Mon Sep 17 00:00:00 2001
From: Bixia Zheng <bixia@google.com>
Date: Sat, 17 Feb 2018 07:46:14 -0800
Subject: [PATCH 045/575] [XLA:GPU] Fix a problem in DoGemmAutotune.

Replace DCHECK with CHECK so that DoGemmWithAlgorithm is also called in
non-debug mode to perform autotune.

PiperOrigin-RevId: 186103809
---
 tensorflow/compiler/xla/service/gpu/gemm_thunk.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
index 8e3aebbc12..ba482793e7 100644
--- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
@@ -137,9 +137,9 @@ StatusOr<se::blas::AlgorithmType> DoGemmAutotune(
     // for all algorithms if we're targeting < sm_50.  But because we pass a
     // non-null ProfileResult, DoGemmWithAlgorithm should always return true,
     // and the actual success-ness is returned in ProfileResult::is_valid.
-    DCHECK(DoGemmWithAlgorithm<Element>(lhs_matrix, rhs_matrix, output_matrix,
-                                        computation_type, algorithm, stream,
-                                        &profile_result));
+    CHECK(DoGemmWithAlgorithm<Element>(lhs_matrix, rhs_matrix, output_matrix,
+                                       computation_type, algorithm, stream,
+                                       &profile_result));
 
     if (profile_result.is_valid() && profile_result.elapsed_time_in_ms() <
                                          best_result.elapsed_time_in_ms()) {
-- 
GitLab


From f126da78c760f812c00027aeaf083988d44e26fe Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 17 Feb 2018 08:42:40 -0800
Subject: [PATCH 046/575] Tweak `tf.slice` documentation.

Add the input argument (`foo`) to `tf.slice` example so that it actually works if it were run.
Previously, the input argument was missing (perhaps implied), but the example is clearer with its inclusion.

PiperOrigin-RevId: 186105694
---
 tensorflow/python/ops/array_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index ad409ad7e5..d63a9ea0dd 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -605,7 +605,7 @@ def slice(input_, begin, size, name=None):
 
   Note that @{tf.Tensor.__getitem__} is typically a more pythonic way to
   perform slices, as it allows you to write `foo[3:7, :-2]` instead of
-  `tf.slice([3, 0], [4, foo.get_shape()[1]-2])`.
+  `tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])`.
 
   `begin` is zero-based; `size` is one-based. If `size[i]` is -1,
   all remaining elements in dimension i are included in the
-- 
GitLab


From c6f6aed789100b47973c19ea5a759ba86f630f4a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Carl=20Thom=C3=A9?= <carlthome@gmail.com>
Date: Sun, 18 Feb 2018 17:18:23 +0100
Subject: [PATCH 047/575] Add missing cast functions

---
 tensorflow/python/ops/math_ops.py | 34 +++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index da9957aa2a..7e3977c7a2 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -901,6 +901,40 @@ def to_bfloat16(x, name="ToBFloat16"):
   return cast(x, dtypes.bfloat16, name=name)
 
 
+@tf_export("to_complex64")
+def to_complex64(x, name="ToComplex64"):
+  """Casts a tensor to type `complex64`.
+
+  Args:
+    x: A `Tensor` or `SparseTensor`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` or `SparseTensor` with same shape as `x` with type `complex64`.
+
+  Raises:
+    TypeError: If `x` cannot be cast to the `complex64`.
+  """
+  return cast(x, dtypes.complex64, name=name)
+
+
+@tf_export("to_complex128")
+def to_complex128(x, name="ToComplex128"):
+  """Casts a tensor to type `complex128`.
+
+  Args:
+    x: A `Tensor` or `SparseTensor`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` or `SparseTensor` with same shape as `x` with type `complex128`.
+
+  Raises:
+    TypeError: If `x` cannot be cast to the `complex128`.
+  """
+  return cast(x, dtypes.complex128, name=name)
+
+
 ops.Tensor._override_operator("__neg__", gen_math_ops._neg)
 ops.Tensor._override_operator("__abs__", abs)
 # __invert__ corresponds to the ~ operator.  Here we follow the numpy convention
-- 
GitLab


From 49c20c5814dd80f81ced493d362d374be9ab0b3e Mon Sep 17 00:00:00 2001
From: ManHyuk <manhyuk@kw.ac.kr>
Date: Mon, 19 Feb 2018 04:33:25 +0900
Subject: [PATCH 048/575] Fix typo (#17008)

* fix typo
---
 tensorflow/contrib/makefile/README.md   | 2 +-
 tensorflow/core/lib/io/record_writer.cc | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md
index b0228c5435..995230dfa8 100644
--- a/tensorflow/contrib/makefile/README.md
+++ b/tensorflow/contrib/makefile/README.md
@@ -155,7 +155,7 @@ CC_PREFIX=ccache tensorflow/contrib/makefile/build_all_android.sh -s tensorflow/
 (add -T on subsequent builds to skip protobuf downloading/building)
 
 
-#### Testing the the CUDA-enabled benchmark via adb:
+#### Testing the CUDA-enabled benchmark via adb:
 Build binaries first as above, then run:
 
 ```bash
diff --git a/tensorflow/core/lib/io/record_writer.cc b/tensorflow/core/lib/io/record_writer.cc
index 3657243c5d..ebc5648269 100644
--- a/tensorflow/core/lib/io/record_writer.cc
+++ b/tensorflow/core/lib/io/record_writer.cc
@@ -49,7 +49,7 @@ RecordWriterOptions RecordWriterOptions::CreateRecordWriterOptions(
 #endif  // IS_SLIM_BUILD
   } else if (compression_type != compression::kNone) {
     LOG(ERROR) << "Unsupported compression_type:" << compression_type
-               << ". No comprression will be used.";
+               << ". No compression will be used.";
   }
   return options;
 }
-- 
GitLab


From dc34d5d0425adfa2aa384b43731c6cfa2c29228d Mon Sep 17 00:00:00 2001
From: Blake Hechtman <blakehechtman@google.com>
Date: Mon, 19 Feb 2018 03:29:08 -0800
Subject: [PATCH 049/575] [TF:XLA] Select the update value instead of the
 buffer to support negative index scatter.

PiperOrigin-RevId: 186202761
---
 .../tests/segment_reduction_ops_test.py       |  6 +++---
 tensorflow/compiler/tf2xla/lib/scatter.cc     | 19 +++++++++++--------
 2 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/tensorflow/compiler/tests/segment_reduction_ops_test.py b/tensorflow/compiler/tests/segment_reduction_ops_test.py
index 23bc39cf3f..4a9c0e7471 100644
--- a/tensorflow/compiler/tests/segment_reduction_ops_test.py
+++ b/tensorflow/compiler/tests/segment_reduction_ops_test.py
@@ -63,10 +63,10 @@ class SegmentReductionOpsTest(XLATestCase):
   def testUnsortedSegmentSum1DIndices1DDataNegativeIndices(self):
     for dtype in self.numeric_types:
       self.assertAllClose(
-          np.array([0, 3, 2, 5], dtype=dtype),
+          np.array([6, 3, 0, 6], dtype=dtype),
           self.UnsortedSegmentSum(
-              np.array([0, 1, 2, 3, 4, 5], dtype=dtype),
-              np.array([3, -1, 2, 1, -1, 3], dtype=np.int32), 4))
+              np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
+              np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
 
   def testUnsortedSegmentSum1DIndices2DDataDisjoint(self):
     for dtype in self.numeric_types:
diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc
index 6009243f97..45699233ea 100644
--- a/tensorflow/compiler/tf2xla/lib/scatter.cc
+++ b/tensorflow/compiler/tf2xla/lib/scatter.cc
@@ -141,6 +141,8 @@ xla::StatusOr<xla::ComputationDataHandle> XlaScatter(
                                 body_builder->ConstantR0<bool>(true),
                                 xla::CreateScalarAndComputation(body_builder));
 
+    // Make the index in bounds to prevent implementation defined behavior.
+    index = body_builder->Max(index, zero_index);
     index = body_builder->Pad(
         index, zero_index,
         xla::MakeEdgePaddingConfig({{0, buffer_shape_post_axes.size()}}));
@@ -157,8 +159,8 @@ xla::StatusOr<xla::ComputationDataHandle> XlaScatter(
     auto update = body_builder->DynamicSlice(updates, updates_offset,
                                              flat_updates_slice_shape);
 
-    // Unflatten the major (iteration) dimensions of the slice to their original
-    // shape.
+    // Unflatten the major (iteration) dimensions of the slice to their
+    // original shape.
     std::vector<int64> updates_slice_shape(num_index_dims, 1);
     updates_slice_shape.insert(updates_slice_shape.end(),
                                buffer_shape_post_axes.begin(),
@@ -167,15 +169,16 @@ xla::StatusOr<xla::ComputationDataHandle> XlaScatter(
 
     // Apply the update to the buffer. If there is a combiner, use it to merge
     // the current values with the update.
+    auto current_value =
+        body_builder->DynamicSlice(buffer, index, updates_slice_shape);
     if (combiner) {
-      auto current_value =
-          body_builder->DynamicSlice(buffer, index, updates_slice_shape);
       update = combiner(current_value, update, body_builder);
     }
-    // Apply the update if it is in range.
-    buffer = body_builder->Select(
-        index_in_range, body_builder->DynamicUpdateSlice(buffer, update, index),
-        buffer);
+    // Use the current value instead of the update if the index is out of
+    // bounds.
+    update = body_builder->Select(index_in_range, update, current_value);
+    // Apply the update.
+    buffer = body_builder->DynamicUpdateSlice(buffer, update, index);
 
     return std::vector<xla::ComputationDataHandle>{indices, updates, buffer};
   };
-- 
GitLab


From f730a0259838558ccefcd48ad132fe039dd2ee6d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Feb 2018 05:54:27 -0800
Subject: [PATCH 050/575] Remove experimental C API from srcs rule as it
 requires other sources

PiperOrigin-RevId: 186213207
---
 tensorflow/c/BUILD | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD
index 85cfa98908..5dfb743681 100644
--- a/tensorflow/c/BUILD
+++ b/tensorflow/c/BUILD
@@ -28,7 +28,11 @@ filegroup(
             "*.cc",
             "*.h",
         ],
-        exclude = ["*test*"],
+        exclude = [
+            "c_api_experimental.cc",
+            "c_api_experimental.h",
+            "*test*",
+        ],
     ),
     visibility = ["//visibility:public"],
 )
-- 
GitLab


From 943a21fcdc1c48c8e95d872911ad52b13f0c037d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 19 Feb 2018 06:14:10 -0800
Subject: [PATCH 051/575] Share Variable objects among collections when
 importing metagraphs.

This mirrors the behavior of usual graph construction where a Variable object is added to multiple collections.

PiperOrigin-RevId: 186214551
---
 tensorflow/python/framework/meta_graph.py     | 23 +++++++++---
 .../python/framework/meta_graph_test.py       | 35 ++++++++++++++++---
 2 files changed, 49 insertions(+), 9 deletions(-)

diff --git a/tensorflow/python/framework/meta_graph.py b/tensorflow/python/framework/meta_graph.py
index 8c03a5f19d..4c1bd736d7 100644
--- a/tensorflow/python/framework/meta_graph.py
+++ b/tensorflow/python/framework/meta_graph.py
@@ -741,6 +741,7 @@ def import_scoped_meta_graph(meta_graph_or_file,
         producer_op_list=producer_op_list)
 
     # Restores all the other collections.
+    variable_objects = {}
     for key, col_def in sorted(meta_graph_def.collection_def.items()):
       # Don't add unbound_inputs to the new graph.
       if key == unbound_inputs_col_name:
@@ -756,11 +757,23 @@ def import_scoped_meta_graph(meta_graph_or_file,
       from_proto = ops.get_from_proto_function(key)
       if from_proto and kind == "bytes_list":
         proto_type = ops.get_collection_proto_type(key)
-        for value in col_def.bytes_list.value:
-          proto = proto_type()
-          proto.ParseFromString(value)
-          graph.add_to_collection(
-              key, from_proto(proto, import_scope=scope_to_prepend_to_names))
+        if key in ops.GraphKeys._VARIABLE_COLLECTIONS:  # pylint: disable=protected-access
+          for value in col_def.bytes_list.value:
+            variable = variable_objects.get(value, None)
+            if variable is None:
+              proto = proto_type()
+              proto.ParseFromString(value)
+              variable = from_proto(
+                  proto, import_scope=scope_to_prepend_to_names)
+              variable_objects[value] = variable
+            graph.add_to_collection(key, variable)
+        else:
+          for value in col_def.bytes_list.value:
+            proto = proto_type()
+            proto.ParseFromString(value)
+            graph.add_to_collection(
+                key, from_proto(
+                    proto, import_scope=scope_to_prepend_to_names))
       else:
         field = getattr(col_def, kind)
         if key in _COMPAT_COLLECTION_LIST:
diff --git a/tensorflow/python/framework/meta_graph_test.py b/tensorflow/python/framework/meta_graph_test.py
index f2f1e83da1..19dcd6a1b3 100644
--- a/tensorflow/python/framework/meta_graph_test.py
+++ b/tensorflow/python/framework/meta_graph_test.py
@@ -261,6 +261,29 @@ class SimpleMetaGraphTest(test.TestCase):
       self.assertEqual(node_def.attr["attr_1"].i, 1)
       self.assertTrue(meta_graph_def.meta_info_def.stripped_default_attrs)
 
+  def testVariableObjectsAreSharedAmongCollections(self):
+    with ops.Graph().as_default() as graph1:
+      v = variables.Variable(3.0)
+      # A single instance of Variable is shared among the collections:
+      global_vars = graph1.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+      trainable_vars = graph1.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertEqual(len(global_vars), 1)
+      self.assertEqual(len(trainable_vars), 1)
+      self.assertIs(global_vars[0], trainable_vars[0])
+      self.assertIs(v, global_vars[0])
+
+    orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(graph=graph1)
+    del graph1  # To avoid accidental references in code involving graph2.
+
+    with ops.Graph().as_default() as graph2:
+      meta_graph.import_scoped_meta_graph(orig_meta_graph)
+      global_vars = graph2.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+      trainable_vars = graph2.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
+      self.assertEqual(len(global_vars), 1)
+      self.assertEqual(len(trainable_vars), 1)
+      # A single instance of Variable is shared among the collections:
+      self.assertIs(global_vars[0], trainable_vars[0])
+
 
 @test_util.with_c_api
 class ScopedMetaGraphTest(test.TestCase):
@@ -883,21 +906,25 @@ class ExportImportAcrossScopesTest(test.TestCase):
         graph_fn(use_resource=use_resource)
 
       if use_resource:
-        # Bringing in a collection that contains ResourceVariables adds ops
-        # to the graph, so mimic the same behavior.
+        # Bringing in collections that contain ResourceVariables will adds ops
+        # to the graph the first time a variable is encountered, so mimic the
+        # same behavior.
+        seen_variables = set()
         for collection_key in sorted([
             ops.GraphKeys.GLOBAL_VARIABLES,
             ops.GraphKeys.TRAINABLE_VARIABLES,
         ]):
           for var in expected_graph.get_collection(collection_key):
-            var._read_variable_op()
+            if var not in seen_variables:
+              var._read_variable_op()
+              seen_variables.add(var)
 
     result = meta_graph.export_scoped_meta_graph(graph=imported_graph)[0]
     expected = meta_graph.export_scoped_meta_graph(graph=expected_graph)[0]
 
     if use_resource:
       # Clear all shared_name attributes before comparing, since they are
-      # supposed to be orthogonal to scopes.
+      # orthogonal to scopes and are not updated on export/import.
       for meta_graph_def in [result, expected]:
         for node in meta_graph_def.graph_def.node:
           shared_name_attr = "shared_name"
-- 
GitLab


From f4fb90c3cb20cc636db90af4bde08c96ae619696 Mon Sep 17 00:00:00 2001
From: terrytangyuan <terrytangyuan@gmail.com>
Date: Mon, 19 Feb 2018 11:14:50 -0500
Subject: [PATCH 052/575] TFTS: Cleanup remaining TODOs in timeseries head

---
 .../timeseries/python/timeseries/head.py      | 55 ++++++++-----------
 1 file changed, 24 insertions(+), 31 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py
index f0330bfbbd..6b526e5450 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/head.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/head.py
@@ -71,12 +71,26 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
     self.input_statistics_generator = input_statistics_generator
     self._name = name
 
-  def _train_ops(self, features):
-    """Add training ops to the graph."""
-    with variable_scope.variable_scope("model"):
+  @property
+  def name(self):
+    return self._name
+
+  def create_loss(self, features, mode, logits=None, labels=None):
+    """See `_Head`."""
+    with variable_scope.variable_scope("model", reuse=variable_scope.AUTO_REUSE):
       model_outputs = self.state_manager.define_loss(
-          self.model, features, estimator_lib.ModeKeys.TRAIN)
+          self.model, features, mode)
+    return model_outputs
+
+  @property
+  def logits_dimension(self):
+    """See `_Head`."""
+    return 1
 
+  def _train_ops(self, features):
+    """Add training ops to the graph."""
+    mode = estimator_lib.ModeKeys.TRAIN
+    model_outputs = self.create_loss(features, mode)
     train_op = optimizers.optimize_loss(
         model_outputs.loss,
         global_step=training_util.get_global_step(),
@@ -85,31 +99,13 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
         learning_rate=None)
     return estimator_lib.EstimatorSpec(
         loss=model_outputs.loss,
-        mode=estimator_lib.ModeKeys.TRAIN,
+        mode=mode,
         train_op=train_op)
 
-  # TODO(terrytangyuan): suffix summary and metrics keys by `"/" + name`
-  @property
-  def name(self):
-    return self._name
-
-  # TODO(terrytangyuan): unused for now. Need to decouple
-  # `state_manager.define_loss` to satisfy the extendable return signature of
-  # `_Head.create_loss`.
-  def create_loss(self, features, mode, logits, labels):
-    """See `_Head`."""
-    return None
-
-  # TODO(terrytangyuan): check label dimension
-  @property
-  def logits_dimension(self):
-    return None
-
   def _evaluate_ops(self, features):
     """Add ops for evaluation (aka filtering) to the graph."""
-    with variable_scope.variable_scope("model"):
-      model_outputs = self.state_manager.define_loss(
-          self.model, features, estimator_lib.ModeKeys.EVAL)
+    mode = estimator_lib.ModeKeys.EVAL
+    model_outputs = self.create_loss(features, mode)
     metrics = {}
     # Just output in-sample predictions for the last chunk seen
     for prediction_key, prediction_value in model_outputs.predictions.items():
@@ -122,7 +118,7 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
                                 model_outputs.end_state))
     return estimator_lib.EstimatorSpec(
         loss=model_outputs.loss,
-        mode=estimator_lib.ModeKeys.EVAL,
+        mode=mode,
         eval_metric_ops=metrics,
         predictions={})
 
@@ -139,10 +135,7 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
     """Add ops for serving to the graph."""
     with variable_scope.variable_scope("model"):
       prediction_outputs = self.model.predict(features=features)
-    with variable_scope.variable_scope("model", reuse=True):
-      filtering_outputs = self.state_manager.define_loss(
-          self.model, features, estimator_lib.ModeKeys.EVAL)
-
+    filtering_outputs = self.create_loss(features, estimator_lib.ModeKeys.EVAL)
     return estimator_lib.EstimatorSpec(
         mode=estimator_lib.ModeKeys.PREDICT,
         export_outputs={
@@ -191,7 +184,7 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
 
   def create_estimator_spec(self, features, mode, labels=None):
     """Performs basic error checking and returns an EstimatorSpec."""
-    with ops.name_scope("head"):
+    with ops.name_scope(self._name, "head"):
       if labels:
         raise ValueError(
             "The model received a `labels` dictionary, which is "
-- 
GitLab


From 39f4ea97f4e903d81bfe093339bf220ce7dd9256 Mon Sep 17 00:00:00 2001
From: terrytangyuan <terrytangyuan@gmail.com>
Date: Mon, 19 Feb 2018 11:36:05 -0500
Subject: [PATCH 053/575] TFTS: Added summary for loss

---
 tensorflow/contrib/timeseries/python/timeseries/BUILD   | 1 +
 tensorflow/contrib/timeseries/python/timeseries/head.py | 6 ++++++
 2 files changed, 7 insertions(+)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index fff972c1f3..25a06b36db 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -145,6 +145,7 @@ py_library(
         "//tensorflow/python/estimator:estimator_py",
         "//tensorflow/python/estimator:export",
         "//tensorflow/python/estimator:head",
+        "//tensorflow/python/estimator:metric_keys"
     ],
 )
 
diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py
index 6b526e5450..8de4b38f9b 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/head.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/head.py
@@ -26,6 +26,7 @@ from tensorflow.contrib.timeseries.python.timeseries import feature_keys
 
 from tensorflow.python.estimator import estimator_lib
 from tensorflow.python.estimator.canned import head as head_lib
+from tensorflow.python.estimator.canned import metric_keys
 from tensorflow.python.estimator.export import export_lib
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -75,11 +76,16 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
   def name(self):
     return self._name
 
+  # TODO(terrytangyuan): consolidate model_outputs and _Head.LossSpec once _Head.create_loss
+  # becomes extendable
   def create_loss(self, features, mode, logits=None, labels=None):
     """See `_Head`."""
     with variable_scope.variable_scope("model", reuse=variable_scope.AUTO_REUSE):
       model_outputs = self.state_manager.define_loss(
           self.model, features, mode)
+      summary.scalar(
+        head_lib._summary_key(self._name, metric_keys.LOSS),
+        model_outputs.loss)
     return model_outputs
 
   @property
-- 
GitLab


From a9323002c99341f5ae1f9f24b791e0dea1e49870 Mon Sep 17 00:00:00 2001
From: terrytangyuan <terrytangyuan@gmail.com>
Date: Mon, 19 Feb 2018 11:44:34 -0500
Subject: [PATCH 054/575] Fixed missing imports

---
 tensorflow/contrib/timeseries/python/timeseries/BUILD   | 1 +
 tensorflow/contrib/timeseries/python/timeseries/head.py | 3 ++-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index 25a06b36db..862a05fa9f 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -140,6 +140,7 @@ py_library(
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:state_ops",
+        "//tensorflow/python:summary",
         "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python/estimator:estimator_py",
diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py
index 8de4b38f9b..7633ca088a 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/head.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/head.py
@@ -36,6 +36,7 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.util import nest
+from tensorflow.python.summary import summary
 
 
 def time_series_regression_head(model,
@@ -84,7 +85,7 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
       model_outputs = self.state_manager.define_loss(
           self.model, features, mode)
       summary.scalar(
-        head_lib._summary_key(self._name, metric_keys.LOSS),
+        head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS),
         model_outputs.loss)
     return model_outputs
 
-- 
GitLab


From a4f1478134cdbf73f0ad7eda3e73407135a54566 Mon Sep 17 00:00:00 2001
From: Michael Zhou <michaelchouqj@gmail.com>
Date: Mon, 19 Feb 2018 12:42:33 -0800
Subject: [PATCH 055/575] make "smart_cond" api public and reusable (#13954)

* make smart_cond api public and reusable

* adjust function comment space

* change `smart_cond` naming schema consistent with `cond`

* Move `smart_cond` and `smart_constant_value` to tf.contrib.framework

* resolve loop import

* remove redundant constant_value

* fix function name typo

* Fix function parameter name usage

* Fix function parameter name usage(2)

* To support 0/1 interger pred

* check integer value after type check

* Lint fixes
---
 tensorflow/contrib/crf/python/ops/crf.py      |  8 +-
 tensorflow/contrib/framework/__init__.py      |  2 +
 tensorflow/python/layers/utils.py             | 77 ++++++++-----------
 tensorflow/python/ops/control_flow_ops.py     | 56 ++++++++++++++
 .../python/ops/control_flow_ops_test.py       | 38 +++++++++
 5 files changed, 133 insertions(+), 48 deletions(-)

diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py
index faa78769b9..a30bf06396 100644
--- a/tensorflow/contrib/crf/python/ops/crf.py
+++ b/tensorflow/contrib/crf/python/ops/crf.py
@@ -105,8 +105,8 @@ def crf_sequence_score(inputs, tag_indices, sequence_lengths,
   return utils.smart_cond(
       pred=math_ops.equal(inputs.shape[1].value or array_ops.shape(inputs)[1],
                           1),
-      fn1=_single_seq_fn,
-      fn2=_multi_seq_fn)
+      true_fn=_single_seq_fn,
+      false_fn=_multi_seq_fn)
 
 
 def crf_log_norm(inputs, sequence_lengths, transition_params):
@@ -513,5 +513,5 @@ def crf_decode(potentials, transition_params, sequence_length):
   return utils.smart_cond(
       pred=math_ops.equal(
           potentials.shape[1].value or array_ops.shape(potentials)[1], 1),
-      fn1=_single_seq_fn,
-      fn2=_multi_seq_fn)
+      true_fn=_single_seq_fn,
+      false_fn=_multi_seq_fn)
diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py
index a49d42cd52..4746cfe072 100644
--- a/tensorflow/contrib/framework/__init__.py
+++ b/tensorflow/contrib/framework/__init__.py
@@ -104,6 +104,8 @@ from tensorflow.contrib.framework.python.ops import *
 
 from tensorflow.python.framework.ops import prepend_name_scope
 from tensorflow.python.framework.ops import strip_name_scope
+from tensorflow.python.ops.control_flow_ops import smart_cond
+from tensorflow.python.ops.control_flow_ops import smart_constant_value
 
 from tensorflow.python.framework.tensor_spec import BoundedTensorSpec
 from tensorflow.python.framework.tensor_spec import TensorSpec
diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py
index 1bbf4e6dff..1a0f211cf3 100644
--- a/tensorflow/python/layers/utils.py
+++ b/tensorflow/python/layers/utils.py
@@ -178,67 +178,56 @@ def deconv_output_length(input_length, filter_size, padding, stride):
   return input_length
 
 
-def smart_cond(pred, fn1, fn2, name=None):
-  """Return either `fn1()` or `fn2()` based on the boolean predicate `pred`.
+def smart_cond(pred, true_fn=None, false_fn=None, name=None):
+  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
 
-  If `pred` is a bool or has a constant value, we return either `fn1()`
-  or `fn2()`, otherwise we use `tf.cond` to dynamically route to both.
+  If `pred` is a bool or has a constant value, we return either `true_fn()`
+  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
 
   Arguments:
-    pred: A scalar determining whether to return the result of `fn1` or `fn2`.
-    fn1: The callable to be performed if pred is true.
-    fn2: The callable to be performed if pred is false.
+    pred: A scalar determining whether to return the result of `true_fn` or
+      `false_fn`.
+    true_fn: The callable to be performed if pred is true.
+    false_fn: The callable to be performed if pred is false.
     name: Optional name prefix when using `tf.cond`.
 
   Returns:
-    Tensors returned by the call to either `fn1` or `fn2`.
+    Tensors returned by the call to either `true_fn` or `false_fn`.
 
   Raises:
-    TypeError: If `fn1` or `fn2` is not callable.
+    TypeError: If `true_fn` or `false_fn` is not callable.
   """
-  if not callable(fn1):
-    raise TypeError('`fn1` must be callable.')
-  if not callable(fn2):
-    raise TypeError('`fn2` must be callable.')
-
-  pred_value = constant_value(pred)
-  if pred_value is not None:
-    if pred_value:
-      return fn1()
-    else:
-      return fn2()
-  else:
-    return control_flow_ops.cond(pred, true_fn=fn1, false_fn=fn2, name=name)
+  if isinstance(pred, variables.Variable):
+    return control_flow_ops.cond(pred, true_fn=true_fn, false_fn=false_fn,
+                                 name=name)
+  return control_flow_ops.smart_cond(pred, true_fn=true_fn,
+                                     false_fn=false_fn, name=name)
 
 
 def constant_value(pred):
   """Return the bool value for `pred`, or None if `pred` had a dynamic value.
 
-  Arguments:
-    pred: A scalar, either a Python bool or a TensorFlow boolean variable
-      or tensor, or the Python integer 1 or 0.
+    Arguments:
+      pred: A scalar, either a Python bool or a TensorFlow boolean variable
+        or tensor, or the Python integer 1 or 0.
 
-  Returns:
-    True or False if `pred` has a constant boolean value, None otherwise.
+    Returns:
+      True or False if `pred` has a constant boolean value, None otherwise.
 
-  Raises:
-    TypeError: If `pred` is not a Variable, Tensor or bool.
-  """
+    Raises:
+      TypeError: If `pred` is not a Variable, Tensor or bool, or Python
+        interger 1 or 0.
+    """
   # Allow integer booleans.
-  if pred == 0:
-    pred = False
-  elif pred == 1:
-    pred = True
-
-  if isinstance(pred, bool):
-    pred_value = pred
-  elif isinstance(pred, variables.Variable):
-    pred_value = None
-  elif isinstance(pred, ops.Tensor):
-    pred_value = tensor_util.constant_value(pred)
-  else:
-    raise TypeError('`pred` must be a Tensor, a Variable, or a Python bool.')
-  return pred_value
+  if isinstance(pred, int):
+    if pred == 1:
+      pred = True
+    elif pred == 0:
+      pred = False
+  
+  if isinstance(pred, variables.Variable):
+    return None
+  return control_flow_ops.smart_constant_value(pred)
 
 
 def object_list_uid(object_list):
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 179f38f035..740a67fc7e 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -23,6 +23,7 @@ See the @{$python/control_flow_ops} guide.
 @@no_op
 @@count_up_to
 @@cond
+@@smart_cond
 @@case
 @@while_loop
 @@logical_and
@@ -2123,6 +2124,61 @@ def cond(pred,
 # pylint: enable=redefined-outer-name
 
 
+def smart_cond(pred, true_fn=None, false_fn=None, name=None):
+  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
+
+  If `pred` is a bool or has a constant value, we return either `true_fn()`
+  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
+
+  Arguments:
+    pred: A scalar determining whether to return the result of `true_fn` or
+      `false_fn`.
+    true_fn: The callable to be performed if pred is true.
+    false_fn: The callable to be performed if pred is false.
+    name: Optional name prefix when using `tf.cond`.
+
+  Returns:
+    Tensors returned by the call to either `true_fn` or `false_fn`.
+
+  Raises:
+    TypeError: If `true_fn` or `false_fn` is not callable.
+  """
+  if not callable(true_fn):
+    raise TypeError('`true_fn` must be callable.')
+  if not callable(false_fn):
+    raise TypeError('`false_fn` must be callable.')
+
+  pred_value = smart_constant_value(pred)
+  if pred_value is not None:
+    if pred_value:
+      return true_fn()
+    else:
+      return false_fn()
+  else:
+    return cond(pred, true_fn=true_fn, false_fn=false_fn, name=name)
+
+
+def smart_constant_value(pred):
+  """Return the bool value for `pred`, or None if `pred` had a dynamic value.
+
+  Arguments:
+    pred: A scalar, either a Python bool or tensor.
+
+  Returns:
+    True or False if `pred` has a constant boolean value, None otherwise.
+
+  Raises:
+    TypeError: If `pred` is not a Tensor or bool.
+  """
+  if isinstance(pred, bool):
+    pred_value = pred
+  elif isinstance(pred, ops.Tensor):
+    pred_value = tensor_util.constant_value(pred)
+  else:
+    raise TypeError('`pred` must be a Tensor or a Python bool.')
+  return pred_value
+
+
 def _resource_safe_shape(t):
   """Returns the shape of t or the variable it points to."""
   if t.dtype == dtypes.resource:
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index f22f3059d1..7775133348 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -349,6 +349,44 @@ class SwitchTestCase(test_util.TensorFlowTestCase):
       self.assertEquals(grad_x_false.eval(), 0.)
 
 
+@test_util.with_c_api
+class SmartCondTest(test_util.TensorFlowTestCase):
+
+  def testSmartCondTrue(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(2)
+        y = constant_op.constant(5)
+        z = control_flow_ops.smart_cond(
+            True, lambda: math_ops.multiply(x, 16),
+            lambda: math_ops.multiply(y, 5))
+        self.assertEqual(z.eval(), 32)
+
+  def testSmartCondFalse(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(4)
+        y = constant_op.constant(3)
+        z = control_flow_ops.smart_cond(
+            False, lambda: math_ops.multiply(x, 16),
+            lambda: math_ops.multiply(y, 3))
+        self.assertEqual(z.eval(), 9)
+
+  def testSmartCondMissingArg1(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(1)
+        with self.assertRaises(TypeError):
+          control_flow_ops.smart_cond(True, false_fn=lambda: x)
+
+  def testSmartCondMissingArg2(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(1)
+        with self.assertRaises(TypeError):
+          control_flow_ops.smart_cond(True, lambda: x)
+
+
 @test_util.with_c_api
 class CondTest(test_util.TensorFlowTestCase):
 
-- 
GitLab


From f16e1ea3f28ad69131cd69db189a1ec88f2b8335 Mon Sep 17 00:00:00 2001
From: Deron Eriksson <deron@us.ibm.com>
Date: Mon, 19 Feb 2018 14:06:04 -0800
Subject: [PATCH 056/575] Fix typos in Operation Semantics docs

Fix MathJax beta display.
Update '( assuming' to '(assuming'.
Update 'in a the first' to 'in the first'.
Change 'nop' to 'no-op' to match other occurrences.
Misc other minor updates (periods, hyphen, etc).
---
 .../performance/xla/operation_semantics.md    | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index 5431572db8..1d4a657b53 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -123,7 +123,7 @@ Normalizes an array across batch and spatial dimensions.
 | `scale`         | `ComputationDataHandle` | 1 dimensional array              |
 :                 :                         : (\\(\gamma\\))                   :
 | `offset`        | `ComputationDataHandle` | 1 dimensional array              |
-:                 :                         : (\\(\beta\\ )                    :
+:                 :                         : (\\(\beta\\))                    :
 | `epsilon`       | `float`                 | Epsilon value (\\(\epsilon\\))   |
 | `feature_index` | `int64`                 | Index to feature dimension       |
 :                 :                         : in `operand`                     :
@@ -135,8 +135,8 @@ element in `operand`. The `feature_index` must be a valid index for the feature
 dimension in `operand`.
 
 The algorithm goes as follows for each batch in `operand` \\(x\\) that
-contains `m` elements with `w` and `h` as the size of spatial dimensions (
-assuming `operand` is an 4 dimensional array):
+contains `m` elements with `w` and `h` as the size of spatial dimensions
+(assuming `operand` is an 4 dimensional array):
 
 - Calculates batch mean \\(\mu_l\\) for each feature `l` in feature dimension:
 \\(\mu_l=\frac{1}{mwh}\sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h x_{ijkl}\\)
@@ -170,7 +170,7 @@ Similar to a `tf.bitcast` in TensorFlow, performs an element-wise bitcast
 operation from a data shape to a target shape. The dimensions must match, and
 the conversion is an element-wise one; e.g. `s32` elements become `f32` elements
 via bitcast routine. Bitcast is implemented as a low-level cast, so machines
-with different floating point representations will give different results.
+with different floating-point representations will give different results.
 
 <b> `BitcastConvertType(operand, new_element_type)` </b>
 
@@ -351,7 +351,7 @@ each other) and contains the arguments in the order that they were specified.
 :             :                         : concatenated between the `operands`. :
 
 With the exception of `dimension` all dimensions must be the same. This is
-because XLA does not support "ragged" arrays Also note that rank-0 values
+because XLA does not support "ragged" arrays. Also note that rank-0 values
 cannot be concatenated (as it's impossible to name the dimension along which the
 concatenation occurs).
 
@@ -468,7 +468,7 @@ filter/kernel/window. The dimensions are, in this order:
     window that moves across the base area.
 
 The `window_strides` argument specifies the stride of the convolutional window
-in the spatial dimensions. For example, if the stride in a the first spatial
+in the spatial dimensions. For example, if the stride in the first spatial
 dimension is 3, then the window can only be placed at coordinates where the
 first spatial index is divisible by 3.
 
@@ -942,7 +942,7 @@ expand the rank of the lower-rank operand up to the rank of the higher-rank
 operand. `broadcast_dimensions` maps the dimensions of the lower-rank shape to
 the dimensions of the higher-rank shape. The unmapped dimensions of the expanded
 shape are filled with dimensions of size one. Degenerate-dimension broadcasting
-then broadcasts the shapes along these degenerate dimension to equalize the
+then broadcasts the shapes along these degenerate dimensions to equalize the
 shapes of both operands. The semantics are described in detail on the
 @{$broadcasting$broadcasting page}.
 
@@ -1081,7 +1081,7 @@ result2 = while (condition, init = result1) {
 ```
 
 Nested tuple shapes are not supported. For an empty tuple shape, the Infeed
-operation is effectively a nop and proceeds without reading any data from the
+operation is effectively a no-op and proceeds without reading any data from the
 Infeed of the device.
 
 > Note: We plan to allow multiple Infeed operations without a total order, in
@@ -1144,7 +1144,7 @@ dimension.
 
 `PaddingConfig` is a repeated field of `PaddingConfigDimension`, which contains
 three fields for each dimension: `edge_padding_low`, `edge_padding_high`, and
-`interior_padding`. `edge_padding_low` and `edge_padding_high` specifies the
+`interior_padding`. `edge_padding_low` and `edge_padding_high` specify the
 amount of padding added at the low-end (next to index 0) and the high-end (next
 to the highest index) of each dimension respectively. The amount of edge padding
 can be negative -- the absolute value of negative padding indicates the number
@@ -1153,8 +1153,8 @@ the amount of padding added between any two elements in each dimension. Interior
 padding occurs logically before edge padding, so in the case of negative edge
 padding elements are removed from the interior-padded operand. This operation is
 a no-op if the edge padding pairs are all (0, 0) and the interior padding values
-are all 0. Figure below shows examples of different `edge_padding` and
-`interior_padding` values for a two dimensional array.
+are all 0. The figure below shows examples of different `edge_padding` and
+`interior_padding` values for a two-dimensional array.
 
 <div style="width:95%; margin:auto; margin-bottom:10px; margin-top:20px;">
   <img style="width:100%" src="https://www.tensorflow.org/images/ops_pad.png">
-- 
GitLab


From eb15dbaf130e4f87727233cced999d19fab6435e Mon Sep 17 00:00:00 2001
From: Henry Spivey <spivotron@gmail.com>
Date: Mon, 19 Feb 2018 14:33:55 -0800
Subject: [PATCH 057/575] Update mobile_intro.md

Found a grammatical/spelling error while reading the documentation.
---
 tensorflow/docs_src/mobile/mobile_intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md
index 17dbf1c3e6..c406c1852f 100644
--- a/tensorflow/docs_src/mobile/mobile_intro.md
+++ b/tensorflow/docs_src/mobile/mobile_intro.md
@@ -235,7 +235,7 @@ TensorFlow [on Github](https://github.com/tensorflow/models) that you can look
 through. Lean towards the simplest model you can find, and try to get started as
 soon as you have even a small amount of labelled data, since you’ll get the best
 results when you’re able to iterate quickly. The shorter the time it takes to
-try training a model and running it in s real application, the better overall
+try training a model and running it in it's real application, the better overall
 results you’ll see. It’s common for an algorithm to get great training accuracy
 numbers but then fail to be useful within a real application because there’s a
 mismatch between the dataset and real usage. Prototype end-to-end usage as soon
-- 
GitLab


From 84bf20f7f6483e3cf42aaf5b522b41b43a066f9f Mon Sep 17 00:00:00 2001
From: Danny Goodman <goodman.danny@gmail.com>
Date: Mon, 19 Feb 2018 17:09:45 -0800
Subject: [PATCH 058/575] fix typo

---
 tensorflow/python/ops/distributions/multinomial.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index 26b5c5aef9..4ae67a009b 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -238,7 +238,7 @@ class Multinomial(distribution.Distribution):
     n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32)
     k = self.event_shape_tensor()[0]
 
-    # boardcast the total_count and logits to same shape
+    # broadcast the total_count and logits to same shape
     n_draws = array_ops.ones_like(
         self.logits[..., 0], dtype=n_draws.dtype) * n_draws
     logits = array_ops.ones_like(
-- 
GitLab


From ff6c4de87cbb23be97c4a10e9cb37fe13d2cb3a4 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Mon, 19 Feb 2018 17:36:56 -0800
Subject: [PATCH 059/575] [tf.data] Delete contrib version of dataset_ops.py,
 which was re-added by a merge from GitHub.

PiperOrigin-RevId: 186249376
---
 .../contrib/data/python/ops/dataset_ops.py    | 691 ------------------
 1 file changed, 691 deletions(-)
 delete mode 100644 tensorflow/contrib/data/python/ops/dataset_ops.py

diff --git a/tensorflow/contrib/data/python/ops/dataset_ops.py b/tensorflow/contrib/data/python/ops/dataset_ops.py
deleted file mode 100644
index bb6b049694..0000000000
--- a/tensorflow/contrib/data/python/ops/dataset_ops.py
+++ /dev/null
@@ -1,691 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Python wrappers for Datasets and Iterators."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.data.python.ops import batching
-from tensorflow.contrib.data.python.ops import enumerate_ops
-from tensorflow.contrib.data.python.ops import error_ops
-from tensorflow.contrib.data.python.ops import grouping
-from tensorflow.python.data.ops import dataset_ops
-from tensorflow.python.data.util import nest
-from tensorflow.python.ops import gen_dataset_ops
-from tensorflow.python.ops import gen_io_ops
-from tensorflow.python.util import deprecation
-
-
-class Dataset(dataset_ops.Dataset):
-  """Represents a potentially large set of elements.
-
-  A `Dataset` can be used to represent an input pipeline as a
-  collection of elements (nested structures of tensors) and a "logical
-  plan" of transformations that act on those elements.
-  """
-
-  def __init__(self, dataset):
-    super(Dataset, self).__init__()
-    self._dataset = dataset
-
-  @deprecation.deprecated(None, "Use `ds._as_variant_tensor()`.")
-  def make_dataset_resource(self):
-    return self._as_variant_tensor()
-
-  def _as_variant_tensor(self):
-    return self._dataset._as_variant_tensor()  # pylint: disable=protected-access
-
-  @property
-  def output_classes(self):
-    return self._dataset.output_classes
-
-  @property
-  def output_shapes(self):
-    return self._dataset.output_shapes
-
-  @property
-  def output_types(self):
-    return self._dataset.output_types
-
-  @staticmethod
-  @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensors()`.")
-  def from_tensors(tensors):
-    """Creates a `Dataset` with a single element, comprising the given tensors.
-
-    Args:
-      tensors: A nested structure of tensors.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.TensorDataset(tensors))
-
-  @staticmethod
-  @deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensor_slices()`.")
-  def from_tensor_slices(tensors):
-    """Creates a `Dataset` whose elements are slices of the given tensors.
-
-    Args:
-      tensors: A nested structure of tensors, each having the same size in the
-        0th dimension.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.TensorSliceDataset(tensors))
-
-  @staticmethod
-  @deprecation.deprecated(None,
-                          "Use `tf.data.Dataset.from_sparse_tensor_slices()`.")
-  def from_sparse_tensor_slices(sparse_tensor):
-    """Splits each rank-N `tf.SparseTensor` in this dataset row-wise.
-
-    Args:
-      sparse_tensor: A `tf.SparseTensor`.
-
-    Returns:
-      A `Dataset` of rank-(N-1) sparse tensors.
-    """
-    return Dataset(dataset_ops.SparseTensorSliceDataset(sparse_tensor))
-
-  @staticmethod
-  @deprecation.deprecated(None, "Use `tf.data.Dataset.from_generator()`.")
-  def from_generator(generator, output_types, output_shapes=None):
-    """Creates a `Dataset` whose elements are generated by `generator`.
-
-    The `generator` argument must be a callable object that returns
-    an object that support the `iter()` protocol (e.g. a generator function).
-    The elements generated by `generator` must be compatible with the given
-    `output_types` and (optional) `output_shapes` arguments.
-
-    For example:
-
-    ```python
-    import itertools
-
-    def gen():
-      for i in itertools.count(1):
-        yield (i, [1] * i)
-
-    ds = Dataset.from_generator(
-        gen, (tf.int64, tf.int64), (tf.TensorShape([]), tf.TensorShape([None])))
-    value = ds.make_one_shot_iterator().get_next()
-
-    sess.run(value)  # (1, array([1]))
-    sess.run(value)  # (2, array([1, 1]))
-    ```
-
-    Args:
-      generator: A callable object that takes no arguments and returns an
-        object that supports the `iter()` protocol.
-      output_types: A nested structure of `tf.DType` objects corresponding to
-        each component of an element yielded by `generator`.
-      output_shapes: (Optional.) A nested structure of `tf.TensorShape`
-        objects corresponding to each component of an element yielded by
-        `generator`.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(
-        dataset_ops.Dataset.from_generator(generator, output_types,
-                                           output_shapes))
-
-  @staticmethod
-  @deprecation.deprecated(None, "Use `tf.data.Dataset.range()`.")
-  def range(*args):
-    """Creates a `Dataset` of a step-separated range of values.
-
-    For example:
-
-    ```python
-    Dataset.range(5) == [0, 1, 2, 3, 4]
-    Dataset.range(2, 5) == [2, 3, 4]
-    Dataset.range(1, 5, 2) == [1, 3]
-    Dataset.range(1, 5, -2) == []
-    Dataset.range(5, 1) == []
-    Dataset.range(5, 1, -2) == [5, 3]
-    ```
-
-    Args:
-      *args: follow same semantics as python's xrange.
-        len(args) == 1 -> start = 0, stop = args[0], step = 1
-        len(args) == 2 -> start = args[0], stop = args[1], step = 1
-        len(args) == 3 -> start = args[0], stop = args[1, stop = args[2]
-
-    Returns:
-      A `RangeDataset`.
-
-    Raises:
-      ValueError: if len(args) == 0.
-    """
-    return Dataset(dataset_ops.RangeDataset(*args))
-
-  @staticmethod
-  @deprecation.deprecated(None, "Use `tf.data.Dataset.zip()`.")
-  def zip(datasets):
-    """Creates a `Dataset` by zipping together the given datasets.
-
-    This method has similar semantics to the built-in `zip()` function
-    in Python, with the main difference being that the `datasets`
-    argument can be an arbitrary nested structure of `Dataset` objects.
-    For example:
-
-    ```python
-    # NOTE: The following examples use `{ ... }` to represent the
-    # contents of a dataset.
-    a = { 1, 2, 3 }
-    b = { 4, 5, 6 }
-    c = { (7, 8), (9, 10), (11, 12) }
-    d = { 13, 14 }
-
-    # The nested structure of the `datasets` argument determines the
-    # structure of elements in the resulting dataset.
-    Dataset.zip((a, b)) == { (1, 4), (2, 5), (3, 6) }
-    Dataset.zip((b, a)) == { (4, 1), (5, 2), (6, 3) }
-
-    # The `datasets` argument may contain an arbitrary number of
-    # datasets.
-    Dataset.zip((a, b, c)) == { (1, 4, (7, 8)),
-                                (2, 5, (9, 10)),
-                                (3, 6, (11, 12)) }
-
-    # The number of elements in the resulting dataset is the same as
-    # the size of the smallest dataset in `datasets`.
-    Dataset.zip((a, d)) == { (1, 13), (2, 14) }
-    ```
-
-    Args:
-      datasets: A nested structure of datasets.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.ZipDataset(datasets))
-
-  def concatenate(self, dataset):
-    """Creates a `Dataset` by concatenating given dataset with this dataset.
-
-    ```python
-    # NOTE: The following examples use `{ ... }` to represent the
-    # contents of a dataset.
-    a = { 1, 2, 3 }
-    b = { 4, 5, 6, 7 }
-
-    # Input dataset and dataset to be concatenated should have same
-    # nested structures and output types.
-    # c = { (8, 9), (10, 11), (12, 13) }
-    # d = { 14.0, 15.0, 16.0 }
-    # a.concatenate(c) and a.concatenate(d) would result in error.
-
-    a.concatenate(b) == { 1, 2, 3, 4, 5, 6, 7 }
-    ```
-
-    Args:
-      dataset: `Dataset` to be concatenated.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.ConcatenateDataset(self._dataset, dataset))
-
-  def prefetch(self, buffer_size):
-    """Creates a `Dataset` that prefetches elements from this dataset.
-
-    Args:
-      buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the
-        maximum number elements that will be buffered when prefetching.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.PrefetchDataset(self._dataset, buffer_size))
-
-  @staticmethod
-  @deprecation.deprecated(None, "Use `tf.data.Dataset.list_files()`.")
-  def list_files(file_pattern):
-    """A dataset of all files matching a pattern.
-
-    Example:
-      If we had the following files on our filesystem:
-        - /path/to/dir/a.txt
-        - /path/to/dir/b.py
-        - /path/to/dir/c.py
-      If we pass "/path/to/dir/*.py" as the directory, the dataset would
-      produce:
-        - /path/to/dir/b.py
-        - /path/to/dir/c.py
-
-    Args:
-      file_pattern: A string or scalar string `tf.Tensor`, representing
-        the filename pattern that will be matched.
-
-    Returns:
-     A `Dataset` of strings corresponding to file names.
-    """
-    return Dataset.from_tensor_slices(gen_io_ops.matching_files(file_pattern))
-
-  def repeat(self, count=None):
-    """Repeats this dataset `count` times.
-
-    Args:
-      count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
-        number of times the elements of this dataset should be repeated. The
-        default behavior (if `count` is `None` or `-1`) is for the elements to
-        be repeated indefinitely.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.RepeatDataset(self._dataset, count))
-
-  @deprecation.deprecated(
-      None, "Use `ds.apply(tf.contrib.data.enumerate_dataset())`.")
-  def enumerate(self, start=0):
-    """Deprecated: Use `Dataset.apply(tf.contrib.data.enumerate_dataset(..)`."""
-
-    return self.apply(enumerate_ops.enumerate_dataset(start))
-
-  def shuffle(self, buffer_size, seed=None):
-    """Randomly shuffles the elements of this dataset.
-
-    Args:
-      buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the
-        number of elements from this dataset from which the new
-        dataset will sample.
-      seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
-        random seed that will be used to create the distribution. See
-        @{tf.set_random_seed} for behavior.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.ShuffleDataset(self._dataset, buffer_size, seed))
-
-  def cache(self, filename=""):
-    """Caches the elements in this dataset.
-
-    Args:
-      filename: A `tf.string` scalar `tf.Tensor`, representing the name of a
-        directory on the filesystem to use for caching tensors in this Dataset.
-        If a filename is not provided, the dataset will be cached in memory.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.CacheDataset(self._dataset, filename))
-
-  def take(self, count):
-    """Creates a `Dataset` with at most `count` elements from this dataset.
-
-    Args:
-      count: A `tf.int64` scalar `tf.Tensor`, representing the number of
-        elements of this dataset that should be taken to form the new dataset.
-        If `count` is -1, or if `count` is greater than the size of this
-        dataset, the new dataset will contain all elements of this dataset.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.TakeDataset(self._dataset, count))
-
-  def skip(self, count):
-    """Creates a `Dataset` that skips `count` elements from this dataset.
-
-    Args:
-      count: A `tf.int64` scalar `tf.Tensor`, representing the number
-        of elements of this dataset that should be skipped to form the
-        new dataset.  If `count` is greater than the size of this
-        dataset, the new dataset will contain no elements.  If `count`
-        is -1, skips the entire dataset.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.SkipDataset(self._dataset, count))
-
-  def shard(self, num_shards, index):
-    """Creates a `Dataset` that includes only 1/`num_shards` of this dataset.
-
-    This dataset operator is very useful when running distributed training, as
-    it allows each worker to read a unique subset.
-
-    When reading a single input file, you can skip elements as follows:
-
-    ```python
-    d = tf.data.TFRecordDataset(FLAGS.input_file)
-    d = d.shard(FLAGS.num_workers, FLAGS.worker_index)
-    d = d.repeat(FLAGS.num_epochs)
-    d = d.shuffle(FLAGS.shuffle_buffer_size)
-    d = d.map(parser_fn, num_parallel_calls=FLAGS.num_map_threads)
-    ```
-
-    Important caveats:
-
-    - Be sure to shard before you use any randomizing operator (such as
-      shuffle).
-    - Generally it is best if the shard operator is used early in the dataset
-      pipeline. For example, when reading from a set of TFRecord files, shard
-      before converting the dataset to input samples. This avoids reading every
-      file on every worker. The following is an example of an efficient
-      sharding strategy within a complete pipeline:
-
-    ```python
-    d = tf.data.Dataset.list_files(FLAGS.pattern)
-    d = d.shard(FLAGS.num_workers, FLAGS.worker_index)
-    d = d.repeat(FLAGS.num_epochs)
-    d = d.shuffle(FLAGS.shuffle_buffer_size)
-    d = d.interleave(tf.data.TFRecordDataset,
-                     cycle_length=FLAGS.num_readers, block_length=1)
-    d = d.map(parser_fn, num_parallel_calls=FLAGS.num_map_threads)
-    ```
-
-    Args:
-      num_shards: A `tf.int64` scalar `tf.Tensor`, representing the number of
-        shards operating in parallel.
-      index: A `tf.int64` scalar `tf.Tensor`, representing the worker index.
-
-    Returns:
-      A `Dataset`.
-
-    Raises:
-      ValueError: if `num_shards` or `index` are illegal values. Note: error
-        checking is done on a best-effort basis, and aren't guaranteed to be
-        caught upon dataset creation. (e.g. providing in a placeholder tensor
-        bypasses the early checking, and will instead result in an error during
-        a session.run call.)
-    """
-    return Dataset(self._dataset.shard(num_shards, index))
-
-  @deprecation.deprecated(None,
-                          "Use `ds.apply(tf.contrib.data.ignore_errors())`.")
-  def ignore_errors(self):
-    """Deprecated: Use `Dataset.apply(tf.contrib.data.ignore_errors())`."""
-
-    return self.apply(error_ops.ignore_errors())
-
-  def batch(self, batch_size):
-    """Combines consecutive elements of this dataset into batches.
-
-    Args:
-      batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
-        consecutive elements of this dataset to combine in a single batch.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.BatchDataset(self._dataset, batch_size))
-
-  def padded_batch(self, batch_size, padded_shapes, padding_values=None):
-    """Combines consecutive elements of this dataset into padded batches.
-
-    Like `Dataset.dense_to_sparse_batch()`, this method combines
-    multiple consecutive elements of this dataset, which might have
-    different shapes, into a single element. The tensors in the
-    resulting element have an additional outer dimension, and are
-    padded to the respective shape in `padded_shapes`.
-
-    Args:
-      batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
-        consecutive elements of this dataset to combine in a single batch.
-      padded_shapes: A nested structure of `tf.TensorShape` or
-        `tf.int64` vector tensor-like objects representing the shape
-        to which the respective component of each input element should
-        be padded prior to batching. Any unknown dimensions
-        (e.g. `tf.Dimension(None)` in a `tf.TensorShape` or `-1` in a
-        tensor-like object) will be padded to the maximum size of that
-        dimension in each batch.
-      padding_values: (Optional.) A nested structure of scalar-shaped
-        `tf.Tensor`, representing the padding values to use for the
-        respective components.  Defaults are `0` for numeric types and
-        the empty string for string types.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(
-        dataset_ops.PaddedBatchDataset(self._dataset, batch_size, padded_shapes,
-                                       padding_values))
-
-  @deprecation.deprecated(
-      None, "Use `ds.apply(tf.contrib.data.dense_to_sparse_batch())`.")
-  def dense_to_sparse_batch(self, batch_size, row_shape):
-    """Use: `Dataset.apply(tf.contrib.data.dense_to_sparse_batch(...))`."""
-
-    return self.apply(batching.dense_to_sparse_batch(batch_size, row_shape))
-
-  @deprecation.deprecated(None,
-                          "Use `ds.apply(tf.contrib.data.group_by_window())`.")
-  def group_by_window(self, key_func, reduce_func, window_size):
-    """Deprecated: Use `Dataset.apply(tf.contrib.data.group_by_window(...))`."""
-
-    return self.apply(
-        grouping.group_by_window(key_func, reduce_func, window_size))
-
-  @deprecation.deprecated_args(
-      None, "Replace `num_threads=T` with `num_parallel_calls=T`. Replace "
-      "`output_buffer_size=N` with `ds.prefetch(N)` on the returned dataset.",
-      "num_threads", "output_buffer_size")
-  def map(self,
-          map_func,
-          num_threads=None,
-          output_buffer_size=None,
-          num_parallel_calls=None):
-    """Maps `map_func` across this dataset.
-
-    Args:
-      map_func: A function mapping a nested structure of tensors (having
-        shapes and types defined by `self.output_shapes` and
-       `self.output_types`) to another nested structure of tensors.
-      num_threads: (Optional.) Deprecated, use `num_parallel_calls` instead.
-      output_buffer_size: (Optional.) A `tf.int64` scalar `tf.Tensor`,
-        representing the maximum number of processed elements that will be
-        buffered.
-      num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
-        representing the number elements to process in parallel. If not
-        specified, elements will be processed sequentially.
-
-    Returns:
-      A `Dataset`.
-    """
-    if num_threads is None and num_parallel_calls is None:
-      ret = Dataset(dataset_ops.MapDataset(self._dataset, map_func))
-    else:
-      if num_threads is None:
-        ret = Dataset(
-            dataset_ops.ParallelMapDataset(self._dataset, map_func,
-                                           num_parallel_calls))
-      else:
-        ret = Dataset(
-            dataset_ops.ParallelMapDataset(self._dataset, map_func,
-                                           num_threads))
-    if output_buffer_size is not None:
-      ret = ret.prefetch(output_buffer_size)
-    return ret
-
-  def flat_map(self, map_func):
-    """Maps `map_func` across this dataset and flattens the result.
-
-    Args:
-      map_func: A function mapping a nested structure of tensors (having shapes
-        and types defined by `self.output_shapes` and `self.output_types`) to a
-        `Dataset`.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.FlatMapDataset(self._dataset, map_func))
-
-  def interleave(self, map_func, cycle_length, block_length=1):
-    """Maps `map_func` across this dataset, and interleaves the results.
-
-    For example, you can use `Dataset.interleave()` to process many input files
-    concurrently:
-
-    ```python
-    # Preprocess 4 files concurrently, and interleave blocks of 16 records from
-    # each file.
-    filenames = ["/var/data/file1.txt", "/var/data/file2.txt", ...]
-    dataset = (Dataset.from_tensor_slices(filenames)
-               .interleave(lambda x:
-                   TextLineDataset(x).map(parse_fn, num_parallel_calls=1),
-                   cycle_length=4, block_length=16))
-    ```
-
-    The `cycle_length` and `block_length` arguments control the order in which
-    elements are produced. `cycle_length` controls the number of input elements
-    that are processed concurrently. If you set `cycle_length` to 1, this
-    transformation will handle one input element at a time, and will produce
-    identical results = to @{tf.data.Dataset.flat_map}. In general,
-    this transformation will apply `map_func` to `cycle_length` input elements,
-    open iterators on the returned `Dataset` objects, and cycle through them
-    producing `block_length` consecutive elements from each iterator, and
-    consuming the next input element each time it reaches the end of an
-    iterator.
-
-    For example:
-
-    ```python
-    # NOTE: The following examples use `{ ... }` to represent the
-    # contents of a dataset.
-    a = { 1, 2, 3, 4, 5 }
-
-    # NOTE: New lines indicate "block" boundaries.
-    a.interleave(lambda x: Dataset.from_tensors(x).repeat(6),
-                 cycle_length=2, block_length=4) == {
-        1, 1, 1, 1,
-        2, 2, 2, 2,
-        1, 1,
-        2, 2,
-        3, 3, 3, 3,
-        4, 4, 4, 4,
-        3, 3,
-        4, 4,
-        5, 5, 5, 5,
-        5, 5,
-    }
-    ```
-
-    NOTE: The order of elements yielded by this transformation is
-    deterministic, as long as `map_func` is a pure function. If
-    `map_func` contains any stateful operations, the order in which
-    that state is accessed is undefined.
-
-    Args:
-      map_func: A function mapping a nested structure of tensors (having shapes
-        and types defined by `self.output_shapes` and `self.output_types`) to a
-        `Dataset`.
-      cycle_length: The number of elements from this dataset that will be
-        processed concurrently.
-      block_length: The number of consecutive elements to produce from each
-        input element before cycling to another input element.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(
-        dataset_ops.InterleaveDataset(self._dataset, map_func, cycle_length,
-                                      block_length))
-
-  @deprecation.deprecated(None, "Use `ds.apply(tf.contrib.data.unbatch())`.")
-  def unbatch(self):
-    """Deprecated: Use `Dataset.apply(tf.contrib.data.unbatch()`."""
-
-    return self.apply(batching.unbatch())
-
-  def filter(self, predicate):
-    """Filters this dataset according to `predicate`.
-
-    Args:
-      predicate: A function mapping a nested structure of tensors (having shapes
-        and types defined by `self.output_shapes` and `self.output_types`) to a
-        scalar `tf.bool` tensor.
-
-    Returns:
-      A `Dataset`.
-    """
-    return Dataset(dataset_ops.FilterDataset(self._dataset, predicate))
-
-  def apply(self, transformation_func):
-    """Apply a transformation function to this dataset.
-
-    `apply` enables chaining of custom `Dataset` transformations, which are
-    represented as functions that take one `Dataset` argument and return a
-    transformed `Dataset`.
-
-    For example:
-
-    ```
-    dataset = (dataset.map(lambda x: x ** 2)
-               .(group_by_window(key_func, reduce_func, window_size))
-               .map(lambda x: x ** 3))
-    ```
-
-    Args:
-      transformation_func: A function that takes one `Dataset` argument and
-        returns a `Dataset`.
-
-    Returns:
-      The `Dataset` returned by applying `transformation_func` to this dataset.
-    """
-    dataset = transformation_func(self)
-    if not isinstance(dataset, dataset_ops.Dataset):
-      raise TypeError("`transformation_func` must return a Dataset.")
-    return Dataset(dataset)
-
-
-def get_single_element(dataset):
-  """Returns the single element in `dataset` as a nested structure of tensors.
-
-  This function enables you to use a @{tf.data.Dataset} in a stateless
-  "tensor-in tensor-out" expression, without creating a @{tf.data.Iterator}.
-  This can be useful when your preprocessing transformations are expressed
-  as a `Dataset`, and you want to use the transformation at serving time.
-  For example:
-
-  ```python
-  input_batch = tf.placeholder(tf.string, shape=[BATCH_SIZE])
-
-  def preprocessing_fn(input_str):
-    # ...
-    return image, label
-
-  dataset = (tf.data.Dataset.from_tensor_slices(input_batch)
-             .map(preprocessing_fn, num_parallel_calls=BATCH_SIZE)
-             .batch(BATCH_SIZE))
-
-  image_batch, label_batch = tf.contrib.data.get_single_element(dataset)
-  ```
-
-  Args:
-    dataset: A @{tf.data.Dataset} object containing a single element.
-
-  Returns:
-    A nested structure of @{tf.Tensor} objects, corresponding to the single
-    element of `dataset`.
-
-  Raises:
-    TypeError: if `dataset` is not a `tf.data.Dataset` object.
-    InvalidArgumentError (at runtime): if `dataset` does not contain exactly
-      one element.
-  """
-  if not isinstance(dataset, dataset_ops.Dataset):
-    raise TypeError("`dataset` must be a `tf.data.Dataset` object.")
-  return nest.pack_sequence_as(
-      dataset.output_types,
-      gen_dataset_ops.dataset_to_single_element(
-          dataset._as_variant_tensor(),  # pylint: disable=protected-access
-          output_types=nest.flatten(dataset.output_types),
-          output_shapes=nest.flatten(dataset.output_shapes)))
-- 
GitLab


From 1ad338200e2643387efe6bebd1fcd59ddd87fdf1 Mon Sep 17 00:00:00 2001
From: Dustin Tran <trandustin@google.com>
Date: Mon, 19 Feb 2018 21:39:03 -0800
Subject: [PATCH 060/575] Reduce tfp.layers boilerplate via programmable
 docstrings.

PiperOrigin-RevId: 186260342
---
 tensorflow/contrib/bayesflow/BUILD            |   10 +
 .../kernel_tests/docstring_util_test.py       |   83 ++
 .../bayesflow/python/ops/docstring_util.py    |   86 ++
 .../python/ops/layers_conv_variational.py     | 1127 +++++------------
 .../python/ops/layers_dense_variational.py    |  391 ++----
 5 files changed, 577 insertions(+), 1120 deletions(-)
 create mode 100644 tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
 create mode 100644 tensorflow/contrib/bayesflow/python/ops/docstring_util.py

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 74712aeb67..fc04933ba0 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -118,6 +118,16 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "docstring_util_test",
+    size = "small",
+    srcs = ["python/kernel_tests/docstring_util_test.py"],
+    additional_deps = [
+        ":bayesflow_py",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
 cuda_py_test(
     name = "layers_dense_variational_test",
     size = "small",
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
new file mode 100644
index 0000000000..09ae6f3952
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
@@ -0,0 +1,83 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for docstring utilities."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.bayesflow.python.ops import docstring_util
+from tensorflow.python.platform import test
+
+
+class DocstringUtil(test.TestCase):
+
+  def _testFunction(self):
+    doc_args = """  x: Input to return as output.
+  y: Baz."""
+    @docstring_util.expand_docstring(args=doc_args)
+    def foo(x):
+      """Hello world.
+
+      Args:
+      @{args}
+
+      Returns:
+        x.
+      """
+      return x
+
+    true_docstring = """Hello world.
+
+    Args:
+      x: Input to return as output.
+      y: Baz.
+
+    Returns:
+      x.
+    """
+    self.assertEqual(foo.__doc__, true_docstring)
+
+  def _testClassInit(self):
+    doc_args = """  x: Input to return as output.
+  y: Baz."""
+
+    class Foo(object):
+
+      @docstring_util.expand_docstring(args=doc_args)
+      def __init__(self, x, y):
+        """Hello world.
+
+        Args:
+        @{args}
+
+        Bar.
+        """
+        pass
+
+    true_docstring = """Hello world.
+
+    Args:
+      x: Input to return as output.
+      y: Baz.
+
+    Bar.
+    """
+    self.assertEqual(Foo.__doc__, true_docstring)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/docstring_util.py b/tensorflow/contrib/bayesflow/python/ops/docstring_util.py
new file mode 100644
index 0000000000..44a1ea2f2a
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/ops/docstring_util.py
@@ -0,0 +1,86 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for programmable docstrings.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+import sys
+import six
+
+
+def expand_docstring(**kwargs):
+  """Decorator to programmatically expand the docstring.
+
+  Args:
+    **kwargs: Keyword arguments to set. For each key-value pair `k` and `v`,
+      the key is found as `@{k}` in the docstring and replaced with `v`.
+
+  Returns:
+    Decorated function.
+  """
+  def _fn_wrapped(fn):
+    """Original function with modified `__doc__` attribute."""
+    doc = _trim(fn.__doc__)
+    for k, v in six.iteritems(kwargs):
+      # Capture each @{k} reference to replace with v.
+      # We wrap the replacement in a function so no backslash escapes
+      # are processed.
+      pattern = r'@\{' + str(k) + r'\}'
+      doc = re.sub(pattern, lambda match: v, doc)  # pylint: disable=cell-var-from-loop
+    fn.__doc__ = doc
+    return fn
+  return _fn_wrapped
+
+
+def _trim(docstring):
+  """Trims docstring indentation.
+
+  In general, multi-line docstrings carry their level of indentation when
+  defined under a function or class method. This function standardizes
+  indentation levels by removing them. Taken from PEP 257 docs.
+
+  Args:
+    docstring: Python string to trim indentation.
+
+  Returns:
+    Trimmed docstring.
+  """
+  if not docstring:
+    return ''
+  # Convert tabs to spaces (following the normal Python rules)
+  # and split into a list of lines:
+  lines = docstring.expandtabs().splitlines()
+  # Determine minimum indentation (first line doesn't count):
+  indent = sys.maxint
+  for line in lines[1:]:
+    stripped = line.lstrip()
+    if stripped:
+      indent = min(indent, len(line) - len(stripped))
+  # Remove indentation (first line is special):
+  trimmed = [lines[0].strip()]
+  if indent < sys.maxint:
+    for line in lines[1:]:
+      trimmed.append(line[indent:].rstrip())
+  # Strip off trailing and leading blank lines:
+  while trimmed and not trimmed[-1]:
+    trimmed.pop()
+  while trimmed and not trimmed[0]:
+    trimmed.pop(0)
+  # Return a single string:
+  return '\n'.join(trimmed)
diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
index 7723cfb442..90219fdfef 100644
--- a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
+++ b/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.bayesflow.python.ops import docstring_util
 from tensorflow.contrib.bayesflow.python.ops import layers_util
 from tensorflow.contrib.distributions.python.ops import independent as independent_lib
 from tensorflow.python.framework import dtypes
@@ -34,6 +35,45 @@ from tensorflow.python.ops.distributions import kullback_leibler as kl_lib
 from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.ops.distributions import util as distribution_util
 
+doc_args = """  activation: Activation function. Set it to None to maintain a
+      linear activation.
+  activity_regularizer: Optional regularizer function for the output.
+  trainable: Boolean, if `True` also add variables to the graph collection
+    `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+  kernel_posterior_fn: Python `callable` which creates
+    `tf.distributions.Distribution` instance representing the surrogate
+    posterior of the `kernel` parameter. Default value:
+    `default_mean_field_normal_fn()`.
+  kernel_posterior_tensor_fn: Python `callable` which takes a
+    `tf.distributions.Distribution` instance and returns a representative
+    value. Default value: `lambda d: d.sample()`.
+  kernel_prior_fn: Python `callable` which creates `tf.distributions`
+    instance. See `default_mean_field_normal_fn` docstring for required
+    parameter signature.
+    Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+  kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+    distribution, prior distribution and random variate sample(s) from the
+    surrogate posterior and computes or approximates the KL divergence. The
+    distributions are `tf.distributions.Distribution`-like instances and the
+    sample is a `Tensor`.
+  bias_posterior_fn: Python `callable` which creates
+    `tf.distributions.Distribution` instance representing the surrogate
+    posterior of the `bias` parameter. Default value:
+    `default_mean_field_normal_fn(is_singular=True)` (which creates an
+    instance of `tf.distributions.Deterministic`).
+  bias_posterior_tensor_fn: Python `callable` which takes a
+    `tf.distributions.Distribution` instance and returns a representative
+    value. Default value: `lambda d: d.sample()`.
+  bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+    See `default_mean_field_normal_fn` docstring for required parameter
+    signature. Default value: `None` (no prior, no variational inference)
+  bias_divergence_fn: Python `callable` which takes the surrogate posterior
+    distribution, prior distribution and random variate sample(s) from the
+    surrogate posterior and computes or approximates the KL divergence. The
+    distributions are `tf.distributions.Distribution`-like instances and the
+    sample is a `Tensor`.
+  name: A string, the name of the layer."""
+
 
 class _ConvVariational(layers_lib.Layer):
   """Abstract nD convolution layer (private, used as implementation base).
@@ -55,65 +95,6 @@ class _ConvVariational(layers_lib.Layer):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of n integers, specifying the
-      length of the convolution window.
-    strides: An integer or tuple/list of n integers,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, ..., channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, ...)`.
-    dilation_rate: An integer or tuple/list of n integers, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: A string, the name of the layer.
-
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -134,6 +115,7 @@ class _ConvVariational(layers_lib.Layer):
     bias_divergence_fn: `callable` returning divergence.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -157,6 +139,31 @@ class _ConvVariational(layers_lib.Layer):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      rank: An integer, the rank of the convolution, e.g. "2" for 2D
+        convolution.
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of n integers, specifying the
+        length of the convolution window.
+      strides: An integer or tuple/list of n integers,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, ...,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, ...)`.
+      dilation_rate: An integer or tuple/list of n integers, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+    @{args}
+    """
     super(_ConvVariational, self).__init__(
         trainable=trainable,
         name=name,
@@ -371,65 +378,6 @@ class _ConvReparameterization(_ConvVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of n integers, specifying the
-      length of the convolution window.
-    strides: An integer or tuple/list of n integers,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, ..., channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, ...)`.
-    dilation_rate: An integer or tuple/list of n integers, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: A string, the name of the layer.
-
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -454,6 +402,7 @@ class _ConvReparameterization(_ConvVariational):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -477,6 +426,31 @@ class _ConvReparameterization(_ConvVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      rank: An integer, the rank of the convolution, e.g. "2" for 2D
+        convolution.
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of n integers, specifying the
+        length of the convolution window.
+      strides: An integer or tuple/list of n integers,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, ...,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, ...)`.
+      dilation_rate: An integer or tuple/list of n integers, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+    @{args}
+    """
     super(_ConvReparameterization, self).__init__(
         rank=rank,
         filters=filters,
@@ -529,63 +503,6 @@ class Conv1DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of a single integer, specifying the
-      length of the 1D convolution window.
-    strides: An integer or tuple/list of a single integer,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, length, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, length)`.
-    dilation_rate: An integer or tuple/list of a single integer, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -639,6 +556,7 @@ class Conv1DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -661,6 +579,29 @@ class Conv1DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of a single integer, specifying the
+        length of the 1D convolution window.
+      strides: An integer or tuple/list of a single integer,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, length,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, length)`.
+      dilation_rate: An integer or tuple/list of a single integer, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+    @{args}
+    """
     super(Conv1DReparameterization, self).__init__(
         rank=1,
         filters=filters,
@@ -683,6 +624,7 @@ class Conv1DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv1d_reparameterization(
     inputs,
     filters,
@@ -726,7 +668,7 @@ def conv1d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -746,43 +688,7 @@ def conv1d_reparameterization(
       the dilation rate to use for dilated convolution.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
+  @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -874,70 +780,6 @@ class Conv2DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of 2 integers, specifying the
-      height and width of the 2D convolution window.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-    strides: An integer or tuple/list of 2 integers,
-      specifying the strides of the convolution along the height and width.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, height, width, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, height, width)`.
-
-    dilation_rate: An integer or tuple/list of 2 integers, specifying
-      the dilation rate to use for dilated convolution.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -994,6 +836,7 @@ class Conv2DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1016,6 +859,35 @@ class Conv2DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of 2 integers, specifying the
+        height and width of the 2D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+      strides: An integer or tuple/list of 2 integers,
+        specifying the strides of the convolution along the height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, height,
+        width, channels)` while `channels_first` corresponds to inputs with
+        shape `(batch, channels, height, width)`.
+      dilation_rate: An integer or tuple/list of 2 integers, specifying
+        the dilation rate to use for dilated convolution.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any stride value != 1.
+    @{args}
+    """
     super(Conv2DReparameterization, self).__init__(
         rank=2,
         filters=filters,
@@ -1038,6 +910,7 @@ class Conv2DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv2d_reparameterization(
     inputs,
     filters,
@@ -1081,7 +954,7 @@ def conv2d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -1101,50 +974,13 @@ def conv2d_reparameterization(
       `channels_last` corresponds to inputs with shape
       `(batch, height, width, channels)` while `channels_first` corresponds to
       inputs with shape `(batch, channels, height, width)`.
-
     dilation_rate: An integer or tuple/list of 2 integers, specifying
       the dilation rate to use for dilated convolution.
       Can be a single integer to specify the same value for
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
+  @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -1240,71 +1076,6 @@ class Conv3DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of 3 integers, specifying the
-      depth, height and width of the 3D convolution window.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-    strides: An integer or tuple/list of 3 integers,
-      specifying the strides of the convolution along the depth,
-      height and width.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, depth, height, width, channels)` while `channels_first`
-      corresponds to inputs with shape
-      `(batch, channels, depth, height, width)`.
-    dilation_rate: An integer or tuple/list of 3 integers, specifying
-      the dilation rate to use for dilated convolution.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -1361,6 +1132,7 @@ class Conv3DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1383,6 +1155,36 @@ class Conv3DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of 3 integers, specifying the
+        depth, height and width of the 3D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+      strides: An integer or tuple/list of 3 integers,
+        specifying the strides of the convolution along the depth,
+        height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, depth,
+        height, width, channels)` while `channels_first` corresponds to inputs
+        with shape `(batch, channels, depth, height, width)`.
+      dilation_rate: An integer or tuple/list of 3 integers, specifying
+        the dilation rate to use for dilated convolution.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any stride value != 1.
+    @{args}
+    """
     super(Conv3DReparameterization, self).__init__(
         rank=3,
         filters=filters,
@@ -1405,6 +1207,7 @@ class Conv3DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv3d_reparameterization(
     inputs,
     filters,
@@ -1448,7 +1251,7 @@ def conv3d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -1476,43 +1279,7 @@ def conv3d_reparameterization(
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
+  @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -1611,67 +1378,6 @@ class _ConvFlipout(_ConvVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of n integers, specifying the
-      length of the convolution window.
-    strides: An integer or tuple/list of n integers,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, ..., channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, ...)`.
-    dilation_rate: An integer or tuple/list of n integers, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
-
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -1694,10 +1400,11 @@ class _ConvFlipout(_ConvVariational):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -1722,6 +1429,31 @@ class _ConvFlipout(_ConvVariational):
       seed=None,
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      rank: An integer, the rank of the convolution, e.g. "2" for 2D
+        convolution.
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of n integers, specifying the
+        length of the convolution window.
+      strides: An integer or tuple/list of n integers,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, ...,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, ...)`.
+      dilation_rate: An integer or tuple/list of n integers, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+    @{args}
+    """
     super(_ConvFlipout, self).__init__(
         rank=rank,
         filters=filters,
@@ -1822,65 +1554,6 @@ class Conv1DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of a single integer, specifying the
-      length of the 1D convolution window.
-    strides: An integer or tuple/list of a single integer,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, length, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, length)`.
-    dilation_rate: An integer or tuple/list of a single integer, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -1932,10 +1605,11 @@ class Conv1DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1959,6 +1633,29 @@ class Conv1DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of a single integer, specifying the
+        length of the 1D convolution window.
+      strides: An integer or tuple/list of a single integer,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, length,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, length)`.
+      dilation_rate: An integer or tuple/list of a single integer, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+    @{args}
+    """
     super(Conv1DFlipout, self).__init__(
         rank=1,
         filters=filters,
@@ -1982,6 +1679,7 @@ class Conv1DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv1d_flipout(
     inputs,
     filters,
@@ -2029,7 +1727,7 @@ def conv1d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -2049,45 +1747,7 @@ def conv1d_flipout(
       the dilation rate to use for dilated convolution.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
+  @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -2130,8 +1790,8 @@ def conv1d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
   layer = Conv1DFlipout(
       filters=filters,
@@ -2184,72 +1844,6 @@ class Conv2DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of 2 integers, specifying the
-      height and width of the 2D convolution window.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-    strides: An integer or tuple/list of 2 integers,
-      specifying the strides of the convolution along the height and width.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, height, width, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, height, width)`.
-
-    dilation_rate: An integer or tuple/list of 2 integers, specifying
-      the dilation rate to use for dilated convolution.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -2304,10 +1898,11 @@ class Conv2DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -2331,6 +1926,35 @@ class Conv2DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of 2 integers, specifying the
+        height and width of the 2D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+      strides: An integer or tuple/list of 2 integers,
+        specifying the strides of the convolution along the height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, height,
+        width, channels)` while `channels_first` corresponds to inputs with
+        shape `(batch, channels, height, width)`.
+      dilation_rate: An integer or tuple/list of 2 integers, specifying
+        the dilation rate to use for dilated convolution.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any stride value != 1.
+    @{args}
+    """
     super(Conv2DFlipout, self).__init__(
         rank=2,
         filters=filters,
@@ -2354,6 +1978,7 @@ class Conv2DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv2d_flipout(
     inputs,
     filters,
@@ -2401,7 +2026,7 @@ def conv2d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -2421,52 +2046,13 @@ def conv2d_flipout(
       `channels_last` corresponds to inputs with shape
       `(batch, height, width, channels)` while `channels_first` corresponds to
       inputs with shape `(batch, channels, height, width)`.
-
     dilation_rate: An integer or tuple/list of 2 integers, specifying
       the dilation rate to use for dilated convolution.
       Can be a single integer to specify the same value for
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
+  @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -2513,8 +2099,8 @@ def conv2d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
   layer = Conv2DFlipout(
       filters=filters,
@@ -2567,73 +2153,6 @@ class Conv3DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of 3 integers, specifying the
-      depth, height and width of the 3D convolution window.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-    strides: An integer or tuple/list of 3 integers,
-      specifying the strides of the convolution along the depth,
-      height and width.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, depth, height, width, channels)` while `channels_first`
-      corresponds to inputs with shape
-      `(batch, channels, depth, height, width)`.
-    dilation_rate: An integer or tuple/list of 3 integers, specifying
-      the dilation rate to use for dilated convolution.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -2688,10 +2207,11 @@ class Conv3DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -2715,6 +2235,36 @@ class Conv3DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of 3 integers, specifying the
+        depth, height and width of the 3D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+      strides: An integer or tuple/list of 3 integers,
+        specifying the strides of the convolution along the depth,
+        height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, depth,
+        height, width, channels)` while `channels_first` corresponds to inputs
+        with shape `(batch, channels, depth, height, width)`.
+      dilation_rate: An integer or tuple/list of 3 integers, specifying
+        the dilation rate to use for dilated convolution.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any stride value != 1.
+    @{args}
+    """
     super(Conv3DFlipout, self).__init__(
         rank=3,
         filters=filters,
@@ -2738,6 +2288,7 @@ class Conv3DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv3d_flipout(
     inputs,
     filters,
@@ -2785,7 +2336,7 @@ def conv3d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -2813,45 +2364,7 @@ def conv3d_flipout(
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
+  @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -2898,8 +2411,8 @@ def conv3d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
   layer = Conv3DFlipout(
       filters=filters,
diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
index 591a8e553d..1e4a445a33 100644
--- a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
+++ b/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.bayesflow.python.ops import docstring_util
 from tensorflow.contrib.bayesflow.python.ops import layers_util
 from tensorflow.contrib.distributions.python.ops import independent as independent_lib
 from tensorflow.python.framework import dtypes
@@ -33,6 +34,53 @@ from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.ops.distributions import util as distribution_util
 
 
+doc_args = """  units: Integer or Long, dimensionality of the output space.
+  activation: Activation function (`callable`). Set it to None to maintain a
+    linear activation.
+  activity_regularizer: Regularizer function for the output.
+  trainable: Boolean, if `True` also add variables to the graph collection
+    `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+  kernel_posterior_fn: Python `callable` which creates
+    `tf.distributions.Distribution` instance representing the surrogate
+    posterior of the `kernel` parameter. Default value:
+    `default_mean_field_normal_fn()`.
+  kernel_posterior_tensor_fn: Python `callable` which takes a
+    `tf.distributions.Distribution` instance and returns a representative
+    value. Default value: `lambda d: d.sample()`.
+  kernel_prior_fn: Python `callable` which creates `tf.distributions`
+    instance. See `default_mean_field_normal_fn` docstring for required
+    parameter signature.
+    Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+  kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+    distribution, prior distribution and random variate sample(s) from the
+    surrogate posterior and computes or approximates the KL divergence. The
+    distributions are `tf.distributions.Distribution`-like instances and the
+    sample is a `Tensor`.
+  bias_posterior_fn: Python `callable` which creates
+    `tf.distributions.Distribution` instance representing the surrogate
+    posterior of the `bias` parameter. Default value:
+    `default_mean_field_normal_fn(is_singular=True)` (which creates an
+    instance of `tf.distributions.Deterministic`).
+  bias_posterior_tensor_fn: Python `callable` which takes a
+    `tf.distributions.Distribution` instance and returns a representative
+    value. Default value: `lambda d: d.sample()`.
+  bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+    See `default_mean_field_normal_fn` docstring for required parameter
+    signature. Default value: `None` (no prior, no variational inference)
+  bias_divergence_fn: Python `callable` which takes the surrogate posterior
+    distribution, prior distribution and random variate sample(s) from the
+    surrogate posterior and computes or approximates the KL divergence. The
+    distributions are `tf.distributions.Distribution`-like instances and the
+    sample is a `Tensor`.
+  seed: Python scalar `int` which initializes the random number
+    generator. Default value: `None` (i.e., use global seed).
+  name: Python `str`, the name of the layer. Layers with the same name will
+    share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+    such cases.
+  reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+    layer by the same name."""
+
+
 class _DenseVariational(layers_lib.Layer):
   """Abstract densely-connected class (private, used as implementation base).
 
@@ -50,51 +98,6 @@ class _DenseVariational(layers_lib.Layer):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
-
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -109,6 +112,7 @@ class _DenseVariational(layers_lib.Layer):
     bias_divergence_fn: `callable` returning divergence.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -126,6 +130,11 @@ class _DenseVariational(layers_lib.Layer):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+    @{args}
+    """
     super(_DenseVariational, self).__init__(
         trainable=trainable,
         name=name,
@@ -274,51 +283,6 @@ class DenseReparameterization(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
-
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -363,6 +327,7 @@ class DenseReparameterization(_DenseVariational):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -381,6 +346,11 @@ class DenseReparameterization(_DenseVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+    @{args}
+    """
     super(DenseReparameterization, self).__init__(
         units=units,
         activation=activation,
@@ -405,6 +375,7 @@ class DenseReparameterization(_DenseVariational):
     return self._matmul(inputs, self.kernel_posterior_tensor)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def dense_reparameterization(
     inputs,
     units,
@@ -444,49 +415,7 @@ def dense_reparameterization(
 
   Args:
     inputs: Tensor input.
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
+  @{args}
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
@@ -563,51 +492,6 @@ class DenseLocalReparameterization(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
-
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -652,6 +536,7 @@ class DenseLocalReparameterization(_DenseVariational):
         Neural Information Processing Systems, 2015.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -670,6 +555,11 @@ class DenseLocalReparameterization(_DenseVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+    @{args}
+    """
     super(DenseLocalReparameterization, self).__init__(
         units=units,
         activation=activation,
@@ -705,6 +595,7 @@ class DenseLocalReparameterization(_DenseVariational):
     return self.kernel_posterior_affine_tensor
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def dense_local_reparameterization(
     inputs,
     units,
@@ -745,49 +636,7 @@ def dense_local_reparameterization(
 
   Args:
     inputs: Tensor input.
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
+  @{args}
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
@@ -866,53 +715,6 @@ class DenseFlipout(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
-
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -959,6 +761,7 @@ class DenseFlipout(_DenseVariational):
         https://openreview.net/forum?id=rJnpifWAb
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -978,6 +781,11 @@ class DenseFlipout(_DenseVariational):
       seed=None,
       name=None,
       **kwargs):
+    """Construct layer.
+
+    Args:
+    @{args}
+    """
     super(DenseFlipout, self).__init__(
         units=units,
         activation=activation,
@@ -1031,6 +839,7 @@ class DenseFlipout(_DenseVariational):
     return outputs
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def dense_flipout(
     inputs,
     units,
@@ -1074,51 +883,7 @@ def dense_flipout(
 
   Args:
     inputs: Tensor input.
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
+  @{args}
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
-- 
GitLab


From c5f8e80a0f58b9b5a606bb8a17032b665e6a3c17 Mon Sep 17 00:00:00 2001
From: Dustin Tran <trandustin@google.com>
Date: Mon, 19 Feb 2018 23:55:21 -0800
Subject: [PATCH 061/575] Automated g4 rollback of changelist 186260342

PiperOrigin-RevId: 186266857
---
 tensorflow/contrib/bayesflow/BUILD            |   10 -
 .../kernel_tests/docstring_util_test.py       |   83 --
 .../bayesflow/python/ops/docstring_util.py    |   86 --
 .../python/ops/layers_conv_variational.py     | 1127 ++++++++++++-----
 .../python/ops/layers_dense_variational.py    |  391 ++++--
 5 files changed, 1120 insertions(+), 577 deletions(-)
 delete mode 100644 tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
 delete mode 100644 tensorflow/contrib/bayesflow/python/ops/docstring_util.py

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index fc04933ba0..74712aeb67 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -118,16 +118,6 @@ cuda_py_test(
     ],
 )
 
-cuda_py_test(
-    name = "docstring_util_test",
-    size = "small",
-    srcs = ["python/kernel_tests/docstring_util_test.py"],
-    additional_deps = [
-        ":bayesflow_py",
-        "//tensorflow/python:client_testlib",
-    ],
-)
-
 cuda_py_test(
     name = "layers_dense_variational_test",
     size = "small",
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
deleted file mode 100644
index 09ae6f3952..0000000000
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for docstring utilities."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.bayesflow.python.ops import docstring_util
-from tensorflow.python.platform import test
-
-
-class DocstringUtil(test.TestCase):
-
-  def _testFunction(self):
-    doc_args = """  x: Input to return as output.
-  y: Baz."""
-    @docstring_util.expand_docstring(args=doc_args)
-    def foo(x):
-      """Hello world.
-
-      Args:
-      @{args}
-
-      Returns:
-        x.
-      """
-      return x
-
-    true_docstring = """Hello world.
-
-    Args:
-      x: Input to return as output.
-      y: Baz.
-
-    Returns:
-      x.
-    """
-    self.assertEqual(foo.__doc__, true_docstring)
-
-  def _testClassInit(self):
-    doc_args = """  x: Input to return as output.
-  y: Baz."""
-
-    class Foo(object):
-
-      @docstring_util.expand_docstring(args=doc_args)
-      def __init__(self, x, y):
-        """Hello world.
-
-        Args:
-        @{args}
-
-        Bar.
-        """
-        pass
-
-    true_docstring = """Hello world.
-
-    Args:
-      x: Input to return as output.
-      y: Baz.
-
-    Bar.
-    """
-    self.assertEqual(Foo.__doc__, true_docstring)
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/docstring_util.py b/tensorflow/contrib/bayesflow/python/ops/docstring_util.py
deleted file mode 100644
index 44a1ea2f2a..0000000000
--- a/tensorflow/contrib/bayesflow/python/ops/docstring_util.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Utilities for programmable docstrings.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import re
-import sys
-import six
-
-
-def expand_docstring(**kwargs):
-  """Decorator to programmatically expand the docstring.
-
-  Args:
-    **kwargs: Keyword arguments to set. For each key-value pair `k` and `v`,
-      the key is found as `@{k}` in the docstring and replaced with `v`.
-
-  Returns:
-    Decorated function.
-  """
-  def _fn_wrapped(fn):
-    """Original function with modified `__doc__` attribute."""
-    doc = _trim(fn.__doc__)
-    for k, v in six.iteritems(kwargs):
-      # Capture each @{k} reference to replace with v.
-      # We wrap the replacement in a function so no backslash escapes
-      # are processed.
-      pattern = r'@\{' + str(k) + r'\}'
-      doc = re.sub(pattern, lambda match: v, doc)  # pylint: disable=cell-var-from-loop
-    fn.__doc__ = doc
-    return fn
-  return _fn_wrapped
-
-
-def _trim(docstring):
-  """Trims docstring indentation.
-
-  In general, multi-line docstrings carry their level of indentation when
-  defined under a function or class method. This function standardizes
-  indentation levels by removing them. Taken from PEP 257 docs.
-
-  Args:
-    docstring: Python string to trim indentation.
-
-  Returns:
-    Trimmed docstring.
-  """
-  if not docstring:
-    return ''
-  # Convert tabs to spaces (following the normal Python rules)
-  # and split into a list of lines:
-  lines = docstring.expandtabs().splitlines()
-  # Determine minimum indentation (first line doesn't count):
-  indent = sys.maxint
-  for line in lines[1:]:
-    stripped = line.lstrip()
-    if stripped:
-      indent = min(indent, len(line) - len(stripped))
-  # Remove indentation (first line is special):
-  trimmed = [lines[0].strip()]
-  if indent < sys.maxint:
-    for line in lines[1:]:
-      trimmed.append(line[indent:].rstrip())
-  # Strip off trailing and leading blank lines:
-  while trimmed and not trimmed[-1]:
-    trimmed.pop()
-  while trimmed and not trimmed[0]:
-    trimmed.pop(0)
-  # Return a single string:
-  return '\n'.join(trimmed)
diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
index 90219fdfef..7723cfb442 100644
--- a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
+++ b/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
@@ -19,7 +19,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.bayesflow.python.ops import docstring_util
 from tensorflow.contrib.bayesflow.python.ops import layers_util
 from tensorflow.contrib.distributions.python.ops import independent as independent_lib
 from tensorflow.python.framework import dtypes
@@ -35,45 +34,6 @@ from tensorflow.python.ops.distributions import kullback_leibler as kl_lib
 from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.ops.distributions import util as distribution_util
 
-doc_args = """  activation: Activation function. Set it to None to maintain a
-      linear activation.
-  activity_regularizer: Optional regularizer function for the output.
-  trainable: Boolean, if `True` also add variables to the graph collection
-    `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-  kernel_posterior_fn: Python `callable` which creates
-    `tf.distributions.Distribution` instance representing the surrogate
-    posterior of the `kernel` parameter. Default value:
-    `default_mean_field_normal_fn()`.
-  kernel_posterior_tensor_fn: Python `callable` which takes a
-    `tf.distributions.Distribution` instance and returns a representative
-    value. Default value: `lambda d: d.sample()`.
-  kernel_prior_fn: Python `callable` which creates `tf.distributions`
-    instance. See `default_mean_field_normal_fn` docstring for required
-    parameter signature.
-    Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-  kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-    distribution, prior distribution and random variate sample(s) from the
-    surrogate posterior and computes or approximates the KL divergence. The
-    distributions are `tf.distributions.Distribution`-like instances and the
-    sample is a `Tensor`.
-  bias_posterior_fn: Python `callable` which creates
-    `tf.distributions.Distribution` instance representing the surrogate
-    posterior of the `bias` parameter. Default value:
-    `default_mean_field_normal_fn(is_singular=True)` (which creates an
-    instance of `tf.distributions.Deterministic`).
-  bias_posterior_tensor_fn: Python `callable` which takes a
-    `tf.distributions.Distribution` instance and returns a representative
-    value. Default value: `lambda d: d.sample()`.
-  bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-    See `default_mean_field_normal_fn` docstring for required parameter
-    signature. Default value: `None` (no prior, no variational inference)
-  bias_divergence_fn: Python `callable` which takes the surrogate posterior
-    distribution, prior distribution and random variate sample(s) from the
-    surrogate posterior and computes or approximates the KL divergence. The
-    distributions are `tf.distributions.Distribution`-like instances and the
-    sample is a `Tensor`.
-  name: A string, the name of the layer."""
-
 
 class _ConvVariational(layers_lib.Layer):
   """Abstract nD convolution layer (private, used as implementation base).
@@ -95,6 +55,65 @@ class _ConvVariational(layers_lib.Layer):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of n integers, specifying the
+      length of the convolution window.
+    strides: An integer or tuple/list of n integers,
+      specifying the stride length of the convolution.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, ..., channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, ...)`.
+    dilation_rate: An integer or tuple/list of n integers, specifying
+      the dilation rate to use for dilated convolution.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any `strides` value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    name: A string, the name of the layer.
+
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -115,7 +134,6 @@ class _ConvVariational(layers_lib.Layer):
     bias_divergence_fn: `callable` returning divergence.
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -139,31 +157,6 @@ class _ConvVariational(layers_lib.Layer):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      rank: An integer, the rank of the convolution, e.g. "2" for 2D
-        convolution.
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of n integers, specifying the
-        length of the convolution window.
-      strides: An integer or tuple/list of n integers,
-        specifying the stride length of the convolution.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, ...,
-        channels)` while `channels_first` corresponds to inputs with shape
-        `(batch, channels, ...)`.
-      dilation_rate: An integer or tuple/list of n integers, specifying
-        the dilation rate to use for dilated convolution.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any `strides` value != 1.
-    @{args}
-    """
     super(_ConvVariational, self).__init__(
         trainable=trainable,
         name=name,
@@ -378,6 +371,65 @@ class _ConvReparameterization(_ConvVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of n integers, specifying the
+      length of the convolution window.
+    strides: An integer or tuple/list of n integers,
+      specifying the stride length of the convolution.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, ..., channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, ...)`.
+    dilation_rate: An integer or tuple/list of n integers, specifying
+      the dilation rate to use for dilated convolution.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any `strides` value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    name: A string, the name of the layer.
+
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -402,7 +454,6 @@ class _ConvReparameterization(_ConvVariational):
         International Conference on Learning Representations, 2014.
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -426,31 +477,6 @@ class _ConvReparameterization(_ConvVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      rank: An integer, the rank of the convolution, e.g. "2" for 2D
-        convolution.
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of n integers, specifying the
-        length of the convolution window.
-      strides: An integer or tuple/list of n integers,
-        specifying the stride length of the convolution.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, ...,
-        channels)` while `channels_first` corresponds to inputs with shape
-        `(batch, channels, ...)`.
-      dilation_rate: An integer or tuple/list of n integers, specifying
-        the dilation rate to use for dilated convolution.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any `strides` value != 1.
-    @{args}
-    """
     super(_ConvReparameterization, self).__init__(
         rank=rank,
         filters=filters,
@@ -503,6 +529,63 @@ class Conv1DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of a single integer, specifying the
+      length of the 1D convolution window.
+    strides: An integer or tuple/list of a single integer,
+      specifying the stride length of the convolution.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, length, channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, length)`.
+    dilation_rate: An integer or tuple/list of a single integer, specifying
+      the dilation rate to use for dilated convolution.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any `strides` value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    name: A string, the name of the layer.
+
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -556,7 +639,6 @@ class Conv1DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -579,29 +661,6 @@ class Conv1DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of a single integer, specifying the
-        length of the 1D convolution window.
-      strides: An integer or tuple/list of a single integer,
-        specifying the stride length of the convolution.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, length,
-        channels)` while `channels_first` corresponds to inputs with shape
-        `(batch, channels, length)`.
-      dilation_rate: An integer or tuple/list of a single integer, specifying
-        the dilation rate to use for dilated convolution.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any `strides` value != 1.
-    @{args}
-    """
     super(Conv1DReparameterization, self).__init__(
         rank=1,
         filters=filters,
@@ -624,7 +683,6 @@ class Conv1DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def conv1d_reparameterization(
     inputs,
     filters,
@@ -668,7 +726,7 @@ def conv1d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
+  Arguments:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -688,7 +746,43 @@ def conv1d_reparameterization(
       the dilation rate to use for dilated convolution.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any `strides` value != 1.
-  @{args}
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    name: A string, the name of the layer.
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -780,6 +874,70 @@ class Conv2DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of 2 integers, specifying the
+      height and width of the 2D convolution window.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+    strides: An integer or tuple/list of 2 integers,
+      specifying the strides of the convolution along the height and width.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, height, width, channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, height, width)`.
+
+    dilation_rate: An integer or tuple/list of 2 integers, specifying
+      the dilation rate to use for dilated convolution.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any stride value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    name: A string, the name of the layer.
+
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -836,7 +994,6 @@ class Conv2DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -859,35 +1016,6 @@ class Conv2DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of 2 integers, specifying the
-        height and width of the 2D convolution window.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-      strides: An integer or tuple/list of 2 integers,
-        specifying the strides of the convolution along the height and width.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, height,
-        width, channels)` while `channels_first` corresponds to inputs with
-        shape `(batch, channels, height, width)`.
-      dilation_rate: An integer or tuple/list of 2 integers, specifying
-        the dilation rate to use for dilated convolution.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any stride value != 1.
-    @{args}
-    """
     super(Conv2DReparameterization, self).__init__(
         rank=2,
         filters=filters,
@@ -910,7 +1038,6 @@ class Conv2DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def conv2d_reparameterization(
     inputs,
     filters,
@@ -954,7 +1081,7 @@ def conv2d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
+  Arguments:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -974,13 +1101,50 @@ def conv2d_reparameterization(
       `channels_last` corresponds to inputs with shape
       `(batch, height, width, channels)` while `channels_first` corresponds to
       inputs with shape `(batch, channels, height, width)`.
+
     dilation_rate: An integer or tuple/list of 2 integers, specifying
       the dilation rate to use for dilated convolution.
       Can be a single integer to specify the same value for
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-  @{args}
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    name: A string, the name of the layer.
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -1076,6 +1240,71 @@ class Conv3DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of 3 integers, specifying the
+      depth, height and width of the 3D convolution window.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+    strides: An integer or tuple/list of 3 integers,
+      specifying the strides of the convolution along the depth,
+      height and width.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, depth, height, width, channels)` while `channels_first`
+      corresponds to inputs with shape
+      `(batch, channels, depth, height, width)`.
+    dilation_rate: An integer or tuple/list of 3 integers, specifying
+      the dilation rate to use for dilated convolution.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any stride value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    name: A string, the name of the layer.
+
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -1132,7 +1361,6 @@ class Conv3DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1155,36 +1383,6 @@ class Conv3DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of 3 integers, specifying the
-        depth, height and width of the 3D convolution window.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-      strides: An integer or tuple/list of 3 integers,
-        specifying the strides of the convolution along the depth,
-        height and width.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, depth,
-        height, width, channels)` while `channels_first` corresponds to inputs
-        with shape `(batch, channels, depth, height, width)`.
-      dilation_rate: An integer or tuple/list of 3 integers, specifying
-        the dilation rate to use for dilated convolution.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any stride value != 1.
-    @{args}
-    """
     super(Conv3DReparameterization, self).__init__(
         rank=3,
         filters=filters,
@@ -1207,7 +1405,6 @@ class Conv3DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def conv3d_reparameterization(
     inputs,
     filters,
@@ -1251,7 +1448,7 @@ def conv3d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
+  Arguments:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -1279,7 +1476,43 @@ def conv3d_reparameterization(
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-  @{args}
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    name: A string, the name of the layer.
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -1378,6 +1611,67 @@ class _ConvFlipout(_ConvVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of n integers, specifying the
+      length of the convolution window.
+    strides: An integer or tuple/list of n integers,
+      specifying the stride length of the convolution.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, ..., channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, ...)`.
+    dilation_rate: An integer or tuple/list of n integers, specifying
+      the dilation rate to use for dilated convolution.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any `strides` value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: A string, the name of the layer.
+
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -1400,11 +1694,10 @@ class _ConvFlipout(_ConvVariational):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
-        International Conference on Learning Representations, 2018.
+        Anonymous. OpenReview, 2017.
+        https://openreview.net/forum?id=rJnpifWAb
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -1429,31 +1722,6 @@ class _ConvFlipout(_ConvVariational):
       seed=None,
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      rank: An integer, the rank of the convolution, e.g. "2" for 2D
-        convolution.
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of n integers, specifying the
-        length of the convolution window.
-      strides: An integer or tuple/list of n integers,
-        specifying the stride length of the convolution.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, ...,
-        channels)` while `channels_first` corresponds to inputs with shape
-        `(batch, channels, ...)`.
-      dilation_rate: An integer or tuple/list of n integers, specifying
-        the dilation rate to use for dilated convolution.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any `strides` value != 1.
-    @{args}
-    """
     super(_ConvFlipout, self).__init__(
         rank=rank,
         filters=filters,
@@ -1554,6 +1822,65 @@ class Conv1DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of a single integer, specifying the
+      length of the 1D convolution window.
+    strides: An integer or tuple/list of a single integer,
+      specifying the stride length of the convolution.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, length, channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, length)`.
+    dilation_rate: An integer or tuple/list of a single integer, specifying
+      the dilation rate to use for dilated convolution.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any `strides` value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: A string, the name of the layer.
+
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -1605,11 +1932,10 @@ class Conv1DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
-        International Conference on Learning Representations, 2018.
+        Anonymous. OpenReview, 2017.
+        https://openreview.net/forum?id=rJnpifWAb
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1633,29 +1959,6 @@ class Conv1DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of a single integer, specifying the
-        length of the 1D convolution window.
-      strides: An integer or tuple/list of a single integer,
-        specifying the stride length of the convolution.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, length,
-        channels)` while `channels_first` corresponds to inputs with shape
-        `(batch, channels, length)`.
-      dilation_rate: An integer or tuple/list of a single integer, specifying
-        the dilation rate to use for dilated convolution.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any `strides` value != 1.
-    @{args}
-    """
     super(Conv1DFlipout, self).__init__(
         rank=1,
         filters=filters,
@@ -1679,7 +1982,6 @@ class Conv1DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def conv1d_flipout(
     inputs,
     filters,
@@ -1727,7 +2029,7 @@ def conv1d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
+  Arguments:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -1747,7 +2049,45 @@ def conv1d_flipout(
       the dilation rate to use for dilated convolution.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any `strides` value != 1.
-  @{args}
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: A string, the name of the layer.
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -1790,8 +2130,8 @@ def conv1d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
-        International Conference on Learning Representations, 2018.
+        Anonymous. OpenReview, 2017.
+        https://openreview.net/forum?id=rJnpifWAb
   """
   layer = Conv1DFlipout(
       filters=filters,
@@ -1844,6 +2184,72 @@ class Conv2DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of 2 integers, specifying the
+      height and width of the 2D convolution window.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+    strides: An integer or tuple/list of 2 integers,
+      specifying the strides of the convolution along the height and width.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, height, width, channels)` while `channels_first` corresponds to
+      inputs with shape `(batch, channels, height, width)`.
+
+    dilation_rate: An integer or tuple/list of 2 integers, specifying
+      the dilation rate to use for dilated convolution.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any stride value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: A string, the name of the layer.
+
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -1898,11 +2304,10 @@ class Conv2DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
-        International Conference on Learning Representations, 2018.
+        Anonymous. OpenReview, 2017.
+        https://openreview.net/forum?id=rJnpifWAb
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1926,35 +2331,6 @@ class Conv2DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of 2 integers, specifying the
-        height and width of the 2D convolution window.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-      strides: An integer or tuple/list of 2 integers,
-        specifying the strides of the convolution along the height and width.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, height,
-        width, channels)` while `channels_first` corresponds to inputs with
-        shape `(batch, channels, height, width)`.
-      dilation_rate: An integer or tuple/list of 2 integers, specifying
-        the dilation rate to use for dilated convolution.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any stride value != 1.
-    @{args}
-    """
     super(Conv2DFlipout, self).__init__(
         rank=2,
         filters=filters,
@@ -1978,7 +2354,6 @@ class Conv2DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def conv2d_flipout(
     inputs,
     filters,
@@ -2026,7 +2401,7 @@ def conv2d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
+  Arguments:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -2046,13 +2421,52 @@ def conv2d_flipout(
       `channels_last` corresponds to inputs with shape
       `(batch, height, width, channels)` while `channels_first` corresponds to
       inputs with shape `(batch, channels, height, width)`.
+
     dilation_rate: An integer or tuple/list of 2 integers, specifying
       the dilation rate to use for dilated convolution.
       Can be a single integer to specify the same value for
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-  @{args}
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: A string, the name of the layer.
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -2099,8 +2513,8 @@ def conv2d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
-        International Conference on Learning Representations, 2018.
+        Anonymous. OpenReview, 2017.
+        https://openreview.net/forum?id=rJnpifWAb
   """
   layer = Conv2DFlipout(
       filters=filters,
@@ -2153,6 +2567,73 @@ class Conv3DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Arguments:
+    filters: Integer, the dimensionality of the output space (i.e. the number
+      of filters in the convolution).
+    kernel_size: An integer or tuple/list of 3 integers, specifying the
+      depth, height and width of the 3D convolution window.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+    strides: An integer or tuple/list of 3 integers,
+      specifying the strides of the convolution along the depth,
+      height and width.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+      Specifying any stride value != 1 is incompatible with specifying
+      any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string, one of `channels_last` (default) or `channels_first`.
+      The ordering of the dimensions in the inputs.
+      `channels_last` corresponds to inputs with shape
+      `(batch, depth, height, width, channels)` while `channels_first`
+      corresponds to inputs with shape
+      `(batch, channels, depth, height, width)`.
+    dilation_rate: An integer or tuple/list of 3 integers, specifying
+      the dilation rate to use for dilated convolution.
+      Can be a single integer to specify the same value for
+      all spatial dimensions.
+      Currently, specifying any `dilation_rate` value != 1 is
+      incompatible with specifying any stride value != 1.
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: A string, the name of the layer.
+
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -2207,11 +2688,10 @@ class Conv3DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
-        International Conference on Learning Representations, 2018.
+        Anonymous. OpenReview, 2017.
+        https://openreview.net/forum?id=rJnpifWAb
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -2235,36 +2715,6 @@ class Conv3DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-      filters: Integer, the dimensionality of the output space (i.e. the number
-        of filters in the convolution).
-      kernel_size: An integer or tuple/list of 3 integers, specifying the
-        depth, height and width of the 3D convolution window.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-      strides: An integer or tuple/list of 3 integers,
-        specifying the strides of the convolution along the depth,
-        height and width.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-        Specifying any stride value != 1 is incompatible with specifying
-        any `dilation_rate` value != 1.
-      padding: One of `"valid"` or `"same"` (case-insensitive).
-      data_format: A string, one of `channels_last` (default) or
-        `channels_first`. The ordering of the dimensions in the inputs.
-        `channels_last` corresponds to inputs with shape `(batch, depth,
-        height, width, channels)` while `channels_first` corresponds to inputs
-        with shape `(batch, channels, depth, height, width)`.
-      dilation_rate: An integer or tuple/list of 3 integers, specifying
-        the dilation rate to use for dilated convolution.
-        Can be a single integer to specify the same value for
-        all spatial dimensions.
-        Currently, specifying any `dilation_rate` value != 1 is
-        incompatible with specifying any stride value != 1.
-    @{args}
-    """
     super(Conv3DFlipout, self).__init__(
         rank=3,
         filters=filters,
@@ -2288,7 +2738,6 @@ class Conv3DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def conv3d_flipout(
     inputs,
     filters,
@@ -2336,7 +2785,7 @@ def conv3d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
+  Arguments:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -2364,7 +2813,45 @@ def conv3d_flipout(
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-  @{args}
+    activation: Activation function. Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Optional regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: A string, the name of the layer.
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -2411,8 +2898,8 @@ def conv3d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
-        International Conference on Learning Representations, 2018.
+        Anonymous. OpenReview, 2017.
+        https://openreview.net/forum?id=rJnpifWAb
   """
   layer = Conv3DFlipout(
       filters=filters,
diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
index 1e4a445a33..591a8e553d 100644
--- a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
+++ b/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
@@ -19,7 +19,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.bayesflow.python.ops import docstring_util
 from tensorflow.contrib.bayesflow.python.ops import layers_util
 from tensorflow.contrib.distributions.python.ops import independent as independent_lib
 from tensorflow.python.framework import dtypes
@@ -34,53 +33,6 @@ from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.ops.distributions import util as distribution_util
 
 
-doc_args = """  units: Integer or Long, dimensionality of the output space.
-  activation: Activation function (`callable`). Set it to None to maintain a
-    linear activation.
-  activity_regularizer: Regularizer function for the output.
-  trainable: Boolean, if `True` also add variables to the graph collection
-    `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-  kernel_posterior_fn: Python `callable` which creates
-    `tf.distributions.Distribution` instance representing the surrogate
-    posterior of the `kernel` parameter. Default value:
-    `default_mean_field_normal_fn()`.
-  kernel_posterior_tensor_fn: Python `callable` which takes a
-    `tf.distributions.Distribution` instance and returns a representative
-    value. Default value: `lambda d: d.sample()`.
-  kernel_prior_fn: Python `callable` which creates `tf.distributions`
-    instance. See `default_mean_field_normal_fn` docstring for required
-    parameter signature.
-    Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-  kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-    distribution, prior distribution and random variate sample(s) from the
-    surrogate posterior and computes or approximates the KL divergence. The
-    distributions are `tf.distributions.Distribution`-like instances and the
-    sample is a `Tensor`.
-  bias_posterior_fn: Python `callable` which creates
-    `tf.distributions.Distribution` instance representing the surrogate
-    posterior of the `bias` parameter. Default value:
-    `default_mean_field_normal_fn(is_singular=True)` (which creates an
-    instance of `tf.distributions.Deterministic`).
-  bias_posterior_tensor_fn: Python `callable` which takes a
-    `tf.distributions.Distribution` instance and returns a representative
-    value. Default value: `lambda d: d.sample()`.
-  bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-    See `default_mean_field_normal_fn` docstring for required parameter
-    signature. Default value: `None` (no prior, no variational inference)
-  bias_divergence_fn: Python `callable` which takes the surrogate posterior
-    distribution, prior distribution and random variate sample(s) from the
-    surrogate posterior and computes or approximates the KL divergence. The
-    distributions are `tf.distributions.Distribution`-like instances and the
-    sample is a `Tensor`.
-  seed: Python scalar `int` which initializes the random number
-    generator. Default value: `None` (i.e., use global seed).
-  name: Python `str`, the name of the layer. Layers with the same name will
-    share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-    such cases.
-  reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-    layer by the same name."""
-
-
 class _DenseVariational(layers_lib.Layer):
   """Abstract densely-connected class (private, used as implementation base).
 
@@ -98,6 +50,51 @@ class _DenseVariational(layers_lib.Layer):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Args:
+    units: Integer or Long, dimensionality of the output space.
+    activation: Activation function (`callable`). Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    name: Python `str`, the name of the layer. Layers with the same name will
+      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+      such cases.
+    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+      layer by the same name.
+
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -112,7 +109,6 @@ class _DenseVariational(layers_lib.Layer):
     bias_divergence_fn: `callable` returning divergence.
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -130,11 +126,6 @@ class _DenseVariational(layers_lib.Layer):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-    @{args}
-    """
     super(_DenseVariational, self).__init__(
         trainable=trainable,
         name=name,
@@ -283,6 +274,51 @@ class DenseReparameterization(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Args:
+    units: Integer or Long, dimensionality of the output space.
+    activation: Activation function (`callable`). Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    name: Python `str`, the name of the layer. Layers with the same name will
+      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+      such cases.
+    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+      layer by the same name.
+
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -327,7 +363,6 @@ class DenseReparameterization(_DenseVariational):
         International Conference on Learning Representations, 2014.
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -346,11 +381,6 @@ class DenseReparameterization(_DenseVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-    @{args}
-    """
     super(DenseReparameterization, self).__init__(
         units=units,
         activation=activation,
@@ -375,7 +405,6 @@ class DenseReparameterization(_DenseVariational):
     return self._matmul(inputs, self.kernel_posterior_tensor)
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def dense_reparameterization(
     inputs,
     units,
@@ -415,7 +444,49 @@ def dense_reparameterization(
 
   Args:
     inputs: Tensor input.
-  @{args}
+    units: Integer or Long, dimensionality of the output space.
+    activation: Activation function (`callable`). Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    name: Python `str`, the name of the layer. Layers with the same name will
+      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+      such cases.
+    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+      layer by the same name.
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
@@ -492,6 +563,51 @@ class DenseLocalReparameterization(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Args:
+    units: Integer or Long, dimensionality of the output space.
+    activation: Activation function (`callable`). Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    name: Python `str`, the name of the layer. Layers with the same name will
+      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+      such cases.
+    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+      layer by the same name.
+
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -536,7 +652,6 @@ class DenseLocalReparameterization(_DenseVariational):
         Neural Information Processing Systems, 2015.
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -555,11 +670,6 @@ class DenseLocalReparameterization(_DenseVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-    @{args}
-    """
     super(DenseLocalReparameterization, self).__init__(
         units=units,
         activation=activation,
@@ -595,7 +705,6 @@ class DenseLocalReparameterization(_DenseVariational):
     return self.kernel_posterior_affine_tensor
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def dense_local_reparameterization(
     inputs,
     units,
@@ -636,7 +745,49 @@ def dense_local_reparameterization(
 
   Args:
     inputs: Tensor input.
-  @{args}
+    units: Integer or Long, dimensionality of the output space.
+    activation: Activation function (`callable`). Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    name: Python `str`, the name of the layer. Layers with the same name will
+      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+      such cases.
+    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+      layer by the same name.
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
@@ -715,6 +866,53 @@ class DenseFlipout(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
+  Args:
+    units: Integer or Long, dimensionality of the output space.
+    activation: Activation function (`callable`). Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: Python `str`, the name of the layer. Layers with the same name will
+      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+      such cases.
+    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+      layer by the same name.
+
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -761,7 +959,6 @@ class DenseFlipout(_DenseVariational):
         https://openreview.net/forum?id=rJnpifWAb
   """
 
-  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -781,11 +978,6 @@ class DenseFlipout(_DenseVariational):
       seed=None,
       name=None,
       **kwargs):
-    """Construct layer.
-
-    Args:
-    @{args}
-    """
     super(DenseFlipout, self).__init__(
         units=units,
         activation=activation,
@@ -839,7 +1031,6 @@ class DenseFlipout(_DenseVariational):
     return outputs
 
 
-@docstring_util.expand_docstring(args=doc_args)
 def dense_flipout(
     inputs,
     units,
@@ -883,7 +1074,51 @@ def dense_flipout(
 
   Args:
     inputs: Tensor input.
-  @{args}
+    units: Integer or Long, dimensionality of the output space.
+    activation: Activation function (`callable`). Set it to None to maintain a
+      linear activation.
+    activity_regularizer: Regularizer function for the output.
+    trainable: Boolean, if `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+    kernel_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `kernel` parameter. Default value:
+      `default_mean_field_normal_fn()`.
+    kernel_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    kernel_prior_fn: Python `callable` which creates `tf.distributions`
+      instance. See `default_mean_field_normal_fn` docstring for required
+      parameter signature.
+      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    bias_posterior_fn: Python `callable` which creates
+      `tf.distributions.Distribution` instance representing the surrogate
+      posterior of the `bias` parameter. Default value:
+      `default_mean_field_normal_fn(is_singular=True)` (which creates an
+      instance of `tf.distributions.Deterministic`).
+    bias_posterior_tensor_fn: Python `callable` which takes a
+      `tf.distributions.Distribution` instance and returns a representative
+      value. Default value: `lambda d: d.sample()`.
+    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+      See `default_mean_field_normal_fn` docstring for required parameter
+      signature. Default value: `None` (no prior, no variational inference)
+    bias_divergence_fn: Python `callable` which takes the surrogate posterior
+      distribution, prior distribution and random variate sample(s) from the
+      surrogate posterior and computes or approximates the KL divergence. The
+      distributions are `tf.distributions.Distribution`-like instances and the
+      sample is a `Tensor`.
+    seed: Python scalar `int` which initializes the random number
+      generator. Default value: `None` (i.e., use global seed).
+    name: Python `str`, the name of the layer. Layers with the same name will
+      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+      such cases.
+    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+      layer by the same name.
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
-- 
GitLab


From 309fb111931bb7aae3e716594e5c53ac4976e76a Mon Sep 17 00:00:00 2001
From: "harumitsu.nobuta" <halhorn@halmidi.com>
Date: Tue, 20 Feb 2018 17:29:29 +0900
Subject: [PATCH 062/575] explicit dtype converting

---
 tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
index d6184d6109..554eb24e52 100644
--- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
+++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py
@@ -724,7 +724,7 @@ def _mask_probs(probs, eos_token, finished):
       eos_token,
       vocab_size,
       dtype=probs.dtype,
-      on_value=0.,
+      on_value=ops.convert_to_tensor(0., dtype=probs.dtype),
       off_value=probs.dtype.min)
   finished_probs = array_ops.tile(
       array_ops.reshape(finished_row, [1, 1, -1]),
-- 
GitLab


From e95eeebbe55d9f4dc2d99d04fd5349842b34feaa Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Yan=20Facai=20=28=E9=A2=9C=E5=8F=91=E6=89=8D=29?=
 <facai.yan@gmail.com>
Date: Tue, 20 Feb 2018 22:02:27 +0800
Subject: [PATCH 063/575] BLD: ci allows bad name user

---
 tensorflow/tools/ci_build/builds/with_the_same_user | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/ci_build/builds/with_the_same_user b/tensorflow/tools/ci_build/builds/with_the_same_user
index 5817716c8d..d4bf546d40 100755
--- a/tensorflow/tools/ci_build/builds/with_the_same_user
+++ b/tensorflow/tools/ci_build/builds/with_the_same_user
@@ -36,8 +36,13 @@ else
   rm /this_is_writable_file_system
 fi
 
+if [ -n "${CI_BUILD_USER_FORCE_BADNAME}" ]; then
+  ADDUSER_OPTS="--force-badname"
+fi
+
 getent group "${CI_BUILD_GID}" || addgroup --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}"
-getent passwd "${CI_BUILD_UID}" || adduser --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" \
+getent passwd "${CI_BUILD_UID}" || adduser ${ADDUSER_OPTS} \
+    --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" \
     --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" \
     --disabled-password --home "${CI_BUILD_HOME}" --quiet "${CI_BUILD_USER}"
 usermod -a -G sudo "${CI_BUILD_USER}"
-- 
GitLab


From 312076e38f8bd5ec582351d04f6b671ead06facb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 07:28:14 -0800
Subject: [PATCH 064/575] Internal change.

PiperOrigin-RevId: 186300438
---
 tensorflow/core/BUILD | 61 ++++++++++++++++++++++++++++++++++++-------
 1 file changed, 52 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 30ac270109..2a8aefa3c4 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -988,22 +988,15 @@ filegroup(
 
 # Core sources for Android builds.
 filegroup(
-    name = "mobile_srcs",
+    name = "mobile_srcs_no_runtime",
     srcs = [
         ":proto_text_srcs_all",
-        "//tensorflow/core/kernels:android_srcs",
         "//tensorflow/core/platform/default/build_config:android_srcs",
-        "//tensorflow/core/util/ctc:android_srcs",
-        "//tensorflow/core/util/tensor_bundle:android_srcs",
     ] + glob(
         [
             "client/**/*.cc",
-            "common_runtime/**/*.h",
-            "common_runtime/**/*.cc",
             "framework/**/*.h",
             "framework/**/*.cc",
-            "graph/**/*.h",
-            "graph/**/*.cc",
             "lib/**/*.h",
             "lib/**/*.cc",
             "platform/**/*.h",
@@ -1019,7 +1012,6 @@ filegroup(
             "**/*main.cc",
             "debug/**/*",
             "framework/op_gen_*",
-            "graph/dot.*",
             "lib/jpeg/**/*",
             "lib/png/**/*",
             "lib/gif/**/*",
@@ -1036,6 +1028,10 @@ filegroup(
             "platform/stream_executor.*",
             "platform/windows/**/*",
             "user_ops/**/*.cu.cc",
+            "util/ctc/*.h",
+            "util/ctc/*.cc",
+            "util/tensor_bundle/*.h",
+            "util/tensor_bundle/*.cc",
             "common_runtime/gpu/**/*",
             "common_runtime/gpu_device_factory.*",
         ],
@@ -1043,6 +1039,41 @@ filegroup(
     visibility = ["//visibility:public"],
 )
 
+filegroup(
+    name = "mobile_srcs_only_runtime",
+    srcs = [
+        "//tensorflow/core/kernels:android_srcs",
+        "//tensorflow/core/util/ctc:android_srcs",
+        "//tensorflow/core/util/tensor_bundle:android_srcs",
+    ] + glob(
+        [
+            "common_runtime/**/*.h",
+            "common_runtime/**/*.cc",
+            "graph/**/*.h",
+            "graph/**/*.cc",
+        ],
+        exclude = [
+            "**/*test.*",
+            "**/*testutil*",
+            "**/*testlib*",
+            "**/*main.cc",
+            "common_runtime/gpu/**/*",
+            "common_runtime/gpu_device_factory.*",
+            "graph/dot.*",
+        ],
+    ),
+    visibility = ["//visibility:public"],
+)
+
+filegroup(
+    name = "mobile_srcs",
+    srcs = [
+        ":mobile_srcs_no_runtime",
+        ":mobile_srcs_only_runtime",
+    ],
+    visibility = ["//visibility:public"],
+)
+
 # Native library support for Android applications.  Does not contain
 # operators, use :android_tensorflow_lib if you want full operator
 # support.
@@ -3642,6 +3673,18 @@ filegroup(
     visibility = ["//tensorflow:__subpackages__"],
 )
 
+alias(
+    name = "android_srcs_no_runtime",
+    actual = ":mobile_srcs_no_runtime",
+    visibility = ["//visibility:public"],
+)
+
+alias(
+    name = "android_srcs_only_runtime",
+    actual = ":mobile_srcs_only_runtime",
+    visibility = ["//visibility:public"],
+)
+
 alias(
     name = "android_srcs",
     actual = ":mobile_srcs",
-- 
GitLab


From 7193af53a38017df9b617be60c4f44414b73bcb4 Mon Sep 17 00:00:00 2001
From: terrytangyuan <terrytangyuan@gmail.com>
Date: Tue, 20 Feb 2018 11:50:13 -0500
Subject: [PATCH 065/575] Fix lint issues and BUILD file

---
 tensorflow/contrib/timeseries/python/timeseries/BUILD |  2 +-
 .../contrib/timeseries/python/timeseries/head.py      | 11 ++++++-----
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD
index 862a05fa9f..ed3ed4c0e1 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/BUILD
+++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD
@@ -146,7 +146,7 @@ py_library(
         "//tensorflow/python/estimator:estimator_py",
         "//tensorflow/python/estimator:export",
         "//tensorflow/python/estimator:head",
-        "//tensorflow/python/estimator:metric_keys"
+        "//tensorflow/python/estimator:metric_keys",
     ],
 )
 
diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py
index 7633ca088a..9e62761e7e 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/head.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/head.py
@@ -77,16 +77,17 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
   def name(self):
     return self._name
 
-  # TODO(terrytangyuan): consolidate model_outputs and _Head.LossSpec once _Head.create_loss
-  # becomes extendable
+  # TODO(terrytangyuan): consolidate `model_outputs` and `_Head.LossSpec`
+  # once `_Head.create_loss` becomes extendable
   def create_loss(self, features, mode, logits=None, labels=None):
     """See `_Head`."""
-    with variable_scope.variable_scope("model", reuse=variable_scope.AUTO_REUSE):
+    with variable_scope.variable_scope(
+        "model", reuse=variable_scope.AUTO_REUSE):
       model_outputs = self.state_manager.define_loss(
           self.model, features, mode)
       summary.scalar(
-        head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS),
-        model_outputs.loss)
+          head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS),
+          model_outputs.loss)
     return model_outputs
 
   @property
-- 
GitLab


From 9ff53934f38362e3c422ef1faab661a3ee50e778 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 09:20:28 -0800
Subject: [PATCH 066/575] Implementation of `len` that uses multiple dispatch.
 Replaces the current blank `tf.shape()[0]` code.

PiperOrigin-RevId: 186313178
---
 .../py2tf/converters/builtin_functions.py     |  2 +-
 .../converters/builtin_functions_test.py      |  2 +
 tensorflow/contrib/py2tf/utils/__init__.py    |  1 +
 tensorflow/contrib/py2tf/utils/misc.py        | 13 ++++++
 tensorflow/contrib/py2tf/utils/misc_test.py   | 41 +++++++++++++++----
 5 files changed, 50 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/py2tf/converters/builtin_functions.py b/tensorflow/contrib/py2tf/converters/builtin_functions.py
index 2eb00f9057..e69038aced 100644
--- a/tensorflow/contrib/py2tf/converters/builtin_functions.py
+++ b/tensorflow/contrib/py2tf/converters/builtin_functions.py
@@ -38,7 +38,7 @@ class BuiltinFunctionTransformer(transformer.Base):
 
   def _convert_len(self, node):
     template = """
-      tf.shape(args)[0]
+      py2tf_utils.dynamic_len(args)
     """
     return templates.replace(template, args=node.args)[0].value
 
diff --git a/tensorflow/contrib/py2tf/converters/builtin_functions_test.py b/tensorflow/contrib/py2tf/converters/builtin_functions_test.py
index b279ff77ef..eb60a1d8ae 100644
--- a/tensorflow/contrib/py2tf/converters/builtin_functions_test.py
+++ b/tensorflow/contrib/py2tf/converters/builtin_functions_test.py
@@ -47,6 +47,8 @@ class BuiltinFunctionsTest(converter_test_base.TestCase):
                          sess.run(
                              result.test_fn(constant_op.constant([0, 0, 0]))))
 
+        self.assertEqual(3, result.test_fn([0, 0, 0]))
+
   def test_print_with_op(self):
 
     def test_fn(a):
diff --git a/tensorflow/contrib/py2tf/utils/__init__.py b/tensorflow/contrib/py2tf/utils/__init__.py
index 0a1b993fd3..d931322bf3 100644
--- a/tensorflow/contrib/py2tf/utils/__init__.py
+++ b/tensorflow/contrib/py2tf/utils/__init__.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 from tensorflow.contrib.py2tf.utils.context_managers import control_dependency_on_returns
 from tensorflow.contrib.py2tf.utils.misc import alias_tensors
+from tensorflow.contrib.py2tf.utils.misc import dynamic_len
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_cond
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_while
 from tensorflow.contrib.py2tf.utils.printing import call_print
diff --git a/tensorflow/contrib/py2tf/utils/misc.py b/tensorflow/contrib/py2tf/utils/misc.py
index 1b06caf0bd..7548048388 100644
--- a/tensorflow/contrib/py2tf/utils/misc.py
+++ b/tensorflow/contrib/py2tf/utils/misc.py
@@ -19,9 +19,22 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 
 
+def dynamic_len(list_or_tensor):
+  """Implementation of len using dynamic dispatch."""
+  if tensor_util.is_tensor(list_or_tensor):
+    shape = list_or_tensor.shape
+    if not shape:
+      raise ValueError(
+          'len requires non-zero rank for tensor "%s"' % list_or_tensor)
+    return array_ops.shape(list_or_tensor)[0]
+
+  return len(list_or_tensor)
+
+
 def alias_tensors(*args):
   """Wrap any Tensor arguments with an identity op.
 
diff --git a/tensorflow/contrib/py2tf/utils/misc_test.py b/tensorflow/contrib/py2tf/utils/misc_test.py
index bfcb304c83..ec88e7cb74 100644
--- a/tensorflow/contrib/py2tf/utils/misc_test.py
+++ b/tensorflow/contrib/py2tf/utils/misc_test.py
@@ -18,29 +18,54 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.py2tf.utils import misc
-from tensorflow.python.framework import constant_op
-from tensorflow.python.ops import variables
+from tensorflow.contrib.py2tf.utils.misc import alias_tensors
+from tensorflow.contrib.py2tf.utils.misc import dynamic_len
+from tensorflow.python.framework.constant_op import constant
+from tensorflow.python.ops.variables import Variable
 from tensorflow.python.platform import test
 
 
 class ContextManagersTest(test.TestCase):
 
+  def test_dynamic_len_tf_scalar(self):
+    a = constant(1)
+
+    with self.assertRaises(ValueError):
+      with self.test_session() as sess:
+        sess.run(dynamic_len(a))
+
+  def test_dynamic_len_tf_array(self):
+    a = constant([1, 2, 3])
+
+    with self.test_session() as sess:
+      self.assertEqual(3, sess.run(dynamic_len(a)))
+
+  def test_dynamic_len_tf_matrix(self):
+    a = constant([[1, 2], [3, 4]])
+
+    with self.test_session() as sess:
+      self.assertEqual(2, sess.run(dynamic_len(a)))
+
+  def test_dynamic_len_py_list(self):
+    a = [3] * 5
+
+    self.assertEqual(5, dynamic_len(a))
+
   def test_alias_single_tensor(self):
-    a = constant_op.constant(1)
+    a = constant(1)
 
-    new_a = misc.alias_tensors(a)
+    new_a = alias_tensors(a)
     self.assertFalse(new_a is a)
     with self.test_session() as sess:
       self.assertEqual(1, sess.run(new_a))
 
   def test_alias_tensors(self):
-    a = constant_op.constant(1)
-    v = variables.Variable(2)
+    a = constant(1)
+    v = Variable(2)
     s = 'a'
     l = [1, 2, 3]
 
-    new_a, new_v, new_s, new_l = misc.alias_tensors(a, v, s, l)
+    new_a, new_v, new_s, new_l = alias_tensors(a, v, s, l)
 
     self.assertFalse(new_a is a)
     self.assertTrue(new_v is v)
-- 
GitLab


From fb8fe7392808c26717388b784ae139c5618867e4 Mon Sep 17 00:00:00 2001
From: Martin Wicke <577277+martinwicke@users.noreply.github.com>
Date: Tue, 20 Feb 2018 09:29:18 -0800
Subject: [PATCH 067/575] Grammar

---
 tensorflow/docs_src/mobile/mobile_intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md
index c406c1852f..69b63ae7d2 100644
--- a/tensorflow/docs_src/mobile/mobile_intro.md
+++ b/tensorflow/docs_src/mobile/mobile_intro.md
@@ -235,7 +235,7 @@ TensorFlow [on Github](https://github.com/tensorflow/models) that you can look
 through. Lean towards the simplest model you can find, and try to get started as
 soon as you have even a small amount of labelled data, since you’ll get the best
 results when you’re able to iterate quickly. The shorter the time it takes to
-try training a model and running it in it's real application, the better overall
+try training a model and running it in its real application, the better overall
 results you’ll see. It’s common for an algorithm to get great training accuracy
 numbers but then fail to be useful within a real application because there’s a
 mismatch between the dataset and real usage. Prototype end-to-end usage as soon
-- 
GitLab


From 35deaa9f7a659e02a6a4b2bf470a9f23a509b1e1 Mon Sep 17 00:00:00 2001
From: Igor Saprykin <isaprykin@google.com>
Date: Tue, 20 Feb 2018 09:34:09 -0800
Subject: [PATCH 068/575] Add API to switch certain parts of Graph state to be
 thread-local.

For example, this can allow two threads to create ops under varying ops.device().

PiperOrigin-RevId: 186314978
---
 tensorflow/python/framework/ops.py            |  88 ++++++++-
 tensorflow/python/framework/ops_test.py       | 175 ++++++++++++++++++
 .../tools/api/golden/tensorflow.-graph.pbtxt  |   4 +
 3 files changed, 263 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 398b3f67e2..b440e149b7 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -2707,15 +2707,21 @@ class Graph(object):
     self._name_stack = ""
     # Maps a name used in the graph to the next id to use for that name.
     self._names_in_use = {}
+    self._stack_state_is_thread_local = False
+    self._thread_local = threading.local()
     # Functions that will be applied to choose a device if none is specified.
-    self._device_function_stack = []
+    # After switch_to_thread_local(), self._thread_local._device_function_stack
+    # is used instead.
+    self._graph_device_function_stack = []
     # Default original_op applied to new ops.
     self._default_original_op = None
     # Current control flow context. It could be either CondContext or
     # WhileContext defined in ops/control_flow_ops.py
     self._control_flow_context = None
     # A new node will depend of the union of all of the nodes in the stack.
-    self._control_dependencies_stack = []
+    # After switch_to_thread_local(),
+    # self._thread_local._control_dependencies_stack is used instead.
+    self._graph_control_dependencies_stack = []
     # Arbitrary collections of objects.
     self._collections = {}
     # The graph-level random seed
@@ -2737,8 +2743,9 @@ class Graph(object):
         producer=versions.GRAPH_DEF_VERSION,
         min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER)
     self._building_function = False
-    # Stack of colocate_with ops
-    self._colocation_stack = []
+    # Stack of colocate_with ops. After switch_to_thread_local(),
+    # self._thread_local._colocation_stack is used instead.
+    self._graph_colocation_stack = []
     # Set of tensors that are dangerous to feed!
     self._unfeedable_tensors = set()
     # Set of operations that are dangerous to fetch!
@@ -4669,6 +4676,79 @@ class Graph(object):
     else:
       return tensor_or_op not in self._unfetchable_ops
 
+  def switch_to_thread_local(self):
+    """Make device, colocation and dependencies stacks thread-local.
+
+    Device, colocation and dependencies stacks are not thread-local be default.
+    If multiple threads access them, then the state is shared.  This means that
+    one thread may affect the behavior of another thread.
+
+    After this method is called, the stacks become thread-local.  If multiple
+    threads access them, then the state is not shared.  Each thread uses its own
+    value; a thread doesn't affect other threads by mutating such a stack.
+
+    The initial value for every thread's stack is set to the current value
+    of the stack when `switch_to_thread_local()` was first called.
+    """
+    if not self._stack_state_is_thread_local:
+      self._stack_state_is_thread_local = True
+
+  @property
+  def _device_function_stack(self):
+    if self._stack_state_is_thread_local:
+      # This may be called from a thread where device_function_stack doesn't yet
+      # exist.
+      if not hasattr(self._thread_local, "_device_function_stack"):
+        self._thread_local._device_function_stack = (
+            self._graph_device_function_stack[:])
+      return self._thread_local._device_function_stack
+    else:
+      return self._graph_device_function_stack
+
+  @_device_function_stack.setter
+  def _device_function_stack(self, device_function_stack):
+    if self._stack_state_is_thread_local:
+      self._thread_local._device_function_stack = device_function_stack
+    else:
+      self._graph_device_function_stack = device_function_stack
+
+  @property
+  def _colocation_stack(self):
+    if self._stack_state_is_thread_local:
+      # This may be called from a thread where colocation_stack doesn't yet
+      # exist.
+      if not hasattr(self._thread_local, "_colocation_stack"):
+        self._thread_local._colocation_stack = self._graph_colocation_stack[:]
+      return self._thread_local._colocation_stack
+    else:
+      return self._graph_colocation_stack
+
+  @_colocation_stack.setter
+  def _colocation_stack(self, colocation_stack):
+    if self._stack_state_is_thread_local:
+      self._thread_local._colocation_stack = colocation_stack
+    else:
+      self._graph_colocation_stack = colocation_stack
+
+  @property
+  def _control_dependencies_stack(self):
+    if self._stack_state_is_thread_local:
+      # This may be called from a thread where control_dependencies_stack
+      # doesn't yet exist.
+      if not hasattr(self._thread_local, "_control_dependencies_stack"):
+        self._thread_local._control_dependencies_stack = (
+            self._graph_control_dependencies_stack[:])
+      return self._thread_local._control_dependencies_stack
+    else:
+      return self._graph_control_dependencies_stack
+
+  @_control_dependencies_stack.setter
+  def _control_dependencies_stack(self, control_dependencies):
+    if self._stack_state_is_thread_local:
+      self._thread_local._control_dependencies_stack = control_dependencies
+    else:
+      self._graph_control_dependencies_stack = control_dependencies
+
 
 # TODO(agarwal): currently device directives in an outer eager scope will not
 # apply to inner graph mode code. Fix that.
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index c6deafd89e..a141fe6340 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import gc
+import threading
 import weakref
 
 from tensorflow.core.framework import attr_value_pb2
@@ -1381,6 +1382,180 @@ class DeviceTest(test_util.TensorFlowTestCase):
     """, gd)
 
 
+@test_util.with_c_api
+class MultithreadedGraphStateTest(test_util.TensorFlowTestCase):
+
+  class TestThread(threading.Thread):
+
+    def __init__(self, graph, replica_id):
+      super(MultithreadedGraphStateTest.TestThread, self).__init__()
+      self._graph = graph
+      self._replica_id = replica_id
+      # This thread sets this event when it mutated the graph.  The caller can
+      # wait for that.
+      self.has_mutated_graph = threading.Event()
+      # This thread waits for when it should continue.  The caller can set this
+      # event.
+      self.should_continue = threading.Event()
+
+    def run(self):
+      # Mutate a graph's stack, then set `has_mutated_graph`, then wait for
+      # `should_continue`, then add an op to the graph affected by the graph's
+      # stack.
+      raise NotImplementedError("must be implemented in descendants")
+
+  def testDeviceFunctionStack(self):
+
+    class DeviceSettingThread(self.TestThread):
+
+      def run(self):
+        with g.device("/job:worker/replica:{}".format(self._replica_id)):
+          self.has_mutated_graph.set()
+          self.should_continue.wait()
+          self.should_continue.clear()
+          g.create_op(
+              "FloatOutput", [], [dtypes.float32],
+              name="FloatOutput_{}".format(self._replica_id))
+
+    g = ops.Graph()
+    # If `switch_to_thread` isn't called, then device placement of the ops
+    # below is not deterministic.
+    g.switch_to_thread_local()
+    threads = [DeviceSettingThread(g, i) for i in range(3)]
+    for t in threads:
+      t.start()
+      t.has_mutated_graph.wait()
+      t.has_mutated_graph.clear()
+    for t in threads:
+      t.should_continue.set()
+      t.join()
+
+    gd = g.as_graph_def()
+    self.assertProtoEqualsVersion("""
+      node { name: "FloatOutput_0" op: "FloatOutput"
+             device: "/job:worker/replica:0" }
+      node { name: "FloatOutput_1" op: "FloatOutput"
+             device: "/job:worker/replica:1" }
+      node { name: "FloatOutput_2" op: "FloatOutput"
+             device: "/job:worker/replica:2" }
+    """, gd)
+
+  def testColocateWith(self):
+
+    class ColocatingThread(self.TestThread):
+
+      def __init__(self, graph, replica_id, op_to_colocate_with):
+        super(ColocatingThread, self).__init__(graph, replica_id)
+        self._op_to_colocate_with = op_to_colocate_with
+
+      def run(self):
+        with g.colocate_with(self._op_to_colocate_with):
+          self.has_mutated_graph.set()
+          self.should_continue.wait()
+          self.should_continue.clear()
+          g.create_op(
+              "FloatOutput", [], [dtypes.float32],
+              name="FloatOutput_{}".format(self._replica_id))
+
+    g = ops.Graph()
+    ops_to_colocate_with = []
+    for i in range(3):
+      with g.device("/job:worker/replica:{}".format(i)):
+        ops_to_colocate_with.append(
+            g.create_op(
+                "FloatOutput", [], [dtypes.float32],
+                name="ColocateWithMe_{}".format(i)))
+
+    # If `switch_to_thread` isn't called, then `device` and `attr` values for
+    # the ops below are not deterministic.
+    g.switch_to_thread_local()
+    threads = [
+        ColocatingThread(g, i, ops_to_colocate_with[i]) for i in range(3)
+    ]
+    for t in threads:
+      t.start()
+      t.has_mutated_graph.wait()
+      t.has_mutated_graph.clear()
+    for t in threads:
+      t.should_continue.set()
+      t.join()
+
+    gd = g.as_graph_def()
+    self.assertProtoEqualsVersion("""
+      node { name: "ColocateWithMe_0" op: "FloatOutput"
+             device: "/job:worker/replica:0" }
+      node { name: "ColocateWithMe_1" op: "FloatOutput"
+             device: "/job:worker/replica:1" }
+      node { name: "ColocateWithMe_2" op: "FloatOutput"
+             device: "/job:worker/replica:2" }
+      node { name: "FloatOutput_0" op: "FloatOutput"
+             device: "/job:worker/replica:0"
+             attr { key: "_class"
+               value { list {
+                 s: "loc:@ColocateWithMe_0"}}}}
+      node { name: "FloatOutput_1" op: "FloatOutput"
+             device: "/job:worker/replica:1"
+             attr { key: "_class"
+               value { list {
+                 s: "loc:@ColocateWithMe_1"}}}}
+      node { name: "FloatOutput_2" op: "FloatOutput"
+             device: "/job:worker/replica:2"
+             attr { key: "_class"
+               value { list {
+                 s: "loc:@ColocateWithMe_2"}}}}
+    """, gd)
+
+  def testControlDependencies(self):
+
+    class DependingThread(self.TestThread):
+
+      def __init__(self, graph, replica_id, dependency_op):
+        super(DependingThread, self).__init__(graph, replica_id)
+        self._dependency_op = dependency_op
+
+      def run(self):
+        with g.control_dependencies([self._dependency_op]):
+          self.has_mutated_graph.set()
+          self.should_continue.wait()
+          self.should_continue.clear()
+          g.create_op(
+              "FloatOutput", [], [dtypes.float32],
+              name="FloatOutput_{}".format(self._replica_id))
+
+    g = ops.Graph()
+    dependency_ops = []
+    for i in range(3):
+      dependency_ops.append(
+          g.create_op(
+              "FloatOutput", [], [dtypes.float32],
+              name="ColocateWithMe_{}".format(i)))
+
+    # If `switch_to_thread` isn't called, then `input` values for the ops below
+    # are not deterministic.
+    g.switch_to_thread_local()
+    threads = [DependingThread(g, i, dependency_ops[i]) for i in range(3)]
+    for t in threads:
+      t.start()
+      t.has_mutated_graph.wait()
+      t.has_mutated_graph.clear()
+    for t in threads:
+      t.should_continue.set()
+      t.join()
+
+    gd = g.as_graph_def()
+    self.assertProtoEqualsVersion("""
+      node { name: "ColocateWithMe_0" op: "FloatOutput" }
+      node { name: "ColocateWithMe_1" op: "FloatOutput" }
+      node { name: "ColocateWithMe_2" op: "FloatOutput" }
+      node { name: "FloatOutput_0" op: "FloatOutput"
+             input: "^ColocateWithMe_0" }
+      node { name: "FloatOutput_1" op: "FloatOutput"
+             input: "^ColocateWithMe_1" }
+      node { name: "FloatOutput_2" op: "FloatOutput"
+             input: "^ColocateWithMe_2" }
+    """, gd)
+
+
 @test_util.with_c_api
 class ObjectWithName(object):
 
diff --git a/tensorflow/tools/api/golden/tensorflow.-graph.pbtxt b/tensorflow/tools/api/golden/tensorflow.-graph.pbtxt
index 75361803a3..cdaeb55e30 100644
--- a/tensorflow/tools/api/golden/tensorflow.-graph.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.-graph.pbtxt
@@ -130,6 +130,10 @@ tf_class {
     name: "prevent_fetching"
     argspec: "args=[\'self\', \'op\'], varargs=None, keywords=None, defaults=None"
   }
+  member_method {
+    name: "switch_to_thread_local"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "unique_name"
     argspec: "args=[\'self\', \'name\', \'mark_as_used\'], varargs=None, keywords=None, defaults=[\'True\'], "
-- 
GitLab


From a2841a64372dce192c0e16e5d60a6c73adce403b Mon Sep 17 00:00:00 2001
From: terrytangyuan <terrytangyuan@gmail.com>
Date: Tue, 20 Feb 2018 13:18:59 -0500
Subject: [PATCH 069/575] Move variable scope to outside of create_loss

---
 .../timeseries/python/timeseries/head.py      | 21 ++++++++++---------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py
index 9e62761e7e..a870fa0c3d 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/head.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/head.py
@@ -81,13 +81,11 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
   # once `_Head.create_loss` becomes extendable
   def create_loss(self, features, mode, logits=None, labels=None):
     """See `_Head`."""
-    with variable_scope.variable_scope(
-        "model", reuse=variable_scope.AUTO_REUSE):
-      model_outputs = self.state_manager.define_loss(
-          self.model, features, mode)
-      summary.scalar(
-          head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS),
-          model_outputs.loss)
+    model_outputs = self.state_manager.define_loss(
+        self.model, features, mode)
+    summary.scalar(
+        head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS),
+        model_outputs.loss)
     return model_outputs
 
   @property
@@ -98,7 +96,8 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
   def _train_ops(self, features):
     """Add training ops to the graph."""
     mode = estimator_lib.ModeKeys.TRAIN
-    model_outputs = self.create_loss(features, mode)
+    with variable_scope.variable_scope("model"):
+      model_outputs = self.create_loss(features, mode)
     train_op = optimizers.optimize_loss(
         model_outputs.loss,
         global_step=training_util.get_global_step(),
@@ -113,7 +112,8 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
   def _evaluate_ops(self, features):
     """Add ops for evaluation (aka filtering) to the graph."""
     mode = estimator_lib.ModeKeys.EVAL
-    model_outputs = self.create_loss(features, mode)
+    with variable_scope.variable_scope("model"):
+      model_outputs = self.create_loss(features, mode)
     metrics = {}
     # Just output in-sample predictions for the last chunk seen
     for prediction_key, prediction_value in model_outputs.predictions.items():
@@ -143,7 +143,8 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
     """Add ops for serving to the graph."""
     with variable_scope.variable_scope("model"):
       prediction_outputs = self.model.predict(features=features)
-    filtering_outputs = self.create_loss(features, estimator_lib.ModeKeys.EVAL)
+    with variable_scope.variable_scope("model", reuse=True):
+      filtering_outputs = self.create_loss(features, estimator_lib.ModeKeys.EVAL)
     return estimator_lib.EstimatorSpec(
         mode=estimator_lib.ModeKeys.PREDICT,
         export_outputs={
-- 
GitLab


From 422dcdacc46d3319baf0c87b25b47da0550a78b1 Mon Sep 17 00:00:00 2001
From: terrytangyuan <terrytangyuan@gmail.com>
Date: Tue, 20 Feb 2018 13:46:51 -0500
Subject: [PATCH 070/575] Fix sanity check

---
 tensorflow/contrib/timeseries/python/timeseries/head.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py
index a870fa0c3d..5c49e903ab 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/head.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/head.py
@@ -144,7 +144,8 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
     with variable_scope.variable_scope("model"):
       prediction_outputs = self.model.predict(features=features)
     with variable_scope.variable_scope("model", reuse=True):
-      filtering_outputs = self.create_loss(features, estimator_lib.ModeKeys.EVAL)
+      filtering_outputs = self.create_loss(
+          features, estimator_lib.ModeKeys.EVAL)
     return estimator_lib.EstimatorSpec(
         mode=estimator_lib.ModeKeys.PREDICT,
         export_outputs={
-- 
GitLab


From 48f7d950333e0ade01053c5915056df7e17cd72d Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Tue, 20 Feb 2018 10:47:06 -0800
Subject: [PATCH 071/575] TFLite: Check if builtin_code is in valid range by
 best effort. PiperOrigin-RevId: 186326496

---
 tensorflow/contrib/lite/model.cc | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index d6522fc077..c100a0c8d0 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -124,14 +124,20 @@ TfLiteStatus InterpreterBuilder::BuildLocalIndexToRegistrationMapping() {
   auto opcodes = model_->operator_codes();
   for (const OperatorCode* opcode : *opcodes) {
     TfLiteRegistration* registration = nullptr;
-
-    if (opcode->builtin_code() != BuiltinOperator_CUSTOM) {
-      auto x = opcode->builtin_code();
-      flatbuffer_op_index_to_registration_types_.push_back(x);
-      registration = op_resolver_.FindOp(x);
+    auto builtin_code = opcode->builtin_code();
+    if (builtin_code > BuiltinOperator_MAX ||
+        builtin_code < BuiltinOperator_MIN) {
+      error_reporter_->Report(
+          "Op builtin_code out or range: %d. Are you using old TFLite binary "
+          "with newer model?",
+          builtin_code);
+      status = kTfLiteError;
+    } else if (builtin_code != BuiltinOperator_CUSTOM) {
+      flatbuffer_op_index_to_registration_types_.push_back(builtin_code);
+      registration = op_resolver_.FindOp(builtin_code);
       if (registration == nullptr) {
         error_reporter_->Report("Didn't find op for builtin opcode '%s'\n",
-                                EnumNameBuiltinOperator(x));
+                                EnumNameBuiltinOperator(builtin_code));
         status = kTfLiteError;
       }
     } else if (!opcode->custom_code()) {
-- 
GitLab


From 65ac3dfa9a48d209edd50178b7477bbfe0435633 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 10:58:39 -0800
Subject: [PATCH 072/575] Replace private method call _ref() with read_value()

PiperOrigin-RevId: 186328404
---
 .../contrib/opt/python/training/variable_clipping_optimizer.py  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/opt/python/training/variable_clipping_optimizer.py b/tensorflow/contrib/opt/python/training/variable_clipping_optimizer.py
index 74036082f0..3c0b8394be 100644
--- a/tensorflow/contrib/opt/python/training/variable_clipping_optimizer.py
+++ b/tensorflow/contrib/opt/python/training/variable_clipping_optimizer.py
@@ -109,7 +109,7 @@ class VariableClippingOptimizer(optimizer.Optimizer):
 
   def _clip_dense(self, var):
     with self._maybe_colocate_with(var):
-      updated_var_value = var._ref()  # pylint: disable=protected-access
+      updated_var_value = var.read_value()
       normalized_var = clip_ops.clip_by_norm(
           updated_var_value, self._max_norm, self._vars_to_clip_dims[var])
       delta = updated_var_value - normalized_var
-- 
GitLab


From 10386781aebfacd5366bf6af9fc40db35625232e Mon Sep 17 00:00:00 2001
From: Yao Zhang <yaozhang@google.com>
Date: Tue, 20 Feb 2018 11:03:08 -0800
Subject: [PATCH 073/575] Support multiple fetch nodes and add a flag for
 memory report.

PiperOrigin-RevId: 186329308
---
 .../python/grappler/cost_analyzer_tool.py     | 28 +++++++++++++------
 1 file changed, 19 insertions(+), 9 deletions(-)

diff --git a/tensorflow/python/grappler/cost_analyzer_tool.py b/tensorflow/python/grappler/cost_analyzer_tool.py
index 86db87d515..0db3c30a27 100644
--- a/tensorflow/python/grappler/cost_analyzer_tool.py
+++ b/tensorflow/python/grappler/cost_analyzer_tool.py
@@ -35,7 +35,8 @@ from tensorflow.python.platform import gfile
 from tensorflow.python.training import saver
 
 
-def main(_):
+def get_metagraph():
+  """Constructs and returns a MetaGraphDef from the input file."""
   if FLAGS.metagraphdef:
     with gfile.GFile(FLAGS.metagraphdef) as meta_file:
       metagraph = meta_graph_pb2.MetaGraphDef()
@@ -45,7 +46,8 @@ def main(_):
         metagraph.ParseFromString(meta_file.read())
     if FLAGS.fetch is not None:
       fetch_collection = meta_graph_pb2.CollectionDef()
-      fetch_collection.node_list.value.append(FLAGS.fetch)
+      for fetch in FLAGS.fetch.split(","):
+        fetch_collection.node_list.value.append(fetch)
       metagraph.collection_def["train_op"].CopyFrom(fetch_collection)
   else:
     with gfile.GFile(FLAGS.graphdef) as graph_file:
@@ -56,11 +58,16 @@ def main(_):
         graph_def.ParseFromString(graph_file.read())
       importer.import_graph_def(graph_def, name="")
       graph = ops.get_default_graph()
-      fetch = graph.get_operation_by_name(FLAGS.fetch)
-      graph.add_to_collection("train_op", fetch)
+      for fetch in FLAGS.fetch.split(","):
+        fetch_op = graph.get_operation_by_name(fetch)
+        graph.add_to_collection("train_op", fetch_op)
       metagraph = saver.export_meta_graph(
           graph_def=graph.as_graph_def(), graph=graph)
+  return metagraph
+
 
+def main(_):
+  metagraph = get_metagraph()
   rewriter_config = rewriter_config_pb2.RewriterConfig()
   if FLAGS.rewriter_config is not None:
     text_format.Merge(FLAGS.rewriter_config, rewriter_config)
@@ -69,8 +76,9 @@ def main(_):
 
   report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report)
   print(report)
-  report = cost_analyzer.GenerateMemoryReport(metagraph)
-  print(report)
+  if FLAGS.memory_report:
+    report = cost_analyzer.GenerateMemoryReport(metagraph)
+    print(report)
 
 
 if __name__ == "__main__":
@@ -89,9 +97,7 @@ if __name__ == "__main__":
       "--fetch",
       type=str,
       default=None,
-      help=
-      "The name of the fetch node."
-  )
+      help="The names of the fetch node delimited by comma.")
   parser.add_argument(
       "--rewriter_config",
       type=str,
@@ -107,5 +113,9 @@ if __name__ == "__main__":
       help="Generate per-node report. By default the report contains stats "
       "aggregated on a per op type basis, per_node_report adds results "
       "for each individual node to the report.")
+  parser.add_argument(
+      "--memory_report",
+      action="store_true",
+      help="Generate memory usage report.")
   FLAGS, unparsed = parser.parse_known_args()
   app.run(main=main, argv=[sys.argv[0]] + unparsed)
-- 
GitLab


From b11b456c96adfb7e3fce15d7f17d060391bc36d7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 11:11:35 -0800
Subject: [PATCH 074/575] Introduce tflite diff test to verify difference
 between tf and tf lite model

PiperOrigin-RevId: 186330891
---
 tensorflow/contrib/lite/testing/BUILD         | 82 +++++++++++++++++
 .../contrib/lite/testing/generate_testspec.cc | 88 +++++++++++++++++++
 .../contrib/lite/testing/generate_testspec.h  | 64 ++++++++++++++
 .../lite/testing/generate_testspec_test.cc    | 54 ++++++++++++
 .../lite/testing/tflite_diff_example_test.cc  | 28 ++++++
 .../contrib/lite/testing/tflite_diff_flags.h  | 70 +++++++++++++++
 .../contrib/lite/testing/tflite_diff_util.cc  | 41 +++++++++
 .../contrib/lite/testing/tflite_diff_util.h   | 51 +++++++++++
 8 files changed, 478 insertions(+)
 create mode 100644 tensorflow/contrib/lite/testing/generate_testspec.cc
 create mode 100644 tensorflow/contrib/lite/testing/generate_testspec.h
 create mode 100644 tensorflow/contrib/lite/testing/generate_testspec_test.cc
 create mode 100644 tensorflow/contrib/lite/testing/tflite_diff_example_test.cc
 create mode 100644 tensorflow/contrib/lite/testing/tflite_diff_flags.h
 create mode 100644 tensorflow/contrib/lite/testing/tflite_diff_util.cc
 create mode 100644 tensorflow/contrib/lite/testing/tflite_diff_util.h

diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index 06570ae9aa..14cb2b3ec3 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -240,6 +240,88 @@ cc_test(
     ],
 )
 
+cc_library(
+    name = "generate_testspec",
+    testonly = 1,
+    srcs = ["generate_testspec.cc"],
+    hdrs = ["generate_testspec.h"],
+    deps = [
+        ":join",
+        ":split",
+        ":tf_driver",
+        "//tensorflow/core:framework",
+    ],
+)
+
+cc_test(
+    name = "generate_testspec_test",
+    size = "small",
+    srcs = ["generate_testspec_test.cc"],
+    deps = [
+        ":generate_testspec",
+        "@com_google_googletest//:gtest_main",
+    ],
+)
+
+cc_library(
+    name = "tflite_diff_util",
+    testonly = 1,
+    srcs = ["tflite_diff_util.cc"],
+    hdrs = ["tflite_diff_util.h"],
+    deps = [
+        ":generate_testspec",
+        ":parse_testdata_lib",
+        ":split",
+        ":tflite_driver",
+        ":util",
+        "//tensorflow/contrib/lite:builtin_op_data",
+        "//tensorflow/contrib/lite:framework",
+        "//tensorflow/contrib/lite:string",
+        "//tensorflow/contrib/lite/kernels:builtin_ops",
+    ],
+)
+
+cc_library(
+    name = "tflite_diff_flags",
+    testonly = 1,
+    hdrs = ["tflite_diff_flags.h"],
+    deps = [
+        ":split",
+        ":tflite_diff_util",
+    ] + select({
+        "//conditions:default": [
+            "//tensorflow/core:framework_internal",
+            "//tensorflow/core:lib",
+        ],
+        "//tensorflow:android": [
+            "//tensorflow/core:android_tensorflow_lib",
+        ],
+    }),
+)
+
+tf_cc_test(
+    name = "tflite_diff_example_test",
+    size = "medium",
+    srcs = ["tflite_diff_example_test.cc"],
+    args = [
+        "--tensorflow_model=third_party/tensorflow/contrib/lite/testdata/multi_add.pb",
+        "--tflite_model=third_party/tensorflow/contrib/lite/testdata/multi_add.bin",
+        "--input_layer=a,b,c,d",
+        "--input_layer_type=float,float,float,float",
+        "--input_layer_shape=1,3,4,3:1,3,4,3:1,3,4,3:1,3,4,3",
+        "--output_layer=x,y",
+    ],
+    data = [
+        "//tensorflow/contrib/lite:testdata/multi_add.bin",
+        "//tensorflow/contrib/lite:testdata/multi_add.pb",
+    ],
+    tags = ["no_oss"],
+    deps = [
+        ":tflite_diff_flags",
+        ":tflite_diff_util",
+    ],
+)
+
 tf_cc_test(
     name = "generated_examples_zip_test",
     size = "large",
diff --git a/tensorflow/contrib/lite/testing/generate_testspec.cc b/tensorflow/contrib/lite/testing/generate_testspec.cc
new file mode 100644
index 0000000000..eb3deafb69
--- /dev/null
+++ b/tensorflow/contrib/lite/testing/generate_testspec.cc
@@ -0,0 +1,88 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/lite/testing/generate_testspec.h"
+#include "tensorflow/contrib/lite/testing/join.h"
+#include "tensorflow/contrib/lite/testing/split.h"
+#include "tensorflow/contrib/lite/testing/tf_driver.h"
+#include "tensorflow/core/framework/types.h"
+
+namespace tflite {
+namespace testing {
+
+void GenerateTestSpecFromTensorflowModel(
+    std::iostream& stream, const string& tensorflow_model_path,
+    const string& tflite_model_path, const std::vector<string>& input_layer,
+    const std::vector<string>& input_layer_type,
+    const std::vector<string>& input_layer_shape,
+    const std::vector<string>& output_layer) {
+  CHECK_EQ(input_layer.size(), input_layer_type.size());
+  CHECK_EQ(input_layer.size(), input_layer_shape.size());
+
+  // Initialize random functions.
+  static unsigned int seed = 0;
+  std::function<float(int)> float_rand = [](int idx) {
+    return static_cast<float>(rand_r(&seed)) / RAND_MAX - 0.5f;
+  };
+
+  // Generate inputs.
+  std::vector<string> input_values;
+  input_values.resize(input_layer.size());
+  for (int i = 0; i < input_layer.size(); i++) {
+    tensorflow::DataType type;
+    CHECK(DataTypeFromString(input_layer_type[i], &type));
+    auto shape = Split<int>(input_layer_shape[i], ",");
+
+    switch (type) {
+      case tensorflow::DT_FLOAT: {
+        const auto& data = GenerateRandomTensor<float>(shape, float_rand);
+        input_values[i] = Join(data.data(), data.size(), ",");
+        break;
+      }
+      default:
+
+        fprintf(stderr, "Unsupported type %d when generating testspec\n", type);
+        return;
+    }
+  }
+
+  // Invoke tensorflow model.
+  TfDriver runner(input_layer, input_layer_type, input_layer_shape,
+                  output_layer);
+  runner.LoadModel(tensorflow_model_path);
+  for (int i = 0; i < input_values.size(); i++) {
+    runner.SetInput(i, input_values[i]);
+  }
+  runner.Invoke();
+
+  // Write test spec.
+  stream << "load_model: " << tflite_model_path << "\n";
+  stream << "reshape {\n";
+  for (const auto& shape : input_layer_shape) {
+    stream << "  input: \"" << shape << "\"\n";
+  }
+  stream << "}\n";
+  stream << "invoke {\n";
+  for (const auto& value : input_values) {
+    stream << "  input: \"" << value << "\"\n";
+  }
+  for (int i = 0; i < output_layer.size(); i++) {
+    stream << "  output: \"" << runner.ReadOutput(i) << "\"\n";
+  }
+  stream << "}\n";
+}
+
+}  // namespace testing
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/testing/generate_testspec.h b/tensorflow/contrib/lite/testing/generate_testspec.h
new file mode 100644
index 0000000000..3529ee709b
--- /dev/null
+++ b/tensorflow/contrib/lite/testing/generate_testspec.h
@@ -0,0 +1,64 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_CONTRIB_LITE_TESTING_GENERATE_TESTSPEC_H_
+#define TENSORFLOW_CONTRIB_LITE_TESTING_GENERATE_TESTSPEC_H_
+
+#include <functional>
+#include <iostream>
+#include <vector>
+
+namespace tflite {
+namespace testing {
+
+// Generate test spec by executing TensorFlow model on random inputs.
+// The test spec can be consumed by ParseAndRunTests.
+// See test spec format in parse_testdata.h
+//
+// Inputs:
+//   stream: mutable iostream that contains the contents of test spec.
+//   tensorflow_model_path: path to TensorFlow model.
+//   tflite_model_path: path to tflite_model_path that the test spec runs
+//   against. input_layer: names of input tensors. Example: input1
+//   input_layer_type: datatypes of input tensors. Example: float
+//   input_layer_shape: shapes of input tensors, separated by comma. example:
+//   1,3,4 output_layer: names of output tensors. Example: output
+void GenerateTestSpecFromTensorflowModel(
+    std::iostream& stream, const string& tensorflow_model_path,
+    const string& tflite_model_path, const std::vector<string>& input_layer,
+    const std::vector<string>& input_layer_type,
+    const std::vector<string>& input_layer_shape,
+    const std::vector<string>& output_layer);
+
+// Generates random values that are filled into the tensor.
+// random_func returns the generated random element at given index.
+template <typename T>
+std::vector<T> GenerateRandomTensor(const std::vector<int>& shape,
+                                    const std::function<T(int)>& random_func) {
+  int64_t num_elements = 1;
+  for (const int dim : shape) {
+    num_elements *= dim;
+  }
+
+  std::vector<T> result(num_elements);
+  for (int i = 0; i < num_elements; i++) {
+    result[i] = random_func(i);
+  }
+  return result;
+}
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_CONTRIB_LITE_TESTING_GENERATE_TESTSPEC_H_
diff --git a/tensorflow/contrib/lite/testing/generate_testspec_test.cc b/tensorflow/contrib/lite/testing/generate_testspec_test.cc
new file mode 100644
index 0000000000..2a97b757a4
--- /dev/null
+++ b/tensorflow/contrib/lite/testing/generate_testspec_test.cc
@@ -0,0 +1,54 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/contrib/lite/testing/generate_testspec.h"
+
+#include <gmock/gmock.h>
+#include <gtest/gtest.h>
+
+namespace tflite {
+namespace testing {
+namespace {
+
+TEST(GenerateRandomTensor, FloatValue) {
+  static unsigned int seed = 0;
+  std::function<float(int)> float_rand = [](int idx) {
+    return static_cast<float>(rand_r(&seed)) / RAND_MAX - 0.5f;
+  };
+
+  std::set<float> values;
+  float sum_x_square = 0.0f;
+  float sum_x = 0.0f;
+  for (int i = 0; i < 100; i++) {
+    const auto& data = GenerateRandomTensor<float>({1, 3, 4}, float_rand);
+    for (float value : data) {
+      values.insert(value);
+      sum_x_square += value * value;
+      sum_x += value;
+    }
+  }
+
+  // Eech round, generated tensor has different values.
+  EXPECT_GT(values.size(), 200);
+  int num = 1 * 3 * 4 * 100;
+  float stddev = sum_x_square / num - (sum_x / num) * (sum_x / num);
+
+  // Stddev is greater than 1/2 stddev of uniform distribution: (B-A)^2 / 12
+  float minstddev = 1.0f / 12 / 2;
+  EXPECT_GT(stddev, minstddev);
+}
+
+}  // namespace
+}  // namespace testing
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc b/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc
new file mode 100644
index 0000000000..3817e68111
--- /dev/null
+++ b/tensorflow/contrib/lite/testing/tflite_diff_example_test.cc
@@ -0,0 +1,28 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/lite/testing/tflite_diff_flags.h"
+#include "tensorflow/contrib/lite/testing/tflite_diff_util.h"
+
+int main(int argc, char** argv) {
+  ::tflite::testing::DiffOptions options =
+      ::tflite::testing::ParseTfliteDiffFlags(&argc, argv);
+  for (int i = 0; i < 100; i++) {
+    if (!tflite::testing::RunDiffTest(options)) {
+      return 1;
+    }
+  }
+  return 0;
+}
diff --git a/tensorflow/contrib/lite/testing/tflite_diff_flags.h b/tensorflow/contrib/lite/testing/tflite_diff_flags.h
new file mode 100644
index 0000000000..5f1129d501
--- /dev/null
+++ b/tensorflow/contrib/lite/testing/tflite_diff_flags.h
@@ -0,0 +1,70 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_
+#define TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_
+
+#include "tensorflow/contrib/lite/testing/split.h"
+#include "tensorflow/contrib/lite/testing/tflite_diff_util.h"
+#include "tensorflow/core/util/command_line_flags.h"
+
+namespace tflite {
+namespace testing {
+
+DiffOptions ParseTfliteDiffFlags(int* argc, char** argv) {
+  struct {
+    string tensorflow_model;
+    string tflite_model;
+    string input_layer;
+    string input_layer_type;
+    string input_layer_shape;
+    string output_layer;
+  } values;
+
+  std::vector<tensorflow::Flag> flags = {
+      tensorflow::Flag("tensorflow_model", &values.tensorflow_model,
+                       "Path of tensorflow model."),
+      tensorflow::Flag("tflite_model", &values.tflite_model,
+                       "Path of tensorflow lite model."),
+      tensorflow::Flag("input_layer", &values.input_layer,
+                       "Names of input tensors, separated by comma. Example: "
+                       "input_1,input_2"),
+      tensorflow::Flag("input_layer_type", &values.input_layer_type,
+                       "Data types of input tensors, separated by comma. "
+                       "Example: float,int"),
+      tensorflow::Flag(
+          "input_layer_shape", &values.input_layer_shape,
+          "Shapes of input tensors, separated by colon. Example: 1,3,4,1:2"),
+      tensorflow::Flag("output_layer", &values.output_layer,
+                       "Names of output tensors, separated by comma. Example "
+                       "output_1,output_2"),
+  };
+
+  bool success = tensorflow::Flags::Parse(argc, argv, flags);
+  if (!success || (*argc == 2 && !strcmp(argv[1], "--helpfull"))) {
+    fprintf(stderr, "%s", tensorflow::Flags::Usage(argv[0], flags).c_str());
+  }
+
+  return {values.tensorflow_model,
+          values.tflite_model,
+          Split<string>(values.input_layer, ","),
+          Split<string>(values.input_layer_type, ","),
+          Split<string>(values.input_layer_shape, ":"),
+          Split<string>(values.output_layer, ",")};
+}
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_
diff --git a/tensorflow/contrib/lite/testing/tflite_diff_util.cc b/tensorflow/contrib/lite/testing/tflite_diff_util.cc
new file mode 100644
index 0000000000..9ef4e1f66c
--- /dev/null
+++ b/tensorflow/contrib/lite/testing/tflite_diff_util.cc
@@ -0,0 +1,41 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include <cstdarg>
+#include <cstdio>
+#include <cstdlib>
+#include <sstream>
+
+#include "tensorflow/contrib/lite/testing/generate_testspec.h"
+#include "tensorflow/contrib/lite/testing/parse_testdata.h"
+#include "tensorflow/contrib/lite/testing/tflite_diff_util.h"
+#include "tensorflow/contrib/lite/testing/tflite_driver.h"
+
+namespace tflite {
+namespace testing {
+
+bool RunDiffTest(const DiffOptions& options) {
+  std::stringstream tflite_stream;
+  GenerateTestSpecFromTensorflowModel(
+      tflite_stream, options.tensorflow_model, options.tflite_model,
+      options.input_layer, options.input_layer_type, options.input_layer_shape,
+      options.output_layer);
+  TfLiteDriver tflite_driver(/*use_nnapi=*/true);
+  tflite_driver.LoadModel(options.tflite_model);
+  std::cout << tflite_stream.str();
+  return tflite::testing::ParseAndRunTests(&tflite_stream, &tflite_driver);
+}
+}  // namespace testing
+
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/testing/tflite_diff_util.h b/tensorflow/contrib/lite/testing/tflite_diff_util.h
new file mode 100644
index 0000000000..326fa6c3e2
--- /dev/null
+++ b/tensorflow/contrib/lite/testing/tflite_diff_util.h
@@ -0,0 +1,51 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_UTIL_H_
+#define TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_UTIL_H_
+
+#include <vector>
+
+#include "tensorflow/contrib/lite/string.h"
+
+namespace tflite {
+namespace testing {
+
+// Configurations to run Tflite diff test.
+struct DiffOptions {
+  // Path of tensorflow model.
+  string tensorflow_model;
+  // Path of tensorflow lite model.
+  string tflite_model;
+  // Names of input tensors.
+  // Example: input_1,input_2
+  std::vector<string> input_layer;
+  // Data types of input tensors.
+  // Example: float,int
+  std::vector<string> input_layer_type;
+  // Shapes of input tensors, separated by comma.
+  // Example: 1,3,4,1
+  std::vector<string> input_layer_shape;
+  // Names of output tensors.
+  // Example output_1,output_2
+  std::vector<string> output_layer;
+};
+
+// Run a single TensorFLow Lite diff test with a given options.
+bool RunDiffTest(const DiffOptions& options);
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_UTIL_H_
-- 
GitLab


From 3ecdd29cc2e7349f75b5b62bf55bb183bafa3875 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Tue, 20 Feb 2018 11:12:53 -0800
Subject: [PATCH 075/575] Doc fixes for switching to 10.12.6 (Sierra) as min
 supported macOS

see: #15933
PiperOrigin-RevId: 186331121
---
 tensorflow/docs_src/install/index.md        | 2 +-
 tensorflow/docs_src/install/install_c.md    | 2 +-
 tensorflow/docs_src/install/install_go.md   | 2 +-
 tensorflow/docs_src/install/install_java.md | 2 +-
 tensorflow/docs_src/install/install_mac.md  | 6 +++++-
 5 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/tensorflow/docs_src/install/index.md b/tensorflow/docs_src/install/index.md
index 3c8488643f..4f85383925 100644
--- a/tensorflow/docs_src/install/index.md
+++ b/tensorflow/docs_src/install/index.md
@@ -3,7 +3,7 @@
 We've built and tested TensorFlow on the following 64-bit laptop/desktop
 operating systems:
 
-  * MacOS X 10.11 (El Capitan) or later.
+  * macOS 10.12.6 (Sierra) or later.
   * Ubuntu 16.04 or later
   * Windows 7 or later.
 
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index a783205b4a..9563eb5017 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -15,7 +15,7 @@ instructions might also work on other variants, we have only tested
 following requirements:
 
   * Linux, 64-bit, x86
-  * macOS X, Version 10.11 (El Capitan) or higher
+  * macOS X, Version 10.12.6 (Sierra) or higher
 
 
 ## Installation
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index 5249e04615..f4207debe0 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -17,7 +17,7 @@ instructions might also work on other variants, we have only tested
 following requirements:
 
   * Linux, 64-bit, x86
-  * macOS X, 10.11 (El Capitan) or higher
+  * macOS X, 10.12.6 (Sierra) or higher
 
 
 ## Installation
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 0c6c773e62..9a80c18aa5 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -18,7 +18,7 @@ instructions might also work on other variants, we have only tested
 following requirements:
 
   * Ubuntu 16.04 or higher; 64-bit, x86
-  * macOS X 10.11 (El Capitan) or higher
+  * macOS 10.12.6 (Sierra) or higher
   * Windows 7 or higher; 64-bit, x86
 
 The installation instructions for Android are in a separate
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index a6ea548cfb..d6df27f8c8 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -5,7 +5,11 @@ instructions might also work on other macOS variants, we have only
 tested (and we only support) these instructions on machines meeting the
 following requirements:
 
-  * macOS X 10.11 (El Capitan) or higher
+  * macOS 10.12.6 (Sierra) or higher
+
+Note: There are known, accuracy-affecting numerical issues before macOS 10.12.6
+(Sierra) that are described in
+[GitHub#15933](https://github.com/tensorflow/tensorflow/issues/15933#issuecomment-366331383).
 
 Note: As of version 1.2, TensorFlow no longer provides GPU support on macOS.
 
-- 
GitLab


From b64da9c1c9f1a3a488526020648855127c03e742 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Tue, 20 Feb 2018 11:13:55 -0800
Subject: [PATCH 076/575] Add numpy compatibility note to transpose operations.

fixes #15994

PiperOrigin-RevId: 186331307
---
 tensorflow/python/ops/array_ops.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index d63a9ea0dd..2aa3ef05ba 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1390,6 +1390,14 @@ def transpose(a, perm=None, name="transpose", conjugate=False):
   `a.dtype` is either `complex64` or `complex128` then the values of `a`
   are conjugated and transposed.
 
+  @compatibility(numpy)
+  In `numpy` transposes are memory-efficient constant time operations as they
+  simply return a new view of the same data with adjusted `strides`.
+
+  TensorFlow does not support strides, so `transpose` returns a new tensor with
+  the items permuted.
+  @end_compatibility
+
   For example:
 
   ```python
@@ -1490,6 +1498,14 @@ def matrix_transpose(a, name="matrix_transpose", conjugate=False):
   tf.matmul(matrix, tf.matrix_transpose(b))
   ```
 
+  @compatibility(numpy)
+  In `numpy` transposes are memory-efficient constant time operations as they
+  simply return a new view of the same data with adjusted `strides`.
+
+  TensorFlow does not support strides, `matrix_transposes` return a new tensor
+  with the items permuted.
+  @end_compatibility
+
   Args:
     a: A `Tensor` with `rank >= 2`.
     name: A name for the operation (optional).
-- 
GitLab


From 075aa8aa5a73113935e5a0962166bfb012e1a86e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 11:40:04 -0800
Subject: [PATCH 077/575] Temporarily disable flaky test.

PiperOrigin-RevId: 186336341
---
 tensorflow/contrib/py2tf/converters/BUILD | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD
index e9a96ec8d1..3cce8be9d5 100644
--- a/tensorflow/contrib/py2tf/converters/BUILD
+++ b/tensorflow/contrib/py2tf/converters/BUILD
@@ -166,6 +166,11 @@ py_test(
     name = "side_effect_guards_test",
     srcs = ["side_effect_guards_test.py"],
     srcs_version = "PY2AND3",
+    tags = [
+        # TODO(mdan): Fix.
+        "flaky",
+        "notap",
+    ],
     deps = [
         ":test_lib",
         "//tensorflow/contrib/py2tf/pyct",
-- 
GitLab


From 23feb7a180288cd3bc0b2afb4d65afdac1fbbf7e Mon Sep 17 00:00:00 2001
From: Yifei Feng <1192265+yifeif@users.noreply.github.com>
Date: Tue, 20 Feb 2018 11:48:43 -0800
Subject: [PATCH 078/575] Disable flaky test

tensorflow/contrib/opt:moving_average_optimizer_test
---
 tensorflow/contrib/opt/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 827279bd47..9f828fe19a 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -71,6 +71,7 @@ py_test(
     srcs_version = "PY2AND3",
     tags = [
         "notsan",  # b/31055119
+        "no_oss",  # b/73507407
     ],
     deps = [
         ":opt_py",
-- 
GitLab


From 706089cf71af3755cc911722b596bae948d1e5b4 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Tue, 20 Feb 2018 11:55:57 -0800
Subject: [PATCH 079/575] [TF:XLA] Bump open source llvm revision to r325553

PiperOrigin-RevId: 186339171
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 2e84d83fe4..0ca19b769f 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -473,11 +473,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/11b0e47b5b79bab22d27b6b2952b1f7582848063.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/11b0e47b5b79bab22d27b6b2952b1f7582848063.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/cd1a39550da51f57a87e2701f09451860dd1d98d.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/cd1a39550da51f57a87e2701f09451860dd1d98d.tar.gz",
       ],
-      sha256 = "b870b6f5df94c4c0cf7c6957046fca354c37d7641e838e905279a7509b0705e9",
-      strip_prefix = "llvm-11b0e47b5b79bab22d27b6b2952b1f7582848063",
+      sha256 = "62507d597053f36592725a515992668e7050b0259db2d4771661a0bd7a47882a",
+      strip_prefix = "llvm-cd1a39550da51f57a87e2701f09451860dd1d98d",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
   )
 
-- 
GitLab


From c07a6e6568b776037f052bc0d385a509ec2647aa Mon Sep 17 00:00:00 2001
From: Chris Ying <chrisying@google.com>
Date: Tue, 20 Feb 2018 12:16:49 -0800
Subject: [PATCH 080/575] Add Timestamp Op which returns the current timestamp
 during graph execution

PiperOrigin-RevId: 186342760
---
 .../api_def/base_api/api_def_Timestamp.pbtxt  | 10 ++++++++
 tensorflow/core/kernels/logging_ops.cc        | 19 ++++++++++++++
 tensorflow/core/kernels/logging_ops_test.cc   | 25 +++++++++++++++++++
 tensorflow/core/ops/logging_ops.cc            |  5 ++++
 tensorflow/python/ops/control_flow_ops.py     |  1 +
 tensorflow/python/ops/logging_ops.py          |  1 +
 tensorflow/python/ops/standard_ops.py         |  5 ++--
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  4 +++
 8 files changed, 68 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/core/api_def/base_api/api_def_Timestamp.pbtxt

diff --git a/tensorflow/core/api_def/base_api/api_def_Timestamp.pbtxt b/tensorflow/core/api_def/base_api/api_def_Timestamp.pbtxt
new file mode 100644
index 0000000000..bf2d07bcf5
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_Timestamp.pbtxt
@@ -0,0 +1,10 @@
+op {
+  graph_op_name: "Timestamp"
+  summary: "Provides the time since epoch in seconds."
+  description: <<END
+Returns the timestamp as a `float64` for seconds since the Unix epoch.
+
+Note: the timestamp is computed when the op is executed, not when it is added
+to the graph.
+END
+}
diff --git a/tensorflow/core/kernels/logging_ops.cc b/tensorflow/core/kernels/logging_ops.cc
index bacf3e7740..6b6a14e9a7 100644
--- a/tensorflow/core/kernels/logging_ops.cc
+++ b/tensorflow/core/kernels/logging_ops.cc
@@ -90,4 +90,23 @@ class PrintOp : public OpKernel {
 
 REGISTER_KERNEL_BUILDER(Name("Print").Device(DEVICE_CPU), PrintOp);
 
+class TimestampOp : public OpKernel {
+ public:
+  explicit TimestampOp(OpKernelConstruction* context) : OpKernel(context) {}
+
+  void Compute(OpKernelContext* context) override {
+    TensorShape output_shape;  // Default shape is 0 dim, 1 element
+    Tensor* output_tensor = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, output_shape, &output_tensor));
+
+    auto output_scalar = output_tensor->scalar<double>();
+    double now_us = static_cast<double>(Env::Default()->NowMicros());
+    double now_s = now_us / 1000000;
+    output_scalar() = now_s;
+  }
+};
+
+REGISTER_KERNEL_BUILDER(Name("Timestamp").Device(DEVICE_CPU), TimestampOp);
+
 }  // end namespace tensorflow
diff --git a/tensorflow/core/kernels/logging_ops_test.cc b/tensorflow/core/kernels/logging_ops_test.cc
index 9cf669a7ef..5e6958f364 100644
--- a/tensorflow/core/kernels/logging_ops_test.cc
+++ b/tensorflow/core/kernels/logging_ops_test.cc
@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include <chrono>
+#include <thread>
+
 #include "tensorflow/core/framework/fake_input.h"
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/tensor.h"
@@ -96,5 +99,27 @@ TEST_F(PrintingGraphTest, FirstNSuccess) {
   test::ExpectTensorEqual<int32>(expected, *GetOutput(0));
 }
 
+class TimestampTest : public OpsTestBase {
+ protected:
+  Status Init() {
+    TF_CHECK_OK(NodeDefBuilder("op", "Timestamp").Finalize(node_def()));
+    return InitOp();
+  }
+};
+
+TEST_F(TimestampTest, WaitAtLeast) {
+  TF_ASSERT_OK(Init());
+  TF_ASSERT_OK(RunOpKernel());
+  double ts1 = *((*GetOutput(0)).flat<double>().data());
+
+  // wait 1 second
+  std::this_thread::sleep_for(std::chrono::seconds(1));
+
+  TF_ASSERT_OK(RunOpKernel());
+  double ts2 = *((*GetOutput(0)).flat<double>().data());
+
+  EXPECT_LE(1.0, ts2 - ts1);
+}
+
 }  // end namespace
 }  // end namespace tensorflow
diff --git a/tensorflow/core/ops/logging_ops.cc b/tensorflow/core/ops/logging_ops.cc
index d263dc25b2..fbde692e95 100644
--- a/tensorflow/core/ops/logging_ops.cc
+++ b/tensorflow/core/ops/logging_ops.cc
@@ -111,4 +111,9 @@ REGISTER_OP("MergeSummary")
     .Attr("N : int >= 1")
     .SetShapeFn(shape_inference::ScalarShape);
 
+REGISTER_OP("Timestamp")
+    .Output("ts: float64")
+    .SetIsStateful()
+    .SetShapeFn(shape_inference::ScalarShape);
+
 }  // end namespace tensorflow
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index c33f351289..f77f0050f7 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -44,6 +44,7 @@ See the @{$python/control_flow_ops} guide.
 @@add_check_numerics_ops
 @@Assert
 @@Print
+@@timestamp
 """
 # pylint: disable=g-bad-name
 from __future__ import absolute_import
diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py
index eadbc1b7c3..3757109c95 100644
--- a/tensorflow/python/ops/logging_ops.py
+++ b/tensorflow/python/ops/logging_ops.py
@@ -356,3 +356,4 @@ ops.NotDifferentiable("AudioSummary")
 ops.NotDifferentiable("AudioSummaryV2")
 ops.NotDifferentiable("MergeSummary")
 ops.NotDifferentiable("ScalarSummary")
+ops.NotDifferentiable("Timestamp")
diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py
index f6d9111009..b62e556967 100644
--- a/tensorflow/python/ops/standard_ops.py
+++ b/tensorflow/python/ops/standard_ops.py
@@ -60,6 +60,7 @@ from tensorflow.python.ops.io_ops import *
 from tensorflow.python.ops.linalg_ops import *
 from tensorflow.python.ops.logging_ops import Print
 from tensorflow.python.ops.logging_ops import get_summary_op
+from tensorflow.python.ops.logging_ops import timestamp
 from tensorflow.python.ops.lookup_ops import initialize_all_tables
 from tensorflow.python.ops.lookup_ops import tables_initializer
 from tensorflow.python.ops.manip_ops import *
@@ -232,7 +233,7 @@ _allowed_symbols_clip_ops = [
     "global_norm",
 ]
 
-_allowed_symbols_image_ops = [
+_allowed_symbols_logging_ops = [
     # Documented in training.py.
     # We are not importing training.py to avoid complex dependencies.
     "audio_summary",
@@ -262,8 +263,8 @@ _allowed_symbols = (_allowed_symbols_array_ops +
                     _allowed_symbols_clip_ops +
                     _allowed_symbols_control_flow_ops +
                     _allowed_symbols_functional_ops +
-                    _allowed_symbols_image_ops +
                     _allowed_symbols_gradients +
+                    _allowed_symbols_logging_ops +
                     _allowed_symbols_math_ops +
                     _allowed_symbols_variable_scope_ops +
                     _allowed_symbols_misc +
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index e8890e9cc0..2333736583 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -1988,6 +1988,10 @@ tf_module {
     name: "tile"
     argspec: "args=[\'input\', \'multiples\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "timestamp"
+    argspec: "args=[\'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "to_bfloat16"
     argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'ToBFloat16\'], "
-- 
GitLab


From 53700ca21a4521ad62904fc596cf5f14c4cc46d1 Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Tue, 20 Feb 2018 12:19:02 -0800
Subject: [PATCH 081/575] Move the `maxout` layer implementation to contrib.

In September 2017, a contributor from GitHub added a MaxOut layer in tf.layers.
It was never added to the public API. Instead, it was only listed as part of the
API of tf.contrib.layers. This CL moves it out of tf.layers.

PiperOrigin-RevId: 186343115
---
 .../contrib/layers/python/layers/layers.py    |  48 +++++++-
 .../layers/python/layers/layers_test.py       |  26 ++++
 tensorflow/python/BUILD                       |  17 ---
 tensorflow/python/layers/maxout.py            | 111 ------------------
 tensorflow/python/layers/maxout_test.py       |  61 ----------
 5 files changed, 73 insertions(+), 190 deletions(-)
 delete mode 100644 tensorflow/python/layers/maxout.py
 delete mode 100644 tensorflow/python/layers/maxout_test.py

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index dcee775337..45ddfbfc9f 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -51,7 +51,6 @@ from tensorflow.python.ops import standard_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.training import moving_averages
-from tensorflow.python.layers.maxout import maxout
 
 # TODO(b/28426988): Replace legacy_* fns migrated from slim.
 # TODO(b/28426988): Remove legacy_* when all uses have migrated to new API.
@@ -2940,6 +2939,53 @@ def unit_norm(inputs, dim, epsilon=1e-7, scope=None):
     return math_ops.div(inputs, array_ops.tile(lengths, multiples))
 
 
+@add_arg_scope
+def maxout(inputs, num_units, axis=-1, scope=None):
+  """Adds a maxout op from https://arxiv.org/abs/1302.4389
+
+  "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron
+  Courville,
+   Yoshua Bengio
+
+  Usually the operation is performed in the filter/channel dimension. This can
+  also be
+  used after fully-connected layers to reduce number of features.
+
+  Arguments:
+    inputs: Tensor input
+    num_units: Specifies how many features will remain after maxout
+      in the `axis` dimension (usually channel).
+      This must be multiple of number of `axis`.
+    axis: The dimension where max pooling will be performed. Default is the
+    last dimension.
+    scope: Optional scope for variable_scope.
+
+  Returns:
+    A `Tensor` representing the results of the pooling operation.
+
+  Raises:
+    ValueError: if num_units is not multiple of number of features.
+  """
+  with variable_scope.variable_scope(scope, 'MaxOut', [inputs]):
+    inputs = ops.convert_to_tensor(inputs)
+    shape = inputs.get_shape().as_list()
+    num_channels = shape[axis]
+    if num_channels % num_units:
+      raise ValueError('number of features({}) is not '
+                       'a multiple of num_units({})'.format(
+                           num_channels, num_units))
+    shape[axis] = -1
+    shape += [num_channels // num_units]
+
+    # Dealing with batches with arbitrary sizes
+    for i in range(len(shape)):
+      if shape[i] is None:
+        shape[i] = array_ops.shape(inputs)[i]
+    outputs = math_ops.reduce_max(
+        array_ops.reshape(inputs, shape), -1, keepdims=False)
+    return outputs
+
+
 def poincare_normalize(x, axis=1, epsilon=1e-5, name=None):
   """Project into the Poincare ball with norm <= 1.0 - epsilon.
 
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 5c0ae9a3f1..ba70432c48 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -4132,5 +4132,31 @@ class LegacyFullyConnectedTest(test.TestCase):
         _layers.legacy_fully_connected(x, 2, activation_fn=nn_ops.softmax)
 
 
+class MaxOutTest(test.TestCase):
+
+  def test_simple(self):
+    inputs = random_ops.random_uniform((64, 10, 36), seed=1)
+    graph = _layers.maxout(inputs, num_units=3)
+    self.assertEqual(graph.get_shape().as_list(), [64, 10, 3])
+
+  def test_fully_connected(self):
+    inputs = random_ops.random_uniform((64, 50), seed=1)
+    graph = _layers.fully_connected(inputs, 50)
+    graph = _layers.maxout(graph, num_units=10)
+    self.assertEqual(graph.get_shape().as_list(), [64, 10])
+
+  def test_nchw(self):
+    inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1)
+    graph = _layers.conv2d(inputs, 10, 3, padding='SAME')
+    graph = _layers.maxout(graph, num_units=1)
+    self.assertEqual(graph.get_shape().as_list(), [10, 100, 100, 1])
+
+  def test_invalid_shape(self):
+    inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1)
+    graph = _layers.conv2d(inputs, 3, 10)
+    with self.assertRaisesRegexp(ValueError, 'number of features'):
+      graph = _layers.maxout(graph, num_units=2)
+
+
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index cee7c47e00..d7cf2c6fea 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -4108,7 +4108,6 @@ py_library(
         "layers/convolutional.py",
         "layers/core.py",
         "layers/layers.py",
-        "layers/maxout.py",
         "layers/network.py",
         "layers/normalization.py",
         "layers/pooling.py",
@@ -4219,22 +4218,6 @@ py_test(
     ],
 )
 
-py_test(
-    name = "layers_maxout_test",
-    size = "small",
-    srcs = ["layers/maxout_test.py"],
-    main = "layers/maxout_test.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":client_testlib",
-        ":framework_for_generated_wrappers",
-        ":layers",
-        ":math_ops",
-        ":nn_ops",
-        ":random_ops",
-    ],
-)
-
 py_test(
     name = "layers_utils_test",
     size = "small",
diff --git a/tensorflow/python/layers/maxout.py b/tensorflow/python/layers/maxout.py
deleted file mode 100644
index 765a1c4fda..0000000000
--- a/tensorflow/python/layers/maxout.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# pylint: disable=unused-import,g-bad-import-order
-"""Contains the maxout layer
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.python.eager import context
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import gen_array_ops
-
-from tensorflow.python.layers import base
-
-
-def maxout(inputs, num_units, axis=-1, name=None):
-  """Adds a maxout op from https://arxiv.org/abs/1302.4389
-
-  "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron
-  Courville,
-   Yoshua Bengio
-
-   Usually the operation is performed in the filter/channel dimension. This can
-   also be
-   used after fully-connected layers to reduce number of features.
-
-   Arguments:
-   inputs: Tensor input
-   num_units: Specifies how many features will remain after maxout in the `axis`
-     dimension
-         (usually channel). This must be multiple of number of `axis`.
-   axis: The dimension where max pooling will be performed. Default is the
-   last dimension.
-   name: Optional scope for name_scope.
-
-   Returns:
-    A `Tensor` representing the results of the pooling operation.
-
-   Raises:
-    ValueError: if num_units is not multiple of number of features.
-  """
-  return MaxOut(num_units=num_units, axis=axis, name=name)(inputs)
-
-
-class MaxOut(base.Layer):
-  """Adds a maxout op from https://arxiv.org/abs/1302.4389
-
-  "Maxout Networks" Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron
-  Courville, Yoshua
-  Bengio
-
-  Usually the operation is performed in the filter/channel dimension. This can
-  also be
-  used after fully-connected layers to reduce number of features.
-
-  Arguments:
-    inputs: Tensor input
-    num_units: Specifies how many features will remain after maxout in the
-      `axis` dimension
-         (usually channel).
-    This must be multiple of number of `axis`.
-    axis: The dimension where max pooling will be performed. Default is the
-    last dimension.
-    name: Optional scope for name_scope.
-
-  Returns:
-    A `Tensor` representing the results of the pooling operation.
-
-  Raises:
-    ValueError: if num_units is not multiple of number of features.
-  """
-
-  def __init__(self, num_units, axis=-1, name=None, **kwargs):
-    super(MaxOut, self).__init__(name=name, trainable=False, **kwargs)
-    self.axis = axis
-    self.num_units = num_units
-
-  def call(self, inputs):
-    inputs = ops.convert_to_tensor(inputs)
-    shape = inputs.get_shape().as_list()
-    num_channels = shape[self.axis]
-    if num_channels % self.num_units:
-      raise ValueError('number of features({}) is not '
-                       'a multiple of num_units({})'.format(
-                           num_channels, self.num_units))
-    shape[self.axis] = -1
-    shape += [num_channels // self.num_units]
-
-    # Dealing with batches with arbitrary sizes
-    for i in range(len(shape)):
-      if shape[i] is None:
-        shape[i] = gen_array_ops.shape(inputs)[i]
-    outputs = math_ops.reduce_max(
-        gen_array_ops.reshape(inputs, shape), -1, keepdims=False)
-
-    return outputs
diff --git a/tensorflow/python/layers/maxout_test.py b/tensorflow/python/layers/maxout_test.py
deleted file mode 100644
index 26acac57c4..0000000000
--- a/tensorflow/python/layers/maxout_test.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-# pylint: disable=unused-import,g-bad-import-order
-
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.python.layers import maxout
-from tensorflow.python.layers import convolutional as conv_layers
-from tensorflow.python.layers import core as core_layers
-
-from tensorflow.python.ops import random_ops
-from tensorflow.python.platform import test
-import numpy as np
-
-"""
-Contains the maxout layer tests
-"""
-
-
-class MaxOutTest(test.TestCase):
-  def test_simple(self):
-    inputs = random_ops.random_uniform((64, 10, 36), seed=1)
-    graph = maxout.maxout(inputs, num_units=3)
-    self.assertEqual(graph.get_shape().as_list(), [64, 10, 3])
-
-  def test_fully_connected(self):
-    inputs = random_ops.random_uniform((64, 50), seed=1)
-    graph = core_layers.dense(inputs, 50)
-    graph = maxout.maxout(graph, num_units=10)
-    self.assertEqual(graph.get_shape().as_list(), [64, 10])
-
-  def test_nchw(self):
-    inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1)
-    graph = conv_layers.conv2d(inputs, 10, 3, padding="SAME")
-    graph = maxout.maxout(graph, num_units=1)
-    self.assertEqual(graph.get_shape().as_list(), [10, 100, 100, 1])
-
-  def test_invalid_shape(self):
-    inputs = random_ops.random_uniform((10, 100, 100, 3), seed=1)
-    graph = conv_layers.conv2d(inputs, 3, 10, strides=(1, 1))
-    with self.assertRaisesRegexp(ValueError, 'number of features'):
-      graph = maxout.maxout(graph, num_units=2)
-
-if __name__ == '__main__':
-  test.main()
-- 
GitLab


From f0dff20a242f74c98706680fd41a80c9b5437191 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 12:25:33 -0800
Subject: [PATCH 082/575] More BcastAdd benchmarks in cwise_ops_test.cc

PiperOrigin-RevId: 186344120
---
 tensorflow/core/kernels/cwise_ops_test.cc | 72 +++++++++++++++++++++--
 1 file changed, 67 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/kernels/cwise_ops_test.cc b/tensorflow/core/kernels/cwise_ops_test.cc
index 39f497e716..696d5840e8 100644
--- a/tensorflow/core/kernels/cwise_ops_test.cc
+++ b/tensorflow/core/kernels/cwise_ops_test.cc
@@ -231,14 +231,22 @@ BM_BIAS_ADD_GRAD_ALL(gpu, NHWC, half, DT_HALF);
 
 Graph* BcastAdd(int rows, int cols, int dim) {
   Graph* g = new Graph(OpRegistry::Global());
-  Tensor lhs(DT_FLOAT, TensorShape({rows, cols}));
-  lhs.flat<float>().setRandom();
-  TensorShape rhs_shape;
-  if (dim == 0) {
+  TensorShape lhs_shape, rhs_shape;
+  if (dim == 0) {  // row
+    lhs_shape = TensorShape({rows, cols});
     rhs_shape = TensorShape({rows, 1});
-  } else {
+  } else if (dim == 1) {  // col
+    lhs_shape = TensorShape({rows, cols});
     rhs_shape = TensorShape({cols});
+  } else if (dim == 2) {  // cross_rc
+    lhs_shape = TensorShape({rows, 1});
+    rhs_shape = TensorShape({1, cols});
+  } else {  // cross_cr
+    lhs_shape = TensorShape({1, cols});
+    rhs_shape = TensorShape({rows, 1});
   }
+  Tensor lhs(DT_FLOAT, lhs_shape);
+  lhs.flat<float>().setRandom();
   Tensor rhs(DT_FLOAT, rhs_shape);
   rhs.flat<float>().setRandom();
   test::graph::Binary(g, "Add", test::graph::Constant(g, lhs),
@@ -298,5 +306,59 @@ BM_BCAST_ADD_COL_ALL(sycl);
 #undef BM_BCAST_ADD_COL_ALL
 #undef BM_BCAST_ADD_COL
 
+#define BM_BCAST_ADD_CROSS_RC(DEVICE, R, C)                            \
+  void BM_##DEVICE##_BcastAddCrossRC_R##R##_C##C(int iters, int arg) { \
+    const int rows = RowsFromArg(arg);                                 \
+    const int cols = ColsFromArg(arg);                                 \
+    const int64 tot = static_cast<int64>(iters) * rows * cols;         \
+    testing::ItemsProcessed(tot);                                      \
+    testing::BytesProcessed(tot * sizeof(float));                      \
+    test::Benchmark(#DEVICE, BcastAdd(rows, cols, 2)).Run(iters);      \
+  }                                                                    \
+  BENCHMARK(BM_##DEVICE##_BcastAddCrossRC_R##R##_C##C)                 \
+      ->Arg(RowsAndColsArg(R, C));
+
+#define BM_BCAST_ADD_CROSS_RC_ALL(DEVICE)   \
+  BM_BCAST_ADD_CROSS_RC(DEVICE, 512, 2048); \
+  BM_BCAST_ADD_CROSS_RC(DEVICE, 512, 4096); \
+  BM_BCAST_ADD_CROSS_RC(DEVICE, 2048, 512); \
+  BM_BCAST_ADD_CROSS_RC(DEVICE, 4096, 512);
+BM_BCAST_ADD_CROSS_RC_ALL(cpu);
+#if GOOGLE_CUDA
+BM_BCAST_ADD_CROSS_RC_ALL(gpu);
+#endif  // GOOGLE_CUDA
+#ifdef TENSORFLOW_USE_SYCL
+BM_BCAST_ADD_CROSS_RC_ALL(sycl);
+#endif  // TENSORFLOW_USE_SYCL
+#undef BM_BCAST_ADD_CROSS_RC_ALL
+#undef BM_BCAST_ADD_CROSS_RC
+
+#define BM_BCAST_ADD_CROSS_CR(DEVICE, R, C)                            \
+  void BM_##DEVICE##_BcastAddCrossCR_R##R##_C##C(int iters, int arg) { \
+    const int rows = RowsFromArg(arg);                                 \
+    const int cols = ColsFromArg(arg);                                 \
+    const int64 tot = static_cast<int64>(iters) * rows * cols;         \
+    testing::ItemsProcessed(tot);                                      \
+    testing::BytesProcessed(tot * sizeof(float));                      \
+    test::Benchmark(#DEVICE, BcastAdd(rows, cols, 3)).Run(iters);      \
+  }                                                                    \
+  BENCHMARK(BM_##DEVICE##_BcastAddCrossCR_R##R##_C##C)                 \
+      ->Arg(RowsAndColsArg(R, C));
+
+#define BM_BCAST_ADD_CROSS_CR_ALL(DEVICE)   \
+  BM_BCAST_ADD_CROSS_CR(DEVICE, 512, 2048); \
+  BM_BCAST_ADD_CROSS_CR(DEVICE, 512, 4096); \
+  BM_BCAST_ADD_CROSS_CR(DEVICE, 2048, 512); \
+  BM_BCAST_ADD_CROSS_CR(DEVICE, 4096, 512);
+BM_BCAST_ADD_CROSS_CR_ALL(cpu);
+#if GOOGLE_CUDA
+BM_BCAST_ADD_CROSS_CR_ALL(gpu);
+#endif  // GOOGLE_CUDA
+#ifdef TENSORFLOW_USE_SYCL
+BM_BCAST_ADD_CROSS_CR_ALL(sycl);
+#endif  // TENSORFLOW_USE_SYCL
+#undef BM_BCAST_ADD_CROSS_CR_ALL
+#undef BM_BCAST_ADD_CROSS_CR
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From 1adc14b317b7578cc7c220b05447795edd8474df Mon Sep 17 00:00:00 2001
From: Yifei Feng <1192265+yifeif@users.noreply.github.com>
Date: Tue, 20 Feb 2018 12:44:17 -0800
Subject: [PATCH 083/575] Fix buildifier format error.

---
 tensorflow/contrib/opt/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 9f828fe19a..86ceda71b7 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -70,8 +70,8 @@ py_test(
     srcs = ["python/training/moving_average_optimizer_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "notsan",  # b/31055119
         "no_oss",  # b/73507407
+        "notsan",  # b/31055119
     ],
     deps = [
         ":opt_py",
-- 
GitLab


From 537166cf2bbb428fca1c5fda7a6ff157bbe5c44f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 12:46:14 -0800
Subject: [PATCH 084/575] Go: Update generated wrapper functions for TensorFlow
 ops. PiperOrigin-RevId: 186346967

---
 tensorflow/go/op/wrappers.go | 102 +++++++++++++++++------------------
 1 file changed, 51 insertions(+), 51 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 13f38dfb32..3f742091f5 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -278,6 +278,57 @@ func FakeQuantWithMinMaxVarsPerChannelGradient(scope *Scope, gradients tf.Output
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// FakeQuantWithMinMaxVarsPerChannelAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannel.
+type FakeQuantWithMinMaxVarsPerChannelAttr func(optionalAttr)
+
+// FakeQuantWithMinMaxVarsPerChannelNumBits sets the optional num_bits attribute to value.
+// If not specified, defaults to 8
+func FakeQuantWithMinMaxVarsPerChannelNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelAttr {
+	return func(m optionalAttr) {
+		m["num_bits"] = value
+	}
+}
+
+// FakeQuantWithMinMaxVarsPerChannelNarrowRange sets the optional narrow_range attribute to value.
+// If not specified, defaults to false
+func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelAttr {
+	return func(m optionalAttr) {
+		m["narrow_range"] = value
+	}
+}
+
+// Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
+//
+// `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]`
+// to 'outputs' tensor of same shape as `inputs`.
+//
+// `[min; max]` define the clamping range for the `inputs` data.
+// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
+// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
+// then de-quantized and output as floats in `[min; max]` interval.
+// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive.
+//
+// This operation has a gradient and thus allows for training `min` and `max`
+// values.
+func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "FakeQuantWithMinMaxVarsPerChannel",
+		Input: []tf.Input{
+			inputs, min, max,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Partitions `data` into `num_partitions` tensors using indices from `partitions`.
 //
 // For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
@@ -15312,57 +15363,6 @@ func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional
 	return op.Output(0)
 }
 
-// FakeQuantWithMinMaxVarsPerChannelAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannel.
-type FakeQuantWithMinMaxVarsPerChannelAttr func(optionalAttr)
-
-// FakeQuantWithMinMaxVarsPerChannelNumBits sets the optional num_bits attribute to value.
-// If not specified, defaults to 8
-func FakeQuantWithMinMaxVarsPerChannelNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelAttr {
-	return func(m optionalAttr) {
-		m["num_bits"] = value
-	}
-}
-
-// FakeQuantWithMinMaxVarsPerChannelNarrowRange sets the optional narrow_range attribute to value.
-// If not specified, defaults to false
-func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelAttr {
-	return func(m optionalAttr) {
-		m["narrow_range"] = value
-	}
-}
-
-// Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
-//
-// `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]`
-// to 'outputs' tensor of same shape as `inputs`.
-//
-// `[min; max]` define the clamping range for the `inputs` data.
-// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
-// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
-// then de-quantized and output as floats in `[min; max]` interval.
-// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive.
-//
-// This operation has a gradient and thus allows for training `min` and `max`
-// values.
-func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "FakeQuantWithMinMaxVarsPerChannel",
-		Input: []tf.Input{
-			inputs, min, max,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // RandomShuffleAttr is an optional argument to RandomShuffle.
 type RandomShuffleAttr func(optionalAttr)
 
-- 
GitLab


From 6d1a1433707b37915207c11c2f0e91fcbc862bea Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 13:00:26 -0800
Subject: [PATCH 085/575] Simplify and enforce diagnostic ArrayDataType
 strings.

PiperOrigin-RevId: 186348846
---
 tensorflow/contrib/lite/toco/dump_graphviz.cc | 10 +--
 tensorflow/contrib/lite/toco/tooling_util.cc  | 74 ++++++++-----------
 tensorflow/contrib/lite/toco/tooling_util.h   |  2 +
 3 files changed, 36 insertions(+), 50 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/dump_graphviz.cc b/tensorflow/contrib/lite/toco/dump_graphviz.cc
index c726eb6d86..2184e8f607 100644
--- a/tensorflow/contrib/lite/toco/dump_graphviz.cc
+++ b/tensorflow/contrib/lite/toco/dump_graphviz.cc
@@ -142,14 +142,8 @@ NodeProperties GetPropertiesForArray(const Model& model,
 
   // Append array shape to the label.
   auto& array = model.GetArray(array_name);
-
-  if (array.data_type == ArrayDataType::kFloat) {
-    AppendF(&node_properties.label, "\\nType: float");
-  } else if (array.data_type == ArrayDataType::kInt32) {
-    AppendF(&node_properties.label, "\\nType: int32");
-  } else if (array.data_type == ArrayDataType::kUint8) {
-    AppendF(&node_properties.label, "\\nType: uint8");
-  }
+  AppendF(&node_properties.label, "\\nType: %s",
+          ArrayDataTypeName(array.data_type));
 
   if (array.has_shape()) {
     auto& array_shape = array.shape();
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index dcb409c84d..eec35b7b59 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -62,6 +62,35 @@ string LogName(const Operator& op) {
   }
 }
 
+string ArrayDataTypeName(ArrayDataType data_type) {
+  switch (data_type) {
+    case ArrayDataType::kFloat:
+      return "Float";
+    case ArrayDataType::kInt8:
+      return "Int8";
+    case ArrayDataType::kUint8:
+      return "Uint8";
+    case ArrayDataType::kInt16:
+      return "Int16";
+    case ArrayDataType::kUint16:
+      return "Uint16";
+    case ArrayDataType::kInt32:
+      return "Int32";
+    case ArrayDataType::kUint32:
+      return "Uint32";
+    case ArrayDataType::kInt64:
+      return "Int64";
+    case ArrayDataType::kUint64:
+      return "Uint64";
+    case ArrayDataType::kString:
+      return "String";
+    case ArrayDataType::kNone:
+      return "None";
+    default:
+      LOG(FATAL) << "Unhandled array data type " << static_cast<int>(data_type);
+  }
+}
+
 bool IsInputArray(const Model& model, const string& name) {
   for (const auto& input_array : model.flags.input_arrays()) {
     if (input_array.name() == name) {
@@ -363,48 +392,9 @@ void LogSummary(int log_level, const Model& model) {
 void LogArray(int log_level, const Model& model, const string& name) {
   const auto& array = model.GetArray(name);
   VLOG(log_level) << "Array: " << name;
-  switch (array.data_type) {
-    case ArrayDataType::kNone:
-      VLOG(log_level) << "  Data type:";
-      break;
-    case ArrayDataType::kFloat:
-      VLOG(log_level) << "  Data type: kFloat";
-      break;
-    case ArrayDataType::kInt32:
-      VLOG(log_level) << "  Data type: kInt32";
-      break;
-    case ArrayDataType::kUint8:
-      VLOG(log_level) << "  Data type: kUint8";
-      break;
-    case ArrayDataType::kString:
-      VLOG(log_level) << "  Data type: kString";
-      break;
-    default:
-      VLOG(log_level) << "  Data type: other (numerical value: "
-                      << static_cast<int>(array.data_type) << ")";
-      break;
-  }
-  switch (array.final_data_type) {
-    case ArrayDataType::kNone:
-      VLOG(log_level) << "  Final type:";
-      break;
-    case ArrayDataType::kFloat:
-      VLOG(log_level) << "  Final type: kFloat";
-      break;
-    case ArrayDataType::kInt32:
-      VLOG(log_level) << "  Final type: kInt32";
-      break;
-    case ArrayDataType::kUint8:
-      VLOG(log_level) << "  Final type: kUint8";
-      break;
-    case ArrayDataType::kString:
-      VLOG(log_level) << "  Final type: kString";
-      break;
-    default:
-      VLOG(log_level) << "  Final type: other (numerical value: "
-                      << static_cast<int>(array.data_type) << ")";
-      break;
-  }
+  VLOG(log_level) << "  Data type: " << ArrayDataTypeName(array.data_type);
+  VLOG(log_level) << "  Final type: "
+                  << ArrayDataTypeName(array.final_data_type);
   if (array.buffer) {
     VLOG(log_level) << "  Constant Buffer";
   }
diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h
index 0aaa0f6a21..11208ed667 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.h
+++ b/tensorflow/contrib/lite/toco/tooling_util.h
@@ -54,6 +54,8 @@ absl::string_view FindLongestCommonPrefix(absl::string_view a,
                                           absl::string_view b);
 string LogName(const Operator& op);
 
+string ArrayDataTypeName(ArrayDataType data_type);
+
 bool IsInputArray(const Model& model, const string& name);
 bool IsArrayConsumed(const Model& model, const string& name);
 int CountTrueOutputs(const Model& model, const Operator& op);
-- 
GitLab


From 0632e92abc4f08ffacf6802205f9880accf7ecd2 Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Tue, 20 Feb 2018 13:04:51 -0800
Subject: [PATCH 086/575] DOCFIX:  hmc.sample_chain kwarg
 num_steps_between_results docstring seemed to indicate a different type of
 thinning than what is actually going on. PiperOrigin-RevId: 186349630

---
 .../contrib/bayesflow/python/ops/hmc_impl.py    | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py b/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py
index f724910c59..9e45c19411 100644
--- a/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py
@@ -109,10 +109,13 @@ def sample_chain(
 
   Note: `target_log_prob_fn` is called exactly twice.
 
-  Only one out of every `num_steps_between_samples + 1` steps is included in the
-  returned results. This "thinning" comes at a cost of reduced statistical
-  power, while reducing memory requirements and autocorrelation. For more
-  discussion see [1].
+  Since HMC states are correlated, it is sometimes desirable to produce
+  additional intermediate states, and then discard them, ending up with a set of
+  states with decreased autocorrelation.  See [1].  Such "thinning" is made
+  possible by setting `num_steps_between_results > 0`.  The chain then takes
+  `num_steps_between_results` extra steps between the steps that make it into
+  the results.  The extra steps are never materialized (in calls to `sess.run`),
+  and thus do not increase memory requirements.
 
   [1]: "Statistically efficient thinning of a Markov chain sampler."
        Art B. Owen. April 2017.
@@ -225,10 +228,8 @@ def sample_chain(
       Default value: 0 (i.e., no burn-in).
     num_steps_between_results: Integer number of chain steps between collecting
       a result. Only one out of every `num_steps_between_samples + 1` steps is
-      included in the returned results. This "thinning" comes at a cost of
-      reduced statistical power, while reducing memory requirements and
-      autocorrelation. For more discussion see [1].
-      Default value: 0 (i.e., no subsampling).
+      included in the returned results.  The number of returned chain states is
+      still equal to `num_results`.  Default value: 0 (i.e., no thinning).
     seed: Python integer to seed the random number generator.
     current_target_log_prob: (Optional) `Tensor` representing the value of
       `target_log_prob_fn` at the `current_state`. The only reason to specify
-- 
GitLab


From d77ce310991b4ef668dd91f7e3b010b77bbcce6d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 13:07:43 -0800
Subject: [PATCH 087/575] Adding Transpose to optimized_ops.

PiperOrigin-RevId: 186350064
---
 .../internal/optimized/optimized_ops.h        | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index 7e8db95760..df389fd0d0 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -4547,6 +4547,35 @@ void ArgMax(const T3* axis, const T1* input_data, const Dims<4>& input_dims,
   }
 }
 
+template <typename T>
+void Transpose(const T* input, const Dims<4>& input_dims, T* output,
+               const Dims<4>& output_dims, const int* permuted_axes) {
+  int out_sizes[4];
+  // Compute the inverse permutation array so we can do an output centered
+  // transpose. Also, check to make sure output_dims is matching input_dims.
+  for (int k = 0; k < 4; k++) {
+    out_sizes[k] =
+        MatchingArraySize(input_dims, permuted_axes[k], output_dims, k);
+  }
+
+  // Naive transpose loop (iterate on output index and compute input index).
+  int o[4];  // loop index (on output).
+  int i[4];
+  for (o[3] = 0; o[3] < out_sizes[3]; o[3]++) {
+    i[permuted_axes[3]] = o[3];
+    for (o[2] = 0; o[2] < out_sizes[2]; o[2]++) {
+      i[permuted_axes[2]] = o[2];
+      for (o[1] = 0; o[1] < out_sizes[1]; o[1]++) {
+        i[permuted_axes[1]] = o[1];
+        for (o[0] = 0; o[0] < out_sizes[0]; o[0]++) {
+          i[permuted_axes[0]] = o[0];
+          output[Offset(output_dims, o)] = input[Offset(input_dims, i)];
+        }
+      }
+    }
+  }
+}
+
 }  // namespace optimized_ops
 }  // namespace tflite
 
-- 
GitLab


From 07c762ac47d1c4364b525e57f87321bc8e194a23 Mon Sep 17 00:00:00 2001
From: Igor Ganichev <iga@google.com>
Date: Tue, 20 Feb 2018 13:23:15 -0800
Subject: [PATCH 088/575] Clarify GpuDeviceInfo struct

PiperOrigin-RevId: 186352333
---
 tensorflow/core/framework/device_base.h | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/core/framework/device_base.h b/tensorflow/core/framework/device_base.h
index 1838a8ad02..fb6d5c69e1 100644
--- a/tensorflow/core/framework/device_base.h
+++ b/tensorflow/core/framework/device_base.h
@@ -128,6 +128,8 @@ class DeviceBase {
   // using a single stream.)
   // "event_mgr" is used to delay deallocation of temporary GPU buffers.
   // TODO(pbar) Work out how to move this out of DeviceBase.
+  // GpuDeviceInfo name is an unfortunate legacy, it is used not only by GPUs
+  // but also by TPU devices (to provide default device context).
   struct GpuDeviceInfo {
     // Make sure all the defaults are NULL, so we can spot missing assignments.
     perftools::gputools::Stream* stream = nullptr;
@@ -230,6 +232,7 @@ class DeviceBase {
  private:
   Env* const env_;
   CpuWorkerThreads* cpu_worker_threads_ = nullptr;
+  // Set by GPUs as well as by TPU devices.
   GpuDeviceInfo* gpu_device_info_ = nullptr;
   thread::ThreadPool* device_thread_pool_ = nullptr;
   Eigen::ThreadPoolDevice* eigen_cpu_device_ = nullptr;
-- 
GitLab


From 526ed81a0c6404d921ab36b6e2fe0a4bfbd4808b Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Tue, 20 Feb 2018 13:29:53 -0800
Subject: [PATCH 089/575] [TF:XLA] Tiny fixes. Add missing compile-time
 constant input annotation to BatchToSpaceND. Make definition of Acosh
 slightly more accurate. Addition/subtraction of numbers with similar
 magnitudes is more accurate, and x^2 likely will be further from 1.0 than x
 due to the doubling of the exponent caused by squaring.

PiperOrigin-RevId: 186353472
---
 tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc | 4 +++-
 tensorflow/compiler/tf2xla/kernels/unary_ops.cc       | 8 +++++---
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
index 344a2ab2b6..cbade79e85 100644
--- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc
@@ -159,7 +159,9 @@ class BatchToSpaceNDOp : public XlaOpKernel {
                  block_shape, crops);
   }
 };
-REGISTER_XLA_OP(Name("BatchToSpaceND").CompileTimeConstInput("crops"),
+REGISTER_XLA_OP(Name("BatchToSpaceND")
+                    .CompileTimeConstInput("block_shape")
+                    .CompileTimeConstInput("crops"),
                 BatchToSpaceNDOp);
 
 class BatchToSpaceOp : public XlaOpKernel {
diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
index 0c5ad9e525..7cb47f908d 100644
--- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc
@@ -60,11 +60,13 @@ XLAJIT_MAKE_UNARY(
                     b->Add(XlaHelpers::One(b, input_type(0)), x))));
 
 // acosh(x) = log(x + sqrt(x^2 - 1))
+//          = log(x + sqrt((x+1)*(x-1)))
 XLAJIT_MAKE_UNARY(
     Acosh,
-    b->Log(b->Add(x, b->Pow(b->Sub(b->Mul(x, x),
-                                   XlaHelpers::One(b, input_type(0))),
-                            XlaHelpers::FloatLiteral(b, input_type(0), 0.5)))));
+    b->Log(b->Add(x,
+                  b->Pow(b->Mul(b->Add(x, XlaHelpers::One(b, input_type(0))),
+                                b->Sub(x, XlaHelpers::One(b, input_type(0)))),
+                         XlaHelpers::FloatLiteral(b, input_type(0), 0.5)))));
 
 // asin(x) = 2 * atan(x / (1 + sqrt(1 - x^2)))
 XLAJIT_MAKE_UNARY(
-- 
GitLab


From 624a2e47329fefa1f17373954ac541b0e42a9fca Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Tue, 20 Feb 2018 13:36:15 -0800
Subject: [PATCH 090/575] Java: Fix #17130

PiperOrigin-RevId: 186354700
---
 tensorflow/java/src/main/native/tensor_jni.cc       |  9 ++++++++-
 .../src/test/java/org/tensorflow/TensorTest.java    | 13 ++++++++++++-
 2 files changed, 20 insertions(+), 2 deletions(-)

diff --git a/tensorflow/java/src/main/native/tensor_jni.cc b/tensorflow/java/src/main/native/tensor_jni.cc
index 745abec244..7e3cf4a88a 100644
--- a/tensorflow/java/src/main/native/tensor_jni.cc
+++ b/tensorflow/java/src/main/native/tensor_jni.cc
@@ -400,7 +400,13 @@ size_t nonScalarTF_STRINGTensorSize(JNIEnv* env, jarray value, int num_dims) {
   for (jsize i = 0; i < len; ++i) {
     jarray elem = static_cast<jarray>(
         env->GetObjectArrayElement(static_cast<jobjectArray>(value), i));
+    if (elem == nullptr) {
+      throwException(env, kNullPointerException,
+                     "null entries in provided array");
+      return ret;
+    }
     ret += nonScalarTF_STRINGTensorSize(env, elem, num_dims - 1);
+    if (env->ExceptionCheck()) return ret;
   }
   return ret;
 }
@@ -421,8 +427,8 @@ void fillNonScalarTF_STRINGTensorData(JNIEnv* env, jarray value, int num_dims,
   for (jsize i = 0; i < len; ++i) {
     jarray elem = static_cast<jarray>(
         env->GetObjectArrayElement(static_cast<jobjectArray>(value), i));
-    if (TF_GetCode(status) != TF_OK) return;
     fillNonScalarTF_STRINGTensorData(env, elem, num_dims - 1, writer, status);
+    if (TF_GetCode(status) != TF_OK) return;
   }
 }
 }  // namespace
@@ -444,6 +450,7 @@ JNIEXPORT jlong JNICALL Java_org_tensorflow_Tensor_allocateNonScalarBytes(
   }
   const size_t encoded_size =
       nonScalarTF_STRINGTensorSize(env, value, num_dims);
+  if (env->ExceptionCheck()) return 0;
   TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                    8 * num_elements + encoded_size);
   if (t == nullptr) {
diff --git a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
index 6538359d11..1bd00a763d 100644
--- a/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
+++ b/tensorflow/java/src/test/java/org/tensorflow/TensorTest.java
@@ -432,7 +432,7 @@ public class TensorTest {
     try (Tensor<Integer> t = Tensor.create(vector, Integer.class)) {
       fail("Tensor.create() should fail because it was given an array of boxed values");
     } catch (IllegalArgumentException e) {
-        // The expected exception
+      // The expected exception
     }
   }
 
@@ -536,4 +536,15 @@ public class TensorTest {
       assertArrayEquals(matrix, cpy.copyTo(new float[2][3]));
     }
   }
+
+  @Test
+  public void gracefullyFailCreationFromNullArrayForStringTensor() {
+    // Motivated by: https://github.com/tensorflow/tensorflow/issues/17130
+    byte[][] array = new byte[1][];
+    try {
+      Tensors.create(array);
+    } catch (NullPointerException e) {
+      // expected.
+    }
+  }
 }
-- 
GitLab


From fe819a1ab15e06728d82fcb2d7087e26e55fd6e1 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Tue, 20 Feb 2018 13:47:01 -0800
Subject: [PATCH 091/575] Turn on swapping heuristic by default to better
 manage memory usage on GPU

PiperOrigin-RevId: 186356358
---
 tensorflow/core/grappler/optimizers/memory_optimizer.cc | 6 ++++--
 tensorflow/core/protobuf/rewriter_config.proto          | 2 +-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 3057ee5fa1..dec4f04a1c 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -1104,7 +1104,8 @@ bool SwappingPass(RewriterConfig::MemOptType optimization_level,
                   Cluster* cluster, GrapplerItem* item,
                   std::unordered_set<string>* skip_list) {
   std::unordered_map<NodeDef*, SwapInfo> nodes_to_swap;
-  if (optimization_level == RewriterConfig::SWAPPING_HEURISTICS ||
+  if (optimization_level == RewriterConfig::DEFAULT_MEM_OPT ||
+      optimization_level == RewriterConfig::SWAPPING_HEURISTICS ||
       optimization_level == RewriterConfig::HEURISTICS) {
     // Use heuristics to figure out what needs to be swapped;
     IdentifySwappingCandidates(cluster, item, skip_list, &nodes_to_swap);
@@ -1240,7 +1241,8 @@ Status MemoryOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
       updated_graph |= SchedulingPass(cluster, &optimized_item);
     }
 
-    if ((optimization_level_ == RewriterConfig::SWAPPING_HEURISTICS ||
+    if ((optimization_level_ == RewriterConfig::DEFAULT_MEM_OPT ||
+         optimization_level_ == RewriterConfig::SWAPPING_HEURISTICS ||
          optimization_level_ == RewriterConfig::HEURISTICS ||
          optimization_level_ == RewriterConfig::MANUAL) &&
         cluster != nullptr) {
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 0e9e202bc9..a61eecaa29 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -43,7 +43,7 @@ message RewriterConfig {
   bool disable_model_pruning = 2;
 
   enum MemOptType {
-    // The default setting (SCHEDULING_HEURISTICS only)
+    // The default setting (SCHEDULING and SWAPPING HEURISTICS only)
     DEFAULT_MEM_OPT = 0;
     // Disabled in the meta-optimizer.
     NO_MEM_OPT = 1;
-- 
GitLab


From fdeab946c0c8146c8040d7e125e5ca9e41b0336a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 13:52:53 -0800
Subject: [PATCH 092/575] Add an inspection helper module for related routines
 not found in the core inspect.

PiperOrigin-RevId: 186357270
---
 tensorflow/contrib/py2tf/pyct/BUILD           |  12 ++
 .../contrib/py2tf/pyct/inspect_utils.py       |  70 +++++++
 .../contrib/py2tf/pyct/inspect_utils_test.py  | 189 ++++++++++++++++++
 3 files changed, 271 insertions(+)
 create mode 100644 tensorflow/contrib/py2tf/pyct/inspect_utils.py
 create mode 100644 tensorflow/contrib/py2tf/pyct/inspect_utils_test.py

diff --git a/tensorflow/contrib/py2tf/pyct/BUILD b/tensorflow/contrib/py2tf/pyct/BUILD
index e3c0da4b10..edec5f7712 100644
--- a/tensorflow/contrib/py2tf/pyct/BUILD
+++ b/tensorflow/contrib/py2tf/pyct/BUILD
@@ -24,6 +24,7 @@ py_library(
         "ast_util.py",
         "compiler.py",
         "context.py",
+        "inspect_utils.py",
         "parser.py",
         "pretty_printer.py",
         "qual_names.py",
@@ -72,6 +73,17 @@ py_test(
     ],
 )
 
+py_test(
+    name = "inspect_utils_test",
+    srcs = ["inspect_utils_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":pyct",
+        "//tensorflow/python:client_testlib",
+        "@gast_archive//:gast",
+    ],
+)
+
 py_test(
     name = "parser_test",
     srcs = ["parser_test.py"],
diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils.py b/tensorflow/contrib/py2tf/pyct/inspect_utils.py
new file mode 100644
index 0000000000..b6552cbbee
--- /dev/null
+++ b/tensorflow/contrib/py2tf/pyct/inspect_utils.py
@@ -0,0 +1,70 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Live entity inspection utilities.
+
+This module contains whatever inspect doesn't offer out of the box.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+
+from tensorflow.python.util import tf_inspect
+
+
+def getmethodclass(m, namespace):
+  """Resolves a function's owner, e.g. a method's class."""
+
+  # Instance method and class methods: should be bound to a non-null "self".
+  # If self is a class, then it's a class method.
+  if hasattr(m, '__self__'):
+    if m.__self__:
+      if tf_inspect.isclass(m.__self__):
+        return m.__self__
+      return type(m.__self__)
+
+  # Class and static methods: platform specific.
+  if hasattr(m, 'im_class'):  # Python 2
+    return m.im_class
+
+  if hasattr(m, '__qualname__'):  # Python 3
+    qn = m.__qualname__.split('.')
+    if len(qn) < 2:
+      return None
+    owner_name, func_name = qn[-2:]
+    assert func_name == m.__name__, (
+        'inconsistent names detected '
+        '(__qualname__[1] = "%s", __name__ = "%s") for %s.' % (func_name,
+                                                               m.__name__, m))
+    if owner_name == '<locals>':
+      return None
+    if owner_name not in namespace:
+      raise ValueError(
+          'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' %
+          (owner_name, m, namespace))
+    return namespace[owner_name]
+
+  if six.PY2:
+    # In Python 2 it's impossible, to our knowledge, to detect the class of a
+    # static function. So we're forced to walk all the objects in the
+    # namespace and see if they own it. If any reader finds a better solution,
+    # please let us know.
+    for _, v in namespace.items():
+      if hasattr(v, m.__name__) and getattr(v, m.__name__) is m:
+        return v
+
+  return None
diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
new file mode 100644
index 0000000000..f0468a04c4
--- /dev/null
+++ b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
@@ -0,0 +1,189 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for unspect_utils module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import wraps
+
+from tensorflow.contrib.py2tf.pyct import inspect_utils
+from tensorflow.python.platform import test
+
+
+def decorator(f):
+  return f
+
+
+def function_decorator():
+  def dec(f):
+    return f
+  return dec
+
+
+def wrapping_decorator():
+  def dec(f):
+    def replacement(*_):
+      return None
+
+    @wraps(f)
+    def wrapper(*args, **kwargs):
+      return replacement(*args, **kwargs)
+    return wrapper
+  return dec
+
+
+class TestClass(object):
+
+  def member_function(self):
+    pass
+
+  @decorator
+  def decorated_member(self):
+    pass
+
+  @function_decorator()
+  def fn_decorated_member(self):
+    pass
+
+  @wrapping_decorator()
+  def wrap_decorated_member(self):
+    pass
+
+  @staticmethod
+  def static_method():
+    pass
+
+  @classmethod
+  def class_method(cls):
+    pass
+
+
+def free_function():
+  pass
+
+
+def free_factory():
+  def local_function():
+    pass
+  return local_function
+
+
+class InspectUtilsTest(test.TestCase):
+
+  def test_getmethodclass(self):
+
+    self.assertEqual(
+        inspect_utils.getmethodclass(free_function, {}), None)
+    self.assertEqual(
+        inspect_utils.getmethodclass(free_factory(), {}), None)
+
+    ns = {'TestClass': TestClass}
+    self.assertEqual(
+        inspect_utils.getmethodclass(TestClass.member_function, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(TestClass.decorated_member, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(TestClass.fn_decorated_member, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(TestClass.wrap_decorated_member, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(TestClass.static_method, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(TestClass.class_method, ns),
+        TestClass)
+
+    test_obj = TestClass()
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.member_function, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.decorated_member, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.fn_decorated_member, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.wrap_decorated_member, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.static_method, ns),
+        TestClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.class_method, ns),
+        TestClass)
+
+  def test_getmethodclass_locals(self):
+
+    def local_function():
+      pass
+
+    class LocalClass(object):
+
+      def member_function(self):
+        pass
+
+      @decorator
+      def decorated_member(self):
+        pass
+
+      @function_decorator()
+      def fn_decorated_member(self):
+        pass
+
+      @wrapping_decorator()
+      def wrap_decorated_member(self):
+        pass
+
+    self.assertEqual(
+        inspect_utils.getmethodclass(local_function, {}), None)
+
+    ns = {'LocalClass': LocalClass}
+    self.assertEqual(
+        inspect_utils.getmethodclass(LocalClass.member_function, ns),
+        LocalClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(LocalClass.decorated_member, ns),
+        LocalClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(LocalClass.fn_decorated_member, ns),
+        LocalClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(LocalClass.wrap_decorated_member, ns),
+        LocalClass)
+
+    test_obj = LocalClass()
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.member_function, ns),
+        LocalClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.decorated_member, ns),
+        LocalClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.fn_decorated_member, ns),
+        LocalClass)
+    self.assertEqual(
+        inspect_utils.getmethodclass(test_obj.wrap_decorated_member, ns),
+        LocalClass)
+
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From 0fad3428f4de84e10524a7ed5ed53b7e4b636edb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 13:57:01 -0800
Subject: [PATCH 093/575] Basic LogSoftmax support

PiperOrigin-RevId: 186357933
---
 tensorflow/contrib/lite/builtin_ops.h         |   1 +
 tensorflow/contrib/lite/kernels/BUILD         |  13 ++
 .../contrib/lite/kernels/activations.cc       |  22 +++
 .../contrib/lite/kernels/activations_test.cc  |  41 ++++++
 .../internal/optimized/optimized_ops.h        |  37 ++++++
 .../internal/reference/reference_ops.h        |  35 +++++
 .../contrib/lite/kernels/log_softmax_test.cc  | 112 ++++++++++++++++
 tensorflow/contrib/lite/kernels/register.cc   |   2 +
 tensorflow/contrib/lite/model.cc              |   1 +
 tensorflow/contrib/lite/nnapi_delegate.cc     |   1 +
 tensorflow/contrib/lite/schema/schema.fbs     |   5 +
 .../contrib/lite/schema/schema_generated.h    | 125 +++++++++++++++++-
 tensorflow/contrib/lite/testing/BUILD         |   1 +
 .../contrib/lite/testing/generate_examples.py |  32 +++++
 .../testing/generated_examples_zip_test.cc    |   1 +
 .../contrib/lite/toco/export_tensorflow.cc    |   3 +-
 .../contrib/lite/toco/tflite/operator.cc      |   2 +
 .../contrib/lite/toco/tflite/operator_test.cc |   2 +
 18 files changed, 429 insertions(+), 7 deletions(-)
 create mode 100644 tensorflow/contrib/lite/kernels/log_softmax_test.cc

diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h
index 4ebd1586de..4f872c79e5 100644
--- a/tensorflow/contrib/lite/builtin_ops.h
+++ b/tensorflow/contrib/lite/builtin_ops.h
@@ -71,6 +71,7 @@ typedef enum {
   kTfLiteBuiltinExp = 47,
   kTfLiteBuiltinTopkV2 = 48,
   kTfLiteBuiltinSplit = 49,
+  kTfLiteBuiltinLogSoftmax = 50,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus
diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD
index b59dc5ffb3..68a53432f0 100644
--- a/tensorflow/contrib/lite/kernels/BUILD
+++ b/tensorflow/contrib/lite/kernels/BUILD
@@ -513,6 +513,19 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "log_softmax_test",
+    size = "small",
+    srcs = ["log_softmax_test.cc"],
+    deps = [
+        ":builtin_ops",
+        "//tensorflow/contrib/lite:framework",
+        "//tensorflow/contrib/lite/kernels:test_util",
+        "//tensorflow/contrib/lite/kernels/internal:reference_base",
+        "@com_google_googletest//:gtest",
+    ],
+)
+
 tf_cc_test(
     name = "lsh_projection_test",
     size = "small",
diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc
index 3c5c77815d..6acded3091 100644
--- a/tensorflow/contrib/lite/kernels/activations.cc
+++ b/tensorflow/contrib/lite/kernels/activations.cc
@@ -337,6 +337,21 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
   }
 }
 
+TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
+  TfLiteTensor* input = GetInput(context, node, 0);
+  TfLiteTensor* output = GetOutput(context, node, 0);
+  switch (input->type) {
+    case kTfLiteFloat32:
+      optimized_ops::LogSoftmax(
+          GetTensorData<float>(input), GetTensorDims(input),
+          GetTensorData<float>(output), GetTensorDims(output));
+      return kTfLiteOk;
+    default:
+      context->ReportError(context, "Only float32 supported currently.");
+      return kTfLiteError;
+  }
+}
+
 }  // namespace activations
 
 TfLiteRegistration* Register_RELU() {
@@ -381,6 +396,13 @@ TfLiteRegistration* Register_SOFTMAX() {
   return &r;
 }
 
+TfLiteRegistration* Register_LOG_SOFTMAX() {
+  static TfLiteRegistration r = {activations::Init, activations::Free,
+                                 activations::GenericPrepare,
+                                 activations::LogSoftmaxEval};
+  return &r;
+}
+
 }  // namespace builtin
 }  // namespace ops
 }  // namespace tflite
diff --git a/tensorflow/contrib/lite/kernels/activations_test.cc b/tensorflow/contrib/lite/kernels/activations_test.cc
index 68d49944e5..302e52b96d 100644
--- a/tensorflow/contrib/lite/kernels/activations_test.cc
+++ b/tensorflow/contrib/lite/kernels/activations_test.cc
@@ -313,6 +313,47 @@ TEST(QuantizedActivationsOpTest, Softmax2D) {
                                              kQuantizedTolerance)));
 }
 
+// This contains the same test values as the Softmax test, but reference answer
+// generated via the following snippet of python:
+//   logits1 = tf.constant([[0, -6, 2, 4],[3, -2, 10, 1]], dtype=tf.float32)
+//   logits2 = tf.constant([[0,-6],[2,4],[3,-2],[10,1]], dtype=tf.float32)
+//   lsm1 = tf.nn.log_softmax(logits1)
+//   lsm2 = tf.nn.log_softmax(logits2)
+//   with tf.Session() as sess:
+//     print('lsm1', sess.run(lsm1))
+//     print('lsm2', sess.run(lsm2))
+
+TEST(FloatActivationsOpTest, LogSoftmax) {
+  FloatActivationsOpModel m(BuiltinOperator_LOG_SOFTMAX,
+                            /*input=*/{TensorType_FLOAT32, {2, 4}});
+  m.SetInput({
+      0, -6, 2, 4,   //
+      3, -2, 10, 1,  //
+  });
+  m.Invoke();
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({
+                                 -4.14297, -10.14297, -2.14297, -.142971,    //
+                                 -7.00104, -12.00104, -.00104087, -9.00104,  //
+                             })));
+
+  // Same input, but a different shape.
+  FloatActivationsOpModel m2(BuiltinOperator_LOG_SOFTMAX,
+                             /*input=*/{TensorType_FLOAT32, {4, 2}});
+  m2.SetInput({
+      0, -6,  //
+      2, 4,   //
+      3, -2,  //
+      10, 1,  //
+  });
+  m2.Invoke();
+  EXPECT_THAT(m2.GetOutput(), ElementsAreArray(ArrayFloatNear({
+                                  -.00247565, -6.00247,   //
+                                  -2.12692, -.126928,     //
+                                  -.00671534, -5.00671,   //
+                                  -.000123374, -9.00012,  //
+                              })));
+}
+
 }  // namespace
 }  // namespace tflite
 
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index df389fd0d0..3cc800fac5 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -3332,6 +3332,43 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims,
   }
 }
 
+// TODO(myenik): This is the same as the reference implementation, not actually
+// optimized yet.
+inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims,
+                       float* output_data, const Dims<4>& output_dims) {
+  const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
+  const int height = MatchingArraySize(input_dims, 2, output_dims, 2);
+  const int width = MatchingArraySize(input_dims, 1, output_dims, 1);
+  const int depth = MatchingArraySize(input_dims, 0, output_dims, 0);
+
+  for (int b = 0; b < batches; ++b) {
+    for (int y = 0; y < height; ++y) {
+      for (int x = 0; x < width; ++x) {
+        // Find max element value which we'll use to ensure numerical stability
+        // taking advantage of the following equality:
+        // log(exp(x[i])/sum(exp(x[i]))) == log(exp(x[i]+C)/sum(exp(x[i]+C)))
+        float max = std::numeric_limits<float>::lowest();
+        for (int c = 0; c < depth; ++c) {
+          max = std::max(max, input_data[Offset(input_dims, c, x, y, b)]);
+        }
+
+        // Compute sum.
+        float sum = 0.f;
+        for (int c = 0; c < depth; ++c) {
+          sum += std::exp(input_data[Offset(input_dims, c, x, y, b)] - max);
+        }
+
+        // Compute result.
+        const float log_sum = std::log(sum);
+        for (int c = 0; c < depth; ++c) {
+          output_data[Offset(output_dims, c, x, y, b)] =
+              input_data[Offset(input_dims, c, x, y, b)] - max - log_sum;
+        }
+      }
+    }
+  }
+}
+
 inline void Logistic(const float* input_data, const Dims<4>& input_dims,
                      float* output_data, const Dims<4>& output_dims) {
   gemmlowp::ScopedProfilingLabel label("Logistic");
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index d8907d5d48..24f6356d5a 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -2216,6 +2216,41 @@ inline void Softmax(const uint8* input_data, const Dims<4>& input_dims,
   }
 }
 
+inline void LogSoftmax(const float* input_data, const Dims<4>& input_dims,
+                       float* output_data, const Dims<4>& output_dims) {
+  const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
+  const int height = MatchingArraySize(input_dims, 2, output_dims, 2);
+  const int width = MatchingArraySize(input_dims, 1, output_dims, 1);
+  const int depth = MatchingArraySize(input_dims, 0, output_dims, 0);
+
+  for (int b = 0; b < batches; ++b) {
+    for (int y = 0; y < height; ++y) {
+      for (int x = 0; x < width; ++x) {
+        // Find max element value which we'll use to ensure numerical stability
+        // taking advantage of the following equality:
+        // log(exp(x[i])/sum(exp(x[i]))) == log(exp(x[i]+C)/sum(exp(x[i]+C)))
+        float max = std::numeric_limits<float>::lowest();
+        for (int c = 0; c < depth; ++c) {
+          max = std::max(max, input_data[Offset(input_dims, c, x, y, b)]);
+        }
+
+        // Compute sum.
+        float sum = 0.f;
+        for (int c = 0; c < depth; ++c) {
+          sum += std::exp(input_data[Offset(input_dims, c, x, y, b)] - max);
+        }
+
+        // Compute result.
+        const float log_sum = std::log(sum);
+        for (int c = 0; c < depth; ++c) {
+          output_data[Offset(output_dims, c, x, y, b)] =
+              input_data[Offset(input_dims, c, x, y, b)] - max - log_sum;
+        }
+      }
+    }
+  }
+}
+
 inline void Logistic(const float* input_data, const Dims<4>& input_dims,
                      float* output_data, const Dims<4>& output_dims) {
   const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
diff --git a/tensorflow/contrib/lite/kernels/log_softmax_test.cc b/tensorflow/contrib/lite/kernels/log_softmax_test.cc
new file mode 100644
index 0000000000..62820a2f51
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/log_softmax_test.cc
@@ -0,0 +1,112 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// Unit test for TFLite LOG_SOFTMAX op.
+
+#include <iomanip>
+#include <memory>
+#include <vector>
+
+#include <gmock/gmock.h>
+#include <gtest/gtest.h>
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+class LogSoftmaxOpModel : public SingleOpModel {
+ public:
+  LogSoftmaxOpModel(int batches, int size)
+      : batches_(batches), input_size_(size) {
+    input_ = AddInput(TensorType_FLOAT32);
+    output_ = AddOutput(TensorType_FLOAT32);
+    SetBuiltinOp(BuiltinOperator_LOG_SOFTMAX, BuiltinOptions_LogSoftmaxOptions,
+                 CreateLogSoftmaxOptions(builder_).Union());
+    BuildInterpreter({{batches_, input_size_}});
+  }
+
+  void SetInput(std::initializer_list<float> data) {
+    PopulateTensor(input_, data);
+  }
+
+  void SetInput(int offset, float* begin, float* end) {
+    PopulateTensor(input_, offset, begin, end);
+  }
+
+  std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
+
+ private:
+  int input_;
+  int output_;
+
+  int batches_;
+  int input_size_;
+};
+
+TEST(LogSoftmaxOpTest, SimpleTest) {
+  LogSoftmaxOpModel m(/*batches=*/2, /*size=*/5);
+  m.SetInput({
+      1.0, 2.0, 3.0, 4.0, 5.0,       // b = 0
+      -1.0, -2.0, -3.0, -4.0, -5.0,  // b = 1
+  });
+
+  m.Invoke();
+
+  EXPECT_THAT(
+      m.GetOutput(),
+      ElementsAreArray(ArrayFloatNear(
+          {-4.45191431, -3.45191431, -2.45191431, -1.45191443, -0.4519144,
+           -0.4519144, -1.45191443, -2.45191431, -3.45191431, -4.45191431},
+          1e-6)));
+}
+
+TEST(LogSoftmaxOpTest, CompareWithTFmini) {
+  const int batch_size = 2;
+  const int input_size = 5;
+  static float input_buffer[] = {
+      1.0,  2.0,  3.0,  4.0,  5.0,   // b = 0
+      -1.0, -2.0, -3.0, -4.0, -5.0,  // b = 1
+  };
+
+  LogSoftmaxOpModel m(batch_size, input_size);
+
+  m.SetInput(0, input_buffer, input_buffer + input_size * batch_size);
+
+  m.Invoke();
+
+  std::unique_ptr<float[]> output_buffer(new float[input_size * batch_size]);
+  static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size},
+                                       {1, 0, 0, input_size}};
+  tflite::reference_ops::LogSoftmax(input_buffer, input_dims,
+                                    output_buffer.get(), input_dims);
+
+  std::vector<float> expected;
+  expected.insert(expected.end(), output_buffer.get(),
+                  output_buffer.get() + input_size * batch_size);
+
+  EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear(expected, 1e-6)));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc
index edc4e26edb..c87a4ac50b 100644
--- a/tensorflow/contrib/lite/kernels/register.cc
+++ b/tensorflow/contrib/lite/kernels/register.cc
@@ -63,6 +63,7 @@ TfLiteRegistration* Register_SQUEEZE();
 TfLiteRegistration* Register_STRIDED_SLICE();
 TfLiteRegistration* Register_EXP();
 TfLiteRegistration* Register_TOPK_V2();
+TfLiteRegistration* Register_LOG_SOFTMAX();
 
 BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_RELU, Register_RELU());
@@ -114,6 +115,7 @@ BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE());
   AddBuiltin(BuiltinOperator_EXP, Register_EXP());
   AddBuiltin(BuiltinOperator_TOPK_V2, Register_TOPK_V2());
+  AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX());
 }
 
 TfLiteRegistration* BuiltinOpResolver::FindOp(
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index c100a0c8d0..239f9df481 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -286,6 +286,7 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type,
     case BuiltinOperator_CONCAT_EMBEDDINGS:
     case BuiltinOperator_EXP:
     case BuiltinOperator_TOPK_V2:
+    case BuiltinOperator_LOG_SOFTMAX:
       break;
     case BuiltinOperator_LSH_PROJECTION: {
       TfLiteLSHProjectionParams* params =
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index 02e8499f61..999fe52ec8 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -344,6 +344,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_SQUEEZE:
       case tflite::BuiltinOperator_STRIDED_SLICE:
       case tflite::BuiltinOperator_EXP:
+      case tflite::BuiltinOperator_LOG_SOFTMAX:
         FATAL("Op code %d is currently not delegated to NNAPI", builtin);
         nn_op_type = -1;  // set to invalid
         break;
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index 75970b4126..a08d87cec4 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -123,6 +123,7 @@ enum BuiltinOperator : byte {
   EXP = 47,
   TOPK_V2 = 48,
   SPLIT = 49,
+  LOG_SOFTMAX = 50,
 }
 
 // Options for the builtin operators.
@@ -162,6 +163,7 @@ union BuiltinOptions {
   ExpOptions,
   TopKV2Options,
   SplitOptions,
+  LogSoftmaxOptions,
 }
 
 enum Padding : byte { SAME, VALID }
@@ -364,6 +366,9 @@ table StridedSliceOptions {
   shrink_axis_mask: int;
 }
 
+table LogSoftmaxOptions {
+}
+
 // An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
 // builtin, or a string if the operator is custom.
 table OperatorCode {
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
index 06989c7b61..dc37f8f9ee 100755
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
+
 // automatically generated by the FlatBuffers compiler, do not modify
 
 
@@ -136,6 +137,9 @@ struct SplitOptionsT;
 struct StridedSliceOptions;
 struct StridedSliceOptionsT;
 
+struct LogSoftmaxOptions;
+struct LogSoftmaxOptionsT;
+
 struct OperatorCode;
 struct OperatorCodeT;
 
@@ -240,11 +244,12 @@ enum BuiltinOperator {
   BuiltinOperator_EXP = 47,
   BuiltinOperator_TOPK_V2 = 48,
   BuiltinOperator_SPLIT = 49,
+  BuiltinOperator_LOG_SOFTMAX = 50,
   BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_SPLIT
+  BuiltinOperator_MAX = BuiltinOperator_LOG_SOFTMAX
 };
 
-inline BuiltinOperator (&EnumValuesBuiltinOperator())[47] {
+inline BuiltinOperator (&EnumValuesBuiltinOperator())[48] {
   static BuiltinOperator values[] = {
     BuiltinOperator_ADD,
     BuiltinOperator_AVERAGE_POOL_2D,
@@ -292,7 +297,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[47] {
     BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN,
     BuiltinOperator_EXP,
     BuiltinOperator_TOPK_V2,
-    BuiltinOperator_SPLIT
+    BuiltinOperator_SPLIT,
+    BuiltinOperator_LOG_SOFTMAX
   };
   return values;
 }
@@ -349,6 +355,7 @@ inline const char **EnumNamesBuiltinOperator() {
     "EXP",
     "TOPK_V2",
     "SPLIT",
+    "LOG_SOFTMAX",
     nullptr
   };
   return names;
@@ -396,11 +403,12 @@ enum BuiltinOptions {
   BuiltinOptions_ExpOptions = 33,
   BuiltinOptions_TopKV2Options = 34,
   BuiltinOptions_SplitOptions = 35,
+  BuiltinOptions_LogSoftmaxOptions = 36,
   BuiltinOptions_MIN = BuiltinOptions_NONE,
-  BuiltinOptions_MAX = BuiltinOptions_SplitOptions
+  BuiltinOptions_MAX = BuiltinOptions_LogSoftmaxOptions
 };
 
-inline BuiltinOptions (&EnumValuesBuiltinOptions())[36] {
+inline BuiltinOptions (&EnumValuesBuiltinOptions())[37] {
   static BuiltinOptions values[] = {
     BuiltinOptions_NONE,
     BuiltinOptions_Conv2DOptions,
@@ -437,7 +445,8 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[36] {
     BuiltinOptions_StridedSliceOptions,
     BuiltinOptions_ExpOptions,
     BuiltinOptions_TopKV2Options,
-    BuiltinOptions_SplitOptions
+    BuiltinOptions_SplitOptions,
+    BuiltinOptions_LogSoftmaxOptions
   };
   return values;
 }
@@ -480,6 +489,7 @@ inline const char **EnumNamesBuiltinOptions() {
     "ExpOptions",
     "TopKV2Options",
     "SplitOptions",
+    "LogSoftmaxOptions",
     nullptr
   };
   return names;
@@ -634,6 +644,10 @@ template<> struct BuiltinOptionsTraits<SplitOptions> {
   static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions;
 };
 
+template<> struct BuiltinOptionsTraits<LogSoftmaxOptions> {
+  static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions;
+};
+
 struct BuiltinOptionsUnion {
   BuiltinOptions type;
   void *value;
@@ -945,6 +959,14 @@ struct BuiltinOptionsUnion {
     return type == BuiltinOptions_SplitOptions ?
       reinterpret_cast<const SplitOptionsT *>(value) : nullptr;
   }
+  LogSoftmaxOptionsT *AsLogSoftmaxOptions() {
+    return type == BuiltinOptions_LogSoftmaxOptions ?
+      reinterpret_cast<LogSoftmaxOptionsT *>(value) : nullptr;
+  }
+  const LogSoftmaxOptionsT *AsLogSoftmaxOptions() const {
+    return type == BuiltinOptions_LogSoftmaxOptions ?
+      reinterpret_cast<const LogSoftmaxOptionsT *>(value) : nullptr;
+  }
 };
 
 bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
@@ -3568,6 +3590,46 @@ inline flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(
 
 flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
 
+struct LogSoftmaxOptionsT : public flatbuffers::NativeTable {
+  typedef LogSoftmaxOptions TableType;
+  LogSoftmaxOptionsT() {
+  }
+};
+
+struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef LogSoftmaxOptionsT NativeTableType;
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           verifier.EndTable();
+  }
+  LogSoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<LogSoftmaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct LogSoftmaxOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  LogSoftmaxOptionsBuilder &operator=(const LogSoftmaxOptionsBuilder &);
+  flatbuffers::Offset<LogSoftmaxOptions> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<LogSoftmaxOptions>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(
+    flatbuffers::FlatBufferBuilder &_fbb) {
+  LogSoftmaxOptionsBuilder builder_(_fbb);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
 struct OperatorCodeT : public flatbuffers::NativeTable {
   typedef OperatorCode TableType;
   BuiltinOperator builtin_code;
@@ -3790,6 +3852,9 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
   const SplitOptions *builtin_options_as_SplitOptions() const {
     return builtin_options_type() == BuiltinOptions_SplitOptions ? static_cast<const SplitOptions *>(builtin_options()) : nullptr;
   }
+  const LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const {
+    return builtin_options_type() == BuiltinOptions_LogSoftmaxOptions ? static_cast<const LogSoftmaxOptions *>(builtin_options()) : nullptr;
+  }
   const flatbuffers::Vector<uint8_t> *custom_options() const {
     return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS);
   }
@@ -3956,6 +4021,10 @@ template<> inline const SplitOptions *Operator::builtin_options_as<SplitOptions>
   return builtin_options_as_SplitOptions();
 }
 
+template<> inline const LogSoftmaxOptions *Operator::builtin_options_as<LogSoftmaxOptions>() const {
+  return builtin_options_as_LogSoftmaxOptions();
+}
+
 struct OperatorBuilder {
   flatbuffers::FlatBufferBuilder &fbb_;
   flatbuffers::uoffset_t start_;
@@ -5415,6 +5484,29 @@ inline flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(flatbu
       _shrink_axis_mask);
 }
 
+inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new LogSoftmaxOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+}
+
+inline flatbuffers::Offset<LogSoftmaxOptions> LogSoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateLogSoftmaxOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  return tflite::CreateLogSoftmaxOptions(
+      _fbb);
+}
+
 inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
   auto _o = new OperatorCodeT();
   UnPackTo(_o, _resolver);
@@ -5735,6 +5827,10 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob
       auto ptr = reinterpret_cast<const SplitOptions *>(obj);
       return verifier.VerifyTable(ptr);
     }
+    case BuiltinOptions_LogSoftmaxOptions: {
+      auto ptr = reinterpret_cast<const LogSoftmaxOptions *>(obj);
+      return verifier.VerifyTable(ptr);
+    }
     default: return false;
   }
 }
@@ -5893,6 +5989,10 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c
       auto ptr = reinterpret_cast<const SplitOptions *>(obj);
       return ptr->UnPack(resolver);
     }
+    case BuiltinOptions_LogSoftmaxOptions: {
+      auto ptr = reinterpret_cast<const LogSoftmaxOptions *>(obj);
+      return ptr->UnPack(resolver);
+    }
     default: return nullptr;
   }
 }
@@ -6039,6 +6139,10 @@ inline flatbuffers::Offset<void> BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff
       auto ptr = reinterpret_cast<const SplitOptionsT *>(value);
       return CreateSplitOptions(_fbb, ptr, _rehasher).Union();
     }
+    case BuiltinOptions_LogSoftmaxOptions: {
+      auto ptr = reinterpret_cast<const LogSoftmaxOptionsT *>(value);
+      return CreateLogSoftmaxOptions(_fbb, ptr, _rehasher).Union();
+    }
     default: return 0;
   }
 }
@@ -6185,6 +6289,10 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL
       value = new SplitOptionsT(*reinterpret_cast<SplitOptionsT *>(u.value));
       break;
     }
+    case BuiltinOptions_LogSoftmaxOptions: {
+      value = new LogSoftmaxOptionsT(*reinterpret_cast<LogSoftmaxOptionsT *>(u.value));
+      break;
+    }
     default:
       break;
   }
@@ -6367,6 +6475,11 @@ inline void BuiltinOptionsUnion::Reset() {
       delete ptr;
       break;
     }
+    case BuiltinOptions_LogSoftmaxOptions: {
+      auto ptr = reinterpret_cast<LogSoftmaxOptionsT *>(value);
+      delete ptr;
+      break;
+    }
     default: break;
   }
   value = nullptr;
diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index 14cb2b3ec3..1ccf7d4d0e 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -33,6 +33,7 @@ gen_zipped_test_files(
         "l2_pool.zip",
         "l2norm.zip",
         "local_response_norm.zip",
+        "log_softmax.zip",
         "max_pool.zip",
         "mean.zip",
         "mul.zip",
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index b6c09306d6..2cbac7caa6 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -783,6 +783,37 @@ def make_exp_tests(zip_path):
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
 
 
+def make_log_softmax_tests(zip_path):
+  """Make a set of tests to do log_softmax."""
+
+  test_parameters = [{
+      "input_dtype": [tf.float32],
+      "input_shape": [[1, 100], [4, 2], [5, 224]],
+  }]
+
+  def build_graph(parameters):
+    """Build the log_softmax op testing graph."""
+    input_tensor = tf.placeholder(
+        dtype=parameters["input_dtype"],
+        name="input",
+        shape=parameters["input_shape"])
+
+    out = tf.nn.log_softmax(input_tensor)
+    return [input_tensor], [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    values = [
+        create_tensor_data(
+            parameters["input_dtype"],
+            parameters["input_shape"],
+            min_value=-100,
+            max_value=9)
+    ]
+    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
+
+  make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
+
+
 def make_binary_op_tests_func(binary_operator):
   """Return a function that does a test on a binary operator."""
   return lambda zip_path: make_binary_op_tests(zip_path, binary_operator)
@@ -1818,6 +1849,7 @@ def main(unused_args):
         "squeeze.zip": make_squeeze_tests,
         "strided_slice.zip": make_strided_slice_tests,
         "exp.zip": make_exp_tests,
+        "log_softmax.zip": make_log_softmax_tests,
     }
     out = FLAGS.zip_to_output
     bin_path = FLAGS.toco
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 49766cedac..89a5841371 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -250,6 +250,7 @@ INSTANTIATE_TESTS(global_batch_norm)
 INSTANTIATE_TESTS(l2norm)
 INSTANTIATE_TESTS(l2_pool)
 INSTANTIATE_TESTS(local_response_norm)
+INSTANTIATE_TESTS(log_softmax)
 INSTANTIATE_TESTS(max_pool)
 INSTANTIATE_TESTS(mul)
 INSTANTIATE_TESTS(pad)
diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc
index 570cc7943b..d54014aaaf 100644
--- a/tensorflow/contrib/lite/toco/export_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc
@@ -720,7 +720,8 @@ void ConvertLogSoftmaxOperator(const Model& model,
                                GraphDef* tensorflow_graph) {
   string softmax_input;
   Operator* providing_op = GetOpWithOutput(model, src_op.inputs[0]);
-  if (providing_op->type == OperatorType::kTensorFlowReshape) {
+  if (providing_op != nullptr &&
+      providing_op->type == OperatorType::kTensorFlowReshape) {
     softmax_input = src_op.inputs[0];
   } else {
     // Insert a reshape operator that reduces the dimensions down to the 2 that
diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc
index aabc7c5109..f2cc4ef71f 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator.cc
@@ -859,6 +859,8 @@ std::vector<std::unique_ptr<BaseOperator>> BuildOperatorList() {
   ops.emplace_back(
       new SimpleOperator<TanhOperator>("TANH", OperatorType::kTanh));
   ops.emplace_back(new SimpleOperator<ExpOperator>("EXP", OperatorType::kExp));
+  ops.emplace_back(new SimpleOperator<LogSoftmaxOperator>(
+      "LOG_SOFTMAX", OperatorType::kLogSoftmax));
 
   return ops;
 }
diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
index 5c486f72ad..9c19f8d464 100644
--- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc
+++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc
@@ -107,6 +107,8 @@ TEST_F(OperatorTest, SimpleOperators) {
   CheckSimpleOperator<LogisticOperator>("LOGISTIC", OperatorType::kLogistic);
   CheckSimpleOperator<TanhOperator>("TANH", OperatorType::kTanh);
   CheckSimpleOperator<ExpOperator>("EXP", OperatorType::kExp);
+  CheckSimpleOperator<LogSoftmaxOperator>("LOG_SOFTMAX",
+                                          OperatorType::kLogSoftmax);
 }
 
 TEST_F(OperatorTest, BuiltinAdd) {
-- 
GitLab


From ce742213f15579034168b7f4271329430a4a32c5 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Tue, 20 Feb 2018 13:58:28 -0800
Subject: [PATCH 094/575] Add documentation to contrib/quantization to reduce
 confusion with contrib/quantize.

PiperOrigin-RevId: 186358131
---
 tensorflow/contrib/quantization/README.md | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 tensorflow/contrib/quantization/README.md

diff --git a/tensorflow/contrib/quantization/README.md b/tensorflow/contrib/quantization/README.md
new file mode 100644
index 0000000000..359950aaf3
--- /dev/null
+++ b/tensorflow/contrib/quantization/README.md
@@ -0,0 +1,7 @@
+The contrib/quantization package exposes a few TensorFlow quantization operations.
+
+If you are looking for quantized training rewrites that allow for training
+quantized models that work with
+[TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/), you should look at
+the [contrib/quantize](https://www.tensorflow.org/api_docs/python/tf/contrib/quantize)
+package.
-- 
GitLab


From 249065a49ed007bf631de453506bfbf22accbb39 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 14:09:55 -0800
Subject: [PATCH 095/575] Fix a memory corruption issue in boosted trees as the
 iterators become invalid after an Add.

PiperOrigin-RevId: 186360144
---
 .../boosted_trees/resources/decision_tree_ensemble_resource.h  | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h b/tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h
index 3ebf28ea44..94aeb2c7bb 100644
--- a/tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h
+++ b/tensorflow/contrib/boosted_trees/resources/decision_tree_ensemble_resource.h
@@ -126,7 +126,8 @@ class DecisionTreeEnsembleResource : public StampedResource {
       return;
     }
     used_ids->Add(handler_id);
-    std::rotate(first, used_ids->end() - 1, used_ids->end());
+    // Keep the list of used handlers sorted.
+    std::sort(used_ids->begin(), used_ids->end());
   }
 
   std::vector<int64> GetUsedHandlers() const {
-- 
GitLab


From be862d5b91e9b9044f4e028dcdae0b6ad283e8b4 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Tue, 20 Feb 2018 14:11:35 -0800
Subject: [PATCH 096/575] [tf.data] Fix memory leak when not all elements of a
 `Dataset.from_generator()` are consumed.

This change introduces a new C++ Dataset implementation
(`GeneratorDataset`) that takes three functions:

1. An initialization function that is called before the first use.
2. A "get next" function that is called to produce the elements, until a call
   raises the OutOfRange error.
3. A finalization function that is called before the iterator is destroyed.

Previously, the generator state would only be cleaned up if the caller
consumed *every* element of the generator. In the new version, the
finalization function ensures that the Python-side state of the
generator is released regardless of how the iterator is disposed.

Fixes #16163.

PiperOrigin-RevId: 186360401
---
 .../base_api/api_def_GeneratorDataset.pbtxt   |   4 +
 tensorflow/core/kernels/data/BUILD            |  14 ++
 .../core/kernels/data/captured_function.cc    |  56 +++++
 .../core/kernels/data/captured_function.h     |  16 ++
 .../core/kernels/data/generator_dataset_op.cc | 201 +++++++++++++++
 tensorflow/core/ops/dataset_ops.cc            |  17 ++
 .../dataset_from_generator_op_test.py         |  86 +++++++
 tensorflow/python/data/ops/dataset_ops.py     | 236 ++++++++++++++++--
 8 files changed, 613 insertions(+), 17 deletions(-)
 create mode 100644 tensorflow/core/api_def/base_api/api_def_GeneratorDataset.pbtxt
 create mode 100644 tensorflow/core/kernels/data/generator_dataset_op.cc

diff --git a/tensorflow/core/api_def/base_api/api_def_GeneratorDataset.pbtxt b/tensorflow/core/api_def/base_api/api_def_GeneratorDataset.pbtxt
new file mode 100644
index 0000000000..4f1cf3e686
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_GeneratorDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "GeneratorDataset"
+  summary: "Creates a dataset that invokes a function to generate elements."
+}
diff --git a/tensorflow/core/kernels/data/BUILD b/tensorflow/core/kernels/data/BUILD
index 1e3b0c231f..9880cc76d3 100644
--- a/tensorflow/core/kernels/data/BUILD
+++ b/tensorflow/core/kernels/data/BUILD
@@ -209,6 +209,19 @@ tf_kernel_library(
     ],
 )
 
+tf_kernel_library(
+    name = "generator_dataset_op",
+    srcs = ["generator_dataset_op.cc"],
+    deps = [
+        ":captured_function",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:dataset_ops_op_lib",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+    ],
+)
+
 tf_kernel_library(
     name = "scan_dataset_op",
     srcs = ["scan_dataset_op.cc"],
@@ -519,6 +532,7 @@ tf_kernel_library(
         ":dense_to_sparse_batch_dataset_op",
         ":filter_dataset_op",
         ":flat_map_dataset_op",
+        ":generator_dataset_op",
         ":group_by_window_dataset_op",
         ":interleave_dataset_op",
         ":iterator_ops",
diff --git a/tensorflow/core/kernels/data/captured_function.cc b/tensorflow/core/kernels/data/captured_function.cc
index c4aa9ec265..dd61b7daee 100644
--- a/tensorflow/core/kernels/data/captured_function.cc
+++ b/tensorflow/core/kernels/data/captured_function.cc
@@ -256,6 +256,62 @@ Status CapturedFunction::RunWithBorrowedArgs(IteratorContext* ctx,
   return frame.ConsumeRetvals(rets);
 }
 
+Status CapturedFunction::Instantiate(IteratorContext* ctx) {
+  FunctionLibraryRuntime::Handle unused_handle;
+  TF_RETURN_IF_ERROR(MaybeInstantiate(ctx, &unused_handle));
+  mutex_lock l(mu_);
+  if (captured_runner_ == nullptr) {
+    captured_runner_ = *ctx->runner();
+  }
+  return Status::OK();
+}
+
+Status CapturedFunction::RunInstantiated(const std::vector<Tensor>& args,
+                                         std::vector<Tensor>* rets) {
+  FunctionLibraryRuntime* lib;
+  FunctionLibraryRuntime::Handle handle;
+  std::function<void(std::function<void()>)>* runner;
+  {
+    tf_shared_lock l(mu_);
+    if (lib_ == nullptr) {
+      return errors::FailedPrecondition(
+          "`CapturedFunction::Instantiate()` must be called before a call to "
+          "`CapturedFunction::RunInstantiated()`.");
+    }
+    lib = lib_;
+    handle = f_handle_;
+    runner = &captured_runner_;
+  }
+
+  FunctionLibraryRuntime::Options f_opts;
+  f_opts.step_id = CapturedFunction::generate_step_id();
+  ScopedStepContainer step_container(f_opts.step_id, [lib](const string& name) {
+    lib->device()->resource_manager()->Cleanup(name).IgnoreError();
+  });
+  f_opts.step_container = &step_container;
+  f_opts.runner = runner;
+  // TODO(mrry): Add cancellation manager support to IteratorContext
+  // so that we can cancel running map functions. The local
+  // cancellation manager here is created so that we can run kernels
+  // (such as queue kernels) that depend on the non-nullness of
+  // `OpKernelContext::cancellation_manager()`, but additional effort
+  // will be required to plumb it through the `IteratorContext`.
+  CancellationManager c_mgr;
+  f_opts.cancellation_manager = &c_mgr;
+
+  BorrowedArgsCallFrame frame(args, &captured_inputs_, ret_types_);
+  Notification n;
+  Status s;
+
+  lib->Run(f_opts, handle, &frame, [&n, &s](Status func_status) {
+    s.Update(func_status);
+    n.Notify();
+  });
+  n.WaitForNotification();
+  TF_RETURN_IF_ERROR(s);
+  return frame.ConsumeRetvals(rets);
+}
+
 void CapturedFunction::RunAsync(IteratorContext* ctx,
                                 std::vector<Tensor>&& args,
                                 std::vector<Tensor>* rets,
diff --git a/tensorflow/core/kernels/data/captured_function.h b/tensorflow/core/kernels/data/captured_function.h
index 32d2bc3aae..490f5cd1e3 100644
--- a/tensorflow/core/kernels/data/captured_function.h
+++ b/tensorflow/core/kernels/data/captured_function.h
@@ -64,6 +64,21 @@ class CapturedFunction {
                              const std::vector<Tensor>& args,
                              std::vector<Tensor>* rets);
 
+  // Explicitly instantiate this function for use in the given
+  // context. This method, and the context-less overload
+  // `RunInstantiated()` below can be useful for calling a captured
+  // function in cases where an `IteratorContext*` is not available
+  // (such as a destructor).
+  Status Instantiate(IteratorContext* ctx);
+
+  // Synchronously runs the captured function on the given `args`, and stores
+  // the results in `*rets`. Prefer to use `Run()` or `RunAsync()` when
+  // possible.
+  //
+  // REQUIRES: `this->Instantiate()` must have been called before this method.
+  Status RunInstantiated(const std::vector<Tensor>& args,
+                         std::vector<Tensor>* rets);
+
   // Asynchronously runs the captured function on the given `args`, stores
   // the results in `*rets`, and calls the given `done` callback when the
   // function returns. This method takes ownership of the tensors in `args`,
@@ -99,6 +114,7 @@ class CapturedFunction {
   FunctionLibraryRuntime::Handle f_handle_ GUARDED_BY(mu_);
   const std::vector<Tensor> captured_inputs_;
   DataTypeSlice ret_types_;
+  std::function<void(std::function<void()>)> captured_runner_ = nullptr;
 
   TF_DISALLOW_COPY_AND_ASSIGN(CapturedFunction);
 };
diff --git a/tensorflow/core/kernels/data/generator_dataset_op.cc b/tensorflow/core/kernels/data/generator_dataset_op.cc
new file mode 100644
index 0000000000..3f1e441b91
--- /dev/null
+++ b/tensorflow/core/kernels/data/generator_dataset_op.cc
@@ -0,0 +1,201 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include <iterator>
+#include <vector>
+
+#include "tensorflow/core/framework/dataset.h"
+#include "tensorflow/core/framework/partial_tensor_shape.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/kernels/data/captured_function.h"
+#include "tensorflow/core/lib/random/random.h"
+
+namespace tensorflow {
+
+namespace {
+
+// See documentation in ../ops/dataset_ops.cc for a high-level
+// description of the following op.
+
+class GeneratorDatasetOp : public DatasetOpKernel {
+ public:
+  explicit GeneratorDatasetOp(OpKernelConstruction* ctx)
+      : DatasetOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("init_func", &init_func_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("next_func", &next_func_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("finalize_func", &finalize_func_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_));
+  }
+
+  void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override {
+    OpInputList init_func_other_args_input;
+    OP_REQUIRES_OK(ctx, ctx->input_list("init_func_other_args",
+                                        &init_func_other_args_input));
+    std::vector<Tensor> init_func_other_args;
+    init_func_other_args.reserve(init_func_other_args_input.size());
+    for (const Tensor& t : init_func_other_args_input) {
+      init_func_other_args.push_back(t);
+    }
+    std::unique_ptr<CapturedFunction> init_func;
+    OP_REQUIRES_OK(
+        ctx, CapturedFunction::Create(
+                 init_func_, std::move(init_func_other_args), &init_func));
+
+    OpInputList next_func_other_args_input;
+    OP_REQUIRES_OK(ctx, ctx->input_list("next_func_other_args",
+                                        &next_func_other_args_input));
+    std::vector<Tensor> next_func_other_args;
+    next_func_other_args.reserve(next_func_other_args_input.size());
+    for (const Tensor& t : next_func_other_args_input) {
+      next_func_other_args.push_back(t);
+    }
+    std::unique_ptr<CapturedFunction> next_func;
+    OP_REQUIRES_OK(
+        ctx, CapturedFunction::Create(
+                 next_func_, std::move(next_func_other_args), &next_func));
+
+    OpInputList finalize_func_other_args_input;
+    OP_REQUIRES_OK(ctx, ctx->input_list("finalize_func_other_args",
+                                        &finalize_func_other_args_input));
+    std::vector<Tensor> finalize_func_other_args;
+    finalize_func_other_args.reserve(finalize_func_other_args_input.size());
+    for (const Tensor& t : finalize_func_other_args_input) {
+      finalize_func_other_args.push_back(t);
+    }
+    std::unique_ptr<CapturedFunction> finalize_func;
+    OP_REQUIRES_OK(ctx, CapturedFunction::Create(
+                            finalize_func_, std::move(finalize_func_other_args),
+                            &finalize_func));
+
+    *output =
+        new Dataset(ctx, std::move(init_func), std::move(next_func),
+                    std::move(finalize_func), output_types_, output_shapes_);
+  }
+
+ private:
+  class Dataset : public GraphDatasetBase {
+   public:
+    Dataset(OpKernelContext* ctx, std::unique_ptr<CapturedFunction> init_func,
+            std::unique_ptr<CapturedFunction> next_func,
+            std::unique_ptr<CapturedFunction> finalize_func,
+            const DataTypeVector& output_types,
+            const std::vector<PartialTensorShape>& output_shapes)
+        : GraphDatasetBase(ctx),
+          init_func_(std::move(init_func)),
+          next_func_(std::move(next_func)),
+          finalize_func_(std::move(finalize_func)),
+          output_types_(output_types),
+          output_shapes_(output_shapes) {}
+
+    std::unique_ptr<IteratorBase> MakeIterator(
+        const string& prefix) const override {
+      return std::unique_ptr<IteratorBase>(
+          new Iterator({this, strings::StrCat(prefix, "::Generator")}));
+    }
+
+    const DataTypeVector& output_dtypes() const override {
+      return output_types_;
+    }
+    const std::vector<PartialTensorShape>& output_shapes() const override {
+      return output_shapes_;
+    }
+
+    string DebugString() override { return "GeneratorDatasetOp::Dataset"; }
+
+   private:
+    class Iterator : public DatasetIterator<Dataset> {
+     public:
+      explicit Iterator(const Params& params)
+          : DatasetIterator<Dataset>(params) {}
+
+      ~Iterator() override {
+        if (!finalized_) {
+          std::vector<Tensor> ignored;
+          Status s =
+              dataset()->finalize_func_->RunInstantiated(state_, &ignored);
+          if (!s.ok()) {
+            LOG(WARNING)
+                << "Error occurred when finalizing GeneratorDataset iterator: "
+                << s;
+          }
+        }
+      }
+
+      Status GetNextInternal(IteratorContext* ctx,
+                             std::vector<Tensor>* out_tensors,
+                             bool* end_of_sequence) override {
+        mutex_lock l(mu_);
+
+        if (!initialized_) {
+          TF_RETURN_IF_ERROR(
+              dataset()->init_func_->RunWithBorrowedArgs(ctx, {}, &state_));
+          // Explicitly instantiate the finalize function here so that
+          // we can invoke it in the destructor.
+          TF_RETURN_IF_ERROR(dataset()->finalize_func_->Instantiate(ctx));
+          initialized_ = true;
+        }
+
+        if (finalized_) {
+          *end_of_sequence = true;
+          return Status::OK();
+        }
+
+        Status s = dataset()->next_func_->RunWithBorrowedArgs(ctx, state_,
+                                                              out_tensors);
+        if (s.ok()) {
+          *end_of_sequence = false;
+        } else if (errors::IsOutOfRange(s)) {
+          // `next_func` may deliberately raise `errors::OutOfRange`
+          // to indicate that we should terminate the iteration.
+          s = Status::OK();
+          *end_of_sequence = true;
+
+          // NOTE(mrry): We ignore any tensors returned by the
+          // finalize function.
+          std::vector<Tensor> ignored;
+          TF_RETURN_IF_ERROR(
+              dataset()->finalize_func_->RunInstantiated(state_, &ignored));
+          finalized_ = true;
+        }
+        return s;
+      }
+
+     private:
+      mutex mu_;
+      bool initialized_ GUARDED_BY(mu_) = false;
+      bool finalized_ GUARDED_BY(mu_) = false;
+      std::vector<Tensor> state_ GUARDED_BY(mu_);
+    };
+
+    const std::unique_ptr<CapturedFunction> init_func_;
+    const std::unique_ptr<CapturedFunction> next_func_;
+    const std::unique_ptr<CapturedFunction> finalize_func_;
+    const DataTypeVector output_types_;
+    const std::vector<PartialTensorShape> output_shapes_;
+  };
+
+  DataTypeVector output_types_;
+  std::vector<PartialTensorShape> output_shapes_;
+  NameAttrList init_func_;
+  NameAttrList next_func_;
+  NameAttrList finalize_func_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("GeneratorDataset").Device(DEVICE_CPU),
+                        GeneratorDatasetOp);
+
+}  // namespace
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index 9e98f56c74..117ae6ba79 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -66,6 +66,23 @@ REGISTER_OP("SparseTensorSliceDataset")
                       // stateful to inhibit constant folding.
     .SetShapeFn(shape_inference::ScalarShape);
 
+REGISTER_OP("GeneratorDataset")
+    .Input("init_func_other_args: Tinit_func_args")
+    .Input("next_func_other_args: Tnext_func_args")
+    .Input("finalize_func_other_args: Tfinalize_func_args")
+    .Output("handle: variant")
+    .Attr("init_func: func")
+    .Attr("next_func: func")
+    .Attr("finalize_func: func")
+    .Attr("Tinit_func_args: list(type) >= 0")
+    .Attr("Tnext_func_args: list(type) >= 0")
+    .Attr("Tfinalize_func_args: list(type) >= 0")
+    .Attr("output_types: list(type) >= 1")
+    .Attr("output_shapes: list(shape) >= 1")
+    .SetIsStateful()  // TODO(b/65524810): Source dataset ops must be marked
+                      // stateful to inhibit constant folding.
+    .SetShapeFn(shape_inference::ScalarShape);
+
 REGISTER_OP("ZipDataset")
     .Input("input_datasets: N * variant")
     .Output("handle: variant")
diff --git a/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py b/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py
index f129d07b57..6aabad2f57 100644
--- a/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py
+++ b/tensorflow/python/data/kernel_tests/dataset_from_generator_op_test.py
@@ -21,9 +21,12 @@ import threading
 
 import numpy as np
 
+from tensorflow.python.client import session
 from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
+from tensorflow.python.ops import script_ops
 from tensorflow.python.platform import test
 
 
@@ -302,6 +305,89 @@ class DatasetConstructorTest(test.TestCase):
       with self.assertRaises(errors.OutOfRangeError):
         sess.run(get_next)
 
+  def testFromGeneratorStopShort(self):
+
+    def generator():
+      yield 0
+      yield 1
+      yield 2
+
+    iterator = (
+        dataset_ops.Dataset.from_generator(
+            generator, output_types=dtypes.int64).make_initializable_iterator())
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      self.assertAllEqual(0, sess.run(get_next))
+      self.assertAllEqual(1, sess.run(get_next))
+
+  def testFromGeneratorDestructorCalled(self):
+    # Use an `Event` to signal that the generator has been deleted.
+    event = threading.Event()
+
+    class GeneratorWrapper(object):
+
+      def __iter__(self):
+        return self
+
+      def next(self):
+        return self.__next__()
+
+      def __next__(self):
+        return 42
+
+      def __del__(self):
+        event.set()
+
+    iterator = dataset_ops.Dataset.from_generator(
+        GeneratorWrapper,
+        output_types=dtypes.int64).take(2).make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with session.Session() as sess:
+      sess.run(init_op)
+      self.assertAllEqual(42, sess.run(get_next))
+      self.assertAllEqual(42, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+      # Test that `GeneratorWrapper` object is destroyed when the
+      # iterator terminates (and the generator iterator is deleted).
+      self.assertTrue(event.is_set())
+
+  def testGeneratorDatasetFinalizeFunctionCalled(self):
+    # NOTE(mrry): This test tests the internal `_GeneratorDataset`,
+    # which affords more control over what the finalize function can do than
+    # the `Dataset.from_generator()` wrapper.
+
+    # Use an `Event` to signal that the generator has been deleted.
+    event = threading.Event()
+
+    def finalize_fn(_):
+      def finalize_py_func():
+        event.set()
+        return 0
+      return script_ops.py_func(finalize_py_func, [], [dtypes.int64],
+                                stateful=True)
+
+    dummy = constant_op.constant(37)
+    iterator = (dataset_ops._GeneratorDataset(dummy, lambda x: x,
+                                              lambda x: x, finalize_fn)
+                .take(2)
+                .make_initializable_iterator())
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      self.assertAllEqual(37, sess.run(get_next))
+      self.assertAllEqual(37, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+        self.assertTrue(event.is_set())
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py
index b665443b7a..3fb1f8d547 100644
--- a/tensorflow/python/data/ops/dataset_ops.py
+++ b/tensorflow/python/data/ops/dataset_ops.py
@@ -331,10 +331,10 @@ class Dataset(object):
 
     generator_state = Dataset._GeneratorState(generator)
 
-    def get_iterator_id_map_fn(unused_dummy):
+    def get_iterator_id_fn(unused_dummy):
       """Creates a unique `iterator_id` for each pass over the dataset.
 
-      The "iterator_id" disambiguates between multiple concurrently
+      The returned `iterator_id` disambiguates between multiple concurrently
       existing iterators.
 
       Args:
@@ -347,7 +347,7 @@ class Dataset(object):
       return script_ops.py_func(
           generator_state.get_next_id, [], dtypes.int64, stateful=True)
 
-    def generator_map_fn(iterator_id_t):
+    def generator_next_fn(iterator_id_t):
       """Generates the next element from iterator with ID `iterator_id_t`.
 
       We map this function across an infinite repetition of the
@@ -363,11 +363,9 @@ class Dataset(object):
 
       def generator_py_func(iterator_id):
         """A `py_func` that will be called to invoke the iterator."""
-        try:
-          values = next(generator_state.get_iterator(iterator_id))
-        except StopIteration:
-          generator_state.iterator_completed(iterator_id)
-          raise StopIteration("Iteration finished.")
+        # `next()` raises `StopIteration` when there are no more
+        # elements remaining to be generated.
+        values = next(generator_state.get_iterator(iterator_id))
 
         # Use the same _convert function from the py_func() implementation to
         # convert the returned values to arrays early, so that we can inspect
@@ -408,17 +406,31 @@ class Dataset(object):
 
       return nest.pack_sequence_as(output_types, flat_values)
 
+    def finalize_fn(iterator_id_t):
+      """Releases host-side state for the iterator with ID `iterator_id_t`."""
+
+      def finalize_py_func(iterator_id):
+        generator_state.iterator_completed(iterator_id)
+        # We return a dummy value so that the `finalize_fn` has a valid
+        # signature.
+        # NOTE(mrry): Explicitly create an array of `np.int64` because implicit
+        # casting in `py_func()` will create an array of `np.int32` on Windows,
+        # leading to a runtime error.
+        return np.array(0, dtype=np.int64)
+
+      return script_ops.py_func(
+          finalize_py_func, [iterator_id_t], dtypes.int64, stateful=True)
+
     # This function associates each traversal of `generator` with a unique
     # iterator ID.
-    def flat_map_fn(iterator_id_t):
-      # First, generate an infinite dataset containing the iterator ID repeated
-      # forever.
-      repeated_id = Dataset.from_tensors(iterator_id_t).repeat(None)
-
-      # The `generator_map_fn` gets the next element from the iterator with the
-      # relevant ID, and raises StopIteration when that iterator contains no
+    def flat_map_fn(dummy_arg):
+      # The `get_iterator_id_fn` gets a unique ID for the current instance of
+      # of the generator.
+      # The `generator_next_fn` gets the next element from the iterator with the
+      # given ID, and raises StopIteration when that iterator contains no
       # more elements.
-      return repeated_id.map(generator_map_fn)
+      return _GeneratorDataset(dummy_arg, get_iterator_id_fn, generator_next_fn,
+                               finalize_fn)
 
     # A single-element dataset that, each time it is evaluated, contains a
     # freshly-generated and unique (for the returned dataset) int64
@@ -426,7 +438,7 @@ class Dataset(object):
     # is encapsulated in `generator_state`, and captured in
     # `get_iterator_id_map_fn`.
     dummy = 0
-    id_dataset = Dataset.from_tensors(dummy).map(get_iterator_id_map_fn)
+    id_dataset = Dataset.from_tensors(dummy)
 
     # A dataset that contains all of the elements generated by a
     # single iterator created from `generator`, identified by the
@@ -1033,6 +1045,196 @@ class SparseTensorSliceDataset(Dataset):
     return (dtypes.int64, self._sparse_tensor.dtype, dtypes.int64)
 
 
+class _GeneratorDataset(Dataset):
+  """A `Dataset` that generates elements by invoking a function."""
+
+  def __init__(self, init_args, init_func, next_func, finalize_func):
+    """Constructs a `_GeneratorDataset`.
+
+    Args:
+      init_args: A nested structure representing the arguments to `init_func`.
+      init_func: A TensorFlow function that will be called on `init_args` each
+        time a C++ iterator over this dataset is constructed. Returns a nested
+        structure representing the "state" of the dataset.
+      next_func: A TensorFlow function that will be called on the result of
+        `init_func` to produce each element, and that raises `OutOfRangeError`
+        to terminate iteration.
+      finalize_func: A TensorFlow function that will be called on the result of
+        `init_func` immediately before a C++ iterator over this dataset is
+        destroyed. The return value is ignored.
+    """
+    super(_GeneratorDataset, self).__init__()
+    # These members will be initialized by `tf_init_func`.
+    self._state_classes = None
+    self._state_shapes = None
+    self._state_types = None
+
+    self._init_args = init_args
+
+    init_args_classes = sparse.get_classes(init_args)
+    init_args_shapes = nest.pack_sequence_as(
+        init_args, [t.get_shape() for t in nest.flatten(init_args)])
+    init_args_types = nest.pack_sequence_as(
+        init_args, [t.dtype for t in nest.flatten(init_args)])
+
+    @function.Defun(*nest.flatten(
+        sparse.as_dense_types(init_args_types, init_args_classes)))
+    def tf_init_func(*args):
+      """A wrapper for Defun that facilitates shape inference."""
+      dense_shapes = sparse.as_dense_shapes(init_args_shapes, init_args_classes)
+      for arg, shape in zip(args, nest.flatten(dense_shapes)):
+        arg.set_shape(shape)
+
+      nested_args = nest.pack_sequence_as(init_args_classes, args)
+      nested_args = sparse.deserialize_sparse_tensors(
+          nested_args, init_args_types, init_args_shapes, init_args_classes)
+      if _should_unpack_args(nested_args):
+        ret = init_func(*nested_args)
+      else:
+        ret = init_func(nested_args)
+
+      # If `init_func` returns a list of tensors, `nest.flatten()` and
+      # `ops.convert_to_tensor()` would conspire to attempt to stack
+      # those tensors into a single tensor, because the customized
+      # version of `nest.flatten()` does not recurse into lists. Since
+      # it is more likely that the list arose from returning the
+      # result of an operation (such as `tf.py_func()`) that returns a
+      # list of not-necessarily-stackable tensors, we treat the
+      # returned value is a `tuple` instead. A user wishing to pack
+      # the return value into a single tensor can use an explicit
+      # `tf.stack()` before returning.
+      if isinstance(ret, list):
+        ret = tuple(ret)
+
+      # Convert any `SparseTensorValue`s to `SparseTensor`s.
+      ret = nest.pack_sequence_as(ret, [
+          sparse_tensor_lib.SparseTensor.from_value(t)
+          if sparse_tensor_lib.is_sparse(t) else t for t in nest.flatten(ret)
+      ])
+
+      self._state_classes = sparse.get_classes(ret)
+      self._state_shapes = nest.pack_sequence_as(
+          ret, [t.get_shape() for t in nest.flatten(ret)])
+      self._state_types = nest.pack_sequence_as(
+          ret, [t.dtype for t in nest.flatten(ret)])
+
+      # Serialize any sparse tensors and convert result to tensors.
+      ret = nest.pack_sequence_as(ret, [
+          ops.convert_to_tensor(t)
+          for t in nest.flatten(sparse.serialize_sparse_tensors(ret))
+      ])
+      return nest.flatten(ret)
+
+    self._init_func = tf_init_func
+    self._init_func.add_to_graph(ops.get_default_graph())
+
+    # These members will be initialized by `tf_next_func`.
+    self._output_classes = None
+    self._output_shapes = None
+    self._output_types = None
+
+    @function.Defun(*nest.flatten(
+        sparse.as_dense_types(self._state_types, self._state_classes)))
+    def tf_next_func(*args):
+      """A wrapper for Defun that facilitates shape inference."""
+      # Pass in shape information from the input_dataset.
+      dense_shapes = sparse.as_dense_shapes(self._state_shapes,
+                                            self._state_classes)
+      for arg, shape in zip(args, nest.flatten(dense_shapes)):
+        arg.set_shape(shape)
+
+      nested_args = nest.pack_sequence_as(self._state_classes, args)
+      nested_args = sparse.deserialize_sparse_tensors(
+          nested_args, self._state_types, self._state_shapes,
+          self._state_classes)
+      if _should_unpack_args(nested_args):
+        ret = next_func(*nested_args)
+      else:
+        ret = next_func(nested_args)
+
+      # If `next_func` returns a list of tensors, `nest.flatten()` and
+      # `ops.convert_to_tensor()` would conspire to attempt to stack
+      # those tensors into a single tensor, because the customized
+      # version of `nest.flatten()` does not recurse into lists. Since
+      # it is more likely that the list arose from returning the
+      # result of an operation (such as `tf.py_func()`) that returns a
+      # list of not-necessarily-stackable tensors, we treat the
+      # returned value is a `tuple` instead. A user wishing to pack
+      # the return value into a single tensor can use an explicit
+      # `tf.stack()` before returning.
+      if isinstance(ret, list):
+        ret = tuple(ret)
+
+      # Convert any `SparseTensorValue`s to `SparseTensor`s.
+      ret = nest.pack_sequence_as(ret, [
+          sparse_tensor_lib.SparseTensor.from_value(t)
+          if sparse_tensor_lib.is_sparse(t) else t for t in nest.flatten(ret)
+      ])
+
+      self._output_classes = sparse.get_classes(ret)
+      self._output_shapes = nest.pack_sequence_as(
+          ret, [t.get_shape() for t in nest.flatten(ret)])
+      self._output_types = nest.pack_sequence_as(
+          ret, [t.dtype for t in nest.flatten(ret)])
+
+      # Serialize any sparse tensors and convert result to tensors.
+      ret = nest.pack_sequence_as(ret, [
+          ops.convert_to_tensor(t)
+          for t in nest.flatten(sparse.serialize_sparse_tensors(ret))
+      ])
+      return nest.flatten(ret)
+
+    self._next_func = tf_next_func
+    self._next_func.add_to_graph(ops.get_default_graph())
+
+    @function.Defun(*nest.flatten(
+        sparse.as_dense_types(self._state_types, self._state_classes)))
+    def tf_finalize_func(*args):
+      """A wrapper for Defun that facilitates shape inference."""
+      # Pass in shape information from the state.
+      dense_shapes = sparse.as_dense_shapes(self._state_shapes,
+                                            self._state_classes)
+      for arg, shape in zip(args, nest.flatten(dense_shapes)):
+        arg.set_shape(shape)
+
+      nested_args = nest.pack_sequence_as(self._state_classes, args)
+      nested_args = sparse.deserialize_sparse_tensors(
+          nested_args, self._state_types, self._state_shapes,
+          self._state_classes)
+      if _should_unpack_args(nested_args):
+        return finalize_func(*nested_args)
+      else:
+        return finalize_func(nested_args)
+
+    self._finalize_func = tf_finalize_func
+    self._finalize_func.add_to_graph(ops.get_default_graph())
+
+  def _as_variant_tensor(self):
+    return gen_dataset_ops.generator_dataset(
+        nest.flatten(self._init_args) + self._init_func.captured_inputs,
+        self._next_func.captured_inputs,
+        self._finalize_func.captured_inputs,
+        init_func=self._init_func,
+        next_func=self._next_func,
+        finalize_func=self._finalize_func,
+        output_types=nest.flatten(
+            sparse.as_dense_types(self.output_types, self.output_classes)),
+        output_shapes=nest.flatten(
+            sparse.as_dense_shapes(self.output_shapes, self.output_classes)))
+
+  @property
+  def output_classes(self):
+    return self._output_classes
+
+  @property
+  def output_shapes(self):
+    return self._output_shapes
+
+  @property
+  def output_types(self):
+    return self._output_types
+
+
 class ZipDataset(Dataset):
   """A `Dataset` that zips its inputs together."""
 
-- 
GitLab


From 724c8304761971b6a9f23bde3908d29a44a952c0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 14:18:23 -0800
Subject: [PATCH 097/575] Update ops-related pbtxt files.

PiperOrigin-RevId: 186361455
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 67 +++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 67 +++++++++++++++++++
 2 files changed, 134 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index fc9e5b02a2..3e9460952c 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -20556,6 +20556,65 @@ op {
     minimum: -1
   }
 }
+op {
+  name: "GeneratorDataset"
+  input_arg {
+    name: "init_func_other_args"
+    type_list_attr: "Tinit_func_args"
+  }
+  input_arg {
+    name: "next_func_other_args"
+    type_list_attr: "Tnext_func_args"
+  }
+  input_arg {
+    name: "finalize_func_other_args"
+    type_list_attr: "Tfinalize_func_args"
+  }
+  output_arg {
+    name: "handle"
+    type: DT_VARIANT
+  }
+  attr {
+    name: "init_func"
+    type: "func"
+  }
+  attr {
+    name: "next_func"
+    type: "func"
+  }
+  attr {
+    name: "finalize_func"
+    type: "func"
+  }
+  attr {
+    name: "Tinit_func_args"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Tnext_func_args"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Tfinalize_func_args"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  is_stateful: true
+}
 op {
   name: "GetSessionHandle"
   input_arg {
@@ -64366,6 +64425,14 @@ op {
     version: 3
   }
 }
+op {
+  name: "Timestamp"
+  output_arg {
+    name: "ts"
+    type: DT_DOUBLE
+  }
+  is_stateful: true
+}
 op {
   name: "TopK"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 45ff08f38b..bbd43e191d 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -9656,6 +9656,65 @@ op {
     minimum: -1
   }
 }
+op {
+  name: "GeneratorDataset"
+  input_arg {
+    name: "init_func_other_args"
+    type_list_attr: "Tinit_func_args"
+  }
+  input_arg {
+    name: "next_func_other_args"
+    type_list_attr: "Tnext_func_args"
+  }
+  input_arg {
+    name: "finalize_func_other_args"
+    type_list_attr: "Tfinalize_func_args"
+  }
+  output_arg {
+    name: "handle"
+    type: DT_VARIANT
+  }
+  attr {
+    name: "init_func"
+    type: "func"
+  }
+  attr {
+    name: "next_func"
+    type: "func"
+  }
+  attr {
+    name: "finalize_func"
+    type: "func"
+  }
+  attr {
+    name: "Tinit_func_args"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Tnext_func_args"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "Tfinalize_func_args"
+    type: "list(type)"
+    has_minimum: true
+  }
+  attr {
+    name: "output_types"
+    type: "list(type)"
+    has_minimum: true
+    minimum: 1
+  }
+  attr {
+    name: "output_shapes"
+    type: "list(shape)"
+    has_minimum: true
+    minimum: 1
+  }
+  is_stateful: true
+}
 op {
   name: "GetSessionHandle"
   input_arg {
@@ -30368,6 +30427,14 @@ op {
     explanation: "TileGrad has been replaced with reduce_sum"
   }
 }
+op {
+  name: "Timestamp"
+  output_arg {
+    name: "ts"
+    type: DT_DOUBLE
+  }
+  is_stateful: true
+}
 op {
   name: "TopK"
   input_arg {
-- 
GitLab


From 02e0edf58331e829de3ece7f1ce02cf281c9177a Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Tue, 20 Feb 2018 14:27:04 -0800
Subject: [PATCH 098/575] Update contrib/quantize docs to add description of
 how to use rewrites.

PiperOrigin-RevId: 186362791
---
 tensorflow/contrib/quantize/README.md         | 101 +++++++++---------
 .../g3doc/drawings/Fake_Quantization.jpg      | Bin 32990 -> 0 bytes
 2 files changed, 49 insertions(+), 52 deletions(-)
 delete mode 100644 tensorflow/contrib/quantize/g3doc/drawings/Fake_Quantization.jpg

diff --git a/tensorflow/contrib/quantize/README.md b/tensorflow/contrib/quantize/README.md
index 40541729da..8b0e7bb68f 100644
--- a/tensorflow/contrib/quantize/README.md
+++ b/tensorflow/contrib/quantize/README.md
@@ -1,9 +1,10 @@
+# Quantized Training Rewrites
+
 tf.contrib.quantize provides tools for transforming graphs to include ops to
 model quantization of weights, biases and activations during both training and
 inference. This is done using the
 [fake quantization op]
-(https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization),
-which is described below:
+(https://www.tensorflow.org/versions/r0.12/api_docs/python/array_ops/fake_quantization).
 
 Recent literature has shown that fixed point networks provide comparable
 performance to floating point networks [1]. This is achieved by modeling the
@@ -14,56 +15,52 @@ updated at high precision as this is needed to ensure sufficient precision in
 accumulating tiny adjustments to the parameters. However, for the forward pass,
 the parameters and activations are quantized to the desired lower precision.
 
-![drawing](g3doc/drawings/Fake_Quantization.jpg)
-
-###Forward pass
-
-
-
-
-\begin{equation*}
-f_Q(x) = \Delta\text{ }round\left(\frac{sat\left(x\right)-x_{min}}{\Delta}\right)
-\end{equation*}
-
-
-where
-
-$$
-\begin{equation*}
-sat(x) =
-\left\{
-	\begin{array}{ll}
-		x_{min}  & \mbox{if } x \le x_{min} \\
-		x & \mbox{if } x_{min} \leq x \leq x_{max} \\
-    x_{max} & \mbox{if } x_{max} \le x
-	\end{array}
-\right.
-\end{equation*}
-$$
-
-
-where $$\Delta$$ is the Quantizer Step size, given by
-$$\Delta =\frac{x_{max} - x_{min} }{255} $$ and $$x_{min} $$ and $$x_{max}$$ are
-the minimum and maximum values of the variable under consideration. Note that
-the rounding performed is deterministic and corresponds to asymmetric rounding,
-which is supported in almost all hardware platforms.
-
-###Backward pass
-For the backward pass, we model the quantizer as a piecewise linear block, with
-derivatives that are non-zero only in the linear region.
-
-
-
-\begin{equation*}
-\frac{df_Q(x)}{dx}=1, x_{min} \leq x \leq x_{max},\text{ 0  elsewhere }
-\end{equation*}
-
-Therefore, the backward pass through the quantizer reduces to passing through
-the gradients as long as the inputs to the quantizer are in the linear region.
-Otherwise, the gradients are set to zero.
-
-Note that the quantizer is fully specified by the min and max values of the
-variables being quantized.
+## How to use the Rewrites
+
+tf.contrib.quantize provides two rewrites, one to train for quantization and
+one to create a [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/)
+compatible eval graph.
+
+```
+# Build forward pass of model.
+…
+loss = tf.losses.get_total_loss()
+
+# Call the training rewrite which rewrites the graph in-place with FakeQuantization nodes
+# and folds batchnorm for training.
+# It is often needed to finetune a floating point model for quantization with this training tool.
+# When training from scratch, quant_delay can be used to activate quantization after
+# training to convergence with the float graph, effectively finetuning the model.
+tf.contrib.quantize.create_training_graph(quant_delay=2000000)
+
+# Call backward pass optimizer as usual.
+optimizer = tf.train.GradientDescentOptimizer(learning_rate)
+optimizer.minimize(loss)
+```
+
+Additionally, the rewritten eval graph is non-trivially different from the
+training graph due the effects of quantization on batch normalization. Thus,
+we offer a separate rewrite for the eval_graph.
+
+```
+# Build eval model
+…
+logits = tf.nn.softmax_cross_entropy_with_logits(...)
+
+# Call the eval rewrite which rewrites the graph in-place with FakeQuantization nodes
+# and fold batchnorm for eval.
+tf.contrib.quantize.create_eval_graph()
+
+# Save the checkpoint and eval graph proto to disk for freezing and providing to TFLite.
+with open(eval_graph_file, ‘w’) as f:
+  f.write(str(g.as_graph_def()))
+saver = tf.train.Saver()
+saver.save(sess, checkpoint_name)
+```
+
+These rewrites are an active area of research and experimentation, so the
+rewrites and quantized training will likely not work across all models, though
+we hope to work towards generalizing these techniques.
 
 
 [1] P.Gysel, "HARDWARE-ORIENTED APPROXIMATION OF CONVOLUTIONAL
diff --git a/tensorflow/contrib/quantize/g3doc/drawings/Fake_Quantization.jpg b/tensorflow/contrib/quantize/g3doc/drawings/Fake_Quantization.jpg
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z>BLrlV$XkuZvy-NGhEu4!Mp&=ZVZZtNU3Bd^Zbv;|8)F1`A#>Ye+Ag_GXEJ)^scSH
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wt|+WyI7$=lwvUF<Xc&#AfzdQDng&MGz-Ss6O#`E8U^ESkrUCNPK>h!l0OS<TV*mgE

-- 
GitLab


From f17238c8a42705e5a160a4185afe30fac8497683 Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Tue, 20 Feb 2018 14:31:00 -0800
Subject: [PATCH 099/575] Fix a typo in the comment (TFLite)

PiperOrigin-RevId: 186363449
---
 tensorflow/contrib/lite/context.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h
index b0c4d3431f..c604cbc39e 100644
--- a/tensorflow/contrib/lite/context.h
+++ b/tensorflow/contrib/lite/context.h
@@ -258,7 +258,7 @@ typedef struct TfLiteContext {
   TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context,
                                    TfLiteIntArray** execution_plan);
 
-  // An tensor of tensors in the interpreter context (of length `tensors_size`)
+  // An array of tensors in the interpreter context (of length `tensors_size`)
   TfLiteTensor* tensors;
 
   // opaque full context ptr (an opaque c++ data structure)
-- 
GitLab


From af4b7d75c40ba305c40fe6faa873374cfc6ec881 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 14:46:43 -0800
Subject: [PATCH 100/575] Go: Update generated wrapper functions for TensorFlow
 ops. PiperOrigin-RevId: 186365924

---
 tensorflow/go/op/wrappers.go | 150 +++++++++++++++++------------------
 1 file changed, 75 insertions(+), 75 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 3f742091f5..34c4e1b3ff 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -329,6 +329,61 @@ func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Ou
 	return op.Output(0)
 }
 
+// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient.
+type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr)
+
+// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value.
+//
+// value: The bitwidth of the quantization; between 2 and 8, inclusive.
+// If not specified, defaults to 8
+func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr {
+	return func(m optionalAttr) {
+		m["num_bits"] = value
+	}
+}
+
+// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value.
+//
+// value: Whether to quantize into 2^num_bits - 1 distinct values.
+// If not specified, defaults to false
+func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr {
+	return func(m optionalAttr) {
+		m["narrow_range"] = value
+	}
+}
+
+// Compute gradients for a FakeQuantWithMinMaxVars operation.
+//
+// Arguments:
+//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
+//	inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation.
+// min, max: Quantization interval, scalar floats.
+//
+//
+//
+// Returns Backpropagated gradients w.r.t. inputs:
+// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter:
+// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter:
+// `sum(gradients * (inputs > max))`.
+func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "FakeQuantWithMinMaxVarsGradient",
+		Input: []tf.Input{
+			gradients, inputs, min, max,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
+}
+
 // Partitions `data` into `num_partitions` tensors using indices from `partitions`.
 //
 // For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
@@ -1695,61 +1750,6 @@ func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
-// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient.
-type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr)
-
-// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value.
-//
-// value: The bitwidth of the quantization; between 2 and 8, inclusive.
-// If not specified, defaults to 8
-func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr {
-	return func(m optionalAttr) {
-		m["num_bits"] = value
-	}
-}
-
-// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value.
-//
-// value: Whether to quantize into 2^num_bits - 1 distinct values.
-// If not specified, defaults to false
-func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr {
-	return func(m optionalAttr) {
-		m["narrow_range"] = value
-	}
-}
-
-// Compute gradients for a FakeQuantWithMinMaxVars operation.
-//
-// Arguments:
-//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
-//	inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation.
-// min, max: Quantization interval, scalar floats.
-//
-//
-//
-// Returns Backpropagated gradients w.r.t. inputs:
-// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter:
-// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter:
-// `sum(gradients * (inputs > max))`.
-func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "FakeQuantWithMinMaxVarsGradient",
-		Input: []tf.Input{
-			gradients, inputs, min, max,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
-}
-
 // LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler.
 type LogUniformCandidateSamplerAttr func(optionalAttr)
 
@@ -2480,26 +2480,6 @@ func ReaderNumWorkUnitsCompletedV2(scope *Scope, reader_handle tf.Output) (units
 	return op.Output(0)
 }
 
-// Returns x / y element-wise for real types.
-//
-// If `x` and `y` are reals, this will return the floating-point division.
-//
-// *NOTE*: `Div` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func RealDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "RealDiv",
-		Input: []tf.Input{
-			x, y,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Computes the log of the absolute value of `Gamma(x)` element-wise.
 func Lgamma(scope *Scope, x tf.Output) (y tf.Output) {
 	if scope.Err() != nil {
@@ -20021,6 +20001,26 @@ func SparseTensorSliceDataset(scope *Scope, indices tf.Output, values tf.Output,
 	return op.Output(0)
 }
 
+// Returns x / y element-wise for real types.
+//
+// If `x` and `y` are reals, this will return the floating-point division.
+//
+// *NOTE*: `Div` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func RealDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "RealDiv",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Creates a dataset that concatenates `input_dataset` with `another_dataset`.
 func ConcatenateDataset(scope *Scope, input_dataset tf.Output, another_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
 	if scope.Err() != nil {
-- 
GitLab


From 776fa148b4772afb3e000ec15c3b3e6eb9f43a52 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 15:08:09 -0800
Subject: [PATCH 101/575] Shorten grappler per-node report by default with
 previous behavior behind --verbose.

PiperOrigin-RevId: 186369380
---
 tensorflow/python/grappler/cost_analyzer.cc   | 59 +++++++++++++++++--
 tensorflow/python/grappler/cost_analyzer.h    |  6 +-
 tensorflow/python/grappler/cost_analyzer.i    |  6 +-
 tensorflow/python/grappler/cost_analyzer.py   | 11 +++-
 .../python/grappler/cost_analyzer_test.py     | 22 ++++++-
 .../python/grappler/cost_analyzer_tool.py     |  7 ++-
 6 files changed, 95 insertions(+), 16 deletions(-)

diff --git a/tensorflow/python/grappler/cost_analyzer.cc b/tensorflow/python/grappler/cost_analyzer.cc
index 88bf900dca..b474e19894 100644
--- a/tensorflow/python/grappler/cost_analyzer.cc
+++ b/tensorflow/python/grappler/cost_analyzer.cc
@@ -30,11 +30,12 @@ CostAnalyzer::CostAnalyzer(const GrapplerItem& item, Cluster* cluster,
       analytical_estimator_(cluster, false),
       suffix_(suffix) {}
 
-Status CostAnalyzer::GenerateReport(std::ostream& os, bool per_node_report) {
+Status CostAnalyzer::GenerateReport(std::ostream& os, bool per_node_report,
+                                    bool verbose) {
   GatherCosts();
   PreprocessCosts();
   AnalyzeCosts();
-  PrintAnalysis(os, per_node_report);
+  PrintAnalysis(os, per_node_report, verbose);
   return Status::OK();
 }
 
@@ -158,7 +159,8 @@ void CostAnalyzer::AnalyzeCosts() {
   }
 }
 
-void CostAnalyzer::PrintAnalysis(std::ostream& os, bool per_node_report) const {
+void CostAnalyzer::PrintAnalysis(std::ostream& os, bool per_node_report,
+                                 bool verbose) const {
   os << std::endl;
   os << std::left << std::setw(50)
      << "Total time measured in ns (serialized): " << std::right
@@ -227,10 +229,55 @@ void CostAnalyzer::PrintAnalysis(std::ostream& os, bool per_node_report) const {
   os << std::endl;
 
   if (per_node_report) {
-    os << "Below is the per-node report:" << std::endl;
-    os << op_perf_.DebugString();
+    if (verbose) {
+      os << "Below is the full per-node report:" << std::endl;
+      os << op_perf_.DebugString();
+    } else {
+      os << "Below is the per-node report summary:" << std::endl;
+      int width = 35;
+      int width_narrow = 15;
+      int width_wide = 20;
+      os << std::setw(width + 1) << "Op,";
+      os << std::setw(width_wide + 1) << "Measured time (ns),";
+      os << std::setw(width_wide + 1) << "Compute time (ns),";
+      os << std::setw(width_wide + 1) << "Memory time (ns),";
+      os << std::setw(width_narrow + 2) << "Compute eff,";
+      os << std::setw(width_narrow + 2) << "Memory eff,";
+      os << "    Inputs" << std::endl;
+      for (int i = 0; i < op_perf_.op_performance_size(); i++) {
+        const auto& perf = op_perf_.op_performance(i);
+        string op_name = perf.op().op();
+        os << std::setw(width) << op_name << ",";
+        os << std::setw(width_wide) << perf.compute_cost() << ",";
+        os << std::setw(width_wide) << perf.compute_time() << ",";
+        os << std::setw(width_wide) << perf.memory_time() << ",";
+        os << std::setw(width_narrow) << std::setprecision(2)
+           << perf.compute_efficiency() * 100 << "%,";
+        os << std::setw(width_narrow) << std::setprecision(2)
+           << perf.memory_efficiency() * 100 << "%,";
+        os << "    [";
+        for (int j = 0; j < perf.op().inputs_size(); j++) {
+          const auto& shape = perf.op().inputs(j).shape();
+          if (shape.dim_size() > 0) {
+            os << "(";
+            std::vector<int> dims;
+            for (int k = 0; k < shape.dim_size(); k++) {
+              os << shape.dim(k).size();
+              if (k < shape.dim_size() - 1) {
+                os << ", ";
+              }
+            }
+            os << ")";
+            if (j < perf.op().inputs_size() - 1) {
+              os << ", ";
+            }
+          }
+        }
+        os << "]" << std::endl;
+      }
+      os << std::endl;
+    }
   }
 }
-
 }  // end namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/python/grappler/cost_analyzer.h b/tensorflow/python/grappler/cost_analyzer.h
index 0e860e0fee..b5364aa37a 100644
--- a/tensorflow/python/grappler/cost_analyzer.h
+++ b/tensorflow/python/grappler/cost_analyzer.h
@@ -19,6 +19,7 @@ limitations under the License.
 #include <iostream>
 #include "tensorflow/core/framework/cost_graph.pb.h"
 #include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/grappler/clusters/cluster.h"
 #include "tensorflow/core/grappler/costs/analytical_cost_estimator.h"
 #include "tensorflow/core/grappler/costs/cost_estimator.h"
@@ -50,7 +51,7 @@ class CostAnalyzer {
  public:
   explicit CostAnalyzer(const GrapplerItem& item, Cluster* cluster,
                         const string& suffix);
-  Status GenerateReport(std::ostream& os, bool per_node_report);
+  Status GenerateReport(std::ostream& os, bool per_node_report, bool verbose);
 
  private:
   void PredictCosts(CostEstimator* cost_estimator, CostGraphDef* cost_graph,
@@ -59,7 +60,8 @@ class CostAnalyzer {
   void PreprocessCosts();
   void AnalyzeCosts();
   void SortOpsByTime(std::map<string, OpPerfSummary> ops);
-  void PrintAnalysis(std::ostream& os, bool per_node_report) const;
+  void PrintAnalysis(std::ostream& os, bool per_node_report,
+                     bool verbose) const;
 
   const GrapplerItem* item_;
   MeasuringCostEstimator measure_estimator_;
diff --git a/tensorflow/python/grappler/cost_analyzer.i b/tensorflow/python/grappler/cost_analyzer.i
index 4c0953435b..8f7fdb47f2 100644
--- a/tensorflow/python/grappler/cost_analyzer.i
+++ b/tensorflow/python/grappler/cost_analyzer.i
@@ -44,7 +44,7 @@ limitations under the License.
 
 %{
 string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph, bool per_node_report,
-                          GCluster cluster) {
+                          bool verbose, GCluster cluster) {
   tensorflow::grappler::ItemConfig cfg;
   cfg.apply_optimizations = false;
   std::unique_ptr<tensorflow::grappler::GrapplerItem> item =
@@ -57,11 +57,11 @@ string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph, bool per_no
   tensorflow::grappler::CostAnalyzer analyzer(*item, cluster.get(), suffix);
 
   std::stringstream os;
-  analyzer.GenerateReport(os, per_node_report);
+  analyzer.GenerateReport(os, per_node_report, verbose);
   return os.str();
 }
 
 %}
 
 string GenerateCostReport(const tensorflow::MetaGraphDef& metagraph, bool per_node_report,
-                          GCluster cluster);
+                          bool verbose, GCluster cluster);
diff --git a/tensorflow/python/grappler/cost_analyzer.py b/tensorflow/python/grappler/cost_analyzer.py
index a1ff915c61..6a4690e91b 100644
--- a/tensorflow/python/grappler/cost_analyzer.py
+++ b/tensorflow/python/grappler/cost_analyzer.py
@@ -24,7 +24,10 @@ from tensorflow.python.grappler import cluster as gcluster
 from tensorflow.python.grappler import item as gitem
 
 
-def GenerateCostReport(metagraph, per_node_report=False, cluster=None):
+def GenerateCostReport(metagraph,
+                       per_node_report=False,
+                       verbose=False,
+                       cluster=None):
   """Analyze the cost of each TensorFlow op and node in the provided metagraph.
 
   Args:
@@ -32,6 +35,7 @@ def GenerateCostReport(metagraph, per_node_report=False, cluster=None):
     per_node_report: by default the report contains stats aggregated on a per op
       type basis, setting per_node_report to True adds results for each
       individual node to the report.
+    verbose: Prints out the entire operation proto instead of a summary table.
     cluster: Analyze the costs using the specified cluster, or the local machine
       if no cluster was specified.
 
@@ -42,8 +46,9 @@ def GenerateCostReport(metagraph, per_node_report=False, cluster=None):
     cluster = gcluster.Cluster(disable_detailed_stats=False)
 
   with errors.raise_exception_on_not_ok_status():
-    ret_from_swig = tf_wrap.GenerateCostReport(
-        metagraph.SerializeToString(), per_node_report, cluster.tf_cluster)
+    ret_from_swig = tf_wrap.GenerateCostReport(metagraph.SerializeToString(),
+                                               per_node_report, verbose,
+                                               cluster.tf_cluster)
   return ret_from_swig
 
 
diff --git a/tensorflow/python/grappler/cost_analyzer_test.py b/tensorflow/python/grappler/cost_analyzer_test.py
index 511908c79c..b8225b81a5 100644
--- a/tensorflow/python/grappler/cost_analyzer_test.py
+++ b/tensorflow/python/grappler/cost_analyzer_test.py
@@ -48,7 +48,7 @@ class CostAnalysisTest(test.TestCase):
     train_op.append(d)
     mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph())
 
-    report = cost_analyzer.GenerateCostReport(mg)
+    report = cost_analyzer.GenerateCostReport(mg, per_node_report=True)
 
     # Check the report headers
     self.assertTrue(b"Total time measured in ns (serialized):" in report)
@@ -57,6 +57,26 @@ class CostAnalysisTest(test.TestCase):
     self.assertTrue(b"Total time analytical in ns (lower bound):" in report)
     self.assertTrue(b"Overall efficiency (analytical upper/actual):" in report)
     self.assertTrue(b"Overall efficiency (analytical lower/actual):" in report)
+    self.assertTrue(b"Below is the per-node report summary:" in report)
+
+    # Also print the report to make it easier to debug
+    print("{}".format(report))
+
+  def testVerbose(self):
+    """Make sure the full report is generated with verbose=True."""
+    a = constant_op.constant(10, name="a")
+    b = constant_op.constant(20, name="b")
+    c = math_ops.add_n([a, b], name="c")
+    d = math_ops.add_n([b, c], name="d")
+    train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
+    train_op.append(d)
+    mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph())
+
+    report = cost_analyzer.GenerateCostReport(
+        mg, per_node_report=True, verbose=True)
+
+    # Check the report headers
+    self.assertTrue(b"Below is the full per-node report:" in report)
 
     # Also print the report to make it easier to debug
     print("{}".format(report))
diff --git a/tensorflow/python/grappler/cost_analyzer_tool.py b/tensorflow/python/grappler/cost_analyzer_tool.py
index 0db3c30a27..0853db2524 100644
--- a/tensorflow/python/grappler/cost_analyzer_tool.py
+++ b/tensorflow/python/grappler/cost_analyzer_tool.py
@@ -74,7 +74,8 @@ def main(_):
   optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, metagraph)
   metagraph.graph_def.CopyFrom(optimized_graph)
 
-  report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report)
+  report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report,
+                                            FLAGS.verbose)
   print(report)
   if FLAGS.memory_report:
     report = cost_analyzer.GenerateMemoryReport(metagraph)
@@ -117,5 +118,9 @@ if __name__ == "__main__":
       "--memory_report",
       action="store_true",
       help="Generate memory usage report.")
+  parser.add_argument(
+      "--verbose",
+      action="store_true",
+      help="Generate verbose reports. By default, succinct reports are used.")
   FLAGS, unparsed = parser.parse_known_args()
   app.run(main=main, argv=[sys.argv[0]] + unparsed)
-- 
GitLab


From 8e44ce68ea102f8d6fde317fbe38e0c58b59b9af Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Tue, 20 Feb 2018 16:13:11 -0800
Subject: [PATCH 102/575] [XLA] Emit saturating shifts on CPU, GPU and
 interpreter

With this change shifting out >= bitwidth for shift left and logical shift right
produces 0, and shifting out >= bitwidth for arithmetic shift right produces -1
if the LHS is negative and 0 otherwise.  Before this we were invoking undefined
behavior for these out-of-bounds shifts in LLVM and the HLO evaluator.

PiperOrigin-RevId: 186379160
---
 .../xla/service/elemental_ir_emitter.cc       | 47 +++++++++++--
 .../compiler/xla/service/hlo_evaluator.cc     | 21 ++++--
 .../xla/tests/array_elementwise_ops_test.cc   | 66 +++++++++++--------
 3 files changed, 97 insertions(+), 37 deletions(-)

diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index 4468adbadb..12b35b2f96 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -1003,6 +1003,30 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitReducePrecision(
                                   ir_builder_);
 }
 
+static llvm::Value* SaturateShiftIfNecessary(llvm::IRBuilder<>* ir_builder,
+                                             llvm::Value* lhs, llvm::Value* rhs,
+                                             llvm::Value* shift_result,
+                                             bool saturate_to_sign_bit) {
+  llvm::IntegerType* integer_type =
+      llvm::cast<llvm::IntegerType>(lhs->getType());
+  unsigned integer_bitsize = integer_type->getBitWidth();
+  llvm::ConstantInt* integer_bitsize_constant =
+      llvm::ConstantInt::get(integer_type, integer_bitsize);
+  llvm::ConstantInt* zero = llvm::ConstantInt::get(integer_type, 0);
+  llvm::ConstantInt* minus_one = llvm::ConstantInt::get(integer_type, -1);
+  llvm::Value* saturated_value;
+  if (saturate_to_sign_bit) {
+    saturated_value = ir_builder->CreateSelect(
+        ir_builder->CreateICmpSLT(lhs, zero), minus_one, zero);
+  } else {
+    saturated_value = zero;
+  }
+  llvm::Value* shift_amt_in_range =
+      ir_builder->CreateICmpULT(rhs, integer_bitsize_constant, "shft.chk");
+  return ir_builder->CreateSelect(shift_amt_in_range, shift_result,
+                                  saturated_value);
+}
+
 StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerBinaryOp(
     const HloInstruction* op, llvm::Value* lhs_value, llvm::Value* rhs_value,
     bool is_signed) const {
@@ -1050,12 +1074,27 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerBinaryOp(
       return ir_builder_->CreateAnd(lhs_value, rhs_value);
     case HloOpcode::kOr:
       return ir_builder_->CreateOr(lhs_value, rhs_value);
-    case HloOpcode::kShiftLeft:
-      return ir_builder_->CreateShl(lhs_value, rhs_value);
+
+    // Shifting out bits >= the number of bits in the type being shifted
+    // produces a poison value in LLVM which is basically "deferred undefined
+    // behavior" -- doing something observable with such a value precipitates
+    // UB.  We replace the poison value with a constant to avoid this deferred
+    // UB.
     case HloOpcode::kShiftRightArithmetic:
-      return ir_builder_->CreateAShr(lhs_value, rhs_value);
+      return SaturateShiftIfNecessary(
+          ir_builder_, lhs_value, rhs_value,
+          ir_builder_->CreateAShr(lhs_value, rhs_value),
+          /*saturate_to_sign_bit=*/true);
+    case HloOpcode::kShiftLeft:
+      return SaturateShiftIfNecessary(
+          ir_builder_, lhs_value, rhs_value,
+          ir_builder_->CreateShl(lhs_value, rhs_value),
+          /*saturate_to_sign_bit=*/false);
     case HloOpcode::kShiftRightLogical:
-      return ir_builder_->CreateLShr(lhs_value, rhs_value);
+      return SaturateShiftIfNecessary(
+          ir_builder_, lhs_value, rhs_value,
+          ir_builder_->CreateLShr(lhs_value, rhs_value),
+          /*saturate_to_sign_bit=*/false);
     default:
       return Unimplemented("binary integer op '%s'",
                            HloOpcodeString(op->opcode()).c_str());
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 296f010a92..15ae53128a 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -740,7 +740,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     TF_ASSIGN_OR_RETURN(
         parent_->evaluated_[shl],
         ElementWiseBinaryOp(shl, [](NativeT lhs_elem, NativeT rhs_elem) {
-          return lhs_elem << rhs_elem;
+          return IsShiftOutOfBounds<NativeT>(rhs_elem) ? 0
+                                                       : (lhs_elem << rhs_elem);
         }));
     return Status::OK();
   }
@@ -765,8 +766,12 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     TF_ASSIGN_OR_RETURN(
         parent_->evaluated_[shr],
         ElementWiseBinaryOp(shr, [](NativeT lhs_elem, NativeT rhs_elem) {
-          return static_cast<NativeT>(static_cast<SignedT>(lhs_elem) >>
-                                      rhs_elem);
+          SignedT lhs_signed = static_cast<SignedT>(lhs_elem);
+          if (IsShiftOutOfBounds<NativeT>(rhs_elem)) {
+            return lhs_signed < 0 ? static_cast<SignedT>(-1) : 0;
+          } else {
+            return lhs_signed >> rhs_elem;
+          }
         }));
     return Status::OK();
   }
@@ -793,7 +798,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
         parent_->evaluated_[shr],
         ElementWiseBinaryOp(shr, [](NativeT lhs_elem, NativeT rhs_elem) {
           // If shift amount is greater than the number of bits, then return 0.
-          if (rhs_elem >= sizeof(UnsignedT) * CHAR_BIT) {
+          if (IsShiftOutOfBounds<NativeT>(rhs_elem)) {
             return static_cast<NativeT>(0);
           }
           return static_cast<NativeT>(static_cast<UnsignedT>(lhs_elem) >>
@@ -2031,6 +2036,14 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return std::move(result);
   }
 
+  template <typename NativeT>
+  static bool IsShiftOutOfBounds(NativeT rhs) {
+    typedef typename std::make_unsigned<NativeT>::type UnsignedT;
+    UnsignedT lhs_size_unsigned = sizeof(NativeT) * CHAR_BIT;
+    UnsignedT rhs_unsigned = static_cast<UnsignedT>(rhs);
+    return rhs_unsigned >= lhs_size_unsigned;
+  }
+
   HloEvaluator* parent_;
 };  // class HloEvaluator::TypedVisitor
 
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 7e9005001d..739d201fad 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -847,68 +847,76 @@ XLA_TEST_F(ArrayElementwiseOpTest, NotZeroElementU32R1) {
 
 XLA_TEST_F(ArrayElementwiseOpTest, ShiftLeftS32) {
   ComputationBuilder builder(client_, TestName());
-  auto a =
-      builder.ConstantR1<int32>({static_cast<int32>(0x12345678),
-                                 static_cast<int32>(0xF0001000), 1, 3, 77});
-  auto b = builder.ConstantR1<int32>({4, 8, 2, 7, 15});
+  auto a = builder.ConstantR1<int32>({static_cast<int32>(0x12345678),
+                                      static_cast<int32>(0xF0001000), 1, 3, 77,
+                                      1, -3, 77});
+  auto b = builder.ConstantR1<int32>({4, 8, 2, 7, 15, 32, 100, -1});
   auto out = builder.ShiftLeft(a, b);
 
-  ComputeAndCompareR1<int32>(
-      &builder,
-      {static_cast<int32>(0x23456780), 0x00100000, 0x4, 0x180, 2523136}, {});
+  ComputeAndCompareR1<int32>(&builder,
+                             {static_cast<int32>(0x23456780), 0x00100000, 0x4,
+                              0x180, 2523136, 0, 0, 0},
+                             {});
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightArithmeticS32) {
   ComputationBuilder builder(client_, TestName());
-  auto a =
-      builder.ConstantR1<int32>({static_cast<int32>(0x92345678),
-                                 static_cast<int32>(0x10001000), 1, 3, 77});
-  auto b = builder.ConstantR1<int32>({4, 8, 2, 7, 2});
+  auto a = builder.ConstantR1<int32>({static_cast<int32>(0x92345678),
+                                      static_cast<int32>(0x10001000), 1, 3, 77,
+                                      1, -3, 77});
+  auto b = builder.ConstantR1<int32>({4, 8, 2, 7, 2, 32, 100, -1});
   auto out = builder.ShiftRightArithmetic(a, b);
 
-  ComputeAndCompareR1<int32>(&builder,
-                             {static_cast<int32>(0xF9234567),
-                              static_cast<int32>(0x00100010), 0, 0, 19},
-                             {});
+  ComputeAndCompareR1<int32>(
+      &builder,
+      {static_cast<int32>(0xF9234567), static_cast<int32>(0x00100010), 0, 0, 19,
+       0, -1, 0},
+      {});
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightLogicalS32) {
   ComputationBuilder builder(client_, TestName());
-  auto a =
-      builder.ConstantR1<int32>({static_cast<int32>(0x92345678),
-                                 static_cast<int32>(0x10001000), 1, 3, 77});
-  auto b = builder.ConstantR1<int32>({4, 8, 2, 7, 5});
+  auto a = builder.ConstantR1<int32>({static_cast<int32>(0x92345678),
+                                      static_cast<int32>(0x10001000), 1, 3, 77,
+                                      1, -3, 77});
+  auto b = builder.ConstantR1<int32>({4, 8, 2, 7, 5, 32, 100, -1});
   auto out = builder.ShiftRightLogical(a, b);
 
-  ComputeAndCompareR1<int32>(&builder, {0x09234567, 0x00100010, 0, 0, 2}, {});
+  ComputeAndCompareR1<int32>(&builder,
+                             {0x09234567, 0x00100010, 0, 0, 2, 0, 0, 0}, {});
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, ShiftLeftU32) {
   ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR1<uint32>({0x12345678, 0xF0001000, 1, 3, 77});
-  auto b = builder.ConstantR1<uint32>({4, 8, 2, 7, 15});
+  auto a = builder.ConstantR1<uint32>(
+      {0x12345678, 0xF0001000, 1, 3, 77, 1, ~3u, 77});
+  auto b = builder.ConstantR1<uint32>({4, 8, 2, 7, 15, 32, 100, ~0u});
   auto out = builder.ShiftLeft(a, b);
 
   ComputeAndCompareR1<uint32>(
-      &builder, {0x23456780, 0x00100000, 0x4, 0x180, 2523136}, {});
+      &builder, {0x23456780, 0x00100000, 0x4, 0x180, 2523136, 0, 0, 0}, {});
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightArithmeticU32) {
   ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR1<uint32>({0x92345678, 0x10001000, 1, 3, 77});
-  auto b = builder.ConstantR1<uint32>({4, 8, 2, 7, 2});
+  auto a = builder.ConstantR1<uint32>(
+      {0x92345678, 0x10001000, 1, 3, 77, 1, ~3u, 77});
+  auto b = builder.ConstantR1<uint32>({4, 8, 2, 7, 2, 32, 100, ~0u});
   auto out = builder.ShiftRightArithmetic(a, b);
 
-  ComputeAndCompareR1<uint32>(&builder, {0xF9234567, 0x00100010, 0, 0, 19}, {});
+  ComputeAndCompareR1<uint32>(
+      &builder, {0xF9234567, 0x00100010, 0, 0, 19, 0, ~0u, 0}, {});
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, ShiftRightLogicalU32) {
   ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR1<uint32>({0x92345678, 0x10001000, 1, 3, 77});
-  auto b = builder.ConstantR1<uint32>({4, 8, 2, 7, 5});
+  auto a = builder.ConstantR1<uint32>(
+      {0x92345678, 0x10001000, 1, 3, 77, 1, ~3u, 77});
+  auto b = builder.ConstantR1<uint32>({4, 8, 2, 7, 5, 32, 100, ~0u});
   auto out = builder.ShiftRightLogical(a, b);
 
-  ComputeAndCompareR1<uint32>(&builder, {0x09234567, 0x00100010, 0, 0, 2}, {});
+  ComputeAndCompareR1<uint32>(&builder,
+                              {0x09234567, 0x00100010, 0, 0, 2, 0, 0, 0}, {});
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, CompareEqF32s) {
-- 
GitLab


From 62f64c0876bced0e8e77324dc17502b69b170206 Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Tue, 20 Feb 2018 17:33:16 -0800
Subject: [PATCH 103/575] Fill the new `custom_initial_data(_size)?` fields in
 TfLiteNode.

PiperOrigin-RevId: 186389819
---
 tensorflow/contrib/lite/interpreter.cc | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc
index 028449211b..0c30f1c64f 100644
--- a/tensorflow/contrib/lite/interpreter.cc
+++ b/tensorflow/contrib/lite/interpreter.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/contrib/lite/kernels/gemm_support.h"
 #include "tensorflow/contrib/lite/memory_planner.h"
 #include "tensorflow/contrib/lite/nnapi_delegate.h"
+#include "tensorflow/contrib/lite/schema/schema_generated.h"
 
 namespace {
 
@@ -298,7 +299,20 @@ TfLiteStatus Interpreter::AddNodeWithParameters(
         OpInit(*registration,
                reinterpret_cast<const char*>(builtin_data_deleter.get()), 0);
   }
+
   node.builtin_data = builtin_data_deleter.release();
+  // TODO(ycling): Filling `custom_initial_data` and `custom_initial_data_size`
+  // properly for nodes generated by ReplaceSubgraphsWithDelegateKernels.
+  if (registration->builtin_code == BuiltinOperator_CUSTOM) {
+    // When it's a CUSTOM op, the `custom_options` field in the Flatbuffer
+    // `Operator` table is passed in.
+    node.custom_initial_data = init_data;
+    node.custom_initial_data_size = init_data_size;
+  } else {
+    node.custom_initial_data = nullptr;
+    node.custom_initial_data_size = 0;
+  }
+
   node_and_reg.second = *registration;
   execution_plan_.push_back(new_node_index);
   return kTfLiteOk;
-- 
GitLab


From d3488849e4f6469c87dc3535f442eee120a36074 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 17:40:06 -0800
Subject: [PATCH 104/575] Add a utility that generalizes getcallargs to
 non-function callables like constructors and __call__ operators.

PiperOrigin-RevId: 186390545
---
 .../contrib/py2tf/pyct/inspect_utils.py       | 20 +++++++++++
 .../contrib/py2tf/pyct/inspect_utils_test.py  | 36 +++++++++++++++++++
 2 files changed, 56 insertions(+)

diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils.py b/tensorflow/contrib/py2tf/pyct/inspect_utils.py
index b6552cbbee..86cf52afd5 100644
--- a/tensorflow/contrib/py2tf/pyct/inspect_utils.py
+++ b/tensorflow/contrib/py2tf/pyct/inspect_utils.py
@@ -26,6 +26,26 @@ import six
 from tensorflow.python.util import tf_inspect
 
 
+def getcallargs(c, *args, **kwargs):
+  """Extension of getcallargs to non-function callables."""
+  if tf_inspect.isfunction(c):
+    # The traditional getcallargs
+    return tf_inspect.getcallargs(c, *args, **kwargs)
+
+  if tf_inspect.isclass(c):
+    # Constructors: pass a fake None for self, then remove it.
+    arg_map = tf_inspect.getcallargs(c.__init__, None, *args, **kwargs)
+    assert 'self' in arg_map, 'no "self" argument, is this not a constructor?'
+    del arg_map['self']
+    return arg_map
+
+  if hasattr(c, '__call__'):
+    # Callable objects: map self to the object itself
+    return tf_inspect.getcallargs(c.__call__, *args, **kwargs)
+
+  raise NotImplementedError('unknown callable "%s"' % type(c))
+
+
 def getmethodclass(m, namespace):
   """Resolves a function's owner, e.g. a method's class."""
 
diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
index f0468a04c4..5d92e75b18 100644
--- a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
+++ b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
@@ -84,6 +84,42 @@ def free_factory():
 
 class InspectUtilsTest(test.TestCase):
 
+  def test_getcallargs_constructor(self):
+
+    class TestSuperclass(object):
+
+      def __init__(self, x):
+        pass
+
+    class TestCallable(TestSuperclass):
+      pass
+
+    self.assertDictEqual({
+        'x': 1
+    }, inspect_utils.getcallargs(TestCallable, 1))
+
+  def test_getcallargs_object(self):
+
+    class TestCallable(object):
+
+      def __call__(self, x):
+        pass
+
+    obj = TestCallable()
+    self.assertDictEqual({
+        'self': obj,
+        'x': 1
+    }, inspect_utils.getcallargs(obj, 1))
+
+  def test_getcallargs_function(self):
+
+    def test_fn(x):
+      return x + 1
+
+    self.assertDictEqual({
+        'x': 1
+    }, inspect_utils.getcallargs(test_fn, 1))
+
   def test_getmethodclass(self):
 
     self.assertEqual(
-- 
GitLab


From 0dc4f2c7c6ba384b42706a092f300875befde037 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 17:40:17 -0800
Subject: [PATCH 105/575] Update decorators transformer with additional
 clarifications in the tests and handling of the more rare cases when multiple
 decorators are applied together, as well as when decorators are used with
 local functions.

PiperOrigin-RevId: 186390564
---
 .../contrib/py2tf/converters/decorators.py    |  32 +++-
 .../py2tf/converters/decorators_test.py       | 147 +++++++++++-------
 tensorflow/contrib/py2tf/impl/conversion.py   |  13 +-
 3 files changed, 131 insertions(+), 61 deletions(-)

diff --git a/tensorflow/contrib/py2tf/converters/decorators.py b/tensorflow/contrib/py2tf/converters/decorators.py
index 3f620c1cd2..68bf241ef3 100644
--- a/tensorflow/contrib/py2tf/converters/decorators.py
+++ b/tensorflow/contrib/py2tf/converters/decorators.py
@@ -33,6 +33,7 @@ class DecoratorsTransformer(gast.NodeTransformer):
 
   def __init__(self, remove_decorators):
     self.remove_decorators = remove_decorators
+    self.additional_dependencies = set()
 
   # pylint:disable=invalid-name
 
@@ -44,13 +45,38 @@ class DecoratorsTransformer(gast.NodeTransformer):
         dec_func = dec.func
       else:
         dec_func = dec
+
+      # Special cases.
+      # TODO(mdan): Is there any way we can treat these more generically?
+      # We may want to forego using decorators altogether if we can't
+      # properly support them.
+      if isinstance(dec_func, gast.Name) and dec_func.id in ('classmethod',):
+        # Assumption: decorators are only visible in the AST when converting
+        # a function inline (via another decorator).
+        # In that case, the converted function is no longer part of the
+        # original object that it was declared into.
+        # This is currently verified by tests.
+        continue
+
       if not anno.hasanno(dec_func, 'live_val'):
         raise ValueError(
             'Could not resolve decorator: %s' % pretty_printer.fmt(dec_func))
+
       dec_value = anno.getanno(dec_func, 'live_val')
       if dec_value not in self.remove_decorators:
-        kept_decorators.append(dec)
-    node.decorator_list = kept_decorators
+        kept_decorators.append((dec, dec_value))
+
+    for _, dec_value in kept_decorators:
+      if dec_value.__module__ == '__main__':
+        raise ValueError(
+            'decorator "%s" was not allowed because it is declared '
+            'in the module "%s". To fix this, declare it in a separate '
+            'module that we can import it from.' % (dec_value,
+                                                    dec_value.__module__))
+      else:
+        self.additional_dependencies.add(dec_value)
+
+    node.decorator_list = [dec for dec, _ in kept_decorators]
     return node
 
   # pylint:enable=invalid-name
@@ -59,4 +85,4 @@ class DecoratorsTransformer(gast.NodeTransformer):
 def transform(node, remove_decorators):
   transformer = DecoratorsTransformer(remove_decorators)
   node = transformer.visit(node)
-  return node
+  return node, transformer.additional_dependencies
diff --git a/tensorflow/contrib/py2tf/converters/decorators_test.py b/tensorflow/contrib/py2tf/converters/decorators_test.py
index 402fa0dda2..c75e546174 100644
--- a/tensorflow/contrib/py2tf/converters/decorators_test.py
+++ b/tensorflow/contrib/py2tf/converters/decorators_test.py
@@ -18,84 +18,121 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import textwrap
+from functools import wraps
 
 from tensorflow.contrib.py2tf.converters import converter_test_base
 from tensorflow.contrib.py2tf.converters import decorators
 from tensorflow.contrib.py2tf.pyct import compiler
 from tensorflow.python.platform import test
-from tensorflow.python.util import tf_inspect
+
+
+# The Python parser only briefly captures decorators into the AST.
+# The interpreter desugars them on load, and the decorated function loses any
+# trace of the decorator (which is notmally what you would expect, since
+# they are meant to be transparent).
+# However, decorators are still visible when you analyze the function
+# from inside a decorator, before it was applied - as is the case
+# with our conversion decorators.
+
+
+def simple_decorator(f):
+  return lambda a: f(a) + 1
+
+
+def self_removing_decorator(removing_wrapper):
+  def decorator(f):
+    @wraps(f)
+    def wrapper(*args):
+      # This removing wrapper is defined in the test below. This setup is so
+      # intricate just to simulate how we use the transformer in practice.
+      transformed_f = removing_wrapper(f, (self_removing_decorator,))
+      return transformed_f(*args) + 1
+    return wrapper
+  return decorator
 
 
 class DecoratorsTest(converter_test_base.TestCase):
 
-  def test_function_decorator(self):
+  def _remover_wrapper(self, f, remove_decorators):
+    namespace = {
+        'self_removing_decorator': self_removing_decorator,
+        'simple_decorator': simple_decorator
+    }
+    node = self.parse_and_analyze(f, namespace)
+    node, _ = decorators.transform(node, remove_decorators=remove_decorators)
+    result, _ = compiler.ast_to_object(node)
+    return getattr(result, f.__name__)
 
-    def function_decorator():
+  def test_noop(self):
 
-      def decorator(f):
-        return lambda a: f(a) + 1
+    def test_fn(a):
+      return a
 
-      return decorator
+    node = self.parse_and_analyze(test_fn, {})
+    node, deps = decorators.transform(node, remove_decorators=())
+    result, _ = compiler.ast_to_object(node)
 
-    # The Python parser does capture decorators into the AST.
-    # However, the interpreter desugars them on load, and refering to the
-    # decorated function at runtime usually loses any trace of the decorator.
-    # Below is an example when that doesn't happen.
-    def static_wrapper():
+    self.assertFalse(deps)
+    self.assertEqual(1, result.test_fn(1))
 
-      @function_decorator()
-      def test_fn(a):  # pylint:disable=unused-variable
-        return a
+  def test_function(self):
 
-    node = self.parse_and_analyze(static_wrapper,
-                                  {'function_decorator': function_decorator})
-    node = node.body[0].body[0]
+    @self_removing_decorator(self._remover_wrapper)
+    def test_fn(a):
+      return a
 
-    node = decorators.transform(node, remove_decorators=())
-    # Since the decorator is not removed, we need to include its source
-    # code. We cannot do it after the fact because decorators are executed
-    # on load.
-    result, _ = compiler.ast_to_object(
-        node,
-        source_prefix=textwrap.dedent(tf_inspect.getsource(function_decorator)))
-    self.assertEqual(2, result.test_fn(1))
+    # 2 = 1 (a) + 1 (decorator applied exactly once)
+    self.assertEqual(2, test_fn(1))
 
-    node = decorators.transform(node, remove_decorators=(function_decorator,))
-    with self.compiled(node) as result:
-      self.assertEqual(1, result.test_fn(1))
+  def test_method(self):
 
-  def test_simple_decorator(self):
+    class TestClass(object):
 
-    def simple_decorator(f):
-      return lambda a: f(a) + 1
+      @self_removing_decorator(self._remover_wrapper)
+      def test_fn(self, a):
+        return a
 
-    # The Python parser does capture decorators into the AST.
-    # However, the interpreter desugars them upon load, and refering to the
-    # decorated function at runtime usually loses any trace of the decorator.
-    # Below is an example when that doesn't happen.
-    def static_wrapper():
+    # 2 = 1 (a) + 1 (decorator applied exactly once)
+    self.assertEqual(2, TestClass().test_fn(1))
 
-      @simple_decorator
-      def test_fn(a):  # pylint:disable=unused-variable
+  def test_multiple_decorators(self):
+
+    class TestClass(object):
+
+      # Note that reversing the order of this two doesn't work.
+      @classmethod
+      @self_removing_decorator(self._remover_wrapper)
+      def test_fn(cls, a):
         return a
 
-    node = self.parse_and_analyze(static_wrapper,
-                                  {'simple_decorator': simple_decorator})
-    node = node.body[0].body[0]
-
-    node = decorators.transform(node, remove_decorators=())
-    # Since the decorator is not removed, we need to include its source
-    # code. We cannot do it after the fact because decorators are executed
-    # on load.
-    result, _ = compiler.ast_to_object(
-        node,
-        source_prefix=textwrap.dedent(tf_inspect.getsource(simple_decorator)))
-    self.assertEqual(2, result.test_fn(1))
-
-    node = decorators.transform(node, remove_decorators=(simple_decorator,))
-    with self.compiled(node) as result:
-      self.assertEqual(1, result.test_fn(1))
+    # 2 = 1 (a) + 1 (decorator applied exactly once)
+    self.assertEqual(2, TestClass.test_fn(1))
+
+  def test_nested_decorators(self):
+
+    @self_removing_decorator(self._remover_wrapper)
+    def test_fn(a):
+      @simple_decorator
+      def inner_fn(b):
+        return b + 11
+      return inner_fn(a)
+
+    with self.assertRaises(ValueError):
+      test_fn(1)
+
+  # TODO(mdan): Uncomment this test once converter_test_base is updated.
+  # (can't do it now because it has unrelated pending changes)
+  # def test_nested_decorators(self):
+  #
+  #   @self_removing_decorator(self._remover_wrapper)
+  #   def test_fn(a):
+  #     @imported_decorator
+  #     def inner_fn(b):
+  #       return b + 11
+  #     return inner_fn(a)
+  #
+  #   # 14 = 1 (a) + 1 (simple_decorator) + 11 (inner_fn)
+  #   self.assertEqual(14, test_fn(1))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/py2tf/impl/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py
index 7610f0427b..f3dc6b4d06 100644
--- a/tensorflow/contrib/py2tf/impl/conversion.py
+++ b/tensorflow/contrib/py2tf/impl/conversion.py
@@ -56,6 +56,9 @@ class ConversionMap(object):
         off.
     dependency_cache: dict[object]: ast; maps original entities to their
         converted AST
+    additional_imports: set(object); additional entities which for any reason
+        cannot be attached after loading and need to be explicitly imported
+        in the generated code
     name_map: dict[string]: string; maps original entities to the name of
         their converted counterparts
     api_module: A reference to the api module. The reference needs to be passed
@@ -70,6 +73,7 @@ class ConversionMap(object):
     self.nocompile_decorators = nocompile_decorators
     self.partial_types = partial_types if partial_types else ()
     self.dependency_cache = {}
+    self.additional_imports = set()
     self.name_map = {}
     self.api_module = api_module
 
@@ -218,7 +222,7 @@ def function_to_graph(f, conversion_map, arg_values, arg_types,
       arg_values=arg_values,
       arg_types=arg_types,
       recursive=conversion_map.recursive)
-  node = node_to_graph(node, ctx, conversion_map.nocompile_decorators)
+  node, deps = node_to_graph(node, ctx, conversion_map.nocompile_decorators)
 
   # TODO(mdan): This somewhat duplicates the call rename logic in call_treest.py
   new_name, did_rename = namer.compiled_function_name(f.__name__, f, owner_type)
@@ -229,6 +233,9 @@ def function_to_graph(f, conversion_map, arg_values, arg_types,
 
   node.name = new_name
   conversion_map.update_name_map(namer)
+  # TODO(mdan): Use this at compilation.
+  conversion_map.additional_imports.update(deps)
+
   return node, new_name
 
 
@@ -271,7 +278,7 @@ def node_to_graph(node, ctx, nocompile_decorators):
   # source.
   # TODO(mdan): Is it feasible to reconstruct intermediate source code?
   ctx.source_code = None
-  node = decorators.transform(node, nocompile_decorators)
+  node, deps = decorators.transform(node, nocompile_decorators)
   node = break_statements.transform(node, ctx)
   node = asserts.transform(node, ctx)
 
@@ -296,4 +303,4 @@ def node_to_graph(node, ctx, nocompile_decorators):
   node = logical_expressions.transform(node)
   node = side_effect_guards.transform(node, ctx)
 
-  return node
+  return node, deps
-- 
GitLab


From 205baa86fe9e559f458dcf534d18c80215890ecd Mon Sep 17 00:00:00 2001
From: Dustin Tran <trandustin@google.com>
Date: Tue, 20 Feb 2018 18:06:02 -0800
Subject: [PATCH 106/575] Automated g4 rollback of changelist 186260342

PiperOrigin-RevId: 186393300
---
 tensorflow/contrib/bayesflow/BUILD            |   10 +
 .../kernel_tests/docstring_util_test.py       |   87 ++
 .../bayesflow/python/ops/docstring_util.py    |   88 ++
 .../python/ops/layers_conv_variational.py     | 1157 +++++------------
 .../python/ops/layers_dense_variational.py    |  405 ++----
 5 files changed, 627 insertions(+), 1120 deletions(-)
 create mode 100644 tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
 create mode 100644 tensorflow/contrib/bayesflow/python/ops/docstring_util.py

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 74712aeb67..d7beb26e1b 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -99,6 +99,16 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "docstring_util_test",
+    size = "small",
+    srcs = ["python/kernel_tests/docstring_util_test.py"],
+    additional_deps = [
+        ":bayesflow_py",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
 cuda_py_test(
     name = "layers_conv_variational_test",
     size = "small",
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
new file mode 100644
index 0000000000..8ed500b19d
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/docstring_util_test.py
@@ -0,0 +1,87 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for docstring utilities."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.bayesflow.python.ops import docstring_util
+from tensorflow.python.platform import test
+
+
+class DocstringUtil(test.TestCase):
+
+  def _testFunction(self):
+    doc_args = """x: Input to return as output.
+  y: Baz."""
+    @docstring_util.expand_docstring(args=doc_args)
+    def foo(x):
+      # pylint: disable=g-doc-args
+      """Hello world.
+
+      Args:
+        @{args}
+
+      Returns:
+        x.
+      """
+      # pylint: enable=g-doc-args
+      return x
+
+    true_docstring = """Hello world.
+
+    Args:
+      x: Input to return as output.
+      y: Baz.
+
+    Returns:
+      x.
+    """
+    self.assertEqual(foo.__doc__, true_docstring)
+
+  def _testClassInit(self):
+    doc_args = """x: Input to return as output.
+  y: Baz."""
+
+    class Foo(object):
+
+      @docstring_util.expand_docstring(args=doc_args)
+      def __init__(self, x, y):
+        # pylint: disable=g-doc-args
+        """Hello world.
+
+        Args:
+          @{args}
+
+        Bar.
+        """
+        # pylint: enable=g-doc-args
+        pass
+
+    true_docstring = """Hello world.
+
+    Args:
+      x: Input to return as output.
+      y: Baz.
+
+    Bar.
+    """
+    self.assertEqual(Foo.__doc__, true_docstring)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/docstring_util.py b/tensorflow/contrib/bayesflow/python/ops/docstring_util.py
new file mode 100644
index 0000000000..081f2d5a8b
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/ops/docstring_util.py
@@ -0,0 +1,88 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for programmable docstrings.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import re
+import six
+
+
+def expand_docstring(**kwargs):
+  """Decorator to programmatically expand the docstring.
+
+  Args:
+    **kwargs: Keyword arguments to set. For each key-value pair `k` and `v`,
+      the key is found as `@{k}` in the docstring and replaced with `v`.
+
+  Returns:
+    Decorated function.
+  """
+  def _fn_wrapped(fn):
+    """Original function with modified `__doc__` attribute."""
+    doc = _trim(fn.__doc__)
+    for k, v in six.iteritems(kwargs):
+      # Capture each @{k} reference to replace with v.
+      # We wrap the replacement in a function so no backslash escapes
+      # are processed.
+      pattern = r'@\{' + str(k) + r'\}'
+      doc = re.sub(pattern, lambda match: v, doc)  # pylint: disable=cell-var-from-loop
+    fn.__doc__ = doc
+    return fn
+  return _fn_wrapped
+
+
+def _trim(docstring):
+  """Trims docstring indentation.
+
+  In general, multi-line docstrings carry their level of indentation when
+  defined under a function or class method. This function standardizes
+  indentation levels by removing them. Taken from PEP 257 docs.
+
+  Args:
+    docstring: Python string to trim indentation.
+
+  Returns:
+    Trimmed docstring.
+  """
+  if not docstring:
+    return ''
+  # Convert tabs to spaces (following the normal Python rules)
+  # and split into a list of lines:
+  lines = docstring.expandtabs().splitlines()
+  # Determine minimum indentation (first line doesn't count):
+  indent = None
+  for line in lines[1:]:
+    stripped = line.lstrip()
+    if stripped:
+      if indent is None:
+        indent = len(line) - len(stripped)
+      else:
+        indent = min(indent, len(line) - len(stripped))
+  # Remove indentation (first line is special):
+  trimmed = [lines[0].strip()]
+  if indent is not None:
+    for line in lines[1:]:
+      trimmed.append(line[indent:].rstrip())
+  # Strip off trailing and leading blank lines:
+  while trimmed and not trimmed[-1]:
+    trimmed.pop()
+  while trimmed and not trimmed[0]:
+    trimmed.pop(0)
+  # Return a single string:
+  return '\n'.join(trimmed)
diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
index 7723cfb442..cb80718f71 100644
--- a/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
+++ b/tensorflow/contrib/bayesflow/python/ops/layers_conv_variational.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.bayesflow.python.ops import docstring_util
 from tensorflow.contrib.bayesflow.python.ops import layers_util
 from tensorflow.contrib.distributions.python.ops import independent as independent_lib
 from tensorflow.python.framework import dtypes
@@ -34,6 +35,45 @@ from tensorflow.python.ops.distributions import kullback_leibler as kl_lib
 from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.ops.distributions import util as distribution_util
 
+doc_args = """activation: Activation function. Set it to None to maintain a
+      linear activation.
+  activity_regularizer: Optional regularizer function for the output.
+  trainable: Boolean, if `True` also add variables to the graph collection
+    `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+  kernel_posterior_fn: Python `callable` which creates
+    `tf.distributions.Distribution` instance representing the surrogate
+    posterior of the `kernel` parameter. Default value:
+    `default_mean_field_normal_fn()`.
+  kernel_posterior_tensor_fn: Python `callable` which takes a
+    `tf.distributions.Distribution` instance and returns a representative
+    value. Default value: `lambda d: d.sample()`.
+  kernel_prior_fn: Python `callable` which creates `tf.distributions`
+    instance. See `default_mean_field_normal_fn` docstring for required
+    parameter signature.
+    Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+  kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+    distribution, prior distribution and random variate sample(s) from the
+    surrogate posterior and computes or approximates the KL divergence. The
+    distributions are `tf.distributions.Distribution`-like instances and the
+    sample is a `Tensor`.
+  bias_posterior_fn: Python `callable` which creates
+    `tf.distributions.Distribution` instance representing the surrogate
+    posterior of the `bias` parameter. Default value:
+    `default_mean_field_normal_fn(is_singular=True)` (which creates an
+    instance of `tf.distributions.Deterministic`).
+  bias_posterior_tensor_fn: Python `callable` which takes a
+    `tf.distributions.Distribution` instance and returns a representative
+    value. Default value: `lambda d: d.sample()`.
+  bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+    See `default_mean_field_normal_fn` docstring for required parameter
+    signature. Default value: `None` (no prior, no variational inference)
+  bias_divergence_fn: Python `callable` which takes the surrogate posterior
+    distribution, prior distribution and random variate sample(s) from the
+    surrogate posterior and computes or approximates the KL divergence. The
+    distributions are `tf.distributions.Distribution`-like instances and the
+    sample is a `Tensor`.
+  name: A string, the name of the layer."""
+
 
 class _ConvVariational(layers_lib.Layer):
   """Abstract nD convolution layer (private, used as implementation base).
@@ -55,65 +95,6 @@ class _ConvVariational(layers_lib.Layer):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of n integers, specifying the
-      length of the convolution window.
-    strides: An integer or tuple/list of n integers,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, ..., channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, ...)`.
-    dilation_rate: An integer or tuple/list of n integers, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: A string, the name of the layer.
-
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -134,6 +115,7 @@ class _ConvVariational(layers_lib.Layer):
     bias_divergence_fn: `callable` returning divergence.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -157,6 +139,33 @@ class _ConvVariational(layers_lib.Layer):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      rank: An integer, the rank of the convolution, e.g. "2" for 2D
+        convolution.
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of n integers, specifying the
+        length of the convolution window.
+      strides: An integer or tuple/list of n integers,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, ...,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, ...)`.
+      dilation_rate: An integer or tuple/list of n integers, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(_ConvVariational, self).__init__(
         trainable=trainable,
         name=name,
@@ -371,65 +380,6 @@ class _ConvReparameterization(_ConvVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of n integers, specifying the
-      length of the convolution window.
-    strides: An integer or tuple/list of n integers,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, ..., channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, ...)`.
-    dilation_rate: An integer or tuple/list of n integers, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: A string, the name of the layer.
-
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -454,6 +404,7 @@ class _ConvReparameterization(_ConvVariational):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -477,6 +428,33 @@ class _ConvReparameterization(_ConvVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      rank: An integer, the rank of the convolution, e.g. "2" for 2D
+        convolution.
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of n integers, specifying the
+        length of the convolution window.
+      strides: An integer or tuple/list of n integers,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, ...,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, ...)`.
+      dilation_rate: An integer or tuple/list of n integers, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(_ConvReparameterization, self).__init__(
         rank=rank,
         filters=filters,
@@ -529,63 +507,6 @@ class Conv1DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of a single integer, specifying the
-      length of the 1D convolution window.
-    strides: An integer or tuple/list of a single integer,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, length, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, length)`.
-    dilation_rate: An integer or tuple/list of a single integer, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -639,6 +560,7 @@ class Conv1DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -661,6 +583,31 @@ class Conv1DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of a single integer, specifying the
+        length of the 1D convolution window.
+      strides: An integer or tuple/list of a single integer,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, length,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, length)`.
+      dilation_rate: An integer or tuple/list of a single integer, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(Conv1DReparameterization, self).__init__(
         rank=1,
         filters=filters,
@@ -683,6 +630,7 @@ class Conv1DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv1d_reparameterization(
     inputs,
     filters,
@@ -705,6 +653,7 @@ def conv1d_reparameterization(
     bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Functional interface for 1D convolution layer (e.g. temporal convolution).
 
   This layer creates a convolution kernel that is convolved
@@ -726,7 +675,7 @@ def conv1d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -746,43 +695,7 @@ def conv1d_reparameterization(
       the dilation rate to use for dilated convolution.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
+    @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -827,6 +740,7 @@ def conv1d_reparameterization(
         Diederik P. Kingma, Max Welling.
         International Conference on Learning Representations, 2014.
   """
+  # pylint: enable=g-doc-args
   layer = Conv1DReparameterization(
       filters=filters,
       kernel_size=kernel_size,
@@ -874,70 +788,6 @@ class Conv2DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of 2 integers, specifying the
-      height and width of the 2D convolution window.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-    strides: An integer or tuple/list of 2 integers,
-      specifying the strides of the convolution along the height and width.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, height, width, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, height, width)`.
-
-    dilation_rate: An integer or tuple/list of 2 integers, specifying
-      the dilation rate to use for dilated convolution.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -994,6 +844,7 @@ class Conv2DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1016,6 +867,37 @@ class Conv2DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of 2 integers, specifying the
+        height and width of the 2D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+      strides: An integer or tuple/list of 2 integers,
+        specifying the strides of the convolution along the height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, height,
+        width, channels)` while `channels_first` corresponds to inputs with
+        shape `(batch, channels, height, width)`.
+      dilation_rate: An integer or tuple/list of 2 integers, specifying
+        the dilation rate to use for dilated convolution.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any stride value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(Conv2DReparameterization, self).__init__(
         rank=2,
         filters=filters,
@@ -1038,6 +920,7 @@ class Conv2DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv2d_reparameterization(
     inputs,
     filters,
@@ -1060,6 +943,7 @@ def conv2d_reparameterization(
     bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Functional interface for the 2D convolution layer.
 
   This layer creates a convolution kernel that is convolved
@@ -1081,7 +965,7 @@ def conv2d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -1101,50 +985,13 @@ def conv2d_reparameterization(
       `channels_last` corresponds to inputs with shape
       `(batch, height, width, channels)` while `channels_first` corresponds to
       inputs with shape `(batch, channels, height, width)`.
-
     dilation_rate: An integer or tuple/list of 2 integers, specifying
       the dilation rate to use for dilated convolution.
       Can be a single integer to specify the same value for
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
+    @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -1193,6 +1040,7 @@ def conv2d_reparameterization(
         Diederik P. Kingma, Max Welling.
         International Conference on Learning Representations, 2014.
   """
+  # pylint: enable=g-doc-args
   layer = Conv2DReparameterization(
       filters=filters,
       kernel_size=kernel_size,
@@ -1240,71 +1088,6 @@ class Conv3DReparameterization(_ConvReparameterization):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of 3 integers, specifying the
-      depth, height and width of the 3D convolution window.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-    strides: An integer or tuple/list of 3 integers,
-      specifying the strides of the convolution along the depth,
-      height and width.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, depth, height, width, channels)` while `channels_first`
-      corresponds to inputs with shape
-      `(batch, channels, depth, height, width)`.
-    dilation_rate: An integer or tuple/list of 3 integers, specifying
-      the dilation rate to use for dilated convolution.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -1361,6 +1144,7 @@ class Conv3DReparameterization(_ConvReparameterization):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1383,6 +1167,38 @@ class Conv3DReparameterization(_ConvReparameterization):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of 3 integers, specifying the
+        depth, height and width of the 3D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+      strides: An integer or tuple/list of 3 integers,
+        specifying the strides of the convolution along the depth,
+        height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, depth,
+        height, width, channels)` while `channels_first` corresponds to inputs
+        with shape `(batch, channels, depth, height, width)`.
+      dilation_rate: An integer or tuple/list of 3 integers, specifying
+        the dilation rate to use for dilated convolution.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any stride value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(Conv3DReparameterization, self).__init__(
         rank=3,
         filters=filters,
@@ -1405,6 +1221,7 @@ class Conv3DReparameterization(_ConvReparameterization):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv3d_reparameterization(
     inputs,
     filters,
@@ -1427,6 +1244,7 @@ def conv3d_reparameterization(
     bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Functional interface for the 3D convolution layer.
 
   This layer creates a convolution kernel that is convolved
@@ -1448,7 +1266,7 @@ def conv3d_reparameterization(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -1476,43 +1294,7 @@ def conv3d_reparameterization(
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    name: A string, the name of the layer.
+    @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -1561,6 +1343,7 @@ def conv3d_reparameterization(
         Diederik P. Kingma, Max Welling.
         International Conference on Learning Representations, 2014.
   """
+  # pylint: enable=g-doc-args
   layer = Conv3DReparameterization(
       filters=filters,
       kernel_size=kernel_size,
@@ -1611,67 +1394,6 @@ class _ConvFlipout(_ConvVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of n integers, specifying the
-      length of the convolution window.
-    strides: An integer or tuple/list of n integers,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, ..., channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, ...)`.
-    dilation_rate: An integer or tuple/list of n integers, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
-
   Properties:
     rank: Python integer, dimensionality of convolution.
     filters: Python integer, dimensionality of the output space.
@@ -1694,10 +1416,11 @@ class _ConvFlipout(_ConvVariational):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       rank,
@@ -1722,6 +1445,33 @@ class _ConvFlipout(_ConvVariational):
       seed=None,
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      rank: An integer, the rank of the convolution, e.g. "2" for 2D
+        convolution.
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of n integers, specifying the
+        length of the convolution window.
+      strides: An integer or tuple/list of n integers,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, ...,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, ...)`.
+      dilation_rate: An integer or tuple/list of n integers, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(_ConvFlipout, self).__init__(
         rank=rank,
         filters=filters,
@@ -1822,65 +1572,6 @@ class Conv1DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of a single integer, specifying the
-      length of the 1D convolution window.
-    strides: An integer or tuple/list of a single integer,
-      specifying the stride length of the convolution.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, length, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, length)`.
-    dilation_rate: An integer or tuple/list of a single integer, specifying
-      the dilation rate to use for dilated convolution.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -1932,10 +1623,11 @@ class Conv1DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -1959,6 +1651,31 @@ class Conv1DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of a single integer, specifying the
+        length of the 1D convolution window.
+      strides: An integer or tuple/list of a single integer,
+        specifying the stride length of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, length,
+        channels)` while `channels_first` corresponds to inputs with shape
+        `(batch, channels, length)`.
+      dilation_rate: An integer or tuple/list of a single integer, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(Conv1DFlipout, self).__init__(
         rank=1,
         filters=filters,
@@ -1982,6 +1699,7 @@ class Conv1DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv1d_flipout(
     inputs,
     filters,
@@ -2005,6 +1723,7 @@ def conv1d_flipout(
     seed=None,
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Functional interface for 1D convolution layer (e.g. temporal convolution).
 
   This layer creates a convolution kernel that is convolved
@@ -2029,7 +1748,7 @@ def conv1d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -2049,45 +1768,7 @@ def conv1d_flipout(
       the dilation rate to use for dilated convolution.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any `strides` value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
+    @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -2130,9 +1811,10 @@ def conv1d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
+  # pylint: enable=g-doc-args
   layer = Conv1DFlipout(
       filters=filters,
       kernel_size=kernel_size,
@@ -2184,72 +1866,6 @@ class Conv2DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of 2 integers, specifying the
-      height and width of the 2D convolution window.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-    strides: An integer or tuple/list of 2 integers,
-      specifying the strides of the convolution along the height and width.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, height, width, channels)` while `channels_first` corresponds to
-      inputs with shape `(batch, channels, height, width)`.
-
-    dilation_rate: An integer or tuple/list of 2 integers, specifying
-      the dilation rate to use for dilated convolution.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -2304,10 +1920,11 @@ class Conv2DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -2331,6 +1948,37 @@ class Conv2DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of 2 integers, specifying the
+        height and width of the 2D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+      strides: An integer or tuple/list of 2 integers,
+        specifying the strides of the convolution along the height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, height,
+        width, channels)` while `channels_first` corresponds to inputs with
+        shape `(batch, channels, height, width)`.
+      dilation_rate: An integer or tuple/list of 2 integers, specifying
+        the dilation rate to use for dilated convolution.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any stride value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(Conv2DFlipout, self).__init__(
         rank=2,
         filters=filters,
@@ -2354,6 +2002,7 @@ class Conv2DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv2d_flipout(
     inputs,
     filters,
@@ -2377,6 +2026,7 @@ def conv2d_flipout(
     seed=None,
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Functional interface for the 2D convolution layer.
 
   This layer creates a convolution kernel that is convolved
@@ -2401,7 +2051,7 @@ def conv2d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -2421,52 +2071,13 @@ def conv2d_flipout(
       `channels_last` corresponds to inputs with shape
       `(batch, height, width, channels)` while `channels_first` corresponds to
       inputs with shape `(batch, channels, height, width)`.
-
     dilation_rate: An integer or tuple/list of 2 integers, specifying
       the dilation rate to use for dilated convolution.
       Can be a single integer to specify the same value for
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
+    @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -2513,9 +2124,10 @@ def conv2d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
+  # pylint: enable=g-doc-args
   layer = Conv2DFlipout(
       filters=filters,
       kernel_size=kernel_size,
@@ -2567,73 +2179,6 @@ class Conv3DFlipout(_ConvFlipout):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
-    filters: Integer, the dimensionality of the output space (i.e. the number
-      of filters in the convolution).
-    kernel_size: An integer or tuple/list of 3 integers, specifying the
-      depth, height and width of the 3D convolution window.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-    strides: An integer or tuple/list of 3 integers,
-      specifying the strides of the convolution along the depth,
-      height and width.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Specifying any stride value != 1 is incompatible with specifying
-      any `dilation_rate` value != 1.
-    padding: One of `"valid"` or `"same"` (case-insensitive).
-    data_format: A string, one of `channels_last` (default) or `channels_first`.
-      The ordering of the dimensions in the inputs.
-      `channels_last` corresponds to inputs with shape
-      `(batch, depth, height, width, channels)` while `channels_first`
-      corresponds to inputs with shape
-      `(batch, channels, depth, height, width)`.
-    dilation_rate: An integer or tuple/list of 3 integers, specifying
-      the dilation rate to use for dilated convolution.
-      Can be a single integer to specify the same value for
-      all spatial dimensions.
-      Currently, specifying any `dilation_rate` value != 1 is
-      incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
-
   Properties:
     filters: Python integer, dimensionality of the output space.
     kernel_size: Size of the convolution window.
@@ -2688,10 +2233,11 @@ class Conv3DFlipout(_ConvFlipout):
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       filters,
@@ -2715,6 +2261,38 @@ class Conv3DFlipout(_ConvFlipout):
       seed=None,
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      kernel_size: An integer or tuple/list of 3 integers, specifying the
+        depth, height and width of the 3D convolution window.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+      strides: An integer or tuple/list of 3 integers,
+        specifying the strides of the convolution along the depth,
+        height and width.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+      padding: One of `"valid"` or `"same"` (case-insensitive).
+      data_format: A string, one of `channels_last` (default) or
+        `channels_first`. The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape `(batch, depth,
+        height, width, channels)` while `channels_first` corresponds to inputs
+        with shape `(batch, channels, depth, height, width)`.
+      dilation_rate: An integer or tuple/list of 3 integers, specifying
+        the dilation rate to use for dilated convolution.
+        Can be a single integer to specify the same value for
+        all spatial dimensions.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any stride value != 1.
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(Conv3DFlipout, self).__init__(
         rank=3,
         filters=filters,
@@ -2738,6 +2316,7 @@ class Conv3DFlipout(_ConvFlipout):
         name=name, **kwargs)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def conv3d_flipout(
     inputs,
     filters,
@@ -2761,6 +2340,7 @@ def conv3d_flipout(
     seed=None,
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Functional interface for the 3D convolution layer.
 
   This layer creates a convolution kernel that is convolved
@@ -2785,7 +2365,7 @@ def conv3d_flipout(
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Arguments:
+  Args:
     inputs: Tensor input.
     filters: Integer, the dimensionality of the output space (i.e. the number
       of filters in the convolution).
@@ -2813,45 +2393,7 @@ def conv3d_flipout(
       all spatial dimensions.
       Currently, specifying any `dilation_rate` value != 1 is
       incompatible with specifying any stride value != 1.
-    activation: Activation function. Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Optional regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: A string, the name of the layer.
+    @{args}
     reuse: Boolean, whether to reuse the weights of a previous layer
       by the same name.
 
@@ -2898,9 +2440,10 @@ def conv3d_flipout(
 
   [1]: "Flipout: Efficient Pseudo-Independent Weight Perturbations on
         Mini-Batches."
-        Anonymous. OpenReview, 2017.
-        https://openreview.net/forum?id=rJnpifWAb
+        Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse.
+        International Conference on Learning Representations, 2018.
   """
+  # pylint: enable=g-doc-args
   layer = Conv3DFlipout(
       filters=filters,
       kernel_size=kernel_size,
diff --git a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py b/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
index 591a8e553d..1f1d8fda2a 100644
--- a/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
+++ b/tensorflow/contrib/bayesflow/python/ops/layers_dense_variational.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.bayesflow.python.ops import docstring_util
 from tensorflow.contrib.bayesflow.python.ops import layers_util
 from tensorflow.contrib.distributions.python.ops import independent as independent_lib
 from tensorflow.python.framework import dtypes
@@ -33,6 +34,53 @@ from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.ops.distributions import util as distribution_util
 
 
+doc_args = """units: Integer or Long, dimensionality of the output space.
+  activation: Activation function (`callable`). Set it to None to maintain a
+    linear activation.
+  activity_regularizer: Regularizer function for the output.
+  trainable: Boolean, if `True` also add variables to the graph collection
+    `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+  kernel_posterior_fn: Python `callable` which creates
+    `tf.distributions.Distribution` instance representing the surrogate
+    posterior of the `kernel` parameter. Default value:
+    `default_mean_field_normal_fn()`.
+  kernel_posterior_tensor_fn: Python `callable` which takes a
+    `tf.distributions.Distribution` instance and returns a representative
+    value. Default value: `lambda d: d.sample()`.
+  kernel_prior_fn: Python `callable` which creates `tf.distributions`
+    instance. See `default_mean_field_normal_fn` docstring for required
+    parameter signature.
+    Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
+  kernel_divergence_fn: Python `callable` which takes the surrogate posterior
+    distribution, prior distribution and random variate sample(s) from the
+    surrogate posterior and computes or approximates the KL divergence. The
+    distributions are `tf.distributions.Distribution`-like instances and the
+    sample is a `Tensor`.
+  bias_posterior_fn: Python `callable` which creates
+    `tf.distributions.Distribution` instance representing the surrogate
+    posterior of the `bias` parameter. Default value:
+    `default_mean_field_normal_fn(is_singular=True)` (which creates an
+    instance of `tf.distributions.Deterministic`).
+  bias_posterior_tensor_fn: Python `callable` which takes a
+    `tf.distributions.Distribution` instance and returns a representative
+    value. Default value: `lambda d: d.sample()`.
+  bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
+    See `default_mean_field_normal_fn` docstring for required parameter
+    signature. Default value: `None` (no prior, no variational inference)
+  bias_divergence_fn: Python `callable` which takes the surrogate posterior
+    distribution, prior distribution and random variate sample(s) from the
+    surrogate posterior and computes or approximates the KL divergence. The
+    distributions are `tf.distributions.Distribution`-like instances and the
+    sample is a `Tensor`.
+  seed: Python scalar `int` which initializes the random number
+    generator. Default value: `None` (i.e., use global seed).
+  name: Python `str`, the name of the layer. Layers with the same name will
+    share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
+    such cases.
+  reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
+    layer by the same name."""
+
+
 class _DenseVariational(layers_lib.Layer):
   """Abstract densely-connected class (private, used as implementation base).
 
@@ -50,51 +98,6 @@ class _DenseVariational(layers_lib.Layer):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
-
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -109,6 +112,7 @@ class _DenseVariational(layers_lib.Layer):
     bias_divergence_fn: `callable` returning divergence.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -126,6 +130,13 @@ class _DenseVariational(layers_lib.Layer):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(_DenseVariational, self).__init__(
         trainable=trainable,
         name=name,
@@ -274,51 +285,6 @@ class DenseReparameterization(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
-
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -363,6 +329,7 @@ class DenseReparameterization(_DenseVariational):
         International Conference on Learning Representations, 2014.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -381,6 +348,13 @@ class DenseReparameterization(_DenseVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(DenseReparameterization, self).__init__(
         units=units,
         activation=activation,
@@ -405,6 +379,7 @@ class DenseReparameterization(_DenseVariational):
     return self._matmul(inputs, self.kernel_posterior_tensor)
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def dense_reparameterization(
     inputs,
     units,
@@ -422,6 +397,7 @@ def dense_reparameterization(
     bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Densely-connected layer with reparameterization estimator.
 
   This layer implements the Bayesian variational inference analogue to
@@ -444,49 +420,7 @@ def dense_reparameterization(
 
   Args:
     inputs: Tensor input.
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
+    @{args}
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
@@ -522,6 +456,7 @@ def dense_reparameterization(
         Diederik P. Kingma, Max Welling.
         International Conference on Learning Representations, 2014.
   """
+  # pylint: enable=g-doc-args
   layer = DenseReparameterization(
       units,
       activation=activation,
@@ -563,51 +498,6 @@ class DenseLocalReparameterization(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
-
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -652,6 +542,7 @@ class DenseLocalReparameterization(_DenseVariational):
         Neural Information Processing Systems, 2015.
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -670,6 +561,13 @@ class DenseLocalReparameterization(_DenseVariational):
       bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(DenseLocalReparameterization, self).__init__(
         units=units,
         activation=activation,
@@ -705,6 +603,7 @@ class DenseLocalReparameterization(_DenseVariational):
     return self.kernel_posterior_affine_tensor
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def dense_local_reparameterization(
     inputs,
     units,
@@ -723,6 +622,7 @@ def dense_local_reparameterization(
     bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p),
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Densely-connected layer with local reparameterization estimator.
 
   This layer implements the Bayesian variational inference analogue to
@@ -745,49 +645,7 @@ def dense_local_reparameterization(
 
   Args:
     inputs: Tensor input.
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
+    @{args}
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
@@ -823,6 +681,7 @@ def dense_local_reparameterization(
         Diederik P. Kingma, Tim Salimans, Max Welling.
         Neural Information Processing Systems, 2015.
   """
+  # pylint: enable=g-doc-args
   layer = DenseLocalReparameterization(
       units,
       activation=activation,
@@ -866,53 +725,6 @@ class DenseFlipout(_DenseVariational):
   (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias`
   distributions.
 
-  Args:
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
-
   Properties:
     units: Python integer, dimensionality of the output space.
     activation: Activation function (`callable`).
@@ -959,6 +771,7 @@ class DenseFlipout(_DenseVariational):
         https://openreview.net/forum?id=rJnpifWAb
   """
 
+  @docstring_util.expand_docstring(args=doc_args)
   def __init__(
       self,
       units,
@@ -978,6 +791,13 @@ class DenseFlipout(_DenseVariational):
       seed=None,
       name=None,
       **kwargs):
+    # pylint: disable=g-doc-args
+    """Construct layer.
+
+    Args:
+      @{args}
+    """
+    # pylint: enable=g-doc-args
     super(DenseFlipout, self).__init__(
         units=units,
         activation=activation,
@@ -1031,6 +851,7 @@ class DenseFlipout(_DenseVariational):
     return outputs
 
 
+@docstring_util.expand_docstring(args=doc_args)
 def dense_flipout(
     inputs,
     units,
@@ -1050,6 +871,7 @@ def dense_flipout(
     seed=None,
     name=None,
     reuse=None):
+  # pylint: disable=g-doc-args
   """Densely-connected layer with Flipout estimator.
 
   This layer implements the Bayesian variational inference analogue to
@@ -1074,51 +896,7 @@ def dense_flipout(
 
   Args:
     inputs: Tensor input.
-    units: Integer or Long, dimensionality of the output space.
-    activation: Activation function (`callable`). Set it to None to maintain a
-      linear activation.
-    activity_regularizer: Regularizer function for the output.
-    trainable: Boolean, if `True` also add variables to the graph collection
-      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
-    kernel_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `kernel` parameter. Default value:
-      `default_mean_field_normal_fn()`.
-    kernel_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    kernel_prior_fn: Python `callable` which creates `tf.distributions`
-      instance. See `default_mean_field_normal_fn` docstring for required
-      parameter signature.
-      Default value: `tf.distributions.Normal(loc=0., scale=1.)`.
-    kernel_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    bias_posterior_fn: Python `callable` which creates
-      `tf.distributions.Distribution` instance representing the surrogate
-      posterior of the `bias` parameter. Default value:
-      `default_mean_field_normal_fn(is_singular=True)` (which creates an
-      instance of `tf.distributions.Deterministic`).
-    bias_posterior_tensor_fn: Python `callable` which takes a
-      `tf.distributions.Distribution` instance and returns a representative
-      value. Default value: `lambda d: d.sample()`.
-    bias_prior_fn: Python `callable` which creates `tf.distributions` instance.
-      See `default_mean_field_normal_fn` docstring for required parameter
-      signature. Default value: `None` (no prior, no variational inference)
-    bias_divergence_fn: Python `callable` which takes the surrogate posterior
-      distribution, prior distribution and random variate sample(s) from the
-      surrogate posterior and computes or approximates the KL divergence. The
-      distributions are `tf.distributions.Distribution`-like instances and the
-      sample is a `Tensor`.
-    seed: Python scalar `int` which initializes the random number
-      generator. Default value: `None` (i.e., use global seed).
-    name: Python `str`, the name of the layer. Layers with the same name will
-      share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in
-      such cases.
-    reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous
-      layer by the same name.
+    @{args}
 
   Returns:
     output: `Tensor` representing a the affine transformed input under a random
@@ -1155,6 +933,7 @@ def dense_flipout(
         Anonymous. OpenReview, 2017.
         https://openreview.net/forum?id=rJnpifWAb
   """
+  # pylint: enable=g-doc-args
   layer = DenseFlipout(
       units,
       activation=activation,
-- 
GitLab


From 3e7ed13c2dac79c05a63a9c25e3c8eb6f1d99ac2 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Tue, 20 Feb 2018 18:17:46 -0800
Subject: [PATCH 107/575] Make sure the nodes that are refered to by a
 collection are preserved during an optimization

PiperOrigin-RevId: 186394467
---
 tensorflow/core/grappler/grappler_item.cc     |  4 +++
 tensorflow/core/grappler/grappler_item.h      |  8 +++++-
 .../core/grappler/grappler_item_builder.cc    |  8 ++++++
 .../grappler/optimizers/constant_folding.cc   | 10 +++++--
 .../core/grappler/optimizers/model_pruner.cc  |  2 +-
 .../python/grappler/tf_optimizer_test.py      | 26 +++++++++++++++++++
 6 files changed, 54 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/grappler/grappler_item.cc b/tensorflow/core/grappler/grappler_item.cc
index 2f8549cf39..ad86356504 100644
--- a/tensorflow/core/grappler/grappler_item.cc
+++ b/tensorflow/core/grappler/grappler_item.cc
@@ -32,6 +32,7 @@ GrapplerItem::GrapplerItem(const GrapplerItem& other, GraphDef&& graphDef) {
   feed = other.feed;
   fetch = other.fetch;
   init_ops = other.init_ops;
+  keep_ops = other.keep_ops;
   expected_init_time = other.expected_init_time;
   save_op = other.save_op;
   restore_op = other.restore_op;
@@ -82,6 +83,9 @@ std::unordered_set<string> GrapplerItem::NodesToPreserve() const {
   for (const auto& node : init_ops) {
     result.insert(NodeName(node));
   }
+  for (const auto& node : keep_ops) {
+    result.insert(NodeName(node));
+  }
   if (!save_op.empty()) {
     result.insert(NodeName(save_op));
   }
diff --git a/tensorflow/core/grappler/grappler_item.h b/tensorflow/core/grappler/grappler_item.h
index 302685972a..06bba544c3 100644
--- a/tensorflow/core/grappler/grappler_item.h
+++ b/tensorflow/core/grappler/grappler_item.h
@@ -58,6 +58,11 @@ struct GrapplerItem {
   // Queue runner(s) required to run the queue(s) of this model.
   std::vector<QueueRunnerDef> queue_runners;
 
+  // List of op names to keep in the graph. This includes nodes that are
+  // referenced in various collections, and therefore must be preserved to
+  // ensure that the optimized metagraph can still be loaded.
+  std::vector<string> keep_ops;
+
   // Return the set of node evaluated during a regular train/inference step.
   std::vector<const NodeDef*> MainOpsFanin() const;
   // Return the set of node run to populate the queues (if any).
@@ -66,7 +71,8 @@ struct GrapplerItem {
   std::vector<const NodeDef*> InitOpsFanin() const;
   // Return the set of variables accessed during a regular train/inference step.
   std::vector<const NodeDef*> MainVariables() const;
-  // Return a set of node names that must be preserved.
+  // Return a set of node names that must be preserved. This includes feed and
+  // fetch nodes, keep_ops, init_ops.
   std::unordered_set<string> NodesToPreserve() const;
 };
 
diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 7ba498dd06..5ac52eefe1 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -296,6 +296,14 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
     }
   }
 
+  // Add each node referenced in a collection to the list of nodes to keep.
+  for (const auto& col : meta_graph.collection_def()) {
+    const CollectionDef& collection = col.second;
+    for (const string& node : collection.node_list().value()) {
+      new_item->keep_ops.push_back(NodeName(node));
+    }
+  }
+
   for (auto& node : *new_item->graph.mutable_node()) {
     if (IsPlaceholder(node) && node.op() != "PlaceholderWithDefault") {
       if (node.attr().count("dtype") == 0) {
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index b8a21ea5a1..7a621bd95d 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -1159,14 +1159,20 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
       continue;
     }
     // We need to record a copy of output nodes before FoldNode() modifies it.
-    std::set<NodeDef*> outputs = node_map_->GetOutputs(node->name());
+    // We also need to ensure that the fanout is sorted deterministically.
+    const std::set<NodeDef*>& outputs = node_map_->GetOutputs(node->name());
+    std::vector<NodeDef*> fanout(outputs.begin(), outputs.end());
+    std::sort(fanout.begin(), fanout.end(),
+              [](const NodeDef* n1, const NodeDef* n2) {
+                return n1->name() < n2->name();
+              });
 
     Status s = FoldNode(node, output);
     processed_nodes.insert(node->name());
     if (!s.ok()) {
       VLOG(1) << "Failed to fold node " << node->name() << ": " << s;
     } else {
-      for (auto& output : outputs) {
+      for (auto& output : fanout) {
         if (IsFoldable(*output)) {
           queue.push_back(output);
         }
diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index f52a2ab862..97f456d2a6 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -50,7 +50,7 @@ bool IsTrivialOp(const NodeDef& node, const GraphRewriter& rewriter) {
 
 Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
                              GraphDef* pruned_graph) {
-  const std::unordered_set<string>& nodes_to_preserve = item.NodesToPreserve();
+  const std::unordered_set<string> nodes_to_preserve = item.NodesToPreserve();
 
   // Prune all the nodes that won't be executed, ie all the nodes that aren't in
   // the fanin of a fetch node. If fetch nodes aren't specified, we'll assume
diff --git a/tensorflow/python/grappler/tf_optimizer_test.py b/tensorflow/python/grappler/tf_optimizer_test.py
index 55dcbe2071..5683ab5a04 100644
--- a/tensorflow/python/grappler/tf_optimizer_test.py
+++ b/tensorflow/python/grappler/tf_optimizer_test.py
@@ -24,6 +24,7 @@ from tensorflow.python.framework import meta_graph
 from tensorflow.python.framework import ops
 from tensorflow.python.grappler import tf_optimizer
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
 
@@ -48,6 +49,31 @@ class PyWrapOptimizeGraphTest(test.TestCase):
     self.assertEqual(len(graph.node), 1)
     self.assertItemsEqual([node.name for node in graph.node], ['d'])
 
+  def testKeepNodes(self):
+    g = ops.Graph()
+    with g.as_default():
+      a1 = variables.Variable(
+          1.0)  # Must be preserved since it's in the collection 'variables'.
+      a2 = constant_op.constant(0, shape=[50, 50], name='keep')
+      ops.add_to_collection('a2', a2)  # Explicitly add to collection.
+      b = constant_op.constant(1, shape=[100, 10])
+      c = constant_op.constant(0, shape=[10, 30])
+      d = math_ops.matmul(b, c)
+      ops.add_to_collection('train_op', d)  # d is the fetch node.
+
+    # Optimize the graph.
+    mg = meta_graph.create_meta_graph_def(graph=g)
+    rewriter_config = rewriter_config_pb2.RewriterConfig()
+    optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, mg)
+
+    # Check that the nodes referenced in various collections have been preserved
+    self.assertEqual(len(optimized_graph.node), 5)
+    self.assertEqual(a2.op.name, optimized_graph.node[0].name)
+    self.assertEqual(a1.op.name, optimized_graph.node[1].name)
+    self.assertEqual('Variable/initial_value', optimized_graph.node[2].name)
+    self.assertEqual(d.op.name, optimized_graph.node[3].name)
+    self.assertEqual('Variable/Assign', optimized_graph.node[4].name)
+
 
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From a75d7bf43bf1ff44566b7587e978a0c22b2ce171 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Tue, 20 Feb 2018 18:56:07 -0800
Subject: [PATCH 108/575] [TF2XLA] Account for input edge of predicate.

PiperOrigin-RevId: 186397549
---
 .../tf2xla/functionalize_control_flow.cc      | 115 ++++++++++--------
 1 file changed, 64 insertions(+), 51 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
index f8169795dd..8b7beef83e 100644
--- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
+++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc
@@ -583,13 +583,15 @@ class FunctionalizeCond {
   // CondArgNode represents a input to the conditional and its corresponding
   // switch nodes.
   struct CondArgNode {
-    explicit CondArgNode(Node* input) : input(input) {}
+    explicit CondArgNode(Node* src, int src_output)
+        : src(src), src_output(src_output) {}
     string ToString() const {
-      return strings::StrCat("input=", input->name(),
+      return strings::StrCat("src=", src->name(), ":", src_output,
                              " switches=", NodesToString(switches));
     }
 
-    Node* input;
+    Node* src;
+    int src_output;
     std::vector<Node*> switches;
   };
   using CondArgNodes = std::vector<CondArgNode>;
@@ -606,14 +608,15 @@ class FunctionalizeCond {
 
   // Group of switch nodes that will be part of the same XlaIf.
   struct SwitchCluster {
-    explicit SwitchCluster(Node* predicate) : predicate(predicate) {}
+    explicit SwitchCluster(const Edge* predicate_edge)
+        : predicate_edge(predicate_edge) {}
     string ToString() const {
-      return strings::StrCat(name, " predicate=", predicate->name(),
+      return strings::StrCat(name, " predicate=", predicate_edge->src()->name(),
                              " switches=", NodesToString(switches));
     }
 
     string name;
-    Node* predicate;
+    const Edge* predicate_edge;
     std::vector<Node*> switches;
   };
 
@@ -653,8 +656,8 @@ class FunctionalizeCond {
                      Graph* body);
 
   // Adds all the input edges to `if_node` corresponding to the arguments.
-  Status AddInputEdges(const CondArgNodes& cond_arg_nodes, Node* predicate,
-                       Node* if_node);
+  Status AddInputEdges(const CondArgNodes& cond_arg_nodes,
+                       const Edge* predicate_edge, Node* if_node);
 
   // Adds all output edges from the `if_node`.
   Status AddOutputEdges(const std::vector<Node*>& outputs, Node* if_node);
@@ -756,8 +759,8 @@ Status FunctionalizeCond::Join(const ForwardFlowNode& src_state,
     if (IsMerge(dst)) {
       dst_state->branch = Branch::kBoth;
     } else {
-      return errors::Internal("Illegal merge: ", src_state.ToString(), " with ",
-                              dst_state->ToString(), " for ",
+      return errors::Internal("Illegal merge:\n", src_state.ToString(),
+                              " with ", dst_state->ToString(), " for\n",
                               dst->DebugString());
     }
   }
@@ -861,8 +864,8 @@ FunctionalizeCond::DeterminePredicateSwitchOrder() {
     if (IsSwitch(n)) {
       Node* input;
       TF_CHECK_OK(n->input_node(0, &input));
-      entry_cluster[n->id()] = &clusters[input->id()];
-      UnionFind<Cluster>* cluster = find_output_cluster(input);
+      entry_cluster[n->id()] = find_output_cluster(input);
+      UnionFind<Cluster>* cluster = entry_cluster[n->id()];
       int cluster_depth = switch_depth[cluster->Get().representative];
       // Merge the inputs of the switch node with one another. This results in
       // predicates and control input residing in the same cluster.
@@ -956,16 +959,21 @@ FunctionalizeCond::DeterminePredicateSwitchOrder() {
   // node whose cluster is later in the topological order of clustered
   // switches).
   for (auto it = switch_order.rbegin(); it != switch_order.rend(); ++it) {
-    Node* pred;
-    TF_CHECK_OK((*it)->input_node(1, &pred));
-    auto repr = std::make_pair(pred, clusters[(*it)->id()].Get());
+    const Edge* pred_edge;
+    TF_CHECK_OK((*it)->input_edge(1, &pred_edge));
+    // The predicate can be preceded by a identity node. Look through identity
+    // nodes to predicate.
+    while (pred_edge->src()->IsIdentity()) {
+      TF_CHECK_OK(pred_edge->src()->input_edge(0, &pred_edge));
+    }
+    auto repr = std::make_pair(pred_edge->src(), clusters[(*it)->id()].Get());
     if (predicate_index.find(repr) == predicate_index.end()) {
       predicate_index[repr] = switch_clusters.size();
-      switch_clusters.emplace_back(pred);
+      switch_clusters.emplace_back(pred_edge);
       // Generate a name by concatenating with the cluster representative as
       // there could be multiple switch clusters with the same predicate.
-      switch_clusters[predicate_index[repr]].name =
-          strings::StrCat(pred->name(), "_", repr.second.representative, "_If");
+      switch_clusters[predicate_index[repr]].name = strings::StrCat(
+          pred_edge->src()->name(), "_", repr.second.representative, "_If");
     }
     switch_clusters[predicate_index[repr]].switches.push_back(*it);
   }
@@ -1044,9 +1052,12 @@ FunctionalizeCond::DetermineBranchMapAndFrontier(
       ForwardFlowNode& ffn = branch_map[out];
       if (IsSwitch(n)) {
         int index = e->IsControlEdge() ? Branch::kNeither : e->src_output();
-        TF_RETURN_IF_ERROR(Join(ForwardFlowNode(Branch(index)), out, &ffn));
+        TF_RETURN_WITH_CONTEXT_IF_ERROR(
+            Join(ForwardFlowNode(Branch(index)), out, &ffn), " when joining ",
+            e->DebugString());
       } else {
-        TF_RETURN_IF_ERROR(Join(branch_map[n], out, &ffn));
+        TF_RETURN_WITH_CONTEXT_IF_ERROR(Join(branch_map[n], out, &ffn),
+                                        " when joining ", e->DebugString());
       }
       if (IsMerge(out)) {
         if (out->in_edges().size() == ffn.count) {
@@ -1083,8 +1094,7 @@ Status FunctionalizeCond::FunctionalizeInternal() {
   for (auto it = predicate_switch_order.rbegin();
        it != predicate_switch_order.rend(); ++it) {
     auto& ps = *it;
-    VLOG(3) << "Flow down from: " << NodesToString(ps.switches) << " ("
-            << ps.predicate->name() << ")";
+    VLOG(3) << "Flow down from: " << ps.ToString();
 
     std::unordered_map<Node*, ForwardFlowNode> branch_map;
     std::unordered_set<Node*> frontier;
@@ -1097,21 +1107,29 @@ Status FunctionalizeCond::FunctionalizeInternal() {
                                                library_);
     TF_RETURN_IF_ERROR(ValidateFrontier(branch_map, frontier));
 
+    struct Hash {
+      size_t operator()(const std::pair<Node*, int>& item) const {
+        return Hash64Combine(hash<Node*>()(item.first),
+                             std::hash<int>()(item.second));
+      }
+    };
+
     // Sort the merge and switch nodes using NodeCmp. The switch-nodes are
     // further grouped (post sorting) by input to the switch node as in the
     // functionalized form each input will be passed in only once. This grouping
     // should retain the sorted order.
     CondArgNodes cond_arg_nodes;
-    std::unordered_map<Node*, int> input_index;
     std::sort(ps.switches.begin(), ps.switches.end(), NodeCmp());
+    std::unordered_map<std::pair<Node*, int>, int, Hash> input_index;
     for (Node* switch_node : ps.switches) {
-      Node* in;
-      TF_RETURN_IF_ERROR(switch_node->input_node(0, &in));
-      if (input_index.find(in) == input_index.end()) {
-        input_index[in] = cond_arg_nodes.size();
-        cond_arg_nodes.emplace_back(in);
+      const Edge* e;
+      TF_RETURN_IF_ERROR(switch_node->input_edge(0, &e));
+      std::pair<Node*, int> key = std::make_pair(e->src(), e->src_output());
+      if (input_index.find(key) == input_index.end()) {
+        input_index[key] = cond_arg_nodes.size();
+        cond_arg_nodes.emplace_back(key.first, key.second);
       }
-      cond_arg_nodes.at(input_index.at(in)).switches.push_back(switch_node);
+      cond_arg_nodes.at(input_index.at(key)).switches.push_back(switch_node);
     }
     std::vector<Node*> merge_nodes(frontier.begin(), frontier.end());
     std::sort(merge_nodes.begin(), merge_nodes.end(), NodeCmp());
@@ -1200,11 +1218,12 @@ StatusOr<Node*> FunctionalizeCond::BuildAndAddXlaIfOp(
   builder.Attr("Tout", out_type);
 
   builder.Attr("Tcond", DT_BOOL);
-  builder.Device(switch_cluster.predicate->assigned_device_name());
+  builder.Device(switch_cluster.predicate_edge->src()->assigned_device_name());
   // Conditional should be the first input ...
-  builder.Input(
-      NodeDefBuilder::NodeOut(switch_cluster.predicate->name(), 0,
-                              switch_cluster.predicate->output_type(0)));
+  builder.Input(NodeDefBuilder::NodeOut(
+      switch_cluster.predicate_edge->src()->name(),
+      switch_cluster.predicate_edge->src_output(),
+      switch_cluster.predicate_edge->src()->output_type(0)));
   // ... followed by the other inputs.
   builder.Input(inputs);
 
@@ -1264,24 +1283,17 @@ Status FunctionalizeCond::ExtractBody(const CondArgNodes& cond_arg_nodes,
 }
 
 Status FunctionalizeCond::AddInputEdges(const CondArgNodes& cond_arg_nodes,
-                                        Node* predicate, Node* if_node) {
+                                        const Edge* predicate_edge,
+                                        Node* if_node) {
   VLOG(3) << "AddInputEdges for " << if_node->name();
   int index = 0;
-  graph_->AddEdge(predicate, 0, if_node, index++);
-  for (auto& kv : cond_arg_nodes) {
-    bool inserted = false;
-    for (const Node* arg : kv.switches) {
-      const Edge* in_edge;
-      TF_RETURN_IF_ERROR(arg->input_edge(0, &in_edge));
-      if (in_edge->IsControlEdge()) {
-        graph_->AddControlEdge(in_edge->src(), if_node);
-      } else {
-        if (!inserted) {
-          graph_->AddEdge(in_edge->src(), in_edge->src_output(), if_node,
-                          index++);
-          inserted = true;
-        }
-      }
+  graph_->AddEdge(predicate_edge->src(), predicate_edge->src_output(), if_node,
+                  index++);
+  for (auto& arg : cond_arg_nodes) {
+    if (arg.src_output == Graph::kControlSlot) {
+      graph_->AddControlEdge(arg.src, if_node);
+    } else {
+      graph_->AddEdge(arg.src, arg.src_output, if_node, index++);
     }
   }
   return Status::OK();
@@ -1302,10 +1314,10 @@ Status FunctionalizeCond::AddOutputEdges(const std::vector<Node*>& outputs,
         return errors::Unimplemented("Output of index (", edge->src_output(),
                                      ") of merge node ", node->name());
       }
-      graph_->RemoveEdge(edge);
 
       int src_output =
           dst_input == Graph::kControlSlot ? Graph::kControlSlot : i;
+      graph_->RemoveEdge(edge);
       graph_->AddEdge(if_node, src_output, dst, dst_input);
     }
   }
@@ -1323,7 +1335,7 @@ StatusOr<Node*> FunctionalizeCond::ConvertToXlaIf(
       Node * if_node,
       BuildAndAddXlaIfOp(cond_arg_nodes, switch_cluster, merge_nodes));
   TF_RETURN_IF_ERROR(
-      AddInputEdges(cond_arg_nodes, switch_cluster.predicate, if_node));
+      AddInputEdges(cond_arg_nodes, switch_cluster.predicate_edge, if_node));
   TF_RETURN_IF_ERROR(AddOutputEdges(merge_nodes, if_node));
 
   return if_node;
@@ -1345,6 +1357,7 @@ Status FunctionalizeControlFlow(Graph* graph,
   VLOG(2) << "FunctionalizeControlFlow (initial): "
           << dump_graph::DumpGraphToFile("functionalize_initial", *graph,
                                          library);
+
   // Note: BuildControlFlowInfo() requires that the graph's source node is
   // connected to all source nodes in the graph. Many graphs violate this
   // invariant.
-- 
GitLab


From 395616ff770318bfe19a9722bc8bc9d792779235 Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Tue, 20 Feb 2018 19:55:06 -0800
Subject: [PATCH 109/575] TFLite Conv2D: Create temporary tensors in Prepare
 phase.

PiperOrigin-RevId: 186402268
---
 tensorflow/contrib/lite/arena_planner.cc |  5 ++
 tensorflow/contrib/lite/kernels/conv.cc  | 95 +++++++++++++++---------
 2 files changed, 66 insertions(+), 34 deletions(-)

diff --git a/tensorflow/contrib/lite/arena_planner.cc b/tensorflow/contrib/lite/arena_planner.cc
index 87b17c338e..8e47e2375e 100644
--- a/tensorflow/contrib/lite/arena_planner.cc
+++ b/tensorflow/contrib/lite/arena_planner.cc
@@ -128,6 +128,11 @@ TfLiteStatus ArenaPlanner::PlanAllocations() {
 }
 
 TfLiteStatus ArenaPlanner::ExecuteAllocations(int first_node, int last_node) {
+  // Grow the size of `allocs_` if necessary. This allows allocating temporary
+  // tensors in op's `prepare` function.
+  TF_LITE_ENSURE(context_, graph_info_->num_tensors() >= allocs_.size());
+  allocs_.resize(graph_info_->num_tensors());
+
   TF_LITE_ENSURE_STATUS(CalculateAllocations(first_node, last_node));
   TF_LITE_ENSURE_STATUS(Commit());
 
diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc
index 66d2c04bba..b2fdd61dc0 100644
--- a/tensorflow/contrib/lite/kernels/conv.cc
+++ b/tensorflow/contrib/lite/kernels/conv.cc
@@ -51,11 +51,13 @@ enum KernelType {
   kCblasOptimized,
 };
 
+const int kTensorNotAllocated = -1;
+
 struct OpData {
   // IDs are the arbitrary identifiers used by TF Lite to identify and access
   // memory buffers.
-  int im2col_id;
-  int hwcn_weights_id;
+  int im2col_id = kTensorNotAllocated;
+  int hwcn_weights_id = kTensorNotAllocated;
 
   TfLitePaddingValues padding;
   // The scaling factor from input to output (aka the 'real multiplier') can
@@ -80,8 +82,6 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) {
   // Instead, we allocate a new object to use as scratch space for im2col, and
   // to carry information from Prepare() to Eval().
   auto* data = new OpData;
-  context->AddTensors(context, 1, &data->im2col_id);
-  context->AddTensors(context, 1, &data->hwcn_weights_id);
   gemm_support::IncrementUsageCounter(context);
   return data;
 }
@@ -107,10 +107,66 @@ void TransposeFloatTensor(TfLiteTensor* input, TfLiteTensor* output) {
   }
 }
 
+// Allocate temporary tensors (`im2col`, `hwcn_weights` if necessary).
+// Note: `context->AddTensors` might invalidate pointers to existing tensors.
+// Therefore the logic to add tensors are isolated into this function.
+static TfLiteStatus AllocateTemporaryTensorsIfRequired(TfLiteContext* context,
+                                                       TfLiteNode* node) {
+  auto* params = reinterpret_cast<TfLiteConvParams*>(node->builtin_data);
+  OpData* data = reinterpret_cast<OpData*>(node->user_data);
+
+  TF_LITE_ENSURE(context, node->inputs->size >= 2);
+  TfLiteTensor* input = &context->tensors[node->inputs->data[0]];
+  TfLiteTensor* filter = &context->tensors[node->inputs->data[1]];
+
+  int filter_width = filter->dims->data[2];
+  int filter_height = filter->dims->data[1];
+
+  // We don't always need to allocate im2col. It is only used in some versions
+  // of the optimized Conv. This test just mimics something that happens inside
+  // optimized_ops.h, in order to avoid a DCHECK(!im2col_data).
+  data->need_im2col =
+      (params->stride_width != 1 || params->stride_height != 1 ||
+       filter_width != 1 || filter_height != 1);
+  // If we're using the optimized multithreaded EigenTensor implementation of
+  // convolution, it expects the filter weights to be transposed compared to
+  // the normal TF Lite buffer format. Typical TF Lite weights are
+  // [filter_count, filter_height, filter_width, input_depth], but for the float
+  // implementation we need them as [filter_height, filter_width, input_depth,
+  // filter_count]. We get to that format by transposing, and create a temporary
+  // buffer to store the results.
+  // This path is only used for float processing, so only create the buffer if
+  // we're running with that data type.
+  data->need_hwcn_weights = (input->type == kTfLiteFloat32);
+
+  int temporaries_count = 0;
+  if (data->need_im2col) {
+    data->im2col_index = temporaries_count;
+    if (data->im2col_id == kTensorNotAllocated) {
+      context->AddTensors(context, 1, &data->im2col_id);
+    }
+    ++temporaries_count;
+  }
+  if (data->need_hwcn_weights) {
+    data->hwcn_weights_index = temporaries_count;
+    if (data->hwcn_weights_id == kTensorNotAllocated) {
+      context->AddTensors(context, 1, &data->hwcn_weights_id);
+    }
+    ++temporaries_count;
+  }
+
+  TfLiteIntArrayFree(node->temporaries);
+  node->temporaries = TfLiteIntArrayCreate(temporaries_count);
+
+  return kTfLiteOk;
+}
+
 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast<TfLiteConvParams*>(node->builtin_data);
   OpData* data = reinterpret_cast<OpData*>(node->user_data);
 
+  TF_LITE_ENSURE_STATUS(AllocateTemporaryTensorsIfRequired(context, node));
+
   bool hasBias = node->inputs->size == 3;
   // Check number of inputs/outputs
   TF_LITE_ENSURE(context, hasBias || node->inputs->size == 2);
@@ -118,6 +174,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TfLiteTensor* output = &context->tensors[node->outputs->data[0]];
   TfLiteTensor* input = &context->tensors[node->inputs->data[0]];
   TfLiteTensor* filter = &context->tensors[node->inputs->data[1]];
+
   // Check dimensionality of input, filter
   TF_LITE_ENSURE_EQ(context, input->dims->size, 4);
   TF_LITE_ENSURE_EQ(context, filter->dims->size, 4);
@@ -199,36 +256,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
 
   if (output_status != kTfLiteOk) return output_status;
 
-  // We don't always need to allocate im2col. It is only used in some versions
-  // of the optimized Conv. This test just mimics something that happens inside
-  // optimized_ops.h, in order to avoid a DCHECK(!im2col_data).
-  data->need_im2col =
-      (params->stride_width != 1 || params->stride_height != 1 ||
-       filter_width != 1 || filter_height != 1);
-  // If we're using the optimized multithreaded EigenTensor implementation of
-  // convolution, it expects the filter weights to be transposed compared to
-  // the normal TF Lite buffer format. Typical TF Lite weights are
-  // [filter_count, filter_height, filter_width, input_depth], but for the float
-  // implementation we need them as [filter_height, filter_width, input_depth,
-  // filter_count]. We get to that format by transposing, and create a temporary
-  // buffer to store the results.
-  // This path is only used for float processing, so only create the buffer if
-  // we're running with that data type.
-  data->need_hwcn_weights = (data_type == kTfLiteFloat32);
-
-  int temporaries_count = 0;
-  if (data->need_im2col) {
-    data->im2col_index = temporaries_count;
-    ++temporaries_count;
-  }
-  if (data->need_hwcn_weights) {
-    data->hwcn_weights_index = temporaries_count;
-    ++temporaries_count;
-  }
-
-  TfLiteIntArrayFree(node->temporaries);
-  node->temporaries = TfLiteIntArrayCreate(temporaries_count);
-
   if (data->need_im2col) {
     node->temporaries->data[data->im2col_index] = data->im2col_id;
 
-- 
GitLab


From a6735131827a15127a7563bf7190c5a4e3a19bff Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Tue, 20 Feb 2018 20:34:05 -0800
Subject: [PATCH 110/575] TFLite: Define a DELEGATE op type. PiperOrigin-RevId:
 186405366

---
 tensorflow/contrib/lite/builtin_ops.h                  |  4 ++++
 tensorflow/contrib/lite/interpreter.cc                 |  3 +++
 tensorflow/contrib/lite/model.cc                       |  5 +++++
 tensorflow/contrib/lite/nnapi_delegate.cc              |  1 +
 .../lite/schema/builtin_ops_header/generator.cc        |  3 +++
 tensorflow/contrib/lite/schema/schema.fbs              |  4 ++++
 tensorflow/contrib/lite/schema/schema_generated.h      | 10 ++++++----
 7 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h
index 4f872c79e5..5f65a9575a 100644
--- a/tensorflow/contrib/lite/builtin_ops.h
+++ b/tensorflow/contrib/lite/builtin_ops.h
@@ -23,6 +23,9 @@ limitations under the License.
 extern "C" {
 #endif  // __cplusplus
 
+// The enum for builtin operators.
+// Note: CUSTOM and DELEGATE are 2 special ops which are not real biultin
+// ops.
 typedef enum {
   kTfLiteBuiltinAdd = 0,
   kTfLiteBuiltinAveragePool2d = 1,
@@ -72,6 +75,7 @@ typedef enum {
   kTfLiteBuiltinTopkV2 = 48,
   kTfLiteBuiltinSplit = 49,
   kTfLiteBuiltinLogSoftmax = 50,
+  kTfLiteBuiltinDelegate = 51,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus
diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc
index 0c30f1c64f..370e495527 100644
--- a/tensorflow/contrib/lite/interpreter.cc
+++ b/tensorflow/contrib/lite/interpreter.cc
@@ -116,6 +116,9 @@ TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels(
 
 TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels(
     TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace) {
+  // Annotate the registration as DELEGATE op.
+  registration.builtin_code = BuiltinOperator_DELEGATE;
+
   // Analyze the graph to find all independent subgraphs that are either
   // fully not-this-delegate or this-delegate computation.
   InterpreterInfo info(this);
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index 239f9df481..520a4c1089 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -573,6 +573,11 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type,
       builtin_data = reinterpret_cast<void*>(params);
       break;
     }
+    case BuiltinOperator_DELEGATE: {
+      // TODO(ycling): Revisit when supporting saving delegated models.
+      error_reporter->Report("DELEGATE op shouldn't exist in model.");
+      break;
+    }
   }
   return builtin_data;
 }
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index 999fe52ec8..4150ffefc1 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -345,6 +345,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_STRIDED_SLICE:
       case tflite::BuiltinOperator_EXP:
       case tflite::BuiltinOperator_LOG_SOFTMAX:
+      case tflite::BuiltinOperator_DELEGATE:
         FATAL("Op code %d is currently not delegated to NNAPI", builtin);
         nn_op_type = -1;  // set to invalid
         break;
diff --git a/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc b/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc
index b983d59d85..08bcfe4516 100644
--- a/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc
+++ b/tensorflow/contrib/lite/schema/builtin_ops_header/generator.cc
@@ -45,6 +45,9 @@ limitations under the License.
 extern "C" {
 #endif  // __cplusplus
 
+// The enum for builtin operators.
+// Note: CUSTOM and DELEGATE are 2 special ops which are not real biultin
+// ops.
 typedef enum {
 )";
 
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index a08d87cec4..03b471926c 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -124,6 +124,10 @@ enum BuiltinOperator : byte {
   TOPK_V2 = 48,
   SPLIT = 49,
   LOG_SOFTMAX = 50,
+  // DELEGATE is a special op type for the operations which are delegated to
+  // other backends.
+  // WARNING: Experimental interface, subject to change
+  DELEGATE = 51,
 }
 
 // Options for the builtin operators.
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
index dc37f8f9ee..052e35fbf0 100755
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-
 // automatically generated by the FlatBuffers compiler, do not modify
 
 
@@ -245,11 +244,12 @@ enum BuiltinOperator {
   BuiltinOperator_TOPK_V2 = 48,
   BuiltinOperator_SPLIT = 49,
   BuiltinOperator_LOG_SOFTMAX = 50,
+  BuiltinOperator_DELEGATE = 51,
   BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_LOG_SOFTMAX
+  BuiltinOperator_MAX = BuiltinOperator_DELEGATE
 };
 
-inline BuiltinOperator (&EnumValuesBuiltinOperator())[48] {
+inline BuiltinOperator (&EnumValuesBuiltinOperator())[49] {
   static BuiltinOperator values[] = {
     BuiltinOperator_ADD,
     BuiltinOperator_AVERAGE_POOL_2D,
@@ -298,7 +298,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[48] {
     BuiltinOperator_EXP,
     BuiltinOperator_TOPK_V2,
     BuiltinOperator_SPLIT,
-    BuiltinOperator_LOG_SOFTMAX
+    BuiltinOperator_LOG_SOFTMAX,
+    BuiltinOperator_DELEGATE
   };
   return values;
 }
@@ -356,6 +357,7 @@ inline const char **EnumNamesBuiltinOperator() {
     "TOPK_V2",
     "SPLIT",
     "LOG_SOFTMAX",
+    "DELEGATE",
     nullptr
   };
   return names;
-- 
GitLab


From ec19c4677e59d0bf1f31535cd33fc05a3b744573 Mon Sep 17 00:00:00 2001
From: Michael Case <mikecase@google.com>
Date: Tue, 20 Feb 2018 20:45:33 -0800
Subject: [PATCH 111/575] Do not add --host_copt=-march=native on Power PC.
 (#17161)

---
 configure.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/configure.py b/configure.py
index 2f268ee9d8..60f144f315 100644
--- a/configure.py
+++ b/configure.py
@@ -502,7 +502,8 @@ def set_cc_opt_flags(environ_cp):
   for opt in cc_opt_flags.split():
     write_to_bazelrc('build:opt --copt=%s' % opt)
   # It should be safe on the same build host.
-  write_to_bazelrc('build:opt --host_copt=-march=native')
+  if not is_ppc64le():
+    write_to_bazelrc('build:opt --host_copt=-march=native')
   write_to_bazelrc('build:opt --define with_default_optimizations=true')
   # TODO(mikecase): Remove these default defines once we are able to get
   # TF Lite targets building without them.
-- 
GitLab


From 3f32d3e0ac69f8073ec8e9ca0ee2410424655cfb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 20 Feb 2018 20:46:26 -0800
Subject: [PATCH 112/575] Clarify the shape of convolution arguments.

Replace n-d with size n, as n-d often means rank n, while the arguments here are arrays of size n.

PiperOrigin-RevId: 186406143
---
 .../docs_src/performance/xla/operation_semantics.md    | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index daa2d4767c..1f7a3a1e2c 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -440,11 +440,13 @@ area and a computation is performed for each possible position of the window.
 | `lhs`            | `ComputationDataHandle` | rank n+2 array of inputs      |
 | `rhs`            | `ComputationDataHandle` | rank n+2 array of kernel      |
 :                  :                         : weights                       :
-| `window_strides` | `ArraySlice<int64>`     | n-d array of kernel strides   |
-| `padding`        | `ArraySlice<pair<int64, | n-d array of (low, high)      |
+| `window_strides` | `ArraySlice<int64>`     | size n array of kernel strides|
+| `padding`        | `ArraySlice<pair<int64, | size n array of (low, high)   |
 :                  : int64>>`                : padding                       :
-| `lhs_dilation`   | `ArraySlice<int64>`     | n-d lhs dilation factor array |
-| `rhs_dilation`   | `ArraySlice<int64>`     | n-d rhs dilation factor array |
+| `lhs_dilation`   | `ArraySlice<int64>`     | size n lhs dilation factor    |
+:                  :                         : array                         |
+| `rhs_dilation`   | `ArraySlice<int64>`     | size n rhs dilation factor
+:                  :                         : array                         |
 
 Let n be the number of spatial dimensions. The `lhs` argument is a rank n+2
 array describing the base area. This is called the input, even though of course
-- 
GitLab


From d5e35145e9016077cbe045968e8de59358040622 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Tue, 20 Feb 2018 21:41:02 -0800
Subject: [PATCH 113/575] Add a small test to ensure that rewrites are
 idempotent.

PiperOrigin-RevId: 186410356
---
 .../contrib/quantize/python/quantize_graph_test.py  | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tensorflow/contrib/quantize/python/quantize_graph_test.py b/tensorflow/contrib/quantize/python/quantize_graph_test.py
index 6b9289ef5f..b9d03c1bc0 100644
--- a/tensorflow/contrib/quantize/python/quantize_graph_test.py
+++ b/tensorflow/contrib/quantize/python/quantize_graph_test.py
@@ -211,6 +211,19 @@ class QuantizeGraphTest(test_util.TensorFlowTestCase):
       self.assertFalse(any(s in op.name for s in update_names))
     self.assertTrue(quant_found)
 
+  def testIdempotent(self):
+    self._RunTestOverAllRewrites(self._TestIdempotent)
+
+  def _TestIdempotent(self, rewrite_fn):
+    with ops.Graph().as_default() as g:
+      self._ConvLayer()
+      rewrite_fn()
+      graph_def_before = str(g.as_graph_def())
+      # Ensuring that calling the rewrite again doesn't add more nodes.
+      rewrite_fn()
+      graph_def_after = str(g.as_graph_def())
+      self.assertEqual(graph_def_before, graph_def_after)
+
   def _ConvLayer(self):
     """Add a basic convolution layer to the default graph."""
     batch_size, height, width, depth = 5, 128, 128, 3
-- 
GitLab


From 3b4bf29952c0a3247776b75d71a694a8d01aeed6 Mon Sep 17 00:00:00 2001
From: Christopher Yeh <chrisyeh96@users.noreply.github.com>
Date: Tue, 20 Feb 2018 22:58:18 -0800
Subject: [PATCH 114/575] Cleaner documentation for tf.confusion_matrix
 (#17126)

---
 tensorflow/python/ops/check_ops.py        |  6 +++---
 tensorflow/python/ops/confusion_matrix.py | 18 ++++++++----------
 2 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py
index 0fd6e29a49..64567ac54a 100644
--- a/tensorflow/python/ops/check_ops.py
+++ b/tensorflow/python/ops/check_ops.py
@@ -334,9 +334,9 @@ def assert_equal(x, y, data=None, summarize=None, message=None, name=None):
     @compatibility{eager} returns None
 
   Raises:
-    InvalidArgumentError if the check can be performed immediately and
-    `x == y` is False. The check can be performed immediately during
-    eager execution or if `x` and `y` are statically known.
+    InvalidArgumentError: if the check can be performed immediately and
+      `x == y` is False. The check can be performed immediately during eager
+      execution or if `x` and `y` are statically known.
   """
   message = message or ''
   with ops.name_scope(name, 'assert_equal', [x, y, data]):
diff --git a/tensorflow/python/ops/confusion_matrix.py b/tensorflow/python/ops/confusion_matrix.py
index e4ce2ab28a..b9a93c3bed 100644
--- a/tensorflow/python/ops/confusion_matrix.py
+++ b/tensorflow/python/ops/confusion_matrix.py
@@ -99,19 +99,16 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32,
                      name=None, weights=None):
   """Computes the confusion matrix from predictions and labels.
 
-  Calculate the Confusion Matrix for a pair of prediction and
-  label 1-D int arrays.
-
   The matrix columns represent the prediction labels and the rows represent the
   real labels. The confusion matrix is always a 2-D array of shape `[n, n]`,
   where `n` is the number of valid labels for a given classification task. Both
   prediction and labels must be 1-D arrays of the same shape in order for this
   function to work.
 
-  If `num_classes` is None, then `num_classes` will be set to the one plus
-  the maximum value in either predictions or labels.
-  Class labels are expected to start at 0. E.g., if `num_classes` was
-  three, then the possible labels would be `[0, 1, 2]`.
+  If `num_classes` is `None`, then `num_classes` will be set to one plus the
+  maximum value in either predictions or labels. Class labels are expected to
+  start at 0. For example, if `num_classes` is 3, then the possible labels
+  would be `[0, 1, 2]`.
 
   If `weights` is not `None`, then each prediction contributes its
   corresponding weight to the total value of the confusion matrix cell.
@@ -141,8 +138,9 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32,
     weights: An optional `Tensor` whose shape matches `predictions`.
 
   Returns:
-    A k X k matrix representing the confusion matrix, where k is the number of
-    possible labels in the classification task.
+    A `Tensor` of type `dtype` with shape `[n, n]` representing the confusion
+    matrix, where `n` is the number of possible labels in the classification
+    task.
 
   Raises:
     ValueError: If both predictions and labels are not 1-D vectors and have
@@ -188,7 +186,7 @@ def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.int32,
       weights = math_ops.cast(weights, dtype)
 
     shape = array_ops.stack([num_classes, num_classes])
-    indices = array_ops.transpose(array_ops.stack([labels, predictions]))
+    indices = array_ops.stack([labels, predictions], axis=1)
     values = (array_ops.ones_like(predictions, dtype)
               if weights is None else weights)
     cm_sparse = sparse_tensor.SparseTensor(
-- 
GitLab


From d100729c309cb22baf1630d9f39cf60516c58cdf Mon Sep 17 00:00:00 2001
From: Daniel Trebbien <dtrebbien@gmail.com>
Date: Tue, 20 Feb 2018 22:59:51 -0800
Subject: [PATCH 115/575] Add missing `override' (#17118)

This fixes a warning produced by clang:
./tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h:470:8: warning: 'InitLeafClassStats' overrides a member function but is not marked 'override' [-Winconsistent-missing-override]
  void InitLeafClassStats(int best_split_index, LeafStat* left_stats,
       ^
./tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h:190:16: note: overridden virtual function is here
  virtual void InitLeafClassStats(int best_split_index, LeafStat* left_stats,
               ^
---
 tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
index 04e6b0a735..dc3e9fe79d 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/grow_stats.h
@@ -468,7 +468,7 @@ class FixedSizeSparseClassificationGrowStats : public ClassificationStats {
   void PackToProto(FertileSlot* slot) const override;
 
   void InitLeafClassStats(int best_split_index, LeafStat* left_stats,
-                          LeafStat* right_stats) const;
+                          LeafStat* right_stats) const override;
 
  protected:
   void ClassificationAddSplitStats() override {
-- 
GitLab


From 241c944a423892c658b47b16958a75194e3d11b1 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Tue, 20 Feb 2018 23:12:57 -0800
Subject: [PATCH 116/575] [XLA:CPU] Add FP32<->FP16 conversion routines

LLVM generates calls to these functions when lowering some fp16 operations on
certain architectures.  These symbols are defined in compiler-rt but we don't
always link to compiler-rt so these symbols are sometimes absent.

This change adds __gnu_f2h_ieee and __gnu_h2f_ieee as weak symbols.  Making them
weak ensures that we are able to build successfully even when linking to a
compiler-rt that defines these symbols.

PiperOrigin-RevId: 186416684
---
 tensorflow/compiler/xla/service/cpu/BUILD     |  15 ++
 .../compiler/xla/service/cpu/runtime_fp16.cc  | 133 ++++++++++++++++++
 .../compiler/xla/service/cpu/runtime_fp16.h   |  27 ++++
 .../xla/service/cpu/simple_orc_jit.cc         |   4 +
 tensorflow/compiler/xla/tests/BUILD           |   4 +-
 tensorflow/compiler/xla/tests/convert_test.cc |  61 ++++++++
 .../compiler/xla/tests/literal_test_util.cc   |   5 +
 7 files changed, 247 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_fp16.cc
 create mode 100644 tensorflow/compiler/xla/service/cpu/runtime_fp16.h

diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index c13a0b1cdf..32be0b0c96 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -163,6 +163,7 @@ cc_library(
         ":disassembler",
         ":external_constant_pool",
         ":orc_jit_memory_mapper",
+        ":runtime_fp16",
         ":runtime_conv2d",
         ":runtime_fft",
         ":runtime_fork_join",
@@ -182,6 +183,20 @@ cc_library(
     ] + ORC_JIT_MEMORY_MAPPER_TARGETS,
 )
 
+cc_library(
+    name = "runtime_fp16",
+    srcs = [
+        "runtime_fp16.cc",
+    ],
+    hdrs = [
+        "runtime_fp16.h",
+    ],
+    copts = runtime_copts(),
+    deps = [
+        "//tensorflow/core:framework_lite",
+    ],
+)
+
 cc_library(
     name = "cpu_executable",
     srcs = ["cpu_executable.cc"],
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fp16.cc b/tensorflow/compiler/xla/service/cpu/runtime_fp16.cc
new file mode 100644
index 0000000000..af0275c8bd
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/runtime_fp16.cc
@@ -0,0 +1,133 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <cstring>
+
+#include "tensorflow/compiler/xla/service/cpu/runtime_fp16.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace {
+using tensorflow::uint16;
+using tensorflow::uint32;
+
+// Helper class that lets us access the underlying bit representation
+// of a float without breaking C++ strict aliasing.
+class AliasedFloatInt {
+ public:
+  static_assert(sizeof(float) == sizeof(uint32), "");
+
+  static AliasedFloatInt FromFloat(float f) {
+    AliasedFloatInt value;
+    value.set_float(f);
+    return value;
+  }
+
+  static AliasedFloatInt FromUInt(uint32 u) {
+    AliasedFloatInt value;
+    value.set_uint(u);
+    return value;
+  }
+
+  void set_float(float f) { memcpy(&value_, &f, sizeof(f)); }
+  float as_float() const {
+    float f;
+    memcpy(&f, &value_, sizeof(f));
+    return f;
+  }
+
+  void set_uint(uint32 u) { value_ = u; }
+  uint32 as_uint() const { return value_; }
+
+ private:
+  uint32 value_;
+};
+}  // namespace
+
+// __gnu_f2h_ieee and __gnu_h2f_ieee are marked as weak symbols so if XLA is
+// built with compiler-rt (that also defines these symbols) we don't get a
+// duplicate definition linker error.  Making these symbols weak also ensures
+// that the compiler-rt definitions "win", but that isn't essential.
+
+// Algorithm copied from Eigen.
+uint16 TF_ATTRIBUTE_WEAK __gnu_f2h_ieee(float float_value) {
+  AliasedFloatInt f = AliasedFloatInt::FromFloat(float_value);
+
+  const AliasedFloatInt f32infty = AliasedFloatInt::FromUInt(255 << 23);
+  const AliasedFloatInt f16max = AliasedFloatInt::FromUInt((127 + 16) << 23);
+  const AliasedFloatInt denorm_magic =
+      AliasedFloatInt::FromUInt(((127 - 15) + (23 - 10) + 1) << 23);
+  unsigned int sign_mask = 0x80000000u;
+  uint32 o = static_cast<uint16>(0x0u);
+
+  unsigned int sign = f.as_uint() & sign_mask;
+  f.set_uint(f.as_uint() ^ sign);
+
+  // NOTE all the integer compares in this function can be safely
+  // compiled into signed compares since all operands are below
+  // 0x80000000. Important if you want fast straight SSE2 code
+  // (since there's no unsigned PCMPGTD).
+
+  if (f.as_uint() >=
+      f16max.as_uint()) {  // result is Inf or NaN (all exponent bits set)
+    o = (f.as_uint() > f32infty.as_uint()) ? 0x7e00
+                                           : 0x7c00;  // NaN->qNaN and Inf->Inf
+  } else {                            // (De)normalized number or zero
+    if (f.as_uint() < (113 << 23)) {  // resulting FP16 is subnormal or zero
+      // use a magic value to align our 10 mantissa bits at the bottom of
+      // the float. as long as FP addition is round-to-nearest-even this
+      // just works.
+      f.set_float(f.as_float() + denorm_magic.as_float());
+
+      // and one integer subtract of the bias later, we have our final float!
+      o = static_cast<uint16>(f.as_uint() - denorm_magic.as_uint());
+    } else {
+      unsigned int mant_odd =
+          (f.as_uint() >> 13) & 1;  // resulting mantissa is odd
+
+      // update exponent, rounding bias part 1
+      f.set_uint(f.as_uint() + (static_cast<unsigned int>(15 - 127) << 23) +
+                 0xfff);
+      // rounding bias part 2
+      f.set_uint(f.as_uint() + mant_odd);
+      // take the bits!
+      o = static_cast<uint16>(f.as_uint() >> 13);
+    }
+  }
+
+  o |= static_cast<uint16>(sign >> 16);
+  return o;
+}
+
+// Algorithm copied from Eigen.
+float TF_ATTRIBUTE_WEAK __gnu_h2f_ieee(uint16 h) {
+  const AliasedFloatInt magic = AliasedFloatInt::FromUInt(113 << 23);
+  const unsigned int shifted_exp = 0x7c00 << 13;  // exponent mask after shift
+  AliasedFloatInt o;
+
+  o.set_uint((h & 0x7fff) << 13);                // exponent/mantissa bits
+  unsigned int exp = shifted_exp & o.as_uint();  // just the exponent
+  o.set_uint(o.as_uint() + ((127 - 15) << 23));  // exponent adjust
+
+  // handle exponent special cases
+  if (exp == shifted_exp) {                        // Inf/NaN?
+    o.set_uint(o.as_uint() + ((128 - 16) << 23));  // extra exp adjust
+  } else if (exp == 0) {                           // Zero/Denormal?
+    o.set_uint(o.as_uint() + (1 << 23));           // extra exp adjust
+    o.set_float(o.as_float() - magic.as_float());  // renormalize
+  }
+
+  o.set_uint(o.as_uint() | (h & 0x8000) << 16);  // sign bit
+  return o.as_float();
+}
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_fp16.h b/tensorflow/compiler/xla/service/cpu/runtime_fp16.h
new file mode 100644
index 0000000000..01d92d0319
--- /dev/null
+++ b/tensorflow/compiler/xla/service/cpu/runtime_fp16.h
@@ -0,0 +1,27 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_FP16_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_FP16_H_
+
+#include "tensorflow/core/platform/types.h"
+
+// Converts an F32 value to a F16.
+extern "C" tensorflow::uint16 __gnu_f2h_ieee(float);
+
+// Converts an F16 value to a F32.
+extern "C" float __gnu_h2f_ieee(tensorflow::uint16);
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_FP16_H_
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index aa8d4ad9dc..e8a375d637 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/cpu/runtime_conv2d.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_fft.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_fork_join.h"
+#include "tensorflow/compiler/xla/service/cpu/runtime_fp16.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_matmul.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.h"
 #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h"
@@ -190,6 +191,9 @@ bool RegisterKnownJITSymbols() {
   REGISTER_CPU_RUNTIME_SYMBOL(ReleaseInfeedBufferAfterDequeue);
   REGISTER_CPU_RUNTIME_SYMBOL(ReleaseOutfeedBufferAfterPopulation);
 
+  registry->Register("__gnu_f2h_ieee", reinterpret_cast<void*>(__gnu_f2h_ieee));
+  registry->Register("__gnu_h2f_ieee", reinterpret_cast<void*>(__gnu_h2f_ieee));
+
 #undef REGISTER_CPU_RUNTIME_SYMBOL
 
 // Register both the f32 (float) and f64 (double) versions of a libm symbol.
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 8339d08ef4..a2c0f834de 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -877,8 +877,7 @@ xla_test(
     name = "half_test",
     srcs = ["half_test.cc"],
     backends = [
-        # TODO(b/72509305): Flaky (fails with SEGV) as of 2018-01-25
-        # "cpu",
+        "cpu",
         "gpu",
     ],
     deps = [
@@ -1367,6 +1366,7 @@ xla_test(
     srcs = ["convert_test.cc"],
     deps = [
         "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc
index f66e3b57bf..1c6e7859a2 100644
--- a/tensorflow/compiler/xla/tests/convert_test.cc
+++ b/tensorflow/compiler/xla/tests/convert_test.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
 #include "tensorflow/compiler/xla/tests/test_macros.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/casts.h"
 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/types.h"
@@ -208,5 +209,65 @@ TEST_F(ConvertTest, ConvertReshape) {
   ComputeAndCompareR0<float>(&builder, 42.0f, {}, ErrorSpec(0.0001));
 }
 
+std::vector<float> GetInterestingF16ConversionTestCases() {
+  float infinity = std::numeric_limits<float>::infinity();
+  float half_min_positive_normal =
+      tensorflow::bit_cast<float, uint32>(0x38800000);
+  float half_max_subnormal = tensorflow::bit_cast<float, uint32>(0x387fc000);
+  float half_min_positive_subnormal =
+      tensorflow::bit_cast<float, uint32>(0x33800000);
+  float half_max = 65504.0f;
+
+  std::vector<float> test_cases(
+      {-infinity, -(half_max * 2 + 1), -half_max, -42.0f, -1.0f,
+       -half_min_positive_subnormal, -half_max_subnormal,
+       -half_min_positive_normal, -0.0f, 0.0f, half_min_positive_subnormal,
+       half_max_subnormal, half_min_positive_normal, 1.0f, 42.0f, half_max,
+       (half_max * 2 + 1), infinity});
+  return test_cases;
+}
+
+XLA_TEST_F(ConvertTest, ConvertR1F16ToR1F32) {
+  std::vector<float> test_cases = GetInterestingF16ConversionTestCases();
+  std::vector<half> input;
+  c_transform(test_cases, std::back_inserter(input),
+              [](float f) { return Eigen::half(f); });
+  std::vector<float> expected_output;
+  c_transform(input, std::back_inserter(expected_output),
+              [](Eigen::half h) { return static_cast<float>(h); });
+
+  TF_ASSERT_OK_AND_ASSIGN(
+      std::unique_ptr<GlobalData> dot_lhs_handle,
+      client_->TransferToServer(*Literal::CreateR1<half>(input)));
+
+  ComputationBuilder builder(client_, TestName());
+  builder.ConvertElementType(
+      builder.Parameter(
+          0, ShapeUtil::MakeShape(F16, {static_cast<int64>(input.size())}),
+          "param"),
+      F32);
+
+  ComputeAndCompareR1<float>(&builder, expected_output, {dot_lhs_handle.get()});
+}
+
+XLA_TEST_F(ConvertTest, ConvertR1F32ToR1F16) {
+  std::vector<float> input = GetInterestingF16ConversionTestCases();
+  std::vector<half> expected_output;
+  c_transform(input, std::back_inserter(expected_output),
+              [](float f) { return Eigen::half(f); });
+
+  TF_ASSERT_OK_AND_ASSIGN(
+      std::unique_ptr<GlobalData> dot_lhs_handle,
+      client_->TransferToServer(*Literal::CreateR1<float>(input)));
+
+  ComputationBuilder builder(client_, TestName());
+  builder.ConvertElementType(
+      builder.Parameter(
+          0, ShapeUtil::MakeShape(F32, {static_cast<int64>(input.size())}),
+          "param"),
+      F16);
+
+  ComputeAndCompareR1<half>(&builder, expected_output, {dot_lhs_handle.get()});
+}
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/literal_test_util.cc b/tensorflow/compiler/xla/tests/literal_test_util.cc
index 5aa71a9261..81630df34c 100644
--- a/tensorflow/compiler/xla/tests/literal_test_util.cc
+++ b/tensorflow/compiler/xla/tests/literal_test_util.cc
@@ -209,6 +209,11 @@ template <>
   return CompareFloatsBitwiseEqual<bfloat16, uint16>(lhs, rhs);
 }
 template <>
+::testing::AssertionResult CompareEqual<Eigen::half>(Eigen::half lhs,
+                                                     Eigen::half rhs) {
+  return CompareFloatsBitwiseEqual<Eigen::half, uint16>(lhs, rhs);
+}
+template <>
 ::testing::AssertionResult CompareEqual<float>(float lhs, float rhs) {
   return CompareFloatsBitwiseEqual<float, uint32>(lhs, rhs);
 }
-- 
GitLab


From e5496b556734bb1d8de85311092804e0150b3009 Mon Sep 17 00:00:00 2001
From: Christopher Yeh <chrisyeh96@users.noreply.github.com>
Date: Wed, 21 Feb 2018 05:27:17 -0800
Subject: [PATCH 117/575] Remove extraneous check for Eager mode (#17125)

The check is already made once at the start of the method
---
 tensorflow/python/ops/control_flow_ops.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 740a67fc7e..4f4a2e35db 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -178,8 +178,6 @@ def Assert(condition, data, summarize=None, name=None):
             condition, data, summarize, name="Assert")
 
       guarded_assert = cond(condition, no_op, true_assert, name="AssertGuard")
-      if context.in_eager_mode():
-        return
       return guarded_assert.op
 
 
-- 
GitLab


From 5ac8d4b219b86715e9124708ce5f5051a8652660 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 06:14:29 -0800
Subject: [PATCH 118/575] Minor corrections in feature_columns doc

PiperOrigin-RevId: 186449350
---
 tensorflow/docs_src/get_started/feature_columns.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/docs_src/get_started/feature_columns.md b/tensorflow/docs_src/get_started/feature_columns.md
index ad3e1fe3e3..d8e4bec863 100644
--- a/tensorflow/docs_src/get_started/feature_columns.md
+++ b/tensorflow/docs_src/get_started/feature_columns.md
@@ -146,10 +146,10 @@ single input number into a four-element vector. Therefore, the model now can
 learn _four individual weights_ rather than just one; four weights creates a
 richer model than one weight. More importantly, bucketizing enables the model
 to clearly distinguish between different year categories since only one of the
-elements is set (1) and the other three elements are cleared (0). When we just
-use a single number (a year) as input, the model can only learn a linear
-relationship. So, bucketing provides the model with additional flexibility that
-the model can use to learn.
+elements is set (1) and the other three elements are cleared (0). For example,
+when we just use a single number (a year) as input, a linear model can only
+learn a linear relationship. So, bucketing provides the model with additional
+flexibility that the model can use to learn.
 
 The following code demonstrates how to create a bucketized feature:
 
@@ -242,7 +242,7 @@ on an explicit vocabulary list. For example:
 # the elements in the vocabulary list.
 vocabulary_feature_column =
     tf.feature_column.categorical_column_with_vocabulary_list(
-        key="a feature returned by input_fn()",
+        key=feature_name_from_input_fn,
         vocabulary_list=["kitchenware", "electronics", "sports"])
 ```
 
@@ -259,7 +259,7 @@ you place the vocabulary words in a separate file. For example:
 # the elements in the vocabulary file
 vocabulary_feature_column =
     tf.feature_column.categorical_column_with_vocabulary_file(
-        key="a feature returned by input_fn()",
+        key=feature_name_from_input_fn,
         vocabulary_file="product_class.txt",
         vocabulary_size=3)
 ```
-- 
GitLab


From e4cf1aea16532e697d6de17d22043b49d4146711 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 08:13:01 -0800
Subject: [PATCH 119/575] Adding some beginner advice to the README.

PiperOrigin-RevId: 186461145
---
 tensorflow/contrib/py2tf/README.md | 39 ++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/tensorflow/contrib/py2tf/README.md b/tensorflow/contrib/py2tf/README.md
index cd50675ad5..c89f2084b4 100644
--- a/tensorflow/contrib/py2tf/README.md
+++ b/tensorflow/contrib/py2tf/README.md
@@ -2,3 +2,42 @@
 
 A compiler for generating TensorFlow numeric and control flow ops from Python
 code.
+
+### Eng Guide
+See tensorflow/contrib/py2tf/impl/api.py for the decorator definition and entry
+point to the conversion code.
+
+See tensorflow/contrib/py2tf/impl/conversion.py for where all of the
+`Transformer`s are called on the AST.
+
+In order to alter the AST one should create a subclass of `transformer.Base`, as
+seen in converters/.  In this subclass if one wants to add code that runs on
+each node then the `visit_<node_name>` method should be overridden, where
+`<node_name>` is the name of the type of node you wish to alter.  See
+https://docs.python.org/2/library/ast.html#ast.NodeTransformer and note that we
+use gast to bridge some Python version differences.  Also
+http://greentreesnakes.readthedocs.io/en/latest/nodes.html has references on
+which visitation functions are supported.  The `visit_<node_name>` function then
+returns the node that will be included in the final AST. An example of this is
+the following `Transformer` that will alter all while loops:
+
+  ```
+  class WhileLoopTransformer(transformer.Base):
+
+  def __init__(self, context):
+    super(WhileLoopTransformer, self).__init__(context)
+
+  def visit_While(self, node):
+    return node
+  ```
+
+Here, `visit_While` will be called on all while loop nodes with the node passed
+in as node.  Because we just return node without altering it, this is a no-op.
+
+One thing to note is that this will not recursively alter nested while loops; in
+order to do this we need to call `self.generic_visit(node)` which is a
+pre-defined function that recursively visits all the children of `node`.
+
+In order to have the new `Transformer` actually be called on the AST, it needs
+to be called from `node_to_graph` in
+tensorflow/contrib/py2tf/impl/conversion.py.
-- 
GitLab


From edd46dd1b3817c847a76bc3b7490e923037e62c2 Mon Sep 17 00:00:00 2001
From: Ilya Biryukov <ibiryukov@google.com>
Date: Tue, 20 Feb 2018 19:24:24 +0100
Subject: [PATCH 120/575] Fix compiler error with cuda-clang

segment_reduction_ops.h requires cuda_kernel_helper.h to be
included in clang because it uses some of the helpers directly in the
header (e.g. CudaAtomicMax). It works with nvcc, because the usage is
in a template context and nvcc checks that function is available only
at template instantiation.
However, clang does more strict erorr-checking for functions found
during template instantiation and requires them to be found either by
ADL or at the point of template declaration.
---
 tensorflow/core/kernels/segment_reduction_ops.h         | 8 ++++++++
 tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc | 5 ++++-
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/segment_reduction_ops.h b/tensorflow/core/kernels/segment_reduction_ops.h
index 51814273b3..fe0a2782f9 100644
--- a/tensorflow/core/kernels/segment_reduction_ops.h
+++ b/tensorflow/core/kernels/segment_reduction_ops.h
@@ -16,6 +16,14 @@ limitations under the License.
 #ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_
 #define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_SEGMENT_REDUCTION_OPS_H_
 
+
+// This file requires the following include because it uses CudaAtomicMax:
+// #include "tensorflow/core/util/cuda_kernel_helper.h"
+
+// Unfortunately we can't add the #include, since it breaks compilation for
+// non-GPU targets. This only breaks in clang, because it's more strict for
+// template code and CudaAtomicMax is used in template context.
+
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
diff --git a/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc b/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc
index ba979e6bb2..3511c85f71 100644
--- a/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc
+++ b/tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc
@@ -17,10 +17,13 @@ limitations under the License.
 
 #define EIGEN_USE_GPU
 
+// We need to include cuda_kernel_helper.h before segment_reduction_ops.h
+// See comment in segment_reduction_ops.h for more details.
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+
 #include "tensorflow/core/kernels/segment_reduction_ops.h"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/util/cuda_device_functions.h"
-#include "tensorflow/core/util/cuda_kernel_helper.h"
 
 
 namespace tensorflow {
-- 
GitLab


From 4688e222ae740f60d21569614324f5ec7903821a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 08:41:26 -0800
Subject: [PATCH 121/575] Modify optimized quantized LSTM implementation so
 that it only needs one instantiation of fixed-point Tanh, for 3 integer bits,
 regardless of the value of StateIntegerBits

PiperOrigin-RevId: 186464604
---
 .../internal/optimized/optimized_ops.h        | 28 ++++++++++++++++---
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index 3cc800fac5..b2dd7e9ec0 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -2435,9 +2435,19 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
       FS new_state = gemmlowp::SaturatingAdd(
           gemmlowp::Rescale<StateIntegerBits>(input_times_input_modulation),
           prev_state_times_forget_state);
-      // Implementation of last internal tanh node, still in fixed-point.
-      F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state);
+      // Implementation of last internal Tanh node, still in fixed-point.
+      // Since a Tanh fixed-point implementation is specialized for a given
+      // number or integer bits, and each specialization can have a substantial
+      // code size, and we already used above a Tanh on an input with 3 integer
+      // bits, and per the table in the above function comment there is no
+      // significant accuracy to be lost by clamping to [-8, +8] for a
+      // 3-integer-bits representation, let us just do that. This helps people
+      // porting this to targets where code footprint must be minimized.
+      F3 new_state_f3 = gemmlowp::Rescale<3>(new_state);
+      F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3);
       // Store the new internal state back to memory, as 16-bit integers.
+      // Note: here we store the original value with StateIntegerBits, not
+      // the rescaled 3-integer-bits value fed to tanh.
       vst1q_s16(output_state_data_ptr, new_state.raw());
       output_state_data_ptr += 8;
       // Down-scale the output activations to 8-bit integers, saturating,
@@ -2494,9 +2504,19 @@ void LstmCell(const uint8* input_data_uint8, const Dims<4>& input_dims,
       FS new_state = gemmlowp::SaturatingAdd(
           gemmlowp::Rescale<StateIntegerBits>(input_times_input_modulation),
           prev_state_times_forget_state);
-      // Implementation of last internal tanh node, still in fixed-point.
-      F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state);
+      // Implementation of last internal Tanh node, still in fixed-point.
+      // Since a Tanh fixed-point implementation is specialized for a given
+      // number or integer bits, and each specialization can have a substantial
+      // code size, and we already used above a Tanh on an input with 3 integer
+      // bits, and per the table in the above function comment there is no
+      // significant accuracy to be lost by clamping to [-8, +8] for a
+      // 3-integer-bits representation, let us just do that. This helps people
+      // porting this to targets where code footprint must be minimized.
+      F3 new_state_f3 = gemmlowp::Rescale<3>(new_state);
+      F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3);
       // Store the new internal state back to memory, as 16-bit integers.
+      // Note: here we store the original value with StateIntegerBits, not
+      // the rescaled 3-integer-bits value fed to tanh.
       *output_state_data_ptr++ = new_state.raw();
       // Down-scale the output activations to 8-bit integers, saturating,
       // and store back to memory.
-- 
GitLab


From d5bdd20f2b4d91aa53e0de97729e5135b1a0edc9 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Wed, 21 Feb 2018 09:04:00 -0800
Subject: [PATCH 122/575] fix not_covered anchor

PiperOrigin-RevId: 186467828
---
 tensorflow/docs_src/programmers_guide/version_compat.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/programmers_guide/version_compat.md
index a28f1385c8..e6613cc69f 100644
--- a/tensorflow/docs_src/programmers_guide/version_compat.md
+++ b/tensorflow/docs_src/programmers_guide/version_compat.md
@@ -60,7 +60,8 @@ patch versions.  The public APIs consist of
     * [`tensor_shape`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor_shape.proto)
     * [`types`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto)
 
-## What is *not* covered {not_covered}
+<a name="not_covered"></a>
+## What is *not* covered
 
 Some API functions are explicitly marked as "experimental" and can change in
 backward incompatible ways between minor releases. These include:
-- 
GitLab


From e0e0f5b625ca73f9dd8b04adbbcd5e654a869bd9 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Feb 2018 09:08:05 -0800
Subject: [PATCH 123/575] memory_size should be expressed in Bytes, but
 port::AvailableRam() returns kB.

PiperOrigin-RevId: 186468461
---
 tensorflow/core/grappler/clusters/utils.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/clusters/utils.cc b/tensorflow/core/grappler/clusters/utils.cc
index aacd2ccb72..607e10e1ab 100644
--- a/tensorflow/core/grappler/clusters/utils.cc
+++ b/tensorflow/core/grappler/clusters/utils.cc
@@ -51,7 +51,7 @@ DeviceProperties GetLocalCPUInfo() {
 
   int64 free_mem = port::AvailableRam();
   if (free_mem < INT64_MAX) {
-    device.set_memory_size(free_mem);
+    device.set_memory_size(free_mem * 1024);
   }
 
   (*device.mutable_environment())["cpu_instruction_set"] =
-- 
GitLab


From 42d473f7d6ee0489109dbf1b8aee9508167c3ab8 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Feb 2018 09:09:24 -0800
Subject: [PATCH 124/575] In the arithmetic optimizer enqueue the fanout of
 optimized nodes in a deterministic order

PiperOrigin-RevId: 186468633
---
 .../core/grappler/optimizers/arithmetic_optimizer.cc       | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index 9c544c82bf..c455f28a5b 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -1077,7 +1077,12 @@ Status ArithmeticOptimizer::SimplifyArithmeticOps() {
       // consumers of `node` are already redirected to `simplified_tensor`.
       // Re-push the consumers into `nodes_to_simplify` for further
       // optimizations.
-      std::set<NodeDef*> consumers = node_map_->GetOutputs(node->name());
+      const std::set<NodeDef*> outputs = node_map_->GetOutputs(node->name());
+      std::vector<NodeDef*> consumers(outputs.begin(), outputs.end());
+      std::sort(consumers.begin(), consumers.end(),
+                [](const NodeDef* n1, const NodeDef* n2) {
+                  return n1->name() < n2->name();
+                });
       for (NodeDef* consumer : consumers) {
         // Update `consumer`'s use of `node` to `input`'s operand.
         for (int i = 0; i < consumer->input_size(); ++i) {
-- 
GitLab


From 04ec855825289caa5ef76a2cb370bc351f10bd74 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 09:33:41 -0800
Subject: [PATCH 125/575] Internal updates.

PiperOrigin-RevId: 186472487
---
 tensorflow/contrib/py2tf/README.md | 39 ------------------------------
 1 file changed, 39 deletions(-)

diff --git a/tensorflow/contrib/py2tf/README.md b/tensorflow/contrib/py2tf/README.md
index c89f2084b4..cd50675ad5 100644
--- a/tensorflow/contrib/py2tf/README.md
+++ b/tensorflow/contrib/py2tf/README.md
@@ -2,42 +2,3 @@
 
 A compiler for generating TensorFlow numeric and control flow ops from Python
 code.
-
-### Eng Guide
-See tensorflow/contrib/py2tf/impl/api.py for the decorator definition and entry
-point to the conversion code.
-
-See tensorflow/contrib/py2tf/impl/conversion.py for where all of the
-`Transformer`s are called on the AST.
-
-In order to alter the AST one should create a subclass of `transformer.Base`, as
-seen in converters/.  In this subclass if one wants to add code that runs on
-each node then the `visit_<node_name>` method should be overridden, where
-`<node_name>` is the name of the type of node you wish to alter.  See
-https://docs.python.org/2/library/ast.html#ast.NodeTransformer and note that we
-use gast to bridge some Python version differences.  Also
-http://greentreesnakes.readthedocs.io/en/latest/nodes.html has references on
-which visitation functions are supported.  The `visit_<node_name>` function then
-returns the node that will be included in the final AST. An example of this is
-the following `Transformer` that will alter all while loops:
-
-  ```
-  class WhileLoopTransformer(transformer.Base):
-
-  def __init__(self, context):
-    super(WhileLoopTransformer, self).__init__(context)
-
-  def visit_While(self, node):
-    return node
-  ```
-
-Here, `visit_While` will be called on all while loop nodes with the node passed
-in as node.  Because we just return node without altering it, this is a no-op.
-
-One thing to note is that this will not recursively alter nested while loops; in
-order to do this we need to call `self.generic_visit(node)` which is a
-pre-defined function that recursively visits all the children of `node`.
-
-In order to have the new `Transformer` actually be called on the AST, it needs
-to be called from `node_to_graph` in
-tensorflow/contrib/py2tf/impl/conversion.py.
-- 
GitLab


From 982c183dee45efe27f02702b53d304cdd0e32ed4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 09:35:44 -0800
Subject: [PATCH 126/575] Internal Change

PiperOrigin-RevId: 186472818
---
 tensorflow/contrib/lite/models/speech_test.cc | 44 +++++++++++++------
 .../contrib/lite/testing/parse_testdata.cc    | 18 ++++++--
 .../contrib/lite/testing/parse_testdata.h     |  3 +-
 3 files changed, 46 insertions(+), 19 deletions(-)

diff --git a/tensorflow/contrib/lite/models/speech_test.cc b/tensorflow/contrib/lite/models/speech_test.cc
index daa8c3100b..a354179a94 100644
--- a/tensorflow/contrib/lite/models/speech_test.cc
+++ b/tensorflow/contrib/lite/models/speech_test.cc
@@ -97,7 +97,12 @@ bool ConvertCsvData(const string& model_name, const string& in_name,
   return true;
 }
 
-TEST(SpeechTest, HotwordOkGoogleRank1Test) {
+class SpeechTest : public ::testing::TestWithParam<int> {
+ protected:
+  int GetMaxInvocations() { return GetParam(); }
+};
+
+TEST_P(SpeechTest, HotwordOkGoogleRank1Test) {
   std::stringstream os;
   ASSERT_TRUE(ConvertCsvData(
       "speech_hotword_model_rank1.tflite", "speech_hotword_model_in.csv",
@@ -105,11 +110,11 @@ TEST(SpeechTest, HotwordOkGoogleRank1Test) {
       /*output_tensor=*/"18", /*persistent_tensors=*/"4",
       /*sequence_size=*/40, &os));
   testing::TfLiteDriver test_driver(/*use_nnapi=*/false);
-  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver))
+  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations()))
       << test_driver.GetErrorMessage();
 }
 
-TEST(SpeechTest, HotwordOkGoogleRank2Test) {
+TEST_P(SpeechTest, HotwordOkGoogleRank2Test) {
   std::stringstream os;
   ASSERT_TRUE(ConvertCsvData(
       "speech_hotword_model_rank2.tflite", "speech_hotword_model_in.csv",
@@ -117,11 +122,11 @@ TEST(SpeechTest, HotwordOkGoogleRank2Test) {
       /*output_tensor=*/"18", /*persistent_tensors=*/"1",
       /*sequence_size=*/40, &os));
   testing::TfLiteDriver test_driver(/*use_nnapi=*/false);
-  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver))
+  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations()))
       << test_driver.GetErrorMessage();
 }
 
-TEST(SpeechTest, SpeakerIdOkGoogleTest) {
+TEST_P(SpeechTest, SpeakerIdOkGoogleTest) {
   std::stringstream os;
   ASSERT_TRUE(ConvertCsvData(
       "speech_speakerid_model.tflite", "speech_speakerid_model_in.csv",
@@ -130,11 +135,11 @@ TEST(SpeechTest, SpeakerIdOkGoogleTest) {
       /*persistent_tensors=*/"19,20,40,41,61,62",
       /*sequence_size=*/80, &os));
   testing::TfLiteDriver test_driver(/*use_nnapi=*/false);
-  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver))
+  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations()))
       << test_driver.GetErrorMessage();
 }
 
-TEST(SpeechTest, AsrAmTest) {
+TEST_P(SpeechTest, AsrAmTest) {
   std::stringstream os;
   ASSERT_TRUE(
       ConvertCsvData("speech_asr_am_model.tflite", "speech_asr_am_model_in.csv",
@@ -143,7 +148,7 @@ TEST(SpeechTest, AsrAmTest) {
                      /*persistent_tensors=*/"19,20,40,41,61,62,82,83,103,104",
                      /*sequence_size=*/320, &os));
   testing::TfLiteDriver test_driver(/*use_nnapi=*/false);
-  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver))
+  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations()))
       << test_driver.GetErrorMessage();
 }
 
@@ -151,15 +156,16 @@ TEST(SpeechTest, AsrAmTest) {
 // through the interpreter and stored the sum of all the output, which was them
 // compared for correctness. In this test we are comparing all the intermediate
 // results.
-TEST(SpeechTest, AsrLmTest) {
+TEST_P(SpeechTest, AsrLmTest) {
   std::ifstream in_file;
   testing::TfLiteDriver test_driver(/*use_nnapi=*/false);
   ASSERT_TRUE(Init("speech_asr_lm_model.test_spec", &test_driver, &in_file));
-  ASSERT_TRUE(testing::ParseAndRunTests(&in_file, &test_driver))
+  ASSERT_TRUE(
+      testing::ParseAndRunTests(&in_file, &test_driver, GetMaxInvocations()))
       << test_driver.GetErrorMessage();
 }
 
-TEST(SpeechTest, EndpointerTest) {
+TEST_P(SpeechTest, EndpointerTest) {
   std::stringstream os;
   ASSERT_TRUE(ConvertCsvData(
       "speech_endpointer_model.tflite", "speech_endpointer_model_in.csv",
@@ -168,11 +174,11 @@ TEST(SpeechTest, EndpointerTest) {
       /*persistent_tensors=*/"28,29,49,50",
       /*sequence_size=*/320, &os));
   testing::TfLiteDriver test_driver(/*use_nnapi=*/false);
-  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver))
+  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations()))
       << test_driver.GetErrorMessage();
 }
 
-TEST(SpeechTest, TtsTest) {
+TEST_P(SpeechTest, TtsTest) {
   std::stringstream os;
   ASSERT_TRUE(ConvertCsvData("speech_tts_model.tflite",
                              "speech_tts_model_in.csv",
@@ -181,9 +187,19 @@ TEST(SpeechTest, TtsTest) {
                              /*persistent_tensors=*/"25,26,46,47,67,68,73",
                              /*sequence_size=*/334, &os));
   testing::TfLiteDriver test_driver(/*use_nnapi=*/false);
-  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver))
+  ASSERT_TRUE(testing::ParseAndRunTests(&os, &test_driver, GetMaxInvocations()))
       << test_driver.GetErrorMessage();
 }
 
+// Define two instantiations. The "ShortTests" instantiations is used when
+// running the tests on Android, in order to prevent timeouts (It takes about
+// 200s just to bring up the Android emulator.)
+static const int kAllInvocations = -1;
+static const int kFirstFewInvocations = 10;
+INSTANTIATE_TEST_CASE_P(LongTests, SpeechTest,
+                        ::testing::Values(kAllInvocations));
+INSTANTIATE_TEST_CASE_P(ShortTests, SpeechTest,
+                        ::testing::Values(kFirstFewInvocations));
+
 }  // namespace
 }  // namespace tflite
diff --git a/tensorflow/contrib/lite/testing/parse_testdata.cc b/tensorflow/contrib/lite/testing/parse_testdata.cc
index 0caef0fe22..c8f2e49f93 100644
--- a/tensorflow/contrib/lite/testing/parse_testdata.cc
+++ b/tensorflow/contrib/lite/testing/parse_testdata.cc
@@ -319,8 +319,9 @@ class Reshape : public Message {
 // This is the top-level message in a test file.
 class TestData : public Message {
  public:
-  explicit TestData(TestRunner* test_runner) : test_runner_(test_runner) {}
-
+  explicit TestData(TestRunner* test_runner)
+      : test_runner_(test_runner), num_invocations_(0), max_invocations_(-1) {}
+  void SetMaxInvocations(int max) { max_invocations_ = max; }
   void SetField(const std::string& name, const std::string& value) override {
     if (name == "load_model") {
       test_runner_->LoadModel(value);
@@ -334,7 +335,12 @@ class TestData : public Message {
   Message* AddChild(const std::string& s) override {
     if (s == "invoke") {
       test_runner_->AllocateTensors();
-      return Store(new Invoke(test_runner_));
+      if (max_invocations_ == -1 || num_invocations_ < max_invocations_) {
+        ++num_invocations_;
+        return Store(new Invoke(test_runner_));
+      } else {
+        return nullptr;
+      }
     } else if (s == "reshape") {
       return Store(new Reshape(test_runner_));
     }
@@ -343,10 +349,14 @@ class TestData : public Message {
 
  private:
   TestRunner* test_runner_;
+  int num_invocations_;
+  int max_invocations_;
 };
 
-bool ParseAndRunTests(std::istream* input, TestRunner* test_runner) {
+bool ParseAndRunTests(std::istream* input, TestRunner* test_runner,
+                      int max_invocations) {
   TestData test_data(test_runner);
+  test_data.SetMaxInvocations(max_invocations);
   Message::Read(input, &test_data);
   return test_runner->IsValid() && test_runner->GetOverallSuccess();
 }
diff --git a/tensorflow/contrib/lite/testing/parse_testdata.h b/tensorflow/contrib/lite/testing/parse_testdata.h
index 7ebf362eb9..d94361d735 100644
--- a/tensorflow/contrib/lite/testing/parse_testdata.h
+++ b/tensorflow/contrib/lite/testing/parse_testdata.h
@@ -66,7 +66,8 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter, const Example&);
 //     output: "12,3,4,545,3"
 //     output: "0.01,0.02"
 //   }
-bool ParseAndRunTests(std::istream* input, TestRunner* test_runner);
+bool ParseAndRunTests(std::istream* input, TestRunner* test_runner,
+                      int max_invocations = -1);
 
 }  // namespace testing
 }  // namespace tflite
-- 
GitLab


From 113fce8885c80d6897a58dd8e0747b964e8cb113 Mon Sep 17 00:00:00 2001
From: "Jeffrey A. Dean" <jeff@google.com>
Date: Wed, 21 Feb 2018 09:47:58 -0800
Subject: [PATCH 127/575] Create fast path for common case of finite values in
 CheckNumericsOp

PiperOrigin-RevId: 186474851
---
 tensorflow/core/kernels/check_numerics_op.cc | 32 ++++++++++++--------
 1 file changed, 19 insertions(+), 13 deletions(-)

diff --git a/tensorflow/core/kernels/check_numerics_op.cc b/tensorflow/core/kernels/check_numerics_op.cc
index 534527c6bd..6040b2b399 100644
--- a/tensorflow/core/kernels/check_numerics_op.cc
+++ b/tensorflow/core/kernels/check_numerics_op.cc
@@ -47,6 +47,8 @@ template <typename Device, typename T>
 class CheckNumericsOp;
 
 // Partial specialization for CPU
+// TODO(jeff,rmlarsen): We should make this variant be an AsyncOpKernel, as
+// was done for the GPU case below.
 template <typename T>
 class CheckNumericsOp<CPUDevice, T> : public OpKernel {
  public:
@@ -67,28 +69,32 @@ class CheckNumericsOp<CPUDevice, T> : public OpKernel {
     int fp_props =
         std::accumulate(data, data + size, 0, [](const int& x, const T& y) {
           int result = x;
-          if (Eigen::numext::isinf(y)) {
+          if (TF_PREDICT_TRUE(Eigen::numext::isfinite(y))) {
+            // Do nothing: common case
+          } else if (Eigen::numext::isinf(y)) {
             result |= kInfBit;
           } else if (Eigen::numext::isnan(y)) {
             result |= kNaNBit;
           }
           return result;
         });
-    string status;
-    if ((fp_props & kInfBit) && (fp_props & kNaNBit)) {
-      status = "Inf and NaN";
-    } else {
-      if (fp_props & kInfBit) {
-        status = "Inf";
+    if (fp_props != 0) {
+      string status;
+      if ((fp_props & kInfBit) && (fp_props & kNaNBit)) {
+        status = "Inf and NaN";
+      } else {
+        if (fp_props & kInfBit) {
+          status = "Inf";
+        }
+        if (fp_props & kNaNBit) {
+          status = "NaN";
+        }
       }
-      if (fp_props & kNaNBit) {
-        status = "NaN";
+      if (!status.empty()) {
+        context->SetStatus(errors::InvalidArgument(message_, " : Tensor had ",
+                                                   status, " values"));
       }
     }
-    if (!status.empty()) {
-      context->SetStatus(errors::InvalidArgument(message_, " : Tensor had ",
-                                                 status, " values"));
-    }
   }
 
  private:
-- 
GitLab


From 8f4e42589a55c6297f704b123458472220f4615f Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Wed, 21 Feb 2018 13:20:33 -0500
Subject: [PATCH 128/575] Fix table format in SECURITY.md

---
 tensorflow/SECURITY.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/SECURITY.md b/tensorflow/SECURITY.md
index 6ddac1f964..fea24b2739 100644
--- a/tensorflow/SECURITY.md
+++ b/tensorflow/SECURITY.md
@@ -233,7 +233,7 @@ v//Fw6ZeY+HmRDFdirjD7wXtIuER4vqCryIqR6Xe9X8oJXz9L/Jhslc=
 
 ### Known vulnerabilities
 
-| Type | Versions affected | Reported by | Additional Information |
-|------|:-----------------:|---------------------------------------|
-| out of bounds read| <=1.4 | TenCent Blade Team | [issue report](https://github.com/tensorflow/tensorflow/issues/14959) |
+| Type              | Versions affected |        Reported by | Additional Information      |
+|-------------------|:-----------------:|--------------------|-----------------------------|
+| out of bounds read|             <=1.4 | TenCent Blade Team | [issue report](https://github.com/tensorflow/tensorflow/issues/14959) |
 
-- 
GitLab


From f604ba67ef3340c29afac74162659f1cf0c9d557 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Wed, 21 Feb 2018 10:29:47 -0800
Subject: [PATCH 129/575] [XLA] Add FindInstruction and FindComputation helpers
 to HloTestBase.

These are useful for tests that create HLOs and then search for a
particular computation/instruction.

While we're at it, add a c_find_if utility and fix up the (lack of)
perfect forwarding in some of our other c_foo utilities.

PiperOrigin-RevId: 186482111
---
 .../xla/service/layout_assignment_test.cc     | 42 +++++++++----------
 .../compiler/xla/tests/hlo_test_base.cc       | 22 ++++++++++
 tensorflow/compiler/xla/tests/hlo_test_base.h |  9 ++++
 tensorflow/compiler/xla/util.h                | 15 +++++--
 4 files changed, 61 insertions(+), 27 deletions(-)

diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index dd0fba2758..88e5caaf47 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -629,33 +629,29 @@ TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) {
       LayoutUtil::MakeLayout({2, 1, 0}));
   AssignLayouts(module.get(), &computation_layout);
 
-  HloComputation* fused_computation = *std::find_if(
-      module->computations().begin(), module->computations().end(),
-      [](const HloComputation* c) { return c->name() == "fused_computation"; });
-
-  auto fused_instr = [&](const string& name) {
-    auto it = std::find_if(
-        fused_computation->instructions().begin(),
-        fused_computation->instructions().end(),
-        [&](const HloInstruction* i) { return i->name() == name; });
-    CHECK(it != fused_computation->instructions().end());
-    return *it;
+  auto layout_of = [&](tensorflow::StringPiece name) {
+    return FindInstruction(module.get(), name)
+        ->shape()
+        .layout()
+        .minor_to_major();
   };
 
-  EXPECT_THAT(fused_instr("gte0")->shape().layout().minor_to_major(),
-              ElementsAre(0, 1, 2));
-  EXPECT_THAT(
-      fused_instr("gte1")->shape().tuple_shapes(0).layout().minor_to_major(),
-      ElementsAre(1, 2, 0));
-  EXPECT_THAT(
-      fused_instr("gte1")->shape().tuple_shapes(1).layout().minor_to_major(),
-      ElementsAre(2, 0, 1));
-  EXPECT_THAT(fused_instr("gte1a")->shape().layout().minor_to_major(),
+  EXPECT_THAT(layout_of("gte0"), ElementsAre(0, 1, 2));
+  EXPECT_THAT(layout_of("gte1a"), ElementsAre(1, 2, 0));
+  EXPECT_THAT(layout_of("gte1b"), ElementsAre(2, 0, 1));
+  EXPECT_THAT(layout_of("fresult"), ElementsAre(2, 1, 0));
+  EXPECT_THAT(FindInstruction(module.get(), "gte1")
+                  ->shape()
+                  .tuple_shapes(0)
+                  .layout()
+                  .minor_to_major(),
               ElementsAre(1, 2, 0));
-  EXPECT_THAT(fused_instr("gte1b")->shape().layout().minor_to_major(),
+  EXPECT_THAT(FindInstruction(module.get(), "gte1")
+                  ->shape()
+                  .tuple_shapes(1)
+                  .layout()
+                  .minor_to_major(),
               ElementsAre(2, 0, 1));
-  EXPECT_THAT(fused_instr("fresult")->shape().layout().minor_to_major(),
-              ElementsAre(2, 1, 0));
 }
 
 TEST_F(LayoutAssignmentTest, ConditionalAsymmetricLayout) {
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc
index 9f5806c5e1..6723c99edb 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.cc
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc
@@ -267,6 +267,28 @@ template <typename LiteralPtr>
                                   reference_preprocessor);
 }
 
+HloComputation* HloTestBase::FindComputation(HloModule* module,
+                                             tensorflow::StringPiece name) {
+  auto it = c_find_if(module->computations(),
+                      [&](HloComputation* c) { return c->name() == name; });
+  if (it == module->computations().end()) {
+    return nullptr;
+  }
+  return *it;
+}
+
+HloInstruction* HloTestBase::FindInstruction(HloModule* module,
+                                             tensorflow::StringPiece name) {
+  for (const HloComputation* c : module->computations()) {
+    auto it = c_find_if(c->instructions(),
+                        [&](HloInstruction* i) { return i->name() == name; });
+    if (it != c->instructions().end()) {
+      return *it;
+    }
+  }
+  return nullptr;
+}
+
 Backend& HloTestBase::backend() { return test_runner_.backend(); }
 
 /* static */
diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h
index 4aea9fc9fd..413bb213fd 100644
--- a/tensorflow/compiler/xla/tests/hlo_test_base.h
+++ b/tensorflow/compiler/xla/tests/hlo_test_base.h
@@ -197,6 +197,15 @@ class HloTestBase : public ::testing::Test {
         ->Clear();
   }
 
+  // Gets the computation/instruction from the given module with the given name.
+  //
+  // This is useful for tests which create HLOs from a string and then want to
+  // inspect a particular computation or instruction.
+  HloComputation* FindComputation(HloModule* module,
+                                  tensorflow::StringPiece name);
+  HloInstruction* FindInstruction(HloModule* module,
+                                  tensorflow::StringPiece name);
+
   // Return an HLO verifier constructed for the test backend.
   HloVerifier& verifier() const { return *hlo_verifier_; }
 
diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index 46ec7af542..e14c8cefa1 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -427,8 +427,9 @@ std::vector<std::pair<int64, int64>> CommonFactors(
 string SanitizeFileName(string file_name);
 
 template <typename Container, typename Predicate>
-bool c_all_of(Container container, Predicate predicate) {
-  return std::all_of(std::begin(container), std::end(container), predicate);
+bool c_all_of(Container container, Predicate&& predicate) {
+  return std::all_of(std::begin(container), std::end(container),
+                     std::forward<Predicate>(predicate));
 }
 
 template <typename InputContainer, typename OutputIterator,
@@ -461,8 +462,9 @@ void c_sort(InputContainer& input_container) {
 }
 
 template <class InputContainer, class Comparator>
-void c_sort(InputContainer& input_container, Comparator comparator) {
-  std::sort(std::begin(input_container), std::end(input_container), comparator);
+void c_sort(InputContainer& input_container, Comparator&& comparator) {
+  std::sort(std::begin(input_container), std::end(input_container),
+            std::forward<Comparator>(comparator));
 }
 
 template <typename Sequence, typename T>
@@ -480,6 +482,11 @@ template <typename C>
 auto c_adjacent_find(const C& c) -> decltype(std::begin(c)) {
   return std::adjacent_find(std::begin(c), std::end(c));
 }
+
+template <typename C, typename Pred>
+auto c_find_if(const C& c, Pred&& pred) -> decltype(std::begin(c)) {
+  return std::find_if(std::begin(c), std::end(c), std::forward<Pred>(pred));
+}
 }  // namespace xla
 
 #define XLA_LOG_LINES(SEV, STRING) \
-- 
GitLab


From 4f527563bcd3e94432428fc65004b5c30e5c5bb2 Mon Sep 17 00:00:00 2001
From: Jeremy Lau <lauj@google.com>
Date: Wed, 21 Feb 2018 10:57:51 -0800
Subject: [PATCH 130/575] Internal change.

PiperOrigin-RevId: 186487354
---
 tensorflow/contrib/distributions/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 7f510c4221..4f413e5512 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -403,7 +403,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "poisson_lognormal_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/poisson_lognormal_test.py"],
     additional_deps = [
         ":distributions_py",
-- 
GitLab


From cf87ec188a31f9ad134e8d4f7198cf4e2860cf80 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Wed, 21 Feb 2018 11:06:22 -0800
Subject: [PATCH 131/575] Add link to SECURITY.md from doc describing model
 loading.

PiperOrigin-RevId: 186489041
---
 tensorflow/docs_src/programmers_guide/saved_model.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/programmers_guide/saved_model.md
index f27a658342..f18d50b282 100644
--- a/tensorflow/docs_src/programmers_guide/saved_model.md
+++ b/tensorflow/docs_src/programmers_guide/saved_model.md
@@ -3,6 +3,9 @@
 This document explains how to save and restore
 @{$variables$variables} and models.
 
+Important: TensorFlow model files are code. Be careful with untrusted code.
+See [Using TensorFlow Securely](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/SECURITY.md)
+for details.
 
 ## Saving and restoring variables
 
-- 
GitLab


From 4ab73c109461d7820b0dd07eee2a4d4145c32e02 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Wed, 21 Feb 2018 11:37:13 -0800
Subject: [PATCH 132/575] Add S64 add/subtract test.

PiperOrigin-RevId: 186494344
---
 .../xla/tests/array_elementwise_ops_test.cc   | 107 ++++++++++++++++++
 tensorflow/compiler/xla/tests/convert_test.cc |  70 +++++++++++-
 .../xla/tests/scalar_computations_test.cc     |   2 +-
 3 files changed, 174 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 739d201fad..8b35259013 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -101,6 +101,33 @@ XLA_TEST_F(ArrayElementwiseOpTest, NegConstantC64) {
       {}, error_spec_);
 }
 
+XLA_TEST_F(ArrayElementwiseOpTest, NegConstantS64) {
+  ComputationBuilder builder(client_, TestName());
+  auto a = builder.ConstantR1<int64>({
+      -1,
+      1,
+      0,
+      0x12345678,
+      static_cast<int64>(0xffffffff12345678l),
+      static_cast<int64>(0x8000000000000000LL),
+      static_cast<int64>(0x8000000000000001LL),
+  });
+  auto result = builder.Neg(a);
+  LOG(INFO) << -static_cast<int64>(0x7FFFFFFFFFFFFFFFLL);
+
+  ComputeAndCompareR1<int64>(&builder,
+                             {
+                                 1,
+                                 -1,
+                                 0,
+                                 -0x12345678,
+                                 0xedcba988,
+                                 static_cast<int64>(0x8000000000000000LL),
+                                 -static_cast<int64>(0x8000000000000001LL),
+                             },
+                             {});
+}
+
 XLA_TEST_F(ArrayElementwiseOpTest, IsFiniteZeroElementF32s) {
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR1<float>({});
@@ -186,6 +213,86 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantZeroElementC64s) {
   ComputeAndCompareR1<complex64>(&builder, {}, {}, error_spec_);
 }
 
+XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantU64s) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<uint64> lhs{0xFFFFFFFF,
+                          static_cast<uint64>(-1),
+                          0,
+                          0,
+                          0x7FFFFFFFFFFFFFFFLL,
+                          0x7FFFFFFFFFFFFFFLL,
+                          0x8000000000000000LL,
+                          0x8000000000000000LL,
+                          1};
+  std::unique_ptr<Literal> lhs_literal = Literal::CreateR1<uint64>({lhs});
+  auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param");
+  std::unique_ptr<GlobalData> lhs_data =
+      client_->TransferToServer(*lhs_literal).ConsumeValueOrDie();
+
+  std::vector<uint64> rhs{1,
+                          0x7FFFFFFFFFFFFFFLL,
+                          0x7FFFFFFFFFFFFFFFLL,
+                          0x8000000000000000LL,
+                          0,
+                          static_cast<uint64>(-1),
+                          0,
+                          1,
+                          0x8000000000000000LL};
+  std::unique_ptr<Literal> rhs_literal = Literal::CreateR1<uint64>({rhs});
+  auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param");
+  std::unique_ptr<GlobalData> rhs_data =
+      client_->TransferToServer(*rhs_literal).ConsumeValueOrDie();
+
+  auto add = b.Add(lhs_param, rhs_param);
+
+  std::vector<uint64> expected(lhs.size());
+  for (int64 i = 0; i < lhs.size(); ++i) {
+    expected[i] = lhs[i] + rhs[i];
+  }
+
+  ComputeAndCompareR1<uint64>(&b, expected, {lhs_data.get(), rhs_data.get()});
+}
+
+XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS64s) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<int64> lhs{static_cast<int64>(0x8000000000000000LL),
+                         static_cast<int64>(0x8000000000000000LL),
+                         -1,
+                         0x7FFFFFFFFFFFFFFLL,
+                         0x7FFFFFFFFFFFFFFFLL,
+                         1,
+                         0,
+                         -1};
+  std::unique_ptr<Literal> lhs_literal = Literal::CreateR1<int64>({lhs});
+  auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param");
+  std::unique_ptr<GlobalData> lhs_data =
+      client_->TransferToServer(*lhs_literal).ConsumeValueOrDie();
+
+  std::vector<int64> rhs{-1,
+                         0,
+                         static_cast<int64>(0x8000000000000000LL),
+                         1,
+                         0,
+                         0x7FFFFFFFFFFFFFFLL,
+                         0x7FFFFFFFFFFFFFFFLL,
+                         0x7FFFFFFFFFFFFFFFLL};
+  std::unique_ptr<Literal> rhs_literal = Literal::CreateR1<int64>({rhs});
+  auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param");
+  std::unique_ptr<GlobalData> rhs_data =
+      client_->TransferToServer(*rhs_literal).ConsumeValueOrDie();
+
+  auto sub = b.Sub(lhs_param, rhs_param);
+
+  std::vector<int64> expected(lhs.size());
+  for (int64 i = 0; i < lhs.size(); ++i) {
+    expected[i] = lhs[i] - rhs[i];
+  }
+
+  ComputeAndCompareR1<int64>(&b, expected, {lhs_data.get(), rhs_data.get()});
+}
+
 TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) {
   const int count = GetParam();
   ComputationBuilder builder(client_, TestName());
diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc
index 1c6e7859a2..f4f9f28565 100644
--- a/tensorflow/compiler/xla/tests/convert_test.cc
+++ b/tensorflow/compiler/xla/tests/convert_test.cc
@@ -107,11 +107,73 @@ TEST_F(ConvertTest, ConvertR1F32ToR1S32) {
 
 XLA_TEST_F(ConvertTest, ConvertR1S64ToR1F32) {
   ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR1<int64>({32, 64});
-  builder.ConvertElementType(a, F32);
+  std::vector<int64> arg{
+      -9223371216516022272,
+      -2,
+      -1,
+      -0x7FFFFFFF,
+      -0x80000000,
+      0,
+      1,
+      2,
+      1073742145,
+      1073742656,
+      0x7FFFFFFF,
+      0x80000000,
+      826720496944058148,
+      4296062029846194332,
+      0x0007FB72E4000000LL,
+      0x0007FB72E4000001LL,
+      0x0007FB72E6000000LL,
+      0x0007FB72E7000000LL,
+      0x0007FB72E7FFFFFFLL,
+      0x0007FB72E8000000LL,
+      0x0007FB72E8000001LL,
+      0x0007FB72EA000000LL,
+      0x0007FB72EB000000LL,
+      0x0007FB72EBFFFFFFLL,
+      0x0007FB72EC000000LL,
+      0x7FFFFF0000000000LL,
+      0x7FFFFF8000000000LL,
+      0x7FFFFFFFFFFFFF00,
+      static_cast<int64>(0xFFFFFFFFFFFFFFFF),
+      static_cast<int64>(0x0000f234e67e0001LL),
+      static_cast<int64>(0x8000000000000000),
+      static_cast<int64>(0x8000000000000000LL),
+      static_cast<int64>(0x8000000000000001LL),
+      static_cast<int64>(0x8000008000000000LL),
+      static_cast<int64>(0x8000010000000000LL),
+  };
+  std::unique_ptr<Literal> arg_literal = Literal::CreateR1<int64>({arg});
+  auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param");
+  std::unique_ptr<GlobalData> arg_data =
+      client_->TransferToServer(*arg_literal).ConsumeValueOrDie();
+
+  builder.ConvertElementType(arg_param, F32);
+
+  std::vector<float> expected(arg.size());
+  for (int64 i = 0; i < arg.size(); ++i) {
+    expected[i] = static_cast<float>(arg[i]);
+  }
+  ComputeAndCompareR1<float>(&builder, expected, {arg_data.get()});
+}
 
-  std::vector<float> expected = {32.0, 64.0};
-  ComputeAndCompareR1<float>(&builder, expected, {});
+XLA_TEST_F(ConvertTest, ConvertR1U32ToR1F32) {
+  ComputationBuilder builder(client_, TestName());
+  std::vector<uint32> arg{0,          1,          0x1000,     0x7fffffff,
+                          0x80000000, 0x80000001, 0x80000002, 0xFFFFFFFF};
+  std::unique_ptr<Literal> arg_literal = Literal::CreateR1<uint32>({arg});
+  auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param");
+  std::unique_ptr<GlobalData> arg_data =
+      client_->TransferToServer(*arg_literal).ConsumeValueOrDie();
+
+  builder.ConvertElementType(arg_param, F32);
+
+  std::vector<float> expected(arg.size());
+  for (int64 i = 0; i < arg.size(); ++i) {
+    expected[i] = static_cast<float>(arg[i]);
+  }
+  ComputeAndCompareR1<float>(&builder, expected, {arg_data.get()});
 }
 
 XLA_TEST_F(ConvertTest, ConvertR1U8ToR1F32) {
diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
index 4da6ee9160..d7bda77e87 100644
--- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc
+++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
@@ -163,7 +163,7 @@ XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) {
   auto a = builder.Parameter(0, ShapeUtil::MakeShape(S64, {}), "a");
   builder.ConvertElementType(a, F32);
 
-  int64 value = 3LL << 32;
+  int64 value = 3LL << 35;
   std::unique_ptr<Literal> a_literal = Literal::CreateR0<int64>(value);
   std::unique_ptr<GlobalData> a_data =
       client_->TransferToServer(*a_literal).ConsumeValueOrDie();
-- 
GitLab


From 47ea851d3faf029d5b23ee70cb3b96bad0128324 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Wed, 21 Feb 2018 11:39:43 -0800
Subject: [PATCH 133/575] Fast-path for losses code.

PiperOrigin-RevId: 186494736
---
 tensorflow/python/kernel_tests/losses_test.py | 15 +++++++++++++
 tensorflow/python/ops/array_ops.py            |  7 +++++++
 tensorflow/python/ops/losses/losses_impl.py   | 21 +++++++++++++++++--
 tensorflow/python/ops/math_ops.py             |  4 ++--
 4 files changed, 43 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/kernel_tests/losses_test.py b/tensorflow/python/kernel_tests/losses_test.py
index 197dbf44af..1123c20a16 100644
--- a/tensorflow/python/kernel_tests/losses_test.py
+++ b/tensorflow/python/kernel_tests/losses_test.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors_impl
@@ -32,11 +33,25 @@ from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.ops.losses import losses
+from tensorflow.python.ops.losses import losses_impl
 from tensorflow.python.ops.losses import util
 from tensorflow.python.platform import test
 from tensorflow.python.training import momentum as momentum_lib
 
 
+safe_div = losses_impl._safe_div  # pylint: disable=protected-access
+
+
+class SafeDivTest(test.TestCase):
+
+  def testEager(self):
+    with context.eager_mode():
+      self.assertAllEqual(safe_div(constant_op.constant(1.0),
+                                   constant_op.constant(0.0)), 0.0)
+      self.assertAllEqual(safe_div(constant_op.constant(1.0),
+                                   0.0), 0.0)
+
+
 class AbsoluteDifferenceLossTest(test.TestCase):
 
   def setUp(self):
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 2aa3ef05ba..08db8a17b5 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -386,6 +386,13 @@ def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
   Returns:
     A `Tensor` of type `out_type`. Defaults to `tf.int32`.
   """
+  if context.in_eager_mode() and not isinstance(
+      input, (sparse_tensor.SparseTensor,
+              sparse_tensor.SparseTensorValue)):
+    size_ = 1
+    for dim in ops.convert_to_tensor(input)._shape_tuple():  # pylint: disable=protected-access
+      size_ *= dim
+    return size_
   with ops.name_scope(name, "Size", [input]) as name:
     if isinstance(input, (sparse_tensor.SparseTensor,
                           sparse_tensor.SparseTensorValue)):
diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index ca408988dd..c86cc92321 100644
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.eager import context
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import confusion_matrix
@@ -88,6 +89,14 @@ def _safe_div(numerator, denominator, name="value"):
   Returns:
     The element-wise value of the numerator divided by the denominator.
   """
+  if isinstance(denominator, float):
+    if math_ops.equal(denominator, 0.0):
+      return ops.convert_to_tensor(0.0, dtype=numerator.dtype)
+    return math_ops.div(numerator, denominator)
+  if context.in_eager_mode() and denominator._rank() == 0:  # pylint: disable=protected-access
+    if math_ops.equal(denominator, 0.0):
+      return ops.convert_to_tensor(0.0, dtype=numerator.dtype)
+    return math_ops.div(numerator, denominator)
   return array_ops.where(
       math_ops.greater(denominator, 0),
       math_ops.div(numerator, array_ops.where(
@@ -134,6 +143,10 @@ def _num_present(losses, weights, per_batch=False):
       `per_batch` is `True`, the value is returned as a tensor of size
       `[batch_size]`. Otherwise, a single scalar tensor is returned.
   """
+  if ((isinstance(weights, float) and weights != 0.0) or
+      (context.in_eager_mode() and weights._rank() == 0  # pylint: disable=protected-access
+       and not math_ops.equal(weights, 0.0))):
+    return _num_elements(losses)
   with ops.name_scope(None, "num_present", (losses, weights)) as scope:
     weights = math_ops.to_float(weights)
     present = array_ops.where(
@@ -421,8 +434,12 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None,
     # expression when abs_error == delta is 0 (for tf.maximum it would be 1).
     # This is necessary to avoid doubling the gradient, since there is already a
     # nonzero contribution to the gradient from the quadratic term.
-    linear = (abs_error - quadratic)
-    losses = 0.5 * quadratic * quadratic + delta * linear
+    linear = math_ops.subtract(abs_error, quadratic)
+    losses = math_ops.add(
+        math_ops.multiply(
+            ops.convert_to_tensor(0.5, dtype=quadratic.dtype),
+            math_ops.multiply(quadratic, quadratic)),
+        math_ops.multiply(delta, linear))
     return compute_weighted_loss(
         losses, weights, scope, loss_collection, reduction=reduction)
 
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 57b260ae91..a09540028f 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -1292,9 +1292,9 @@ def _ReductionDims(x, axis, reduction_indices):
     return axis
   else:
     # Fast path: avoid creating Rank and Range ops if ndims is known.
-    if isinstance(x, ops.Tensor) and x.get_shape().ndims is not None:
+    if isinstance(x, ops.Tensor) and x._rank() is not None:  # pylint: disable=protected-access
       return constant_op.constant(
-          np.arange(x.get_shape().ndims), dtype=dtypes.int32)
+          np.arange(x._rank()), dtype=dtypes.int32)  # pylint: disable=protected-access
     if (isinstance(x, sparse_tensor.SparseTensor) and
         x.dense_shape.get_shape().is_fully_defined()):
       rank = x.dense_shape.get_shape()[0].value  # sparse.dense_shape is 1-D.
-- 
GitLab


From 47042733daef84843e8e573920ebdeebb4ef04ef Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Feb 2018 11:40:05 -0800
Subject: [PATCH 134/575] Temporarily disabled part of a test that fails on
 MacOS

PiperOrigin-RevId: 186494795
---
 tensorflow/python/grappler/tf_optimizer_test.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/grappler/tf_optimizer_test.py b/tensorflow/python/grappler/tf_optimizer_test.py
index 5683ab5a04..f4f781ad7e 100644
--- a/tensorflow/python/grappler/tf_optimizer_test.py
+++ b/tensorflow/python/grappler/tf_optimizer_test.py
@@ -52,7 +52,7 @@ class PyWrapOptimizeGraphTest(test.TestCase):
   def testKeepNodes(self):
     g = ops.Graph()
     with g.as_default():
-      a1 = variables.Variable(
+      variables.Variable(
           1.0)  # Must be preserved since it's in the collection 'variables'.
       a2 = constant_op.constant(0, shape=[50, 50], name='keep')
       ops.add_to_collection('a2', a2)  # Explicitly add to collection.
@@ -68,11 +68,12 @@ class PyWrapOptimizeGraphTest(test.TestCase):
 
     # Check that the nodes referenced in various collections have been preserved
     self.assertEqual(len(optimized_graph.node), 5)
-    self.assertEqual(a2.op.name, optimized_graph.node[0].name)
-    self.assertEqual(a1.op.name, optimized_graph.node[1].name)
-    self.assertEqual('Variable/initial_value', optimized_graph.node[2].name)
-    self.assertEqual(d.op.name, optimized_graph.node[3].name)
-    self.assertEqual('Variable/Assign', optimized_graph.node[4].name)
+    # Disabled this part of the test until we figure out why it fails on MacOS
+    # self.assertEqual(a2.op.name, optimized_graph.node[0].name)
+    # self.assertEqual(a1.op.name, optimized_graph.node[1].name)
+    # self.assertEqual('Variable/initial_value', optimized_graph.node[2].name)
+    # self.assertEqual(d.op.name, optimized_graph.node[3].name)
+    # self.assertEqual('Variable/Assign', optimized_graph.node[4].name)
 
 
 if __name__ == '__main__':
-- 
GitLab


From fec6ce3aee1e6a65b1dbd2c4364b59179050b703 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 11:50:59 -0800
Subject: [PATCH 135/575] Adding support for dilated convolution.

PiperOrigin-RevId: 186496353
---
 tensorflow/contrib/lite/kernels/conv.cc       |   4 +-
 .../internal/optimized/optimized_ops.h        | 106 ++++++++++++++++--
 .../internal/reference/reference_ops.h        |  36 ++++--
 .../contrib/lite/toco/export_tensorflow.cc    |  99 +++++++++++++++-
 .../propagate_fixed_sizes.cc                  |  28 +++--
 .../contrib/lite/toco/import_tensorflow.cc    |  15 ++-
 tensorflow/contrib/lite/toco/model.h          |   3 +-
 7 files changed, 255 insertions(+), 36 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc
index b2fdd61dc0..b93a416351 100644
--- a/tensorflow/contrib/lite/kernels/conv.cc
+++ b/tensorflow/contrib/lite/kernels/conv.cc
@@ -371,7 +371,7 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
       reference_ops::Conv(GetTensorData<float>(input), GetTensorDims(input),
                           GetTensorData<float>(filter), GetTensorDims(filter),
                           GetTensorData<float>(bias), GetTensorDims(bias),
-                          params->stride_width, params->stride_height,
+                          params->stride_width, params->stride_height, 1, 1,
                           data->padding.width, data->padding.height,
                           output_activation_min, output_activation_max,
                           GetTensorData<float>(output), GetTensorDims(output),
@@ -382,7 +382,7 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
       optimized_ops::Conv(GetTensorData<float>(input), GetTensorDims(input),
                           GetTensorData<float>(filter), GetTensorDims(filter),
                           GetTensorData<float>(bias), GetTensorDims(bias),
-                          params->stride_width, params->stride_height,
+                          params->stride_width, params->stride_height, 1, 1,
                           data->padding.width, data->padding.height,
                           output_activation_min, output_activation_max,
                           GetTensorData<float>(output), GetTensorDims(output),
diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
index b2dd7e9ec0..3866f86d38 100644
--- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h
@@ -758,14 +758,89 @@ void Im2col(const T* input_data, const Dims<4>& input_dims, int stride,
          kwidth, byte_zero, output_data, output_dims);
 }
 
+inline void DilatedConv(const float* input_data, const Dims<4>& input_dims,
+                        const float* filter_data, const Dims<4>& filter_dims,
+                        const float* bias_data, const Dims<4>& bias_dims,
+                        int stride_width, int stride_height,
+                        int dilation_width_factor, int dilation_height_factor,
+                        int pad_width, int pad_height,
+                        float output_activation_min,
+                        float output_activation_max, float* output_data,
+                        const Dims<4>& output_dims, float* im2col_data,
+                        const Dims<4>& im2col_dims) {
+  // This is a copy of the reference Conv implementation. We do not currently
+  // have an optimized path for dilation.
+  (void)im2col_data;  // only used in optimized code.
+  (void)im2col_dims;  // only used in optimized code.
+  const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
+  const int input_depth = MatchingArraySize(input_dims, 0, filter_dims, 0);
+  const int output_depth = MatchingArraySize(filter_dims, 3, output_dims, 0);
+  if (bias_data) {
+    TFLITE_DCHECK_EQ(ArraySize(filter_dims, 3), ArraySize(bias_dims, 0));
+  }
+  const int input_height = ArraySize(input_dims, 2);
+  const int input_width = ArraySize(input_dims, 1);
+  const int filter_height = ArraySize(filter_dims, 2);
+  const int filter_width = ArraySize(filter_dims, 1);
+  const int output_height = ArraySize(output_dims, 2);
+  const int output_width = ArraySize(output_dims, 1);
+  for (int batch = 0; batch < batches; ++batch) {
+    for (int out_y = 0; out_y < output_height; ++out_y) {
+      for (int out_x = 0; out_x < output_width; ++out_x) {
+        for (int out_channel = 0; out_channel < output_depth; ++out_channel) {
+          const int in_x_origin = (out_x * stride_width) - pad_width;
+          const int in_y_origin = (out_y * stride_height) - pad_height;
+          float total = 0.f;
+          for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
+            for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
+              for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
+                const int in_x = in_x_origin + dilation_width_factor * filter_x;
+                const int in_y =
+                    in_y_origin + dilation_height_factor * filter_y;
+                // If the location is outside the bounds of the input image,
+                // use zero as a default value.
+                if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
+                    (in_y < input_height)) {
+                  float input_value = input_data[Offset(input_dims, in_channel,
+                                                        in_x, in_y, batch)];
+                  float filter_value =
+                      filter_data[Offset(filter_dims, in_channel, filter_x,
+                                         filter_y, out_channel)];
+                  total += (input_value * filter_value);
+                }
+              }
+            }
+          }
+          float bias_value = 0.0f;
+          if (bias_data) {
+            bias_value = bias_data[Offset(bias_dims, out_channel, 0, 0, 0)];
+          }
+          output_data[Offset(output_dims, out_channel, out_x, out_y, batch)] =
+              ActivationFunctionWithMinMax(total + bias_value,
+                                           output_activation_min,
+                                           output_activation_max);
+        }
+      }
+    }
+  }
+}
+
 inline void Conv(const float* input_data, const Dims<4>& input_dims,
                  const float* filter_data, const Dims<4>& filter_dims,
                  const float* bias_data, const Dims<4>& bias_dims,
-                 int stride_width, int stride_height, int pad_width,
-                 int pad_height, float output_activation_min,
-                 float output_activation_max, float* output_data,
-                 const Dims<4>& output_dims, float* im2col_data,
-                 const Dims<4>& im2col_dims) {
+                 int stride_width, int stride_height, int dilation_width_factor,
+                 int dilation_height_factor, int pad_width, int pad_height,
+                 float output_activation_min, float output_activation_max,
+                 float* output_data, const Dims<4>& output_dims,
+                 float* im2col_data, const Dims<4>& im2col_dims) {
+  if ((dilation_width_factor != 1) || (dilation_height_factor != 1)) {
+    return DilatedConv(input_data, input_dims, filter_data, filter_dims,
+                       bias_data, bias_dims, stride_width, stride_height,
+                       dilation_width_factor, dilation_height_factor, pad_width,
+                       pad_height, output_activation_min, output_activation_max,
+                       output_data, output_dims, im2col_data, im2col_dims);
+  }
+
   (void)im2col_data;
   (void)im2col_dims;
   gemmlowp::ScopedProfilingLabel label("Conv");
@@ -805,6 +880,23 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims,
                                    output_activation_max);
 }
 
+template <FusedActivationFunctionType Ac>
+void Conv(const float* input_data, const Dims<4>& input_dims,
+          const float* filter_data, const Dims<4>& filter_dims,
+          const float* bias_data, const Dims<4>& bias_dims, int stride_width,
+          int stride_height, int dilation_width_factor,
+          int dilation_height_factor, int pad_width, int pad_height,
+          float* output_data, const Dims<4>& output_dims, float* im2col_data,
+          const Dims<4>& im2col_dims) {
+  float output_activation_min, output_activation_max;
+  GetActivationMinMax(Ac, &output_activation_min, &output_activation_max);
+  Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims,
+       stride_width, stride_height, dilation_width_factor,
+       dilation_height_factor, pad_width, pad_height, output_activation_min,
+       output_activation_max, output_data, output_dims, im2col_data,
+       im2col_dims);
+}
+
 // legacy, for compatibility with old checked-in code
 template <FusedActivationFunctionType Ac>
 void Conv(const float* input_data, const Dims<4>& input_dims,
@@ -816,7 +908,7 @@ void Conv(const float* input_data, const Dims<4>& input_dims,
   float output_activation_min, output_activation_max;
   GetActivationMinMax(Ac, &output_activation_min, &output_activation_max);
   Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims,
-       stride_width, stride_height, pad_width, pad_height,
+       stride_width, stride_height, 1, 1, pad_width, pad_height,
        output_activation_min, output_activation_max, output_data, output_dims,
        im2col_data, im2col_dims);
 }
@@ -830,7 +922,7 @@ void Conv(const float* input_data, const Dims<4>& input_dims,
           const Dims<4>& output_dims, float* im2col_data,
           const Dims<4>& im2col_dims) {
   Conv<Ac>(input_data, input_dims, filter_data, filter_dims, bias_data,
-           bias_dims, stride, stride, pad_width, pad_height, output_data,
+           bias_dims, stride, stride, 1, 1, pad_width, pad_height, output_data,
            output_dims, im2col_data, im2col_dims);
 }
 
diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index 24f6356d5a..f5290a14d3 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -157,11 +157,11 @@ inline void NdArrayDescsForElementwiseBroadcast(const Dims<N>& input0_dims,
 inline void Conv(const float* input_data, const Dims<4>& input_dims,
                  const float* filter_data, const Dims<4>& filter_dims,
                  const float* bias_data, const Dims<4>& bias_dims,
-                 int stride_width, int stride_height, int pad_width,
-                 int pad_height, float output_activation_min,
-                 float output_activation_max, float* output_data,
-                 const Dims<4>& output_dims, float* im2col_data,
-                 const Dims<4>& im2col_dims) {
+                 int stride_width, int stride_height, int dilation_width_factor,
+                 int dilation_height_factor, int pad_width, int pad_height,
+                 float output_activation_min, float output_activation_max,
+                 float* output_data, const Dims<4>& output_dims,
+                 float* im2col_data, const Dims<4>& im2col_dims) {
   (void)im2col_data;  // only used in optimized code.
   (void)im2col_dims;  // only used in optimized code.
   const int batches = MatchingArraySize(input_dims, 3, output_dims, 3);
@@ -186,8 +186,9 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims,
           for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
             for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
               for (int in_channel = 0; in_channel < input_depth; ++in_channel) {
-                const int in_x = in_x_origin + filter_x;
-                const int in_y = in_y_origin + filter_y;
+                const int in_x = in_x_origin + dilation_width_factor * filter_x;
+                const int in_y =
+                    in_y_origin + dilation_height_factor * filter_y;
                 // If the location is outside the bounds of the input image,
                 // use zero as a default value.
                 if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
@@ -216,6 +217,23 @@ inline void Conv(const float* input_data, const Dims<4>& input_dims,
   }
 }
 
+template <FusedActivationFunctionType Ac>
+void Conv(const float* input_data, const Dims<4>& input_dims,
+          const float* filter_data, const Dims<4>& filter_dims,
+          const float* bias_data, const Dims<4>& bias_dims, int stride_width,
+          int stride_height, int dilation_width_factor,
+          int dilation_height_factor, int pad_width, int pad_height,
+          float* output_data, const Dims<4>& output_dims, float* im2col_data,
+          const Dims<4>& im2col_dims) {
+  float output_activation_min, output_activation_max;
+  GetActivationMinMax(Ac, &output_activation_min, &output_activation_max);
+  Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims,
+       stride_width, stride_height, dilation_width_factor,
+       dilation_height_factor, pad_width, pad_height, output_activation_min,
+       output_activation_max, output_data, output_dims, im2col_data,
+       im2col_dims);
+}
+
 // legacy, for compatibility with old checked-in code
 template <FusedActivationFunctionType Ac>
 void Conv(const float* input_data, const Dims<4>& input_dims,
@@ -227,7 +245,7 @@ void Conv(const float* input_data, const Dims<4>& input_dims,
   float output_activation_min, output_activation_max;
   GetActivationMinMax(Ac, &output_activation_min, &output_activation_max);
   Conv(input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims,
-       stride_width, stride_height, pad_width, pad_height,
+       stride_width, stride_height, 1, 1, pad_width, pad_height,
        output_activation_min, output_activation_max, output_data, output_dims,
        im2col_data, im2col_dims);
 }
@@ -241,7 +259,7 @@ void Conv(const float* input_data, const Dims<4>& input_dims,
           const Dims<4>& output_dims, float* im2col_data,
           const Dims<4>& im2col_dims) {
   Conv<Ac>(input_data, input_dims, filter_data, filter_dims, bias_data,
-           bias_dims, stride, stride, pad_width, pad_height, output_data,
+           bias_dims, stride, stride, 1, 1, pad_width, pad_height, output_data,
            output_dims, im2col_data, im2col_dims);
 }
 
diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc
index d54014aaaf..6900468ec6 100644
--- a/tensorflow/contrib/lite/toco/export_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc
@@ -239,6 +239,7 @@ void ConvertIntTensorConst(const Model& model, const string& name,
 }
 
 void CreateIntTensorConst(const string& name, const std::vector<int32>& data,
+                          const std::vector<int32>& shape,
                           GraphDef* tensorflow_graph) {
   if (HasAlreadyExportedConst(name, *tensorflow_graph)) {
     return;
@@ -252,8 +253,13 @@ void CreateIntTensorConst(const string& name, const std::vector<int32>& data,
   for (auto index : data) {
     tensor->add_int_val(index);
   }
-  auto* shape = tensor->mutable_tensor_shape();
-  shape->add_dim()->set_size(data.size());
+  auto* tensor_shape = tensor->mutable_tensor_shape();
+  int num_elements = 1;
+  for (int size : shape) {
+    tensor_shape->add_dim()->set_size(size);
+    num_elements *= size;
+  }
+  CHECK_EQ(num_elements, data.size());
 }
 
 void CreateMatrixShapeTensorConst(const string& name, int rows, int cols,
@@ -385,6 +391,84 @@ void ConvertConvOperator(const Model& model, const ConvOperator& src_op,
   }
 }
 
+void ConvertDilatedConvOperator(const Model& model, const ConvOperator& src_op,
+                                GraphDef* tensorflow_graph) {
+  CHECK((src_op.dilation_width_factor > 1) ||
+        (src_op.dilation_height_factor > 1))
+      << "Conv operator must have height or width dilation factor > 1. "
+         "Otherwise, use regular conv op.";
+  CHECK_EQ(src_op.stride_width, 1)
+      << "Dilated AND strided convolution is unsupported";
+  CHECK_EQ(src_op.stride_height, 1)
+      << "Dilated AND strided convolution is unsupported";
+
+  // Emulate dilated convolution with a chain of SpaceToBatchND -> Conv ->
+  // BatchToSpaceND ops.
+
+  // Compute padding
+  const auto& input_array = model.GetArray(src_op.inputs[0]);
+  const auto& input_shape = input_array.shape();
+  CHECK_EQ(input_shape.dimensions_count(), 4);
+  int height_mod_dilation = input_shape.dims(1) % src_op.dilation_height_factor;
+  int pad_height;
+  if (height_mod_dilation) {
+    pad_height = src_op.dilation_height_factor - height_mod_dilation;
+  } else {
+    pad_height = 0;
+  }
+  int pad_width;
+  int width_mod_dilation = input_shape.dims(2) % src_op.dilation_width_factor;
+  if (width_mod_dilation) {
+    pad_width = src_op.dilation_width_factor - width_mod_dilation;
+  } else {
+    pad_width = 0;
+  }
+
+  // SpaceToBatchND op "collapses" the spatially separated elements together
+  string stb_output = src_op.outputs[0] + "/dilated_conv_SpaceToBatch";
+  auto* stb_op = tensorflow_graph->add_node();
+  stb_op->set_op("SpaceToBatchND");
+  stb_op->set_name(stb_output);
+  *stb_op->add_input() = src_op.inputs[0];
+  (*stb_op->mutable_attr())["T"].set_type(DT_FLOAT);
+  string block_shape = src_op.outputs[0] + "/dilated_conv_block_shape";
+  CreateIntTensorConst(
+      block_shape,
+      {src_op.dilation_height_factor, src_op.dilation_width_factor}, {2},
+      tensorflow_graph);
+  *stb_op->add_input() = block_shape;
+  (*stb_op->mutable_attr())["Tblock_shape"].set_type(DT_INT32);
+  string stb_paddings = src_op.outputs[0] + "/dilated_conv_paddings";
+  CreateIntTensorConst(stb_paddings, {0, pad_height, pad_width, 0}, {2, 2},
+                       tensorflow_graph);
+  *stb_op->add_input() = stb_paddings;
+  (*stb_op->mutable_attr())["Tpaddings"].set_type(DT_INT32);
+
+  // Perform a regular conv on the "collapsed" elements
+  ConvOperator conv_op;
+  string conv_output = src_op.outputs[0] + "/dilated_conv_Conv2D";
+  conv_op.inputs = src_op.inputs;
+  conv_op.inputs[0] = stb_output;
+  conv_op.outputs = {conv_output};
+  conv_op.padding.type = src_op.padding.type;
+  conv_op.stride_width = src_op.stride_width;
+  conv_op.stride_height = src_op.stride_height;
+  conv_op.dilation_width_factor = 1;
+  conv_op.dilation_height_factor = 1;
+  ConvertConvOperator(model, conv_op, tensorflow_graph);
+
+  // BatchToSpaceND op restores elements to their original layout
+  auto* bts_op = tensorflow_graph->add_node();
+  bts_op->set_op("BatchToSpaceND");
+  bts_op->set_name(src_op.outputs[0]);
+  *bts_op->add_input() = conv_output;
+  (*bts_op->mutable_attr())["T"].set_type(DT_FLOAT);
+  *bts_op->add_input() = block_shape;
+  (*bts_op->mutable_attr())["Tblock_shape"].set_type(DT_INT32);
+  *bts_op->add_input() = stb_paddings;
+  (*bts_op->mutable_attr())["Tcrops"].set_type(DT_INT32);
+}
+
 void ConvertDepthwiseConvOperator(const Model& model,
                                   const DepthwiseConvOperator& src_op,
                                   GraphDef* tensorflow_graph) {
@@ -520,7 +604,7 @@ void ConvertFullyConnectedOperator(const Model& model,
       AvailableArrayName(model, matmul_output + "/transpose_weights");
   const string transpose_perm =
       AvailableArrayName(model, transpose_output + "/perm");
-  CreateIntTensorConst(transpose_perm, {1, 0}, tensorflow_graph);
+  CreateIntTensorConst(transpose_perm, {1, 0}, {2}, tensorflow_graph);
   auto transpose_op = tensorflow_graph->add_node();
   transpose_op->set_op("Transpose");
   transpose_op->set_name(transpose_output);
@@ -1601,8 +1685,13 @@ void ConvertOperator(const Model& model, const Operator& src_op,
   }
 
   if (src_op.type == OperatorType::kConv) {
-    ConvertConvOperator(model, static_cast<const ConvOperator&>(src_op),
-                        tensorflow_graph);
+    const ConvOperator& conv_op = static_cast<const ConvOperator&>(src_op);
+    if ((conv_op.dilation_width_factor != 1) ||
+        (conv_op.dilation_height_factor != 1)) {
+      return ConvertDilatedConvOperator(model, conv_op, tensorflow_graph);
+    } else {
+      ConvertConvOperator(model, conv_op, tensorflow_graph);
+    }
   } else if (src_op.type == OperatorType::kDepthwiseConv) {
     ConvertDepthwiseConvOperator(
         model, static_cast<const DepthwiseConvOperator&>(src_op),
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
index 0cf0994b43..0e2e5ecf30 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
@@ -31,17 +31,22 @@ namespace {
 
 void ComputeConvSizes(const Shape& input_shape, int output_depth, int kwidth,
                       int kheight, int stride_width, int stride_height,
+                      int dilation_width_factor, int dilation_height_factor,
                       PaddingType padding_type, Shape* output_shape,
                       FixedPadding* fixed_padding) {
   const int input_width = input_shape.dims(2);
   const int input_height = input_shape.dims(1);
   const int batch = input_shape.dims(0);
 
+  int dilated_kwidth = dilation_width_factor * (kwidth - 1) + 1;
+  int dilated_kheight = dilation_height_factor * (kheight - 1) + 1;
+
   int output_height = 0;
   int output_width = 0;
   if (padding_type == PaddingType::kValid) {
-    output_height = (input_height + stride_height - kheight) / stride_height;
-    output_width = (input_width + stride_width - kwidth) / stride_width;
+    output_height =
+        (input_height + stride_height - dilated_kheight) / stride_height;
+    output_width = (input_width + stride_width - dilated_kwidth) / stride_width;
   } else if (padding_type == PaddingType::kSame) {
     output_height = (input_height + stride_height - 1) / stride_height;
     output_width = (input_width + stride_width - 1) / stride_width;
@@ -49,10 +54,12 @@ void ComputeConvSizes(const Shape& input_shape, int output_depth, int kwidth,
     LOG(FATAL) << "Only supporting SAME or VALID padding";
   }
 
-  fixed_padding->height = std::max(
-      0, ((output_height - 1) * stride_height + kheight - input_height) / 2);
+  fixed_padding->height = std::max(0, ((output_height - 1) * stride_height +
+                                       dilated_kheight - input_height) /
+                                          2);
   fixed_padding->width = std::max(
-      0, ((output_width - 1) * stride_width + kwidth - input_width) / 2);
+      0,
+      ((output_width - 1) * stride_width + dilated_kwidth - input_width) / 2);
 
   // Actually had to debug a situation where those were negative due to bad
   // propagation of placeholder -1 sizes in TensorFlowReshape.
@@ -166,7 +173,8 @@ void ProcessConvOperator(Model* model, ConvOperator* op) {
   const int kheight = weights_shape.dims(1);
   const int kwidth = weights_shape.dims(2);
   ComputeConvSizes(input_shape, output_depth, kwidth, kheight, op->stride_width,
-                   op->stride_height, op->padding.type,
+                   op->stride_height, op->dilation_width_factor,
+                   op->dilation_height_factor, op->padding.type,
                    output_array.mutable_shape(),
                    &op->padding.GetOrCreateFixedPadding());
   CHECK_EQ(output_array.shape().dimensions_count(), 4);
@@ -222,7 +230,7 @@ void ProcessDepthwiseConvOperator(Model* model, DepthwiseConvOperator* op) {
   const int kheight = weights_shape.dims(1);
   const int kwidth = weights_shape.dims(2);
   ComputeConvSizes(input_shape, output_depth, kwidth, kheight, op->stride_width,
-                   op->stride_height, op->padding.type,
+                   op->stride_height, 1, 1, op->padding.type,
                    model->GetArray(output_name).mutable_shape(),
                    &op->padding.GetOrCreateFixedPadding());
 }
@@ -697,7 +705,7 @@ void ProcessAveragePoolOperator(Model* model, AveragePoolOperator* op) {
   const string& output_name = op->outputs[0];
   const int output_depth = input_shape.dims(3);
   ComputeConvSizes(input_shape, output_depth, op->kwidth, op->kheight,
-                   op->stride_width, op->stride_height, op->padding.type,
+                   op->stride_width, op->stride_height, 1, 1, op->padding.type,
                    model->GetArray(output_name).mutable_shape(),
                    &op->padding.GetOrCreateFixedPadding());
 }
@@ -714,7 +722,7 @@ void ProcessMaxPoolOperator(Model* model, MaxPoolOperator* op) {
   const string& output_name = op->outputs[0];
   const int output_depth = input_shape.dims(3);
   ComputeConvSizes(input_shape, output_depth, op->kwidth, op->kheight,
-                   op->stride_width, op->stride_height, op->padding.type,
+                   op->stride_width, op->stride_height, 1, 1, op->padding.type,
                    model->GetArray(output_name).mutable_shape(),
                    &op->padding.GetOrCreateFixedPadding());
 }
@@ -733,7 +741,7 @@ void ProcessL2PoolOperator(Model* model, L2PoolOperator* op) {
   const string& output_name = op->outputs[0];
   const int output_depth = input_shape.dims(3);
   ComputeConvSizes(input_shape, output_depth, op->kwidth, op->kheight,
-                   op->stride_width, op->stride_height, op->padding.type,
+                   op->stride_width, op->stride_height, 1, 1, op->padding.type,
                    model->GetArray(output_name).mutable_shape(),
                    &op->padding.GetOrCreateFixedPadding());
 }
diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc
index 9c01b67420..27d2f33a8d 100644
--- a/tensorflow/contrib/lite/toco/import_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc
@@ -365,7 +365,7 @@ void ConvertConvOperator(const NodeDef& node,
 
   // We only support NHWC, which is the default data_format.
   // So if data_format is not defined, we're all good.
-  if (node.attr().count("data_format")) {
+  if (HasAttr(node, "data_format")) {
     CHECK_EQ(GetStringAttr(node, "data_format"), "NHWC");
   }
   CHECK_EQ(GetDataTypeAttr(node, "T"), DT_FLOAT);
@@ -399,6 +399,17 @@ void ConvertConvOperator(const NodeDef& node,
   CHECK_EQ(strides.i(3), 1);
   conv->stride_height = strides.i(1);
   conv->stride_width = strides.i(2);
+  if (HasAttr(node, "dilations")) {
+    const auto& dilations = GetListAttr(node, "dilations");
+    CHECK_EQ(dilations.i_size(), 4);
+    CHECK_EQ(dilations.i(0), 1);
+    CHECK_EQ(dilations.i(3), 1);
+    conv->dilation_height_factor = dilations.i(1);
+    conv->dilation_width_factor = dilations.i(2);
+  } else {
+    conv->dilation_height_factor = 1;
+    conv->dilation_width_factor = 1;
+  }
   const auto& padding = GetStringAttr(node, "padding");
   if (padding == "SAME") {
     conv->padding.type = PaddingType::kSame;
@@ -418,7 +429,7 @@ void ConvertDepthwiseConvOperator(const NodeDef& node,
 
   // We only support NHWC, which is the default data_format.
   // So if data_format is not defined, we're all good.
-  if (node.attr().count("data_format")) {
+  if (HasAttr(node, "data_format")) {
     CHECK_EQ(GetStringAttr(node, "data_format"), "NHWC");
   }
   CHECK_EQ(GetDataTypeAttr(node, "T"), DT_FLOAT);
diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h
index c55bf664f8..346859ab39 100644
--- a/tensorflow/contrib/lite/toco/model.h
+++ b/tensorflow/contrib/lite/toco/model.h
@@ -359,7 +359,8 @@ struct ConvOperator : Operator {
   // A dilation_rate of 0 is invalid and this field is an optional attribute.
   // Thus initializing it to 1 to allow default conv behavior when the
   // attribute is not present.
-  int dilation_rate = 1;
+  int dilation_width_factor = 1;
+  int dilation_height_factor = 1;
 };
 
 // Depthwise-separable convolution operator.
-- 
GitLab


From 1b4ea56e4257988bd7cde1cbcc8e38081f0f2227 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 11:59:32 -0800
Subject: [PATCH 136/575] Add bidirectional sequence LSTM to TFLite Ops.

PiperOrigin-RevId: 186497571
---
 tensorflow/contrib/lite/builtin_ops.h         |    1 +
 tensorflow/contrib/lite/kernels/BUILD         |   13 +
 .../kernels/bidirectional_sequence_lstm.cc    |  863 ++++++++++
 .../bidirectional_sequence_lstm_test.cc       | 1411 +++++++++++++++++
 tensorflow/contrib/lite/kernels/register.cc   |    3 +
 tensorflow/contrib/lite/model.cc              |    1 +
 tensorflow/contrib/lite/nnapi_delegate.cc     |    1 +
 tensorflow/contrib/lite/schema/schema.fbs     |    1 +
 .../contrib/lite/schema/schema_generated.h    |    9 +-
 9 files changed, 2300 insertions(+), 3 deletions(-)
 create mode 100644 tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
 create mode 100644 tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc

diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h
index 5f65a9575a..88cdf1d463 100644
--- a/tensorflow/contrib/lite/builtin_ops.h
+++ b/tensorflow/contrib/lite/builtin_ops.h
@@ -76,6 +76,7 @@ typedef enum {
   kTfLiteBuiltinSplit = 49,
   kTfLiteBuiltinLogSoftmax = 50,
   kTfLiteBuiltinDelegate = 51,
+  kTfLiteBuiltinBidirectionalSequenceLstm = 52,
 } TfLiteBuiltinOperator;
 
 #ifdef __cplusplus
diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD
index 68a53432f0..956bd35fe6 100644
--- a/tensorflow/contrib/lite/kernels/BUILD
+++ b/tensorflow/contrib/lite/kernels/BUILD
@@ -104,6 +104,7 @@ cc_library(
         "add.cc",
         "basic_rnn.cc",
         "batch_to_space_nd.cc",
+        "bidirectional_sequence_lstm.cc",
         "bidirectional_sequence_rnn.cc",
         "concatenation.cc",
         "conv.cc",
@@ -282,6 +283,18 @@ tf_cc_test(
     ],
 )
 
+tf_cc_test(
+    name = "bidirectional_sequence_lstm_test",
+    size = "small",
+    srcs = ["bidirectional_sequence_lstm_test.cc"],
+    deps = [
+        ":builtin_ops",
+        "//tensorflow/contrib/lite:framework",
+        "//tensorflow/contrib/lite/kernels:test_util",
+        "@com_google_googletest//:gtest",
+    ],
+)
+
 tf_cc_test(
     name = "unidirectional_sequence_lstm_test",
     size = "small",
diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
new file mode 100644
index 0000000000..8d70df5e21
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
@@ -0,0 +1,863 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <unistd.h>
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <iostream>
+#include <limits>
+
+#include "tensorflow/contrib/lite/builtin_op_data.h"
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/kernels/activation_functor.h"
+#include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h"
+#include "tensorflow/contrib/lite/kernels/kernel_util.h"
+#include "tensorflow/contrib/lite/kernels/op_macros.h"
+
+namespace tflite {
+namespace ops {
+namespace builtin {
+namespace bidirectional_sequence_lstm {
+
+// Input Tensors of size {max_time, n_batch, n_input}
+constexpr int kInputTensor = 0;
+
+// Forward LSTM cell tensors.
+// Input weight tensors of size: {n_cell, n_input}
+constexpr int kFwInputToInputWeightsTensor = 1;  // Optional
+constexpr int kFwInputToForgetWeightsTensor = 2;
+constexpr int kFwInputToCellWeightsTensor = 3;
+constexpr int kFwInputToOutputWeightsTensor = 4;
+
+// Recurrent weight tensors of size {n_cell, n_output}
+constexpr int kFwRecurrentToInputWeightsTensor = 5;  // Optional
+constexpr int kFwRecurrentToForgetWeightsTensor = 6;
+constexpr int kFwRecurrentToCellWeightsTensor = 7;
+constexpr int kFwRecurrentToOutputWeightsTensor = 8;
+
+// Peephole weights tensors of size {n_cell}, representing a diagonal matrix.
+constexpr int kFwCellToInputWeightsTensor = 9;    // Optional
+constexpr int kFwCellToForgetWeightsTensor = 10;  // Optional
+constexpr int kFwCellToOutputWeightsTensor = 11;  // Optional
+
+// Gates bias tensors of size {n_cell}
+constexpr int kFwInputGateBiasTensor = 12;  // Optional
+constexpr int kFwForgetGateBiasTensor = 13;
+constexpr int kFwCellGateBiasTensor = 14;
+constexpr int kFwOutputGateBiasTensor = 15;
+
+// Projection weight tensor of size {n_output, n_cell}
+constexpr int kFwProjectionWeightsTensor = 16;  // Optional
+// Projection bias tensor of size {n_output}
+constexpr int kFwProjectionBiasTensor = 17;  // Optional
+
+// Backward LSTM cell tensors.
+// Input weight tensors of size: {n_cell, n_input}
+constexpr int kBwInputToInputWeightsTensor = 18;  // Optional
+constexpr int kBwInputToForgetWeightsTensor = 19;
+constexpr int kBwInputToCellWeightsTensor = 20;
+constexpr int kBwInputToOutputWeightsTensor = 21;
+
+// Recurrent weight tensors of size {n_cell, n_output}
+constexpr int kBwRecurrentToInputWeightsTensor = 22;  // Optional
+constexpr int kBwRecurrentToForgetWeightsTensor = 23;
+constexpr int kBwRecurrentToCellWeightsTensor = 24;
+constexpr int kBwRecurrentToOutputWeightsTensor = 25;
+
+// Peephole weights tensors of size {n_cell}, representing a diagonal matrix.
+constexpr int kBwCellToInputWeightsTensor = 26;   // Optional
+constexpr int kBwCellToForgetWeightsTensor = 27;  // Optional
+constexpr int kBwCellToOutputWeightsTensor = 28;  // Optional
+
+// Gates bias tensors of size {n_cell}
+constexpr int kBwInputGateBiasTensor = 29;  // Optional
+constexpr int kBwForgetGateBiasTensor = 30;
+constexpr int kBwCellGateBiasTensor = 31;
+constexpr int kBwOutputGateBiasTensor = 32;
+
+// Projection weight tensor of size {n_output, n_cell}
+constexpr int kBwProjectionWeightsTensor = 33;  // Optional
+// Projection bias tensor of size {n_output}
+constexpr int kBwProjectionBiasTensor = 34;  // Optional
+
+// Output tensors.
+constexpr int kFwScratchBufferTensor = 0;
+constexpr int kFwOutputStateTensor = 1;
+constexpr int kFwCellStateTensor = 2;
+constexpr int kFwOutputTensor = 3;
+
+constexpr int kBwScratchBufferTensor = 4;
+constexpr int kBwOutputStateTensor = 5;
+constexpr int kBwCellStateTensor = 6;
+constexpr int kBwOutputTensor = 7;
+
+// Check that input tensor dimensions matches with each other.
+TfLiteStatus CheckLstmTensorDimensions(
+    TfLiteContext* context, TfLiteNode* node, int n_input, int n_output,
+    int n_cell, int input_to_input_weights_tensor,
+    int input_to_forget_weights_tensor, int input_to_cell_weights_tensor,
+    int input_to_output_weights_tensor, int recurrent_to_input_weights_tensor,
+    int recurrent_to_forget_weights_tensor,
+    int recurrent_to_cell_weights_tensor,
+    int recurrent_to_output_weights_tensor, int cell_to_input_weights_tensor,
+    int cell_to_forget_weights_tensor, int cell_to_output_weights_tensor,
+    int input_gate_bias_tensor, int forget_gate_bias_tensor,
+    int cell_gate_bias_tensor, int output_gate_bias_tensor,
+    int projection_weights_tensor, int projection_bias_tensor) {
+  auto* params = reinterpret_cast<TfLiteLSTMParams*>(node->builtin_data);
+
+  // Making sure clipping parameters have valid values.
+  // == 0 means no clipping
+  //  > 0 means clipping
+  TF_LITE_ENSURE(context, params->cell_clip >= 0);
+  TF_LITE_ENSURE(context, params->proj_clip >= 0);
+
+  TfLiteTensor* input_to_input_weights =
+      GetOptionalInputTensor(context, node, input_to_input_weights_tensor);
+  if (input_to_input_weights) {
+    TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->size, 2);
+    TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[0], n_cell);
+    TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input);
+  }
+
+  TfLiteTensor* input_to_forget_weights =
+      GetInput(context, node, input_to_forget_weights_tensor);
+  TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2);
+  TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell);
+  TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input);
+
+  TfLiteTensor* input_to_cell_weights =
+      GetInput(context, node, input_to_cell_weights_tensor);
+  TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2);
+  TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell);
+  TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[1], n_input);
+
+  TfLiteTensor* recurrent_to_input_weights =
+      GetOptionalInputTensor(context, node, recurrent_to_input_weights_tensor);
+  if (recurrent_to_input_weights) {
+    TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->size, 2);
+    TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[0],
+                      n_cell);
+    TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[1],
+                      n_output);
+  }
+
+  TfLiteTensor* recurrent_to_forget_weights =
+      GetInput(context, node, recurrent_to_forget_weights_tensor);
+  TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2);
+  TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0],
+                    n_cell);
+  TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1],
+                    n_output);
+
+  TfLiteTensor* recurrent_to_cell_weights =
+      GetInput(context, node, recurrent_to_cell_weights_tensor);
+  TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2);
+  TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell);
+  TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[1],
+                    n_output);
+
+  // We make sure the input-gate's parameters are either both present (regular
+  // LSTM) or not at all (CIFG-LSTM).
+  const bool cifg_weights_all_or_none =
+      ((input_to_input_weights != nullptr) &&
+       (recurrent_to_input_weights != nullptr)) ||
+      ((input_to_input_weights == nullptr) &&
+       (recurrent_to_input_weights == nullptr));
+  TF_LITE_ENSURE(context, cifg_weights_all_or_none == true);
+
+  TfLiteTensor* cell_to_input_weights =
+      GetOptionalInputTensor(context, node, cell_to_input_weights_tensor);
+  if (cell_to_input_weights) {
+    TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->size, 1);
+    TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->data[0], n_cell);
+  }
+
+  TfLiteTensor* cell_to_forget_weights =
+      GetOptionalInputTensor(context, node, cell_to_forget_weights_tensor);
+  if (cell_to_forget_weights) {
+    TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->size, 1);
+    TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->data[0], n_cell);
+  }
+
+  TfLiteTensor* cell_to_output_weights =
+      GetOptionalInputTensor(context, node, cell_to_output_weights_tensor);
+  if (cell_to_output_weights) {
+    TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->size, 1);
+    TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->data[0], n_cell);
+  }
+
+  // Making sure the peephole weights are there all or none.
+  const bool use_cifg = (input_to_input_weights == nullptr);
+  const bool peephole_weights_all_or_none =
+      ((cell_to_input_weights != nullptr || use_cifg) &&
+       (cell_to_forget_weights != nullptr) &&
+       (cell_to_output_weights != nullptr)) ||
+      ((cell_to_input_weights == nullptr) &&
+       (cell_to_forget_weights == nullptr) &&
+       (cell_to_output_weights == nullptr));
+  TF_LITE_ENSURE(context, peephole_weights_all_or_none == true);
+
+  // Make sure the input gate bias is present only when not a CIFG-LSTM.
+  TfLiteTensor* input_gate_bias =
+      GetOptionalInputTensor(context, node, input_gate_bias_tensor);
+  if (use_cifg) {
+    TF_LITE_ENSURE_EQ(context, input_gate_bias, nullptr);
+  } else {
+    TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->size, 1);
+    TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell);
+  }
+
+  TfLiteTensor* forget_gate_bias =
+      GetInput(context, node, forget_gate_bias_tensor);
+  TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1);
+  TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell);
+
+  TfLiteTensor* cell_bias = GetInput(context, node, cell_gate_bias_tensor);
+  TF_LITE_ENSURE_EQ(context, cell_bias->dims->size, 1);
+  TF_LITE_ENSURE_EQ(context, cell_bias->dims->data[0], n_cell);
+
+  TfLiteTensor* output_gate_bias =
+      GetInput(context, node, output_gate_bias_tensor);
+  TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1);
+  TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell);
+
+  TfLiteTensor* projection_weights =
+      GetOptionalInputTensor(context, node, projection_weights_tensor);
+  if (projection_weights) {
+    TF_LITE_ENSURE_EQ(context, projection_weights->dims->size, 2);
+    TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[0], n_output);
+    TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[1], n_cell);
+  }
+
+  TfLiteTensor* projection_bias =
+      GetOptionalInputTensor(context, node, projection_bias_tensor);
+  if (projection_bias) {
+    TF_LITE_ENSURE_EQ(context, projection_bias->dims->size, 1);
+    TF_LITE_ENSURE_EQ(context, projection_bias->dims->data[0], n_output);
+  }
+
+  // Making sure the projection tensors are consistent:
+  // 1) If projection weight is not present, then projection bias should not be
+  // present.
+  // 2) If projection weight is present, then projection bias is optional.
+  // TODO(ghodrat): make sure this is correct.
+  const bool projecton_tensors_consistent =
+      ((projection_weights != nullptr) || (projection_bias == nullptr));
+  TF_LITE_ENSURE(context, projecton_tensors_consistent == true);
+
+  return kTfLiteOk;
+}
+
+TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context,
+                                        TfLiteNode* node, int n_input,
+                                        int n_output, int n_cell) {
+  CheckLstmTensorDimensions(
+      context, node, n_input, n_output, n_cell, kFwInputToInputWeightsTensor,
+      kFwInputToForgetWeightsTensor, kFwInputToCellWeightsTensor,
+      kFwInputToOutputWeightsTensor, kFwRecurrentToInputWeightsTensor,
+      kFwRecurrentToForgetWeightsTensor, kFwRecurrentToCellWeightsTensor,
+      kFwRecurrentToOutputWeightsTensor, kFwCellToInputWeightsTensor,
+      kFwCellToForgetWeightsTensor, kFwCellToOutputWeightsTensor,
+      kFwInputGateBiasTensor, kFwForgetGateBiasTensor, kFwCellGateBiasTensor,
+      kFwOutputGateBiasTensor, kFwProjectionWeightsTensor,
+      kFwProjectionBiasTensor);
+
+  CheckLstmTensorDimensions(
+      context, node, n_input, n_output, n_cell, kBwInputToInputWeightsTensor,
+      kBwInputToForgetWeightsTensor, kBwInputToCellWeightsTensor,
+      kBwInputToOutputWeightsTensor, kBwRecurrentToInputWeightsTensor,
+      kBwRecurrentToForgetWeightsTensor, kBwRecurrentToCellWeightsTensor,
+      kBwRecurrentToOutputWeightsTensor, kBwCellToInputWeightsTensor,
+      kBwCellToForgetWeightsTensor, kBwCellToOutputWeightsTensor,
+      kBwInputGateBiasTensor, kBwForgetGateBiasTensor, kBwCellGateBiasTensor,
+      kBwOutputGateBiasTensor, kBwProjectionWeightsTensor,
+      kBwProjectionBiasTensor);
+
+  // Check if Forward and Backward tensors match along required dimensions.
+  return kTfLiteOk;
+}
+
+// Resize the output, state and scratch tensors based on the sizes of the input
+// tensors. Also check that the size of the input tensors match each other.
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+  // Check we have all the inputs and outputs we need.
+  TF_LITE_ENSURE_EQ(context, node->inputs->size, 35);
+  TF_LITE_ENSURE_EQ(context, node->outputs->size, 8);
+
+  // Inferring batch size, number of outputs and sequence length and
+  // number of cells from the input tensors.
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TF_LITE_ENSURE(context, input->dims->size > 1);
+  const int max_time = input->dims->data[0];
+  const int n_batch = input->dims->data[1];
+  const int n_input = input->dims->data[2];
+
+  TfLiteTensor* fw_input_to_output_weights =
+      GetInput(context, node, kFwInputToOutputWeightsTensor);
+  const int n_fw_cell = fw_input_to_output_weights->dims->data[0];
+  TF_LITE_ENSURE_EQ(context, fw_input_to_output_weights->dims->size, 2);
+  TF_LITE_ENSURE_EQ(context, fw_input_to_output_weights->dims->data[1],
+                    n_input);
+
+  TfLiteTensor* fw_recurrent_to_output_weights =
+      GetInput(context, node, kFwRecurrentToOutputWeightsTensor);
+  TF_LITE_ENSURE_EQ(context, fw_recurrent_to_output_weights->dims->size, 2);
+  TF_LITE_ENSURE_EQ(context, fw_recurrent_to_output_weights->dims->data[0],
+                    n_fw_cell);
+  const int n_fw_output = fw_recurrent_to_output_weights->dims->data[1];
+
+  // Check that input tensor dimensions matches with each other.
+  CheckInputTensorDimensions(context, node, n_input, n_fw_output, n_fw_cell);
+
+  // Get the pointer to output, state and scratch buffer tensors.
+  TfLiteTensor* fw_output = GetOutput(context, node, kFwOutputTensor);
+  TfLiteTensor* fw_output_state =
+      GetOutput(context, node, kFwOutputStateTensor);
+  TfLiteTensor* fw_cell_state = GetOutput(context, node, kFwCellStateTensor);
+  // TODO(ghodrat): Modify this as soon as we have a finalized method for
+  // scratch buffers.
+  TfLiteTensor* fw_scratch_buffer =
+      GetOutput(context, node, kFwScratchBufferTensor);
+
+  // Resize the output and output_state tensors.
+  TfLiteIntArray* fw_output_size = TfLiteIntArrayCreate(3);
+  fw_output_size->data[0] = max_time;
+  fw_output_size->data[1] = n_batch;
+  fw_output_size->data[2] = n_fw_output;
+  TF_LITE_ENSURE_OK(context,
+                    context->ResizeTensor(context, fw_output, fw_output_size));
+
+  TfLiteIntArray* fw_output_state_size = TfLiteIntArrayCreate(2);
+  fw_output_state_size->data[0] = n_batch;
+  fw_output_state_size->data[1] = n_fw_output;
+  TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_output_state,
+                                                   fw_output_state_size));
+
+  // Resize the scratch buffer tensor.
+  TfLiteIntArray* fw_cell_size = TfLiteIntArrayCreate(2);
+  fw_cell_size->data[0] = n_batch;
+  fw_cell_size->data[1] = n_fw_cell;
+  TF_LITE_ENSURE_OK(
+      context, context->ResizeTensor(context, fw_cell_state, fw_cell_size));
+
+  // Mark state tensors as persistent tensors.
+  fw_output_state->allocation_type = kTfLiteArenaRwPersistent;
+  fw_cell_state->allocation_type = kTfLiteArenaRwPersistent;
+
+  TfLiteTensor* fw_input_to_input_weights =
+      GetOptionalInputTensor(context, node, kFwInputToInputWeightsTensor);
+  const bool fw_use_cifg = (fw_input_to_input_weights == nullptr);
+  TfLiteIntArray* fw_scratch_buffer_size = TfLiteIntArrayCreate(2);
+  fw_scratch_buffer_size->data[0] = n_batch;
+  if (fw_use_cifg) {
+    // Reserving space for Cell, Forget, Output gates
+    fw_scratch_buffer_size->data[1] = n_fw_cell * 3;
+  } else {
+    // Reserving space for Input, Cell, Forget, Output gates
+    fw_scratch_buffer_size->data[1] = n_fw_cell * 4;
+  }
+  TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, fw_scratch_buffer,
+                                                   fw_scratch_buffer_size));
+  // Same for the backward cell.
+  TfLiteTensor* bw_input_to_output_weights =
+      GetInput(context, node, kBwInputToOutputWeightsTensor);
+  const int n_bw_cell = bw_input_to_output_weights->dims->data[0];
+  TF_LITE_ENSURE_EQ(context, bw_input_to_output_weights->dims->size, 2);
+  TF_LITE_ENSURE_EQ(context, bw_input_to_output_weights->dims->data[1],
+                    n_input);
+
+  TfLiteTensor* bw_recurrent_to_output_weights =
+      GetInput(context, node, kBwRecurrentToOutputWeightsTensor);
+  TF_LITE_ENSURE_EQ(context, bw_recurrent_to_output_weights->dims->size, 2);
+  TF_LITE_ENSURE_EQ(context, bw_recurrent_to_output_weights->dims->data[0],
+                    n_bw_cell);
+  const int n_bw_output = bw_recurrent_to_output_weights->dims->data[1];
+
+  // Check that input tensor dimensions matches with each other.
+  CheckInputTensorDimensions(context, node, n_input, n_bw_output, n_bw_cell);
+
+  // Get the pointer to output, state and scratch buffer tensors.
+  TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor);
+  TfLiteTensor* bw_output_state =
+      GetOutput(context, node, kBwOutputStateTensor);
+  TfLiteTensor* bw_cell_state = GetOutput(context, node, kBwCellStateTensor);
+  // TODO(ghodrat): Modify this as soon as we have a finalized method for
+  // scratch buffers.
+  TfLiteTensor* bw_scratch_buffer =
+      GetOutput(context, node, kBwScratchBufferTensor);
+
+  // Resize the output and output_state tensors.
+  TfLiteIntArray* bw_output_size = TfLiteIntArrayCreate(3);
+  bw_output_size->data[0] = max_time;
+  bw_output_size->data[1] = n_batch;
+  bw_output_size->data[2] = n_bw_output;
+  TF_LITE_ENSURE_OK(context,
+                    context->ResizeTensor(context, bw_output, bw_output_size));
+
+  TfLiteIntArray* bw_output_state_size = TfLiteIntArrayCreate(2);
+  bw_output_state_size->data[0] = n_batch;
+  bw_output_state_size->data[1] = n_bw_output;
+  TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_output_state,
+                                                   bw_output_state_size));
+
+  // Resize the scratch buffer tensor.
+  TfLiteIntArray* bw_cell_size = TfLiteIntArrayCreate(2);
+  bw_cell_size->data[0] = n_batch;
+  bw_cell_size->data[1] = n_bw_cell;
+  TF_LITE_ENSURE_OK(
+      context, context->ResizeTensor(context, bw_cell_state, bw_cell_size));
+
+  // Mark state tensors as persistent tensors.
+  bw_output_state->allocation_type = kTfLiteArenaRwPersistent;
+  bw_cell_state->allocation_type = kTfLiteArenaRwPersistent;
+
+  TfLiteTensor* bw_input_to_input_weights =
+      GetOptionalInputTensor(context, node, kBwInputToInputWeightsTensor);
+  const bool bw_use_cifg = (bw_input_to_input_weights == nullptr);
+  TfLiteIntArray* bw_scratch_buffer_size = TfLiteIntArrayCreate(2);
+  bw_scratch_buffer_size->data[0] = n_batch;
+  if (bw_use_cifg) {
+    // Reserving space for Cell, Forget, Output gates
+    bw_scratch_buffer_size->data[1] = n_bw_cell * 3;
+  } else {
+    // Reserving space for Input, Cell, Forget, Output gates
+    bw_scratch_buffer_size->data[1] = n_bw_cell * 4;
+  }
+  TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, bw_scratch_buffer,
+                                                   bw_scratch_buffer_size));
+  return kTfLiteOk;
+}
+
+// Performs an LSTM batch inference step for input specified by input_ptr_batch.
+// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and
+// biases (*_bias_ptr), and buffers (*_scratch), along with additional
+// parameters:
+//  - params: various LSTM params including activation, clipping, etc.,
+//  - use_cifg: use coupled input forget gates,
+//  - use_peephole: whether to use peephole connection or not,
+//  - n_batch: size of batch,
+//  - n_cell: number of cells (or units),
+//  - n_input: the input size,
+//  - n_output: the output size.
+//
+// The pointers to the hidden state and the output are updated as a result.
+//
+// The pointers with the suffix "_batch" point to data aligned in batch_major
+// order, and each step processes batch_size many inputs from input_ptr_batch,
+// and updates batch_size many outputs and hidden states.
+void LstmBatchStep(
+    const float* input_ptr_batch, const float* input_to_input_weights_ptr,
+    const float* input_to_forget_weights_ptr,
+    const float* input_to_cell_weights_ptr,
+    const float* input_to_output_weights_ptr,
+    const float* recurrent_to_input_weights_ptr,
+    const float* recurrent_to_forget_weights_ptr,
+    const float* recurrent_to_cell_weights_ptr,
+    const float* recurrent_to_output_weights_ptr,
+    const float* cell_to_input_weights_ptr,
+    const float* cell_to_forget_weights_ptr,
+    const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr,
+    const float* forget_gate_bias_ptr, const float* cell_bias_ptr,
+    const float* output_gate_bias_ptr, const float* projection_weights_ptr,
+    const float* projection_bias_ptr, const TfLiteLSTMParams* params,
+    bool use_cifg, bool use_peephole, int n_batch, int n_cell, int n_input,
+    int n_output, float* output_state_ptr, float* cell_state_ptr,
+    float* input_gate_scratch, float* forget_gate_scratch, float* cell_scratch,
+    float* output_gate_scratch, float* output_ptr_time) {
+  // Initialize scratch buffers with bias.
+  if (!use_cifg) {
+    tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch,
+                                          input_gate_scratch);
+  }
+  tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch,
+                                        forget_gate_scratch);
+  tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch,
+                                        cell_scratch);
+  tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch,
+                                        output_gate_scratch);
+
+  // For each batch and cell: compute input_weight * input.
+  if (!use_cifg) {
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+        input_gate_scratch, /*result_stride=*/1);
+  }
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+      forget_gate_scratch, /*result_stride=*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+      cell_scratch, /*result_stride=*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+      output_gate_scratch, /*result_stride=*/1);
+
+  // For each batch and cell: compute recurrent_weight * output_state.
+  if (!use_cifg) {
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr,
+        n_batch, input_gate_scratch,
+        /*result_stride=*/1);
+  }
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr,
+      n_batch, forget_gate_scratch,
+      /*result_stride=*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr,
+      n_batch, cell_scratch, /*result_stride=*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr,
+      n_batch, output_gate_scratch,
+      /*result_stride=*/1);
+
+  // For each batch and cell: update input gate.
+  if (!use_cifg) {
+    if (use_peephole) {
+      tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+          cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch,
+          input_gate_scratch);
+    }
+    tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
+                                       input_gate_scratch);
+  }
+
+  // For each batch and cell: update forget gate.
+  if (use_peephole) {
+    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+        cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch,
+        forget_gate_scratch);
+  }
+  tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
+                                     forget_gate_scratch);
+
+  // For each batch and cell: update the cell.
+  tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr,
+                                         n_batch * n_cell, cell_state_ptr);
+  tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell,
+                                        params->activation, cell_scratch);
+  if (use_cifg) {
+    tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell,
+                             forget_gate_scratch);
+    tensor_utils::VectorVectorCwiseProductAccumulate(
+        cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr);
+  } else {
+    tensor_utils::VectorVectorCwiseProductAccumulate(
+        cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr);
+  }
+  if (params->cell_clip > 0.0) {
+    tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell,
+                             params->cell_clip, cell_state_ptr);
+  }
+
+  // For each batch and cell: update the output gate.
+  if (use_peephole) {
+    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+        cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch,
+        output_gate_scratch);
+  }
+  tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
+                                     output_gate_scratch);
+  tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell,
+                                        params->activation, cell_scratch);
+  tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch,
+                                         n_batch * n_cell, output_gate_scratch);
+
+  // For each batch: update the projection and output_state.
+  const bool use_projection_weight = (projection_weights_ptr != nullptr);
+  const bool use_projection_bias = (projection_bias_ptr != nullptr);
+  if (use_projection_weight) {
+    if (use_projection_bias) {
+      tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output,
+                                            n_batch, output_ptr_time);
+    } else {
+      tensor_utils::ZeroVector(output_ptr_time, n_batch * n_output);
+    }
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch,
+        output_ptr_time, /*result_stride=*/1);
+    if (params->proj_clip > 0.0) {
+      tensor_utils::ClipVector(output_ptr_time, n_batch * n_output,
+                               params->proj_clip, output_ptr_time);
+    }
+  } else {
+    tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output,
+                             output_ptr_time);
+  }
+  tensor_utils::CopyVector(output_ptr_time, n_batch * n_output,
+                           output_state_ptr);
+}
+
+// The LSTM Op engine.
+TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+  auto* params = reinterpret_cast<TfLiteLSTMParams*>(node->builtin_data);
+
+  // Input tensor.
+  TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  const int max_time = input->dims->data[0];
+  const int n_batch = input->dims->data[1];
+  const int n_input = input->dims->data[2];
+
+  // Tensors for the forward cell.
+  TfLiteTensor* fw_input_to_input_weights =
+      GetOptionalInputTensor(context, node, kFwInputToInputWeightsTensor);
+  TfLiteTensor* fw_input_to_forget_weights =
+      GetInput(context, node, kFwInputToForgetWeightsTensor);
+  TfLiteTensor* fw_input_to_cell_weights =
+      GetInput(context, node, kFwInputToCellWeightsTensor);
+  TfLiteTensor* fw_input_to_output_weights =
+      GetInput(context, node, kFwInputToOutputWeightsTensor);
+
+  TfLiteTensor* fw_recurrent_to_input_weights =
+      GetOptionalInputTensor(context, node, kFwRecurrentToInputWeightsTensor);
+  TfLiteTensor* fw_recurrent_to_forget_weights =
+      GetInput(context, node, kFwRecurrentToForgetWeightsTensor);
+  TfLiteTensor* fw_recurrent_to_cell_weights =
+      GetInput(context, node, kFwRecurrentToCellWeightsTensor);
+  TfLiteTensor* fw_recurrent_to_output_weights =
+      GetInput(context, node, kFwRecurrentToOutputWeightsTensor);
+
+  TfLiteTensor* fw_cell_to_input_weights =
+      GetOptionalInputTensor(context, node, kFwCellToInputWeightsTensor);
+  TfLiteTensor* fw_cell_to_forget_weights =
+      GetOptionalInputTensor(context, node, kFwCellToForgetWeightsTensor);
+  TfLiteTensor* fw_cell_to_output_weights =
+      GetOptionalInputTensor(context, node, kFwCellToOutputWeightsTensor);
+
+  TfLiteTensor* fw_input_gate_bias =
+      GetOptionalInputTensor(context, node, kFwInputGateBiasTensor);
+  TfLiteTensor* fw_forget_gate_bias =
+      GetInput(context, node, kFwForgetGateBiasTensor);
+  TfLiteTensor* fw_cell_bias = GetInput(context, node, kFwCellGateBiasTensor);
+  TfLiteTensor* fw_output_gate_bias =
+      GetInput(context, node, kFwOutputGateBiasTensor);
+
+  TfLiteTensor* fw_projection_weights =
+      GetOptionalInputTensor(context, node, kFwProjectionWeightsTensor);
+  TfLiteTensor* fw_projection_bias =
+      GetOptionalInputTensor(context, node, kFwProjectionBiasTensor);
+
+  TfLiteTensor* fw_output_state =
+      GetOutput(context, node, kFwOutputStateTensor);
+  TfLiteTensor* fw_cell_state = GetOutput(context, node, kFwCellStateTensor);
+  TfLiteTensor* fw_output = GetOutput(context, node, kFwOutputTensor);
+
+  // Tensors for the backward cell.
+  TfLiteTensor* bw_input_to_input_weights =
+      GetOptionalInputTensor(context, node, kBwInputToInputWeightsTensor);
+  TfLiteTensor* bw_input_to_forget_weights =
+      GetInput(context, node, kBwInputToForgetWeightsTensor);
+  TfLiteTensor* bw_input_to_cell_weights =
+      GetInput(context, node, kBwInputToCellWeightsTensor);
+  TfLiteTensor* bw_input_to_output_weights =
+      GetInput(context, node, kBwInputToOutputWeightsTensor);
+
+  TfLiteTensor* bw_recurrent_to_input_weights =
+      GetOptionalInputTensor(context, node, kBwRecurrentToInputWeightsTensor);
+  TfLiteTensor* bw_recurrent_to_forget_weights =
+      GetInput(context, node, kBwRecurrentToForgetWeightsTensor);
+  TfLiteTensor* bw_recurrent_to_cell_weights =
+      GetInput(context, node, kBwRecurrentToCellWeightsTensor);
+  TfLiteTensor* bw_recurrent_to_output_weights =
+      GetInput(context, node, kBwRecurrentToOutputWeightsTensor);
+
+  TfLiteTensor* bw_cell_to_input_weights =
+      GetOptionalInputTensor(context, node, kBwCellToInputWeightsTensor);
+  TfLiteTensor* bw_cell_to_forget_weights =
+      GetOptionalInputTensor(context, node, kBwCellToForgetWeightsTensor);
+  TfLiteTensor* bw_cell_to_output_weights =
+      GetOptionalInputTensor(context, node, kBwCellToOutputWeightsTensor);
+
+  TfLiteTensor* bw_input_gate_bias =
+      GetOptionalInputTensor(context, node, kBwInputGateBiasTensor);
+  TfLiteTensor* bw_forget_gate_bias =
+      GetInput(context, node, kBwForgetGateBiasTensor);
+  TfLiteTensor* bw_cell_bias = GetInput(context, node, kBwCellGateBiasTensor);
+  TfLiteTensor* bw_output_gate_bias =
+      GetInput(context, node, kBwOutputGateBiasTensor);
+
+  TfLiteTensor* bw_projection_weights =
+      GetOptionalInputTensor(context, node, kBwProjectionWeightsTensor);
+  TfLiteTensor* bw_projection_bias =
+      GetOptionalInputTensor(context, node, kBwProjectionBiasTensor);
+
+  TfLiteTensor* bw_output_state =
+      GetOutput(context, node, kBwOutputStateTensor);
+  TfLiteTensor* bw_cell_state = GetOutput(context, node, kBwCellStateTensor);
+  TfLiteTensor* bw_output = GetOutput(context, node, kBwOutputTensor);
+
+  // n_cell and n_output will be the same size when there is no projection.
+  const int n_fw_cell = fw_input_to_output_weights->dims->data[0];
+  const int n_fw_output = fw_recurrent_to_output_weights->dims->data[1];
+
+  // Since we have already checked that weights are all there or none, we can
+  // check the existense of only one to the get the condition.
+  const bool fw_use_cifg = (fw_input_to_input_weights == nullptr);
+  const bool fw_use_peephole = (fw_cell_to_output_weights != nullptr);
+
+  // Index the scratch buffers pointers to the global scratch buffer.
+  TfLiteTensor* fw_scratch_buffer =
+      GetOutput(context, node, kFwScratchBufferTensor);
+  float* fw_input_gate_scratch = nullptr;
+  float* fw_cell_scratch = nullptr;
+  float* fw_forget_gate_scratch = nullptr;
+  float* fw_output_gate_scratch = nullptr;
+  if (fw_use_cifg) {
+    fw_cell_scratch = fw_scratch_buffer->data.f;
+    fw_forget_gate_scratch = fw_scratch_buffer->data.f + n_fw_cell * n_batch;
+    fw_output_gate_scratch =
+        fw_scratch_buffer->data.f + 2 * n_fw_cell * n_batch;
+  } else {
+    fw_input_gate_scratch = fw_scratch_buffer->data.f;
+    fw_cell_scratch = fw_scratch_buffer->data.f + n_fw_cell * n_batch;
+    fw_forget_gate_scratch =
+        fw_scratch_buffer->data.f + 2 * n_fw_cell * n_batch;
+    fw_output_gate_scratch =
+        fw_scratch_buffer->data.f + 3 * n_fw_cell * n_batch;
+  }
+
+  // Check optional tensors, the respective pointers can be null.
+  const float* fw_input_to_input_weights_ptr =
+      (fw_use_cifg) ? nullptr : fw_input_to_input_weights->data.f;
+  const float* fw_recurrent_to_input_weights_ptr =
+      (fw_use_cifg) ? nullptr : fw_recurrent_to_input_weights->data.f;
+  const float* fw_input_gate_bias_ptr =
+      (fw_use_cifg) ? nullptr : fw_input_gate_bias->data.f;
+  const float* fw_cell_to_input_weights_ptr =
+      (fw_use_peephole && !fw_use_cifg) ? fw_cell_to_input_weights->data.f
+                                        : nullptr;
+  const float* fw_cell_to_forget_weights_ptr =
+      (fw_use_peephole) ? fw_cell_to_forget_weights->data.f : nullptr;
+  const float* fw_cell_to_output_weights_ptr =
+      (fw_use_peephole) ? fw_cell_to_output_weights->data.f : nullptr;
+  const float* fw_projection_weights_ptr = (fw_projection_weights == nullptr)
+                                               ? nullptr
+                                               : fw_projection_weights->data.f;
+  const float* fw_projection_bias_ptr =
+      (fw_projection_bias == nullptr) ? nullptr : fw_projection_bias->data.f;
+
+  // Loop through the sequence.
+  for (int t = 0; t < max_time; t++) {
+    const float* input_ptr_batch = input->data.f + t * n_batch * n_input;
+    float* output_ptr_time = fw_output->data.f + t * n_batch * n_fw_output;
+
+    LstmBatchStep(
+        input_ptr_batch, fw_input_to_input_weights_ptr,
+        fw_input_to_forget_weights->data.f, fw_input_to_cell_weights->data.f,
+        fw_input_to_output_weights->data.f, fw_recurrent_to_input_weights_ptr,
+        fw_recurrent_to_forget_weights->data.f,
+        fw_recurrent_to_cell_weights->data.f,
+        fw_recurrent_to_output_weights->data.f, fw_cell_to_input_weights_ptr,
+        fw_cell_to_forget_weights_ptr, fw_cell_to_output_weights_ptr,
+        fw_input_gate_bias_ptr, fw_forget_gate_bias->data.f,
+        fw_cell_bias->data.f, fw_output_gate_bias->data.f,
+        fw_projection_weights_ptr, fw_projection_bias_ptr, params, fw_use_cifg,
+        fw_use_peephole, n_batch, n_fw_cell, n_input, n_fw_output,
+        fw_output_state->data.f, fw_cell_state->data.f, fw_input_gate_scratch,
+        fw_forget_gate_scratch, fw_cell_scratch, fw_output_gate_scratch,
+        output_ptr_time);
+  }
+
+  // n_cell and n_output will be the same size when there is no projection.
+  const int n_bw_cell = bw_input_to_output_weights->dims->data[0];
+  const int n_bw_output = bw_recurrent_to_output_weights->dims->data[1];
+
+  // Since we have already checked that weights are all there or none, we can
+  // check the existense of only one to the get the condition.
+  const bool bw_use_cifg = (bw_input_to_input_weights == nullptr);
+  const bool bw_use_peephole = (bw_cell_to_output_weights != nullptr);
+
+  // Index the scratch buffers pointers to the global scratch buffer.
+  TfLiteTensor* bw_scratch_buffer =
+      GetOutput(context, node, kBwScratchBufferTensor);
+  float* bw_input_gate_scratch = nullptr;
+  float* bw_cell_scratch = nullptr;
+  float* bw_forget_gate_scratch = nullptr;
+  float* bw_output_gate_scratch = nullptr;
+  if (bw_use_cifg) {
+    bw_cell_scratch = bw_scratch_buffer->data.f;
+    bw_forget_gate_scratch = bw_scratch_buffer->data.f + n_bw_cell * n_batch;
+    bw_output_gate_scratch =
+        bw_scratch_buffer->data.f + 2 * n_bw_cell * n_batch;
+  } else {
+    bw_input_gate_scratch = bw_scratch_buffer->data.f;
+    bw_cell_scratch = bw_scratch_buffer->data.f + n_bw_cell * n_batch;
+    bw_forget_gate_scratch =
+        bw_scratch_buffer->data.f + 2 * n_bw_cell * n_batch;
+    bw_output_gate_scratch =
+        bw_scratch_buffer->data.f + 3 * n_bw_cell * n_batch;
+  }
+
+  // Check optional tensors, the respective pointers can be null.
+  const float* bw_input_to_input_weights_ptr =
+      (bw_use_cifg) ? nullptr : bw_input_to_input_weights->data.f;
+  const float* bw_recurrent_to_input_weights_ptr =
+      (bw_use_cifg) ? nullptr : bw_recurrent_to_input_weights->data.f;
+  const float* bw_input_gate_bias_ptr =
+      (bw_use_cifg) ? nullptr : bw_input_gate_bias->data.f;
+  const float* bw_cell_to_input_weights_ptr =
+      (bw_use_peephole && !bw_use_cifg) ? bw_cell_to_input_weights->data.f
+                                        : nullptr;
+  const float* bw_cell_to_forget_weights_ptr =
+      (bw_use_peephole) ? bw_cell_to_forget_weights->data.f : nullptr;
+  const float* bw_cell_to_output_weights_ptr =
+      (bw_use_peephole) ? bw_cell_to_output_weights->data.f : nullptr;
+  const float* bw_projection_weights_ptr = (bw_projection_weights == nullptr)
+                                               ? nullptr
+                                               : bw_projection_weights->data.f;
+  const float* bw_projection_bias_ptr =
+      (bw_projection_bias == nullptr) ? nullptr : bw_projection_bias->data.f;
+
+  // Loop through the sequence backwards.
+  for (int t = max_time - 1; t >= 0; t--) {
+    const float* input_ptr_batch = input->data.f + t * n_batch * n_input;
+    float* output_ptr_time = bw_output->data.f + t * n_batch * n_bw_output;
+
+    LstmBatchStep(
+        input_ptr_batch, bw_input_to_input_weights_ptr,
+        bw_input_to_forget_weights->data.f, bw_input_to_cell_weights->data.f,
+        bw_input_to_output_weights->data.f, bw_recurrent_to_input_weights_ptr,
+        bw_recurrent_to_forget_weights->data.f,
+        bw_recurrent_to_cell_weights->data.f,
+        bw_recurrent_to_output_weights->data.f, bw_cell_to_input_weights_ptr,
+        bw_cell_to_forget_weights_ptr, bw_cell_to_output_weights_ptr,
+        bw_input_gate_bias_ptr, bw_forget_gate_bias->data.f,
+        bw_cell_bias->data.f, bw_output_gate_bias->data.f,
+        bw_projection_weights_ptr, bw_projection_bias_ptr, params, bw_use_cifg,
+        bw_use_peephole, n_batch, n_bw_cell, n_input, n_bw_output,
+        bw_output_state->data.f, bw_cell_state->data.f, bw_input_gate_scratch,
+        bw_forget_gate_scratch, bw_cell_scratch, bw_output_gate_scratch,
+        output_ptr_time);
+  }
+
+  // Backward step.
+  return kTfLiteOk;
+}
+
+}  // namespace bidirectional_sequence_lstm
+
+TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_LSTM() {
+  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
+                                 bidirectional_sequence_lstm::Prepare,
+                                 bidirectional_sequence_lstm::Eval};
+  return &r;
+}
+
+}  // namespace builtin
+}  // namespace ops
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc
new file mode 100644
index 0000000000..cca857bac0
--- /dev/null
+++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm_test.cc
@@ -0,0 +1,1411 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// Unit test for TFLite Bidirectional LSTM op.
+
+#include <iomanip>
+#include <memory>
+#include <vector>
+
+#include <gmock/gmock.h>
+#include <gtest/gtest.h>
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/kernels/test_util.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAreArray;
+
+class BidirectionalLSTMOpModel : public SingleOpModel {
+ public:
+  BidirectionalLSTMOpModel(int n_batch, int n_input, int n_cell, int n_output,
+                           int sequence_length, bool use_cifg,
+                           bool use_peephole, bool use_projection_weights,
+                           bool use_projection_bias, float cell_clip,
+                           float proj_clip,
+                           const std::vector<std::vector<int>>& input_shapes)
+      : n_batch_(n_batch),
+        n_input_(n_input),
+        n_fw_cell_(n_cell),
+        n_bw_cell_(n_cell),
+        n_fw_output_(n_output),
+        n_bw_output_(n_output),
+        sequence_length_(sequence_length) {
+    input_ = AddInput(TensorType_FLOAT32);
+
+    if (use_cifg) {
+      fw_input_to_input_weights_ = AddNullInput();
+    } else {
+      fw_input_to_input_weights_ = AddInput(TensorType_FLOAT32);
+    }
+
+    fw_input_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+    fw_input_to_cell_weights_ = AddInput(TensorType_FLOAT32);
+    fw_input_to_output_weights_ = AddInput(TensorType_FLOAT32);
+
+    if (use_cifg) {
+      fw_recurrent_to_input_weights_ = AddNullInput();
+    } else {
+      fw_recurrent_to_input_weights_ = AddInput(TensorType_FLOAT32);
+    }
+
+    fw_recurrent_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+    fw_recurrent_to_cell_weights_ = AddInput(TensorType_FLOAT32);
+    fw_recurrent_to_output_weights_ = AddInput(TensorType_FLOAT32);
+
+    if (use_peephole) {
+      if (use_cifg) {
+        fw_cell_to_input_weights_ = AddNullInput();
+      } else {
+        fw_cell_to_input_weights_ = AddInput(TensorType_FLOAT32);
+      }
+      fw_cell_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+      fw_cell_to_output_weights_ = AddInput(TensorType_FLOAT32);
+    } else {
+      fw_cell_to_input_weights_ = AddNullInput();
+      fw_cell_to_forget_weights_ = AddNullInput();
+      fw_cell_to_output_weights_ = AddNullInput();
+    }
+
+    if (use_cifg) {
+      fw_input_gate_bias_ = AddNullInput();
+    } else {
+      fw_input_gate_bias_ = AddInput(TensorType_FLOAT32);
+    }
+    fw_forget_gate_bias_ = AddInput(TensorType_FLOAT32);
+    fw_cell_bias_ = AddInput(TensorType_FLOAT32);
+    fw_output_gate_bias_ = AddInput(TensorType_FLOAT32);
+
+    if (use_projection_weights) {
+      fw_projection_weights_ = AddInput(TensorType_FLOAT32);
+      if (use_projection_bias) {
+        fw_projection_bias_ = AddInput(TensorType_FLOAT32);
+      } else {
+        fw_projection_bias_ = AddNullInput();
+      }
+    } else {
+      fw_projection_weights_ = AddNullInput();
+      fw_projection_bias_ = AddNullInput();
+    }
+
+    fw_scratch_buffer_ = AddOutput(TensorType_FLOAT32);
+    // TODO(ghodrat): Modify these states when we have a permanent solution for
+    // persistent buffer.
+    fw_output_state_ = AddOutput(TensorType_FLOAT32);
+    fw_cell_state_ = AddOutput(TensorType_FLOAT32);
+    fw_output_ = AddOutput(TensorType_FLOAT32);
+
+    if (use_cifg) {
+      bw_input_to_input_weights_ = AddNullInput();
+    } else {
+      bw_input_to_input_weights_ = AddInput(TensorType_FLOAT32);
+    }
+
+    bw_input_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+    bw_input_to_cell_weights_ = AddInput(TensorType_FLOAT32);
+    bw_input_to_output_weights_ = AddInput(TensorType_FLOAT32);
+
+    if (use_cifg) {
+      bw_recurrent_to_input_weights_ = AddNullInput();
+    } else {
+      bw_recurrent_to_input_weights_ = AddInput(TensorType_FLOAT32);
+    }
+
+    bw_recurrent_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+    bw_recurrent_to_cell_weights_ = AddInput(TensorType_FLOAT32);
+    bw_recurrent_to_output_weights_ = AddInput(TensorType_FLOAT32);
+
+    if (use_peephole) {
+      if (use_cifg) {
+        bw_cell_to_input_weights_ = AddNullInput();
+      } else {
+        bw_cell_to_input_weights_ = AddInput(TensorType_FLOAT32);
+      }
+      bw_cell_to_forget_weights_ = AddInput(TensorType_FLOAT32);
+      bw_cell_to_output_weights_ = AddInput(TensorType_FLOAT32);
+    } else {
+      bw_cell_to_input_weights_ = AddNullInput();
+      bw_cell_to_forget_weights_ = AddNullInput();
+      bw_cell_to_output_weights_ = AddNullInput();
+    }
+
+    if (use_cifg) {
+      bw_input_gate_bias_ = AddNullInput();
+    } else {
+      bw_input_gate_bias_ = AddInput(TensorType_FLOAT32);
+    }
+    bw_forget_gate_bias_ = AddInput(TensorType_FLOAT32);
+    bw_cell_bias_ = AddInput(TensorType_FLOAT32);
+    bw_output_gate_bias_ = AddInput(TensorType_FLOAT32);
+
+    if (use_projection_weights) {
+      bw_projection_weights_ = AddInput(TensorType_FLOAT32);
+      if (use_projection_bias) {
+        bw_projection_bias_ = AddInput(TensorType_FLOAT32);
+      } else {
+        bw_projection_bias_ = AddNullInput();
+      }
+    } else {
+      bw_projection_weights_ = AddNullInput();
+      bw_projection_bias_ = AddNullInput();
+    }
+
+    bw_scratch_buffer_ = AddOutput(TensorType_FLOAT32);
+    // TODO(ghodrat): Modify these states when we have a permanent solution for
+    // persistent buffer.
+    bw_output_state_ = AddOutput(TensorType_FLOAT32);
+    bw_cell_state_ = AddOutput(TensorType_FLOAT32);
+    bw_output_ = AddOutput(TensorType_FLOAT32);
+
+    SetBuiltinOp(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM,
+                 BuiltinOptions_LSTMOptions,
+                 CreateLSTMOptions(builder_, ActivationFunctionType_TANH,
+                                   cell_clip, proj_clip)
+                     .Union());
+    BuildInterpreter(input_shapes);
+  }
+
+  // Set weights in forward and backward cells to be the same.
+  void SetInputToInputWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_input_to_input_weights_, f);
+    PopulateTensor(bw_input_to_input_weights_, f);
+  }
+
+  void SetInputToForgetWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_input_to_forget_weights_, f);
+    PopulateTensor(bw_input_to_forget_weights_, f);
+  }
+
+  void SetInputToCellWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_input_to_cell_weights_, f);
+    PopulateTensor(bw_input_to_cell_weights_, f);
+  }
+
+  void SetInputToOutputWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_input_to_output_weights_, f);
+    PopulateTensor(bw_input_to_output_weights_, f);
+  }
+
+  void SetRecurrentToInputWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_recurrent_to_input_weights_, f);
+    PopulateTensor(bw_recurrent_to_input_weights_, f);
+  }
+
+  void SetRecurrentToForgetWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_recurrent_to_forget_weights_, f);
+    PopulateTensor(bw_recurrent_to_forget_weights_, f);
+  }
+
+  void SetRecurrentToCellWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_recurrent_to_cell_weights_, f);
+    PopulateTensor(bw_recurrent_to_cell_weights_, f);
+  }
+
+  void SetRecurrentToOutputWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_recurrent_to_output_weights_, f);
+    PopulateTensor(bw_recurrent_to_output_weights_, f);
+  }
+
+  void SetCellToInputWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_cell_to_input_weights_, f);
+    PopulateTensor(bw_cell_to_input_weights_, f);
+  }
+
+  void SetCellToForgetWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_cell_to_forget_weights_, f);
+    PopulateTensor(bw_cell_to_forget_weights_, f);
+  }
+
+  void SetCellToOutputWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_cell_to_output_weights_, f);
+    PopulateTensor(bw_cell_to_output_weights_, f);
+  }
+
+  void SetInputGateBias(std::initializer_list<float> f) {
+    PopulateTensor(fw_input_gate_bias_, f);
+    PopulateTensor(bw_input_gate_bias_, f);
+  }
+
+  void SetForgetGateBias(std::initializer_list<float> f) {
+    PopulateTensor(fw_forget_gate_bias_, f);
+    PopulateTensor(bw_forget_gate_bias_, f);
+  }
+
+  void SetCellBias(std::initializer_list<float> f) {
+    PopulateTensor(fw_cell_bias_, f);
+    PopulateTensor(bw_cell_bias_, f);
+  }
+
+  void SetOutputGateBias(std::initializer_list<float> f) {
+    PopulateTensor(fw_output_gate_bias_, f);
+    PopulateTensor(bw_output_gate_bias_, f);
+  }
+
+  void SetProjectionWeights(std::initializer_list<float> f) {
+    PopulateTensor(fw_projection_weights_, f);
+    PopulateTensor(bw_projection_weights_, f);
+  }
+
+  void SetProjectionBias(std::initializer_list<float> f) {
+    PopulateTensor(fw_projection_bias_, f);
+    PopulateTensor(bw_projection_bias_, f);
+  }
+
+  void ResetFwOutputAndCellStates() {
+    const int zero_buffer_size = n_fw_cell_ * n_batch_;
+    std::unique_ptr<float[]> zero_buffer(new float[zero_buffer_size]);
+    memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float));
+    PopulateTensor(fw_output_state_, 0, zero_buffer.get(),
+                   zero_buffer.get() + zero_buffer_size);
+    PopulateTensor(fw_cell_state_, 0, zero_buffer.get(),
+                   zero_buffer.get() + zero_buffer_size);
+  }
+
+  void ResetBwOutputAndCellStates() {
+    const int zero_buffer_size = n_bw_cell_ * n_batch_;
+    std::unique_ptr<float[]> zero_buffer(new float[zero_buffer_size]);
+    memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float));
+    PopulateTensor(bw_output_state_, 0, zero_buffer.get(),
+                   zero_buffer.get() + zero_buffer_size);
+    PopulateTensor(bw_cell_state_, 0, zero_buffer.get(),
+                   zero_buffer.get() + zero_buffer_size);
+  }
+
+  void SetInput(int offset, float* begin, float* end) {
+    PopulateTensor(input_, offset, begin, end);
+  }
+
+  std::vector<float> GetFwOutput() { return ExtractVector<float>(fw_output_); }
+  std::vector<float> GetBwOutput() { return ExtractVector<float>(bw_output_); }
+
+  int num_inputs() { return n_input_; }
+  int num_fw_outputs() { return n_fw_output_; }
+  int num_bw_outputs() { return n_bw_output_; }
+  int num_fw_cells() { return n_fw_cell_; }
+  int num_bw_cells() { return n_bw_cell_; }
+  int num_batches() { return n_batch_; }
+  int sequence_length() { return sequence_length_; }
+
+ private:
+  int input_;
+  int fw_input_to_input_weights_;
+  int fw_input_to_forget_weights_;
+  int fw_input_to_cell_weights_;
+  int fw_input_to_output_weights_;
+
+  int fw_recurrent_to_input_weights_;
+  int fw_recurrent_to_forget_weights_;
+  int fw_recurrent_to_cell_weights_;
+  int fw_recurrent_to_output_weights_;
+
+  int fw_cell_to_input_weights_;
+  int fw_cell_to_forget_weights_;
+  int fw_cell_to_output_weights_;
+
+  int fw_input_gate_bias_;
+  int fw_forget_gate_bias_;
+  int fw_cell_bias_;
+  int fw_output_gate_bias_;
+
+  int fw_projection_weights_;
+  int fw_projection_bias_;
+
+  int bw_input_to_input_weights_;
+  int bw_input_to_forget_weights_;
+  int bw_input_to_cell_weights_;
+  int bw_input_to_output_weights_;
+
+  int bw_recurrent_to_input_weights_;
+  int bw_recurrent_to_forget_weights_;
+  int bw_recurrent_to_cell_weights_;
+  int bw_recurrent_to_output_weights_;
+
+  int bw_cell_to_input_weights_;
+  int bw_cell_to_forget_weights_;
+  int bw_cell_to_output_weights_;
+
+  int bw_input_gate_bias_;
+  int bw_forget_gate_bias_;
+  int bw_cell_bias_;
+  int bw_output_gate_bias_;
+
+  int bw_projection_weights_;
+  int bw_projection_bias_;
+
+  int fw_output_;
+  int fw_output_state_;
+  int fw_cell_state_;
+  int fw_scratch_buffer_;
+
+  int bw_output_;
+  int bw_output_state_;
+  int bw_cell_state_;
+  int bw_scratch_buffer_;
+
+  int n_batch_;
+  int n_input_;
+  int n_fw_cell_;
+  int n_bw_cell_;
+  int n_fw_output_;
+  int n_bw_output_;
+  int sequence_length_;
+};
+
+TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) {
+  const int n_batch = 1;
+  const int n_input = 2;
+  // n_cell and n_output have the same size when there is no projection.
+  const int n_cell = 4;
+  const int n_output = 4;
+  const int sequence_length = 3;
+
+  BidirectionalLSTMOpModel lstm(
+      n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/false,
+      /*use_peephole=*/false, /*use_projection_weights=*/false,
+      /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0,
+      {
+          {sequence_length, n_batch, n_input},  // input tensor
+
+          // Forward cell
+          {n_cell, n_input},  // input_to_input_weight tensor
+          {n_cell, n_input},  // input_to_forget_weight tensor
+          {n_cell, n_input},  // input_to_cell_weight tensor
+          {n_cell, n_input},  // input_to_output_weight tensor
+
+          {n_cell, n_output},  // recurrent_to_input_weight tensor
+          {n_cell, n_output},  // recurrent_to_forget_weight tensor
+          {n_cell, n_output},  // recurrent_to_cell_weight tensor
+          {n_cell, n_output},  // recurrent_to_output_weight tensor
+
+          {0},  // cell_to_input_weight tensor
+          {0},  // cell_to_forget_weight tensor
+          {0},  // cell_to_output_weight tensor
+
+          {n_cell},  // input_gate_bias tensor
+          {n_cell},  // forget_gate_bias tensor
+          {n_cell},  // cell_bias tensor
+          {n_cell},  // output_gate_bias tensor
+
+          {0, 0},  // projection_weight tensor
+          {0},     // projection_bias tensor
+
+          // Backward cell
+          {n_cell, n_input},  // input_to_input_weight tensor
+          {n_cell, n_input},  // input_to_forget_weight tensor
+          {n_cell, n_input},  // input_to_cell_weight tensor
+          {n_cell, n_input},  // input_to_output_weight tensor
+
+          {n_cell, n_output},  // recurrent_to_input_weight tensor
+          {n_cell, n_output},  // recurrent_to_forget_weight tensor
+          {n_cell, n_output},  // recurrent_to_cell_weight tensor
+          {n_cell, n_output},  // recurrent_to_output_weight tensor
+
+          {0},  // cell_to_input_weight tensor
+          {0},  // cell_to_forget_weight tensor
+          {0},  // cell_to_output_weight tensor
+
+          {n_cell},  // input_gate_bias tensor
+          {n_cell},  // forget_gate_bias tensor
+          {n_cell},  // cell_bias tensor
+          {n_cell},  // output_gate_bias tensor
+
+          {0, 0},  // projection_weight tensor
+          {0},     // projection_bias tensor
+      });
+
+  lstm.SetInputToInputWeights({-0.45018822, -0.02338299, -0.0870589,
+                               -0.34550029, 0.04266912, -0.15680569,
+                               -0.34856534, 0.43890524});
+
+  lstm.SetInputToCellWeights({-0.50013041, 0.1370284, 0.11810488, 0.2013163,
+                              -0.20583314, 0.44344562, 0.22077113,
+                              -0.29909778});
+
+  lstm.SetInputToForgetWeights({0.09701663, 0.20334584, -0.50592935,
+                                -0.31343272, -0.40032279, 0.44781327,
+                                0.01387155, -0.35593212});
+
+  lstm.SetInputToOutputWeights({-0.25065863, -0.28290087, 0.04613829,
+                                0.40525138, 0.44272184, 0.03897077, -0.1556896,
+                                0.19487578});
+
+  lstm.SetInputGateBias({0., 0., 0., 0.});
+
+  lstm.SetCellBias({0., 0., 0., 0.});
+
+  lstm.SetForgetGateBias({1., 1., 1., 1.});
+
+  lstm.SetOutputGateBias({0., 0., 0., 0.});
+
+  lstm.SetRecurrentToInputWeights(
+      {-0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324,
+       -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322,
+       -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296});
+
+  lstm.SetRecurrentToCellWeights(
+      {-0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841,
+       -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659,
+       -0.46367589, 0.26016325, -0.03894562, -0.16368064});
+
+  lstm.SetRecurrentToForgetWeights(
+      {-0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892,
+       -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436,
+       0.28053468, 0.01560611, -0.20127171, -0.01140004});
+
+  lstm.SetRecurrentToOutputWeights(
+      {0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793,
+       0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421,
+       -0.51818722, -0.15390486, 0.0468148, 0.39922136});
+
+  // Input should have n_input * sequence_length many values.
+  static float lstm_input[] = {2., 3., 3., 4., 1., 1.};
+  static float lstm_fw_golden_output[] = {
+      -0.02973187, 0.1229473,  0.20885126, -0.15358765,
+      -0.03716109, 0.12507336, 0.41193449, -0.20860538,
+      -0.15053082, 0.09120187, 0.24278517, -0.12222792};
+  static float lstm_bw_golden_output[] = {
+      -0.0806187, 0.139077, 0.400476, -0.197842,
+      -0.0332076, 0.123838, 0.309777, -0.17621,
+      -0.0490733, 0.0739237, 0.067706, -0.0208124};
+
+  // Resetting cell_state and output_state
+  lstm.ResetFwOutputAndCellStates();
+  lstm.ResetBwOutputAndCellStates();
+
+  float* batch0_start = lstm_input;
+  float* batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length();
+
+  lstm.SetInput(0, batch0_start, batch0_end);
+
+  lstm.Invoke();
+
+  float* fw_golden_start = lstm_fw_golden_output;
+  float* fw_golden_end =
+      fw_golden_start + lstm.num_fw_outputs() * lstm.sequence_length();
+  std::vector<float> fw_expected;
+  fw_expected.insert(fw_expected.end(), fw_golden_start, fw_golden_end);
+  EXPECT_THAT(lstm.GetFwOutput(),
+              ElementsAreArray(ArrayFloatNear(fw_expected)));
+
+  float* bw_golden_start = lstm_bw_golden_output;
+  float* bw_golden_end =
+      bw_golden_start + lstm.num_bw_outputs() * lstm.sequence_length();
+  std::vector<float> bw_expected;
+  bw_expected.insert(bw_expected.end(), bw_golden_start, bw_golden_end);
+  EXPECT_THAT(lstm.GetBwOutput(),
+              ElementsAreArray(ArrayFloatNear(bw_expected)));
+
+  // Check reversed inputs.
+  static float lstm_input_reversed[] = {1., 1., 3., 4., 2., 3.};
+
+  // Resetting cell_state and output_state
+  lstm.ResetFwOutputAndCellStates();
+  lstm.ResetBwOutputAndCellStates();
+
+  batch0_start = lstm_input_reversed;
+  batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length();
+
+  lstm.SetInput(0, batch0_start, batch0_end);
+
+  lstm.Invoke();
+
+  fw_expected.clear();
+  for (int s = 0; s < lstm.sequence_length(); s++) {
+    fw_golden_start = lstm_fw_golden_output + s * lstm.num_fw_outputs();
+    fw_golden_end = fw_golden_start + lstm.num_fw_outputs();
+    fw_expected.insert(fw_expected.begin(), fw_golden_start, fw_golden_end);
+  }
+  EXPECT_THAT(lstm.GetBwOutput(),
+              ElementsAreArray(ArrayFloatNear(fw_expected)));
+
+  bw_expected.clear();
+  for (int s = 0; s < lstm.sequence_length(); s++) {
+    bw_golden_start = lstm_bw_golden_output + s * lstm.num_bw_outputs();
+    bw_golden_end = bw_golden_start + lstm.num_bw_outputs();
+    bw_expected.insert(bw_expected.begin(), bw_golden_start, bw_golden_end);
+  }
+  EXPECT_THAT(lstm.GetFwOutput(),
+              ElementsAreArray(ArrayFloatNear(bw_expected)));
+}
+
+TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) {
+  const int n_batch = 1;
+  const int n_input = 2;
+  // n_cell and n_output have the same size when there is no projection.
+  const int n_cell = 4;
+  const int n_output = 4;
+  const int sequence_length = 3;
+
+  BidirectionalLSTMOpModel lstm(
+      n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/true,
+      /*use_peephole=*/true, /*use_projection_weights=*/false,
+      /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0,
+      {
+          {sequence_length, n_batch, n_input},  // input tensor
+
+          {0, 0},             // input_to_input_weight tensor
+          {n_cell, n_input},  // input_to_forget_weight tensor
+          {n_cell, n_input},  // input_to_cell_weight tensor
+          {n_cell, n_input},  // input_to_output_weight tensor
+
+          {0, 0},              // recurrent_to_input_weight tensor
+          {n_cell, n_output},  // recurrent_to_forget_weight tensor
+          {n_cell, n_output},  // recurrent_to_cell_weight tensor
+          {n_cell, n_output},  // recurrent_to_output_weight tensor
+
+          {0},       // cell_to_input_weight tensor
+          {n_cell},  // cell_to_forget_weight tensor
+          {n_cell},  // cell_to_output_weight tensor
+
+          {0},       // input_gate_bias tensor
+          {n_cell},  // forget_gate_bias tensor
+          {n_cell},  // cell_bias tensor
+          {n_cell},  // output_gate_bias tensor
+
+          {0, 0},  // projection_weight tensor
+          {0},     // projection_bias tensor
+
+          {0, 0},             // input_to_input_weight tensor
+          {n_cell, n_input},  // input_to_forget_weight tensor
+          {n_cell, n_input},  // input_to_cell_weight tensor
+          {n_cell, n_input},  // input_to_output_weight tensor
+
+          {0, 0},              // recurrent_to_input_weight tensor
+          {n_cell, n_output},  // recurrent_to_forget_weight tensor
+          {n_cell, n_output},  // recurrent_to_cell_weight tensor
+          {n_cell, n_output},  // recurrent_to_output_weight tensor
+
+          {0},       // cell_to_input_weight tensor
+          {n_cell},  // cell_to_forget_weight tensor
+          {n_cell},  // cell_to_output_weight tensor
+
+          {0},       // input_gate_bias tensor
+          {n_cell},  // forget_gate_bias tensor
+          {n_cell},  // cell_bias tensor
+          {n_cell},  // output_gate_bias tensor
+
+          {0, 0},  // projection_weight tensor
+          {0},     // projection_bias tensor
+      });
+
+  lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781,
+                              0.04717243, 0.48944736, -0.38535351,
+                              -0.17212132});
+
+  lstm.SetInputToForgetWeights({-0.55291498, -0.42866567, 0.13056988,
+                                -0.3633365, -0.22755712, 0.28253698, 0.24407166,
+                                0.33826375});
+
+  lstm.SetInputToOutputWeights({0.10725588, -0.02335852, -0.55932593,
+                                -0.09426838, -0.44257352, 0.54939759,
+                                0.01533556, 0.42751634});
+
+  lstm.SetCellBias({0., 0., 0., 0.});
+
+  lstm.SetForgetGateBias({1., 1., 1., 1.});
+
+  lstm.SetOutputGateBias({0., 0., 0., 0.});
+
+  lstm.SetRecurrentToCellWeights(
+      {0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711,
+       0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004,
+       0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288,
+       0.21193194});
+
+  lstm.SetRecurrentToForgetWeights(
+      {-0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827,
+       0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795,
+       -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349});
+
+  lstm.SetRecurrentToOutputWeights(
+      {0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908,
+       -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835,
+       0.50248802, 0.26114327, -0.43736315, 0.33149987});
+
+  lstm.SetCellToForgetWeights(
+      {0.47485286, -0.51955009, -0.24458408, 0.31544167});
+  lstm.SetCellToOutputWeights(
+      {-0.17135078, 0.82760304, 0.85573703, -0.77109635});
+
+  static float lstm_input[] = {2., 3., 3., 4., 1., 1.};
+  static float lstm_fw_golden_output[] = {
+      -0.36444446, -0.00352185, 0.12886585, -0.05163646,
+      -0.42312205, -0.01218222, 0.24201041, -0.08124574,
+      -0.358325,   -0.04621704, 0.21641694, -0.06471302};
+  static float lstm_bw_golden_output[] = {
+      -0.401685, -0.0232794, 0.288642,  -0.123074,   -0.42915,  -0.00871577,
+      0.20912,   -0.103567,  -0.166398, -0.00486649, 0.0697471, -0.0537578};
+
+  // Resetting cell_state and output_state
+  lstm.ResetFwOutputAndCellStates();
+  lstm.ResetBwOutputAndCellStates();
+
+  float* batch0_start = lstm_input;
+  float* batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length();
+
+  lstm.SetInput(0, batch0_start, batch0_end);
+
+  lstm.Invoke();
+
+  float* fw_golden_start = lstm_fw_golden_output;
+  float* fw_golden_end =
+      fw_golden_start + lstm.num_fw_outputs() * lstm.sequence_length();
+  std::vector<float> fw_expected;
+  fw_expected.insert(fw_expected.end(), fw_golden_start, fw_golden_end);
+  EXPECT_THAT(lstm.GetFwOutput(),
+              ElementsAreArray(ArrayFloatNear(fw_expected)));
+
+  float* bw_golden_start = lstm_bw_golden_output;
+  float* bw_golden_end =
+      bw_golden_start + lstm.num_bw_outputs() * lstm.sequence_length();
+  std::vector<float> bw_expected;
+  bw_expected.insert(bw_expected.end(), bw_golden_start, bw_golden_end);
+  EXPECT_THAT(lstm.GetBwOutput(),
+              ElementsAreArray(ArrayFloatNear(bw_expected)));
+
+  // Check reversed inputs.
+  static float lstm_input_reversed[] = {1., 1., 3., 4., 2., 3.};
+
+  // Resetting cell_state and output_state
+  lstm.ResetFwOutputAndCellStates();
+  lstm.ResetBwOutputAndCellStates();
+
+  batch0_start = lstm_input_reversed;
+  batch0_end = batch0_start + lstm.num_inputs() * lstm.sequence_length();
+
+  lstm.SetInput(0, batch0_start, batch0_end);
+
+  lstm.Invoke();
+
+  fw_expected.clear();
+  for (int s = 0; s < lstm.sequence_length(); s++) {
+    fw_golden_start = lstm_fw_golden_output + s * lstm.num_fw_outputs();
+    fw_golden_end = fw_golden_start + lstm.num_fw_outputs();
+    fw_expected.insert(fw_expected.begin(), fw_golden_start, fw_golden_end);
+  }
+  EXPECT_THAT(lstm.GetBwOutput(),
+              ElementsAreArray(ArrayFloatNear(fw_expected)));
+
+  bw_expected.clear();
+  for (int s = 0; s < lstm.sequence_length(); s++) {
+    bw_golden_start = lstm_bw_golden_output + s * lstm.num_bw_outputs();
+    bw_golden_end = bw_golden_start + lstm.num_bw_outputs();
+    bw_expected.insert(bw_expected.begin(), bw_golden_start, bw_golden_end);
+  }
+  EXPECT_THAT(lstm.GetFwOutput(),
+              ElementsAreArray(ArrayFloatNear(bw_expected)));
+}
+
+TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) {
+  const int n_batch = 2;
+  const int n_input = 5;
+  const int n_cell = 20;
+  const int n_output = 16;
+  const int sequence_length = 4;
+
+  BidirectionalLSTMOpModel lstm(
+      n_batch, n_input, n_cell, n_output, sequence_length, /*use_cifg=*/false,
+      /*use_peephole=*/true, /*use_projection_weights=*/true,
+      /*use_projection_bias=*/false, /*cell_clip=*/0.0, /*proj_clip=*/0.0,
+      {
+          {sequence_length, n_batch, n_input},  // input tensor
+
+          {n_cell, n_input},  // input_to_input_weight tensor
+          {n_cell, n_input},  // input_to_forget_weight tensor
+          {n_cell, n_input},  // input_to_cell_weight tensor
+          {n_cell, n_input},  // input_to_output_weight tensor
+
+          {n_cell, n_output},  // recurrent_to_input_weight tensor
+          {n_cell, n_output},  // recurrent_to_forget_weight tensor
+          {n_cell, n_output},  // recurrent_to_cell_weight tensor
+          {n_cell, n_output},  // recurrent_to_output_weight tensor
+
+          {n_cell},  // cell_to_input_weight tensor
+          {n_cell},  // cell_to_forget_weight tensor
+          {n_cell},  // cell_to_output_weight tensor
+
+          {n_cell},  // input_gate_bias tensor
+          {n_cell},  // forget_gate_bias tensor
+          {n_cell},  // cell_bias tensor
+          {n_cell},  // output_gate_bias tensor
+
+          {n_output, n_cell},  // projection_weight tensor
+          {0},                 // projection_bias tensor
+
+          {n_cell, n_input},  // input_to_input_weight tensor
+          {n_cell, n_input},  // input_to_forget_weight tensor
+          {n_cell, n_input},  // input_to_cell_weight tensor
+          {n_cell, n_input},  // input_to_output_weight tensor
+
+          {n_cell, n_output},  // recurrent_to_input_weight tensor
+          {n_cell, n_output},  // recurrent_to_forget_weight tensor
+          {n_cell, n_output},  // recurrent_to_cell_weight tensor
+          {n_cell, n_output},  // recurrent_to_output_weight tensor
+
+          {n_cell},  // cell_to_input_weight tensor
+          {n_cell},  // cell_to_forget_weight tensor
+          {n_cell},  // cell_to_output_weight tensor
+
+          {n_cell},  // input_gate_bias tensor
+          {n_cell},  // forget_gate_bias tensor
+          {n_cell},  // cell_bias tensor
+          {n_cell},  // output_gate_bias tensor
+
+          {n_output, n_cell},  // projection_weight tensor
+          {0},                 // projection_bias tensor
+      });
+
+  lstm.SetInputToInputWeights(
+      {0.021393683,  0.06124551,    0.046905167,  -0.014657677,  -0.03149463,
+       0.09171803,   0.14647801,    0.10797193,   -0.0057968358, 0.0019193048,
+       -0.2726754,   0.10154029,    -0.018539885, 0.080349885,   -0.10262385,
+       -0.022599787, -0.09121155,   -0.008675967, -0.045206103,  -0.0821282,
+       -0.008045952, 0.015478081,   0.055217247,  0.038719587,   0.044153627,
+       -0.06453243,  0.05031825,    -0.046935108, -0.008164439,  0.014574226,
+       -0.1671009,   -0.15519552,   -0.16819797,  -0.13971269,   -0.11953059,
+       0.25005487,   -0.22790983,   0.009855087,  -0.028140958,  -0.11200698,
+       0.11295408,   -0.0035217577, 0.054485075,  0.05184695,    0.064711206,
+       0.10989193,   0.11674786,    0.03490607,   0.07727357,    0.11390585,
+       -0.1863375,   -0.1034451,    -0.13945189,  -0.049401227,  -0.18767063,
+       0.042483903,  0.14233552,    0.13832581,   0.18350165,    0.14545603,
+       -0.028545704, 0.024939531,   0.050929718,  0.0076203286,  -0.0029723682,
+       -0.042484224, -0.11827596,   -0.09171104,  -0.10808628,   -0.16327988,
+       -0.2273378,   -0.0993647,    -0.017155107, 0.0023917493,  0.049272764,
+       0.0038534778, 0.054764505,   0.089753784,  0.06947234,    0.08014476,
+       -0.04544234,  -0.0497073,    -0.07135631,  -0.048929106,  -0.004042012,
+       -0.009284026, 0.018042054,   0.0036860977, -0.07427302,   -0.11434604,
+       -0.018995456, 0.031487543,   0.012834908,  0.019977754,   0.044256654,
+       -0.39292613,  -0.18519334,   -0.11651281,  -0.06809892,   0.011373677});
+
+  lstm.SetInputToForgetWeights(
+      {-0.0018401089, -0.004852237,  0.03698424,   0.014181704,   0.028273236,
+       -0.016726194,  -0.05249759,   -0.10204261,  0.00861066,    -0.040979505,
+       -0.009899187,  0.01923892,    -0.028177269, -0.08535103,   -0.14585495,
+       0.10662567,    -0.01909731,   -0.017883534, -0.0047269356, -0.045103323,
+       0.0030784295,  0.076784775,   0.07463696,   0.094531395,   0.0814421,
+       -0.12257899,   -0.033945758,  -0.031303465, 0.045630626,   0.06843887,
+       -0.13492945,   -0.012480007,  -0.0811829,   -0.07224499,   -0.09628791,
+       0.045100946,   0.0012300825,  0.013964662,  0.099372394,   0.02543059,
+       0.06958324,    0.034257296,   0.0482646,    0.06267997,    0.052625068,
+       0.12784666,    0.07077897,    0.025725935,  0.04165009,    0.07241905,
+       0.018668644,   -0.037377294,  -0.06277783,  -0.08833636,   -0.040120605,
+       -0.011405586,  -0.007808335,  -0.010301386, -0.005102167,  0.027717464,
+       0.05483423,    0.11449111,    0.11289652,   0.10939839,    0.13396506,
+       -0.08402166,   -0.01901462,   -0.044678304, -0.07720565,   0.014350063,
+       -0.11757958,   -0.0652038,    -0.08185733,  -0.076754324,  -0.092614375,
+       0.10405491,    0.052960336,   0.035755895,  0.035839386,   -0.012540553,
+       0.036881298,   0.02913376,    0.03420159,   0.05448447,    -0.054523353,
+       0.02582715,    0.02327355,    -0.011857179, -0.0011980024, -0.034641717,
+       -0.026125094,  -0.17582615,   -0.15923657,  -0.27486774,   -0.0006143371,
+       0.0001771948,  -8.470171e-05, 0.02651807,   0.045790765,   0.06956496});
+
+  lstm.SetInputToCellWeights(
+      {-0.04580283,   -0.09549462,   -0.032418985,  -0.06454633,
+       -0.043528453,  0.043018587,   -0.049152344,  -0.12418144,
+       -0.078985475,  -0.07596889,   0.019484362,   -0.11434962,
+       -0.0074034138, -0.06314844,   -0.092981495,  0.0062155537,
+       -0.025034338,  -0.0028890965, 0.048929527,   0.06235075,
+       0.10665918,    -0.032036792,  -0.08505916,   -0.10843358,
+       -0.13002433,   -0.036816437,  -0.02130134,   -0.016518239,
+       0.0047691227,  -0.0025825808, 0.066017866,   0.029991534,
+       -0.10652836,   -0.1037554,    -0.13056071,   -0.03266643,
+       -0.033702414,  -0.006473424,  -0.04611692,   0.014419339,
+       -0.025174323,  0.0396852,     0.081777506,   0.06157468,
+       0.10210095,    -0.009658194,  0.046511717,   0.03603906,
+       0.0069369148,  0.015960095,   -0.06507666,   0.09551598,
+       0.053568836,   0.06408714,    0.12835667,    -0.008714329,
+       -0.20211966,   -0.12093674,   0.029450472,   0.2849013,
+       -0.029227901,  0.1164364,     -0.08560263,   0.09941786,
+       -0.036999565,  -0.028842626,  -0.0033637602, -0.017012902,
+       -0.09720865,   -0.11193351,   -0.029155117,  -0.017936034,
+       -0.009768936,  -0.04223324,   -0.036159635,  0.06505112,
+       -0.021742892,  -0.023377212,  -0.07221364,   -0.06430552,
+       0.05453865,    0.091149814,   0.06387331,    0.007518393,
+       0.055960953,   0.069779344,   0.046411168,   0.10509911,
+       0.07463894,    0.0075130584,  0.012850982,   0.04555431,
+       0.056955688,   0.06555285,    0.050801456,   -0.009862683,
+       0.00826772,    -0.026555609,  -0.0073611983, -0.0014897042});
+
+  lstm.SetInputToOutputWeights(
+      {-0.0998932,   -0.07201956,  -0.052803773,  -0.15629593,  -0.15001918,
+       -0.07650751,  0.02359855,   -0.075155355,  -0.08037709,  -0.15093534,
+       0.029517552,  -0.04751393,  0.010350531,   -0.02664851,  -0.016839722,
+       -0.023121163, 0.0077019283, 0.012851257,   -0.05040649,  -0.0129761,
+       -0.021737747, -0.038305793, -0.06870586,   -0.01481247,  -0.001285394,
+       0.10124236,   0.083122835,  0.053313006,   -0.062235646, -0.075637154,
+       -0.027833903, 0.029774971,  0.1130802,     0.09218906,   0.09506135,
+       -0.086665764, -0.037162706, -0.038880914,  -0.035832845, -0.014481564,
+       -0.09825003,  -0.12048569,  -0.097665586,  -0.05287633,  -0.0964047,
+       -0.11366429,  0.035777505,  0.13568819,    0.052451383,  0.050649304,
+       0.05798951,   -0.021852335, -0.099848844,  0.014740475,  -0.078897946,
+       0.04974699,   0.014160473,  0.06973932,    0.04964942,   0.033364646,
+       0.08190124,   0.025535367,  0.050893165,   0.048514254,  0.06945813,
+       -0.078907564, -0.06707616,  -0.11844508,   -0.09986688,  -0.07509403,
+       0.06263226,   0.14925587,   0.20188436,    0.12098451,   0.14639415,
+       0.0015017595, -0.014267382, -0.03417257,   0.012711468,  0.0028300495,
+       -0.024758482, -0.05098548,  -0.0821182,    0.014225672,  0.021544158,
+       0.08949725,   0.07505268,   -0.0020780868, 0.04908258,   0.06476295,
+       -0.022907063, 0.027562456,  0.040185735,   0.019567577,  -0.015598739,
+       -0.049097303, -0.017121866, -0.083368234,  -0.02332002,  -0.0840956});
+
+  lstm.SetInputGateBias(
+      {0.02234832,  0.14757581,   0.18176508,  0.10380666,  0.053110216,
+       -0.06928846, -0.13942584,  -0.11816189, 0.19483899,  0.03652339,
+       -0.10250295, 0.036714908,  -0.18426876, 0.036065217, 0.21810818,
+       0.02383196,  -0.043370757, 0.08690144,  -0.04444982, 0.00030581196});
+
+  lstm.SetForgetGateBias({0.035185695, -0.042891346, -0.03032477, 0.23027696,
+                          0.11098921,  0.15378423,   0.09263801,  0.09790885,
+                          0.09508917,  0.061199076,  0.07665568,  -0.015443159,
+                          -0.03499149, 0.046190713,  0.08895977,  0.10899629,
+                          0.40694186,  0.06030037,   0.012413437, -0.06108739});
+
+  lstm.SetCellBias({-0.024379363, 0.0055531194, 0.23377132,   0.033463873,
+                    -0.1483596,   -0.10639995,  -0.091433935, 0.058573797,
+                    -0.06809782,  -0.07889636,  -0.043246906, -0.09829136,
+                    -0.4279842,   0.034901652,  0.18797937,   0.0075234566,
+                    0.016178843,  0.1749513,    0.13975595,   0.92058027});
+
+  lstm.SetOutputGateBias(
+      {0.046159424,  -0.0012809046, 0.03563469,   0.12648113, 0.027195795,
+       0.35373217,   -0.018957434,  0.008907322,  -0.0762701, 0.12018895,
+       0.04216877,   0.0022856654,  0.040952638,  0.3147856,  0.08225149,
+       -0.057416286, -0.14995944,   -0.008040261, 0.13208859, 0.029760877});
+
+  lstm.SetRecurrentToInputWeights(
+      {-0.001374326,   -0.078856036,   0.10672688,    0.029162422,
+       -0.11585556,    0.02557986,     -0.13446963,   -0.035785314,
+       -0.01244275,    0.025961924,    -0.02337298,   -0.044228926,
+       -0.055839065,   -0.046598054,   -0.010546039,  -0.06900766,
+       0.027239809,    0.022582639,    -0.013296484,  -0.05459212,
+       0.08981,        -0.045407712,   0.08682226,    -0.06867011,
+       -0.14390695,    -0.02916037,    0.000996957,   0.091420636,
+       0.14283475,     -0.07390571,    -0.06402044,   0.062524505,
+       -0.093129106,   0.04860203,     -0.08364217,   -0.08119002,
+       0.009352075,    0.22920375,     0.0016303885,  0.11583097,
+       -0.13732095,    0.012405723,    -0.07551853,   0.06343048,
+       0.12162708,     -0.031923793,   -0.014335606,  0.01790974,
+       -0.10650317,    -0.0724401,     0.08554849,    -0.05727212,
+       0.06556731,     -0.042729504,   -0.043227166,  0.011683251,
+       -0.013082158,   -0.029302018,   -0.010899579,  -0.062036745,
+       -0.022509435,   -0.00964907,    -0.01567329,   0.04260106,
+       -0.07787477,    -0.11576462,    0.017356863,   0.048673786,
+       -0.017577527,   -0.05527947,    -0.082487635,  -0.040137455,
+       -0.10820036,    -0.04666372,    0.022746278,   -0.07851417,
+       0.01068115,     0.032956902,    0.022433773,   0.0026891115,
+       0.08944216,     -0.0685835,     0.010513544,   0.07228705,
+       0.02032331,     -0.059686817,   -0.0005566496, -0.086984694,
+       0.040414046,    -0.1380399,     0.094208956,   -0.05722982,
+       0.012092817,    -0.04989123,    -0.086576,     -0.003399834,
+       -0.04696032,    -0.045747425,   0.10091314,    0.048676282,
+       -0.029037097,   0.031399418,    -0.0040285117, 0.047237843,
+       0.09504992,     0.041799378,    -0.049185462,  -0.031518843,
+       -0.10516937,    0.026374253,    0.10058866,    -0.0033195973,
+       -0.041975245,   0.0073591834,   0.0033782164,  -0.004325073,
+       -0.10167381,    0.042500053,    -0.01447153,   0.06464186,
+       -0.017142897,   0.03312627,     0.009205989,   0.024138335,
+       -0.011337001,   0.035530265,    -0.010912711,  0.0706555,
+       -0.005894094,   0.051841937,    -0.1401738,    -0.02351249,
+       0.0365468,      0.07590991,     0.08838724,    0.021681072,
+       -0.10086113,    0.019608743,    -0.06195883,   0.077335775,
+       0.023646897,    -0.095322326,   0.02233014,    0.09756986,
+       -0.048691444,   -0.009579111,   0.07595467,    0.11480546,
+       -0.09801813,    0.019894179,    0.08502348,    0.004032281,
+       0.037211012,    0.068537936,    -0.048005626,  -0.091520436,
+       -0.028379958,   -0.01556313,    0.06554592,    -0.045599163,
+       -0.01672207,    -0.020169014,   -0.011877351,  -0.20212261,
+       0.010889619,    0.0047078193,   0.038385306,   0.08540671,
+       -0.017140968,   -0.0035865551,  0.016678626,   0.005633034,
+       0.015963363,    0.00871737,     0.060130805,   0.028611384,
+       0.10109069,     -0.015060172,   -0.07894427,   0.06401885,
+       0.011584063,    -0.024466386,   0.0047652307,  -0.09041358,
+       0.030737216,    -0.0046374933,  0.14215417,    -0.11823516,
+       0.019899689,    0.006106124,    -0.027092824,  0.0786356,
+       0.05052217,     -0.058925,      -0.011402121,  -0.024987547,
+       -0.0013661642,  -0.06832946,    -0.015667673,  -0.1083353,
+       -0.00096863037, -0.06988685,    -0.053350925,  -0.027275559,
+       -0.033664223,   -0.07978348,    -0.025200296,  -0.017207067,
+       -0.058403496,   -0.055697463,   0.005798788,   0.12965427,
+       -0.062582195,   0.0013350133,   -0.10482091,   0.0379771,
+       0.072521195,    -0.0029455067,  -0.13797039,   -0.03628521,
+       0.013806405,    -0.017858358,   -0.01008298,   -0.07700066,
+       -0.017081132,   0.019358726,    0.0027079724,  0.004635139,
+       0.062634714,    -0.02338735,    -0.039547626,  -0.02050681,
+       0.03385117,     -0.083611414,   0.002862572,   -0.09421313,
+       0.058618143,    -0.08598433,    0.00972939,    0.023867095,
+       -0.053934585,   -0.023203006,   0.07452513,    -0.048767887,
+       -0.07314807,    -0.056307215,   -0.10433547,   -0.06440842,
+       0.04328182,     0.04389765,     -0.020006588,  -0.09076438,
+       -0.11652589,    -0.021705797,   0.03345259,    -0.010329105,
+       -0.025767034,   0.013057034,    -0.07316461,   -0.10145612,
+       0.06358255,     0.18531723,     0.07759293,    0.12006465,
+       0.1305557,      0.058638252,    -0.03393652,   0.09622831,
+       -0.16253184,    -2.4580743e-06, 0.079869635,   -0.070196845,
+       -0.005644518,   0.06857898,     -0.12598175,   -0.035084512,
+       0.03156317,     -0.12794146,    -0.031963028,  0.04692781,
+       0.030070418,    0.0071660685,   -0.095516115,  -0.004643372,
+       0.040170413,    -0.062104587,   -0.0037324072, 0.0554317,
+       0.08184801,     -0.019164372,   0.06791302,    0.034257166,
+       -0.10307039,    0.021943003,    0.046745934,   0.0790918,
+       -0.0265588,     -0.007824208,   0.042546265,   -0.00977924,
+       -0.0002440307,  -0.017384544,   -0.017990116,  0.12252321,
+       -0.014512694,   -0.08251313,    0.08861942,    0.13589665,
+       0.026351685,    0.012641483,    0.07466548,    0.044301085,
+       -0.045414884,   -0.051112458,   0.03444247,    -0.08502782,
+       -0.04106223,    -0.028126027,   0.028473156,   0.10467447});
+
+  lstm.SetRecurrentToForgetWeights(
+      {-0.057784554,  -0.026057621,  -0.068447545,   -0.022581743,
+       0.14811787,    0.10826372,    0.09471067,     0.03987225,
+       -0.0039523416, 0.00030638507, 0.053185795,    0.10572994,
+       0.08414449,    -0.022036452,  -0.00066928595, -0.09203576,
+       0.032950465,   -0.10985798,   -0.023809856,   0.0021431844,
+       -0.02196096,   -0.00326074,   0.00058621005,  -0.074678116,
+       -0.06193199,   0.055729095,   0.03736828,     0.020123724,
+       0.061878487,   -0.04729229,   0.034919553,    -0.07585433,
+       -0.04421272,   -0.044019096,  0.085488975,    0.04058006,
+       -0.06890133,   -0.030951202,  -0.024628663,   -0.07672815,
+       0.034293607,   0.08556707,    -0.05293577,    -0.033561368,
+       -0.04899627,   0.0241671,     0.015736353,    -0.095442444,
+       -0.029564252,  0.016493602,   -0.035026584,   0.022337519,
+       -0.026871363,  0.004780428,   0.0077918363,   -0.03601621,
+       0.016435321,   -0.03263031,   -0.09543275,    -0.047392778,
+       0.013454138,   0.028934088,   0.01685226,     -0.086110644,
+       -0.046250615,  -0.01847454,   0.047608484,    0.07339695,
+       0.034546845,   -0.04881143,   0.009128804,    -0.08802852,
+       0.03761666,    0.008096139,   -0.014454086,   0.014361001,
+       -0.023502491,  -0.0011840804, -0.07607001,    0.001856849,
+       -0.06509276,   -0.006021153,  -0.08570962,    -0.1451793,
+       0.060212336,   0.055259194,   0.06974018,     0.049454916,
+       -0.027794661,  -0.08077226,   -0.016179763,   0.1169753,
+       0.17213494,    -0.0056326236, -0.053934924,   -0.0124349,
+       -0.11520337,   0.05409887,    0.088759385,    0.0019655675,
+       0.0042065294,  0.03881498,    0.019844765,    0.041858196,
+       -0.05695512,   0.047233116,   0.038937137,    -0.06542224,
+       0.014429736,   -0.09719407,   0.13908425,     -0.05379757,
+       0.012321099,   0.082840554,   -0.029899208,   0.044217527,
+       0.059855383,   0.07711018,    -0.045319796,   0.0948846,
+       -0.011724666,  -0.0033288454, -0.033542685,   -0.04764985,
+       -0.13873616,   0.040668588,   0.034832682,    -0.015319203,
+       -0.018715994,  0.046002675,   0.0599172,      -0.043107376,
+       0.0294216,     -0.002314414,  -0.022424703,   0.0030315618,
+       0.0014641669,  0.0029166266,  -0.11878115,    0.013738511,
+       0.12375372,    -0.0006038222, 0.029104086,    0.087442465,
+       0.052958444,   0.07558703,    0.04817258,     0.044462286,
+       -0.015213451,  -0.08783778,   -0.0561384,     -0.003008196,
+       0.047060397,   -0.002058388,  0.03429439,     -0.018839769,
+       0.024734668,   0.024614193,   -0.042046934,   0.09597743,
+       -0.0043254104, 0.04320769,    0.0064070094,   -0.0019131786,
+       -0.02558259,   -0.022822596,  -0.023273505,   -0.02464396,
+       -0.10991725,   -0.006240552,  0.0074488563,   0.024044557,
+       0.04383914,    -0.046476185,  0.028658995,    0.060410924,
+       0.050786525,   0.009452605,   -0.0073054377,  -0.024810238,
+       0.0052906186,  0.0066939713,  -0.0020913032,  0.014515517,
+       0.015898481,   0.021362653,   -0.030262267,   0.016587038,
+       -0.011442813,  0.041154444,   -0.007631438,   -0.03423484,
+       -0.010977775,  0.036152758,   0.0066366293,   0.11915515,
+       0.02318443,    -0.041350313,  0.021485701,    -0.10906167,
+       -0.028218046,  -0.00954771,   0.020531068,    -0.11995105,
+       -0.03672871,   0.024019798,   0.014255957,    -0.05221243,
+       -0.00661567,   -0.04630967,   0.033188973,    0.10107534,
+       -0.014027541,  0.030796422,   -0.10270911,    -0.035999842,
+       0.15443139,    0.07684145,    0.036571592,    -0.035900835,
+       -0.0034699554, 0.06209149,    0.015920248,    -0.031122351,
+       -0.03858649,   0.01849943,    0.13872518,     0.01503974,
+       0.069941424,   -0.06948533,   -0.0088794185,  0.061282158,
+       -0.047401894,  0.03100163,    -0.041533746,   -0.10430945,
+       0.044574402,   -0.01425562,   -0.024290353,   0.034563623,
+       0.05866852,    0.023947537,   -0.09445152,    0.035450947,
+       0.02247216,    -0.0042998926, 0.061146557,    -0.10250651,
+       0.020881841,   -0.06747029,   0.10062043,     -0.0023941975,
+       0.03532124,    -0.016341697,  0.09685456,     -0.016764693,
+       0.051808182,   0.05875331,    -0.04536488,    0.001626336,
+       -0.028892258,  -0.01048663,   -0.009793449,   -0.017093895,
+       0.010987891,   0.02357273,    -0.00010856845, 0.0099760275,
+       -0.001845119,  -0.03551521,   0.0018358806,   0.05763657,
+       -0.01769146,   0.040995963,   0.02235177,     -0.060430344,
+       0.11475477,    -0.023854522,  0.10071741,     0.0686208,
+       -0.014250481,  0.034261297,   0.047418304,    0.08562733,
+       -0.030519066,  0.0060542435,  0.014653856,    -0.038836084,
+       0.04096551,    0.032249358,   -0.08355519,    -0.026823482,
+       0.056386515,   -0.010401743,  -0.028396193,   0.08507674,
+       0.014410365,   0.020995233,   0.17040324,     0.11511526,
+       0.02459721,    0.0066619175,  0.025853224,    -0.023133837,
+       -0.081302024,  0.017264642,   -0.009585969,   0.09491168,
+       -0.051313367,  0.054532815,   -0.014298593,   0.10657464,
+       0.007076659,   0.10964551,    0.0409152,      0.008275321,
+       -0.07283536,   0.07937492,    0.04192024,     -0.1075027});
+
+  lstm.SetRecurrentToCellWeights(
+      {-0.037322544,   0.018592842,   0.0056175636,  -0.06253426,
+       0.055647098,    -0.05713207,   -0.05626563,   0.005559383,
+       0.03375411,     -0.025757805,  -0.088049285,  0.06017052,
+       -0.06570978,    0.007384076,   0.035123326,   -0.07920549,
+       0.053676967,    0.044480428,   -0.07663568,   0.0071805613,
+       0.08089997,     0.05143358,    0.038261272,   0.03339287,
+       -0.027673481,   0.044746667,   0.028349208,   0.020090483,
+       -0.019443132,   -0.030755889,  -0.0040000007, 0.04465846,
+       -0.021585021,   0.0031670958,  0.0053199246,  -0.056117613,
+       -0.10893326,    0.076739706,   -0.08509834,   -0.027997585,
+       0.037871376,    0.01449768,    -0.09002357,   -0.06111149,
+       -0.046195522,   0.0422062,     -0.005683705,  -0.1253618,
+       -0.012925729,   -0.04890792,   0.06985068,    0.037654128,
+       0.03398274,     -0.004781977,  0.007032333,   -0.031787455,
+       0.010868644,    -0.031489216,  0.09525667,    0.013939797,
+       0.0058680447,   0.0167067,     0.02668468,    -0.04797466,
+       -0.048885044,   -0.12722108,   0.035304096,   0.06554885,
+       0.00972396,     -0.039238118,  -0.05159735,   -0.11329045,
+       0.1613692,      -0.03750952,   0.06529313,    -0.071974665,
+       -0.11769596,    0.015524369,   -0.0013754242, -0.12446318,
+       0.02786344,     -0.014179351,  0.005264273,   0.14376344,
+       0.015983658,    0.03406988,    -0.06939408,   0.040699873,
+       0.02111075,     0.09669095,    0.041345075,   -0.08316494,
+       -0.07684199,    -0.045768797,  0.032298047,   -0.041805092,
+       0.0119405,      0.0061010392,  0.12652606,    0.0064572375,
+       -0.024950314,   0.11574242,    0.04508852,    -0.04335324,
+       0.06760663,     -0.027437469,  0.07216407,    0.06977076,
+       -0.05438599,    0.034033038,   -0.028602652,  0.05346137,
+       0.043184172,    -0.037189785,  0.10420091,    0.00882477,
+       -0.054019816,   -0.074273005,  -0.030617684,  -0.0028467078,
+       0.024302477,    -0.0038869337, 0.005332455,   0.0013399826,
+       0.04361412,     -0.007001822,  0.09631092,    -0.06702025,
+       -0.042049985,   -0.035070654,  -0.04103342,   -0.10273396,
+       0.0544271,      0.037184782,   -0.13150354,   -0.0058036847,
+       -0.008264958,   0.042035464,   0.05891794,    0.029673764,
+       0.0063542654,   0.044788733,   0.054816857,   0.062257513,
+       -0.00093483756, 0.048938446,   -0.004952862,  -0.007730018,
+       -0.04043371,    -0.017094059,  0.07229206,    -0.023670016,
+       -0.052195564,   -0.025616996,  -0.01520939,   0.045104615,
+       -0.007376126,   0.003533447,   0.006570588,   0.056037236,
+       0.12436656,     0.051817212,   0.028532185,   -0.08686856,
+       0.11868599,     0.07663395,    -0.07323171,   0.03463402,
+       -0.050708205,   -0.04458982,   -0.11590894,   0.021273347,
+       0.1251325,      -0.15313013,   -0.12224372,   0.17228661,
+       0.023029093,    0.086124025,   0.006445803,   -0.03496501,
+       0.028332196,    0.04449512,    -0.042436164,  -0.026587414,
+       -0.006041347,   -0.09292539,   -0.05678812,   0.03897832,
+       0.09465633,     0.008115513,   -0.02171956,   0.08304309,
+       0.071401566,    0.019622514,   0.032163795,   -0.004167056,
+       0.02295182,     0.030739572,   0.056506045,   0.004612461,
+       0.06524936,     0.059999723,   0.046395954,   -0.0045512207,
+       -0.1335546,     -0.030136576,  0.11584653,    -0.014678886,
+       0.0020118146,   -0.09688814,   -0.0790206,    0.039770417,
+       -0.0329582,     0.07922767,    0.029322514,   0.026405897,
+       0.04207835,     -0.07073373,   0.063781224,   0.0859677,
+       -0.10925287,    -0.07011058,   0.048005477,   0.03438226,
+       -0.09606514,    -0.006669445,  -0.043381985,  0.04240257,
+       -0.06955775,    -0.06769346,   0.043903265,   -0.026784198,
+       -0.017840602,   0.024307009,   -0.040079936,  -0.019946516,
+       0.045318738,    -0.12233574,   0.026170589,   0.0074471775,
+       0.15978073,     0.10185836,    0.10298046,    -0.015476589,
+       -0.039390966,   -0.072174534,  0.0739445,     -0.1211869,
+       -0.0347889,     -0.07943156,   0.014809798,   -0.12412325,
+       -0.0030663363,  0.039695457,   0.0647603,     -0.08291318,
+       -0.018529687,   -0.004423833,  0.0037507233,  0.084633216,
+       -0.01514876,    -0.056505352,  -0.012800942,  -0.06994386,
+       0.012962922,    -0.031234352,  0.07029052,    0.016418684,
+       0.03618972,     0.055686004,   -0.08663945,   -0.017404709,
+       -0.054761406,   0.029065743,   0.052404847,   0.020238016,
+       0.0048197987,   -0.0214882,    0.07078733,    0.013016777,
+       0.06262858,     0.009184685,   0.020785125,   -0.043904778,
+       -0.0270329,     -0.03299152,   -0.060088247,  -0.015162964,
+       -0.001828936,   0.12642565,    -0.056757294,  0.013586685,
+       0.09232601,     -0.035886683,  0.06000002,    0.05229691,
+       -0.052580316,   -0.082029596,  -0.010794592,  0.012947712,
+       -0.036429964,   -0.085508935,  -0.13127148,   -0.017744139,
+       0.031502828,    0.036232427,   -0.031581745,  0.023051167,
+       -0.05325106,    -0.03421577,   0.028793324,   -0.034633752,
+       -0.009881397,   -0.043551125,  -0.018609839,  0.0019097115,
+       -0.008799762,   0.056595087,   0.0022273948,  0.055752404});
+
+  lstm.SetRecurrentToOutputWeights({
+      0.025825322,   -0.05813119,  0.09495884,   -0.045984812,   -0.01255415,
+      -0.0026479573, -0.08196161,  -0.054914974, -0.0046604523,  -0.029587349,
+      -0.044576716,  -0.07480124,  -0.082868785, 0.023254942,    0.027502948,
+      -0.0039728214, -0.08683098,  -0.08116779,  -0.014675607,   -0.037924774,
+      -0.023314456,  -0.007401714, -0.09255757,  0.029460307,    -0.08829125,
+      -0.005139627,  -0.08989442,  -0.0555066,   0.13596267,     -0.025062224,
+      -0.048351806,  -0.03850004,  0.07266485,   -0.022414139,   0.05940088,
+      0.075114764,   0.09597592,   -0.010211725, -0.0049794707,  -0.011523867,
+      -0.025980417,  0.072999895,  0.11091378,   -0.081685916,   0.014416728,
+      0.043229222,   0.034178585,  -0.07530371,  0.035837382,    -0.085607,
+      -0.007721233,  -0.03287832,  -0.043848954, -0.06404588,    -0.06632928,
+      -0.073643476,  0.008214239,  -0.045984086, 0.039764922,    0.03474462,
+      0.060612556,   -0.080590084, 0.049127717,  0.04151091,     -0.030063879,
+      0.008801774,   -0.023021035, -0.019558564, 0.05158114,     -0.010947698,
+      -0.011825728,  0.0075720972, 0.0699727,    -0.0039981045,  0.069350146,
+      0.08799282,    0.016156472,  0.035502106,  0.11695009,     0.006217345,
+      0.13392477,    -0.037875112, 0.025745004,  0.08940699,     -0.00924166,
+      0.0046702605,  -0.036598757, -0.08811812,  0.10522024,     -0.032441203,
+      0.008176899,   -0.04454919,  0.07058152,   0.0067963637,   0.039206743,
+      0.03259838,    0.03725492,   -0.09515802,  0.013326398,    -0.052055415,
+      -0.025676316,  0.03198509,   -0.015951829, -0.058556724,   0.036879618,
+      0.043357447,   0.028362012,  -0.05908629,  0.0059240665,   -0.04995891,
+      -0.019187413,  0.0276265,    -0.01628143,  0.0025863599,   0.08800015,
+      0.035250366,   -0.022165963, -0.07328642,  -0.009415526,   -0.07455109,
+      0.11690406,    0.0363299,    0.07411125,   0.042103454,    -0.009660886,
+      0.019076364,   0.018299393,  -0.046004917, 0.08891175,     0.0431396,
+      -0.026327137,  -0.051502608, 0.08979574,   -0.051670972,   0.04940282,
+      -0.07491107,   -0.021240504, 0.022596184,  -0.034280192,   0.060163025,
+      -0.058211457,  -0.051837247, -0.01349775,  -0.04639988,    -0.035936575,
+      -0.011681591,  0.064818054,  0.0073146066, -0.021745546,   -0.043124277,
+      -0.06471268,   -0.07053354,  -0.029321948, -0.05330136,    0.016933719,
+      -0.053782392,  0.13747959,   -0.1361751,   -0.11569455,    0.0033329215,
+      0.05693899,    -0.053219706, 0.063698,     0.07977434,     -0.07924483,
+      0.06936997,    0.0034815092, -0.007305279, -0.037325785,   -0.07251102,
+      -0.033633437,  -0.08677009,  0.091591336,  -0.14165086,    0.021752775,
+      0.019683983,   0.0011612234, -0.058154266, 0.049996935,    0.0288841,
+      -0.0024567875, -0.14345716,  0.010955264,  -0.10234828,    0.1183656,
+      -0.0010731248, -0.023590032, -0.072285876, -0.0724771,     -0.026382286,
+      -0.0014920527, 0.042667855,  0.0018776858, 0.02986552,     0.009814309,
+      0.0733756,     0.12289186,   0.018043943,  -0.0458958,     0.049412545,
+      0.033632483,   0.05495232,   0.036686596,  -0.013781798,   -0.010036754,
+      0.02576849,    -0.08307328,  0.010112348,  0.042521734,    -0.05869831,
+      -0.071689695,  0.03876447,   -0.13275425,  -0.0352966,     -0.023077697,
+      0.10285965,    0.084736146,  0.15568255,   -0.00040734606, 0.027835453,
+      -0.10292561,   -0.032401145, 0.10053256,   -0.026142767,   -0.08271222,
+      -0.0030240538, -0.016368777, 0.1070414,    0.042672627,    0.013456989,
+      -0.0437609,    -0.022309763, 0.11576483,   0.04108048,     0.061026827,
+      -0.0190714,    -0.0869359,   0.037901703,  0.0610107,      0.07202949,
+      0.01675338,    0.086139716,  -0.08795751,  -0.014898893,   -0.023771819,
+      -0.01965048,   0.007955471,  -0.043740474, 0.03346837,     -0.10549954,
+      0.090567775,   0.042013682,  -0.03176985,  0.12569028,     -0.02421228,
+      -0.029526481,  0.023851605,  0.031539805,  0.05292009,     -0.02344001,
+      -0.07811758,   -0.08834428,  0.10094801,   0.16594367,     -0.06861939,
+      -0.021256343,  -0.041093912, -0.06669611,  0.035498552,    0.021757556,
+      -0.09302526,   -0.015403468, -0.06614931,  -0.051798206,   -0.013874718,
+      0.03630673,    0.010412845,  -0.08077351,  0.046185967,    0.0035662893,
+      0.03541868,    -0.094149634, -0.034814864, 0.003128424,    -0.020674974,
+      -0.03944324,   -0.008110165, -0.11113267,  0.08484226,     0.043586485,
+      0.040582247,   0.0968012,    -0.065249965, -0.028036479,   0.0050708856,
+      0.0017462453,  0.0326779,    0.041296225,  0.09164146,     -0.047743853,
+      -0.015952192,  -0.034451712, 0.084197424,  -0.05347844,    -0.11768019,
+      0.085926116,   -0.08251791,  -0.045081906, 0.0948852,      0.068401024,
+      0.024856757,   0.06978981,   -0.057309967, -0.012775832,   -0.0032452994,
+      0.01977615,    -0.041040014, -0.024264973, 0.063464895,    0.05431621,
+  });
+
+  lstm.SetCellToInputWeights(
+      {0.040369894, 0.030746894,  0.24704495,  0.018586371,  -0.037586458,
+       -0.15312155, -0.11812848,  -0.11465643, 0.20259799,   0.11418174,
+       -0.10116027, -0.011334949, 0.12411352,  -0.076769054, -0.052169047,
+       0.21198851,  -0.38871562,  -0.09061183, -0.09683246,  -0.21929175});
+
+  lstm.SetCellToForgetWeights(
+      {-0.01998659,  -0.15568835,  -0.24248174,   -0.012770197, 0.041331276,
+       -0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766,
+       -0.047248036, 0.021479502,  0.033189066,   0.11952997,   -0.020432774,
+       0.64658105,   -0.06650122,  -0.03467612,   0.095340036,  0.23647355});
+
+  lstm.SetCellToOutputWeights(
+      {0.08286371,  -0.08261836, -0.51210177, 0.002913762, 0.17764764,
+       -0.5495371,  -0.08460716, -0.24552552, 0.030037103, 0.04123544,
+       -0.11940523, 0.007358328, 0.1890978,   0.4833202,   -0.34441817,
+       0.36312827,  -0.26375428, 0.1457655,   -0.19724406, 0.15548733});
+
+  lstm.SetProjectionWeights(
+      {-0.009802181,  0.09401916,    0.0717386,     -0.13895074,  0.09641832,
+       0.060420845,   0.08539281,    0.054285463,   0.061395317,  0.034448683,
+       -0.042991187,  0.019801661,   -0.16840284,   -0.015726732, -0.23041931,
+       -0.024478018,  -0.10959692,   -0.013875541,  0.18600968,   -0.061274476,
+       0.0138165,     -0.08160894,   -0.07661644,   0.032372914,  0.16169067,
+       0.22465782,    -0.03993472,   -0.004017731,  0.08633481,   -0.28869787,
+       0.08682067,    0.17240396,    0.014975425,   0.056431185,  0.031037588,
+       0.16702051,    0.0077946745,  0.15140012,    0.29405436,   0.120285,
+       -0.188994,     -0.027265169,  0.043389652,   -0.022061434, 0.014777949,
+       -0.20203483,   0.094781205,   0.19100232,    0.13987629,   -0.036132768,
+       -0.06426278,   -0.05108664,   0.13221376,    0.009441198,  -0.16715929,
+       0.15859416,    -0.040437475,  0.050779544,   -0.022187516, 0.012166504,
+       0.027685808,   -0.07675938,   -0.0055694645, -0.09444123,  0.0046453946,
+       0.050794356,   0.10770313,    -0.20790008,   -0.07149004,  -0.11425117,
+       0.008225835,   -0.035802525,  0.14374903,    0.15262283,   0.048710253,
+       0.1847461,     -0.007487823,  0.11000021,    -0.09542012,  0.22619456,
+       -0.029149994,  0.08527916,    0.009043713,   0.0042746216, 0.016261552,
+       0.022461696,   0.12689082,    -0.043589946,  -0.12035478,  -0.08361797,
+       -0.050666027,  -0.1248618,    -0.1275799,    -0.071875185, 0.07377272,
+       0.09944291,    -0.18897448,   -0.1593054,    -0.06526116,  -0.040107165,
+       -0.004618631,  -0.067624845,  -0.007576253,  0.10727444,   0.041546922,
+       -0.20424393,   0.06907816,    0.050412357,   0.00724631,   0.039827548,
+       0.12449835,    0.10747581,    0.13708383,    0.09134148,   -0.12617786,
+       -0.06428341,   0.09956831,    0.1208086,     -0.14676677,  -0.0727722,
+       0.1126304,     0.010139365,   0.015571211,   -0.038128063, 0.022913318,
+       -0.042050496,  0.16842307,    -0.060597885,  0.10531834,   -0.06411776,
+       -0.07451711,   -0.03410368,   -0.13393489,   0.06534304,   0.003620307,
+       0.04490757,    0.05970546,    0.05197996,    0.02839995,   0.10434969,
+       -0.013699693,  -0.028353551,  -0.07260381,   0.047201227,  -0.024575593,
+       -0.036445823,  0.07155557,    0.009672501,   -0.02328883,  0.009533515,
+       -0.03606021,   -0.07421458,   -0.028082801,  -0.2678904,   -0.13221288,
+       0.18419984,    -0.13012612,   -0.014588381,  -0.035059117, -0.04824723,
+       0.07830115,    -0.056184657,  0.03277091,    0.025466874,  0.14494097,
+       -0.12522776,   -0.098633975,  -0.10766018,   -0.08317623,  0.08594209,
+       0.07749552,    0.039474737,   0.1776665,     -0.07409566,  -0.0477268,
+       0.29323658,    0.10801441,    0.1154011,     0.013952499,  0.10739139,
+       0.10708251,    -0.051456142,  0.0074137426,  -0.10430189,  0.10034707,
+       0.045594677,   0.0635285,     -0.0715442,    -0.089667566, -0.10811871,
+       0.00026344223, 0.08298446,    -0.009525053,  0.006585689,  -0.24567553,
+       -0.09450807,   0.09648481,    0.026996298,   -0.06419476,  -0.04752702,
+       -0.11063944,   -0.23441927,   -0.17608605,   -0.052156363, 0.067035615,
+       0.19271925,    -0.0032889997, -0.043264326,  0.09663576,   -0.057112187,
+       -0.10100678,   0.0628376,     0.04447668,    0.017961001,  -0.10094388,
+       -0.10190601,   0.18335468,    0.10494553,    -0.052095775, -0.0026118709,
+       0.10539724,    -0.04383912,   -0.042349473,  0.08438151,   -0.1947263,
+       0.02251204,    0.11216432,    -0.10307853,   0.17351969,   -0.039091777,
+       0.08066188,    -0.00561982,   0.12633002,    0.11335965,   -0.0088127935,
+       -0.019777594,  0.06864014,    -0.059751723,  0.016233567,  -0.06894641,
+       -0.28651384,   -0.004228674,  0.019708522,   -0.16305895,  -0.07468996,
+       -0.0855457,    0.099339016,   -0.07580735,   -0.13775392,  0.08434318,
+       0.08330512,    -0.12131499,   0.031935584,   0.09180414,   -0.08876437,
+       -0.08049874,   0.008753825,   0.03498998,    0.030215185,  0.03907079,
+       0.089751154,   0.029194152,   -0.03337423,   -0.019092513, 0.04331237,
+       0.04299654,    -0.036394123,  -0.12915532,   0.09793732,   0.07512415,
+       -0.11319543,   -0.032502122,  0.15661901,    0.07671967,   -0.005491124,
+       -0.19379048,   -0.218606,     0.21448623,    0.017840758,  0.1416943,
+       -0.07051762,   0.19488361,    0.02664691,    -0.18104725,  -0.09334311,
+       0.15026465,    -0.15493552,   -0.057762887,  -0.11604192,  -0.262013,
+       -0.01391798,   0.012185008,   0.11156489,    -0.07483202,  0.06693364,
+       -0.26151478,   0.046425626,   0.036540434,   -0.16435726,  0.17338543,
+       -0.21401681,   -0.11385144,   -0.08283257,   -0.069031075, 0.030635102,
+       0.010969227,   0.11109743,    0.010919218,   0.027526086,  0.13519906,
+       0.01891392,    -0.046839405,  -0.040167913,  0.017953383,  -0.09700955,
+       0.0061885654,  -0.07000971,   0.026893595,   -0.038844477, 0.14543656});
+
+  static float lstm_input[][20] = {
+      {// Batch0: 4 (input_sequence_size) * 5 (n_input)
+       0.787926, 0.151646, 0.071352, 0.118426, 0.458058, 0.596268, 0.998386,
+       0.568695, 0.864524, 0.571277, 0.073204, 0.296072, 0.743333, 0.069199,
+       0.045348, 0.867394, 0.291279, 0.013714, 0.482521, 0.626339},
+
+      {// Batch1: 4 (input_sequence_size) * 5 (n_input)
+       0.295743, 0.544053, 0.690064, 0.858138, 0.497181, 0.642421, 0.524260,
+       0.134799, 0.003639, 0.162482, 0.640394, 0.930399, 0.050782, 0.432485,
+       0.988078, 0.082922, 0.563329, 0.865614, 0.333232, 0.259916}};
+
+  static float lstm_fw_golden_output[][64] = {
+      {// Batch0: 4 (input_sequence_size) * 16 (n_output)
+       -0.00396806, 0.029352,     -0.00279226, 0.0159977,   -0.00835576,
+       -0.0211779,  0.0283512,    -0.0114597,  0.00907307,  -0.0244004,
+       -0.0152191,  -0.0259063,   0.00914318,  0.00415118,  0.017147,
+       0.0134203,   -0.0166936,   0.0381209,   0.000889694, 0.0143363,
+       -0.0328911,  -0.0234288,   0.0333051,   -0.012229,   0.0110322,
+       -0.0457725,  -0.000832209, -0.0202817,  0.0327257,   0.0121308,
+       0.0155969,   0.0312091,    -0.0213783,  0.0350169,   0.000324794,
+       0.0276012,   -0.0263374,   -0.0371449,  0.0446149,   -0.0205474,
+       0.0103729,   -0.0576349,   -0.0150052,  -0.0292043,  0.0376827,
+       0.0136115,   0.0243435,    0.0354492,   -0.0189322,  0.0464512,
+       -0.00251373, 0.0225745,    -0.0308346,  -0.0317124,  0.0460407,
+       -0.0189395,  0.0149363,    -0.0530162,  -0.0150767,  -0.0340193,
+       0.0286833,   0.00824207,   0.0264887,   0.0305169},
+      {// Batch1: 4 (input_sequence_size) * 16 (n_output)
+       -0.013869,    0.0287268,   -0.00334693, 0.00733398,  -0.0287926,
+       -0.0186926,   0.0193662,   -0.0115437,  0.00422612,  -0.0345232,
+       0.00223253,   -0.00957321, 0.0210624,   0.013331,    0.0150954,
+       0.02168,      -0.0141913,  0.0322082,   0.00227024,  0.0260507,
+       -0.0188721,   -0.0296489,  0.0399134,   -0.0160509,  0.0116039,
+       -0.0447318,   -0.0150515,  -0.0277406,  0.0316596,   0.0118233,
+       0.0214762,    0.0293641,   -0.0204549,  0.0450315,   -0.00117378,
+       0.0167673,    -0.0375007,  -0.0238314,  0.038784,    -0.0174034,
+       0.0131743,    -0.0506589,  -0.0048447,  -0.0240239,  0.0325789,
+       0.00790065,   0.0220157,   0.0333314,   -0.0264787,  0.0387855,
+       -0.000764675, 0.0217599,   -0.037537,   -0.0335206,  0.0431679,
+       -0.0211424,   0.010203,    -0.062785,   -0.00832363, -0.025181,
+       0.0412031,    0.0118723,   0.0239643,   0.0394009}};
+
+  static float lstm_combined_golden_output[][64] = {
+    {
+      -0.022014,  0.073544, -0.002235,  0.040068, -0.037136, -0.052788,
+      0.075325, -0.029378,  0.024298, -0.07733 , -0.030674, -0.060229,
+      0.040599,  0.011608,  0.042005,  0.045977, -0.039225,  0.076294,
+      0.000735,  0.032852, -0.069869, -0.053312,  0.073527, -0.028136,
+      0.021585, -0.102679, -0.004327, -0.043304,  0.072861,  0.027077,
+      0.034558,  0.068292, -0.036292,  0.069832, -0.003032,  0.053829,
+      -0.043821, -0.072713,  0.085029, -0.040374,  0.020014, -0.104521,
+      -0.034504, -0.059759,  0.062569,  0.025652,  0.049306,  0.061189,
+      -0.025146,  0.079643, -0.005188,  0.033080, -0.048079, -0.048082,
+      0.069369, -0.028900,  0.024572, -0.077547, -0.022517, -0.054477,
+      0.038857,  0.013336,  0.043234,  0.044788},
+    {
+      -0.039186,  0.070792, -0.005913,  0.02642,  -0.068274, -0.05022,
+      0.061444, -0.031241,  0.014996, -0.094544, -0.004146, -0.03464,
+      0.058981,  0.026097,  0.039781,  0.058408, -0.031887,  0.069252,
+      0.00576,   0.054062, -0.042801, -0.059974,  0.085272, -0.034453,
+      0.026097, -0.0959,   -0.031164, -0.058699,  0.06839,   0.020512,
+      0.044727,  0.063609, -0.039863,  0.084819, -0.003909,  0.028666,
+      -0.075677, -0.045125,  0.070379, -0.033895,  0.022111, -0.097184,
+      -0.004921, -0.040851,  0.062316,  0.017435,  0.041437,  0.064568,
+      -0.039656,  0.060726, -0.003402,  0.036854, -0.056503, -0.058554,
+      0.068588, -0.034879,  0.01352,  -0.09962,  -0.01434,  -0.039505,
+      0.065133,  0.024321,  0.038473,  0.062438
+    }};
+
+  // Resetting cell_state and output_state
+  lstm.ResetFwOutputAndCellStates();
+  lstm.ResetBwOutputAndCellStates();
+
+  for (int i = 0; i < lstm.sequence_length(); i++) {
+    float* batch0_start = lstm_input[0] + i * lstm.num_inputs();
+    float* batch0_end = batch0_start + lstm.num_inputs();
+
+    lstm.SetInput(2 * i * lstm.num_inputs(), batch0_start, batch0_end);
+
+    float* batch1_start = lstm_input[1] + i * lstm.num_inputs();
+    float* batch1_end = batch1_start + lstm.num_inputs();
+    lstm.SetInput((2 * i + 1) * lstm.num_inputs(), batch1_start, batch1_end);
+  }
+
+  lstm.Invoke();
+
+  std::vector<float> expected;
+  for (int i = 0; i < lstm.sequence_length(); i++) {
+    float* golden_start_batch0 =
+        lstm_fw_golden_output[0] + i * lstm.num_fw_outputs();
+    float* golden_end_batch0 = golden_start_batch0 + lstm.num_fw_outputs();
+    float* golden_start_batch1 =
+        lstm_fw_golden_output[1] + i * lstm.num_fw_outputs();
+    float* golden_end_batch1 = golden_start_batch1 + lstm.num_fw_outputs();
+    expected.insert(expected.end(), golden_start_batch0, golden_end_batch0);
+    expected.insert(expected.end(), golden_start_batch1, golden_end_batch1);
+  }
+  EXPECT_THAT(lstm.GetFwOutput(), ElementsAreArray(ArrayFloatNear(expected)));
+
+  // Check if the sum of forward backward matches the golden.
+  expected.clear();
+  for (int i = 0; i < lstm.sequence_length(); i++) {
+    float* golden_start_batch0 =
+        lstm_combined_golden_output[0] + i * lstm.num_fw_outputs();
+    float* golden_end_batch0 = golden_start_batch0 + lstm.num_fw_outputs();
+    float* golden_start_batch1 =
+        lstm_combined_golden_output[1] + i * lstm.num_fw_outputs();
+    float* golden_end_batch1 = golden_start_batch1 + lstm.num_fw_outputs();
+    expected.insert(expected.end(), golden_start_batch0, golden_end_batch0);
+    expected.insert(expected.end(), golden_start_batch1, golden_end_batch1);
+  }
+
+  std::vector<float> combined;
+  for (int i = 0; i < lstm.GetFwOutput().size(); ++i) {
+    combined.push_back(lstm.GetFwOutput()[i] + lstm.GetBwOutput()[i]);
+  }
+  EXPECT_THAT(combined, ElementsAreArray(ArrayFloatNear(expected)));
+}
+
+}  // namespace
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/contrib/lite/kernels/register.cc b/tensorflow/contrib/lite/kernels/register.cc
index c87a4ac50b..aea6f8d9d3 100644
--- a/tensorflow/contrib/lite/kernels/register.cc
+++ b/tensorflow/contrib/lite/kernels/register.cc
@@ -49,6 +49,7 @@ TfLiteRegistration* Register_MUL();
 TfLiteRegistration* Register_L2_NORMALIZATION();
 TfLiteRegistration* Register_LOCAL_RESPONSE_NORMALIZATION();
 TfLiteRegistration* Register_LSTM();
+TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_LSTM();
 TfLiteRegistration* Register_UNIDIRECTIONAL_SEQUENCE_LSTM();
 TfLiteRegistration* Register_PAD();
 TfLiteRegistration* Register_RESHAPE();
@@ -98,6 +99,8 @@ BuiltinOpResolver::BuiltinOpResolver() {
   AddBuiltin(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION,
              Register_LOCAL_RESPONSE_NORMALIZATION());
   AddBuiltin(BuiltinOperator_LSTM, Register_LSTM());
+  AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM,
+             Register_BIDIRECTIONAL_SEQUENCE_LSTM());
   AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM,
              Register_UNIDIRECTIONAL_SEQUENCE_LSTM());
   AddBuiltin(BuiltinOperator_PAD, Register_PAD());
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index 520a4c1089..7dae9f4d18 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -462,6 +462,7 @@ void* ParseOpData(const Operator* op, BuiltinOperator op_type,
       builtin_data = reinterpret_cast<void*>(params);
       break;
     }
+    case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM:
     case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM:
     case BuiltinOperator_LSTM: {
       TfLiteLSTMParams* params = MallocPOD<TfLiteLSTMParams>();
diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc
index 4150ffefc1..e631ffd845 100644
--- a/tensorflow/contrib/lite/nnapi_delegate.cc
+++ b/tensorflow/contrib/lite/nnapi_delegate.cc
@@ -323,6 +323,7 @@ void AddOpsAndParams(tflite::Interpreter* interpreter,
       case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN:
       case tflite::BuiltinOperator_EMBEDDING_LOOKUP:
       case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE:
+      case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM:
       case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM:
       case tflite::BuiltinOperator_L2_NORMALIZATION:
       case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION:
diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs
index 03b471926c..98ac0469d1 100644
--- a/tensorflow/contrib/lite/schema/schema.fbs
+++ b/tensorflow/contrib/lite/schema/schema.fbs
@@ -128,6 +128,7 @@ enum BuiltinOperator : byte {
   // other backends.
   // WARNING: Experimental interface, subject to change
   DELEGATE = 51,
+  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
 }
 
 // Options for the builtin operators.
diff --git a/tensorflow/contrib/lite/schema/schema_generated.h b/tensorflow/contrib/lite/schema/schema_generated.h
index 052e35fbf0..99e1accaa7 100755
--- a/tensorflow/contrib/lite/schema/schema_generated.h
+++ b/tensorflow/contrib/lite/schema/schema_generated.h
@@ -245,11 +245,12 @@ enum BuiltinOperator {
   BuiltinOperator_SPLIT = 49,
   BuiltinOperator_LOG_SOFTMAX = 50,
   BuiltinOperator_DELEGATE = 51,
+  BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52,
   BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_DELEGATE
+  BuiltinOperator_MAX = BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM
 };
 
-inline BuiltinOperator (&EnumValuesBuiltinOperator())[49] {
+inline BuiltinOperator (&EnumValuesBuiltinOperator())[50] {
   static BuiltinOperator values[] = {
     BuiltinOperator_ADD,
     BuiltinOperator_AVERAGE_POOL_2D,
@@ -299,7 +300,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[49] {
     BuiltinOperator_TOPK_V2,
     BuiltinOperator_SPLIT,
     BuiltinOperator_LOG_SOFTMAX,
-    BuiltinOperator_DELEGATE
+    BuiltinOperator_DELEGATE,
+    BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM
   };
   return values;
 }
@@ -358,6 +360,7 @@ inline const char **EnumNamesBuiltinOperator() {
     "SPLIT",
     "LOG_SOFTMAX",
     "DELEGATE",
+    "BIDIRECTIONAL_SEQUENCE_LSTM",
     nullptr
   };
   return names;
-- 
GitLab


From f1997ccfa3e00c71f522263b775946c67c4bd730 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 12:00:04 -0800
Subject: [PATCH 137/575] Adds inverse_link_fn argument in regression_head.
 This is used in generalized regression.

PiperOrigin-RevId: 186497656
---
 .../estimator/python/estimator/head.py        | 14 ++++-
 tensorflow/python/estimator/canned/head.py    | 30 +++++++++--
 .../python/estimator/canned/head_test.py      | 53 +++++++++++++++++--
 3 files changed, 88 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py
index 238cf287b7..a45f6934cc 100644
--- a/tensorflow/contrib/estimator/python/estimator/head.py
+++ b/tensorflow/contrib/estimator/python/estimator/head.py
@@ -177,6 +177,7 @@ def regression_head(weight_column=None,
                     label_dimension=1,
                     loss_reduction=losses.Reduction.SUM,
                     loss_fn=None,
+                    inverse_link_fn=None,
                     name=None):
   """Creates a `_Head` for regression using the `mean_squared_error` loss.
 
@@ -195,10 +196,16 @@ def regression_head(weight_column=None,
   `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or
   `[D0, D1, ... DN, label_dimension]`.
 
-  Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or
+  Supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or
   `(labels, logits, features)` as arguments and returns unreduced loss with
   shape `[D0, D1, ... DN, label_dimension]`.
 
+  Also supports custom `inverse_link_fn`, also known as 'mean function'.
+  `inverse_link_fn` takes `logits` as argument and returns predicted values.
+  This function is the inverse of the link function defined in
+  https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function
+  Namely, for poisson regression, set `inverse_link_fn=tf.exp`.
+
   Args:
     weight_column: A string or a `_NumericColumn` created by
       `tf.feature_column.numeric_column` defining feature column representing
@@ -209,7 +216,9 @@ def regression_head(weight_column=None,
       `[batch_size, label_dimension]`).
     loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to
       reduce training loss over batch. Defaults to `SUM`.
-    loss_fn: Optional loss function.
+    loss_fn: Optional loss function. Defaults to `mean_squared_error`.
+    inverse_link_fn: Optional inverse link function, also known as 'mean
+      function'. Defaults to identity.
     name: name of the head. If provided, summary and metrics keys will be
       suffixed by `"/" + name`. Also used as `name_scope` when creating ops.
 
@@ -224,6 +233,7 @@ def regression_head(weight_column=None,
       label_dimension=label_dimension,
       loss_reduction=loss_reduction,
       loss_fn=loss_fn,
+      inverse_link_fn=inverse_link_fn,
       name=name)
 
 
diff --git a/tensorflow/python/estimator/canned/head.py b/tensorflow/python/estimator/canned/head.py
index cb9e3fc6ca..8d742a2c61 100644
--- a/tensorflow/python/estimator/canned/head.py
+++ b/tensorflow/python/estimator/canned/head.py
@@ -1156,6 +1156,7 @@ def _regression_head_with_mean_squared_error_loss(
     label_dimension=1,
     loss_reduction=losses.Reduction.SUM,
     loss_fn=None,
+    inverse_link_fn=None,
     name=None):
   """Creates a `_Head` for regression using the `mean_squared_error` loss.
 
@@ -1174,10 +1175,16 @@ def _regression_head_with_mean_squared_error_loss(
   `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or
   `[D0, D1, ... DN, label_dimension]`.
 
-  Also supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or
+  Supports custom `loss_fn`. `loss_fn` takes `(labels, logits)` or
   `(labels, logits, features)` as arguments and returns unreduced loss with
   shape `[D0, D1, ... DN, label_dimension]`.
 
+  Also supports custom `inverse_link_fn`, also known as 'mean function'.
+  `inverse_link_fn` takes `logits` as argument and returns predicted values.
+  This function is the inverse of the link function defined in
+  https://en.wikipedia.org/wiki/Generalized_linear_model#Link_function
+  Namely, for poisson regression, set `inverse_link_fn=tf.exp`.
+
   Args:
     weight_column: A string or a `_NumericColumn` created by
       `tf.feature_column.numeric_column` defining feature column representing
@@ -1188,7 +1195,9 @@ def _regression_head_with_mean_squared_error_loss(
       `[batch_size, label_dimension]`).
     loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to
       reduce training loss over batch. Defaults to `SUM`.
-    loss_fn: Optional loss function.
+    loss_fn: Optional loss function. Defaults to `mean_squared_error`.
+    inverse_link_fn: Optional inverse link function, also known as 'mean
+      function'. Defaults to identity.
     name: name of the head. If provided, summary and metrics keys will be
       suffixed by `"/" + name`. Also used as `name_scope` when creating ops.
 
@@ -1208,6 +1217,7 @@ def _regression_head_with_mean_squared_error_loss(
       label_dimension=label_dimension,
       loss_reduction=loss_reduction,
       loss_fn=loss_fn,
+      inverse_link_fn=inverse_link_fn,
       name=name)
 
 
@@ -1220,6 +1230,7 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
       weight_column=None,
       loss_reduction=losses.Reduction.SUM,
       loss_fn=None,
+      inverse_link_fn=None,
       name=None):
     """`Head` for regression."""
     if label_dimension < 1:
@@ -1228,6 +1239,7 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
     self._weight_column = weight_column
     self._loss_reduction = loss_reduction
     self._loss_fn = loss_fn
+    self._inverse_link_fn = inverse_link_fn
     self._name = name
 
   @property
@@ -1294,9 +1306,19 @@ class _RegressionHeadWithMeanSquaredErrorLoss(_Head):
     # Predict.
     with ops.name_scope(self._name, 'head'):
       logits = _check_logits_final_dim(logits, self._logits_dimension)
-      predictions = {prediction_keys.PredictionKeys.PREDICTIONS: logits}
+      if self._inverse_link_fn:
+        predicted_value = self._inverse_link_fn(logits)
+        predictions = {
+            prediction_keys.PredictionKeys.PREDICTIONS: predicted_value,
+            prediction_keys.PredictionKeys.LOGITS: logits,
+        }
+      else:
+        predicted_value = logits
+        predictions = {
+            prediction_keys.PredictionKeys.PREDICTIONS: predicted_value}
       if mode == model_fn.ModeKeys.PREDICT:
-        regression_output = export_output.RegressionOutput(value=logits)
+        regression_output = export_output.RegressionOutput(
+            value=predicted_value)
         return model_fn.EstimatorSpec(
             mode=model_fn.ModeKeys.PREDICT,
             predictions=predictions,
diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py
index c09f88262a..a300f315c1 100644
--- a/tensorflow/python/estimator/canned/head_test.py
+++ b/tensorflow/python/estimator/canned/head_test.py
@@ -2703,10 +2703,9 @@ class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase):
     self.assertIsNone(spec.loss)
     self.assertEqual({}, spec.eval_metric_ops)
     self.assertIsNone(spec.train_op)
+    default_serving_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
     self.assertItemsEqual(
-        (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,
-         'predict',
-         'regression'),
+        (default_serving_key, 'predict', 'regression'),
         spec.export_outputs.keys())
     _assert_no_hooks(self, spec)
 
@@ -2714,6 +2713,54 @@ class RegressionHeadWithMeanSquaredErrorLossTest(test.TestCase):
     with self.test_session():
       _initialize_variables(self, spec.scaffold)
       self.assertAllClose(logits, spec.predictions[prediction_key].eval())
+      self.assertAllClose(
+          logits, spec.export_outputs[default_serving_key].value.eval())
+      self.assertAllClose(
+          logits, spec.export_outputs['regression'].value.eval())
+      self.assertAllClose(
+          logits, spec.export_outputs['predict'].outputs['predictions'].eval())
+
+  def test_predict_with_inverse_link_fn(self):
+    def _inverse_link_fn(logits):
+      return logits - 10.
+    head = head_lib._regression_head_with_mean_squared_error_loss(
+        inverse_link_fn=_inverse_link_fn)
+
+    # Create estimator spec.
+    logits = np.array(((45,), (41,),), dtype=np.int32)
+    expected_predictions = np.array(((35,), (31,),), dtype=np.int32)
+    spec = head.create_estimator_spec(
+        features={'x': np.array(((42.,),), dtype=np.int32)},
+        mode=model_fn.ModeKeys.PREDICT,
+        logits=logits)
+
+    # Assert spec contains expected tensors.
+    keys = prediction_keys.PredictionKeys
+    self.assertItemsEqual(
+        (keys.PREDICTIONS, keys.LOGITS), spec.predictions.keys())
+    self.assertEqual(dtypes.float32, spec.predictions[keys.PREDICTIONS].dtype)
+    self.assertEqual(dtypes.float32, spec.predictions[keys.LOGITS].dtype)
+    default_serving_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+    self.assertItemsEqual(
+        (default_serving_key, 'predict', 'regression'),
+        spec.export_outputs.keys())
+
+    # Assert predictions.
+    with self.test_session():
+      _initialize_variables(self, spec.scaffold)
+      self.assertAllClose(
+          expected_predictions, spec.predictions[keys.PREDICTIONS].eval())
+      self.assertAllClose(logits, spec.predictions[keys.LOGITS].eval())
+      self.assertAllClose(
+          expected_predictions,
+          spec.export_outputs[default_serving_key].value.eval())
+      self.assertAllClose(
+          expected_predictions, spec.export_outputs['regression'].value.eval())
+      self.assertAllClose(
+          expected_predictions,
+          spec.export_outputs['predict'].outputs['predictions'].eval())
+      self.assertAllClose(
+          logits, spec.export_outputs['predict'].outputs['logits'].eval())
 
   def test_eval_create_loss(self):
     head = head_lib._regression_head_with_mean_squared_error_loss()
-- 
GitLab


From 91213cf9e3d7ded171c6c46fdb95521b40b8c4b9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 12:13:48 -0800
Subject: [PATCH 138/575] Automatically add a tf.name_scope of the function or
 class name as the first line of each function.  For functions inside a class,
 the class name is also prepended to the name scope.  Also adds a decorator
 param to turn this off (it defaults to True.)

PiperOrigin-RevId: 186499660
---
 tensorflow/contrib/py2tf/converters/BUILD     | 11 +++
 .../py2tf/converters/converter_test_base.py   |  2 +
 .../contrib/py2tf/converters/name_scopes.py   | 52 +++++++++++
 .../py2tf/converters/name_scopes_test.py      | 92 +++++++++++++++++++
 tensorflow/contrib/py2tf/impl/api_test.py     |  4 +-
 tensorflow/contrib/py2tf/impl/conversion.py   |  3 +
 .../contrib/py2tf/impl/conversion_test.py     |  5 +-
 tensorflow/contrib/py2tf/pyct/context.py      |  4 +-
 .../pyct/static_analysis/activity_test.py     |  1 +
 .../pyct/static_analysis/live_values_test.py  |  1 +
 .../pyct/static_analysis/type_info_test.py    |  1 +
 11 files changed, 172 insertions(+), 4 deletions(-)
 create mode 100644 tensorflow/contrib/py2tf/converters/name_scopes.py
 create mode 100644 tensorflow/contrib/py2tf/converters/name_scopes_test.py

diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD
index 3cce8be9d5..42baaaaba7 100644
--- a/tensorflow/contrib/py2tf/converters/BUILD
+++ b/tensorflow/contrib/py2tf/converters/BUILD
@@ -27,6 +27,7 @@ py_library(
         "for_loops.py",
         "list_comprehension.py",
         "logical_expressions.py",
+        "name_scopes.py",
         "side_effect_guards.py",
     ],
     srcs_version = "PY2AND3",
@@ -140,6 +141,16 @@ py_test(
     ],
 )
 
+py_test(
+    name = "name_scopes_test",
+    srcs = ["name_scopes_test.py"],
+    deps = [
+        ":test_lib",
+        "//tensorflow/contrib/py2tf/pyct",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
 py_test(
     name = "list_comprehension_test",
     srcs = ["list_comprehension_test.py"],
diff --git a/tensorflow/contrib/py2tf/converters/converter_test_base.py b/tensorflow/contrib/py2tf/converters/converter_test_base.py
index 67747183dd..afa5c2f96f 100644
--- a/tensorflow/contrib/py2tf/converters/converter_test_base.py
+++ b/tensorflow/contrib/py2tf/converters/converter_test_base.py
@@ -83,6 +83,7 @@ class TestCase(test.TestCase):
                         namer=None,
                         arg_types=None,
                         include_type_analysis=True,
+                        owner_type=None,
                         recursive=True):
     node, source = parser.parse_entity(test_fn)
     ctx = context.EntityContext(
@@ -92,6 +93,7 @@ class TestCase(test.TestCase):
         namespace=namespace,
         arg_values=None,
         arg_types=arg_types,
+        owner_type=owner_type,
         recursive=recursive)
     node = qual_names.resolve(node)
     node = activity.resolve(node, ctx)
diff --git a/tensorflow/contrib/py2tf/converters/name_scopes.py b/tensorflow/contrib/py2tf/converters/name_scopes.py
new file mode 100644
index 0000000000..c702823fcf
--- /dev/null
+++ b/tensorflow/contrib/py2tf/converters/name_scopes.py
@@ -0,0 +1,52 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Wraps a function body with a `name_scope` of the function name.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import gast
+
+from tensorflow.contrib.py2tf.pyct import templates
+from tensorflow.contrib.py2tf.pyct import transformer
+
+
+class FunctionNameScopeTransformer(transformer.Base):
+  """Wrap a function body with a `name_scope` of the function name."""
+
+  def __init__(self, context):
+    super(FunctionNameScopeTransformer, self).__init__(context)
+    self._function_level = 0
+
+  def visit_FunctionDef(self, node):
+    self._function_level += 1
+    try:
+      self.generic_visit(node)
+    finally:
+      self._function_level -= 1
+    scope_name = node.name
+    if self._function_level == 0 and self.context.owner_type is not None:
+      scope_name = '{}/{}'.format(self.context.owner_type.__name__, scope_name)
+    node.body = templates.replace(
+        'with tf.name_scope(scope_name): body',
+        scope_name=gast.Str(scope_name),
+        body=node.body)
+    return node
+
+
+def transform(node, context):
+  return FunctionNameScopeTransformer(context).visit(node)
diff --git a/tensorflow/contrib/py2tf/converters/name_scopes_test.py b/tensorflow/contrib/py2tf/converters/name_scopes_test.py
new file mode 100644
index 0000000000..a8ca341602
--- /dev/null
+++ b/tensorflow/contrib/py2tf/converters/name_scopes_test.py
@@ -0,0 +1,92 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for for_canonicalization module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.py2tf.converters import converter_test_base
+from tensorflow.contrib.py2tf.converters import name_scopes
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.platform import test
+
+
+class FunctionNameScopeTransformer(converter_test_base.TestCase):
+
+  def test_basic_name(self):
+
+    def test_fn(l):
+      a = 5
+      l += a
+      return l
+
+    node = self.parse_and_analyze(test_fn, {})
+    node = name_scopes.transform(node, self.ctx)
+
+    with self.compiled(node, ops.name_scope) as result:
+      result_op = result.test_fn(constant_op.constant([1, 2, 3]))
+      self.assertIn('test_fn/', result_op.op.name)
+
+  def test_nested_name(self):
+
+    def test_fn(l):
+
+      def body(i):
+        return i**2
+
+      l += [4]
+      return body(l)
+
+    node = self.parse_and_analyze(test_fn, {})
+    node = name_scopes.transform(node, self.ctx)
+
+    with self.compiled(node, ops.name_scope) as result:
+      result_op = result.test_fn(constant_op.constant([1, 2, 3]))
+      first_result_input_name = result_op.op.inputs[0].name
+      second_result_input_name = result_op.op.inputs[1].name
+      self.assertIn('test_fn/', first_result_input_name)
+      self.assertNotIn('body/', first_result_input_name)
+      self.assertIn('test_fn/body/', second_result_input_name)
+
+  def test_class_name(self):
+
+    class TestClass(object):
+
+      def test_fn(self, l):
+
+        def body(i):
+          return i**2
+
+        l += [4]
+        return body(l)
+
+    # Note that 'TestClass' was needed in the namespace here.
+    node = self.parse_and_analyze(
+        TestClass, {'TestClass': TestClass}, owner_type=TestClass)
+    node = name_scopes.transform(node, self.ctx)
+
+    with self.compiled(node, ops.name_scope) as result:
+      result_op = result.TestClass().test_fn(constant_op.constant([1, 2, 3]))
+      first_result_input_name = result_op.op.inputs[0].name
+      second_result_input_name = result_op.op.inputs[1].name
+      self.assertIn('TestClass/test_fn/', first_result_input_name)
+      self.assertNotIn('body/', first_result_input_name)
+      self.assertIn('TestClass/test_fn/body/', second_result_input_name)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/py2tf/impl/api_test.py b/tensorflow/contrib/py2tf/impl/api_test.py
index 02cd8ed2d0..51e99864ad 100644
--- a/tensorflow/contrib/py2tf/impl/api_test.py
+++ b/tensorflow/contrib/py2tf/impl/api_test.py
@@ -31,8 +31,8 @@ class ApiTest(test.TestCase):
   def setUp(self):
     config.DEFAULT_UNCOMPILED_MODULES.add((math_ops.__name__,))
     config.COMPILED_IMPORT_STATEMENTS = (
-        'from tensorflow.python.ops '
-        'import control_flow_ops as tf',
+        'from tensorflow.python.framework '
+        'import ops as tf',
         'from tensorflow.contrib.py2tf import utils as '
         'py2tf_utils')
 
diff --git a/tensorflow/contrib/py2tf/impl/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py
index f3dc6b4d06..4bf698f207 100644
--- a/tensorflow/contrib/py2tf/impl/conversion.py
+++ b/tensorflow/contrib/py2tf/impl/conversion.py
@@ -31,6 +31,7 @@ from tensorflow.contrib.py2tf.converters import control_flow
 from tensorflow.contrib.py2tf.converters import decorators
 from tensorflow.contrib.py2tf.converters import for_loops
 from tensorflow.contrib.py2tf.converters import logical_expressions
+from tensorflow.contrib.py2tf.converters import name_scopes
 from tensorflow.contrib.py2tf.converters import side_effect_guards
 from tensorflow.contrib.py2tf.impl import config
 from tensorflow.contrib.py2tf.impl import naming
@@ -221,6 +222,7 @@ def function_to_graph(f, conversion_map, arg_values, arg_types,
       namespace=namespace,
       arg_values=arg_values,
       arg_types=arg_types,
+      owner_type=owner_type,
       recursive=conversion_map.recursive)
   node, deps = node_to_graph(node, ctx, conversion_map.nocompile_decorators)
 
@@ -302,5 +304,6 @@ def node_to_graph(node, ctx, nocompile_decorators):
   node = _static_analysis_pass(node, ctx)
   node = logical_expressions.transform(node)
   node = side_effect_guards.transform(node, ctx)
+  node = name_scopes.transform(node, ctx)
 
   return node, deps
diff --git a/tensorflow/contrib/py2tf/impl/conversion_test.py b/tensorflow/contrib/py2tf/impl/conversion_test.py
index 75e95ed888..7816f95857 100644
--- a/tensorflow/contrib/py2tf/impl/conversion_test.py
+++ b/tensorflow/contrib/py2tf/impl/conversion_test.py
@@ -55,8 +55,11 @@ class ConversionTest(test.TestCase):
     self.assertTrue(f in conversion_map.dependency_cache)
     self.assertTrue(g in conversion_map.dependency_cache)
     self.assertEqual('tf__f', conversion_map.dependency_cache[f].name)
+    # need the extra .body[0] in order to step past the with tf.name_scope('f')
+    # that is added automatically
     self.assertEqual(
-        'tf__g', conversion_map.dependency_cache[f].body[0].value.func.id)
+        'tf__g',
+        conversion_map.dependency_cache[f].body[0].body[0].value.func.id)
     self.assertEqual('tf__g', conversion_map.dependency_cache[g].name)
 
 
diff --git a/tensorflow/contrib/py2tf/pyct/context.py b/tensorflow/contrib/py2tf/pyct/context.py
index fef74ebefa..4fcf2a687d 100644
--- a/tensorflow/contrib/py2tf/pyct/context.py
+++ b/tensorflow/contrib/py2tf/pyct/context.py
@@ -30,14 +30,16 @@ class EntityContext(object):
         (excluding parameters).
     arg_values: Dict[str->*], containing parameter values, if known.
     arg_types: Dict[str->*], containing parameter types, if known.
+    owner_type: The surrounding class type of the function, if present.
   """
 
   def __init__(self, namer, source_code, source_file, namespace, arg_values,
-               arg_types, recursive):
+               arg_types, owner_type, recursive):
     self.namer = namer
     self.source_code = source_code
     self.source_file = source_file
     self.namespace = namespace
     self.arg_values = {} if arg_values is None else arg_values
     self.arg_types = {} if arg_types is None else arg_types
+    self.owner_type = owner_type
     self.recursive = recursive
diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py
index e1eb954a5e..029e4eb480 100644
--- a/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py
+++ b/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py
@@ -108,6 +108,7 @@ class ActivityAnalizerTest(test.TestCase):
         namespace={},
         arg_values=None,
         arg_types=None,
+        owner_type=None,
         recursive=True)
     node = qual_names.resolve(node)
     node = activity.resolve(node, ctx)
diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py
index 9f64689401..1e81bc70a8 100644
--- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py
+++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py
@@ -46,6 +46,7 @@ class LiveValuesResolverTest(test.TestCase):
         namespace=namespace,
         arg_values=None,
         arg_types=arg_types,
+        owner_type=None,
         recursive=True)
     node = qual_names.resolve(node)
     node = activity.resolve(node, ctx)
diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py
index 3659f949db..a3e78202c8 100644
--- a/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py
+++ b/tensorflow/contrib/py2tf/pyct/static_analysis/type_info_test.py
@@ -65,6 +65,7 @@ class TypeInfoResolverTest(test.TestCase):
         namespace=namespace,
         arg_values=None,
         arg_types=arg_types,
+        owner_type=None,
         recursive=True)
     node = qual_names.resolve(node)
     node = activity.resolve(node, ctx)
-- 
GitLab


From 80e100b4fb98f47e8e843762651530741c1d66bb Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 21 Feb 2018 12:31:27 -0800
Subject: [PATCH 139/575] framework/ops.py: Stricter check for use of the C API
 for graph construction.

Fixes #16913

Didn't add an explicit test for this since arguably use of the C API for graph
construction will soon become the default, so I figured testing that
tfe.defun's use of _use_c_api_hack() would only add code that should be deleted
soon.

PiperOrigin-RevId: 186502140
---
 tensorflow/python/framework/ops.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index b440e149b7..afd553bede 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -368,8 +368,8 @@ class Tensor(_TensorLike):
       A `TensorShape` representing the shape of this tensor.
 
     """
-    if _USE_C_API:
-      graph = self._op._graph._c_graph  # pylint: disable=protected-access
+    graph = self._op._graph._c_graph # pylint: disable=protected-access
+    if graph:
       with errors.raise_exception_on_not_ok_status() as status:
         num_dims = c_api.TF_GraphGetTensorNumDims(graph, self._as_tf_output(),
                                                   status)
@@ -466,7 +466,7 @@ class Tensor(_TensorLike):
       ValueError: If `shape` is not compatible with the current shape of
         this tensor.
     """
-    if not _USE_C_API:
+    if not self._op._graph._c_graph:  # pylint: disable=protected-access # ASIM
       self._shape_val = self._shape_val.merge_with(shape)
       return
     if not isinstance(shape, tensor_shape.TensorShape):
@@ -2768,7 +2768,7 @@ class Graph(object):
 
     # TODO(skyewm): fold as much of the above as possible into the C
     # implementation
-    if _USE_C_API or self._use_c_api_hack():
+    if self._use_c_api_hack():
       self._scoped_c_graph = c_api_util.ScopedTFGraph()
     else:
       self._scoped_c_graph = None
@@ -2777,7 +2777,7 @@ class Graph(object):
   # TODO(apassos) remove once the C API is used by default.
   def _use_c_api_hack(self):
     """Temporary hack; can be overridden to force C API usage."""
-    return False
+    return _USE_C_API
 
   def _convert_stack(self, stack, include_func_start_lineno=False):
     """Converts a stack extracted using _extract_stack() to a traceback stack.
@@ -3037,7 +3037,7 @@ class Graph(object):
 
     """
     # pylint: enable=line-too-long
-    if _USE_C_API:
+    if self._c_graph:
       with self._lock:
         with c_api_util.tf_buffer() as buf:
           with errors.raise_exception_on_not_ok_status() as status:
-- 
GitLab


From b34c2788fc4b879f47048f2cac6139b6052d5f1b Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 21 Feb 2018 12:32:52 -0800
Subject: [PATCH 140/575] eager/mnist: Point to the example in
 tensorflow/models instead.

PiperOrigin-RevId: 186502375
---
 .../contrib/eager/python/examples/BUILD       |   1 -
 .../contrib/eager/python/examples/mnist/BUILD |  36 ---
 .../eager/python/examples/mnist/README.md     |  11 +-
 .../eager/python/examples/mnist/mnist.py      | 264 ------------------
 .../python/examples/mnist/mnist_graph_test.py |  65 -----
 .../eager/python/examples/mnist/mnist_test.py |  80 ------
 .../contrib/eager/python/g3doc/guide.md       |  12 +-
 7 files changed, 6 insertions(+), 463 deletions(-)
 delete mode 100644 tensorflow/contrib/eager/python/examples/mnist/BUILD
 delete mode 100644 tensorflow/contrib/eager/python/examples/mnist/mnist.py
 delete mode 100644 tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py
 delete mode 100644 tensorflow/contrib/eager/python/examples/mnist/mnist_test.py

diff --git a/tensorflow/contrib/eager/python/examples/BUILD b/tensorflow/contrib/eager/python/examples/BUILD
index 15a21885f6..c1fd9e0ed0 100644
--- a/tensorflow/contrib/eager/python/examples/BUILD
+++ b/tensorflow/contrib/eager/python/examples/BUILD
@@ -8,7 +8,6 @@ py_library(
     deps = [
         "//tensorflow/contrib/eager/python/examples/gan:mnist",
         "//tensorflow/contrib/eager/python/examples/linear_regression",
-        "//tensorflow/contrib/eager/python/examples/mnist",
         "//tensorflow/contrib/eager/python/examples/resnet50",
         "//tensorflow/contrib/eager/python/examples/rnn_colorbot",
         "//tensorflow/contrib/eager/python/examples/rnn_ptb",
diff --git a/tensorflow/contrib/eager/python/examples/mnist/BUILD b/tensorflow/contrib/eager/python/examples/mnist/BUILD
deleted file mode 100644
index c61ec2dbae..0000000000
--- a/tensorflow/contrib/eager/python/examples/mnist/BUILD
+++ /dev/null
@@ -1,36 +0,0 @@
-licenses(["notice"])  # Apache 2.0
-
-package(default_visibility = ["//tensorflow:internal"])
-
-load("//tensorflow:tensorflow.bzl", "cuda_py_test")
-
-py_binary(
-    name = "mnist",
-    srcs = ["mnist.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        "//tensorflow:tensorflow_py",
-        "//tensorflow/contrib/eager/python:tfe",
-        "//tensorflow/examples/tutorials/mnist:input_data",
-    ],
-)
-
-cuda_py_test(
-    name = "mnist_test",
-    srcs = ["mnist_test.py"],
-    additional_deps = [
-        ":mnist",
-        "//tensorflow/contrib/eager/python:tfe",
-        "//tensorflow:tensorflow_py",
-    ],
-)
-
-cuda_py_test(
-    name = "mnist_graph_test",
-    srcs = ["mnist_graph_test.py"],
-    additional_deps = [
-        ":mnist",
-        "//third_party/py/numpy",
-        "//tensorflow:tensorflow_py",
-    ],
-)
diff --git a/tensorflow/contrib/eager/python/examples/mnist/README.md b/tensorflow/contrib/eager/python/examples/mnist/README.md
index e987996b88..d1c079ff6b 100644
--- a/tensorflow/contrib/eager/python/examples/mnist/README.md
+++ b/tensorflow/contrib/eager/python/examples/mnist/README.md
@@ -1,10 +1 @@
-Classification model for the MNIST dataset using eager execution.
-
-To run:
-
-```
-python mnist.py
-```
-
-`mnist_graph_test.py` demonstrates that the same code that is executed eagerly
-in `mnist.py` is used to construct a TensorFlow graph.
+See https://github.com/tensorflow/models/tree/master/official/mnist/mnist_eager.py
diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist.py b/tensorflow/contrib/eager/python/examples/mnist/mnist.py
deleted file mode 100644
index 58b1e89d15..0000000000
--- a/tensorflow/contrib/eager/python/examples/mnist/mnist.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""A deep MNIST classifier using convolutional layers.
-
-Sample usage:
-  python mnist.py --help
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import os
-import sys
-import time
-
-import tensorflow as tf
-
-import tensorflow.contrib.eager as tfe
-from tensorflow.examples.tutorials.mnist import input_data
-
-FLAGS = None
-
-
-class MNISTModel(tf.keras.Model):
-  """MNIST Network.
-
-  Network structure is equivalent to:
-  https://github.com/tensorflow/tensorflow/blob/r1.6/tensorflow/examples/tutorials/mnist/mnist_deep.py
-  and
-  https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
-
-  But written using the tf.layers API.
-  """
-
-  def __init__(self, data_format):
-    """Creates a model for classifying a hand-written digit.
-
-    Args:
-      data_format: Either 'channels_first' or 'channels_last'.
-        'channels_first' is typically faster on GPUs while 'channels_last' is
-        typically faster on CPUs. See
-        https://www.tensorflow.org/performance/performance_guide#data_formats
-    """
-    super(MNISTModel, self).__init__(name='')
-    if data_format == 'channels_first':
-      self._input_shape = [-1, 1, 28, 28]
-    else:
-      assert data_format == 'channels_last'
-      self._input_shape = [-1, 28, 28, 1]
-    self.conv1 = tf.layers.Conv2D(
-        32, 5, data_format=data_format, activation=tf.nn.relu)
-    self.conv2 = tf.layers.Conv2D(
-        64, 5, data_format=data_format, activation=tf.nn.relu)
-    self.fc1 = tf.layers.Dense(1024, activation=tf.nn.relu)
-    self.fc2 = tf.layers.Dense(10)
-    self.dropout = tf.layers.Dropout(0.5)
-    self.max_pool2d = tf.layers.MaxPooling2D(
-        (2, 2), (2, 2), padding='SAME', data_format=data_format)
-
-  def call(self, inputs, training=False):
-    """Computes labels from inputs.
-
-    Users should invoke __call__ to run the network, which delegates to this
-    method (and not call this method directly).
-
-    Args:
-      inputs: A batch of images as a Tensor with shape [batch_size, 784].
-      training: True if invoked in the context of training (causing dropout to
-        be applied).  False otherwise.
-
-    Returns:
-      A Tensor with shape [batch_size, 10] containing the predicted logits
-      for each image in the batch, for each of the 10 classes.
-    """
-
-    x = tf.reshape(inputs, self._input_shape)
-    x = self.conv1(x)
-    x = self.max_pool2d(x)
-    x = self.conv2(x)
-    x = self.max_pool2d(x)
-    x = tf.layers.flatten(x)
-    x = self.fc1(x)
-    x = self.dropout(x, training=training)
-    x = self.fc2(x)
-    return x
-
-
-def loss(predictions, labels):
-  return tf.reduce_mean(
-      tf.nn.softmax_cross_entropy_with_logits(
-          logits=predictions, labels=labels))
-
-
-def compute_accuracy(predictions, labels):
-  return tf.reduce_sum(
-      tf.cast(
-          tf.equal(
-              tf.argmax(predictions, axis=1,
-                        output_type=tf.int64),
-              tf.argmax(labels, axis=1,
-                        output_type=tf.int64)),
-          dtype=tf.float32)) / float(predictions.shape[0].value)
-
-
-def train_one_epoch(model, optimizer, dataset, log_interval=None):
-  """Trains model on `dataset` using `optimizer`."""
-
-  tf.train.get_or_create_global_step()
-
-  for (batch, (images, labels)) in enumerate(tfe.Iterator(dataset)):
-    with tf.contrib.summary.record_summaries_every_n_global_steps(10):
-      with tfe.GradientTape() as tape:
-        prediction = model(images, training=True)
-        loss_value = loss(prediction, labels)
-        tf.contrib.summary.scalar('loss', loss_value)
-        tf.contrib.summary.scalar('accuracy',
-                                  compute_accuracy(prediction, labels))
-      grads = tape.gradient(loss_value, model.variables)
-      optimizer.apply_gradients(zip(grads, model.variables))
-      if log_interval and batch % log_interval == 0:
-        print('Batch #%d\tLoss: %.6f' % (batch, loss_value))
-
-
-def test(model, dataset):
-  """Perform an evaluation of `model` on the examples from `dataset`."""
-  avg_loss = tfe.metrics.Mean('loss')
-  accuracy = tfe.metrics.Accuracy('accuracy')
-
-  for (images, labels) in tfe.Iterator(dataset):
-    predictions = model(images, training=False)
-    avg_loss(loss(predictions, labels))
-    accuracy(tf.argmax(predictions, axis=1, output_type=tf.int64),
-             tf.argmax(labels, axis=1, output_type=tf.int64))
-  print('Test set: Average loss: %.4f, Accuracy: %4f%%\n' %
-        (avg_loss.result(), 100 * accuracy.result()))
-  with tf.contrib.summary.always_record_summaries():
-    tf.contrib.summary.scalar('loss', avg_loss.result())
-    tf.contrib.summary.scalar('accuracy', accuracy.result())
-
-
-def load_data(data_dir):
-  """Returns training and test tf.data.Dataset objects."""
-  data = input_data.read_data_sets(data_dir, one_hot=True)
-  train_ds = tf.data.Dataset.from_tensor_slices((data.train.images,
-                                                 data.train.labels))
-  test_ds = tf.data.Dataset.from_tensors((data.test.images, data.test.labels))
-  return (train_ds, test_ds)
-
-
-def main(_):
-  tfe.enable_eager_execution()
-
-  (device, data_format) = ('/gpu:0', 'channels_first')
-  if FLAGS.no_gpu or tfe.num_gpus() <= 0:
-    (device, data_format) = ('/cpu:0', 'channels_last')
-  print('Using device %s, and data format %s.' % (device, data_format))
-
-  # Load the datasets
-  (train_ds, test_ds) = load_data(FLAGS.data_dir)
-  train_ds = train_ds.shuffle(60000).batch(FLAGS.batch_size)
-
-  # Create the model and optimizer
-  model = MNISTModel(data_format)
-  optimizer = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.momentum)
-
-  if FLAGS.output_dir:
-    train_dir = os.path.join(FLAGS.output_dir, 'train')
-    test_dir = os.path.join(FLAGS.output_dir, 'eval')
-    tf.gfile.MakeDirs(FLAGS.output_dir)
-  else:
-    train_dir = None
-    test_dir = None
-  summary_writer = tf.contrib.summary.create_file_writer(
-      train_dir, flush_millis=10000)
-  test_summary_writer = tf.contrib.summary.create_file_writer(
-      test_dir, flush_millis=10000, name='test')
-  checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt')
-
-  with tf.device(device):
-    for epoch in range(1, 11):
-      with tfe.restore_variables_on_create(
-          tf.train.latest_checkpoint(FLAGS.checkpoint_dir)):
-        global_step = tf.train.get_or_create_global_step()
-        start = time.time()
-        with summary_writer.as_default():
-          train_one_epoch(model, optimizer, train_ds, FLAGS.log_interval)
-        end = time.time()
-        print('\nTrain time for epoch #%d (global step %d): %f' % (
-            epoch, global_step.numpy(), end - start))
-      with test_summary_writer.as_default():
-        test(model, test_ds)
-      all_variables = (
-          model.variables
-          + optimizer.variables()
-          + [global_step])
-      tfe.Saver(all_variables).save(
-          checkpoint_prefix, global_step=global_step)
-
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument(
-      '--data-dir',
-      type=str,
-      default='/tmp/tensorflow/mnist/input_data',
-      help='Directory for storing input data')
-  parser.add_argument(
-      '--batch-size',
-      type=int,
-      default=64,
-      metavar='N',
-      help='input batch size for training (default: 64)')
-  parser.add_argument(
-      '--log-interval',
-      type=int,
-      default=10,
-      metavar='N',
-      help='how many batches to wait before logging training status')
-  parser.add_argument(
-      '--output_dir',
-      type=str,
-      default=None,
-      metavar='N',
-      help='Directory to write TensorBoard summaries')
-  parser.add_argument(
-      '--checkpoint_dir',
-      type=str,
-      default='/tmp/tensorflow/mnist/checkpoints/',
-      metavar='N',
-      help='Directory to save checkpoints in (once per epoch)')
-  parser.add_argument(
-      '--lr',
-      type=float,
-      default=0.01,
-      metavar='LR',
-      help='learning rate (default: 0.01)')
-  parser.add_argument(
-      '--momentum',
-      type=float,
-      default=0.5,
-      metavar='M',
-      help='SGD momentum (default: 0.5)')
-  parser.add_argument(
-      '--no-gpu',
-      action='store_true',
-      default=False,
-      help='disables GPU usage even if a GPU is available')
-
-  FLAGS, unparsed = parser.parse_known_args()
-  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py b/tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py
deleted file mode 100644
index 1af2655312..0000000000
--- a/tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import tensorflow as tf
-from tensorflow.contrib.eager.python.examples.mnist import mnist
-
-
-def data_format():
-  return "channels_first" if tf.test.is_gpu_available() else "channels_last"
-
-
-class MNISTGraphTest(tf.test.TestCase):
-
-  def testTrainGraph(self):
-    # The MNISTModel class can be executed eagerly (as in mnist.py and
-    # mnist_test.py) and also be used to construct a TensorFlow graph, which is
-    # then trained in a session.
-    with tf.Graph().as_default():
-      # Generate some random data.
-      batch_size = 64
-      images = np.random.randn(batch_size, 784).astype(np.float32)
-      digits = np.random.randint(low=0, high=10, size=batch_size)
-      labels = np.zeros((batch_size, 10))
-      labels[np.arange(batch_size), digits] = 1.
-
-      # Create a model, optimizer, and dataset as would be done
-      # for eager execution as well.
-      model = mnist.MNISTModel(data_format())
-      optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
-      dataset = tf.data.Dataset.from_tensors((images, labels))
-
-      # Define the loss tensor (as opposed to a loss function when
-      # using eager execution).
-      (images, labels) = dataset.make_one_shot_iterator().get_next()
-      predictions = model(images, training=True)
-      loss = mnist.loss(predictions, labels)
-
-      train_op = optimizer.minimize(loss)
-      init = tf.global_variables_initializer()
-      with tf.Session() as sess:
-        # Variables have to be initialized in the session.
-        sess.run(init)
-        # Train using the optimizer.
-        sess.run(train_op)
-
-
-if __name__ == "__main__":
-  tf.test.main()
diff --git a/tensorflow/contrib/eager/python/examples/mnist/mnist_test.py b/tensorflow/contrib/eager/python/examples/mnist/mnist_test.py
deleted file mode 100644
index 136085eba2..0000000000
--- a/tensorflow/contrib/eager/python/examples/mnist/mnist_test.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-import tensorflow.contrib.eager as tfe
-from tensorflow.contrib.eager.python.examples.mnist import mnist
-
-
-def device():
-  return "/device:GPU:0" if tfe.num_gpus() else "/device:CPU:0"
-
-
-def data_format():
-  return "channels_first" if tfe.num_gpus() else "channels_last"
-
-
-def random_dataset():
-  batch_size = 64
-  images = tf.random_normal([batch_size, 784])
-  digits = tf.random_uniform([batch_size], minval=0, maxval=10, dtype=tf.int32)
-  labels = tf.one_hot(digits, 10)
-  return tf.data.Dataset.from_tensors((images, labels))
-
-
-def train_one_epoch(defun=False):
-  model = mnist.MNISTModel(data_format())
-  if defun:
-    model.call = tfe.defun(model.call)
-  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
-  dataset = random_dataset()
-  with tf.device(device()):
-    tf.train.get_or_create_global_step()
-    mnist.train_one_epoch(model, optimizer, dataset)
-
-
-def evaluate(defun=False):
-  model = mnist.MNISTModel(data_format())
-  dataset = random_dataset()
-  if defun:
-    model.call = tfe.defun(model.call)
-  with tf.device(device()):
-    tf.train.get_or_create_global_step()
-    mnist.test(model, dataset)
-
-
-class MNISTTest(tf.test.TestCase):
-
-  def testTrainOneEpoch(self):
-    train_one_epoch(defun=False)
-
-  def testTest(self):
-    evaluate(defun=False)
-
-  def testTrainOneEpochWithDefunCall(self):
-    train_one_epoch(defun=True)
-
-  def testTestWithDefunCall(self):
-    evaluate(defun=True)
-
-
-if __name__ == "__main__":
-  tfe.enable_eager_execution()
-  tf.test.main()
diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md
index ffc1d0332e..4724aa4aee 100644
--- a/tensorflow/contrib/eager/python/g3doc/guide.md
+++ b/tensorflow/contrib/eager/python/g3doc/guide.md
@@ -570,8 +570,8 @@ for i in range(20001):
 print("Loss on test set: %f" % loss(model, data.test.images, data.test.labels).numpy())
 ```
 
-For a more complete example, see
-[`tensorflow/contrib/eager/python/examples/mnist.py`](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/mnist/mnist.py)
+For a more complete example, see [the example in the tensorflow/models
+repository](https://github.com/tensorflow/models/tree/master/official/mnist/mnist_eager.py).
 
 ### Checkpointing trained variables
 
@@ -860,11 +860,9 @@ eagerly or constructing graphs. This means that you can iteratively develop your
 model with eager execution enabled and later, if needed, use the same code to
 reap the benefits of representing models as computational graphs.
 
-For example,
-[`mnist.py`](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/mnist/mnist.py)
-defines a model that is eagerly executed. That same code is used to construct
-and execute a graph in
-[`mnist_graph_test.py`](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/mnist/mnist_graph_test.py).
+For example, the same model definition used to construct a graph in
+[mnist.py`](https://github.com/tensorflow/models/tree/master/official/mnist/mnist.py)
+can be trained with eager execution enabled as in [`mnist_eager.py`](https://github.com/tensorflow/models/tree/master/official/mnist/mnist_eager.py).
 
 Other models in the [examples
 directory](https://www.tensorflow.org/code/tensorflow/contrib/eager/python/examples/)
-- 
GitLab


From 32ccc6a0699d6cdb4a15c328454effaacda47aaa Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Wed, 21 Feb 2018 12:33:41 -0800
Subject: [PATCH 141/575] Merge tf.layers.GraphNetwork into tf.keras.Network
 and remove it.

PiperOrigin-RevId: 186502476
---
 tensorflow/python/BUILD                       |   20 -
 .../keras/_impl/keras/engine/topology.py      |  950 ++++++++++++++-
 .../keras/_impl/keras/engine/topology_test.py |  539 +++++++--
 .../keras/_impl/keras/integration_test.py     |    5 +-
 tensorflow/python/keras/_impl/keras/models.py |   14 +-
 tensorflow/python/layers/layers.py            |    1 -
 tensorflow/python/layers/network.py           | 1024 -----------------
 tensorflow/python/layers/network_test.py      |  633 ----------
 .../api/golden/tensorflow.keras.-model.pbtxt  |    1 -
 .../golden/tensorflow.keras.-sequential.pbtxt |    1 -
 ...tensorflow.keras.layers.-input-layer.pbtxt |    1 -
 .../tensorflow.keras.models.-model.pbtxt      |    1 -
 .../tensorflow.keras.models.-sequential.pbtxt |    1 -
 .../tools/api/golden/tensorflow.layers.pbtxt  |    4 -
 14 files changed, 1334 insertions(+), 1861 deletions(-)
 delete mode 100644 tensorflow/python/layers/network.py
 delete mode 100644 tensorflow/python/layers/network_test.py

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index d7cf2c6fea..9b0c800ec7 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -4108,7 +4108,6 @@ py_library(
         "layers/convolutional.py",
         "layers/core.py",
         "layers/layers.py",
-        "layers/network.py",
         "layers/normalization.py",
         "layers/pooling.py",
     ],
@@ -4161,25 +4160,6 @@ py_test(
     ],
 )
 
-py_test(
-    name = "layers_network_test",
-    size = "small",
-    srcs = ["layers/network_test.py"],
-    main = "layers/network_test.py",
-    srcs_version = "PY2AND3",
-    deps = [
-        ":array_ops",
-        ":client_testlib",
-        ":framework_for_generated_wrappers",
-        ":framework_test_lib",
-        ":layers",
-        ":layers_base",
-        ":sparse_ops",
-        "//tensorflow/python/eager:context",
-        "//third_party/py/numpy",
-    ],
-)
-
 py_test(
     name = "layers_core_test",
     size = "small",
diff --git a/tensorflow/python/keras/_impl/keras/engine/topology.py b/tensorflow/python/keras/_impl/keras/engine/topology.py
index 7de5af41c5..dbf9652a5b 100644
--- a/tensorflow/python/keras/_impl/keras/engine/topology.py
+++ b/tensorflow/python/keras/_impl/keras/engine/topology.py
@@ -27,6 +27,7 @@ import numpy as np
 from six.moves import zip  # pylint: disable=redefined-builtin
 
 from tensorflow.python.eager import context
+from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras import constraints
@@ -36,9 +37,10 @@ from tensorflow.python.keras._impl.keras.utils import conv_utils
 from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite
 from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary as print_layer_summary
 from tensorflow.python.layers import base as tf_base_layers
-from tensorflow.python.layers import network as tf_network
 from tensorflow.python.layers import utils as tf_layers_util
+from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util import nest
 from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import tf_export
 
@@ -267,9 +269,9 @@ class Layer(tf_base_layers.Layer):
       self._set_inputs(inputs, training=kwargs.get('training'))
 
     # Update learning phase info.
-    output_tensors = _to_list(output)
+    output_tensors = to_list(output)
     uses_lp = any(
-        [getattr(x, '_uses_learning_phase', False) for x in _to_list(inputs)])
+        [getattr(x, '_uses_learning_phase', False) for x in to_list(inputs)])
     uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp
     for i in range(len(output_tensors)):
       output_tensors[i]._uses_learning_phase = getattr(
@@ -497,15 +499,19 @@ class Layer(tf_base_layers.Layer):
     self._activity_regularizer = activity_regularizer
 
 
-@tf_export('keras.layers.InputLayer')
-class InputLayer(tf_network.InputLayer, Layer):
-  """Layer to be used as an entry point into a graph.
+class InputLayer(Layer):
+  """Layer to be used as an entry point into a Network (a graph of layers).
 
   It can either wrap an existing tensor (pass an `input_tensor` argument)
-  or create its a placeholder tensor (pass argument `input_shape`.
+  or create its a placeholder tensor (pass arguments `input_shape`, and
+  optionally, `dtype`).
+
+  It is generally recommend to use the functional layer API via `Input`,
+  (which creates an `InputLayer`) without directly using `InputLayer`.
 
   Arguments:
-      input_shape: Shape tuple, not including the batch axis.
+      input_shape: Shape tuple (not including the batch axis), or `TensorShape`
+        instance (not including the batch axis).
       batch_size: Optional input batch size (integer or None).
       dtype: Datatype of the input.
       input_tensor: Optional tensor to use as layer input
@@ -543,12 +549,57 @@ class InputLayer(tf_network.InputLayer, Layer):
         dtype = K.floatx()
       else:
         dtype = K.dtype(input_tensor)
-    super(InputLayer, self).__init__(input_shape=input_shape,
-                                     batch_size=batch_size,
-                                     dtype=dtype,
-                                     input_tensor=input_tensor,
-                                     sparse=sparse,
-                                     name=name)
+    super(InputLayer, self).__init__(dtype=dtype, name=name)
+    self.built = True
+    self.sparse = sparse
+    self.batch_size = batch_size
+
+    if isinstance(input_shape, tensor_shape.TensorShape):
+      input_shape = tuple(input_shape.as_list())
+
+    if input_tensor is None:
+      if input_shape is not None:
+        batch_input_shape = (batch_size,) + tuple(input_shape)
+      else:
+        batch_input_shape = None
+
+      if context.in_eager_mode():
+        # In eager mode, create a temporary placeholder to call the layer on.
+        input_tensor = tf_base_layers._DeferredTensor(  # pylint: disable=protected-access
+            shape=batch_input_shape,
+            dtype=dtype,
+            name=self.name)
+      else:
+        # In graph mode, create a graph placeholder to call the layer on.
+        if sparse:
+          input_tensor = array_ops.sparse_placeholder(
+              shape=batch_input_shape,
+              dtype=dtype,
+              name=self.name)
+        else:
+          input_tensor = array_ops.placeholder(
+              shape=batch_input_shape,
+              dtype=dtype,
+              name=self.name)
+
+      # For compatibility with Keras API.
+      self.is_placeholder = True
+      self._batch_input_shape = batch_input_shape
+    else:
+      # For compatibility with Keras API.
+      self.is_placeholder = False
+      self._batch_input_shape = tuple(input_tensor.get_shape().as_list())
+
+    # Create an input node to add to self.outbound_node
+    # and set output_tensors' _keras_history.
+    input_tensor._keras_history = (self, 0, 0)  # pylint: disable=protected-access
+    tf_base_layers.Node(
+        self,
+        inbound_layers=[],
+        node_indices=[],
+        tensor_indices=[],
+        input_tensors=[input_tensor],
+        output_tensors=[input_tensor])
 
   def get_config(self):
     config = {
@@ -650,7 +701,7 @@ def Input(  # pylint: disable=invalid-name
     return outputs
 
 
-class Network(tf_network.GraphNetwork, Layer):
+class Network(Layer):
   """A Network is a directed acyclic graph of layers.
 
   It is the topological form of a "model". A Model
@@ -697,27 +748,188 @@ class Network(tf_network.GraphNetwork, Layer):
       # Subclassed network
       self._init_subclassed_network(**kwargs)
 
-  def _init_graph_network(self, inputs, outputs, name=None):
-    # TODO(fchollet): merge back tf.layers.Network and tf.keras.Network
-    # into a single class tf.keras.Network
-    super(Network, self).__init__(inputs, outputs, name=name)
+  def _base_init(self, name=None):
+    # The following are implemented as property functions:
+    # self.trainable_weights
+    # self.non_trainable_weights
+    # self.input_spec
+    # self.losses
+    # self.updates
 
+    self._init_set_name(name)
+    self._activity_regularizer = None
+    # This acts just like the `trainable` attribute of any layer instance.
+    # It does not affect users of the underlying layers, only users of the
+    # Network instance.
+    self.trainable = True
     self._is_compiled = False
     self._expects_training_arg = False
 
     self.supports_masking = False
     self.optimizer = None
 
+    # Private attributes to implement compatibility with Layer.
+    self._updates = []  # Used in symbolic mode only.
+    self._losses = []   # Used in symbolic mode only.
+    self._scope = None  # Never used.
+    self._reuse = None  # Never used.
+    if context.in_eager_mode:
+      self._graph = None
+    else:
+      self._graph = ops.get_default_graph()  # Used in symbolic mode only.
+        # A Network does not create weights of its own, thus has no dtype.
+    self._dtype = None
+
+    # All layers in order of horizontal graph traversal.
+    # Entries are unique. Includes input and output layers.
+    self._layers = []
+
+    # Used in symbolic mode only, only in conjonction with graph-networks
+    self._outbound_nodes = []
+    self._inbound_nodes = []
+
+  def _init_graph_network(self, inputs, outputs, name=None):
+    # Normalize and set self.inputs, self.outputs.
+    if isinstance(inputs, (list, tuple)):
+      self.inputs = list(inputs)  # Tensor or list of tensors.
+    else:
+      self.inputs = [inputs]
+    if isinstance(outputs, (list, tuple)):
+      self.outputs = list(outputs)
+    else:
+      self.outputs = [outputs]
+
+    # User-prodived argument validation.
+    if context.in_eager_mode():
+      # Check that all inputs/outputs are DeferredTensors.
+      for tensor in self.inputs:
+        if not isinstance(tensor, tf_base_layers._DeferredTensor):  # pylint: disable=protected-access
+          raise TypeError('When eager execution is enabled, '
+                          'inputs must come from a call to '
+                          '`tf.keras.Input` (called after '
+                          'tfe.enable_eager_execution()). '
+                          'Received invalid input: ' + str(tensor))
+      for tensor in self.outputs:
+        if not isinstance(tensor, tf_base_layers._DeferredTensor):  # pylint: disable=protected-access
+          raise TypeError('When eager execution is enabled, '
+                          'outputs must come from a call to '
+                          'a layer (called after '
+                          'tfe.enable_eager_execution()). '
+                          'Received invalid output: ' + str(tensor))
+    # Check for redundancy in inputs.
+    if len(set(self.inputs)) != len(self.inputs):
+      raise ValueError('The list of inputs passed to the model '
+                       'is redundant. '
+                       'All inputs should only appear once.'
+                       ' Found: ' + str(self.inputs))
+    for x in self.inputs:
+      # Check that x has appropriate `_keras_history` metadata.
+      if not hasattr(x, '_keras_history'):
+        cls_name = self.__class__.__name__
+        raise ValueError('Input tensors to a ' + cls_name + ' ' +
+                         'must come from `tf.layers.Input`. '
+                         'Received: ' + str(x) +
+                         ' (missing previous layer metadata).')
+      # Check that x is an input tensor.
+      # pylint: disable=protected-access
+      layer, node_index, tensor_index = x._keras_history
+      if len(layer._inbound_nodes) > 1 or (
+          layer._inbound_nodes and layer._inbound_nodes[0].inbound_layers):
+        cls_name = self.__class__.__name__
+        logging.warning(cls_name + ' inputs must come from '
+                        '`tf.layers.Input` (thus holding past layer metadata), '
+                        'they cannot be the output of '
+                        'a previous non-Input layer. '
+                        'Here, a tensor specified as '
+                        'input to "' + self.name + '" was not an Input tensor, '
+                        'it was generated by layer ' + layer.name + '.\n'
+                        'Note that input tensors are '
+                        'instantiated via `tensor = tf.layers.Input(shape)`.\n'
+                        'The tensor that caused the issue was: ' + str(x.name))
+    for x in self.outputs:
+      if not hasattr(x, '_keras_history'):
+        cls_name = self.__class__.__name__
+        raise ValueError('Output tensors to a ' + cls_name + ' must be '
+                         'the output of a TensorFlow `Layer` '
+                         '(thus holding past layer metadata). Found: ' + str(x))
+
+    self._base_init(name=name)
+    self._compute_previous_mask = (
+        'mask' in tf_inspect.getargspec(self.call).args or
+        hasattr(self, 'compute_mask'))
+    # A Network does not create weights of its own, thus it is already
+    # built.
+    self.built = True
+    self._is_graph_network = True
+
+    # # List of initial layers (1 to 1 mapping with self.inputs,
+    # # hence the same layer might appear twice)
+    # self._input_layers = []
+    # self._input_layers_node_indices = []
+    # self._input_layers_tensor_indices = []
+    # # list of layers (1 to 1 mapping with self.inputs,
+    # # hence the same layer might appear twice)
+    # self._output_layers = []
+    # self._output_layers_node_indices = []
+    # self._output_layers_tensor_indices = []
+
+    self._input_layers = []
+    self._output_layers = []
+    self._input_coordinates = []
+    self._output_coordinates = []
+
+    # This is for performance optimization when calling the Network on new
+    # inputs. Every time the Network is called on a set on input tensors,
+    # we compute the output tensors, output masks and output shapes in one pass,
+    # then cache them here. When any of these outputs is queried later, we
+    # retrieve it from there instead of recomputing it.
+    self._output_mask_cache = {}
+    self._output_tensor_cache = {}
+    self._output_shape_cache = {}
+
+    # Build self._output_layers:
+    for x in self.outputs:
+      layer, node_index, tensor_index = x._keras_history  # pylint: disable=protected-access
+      self._output_layers.append(layer)
+      self._output_coordinates.append((layer, node_index, tensor_index))
+
+    # Build self._input_layers:
+    for x in self.inputs:
+      layer, node_index, tensor_index = x._keras_history  # pylint: disable=protected-access
+      # It's supposed to be an input layer, so only one node
+      # and one tensor output.
+      assert node_index == 0
+      assert tensor_index == 0
+      self._input_layers.append(layer)
+      self._input_coordinates.append((layer, node_index, tensor_index))
+
+    # Keep track of the network's nodes and layers.
+    nodes, nodes_by_depth, layers, layers_by_depth = _map_graph_network(
+        self.inputs, self.outputs)
+    self._network_nodes = nodes
+    self._nodes_by_depth = nodes_by_depth
+    self._layers = layers
+    self._layers_by_depth = layers_by_depth
+
+    # Create the node linking internal inputs to internal outputs.
+    tf_base_layers.Node(
+        outbound_layer=self,
+        inbound_layers=[],
+        node_indices=[],
+        tensor_indices=[],
+        input_tensors=self.inputs,
+        output_tensors=self.outputs)
+
     # Fill in the output mask cache.
     masks = []
     for x in self.inputs:
-      mask = x._keras_mask if hasattr(x, '_keras_mask') else None
+      mask = x._keras_mask if hasattr(x, '_keras_mask') else None  # pylint: disable=protected-access
       masks.append(mask)
     mask_cache_key = (tf_layers_util.object_list_uid(self.inputs) + '_' +
                       tf_layers_util.object_list_uid(masks))
     masks = []
     for x in self.outputs:
-      mask = x._keras_mask if hasattr(x, '_keras_mask') else None
+      mask = x._keras_mask if hasattr(x, '_keras_mask') else None  # pylint: disable=protected-access
       masks.append(mask)
     if len(masks) == 1:
       mask = masks[0]
@@ -743,10 +955,8 @@ class Network(tf_network.GraphNetwork, Layer):
       self.output_names.append(layer.name)
 
   def _init_subclassed_network(self, name=None):
-    self._init_set_name(name)
-    self._layers = []
+    self._base_init(name=name)
     self._is_graph_network = False
-    self._is_compiled = False
     if 'training' in tf_inspect.getargspec(self.call).args:
       self._expects_training_arg = True
     else:
@@ -754,26 +964,7 @@ class Network(tf_network.GraphNetwork, Layer):
 
     self.outputs = None
     self.inputs = None
-    self.trainable = True
-    self.supports_masking = False
     self.built = False
-    self.optimizer = None
-
-    # Not used, exists for compatibility purposes due to implementation of
-    # the base layer tf.layers.Layer - TODO(fchollet): clean up when refactoring
-    self._scope = None
-    self._reuse = None
-    self._dtype = None
-    self._graph = None
-    self._activity_regularizer = None
-
-    # Used in symbolic mode only
-    self._updates = []
-    self._losses = []
-
-    # Used in symbolic mode only, only in conjonction with graph-networks
-    self._outbound_nodes = []
-    self._inbound_nodes = []
 
   def __setattr__(self, name, value):
     if isinstance(value, (tf_base_layers.Layer, Network)):
@@ -790,12 +981,12 @@ class Network(tf_network.GraphNetwork, Layer):
 
   def add_variable(self, name, shape, dtype=None, initializer=None,
                    regularizer=None, trainable=True, constraint=None):
-    raise NotImplementedError('`add_variable` is not supported on Networks')
+    raise NotImplementedError('`add_variable` is not supported on Networks.')
 
   def add_loss(self, *args, **kwargs):
     if context.in_eager_mode():
-      raise NotImplementedError('`add_loss` is not supported in eager-mode '
-                                'on Networks')
+      raise NotImplementedError('`add_loss` is not supported on Networks '
+                                'when eager execution is enabled.')
     super(Network, self).add_loss(*args, **kwargs)
 
   @property
@@ -862,11 +1053,11 @@ class Network(tf_network.GraphNetwork, Layer):
     if not self._is_graph_network:
       return None
 
-    inputs = _to_list(inputs)
+    inputs = to_list(inputs)
     if mask is None:
       masks = [None for _ in range(len(inputs))]
     else:
-      masks = _to_list(mask)
+      masks = to_list(mask)
     cache_key = (tf_layers_util.object_list_uid(inputs)
                  + '_' + tf_layers_util.object_list_uid(masks))
     if cache_key in self._output_mask_cache:
@@ -875,6 +1066,465 @@ class Network(tf_network.GraphNetwork, Layer):
       _, output_masks = self._run_internal_graph(inputs, masks)
       return output_masks
 
+  @property
+  def layers(self):
+    return self._layers
+
+  def get_layer(self, name=None, index=None):
+    """Retrieves a layer based on either its name (unique) or index.
+
+    Indices are based on order of horizontal graph traversal (bottom-up).
+
+    Arguments:
+        name: String, name of layer.
+        index: Integer, index of layer.
+
+    Returns:
+        A layer instance.
+
+    Raises:
+        ValueError: In case of invalid layer name or index.
+    """
+    # TODO(fchollet): We could build a dictionary based on layer names
+    # since they are constant, but we have not done that yet.
+    if index is not None:
+      if len(self.layers) <= index:
+        raise ValueError('Was asked to retrieve layer at index ' + str(index) +
+                         ' but model only has ' + str(len(self.layers)) +
+                         ' layers.')
+      else:
+        return self.layers[index]
+    else:
+      if not name:
+        raise ValueError('Provide either a layer name or layer index.')
+    for layer in self.layers:
+      if layer.name == name:
+        return layer
+    raise ValueError('No such layer: ' + name)
+
+  @property
+  def updates(self):
+    """Retrieve the network's updates.
+
+    Will only include updates that are either
+    unconditional, or conditional on inputs to this model
+    (e.g. will not include updates that were created by layers of this model
+    outside of the model).
+
+    Effectively, `network.updates` behaves like `layer.updates`.
+
+    Concrete example:
+
+    ```python
+      bn = keras.layers.BatchNormalization()
+      x1 = keras.layers.Input(shape=(10,))
+      _ = bn(x1)  # This creates 2 updates.
+
+      x2 = keras.layers.Input(shape=(10,))
+      y2 = bn(x2)  # This creates 2 more updates.
+
+      # The BN layer has now 4 updates.
+      self.assertEqual(len(bn.updates), 4)
+
+      # Let's create a model from x2 to y2.
+      model = keras.models.Model(x2, y2)
+
+      # The model does not list all updates from its underlying layers,
+      # but only the updates that are relevant to it. Updates created by layers
+      # outside of the model are discarded.
+      self.assertEqual(len(model.updates), 2)
+
+      # If you keep calling the model, you append to its updates, just like
+      # what happens for a layer.
+      x3 = keras.layers.Input(shape=(10,))
+      y3 = model(x3)
+      self.assertEqual(len(model.updates), 4)
+
+      # But if you call the inner BN layer independently, you don't affect
+      # the model's updates.
+      x4 = keras.layers.Input(shape=(10,))
+      _ = bn(x4)
+      self.assertEqual(len(model.updates), 4)
+    ```
+
+    Returns:
+        A list of update ops.
+    """
+    if context.in_eager_mode():
+      return []
+
+    if not self.trainable and not self.stateful:
+      return []
+
+    updates = []
+    for layer in self.layers:
+      updates += layer.updates
+
+    # `updates` might contain irrelevant updates, so it needs to be filtered
+    # with respect to inputs the model has been called on.
+    relevant_inputs = self.inputs or []
+    for i in range(1, len(self._inbound_nodes)):
+      inputs = self.get_input_at(i)
+      if isinstance(inputs, list):
+        relevant_inputs += inputs
+      else:
+        relevant_inputs.append(inputs)
+    reachable = tf_layers_util.get_reachable_from_inputs(relevant_inputs,
+                                                         updates)
+    relevant_conditional_updates = [x for x in updates if x in reachable]
+    unconditional_updates = [
+        x for x in updates if x._unconditional_update]  # pylint: disable=protected-access
+    # A layer could be used multiple times in a nested structure,
+    # so the updates list must be de-duped.
+    return list(set(
+        relevant_conditional_updates + unconditional_updates + self._updates))
+
+  @property
+  def losses(self):
+    """Retrieve the network's losses.
+
+    Will only include losses that are either
+    unconditional, or conditional on inputs to this model
+    (e.g. will not include losses that depend on tensors
+    that aren't inputs to this model).
+
+    Returns:
+        A list of loss tensors.
+    """
+    losses = []
+    for layer in self.layers:
+      losses += layer.losses
+    if context.in_eager_mode():
+      return losses
+
+    relevant_inputs = self.inputs or []
+    for i in range(1, len(self._inbound_nodes)):
+      inputs = self.get_input_at(i)
+      if isinstance(inputs, list):
+        relevant_inputs += inputs
+      else:
+        relevant_inputs.append(inputs)
+    reachable = tf_layers_util.get_reachable_from_inputs(relevant_inputs,
+                                                         losses)
+    relevant_conditional_losses = [x for x in losses if x in reachable]
+    unconditional_losses = [
+        x for x in losses if x._unconditional_loss]  # pylint: disable=protected-access
+    return list(set(
+        relevant_conditional_losses + unconditional_losses + self._losses))
+
+  @property
+  def trainable_weights(self):
+    if not self.trainable:
+      return []
+    weights = []
+    for layer in self.layers:
+      weights += layer.trainable_weights
+    return weights
+
+  @property
+  def non_trainable_weights(self):
+    weights = []
+    for layer in self.layers:
+      weights += layer.non_trainable_weights
+    if not self.trainable:
+      trainable_weights = []
+      for layer in self.layers:
+        trainable_weights += layer.trainable_weights
+      return trainable_weights + weights
+    return weights
+
+  @property
+  def input_spec(self):
+    """Gets the network's input specs.
+
+    Returns:
+        A list of `InputSpec` instances (one per input to the model)
+            or a single instance if the model has only one input.
+    """
+    # If not a graph network, can't assume anything.
+    if not self._is_graph_network:
+      return None
+
+    specs = []
+    for layer in self._input_layers:
+      if layer.input_spec is None:
+        specs.append(None)
+      else:
+        if not isinstance(layer.input_spec, list):
+          raise TypeError('Layer ' + layer.name +
+                          ' has an input_spec attribute that '
+                          'is not a list. We expect a list. '
+                          'Found input_spec = ' + str(layer.input_spec))
+        specs += layer.input_spec
+    if len(specs) == 1:
+      return specs[0]
+    return specs
+
+  def call(self, inputs, mask=None):
+    """Call the model on new inputs.
+
+    In this case `call` just reapplies
+    all ops in the graph to the new inputs
+    (e.g. build a new computational graph from the provided inputs).
+
+    Arguments:
+        inputs: A tensor or list of tensors.
+        mask: A mask or list of masks. A mask can be
+            either a tensor or None (no mask).
+
+    Returns:
+        A tensor if there is a single output, or
+        a list of tensors if there are more than one outputs.
+    """
+    inputs = nest.flatten(inputs)
+    if mask is None:
+      masks = [None for _ in range(len(inputs))]
+    else:
+      masks = nest.flatten(mask)
+
+    if context.in_graph_mode():
+      # Try to retrieve cached outputs if the layer has already been called
+      # on these exact inputs.
+      cache_key = (tf_layers_util.object_list_uid(inputs)
+                   + '_' + tf_layers_util.object_list_uid(masks))
+      if cache_key in self._output_tensor_cache:
+        # Cache hit.
+        return self._output_tensor_cache[cache_key]
+    # Actually apply the network graph to the new inputs.
+    outputs, _ = self._run_internal_graph(inputs, masks)
+    return outputs
+
+  def compute_output_shape(self, input_shape):
+    if not self._is_graph_network:
+      raise NotImplementedError
+
+    if isinstance(input_shape, list):
+      input_shapes = []
+      for shape in input_shape:
+        if shape is not None:
+          input_shapes.append(tuple(tensor_shape.TensorShape(shape).as_list()))
+        else:
+          input_shapes.append(None)
+    else:
+      if input_shape is not None:
+        input_shapes = [tuple(tensor_shape.TensorShape(input_shape).as_list())]
+      else:
+        input_shapes = [None]
+
+    if len(input_shapes) != len(self._input_layers):
+      raise ValueError('Invalid input_shape argument ' + str(input_shape) +
+                       ': model has ' + str(len(self._input_layers)) +
+                       ' tensor inputs.')
+
+    cache_key = tf_layers_util.object_list_uid(input_shapes)
+    if cache_key not in self._output_shape_cache:
+      # Cache miss. We have to run the network graph manually (recursive calls
+      # to `compute_output_shape`).
+      layers_to_output_shapes = {}
+      for i in range(len(input_shapes)):
+        layer = self._input_layers[i]
+        input_shape = input_shapes[i]
+        # It's an input layer: then `compute_output_shape` is identity,
+        # and there is only one node and one tensor output.
+        shape_key = layer.name + '_0_0'
+        layers_to_output_shapes[shape_key] = input_shape
+
+      depth_keys = list(self._nodes_by_depth.keys())
+      depth_keys.sort(reverse=True)
+      # Iterate over nodes, by depth level.
+      if len(depth_keys) > 1:
+        for depth in depth_keys:
+          nodes = self._nodes_by_depth[depth]
+          for node in nodes:
+            # This is always a single layer, never a list.
+            layer = node.outbound_layer
+            if layer in self._input_layers:
+              # We've already covered the input layers
+              # a few lines above.
+              continue
+            # Potentially redundant list,
+            # same size as node.input_tensors.
+            input_shapes = []
+            for j in range(len(node.inbound_layers)):
+              inbound_layer = node.inbound_layers[j]
+              node_index = node.node_indices[j]
+              tensor_index = node.tensor_indices[j]
+              shape_key = inbound_layer.name + '_%s_%s' % (node_index,
+                                                           tensor_index)
+              input_shape = layers_to_output_shapes[shape_key]
+              input_shapes.append(input_shape)
+
+            if len(input_shapes) == 1:
+              output_shape = layer.compute_output_shape(input_shapes[0])
+            else:
+              output_shape = layer.compute_output_shape(input_shapes)
+            if isinstance(output_shape, list):
+              output_shapes = [
+                  tuple(tensor_shape.TensorShape(shape).as_list())
+                  for shape in output_shape
+              ]
+            else:
+              output_shapes = [
+                  tuple(tensor_shape.TensorShape(output_shape).as_list())
+              ]
+
+            node_index = layer._inbound_nodes.index(node)  # pylint: disable=protected-access
+            for j in range(len(output_shapes)):
+              shape_key = layer.name + '_%s_%s' % (node_index, j)
+              layers_to_output_shapes[shape_key] = output_shapes[j]
+
+        # Read final output shapes from layers_to_output_shapes.
+        output_shapes = []
+        for i in range(len(self._output_layers)):
+          layer, node_index, tensor_index = self._output_coordinates[i]
+          shape_key = layer.name + '_%s_%s' % (node_index, tensor_index)
+          output_shapes.append(layers_to_output_shapes[shape_key])
+        # Store in cache.
+        self._output_shape_cache[cache_key] = output_shapes
+    else:
+      # Cache hit.
+      output_shapes = self._output_shape_cache[cache_key]
+
+    if isinstance(output_shapes, list):
+      if len(output_shapes) == 1:
+        return tensor_shape.TensorShape(output_shapes[0])
+      else:
+        return [tensor_shape.TensorShape(shape) for shape in output_shapes]
+    else:
+      return tensor_shape.TensorShape(output_shapes)
+
+  def _run_internal_graph(self, inputs, masks=None):
+    """Computes output tensors for new inputs.
+
+    # Note:
+        - Expects `inputs` to be a list (potentially with 1 element).
+        - Can be run on non-Keras tensors.
+
+    Arguments:
+        inputs: List of tensors
+        masks: List of masks (tensors or None).
+
+    Returns:
+        Three lists: output_tensors, output_masks, output_shapes
+    """
+    # Note: masking support is relevant mainly for Keras.
+    # It cannot be factored out without having the fully reimplement the network
+    # calling logic on the Keras side. We choose to incorporate it in
+    # Network because 1) it may be useful to fully support in tf.layers in
+    # the future and 2) Keras is a major user of Network.  If you don't
+    # use masking, it does not interfere with regular behavior at all and you
+    # can ignore it.
+    if masks is None:
+      masks = [None for _ in range(len(inputs))]
+
+    # Dictionary mapping reference tensors to tuples
+    # (computed tensor, compute mask)
+    # we assume a 1:1 mapping from tensor to mask
+    # TODO(fchollet): raise exception when a `.compute_mask()` call
+    # does not return a list the same size as `call`
+    tensor_map = {}
+    for x, y, mask in zip(self.inputs, inputs, masks):
+      tensor_map[str(id(x))] = (y, mask)
+
+    depth_keys = list(self._nodes_by_depth.keys())
+    depth_keys.sort(reverse=True)
+    for depth in depth_keys:
+      nodes = self._nodes_by_depth[depth]
+      for node in nodes:
+        # This is always a single layer, never a list.
+        layer = node.outbound_layer
+        reference_input_tensors = node.input_tensors
+        reference_output_tensors = node.output_tensors
+
+        # If all previous input tensors are available in tensor_map,
+        # then call node.inbound_layer on them.
+        computed_data = []  # List of tuples (input, mask).
+        for x in reference_input_tensors:
+          if str(id(x)) in tensor_map:
+            computed_data.append(tensor_map[str(id(x))])
+
+        if len(computed_data) == len(reference_input_tensors):
+          # Call layer (reapplying ops to new inputs).
+          with ops.name_scope(layer.name):
+            if node.arguments:
+              kwargs = node.arguments
+            else:
+              kwargs = {}
+            if len(computed_data) == 1:
+              computed_tensor, computed_mask = computed_data[0]
+              # Ensure mask propagation if applicable.
+              if 'mask' in tf_inspect.getargspec(layer.call).args:
+                if 'mask' not in kwargs:
+                  kwargs['mask'] = computed_mask
+
+              output_tensors = nest.flatten(
+                  layer.call(computed_tensor, **kwargs))
+              if hasattr(layer, 'compute_mask'):
+                output_masks = nest.flatten(
+                    layer.compute_mask(computed_tensor, computed_mask))
+              else:
+                output_masks = [None for _ in range(len(output_tensors))]
+              computed_tensors = [computed_tensor]
+              computed_masks = [computed_mask]
+            else:
+              computed_tensors = [x[0] for x in computed_data]
+              computed_masks = [x[1] for x in computed_data]
+              if 'mask' in tf_inspect.getargspec(layer.call).args:
+                if 'mask' not in kwargs:
+                  kwargs['mask'] = computed_masks
+              output_tensors = nest.flatten(
+                  layer.call(computed_tensors, **kwargs))
+              if hasattr(layer, 'compute_mask'):
+                output_masks = nest.flatten(
+                    layer.compute_mask(computed_tensors, computed_masks))
+              else:
+                output_masks = [None for _ in range(len(output_tensors))]
+
+            if context.in_graph_mode():
+              if layer.activity_regularizer is not None:
+                regularization_losses = [
+                    layer.activity_regularizer(x) for x in output_tensors
+                ]
+                # Apply activity regularizer if any:
+                layer.add_loss(regularization_losses, computed_tensors)
+
+          # Update tensor_map.
+          for x, y, mask in zip(reference_output_tensors, output_tensors,
+                                output_masks):
+            tensor_map[str(id(x))] = (y, mask)
+
+    output_tensors = []
+    output_masks = []
+    output_shapes = []
+    for x in self.outputs:
+      assert str(id(x)) in tensor_map, 'Could not compute output ' + str(x)
+      tensor, mask = tensor_map[str(id(x))]
+      output_shapes.append(tf_layers_util.static_shape(x))
+      output_tensors.append(tensor)
+      output_masks.append(mask)
+
+    if len(output_tensors) == 1:
+      output_tensors = output_tensors[0]
+      if output_shapes is not None:
+        output_shapes = output_shapes[0]
+      if output_masks is not None:
+        output_masks = output_masks[0]
+
+    if context.in_graph_mode():
+      # Update cache;
+      # keys are based on ids on input tensors and inputs masks.
+      cache_key = (tf_layers_util.object_list_uid(inputs)
+                   + '_' + tf_layers_util.object_list_uid(masks))
+      self._output_tensor_cache[cache_key] = output_tensors
+      self._output_mask_cache[cache_key] = output_masks
+
+      if output_shapes is not None:
+        input_shapes = [tf_layers_util.static_shape(x) for x in inputs]
+        cache_key = tf_layers_util.object_list_uid(input_shapes)
+        self._output_shape_cache[cache_key] = output_shapes
+
+    return output_tensors, output_masks
+
   def get_config(self):
     if not self._is_graph_network:
       raise NotImplementedError
@@ -891,8 +1541,7 @@ class Network(tf_network.GraphNetwork, Layer):
       else:
         kept_nodes = 0
       for original_node_index, node in enumerate(layer._inbound_nodes):
-        node_key = tf_network._make_node_key(layer.name,
-                                             original_node_index)
+        node_key = _make_node_key(layer.name, original_node_index)
         if node_key in self._network_nodes:
           node_conversion_map[node_key] = kept_nodes
           kept_nodes += 1
@@ -902,8 +1551,7 @@ class Network(tf_network.GraphNetwork, Layer):
       layer_config = layer.get_config()
       filtered_inbound_nodes = []
       for original_node_index, node in enumerate(layer._inbound_nodes):
-        node_key = tf_network._make_node_key(layer.name,
-                                             original_node_index)
+        node_key = _make_node_key(layer.name, original_node_index)
         if node_key in self._network_nodes:
           # The node is relevant to the model:
           # add to filtered_inbound_nodes.
@@ -927,8 +1575,7 @@ class Network(tf_network.GraphNetwork, Layer):
               inbound_layer = node.inbound_layers[i]
               node_index = node.node_indices[i]
               tensor_index = node.tensor_indices[i]
-              node_key = tf_network._make_node_key(inbound_layer.name,
-                                                   node_index)
+              node_key = _make_node_key(inbound_layer.name, node_index)
               new_node_index = node_conversion_map.get(node_key, 0)
               node_data.append(
                   [inbound_layer.name, new_node_index, tensor_index, kwargs])
@@ -945,8 +1592,7 @@ class Network(tf_network.GraphNetwork, Layer):
     model_inputs = []
     for i in range(len(self._input_layers)):
       layer, node_index, tensor_index = self._input_coordinates[i]
-      node_key = tf_network._make_node_key(layer.name,
-                                           node_index)
+      node_key = _make_node_key(layer.name, node_index)
       if node_key not in self._network_nodes:
         continue
       new_node_index = node_conversion_map[node_key]
@@ -955,8 +1601,7 @@ class Network(tf_network.GraphNetwork, Layer):
     model_outputs = []
     for i in range(len(self._output_layers)):
       layer, node_index, tensor_index = self._output_coordinates[i]
-      node_key = tf_network._make_node_key(layer.name,
-                                           node_index)
+      node_key = _make_node_key(layer.name, node_index)
       if node_key not in self._network_nodes:
         continue
       new_node_index = node_conversion_map[node_key]
@@ -1334,7 +1979,7 @@ def get_source_inputs(tensor, layer=None, node_index=None):
       return source_tensors
 
 
-def _to_list(x):
+def to_list(x):
   """Normalizes a list/tensor into a list.
 
   If a tensor is passed, we return
@@ -1690,3 +2335,190 @@ def shape_type_conversion(fn):
       return tensor_shape.TensorShape(output_shape)
 
   return wrapper
+
+
+def _make_node_key(layer_name, node_index):
+  return layer_name + '_ib-' + str(node_index)
+
+
+def _map_graph_network(inputs, outputs):
+  """Validate a network's topology and gather its layers and nodes.
+
+  Arguments:
+    inputs: List of input tensors.
+    outputs: List of outputs tensors.
+
+  Returns:
+    A tuple `(nodes, nodes_by_depth, layers, layers_by_depth)`.
+    - nodes: list of Node instances.
+    - nodes_by_depth: dict mapping ints (depth) to lists of node instances.
+    - layers: list of Layer instances.
+    - layers_by_depth: dict mapping ints (depth) to lists of layer instances.
+
+  Raises:
+    ValueError: In case the network is not valid (e.g. disconnected graph).
+  """
+  # Network_nodes: set of nodes included in the graph of layers
+  # (not all nodes included in the layers are relevant to the current graph).
+  network_nodes = set()  # ids of all nodes relevant to the Network
+  nodes_depths = {}  # dict {node: depth value}
+  layers_depths = {}  # dict {layer: depth value}
+  layer_indices = {}  # dict {layer: index in traversal}
+  nodes_in_decreasing_depth = []
+
+  def build_map(tensor,
+                finished_nodes,
+                nodes_in_progress,
+                layer,
+                node_index,
+                tensor_index):
+    """Builds a map of the graph of layers.
+
+    This recursively updates the map `layer_indices`,
+    the list `nodes_in_decreasing_depth` and the set `network_nodes`.
+
+    Arguments:
+        tensor: Some tensor in a graph.
+        finished_nodes: Set of nodes whose subgraphs have been traversed
+            completely. Useful to prevent duplicated work.
+        nodes_in_progress: Set of nodes that are currently active on the
+            recursion stack. Useful to detect cycles.
+        layer: Layer from which `tensor` comes from. If not provided,
+            will be obtained from `tensor._keras_history`.
+        node_index: Node index from which `tensor` comes from.
+        tensor_index: Tensor_index from which `tensor` comes from.
+
+    Raises:
+        ValueError: if a cycle is detected.
+    """
+    node = layer._inbound_nodes[node_index]  # pylint: disable=protected-access
+
+    # Prevent cycles.
+    if node in nodes_in_progress:
+      raise ValueError('The tensor ' + str(tensor) + ' at layer "' +
+                       layer.name + '" is part of a cycle.')
+
+    # Don't repeat work for shared subgraphs
+    if node in finished_nodes:
+      return
+
+    node_key = _make_node_key(layer.name, node_index)
+    # Update network_nodes.
+    network_nodes.add(node_key)
+
+    # Store the traversal order for layer sorting.
+    if layer not in layer_indices:
+      layer_indices[layer] = len(layer_indices)
+
+    nodes_in_progress.add(node)
+
+    # Propagate to all previous tensors connected to this node.
+    for i in range(len(node.inbound_layers)):
+      x = node.input_tensors[i]
+      layer = node.inbound_layers[i]
+      node_index = node.node_indices[i]
+      tensor_index = node.tensor_indices[i]
+      build_map(x, finished_nodes, nodes_in_progress, layer,
+                node_index, tensor_index)
+
+    finished_nodes.add(node)
+    nodes_in_progress.remove(node)
+    nodes_in_decreasing_depth.append(node)
+
+  finished_nodes = set()
+  nodes_in_progress = set()
+  for x in outputs:
+    layer, node_index, tensor_index = x._keras_history  # pylint: disable=protected-access
+    build_map(x, finished_nodes, nodes_in_progress,
+              layer=layer,
+              node_index=node_index,
+              tensor_index=tensor_index)
+
+  for node in reversed(nodes_in_decreasing_depth):
+    # If the depth is not set, the node has no outbound nodes (depth 0).
+    depth = nodes_depths.setdefault(node, 0)
+
+    # Update the depth of the corresponding layer
+    previous_depth = layers_depths.get(node.outbound_layer, 0)
+    # If we've seen this layer before at a higher depth,
+    # we should use that depth instead of the node depth.
+    # This is necessary for shared layers that have inputs at different
+    # depth levels in the graph.
+    depth = max(depth, previous_depth)
+    layers_depths[node.outbound_layer] = depth
+    nodes_depths[node] = depth
+
+    # Update the depth of inbound nodes.
+    # The "depth" of a node is the max of the depths
+    # of all layers it is connected to.
+    for i in range(len(node.inbound_layers)):
+      inbound_layer = node.inbound_layers[i]
+      node_index = node.node_indices[i]
+      inbound_node = inbound_layer._inbound_nodes[node_index]  # pylint: disable=protected-access
+      previous_depth = nodes_depths.get(inbound_node, 0)
+      nodes_depths[inbound_node] = max(depth + 1, previous_depth)
+
+  # Build a dict {depth: list of nodes with this depth}
+  nodes_by_depth = {}
+  for node, depth in nodes_depths.items():
+    if depth not in nodes_by_depth:
+      nodes_by_depth[depth] = []
+    nodes_by_depth[depth].append(node)
+
+  # Build a dict {depth: list of layers with this depth}
+  layers_by_depth = {}
+  for layer, depth in layers_depths.items():
+    if depth not in layers_by_depth:
+      layers_by_depth[depth] = []
+    layers_by_depth[depth].append(layer)
+
+  # Get sorted list of layer depths.
+  depth_keys = list(layers_by_depth.keys())
+  depth_keys.sort(reverse=True)
+
+  # Set self.layers and self._layers_by_depth.
+  layers = []
+  for depth in depth_keys:
+    layers_for_depth = layers_by_depth[depth]
+    # Network.layers needs to have a deterministic order:
+    # here we order them by traversal order.
+    layers_for_depth.sort(key=lambda x: layer_indices[x])
+    layers.extend(layers_for_depth)
+
+  # Get sorted list of node depths.
+  depth_keys = list(nodes_by_depth.keys())
+  depth_keys.sort(reverse=True)
+
+  # Check that all tensors required are computable.
+  # computable_tensors: all tensors in the graph
+  # that can be computed from the inputs provided.
+  computable_tensors = []
+  for x in inputs:
+    computable_tensors.append(x)
+
+  layers_with_complete_input = []  # To provide a better error msg.
+  for depth in depth_keys:
+    for node in nodes_by_depth[depth]:
+      layer = node.outbound_layer
+      if layer:
+        for x in node.input_tensors:
+          if x not in computable_tensors:
+            raise ValueError('Graph disconnected: '
+                             'cannot obtain value for tensor ' + str(x) +
+                             ' at layer "' + layer.name + '". '
+                             'The following previous layers '
+                             'were accessed without issue: ' +
+                             str(layers_with_complete_input))
+        for x in node.output_tensors:
+          computable_tensors.append(x)
+        layers_with_complete_input.append(layer.name)
+
+  # Ensure name unicity, which will be crucial for serialization
+  # (since serialized nodes refer to layers by their name).
+  all_names = [layer.name for layer in layers]
+  for name in all_names:
+    if all_names.count(name) != 1:
+      raise ValueError('The name "' + name + '" is used ' +
+                       str(all_names.count(name)) + ' times in the model. '
+                       'All layer names should be unique.')
+  return network_nodes, nodes_by_depth, layers, layers_by_depth
diff --git a/tensorflow/python/keras/_impl/keras/engine/topology_test.py b/tensorflow/python/keras/_impl/keras/engine/topology_test.py
index 28ddc094ee..ba4d427a19 100644
--- a/tensorflow/python/keras/_impl/keras/engine/topology_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/topology_test.py
@@ -23,8 +23,12 @@ import shutil
 
 import numpy as np
 
+from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import test_util
 from tensorflow.python.keras._impl import keras
+from tensorflow.python.layers import base as base_layers
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
@@ -43,29 +47,252 @@ except ImportError:
 
 class TopologyConstructionTest(test.TestCase):
 
-  def test_get_updates_for(self):
-    a = keras.layers.Input(shape=(1,))
-    dense_layer = keras.layers.Dense(1)
-    dense_layer.build((None, 1))
-    update_1 = state_ops.assign_add(dense_layer.kernel, a)
-    update_2 = state_ops.assign_add(dense_layer.kernel, [[1.]])
-    dense_layer.add_update(update_1, inputs=a)
-    dense_layer.add_update(update_2, inputs=None)
-
-    self.assertListEqual(dense_layer.get_updates_for(a), [update_1])
-    self.assertListEqual(dense_layer.get_updates_for(None), [update_2])
-
-  def test_get_losses_for(self):
-    a = keras.layers.Input(shape=(1,))
-    dense_layer = keras.layers.Dense(1)
-    dense_layer.build((None, 1))
-    loss_1 = math_ops.reduce_sum(a)
-    loss_2 = math_ops.reduce_sum(dense_layer.kernel)
-    dense_layer.add_loss(loss_1, inputs=a)
-    dense_layer.add_loss(loss_2, inputs=None)
-
-    self.assertListEqual(dense_layer.get_losses_for(a), [loss_1])
-    self.assertListEqual(dense_layer.get_losses_for(None), [loss_2])
+  def test_get_updates(self):
+
+    class MyLayer(keras.layers.Layer):
+
+      def build(self, input_shape):
+        self.a = self.add_variable('a',
+                                   (1, 1),
+                                   'float32',
+                                   trainable=False)
+        self.b = self.add_variable('b',
+                                   (1, 1),
+                                   'float32',
+                                   trainable=False)
+        self.add_update(state_ops.assign_add(self.a, [[1.]]))
+        self.built = True
+
+      def call(self, inputs):
+        self.add_update(state_ops.assign_add(self.a, inputs),
+                        inputs=True)
+        return inputs + 1
+
+    x1 = keras.Input(shape=(1,))
+    layer = MyLayer()
+    _ = layer.apply(x1)
+
+    self.assertEqual(len(layer.updates), 2)
+    self.assertEqual(len(layer.get_updates_for(x1)), 1)
+    self.assertEqual(len(layer.get_updates_for(None)), 1)
+
+    x2 = keras.Input(shape=(1,))
+    y2 = layer.apply(x2)
+
+    self.assertEqual(len(layer.updates), 3)
+    self.assertEqual(len(layer.get_updates_for(x1)), 1)
+    self.assertEqual(len(layer.get_updates_for(x2)), 1)
+    self.assertEqual(len(layer.get_updates_for(None)), 1)
+
+    network = keras.engine.topology.Network(x2, y2)
+    self.assertEqual(len(network.updates), 2)
+    self.assertEqual(len(network.get_updates_for(x1)), 0)
+    self.assertEqual(len(network.get_updates_for(x2)), 1)
+    self.assertEqual(len(network.get_updates_for(None)), 1)
+
+    x3 = keras.Input(shape=(1,))
+    _ = layer.apply(x3)
+    self.assertEqual(len(network.updates), 2)
+
+    x4 = keras.Input(shape=(1,))
+    _ = network(x4)
+    self.assertEqual(len(network.updates), 3)
+    self.assertEqual(len(network.get_updates_for(x2)), 1)
+    self.assertEqual(len(network.get_updates_for(x4)), 1)
+    self.assertEqual(len(network.get_updates_for(None)), 1)
+
+    network.add_update(state_ops.assign_add(layer.a, [[1]]))
+    self.assertEqual(len(network.updates), 4)
+    self.assertEqual(len(network.get_updates_for(None)), 2)
+
+    network.add_update(state_ops.assign_add(layer.a, x4), inputs=True)
+    self.assertEqual(len(network.updates), 5)
+    self.assertEqual(len(network.get_updates_for(x4)), 2)
+
+  def test_get_losses(self):
+
+    class MyLayer(keras.layers.Layer):
+
+      def build(self, input_shape):
+        self.a = self.add_variable('a',
+                                   (1, 1),
+                                   'float32',
+                                   trainable=False)
+        self.b = self.add_variable('b',
+                                   (1, 1),
+                                   'float32',
+                                   trainable=False)
+        self.add_loss(math_ops.reduce_sum(self.a))
+        self.built = True
+
+      def call(self, inputs):
+        self.add_loss(math_ops.reduce_sum(inputs),
+                      inputs=True)
+        return inputs + 1
+
+    x1 = keras.Input(shape=(1,))
+    layer = MyLayer()
+    _ = layer.apply(x1)
+
+    self.assertEqual(len(layer.losses), 2)
+    self.assertEqual(len(layer.get_losses_for(x1)), 1)
+    self.assertEqual(len(layer.get_losses_for(None)), 1)
+
+    x2 = keras.Input(shape=(1,))
+    y2 = layer.apply(x2)
+
+    self.assertEqual(len(layer.losses), 3)
+    self.assertEqual(len(layer.get_losses_for(x1)), 1)
+    self.assertEqual(len(layer.get_losses_for(x2)), 1)
+    self.assertEqual(len(layer.get_losses_for(None)), 1)
+
+    network = keras.engine.topology.Network(x2, y2)
+    self.assertEqual(len(network.losses), 2)
+    self.assertEqual(len(network.get_losses_for(x1)), 0)
+    self.assertEqual(len(network.get_losses_for(x2)), 1)
+    self.assertEqual(len(network.get_losses_for(None)), 1)
+
+    x3 = keras.Input(shape=(1,))
+    _ = layer.apply(x3)
+    self.assertEqual(len(network.losses), 2)
+
+    x4 = keras.Input(shape=(1,))
+    _ = network(x4)
+    self.assertEqual(len(network.losses), 3)
+    self.assertEqual(len(network.get_losses_for(x2)), 1)
+    self.assertEqual(len(network.get_losses_for(x4)), 1)
+    self.assertEqual(len(network.get_losses_for(None)), 1)
+
+    network.add_loss(math_ops.reduce_sum(layer.a))
+    self.assertEqual(len(network.losses), 4)
+    self.assertEqual(len(network.get_losses_for(None)), 2)
+
+    network.add_loss(math_ops.reduce_sum(x4), inputs=True)
+    self.assertEqual(len(network.losses), 5)
+    self.assertEqual(len(network.get_losses_for(x4)), 2)
+
+  def testTopologicalAttributes(self):
+    # test layer attributes / methods related to cross-layer connectivity.
+    a = keras.Input(shape=(32,), name='input_a')
+    b = keras.Input(shape=(32,), name='input_b')
+
+    # test input, output, input_shape, output_shape
+    test_layer = keras.layers.Dense(16, name='test_layer')
+    a_test = test_layer(a)
+    self.assertEqual(test_layer.input, a)
+    self.assertEqual(test_layer.output, a_test)
+    self.assertEqual(test_layer.input_shape, (None, 32))
+    self.assertEqual(test_layer.output_shape, (None, 16))
+
+    # test `get_*_at` methods
+    dense = keras.layers.Dense(16, name='dense_1')
+    a_2 = dense(a)
+    b_2 = dense(b)
+
+    self.assertEqual(dense.get_input_at(0), a)
+    self.assertEqual(dense.get_input_at(1), b)
+    self.assertEqual(dense.get_output_at(0), a_2)
+    self.assertEqual(dense.get_output_at(1), b_2)
+    self.assertEqual(dense.get_input_shape_at(0), (None, 32))
+    self.assertEqual(dense.get_input_shape_at(1), (None, 32))
+    self.assertEqual(dense.get_output_shape_at(0), (None, 16))
+    self.assertEqual(dense.get_output_shape_at(1), (None, 16))
+
+    # Test invalid value for attribute retrieval.
+    with self.assertRaises(ValueError):
+      dense.get_input_at(2)
+    with self.assertRaises(AttributeError):
+      new_dense = keras.layers.Dense(16)
+      _ = new_dense.input
+    with self.assertRaises(AttributeError):
+      new_dense = keras.layers.Dense(16)
+      _ = new_dense.output
+    with self.assertRaises(AttributeError):
+      new_dense = keras.layers.Dense(16)
+      _ = new_dense.output_shape
+    with self.assertRaises(AttributeError):
+      new_dense = keras.layers.Dense(16)
+      _ = new_dense.input_shape
+    with self.assertRaises(AttributeError):
+      new_dense = keras.layers.Dense(16)
+      a = keras.Input(shape=(3, 32))
+      a = keras.Input(shape=(5, 32))
+      a_2 = dense(a)
+      b_2 = dense(b)
+      _ = new_dense.input_shape
+    with self.assertRaises(AttributeError):
+      new_dense = keras.layers.Dense(16)
+      a = keras.Input(shape=(3, 32))
+      a = keras.Input(shape=(5, 32))
+      a_2 = dense(a)
+      b_2 = dense(b)
+      _ = new_dense.output_shape
+
+  def testTopologicalAttributesMultiOutputLayer(self):
+
+    class PowersLayer(keras.layers.Layer):
+
+      def call(self, inputs):
+        return [inputs**2, inputs**3]
+
+    x = keras.Input(shape=(32,))
+    test_layer = PowersLayer()
+    p1, p2 = test_layer(x)  # pylint: disable=not-callable
+
+    self.assertEqual(test_layer.input, x)
+    self.assertEqual(test_layer.output, [p1, p2])
+    self.assertEqual(test_layer.input_shape, (None, 32))
+    self.assertEqual(test_layer.output_shape, [(None, 32), (None, 32)])
+
+  def testTopologicalAttributesMultiInputLayer(self):
+
+    class AddLayer(keras.layers.Layer):
+
+      def call(self, inputs):
+        assert len(inputs) == 2
+        return inputs[0] + inputs[1]
+
+    a = keras.Input(shape=(32,))
+    b = keras.Input(shape=(32,))
+    test_layer = AddLayer()
+    y = test_layer([a, b])  # pylint: disable=not-callable
+
+    self.assertEqual(test_layer.input, [a, b])
+    self.assertEqual(test_layer.output, y)
+    self.assertEqual(test_layer.input_shape, [(None, 32), (None, 32)])
+    self.assertEqual(test_layer.output_shape, (None, 32))
+
+  def testBasicNetwork(self):
+    # minimum viable network
+    x = keras.Input(shape=(32,))
+    dense = keras.layers.Dense(2)
+    y = dense(x)
+    network = keras.engine.topology.Network(x, y, name='dense_network')
+
+    # test basic attributes
+    self.assertEqual(network.name, 'dense_network')
+    self.assertEqual(len(network.layers), 2)  # InputLayer + Dense
+    self.assertEqual(network.layers[1], dense)
+    self.assertEqual(network.weights, dense.weights)
+    self.assertEqual(network.trainable_weights, dense.trainable_weights)
+    self.assertEqual(network.non_trainable_weights, dense.non_trainable_weights)
+
+    # test callability on Input
+    x_2 = keras.Input(shape=(32,))
+    y_2 = network(x_2)
+    self.assertEqual(y_2.get_shape().as_list(), [None, 2])
+
+    # test callability on regular tensor
+    x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32))
+    y_2 = network(x_2)
+    self.assertEqual(y_2.get_shape().as_list(), [None, 2])
+
+    # test network `trainable` attribute
+    network.trainable = False
+    self.assertEqual(network.weights, dense.weights)
+    self.assertEqual(network.trainable_weights, [])
+    self.assertEqual(network.non_trainable_weights,
+                     dense.trainable_weights + dense.non_trainable_weights)
 
   def test_trainable_weights(self):
     a = keras.layers.Input(shape=(2,))
@@ -108,41 +335,6 @@ class TopologyConstructionTest(test.TestCase):
     self.assertListEqual(model.trainable_weights, [])
     self.assertListEqual(model.non_trainable_weights, weights)
 
-  def test_weight_loading(self):
-    with self.test_session():
-      a = keras.layers.Input(shape=(2,))
-      x = keras.layers.Dense(3)(a)
-      b = keras.layers.Dense(1)(x)
-      model = keras.models.Model(a, b)
-
-      x = np.random.random((3, 2))
-      ref_y = model.predict(x)
-      weights = model.get_weights()
-      model.set_weights(weights)
-      y = model.predict(x)
-      self.assertAllClose(ref_y, y)
-
-      with self.assertRaises(ValueError):
-        model.set_weights(weights[1:])
-      with self.assertRaises(ValueError):
-        model.set_weights(weights[::-1])
-
-      if h5py is None:
-        return  # Skip rest of test if H5py isn't available.
-
-      temp_dir = self.get_temp_dir()
-      self.addCleanup(shutil.rmtree, temp_dir)
-
-      h5_path = os.path.join(temp_dir, 'test.h5')
-      model.save_weights(h5_path)
-      model.load_weights(h5_path)
-      y = model.predict(x)
-      self.assertAllClose(ref_y, y)
-
-      model.load_weights(h5_path, by_name=True)
-      y = model.predict(x)
-      self.assertAllClose(ref_y, y)
-
   def test_learning_phase(self):
     with self.test_session():
       a = keras.layers.Input(shape=(32,), name='input_a')
@@ -348,7 +540,7 @@ class TopologyConstructionTest(test.TestCase):
       e = keras.layers.Input(shape=(32,), name='input_e')
       f = keras.layers.Input(shape=(32,), name='input_f')
       g, h = model([e, f])
-      self.assertEqual(g.name, 'model_1/dense_2/BiasAdd:0')
+      self.assertEqual(g.name, 'model/dense_2/BiasAdd:0')
 
       self.assertListEqual(g.get_shape().as_list(), c.get_shape().as_list())
       self.assertListEqual(h.get_shape().as_list(), d.get_shape().as_list())
@@ -555,6 +747,42 @@ class TopologyConstructionTest(test.TestCase):
     model = keras.models.Model(a, b)
     self.assertEqual(model.output_mask.get_shape().as_list(), [None, 10])
 
+  def testMaskingSingleInput(self):
+
+    class MaskedLayer(keras.layers.Layer):
+
+      def call(self, inputs, mask=None):
+        if mask is not None:
+          return inputs * mask
+        return inputs
+
+      def compute_mask(self, inputs, mask=None):
+        return array_ops.ones_like(inputs)
+
+    if context.in_graph_mode():
+      x = keras.Input(shape=(32,))
+      y = MaskedLayer()(x)  # pylint: disable=not-callable
+      network = keras.engine.topology.Network(x, y)
+
+      # test callability on Input
+      x_2 = keras.Input(shape=(32,))
+      y_2 = network(x_2)
+      self.assertEqual(y_2.get_shape().as_list(), [None, 32])
+
+      # test callability on regular tensor
+      x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32))
+      y_2 = network(x_2)
+      self.assertEqual(y_2.get_shape().as_list(), [None, 32])
+    else:
+      a = constant_op.constant([2] * 32)
+      mask = constant_op.constant([0, 1] * 16)
+      a._keras_mask = mask
+      b = MaskedLayer().apply(a)
+      self.assertTrue(hasattr(b, '_keras_mask'))
+      self.assertAllEqual(self.evaluate(array_ops.ones_like(mask)),
+                          self.evaluate(getattr(b, '_keras_mask')))
+      self.assertAllEqual(self.evaluate(a * mask), self.evaluate(b))
+
   def test_activity_regularization_with_model_composition(self):
 
     def reg(x):
@@ -576,6 +804,92 @@ class TopologyConstructionTest(test.TestCase):
     loss = model_b.evaluate(x)
     self.assertEqual(loss, 4.)
 
+  def test_layer_sharing_at_heterogenous_depth(self):
+    with self.test_session():
+      x_val = np.random.random((10, 5))
+
+      x = keras.Input(shape=(5,))
+      a = keras.layers.Dense(5, name='A')
+      b = keras.layers.Dense(5, name='B')
+      output = a(b(a(b(x))))
+      m = keras.models.Model(x, output)
+
+      output_val = m.predict(x_val)
+
+      config = m.get_config()
+      weights = m.get_weights()
+
+      m2 = keras.models.Model.from_config(config)
+      m2.set_weights(weights)
+
+      output_val_2 = m2.predict(x_val)
+      self.assertAllClose(output_val, output_val_2, atol=1e-6)
+
+  def test_layer_sharing_at_heterogenous_depth_with_concat(self):
+    with self.test_session():
+      input_shape = (16, 9, 3)
+      input_layer = keras.Input(shape=input_shape)
+
+      a = keras.layers.Dense(3, name='dense_A')
+      b = keras.layers.Dense(3, name='dense_B')
+      c = keras.layers.Dense(3, name='dense_C')
+
+      x1 = b(a(input_layer))
+      x2 = a(c(input_layer))
+      output = keras.layers.concatenate([x1, x2])
+
+      m = keras.models.Model(inputs=input_layer, outputs=output)
+
+      x_val = np.random.random((10, 16, 9, 3))
+      output_val = m.predict(x_val)
+
+      config = m.get_config()
+      weights = m.get_weights()
+
+      m2 = keras.models.Model.from_config(config)
+      m2.set_weights(weights)
+
+      output_val_2 = m2.predict(x_val)
+      self.assertAllClose(output_val, output_val_2, atol=1e-6)
+
+
+class TestSaving(test.TestCase):
+
+  def test_weight_loading(self):
+    with self.test_session():
+      a = keras.layers.Input(shape=(2,))
+      x = keras.layers.Dense(3)(a)
+      b = keras.layers.Dense(1)(x)
+      model = keras.models.Model(a, b)
+
+      x = np.random.random((3, 2))
+      ref_y = model.predict(x)
+      weights = model.get_weights()
+      model.set_weights(weights)
+      y = model.predict(x)
+      self.assertAllClose(ref_y, y)
+
+      with self.assertRaises(ValueError):
+        model.set_weights(weights[1:])
+      with self.assertRaises(ValueError):
+        model.set_weights(weights[::-1])
+
+      if h5py is None:
+        return  # Skip rest of test if H5py isn't available.
+
+      temp_dir = self.get_temp_dir()
+      self.addCleanup(shutil.rmtree, temp_dir)
+
+      h5_path = os.path.join(temp_dir, 'test.h5')
+      model.save_weights(h5_path)
+      model.load_weights(h5_path)
+      y = model.predict(x)
+      self.assertAllClose(ref_y, y)
+
+      model.load_weights(h5_path, by_name=True)
+      y = model.predict(x)
+      self.assertAllClose(ref_y, y)
+
   def test_weight_preprocessing(self):
     input_dim = 3
     output_dim = 3
@@ -667,53 +981,68 @@ class TopologyConstructionTest(test.TestCase):
     _ = keras.engine.topology.preprocess_weights_for_loading(
         model, model.weights, original_keras_version='1')
 
-  def test_layer_sharing_at_heterogenous_depth(self):
-    with self.test_session():
-      x_val = np.random.random((10, 5))
 
-      x = keras.Input(shape=(5,))
-      a = keras.layers.Dense(5, name='A')
-      b = keras.layers.Dense(5, name='B')
-      output = a(b(a(b(x))))
-      m = keras.models.Model(x, output)
-
-      output_val = m.predict(x_val)
-
-      config = m.get_config()
-      weights = m.get_weights()
-
-      m2 = keras.models.Model.from_config(config)
-      m2.set_weights(weights)
-
-      output_val_2 = m2.predict(x_val)
-      self.assertAllClose(output_val, output_val_2, atol=1e-6)
-
-  def test_layer_sharing_at_heterogenous_depth_with_concat(self):
-    with self.test_session():
-      input_shape = (16, 9, 3)
-      input_layer = keras.Input(shape=input_shape)
-
-      a = keras.layers.Dense(3, name='dense_A')
-      b = keras.layers.Dense(3, name='dense_B')
-      c = keras.layers.Dense(3, name='dense_C')
-
-      x1 = b(a(input_layer))
-      x2 = a(c(input_layer))
-      output = keras.layers.concatenate([x1, x2])
-
-      m = keras.models.Model(inputs=input_layer, outputs=output)
-
-      x_val = np.random.random((10, 16, 9, 3))
-      output_val = m.predict(x_val)
-
-      config = m.get_config()
-      weights = m.get_weights()
-
-      m2 = keras.models.Model.from_config(config)
-      m2.set_weights(weights)
-
-      output_val_2 = m2.predict(x_val)
-      self.assertAllClose(output_val, output_val_2, atol=1e-6)
+class DeferredModeTest(test.TestCase):
+
+  def testDeferredTensorAttributes(self):
+    x = base_layers._DeferredTensor(shape=(None, 2), dtype='float32', name='x')
+    self.assertEqual(str(x),
+                     'DeferredTensor(\'x\', shape=(?, 2), dtype=float32)')
+    self.assertEqual(repr(x),
+                     '<_DeferredTensor \'x\' shape=(?, 2) dtype=float32>')
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testSimpleNetworkBuilding(self):
+    inputs = keras.engine.topology.Input(shape=(32,))
+    if context.in_eager_mode():
+      self.assertIsInstance(inputs, base_layers._DeferredTensor)
+      self.assertEqual(inputs.dtype.name, 'float32')
+      self.assertEqual(inputs.shape.as_list(), [None, 32])
+
+    x = keras.layers.Dense(2)(inputs)
+    if context.in_eager_mode():
+      self.assertIsInstance(x, base_layers._DeferredTensor)
+      self.assertEqual(x.dtype.name, 'float32')
+      self.assertEqual(x.shape.as_list(), [None, 2])
+
+    outputs = keras.layers.Dense(4)(x)
+    network = keras.engine.topology.Network(inputs, outputs)
+    self.assertIsInstance(network, keras.engine.topology.Network)
+
+    if context.in_eager_mode():
+      # It should be possible to call such a network on EagerTensors.
+      inputs = constant_op.constant(
+          np.random.random((10, 32)).astype('float32'))
+      outputs = network(inputs)
+      self.assertEqual(outputs.shape.as_list(), [10, 4])
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testMultiIONetworkbuilding(self):
+    input_a = keras.engine.topology.Input(shape=(32,))
+    input_b = keras.engine.topology.Input(shape=(16,))
+    a = keras.layers.Dense(16)(input_a)
+
+    class AddLayer(keras.layers.Layer):
+
+      def call(self, inputs):
+        return inputs[0] + inputs[1]
+
+      def compute_output_shape(self, input_shape):
+        return input_shape[0]
+
+    c = AddLayer()([a, input_b])  # pylint: disable=not-callable
+    c = keras.layers.Dense(2)(c)
+
+    network = keras.engine.topology.Network([input_a, input_b], [a, c])
+    if context.in_eager_mode():
+      a_val = constant_op.constant(
+          np.random.random((10, 32)).astype('float32'))
+      b_val = constant_op.constant(
+          np.random.random((10, 16)).astype('float32'))
+      outputs = network([a_val, b_val])
+      self.assertEqual(len(outputs), 2)
+      self.assertEqual(outputs[0].shape.as_list(), [10, 16])
+      self.assertEqual(outputs[1].shape.as_list(), [10, 2])
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/keras/_impl/keras/integration_test.py b/tensorflow/python/keras/_impl/keras/integration_test.py
index 15c3d14727..280f7ed1b1 100644
--- a/tensorflow/python/keras/_impl/keras/integration_test.py
+++ b/tensorflow/python/keras/_impl/keras/integration_test.py
@@ -23,7 +23,6 @@ import numpy as np
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.layers import core as tf_core_layers
-from tensorflow.python.layers import network as tf_network_layers
 from tensorflow.python.ops import nn
 from tensorflow.python.platform import test
 
@@ -275,10 +274,10 @@ class KerasIntegrationTest(test.TestCase):
       y_train = keras.utils.to_categorical(y_train)
       y_test = keras.utils.to_categorical(y_test)
 
-      inputs = tf_network_layers.Input(shape=(10,))
+      inputs = keras.Input(shape=(10,))
       x = tf_core_layers.Dense(32, activation=nn.relu)(inputs)
       outputs = tf_core_layers.Dense(2, activation=nn.softmax)(x)
-      model = keras.models.Model(inputs, outputs)
+      model = keras.Model(inputs, outputs)
       model.summary()
 
       model.compile(loss='categorical_crossentropy',
diff --git a/tensorflow/python/keras/_impl/keras/models.py b/tensorflow/python/keras/_impl/keras/models.py
index 4c3ec7dbe4..05912b2ec3 100644
--- a/tensorflow/python/keras/_impl/keras/models.py
+++ b/tensorflow/python/keras/_impl/keras/models.py
@@ -1365,7 +1365,7 @@ def _clone_functional_model(model, input_tensors=None):
   else:
     # Make sure that all input tensors come from a Keras layer.
     # If tensor comes from an input layer: cache the input layer.
-    input_tensors = topology._to_list(input_tensors)
+    input_tensors = topology.to_list(input_tensors)
     input_tensors_ = []
     for i, x in enumerate(input_tensors):
       if not K.is_keras_tensor(x):
@@ -1427,8 +1427,8 @@ def _clone_functional_model(model, input_tensors=None):
           if has_arg(layer.call, 'mask'):
             if 'mask' not in kwargs:
               kwargs['mask'] = computed_mask
-          output_tensors = topology._to_list(layer(computed_tensor, **kwargs))
-          output_masks = topology._to_list(
+          output_tensors = topology.to_list(layer(computed_tensor, **kwargs))
+          output_masks = topology.to_list(
               layer.compute_mask(computed_tensor, computed_mask))
           computed_tensors = [computed_tensor]
           computed_masks = [computed_mask]
@@ -1438,8 +1438,8 @@ def _clone_functional_model(model, input_tensors=None):
           if has_arg(layer.call, 'mask'):
             if 'mask' not in kwargs:
               kwargs['mask'] = computed_masks
-          output_tensors = topology._to_list(layer(computed_tensors, **kwargs))
-          output_masks = topology._to_list(
+          output_tensors = topology.to_list(layer(computed_tensors, **kwargs))
+          output_masks = topology.to_list(
               layer.compute_mask(computed_tensors, computed_masks))
         # Update tensor_map.
         for x, y, mask in zip(reference_output_tensors, output_tensors,
@@ -1489,11 +1489,11 @@ def _clone_sequential_model(model, input_tensors=None):
   if input_tensors is None:
     return Sequential(layers=layers, name=model.name)
   else:
-    if len(topology._to_list(input_tensors)) != 1:
+    if len(topology.to_list(input_tensors)) != 1:
       raise ValueError('To clone a `Sequential` model, we expect '
                        ' at most one tensor '
                        'as part of `input_tensors`.')
-    x = topology._to_list(input_tensors)[0]
+    x = topology.to_list(input_tensors)[0]
     if K.is_keras_tensor(x):
       origin_layer = x._keras_history[0]
       if isinstance(origin_layer, topology.InputLayer):
diff --git a/tensorflow/python/layers/layers.py b/tensorflow/python/layers/layers.py
index 1555846efd..13a8e8e39c 100644
--- a/tensorflow/python/layers/layers.py
+++ b/tensorflow/python/layers/layers.py
@@ -68,7 +68,6 @@ from tensorflow.python.util.all_util import remove_undocumented
 # Base objects.
 from tensorflow.python.layers.base import Layer
 from tensorflow.python.layers.base import InputSpec
-from tensorflow.python.layers.network import Input
 
 # Core layers.
 from tensorflow.python.layers.core import Dense
diff --git a/tensorflow/python/layers/network.py b/tensorflow/python/layers/network.py
deleted file mode 100644
index 9f16559687..0000000000
--- a/tensorflow/python/layers/network.py
+++ /dev/null
@@ -1,1024 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-"""Contains Network, a composition of layers."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from tensorflow.python.eager import context
-from tensorflow.python.estimator import util as estimator_util
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.layers import base
-from tensorflow.python.layers import utils as layers_util
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import variable_scope as vs
-from tensorflow.python.platform import tf_logging as logging
-from tensorflow.python.util import nest
-from tensorflow.python.util.tf_export import tf_export
-
-
-class InputLayer(base.Layer):
-  """Layer to be used as an entry point into a Network (a graph of layers).
-
-  It can either wrap an existing tensor (pass an `input_tensor` argument)
-  or create its a placeholder tensor (pass arguments `input_shape`
-  as well as `dtype`).
-
-  It is generally recommend to use the functional layer API via `Input`,
-  (which creates an `InputLayer`) without directly using `InputLayer`.
-
-  Arguments:
-      input_shape: Shape tuple (not including the batch axis), or `TensorShape`
-        instance (not including the batch axis).
-      batch_size: Optional input batch size (integer or None).
-      dtype: Datatype of the input.
-      input_tensor: Optional tensor to use as layer input
-          instead of creating a placeholder.
-      sparse: Boolean, whether the placeholder created
-          is meant to be sparse.
-      name: Name of the layer (string).
-
-    Raises:
-      RuntimeError: If created in Eager mode.
-  """
-
-  def __init__(self,
-               input_shape=None,
-               batch_size=None,
-               dtype=dtypes.float32,
-               input_tensor=None,
-               sparse=False,
-               name=None):
-    super(InputLayer, self).__init__(dtype=dtype, name=name)
-    self.built = True
-    self.sparse = sparse
-    self.batch_size = batch_size
-
-    if isinstance(input_shape, tensor_shape.TensorShape):
-      input_shape = tuple(input_shape.as_list())
-
-    if input_tensor is None:
-      if input_shape is not None:
-        batch_input_shape = (batch_size,) + tuple(input_shape)
-      else:
-        batch_input_shape = None
-
-      if context.in_eager_mode():
-        # In eager mode, create a temporary placeholder to call the layer on.
-        input_tensor = base._DeferredTensor(  # pylint: disable=protected-access
-            shape=batch_input_shape,
-            dtype=dtype,
-            name=self.name)
-      else:
-        # In graph mode, create a graph placeholder to call the layer on.
-        if sparse:
-          input_tensor = array_ops.sparse_placeholder(
-              shape=batch_input_shape,
-              dtype=dtype,
-              name=self.name)
-        else:
-          input_tensor = array_ops.placeholder(
-              shape=batch_input_shape,
-              dtype=dtype,
-              name=self.name)
-
-      # For compatibility with Keras API.
-      self.is_placeholder = True
-      self._batch_input_shape = batch_input_shape
-    else:
-      # For compatibility with Keras API.
-      self.is_placeholder = False
-      self._batch_input_shape = tuple(input_tensor.get_shape().as_list())
-
-    # Create an input node to add to self.outbound_node
-    # and set output_tensors' _keras_history.
-    input_tensor._keras_history = (self, 0, 0)  # pylint: disable=protected-access
-    base.Node(
-        self,
-        inbound_layers=[],
-        node_indices=[],
-        tensor_indices=[],
-        input_tensors=[input_tensor],
-        output_tensors=[input_tensor])
-
-
-@tf_export('layers.Input')
-def Input(  # pylint: disable=invalid-name
-    shape=None,
-    batch_size=None,
-    name=None,
-    dtype=dtypes.float32,
-    sparse=False,
-    tensor=None):
-  """`Input()` is used to instantiate an input tensor for use with a `Network`.
-
-  For instance, if a, b and c are tensors created via `Input`,
-  it becomes possible to do:
-
-  `network = Network(inputs=[a, b], outputs=c)`
-
-  Example:
-
-      ```python
-      # This is a logistic regression
-      x = tf.layers.Input(shape=(32,))
-      y = tf.layers.Dense(16, activation='softmax')(x)
-      network = tf.layers.Network(x, y)
-      ```
-
-  Arguments:
-      shape: A shape tuple (integer), not including the batch size.
-          For instance, `shape=(32,)` indicates that the expected input
-          will be batches of 32-dimensional vectors.
-      batch_size: Optional input batch size (integer or None).
-      name: An optional name string for the layer.
-          Should be unique in a model (do not reuse the same name twice).
-          It will be autogenerated if it isn't provided.
-      dtype: The data type expected by the input, as a string
-          (`float32`, `float64`, `int32`...)
-      sparse: A boolean specifying whether the placeholder
-          to be created is sparse.
-      tensor: Optional existing tensor to wrap into the `Input` layer.
-          If set, the layer will not create a placeholder tensor.
-
-  Returns:
-      A tensor: either a new placeholder (with history metadata) or
-      `tensor` (if passed), with added history metadata.
-
-  Raises:
-    RuntimeError: If called in Eager mode.
-  """
-  input_layer = InputLayer(
-      input_shape=shape,
-      batch_size=batch_size,
-      name=name,
-      dtype=dtype,
-      sparse=sparse,
-      input_tensor=tensor)
-  # Return tensor including `_keras_history` metadata.
-  # Note that in this case train_output and test_output are the same pointer.
-  outputs = input_layer._inbound_nodes[0].output_tensors  # pylint: disable=protected-access
-  if len(outputs) == 1:
-    return outputs[0]
-  else:
-    return outputs
-
-
-class GraphNetwork(base.Layer):
-  """A GraphNetwork is a directed acyclic graph of layers.
-
-  It is the topological form of a `tf.keras.models.Model`. A `Model` is simply a
-  `GraphNetwork` with added training/evaluation routines.
-
-  A `GraphNetwork` instance implements the full `Layer` API. In particular, a
-  `GraphNetwork` can be called on new inputs.
-
-  Example:
-
-      ```python
-      # This is a logistic regression
-      x = tf.layers.Input(shape=(32,))
-      y = tf.layers.Dense(16, activation='softmax')(x)
-      network = tf.layers.GraphNetwork(x, y)
-
-      # It is then possible to call the network on compatible inputs:
-      z = tf.layers.Input(shape=(32,))
-      w = network(z)
-
-      # It is possible to retrieve the same properties as a layer:
-      weights = network.trainable_weights
-      ```
-
-  Arguments:
-      inputs: Input tensor or list of input tensors.
-        Must come from `tf.layers.Input`.
-      output: Output tensor or list of output tensors. Must come from
-        tf.layers Layers or Keras layers.
-      name: Optional name of the model (string).
-
-  Attributes:
-    GraphNetwork has the same attributes as Layer. On top of it, it also has:
-      - layers: a list of the children layers of the network,
-        a list of layer instances, ordered from "earlier in the graph"
-        to "later in the graph".
-
-  Methods:
-    GraphNetwork has the same methods as Layer. On top of it, it also has:
-      - get_layer: retrieves a child layer by name or index in the graph.
-
-  Raises:
-    TypeError: If created when eager execution is enabled, with inputs that
-      don't come from a call to `Input` or outputs that don't come from layers.
-  """
-
-  def __init__(self, inputs, outputs, name=None):  # pylint: disable=super-init-not-called
-    if isinstance(inputs, (list, tuple)):
-      self.inputs = list(inputs)  # Tensor or list of tensors.
-    else:
-      self.inputs = [inputs]
-    if isinstance(outputs, (list, tuple)):
-      self.outputs = list(outputs)
-    else:
-      self.outputs = [outputs]
-
-    if context.in_eager_mode():
-      # Check that all inputs/outputs are DeferredTensors.
-      for tensor in self.inputs:
-        if not isinstance(tensor, base._DeferredTensor):  # pylint: disable=protected-access
-          raise TypeError('When eager execution is enabled, '
-                          'inputs must come from a call to '
-                          '`tf.keras.Input` (called after '
-                          'tfe.enable_eager_execution()). '
-                          'Received invalid input: ' + str(tensor))
-      for tensor in self.outputs:
-        if not isinstance(tensor, base._DeferredTensor):  # pylint: disable=protected-access
-          raise TypeError('When eager execution is enabled, '
-                          'outputs must come from a call to '
-                          'a layer (called after '
-                          'tfe.enable_eager_execution()). '
-                          'Received invalid output: ' + str(tensor))
-
-    self._init_set_name(name)
-    self._activity_regularizer = None
-    with vs.variable_scope(
-        None, default_name=self._base_name) as captured_scope:
-      self._scope = captured_scope
-    call_fn_args = estimator_util.fn_args(self.call)
-    self._compute_previous_mask = ('mask' in call_fn_args or
-                                   hasattr(self, 'compute_mask'))
-    self._call_has_scope_arg = 'scope' in call_fn_args
-
-    # This acts just like the `trainable` attribute of any layer instance.
-    # It does not affect users of the underlying layers, only users of the
-    # GraphNetwork instance.
-    self.trainable = True
-    # A GraphNetwork does not create weights of its own, thus it is already
-    # built.
-    self.built = True
-    # A GraphNetwork does not create weights of its own, thus has no dtype.
-    self._dtype = None
-    self._is_graph_network = True
-    # The following are implemented as property functions:
-    # self.trainable_weights
-    # self.non_trainable_weights
-    # self.input_spec
-
-    # Private attributes to implement compatibility with Layer.
-    self._updates = []
-    self._losses = []
-    self._scope = None
-    self._reuse = None
-    self._graph = ops.get_default_graph()
-
-    # All layers in order of horizontal graph traversal.
-    # Entries are unique. Includes input and output layers.
-    self._layers = []
-
-    # Check for redundancy in inputs.
-    if len(set(self.inputs)) != len(self.inputs):
-      raise ValueError('The list of inputs passed to the model '
-                       'is redundant. '
-                       'All inputs should only appear once.'
-                       ' Found: ' + str(self.inputs))
-
-    # # List of initial layers (1 to 1 mapping with self.inputs,
-    # # hence the same layer might appear twice)
-    # self._input_layers = []
-    # self._input_layers_node_indices = []
-    # self._input_layers_tensor_indices = []
-    # # list of layers (1 to 1 mapping with self.inputs,
-    # # hence the same layer might appear twice)
-    # self._output_layers = []
-    # self._output_layers_node_indices = []
-    # self._output_layers_tensor_indices = []
-
-    self._input_layers = []
-    self._output_layers = []
-    self._input_coordinates = []
-    self._output_coordinates = []
-
-    # This is for performance optimization when calling the GraphNetwork on new
-    # inputs. Every time the GraphNetwork is called on a set on input tensors,
-    # we compute the output tensors, output masks and output shapes in one pass,
-    # then cache them here. When any of these outputs is queried later, we
-    # retrieve it from there instead of recomputing it.
-    self._output_mask_cache = {}
-    self._output_tensor_cache = {}
-    self._output_shape_cache = {}
-
-    # User-provided arguments validation.
-    for x in self.inputs:
-      # Check that x has appropriate `_keras_history` metadata.
-      if not hasattr(x, '_keras_history'):
-        cls_name = self.__class__.__name__
-        raise ValueError('Input tensors to a ' + cls_name + ' ' +
-                         'must come from `tf.layers.Input`. '
-                         'Received: ' + str(x) +
-                         ' (missing previous layer metadata).')
-      # Check that x is an input tensor.
-      # pylint: disable=protected-access
-      layer, node_index, tensor_index = x._keras_history
-      if len(layer._inbound_nodes) > 1 or (
-          layer._inbound_nodes and layer._inbound_nodes[0].inbound_layers):
-        cls_name = self.__class__.__name__
-        logging.warning(cls_name + ' inputs must come from '
-                        '`tf.layers.Input` (thus holding past layer metadata), '
-                        'they cannot be the output of '
-                        'a previous non-Input layer. '
-                        'Here, a tensor specified as '
-                        'input to "' + self.name + '" was not an Input tensor, '
-                        'it was generated by layer ' + layer.name + '.\n'
-                        'Note that input tensors are '
-                        'instantiated via `tensor = tf.layers.Input(shape)`.\n'
-                        'The tensor that caused the issue was: ' + str(x.name))
-      # pylint: enable=protected-access
-    for x in self.outputs:
-      if not hasattr(x, '_keras_history'):
-        cls_name = self.__class__.__name__
-        raise ValueError('Output tensors to a ' + cls_name + ' must be '
-                         'the output of a TensorFlow `Layer` '
-                         '(thus holding past layer metadata). Found: ' + str(x))
-
-    # Build self._output_layers:
-    for x in self.outputs:
-      layer, node_index, tensor_index = x._keras_history  # pylint: disable=protected-access
-      self._output_layers.append(layer)
-      self._output_coordinates.append((layer, node_index, tensor_index))
-
-    # Build self._input_layers:
-    for x in self.inputs:
-      layer, node_index, tensor_index = x._keras_history  # pylint: disable=protected-access
-      # It's supposed to be an input layer, so only one node
-      # and one tensor output.
-      assert node_index == 0
-      assert tensor_index == 0
-      self._input_layers.append(layer)
-      self._input_coordinates.append((layer, node_index, tensor_index))
-
-    # Network_nodes: set of nodes included in the graph
-    # (not all nodes included in the layers
-    # are relevant to the current graph).
-    network_nodes = set()  # ids of all nodes relevant to the GraphNetwork
-    nodes_depths = {}  # dict {node: depth value}
-    layers_depths = {}  # dict {layer: depth value}
-    layer_indices = {}  # dict {layer: index in traversal}
-    nodes_in_decreasing_depth = []
-
-    def build_map_of_graph(tensor,
-                           finished_nodes,
-                           nodes_in_progress,
-                           layer,
-                           node_index,
-                           tensor_index):
-      """Builds a map of the graph of layers.
-
-      This recursively updates the map `layer_indices`,
-      the list `nodes_in_decreasing_depth` and the set `network_nodes`.
-
-      Arguments:
-          tensor: Some tensor in a graph.
-          finished_nodes: Set of nodes whose subgraphs have been traversed
-              completely. Useful to prevent duplicated work.
-          nodes_in_progress: Set of nodes that are currently active on the
-              recursion stack. Useful to detect cycles.
-          layer: Layer from which `tensor` comes from. If not provided,
-              will be obtained from `tensor._keras_history`.
-          node_index: Node index from which `tensor` comes from.
-          tensor_index: Tensor_index from which `tensor` comes from.
-
-      Raises:
-          ValueError: if a cycle is detected.
-      """
-      node = layer._inbound_nodes[node_index]  # pylint: disable=protected-access
-
-      # Prevent cycles.
-      if node in nodes_in_progress:
-        raise ValueError('The tensor ' + str(tensor) + ' at layer "' +
-                         layer.name + '" is part of a cycle.')
-
-      # Don't repeat work for shared subgraphs
-      if node in finished_nodes:
-        return
-
-      node_key = _make_node_key(layer.name, node_index)
-      # Update network_nodes.
-      network_nodes.add(node_key)
-
-      # Store the traversal order for layer sorting.
-      if layer not in layer_indices:
-        layer_indices[layer] = len(layer_indices)
-
-      nodes_in_progress.add(node)
-
-      # Propagate to all previous tensors connected to this node.
-      for i in range(len(node.inbound_layers)):
-        x = node.input_tensors[i]
-        layer = node.inbound_layers[i]
-        node_index = node.node_indices[i]
-        tensor_index = node.tensor_indices[i]
-        build_map_of_graph(x, finished_nodes, nodes_in_progress, layer,
-                           node_index, tensor_index)
-
-      finished_nodes.add(node)
-      nodes_in_progress.remove(node)
-      nodes_in_decreasing_depth.append(node)
-
-    finished_nodes = set()
-    nodes_in_progress = set()
-    for x in self.outputs:
-      layer, node_index, tensor_index = x._keras_history  # pylint: disable=protected-access
-      build_map_of_graph(x, finished_nodes, nodes_in_progress,
-                         layer=layer,
-                         node_index=node_index,
-                         tensor_index=tensor_index)
-
-    for node in reversed(nodes_in_decreasing_depth):
-      # If the depth is not set, the node has no outbound nodes (depth 0).
-      depth = nodes_depths.setdefault(node, 0)
-
-      # Update the depth of the corresponding layer
-      previous_depth = layers_depths.get(node.outbound_layer, 0)
-      # If we've seen this layer before at a higher depth,
-      # we should use that depth instead of the node depth.
-      # This is necessary for shared layers that have inputs at different
-      # depth levels in the graph.
-      depth = max(depth, previous_depth)
-      layers_depths[node.outbound_layer] = depth
-      nodes_depths[node] = depth
-
-      # Update the depth of inbound nodes.
-      # The "depth" of a node is the max of the depths
-      # of all layers it is connected to.
-      for i in range(len(node.inbound_layers)):
-        inbound_layer = node.inbound_layers[i]
-        node_index = node.node_indices[i]
-        inbound_node = inbound_layer._inbound_nodes[node_index]  # pylint: disable=protected-access
-        previous_depth = nodes_depths.get(inbound_node, 0)
-        nodes_depths[inbound_node] = max(depth + 1, previous_depth)
-
-    # Build a dict {depth: list of nodes with this depth}
-    nodes_by_depth = {}
-    for node, depth in nodes_depths.items():
-      if depth not in nodes_by_depth:
-        nodes_by_depth[depth] = []
-      nodes_by_depth[depth].append(node)
-
-    # Build a dict {depth: list of layers with this depth}
-    layers_by_depth = {}
-    for layer, depth in layers_depths.items():
-      if depth not in layers_by_depth:
-        layers_by_depth[depth] = []
-      layers_by_depth[depth].append(layer)
-
-    # Get sorted list of layer depths.
-    depth_keys = list(layers_by_depth.keys())
-    depth_keys.sort(reverse=True)
-
-    # Set self.layers and self._layers_by_depth.
-    layers = []
-    for depth in depth_keys:
-      layers_for_depth = layers_by_depth[depth]
-      # GraphNetwork.layers needs to have a deterministic order:
-      # here we order them by traversal order.
-      layers_for_depth.sort(key=lambda x: layer_indices[x])
-      layers.extend(layers_for_depth)
-    self._layers = layers
-    self._layers_by_depth = layers_by_depth
-
-    # Get sorted list of node depths.
-    depth_keys = list(nodes_by_depth.keys())
-    depth_keys.sort(reverse=True)
-
-    # Check that all tensors required are computable.
-    # computable_tensors: all tensors in the graph
-    # that can be computed from the inputs provided.
-    computable_tensors = []
-    for x in self.inputs:
-      computable_tensors.append(x)
-
-    layers_with_complete_input = []  # To provide a better error msg.
-    for depth in depth_keys:
-      for node in nodes_by_depth[depth]:
-        layer = node.outbound_layer
-        if layer:
-          for x in node.input_tensors:
-            if x not in computable_tensors:
-              raise ValueError('Graph disconnected: '
-                               'cannot obtain value for tensor ' + str(x) +
-                               ' at layer "' + layer.name + '". '
-                               'The following previous layers '
-                               'were accessed without issue: ' +
-                               str(layers_with_complete_input))
-          for x in node.output_tensors:
-            computable_tensors.append(x)
-          layers_with_complete_input.append(layer.name)
-
-    # Keep track of the network's nodes.
-    self._network_nodes = network_nodes
-    self._nodes_by_depth = nodes_by_depth
-
-    # Ensure name unicity, which will be crucial for serialization
-    # (since serialized nodes refer to layers by their name).
-    all_names = [layer.name for layer in self.layers]
-    for name in all_names:
-      if all_names.count(name) != 1:
-        raise ValueError('The name "' + name + '" is used ' +
-                         str(all_names.count(name)) + ' times in the model. '
-                         'All layer names should be unique.')
-
-    # Layer parameters.
-    # The new network starts with a single inbound node
-    # for its inputs, and no outbound nodes.
-    self._outbound_nodes = []  # Will be appended to by future calls to __call__
-    self._inbound_nodes = [
-    ]  # Will be appended to below, and by future calls to __call__
-    # Create the node linking internal inputs to internal outputs.
-    base.Node(
-        outbound_layer=self,
-        inbound_layers=[],
-        node_indices=[],
-        tensor_indices=[],
-        input_tensors=self.inputs,
-        output_tensors=self.outputs)
-
-  @property
-  def layers(self):
-    return self._layers
-
-  def get_layer(self, name=None, index=None):
-    """Retrieves a layer based on either its name (unique) or index.
-
-    Indices are based on order of horizontal graph traversal (bottom-up).
-
-    Arguments:
-        name: String, name of layer.
-        index: Integer, index of layer.
-
-    Returns:
-        A layer instance.
-
-    Raises:
-        ValueError: In case of invalid layer name or index.
-    """
-    # TODO(fchollet): We could build a dictionary based on layer names
-    # since they are constant, but we have not done that yet.
-    if index is not None:
-      if len(self.layers) <= index:
-        raise ValueError('Was asked to retrieve layer at index ' + str(index) +
-                         ' but model only has ' + str(len(self.layers)) +
-                         ' layers.')
-      else:
-        return self.layers[index]
-    else:
-      if not name:
-        raise ValueError('Provide either a layer name or layer index.')
-    for layer in self.layers:
-      if layer.name == name:
-        return layer
-    raise ValueError('No such layer: ' + name)
-
-  @property
-  def stateful(self):
-    return any([(hasattr(layer, 'stateful') and layer.stateful)
-                for layer in self.layers])
-
-  @property
-  def updates(self):
-    """Retrieve the network's updates.
-
-    Will only include updates that are either
-    unconditional, or conditional on inputs to this model
-    (e.g. will not include updates that were created by layers of this model
-    outside of the model).
-
-    Effectively, `network.updates` behaves like `layer.updates`.
-
-    Concrete example:
-
-    ```python
-      bn = keras.layers.BatchNormalization()
-      x1 = keras.layers.Input(shape=(10,))
-      _ = bn(x1)  # This creates 2 updates.
-
-      x2 = keras.layers.Input(shape=(10,))
-      y2 = bn(x2)  # This creates 2 more updates.
-
-      # The BN layer has now 4 updates.
-      self.assertEqual(len(bn.updates), 4)
-
-      # Let's create a model from x2 to y2.
-      model = keras.models.Model(x2, y2)
-
-      # The model does not list all updates from its underlying layers,
-      # but only the updates that are relevant to it. Updates created by layers
-      # outside of the model are discarded.
-      self.assertEqual(len(model.updates), 2)
-
-      # If you keep calling the model, you append to its updates, just like
-      # what happens for a layer.
-      x3 = keras.layers.Input(shape=(10,))
-      y3 = model(x3)
-      self.assertEqual(len(model.updates), 4)
-
-      # But if you call the inner BN layer independently, you don't affect
-      # the model's updates.
-      x4 = keras.layers.Input(shape=(10,))
-      _ = bn(x4)
-      self.assertEqual(len(model.updates), 4)
-    ```
-
-    Returns:
-        A list of update ops.
-    """
-    if context.in_eager_mode():
-      return []
-
-    if not self.trainable and not self.stateful:
-      return []
-
-    updates = []
-    for layer in self.layers:
-      updates += layer.updates
-
-    # `updates` might contain irrelevant updates, so it needs to be filtered
-    # with respect to inputs the model has been called on.
-    relevant_inputs = self.inputs or []
-    for i in range(1, len(self._inbound_nodes)):
-      inputs = self.get_input_at(i)
-      if isinstance(inputs, list):
-        relevant_inputs += inputs
-      else:
-        relevant_inputs.append(inputs)
-    reachable = layers_util.get_reachable_from_inputs(relevant_inputs, updates)
-    relevant_conditional_updates = [x for x in updates if x in reachable]
-    unconditional_updates = [
-        x for x in updates if x._unconditional_update]  # pylint: disable=protected-access
-    # A layer could be used multiple times in a nested structure,
-    # so the updates list must be de-duped.
-    return list(set(
-        relevant_conditional_updates + unconditional_updates + self._updates))
-
-  @property
-  def losses(self):
-    """Retrieve the network's losses.
-
-    Will only include losses that are either
-    unconditional, or conditional on inputs to this model
-    (e.g. will not include losses that depend on tensors
-    that aren't inputs to this model).
-
-    Returns:
-        A list of loss tensors.
-    """
-    losses = []
-    for layer in self.layers:
-      losses += layer.losses
-    if context.in_eager_mode():
-      return losses
-
-    relevant_inputs = self.inputs or []
-    for i in range(1, len(self._inbound_nodes)):
-      inputs = self.get_input_at(i)
-      if isinstance(inputs, list):
-        relevant_inputs += inputs
-      else:
-        relevant_inputs.append(inputs)
-    reachable = layers_util.get_reachable_from_inputs(relevant_inputs, losses)
-    relevant_conditional_losses = [x for x in losses if x in reachable]
-    unconditional_losses = [
-        x for x in losses if x._unconditional_loss]  # pylint: disable=protected-access
-    return list(set(
-        relevant_conditional_losses + unconditional_losses + self._losses))
-
-  @property
-  def trainable_weights(self):
-    if not self.trainable:
-      return []
-    weights = []
-    for layer in self.layers:
-      weights += layer.trainable_weights
-    return weights
-
-  @property
-  def non_trainable_weights(self):
-    weights = []
-    for layer in self.layers:
-      weights += layer.non_trainable_weights
-    if not self.trainable:
-      trainable_weights = []
-      for layer in self.layers:
-        trainable_weights += layer.trainable_weights
-      return trainable_weights + weights
-    return weights
-
-  @property
-  def input_spec(self):
-    """Gets the network's input specs.
-
-    Returns:
-        A list of `InputSpec` instances (one per input to the model)
-            or a single instance if the model has only one input.
-    """
-    # If not a graph network, can't assume anything.
-    if not self._is_graph_network:
-      return None
-
-    specs = []
-    for layer in self._input_layers:
-      if layer.input_spec is None:
-        specs.append(None)
-      else:
-        if not isinstance(layer.input_spec, list):
-          raise TypeError('Layer ' + layer.name +
-                          ' has an input_spec attribute that '
-                          'is not a list. We expect a list. '
-                          'Found input_spec = ' + str(layer.input_spec))
-        specs += layer.input_spec
-    if len(specs) == 1:
-      return specs[0]
-    return specs
-
-  def call(self, inputs, mask=None):
-    """Call the model on new inputs.
-
-    In this case `call` just reapplies
-    all ops in the graph to the new inputs
-    (e.g. build a new computational graph from the provided inputs).
-
-    Arguments:
-        inputs: A tensor or list of tensors.
-        mask: A mask or list of masks. A mask can be
-            either a tensor or None (no mask).
-
-    Returns:
-        A tensor if there is a single output, or
-        a list of tensors if there are more than one outputs.
-    """
-    inputs = nest.flatten(inputs)
-    if mask is None:
-      masks = [None for _ in range(len(inputs))]
-    else:
-      masks = nest.flatten(mask)
-
-    if context.in_graph_mode():
-      # Try to retrieve cached outputs if the layer has already been called
-      # on these exact inputs.
-      cache_key = (layers_util.object_list_uid(inputs)
-                   + '_' + layers_util.object_list_uid(masks))
-      if cache_key in self._output_tensor_cache:
-        # Cache hit.
-        return self._output_tensor_cache[cache_key]
-    # Actually apply the network graph to the new inputs.
-    outputs, _ = self._run_internal_graph(inputs, masks)
-    return outputs
-
-  def compute_output_shape(self, input_shape):
-    if not self._is_graph_network:
-      raise NotImplementedError
-
-    if isinstance(input_shape, list):
-      input_shapes = []
-      for shape in input_shape:
-        if shape is not None:
-          input_shapes.append(tuple(tensor_shape.TensorShape(shape).as_list()))
-        else:
-          input_shapes.append(None)
-    else:
-      if input_shape is not None:
-        input_shapes = [tuple(tensor_shape.TensorShape(input_shape).as_list())]
-      else:
-        input_shapes = [None]
-
-    if len(input_shapes) != len(self._input_layers):
-      raise ValueError('Invalid input_shape argument ' + str(input_shape) +
-                       ': model has ' + str(len(self._input_layers)) +
-                       ' tensor inputs.')
-
-    cache_key = layers_util.object_list_uid(input_shapes)
-    if cache_key not in self._output_shape_cache:
-      # Cache miss. We have to run the network graph manually (recursive calls
-      # to `compute_output_shape`).
-      layers_to_output_shapes = {}
-      for i in range(len(input_shapes)):
-        layer = self._input_layers[i]
-        input_shape = input_shapes[i]
-        # It's an input layer: then `compute_output_shape` is identity,
-        # and there is only one node and one tensor output.
-        shape_key = layer.name + '_0_0'
-        layers_to_output_shapes[shape_key] = input_shape
-
-      depth_keys = list(self._nodes_by_depth.keys())
-      depth_keys.sort(reverse=True)
-      # Iterate over nodes, by depth level.
-      if len(depth_keys) > 1:
-        for depth in depth_keys:
-          nodes = self._nodes_by_depth[depth]
-          for node in nodes:
-            # This is always a single layer, never a list.
-            layer = node.outbound_layer
-            if layer in self._input_layers:
-              # We've already covered the input layers
-              # a few lines above.
-              continue
-            # Potentially redundant list,
-            # same size as node.input_tensors.
-            input_shapes = []
-            for j in range(len(node.inbound_layers)):
-              inbound_layer = node.inbound_layers[j]
-              node_index = node.node_indices[j]
-              tensor_index = node.tensor_indices[j]
-              shape_key = inbound_layer.name + '_%s_%s' % (node_index,
-                                                           tensor_index)
-              input_shape = layers_to_output_shapes[shape_key]
-              input_shapes.append(input_shape)
-
-            if len(input_shapes) == 1:
-              output_shape = layer.compute_output_shape(input_shapes[0])
-            else:
-              output_shape = layer.compute_output_shape(input_shapes)
-            if isinstance(output_shape, list):
-              output_shapes = [
-                  tuple(tensor_shape.TensorShape(shape).as_list())
-                  for shape in output_shape
-              ]
-            else:
-              output_shapes = [
-                  tuple(tensor_shape.TensorShape(output_shape).as_list())
-              ]
-
-            node_index = layer._inbound_nodes.index(node)  # pylint: disable=protected-access
-            for j in range(len(output_shapes)):
-              shape_key = layer.name + '_%s_%s' % (node_index, j)
-              layers_to_output_shapes[shape_key] = output_shapes[j]
-
-        # Read final output shapes from layers_to_output_shapes.
-        output_shapes = []
-        for i in range(len(self._output_layers)):
-          layer, node_index, tensor_index = self._output_coordinates[i]
-          shape_key = layer.name + '_%s_%s' % (node_index, tensor_index)
-          output_shapes.append(layers_to_output_shapes[shape_key])
-        # Store in cache.
-        self._output_shape_cache[cache_key] = output_shapes
-    else:
-      # Cache hit.
-      output_shapes = self._output_shape_cache[cache_key]
-
-    if isinstance(output_shapes, list):
-      if len(output_shapes) == 1:
-        return tensor_shape.TensorShape(output_shapes[0])
-      else:
-        return [tensor_shape.TensorShape(shape) for shape in output_shapes]
-    else:
-      return tensor_shape.TensorShape(output_shapes)
-
-  def _run_internal_graph(self, inputs, masks=None):
-    """Computes output tensors for new inputs.
-
-    # Note:
-        - Expects `inputs` to be a list (potentially with 1 element).
-        - Can be run on non-Keras tensors.
-
-    Arguments:
-        inputs: List of tensors
-        masks: List of masks (tensors or None).
-
-    Returns:
-        Three lists: output_tensors, output_masks, output_shapes
-    """
-    # Note: masking support is relevant mainly for Keras.
-    # It cannot be factored out without having the fully reimplement the network
-    # calling logic on the Keras side. We choose to incorporate it in
-    # GraphNetwork because 1) it may be useful to fully support in tf.layers in
-    # the future and 2) Keras is a major user of GraphNetwork.  If you don't
-    # use masking, it does not interfere with regular behavior at all and you
-    # can ignore it.
-    if masks is None:
-      masks = [None for _ in range(len(inputs))]
-
-    # Dictionary mapping reference tensors to tuples
-    # (computed tensor, compute mask)
-    # we assume a 1:1 mapping from tensor to mask
-    # TODO(fchollet): raise exception when a `.compute_mask()` call
-    # does not return a list the same size as `call`
-    tensor_map = {}
-    for x, y, mask in zip(self.inputs, inputs, masks):
-      tensor_map[str(id(x))] = (y, mask)
-
-    depth_keys = list(self._nodes_by_depth.keys())
-    depth_keys.sort(reverse=True)
-    for depth in depth_keys:
-      nodes = self._nodes_by_depth[depth]
-      for node in nodes:
-        # This is always a single layer, never a list.
-        layer = node.outbound_layer
-        reference_input_tensors = node.input_tensors
-        reference_output_tensors = node.output_tensors
-
-        # If all previous input tensors are available in tensor_map,
-        # then call node.inbound_layer on them.
-        computed_data = []  # List of tuples (input, mask).
-        for x in reference_input_tensors:
-          if str(id(x)) in tensor_map:
-            computed_data.append(tensor_map[str(id(x))])
-
-        if len(computed_data) == len(reference_input_tensors):
-          # Call layer (reapplying ops to new inputs).
-          with ops.name_scope(layer.name):
-            if node.arguments:
-              kwargs = node.arguments
-            else:
-              kwargs = {}
-            if len(computed_data) == 1:
-              computed_tensor, computed_mask = computed_data[0]
-              # Ensure mask propagation if applicable.
-              if 'mask' in estimator_util.fn_args(layer.call):
-                if 'mask' not in kwargs:
-                  kwargs['mask'] = computed_mask
-
-              output_tensors = nest.flatten(
-                  layer.call(computed_tensor, **kwargs))
-              if hasattr(layer, 'compute_mask'):
-                output_masks = nest.flatten(
-                    layer.compute_mask(computed_tensor, computed_mask))
-              else:
-                output_masks = [None for _ in range(len(output_tensors))]
-              computed_tensors = [computed_tensor]
-              computed_masks = [computed_mask]
-            else:
-              computed_tensors = [x[0] for x in computed_data]
-              computed_masks = [x[1] for x in computed_data]
-              if 'mask' in estimator_util.fn_args(layer.call):
-                if 'mask' not in kwargs:
-                  kwargs['mask'] = computed_masks
-              output_tensors = nest.flatten(
-                  layer.call(computed_tensors, **kwargs))
-              if hasattr(layer, 'compute_mask'):
-                output_masks = nest.flatten(
-                    layer.compute_mask(computed_tensors, computed_masks))
-              else:
-                output_masks = [None for _ in range(len(output_tensors))]
-
-            if context.in_graph_mode():
-              if layer.activity_regularizer is not None:
-                regularization_losses = [
-                    layer.activity_regularizer(x) for x in output_tensors
-                ]
-                # Apply activity regularizer if any:
-                layer.add_loss(regularization_losses, computed_tensors)
-
-          # Update tensor_map.
-          for x, y, mask in zip(reference_output_tensors, output_tensors,
-                                output_masks):
-            tensor_map[str(id(x))] = (y, mask)
-
-    output_tensors = []
-    output_masks = []
-    output_shapes = []
-    for x in self.outputs:
-      assert str(id(x)) in tensor_map, 'Could not compute output ' + str(x)
-      tensor, mask = tensor_map[str(id(x))]
-      output_shapes.append(layers_util.static_shape(x))
-      output_tensors.append(tensor)
-      output_masks.append(mask)
-
-    if len(output_tensors) == 1:
-      output_tensors = output_tensors[0]
-      if output_shapes is not None:
-        output_shapes = output_shapes[0]
-      if output_masks is not None:
-        output_masks = output_masks[0]
-
-    if context.in_graph_mode():
-      # Update cache;
-      # keys are based on ids on input tensors and inputs masks.
-      cache_key = (layers_util.object_list_uid(inputs)
-                   + '_' + layers_util.object_list_uid(masks))
-      self._output_tensor_cache[cache_key] = output_tensors
-      self._output_mask_cache[cache_key] = output_masks
-
-      if output_shapes is not None:
-        input_shapes = [layers_util.static_shape(x) for x in inputs]
-        cache_key = layers_util.object_list_uid(input_shapes)
-        self._output_shape_cache[cache_key] = output_shapes
-
-    return output_tensors, output_masks
-
-
-def _make_node_key(layer_name, node_index):
-  return layer_name + '_ib-' + str(node_index)
diff --git a/tensorflow/python/layers/network_test.py b/tensorflow/python/layers/network_test.py
deleted file mode 100644
index cc6e8ca9f4..0000000000
--- a/tensorflow/python/layers/network_test.py
+++ /dev/null
@@ -1,633 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for tf.layers.network."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.python.eager import context
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import test_util
-from tensorflow.python.layers import base as base_layers
-from tensorflow.python.layers import core as core_layers
-from tensorflow.python.layers import network as network_layers
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import sparse_ops
-from tensorflow.python.ops import state_ops
-from tensorflow.python.platform import test
-
-
-class BaseLayerCompatibilityTest(test.TestCase):
-
-  def test_get_updates(self):
-
-    class MyLayer(base_layers.Layer):
-
-      def build(self, input_shape):
-        self.a = self.add_variable('a',
-                                   (1, 1),
-                                   'float32',
-                                   trainable=False)
-        self.b = self.add_variable('b',
-                                   (1, 1),
-                                   'float32',
-                                   trainable=False)
-        self.add_update(state_ops.assign_add(self.a, [[1.]]))
-        self.built = True
-
-      def call(self, inputs):
-        self.add_update(state_ops.assign_add(self.a, inputs),
-                        inputs=True)
-        return inputs + 1
-
-    x1 = network_layers.Input(shape=(1,))
-    layer = MyLayer()
-    _ = layer.apply(x1)
-
-    self.assertEqual(len(layer.updates), 2)
-    self.assertEqual(len(layer.get_updates_for(x1)), 1)
-    self.assertEqual(len(layer.get_updates_for(None)), 1)
-
-    x2 = network_layers.Input(shape=(1,))
-    y2 = layer.apply(x2)
-
-    self.assertEqual(len(layer.updates), 3)
-    self.assertEqual(len(layer.get_updates_for(x1)), 1)
-    self.assertEqual(len(layer.get_updates_for(x2)), 1)
-    self.assertEqual(len(layer.get_updates_for(None)), 1)
-
-    network = network_layers.GraphNetwork(x2, y2)
-    self.assertEqual(len(network.updates), 2)
-    self.assertEqual(len(network.get_updates_for(x1)), 0)
-    self.assertEqual(len(network.get_updates_for(x2)), 1)
-    self.assertEqual(len(network.get_updates_for(None)), 1)
-
-    x3 = network_layers.Input(shape=(1,))
-    _ = layer.apply(x3)
-    self.assertEqual(len(network.updates), 2)
-
-    x4 = network_layers.Input(shape=(1,))
-    _ = network(x4)
-    self.assertEqual(len(network.updates), 3)
-    self.assertEqual(len(network.get_updates_for(x2)), 1)
-    self.assertEqual(len(network.get_updates_for(x4)), 1)
-    self.assertEqual(len(network.get_updates_for(None)), 1)
-
-    network.add_update(state_ops.assign_add(layer.a, [[1]]))
-    self.assertEqual(len(network.updates), 4)
-    self.assertEqual(len(network.get_updates_for(None)), 2)
-
-    network.add_update(state_ops.assign_add(layer.a, x4), inputs=True)
-    self.assertEqual(len(network.updates), 5)
-    self.assertEqual(len(network.get_updates_for(x4)), 2)
-
-  def test_get_losses(self):
-
-    class MyLayer(base_layers.Layer):
-
-      def build(self, input_shape):
-        self.a = self.add_variable('a',
-                                   (1, 1),
-                                   'float32',
-                                   trainable=False)
-        self.b = self.add_variable('b',
-                                   (1, 1),
-                                   'float32',
-                                   trainable=False)
-        self.add_loss(math_ops.reduce_sum(self.a))
-        self.built = True
-
-      def call(self, inputs):
-        self.add_loss(math_ops.reduce_sum(inputs),
-                      inputs=True)
-        return inputs + 1
-
-    x1 = network_layers.Input(shape=(1,))
-    layer = MyLayer()
-    _ = layer.apply(x1)
-
-    self.assertEqual(len(layer.losses), 2)
-    self.assertEqual(len(layer.get_losses_for(x1)), 1)
-    self.assertEqual(len(layer.get_losses_for(None)), 1)
-
-    x2 = network_layers.Input(shape=(1,))
-    y2 = layer.apply(x2)
-
-    self.assertEqual(len(layer.losses), 3)
-    self.assertEqual(len(layer.get_losses_for(x1)), 1)
-    self.assertEqual(len(layer.get_losses_for(x2)), 1)
-    self.assertEqual(len(layer.get_losses_for(None)), 1)
-
-    network = network_layers.GraphNetwork(x2, y2)
-    self.assertEqual(len(network.losses), 2)
-    self.assertEqual(len(network.get_losses_for(x1)), 0)
-    self.assertEqual(len(network.get_losses_for(x2)), 1)
-    self.assertEqual(len(network.get_losses_for(None)), 1)
-
-    x3 = network_layers.Input(shape=(1,))
-    _ = layer.apply(x3)
-    self.assertEqual(len(network.losses), 2)
-
-    x4 = network_layers.Input(shape=(1,))
-    _ = network(x4)
-    self.assertEqual(len(network.losses), 3)
-    self.assertEqual(len(network.get_losses_for(x2)), 1)
-    self.assertEqual(len(network.get_losses_for(x4)), 1)
-    self.assertEqual(len(network.get_losses_for(None)), 1)
-
-    network.add_loss(math_ops.reduce_sum(layer.a))
-    self.assertEqual(len(network.losses), 4)
-    self.assertEqual(len(network.get_losses_for(None)), 2)
-
-    network.add_loss(math_ops.reduce_sum(x4), inputs=True)
-    self.assertEqual(len(network.losses), 5)
-    self.assertEqual(len(network.get_losses_for(x4)), 2)
-
-  def testTopologicalAttributes(self):
-    # test layer attributes / methods related to cross-layer connectivity.
-    a = network_layers.Input(shape=(32,), name='input_a')
-    b = network_layers.Input(shape=(32,), name='input_b')
-
-    # test input, output, input_shape, output_shape
-    test_layer = core_layers.Dense(16, name='test_layer')
-    a_test = test_layer(a)
-    self.assertEqual(test_layer.input, a)
-    self.assertEqual(test_layer.output, a_test)
-    self.assertEqual(test_layer.input_shape, (None, 32))
-    self.assertEqual(test_layer.output_shape, (None, 16))
-
-    # test `get_*_at` methods
-    dense = core_layers.Dense(16, name='dense_1')
-    a_2 = dense(a)
-    b_2 = dense(b)
-
-    self.assertEqual(dense.get_input_at(0), a)
-    self.assertEqual(dense.get_input_at(1), b)
-    self.assertEqual(dense.get_output_at(0), a_2)
-    self.assertEqual(dense.get_output_at(1), b_2)
-    self.assertEqual(dense.get_input_shape_at(0), (None, 32))
-    self.assertEqual(dense.get_input_shape_at(1), (None, 32))
-    self.assertEqual(dense.get_output_shape_at(0), (None, 16))
-    self.assertEqual(dense.get_output_shape_at(1), (None, 16))
-
-    # Test invalid value for attribute retrieval.
-    with self.assertRaises(ValueError):
-      dense.get_input_at(2)
-    with self.assertRaises(AttributeError):
-      new_dense = core_layers.Dense(16)
-      _ = new_dense.input
-    with self.assertRaises(AttributeError):
-      new_dense = core_layers.Dense(16)
-      _ = new_dense.output
-    with self.assertRaises(AttributeError):
-      new_dense = core_layers.Dense(16)
-      _ = new_dense.output_shape
-    with self.assertRaises(AttributeError):
-      new_dense = core_layers.Dense(16)
-      _ = new_dense.input_shape
-    with self.assertRaises(AttributeError):
-      new_dense = core_layers.Dense(16)
-      a = network_layers.Input(shape=(3, 32))
-      a = network_layers.Input(shape=(5, 32))
-      a_2 = dense(a)
-      b_2 = dense(b)
-      _ = new_dense.input_shape
-    with self.assertRaises(AttributeError):
-      new_dense = core_layers.Dense(16)
-      a = network_layers.Input(shape=(3, 32))
-      a = network_layers.Input(shape=(5, 32))
-      a_2 = dense(a)
-      b_2 = dense(b)
-      _ = new_dense.output_shape
-
-  def testTopologicalAttributesMultiOutputLayer(self):
-
-    class PowersLayer(base_layers.Layer):
-
-      def call(self, inputs):
-        return [inputs**2, inputs**3]
-
-    x = network_layers.Input(shape=(32,))
-    test_layer = PowersLayer()
-    p1, p2 = test_layer(x)  # pylint: disable=not-callable
-
-    self.assertEqual(test_layer.input, x)
-    self.assertEqual(test_layer.output, [p1, p2])
-    self.assertEqual(test_layer.input_shape, (None, 32))
-    self.assertEqual(test_layer.output_shape, [(None, 32), (None, 32)])
-
-  def testTopologicalAttributesMultiInputLayer(self):
-
-    class AddLayer(base_layers.Layer):
-
-      def call(self, inputs):
-        assert len(inputs) == 2
-        return inputs[0] + inputs[1]
-
-    a = network_layers.Input(shape=(32,))
-    b = network_layers.Input(shape=(32,))
-    test_layer = AddLayer()
-    y = test_layer([a, b])  # pylint: disable=not-callable
-
-    self.assertEqual(test_layer.input, [a, b])
-    self.assertEqual(test_layer.output, y)
-    self.assertEqual(test_layer.input_shape, [(None, 32), (None, 32)])
-    self.assertEqual(test_layer.output_shape, (None, 32))
-
-
-class NetworkTest(test.TestCase):
-
-  def testBasicNetwork(self):
-    # minimum viable network
-    x = network_layers.Input(shape=(32,))
-    dense = core_layers.Dense(2)
-    y = dense(x)
-    network = network_layers.GraphNetwork(x, y, name='dense_network')
-
-    # test basic attributes
-    self.assertEqual(network.name, 'dense_network')
-    self.assertEqual(len(network.layers), 2)  # InputLayer + Dense
-    self.assertEqual(network.layers[1], dense)
-    self.assertEqual(network.weights, dense.weights)
-    self.assertEqual(network.trainable_weights, dense.trainable_weights)
-    self.assertEqual(network.non_trainable_weights, dense.non_trainable_weights)
-
-    # test callability on Input
-    x_2 = network_layers.Input(shape=(32,))
-    y_2 = network(x_2)
-    self.assertEqual(y_2.get_shape().as_list(), [None, 2])
-
-    # test callability on regular tensor
-    x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32))
-    y_2 = network(x_2)
-    self.assertEqual(y_2.get_shape().as_list(), [None, 2])
-
-    # test network `trainable` attribute
-    network.trainable = False
-    self.assertEqual(network.weights, dense.weights)
-    self.assertEqual(network.trainable_weights, [])
-    self.assertEqual(network.non_trainable_weights,
-                     dense.trainable_weights + dense.non_trainable_weights)
-
-  def test_node_construction(self):
-    # test graph topology construction basics
-    a = network_layers.Input(shape=(32,), name='input_a')
-    b = network_layers.Input(shape=(32,), name='input_b')
-
-    self.assertEqual(a.get_shape().as_list(), [None, 32])
-    a_layer, a_node_index, a_tensor_index = a._keras_history
-    b_layer, _, _ = b._keras_history
-    self.assertEqual(len(a_layer._inbound_nodes), 1)
-    self.assertEqual(a_tensor_index, 0)
-    node = a_layer._inbound_nodes[a_node_index]
-    self.assertEqual(node.outbound_layer, a_layer)
-
-    self.assertEqual(node.inbound_layers, [])
-    self.assertEqual(node.input_tensors, [a])
-    self.assertEqual(node.input_shapes, [(None, 32)])
-    self.assertEqual(node.output_tensors, [a])
-    self.assertEqual(node.output_shapes, [(None, 32)])
-
-    dense = core_layers.Dense(16, name='dense_1')
-    dense(a)
-    dense(b)
-
-    self.assertEqual(len(dense._inbound_nodes), 2)
-    self.assertEqual(len(dense._outbound_nodes), 0)
-    self.assertEqual(dense._inbound_nodes[0].inbound_layers, [a_layer])
-    self.assertEqual(dense._inbound_nodes[0].outbound_layer, dense)
-    self.assertEqual(dense._inbound_nodes[1].inbound_layers, [b_layer])
-    self.assertEqual(dense._inbound_nodes[1].outbound_layer, dense)
-    self.assertEqual(dense._inbound_nodes[0].input_tensors, [a])
-    self.assertEqual(dense._inbound_nodes[1].input_tensors, [b])
-
-    # Test config
-    config_0 = dense._inbound_nodes[0].get_config()
-    self.assertEqual(config_0['outbound_layer'], dense.name)
-
-  def testMultiInputNetwork(self):
-    a = network_layers.Input(shape=(32,), name='input_a')
-    b = network_layers.Input(shape=(32,), name='input_b')
-
-    class AddLayer(base_layers.Layer):
-
-      def call(self, inputs):
-        assert len(inputs) == 2
-        return inputs[0] + inputs[1]
-
-    c = AddLayer()([a, b])  # pylint: disable=not-callable
-    network = network_layers.GraphNetwork([a, b], c)
-    self.assertEqual(len(network.layers), 3)  # 2 * InputLayer + AddLayer
-
-    # Test callability.
-    a2 = network_layers.Input(shape=(32,))
-    b2 = network_layers.Input(shape=(32,))
-    c2 = network([a2, b2])
-    self.assertEqual(c2.get_shape().as_list(), [None, 32])
-
-  def testMultiOutputNetwork(self):
-    x = network_layers.Input(shape=(32,))
-    y1 = core_layers.Dense(2)(x)
-    y2 = core_layers.Dense(3)(x)
-    network = network_layers.GraphNetwork(x, [y1, y2])
-
-    self.assertEqual(len(network.layers), 3)  # InputLayer + 2 * Dense
-
-    # Test callability.
-    x2 = network_layers.Input(shape=(32,))
-    outputs = network(x2)
-
-    self.assertEqual(type(outputs), list)
-    self.assertEqual(len(outputs), 2)
-    self.assertEqual(outputs[0].get_shape().as_list(), [None, 2])
-    self.assertEqual(outputs[1].get_shape().as_list(), [None, 3])
-
-  def testMultiInputMultiOutputNetworkSharedLayer(self):
-    a = network_layers.Input(shape=(32,), name='input_a')
-    b = network_layers.Input(shape=(32,), name='input_b')
-
-    dense = core_layers.Dense(2)
-
-    y1 = dense(a)
-    y2 = dense(b)
-    network = network_layers.GraphNetwork([a, b], [y1, y2])
-    self.assertEqual(len(network.layers), 3)  # 2 * InputLayer + Dense
-
-    # Test callability.
-    a2 = network_layers.Input(shape=(32,))
-    b2 = network_layers.Input(shape=(32,))
-    outputs = network([a2, b2])
-
-    self.assertEqual(type(outputs), list)
-    self.assertEqual(len(outputs), 2)
-    self.assertEqual(outputs[0].get_shape().as_list(), [None, 2])
-    self.assertEqual(outputs[1].get_shape().as_list(), [None, 2])
-
-  def testCrossDataFlows(self):
-    # Test the ability to have multi-output layers with outputs that get routed
-    # to separate layers
-
-    class PowersLayer(base_layers.Layer):
-
-      def call(self, inputs):
-        return [inputs**2, inputs**3]
-
-    x = network_layers.Input(shape=(32,))
-    p1, p2 = PowersLayer()(x)  # pylint: disable=not-callable
-    y1 = core_layers.Dense(2)(p1)
-    y2 = core_layers.Dense(3)(p2)
-    network = network_layers.GraphNetwork(x, [y1, y2])
-
-    self.assertEqual(len(network.layers), 4)  # InputLayer + 2 * Dense + PLayer
-
-    # Test callability.
-    x2 = network_layers.Input(shape=(32,))
-    outputs = network(x2)
-
-    self.assertEqual(type(outputs), list)
-    self.assertEqual(len(outputs), 2)
-    self.assertEqual(outputs[0].get_shape().as_list(), [None, 2])
-    self.assertEqual(outputs[1].get_shape().as_list(), [None, 3])
-
-  def testNetworkAttributes(self):
-    x = network_layers.Input(shape=(32,))
-    layer = core_layers.Dense(2, kernel_regularizer=lambda x: 0.01 * (x**2))
-    z = layer(x)
-    dense = core_layers.Dense(2, name='dense')
-    dense.add_update(state_ops.assign_add(layer.kernel, layer.kernel * 2.))
-    y = dense(z)
-    net = network_layers.GraphNetwork(x, y)
-
-    # losses
-    self.assertEqual(len(net.losses), 1)
-
-    # updates
-    self.assertEqual(len(net.updates), 1)
-
-    # get_layer
-    self.assertEqual(net.get_layer('dense'), dense)
-    self.assertEqual(net.get_layer(index=2), dense)
-    with self.assertRaises(ValueError):
-      net.get_layer('dense_unknown')
-    with self.assertRaises(ValueError):
-      net.get_layer()
-    with self.assertRaises(ValueError):
-      net.get_layer(index=4)
-
-    # input, output
-    self.assertEqual(net.input, x)
-    self.assertEqual(net.output, y)
-
-    # input_shape, output_shape
-    self.assertEqual(net.input_shape, (None, 32))
-    self.assertEqual(net.output_shape, (None, 2))
-
-    # get_*_at
-    self.assertEqual(net.get_input_at(0), x)
-    self.assertEqual(net.get_output_at(0), y)
-
-    # compute_output_shape
-    self.assertEqual(net.compute_output_shape((3, 32)).as_list(), [3, 2])
-
-  def testInvalidNetworks(self):
-    # redundant inputs
-    x = network_layers.Input(shape=(32,))
-    y = core_layers.Dense(2)(x)
-    with self.assertRaises(ValueError):
-      network_layers.GraphNetwork([x, x], y)
-
-    # inputs that don't come from Input
-    x = array_ops.placeholder(dtype='float32', shape=(None, 32))
-    y = core_layers.Dense(2)(x)
-    with self.assertRaises(ValueError):
-      network_layers.GraphNetwork(x, y)
-
-    # inputs that don't come from Input but have a layer history
-    x = network_layers.Input(shape=(32,))
-    x = core_layers.Dense(32)(x)
-    y = core_layers.Dense(2)(x)
-    with self.assertRaises(ValueError):
-      network_layers.GraphNetwork(x, y)
-
-    # outputs that don't come from layers
-    x = network_layers.Input(shape=(32,))
-    y = core_layers.Dense(2)(x)
-    y = 2 * y
-    with self.assertRaises(ValueError):
-      network_layers.GraphNetwork(x, y)
-
-    # disconnected graphs
-    x1 = network_layers.Input(shape=(32,))
-    x2 = network_layers.Input(shape=(32,))
-    y = core_layers.Dense(2)(x1)
-    with self.assertRaises(ValueError):
-      network_layers.GraphNetwork(x2, y)
-
-    # redundant layer names
-    x = network_layers.Input(shape=(32,))
-    z = core_layers.Dense(2, name='dense')(x)
-    y = core_layers.Dense(2, name='dense')(z)
-    with self.assertRaises(ValueError):
-      network_layers.GraphNetwork(x, y)
-
-  def testInputTensorWrapping(self):
-    x = array_ops.placeholder(dtype='float32', shape=(None, 32))
-    x = network_layers.Input(tensor=x)
-    y = core_layers.Dense(2)(x)
-    network_layers.GraphNetwork(x, y)
-
-  def testExplicitBatchSize(self):
-    x = network_layers.Input(shape=(32,), batch_size=3)
-    y = core_layers.Dense(2)(x)
-    self.assertEqual(y.get_shape().as_list(), [3, 2])
-
-  def testNetworkRecursion(self):
-    # test the ability of networks to be used as layers inside networks.
-    a = network_layers.Input(shape=(32,))
-    b = core_layers.Dense(2)(a)
-    net = network_layers.GraphNetwork(a, b)
-
-    c = network_layers.Input(shape=(32,))
-    d = net(c)
-
-    recursive_net = network_layers.GraphNetwork(c, d)
-    self.assertEqual(len(recursive_net.layers), 2)
-    self.assertEqual(recursive_net.layers[1], net)
-    self.assertEqual(len(recursive_net.weights), 2)
-
-    # test callability
-    x = array_ops.placeholder(dtype='float32', shape=(None, 32))
-    y = recursive_net(x)
-    self.assertEqual(y.get_shape().as_list(), [None, 2])
-
-  def testSparseInput(self):
-
-    class SparseSoftmax(base_layers.Layer):
-
-      def call(self, inputs):
-        return sparse_ops.sparse_softmax(inputs)
-
-    x = network_layers.Input(shape=(32,), sparse=True)
-    y = SparseSoftmax()(x)  # pylint: disable=not-callable
-    network = network_layers.GraphNetwork(x, y)
-
-    self.assertEqual(len(network.layers), 2)
-    self.assertEqual(network.layers[0].sparse, True)
-
-  def testMaskingSingleInput(self):
-
-    class MaskedLayer(base_layers.Layer):
-
-      def call(self, inputs, mask=None):
-        if mask is not None:
-          return inputs * mask
-        return inputs
-
-      def compute_mask(self, inputs, mask=None):
-        return array_ops.ones_like(inputs)
-
-    if context.in_graph_mode():
-      x = network_layers.Input(shape=(32,))
-      y = MaskedLayer()(x)  # pylint: disable=not-callable
-      network = network_layers.GraphNetwork(x, y)
-
-      # test callability on Input
-      x_2 = network_layers.Input(shape=(32,))
-      y_2 = network(x_2)
-      self.assertEqual(y_2.get_shape().as_list(), [None, 32])
-
-      # test callability on regular tensor
-      x_2 = array_ops.placeholder(dtype='float32', shape=(None, 32))
-      y_2 = network(x_2)
-      self.assertEqual(y_2.get_shape().as_list(), [None, 32])
-    else:
-      a = constant_op.constant([2] * 32)
-      mask = constant_op.constant([0, 1] * 16)
-      a._keras_mask = mask
-      b = MaskedLayer().apply(a)
-      self.assertTrue(hasattr(b, '_keras_mask'))
-      self.assertAllEqual(self.evaluate(array_ops.ones_like(mask)),
-                          self.evaluate(getattr(b, '_keras_mask')))
-      self.assertAllEqual(self.evaluate(a * mask), self.evaluate(b))
-
-
-class DeferredModeTest(test.TestCase):
-
-  def testDeferredTensorAttributes(self):
-    x = base_layers._DeferredTensor(shape=(None, 2), dtype='float32', name='x')
-    self.assertEqual(str(x),
-                     'DeferredTensor(\'x\', shape=(?, 2), dtype=float32)')
-    self.assertEqual(repr(x),
-                     '<_DeferredTensor \'x\' shape=(?, 2) dtype=float32>')
-
-  @test_util.run_in_graph_and_eager_modes()
-  def testSimpleNetworkBuilding(self):
-    inputs = network_layers.Input(shape=(32,))
-    if context.in_eager_mode():
-      self.assertIsInstance(inputs, base_layers._DeferredTensor)
-      self.assertEqual(inputs.dtype.name, 'float32')
-      self.assertEqual(inputs.shape.as_list(), [None, 32])
-
-    x = core_layers.Dense(2)(inputs)
-    if context.in_eager_mode():
-      self.assertIsInstance(x, base_layers._DeferredTensor)
-      self.assertEqual(x.dtype.name, 'float32')
-      self.assertEqual(x.shape.as_list(), [None, 2])
-
-    outputs = core_layers.Dense(4)(x)
-    network = network_layers.GraphNetwork(inputs, outputs)
-    self.assertIsInstance(network, network_layers.GraphNetwork)
-
-    if context.in_eager_mode():
-      # It should be possible to call such a network on EagerTensors.
-      inputs = constant_op.constant(
-          np.random.random((10, 32)).astype('float32'))
-      outputs = network(inputs)
-      self.assertEqual(outputs.shape.as_list(), [10, 4])
-
-  @test_util.run_in_graph_and_eager_modes()
-  def testMultiIONetworkbuilding(self):
-    input_a = network_layers.Input(shape=(32,))
-    input_b = network_layers.Input(shape=(16,))
-    a = core_layers.Dense(16)(input_a)
-
-    class AddLayer(base_layers.Layer):
-
-      def call(self, inputs):
-        return inputs[0] + inputs[1]
-
-      def compute_output_shape(self, input_shape):
-        return input_shape[0]
-
-    c = AddLayer()([a, input_b])  # pylint: disable=not-callable
-    c = core_layers.Dense(2)(c)
-
-    network = network_layers.GraphNetwork([input_a, input_b], [a, c])
-    if context.in_eager_mode():
-      a_val = constant_op.constant(
-          np.random.random((10, 32)).astype('float32'))
-      b_val = constant_op.constant(
-          np.random.random((10, 16)).astype('float32'))
-      outputs = network([a_val, b_val])
-      self.assertEqual(len(outputs), 2)
-      self.assertEqual(outputs[0].shape.as_list(), [10, 16])
-      self.assertEqual(outputs[1].shape.as_list(), [10, 2])
-
-if __name__ == '__main__':
-  test.main()
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
index a13bfe0a92..5fb6fa3f19 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.keras.Model"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.training.Model\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Network\'>"
-  is_instance: "<class \'tensorflow.python.layers.network.GraphNetwork\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
index fb6c8d70dd..16f1afbd26 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
@@ -3,7 +3,6 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.models.Sequential\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.training.Model\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Network\'>"
-  is_instance: "<class \'tensorflow.python.layers.network.GraphNetwork\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
index d46fd41a3f..1e9370b02f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
@@ -1,7 +1,6 @@
 path: "tensorflow.keras.layers.InputLayer"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.InputLayer\'>"
-  is_instance: "<class \'tensorflow.python.layers.network.InputLayer\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
index f85b328e34..4260da31d9 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.keras.models.Model"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.training.Model\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Network\'>"
-  is_instance: "<class \'tensorflow.python.layers.network.GraphNetwork\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
index 2e044d78bb..02ddb37423 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
@@ -3,7 +3,6 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.models.Sequential\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.training.Model\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Network\'>"
-  is_instance: "<class \'tensorflow.python.layers.network.GraphNetwork\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.pbtxt
index 59134f8489..df74c32e1f 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.pbtxt
@@ -76,10 +76,6 @@ tf_module {
     name: "SeparableConv2D"
     mtype: "<type \'type\'>"
   }
-  member_method {
-    name: "Input"
-    argspec: "args=[\'shape\', \'batch_size\', \'name\', \'dtype\', \'sparse\', \'tensor\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \"<dtype: \'float32\'>\", \'False\', \'None\'], "
-  }
   member_method {
     name: "average_pooling1d"
     argspec: "args=[\'inputs\', \'pool_size\', \'strides\', \'padding\', \'data_format\', \'name\'], varargs=None, keywords=None, defaults=[\'valid\', \'channels_last\', \'None\'], "
-- 
GitLab


From e3b0a4291984f1af0cb8bf512542dffaca2d6cb5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 12:36:25 -0800
Subject: [PATCH 142/575] Allow non-integer values for Poisson CDF/PMF.

PiperOrigin-RevId: 186502845
---
 .../python/kernel_tests/poisson_test.py       | 24 +++++++++++++++++--
 .../distributions/python/ops/poisson.py       | 19 +++++++--------
 2 files changed, 31 insertions(+), 12 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/poisson_test.py b/tensorflow/contrib/distributions/python/kernel_tests/poisson_test.py
index d9c9008417..19a7472d91 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/poisson_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/poisson_test.py
@@ -18,6 +18,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+from scipy import special
 from scipy import stats
 from tensorflow.contrib.distributions.python.ops import poisson as poisson_lib
 from tensorflow.python.framework import constant_op
@@ -110,7 +111,7 @@ class PoissonTest(test.TestCase):
       batch_size = 6
       lam = constant_op.constant([3.0] * batch_size)
       lam_v = 3.0
-      x = [2.2, 3.1, 4., 5.5, 6., 7.]
+      x = [2., 3., 4., 5., 6., 7.]
 
       poisson = self._make_poisson(rate=lam)
       log_cdf = poisson.log_cdf(x)
@@ -121,12 +122,31 @@ class PoissonTest(test.TestCase):
       self.assertEqual(cdf.get_shape(), (6,))
       self.assertAllClose(cdf.eval(), stats.poisson.cdf(x, lam_v))
 
+  def testPoissonCDFNonIntegerValues(self):
+    with self.test_session():
+      batch_size = 6
+      lam = constant_op.constant([3.0] * batch_size)
+      lam_v = 3.0
+      x = np.array([2.2, 3.1, 4., 5.5, 6., 7.], dtype=np.float32)
+
+      poisson = self._make_poisson(rate=lam)
+      cdf = poisson.cdf(x)
+      self.assertEqual(cdf.get_shape(), (6,))
+
+      # The Poisson CDF should be valid on these non-integer values, and
+      # equal to igammac(1 + x, rate).
+      self.assertAllClose(cdf.eval(), special.gammaincc(1. + x, lam_v))
+
+      with self.assertRaisesOpError("cannot contain fractional components"):
+        poisson_validate = self._make_poisson(rate=lam, validate_args=True)
+        poisson_validate.cdf(x).eval()
+
   def testPoissonCdfMultidimensional(self):
     with self.test_session():
       batch_size = 6
       lam = constant_op.constant([[2.0, 4.0, 5.0]] * batch_size)
       lam_v = [2.0, 4.0, 5.0]
-      x = np.array([[2.2, 3.1, 4., 5.5, 6., 7.]], dtype=np.float32).T
+      x = np.array([[2., 3., 4., 5., 6., 7.]], dtype=np.float32).T
 
       poisson = self._make_poisson(rate=lam)
       log_cdf = poisson.log_cdf(x)
diff --git a/tensorflow/contrib/distributions/python/ops/poisson.py b/tensorflow/contrib/distributions/python/ops/poisson.py
index e967dcc90d..02e97c0a2f 100644
--- a/tensorflow/contrib/distributions/python/ops/poisson.py
+++ b/tensorflow/contrib/distributions/python/ops/poisson.py
@@ -35,9 +35,15 @@ __all__ = [
 
 
 _poisson_sample_note = """
-Note that the input value must be a non-negative floating point tensor with
-dtype `dtype` and whose shape can be broadcast with `self.rate`. `x` is only
-legal if it is non-negative and its components are equal to integer values.
+The Poisson distribution is technically only defined for non-negative integer
+values. When `validate_args=False`, non-integral inputs trigger an assertion.
+
+When `validate_args=False` calculations are otherwise unchanged despite
+integral or non-integral inputs.
+
+When `validate_args=False`, evaluating the pmf at non-integral values,
+corresponds to evaluations of an unnormalized distribution, that does not
+correspond to evaluations of the cdf.
 """
 
 
@@ -150,10 +156,6 @@ class Poisson(distribution.Distribution):
   def _cdf(self, x):
     if self.validate_args:
       x = distribution_util.embed_check_nonnegative_integer_form(x)
-    else:
-      # Whether or not x is integer-form, the following is well-defined.
-      # However, scipy takes the floor, so we do too.
-      x = math_ops.floor(x)
     return math_ops.igammac(1. + x, self.rate)
 
   def _log_normalization(self):
@@ -162,9 +164,6 @@ class Poisson(distribution.Distribution):
   def _log_unnormalized_prob(self, x):
     if self.validate_args:
       x = distribution_util.embed_check_nonnegative_integer_form(x)
-    else:
-      # For consistency with cdf, we take the floor.
-      x = math_ops.floor(x)
     return x * self.log_rate - math_ops.lgamma(1. + x)
 
   def _mean(self):
-- 
GitLab


From 2a104e284c455615ba68c714e60a69f458be56ba Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 12:42:51 -0800
Subject: [PATCH 143/575] Fix a bug in tf.metrics.mean_tensor for case that the
 weights are very small.

We have renamed metrics_test.MeanTensorTest.testWeighted1d as metrics_test.MeanTensorTest.testBinaryWeighted1d, since the weights on the instances are zeros and ones.

We have added a new metrics_test.MeanTensorTest.testWeighted1d that has small weights. It was failing for the previous implementation, but passes now.

Now the code for mean_tensor() and mean() now use the same _safe_div method. Previously, mean_tensor() used a different means to ensure that we don't divide by zero. This set the denominator to max(1., sum(weights)), which was inaccurate when sum(weights) is non-zero, but less than one.

PiperOrigin-RevId: 186503714
---
 .../python/kernel_tests/metrics_test.py       | 29 ++++++++++++++++++-
 tensorflow/python/ops/metrics_impl.py         |  9 ++----
 2 files changed, 30 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/kernel_tests/metrics_test.py b/tensorflow/python/kernel_tests/metrics_test.py
index fd78c026c2..59e7afa2dc 100644
--- a/tensorflow/python/kernel_tests/metrics_test.py
+++ b/tensorflow/python/kernel_tests/metrics_test.py
@@ -417,7 +417,7 @@ class MeanTensorTest(test.TestCase):
 
       self.assertAllClose([[-0.9 / 4., 3.525]], sess.run(mean), 5)
 
-  def testWeighted1d(self):
+  def testBinaryWeighted1d(self):
     with self.test_session() as sess:
       # Create the queue that populates the values.
       values_queue = data_flow_ops.FIFOQueue(
@@ -444,6 +444,33 @@ class MeanTensorTest(test.TestCase):
         sess.run(update_op)
       self.assertAllClose([[3.25, 0.5]], sess.run(mean), 5)
 
+  def testWeighted1d(self):
+    with self.test_session() as sess:
+      # Create the queue that populates the values.
+      values_queue = data_flow_ops.FIFOQueue(
+          4, dtypes=dtypes_lib.float32, shapes=(1, 2))
+      _enqueue_vector(sess, values_queue, [0, 1])
+      _enqueue_vector(sess, values_queue, [-4.2, 9.1])
+      _enqueue_vector(sess, values_queue, [6.5, 0])
+      _enqueue_vector(sess, values_queue, [-3.2, 4.0])
+      values = values_queue.dequeue()
+
+      # Create the queue that populates the weights.
+      weights_queue = data_flow_ops.FIFOQueue(
+          4, dtypes=dtypes_lib.float32, shapes=(1, 1))
+      _enqueue_vector(sess, weights_queue, [[0.0025]])
+      _enqueue_vector(sess, weights_queue, [[0.005]])
+      _enqueue_vector(sess, weights_queue, [[0.01]])
+      _enqueue_vector(sess, weights_queue, [[0.0075]])
+      weights = weights_queue.dequeue()
+
+      mean, update_op = metrics.mean_tensor(values, weights)
+
+      sess.run(variables.local_variables_initializer())
+      for _ in range(4):
+        sess.run(update_op)
+      self.assertAllClose([[0.8, 3.52]], sess.run(mean), 5)
+
   def testWeighted2d_1(self):
     with self.test_session() as sess:
       # Create the queue that populates the values.
diff --git a/tensorflow/python/ops/metrics_impl.py b/tensorflow/python/ops/metrics_impl.py
index 44c2f304cf..043c0e30cd 100644
--- a/tensorflow/python/ops/metrics_impl.py
+++ b/tensorflow/python/ops/metrics_impl.py
@@ -1247,13 +1247,8 @@ def mean_tensor(values,
     with ops.control_dependencies([values]):
       update_count_op = state_ops.assign_add(count, num_values)
 
-    def compute_mean(total, count, name):
-      non_zero_count = math_ops.maximum(
-          count, array_ops.ones_like(count), name=name)
-      return math_ops.truediv(total, non_zero_count, name=name)
-
-    mean_t = compute_mean(total, count, 'value')
-    update_op = compute_mean(update_total_op, update_count_op, 'update_op')
+    mean_t = _safe_div(total, count, 'value')
+    update_op = _safe_div(update_total_op, update_count_op, 'update_op')
 
     if metrics_collections:
       ops.add_to_collections(metrics_collections, mean_t)
-- 
GitLab


From 9dfb73b26c846038ef8101b2624de3b2cbf49c61 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Wed, 21 Feb 2018 12:57:05 -0800
Subject: [PATCH 144/575] Ensure that final layer of networks (which doesn't
 have an activation) get correctly quantized.

PiperOrigin-RevId: 186505814
---
 .../contrib/quantize/python/quantize.py       | 12 ++++++++++
 .../contrib/quantize/python/quantize_test.py  | 22 +++++++++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py
index 7a3f92f503..5fd806d195 100644
--- a/tensorflow/contrib/quantize/python/quantize.py
+++ b/tensorflow/contrib/quantize/python/quantize.py
@@ -207,6 +207,18 @@ def _FindLayersToQuantize(graph):
     yield _LayerMatch(layer_op, weight_tensor, activation_op, bypass_op,
                       bias_add_op)
 
+  # Match the final layer, where there will not be an activation and instead
+  # the output of the final BiasAdd must be quantized, so we treat it as the
+  # 'activation_op' in the _LayerMatch.
+  # TODO(suharshs): Figure out how to quantize this final layer across many
+  # models.
+  final_layer_matcher = graph_matcher.GraphMatcher(bias_add_pattern)
+  for match_result in final_layer_matcher.match_graph(graph):
+    layer_op = match_result.get_op(layer_pattern)
+    weight_tensor = match_result.get_tensor(weight_pattern)
+    activation_op = match_result.get_op(bias_add_pattern)
+    yield _LayerMatch(layer_op, weight_tensor, activation_op, None, None)
+
 
 class _LayerMatch(object):
   """Contains all information related to a matched Layer."""
diff --git a/tensorflow/contrib/quantize/python/quantize_test.py b/tensorflow/contrib/quantize/python/quantize_test.py
index bb7be08094..ef59475167 100644
--- a/tensorflow/contrib/quantize/python/quantize_test.py
+++ b/tensorflow/contrib/quantize/python/quantize_test.py
@@ -113,6 +113,28 @@ class QuantizeTest(test_util.TensorFlowTestCase):
                                             quantization_node_name)
     self.assertEqual(add_quant.type, quantization_node_name)
 
+  def testFinalLayerQuantized(self):
+    self._RunTestOverParameters(self._TestFinalLayerQuantized)
+
+  def _TestFinalLayerQuantized(self, is_training):
+    graph = ops.Graph()
+    with graph.as_default():
+      batch_size, height, width, depth = 5, 128, 128, 3
+      input1 = array_ops.zeros((batch_size, height, width, depth))
+      _ = conv2d(
+          input1,
+          32, [5, 5],
+          stride=2,
+          padding='SAME',
+          weights_initializer=self._WeightInit(0.09),
+          activation_fn=None,
+          scope='test')
+      # Ensure that the a FakeQuant operation is in the outputs of the BiasAdd.
+      bias_add_op = graph.get_operation_by_name('test/BiasAdd')
+      quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
+      self.assertTrue('FakeQuantWithMinMaxVars' in
+                      [op.type for op in bias_add_op.outputs[0].consumers()])
+
   def _WeightInit(self, stddev):
     """Returns truncated normal variable initializer.
 
-- 
GitLab


From 7e8b4a09416e453555073a88b0fd47625e0c5036 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 12:57:26 -0800
Subject: [PATCH 145/575] Change node to Identity operation for shuffle/reverse
 operations on scalar values, but not directly removing those nodes from the
 graph.

PiperOrigin-RevId: 186505857
---
 tensorflow/core/grappler/op_types.cc          |  8 ++++
 tensorflow/core/grappler/op_types.h           |  2 +
 .../grappler/optimizers/constant_folding.cc   | 15 ++++++++
 .../optimizers/constant_folding_test.cc       | 34 +++++++++++++++++
 .../core/grappler/utils/grappler_test.cc      | 38 +++++++++++++++++++
 .../core/grappler/utils/grappler_test.h       |  5 +++
 6 files changed, 102 insertions(+)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index fdf4540540..e225e99a9e 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -256,6 +256,10 @@ bool IsRestore(const NodeDef& node) {
           node.op() == "RestoreSlice");
 }
 
+bool IsReverse(const NodeDef& node) {
+  return node.op() == "Reverse" || node.op() == "ReverseV2";
+}
+
 bool IsReverseV2(const NodeDef& node) { return node.op() == "ReverseV2"; }
 
 bool IsRsqrtGrad(const NodeDef& node) { return node.op() == "RsqrtGrad"; }
@@ -272,6 +276,10 @@ bool IsShape(const NodeDef& node) { return node.op() == "Shape"; }
 
 bool IsShapeN(const NodeDef& node) { return node.op() == "ShapeN"; }
 
+bool IsShuffle(const NodeDef& node) {
+  return node.op() == "Shuffle" || node.op() == "RandomShuffle";
+}
+
 bool IsSigmoidGrad(const NodeDef& node) { return node.op() == "SigmoidGrad"; }
 
 bool IsSlice(const NodeDef& node) { return node.op() == "Slice"; }
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index 9cda40c0a6..1fa43a9b66 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -100,6 +100,7 @@ bool IsRecv(const NodeDef& node);
 bool IsReduction(const NodeDef& node);
 bool IsReshape(const NodeDef& node);
 bool IsRestore(const NodeDef& node);
+bool IsReverse(const NodeDef& node);
 bool IsReverseV2(const NodeDef& node);
 bool IsRsqrtGrad(const NodeDef& node);
 bool IsSelect(const NodeDef& node);
@@ -108,6 +109,7 @@ bool IsSend(const NodeDef& node);
 bool IsSlice(const NodeDef& node);
 bool IsShape(const NodeDef& node);
 bool IsShapeN(const NodeDef& node);
+bool IsShuffle(const NodeDef& node);
 bool IsSigmoidGrad(const NodeDef& node);
 bool IsSoftplusGrad(const NodeDef& node);
 bool IsSoftsignGrad(const NodeDef& node);
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 7a621bd95d..95eaa31a46 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -1446,6 +1446,20 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
   const bool is_aggressive = opt_level_ == RewriterConfig::AGGRESSIVE;
   for (int i = 0; i < output->node_size(); ++i) {
     NodeDef* node = output->mutable_node(i);
+    // Remove Shuffle or Reverse op over scalar values.
+    if (use_shape_info &&
+        (IsShuffle(*node) || IsReverse(*node) || IsTranspose(*node))) {
+      const auto& shape =
+          properties.GetInputProperties(node->name())[0].shape();
+      // The node is replaceable iff
+      // unknown_rank == false && (dim_size == 0 || all dims have size 1)
+      bool replaceable = !shape.unknown_rank();
+      for (int j = 0; j < shape.dim_size(); ++j) {
+        replaceable &= shape.dim(j).size() == 1;
+      }
+      if (replaceable) ReplaceOperationWithIdentity(0, node, output);
+    }
+
     if (IsSimplifiableReduction(*node)) {
       // Replace the reduction node with an identity node, that can be further
       // optimized by the model pruner.
@@ -1713,6 +1727,7 @@ Status ConstantFolding::RunOptimizationPass(Cluster* cluster,
   TF_RETURN_IF_ERROR(FoldGraph(output));
   node_map_.reset(new NodeMap(output));
   TF_RETURN_IF_ERROR(SimplifyGraph(output, properties, can_use_shape_info));
+
   return Status::OK();
 }
 
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index d8df19fe6a..3afc176402 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -1177,6 +1177,40 @@ TEST_F(ConstantFoldingTest, MergeNodes) {
   EXPECT_EQ(2, out_idx.flat<int32>()(0));
 }
 
+TEST_F(ConstantFoldingTest, ShuffleReverseOnScalarRemoval) {
+  tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
+
+  Output in1 =
+      ops::Variable(scope.WithOpName("in1"), TensorShape({}), DT_FLOAT);
+  Output in2 =
+      ops::Variable(scope.WithOpName("in2"), TensorShape({}), DT_FLOAT);
+  ops::RandomShuffle s1(scope.WithOpName("s1"), in1);
+  ops::RandomShuffle s2(scope.WithOpName("s2").WithControlDependencies({in1}),
+                        in2);
+
+  ops::Add out1(scope.WithOpName("out1"), s1, s2);
+  ops::Identity out2(scope.WithOpName("out2"), s2);
+
+  GrapplerItem item;
+  item.fetch = {"out1", "out2"};
+  TF_CHECK_OK(scope.ToGraphDef(&item.graph));
+
+  ConstantFolding fold(nullptr /* cpu_device */);
+  GraphDef got;
+  Status status = fold.Optimize(nullptr, item, &got);
+  TF_EXPECT_OK(status);
+
+  GraphDef want;
+  AddNode("in1", "VariableV2", {}, &want);
+  AddNode("in2", "VariableV2", {}, &want);
+  AddNode("s1", "Identity", {"in1"}, &want);
+  AddNode("s2", "Identity", {"in2", AsControlDependency("in1")}, &want);
+  AddNode("out1", "Add", {"s1", "s2"}, &want);
+  AddNode("out2", "Identity", {"s2"}, &want);
+
+  CompareGraphs(want, got);
+}
+
 TEST_F(ConstantFoldingTest, NoOpReduction) {
   // Build a simple graph with a reduction that can be reduced to the identity.
   tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
diff --git a/tensorflow/core/grappler/utils/grappler_test.cc b/tensorflow/core/grappler/utils/grappler_test.cc
index 813f65f825..fed46c05fb 100644
--- a/tensorflow/core/grappler/utils/grappler_test.cc
+++ b/tensorflow/core/grappler/utils/grappler_test.cc
@@ -35,5 +35,43 @@ std::vector<Tensor> GrapplerTest::EvaluateNodes(
   return output_tensors;
 }
 
+void GrapplerTest::AddNode(const string& name, const string& op,
+                           const std::vector<string>& inputs, GraphDef* graph) {
+  auto* node = graph->add_node();
+  node->set_name(name);
+  node->set_op(op);
+  for (const auto& input : inputs) {
+    node->add_input(input);
+  }
+}
+
+void GrapplerTest::CompareGraphs(GraphDef want, GraphDef got) {
+  auto comparator = [](const NodeDef& n1, const NodeDef& n2) -> bool {
+    return n1.name() < n2.name();
+  };
+  std::sort(want.mutable_node()->begin(), want.mutable_node()->end(),
+            comparator);
+  std::sort(got.mutable_node()->begin(), got.mutable_node()->end(), comparator);
+
+  for (int i = 0; i < want.node_size(); ++i) {
+    std::sort(want.mutable_node(i)->mutable_input()->begin(),
+              want.mutable_node(i)->mutable_input()->end());
+  }
+  for (int i = 0; i < got.node_size(); ++i) {
+    std::sort(got.mutable_node(i)->mutable_input()->begin(),
+              got.mutable_node(i)->mutable_input()->end());
+  }
+
+  ASSERT_EQ(want.node_size(), got.node_size());
+  for (int i = 0; i < want.node_size(); ++i) {
+    EXPECT_EQ(want.node(i).op(), got.node(i).op());
+    EXPECT_EQ(want.node(i).name(), got.node(i).name());
+    ASSERT_EQ(want.node(i).input_size(), got.node(i).input_size());
+    for (int j = 0; j < want.node(i).input_size(); ++j) {
+      EXPECT_TRUE(IsSameInput(want.node(i).input(j), got.node(i).input(j)));
+    }
+  }
+}
+
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/utils/grappler_test.h b/tensorflow/core/grappler/utils/grappler_test.h
index 46ce47c8c3..042b616aa4 100644
--- a/tensorflow/core/grappler/utils/grappler_test.h
+++ b/tensorflow/core/grappler/utils/grappler_test.h
@@ -29,6 +29,11 @@ class GrapplerTest : public ::testing::Test {
  protected:
   std::vector<Tensor> EvaluateNodes(const GraphDef& graph,
                                     const std::vector<string>& node_names);
+
+  void AddNode(const string& name, const string& op,
+               const std::vector<string>& inputs, GraphDef* graph);
+
+  void CompareGraphs(GraphDef want, GraphDef got);
 };
 
 }  // end namespace grappler
-- 
GitLab


From 5196f05d3c150b66a9a5f7eeb9b476780f771b81 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Wed, 21 Feb 2018 13:14:27 -0800
Subject: [PATCH 146/575] Add test that checks all core ops have shape
 functions.

This is meant to be a replacement for the current Python code that
checks that core ops have shape functions registered. Some ops were
missing a shape function, so I added UnknownShape.

This also adds an OpRegistry::GetOpRegistrationData() method for
fetching all the shape functions.

PiperOrigin-RevId: 186508356
---
 tensorflow/core/BUILD                         |  1 +
 .../core/common_runtime/function_testlib.cc   |  5 ++-
 tensorflow/core/framework/op.cc               |  9 +++++
 tensorflow/core/framework/op.h                |  3 ++
 tensorflow/core/graph/testlib.cc              |  4 ++-
 tensorflow/core/ops/function_ops.cc           |  3 ++
 tensorflow/core/ops/shape_function_test.cc    | 34 +++++++++++++++++++
 tensorflow/core/ops/spectral_ops.cc           |  6 ++++
 tensorflow/core/ops/word2vec_ops.cc           |  7 ++--
 tensorflow/core/user_ops/fact.cc              |  5 ++-
 10 files changed, 72 insertions(+), 5 deletions(-)
 create mode 100644 tensorflow/core/ops/shape_function_test.cc

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 2a8aefa3c4..04307db24c 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -3515,6 +3515,7 @@ tf_cc_tests(
         "ops/parsing_ops_test.cc",
         "ops/random_ops_test.cc",
         "ops/set_ops_test.cc",
+        "ops/shape_function_test.cc",
         "ops/sparse_ops_test.cc",
         "ops/spectral_ops_test.cc",
         "ops/state_ops_test.cc",
diff --git a/tensorflow/core/common_runtime/function_testlib.cc b/tensorflow/core/common_runtime/function_testlib.cc
index 87c2476b04..87733ed2db 100644
--- a/tensorflow/core/common_runtime/function_testlib.cc
+++ b/tensorflow/core/common_runtime/function_testlib.cc
@@ -15,6 +15,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/function_testlib.h"
 
 #include "tensorflow/core/common_runtime/device.h"
+#include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/op_kernel.h"
@@ -39,7 +40,9 @@ class FindDeviceOpKernel : public OpKernel {
 
 REGISTER_KERNEL_BUILDER(Name("FindDeviceOp").Device(tensorflow::DEVICE_CPU),
                         FindDeviceOpKernel);
-REGISTER_OP("FindDeviceOp").Output("device_name: string");
+REGISTER_OP("FindDeviceOp")
+    .Output("device_name: string")
+    .SetShapeFn(shape_inference::UnknownShape);
 
 FunctionDef FindDevice() {
   return FDH::Define(
diff --git a/tensorflow/core/framework/op.cc b/tensorflow/core/framework/op.cc
index fadb60d744..fc5467b3c8 100644
--- a/tensorflow/core/framework/op.cc
+++ b/tensorflow/core/framework/op.cc
@@ -110,6 +110,15 @@ void OpRegistry::GetRegisteredOps(std::vector<OpDef>* op_defs) {
   }
 }
 
+void OpRegistry::GetOpRegistrationData(
+    std::vector<OpRegistrationData>* op_data) {
+  mutex_lock lock(mu_);
+  MustCallDeferred();
+  for (const auto& p : registry_) {
+    op_data->push_back(*p.second);
+  }
+}
+
 Status OpRegistry::SetWatcher(const Watcher& watcher) {
   mutex_lock lock(mu_);
   if (watcher_ && watcher) {
diff --git a/tensorflow/core/framework/op.h b/tensorflow/core/framework/op.h
index f7f1ed2a88..3ccca4090d 100644
--- a/tensorflow/core/framework/op.h
+++ b/tensorflow/core/framework/op.h
@@ -89,6 +89,9 @@ class OpRegistry : public OpRegistryInterface {
   // Get all registered ops.
   void GetRegisteredOps(std::vector<OpDef>* op_defs);
 
+  // Get all `OpRegistrationData`s.
+  void GetOpRegistrationData(std::vector<OpRegistrationData>* op_data);
+
   // Watcher, a function object.
   // The watcher, if set by SetWatcher(), is called every time an op is
   // registered via the Register function. The watcher is passed the Status
diff --git a/tensorflow/core/graph/testlib.cc b/tensorflow/core/graph/testlib.cc
index 0d88d1ff72..67b252cb6c 100644
--- a/tensorflow/core/graph/testlib.cc
+++ b/tensorflow/core/graph/testlib.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/graph/testlib.h"
 
 #include <vector>
+#include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def_builder.h"
 #include "tensorflow/core/framework/node_def_util.h"
@@ -50,7 +51,8 @@ REGISTER_KERNEL_BUILDER(
 REGISTER_OP("HostConst")
     .Output("output: dtype")
     .Attr("value: tensor")
-    .Attr("dtype: type");
+    .Attr("dtype: type")
+    .SetShapeFn(shape_inference::UnknownShape);
 
 namespace test {
 namespace graph {
diff --git a/tensorflow/core/ops/function_ops.cc b/tensorflow/core/ops/function_ops.cc
index ada96fa1d2..a6914d9383 100644
--- a/tensorflow/core/ops/function_ops.cc
+++ b/tensorflow/core/ops/function_ops.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference.h"
 
@@ -55,6 +56,7 @@ REGISTER_OP("_ListToArray")
     .Attr("Tin: list(type)")
     .Attr("T: type")
     .Attr("N: int >= 1")
+    .SetShapeFn(shape_inference::UnknownShape)
     .Doc(R"doc(
 Converts a list of tensors to an array of tensors.
 )doc");
@@ -65,6 +67,7 @@ REGISTER_OP("_ArrayToList")
     .Attr("T: type")
     .Attr("N: int >= 1")
     .Attr("out_types: list(type)")
+    .SetShapeFn(shape_inference::UnknownShape)
     .Doc(R"doc(
 Converts an array of tensors to a list of tensors.
 )doc");
diff --git a/tensorflow/core/ops/shape_function_test.cc b/tensorflow/core/ops/shape_function_test.cc
new file mode 100644
index 0000000000..120995f3aa
--- /dev/null
+++ b/tensorflow/core/ops/shape_function_test.cc
@@ -0,0 +1,34 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/platform/test.h"
+
+// Test to ensure that all core ops have shape functions defined. This is done
+// by looking at all ops registered in the test binary.
+
+namespace tensorflow {
+
+TEST(ShapeFunctionTest, RegisteredOpsHaveShapeFns) {
+  OpRegistry* op_registry = OpRegistry::Global();
+  std::vector<OpRegistrationData> op_data;
+  op_registry->GetOpRegistrationData(&op_data);
+  for (const OpRegistrationData& op_reg_data : op_data) {
+    EXPECT_TRUE(op_reg_data.shape_inference_fn != nullptr)
+        << op_reg_data.op_def.name();
+  }
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/ops/spectral_ops.cc b/tensorflow/core/ops/spectral_ops.cc
index 508cea3495..2790aee37e 100644
--- a/tensorflow/core/ops/spectral_ops.cc
+++ b/tensorflow/core/ops/spectral_ops.cc
@@ -142,26 +142,32 @@ REGISTER_OP("IRFFT3D")
 REGISTER_OP("BatchFFT")
     .Input("input: complex64")
     .Output("output: complex64")
+    .SetShapeFn(shape_inference::UnknownShape)
     .Deprecated(15, "Use FFT");
 REGISTER_OP("BatchIFFT")
     .Input("input: complex64")
     .Output("output: complex64")
+    .SetShapeFn(shape_inference::UnknownShape)
     .Deprecated(15, "Use IFFT");
 REGISTER_OP("BatchFFT2D")
     .Input("input: complex64")
     .Output("output: complex64")
+    .SetShapeFn(shape_inference::UnknownShape)
     .Deprecated(15, "Use FFT2D");
 REGISTER_OP("BatchIFFT2D")
     .Input("input: complex64")
     .Output("output: complex64")
+    .SetShapeFn(shape_inference::UnknownShape)
     .Deprecated(15, "Use IFFT2D");
 REGISTER_OP("BatchFFT3D")
     .Input("input: complex64")
     .Output("output: complex64")
+    .SetShapeFn(shape_inference::UnknownShape)
     .Deprecated(15, "Use FFT3D");
 REGISTER_OP("BatchIFFT3D")
     .Input("input: complex64")
     .Output("output: complex64")
+    .SetShapeFn(shape_inference::UnknownShape)
     .Deprecated(15, "Use IFFT3D");
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/word2vec_ops.cc b/tensorflow/core/ops/word2vec_ops.cc
index ed685dcf0a..e469771103 100644
--- a/tensorflow/core/ops/word2vec_ops.cc
+++ b/tensorflow/core/ops/word2vec_ops.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/op.h"
 
 namespace tensorflow {
@@ -33,7 +34,8 @@ REGISTER_OP("Skipgram")
     .Attr("batch_size: int")
     .Attr("window_size: int = 5")
     .Attr("min_count: int = 5")
-    .Attr("subsample: float = 1e-3");
+    .Attr("subsample: float = 1e-3")
+    .SetShapeFn(shape_inference::UnknownShape);
 
 REGISTER_OP("NegTrain")
     .Deprecated(19,
@@ -46,6 +48,7 @@ REGISTER_OP("NegTrain")
     .Input("lr: float")
     .SetIsStateful()
     .Attr("vocab_count: list(int)")
-    .Attr("num_negative_samples: int");
+    .Attr("num_negative_samples: int")
+    .SetShapeFn(shape_inference::UnknownShape);
 
 }  // end namespace tensorflow
diff --git a/tensorflow/core/user_ops/fact.cc b/tensorflow/core/user_ops/fact.cc
index 3a4fc8115a..2e8b22a49b 100644
--- a/tensorflow/core/user_ops/fact.cc
+++ b/tensorflow/core/user_ops/fact.cc
@@ -15,10 +15,13 @@ limitations under the License.
 
 // An example Op.
 
+#include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 
-REGISTER_OP("Fact").Output("fact: string");
+REGISTER_OP("Fact")
+    .Output("fact: string")
+    .SetShapeFn(tensorflow::shape_inference::UnknownShape);
 
 class FactOp : public tensorflow::OpKernel {
  public:
-- 
GitLab


From c8ccab3bda96bbda7adc281eaf095390806b06d7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 13:16:52 -0800
Subject: [PATCH 147/575] Made tf.HParams compatible with
 https://github.com/google/pytype.

PiperOrigin-RevId: 186508693
---
 tensorflow/contrib/training/python/training/hparam.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/contrib/training/python/training/hparam.py b/tensorflow/contrib/training/python/training/hparam.py
index fdfd27d6a4..95e051e3b5 100644
--- a/tensorflow/contrib/training/python/training/hparam.py
+++ b/tensorflow/contrib/training/python/training/hparam.py
@@ -358,6 +358,8 @@ class HParams(object):
   ```
   """
 
+  _HAS_DYNAMIC_ATTRIBUTES = True  # Required for pytype checks.
+
   def __init__(self, hparam_def=None, model_structure=None, **kwargs):
     """Create an instance of `HParams` from keyword arguments.
 
-- 
GitLab


From 6419fd98883cd051213f0daeaea465728cf7a27c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 13:20:58 -0800
Subject: [PATCH 148/575] K-FAC: LM algorithm for adapting damping, Example to
 train MNIST autoencoder model using variable size training data and update
 damping parameter, add KFACOptimizer.{update_damping}.

PiperOrigin-RevId: 186509305
---
 .../python/kernel_tests/estimator_test.py     |  26 +-
 tensorflow/contrib/kfac/python/ops/BUILD      |   3 +
 .../contrib/kfac/python/ops/estimator.py      |  13 +-
 .../contrib/kfac/python/ops/optimizer.py      | 250 ++++++++++++++++--
 tensorflow/contrib/kfac/python/ops/utils.py   |  29 +-
 5 files changed, 263 insertions(+), 58 deletions(-)

diff --git a/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py b/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py
index bfdb69ad02..b12f7be769 100644
--- a/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py
+++ b/tensorflow/contrib/kfac/python/kernel_tests/estimator_test.py
@@ -90,49 +90,51 @@ class EstimatorTest(test.TestCase):
   def testEstimatorInitManualRegistration(self):
     with self._graph.as_default():
       # We should be able to build an estimator for only the registered vars.
-      estimator.FisherEstimator([self.weights], 0.1, 0.2, self.layer_collection)
+      estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1,
+                                self.layer_collection)
 
       # Check that we throw an error if we try to build an estimator for vars
       # that were not manually registered.
       with self.assertRaises(ValueError):
-        estimator.FisherEstimator([self.weights, self.bias], 0.1, 0.2,
+        estimator.FisherEstimator(lambda: 0.2, [self.weights, self.bias], 0.1,
                                   self.layer_collection)
 
       # Check that we throw an error if we don't include registered variables,
       # i.e. self.weights
       with self.assertRaises(ValueError):
-        estimator.FisherEstimator([], 0.1, 0.2, self.layer_collection)
+        estimator.FisherEstimator(lambda: 0.2, [], 0.1, self.layer_collection)
 
   @test.mock.patch.object(utils.SubGraph, "variable_uses", return_value=42)
   def testVariableWrongNumberOfUses(self, mock_uses):
     with self.assertRaises(ValueError):
-      estimator.FisherEstimator([self.weights], 0.1, 0.2, self.layer_collection)
+      estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1,
+                                self.layer_collection)
 
   def testInvalidEstimationMode(self):
     with self.assertRaises(ValueError):
-      estimator.FisherEstimator([self.weights], 0.1, 0.2, self.layer_collection,
-                                "not_a_real_mode")
+      estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1,
+                                self.layer_collection, "not_a_real_mode")
 
   def testModeListCorrect(self):
     with self._graph.as_default():
-      est = estimator.FisherEstimator([self.weights], 0.1, 0.2,
+      est = estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1,
                                       self.layer_collection)
     self.assertItemsEqual(_ALL_ESTIMATION_MODES, est._gradient_fns.keys())
 
   def testAllModesBuild(self):
     for mode in _ALL_ESTIMATION_MODES:
       with self._graph.as_default():
-        estimator.FisherEstimator([self.weights], 0.1, 0.2,
+        estimator.FisherEstimator(lambda: 0.2, [self.weights], 0.1,
                                   self.layer_collection, mode)
 
   def test_cov_update_thunks(self):
     """Ensures covariance update ops run once per global_step."""
     with self._graph.as_default(), self.test_session() as sess:
       fisher_estimator = estimator.FisherEstimator(
+          damping_fn=lambda: 0.2,
           variables=[self.weights],
           layer_collection=self.layer_collection,
-          cov_ema_decay=0.0,
-          damping=0.0)
+          cov_ema_decay=0.0)
 
       # Construct an op that executes one covariance update per step.
       global_step = training_util.get_or_create_global_step()
@@ -176,10 +178,10 @@ class EstimatorTest(test.TestCase):
     """Ensures inverse update ops run once per global_step."""
     with self._graph.as_default(), self.test_session() as sess:
       fisher_estimator = estimator.FisherEstimator(
+          damping_fn=lambda: 0.2,
           variables=[self.weights],
           layer_collection=self.layer_collection,
-          cov_ema_decay=0.0,
-          damping=0.0)
+          cov_ema_decay=0.0)
 
       # Construct op that updates one inverse per global step.
       global_step = training_util.get_or_create_global_step()
diff --git a/tensorflow/contrib/kfac/python/ops/BUILD b/tensorflow/contrib/kfac/python/ops/BUILD
index ee6549b109..c26230c2a8 100644
--- a/tensorflow/contrib/kfac/python/ops/BUILD
+++ b/tensorflow/contrib/kfac/python/ops/BUILD
@@ -144,10 +144,13 @@ py_library(
         ":fisher_estimator",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:linalg_ops",
         "//tensorflow/python:math_ops",
+        "//tensorflow/python:state_ops",
         "//tensorflow/python:training",
+        "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
     ],
 )
diff --git a/tensorflow/contrib/kfac/python/ops/estimator.py b/tensorflow/contrib/kfac/python/ops/estimator.py
index a7b1f9d35c..a7e268c48a 100644
--- a/tensorflow/contrib/kfac/python/ops/estimator.py
+++ b/tensorflow/contrib/kfac/python/ops/estimator.py
@@ -83,9 +83,9 @@ class FisherEstimator(object):
   """
 
   def __init__(self,
+               damping_fn,
                variables,
                cov_ema_decay,
-               damping,
                layer_collection,
                estimation_mode="gradients",
                colocate_gradients_with_ops=True,
@@ -94,16 +94,12 @@ class FisherEstimator(object):
     """Create a FisherEstimator object.
 
     Args:
+      damping_fn: Function, accepts no arguments and returns damping value.
       variables: A list of the variables for which to estimate the Fisher. This
           must match the variables registered in layer_collection (if it is not
           None).
       cov_ema_decay: The decay factor used when calculating the covariance
           estimate moving averages.
-      damping: The damping factor used to stabilize training due to errors in
-          the local approximation with the Fisher information matrix, and to
-          regularize the update direction by making it closer to the gradient.
-          (Higher damping means the update looks more like a standard gradient
-          update - see Tikhonov regularization.)
       layer_collection: The layer collection object, which holds the fisher
           blocks, kronecker factors, and losses associated with the
           graph.
@@ -135,10 +131,9 @@ class FisherEstimator(object):
     Raises:
       ValueError: If no losses have been registered with layer_collection.
     """
-
+    self._damping_fn = damping_fn
     self._cov_ema_decay = cov_ema_decay
     self._variables = variables
-    self._damping = damping
     self._estimation_mode = estimation_mode
     self._layers = layer_collection
     self._layers.create_subgraph()
@@ -182,7 +177,7 @@ class FisherEstimator(object):
 
   @property
   def damping(self):
-    return self._damping
+    return self._damping_fn()
 
   def _apply_transformation(self, vecs_and_vars, transform):
     """Applies an block-wise transformation to the corresponding vectors.
diff --git a/tensorflow/contrib/kfac/python/ops/optimizer.py b/tensorflow/contrib/kfac/python/ops/optimizer.py
index 1974b07acf..5d456bcb79 100644
--- a/tensorflow/contrib/kfac/python/ops/optimizer.py
+++ b/tensorflow/contrib/kfac/python/ops/optimizer.py
@@ -23,11 +23,14 @@ from tensorflow.contrib.kfac.python.ops import curvature_matrix_vector_products
 from tensorflow.contrib.kfac.python.ops import estimator as est
 # pylint enable=long-line
 
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.training import gradient_descent
 
@@ -61,6 +64,8 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
       damping: The damping factor used to stabilize training due to errors in
           the local approximation with the Fisher information matrix, and to
           regularize the update direction by making it closer to the gradient.
+          If damping is adapted during training then this value is used for
+          initializing damping varaible.
           (Higher damping means the update looks more like a standard gradient
           update - see Tikhonov regularization.)
       layer_collection: The layer collection object, which holds the fisher
@@ -105,10 +110,31 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
     if variables is None:
       variables = tf_variables.trainable_variables()
 
+    # The below paramaters are required only if damping needs to be adapated.
+    # These parameters can be set by calling
+    # set_damping_adaptation_params() explicitly.
+    self._damping_adaptation_decay = 0.95
+    self._damping_adaptation_interval = 5
+    # Check section 6.5 KFAC paper. omega(1) = pow(damping decay, interval)
+    self._omega = (
+        self._damping_adaptation_decay**self._damping_adaptation_interval)
+    self._adapt_damping = False
+    self._min_damping = 1e-5
+    self._prev_train_batch = None
+    self._is_chief = False
+    self._loss_fn = None
+    self._damping_constant = damping
+    self._damping = None
+    self._rho = None
+    self._prev_loss = None
+    self._q_model_change = None
+    self._update_damping_op = None
+
+    self._layers = layer_collection
     self._fisher_est = est.FisherEstimator(
+        lambda: self.damping,
         variables,
         cov_ema_decay,
-        damping,
         layer_collection,
         estimation_mode=estimation_mode,
         colocate_gradients_with_ops=colocate_gradients_with_ops,
@@ -139,6 +165,60 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
 
     super(KfacOptimizer, self).__init__(learning_rate, name=name)
 
+  def set_damping_adaptation_params(self,
+                                    is_chief,
+                                    prev_train_batch,
+                                    loss_fn,
+                                    min_damping=1e-5,
+                                    damping_adaptation_decay=0.99,
+                                    damping_adaptation_interval=5):
+    """Sets parameters required to adapt damping during training.
+
+    When called, enables damping adaptation according to the Levenberg-Marquardt
+    style rule described in Section 6.5 of "Optimizing Neural Networks with
+    Kronecker-factored Approximate Curvature".
+
+    Args:
+      is_chief: `Boolean`, `True` if the worker is chief.
+      prev_train_batch: Training data used to minimize loss in the previous
+        step. This will be used to evaluate loss by calling
+        `loss_fn(prev_train_batch)`.
+      loss_fn: `function` that takes as input training data tensor and returns
+        a scalar loss.
+      min_damping: `float`(Optional), Minimum value the damping parameter
+        can take. Default value 1e-5.
+      damping_adaptation_decay: `float`(Optional), The `damping` parameter is
+        multipled by the `damping_adaptation_decay` every
+        `damping_adaptation_interval` number of iterations. Default value 0.99.
+      damping_adaptation_interval: `int`(Optional), Number of steps in between
+        updating the `damping` parameter. Default value 5.
+
+    Raises:
+      ValueError: If `set_damping_adaptation_params` is already called and the
+        the `adapt_damping` is `True`.
+    """
+    if self._adapt_damping:
+      raise ValueError("Damping adaptation parameters already set.")
+    with variable_scope.variable_scope(self.get_name()):
+      self._adapt_damping = True
+      self._is_chief = is_chief
+      self._prev_train_batch = prev_train_batch
+      self._loss_fn = loss_fn
+      self._damping_adaptation_decay = damping_adaptation_decay
+      self._damping_adaptation_interval = damping_adaptation_interval
+      self._omega = (
+          self._damping_adaptation_decay**self._damping_adaptation_interval)
+      self._min_damping = min_damping
+
+      self._rho = variable_scope.get_variable(
+          "rho", shape=(), dtype=dtypes.float32, trainable=False)  # LM ratio.
+      self._prev_loss = variable_scope.get_variable(
+          "prev_loss", shape=(), dtype=dtypes.float32, trainable=False)
+      self._q_model_change = variable_scope.get_variable(
+          "q_model_change", shape=(), dtype=dtypes.float32, trainable=False)
+      self._damping = variable_scope.get_variable(
+          "damping", initializer=self._damping_constant, trainable=False)
+
   @property
   def cov_update_thunks(self):
     return self._fisher_est.cov_update_thunks
@@ -169,14 +249,34 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
 
   @property
   def damping(self):
-    return self._fisher_est.damping
+    if self._damping:
+      return self._damping
+    else:
+      return self._damping_constant
+
+  @property
+  def damping_adaptation_interval(self):
+    return self._damping_adaptation_interval
 
   def minimize(self, *args, **kwargs):
     kwargs["var_list"] = kwargs.get("var_list") or self.variables
     if set(kwargs["var_list"]) != set(self.variables):
       raise ValueError("var_list doesn't match with set of Fisher-estimating "
                        "variables.")
-    return super(KfacOptimizer, self).minimize(*args, **kwargs)
+    if self._adapt_damping and self._is_chief:
+      global_step = kwargs.get("global_step", None)
+      if not global_step:
+        raise KeyError("global_step needs to be passed to optimizer.minimize "
+                       "if damping parameter is adapted.")
+      update_damping_op = self._update_damping(self._prev_train_batch,
+                                               global_step)
+      with ops.control_dependencies([update_damping_op]):
+        loss = args[0]
+        loss_assign_op = state_ops.assign(self._prev_loss, loss)
+        train_op = super(KfacOptimizer, self).minimize(*args, **kwargs)
+        return control_flow_ops.group(loss_assign_op, train_op)
+    else:
+      return super(KfacOptimizer, self).minimize(*args, **kwargs)
 
   def compute_gradients(self, *args, **kwargs):
     # args[1] could be our var_list
@@ -296,6 +396,20 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
     coeff = self._update_clip_coeff(grads_and_vars, precon_grads_and_vars)
     return [(pgrad * coeff, var) for pgrad, var in precon_grads_and_vars]
 
+  def _compute_prev_updates(self, variables):
+    """Computes previous updates as negative velocities scaled by learning rate.
+
+    Args:
+      variables: List of variables in the graph that the update will be
+          applied to.
+
+    Returns:
+      List of previous updates applied to the `variables`.
+    """
+    return list(
+        -1 * self._learning_rate * self._zeros_slot(var, "velocity", self._name)
+        for var in variables)
+
   def _compute_qmodel_hyperparams(self, precon_grads, prev_updates, grads,
                                   variables):
     """Compute optimal update hyperparameters from the quadratic model.
@@ -374,9 +488,9 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
       c = ops.convert_to_tensor([[_inner_product_list(grads, precon_grads)],
                                  [_inner_product_list(grads, prev_updates)]])
 
-      sol = _two_by_two_solve(m, c)
-      alpha = -sol[0]
-      mu = -sol[1]
+      sol = -1. * _two_by_two_solve(m, c)
+      alpha = sol[0]
+      mu = sol[1]
       qmodel_change = 0.5 * math_ops.reduce_sum(sol * c)
 
       return alpha, mu, qmodel_change
@@ -404,6 +518,52 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
     return control_flow_ops.cond(
         math_ops.equal(m_22, 0.0), zero_prevupd_case, non_zero_prevupd_case)
 
+  def _assign_q_model_change(self, q_model_change):
+    """Assigns `q_model_change` to `self._q_model_change` if damping is adapted.
+
+    Note only the chief worker does the assignment.
+
+    Args:
+      q_model_change: Scalar tensor of type `float32`.
+
+    Returns:
+      If `adapt_damping` is `True` then returns an assign op, Otherwise returns
+      a no_op().
+    """
+    if self._adapt_damping and self._is_chief:
+      q_model_assign_op = state_ops.assign(self._q_model_change, q_model_change)
+    else:
+      q_model_assign_op = control_flow_ops.no_op()
+    return q_model_assign_op
+
+  def _compute_qmodel_hyperparams_wrapper(self, grads_and_vars,
+                                          precon_grads_and_vars):
+    """Wrapper function for `self._compute_qmodel_hyperparams`.
+
+    Constructs a list of preconditioned gradients and variables. Also creates a
+    op to asssign the computed q model change to `self._q_model_change`.
+
+    Args:
+      grads_and_vars: List of (gradient, variable) pairs.
+      precon_grads_and_vars: List of (preconditioned gradients, variable)
+        pairs.
+
+    Returns:
+      (alpha, mu, q_model_assign_op), where alpha and mu are chosen to optimize
+      the quadratic model, `q_model_assign_op` assigns the computed q model
+      change to `self._q_model_change`.
+    """
+    precon_grads = list(
+        precon_grad for (precon_grad, _) in precon_grads_and_vars)
+    grads = list(grad for (grad, _) in grads_and_vars)
+    variables = list(var for (_, var) in grads_and_vars)
+    prev_updates = self._compute_prev_updates(variables)
+    # Compute optimal velocity update parameters according to quadratic model
+    alpha, mu, q_model_change = self._compute_qmodel_hyperparams(
+        precon_grads, prev_updates, grads, variables)
+
+    return alpha, mu, self._assign_q_model_change(q_model_change)
+
   def _compute_update_steps(self, grads_and_vars):
     """Computes the update steps for the variables given the gradients.
 
@@ -411,8 +571,10 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
       grads_and_vars: List of (gradient, variable) pairs.
 
     Returns:
-      An 'Operation that computes the update steps for the given variables.
+      A list of tuple (assign_op ,var) where `assign_op` assigns the update
+      steps to `var`.
     """
+
     if self._momentum_type == "regular":
       # Compute "preconditioned" gradient.
       precon_grads_and_vars = self._fisher_est.multiply_inverse(grads_and_vars)
@@ -423,8 +585,13 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
                                                    precon_grads_and_vars)
 
       # Update the velocity with this and return it as the step.
-      return self._update_velocities(precon_grads_and_vars, self._momentum)
-
+      if self._adapt_damping and self._is_chief:
+        _, _, q_model_assign_op = self._compute_qmodel_hyperparams_wrapper(
+            grads_and_vars, precon_grads_and_vars)
+        with ops.control_dependencies([q_model_assign_op]):
+          return self._update_velocities(precon_grads_and_vars, self._momentum)
+      else:
+        return self._update_velocities(precon_grads_and_vars, self._momentum)
     elif self._momentum_type == "adam":
       # Update velocity.
       velocities_and_vars = self._update_velocities(grads_and_vars,
@@ -436,23 +603,13 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
       # Compute "preconditioned" gradient.
       precon_grads_and_vars = self._fisher_est.multiply_inverse(grads_and_vars)
 
-      # Extract out singleton lists from the tuple-lists
-      precon_grads = list(
-          precon_grad for (precon_grad, _) in precon_grads_and_vars)
-      grads = list(grad for (grad, _) in grads_and_vars)
-      variables = list(var for (_, var) in grads_and_vars)
-      # previous updates are the negative velocities (up to scaling by LR)
-      prev_updates = list(
-          -self._zeros_slot(var, "velocity", self._name) for var in variables)
-
       # Compute optimal velocity update parameters according to quadratic model
-      alpha, mu, _ = self._compute_qmodel_hyperparams(
-          precon_grads, prev_updates, grads, variables)
+      alpha, mu, q_model_assign_op = self._compute_qmodel_hyperparams_wrapper(
+          grads_and_vars, precon_grads_and_vars)
 
-      # Update the velocity with precon_grads according to these params
-      # and return it as the step.
-      return self._update_velocities(
-          precon_grads_and_vars, mu, vec_coeff=-alpha)
+      with ops.control_dependencies([q_model_assign_op]):
+        return self._update_velocities(
+            precon_grads_and_vars, mu, vec_coeff=-alpha)
 
   def _update_velocities(self, vecs_and_vars, decay, vec_coeff=1.0):
     """Updates the velocities of the variables with the given vectors.
@@ -482,6 +639,51 @@ class KfacOptimizer(gradient_descent.GradientDescentOptimizer):
     # Go through variable and update its associated part of the velocity vector.
     return [_update_velocity(vec, var) for vec, var in vecs_and_vars]
 
+  # TODO(b/73448937): Move all update damping code to a separate class/function.
+  def _update_damping(self, prev_batch, global_step):
+    """Adapts damping parameter. Check KFAC (Section 6.5) for the details.
+
+    The damping parameter is updated according to the Levenberg-Marquardt rule
+    every `self._damping_adaptation_interval` iterations.
+
+    Args:
+      prev_batch: Tensor or tuple of tensors which can be passed to
+        `self._loss_fn` to evaluate loss.
+      global_step: `Variable` which keeps track of number of times the training
+        variables have been updated.
+    Returns:
+      A `tf.cond` op which updates the damping parameter.
+    """
+    def compute_damping():
+      """"Adapts damping parameter based on "reduction ratio".
+
+      Reduction ratio captures how closely the quadratic approximation to the
+      loss function approximates the actual loss within a trust region. The
+      damping update tries to make the damping as small as possible while
+      maintaining the property that the quadratic model remains a good local
+      approximation to the loss function.
+
+      Returns:
+        An Op to assign newly computed damping value to `self._damping`.
+      """
+      prev_batch_loss = self._loss_fn(prev_batch)
+      with ops.control_dependencies([prev_batch_loss]):
+        rho_assign = self._rho.assign(
+            (prev_batch_loss - self._prev_loss) / self._q_model_change)
+        with ops.control_dependencies([rho_assign]):
+          new_damping = control_flow_ops.case(
+              [(self._rho < 0.25, lambda: self.damping / self._omega),
+               (self._rho > 0.75, lambda: self.damping * self._omega)],
+              lambda: self.damping)
+          with ops.control_dependencies([new_damping]):
+            new_damping_min = math_ops.maximum(new_damping, self._min_damping)
+            return control_flow_ops.group(self._damping.assign(new_damping_min))
+
+    return control_flow_ops.cond(
+        math_ops.equal(
+            math_ops.mod(global_step + 1, self._damping_adaptation_interval),
+            0), compute_damping, control_flow_ops.no_op)
+
 
 def _inner_product_list(list1, list2):
   return math_ops.add_n(
diff --git a/tensorflow/contrib/kfac/python/ops/utils.py b/tensorflow/contrib/kfac/python/ops/utils.py
index f5bd97cb4e..88e6fb20e8 100644
--- a/tensorflow/contrib/kfac/python/ops/utils.py
+++ b/tensorflow/contrib/kfac/python/ops/utils.py
@@ -241,19 +241,22 @@ class SubGraph(object):
     # Set of all ancestor Tensors, Ops to 'outputs'.
     self._members = set()
 
-    self._recurse_add(outputs)
-
-  def _recurse_add(self, nodes):
-    """Recursively adds all of nodes' ancestors."""
-    for node in nodes:
-      if node in self._members:
-        continue
-      self._members.add(node)
-
-      if isinstance(node, ops.Tensor):
-        self._recurse_add((node.op,))
-      elif isinstance(node, ops.Operation):
-        self._recurse_add(node.inputs)
+    self._iter_add(outputs)
+
+  def _iter_add(self, root):
+    """Iteratively adds all of nodes' ancestors using depth first search."""
+    stack = [root]
+    while stack:
+      nodes = stack.pop()
+      for node in nodes:
+        if node in self._members:
+          continue
+        self._members.add(node)
+
+        if isinstance(node, ops.Tensor):
+          stack.append((node.op,))
+        elif isinstance(node, ops.Operation):
+          stack.append(node.inputs)
 
   def is_member(self, node):
     """Check if 'node' is in this subgraph."""
-- 
GitLab


From ae6ffcadafcd83f3488ceb3f47a670f5c6ea45cd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 13:24:35 -0800
Subject: [PATCH 149/575] In VirtualScheduler, if there is a Recv without a
 Send, handle the Recv as an initially ready node.

PiperOrigin-RevId: 186509851
---
 .../core/grappler/costs/virtual_scheduler.cc  | 18 +++--
 .../grappler/costs/virtual_scheduler_test.cc  | 69 +++++++++++++++++++
 2 files changed, 83 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index 14b4ed7507..b9a80fbff2 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -366,8 +366,16 @@ Status VirtualScheduler::Init() {
     std::vector<string> inputs;
     if (IsRecv(*curr_node)) {
       const auto& attr = curr_node->attr();
-      const NodeDef* send = name_to_send[attr.at("tensor_name").s()];
-      inputs = {send->name()};
+      if (attr.count("tensor_name")) {
+        const auto& send_node_name = attr.at("tensor_name").s();
+        auto it = name_to_send.find(send_node_name);
+        // If there is a _Send associated with the curr_node (_Recv), add it as
+        // input.
+        if (it != name_to_send.end()) {
+          const NodeDef* send = it->second;
+          inputs = {send->name()};
+        }
+      }
     } else {
       for (const string& input : curr_node->input()) {
         inputs.push_back(input);
@@ -426,9 +434,11 @@ Status VirtualScheduler::Init() {
         feed_nodes.find(curr_node->name()) != feed_nodes.end();
 
     // Default case: node without inputs are ready at time 0.
-    const bool has_no_inputs = curr_node->input().empty();
+    // Note that we check inputs vector which may be different to
+    // curr_node->input(); e.g., we add Send as input to Recv.
+    const bool has_no_inputs = inputs.empty();
 
-    if (!IsRecv(*curr_node) && (given_as_feed || has_no_inputs)) {
+    if (given_as_feed || has_no_inputs) {
       curr_node_state.time_ready = Costs::Duration();
       ready_nodes_->AddNode(curr_node);
       VLOG(3) << "Added ready node: " << curr_node->name();
diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index 53dcb497a6..d44b83d035 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -394,6 +394,63 @@ versions {
     grappler_item_->fetch = {"Recv"};
   }
 
+  void CreateGrapplerItemWithRecvWithoutSend() {
+    const string gdef_ascii = R"EOF(
+node {
+  name: "Recv"
+  op: "_Recv"
+  device: "/job:localhost/replica:0/task:0/device:CPU:0"
+  attr {
+    key: "client_terminated"
+    value {
+      b: false
+    }
+  }
+  attr {
+    key: "recv_device"
+    value {
+      s: "/job:localhost/replica:0/task:0/device:CPU:0"
+    }
+  }
+  attr {
+    key: "send_device"
+    value {
+      s: "/job:localhost/replica:0/task:0/device:CPU:0"
+    }
+  }
+  attr {
+    key: "send_device_incarnation"
+    value {
+      i: 0
+    }
+  }
+  attr {
+    key: "tensor_name"
+    value {
+      s: "test"
+    }
+  }
+  attr {
+    key: "tensor_type"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+library {
+}
+versions {
+  producer: 24
+}
+    )EOF";
+
+    grappler_item_.reset(new GrapplerItem);
+    CHECK(protobuf::TextFormat::ParseFromString(gdef_ascii,
+                                                &grappler_item_->graph));
+    grappler_item_->id = "test_graph";
+    grappler_item_->fetch = {"Recv"};
+  }
+
   // A simple while loop
   void CreateGrapplerItemWithLoop() {
     // Test graph produced in python using:
@@ -2015,5 +2072,17 @@ TEST_F(VirtualSchedulerTest, GraphWithSendRecvDifferentDevice) {
             0);
   EXPECT_GT(ops_executed.count("Recv"), 0);
 }
+
+TEST_F(VirtualSchedulerTest, GraphWihtOnlyRecv) {
+  // Init.
+  CreateGrapplerItemWithRecvWithoutSend();
+  InitScheduler();
+
+  // Run the scheduler.
+  auto ops_executed = RunScheduler("");
+
+  // Recv without Send will be treated as initially ready node.
+  EXPECT_GT(ops_executed.count("Recv"), 0);
+}
 }  // end namespace grappler
 }  // end namespace tensorflow
-- 
GitLab


From eb06f2fc74cfa020ff76e7cf2c4927a496ebf80f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 13:25:47 -0800
Subject: [PATCH 150/575] Activity analysis annotation on FunctionDef

PiperOrigin-RevId: 186510035
---
 .../py2tf/pyct/static_analysis/activity.py    | 13 +++++++++
 .../pyct/static_analysis/activity_test.py     | 27 +++++++++++++++++++
 2 files changed, 40 insertions(+)

diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py b/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py
index 1c93e16031..02ea6fdeaf 100644
--- a/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py
+++ b/tensorflow/contrib/py2tf/pyct/static_analysis/activity.py
@@ -24,6 +24,7 @@ import gast
 
 from tensorflow.contrib.py2tf.pyct import anno
 from tensorflow.contrib.py2tf.pyct import transformer
+from tensorflow.contrib.py2tf.pyct.qual_names import QN
 from tensorflow.contrib.py2tf.pyct.static_analysis.annos import NodeAnno
 
 # TODO(mdan): Add support for PY3 (e.g. Param vs arg).
@@ -237,6 +238,18 @@ class ActivityAnalizer(transformer.Base):
       self.scope.merge_from(after_child)
     return parent
 
+  def visit_FunctionDef(self, node):
+    if self.scope:
+      qn = QN(node.name)
+      self.scope.mark_write(qn)
+    current_scope = self.scope
+    fndef_scope = Scope(current_scope, isolated=True)
+    self.scope = fndef_scope
+    self.generic_visit(node)
+    anno.setanno(node, NodeAnno.BODY_SCOPE, fndef_scope)
+    self.scope = current_scope
+    return node
+
   def visit_If(self, node):
     self.visit(node.test)
     node = self._process_parallel_blocks(node,
diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py
index 029e4eb480..69f5f4fc58 100644
--- a/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py
+++ b/tensorflow/contrib/py2tf/pyct/static_analysis/activity_test.py
@@ -240,6 +240,33 @@ class ActivityAnalizerTest(test.TestCase):
         anno.getanno(if_node, NodeAnno.ORELSE_SCOPE).parent, ('x', 'z', 'u'),
         ('x', 'y', 'z', 'u'), ('x', 'y', 'z', 'u'))
 
+  def test_functiondef(self):
+
+    def test_fn(a):
+
+      def f(x):
+        y = x * x
+        return y
+
+      b = a
+      for i in a:
+        c = b
+        b -= f(i)
+      return b, c
+
+    node = self._parse_and_analyze(test_fn)
+    fndef_node = node.body[0].body[0]
+
+    self.assertScopeIs(
+        anno.getanno(fndef_node,
+                     NodeAnno.BODY_SCOPE).parent, ('b', 'i', 'f', 'c', 'a'),
+        ('f', 'b', 'c', 'i'), ('f', 'a', 'b', 'c', 'i'))
+    self.assertScopeIs(
+        anno.getanno(fndef_node, NodeAnno.BODY_SCOPE), ('x', 'y'), ('y',), (
+            'x',
+            'y',
+        ))
+
   def test_call_with_composite_names(self):
 
     def foo(*_):
-- 
GitLab


From b2411bc90026560a9db4b1fddd3e7da8f04f6c03 Mon Sep 17 00:00:00 2001
From: Jeremy Lau <lauj@google.com>
Date: Wed, 21 Feb 2018 13:28:23 -0800
Subject: [PATCH 151/575] Internal change.

PiperOrigin-RevId: 186510594
---
 tensorflow/python/debug/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD
index f0e90f6777..253588fc3b 100644
--- a/tensorflow/python/debug/BUILD
+++ b/tensorflow/python/debug/BUILD
@@ -957,7 +957,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "session_debug_grpc_test",
-    size = "medium",
+    size = "large",
     srcs = ["lib/session_debug_grpc_test.py"],
     additional_deps = [
         ":debug_data",
-- 
GitLab


From 469daa466cf2796cbb37490b0da1fa1b36860c88 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Wed, 21 Feb 2018 13:33:07 -0800
Subject: [PATCH 152/575] [XLA:CPU] Don't hard-code lane width in horizontal
 sum routine

The bulk of change is actually in dot_operation_test to get it to the point
where it would have caught this bug.  I made the following changes:

 - Moved some tests under a "no layout assignment pass runs" mode.  This lets
   us test the layout specific aspects of the dot operation more thoroughly.
   Unfortunately not many tests can be run in this mode -- for instance dot
   tests that calls into Eigen won't work here because they need a specific
   layout assignment for correctness.  Tests that runs with layout assignment
   enabled but with non-default layouts are essentially integration tests that
   check that the layout assignment pass + the dot lowering work correctly.

 - Changed the matrix-vector dot tests to check all 4 layouts and the
   matrix-vector Ax+b fusion tests to check all 8 layouts.

 - Duplicated some of the F32 tests to run for F64.

 - Added some new test shapes.

PiperOrigin-RevId: 186511289
---
 tensorflow/compiler/xla/BUILD                 |   1 -
 tensorflow/compiler/xla/array2d.cc            |  36 -----
 tensorflow/compiler/xla/array2d.h             |  19 ++-
 .../xla/service/cpu/vector_support_library.cc |  11 +-
 .../compiler/xla/tests/dot_operation_test.cc  | 141 +++++++++++++-----
 5 files changed, 130 insertions(+), 78 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/array2d.cc

diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD
index 34e733bc8d..c7cb69215f 100644
--- a/tensorflow/compiler/xla/BUILD
+++ b/tensorflow/compiler/xla/BUILD
@@ -372,7 +372,6 @@ tf_cc_test(
 
 cc_library(
     name = "array2d",
-    srcs = ["array2d.cc"],
     hdrs = ["array2d.h"],
     visibility = ["//visibility:public"],
     deps = [
diff --git a/tensorflow/compiler/xla/array2d.cc b/tensorflow/compiler/xla/array2d.cc
deleted file mode 100644
index 418587c1f7..0000000000
--- a/tensorflow/compiler/xla/array2d.cc
+++ /dev/null
@@ -1,36 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/compiler/xla/array2d.h"
-#include "tensorflow/compiler/xla/ptr_util.h"
-
-namespace xla {
-
-std::unique_ptr<Array2D<float>> MakeLinspaceArray2D(float from, float to,
-                                                    int64 n1, int64 n2) {
-  auto array = MakeUnique<Array2D<float>>(n1, n2);
-  int64 count = n1 * n2;
-  float step = (count > 1) ? (to - from) / (count - 1) : 0.0f;
-  auto set = [&array, n1, n2](int64 index, float value) {
-    (*array)(index / n2, index % n2) = value;
-  };
-  for (int64 i = 0; i < count - 1; ++i) {
-    set(i, from + i * step);
-  }
-  set(count - 1, to);
-  return array;
-}
-
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/array2d.h b/tensorflow/compiler/xla/array2d.h
index 41f563486d..d30e78ecde 100644
--- a/tensorflow/compiler/xla/array2d.h
+++ b/tensorflow/compiler/xla/array2d.h
@@ -25,6 +25,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/array.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/lib/core/bits.h"
 #include "tensorflow/core/lib/strings/str_util.h"
@@ -94,9 +95,21 @@ class Array2D : public Array<T> {
 
 // Returns a linspace-populated Array2D in the range [from, to] (inclusive)
 // with dimensions n1 x n2.
-std::unique_ptr<Array2D<float>> MakeLinspaceArray2D(float from, float to,
-                                                    int64 n1, int64 n2);
-
+template <typename NativeT = float>
+std::unique_ptr<Array2D<NativeT>> MakeLinspaceArray2D(double from, double to,
+                                                      int64 n1, int64 n2) {
+  auto array = MakeUnique<Array2D<NativeT>>(n1, n2);
+  int64 count = n1 * n2;
+  NativeT step = (count > 1) ? (to - from) / (count - 1) : 0.0f;
+  auto set = [&array, n1, n2](int64 index, NativeT value) {
+    (*array)(index / n2, index % n2) = value;
+  };
+  for (int64 i = 0; i < count - 1; ++i) {
+    set(i, static_cast<NativeT>(from + i * step));
+  }
+  set(count - 1, to);
+  return array;
+}
 }  // namespace xla
 
 #endif  // TENSORFLOW_COMPILER_XLA_ARRAY2D_H_
diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc
index 150db1cb6e..cd1165e238 100644
--- a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc
+++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc
@@ -370,6 +370,9 @@ std::vector<llvm::Value*> VectorSupportLibrary::ComputeHorizontalSums(
 std::vector<llvm::Value*>
 VectorSupportLibrary::ComputeAvxOptimizedHorizontalSums(
     std::vector<llvm::Value*> vectors, llvm::Value* init_values) {
+  // vectors are N llvm vector values, each with N elements.
+  int64 lane_width = vectors.size();
+
   while (vectors.size() != 2) {
     std::vector<llvm::Value*> new_vectors;
     for (int i = 0; i < vectors.size(); i += 2) {
@@ -390,10 +393,14 @@ VectorSupportLibrary::ComputeAvxOptimizedHorizontalSums(
     high = AddInternal(ExtractHighHalf(init_values), high);
   }
 
+  // `low` has the first `lane_width / 2` horizontal reductions, and `high` has
+  // the next `lane_width / 2` horizontal reductions.
+
   std::vector<llvm::Value*> results;
-  for (int i = 0; i < 8; i++) {
+  for (int i = 0; i < lane_width; i++) {
     llvm::Value* scalar_result = ir_builder()->CreateExtractElement(
-        i < 4 ? low : high, ir_builder()->getInt32(i % 4), name());
+        i < (lane_width / 2) ? low : high,
+        ir_builder()->getInt32(i % (lane_width / 2)), name());
     results.push_back(scalar_result);
   }
 
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 6b0c04c2c0..815962094a 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -225,33 +225,39 @@ string PrintDotTestParam(
 }
 
 class ParametricDotTest : public DotOperationTest,
-                          public ::testing::WithParamInterface<DotTestParam> {};
+                          public ::testing::WithParamInterface<DotTestParam> {
+ protected:
+  template <typename NativeT>
+  void TestImpl();
+};
 
-XLA_TEST_P(ParametricDotTest, TestF32) {
+template <typename NativeT>
+void ParametricDotTest::TestImpl() {
   DotTestParam param = GetParam();
 
-  std::unique_ptr<Array2D<float>> dot_lhs_data =
-      MakeLinspaceArray2D(0.0, 1.0, param.m, param.k);
+  std::unique_ptr<Array2D<NativeT>> dot_lhs_data =
+      MakeLinspaceArray2D<NativeT>(0.0, 1.0, param.m, param.k);
   std::unique_ptr<Literal> dot_lhs_lit = Literal::CreateR2FromArray2DWithLayout(
       *dot_lhs_data, LayoutUtil::MakeLayout(
                          MinorToMajorForIsRowMajor(param.dot_lhs_row_major)));
   std::unique_ptr<GlobalData> dot_lhs_handle =
       client_->TransferToServer(*dot_lhs_lit).ConsumeValueOrDie();
 
-  std::unique_ptr<Array2D<float>> dot_rhs_data =
-      MakeLinspaceArray2D(0.0, 1.0, param.k, param.n);
-  std::unique_ptr<Literal> dot_rhs_lit = Literal::CreateR2FromArray2DWithLayout(
-      *dot_rhs_data, LayoutUtil::MakeLayout(
-                         MinorToMajorForIsRowMajor(param.dot_rhs_row_major)));
+  std::unique_ptr<Array2D<NativeT>> dot_rhs_data =
+      MakeLinspaceArray2D<NativeT>(0.0, 1.0, param.k, param.n);
+  Layout rhs_layout = LayoutUtil::MakeLayout(
+      MinorToMajorForIsRowMajor(param.dot_rhs_row_major));
+  std::unique_ptr<Literal> dot_rhs_lit =
+      Literal::CreateR2FromArray2DWithLayout(*dot_rhs_data, rhs_layout);
   std::unique_ptr<GlobalData> dot_rhs_handle =
       client_->TransferToServer(*dot_rhs_lit).ConsumeValueOrDie();
 
-  std::unique_ptr<Array2D<float>> addend_data;
+  std::unique_ptr<Array2D<NativeT>> addend_data;
   std::unique_ptr<Literal> addend_lit;
   std::unique_ptr<GlobalData> addend_handle;
 
   if (param.has_addend) {
-    addend_data = MakeLinspaceArray2D(0.0, 1.0, param.m, param.n);
+    addend_data = MakeLinspaceArray2D<NativeT>(0.0, 1.0, param.m, param.n);
     addend_lit = Literal::CreateR2FromArray2DWithLayout(
         *addend_data, LayoutUtil::MakeLayout(
                           MinorToMajorForIsRowMajor(param.addend_row_major)));
@@ -259,24 +265,33 @@ XLA_TEST_P(ParametricDotTest, TestF32) {
   }
 
   ComputationBuilder builder(client_, TestName());
-  auto prim_type = primitive_util::NativeToPrimitiveType<float>();
+  auto prim_type = primitive_util::NativeToPrimitiveType<NativeT>();
   auto result = builder.Dot(
-      builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {param.m, param.k}),
+      builder.Parameter(0,
+                        ShapeUtil::MakeShapeWithLayout(
+                            prim_type, {param.m, param.k},
+                            MinorToMajorForIsRowMajor(param.dot_lhs_row_major)),
                         "dot_lhs"),
-      builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {param.k, param.n}),
+      builder.Parameter(1,
+                        ShapeUtil::MakeShapeWithLayout(
+                            prim_type, {param.k, param.n},
+                            MinorToMajorForIsRowMajor(param.dot_rhs_row_major)),
                         "dot_rhs"));
 
   if (param.has_addend) {
     result = builder.Add(
-        result,
-        builder.Parameter(
-            2, ShapeUtil::MakeShape(prim_type, {param.m, param.n}), "addend"));
+        result, builder.Parameter(
+                    2,
+                    ShapeUtil::MakeShapeWithLayout(
+                        prim_type, {param.m, param.n},
+                        MinorToMajorForIsRowMajor(param.addend_row_major)),
+                    "addend"));
   }
 
-  std::unique_ptr<Array2D<float>> expected;
+  std::unique_ptr<Array2D<NativeT>> expected;
   if (param.has_addend) {
     expected = ReferenceUtil::ApplyElementwise2D(
-        std::plus<float>(),
+        std::plus<NativeT>(),
         *ReferenceUtil::MatmulArray2D(*dot_lhs_data, *dot_rhs_data),
         *addend_data);
   } else {
@@ -288,9 +303,13 @@ XLA_TEST_P(ParametricDotTest, TestF32) {
     args.push_back(addend_handle.get());
   }
 
-  ComputeAndCompareR2<float>(&builder, *expected, args, ErrorSpec(0.3, 3e-3));
+  ComputeAndCompareR2<NativeT>(&builder, *expected, args, ErrorSpec(0.3, 3e-3));
 }
 
+XLA_TEST_P(ParametricDotTest, TestF32) { TestImpl<float>(); }
+
+XLA_TEST_P(ParametricDotTest, TestF64) { TestImpl<double>(); }
+
 std::vector<DotTestParam> CreateDotTestParameters() {
   std::vector<DotTestParam> params;
 
@@ -305,30 +324,79 @@ std::vector<DotTestParam> CreateDotTestParameters() {
     }
   };
 
+  add_matrix_matrix_dot_test(/*m=*/12, /*k=*/117, /*n=*/7);
+  add_matrix_matrix_dot_test(/*m=*/270, /*k=*/270, /*n=*/520);
+  add_matrix_matrix_dot_test(/*m=*/260, /*k=*/3, /*n=*/520);
+
+  return params;
+}
+
+INSTANTIATE_TEST_CASE_P(DotTests, ParametricDotTest,
+                        ::testing::ValuesIn(CreateDotTestParameters()),
+                        PrintDotTestParam);
+
+class ParametricDotTestWithoutLayoutAssignment : public ParametricDotTest {
+ public:
+  ParametricDotTestWithoutLayoutAssignment() {
+    execution_options_.mutable_debug_options()->add_xla_disable_hlo_passes(
+        "layout-assignment");
+  }
+};
+
+XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF32) {
+  TestImpl<float>();
+}
+
+XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF64) {
+  TestImpl<double>();
+}
+
+std::vector<DotTestParam> CreateNoLayoutAssignmentDotTestParameters() {
+  std::vector<DotTestParam> params;
+
   auto add_matrix_vector_dot_test = [&](int k, int n) {
-    for (bool has_addend : {false, true}) {
-      params.push_back({/*m=*/1, /*k=*/k, /*n=*/n,
-                        /*dot_lhs_row_major=*/true, /*dot_rhs_row_major=*/true,
-                        /*has_addend=*/has_addend, /*addend_row_major=*/true});
-      if (n != 1) {
-        params.push_back(
-            {/*m=*/n, /*k=*/k, /*n=*/1,
-             /*dot_lhs_row_major=*/true, /*dot_rhs_row_major=*/true,
-             /*has_addend=*/has_addend, /*addend_row_major=*/true});
+    for (bool lhs_row_major : {true, false}) {
+      for (bool rhs_row_major : {true, false}) {
+        for (bool has_addend : {true, false}) {
+          params.push_back({/*m=*/1, /*k=*/k, /*n=*/n,
+                            /*dot_lhs_row_major=*/lhs_row_major,
+                            /*dot_rhs_row_major=*/rhs_row_major,
+                            /*has_addend=*/has_addend,
+                            /*addend_row_major=*/true});
+          if (has_addend) {
+            params.push_back({/*m=*/1, /*k=*/k, /*n=*/n,
+                              /*dot_lhs_row_major=*/lhs_row_major,
+                              /*dot_rhs_row_major=*/rhs_row_major,
+                              /*has_addend=*/has_addend,
+                              /*addend_row_major=*/false});
+          }
+          if (n != 1) {
+            params.push_back({/*m=*/n, /*k=*/k, /*n=*/1,
+                              /*dot_lhs_row_major=*/lhs_row_major,
+                              /*dot_rhs_row_major=*/rhs_row_major,
+                              /*has_addend=*/has_addend,
+                              /*addend_row_major=*/true});
+            if (has_addend) {
+              params.push_back({/*m=*/n, /*k=*/k, /*n=*/1,
+                                /*dot_lhs_row_major=*/lhs_row_major,
+                                /*dot_rhs_row_major=*/rhs_row_major,
+                                /*has_addend=*/has_addend,
+                                /*addend_row_major=*/false});
+            }
+          }
+        }
       }
     }
   };
 
-  add_matrix_matrix_dot_test(/*m=*/12, /*k=*/117, /*n=*/7);
-  add_matrix_matrix_dot_test(/*m=*/270, /*k=*/270, /*n=*/520);
-  add_matrix_matrix_dot_test(/*m=*/260, /*k=*/3, /*n=*/520);
-
   add_matrix_vector_dot_test(/*k=*/8, /*n=*/8);
   add_matrix_vector_dot_test(/*k=*/130, /*n=*/8);
   add_matrix_vector_dot_test(/*k=*/8, /*n=*/130);
   add_matrix_vector_dot_test(/*k=*/290, /*n=*/130);
   add_matrix_vector_dot_test(/*k=*/1, /*n=*/1);
   add_matrix_vector_dot_test(/*k=*/1, /*n=*/16);
+  add_matrix_vector_dot_test(/*k=*/1, /*n=*/4);
+  add_matrix_vector_dot_test(/*k=*/1, /*n=*/3);
   add_matrix_vector_dot_test(/*k=*/3, /*n=*/16);
   add_matrix_vector_dot_test(/*k=*/3, /*n=*/3);
   add_matrix_vector_dot_test(/*k=*/29, /*n=*/29);
@@ -339,9 +407,10 @@ std::vector<DotTestParam> CreateDotTestParameters() {
   return params;
 }
 
-INSTANTIATE_TEST_CASE_P(DotTests, ParametricDotTest,
-                        ::testing::ValuesIn(CreateDotTestParameters()),
-                        PrintDotTestParam);
+INSTANTIATE_TEST_CASE_P(
+    DotTests, ParametricDotTestWithoutLayoutAssignment,
+    ::testing::ValuesIn(CreateNoLayoutAssignmentDotTestParameters()),
+    PrintDotTestParam);
 
 XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorFF) {
   TestSquareMatrixDot<float>(false, false);
-- 
GitLab


From 6c8879296135ddcc5e94c1f0561168846a703fcb Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Wed, 21 Feb 2018 14:07:48 -0800
Subject: [PATCH 153/575] Make with_c_api a no-op if the C API is already
 enabled.

This will help transition to turning the C API on by default by
preventing new tests from breaking.

PiperOrigin-RevId: 186516976
---
 tensorflow/python/framework/test_util.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index 1560766fc9..ad9b1291f0 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -419,6 +419,11 @@ def with_c_api(cls):
   Returns:
     cls with new test methods added
   """
+  # If the C API is already enabled, don't do anything. Some tests break if the
+  # same test is run twice, so this allows us to turn on the C API by default
+  # without breaking these tests.
+  if ops._USE_C_API: return cls
+
   for name, value in cls.__dict__.copy().items():
     if callable(value) and name.startswith("test"):
       setattr(cls, name + "WithCApi", enable_c_api(value))
-- 
GitLab


From 6583044d980686c04a20085098b335c98618d106 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 14:14:42 -0800
Subject: [PATCH 154/575] Make CPUAllocator VisitableAllocator, for better RDMA
 networking.

PiperOrigin-RevId: 186518037
---
 tensorflow/core/BUILD                         |  3 +-
 .../core/common_runtime/bfc_allocator.h       |  2 +-
 .../gpu/gpu_cudamalloc_allocator.h            |  2 +-
 .../common_runtime/gpu/gpu_debug_allocator.h  |  2 +-
 .../core/common_runtime/gpu/pool_allocator.h  |  2 +-
 .../core/common_runtime/mkl_cpu_allocator.h   |  2 +-
 tensorflow/core/framework/allocator.cc        | 62 +++++++++++++++++--
 .../visitable_allocator.h                     |  6 +-
 8 files changed, 67 insertions(+), 14 deletions(-)
 rename tensorflow/core/{common_runtime => framework}/visitable_allocator.h (94%)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 04307db24c..1893967cdd 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -480,6 +480,7 @@ tf_cuda_library(
         "framework/type_index.h",
         "framework/type_traits.h",
         "framework/types.h",
+        "framework/visitable_allocator.h",
         "public/version.h",
         "util/activation_mode.h",
         "util/bcast.h",
@@ -1812,6 +1813,7 @@ FRAMEWORK_INTERNAL_PUBLIC_HEADERS = [
     "framework/tracking_allocator.h",  # only needed for tests
     "framework/unique_tensor_references.h",
     "framework/variant.h",
+    "framework/visitable_allocator.h",
     "platform/variant_coding.h",
     "util/command_line_flags.h",
     "util/env_var.h",
@@ -2107,7 +2109,6 @@ CORE_CPU_LIB_HEADERS = CORE_CPU_BASE_HDRS + [
     "common_runtime/stats_publisher_interface.h",
     "common_runtime/step_stats_collector.h",
     "common_runtime/threadpool_device.h",
-    "common_runtime/visitable_allocator.h",
     "graph/gradients.h",
     "graph/quantize_training.h",
 ] + if_mkl(["graph/mkl_graph_util.h"])
diff --git a/tensorflow/core/common_runtime/bfc_allocator.h b/tensorflow/core/common_runtime/bfc_allocator.h
index b8e773503c..e34945dd48 100644
--- a/tensorflow/core/common_runtime/bfc_allocator.h
+++ b/tensorflow/core/common_runtime/bfc_allocator.h
@@ -23,7 +23,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/common_runtime/allocator_retry.h"
-#include "tensorflow/core/common_runtime/visitable_allocator.h"
+#include "tensorflow/core/framework/visitable_allocator.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/macros.h"
diff --git a/tensorflow/core/common_runtime/gpu/gpu_cudamalloc_allocator.h b/tensorflow/core/common_runtime/gpu/gpu_cudamalloc_allocator.h
index 208697361d..0a586344cc 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_cudamalloc_allocator.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_cudamalloc_allocator.h
@@ -19,7 +19,7 @@ limitations under the License.
 #include <memory>
 
 #include "tensorflow/core/common_runtime/gpu/gpu_id.h"
-#include "tensorflow/core/common_runtime/visitable_allocator.h"
+#include "tensorflow/core/framework/visitable_allocator.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/stream_executor.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/core/common_runtime/gpu/gpu_debug_allocator.h b/tensorflow/core/common_runtime/gpu/gpu_debug_allocator.h
index adce3a8436..0db08dc975 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_debug_allocator.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_debug_allocator.h
@@ -21,7 +21,7 @@ limitations under the License.
 #include <unordered_map>
 
 #include "tensorflow/core/common_runtime/gpu/gpu_id.h"
-#include "tensorflow/core/common_runtime/visitable_allocator.h"
+#include "tensorflow/core/framework/visitable_allocator.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/stream_executor.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/core/common_runtime/gpu/pool_allocator.h b/tensorflow/core/common_runtime/gpu/pool_allocator.h
index 91ce830df8..38d669ea07 100644
--- a/tensorflow/core/common_runtime/gpu/pool_allocator.h
+++ b/tensorflow/core/common_runtime/gpu/pool_allocator.h
@@ -24,7 +24,7 @@ limitations under the License.
 #include <map>
 #include <memory>
 #include <vector>
-#include "tensorflow/core/common_runtime/visitable_allocator.h"
+#include "tensorflow/core/framework/visitable_allocator.h"
 #include "tensorflow/core/lib/core/bits.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
diff --git a/tensorflow/core/common_runtime/mkl_cpu_allocator.h b/tensorflow/core/common_runtime/mkl_cpu_allocator.h
index 2a67c039ac..77eeb56b19 100644
--- a/tensorflow/core/common_runtime/mkl_cpu_allocator.h
+++ b/tensorflow/core/common_runtime/mkl_cpu_allocator.h
@@ -25,7 +25,7 @@ limitations under the License.
 #include <cstdlib>
 #include <string>
 #include "tensorflow/core/common_runtime/bfc_allocator.h"
-#include "tensorflow/core/common_runtime/visitable_allocator.h"
+#include "tensorflow/core/framework/visitable_allocator.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/mem.h"
diff --git a/tensorflow/core/framework/allocator.cc b/tensorflow/core/framework/allocator.cc
index 94bf34afa4..a382b8be95 100644
--- a/tensorflow/core/framework/allocator.cc
+++ b/tensorflow/core/framework/allocator.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/core/framework/allocator.h"
+#include "tensorflow/core/framework/visitable_allocator.h"
 
 #include "tensorflow/core/framework/allocator_registry.h"
 #include "tensorflow/core/framework/log_memory.h"
@@ -68,15 +68,19 @@ void EnableCPUAllocatorFullStats(bool enable) {
   cpu_allocator_collect_full_stats = enable;
 }
 
-class CPUAllocator : public Allocator {
+class CPUAllocator : public VisitableAllocator {
  public:
-  CPUAllocator() {}
+  CPUAllocator() : allocation_begun_(false) {}
 
   ~CPUAllocator() override {}
 
   string Name() override { return "cpu"; }
 
   void* AllocateRaw(size_t alignment, size_t num_bytes) override {
+    if (!allocation_begun_) {
+      allocation_begun_ = true;
+    }
+
     void* p = port::AlignedMalloc(num_bytes, alignment);
     if (cpu_allocator_collect_stats) {
       const std::size_t alloc_size = port::MallocExtension_GetAllocatedSize(p);
@@ -88,16 +92,38 @@ class CPUAllocator : public Allocator {
       stats_.max_alloc_size =
           std::max<int64>(stats_.max_alloc_size, alloc_size);
     }
+
+    // visit each Visitor in alloc_visitors_
+    if (p != nullptr) {
+      for (const Visitor& v : alloc_visitors_) {
+        v(p, num_bytes);
+      }
+    }
+
     return p;
   }
 
   void DeallocateRaw(void* ptr) override {
+    std::size_t alloc_size;
+    bool init_alloc_size = false;
     if (cpu_allocator_collect_stats) {
-      const std::size_t alloc_size =
-          port::MallocExtension_GetAllocatedSize(ptr);
+      alloc_size = port::MallocExtension_GetAllocatedSize(ptr);
+      init_alloc_size = true;
       mutex_lock l(mu_);
       stats_.bytes_in_use -= alloc_size;
     }
+
+    // visit each Visitor in free_visitors_
+    if (ptr != nullptr) {
+      if (!init_alloc_size) {
+        alloc_size = port::MallocExtension_GetAllocatedSize(ptr);
+        init_alloc_size = true;
+      }
+      for (const Visitor& v : free_visitors_) {
+        v(ptr, alloc_size);
+      }
+    }
+
     port::AlignedFree(ptr);
   }
 
@@ -117,10 +143,36 @@ class CPUAllocator : public Allocator {
     return port::MallocExtension_GetAllocatedSize(ptr);
   }
 
+  // REQUIRES: can only add visitors before the first Allocate call
+
+  void AddAllocVisitor(Visitor visitor) override {
+    mutex_lock lock(visitor_mutex_);
+    CHECK(!allocation_begun_)
+        << "AddAllocVisitor may not be called after allocation has begun.";
+    alloc_visitors_.push_back(visitor);
+  }
+
+  void AddFreeVisitor(Visitor visitor) override {
+    mutex_lock lock(visitor_mutex_);
+    CHECK(!allocation_begun_)
+        << "AddFreeVisitor may not be called after allocation has begun.";
+    free_visitors_.push_back(visitor);
+  }
+
  private:
   mutex mu_;
   AllocatorStats stats_ GUARDED_BY(mu_);
 
+  // visitor_mutex_ protects write access to alloc_visitors_ and free_visitors_.
+  // While write access is mutually exclusive, reads may happen concurrently.
+  // This is okay because we may only append to alloc_visitors_ and
+  // free_visitors_ before first allocation, and subsequently we only read these
+  // vectors.
+  mutex visitor_mutex_;
+  std::vector<Visitor> alloc_visitors_;
+  std::vector<Visitor> free_visitors_;
+  std::atomic<bool> allocation_begun_;
+
   TF_DISALLOW_COPY_AND_ASSIGN(CPUAllocator);
 };
 
diff --git a/tensorflow/core/common_runtime/visitable_allocator.h b/tensorflow/core/framework/visitable_allocator.h
similarity index 94%
rename from tensorflow/core/common_runtime/visitable_allocator.h
rename to tensorflow/core/framework/visitable_allocator.h
index 8edf922d11..ed41b05531 100644
--- a/tensorflow/core/common_runtime/visitable_allocator.h
+++ b/tensorflow/core/framework/visitable_allocator.h
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_COMMON_RUNTIME_VISITABLE_ALLOCATOR_H_
-#define TENSORFLOW_COMMON_RUNTIME_VISITABLE_ALLOCATOR_H_
+#ifndef TENSORFLOW_CORE_FRAMEWORK_VISITABLE_ALLOCATOR_H_
+#define TENSORFLOW_CORE_FRAMEWORK_VISITABLE_ALLOCATOR_H_
 
 #include <functional>
 #include "tensorflow/core/framework/allocator.h"
@@ -76,4 +76,4 @@ class TrackingVisitableAllocator : public TrackingAllocator,
   VisitableAllocator* allocator_;
 };
 }  // namespace tensorflow
-#endif  // TENSORFLOW_COMMON_RUNTIME_VISITABLE_ALLOCATOR_H_
+#endif  // TENSORFLOW_CORE_FRAMEWORK_VISITABLE_ALLOCATOR_H_
-- 
GitLab


From 042c60a564d014a19575884f2a0b2cba987b0f7a Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Feb 2018 14:29:27 -0800
Subject: [PATCH 155/575] Ensured that the model pruner outputs the nodes of
 the optimized graph in a deterministic order

PiperOrigin-RevId: 186520272
---
 tensorflow/core/grappler/optimizers/model_pruner.cc |  1 +
 tensorflow/python/grappler/tf_optimizer_test.py     | 13 ++++++-------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/model_pruner.cc b/tensorflow/core/grappler/optimizers/model_pruner.cc
index 97f456d2a6..3311e97010 100644
--- a/tensorflow/core/grappler/optimizers/model_pruner.cc
+++ b/tensorflow/core/grappler/optimizers/model_pruner.cc
@@ -59,6 +59,7 @@ Status ModelPruner::Optimize(Cluster* cluster, const GrapplerItem& item,
   if (!nodes_to_preserve.empty()) {
     std::vector<string> terminal_nodes(nodes_to_preserve.begin(),
                                        nodes_to_preserve.end());
+    std::sort(terminal_nodes.begin(), terminal_nodes.end());
     bool ill_formed = false;
     std::vector<const NodeDef*> keep =
         ComputeTransitiveFanin(item.graph, terminal_nodes, &ill_formed);
diff --git a/tensorflow/python/grappler/tf_optimizer_test.py b/tensorflow/python/grappler/tf_optimizer_test.py
index f4f781ad7e..3ee4d7807e 100644
--- a/tensorflow/python/grappler/tf_optimizer_test.py
+++ b/tensorflow/python/grappler/tf_optimizer_test.py
@@ -52,7 +52,7 @@ class PyWrapOptimizeGraphTest(test.TestCase):
   def testKeepNodes(self):
     g = ops.Graph()
     with g.as_default():
-      variables.Variable(
+      a1 = variables.Variable(
           1.0)  # Must be preserved since it's in the collection 'variables'.
       a2 = constant_op.constant(0, shape=[50, 50], name='keep')
       ops.add_to_collection('a2', a2)  # Explicitly add to collection.
@@ -68,12 +68,11 @@ class PyWrapOptimizeGraphTest(test.TestCase):
 
     # Check that the nodes referenced in various collections have been preserved
     self.assertEqual(len(optimized_graph.node), 5)
-    # Disabled this part of the test until we figure out why it fails on MacOS
-    # self.assertEqual(a2.op.name, optimized_graph.node[0].name)
-    # self.assertEqual(a1.op.name, optimized_graph.node[1].name)
-    # self.assertEqual('Variable/initial_value', optimized_graph.node[2].name)
-    # self.assertEqual(d.op.name, optimized_graph.node[3].name)
-    # self.assertEqual('Variable/Assign', optimized_graph.node[4].name)
+    self.assertEqual(d.op.name, optimized_graph.node[0].name)
+    self.assertEqual(a1.op.name, optimized_graph.node[1].name)
+    self.assertEqual('Variable/initial_value', optimized_graph.node[2].name)
+    self.assertEqual(a2.op.name, optimized_graph.node[3].name)
+    self.assertEqual('Variable/Assign', optimized_graph.node[4].name)
 
 
 if __name__ == '__main__':
-- 
GitLab


From 24edd50d23416b2c15a4e3509ce079e558500894 Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Wed, 21 Feb 2018 14:40:18 -0800
Subject: [PATCH 156/575] Improve error message.

PiperOrigin-RevId: 186521902
---
 tensorflow/python/estimator/training.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index 63328dcfb5..2cc3331a15 100644
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -455,15 +455,21 @@ class _TrainingExecutor(object):
                train_hooks=None,
                continuous_eval_listener=None):
     if not isinstance(estimator, estimator_lib.Estimator):
-      raise TypeError('`estimator` must have type `tf.estimator.Estimator`.')
+      raise TypeError(
+          '`estimator` must have type `tf.estimator.Estimator`. '
+          'Got: {}'.format(type(estimator)))
     self._estimator = estimator
 
     if not isinstance(train_spec, TrainSpec):
-      raise TypeError('`train_spec` must have type `tf.estimator.TrainSpec`.')
+      raise TypeError(
+          '`train_spec` must have type `tf.estimator.TrainSpec`. '
+          'Got: {}'.format(type(train_spec)))
     self._train_spec = train_spec
 
     if not isinstance(eval_spec, EvalSpec):
-      raise TypeError('`eval_spec` must have type `tf.estimator.EvalSpec`.')
+      raise TypeError(
+          '`eval_spec` must have type `tf.estimator.EvalSpec`. '
+          'Got: {}'.format(type(eval_spec)))
     self._eval_spec = eval_spec
 
     self._train_hooks = _validate_hooks(train_hooks)
-- 
GitLab


From 32e39947c80ad410042a5aea266f197e9ecd289d Mon Sep 17 00:00:00 2001
From: Akshay Modi <nareshmodi@google.com>
Date: Wed, 21 Feb 2018 14:42:11 -0800
Subject: [PATCH 157/575] Record gradient in C

PiperOrigin-RevId: 186522240
---
 tensorflow/python/eager/backprop.py       | 160 ++-------------
 tensorflow/python/eager/pywrap_tfe.h      |  14 ++
 tensorflow/python/eager/pywrap_tfe_src.cc | 232 +++++++++++++++++++---
 tensorflow/python/pywrap_tfe.i            |   2 +
 4 files changed, 235 insertions(+), 173 deletions(-)

diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index d8e13d7231..5505661dbb 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -137,110 +137,6 @@ _gradient_functions_lock = threading.Lock()
 _tracing = False
 
 
-# TODO(apassos) replace this with a mechanism which can happen at the op
-# gradient function registration site, to be less error-prone
-# TODO(apassos) add ops other than those in nn_grad and math_grad
-_ops_which_dont_need_outputs = set([
-    "Identity",
-    "MatMul",
-    "Conv2DBackpropInput",
-    "Conv2DBackpropFilter",
-    "Conv3D",
-    "Conv3DBackpropInputV2",
-    "AvgPool3D",
-    "AvgPool3DGrad",
-    "MaxPool3D",
-    "MaxPool3DGrad",
-    "MaxPool3DGradGrad",
-    "BiasAdd",
-    "BiasAddV1",
-    "BiasAddGrad",
-    "Relu6",
-    "Softplus",
-    "SoftplusGrad",
-    "Softsign",
-    "ReluGrad",
-    "Conv2D",
-    "DepthwiseConv2dNative",
-    "Dilation2D",
-    "AvgPool",
-    "AvgPoolGrad",
-    "BatchNormWithGlobalNormalization",
-    "L2Loss",
-    "Sum",
-    "Prod",
-    "SegmentSum",
-    "SegmentMean",
-    "SparseSegmentSum",
-    "SparseSegmentMean",
-    "SparseSegmentSqrtN",
-    "SegmentMin",
-    "SegmentMax",
-    "UnsortedSegmentSum",
-    "UnsortedSegmentMax",
-    "Abs",
-    "Neg",
-    "ReciprocalGrad",
-    "Square",
-    "Expm1",
-    "Log",
-    "Log1p",
-    "TanhGrad",
-    "SigmoidGrad",
-    "Sign",
-    "Sin",
-    "Cos",
-    "Tan",
-    "Add",
-    "Sub",
-    "Mul",
-    "Div",
-    "RealDiv",
-    "Maximum",
-    "Minimum",
-    "SquaredDifference",
-    "Select",
-    "SparseMatMul",
-    "BatchMatMul",
-    "Complex",
-    "Real",
-    "Imag",
-    "Angle",
-    "Conj",
-    "Cast",
-    "Cross",
-    "Cumsum",
-    "Cumprod",
-    "ReadVariableOp",
-    "VarHandleOp",
-    "Shape",
-])
-
-_ops_which_dont_need_inputs = set([
-    "Identity",
-    "Softmax",
-    "LogSoftmax",
-    "BiasAdd",
-    "Relu",
-    "Elu",
-    "Selu",
-    "SparseSoftmaxCrossEntropyWithLogits",
-    "Neg",
-    "Inv",
-    "Reciprocal",
-    "Sqrt",
-    "Exp",
-    "Tanh",
-    "Sigmoid",
-    "Real",
-    "Imag",
-    "Conj",
-    "ReadVariableOp",
-    "VarHandleOp",
-    "Shape",
-])
-
-
 # TODO(agarwal): use an automatic mechanism for handling None arguments to
 # gradient functions.
 # Some gradient functions can accept None arguments for gradients. The following
@@ -259,57 +155,25 @@ _grad_fn_accepts_none_for_indices = {
 }
 
 
-def _record_gradient(op_name, inputs, attrs, results, name):
-  """Records gradients for a TensorFlow operation.
-
-  Args:
-    op_name: Name of the TensorFlow operation (see REGISTER_OP in C++ code) to
-      execute.
-    inputs: A flat list of Tensor object inputs to the operation.
-    attrs: A tuple with alternating string attr names and attr values for this
-      operation.
-    results: The results of the operation (as a flat list).
-    name: Customized name for the operation.
-
-  Returns:
-    A list of maybe-wrapped results. Either Tensors or TensorNodes.
-
-  Raises:
-    An exception on error.
-  """
-  if not tape.could_possibly_record():
-    return
-
-  if op_name in _ops_which_dont_need_outputs:
-    op_outputs = None
-  else:
-    # TODO(apassos) this line creates a weak circular reference where the
-    # backprop function keeps an output alive which in turn keeps the tape entry
-    # alive which keeps the backprop function alive. Figure out how to break
-    # this up without breaking second derivatives of ops like Exp whose
-    # gradients depend only on the outputs.
-    op_outputs = results
-
-  if op_name in _ops_which_dont_need_inputs:
-    op_inputs = None
-  else:
-    op_inputs = inputs
-
-  num_inputs = len(inputs)
+def _get_backward_fn(op_name, attrs, num_inputs, op_inputs, op_outputs):
 
   def grad_fn(*orig_outputs):
-    """Generated gradient function."""
     result = _magic_gradient_function(op_name, attrs, num_inputs,
                                       op_inputs, op_outputs, orig_outputs)
     if _tracing:
-      print("Gradient for", (name if name else op_name), "inputs", op_inputs,
-            "output_grads", orig_outputs, "gradients", result)
+      print("Gradient for", op_name, "inputs", op_inputs, "output_grads",
+            orig_outputs, "gradients", result)
     return nest.flatten(result)
 
-  tape.record_operation(op_name, results, inputs, grad_fn)
-  if _tracing:
-    print("Computed op", (name if name else op_name), "inputs", inputs,
-          "outputs", results)
+  return grad_fn
+
+
+pywrap_tensorflow.TFE_Py_RegisterBackwardFunctionGetter(_get_backward_fn)
+
+
+def _record_gradient(op_name, inputs, attrs, results, name):
+  return pywrap_tensorflow.TFE_Py_RecordGradient(op_name, inputs, attrs,
+                                                 results, name)
 
 
 execute.record_gradient = _record_gradient
diff --git a/tensorflow/python/eager/pywrap_tfe.h b/tensorflow/python/eager/pywrap_tfe.h
index 16b7d1a119..f9692a8910 100644
--- a/tensorflow/python/eager/pywrap_tfe.h
+++ b/tensorflow/python/eager/pywrap_tfe.h
@@ -59,6 +59,15 @@ PyObject* TFE_Py_RegisterExceptionClass(PyObject* e);
 // This function is not thread-safe.
 PyObject* TFE_Py_RegisterFallbackExceptionClass(PyObject* e);
 
+// Registers e as the backward_function_getter.
+// The registered function creates a backward function (a function that can
+// return the gradient of the inputs an op given the gradient of it's outputs).
+// The registered function will be passed the following arguments:
+//    op_name, attrs, num_inputs, op_inputs, op_outputs
+//
+// This function is not thread-safe.
+PyObject* TFE_Py_RegisterBackwardFunctionGetter(PyObject* e);
+
 // Returns 0 if 'status' is TF_OK. Otherwise, raises an exception (using
 // `exception` if not nullptr, else using the class registered via
 // TFE_Py_RegisterExceptionClass), and returns -1.
@@ -165,6 +174,11 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace,
 // directive.
 PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args);
 
+// Record the gradient for a given op.
+PyObject* TFE_Py_RecordGradient(PyObject* op_name, PyObject* inputs,
+                                PyObject* attrs, PyObject* results,
+                                PyObject* name);
+
 // Returns the set of variables watched by the given tape.
 PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape);
 
diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc
index cabbcc48fd..30e08c8e65 100644
--- a/tensorflow/python/eager/pywrap_tfe_src.cc
+++ b/tensorflow/python/eager/pywrap_tfe_src.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/cleanup.h"
 #include "tensorflow/core/lib/gtl/compactptrset.h"
 #include "tensorflow/core/lib/gtl/flatmap.h"
+#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -575,6 +576,9 @@ PyObject* exception_class GUARDED_BY(exception_class_mutex) = nullptr;
 // Python subclass of Exception that is created to signal fallback.
 PyObject* fallback_exception_class = nullptr;
 
+// Python function that returns a backward_function.
+PyObject* backward_function_getter = nullptr;
+
 tensorflow::mutex _uid_mutex(tensorflow::LINKER_INITIALIZED);
 tensorflow::int64 _uid GUARDED_BY(_uid_mutex) = 0;
 
@@ -647,6 +651,23 @@ PyObject* TFE_Py_RegisterFallbackExceptionClass(PyObject* e) {
   }
 }
 
+PyObject* TFE_Py_RegisterBackwardFunctionGetter(PyObject* e) {
+  if (backward_function_getter != nullptr) {
+    Py_DECREF(backward_function_getter);
+  }
+  if (!PyCallable_Check(e)) {
+    backward_function_getter = nullptr;
+    PyErr_SetString(PyExc_TypeError,
+                    "TFE_Py_RegisterBackwardFunctionGetter: "
+                    "Registered object should be function.");
+    return nullptr;
+  } else {
+    Py_INCREF(e);
+    backward_function_getter = e;
+    Py_RETURN_NONE;
+  }
+}
+
 void RaiseFallbackException(const char* message) {
   if (fallback_exception_class != nullptr) {
     PyErr_SetObject(fallback_exception_class, Py_BuildValue("s", message));
@@ -1062,16 +1083,10 @@ PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape) {
   return result;
 }
 
-void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
-                                   PyObject* input_tensors,
-                                   PyObject* backward_function) {
-  if (GetTapeSet()->empty() || *ThreadTapeIsStopped()) {
-    return;
-  }
-  std::vector<tensorflow::int64> input_ids = MakeTensorIDList(input_tensors);
-  if (PyErr_Occurred()) {
-    return;
-  }
+namespace {
+void TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
+                            const std::vector<tensorflow::int64>& input_ids,
+                            PyObject* backward_function) {
   std::vector<tensorflow::eager::TapeTensor> output_info;
   PyObject* seq = PySequence_Fast(output_tensors,
                                   "expected a sequence of integer tensor ids");
@@ -1110,6 +1125,19 @@ void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
         [backward_function]() { Py_DECREF(backward_function); });
   }
 }
+}  // namespace
+
+void TFE_Py_TapeSetRecordOperation(PyObject* op_type, PyObject* output_tensors,
+                                   PyObject* input_tensors,
+                                   PyObject* backward_function) {
+  if (GetTapeSet()->empty() || *ThreadTapeIsStopped()) {
+    return;
+  }
+  std::vector<tensorflow::int64> input_ids = MakeTensorIDList(input_tensors);
+  if (PyErr_Occurred()) return;
+
+  TapeSetRecordOperation(op_type, output_tensors, input_ids, backward_function);
+}
 
 void TFE_Py_TapeSetDeleteTrace(tensorflow::int64 tensor_id) {
   for (TFE_Py_Tape* tape : SafeTapeSet()) {
@@ -1430,6 +1458,164 @@ bool RaiseIfNotPyList(PyObject* list, const string& attr_name) {
   return true;
 }
 
+bool OpDoesntRequireOutput(const string& op_name) {
+  static tensorflow::gtl::FlatSet<string>* ops_that_dont_require_outputs =
+      new tensorflow::gtl::FlatSet<string>({
+          "Identity",
+          "MatMul",
+          "Conv2DBackpropInput",
+          "Conv2DBackpropFilter",
+          "Conv3D",
+          "Conv3DBackpropInputV2",
+          "AvgPool3D",
+          "AvgPool3DGrad",
+          "MaxPool3D",
+          "MaxPool3DGrad",
+          "MaxPool3DGradGrad",
+          "BiasAdd",
+          "BiasAddV1",
+          "BiasAddGrad",
+          "Relu6",
+          "Softplus",
+          "SoftplusGrad",
+          "Softsign",
+          "ReluGrad",
+          "Conv2D",
+          "DepthwiseConv2dNative",
+          "Dilation2D",
+          "AvgPool",
+          "AvgPoolGrad",
+          "BatchNormWithGlobalNormalization",
+          "L2Loss",
+          "Sum",
+          "Prod",
+          "SegmentSum",
+          "SegmentMean",
+          "SparseSegmentSum",
+          "SparseSegmentMean",
+          "SparseSegmentSqrtN",
+          "SegmentMin",
+          "SegmentMax",
+          "UnsortedSegmentSum",
+          "UnsortedSegmentMax",
+          "Abs",
+          "Neg",
+          "ReciprocalGrad",
+          "Square",
+          "Expm1",
+          "Log",
+          "Log1p",
+          "TanhGrad",
+          "SigmoidGrad",
+          "Sign",
+          "Sin",
+          "Cos",
+          "Tan",
+          "Add",
+          "Sub",
+          "Mul",
+          "Div",
+          "RealDiv",
+          "Maximum",
+          "Minimum",
+          "SquaredDifference",
+          "Select",
+          "SparseMatMul",
+          "BatchMatMul",
+          "Complex",
+          "Real",
+          "Imag",
+          "Angle",
+          "Conj",
+          "Cast",
+          "Cross",
+          "Cumsum",
+          "Cumprod",
+          "ReadVariableOp",
+          "VarHandleOp",
+          "Shape",
+      });
+
+  return ops_that_dont_require_outputs->find(op_name) !=
+         ops_that_dont_require_outputs->end();
+}
+
+bool OpDoesntRequireInput(const string& op_name) {
+  static tensorflow::gtl::FlatSet<string>* ops_that_dont_require_inputs =
+      new tensorflow::gtl::FlatSet<string>({
+          "Identity",
+          "Softmax",
+          "LogSoftmax",
+          "BiasAdd",
+          "Relu",
+          "Elu",
+          "Selu",
+          "SparseSoftmaxCrossEntropyWithLogits",
+          "Neg",
+          "Inv",
+          "Reciprocal",
+          "Sqrt",
+          "Exp",
+          "Tanh",
+          "Sigmoid",
+          "Real",
+          "Imag",
+          "Conj",
+          "ReadVariableOp",
+          "VarHandleOp",
+          "Shape",
+      });
+
+  return ops_that_dont_require_inputs->find(op_name) !=
+         ops_that_dont_require_inputs->end();
+}
+
+PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
+                         PyObject* results, PyObject* name) {
+  std::vector<tensorflow::int64> input_ids = MakeTensorIDList(inputs);
+  if (PyErr_Occurred()) return nullptr;
+
+  bool should_record = false;
+  for (TFE_Py_Tape* tape : SafeTapeSet()) {
+    if (tape->tape->ShouldRecord(input_ids)) {
+      should_record = true;
+      break;
+    }
+  }
+
+  if (!should_record) Py_RETURN_NONE;
+
+  string c_op_name = TFE_GetPythonString(op_name);
+  PyObject* op_outputs;
+  if (OpDoesntRequireOutput(c_op_name)) {
+    op_outputs = Py_None;
+  } else {
+    op_outputs = results;
+  }
+
+  PyObject* op_inputs;
+  if (OpDoesntRequireInput(c_op_name)) {
+    op_inputs = Py_None;
+  } else {
+    op_inputs = inputs;
+  }
+
+  PyObject* num_inputs = PyLong_FromLong(PySequence_Size(inputs));
+  PyObject* callback_args =
+      Py_BuildValue("OOOOO", op_name, attrs, num_inputs, op_inputs, op_outputs);
+
+  PyObject* backward_function =
+      PyObject_CallObject(backward_function_getter, callback_args);
+  Py_DECREF(callback_args);
+  if (backward_function == nullptr) return nullptr;
+
+  TapeSetRecordOperation(op_name, results, input_ids, backward_function);
+
+  Py_DECREF(backward_function);
+
+  Py_RETURN_NONE;
+}
+
 bool RunCallbacks(bool run_gradient_callback, bool run_post_exec_callbacks,
                   const tensorflow::OpDef* op_def, PyObject* args,
                   const std::vector<PyObject*>& flattened_inputs,
@@ -1471,21 +1657,7 @@ bool RunCallbacks(bool run_gradient_callback, bool run_post_exec_callbacks,
   });
 
   if (run_gradient_callback) {
-    if (!PyCallable_Check(record_gradient_callback)) {
-      PyErr_SetString(PyExc_TypeError,
-                      Printf("expected a function for "
-                             "record_gradient_callback, got %s instead",
-                             record_gradient_callback->ob_type->tp_name)
-                          .c_str());
-      return false;
-    }
-
-    PyObject* callback_result =
-        PyObject_CallObject(record_gradient_callback, callback_args);
-    if (!callback_result) {
-      return false;
-    }
-    Py_DECREF(callback_result);
+    RecordGradient(op_name, inputs, attrs, flattened_result, name);
   }
 
   if (run_post_exec_callbacks) {
@@ -1796,3 +1968,13 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
   Py_DECREF(flat_result);
   return result;
 }
+
+PyObject* TFE_Py_RecordGradient(PyObject* op_name, PyObject* inputs,
+                                PyObject* attrs, PyObject* results,
+                                PyObject* name) {
+  if (*ThreadTapeIsStopped() || GetTapeSet()->empty()) {
+    Py_RETURN_NONE;
+  }
+
+  return RecordGradient(op_name, inputs, attrs, results, name);
+}
diff --git a/tensorflow/python/pywrap_tfe.i b/tensorflow/python/pywrap_tfe.i
index 50f481d29e..7ab0db5268 100644
--- a/tensorflow/python/pywrap_tfe.i
+++ b/tensorflow/python/pywrap_tfe.i
@@ -29,9 +29,11 @@ limitations under the License.
 %rename("%s") TFE_OpNameGetAttrType;
 %rename("%s") TFE_Py_InitEagerTensor;
 %rename("%s") TFE_Py_RegisterExceptionClass;
+%rename("%s") TFE_Py_RegisterBackwardFunctionGetter;
 %rename("%s") TFE_Py_RegisterFallbackExceptionClass;
 %rename("%s") TFE_Py_Execute;
 %rename("%s") TFE_Py_FastPathExecute;
+%rename("%s") TFE_Py_RecordGradient;
 %rename("%s") TFE_Py_UID;
 %rename("%s") TFE_Py_TapeSetNew;
 %rename("%s") TFE_Py_TapeSetRemove;
-- 
GitLab


From 8017c247c84c4c80fa11744b1b913aec3ee88f3e Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 21 Feb 2018 14:46:29 -0800
Subject: [PATCH 158/575] Mark the `SerializeSparseOp<Variant>` kernel as
 inexpensive.

Since this op only performs a constant amount of work, and typically
executes in a few microseconds, it should be profitable to execute
this op inline, rather than scheduling it on a remote thread.

PiperOrigin-RevId: 186522885
---
 tensorflow/core/kernels/serialize_sparse_op.cc | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/tensorflow/core/kernels/serialize_sparse_op.cc b/tensorflow/core/kernels/serialize_sparse_op.cc
index 799c574d15..64e0a68c2c 100644
--- a/tensorflow/core/kernels/serialize_sparse_op.cc
+++ b/tensorflow/core/kernels/serialize_sparse_op.cc
@@ -44,6 +44,8 @@ class SerializeSparseOp : public OpKernel {
   explicit SerializeSparseOp(OpKernelConstruction* context)
       : OpKernel(context) {}
 
+  bool IsExpensive() override;
+
   Status Initialize(Tensor* result);
   Status Serialize(const Tensor& input, T* result);
 
@@ -82,6 +84,21 @@ class SerializeSparseOp : public OpKernel {
   }
 };
 
+// NOTE(mrry): We specialize the IsExpensive() method differently for
+// the string and variant cases, because (i) the string version
+// actually performs memory copies as part of its serialization (and
+// is hence potentially expensive), and (ii) the variant version
+// performs O(1) shallow copies (and hence is much cheaper than
+// dispatching to another thread would be).
+template <>
+bool SerializeSparseOp<string>::IsExpensive() {
+  return true;
+}
+template <>
+bool SerializeSparseOp<Variant>::IsExpensive() {
+  return false;
+}
+
 template <>
 Status SerializeSparseOp<string>::Initialize(Tensor* result) {
   *result = Tensor(DT_STRING, TensorShape({3}));
-- 
GitLab


From 3c3da104f14709dd0495c5ae0783b69e7da21fb9 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Wed, 21 Feb 2018 14:56:13 -0800
Subject: [PATCH 159/575] Don't require shape functions when creating ops from
 Python using the C API.

There are many ops out there without shape functions, and it's very
onerous to add UnknownShape to all of them.

PiperOrigin-RevId: 186524294
---
 tensorflow/c/python_api.cc            | 5 +++++
 tensorflow/c/python_api.h             | 4 ++++
 tensorflow/python/client/tf_session.i | 2 ++
 tensorflow/python/framework/ops.py    | 4 ++++
 4 files changed, 15 insertions(+)

diff --git a/tensorflow/c/python_api.cc b/tensorflow/c/python_api.cc
index 6e37cdb5f4..f553142d15 100644
--- a/tensorflow/c/python_api.cc
+++ b/tensorflow/c/python_api.cc
@@ -99,4 +99,9 @@ void RemoveAllControlInputs(TF_Graph* graph, TF_Operation* op) {
   }
 }
 
+void SetRequireShapeInferenceFns(TF_Graph* graph, bool require) {
+  mutex_lock l(graph->mu);
+  graph->refiner.set_require_shape_inference_fns(require);
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/c/python_api.h b/tensorflow/c/python_api.h
index aa9d9e06b2..542d70f42c 100644
--- a/tensorflow/c/python_api.h
+++ b/tensorflow/c/python_api.h
@@ -37,6 +37,10 @@ void UpdateEdge(TF_Graph* graph, TF_Output new_src, TF_Input dst,
 
 void RemoveAllControlInputs(TF_Graph* graph, TF_Operation* op);
 
+// Sets whether ops missing a shape inference function should trigger an
+// error. The default is true.
+void SetRequireShapeInferenceFns(TF_Graph* graph, bool require);
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_C_PYTHON_API_H_
diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i
index 1fd488e7b6..f305cd271f 100644
--- a/tensorflow/python/client/tf_session.i
+++ b/tensorflow/python/client/tf_session.i
@@ -719,6 +719,8 @@ def TF_Reset(target, containers=None, config=None):
   $1 = &types_local;
 }
 
+%unignore SetRequireShapeInferenceFns;
+
 %include "tensorflow/python/client/tf_session_helper.h"
 
 %unignoreall
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index afd553bede..013a4dfd94 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -2770,6 +2770,10 @@ class Graph(object):
     # implementation
     if self._use_c_api_hack():
       self._scoped_c_graph = c_api_util.ScopedTFGraph()
+      # The C API requires all ops to have shape functions. Disable this
+      # requirement (many custom ops do not have shape functions, and we don't
+      # want to break these existing cases).
+      c_api.SetRequireShapeInferenceFns(self._c_graph, False)
     else:
       self._scoped_c_graph = None
     self._variable_creator_stack = []
-- 
GitLab


From 5e8aaa66af43b6b66e61ca7d589002eac6b4fb69 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Wed, 21 Feb 2018 15:07:05 -0800
Subject: [PATCH 160/575] Don't assign device for the keras part of
 _saved_first_checkpoint. Fix #14504.

PiperOrigin-RevId: 186526175
---
 .../python/keras/_impl/keras/estimator.py     | 24 ++++++++---------
 .../keras/_impl/keras/estimator_test.py       | 27 ++++++++++++++++++-
 2 files changed, 38 insertions(+), 13 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/estimator.py b/tensorflow/python/keras/_impl/keras/estimator.py
index db0140c2df..0bf5bd41dc 100644
--- a/tensorflow/python/keras/_impl/keras/estimator.py
+++ b/tensorflow/python/keras/_impl/keras/estimator.py
@@ -222,18 +222,18 @@ def _save_first_checkpoint(keras_model, estimator, custom_objects,
   Returns:
     The model_fn for a keras Estimator.
   """
-  with ops.Graph().as_default() as g, g.device(estimator._device_fn):
-    random_seed.set_random_seed(estimator.config.tf_random_seed)
-    training_util.create_global_step()
-    model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model,
-                                   custom_objects)
-
-    if isinstance(model, models.Sequential):
-      model = model.model
-    # Load weights and save to checkpoint if there is no checkpoint
-    latest_path = saver_lib.latest_checkpoint(estimator.model_dir)
-    if not latest_path:
-      with session.Session() as sess:
+  # Load weights and save to checkpoint if there is no checkpoint
+  latest_path = saver_lib.latest_checkpoint(estimator.model_dir)
+  if not latest_path:
+    with ops.Graph().as_default():
+      random_seed.set_random_seed(estimator.config.tf_random_seed)
+      training_util.create_global_step()
+      model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model,
+                                     custom_objects)
+      if isinstance(model, models.Sequential):
+        model = model.model
+      # save to checkpoint
+      with session.Session(config=estimator._session_config) as sess:
         model.set_weights(keras_weights)
         # Make update ops and initialize all variables.
         if not model.train_function:
diff --git a/tensorflow/python/keras/_impl/keras/estimator_test.py b/tensorflow/python/keras/_impl/keras/estimator_test.py
index 9fc48b4117..88dd14b856 100644
--- a/tensorflow/python/keras/_impl/keras/estimator_test.py
+++ b/tensorflow/python/keras/_impl/keras/estimator_test.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import json
 from math import log10
 import os
 import tempfile
@@ -62,7 +63,7 @@ def simple_functional_model():
   return model
 
 
-def get_resource_for_simple_model(is_sequential, is_evaluate):
+def get_resource_for_simple_model(is_sequential=True, is_evaluate=False):
   model = simple_sequential_model(
   ) if is_sequential else simple_functional_model()
   if is_sequential:
@@ -352,6 +353,30 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
           model_dir=tempfile.mkdtemp(dir=self._base_dir),
           custom_objects=custom_objects)
 
+  def test_tf_config(self):
+    keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model()
+    keras_model.compile(
+        loss='categorical_crossentropy',
+        optimizer='rmsprop',
+        metrics=['mse', keras.metrics.categorical_accuracy])
+
+    tf_config = json.dumps({
+        'cluster': {
+            run_config_lib.TaskType.PS: ['localhost:1234'],
+            run_config_lib.TaskType.WORKER: ['localhost:1236'],
+            run_config_lib.TaskType.MASTER: ['localhost:1238']
+        },
+        'task': {
+            'type': run_config_lib.TaskType.MASTER,
+            'index': 0
+        }
+    })
+    with test.mock.patch.dict('os.environ', {'TF_CONFIG': tf_config}):
+      with self.test_session():
+        keras.estimator.model_to_estimator(
+            keras_model=keras_model,
+            model_dir=tempfile.mkdtemp(dir=self._base_dir))
+
 
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From 71455977f7b8f72e349382344159e6e738044aaf Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Wed, 21 Feb 2018 15:12:16 -0800
Subject: [PATCH 161/575] Add the ability to specify an explicit `training`
 argument when calling a Model (including Sequential).

PiperOrigin-RevId: 186526925
---
 .../keras/_impl/keras/engine/topology.py      | 28 +++++++++++++------
 .../keras/_impl/keras/engine/topology_test.py | 22 +++++++++++++++
 tensorflow/python/keras/_impl/keras/models.py |  4 +--
 .../api/golden/tensorflow.keras.-model.pbtxt  |  2 +-
 .../golden/tensorflow.keras.-sequential.pbtxt |  2 +-
 .../tensorflow.keras.models.-model.pbtxt      |  2 +-
 .../tensorflow.keras.models.-sequential.pbtxt |  2 +-
 7 files changed, 47 insertions(+), 15 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/engine/topology.py b/tensorflow/python/keras/_impl/keras/engine/topology.py
index dbf9652a5b..f562a19cf5 100644
--- a/tensorflow/python/keras/_impl/keras/engine/topology.py
+++ b/tensorflow/python/keras/_impl/keras/engine/topology.py
@@ -1260,7 +1260,7 @@ class Network(Layer):
       return specs[0]
     return specs
 
-  def call(self, inputs, mask=None):
+  def call(self, inputs, training=None, mask=None):
     """Call the model on new inputs.
 
     In this case `call` just reapplies
@@ -1269,6 +1269,8 @@ class Network(Layer):
 
     Arguments:
         inputs: A tensor or list of tensors.
+        training: Boolean or boolean scalar tensor, indicating whether to run
+          the `Network` in training mode or inference mode.
         mask: A mask or list of masks. A mask can be
             either a tensor or None (no mask).
 
@@ -1291,7 +1293,9 @@ class Network(Layer):
         # Cache hit.
         return self._output_tensor_cache[cache_key]
     # Actually apply the network graph to the new inputs.
-    outputs, _ = self._run_internal_graph(inputs, masks)
+    outputs, _ = self._run_internal_graph(inputs,
+                                          training=training,
+                                          mask=masks)
     return outputs
 
   def compute_output_shape(self, input_shape):
@@ -1393,7 +1397,7 @@ class Network(Layer):
     else:
       return tensor_shape.TensorShape(output_shapes)
 
-  def _run_internal_graph(self, inputs, masks=None):
+  def _run_internal_graph(self, inputs, training=None, mask=None):
     """Computes output tensors for new inputs.
 
     # Note:
@@ -1402,7 +1406,8 @@ class Network(Layer):
 
     Arguments:
         inputs: List of tensors
-        masks: List of masks (tensors or None).
+        training: Boolean learning phase.
+        mask: List of masks (tensors or None).
 
     Returns:
         Three lists: output_tensors, output_masks, output_shapes
@@ -1414,8 +1419,10 @@ class Network(Layer):
     # the future and 2) Keras is a major user of Network.  If you don't
     # use masking, it does not interfere with regular behavior at all and you
     # can ignore it.
-    if masks is None:
+    if mask is None:
       masks = [None for _ in range(len(inputs))]
+    else:
+      masks = mask
 
     # Dictionary mapping reference tensors to tuples
     # (computed tensor, compute mask)
@@ -1454,8 +1461,9 @@ class Network(Layer):
               computed_tensor, computed_mask = computed_data[0]
               # Ensure mask propagation if applicable.
               if 'mask' in tf_inspect.getargspec(layer.call).args:
-                if 'mask' not in kwargs:
-                  kwargs['mask'] = computed_mask
+                kwargs.setdefault('mask', computed_mask)
+              if 'training' in tf_inspect.getargspec(layer.call).args:
+                kwargs.setdefault('training', training)
 
               output_tensors = nest.flatten(
                   layer.call(computed_tensor, **kwargs))
@@ -1470,8 +1478,10 @@ class Network(Layer):
               computed_tensors = [x[0] for x in computed_data]
               computed_masks = [x[1] for x in computed_data]
               if 'mask' in tf_inspect.getargspec(layer.call).args:
-                if 'mask' not in kwargs:
-                  kwargs['mask'] = computed_masks
+                kwargs.setdefault('mask', computed_masks)
+              if 'training' in tf_inspect.getargspec(layer.call).args:
+                kwargs.setdefault('training', training)
+
               output_tensors = nest.flatten(
                   layer.call(computed_tensors, **kwargs))
               if hasattr(layer, 'compute_mask'):
diff --git a/tensorflow/python/keras/_impl/keras/engine/topology_test.py b/tensorflow/python/keras/_impl/keras/engine/topology_test.py
index ba4d427a19..139621db6d 100644
--- a/tensorflow/python/keras/_impl/keras/engine/topology_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/topology_test.py
@@ -852,6 +852,28 @@ class TopologyConstructionTest(test.TestCase):
       output_val_2 = m2.predict(x_val)
       self.assertAllClose(output_val, output_val_2, atol=1e-6)
 
+  def test_explicit_training_argument(self):
+    with self.test_session():
+      a = keras.layers.Input(shape=(2,))
+      b = keras.layers.Dropout(0.5)(a)
+      base_model = keras.models.Model(a, b)
+
+      a = keras.layers.Input(shape=(2,))
+      b = base_model(a, training=False)
+      model = keras.models.Model(a, b)
+
+      x = np.ones((100, 2))
+      y = np.ones((100, 2))
+      model.compile(optimizer='sgd', loss='mse')
+      loss = model.train_on_batch(x, y)
+      self.assertEqual(loss, 0)  # In inference mode, output is equal to input.
+
+      a = keras.layers.Input(shape=(2,))
+      b = base_model(a, training=True)
+      model = keras.models.Model(a, b)
+      preds = model.predict(x)
+      self.assertEqual(np.min(preds), 0.)  # At least one unit was dropped.
+
 
 class TestSaving(test.TestCase):
 
diff --git a/tensorflow/python/keras/_impl/keras/models.py b/tensorflow/python/keras/_impl/keras/models.py
index 05912b2ec3..8000eaabab 100644
--- a/tensorflow/python/keras/_impl/keras/models.py
+++ b/tensorflow/python/keras/_impl/keras/models.py
@@ -572,10 +572,10 @@ class Sequential(Model):
       self.build()
     return self.model.get_layer(name, index)
 
-  def call(self, inputs, mask=None):
+  def call(self, inputs, **kwargs):
     if not self.built:
       self.build()
-    return self.model.call(inputs, mask)
+    return self.model.call(inputs, **kwargs)
 
   def build(self, input_shape=None):
     if not self.inputs or not self.outputs:
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
index 5fb6fa3f19..04724e3a1a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
@@ -139,7 +139,7 @@ tf_class {
   }
   member_method {
     name: "call"
-    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
   member_method {
     name: "compile"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
index 16f1afbd26..c94bd2faa4 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
@@ -152,7 +152,7 @@ tf_class {
   }
   member_method {
     name: "call"
-    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
   }
   member_method {
     name: "compile"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
index 4260da31d9..88eb237cec 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
@@ -139,7 +139,7 @@ tf_class {
   }
   member_method {
     name: "call"
-    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
   }
   member_method {
     name: "compile"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
index 02ddb37423..34f10f01ad 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
@@ -152,7 +152,7 @@ tf_class {
   }
   member_method {
     name: "call"
-    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None"
   }
   member_method {
     name: "compile"
-- 
GitLab


From 1051fcb74fd6763382ccc83dda34ae5376ce34c9 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Wed, 21 Feb 2018 15:13:03 -0800
Subject: [PATCH 162/575] Disable flaky test
 tensorflow/contrib/opt:moving_average_optimizer_test

PiperOrigin-RevId: 186527039
---
 tensorflow/contrib/opt/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 827279bd47..86ceda71b7 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -70,6 +70,7 @@ py_test(
     srcs = ["python/training/moving_average_optimizer_test.py"],
     srcs_version = "PY2AND3",
     tags = [
+        "no_oss",  # b/73507407
         "notsan",  # b/31055119
     ],
     deps = [
-- 
GitLab


From 4f1e771c10d75cc9014662e49b7906e0a16e2fe5 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Wed, 21 Feb 2018 15:19:47 -0800
Subject: [PATCH 163/575] Internal-only change.

PiperOrigin-RevId: 186528023
---
 tensorflow/contrib/cluster_resolver/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD
index 80e18a43a7..6b03df2b8e 100644
--- a/tensorflow/contrib/cluster_resolver/BUILD
+++ b/tensorflow/contrib/cluster_resolver/BUILD
@@ -30,6 +30,7 @@ py_library(
         "python/training/__init__.py",
     ],
     srcs_version = "PY2AND3",
+    visibility = ["//visibility:public"],
     deps = [
         ":cluster_resolver_py",
         ":gce_cluster_resolver_py",
-- 
GitLab


From a3d29347a3e61a6aa8ccac70e075cf532e7d36fc Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 21 Feb 2018 15:36:55 -0800
Subject: [PATCH 164/575] [tf.data] Move the `tf.contrib.data.unique()` C++
 implementation to contrib.

PiperOrigin-RevId: 186530750
---
 .../contrib/cmake/tf_core_kernels.cmake       |  1 +
 tensorflow/contrib/data/kernels/BUILD         | 13 +++++++++--
 .../data/kernels}/unique_dataset_op.cc        |  2 +-
 tensorflow/contrib/data/ops/dataset_ops.cc    | 10 ++++++++
 tensorflow/contrib/data/python/ops/unique.py  |  3 ++-
 .../base_api/api_def_UniqueDataset.pbtxt      |  4 ----
 tensorflow/core/kernels/data/BUILD            | 13 -----------
 .../core/ops/compat/ops_history.v1.pbtxt      | 23 -------------------
 tensorflow/core/ops/dataset_ops.cc            |  7 ------
 9 files changed, 25 insertions(+), 51 deletions(-)
 rename tensorflow/{core/kernels/data => contrib/data/kernels}/unique_dataset_op.cc (99%)
 delete mode 100644 tensorflow/core/api_def/base_api/api_def_UniqueDataset.pbtxt

diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index f219d5eb57..7cae0afe43 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -71,6 +71,7 @@ if(tensorflow_BUILD_CONTRIB_KERNELS)
       "${tensorflow_source_dir}/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/prefetching_kernels.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/unique_dataset_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/ops/dataset_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/clustering_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/masked_matmul_ops.cc"
diff --git a/tensorflow/contrib/data/kernels/BUILD b/tensorflow/contrib/data/kernels/BUILD
index 56471911c5..8b0556330e 100644
--- a/tensorflow/contrib/data/kernels/BUILD
+++ b/tensorflow/contrib/data/kernels/BUILD
@@ -28,13 +28,22 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_library(
+    name = "unique_dataset_op",
+    srcs = ["unique_dataset_op.cc"],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+        "@protobuf_archive//:protobuf_headers",
+    ],
+)
+
 cc_library(
     name = "dataset_kernels",
     deps = [
         ":ignore_errors_dataset_op",
         ":prefetching_kernels",
-        "//tensorflow/core:framework_headers_lib",
-        "//third_party/eigen3",
+        ":unique_dataset_op",
         "@protobuf_archive//:protobuf_headers",
     ],
 )
diff --git a/tensorflow/core/kernels/data/unique_dataset_op.cc b/tensorflow/contrib/data/kernels/unique_dataset_op.cc
similarity index 99%
rename from tensorflow/core/kernels/data/unique_dataset_op.cc
rename to tensorflow/contrib/data/kernels/unique_dataset_op.cc
index 7726ee0edf..69fbb0fcdc 100644
--- a/tensorflow/core/kernels/data/unique_dataset_op.cc
+++ b/tensorflow/contrib/data/kernels/unique_dataset_op.cc
@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
+#include "tensorflow/core/framework/dataset.h"
 #include "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
-#include "tensorflow/core/kernels/data/dataset.h"
 #include "tensorflow/core/lib/hash/hash.h"
 
 namespace tensorflow {
diff --git a/tensorflow/contrib/data/ops/dataset_ops.cc b/tensorflow/contrib/data/ops/dataset_ops.cc
index 289ffa1d9c..d97a2a6589 100644
--- a/tensorflow/contrib/data/ops/dataset_ops.cc
+++ b/tensorflow/contrib/data/ops/dataset_ops.cc
@@ -27,6 +27,16 @@ REGISTER_OP("IgnoreErrorsDataset")
 Creates a dataset that contains the elements of `input_dataset` ignoring errors.
 )doc");
 
+REGISTER_OP("UniqueDataset")
+    .Input("input_dataset: variant")
+    .Output("handle: variant")
+    .Attr("output_types: list(type) >= 1")
+    .Attr("output_shapes: list(shape) >= 1")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Creates a dataset that contains the unique elements of `input_dataset`.
+)doc");
+
 REGISTER_OP("FunctionBufferingResource")
     .Input("string_arg: string")
     .Input("target_device: string")
diff --git a/tensorflow/contrib/data/python/ops/unique.py b/tensorflow/contrib/data/python/ops/unique.py
index 133e17d20d..765ef3f9b6 100644
--- a/tensorflow/contrib/data/python/ops/unique.py
+++ b/tensorflow/contrib/data/python/ops/unique.py
@@ -17,11 +17,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.data.python.ops import contrib_op_loader  # pylint: disable=unused-import
+from tensorflow.contrib.data.python.ops import gen_dataset_ops
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.util import nest
 from tensorflow.python.data.util import sparse
 from tensorflow.python.framework import dtypes
-from tensorflow.python.ops import gen_dataset_ops
 
 
 def unique():
diff --git a/tensorflow/core/api_def/base_api/api_def_UniqueDataset.pbtxt b/tensorflow/core/api_def/base_api/api_def_UniqueDataset.pbtxt
deleted file mode 100644
index 0092569169..0000000000
--- a/tensorflow/core/api_def/base_api/api_def_UniqueDataset.pbtxt
+++ /dev/null
@@ -1,4 +0,0 @@
-op {
-  graph_op_name: "UniqueDataset"
-  summary: "Creates a dataset that contains the unique elements of `input_dataset`."
-}
diff --git a/tensorflow/core/kernels/data/BUILD b/tensorflow/core/kernels/data/BUILD
index 9880cc76d3..253399c1e4 100644
--- a/tensorflow/core/kernels/data/BUILD
+++ b/tensorflow/core/kernels/data/BUILD
@@ -511,18 +511,6 @@ tf_kernel_library(
     ],
 )
 
-tf_kernel_library(
-    name = "unique_dataset_op",
-    srcs = ["unique_dataset_op.cc"],
-    deps = [
-        ":dataset",
-        "//tensorflow/core:dataset_ops_op_lib",
-        "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:lib_internal",
-    ],
-)
-
 tf_kernel_library(
     name = "dataset_ops",
     deps = [
@@ -557,7 +545,6 @@ tf_kernel_library(
         ":tensor_dataset_op",
         ":tensor_queue_dataset_op",
         ":tensor_slice_dataset_op",
-        ":unique_dataset_op",
         ":zip_dataset_op",
     ],
 )
diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 3e9460952c..7da2365f62 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -65285,29 +65285,6 @@ op {
     }
   }
 }
-op {
-  name: "UniqueDataset"
-  input_arg {
-    name: "input_dataset"
-    type: DT_VARIANT
-  }
-  output_arg {
-    name: "handle"
-    type: DT_VARIANT
-  }
-  attr {
-    name: "output_types"
-    type: "list(type)"
-    has_minimum: true
-    minimum: 1
-  }
-  attr {
-    name: "output_shapes"
-    type: "list(shape)"
-    has_minimum: true
-    minimum: 1
-  }
-}
 op {
   name: "UniqueV2"
   input_arg {
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc
index 117ae6ba79..bdbbf6d7c3 100644
--- a/tensorflow/core/ops/dataset_ops.cc
+++ b/tensorflow/core/ops/dataset_ops.cc
@@ -346,13 +346,6 @@ REGISTER_OP("CacheDataset")
     .Attr("output_shapes: list(shape) >= 1")
     .SetShapeFn(shape_inference::ScalarShape);
 
-REGISTER_OP("UniqueDataset")
-    .Input("input_dataset: variant")
-    .Output("handle: variant")
-    .Attr("output_types: list(type) >= 1")
-    .Attr("output_shapes: list(shape) >= 1")
-    .SetShapeFn(shape_inference::ScalarShape);
-
 REGISTER_OP("TextLineDataset")
     .Input("filenames: string")
     .Input("compression_type: string")
-- 
GitLab


From 6907c18e8f4e9318f0546f2558992711fcfc02da Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Wed, 21 Feb 2018 15:37:05 -0800
Subject: [PATCH 165/575] Automated g4 rollback of changelist 186494344

PiperOrigin-RevId: 186530782
---
 .../xla/tests/array_elementwise_ops_test.cc   | 107 ------------------
 tensorflow/compiler/xla/tests/convert_test.cc |  70 +-----------
 .../xla/tests/scalar_computations_test.cc     |   2 +-
 3 files changed, 5 insertions(+), 174 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 8b35259013..739d201fad 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -101,33 +101,6 @@ XLA_TEST_F(ArrayElementwiseOpTest, NegConstantC64) {
       {}, error_spec_);
 }
 
-XLA_TEST_F(ArrayElementwiseOpTest, NegConstantS64) {
-  ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR1<int64>({
-      -1,
-      1,
-      0,
-      0x12345678,
-      static_cast<int64>(0xffffffff12345678l),
-      static_cast<int64>(0x8000000000000000LL),
-      static_cast<int64>(0x8000000000000001LL),
-  });
-  auto result = builder.Neg(a);
-  LOG(INFO) << -static_cast<int64>(0x7FFFFFFFFFFFFFFFLL);
-
-  ComputeAndCompareR1<int64>(&builder,
-                             {
-                                 1,
-                                 -1,
-                                 0,
-                                 -0x12345678,
-                                 0xedcba988,
-                                 static_cast<int64>(0x8000000000000000LL),
-                                 -static_cast<int64>(0x8000000000000001LL),
-                             },
-                             {});
-}
-
 XLA_TEST_F(ArrayElementwiseOpTest, IsFiniteZeroElementF32s) {
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR1<float>({});
@@ -213,86 +186,6 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantZeroElementC64s) {
   ComputeAndCompareR1<complex64>(&builder, {}, {}, error_spec_);
 }
 
-XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantU64s) {
-  ComputationBuilder b(client_, TestName());
-
-  std::vector<uint64> lhs{0xFFFFFFFF,
-                          static_cast<uint64>(-1),
-                          0,
-                          0,
-                          0x7FFFFFFFFFFFFFFFLL,
-                          0x7FFFFFFFFFFFFFFLL,
-                          0x8000000000000000LL,
-                          0x8000000000000000LL,
-                          1};
-  std::unique_ptr<Literal> lhs_literal = Literal::CreateR1<uint64>({lhs});
-  auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param");
-  std::unique_ptr<GlobalData> lhs_data =
-      client_->TransferToServer(*lhs_literal).ConsumeValueOrDie();
-
-  std::vector<uint64> rhs{1,
-                          0x7FFFFFFFFFFFFFFLL,
-                          0x7FFFFFFFFFFFFFFFLL,
-                          0x8000000000000000LL,
-                          0,
-                          static_cast<uint64>(-1),
-                          0,
-                          1,
-                          0x8000000000000000LL};
-  std::unique_ptr<Literal> rhs_literal = Literal::CreateR1<uint64>({rhs});
-  auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param");
-  std::unique_ptr<GlobalData> rhs_data =
-      client_->TransferToServer(*rhs_literal).ConsumeValueOrDie();
-
-  auto add = b.Add(lhs_param, rhs_param);
-
-  std::vector<uint64> expected(lhs.size());
-  for (int64 i = 0; i < lhs.size(); ++i) {
-    expected[i] = lhs[i] + rhs[i];
-  }
-
-  ComputeAndCompareR1<uint64>(&b, expected, {lhs_data.get(), rhs_data.get()});
-}
-
-XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS64s) {
-  ComputationBuilder b(client_, TestName());
-
-  std::vector<int64> lhs{static_cast<int64>(0x8000000000000000LL),
-                         static_cast<int64>(0x8000000000000000LL),
-                         -1,
-                         0x7FFFFFFFFFFFFFFLL,
-                         0x7FFFFFFFFFFFFFFFLL,
-                         1,
-                         0,
-                         -1};
-  std::unique_ptr<Literal> lhs_literal = Literal::CreateR1<int64>({lhs});
-  auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param");
-  std::unique_ptr<GlobalData> lhs_data =
-      client_->TransferToServer(*lhs_literal).ConsumeValueOrDie();
-
-  std::vector<int64> rhs{-1,
-                         0,
-                         static_cast<int64>(0x8000000000000000LL),
-                         1,
-                         0,
-                         0x7FFFFFFFFFFFFFFLL,
-                         0x7FFFFFFFFFFFFFFFLL,
-                         0x7FFFFFFFFFFFFFFFLL};
-  std::unique_ptr<Literal> rhs_literal = Literal::CreateR1<int64>({rhs});
-  auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param");
-  std::unique_ptr<GlobalData> rhs_data =
-      client_->TransferToServer(*rhs_literal).ConsumeValueOrDie();
-
-  auto sub = b.Sub(lhs_param, rhs_param);
-
-  std::vector<int64> expected(lhs.size());
-  for (int64 i = 0; i < lhs.size(); ++i) {
-    expected[i] = lhs[i] - rhs[i];
-  }
-
-  ComputeAndCompareR1<int64>(&b, expected, {lhs_data.get(), rhs_data.get()});
-}
-
 TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) {
   const int count = GetParam();
   ComputationBuilder builder(client_, TestName());
diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc
index f4f9f28565..1c6e7859a2 100644
--- a/tensorflow/compiler/xla/tests/convert_test.cc
+++ b/tensorflow/compiler/xla/tests/convert_test.cc
@@ -107,73 +107,11 @@ TEST_F(ConvertTest, ConvertR1F32ToR1S32) {
 
 XLA_TEST_F(ConvertTest, ConvertR1S64ToR1F32) {
   ComputationBuilder builder(client_, TestName());
-  std::vector<int64> arg{
-      -9223371216516022272,
-      -2,
-      -1,
-      -0x7FFFFFFF,
-      -0x80000000,
-      0,
-      1,
-      2,
-      1073742145,
-      1073742656,
-      0x7FFFFFFF,
-      0x80000000,
-      826720496944058148,
-      4296062029846194332,
-      0x0007FB72E4000000LL,
-      0x0007FB72E4000001LL,
-      0x0007FB72E6000000LL,
-      0x0007FB72E7000000LL,
-      0x0007FB72E7FFFFFFLL,
-      0x0007FB72E8000000LL,
-      0x0007FB72E8000001LL,
-      0x0007FB72EA000000LL,
-      0x0007FB72EB000000LL,
-      0x0007FB72EBFFFFFFLL,
-      0x0007FB72EC000000LL,
-      0x7FFFFF0000000000LL,
-      0x7FFFFF8000000000LL,
-      0x7FFFFFFFFFFFFF00,
-      static_cast<int64>(0xFFFFFFFFFFFFFFFF),
-      static_cast<int64>(0x0000f234e67e0001LL),
-      static_cast<int64>(0x8000000000000000),
-      static_cast<int64>(0x8000000000000000LL),
-      static_cast<int64>(0x8000000000000001LL),
-      static_cast<int64>(0x8000008000000000LL),
-      static_cast<int64>(0x8000010000000000LL),
-  };
-  std::unique_ptr<Literal> arg_literal = Literal::CreateR1<int64>({arg});
-  auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param");
-  std::unique_ptr<GlobalData> arg_data =
-      client_->TransferToServer(*arg_literal).ConsumeValueOrDie();
-
-  builder.ConvertElementType(arg_param, F32);
-
-  std::vector<float> expected(arg.size());
-  for (int64 i = 0; i < arg.size(); ++i) {
-    expected[i] = static_cast<float>(arg[i]);
-  }
-  ComputeAndCompareR1<float>(&builder, expected, {arg_data.get()});
-}
+  auto a = builder.ConstantR1<int64>({32, 64});
+  builder.ConvertElementType(a, F32);
 
-XLA_TEST_F(ConvertTest, ConvertR1U32ToR1F32) {
-  ComputationBuilder builder(client_, TestName());
-  std::vector<uint32> arg{0,          1,          0x1000,     0x7fffffff,
-                          0x80000000, 0x80000001, 0x80000002, 0xFFFFFFFF};
-  std::unique_ptr<Literal> arg_literal = Literal::CreateR1<uint32>({arg});
-  auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param");
-  std::unique_ptr<GlobalData> arg_data =
-      client_->TransferToServer(*arg_literal).ConsumeValueOrDie();
-
-  builder.ConvertElementType(arg_param, F32);
-
-  std::vector<float> expected(arg.size());
-  for (int64 i = 0; i < arg.size(); ++i) {
-    expected[i] = static_cast<float>(arg[i]);
-  }
-  ComputeAndCompareR1<float>(&builder, expected, {arg_data.get()});
+  std::vector<float> expected = {32.0, 64.0};
+  ComputeAndCompareR1<float>(&builder, expected, {});
 }
 
 XLA_TEST_F(ConvertTest, ConvertR1U8ToR1F32) {
diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
index d7bda77e87..4da6ee9160 100644
--- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc
+++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
@@ -163,7 +163,7 @@ XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) {
   auto a = builder.Parameter(0, ShapeUtil::MakeShape(S64, {}), "a");
   builder.ConvertElementType(a, F32);
 
-  int64 value = 3LL << 35;
+  int64 value = 3LL << 32;
   std::unique_ptr<Literal> a_literal = Literal::CreateR0<int64>(value);
   std::unique_ptr<GlobalData> a_data =
       client_->TransferToServer(*a_literal).ConsumeValueOrDie();
-- 
GitLab


From 2e22c7c9b02d9153aac12c0483d6baeb0cada318 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 15:54:54 -0800
Subject: [PATCH 166/575] Move sorting of variables from the implicit_grad
 family of functions up to GradientTape.watched_variables() so we also get
 deterministic behavior when using the GradientTape as is done in
 Optimizer.compute_gradients().

PiperOrigin-RevId: 186533323
---
 tensorflow/python/eager/backprop.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index 5505661dbb..eebdc5813d 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -240,6 +240,7 @@ def implicit_val_and_grad(f):
       tape.pop_tape(this_tape)
     # Sorting variables by id, which is monotonically increasing in construction
     # order. This ensures unique order across executions.
+    # TODO(josh11b): Move the sort to the C++ implementation in pywrap_tfe_src.cc.
     variables = list(sorted(this_tape.watched_variables(),
                             key=lambda v: v.handle._id))  # pylint: disable=protected-access
     sources = [x.handle for x in variables]
@@ -746,7 +747,11 @@ class GradientTape(object):
       tape.watch(t)
 
   def watched_variables(self):
-    return self._tape.watched_variables()
+    # Sorting variables by id, which is monotonically increasing in construction
+    # order. This ensures unique order across executions.
+    # TODO(josh11b): Move the sort to the C++ implementation in pywrap_tfe_src.cc.
+    return list(sorted(self._tape.watched_variables(),
+                       key=lambda v: v.handle._id))  # pylint: disable=protected-access
 
   def gradient(self, target, sources, output_gradients=None):
     """Computes the gradient using information traced by the tape.
-- 
GitLab


From 83486cb183099c3dc2dcfd036ded4e6526761918 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 16:03:32 -0800
Subject: [PATCH 167/575] Internal change.

PiperOrigin-RevId: 186534524
---
 tensorflow/contrib/lite/interpreter.h | 12 ++++++++++++
 tensorflow/contrib/lite/model.cc      |  2 ++
 2 files changed, 14 insertions(+)

diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h
index bab56a9d72..a9df2627e0 100644
--- a/tensorflow/contrib/lite/interpreter.h
+++ b/tensorflow/contrib/lite/interpreter.h
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/contrib/lite/context.h"
 #include "tensorflow/contrib/lite/error_reporter.h"
 #include "tensorflow/contrib/lite/memory_planner.h"
+#include "tensorflow/contrib/lite/schema/schema_generated.h"
 
 namespace tflite {
 
@@ -258,6 +259,12 @@ class Interpreter {
   // contain new nodes that replace 1 more nodes.
   TfLiteStatus ModifyGraphWithDelegate(TfLiteDelegate* delegate);
 
+  // WARNING: This is a deprecated interface and will be removed as soon as
+  // possible.  Please do not use it.
+  // TODO(impjdi): Remove this interface after resolving dependencies.
+  void set_model(const Model* model) { model_ = const_cast<Model*>(model); }
+  Model* model() const { return model_; }
+
  private:
   // Give 'op_reg' a chance to initialize itself using the contents of
   // 'buffer'.
@@ -425,6 +432,11 @@ class Interpreter {
   std::unique_ptr<NNAPIDelegate> nnapi_delegate_;
 
   std::unique_ptr<MemoryPlanner> memory_planner_;
+
+  // WARNING: This is a deprecated interface and will be removed as soon as
+  // possible.  Please do not use it.
+  // TODO(impjdi): Remove this interface after resolving dependencies.
+  Model* model_ = nullptr;
 };
 
 }  // namespace tflite
diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc
index 7dae9f4d18..725f2838c5 100644
--- a/tensorflow/contrib/lite/model.cc
+++ b/tensorflow/contrib/lite/model.cc
@@ -792,6 +792,8 @@ TfLiteStatus InterpreterBuilder::operator()(
     return cleanup_and_error();
   }
 
+  (**interpreter).set_model(model_);
+
   // Parse inputs/outputs
   (**interpreter).SetInputs(FlatBufferIntArrayToVector(subgraph->inputs()));
   (**interpreter).SetOutputs(FlatBufferIntArrayToVector(subgraph->outputs()));
-- 
GitLab


From 5a474209be6a1db6dc81080b9a5f965b28dfb88e Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 21 Feb 2018 16:20:27 -0800
Subject: [PATCH 168/575] Fix lint errors and improve docs in
 fully_connected_reader.py.

PiperOrigin-RevId: 186537109
---
 .../reading_data/fully_connected_reader.py    | 24 ++++++++++++-------
 1 file changed, 15 insertions(+), 9 deletions(-)

diff --git a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
index 461fb1c517..307eede5c0 100644
--- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
+++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
@@ -1,4 +1,4 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -45,6 +45,7 @@ VALIDATION_FILE = 'validation.tfrecords'
 
 
 def decode(serialized_example):
+  """Parses an image and label from the given `serialized_example`."""
   features = tf.parse_single_example(
       serialized_example,
       # Defaults are not specified since both keys are required.
@@ -66,6 +67,7 @@ def decode(serialized_example):
 
 
 def augment(image, label):
+  """Placeholder for data augmentation."""
   # OPTIONAL: Could reshape into a 28x28 image and apply distortions
   # here.  Since we are not applying any distortions in this
   # example, and the next step expects the image to be flattened
@@ -74,9 +76,8 @@ def augment(image, label):
 
 
 def normalize(image, label):
-  # Convert from [0, 255] -> [-0.5, 0.5] floats.
+  """Convert `image` from [0, 255] -> [-0.5, 0.5] floats."""
   image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
-
   return image, label
 
 
@@ -106,18 +107,23 @@ def inputs(train, batch_size, num_epochs):
                           if train else VALIDATION_FILE)
 
   with tf.name_scope('input'):
-    # TFRecordDataset opens a protobuf and reads entries line by line
-    # could also be [list, of, filenames]
+    # TFRecordDataset opens a binary file and reads one record at a time.
+    # `filename` could also be a list of filenames, which will be read in order.
     dataset = tf.data.TFRecordDataset(filename)
-    dataset = dataset.repeat(num_epochs)
 
-    # map takes a python function and applies it to every sample
+    # The map transformation takes a function and applies it to every element
+    # of the dataset.
     dataset = dataset.map(decode)
     dataset = dataset.map(augment)
     dataset = dataset.map(normalize)
 
-    #the parameter is the queue size
+    # The shuffle transformation uses a finite-sized buffer to shuffle elements
+    # in memory. The parameter is the number of elements in the buffer. For
+    # completely uniform shuffling, set the parameter to be the same as the
+    # number of elements in the dataset.
     dataset = dataset.shuffle(1000 + 3 * batch_size)
+
+    dataset = dataset.repeat(num_epochs)
     dataset = dataset.batch(batch_size)
 
     iterator = dataset.make_one_shot_iterator()
@@ -153,7 +159,7 @@ def run_training():
       sess.run(init_op)
       try:
         step = 0
-        while True:  #train until OutOfRangeError
+        while True:  # Train until OutOfRangeError
           start_time = time.time()
 
           # Run one step of the model.  The return values are
-- 
GitLab


From 37bc5a7eda00a2f87c538381a4e28b3a93095ab1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 16:24:20 -0800
Subject: [PATCH 169/575] Update ops-related pbtxt files.

PiperOrigin-RevId: 186537602
---
 tensorflow/core/ops/ops.pbtxt | 23 -----------------------
 1 file changed, 23 deletions(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index bbd43e191d..14d8598aa1 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -30845,29 +30845,6 @@ op {
     }
   }
 }
-op {
-  name: "UniqueDataset"
-  input_arg {
-    name: "input_dataset"
-    type: DT_VARIANT
-  }
-  output_arg {
-    name: "handle"
-    type: DT_VARIANT
-  }
-  attr {
-    name: "output_types"
-    type: "list(type)"
-    has_minimum: true
-    minimum: 1
-  }
-  attr {
-    name: "output_shapes"
-    type: "list(shape)"
-    has_minimum: true
-    minimum: 1
-  }
-}
 op {
   name: "UniqueV2"
   input_arg {
-- 
GitLab


From 483174ca1af74669ba0abc1bbace93952ccc25c5 Mon Sep 17 00:00:00 2001
From: Blake Hechtman <blakehechtman@google.com>
Date: Wed, 21 Feb 2018 16:39:43 -0800
Subject: [PATCH 170/575] [XLA] Convert large constants of the same value into
 broadcasts.

PiperOrigin-RevId: 186539902
---
 tensorflow/compiler/xla/literal_util.cc       | 129 ++++++++++++++++++
 tensorflow/compiler/xla/literal_util.h        |   6 +
 tensorflow/compiler/xla/literal_util_test.cc  |  18 +++
 .../xla/service/algebraic_simplifier.cc       |  12 ++
 .../xla/service/algebraic_simplifier_test.cc  |  31 +++++
 5 files changed, 196 insertions(+)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 09db011719..ed9d2a187a 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -1008,6 +1008,49 @@ void Literal::SortSparseElements(const ShapeIndex& shape_index) {
   piece(shape_index).SortSparseElements();
 }
 
+Literal Literal::GetFirstScalarLiteral() const {
+  CHECK(ShapeUtil::IsArray(shape_));
+  CHECK_GT(ShapeUtil::ElementsIn(shape_), 0);
+  switch (shape_.element_type()) {
+    case PRED:
+      return std::move(*Literal::CreateR0<bool>(GetFirstElement<bool>()));
+    // 8 bit types.
+    case S8:
+      return std::move(*Literal::CreateR0<int8>(GetFirstElement<int8>()));
+    case U8:
+      return std::move(*Literal::CreateR0<uint8>(GetFirstElement<uint8>()));
+    // 16 bit types.
+    case BF16:
+      return std::move(
+          *Literal::CreateR0<bfloat16>(GetFirstElement<bfloat16>()));
+    case F16:
+      return std::move(*Literal::CreateR0<half>(GetFirstElement<half>()));
+    case S16:
+      return std::move(*Literal::CreateR0<int16>(GetFirstElement<int16>()));
+    case U16:
+      return std::move(*Literal::CreateR0<uint16>(GetFirstElement<uint16>()));
+    // 32 bit types.
+    case F32:
+      return std::move(*Literal::CreateR0<float>(GetFirstElement<float>()));
+    case S32:
+      return std::move(*Literal::CreateR0<int32>(GetFirstElement<int32>()));
+    case U32:
+      return std::move(*Literal::CreateR0<uint32>(GetFirstElement<uint32>()));
+    // 64 bit types.
+    case C64:
+      return std::move(
+          *Literal::CreateR0<complex64>(GetFirstElement<complex64>()));
+    case F64:
+      return std::move(*Literal::CreateR0<double>(GetFirstElement<double>()));
+    case S64:
+      return std::move(*Literal::CreateR0<int64>(GetFirstElement<int64>()));
+    case U64:
+      return std::move(*Literal::CreateR0<uint64>(GetFirstElement<uint64>()));
+    default:
+      LOG(FATAL) << "Unhandled primitive type " << shape_.element_type();
+  }
+}
+
 void Literal::Piece::SortSparseElements() {
   switch (subshape().element_type()) {
     case PRED:
@@ -1570,6 +1613,92 @@ bool Literal::IsAllComplex(complex64 value) const {
   }
 }
 
+bool Literal::IsAllFirst() const {
+  for (const auto& pair : pieces_) {
+    const Piece& piece = pair.second;
+    if (!ShapeUtil::IsArray(piece.subshape())) {
+      continue;
+    }
+
+    // Empty shapes are not all the first element since there is no first
+    // element.
+    if (ShapeUtil::HasZeroElements(piece.subshape())) {
+      return false;
+    }
+    auto piece_is_all = [&]() {
+      switch (piece.subshape().element_type()) {
+        case PRED: {
+          auto data = piece.data<bool>();
+          return AllElementsEqualValue<bool>(data, data[0]);
+        }
+        // 8 bit types
+        case S8: {
+          auto data = piece.data<int8>();
+          return AllElementsEqualValue<int8>(data, data[0]);
+        }
+        case U8: {
+          auto data = piece.data<uint8>();
+          return AllElementsEqualValue<uint8>(data, data[0]);
+        }
+        // 16 bit types
+        case BF16: {
+          auto data = piece.data<bfloat16>();
+          return AllElementsEqualValue<bfloat16>(data, data[0]);
+        }
+        case F16: {
+          auto data = piece.data<half>();
+          return AllElementsEqualValue<half>(data, data[0]);
+        }
+        case S16: {
+          auto data = piece.data<int16>();
+          return AllElementsEqualValue<int16>(data, data[0]);
+        }
+        case U16: {
+          auto data = piece.data<uint16>();
+          return AllElementsEqualValue<uint16>(data, data[0]);
+        }
+        // 32 bit types
+        case F32: {
+          auto data = piece.data<float>();
+          return AllElementsEqualValue<float>(data, data[0]);
+        }
+        case U32: {
+          auto data = piece.data<uint32>();
+          return AllElementsEqualValue<uint32>(data, data[0]);
+        }
+        case S32: {
+          auto data = piece.data<int32>();
+          return AllElementsEqualValue<int32>(data, data[0]);
+        }
+        // 64 bit types
+        case C64: {
+          auto data = piece.data<complex64>();
+          return AllElementsEqualValue<complex64>(data, data[0]);
+        }
+        case F64: {
+          auto data = piece.data<double>();
+          return AllElementsEqualValue<double>(data, data[0]);
+        }
+        case S64: {
+          auto data = piece.data<int64>();
+          return AllElementsEqualValue<int64>(data, data[0]);
+        }
+        case U64: {
+          auto data = piece.data<uint64>();
+          return AllElementsEqualValue<uint64>(data, data[0]);
+        }
+        default:
+          return false;
+      }
+    };
+
+    if (!piece_is_all()) {
+      return false;
+    }
+  }
+  return true;
+}
+
 bool Literal::IsZero(tensorflow::gtl::ArraySlice<int64> indices) const {
   CHECK(ShapeUtil::IsArray(shape()));
   switch (shape().element_type()) {
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index d996004888..d5ae3fd723 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -451,6 +451,9 @@ class Literal {
   template <typename NativeT>
   NativeT GetFirstElement() const;
 
+  // Returns a literal scalar representing the first element.
+  Literal GetFirstScalarLiteral() const;
+
   // As Get(), but determines the correct type and converts the value
   // into text.
   string GetAsString(tensorflow::gtl::ArraySlice<int64> multi_index,
@@ -602,6 +605,9 @@ class Literal {
   // This literal must have a dense layout.
   bool IsAllComplex(complex64 value) const;
 
+  // Literal consists entirely of the first element of the literal.
+  bool IsAllFirst() const;
+
   // Returns whether this literal is zero at the specified index. This literal
   // must be an array with a dense layout.
   bool IsZero(tensorflow::gtl::ArraySlice<int64> indices) const;
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index b3583c2eb7..ee2f4fe874 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -501,6 +501,24 @@ TEST_F(LiteralUtilTest, IsAllComplex) {
                    ->IsAllComplex({8.0f, 9.0f}));
 }
 
+TEST_F(LiteralUtilTest, IsAllFirst) {
+  // IsAllComplex always returns false when the literal is not complex.
+  EXPECT_FALSE(Literal::CreateR1<bool>({false, true})->IsAllFirst());
+  EXPECT_TRUE(Literal::CreateR1<bool>({false, false})->IsAllFirst());
+  EXPECT_FALSE(Literal::CreateR1<int8>({1, 1, 2})->IsAllFirst());
+  EXPECT_TRUE(Literal::CreateR1<int8>({5, 5, 5, 5})->IsAllFirst());
+  EXPECT_FALSE(Literal::CreateR1<uint8>({1, 1, 2})->IsAllFirst());
+  EXPECT_TRUE(Literal::CreateR1<int32>({5, 5, 5, 5})->IsAllFirst());
+  EXPECT_FALSE(Literal::CreateR1<int32>({1, 1, 2})->IsAllFirst());
+  EXPECT_TRUE(Literal::CreateR1<uint32>({5, 5, 5, 5})->IsAllFirst());
+  EXPECT_FALSE(Literal::CreateR1<uint32>({1, 1, 2})->IsAllFirst());
+
+  complex64 c8_9 = {8, 9};
+  complex64 c7_9 = {7, 9};
+  EXPECT_TRUE(Literal::CreateR2<complex64>({{c8_9}, {c8_9}})->IsAllFirst());
+  EXPECT_FALSE(Literal::CreateR2<complex64>({{c7_9}, {c8_9}})->IsAllFirst());
+}
+
 TEST_F(LiteralUtilTest, IsZero) {
   auto scalar_zero = Literal::CreateR0<float>(0.0f);
   auto scalar_one = Literal::CreateR0<float>(1.0f);
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index fb857559f9..4391462c1c 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -516,6 +516,18 @@ Status AlgebraicSimplifierVisitor::HandleConstant(HloInstruction* constant) {
     return ReplaceInstruction(
         constant, BuildTupleConstant(computation_, constant->literal()));
   }
+
+  // If a literal is all the same element replace it with a scalar broadcast.
+  if (ShapeUtil::ElementsIn(constant->shape()) > 1 &&
+      constant->literal().IsAllFirst()) {
+    std::unique_ptr<Literal> unique_scalar =
+        MakeUnique<Literal>(constant->literal().GetFirstScalarLiteral());
+    HloInstruction* scalar = computation_->AddInstruction(
+        HloInstruction::CreateConstant(std::move(unique_scalar)));
+    return ReplaceWithNewInstruction(
+        constant,
+        HloInstruction::CreateBroadcast(constant->shape(), scalar, {}));
+  }
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index 0f08eb3a32..667ae01993 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -162,6 +162,37 @@ TEST_F(AlgebraicSimplifierTest, AddBroadcastZeroR1Operand) {
   EXPECT_EQ(root, param0);
 }
 
+TEST_F(AlgebraicSimplifierTest, ConstantToBroadcast) {
+  HloComputation::Builder builder(TestName());
+  builder.AddInstruction(HloInstruction::CreateConstant(
+      Literal::CreateR1<float>({3.14f, 3.14f, 3.14f})));
+
+  auto computation = module().AddEntryComputation(builder.Build());
+  HloInstruction* root = computation->root_instruction();
+  EXPECT_THAT(root, op::Constant());
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie());
+  root = computation->root_instruction();
+  EXPECT_THAT(root, op::Broadcast(op::Constant()));
+  EXPECT_EQ(3.14f, root->operand(0)->literal().GetFirstElement<float>());
+}
+
+TEST_F(AlgebraicSimplifierTest, ConstantNotToBroadcast) {
+  HloComputation::Builder builder(TestName());
+  builder.AddInstruction(HloInstruction::CreateConstant(
+      Literal::CreateR1<float>({3.14, 3.14, 4})));
+
+  auto computation = module().AddEntryComputation(builder.Build());
+  HloInstruction* root = computation->root_instruction();
+  EXPECT_THAT(root, op::Constant());
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_FALSE(simplifier.Run(&module()).ValueOrDie());
+  root = computation->root_instruction();
+  EXPECT_THAT(root, op::Constant());
+}
+
 // Test that A - 0 is simplified to A
 TEST_F(AlgebraicSimplifierTest, SubZero) {
   Shape r0f32 = ShapeUtil::MakeShape(F32, {});
-- 
GitLab


From aabd3022b35147581e1f58112d2a7a24035deb46 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 21 Feb 2018 16:57:01 -0800
Subject: [PATCH 171/575] Go: Update generated wrapper functions for TensorFlow
 ops. PiperOrigin-RevId: 186542037

---
 tensorflow/go/op/wrappers.go | 127 +++++++++++++++--------------------
 1 file changed, 55 insertions(+), 72 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 34c4e1b3ff..04c20511ba 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -329,61 +329,6 @@ func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Ou
 	return op.Output(0)
 }
 
-// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient.
-type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr)
-
-// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value.
-//
-// value: The bitwidth of the quantization; between 2 and 8, inclusive.
-// If not specified, defaults to 8
-func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr {
-	return func(m optionalAttr) {
-		m["num_bits"] = value
-	}
-}
-
-// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value.
-//
-// value: Whether to quantize into 2^num_bits - 1 distinct values.
-// If not specified, defaults to false
-func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr {
-	return func(m optionalAttr) {
-		m["narrow_range"] = value
-	}
-}
-
-// Compute gradients for a FakeQuantWithMinMaxVars operation.
-//
-// Arguments:
-//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
-//	inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation.
-// min, max: Quantization interval, scalar floats.
-//
-//
-//
-// Returns Backpropagated gradients w.r.t. inputs:
-// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter:
-// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter:
-// `sum(gradients * (inputs > max))`.
-func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "FakeQuantWithMinMaxVarsGradient",
-		Input: []tf.Input{
-			gradients, inputs, min, max,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
-}
-
 // Partitions `data` into `num_partitions` tensors using indices from `partitions`.
 //
 // For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
@@ -1750,6 +1695,61 @@ func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
+// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient.
+type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr)
+
+// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value.
+//
+// value: The bitwidth of the quantization; between 2 and 8, inclusive.
+// If not specified, defaults to 8
+func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr {
+	return func(m optionalAttr) {
+		m["num_bits"] = value
+	}
+}
+
+// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value.
+//
+// value: Whether to quantize into 2^num_bits - 1 distinct values.
+// If not specified, defaults to false
+func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr {
+	return func(m optionalAttr) {
+		m["narrow_range"] = value
+	}
+}
+
+// Compute gradients for a FakeQuantWithMinMaxVars operation.
+//
+// Arguments:
+//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
+//	inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation.
+// min, max: Quantization interval, scalar floats.
+//
+//
+//
+// Returns Backpropagated gradients w.r.t. inputs:
+// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter:
+// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter:
+// `sum(gradients * (inputs > max))`.
+func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "FakeQuantWithMinMaxVarsGradient",
+		Input: []tf.Input{
+			gradients, inputs, min, max,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1), op.Output(2)
+}
+
 // LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler.
 type LogUniformCandidateSamplerAttr func(optionalAttr)
 
@@ -17740,23 +17740,6 @@ func SoftplusGrad(scope *Scope, gradients tf.Output, features tf.Output) (backpr
 	return op.Output(0)
 }
 
-// Creates a dataset that contains the unique elements of `input_dataset`.
-func UniqueDataset(scope *Scope, input_dataset tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
-	opspec := tf.OpSpec{
-		Type: "UniqueDataset",
-		Input: []tf.Input{
-			input_dataset,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // SelfAdjointEigV2Attr is an optional argument to SelfAdjointEigV2.
 type SelfAdjointEigV2Attr func(optionalAttr)
 
-- 
GitLab


From cb7ae9e5af12055bbb14284b0fd5e7d2ac292415 Mon Sep 17 00:00:00 2001
From: Michael Kuperstein <mkuper@google.com>
Date: Wed, 21 Feb 2018 16:58:47 -0800
Subject: [PATCH 172/575] Add more strided R1 tests.

PiperOrigin-RevId: 186542219
---
 tensorflow/compiler/xla/tests/slice_test.cc | 42 ++++++++++++++++-----
 1 file changed, 33 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc
index ac163df127..fe36df160d 100644
--- a/tensorflow/compiler/xla/tests/slice_test.cc
+++ b/tensorflow/compiler/xla/tests/slice_test.cc
@@ -237,6 +237,12 @@ INSTANTIATE_TEST_CASE_P(
     SliceR1TestInstantiation,
     SliceR1Test,
     ::testing::Values(
+// TODO(b/69425338): This uses too much memory on GPU.
+#ifndef XLA_TEST_BACKEND_GPU
+        R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024, 12 * 1024 * 1024, 1},
+        R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024 + 1, 12 * 1024 * 1024 - 1, 1},
+        R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024 - 1, 12 * 1024 * 1024 + 1, 1},
+#endif
         R1Spec{10, 0, 0, 1},
         R1Spec{10, 7, 7, 1},
         R1Spec{10, 0, 5, 1},
@@ -267,13 +273,15 @@ INSTANTIATE_TEST_CASE_P(
         R1Spec{64 * 1024, 1024 + 1, 63 * 1024 - 1, 1},
         R1Spec{64 * 1024, 32 * 1024, 33 * 1024, 1},
         R1Spec{64 * 1024, 32 * 1024 + 1, 33 * 1024 - 1, 1},
-        R1Spec{64 * 1024, 32 * 1024 - 17, 36 * 1024 - 18, 1},
-// TODO(b/69425338): This uses too much memory on GPU.
-#ifndef XLA_TEST_BACKEND_GPU
-        R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024, 12 * 1024 * 1024, 1},
-        R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024 + 1, 12 * 1024 * 1024 - 1, 1},
-        R1Spec{16 * 1024 * 1024, 4 * 1024 * 1024 - 1, 12 * 1024 * 1024 + 1, 1},
-#endif
+        R1Spec{64 * 1024, 32 * 1024 - 17, 36 * 1024 - 18, 1}
+    ),
+    SliceR1TestDataToString
+);
+
+INSTANTIATE_TEST_CASE_P(
+    SliceStridedR1TestInstantiation,
+    SliceR1Test,
+    ::testing::Values(
         R1Spec{10, 2, 4, 2},
         R1Spec{10, 0, 10, 2},
         R1Spec{10, 0, 10, 3},
@@ -285,8 +293,24 @@ INSTANTIATE_TEST_CASE_P(
         R1Spec{2047, 1024 - 24, 1024 + 160, 31},
         R1Spec{2047, 1, 2046, 3 * 128},
         R1Spec{4096, 1024 + 3, 4095, 500},
-        R1Spec{8192, 0, 8192, 1024 * 3 + 400}
-        ),
+        R1Spec{8192, 0, 8192, 1024 * 3 + 400},
+        R1Spec{1024 * 1024, 0, 1024 * 1024, 2},
+        R1Spec{1024 * 1024, 0, 1024 * 1024, 8},
+        R1Spec{1024 * 1024, 0, 1024 * 1024, 7},
+        R1Spec{1024 * 1024, 0, 1024 * 1024, 125},
+        R1Spec{1024 * 1024, 3, 1024 - 9, 2},
+        R1Spec{1024 * 1024, 3, 1024 - 9, 8},
+        R1Spec{1024 * 1024, 3, 1024 - 9, 7},
+        R1Spec{1024 * 1024, 3, 1024 - 9, 125},
+        R1Spec{1024 * 1024, 3, 1024 * 512 - 9, 2},
+        R1Spec{1024 * 1024, 3, 1024 * 512 - 9, 8},
+        R1Spec{1024 * 1024, 3, 1024 * 512 - 9, 7},
+        R1Spec{1024 * 1024, 3, 1024 * 512 - 9, 125},
+        R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 2},
+        R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 8},
+        R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 7},
+        R1Spec{1024 * 1024 + 71, 3, 1024 * 512 - 9, 125}
+    ),
     SliceR1TestDataToString
 );
 // clang-format on
-- 
GitLab


From 913323ba96034108c0c85cadbfd879b35858aa26 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Wed, 21 Feb 2018 17:19:40 -0800
Subject: [PATCH 173/575] Fix subtle race condition in
 ResourceVariable.is_initialized

PiperOrigin-RevId: 186544846
---
 tensorflow/contrib/opt/BUILD                  |  1 -
 .../core/kernels/resource_variable_ops.cc     | 26 +++++++++++++++++--
 tensorflow/core/kernels/variable_ops.h        |  8 ++++++
 3 files changed, 32 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 86ceda71b7..827279bd47 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -70,7 +70,6 @@ py_test(
     srcs = ["python/training/moving_average_optimizer_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "no_oss",  # b/73507407
         "notsan",  # b/31055119
     ],
     deps = [
diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc
index 702fb89aac..2041fb9094 100644
--- a/tensorflow/core/kernels/resource_variable_ops.cc
+++ b/tensorflow/core/kernels/resource_variable_ops.cc
@@ -253,6 +253,7 @@ class AssignVariableOp : public OpKernel {
     std::unique_ptr<Tensor> input_alias =
         context->forward_input(1, dtype_, value.shape(), DEVICE_MEMORY, attr);
     mutex_lock ml(*variable->mu());
+    variable->is_initialized = true;
     if (input_alias) {
       *variable->tensor() = *input_alias;
       return;
@@ -363,7 +364,7 @@ class AssignVariableOp<Device, Variant> : public OpKernel {
                     DataTypeString(DT_VARIANT)));
 
     mutex_lock ml(*variable->mu());
-
+    variable->is_initialized = true;
     *variable->tensor() = Tensor(DT_VARIANT, value.shape());
     const auto elements_in = value.flat<Variant>();
     auto elements_out = variable->tensor()->flat<Variant>();
@@ -462,8 +463,29 @@ TF_CALL_int64(REGISTER_GPU_KERNELS);
 #undef REGISTER_GPU_KERNELS
 #endif  // GOOGLE_CUDA
 
+class VarIsInitializedOp : public OpKernel {
+ public:
+  explicit VarIsInitializedOp(OpKernelConstruction* c) : OpKernel(c) {}
+
+  void Compute(OpKernelContext* context) override {
+    Tensor* output = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(0, TensorShape({}), &output));
+    auto output_tensor = output->tensor<bool, 0>();
+    Var* variable = nullptr;
+    Status s = LookupResource(context, HandleFromInput(context, 0), &variable);
+    if (!s.ok()) {
+      output_tensor() = false;
+      return;
+    }
+    core::ScopedUnref su(variable);
+    mutex_lock ml(*variable->mu());
+    output_tensor() = variable->is_initialized;
+  }
+};
+
 REGISTER_KERNEL_BUILDER(Name("VarIsInitializedOp").Device(DEVICE_CPU),
-                        IsResourceInitialized<Var>);
+                        VarIsInitializedOp);
 
 #if GOOGLE_CUDA
 REGISTER_KERNEL_BUILDER(Name("VarIsInitializedOp")
diff --git a/tensorflow/core/kernels/variable_ops.h b/tensorflow/core/kernels/variable_ops.h
index 83134bad37..8b406e5311 100644
--- a/tensorflow/core/kernels/variable_ops.h
+++ b/tensorflow/core/kernels/variable_ops.h
@@ -45,6 +45,14 @@ class Var : public ResourceBase {
                            tensor_.shape().DebugString());
   }
 
+  // Only used in the resource variable path. In resource variables,
+  // tensor.IsInitialized() can be true (i.e. have memory allocated to it) while
+  // there is not a good value there due to a race condition, and it's possible
+  // to stumble upon this during variable.initialized_value(). So it's best to
+  // just store directly whether the variable is initialized.
+  bool is_initialized = false;  // GUARDED_BY(mu_) but annotalysis doesn't like
+                                // it.
+
  private:
   mutex mu_;
   Tensor tensor_;
-- 
GitLab


From 0137f7d281b22192d652697bf9a14366bd16fe4f Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Wed, 21 Feb 2018 17:20:32 -0800
Subject: [PATCH 174/575] Fix control flow bug.

Without this change, the newly added test would fail with a "Reval[0]"
error. This was due to extra Enter nodes being added in the gradients
graph in order to enter the outer while contexts for a value that was
already in those contexts. The extra Enter nodes would cause the
frames of the execution to be messed up, which prevented the final
value from being propagated to the outermost Exit node.

A similar change is probably needed in WhileContext, although I don't
have a test case for this yet.

PiperOrigin-RevId: 186544961
---
 .../kernel_tests/control_flow_ops_py_test.py    | 17 +++++++++++++++++
 tensorflow/python/ops/control_flow_ops.py       | 13 +++++++++----
 2 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
index 15ff0ec09b..58f38650eb 100644
--- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
@@ -1840,6 +1840,23 @@ class ControlFlowTest(test.TestCase):
                                       [tensor_shape.unknown_shape()])
       self.assertAllClose(9.0, r.eval(feed_dict={x: 1.0}))
 
+  def testCondGradInNestedWhiles(self):
+    def outer_body(i, x):
+      _, x = control_flow_ops.while_loop(
+          lambda j, x: j < 3, inner_body, [0, 0.0])
+      return i + 1, x
+
+    def inner_body(j, x):
+      y = control_flow_ops.cond(math_ops.less(x, 1), lambda: 2 * x, lambda: x)
+      return j + 1, gradients_impl.gradients(y, x)[0]
+
+    i, x = control_flow_ops.while_loop(lambda i, x: i < 3, outer_body, [0, 0.0])
+
+    with self.test_session() as sess:
+      i_val, x_val = sess.run([i, x])
+      self.assertEqual(i_val, 3)
+      self.assertAllClose(x_val, 1.0)
+
   def testWhile_NestedInput(self):
     with self.test_session() as sess:
       named = collections.namedtuple("named", ("a", "b"))
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index f77f0050f7..a2d605532a 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1836,8 +1836,6 @@ class CondContext(ControlFlowContext):
       # pylint: disable=protected-access
       op._add_control_input(self._pivot.op)
       # pylint: enable=protected-access
-      for x in op.outputs:
-        self._values.add(x.name)
     else:
       for index in range(len(op.inputs)):
         x = op.inputs[index]
@@ -1848,13 +1846,20 @@ class CondContext(ControlFlowContext):
           # pylint: enable=protected-access
       # Remove any external control dependency on this op.
       self._RemoveExternalControlEdges(op)
-      for x in op.outputs:
-        self._values.add(x.name)
       # pylint: disable=protected-access
       if op.graph._is_function(op.type) or op.type == "SymbolicGradient":
         op._add_control_input(self._pivot.op)
       # pylint: enable=protected-access
 
+    # Mark op's outputs as seen by this context and any outer contexts.
+    output_names = [x.name for x in op.outputs]
+    ctxt = self
+    while ctxt is not None:
+      # pylint: disable=protected-access
+      ctxt._values.update(output_names)
+      ctxt = ctxt._outer_context
+      # pylint: enable=protected-access
+
     if self._outer_context or not util.IsLoopExit(op):
       op.graph.prevent_fetching(op)
 
-- 
GitLab


From 11a0b760a07805b7a1d48adbd7ed052d2c6d65fa Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Wed, 21 Feb 2018 17:36:14 -0800
Subject: [PATCH 175/575] Delete dot_operation_runtime_test -- it is identical
 to dot_operation_test.

PiperOrigin-RevId: 186546771
---
 tensorflow/compiler/xla/tests/BUILD | 31 +++++------------------------
 1 file changed, 5 insertions(+), 26 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index a2c0f834de..f955d54c64 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -622,8 +622,10 @@ xla_test(
 xla_test(
     name = "dot_operation_test",
     srcs = ["dot_operation_test.cc"],
+    shard_count = 20,
     tags = [
         "enable_for_xla_interpreter",
+        "optonly",
     ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
@@ -642,32 +644,7 @@ xla_test(
     ],
 )
 
-# Tests the dot operation in some cases that can be performed via a
-# runtime call on some backends - e.g. a runtime call to Eigen.
-xla_test(
-    name = "dot_operation_runtime_test",
-    srcs = ["dot_operation_test.cc"],
-    tags = [
-        "enable_for_xla_interpreter",
-    ],
-    deps = [
-        "//tensorflow/compiler/xla:array2d",
-        "//tensorflow/compiler/xla:array3d",
-        "//tensorflow/compiler/xla:reference_util",
-        "//tensorflow/compiler/xla:shape_util",
-        "//tensorflow/compiler/xla/client:computation_builder",
-        "//tensorflow/compiler/xla/client:local_client",
-        "//tensorflow/compiler/xla/tests:client_library_test_base",
-        "//tensorflow/compiler/xla/tests:literal_test_util",
-        "//tensorflow/compiler/xla/tests:test_utils",
-        "//tensorflow/compiler/xla/tests:xla_internal_test_main",
-        "//tensorflow/core:framework_internal",
-        "//tensorflow/core:lib",
-        "//tensorflow/core:test",
-    ],
-)
-
-# Repeat dot_operation_runtime_test with single-threded eigen.
+# Repeat dot_operation_runtime_test with single-threaded eigen.
 xla_test(
     name = "dot_operation_single_threaded_runtime_test",
     srcs = ["dot_operation_test.cc"],
@@ -679,6 +656,8 @@ xla_test(
             "--xla_cpu_multi_thread_eigen=false",
         ],
     },
+    shard_count = 20,
+    tags = ["optonly"],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
-- 
GitLab


From 68e13d6bed42e53091ef9ef8bac248b380fb66a8 Mon Sep 17 00:00:00 2001
From: Yangzihao Wang <yangzihao@google.com>
Date: Wed, 21 Feb 2018 17:42:46 -0800
Subject: [PATCH 176/575] Do not set cudnn batch norm persistent mode when
 doing inference.

PiperOrigin-RevId: 186547439
---
 tensorflow/stream_executor/cuda/cuda_dnn.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index 58b4706766..61cf4ba7ea 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -2793,7 +2793,7 @@ bool CudnnSupport::DoBatchNormalizationForwardImpl(
       parent_, scale_offset_desc, ToCudnnDataType(scale_data_type)};
   cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL;
 #if CUDNN_VERSION >= 7000
-  if (BatchnormSpatialPersistentEnabled()) {
+  if (BatchnormSpatialPersistentEnabled() && is_training) {
     mode = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
   }
 #endif
-- 
GitLab


From ddd66709a396644112e3dda165d53fdd485d7de3 Mon Sep 17 00:00:00 2001
From: Tatiana Shpeisman <shpeisman@google.com>
Date: Wed, 21 Feb 2018 19:56:00 -0800
Subject: [PATCH 177/575] Deleting test that checks that 2D convolution with
 NCHW format is not implemented on CPU. The tests fail with MKL because the
 operation is implemented.

PiperOrigin-RevId: 186558730
---
 tensorflow/python/kernel_tests/conv_ops_test.py | 15 ---------------
 1 file changed, 15 deletions(-)

diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py
index 2785798916..f4fe01f868 100644
--- a/tensorflow/python/kernel_tests/conv_ops_test.py
+++ b/tensorflow/python/kernel_tests/conv_ops_test.py
@@ -1513,21 +1513,6 @@ class Conv2DTest(test.TestCase):
                 strides=[1, 1, 1, 1],
                 padding="VALID"))
 
-  def testCPUConv2DNCHWUnimplemented(self):
-    with self.test_session(use_gpu=False):
-      with self.assertRaisesRegexp(errors_impl.UnimplementedError,
-                                   "NHWC tensor format for now"):
-        conv = self._SetupValuesForDevice(
-            tensor_in_sizes=[1, 4, 4, 1],
-            filter_in_sizes=[2, 2, 1, 1],
-            dilations=[1, 1],
-            strides=[1, 1],
-            padding="VALID",
-            data_format="NCHW",
-            dtype=dtypes.float32,
-            use_gpu=False)
-        self.evaluate(conv)
-
 
 class DepthwiseConv2DTest(test.TestCase):
 
-- 
GitLab


From 3d68260b2c3e48d9a56d4cbb29241e28312375dc Mon Sep 17 00:00:00 2001
From: Martin Pool <mbp@sourcefrog.net>
Date: Wed, 21 Feb 2018 20:13:38 -0800
Subject: [PATCH 178/575] Fix markdown nit (#17163)

Without a leading blank line, it doesn't render properly in https://www.tensorflow.org/programmers_guide/variables#variable_collections.
---
 tensorflow/docs_src/programmers_guide/variables.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md
index 6425073805..e8cf771155 100644
--- a/tensorflow/docs_src/programmers_guide/variables.md
+++ b/tensorflow/docs_src/programmers_guide/variables.md
@@ -62,9 +62,10 @@ them. For this reason TensorFlow provides **collections**, which are named lists
 of tensors or other objects, such as `tf.Variable` instances.
 
 By default every `tf.Variable` gets placed in the following two collections:
+
  * `tf.GraphKeys.GLOBAL_VARIABLES` --- variables that can be shared across
-multiple devices,
- * `tf.GraphKeys.TRAINABLE_VARIABLES`--- variables for which TensorFlow will
+   multiple devices,
+ * `tf.GraphKeys.TRAINABLE_VARIABLES` --- variables for which TensorFlow will
    calculate gradients.
 
 If you don't want a variable to be trainable, add it to the
-- 
GitLab


From 671baf080238025da9698ea980cd9504005f727c Mon Sep 17 00:00:00 2001
From: Michael Case <mikecase@google.com>
Date: Wed, 21 Feb 2018 20:39:21 -0800
Subject: [PATCH 179/575] Make configure script runnable from external
 workspace. (#17172)

To run from external workspace, you should now be able to invoke
script like the following. This will generate some TensorFlow specfic
bazel options and import them into your project's .bazelrc.

$(bazel info output_base)/external/org_tensorflow/configure.py --workspace=$(PWD)
---
 configure    |  3 ++-
 configure.py | 65 ++++++++++++++++++++++++----------------------------
 2 files changed, 32 insertions(+), 36 deletions(-)

diff --git a/configure b/configure
index 9c21d2b03a..66b66ba54e 100755
--- a/configure
+++ b/configure
@@ -8,7 +8,8 @@ if [ -z "$PYTHON_BIN_PATH" ]; then
 fi
 
 # Set all env variables
-"$PYTHON_BIN_PATH" configure.py
+CONFIGURE_DIR=$(dirname "$0")
+"$PYTHON_BIN_PATH" "${CONFIGURE_DIR}/configure.py" "$@"
 
 echo "Configuration finished"
 
diff --git a/configure.py b/configure.py
index 60f144f315..f77a048d86 100644
--- a/configure.py
+++ b/configure.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import argparse
 import errno
 import os
 import platform
@@ -32,10 +33,6 @@ except ImportError:
   from distutils.spawn import find_executable as which
 # pylint: enable=g-import-not-at-top
 
-_TF_BAZELRC = os.path.join(os.path.dirname(os.path.abspath(__file__)),
-                           '.tf_configure.bazelrc')
-_TF_WORKSPACE = os.path.join(os.path.dirname(os.path.abspath(__file__)),
-                             'WORKSPACE')
 _DEFAULT_CUDA_VERSION = '9.0'
 _DEFAULT_CUDNN_VERSION = '7'
 _DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,5.2'
@@ -51,6 +48,11 @@ _SUPPORTED_ANDROID_NDK_VERSIONS = [10, 11, 12, 13, 14, 15]
 
 _DEFAULT_PROMPT_ASK_ATTEMPTS = 10
 
+_TF_WORKSPACE_ROOT = os.path.abspath(os.path.dirname(__file__))
+_TF_BAZELRC_FILENAME = '.tf_configure.bazelrc'
+_TF_BAZELRC = os.path.join(_TF_WORKSPACE_ROOT, _TF_BAZELRC_FILENAME)
+_TF_WORKSPACE = os.path.join(_TF_WORKSPACE_ROOT, 'WORKSPACE')
+
 
 class UserInputError(Exception):
   pass
@@ -119,22 +121,6 @@ def sed_in_place(filename, old, new):
     f.write(newdata)
 
 
-def remove_line_with(filename, token):
-  """Remove lines that contain token from file.
-
-  Args:
-    filename: string for filename.
-    token: string token to check if to remove a line from file or not.
-  """
-  with open(filename, 'r') as f:
-    filedata = f.read()
-
-  with open(filename, 'w') as f:
-    for line in filedata.strip().split('\n'):
-      if token not in line:
-        f.write(line + '\n')
-
-
 def write_to_bazelrc(line):
   with open(_TF_BAZELRC, 'a') as f:
     f.write(line + '\n')
@@ -245,25 +231,26 @@ def setup_python(environ_cp):
   environ_cp['PYTHON_BIN_PATH'] = python_bin_path
 
   # Write tools/python_bin_path.sh
-  with open('tools/python_bin_path.sh', 'w') as f:
+  with open(os.path.join(
+      _TF_WORKSPACE_ROOT, 'tools', 'python_bin_path.sh'), 'w') as f:
     f.write('export PYTHON_BIN_PATH="%s"' % python_bin_path)
 
 
-def reset_tf_configure_bazelrc():
+def reset_tf_configure_bazelrc(workspace_path):
   """Reset file that contains customized config settings."""
   open(_TF_BAZELRC, 'w').close()
+  bazelrc_path = os.path.join(workspace_path, '.bazelrc')
 
-  home = os.path.expanduser('~')
-  if not os.path.exists('.bazelrc'):
-    if os.path.exists(os.path.join(home, '.bazelrc')):
-      with open('.bazelrc', 'a') as f:
-        f.write('import %s/.bazelrc\n' % home.replace('\\', '/'))
-    else:
-      open('.bazelrc', 'w').close()
-
-  remove_line_with('.bazelrc', 'tf_configure')
-  with open('.bazelrc', 'a') as f:
-    f.write('import %workspace%/.tf_configure.bazelrc\n')
+  data = []
+  if os.path.exists(bazelrc_path):
+    with open(bazelrc_path, 'r') as f:
+      data = f.read().splitlines()
+  with open(bazelrc_path, 'w') as f:
+    for l in data:
+      if _TF_BAZELRC_FILENAME in l:
+        continue
+      f.write('%s\n' % l)
+    f.write('import %s\n' % _TF_BAZELRC)
 
 
 def cleanup_makefile():
@@ -271,7 +258,8 @@ def cleanup_makefile():
 
   These files could interfere with Bazel parsing.
   """
-  makefile_download_dir = 'tensorflow/contrib/makefile/downloads'
+  makefile_download_dir = os.path.join(
+      _TF_WORKSPACE_ROOT, 'tensorflow', 'contrib', 'makefile', 'downloads')
   if os.path.isdir(makefile_download_dir):
     for root, _, filenames in os.walk(makefile_download_dir):
       for f in filenames:
@@ -1373,13 +1361,20 @@ def config_info_line(name, help_text):
 
 
 def main():
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--workspace",
+                      type=str,
+                      default=_TF_WORKSPACE_ROOT,
+                      help="The absolute path to your active Bazel workspace.")
+  args = parser.parse_args()
+
   # Make a copy of os.environ to be clear when functions and getting and setting
   # environment variables.
   environ_cp = dict(os.environ)
 
   check_bazel_version('0.5.4')
 
-  reset_tf_configure_bazelrc()
+  reset_tf_configure_bazelrc(args.workspace)
   cleanup_makefile()
   setup_python(environ_cp)
 
-- 
GitLab


From b3df3aa4f5842fe3184088ef2fa0bb5d6edc21d5 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Feb 2018 21:05:42 -0800
Subject: [PATCH 180/575] Started to open source the RL placer.

PiperOrigin-RevId: 186563773
---
 tensorflow/python/BUILD                       |   28 +
 tensorflow/python/grappler/cluster.i          |   13 +-
 tensorflow/python/grappler/cluster_test.py    |    4 +-
 tensorflow/python/grappler/controller.py      |  142 +++
 tensorflow/python/grappler/graph_placer.py    |  110 ++
 .../python/grappler/graph_placer_test.py      |  140 +++
 .../grappler/hierarchical_controller.py       | 1098 +++++++++++++++++
 tensorflow/python/grappler/item.i             |   16 +-
 tensorflow/python/grappler/item_test.py       |    2 +-
 9 files changed, 1542 insertions(+), 11 deletions(-)
 create mode 100644 tensorflow/python/grappler/controller.py
 create mode 100644 tensorflow/python/grappler/graph_placer.py
 create mode 100644 tensorflow/python/grappler/graph_placer_test.py
 create mode 100644 tensorflow/python/grappler/hierarchical_controller.py

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 9b0c800ec7..6a7ece457d 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -4593,6 +4593,34 @@ py_test(
     ],
 )
 
+py_library(
+    name = "graph_placer",
+    srcs = [
+        "grappler/controller.py",
+        "grappler/graph_placer.py",
+        "grappler/hierarchical_controller.py",
+    ],
+    deps = [
+        ":python",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "graph_placer_test",
+    size = "large",
+    srcs = ["grappler/graph_placer_test.py"],
+    tags = [
+        "grappler",
+        "no_pip",  # graph_placer is not available in pip.
+    ],
+    deps = [
+        ":client_testlib",
+        ":graph_placer",
+        "//tensorflow/python:math_ops",
+    ],
+)
+
 py_test(
     name = "memory_optimizer_test",
     size = "medium",
diff --git a/tensorflow/python/grappler/cluster.i b/tensorflow/python/grappler/cluster.i
index 8079cb307b..067c8213d4 100644
--- a/tensorflow/python/grappler/cluster.i
+++ b/tensorflow/python/grappler/cluster.i
@@ -206,7 +206,7 @@ static PyObject* TF_ListDevices(GCluster cluster) {
   return result;
 }
 
-static std::vector<string> TF_ListAvailableOps() {
+static PyObject* TF_ListAvailableOps() {
   tensorflow::OpRegistry* registry = tensorflow::OpRegistry::Global();
   std::vector<tensorflow::OpDef> ops;
   registry->GetRegisteredOps(&ops);
@@ -215,7 +215,14 @@ static std::vector<string> TF_ListAvailableOps() {
     op_names.push_back(op.name());
   }
   std::sort(op_names.begin(), op_names.end());
-  return op_names;
+
+  PyGILState_STATE gstate = PyGILState_Ensure();
+  PyObject* result = PyList_New(op_names.size());
+  for (int i = 0; i < op_names.size(); ++i) {
+    PyList_SetItem(result, i, PyString_FromString(op_names[i].c_str()));
+  }
+  PyGILState_Release(gstate);
+  return result;
 }
 
 static PyObject* TF_GetSupportedDevices(GCluster cluster, GItem item) {
@@ -432,7 +439,7 @@ static GCluster TF_NewVirtualCluster(
     TF_Status* out_status);
 static void TF_ShutdownCluster(GCluster cluster);
 static PyObject* TF_ListDevices(GCluster cluster);
-static std::vector<string> TF_ListAvailableOps();
+static PyObject* TF_ListAvailableOps();
 static PyObject* TF_GetSupportedDevices(GCluster cluster, GItem item);
 static float TF_EstimatePerformance(const tensorflow::NamedDevice& device);
 static PyObject* TF_MeasureCosts(
diff --git a/tensorflow/python/grappler/cluster_test.py b/tensorflow/python/grappler/cluster_test.py
index caae5b114e..a3c4c2bbeb 100644
--- a/tensorflow/python/grappler/cluster_test.py
+++ b/tensorflow/python/grappler/cluster_test.py
@@ -131,8 +131,8 @@ class ClusterTest(test.TestCase):
   def testAvailableOps(self):
     with cluster.Provision() as gcluster:
       op_names = gcluster.ListAvailableOps()
-      self.assertTrue(b'Add' in op_names)
-      self.assertTrue(b'MatMul' in op_names)
+      self.assertTrue('Add' in op_names)
+      self.assertTrue('MatMul' in op_names)
       self.assertEqual(op_names, sorted(op_names))
 
   def testSupportDevices(self):
diff --git a/tensorflow/python/grappler/controller.py b/tensorflow/python/grappler/controller.py
new file mode 100644
index 0000000000..5677f4f523
--- /dev/null
+++ b/tensorflow/python/grappler/controller.py
@@ -0,0 +1,142 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Controller Class."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import defaultdict
+
+
+class Controller(object):
+  """Controller class."""
+
+  def __init__(self, item, cluster):
+    """Controller class initializer.
+
+    Args:
+      item: The metagraph to place wrapped in a cluster.
+      cluster: A cluster of devices on which to place the item.
+    """
+    self.item = item
+
+    self._node = {}
+    for node in item.metagraph.graph_def.node:
+      self._node[node.name] = node
+
+    self._fanout = defaultdict(lambda: [])
+    for node in item.metagraph.graph_def.node:
+      for fanin in self._get_node_fanin(node):
+        self._fanout[fanin.name].append(node)
+
+    important_op_names = item.IdentifyImportantOps(sort_topologically=True)
+
+    # List of important ops (these are the ops to place) sorted in topological
+    # order. The order of this collection is deterministic.
+    self.important_ops = []
+    for name in important_op_names:
+      self.important_ops.append(self._node[name])
+
+    self.node_properties = item.GetOpProperties()
+
+    self.cluster = cluster
+    self.devices = cluster.ListDevices()
+
+    self.colocation_constraints = item.GetColocationGroups()
+
+    self.placement_constraints = cluster.GetSupportedDevices(item)
+    for node_name, dev in self.placement_constraints.items():
+      if len(dev) == 1:
+        # Place the node on the supported device
+        node = self._node[node_name]
+        node.device = dev[0]
+        fanout = self.get_node_fanout(node)
+        # Update the fanout of the fanin to bypass the node
+        for fanin in self._get_node_fanin(node):
+          fanout_of_fanin = self.get_node_fanout(fanin)
+          fanout_of_fanin += fanout
+          fanout_of_fanin.remove(node)
+        # Remove node from the list of important ops since we don't need to
+        # place the node.
+        if node in self.important_ops:
+          self.important_ops.remove(node)
+          important_op_names.remove(node.name)
+
+    # List of important op names, in non deterministic order.
+    self.important_op_names = frozenset(important_op_names)
+
+  @property
+  def input_graph_def(self):
+    return self.item.metagraph.graph_def
+
+  @property
+  def num_devices(self):
+    return len(self.devices)
+
+  def get_node_by_name(self, node_name):
+    return self._node[node_name]
+
+  def get_node_fanout(self, node):
+    return self._fanout[node.name]
+
+  def get_placements(self, *args, **kwargs):
+    """Returns: Two TF ops.
+
+    Args:
+      *args: "".
+      **kwargs: "".
+
+    Returns:
+      y_preds: tensor of size [batch_size, num_ops]
+      log_probs: python dict of at least two fields: "sample", "target" each
+      containing a tensor of size [batch_size], corresponding to the log_probs.
+    """
+    raise NotImplementedError
+
+  def eval_placement(self, sess, *args, **kwargs):
+    """At this time, this method evaluates ONLY ONE placement.
+
+    Args:
+      sess: a tf.Session() object used to retrieve cached assignment info.
+      *args: "".
+      **kwargs: "".
+
+    Returns:
+      run_time: scalar
+    """
+    raise NotImplementedError
+
+  def export_placement(self, metagraph):
+    """Annotate the placement onto the specified metagraph.
+
+    Args:
+      metagraph: the metagraph to annotate with the placement.
+    """
+    for node in metagraph.graph_def.node:
+      if node.name in self.important_op_names:
+        node.device = self.get_node_by_name(node.name).device
+
+  # Get the nodes in the immediate fanin of node.
+  # Beware: this doesn't take into account the nodes that may be skipped
+  # since placement constraints force their placement.
+  def _get_node_fanin(self, node):
+    input_ops = []
+    for fanin_name in node.input:
+      if fanin_name[0] == "^":
+        fanin_name = fanin_name[1:]
+      fanin_name = fanin_name.split(":")[0]
+      input_ops.append(self.get_node_by_name(fanin_name))
+    return input_ops
diff --git a/tensorflow/python/grappler/graph_placer.py b/tensorflow/python/grappler/graph_placer.py
new file mode 100644
index 0000000000..2cc3536792
--- /dev/null
+++ b/tensorflow/python/grappler/graph_placer.py
@@ -0,0 +1,110 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Graph Placer."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops as tf_ops
+from tensorflow.python.grappler import cluster as gcluster
+from tensorflow.python.grappler import hierarchical_controller
+from tensorflow.python.grappler import item as gitem
+from tensorflow.python.grappler import tf_optimizer
+from tensorflow.python.training import training
+
+
+def PlaceGraph(metagraph,
+               cluster=None,
+               allotted_time=3600,
+               hparams=None,
+               verbose=False):
+  """Place the provided metagraph.
+
+  Args:
+    metagraph: the metagraph to place.
+    cluster: an optional set of hardware resource to optimize the placement for.
+      If none is specified, we'll optimize the placement for the hardware
+      available on the local machine.
+    allotted_time: the maximum amount to time in seconds to spend optimizing
+      the placement.
+    hparams: hyperparameters used to fine tune the placer.
+    verbose: prints debug information if True.
+
+  Returns:
+    The placed metagraph.
+  """
+  if cluster is None:
+    cluster = gcluster.Cluster()
+
+  # Optimize the metagraph to speedup the placement
+  rewriter_config = rewriter_config_pb2.RewriterConfig()
+  rewriter_config.optimizers.append("pruning")
+  rewriter_config.optimizers.append("constfold")
+  rewriter_config.optimizers.append("arithmetic")
+  rewriter_config.optimizers.append("dependency")
+  rewriter_config.optimizers.append("pruning")
+  optimized_graph = tf_optimizer.OptimizeGraph(
+      rewriter_config, metagraph, verbose=verbose, cluster=cluster)
+  optimized_metagraph = meta_graph_pb2.MetaGraphDef()
+  optimized_metagraph.CopyFrom(metagraph)
+  optimized_metagraph.graph_def.CopyFrom(optimized_graph)
+
+  item = gitem.Item(optimized_metagraph)
+
+  if hparams is None:
+    hparams = hierarchical_controller.hierarchical_controller_hparams()
+  # We run with a single child
+  hparams.num_children = 1
+
+  with tf_ops.Graph().as_default():
+    # Place all the nodes of the controller on the CPU. We don't want them to
+    # fight for accelerator memory with the model to optimize.
+    with tf_ops.device("/device:CPU:0"):
+      model = hierarchical_controller.HierarchicalController(
+          hparams, item, cluster)
+      ops = model.build_controller()
+      session_creator = training.ChiefSessionCreator()
+      with training.MonitoredSession(session_creator=session_creator) as sess:
+        start_time = time.time()
+        current_time = start_time
+        while current_time - start_time < allotted_time:
+          grouping_actions = model.generate_grouping(sess)
+          input_to_seq2seq = model.create_group_embeddings(
+              grouping_actions, verbose=verbose)
+          model.generate_placement(input_to_seq2seq, sess)
+          try:
+            run_time = model.eval_placement(
+                sess,
+                verbose=verbose)
+          except errors.OpError as e:
+            if verbose:
+              print("Failed to run graph:" + str(e))
+            run_time = hparams.failing_signal
+          updated = model.update_reward(sess, run_time, verbose=verbose)
+          if updated:
+            if verbose:
+              print("Found better placement, with runtime " + str(run_time))
+            model.export_placement(metagraph)
+
+          model.process_reward(sess)
+
+          current_time = time.time()
+
+  return metagraph
diff --git a/tensorflow/python/grappler/graph_placer_test.py b/tensorflow/python/grappler/graph_placer_test.py
new file mode 100644
index 0000000000..9eabe3cd54
--- /dev/null
+++ b/tensorflow/python/grappler/graph_placer_test.py
@@ -0,0 +1,140 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests the graph placer."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from tensorflow.core.protobuf import device_properties_pb2
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import meta_graph
+from tensorflow.python.framework import ops as tf_ops
+from tensorflow.python.grappler import cluster
+from tensorflow.python.grappler import graph_placer
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.platform import test
+
+
+class GraphPlacerTest(test.TestCase):
+
+  @staticmethod
+  def _buildMnist(batch_size=128,
+                  input_size=256,
+                  num_classes=1024,
+                  num_layers=10,
+                  hidden_size=256,
+                  name='mnist'):
+    g = tf_ops.get_default_graph()
+    with g.as_default():
+      ops = {}
+      x = random_ops.random_uniform(
+          [batch_size, input_size], -0.1, 0.1, dtype=dtypes.float32)
+      for layer_id in range(num_layers):
+        with variable_scope.variable_scope('layer_{}'.format(layer_id)):
+          a = input_size if layer_id == 0 else hidden_size
+          b = hidden_size if layer_id < num_layers - 1 else num_classes
+          w = variable_scope.get_variable('w', [a, b])
+          x = math_ops.matmul(x, w)
+          x = nn_ops.relu(x)
+      ops['y_preds'] = math_ops.argmax(x, axis=1)
+
+    train_op = g.get_collection_ref(tf_ops.GraphKeys.TRAIN_OP)
+    train_op.append(ops['y_preds'])
+    return g
+
+  @staticmethod
+  def _buildCluster(num_cpus=1, num_gpus=1):
+    devices = []
+    if num_gpus > 0:
+      device_properties = device_properties_pb2.DeviceProperties(
+          type='GPU',
+          vendor='NVidia',
+          model='GeForce GTX TITAN X',
+          frequency=1076,
+          num_cores=24,
+          environment={'architecture': '5.2',
+                       'cuda': '8000',
+                       'cudnn': '6021'},
+          num_registers=65536,
+          l1_cache_size=24576,
+          l2_cache_size=3145728,
+          shared_memory_size_per_multiprocessor=98304,
+          memory_size=12783648768,
+          bandwidth=336480000)
+      for i in range(num_gpus):
+        devices.append(
+            device_properties_pb2.NamedDevice(
+                properties=device_properties, name='/GPU:' + str(i)))
+
+    assert num_cpus > 0
+    device_properties = device_properties_pb2.DeviceProperties(
+        type='CPU',
+        frequency=2000,
+        num_cores=4,
+        l1_cache_size=32768,
+        l2_cache_size=262144,
+        l3_cache_size=12582912)
+    for i in range(num_cpus):
+      devices.append(
+          device_properties_pb2.NamedDevice(
+              properties=device_properties, name='/CPU:' + str(i)))
+
+    return cluster.Cluster(devices=devices)
+
+  def testBasic(self):
+    """Place a trivial graph."""
+    a = constant_op.constant(10, name='a')
+    b = constant_op.constant(20, name='b')
+    c = math_ops.add_n([a, b], name='c')
+    d = math_ops.add_n([b, c], name='d')
+    train_op = tf_ops.get_collection_ref(tf_ops.GraphKeys.TRAIN_OP)
+    train_op.append(d)
+    mg = meta_graph.create_meta_graph_def(graph=tf_ops.get_default_graph())
+
+    gcluster = cluster.Cluster()
+    placed_mg = graph_placer.PlaceGraph(mg, allotted_time=15, cluster=gcluster)
+
+    self.assertEqual(4, len(placed_mg.graph_def.node))
+    self.assertItemsEqual([node.name for node in placed_mg.graph_def.node],
+                          [node.name for node in mg.graph_def.node])
+
+    available_devices = [device.name for device in gcluster.ListDevices()]
+    for node in placed_mg.graph_def.node:
+      # The constant nodes are optimized away before the placer is run, and
+      # therefore won't be placed.
+      self.assertTrue(not node.device or node.device in available_devices)
+
+  def testMNIST(self):
+    graph = GraphPlacerTest._buildMnist()
+    mg = meta_graph.create_meta_graph_def(graph=graph)
+    gcluster = GraphPlacerTest._buildCluster(num_gpus=1)
+    # Spend 15 seconds trying to optimize the placement of the model. This
+    # should give us enough time to exercise the code, but not enough to find
+    # a good placement, so we'll just check for legality.
+    placed_mg = graph_placer.PlaceGraph(mg, allotted_time=15, cluster=gcluster)
+    self.assertEqual(len(placed_mg.graph_def.node), len(mg.graph_def.node))
+    self.assertItemsEqual([node.name for node in placed_mg.graph_def.node],
+                          [node.name for node in mg.graph_def.node])
+    available_devices = [device.name for device in gcluster.ListDevices()]
+    for node in placed_mg.graph_def.node:
+      self.assertTrue(not node.device or node.device in available_devices)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/grappler/hierarchical_controller.py b/tensorflow/python/grappler/hierarchical_controller.py
new file mode 100644
index 0000000000..655e43e78f
--- /dev/null
+++ b/tensorflow/python/grappler/hierarchical_controller.py
@@ -0,0 +1,1098 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""HierarchicalController Class.
+
+The HierarchicalController encompasses the entire lifecycle of training the
+device placement policy, including generating op embeddings, getting groups for
+each op, placing those groups and running the predicted placements.
+
+Different assignment models can inherit from this class.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import numpy as np
+import six
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops as tf_ops
+from tensorflow.python.grappler.controller import Controller
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import clip_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import embedding_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import tensor_array_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.summary import summary
+from tensorflow.python.training import adam
+from tensorflow.python.training import gradient_descent
+from tensorflow.python.training import learning_rate_decay
+from tensorflow.python.training import training_util
+
+
+class PlacerParams(object):
+  """Class to hold a set of placement parameters as name-value pairs.
+
+  A typical usage is as follows:
+
+  ```python
+  # Create a PlacerParams object specifying names and values of the model
+  # parameters:
+  params = PlacerParams(hidden_size=128, decay_steps=50)
+
+  # The parameters are available as attributes of the PlacerParams object:
+  hparams.hidden_size ==> 128
+  hparams.decay_steps ==> 50
+  ```
+
+  """
+
+  def __init__(self, **kwargs):
+    """Create an instance of `PlacerParams` from keyword arguments.
+
+    The keyword arguments specify name-values pairs for the parameters.
+    The parameter types are inferred from the type of the values passed.
+
+    The parameter names are added as attributes of `PlacerParams` object,
+    and they can be accessed directly with the dot notation `params._name_`.
+
+    Example:
+
+    ```python
+    # Define 1 parameter: 'hidden_size'
+    params = PlacerParams(hidden_size=128)
+    params.hidden_size ==> 128
+    ```
+
+    Args:
+      **kwargs: Key-value pairs where the key is the parameter name and
+        the value is the value for the parameter.
+    """
+    for name, value in six.iteritems(kwargs):
+      self.add_param(name, value)
+
+  def add_param(self, name, value):
+    """Adds {name, value} pair to hyperparameters.
+
+    Args:
+      name: Name of the hyperparameter.
+      value: Value of the hyperparameter. Can be one of the following types:
+        int, float, string, int list, float list, or string list.
+
+    Raises:
+      ValueError: if one of the arguments is invalid.
+    """
+    # Keys in kwargs are unique, but 'name' could be the name of a pre-existing
+    # attribute of this object.  In that case we refuse to use it as a
+    # parameter name.
+    if getattr(self, name, None) is not None:
+      raise ValueError("Parameter name is reserved: %s" % name)
+    setattr(self, name, value)
+
+
+def hierarchical_controller_hparams():
+  """Hyperparameters for hierarchical planner."""
+  return PlacerParams(
+      hidden_size=512,
+      forget_bias_init=1.0,
+      temperature=1.0,
+      logits_std_noise=0.5,
+      stop_noise_step=750,
+      decay_steps=50,
+      max_num_outputs=5,
+      max_output_size=5,
+      tanh_constant=1.0,
+      adj_embed_dim=20,
+      grouping_hidden_size=64,
+      num_groups=None,
+      bi_lstm=True,
+      failing_signal=100,
+      stop_sampling=500,
+      start_with_failing_signal=True,
+      always_update_baseline=False,
+      bl_dec=0.9,
+      grad_bound=1.0,
+      lr=0.1,
+      lr_dec=0.95,
+      start_decay_step=400,
+      optimizer_type="adam",
+      stop_updating_after_steps=1000,
+      name="hierarchical_controller",
+      keep_prob=1.0,
+      reward_function="sqrt",
+      seed=1234,
+      # distributed training params
+      num_children=1)
+
+
+class HierarchicalController(Controller):
+  """HierarchicalController class."""
+
+  def __init__(self, hparams, item, cluster, controller_id=0):
+    """HierarchicalController class initializer.
+
+    Args:
+      hparams: All hyper-parameters.
+      item: The metagraph to place.
+      cluster: The cluster of hardware devices to optimize for.
+      controller_id: the id of the controller in a multi-controller setup.
+    """
+    super(HierarchicalController, self).__init__(item, cluster)
+    self.ctrl_id = controller_id
+    self.hparams = hparams
+
+    if self.hparams.num_groups is None:
+      self.num_groups = min(256, 20 * self.num_devices)
+    else:
+      self.num_groups = self.hparams.num_groups
+
+    # creates self.op_embeddings and self.type_dict
+    self.create_op_embeddings(verbose=False)
+    # TODO(azalia) clean up embedding/group_embedding_size names
+    self.group_emb_size = (
+        2 * self.num_groups + len(self.type_dict) +
+        self.hparams.max_num_outputs * self.hparams.max_output_size)
+    self.embedding_size = self.group_emb_size
+    self.initializer = init_ops.glorot_uniform_initializer(
+        seed=self.hparams.seed)
+
+    with variable_scope.variable_scope(
+        self.hparams.name,
+        initializer=self.initializer,
+        reuse=variable_scope.AUTO_REUSE):
+      # define parameters of feedforward
+      variable_scope.get_variable("w_grouping_ff", [
+          1 + self.hparams.max_num_outputs * self.hparams.max_output_size +
+          self.hparams.adj_embed_dim, self.hparams.grouping_hidden_size
+      ])
+      variable_scope.get_variable(
+          "w_grouping_softmax",
+          [self.hparams.grouping_hidden_size, self.num_groups])
+      if self.hparams.bi_lstm:
+        variable_scope.get_variable("encoder_lstm_forward", [
+            self.embedding_size + self.hparams.hidden_size / 2,
+            2 * self.hparams.hidden_size
+        ])
+        variable_scope.get_variable("encoder_lstm_backward", [
+            self.embedding_size + self.hparams.hidden_size / 2,
+            2 * self.hparams.hidden_size
+        ])
+        variable_scope.get_variable(
+            "device_embeddings", [self.num_devices, self.hparams.hidden_size])
+        variable_scope.get_variable(
+            "decoder_lstm",
+            [2 * self.hparams.hidden_size, 4 * self.hparams.hidden_size])
+        variable_scope.get_variable(
+            "device_softmax", [2 * self.hparams.hidden_size, self.num_devices])
+        variable_scope.get_variable("device_go_embedding",
+                                    [1, self.hparams.hidden_size])
+        variable_scope.get_variable(
+            "encoder_forget_bias",
+            shape=1,
+            dtype=dtypes.float32,
+            initializer=init_ops.constant_initializer(
+                self.hparams.forget_bias_init))
+        variable_scope.get_variable(
+            "decoder_forget_bias",
+            shape=1,
+            dtype=dtypes.float32,
+            initializer=init_ops.constant_initializer(
+                self.hparams.forget_bias_init))
+        variable_scope.get_variable(
+            "attn_w_1", [self.hparams.hidden_size, self.hparams.hidden_size])
+        variable_scope.get_variable(
+            "attn_w_2", [self.hparams.hidden_size, self.hparams.hidden_size])
+        variable_scope.get_variable("attn_v", [self.hparams.hidden_size, 1])
+
+      else:
+        variable_scope.get_variable("encoder_lstm", [
+            self.embedding_size + self.hparams.hidden_size,
+            4 * self.hparams.hidden_size
+        ])
+        variable_scope.get_variable(
+            "device_embeddings", [self.num_devices, self.hparams.hidden_size])
+        variable_scope.get_variable(
+            "decoder_lstm",
+            [2 * self.hparams.hidden_size, 4 * self.hparams.hidden_size])
+        variable_scope.get_variable(
+            "device_softmax", [2 * self.hparams.hidden_size, self.num_devices])
+        variable_scope.get_variable("device_go_embedding",
+                                    [1, self.hparams.hidden_size])
+        variable_scope.get_variable(
+            "encoder_forget_bias",
+            shape=1,
+            dtype=dtypes.float32,
+            initializer=init_ops.constant_initializer(
+                self.hparams.forget_bias_init))
+        variable_scope.get_variable(
+            "decoder_forget_bias",
+            shape=1,
+            dtype=dtypes.float32,
+            initializer=init_ops.constant_initializer(
+                self.hparams.forget_bias_init))
+        variable_scope.get_variable(
+            "attn_w_1", [self.hparams.hidden_size, self.hparams.hidden_size])
+        variable_scope.get_variable(
+            "attn_w_2", [self.hparams.hidden_size, self.hparams.hidden_size])
+        variable_scope.get_variable("attn_v", [self.hparams.hidden_size, 1])
+    seq2seq_input_layer = array_ops.placeholder_with_default(
+        array_ops.zeros([1, self.num_groups, self.group_emb_size],
+                        dtypes.float32),
+        shape=(1, self.num_groups, self.group_emb_size))
+    self.seq2seq_input_layer = seq2seq_input_layer
+
+  def compute_reward(self, run_time):
+    if self.hparams.reward_function == "id":
+      reward = run_time
+    elif self.hparams.reward_function == "sqrt":
+      reward = math.sqrt(run_time)
+    elif self.hparams.reward_function == "log":
+      reward = math.log1p(run_time)
+    else:
+      raise NotImplementedError(
+          "Unrecognized reward function '%s', consider your "
+          "--reward_function flag value." % self.hparams.reward_function)
+    return reward
+
+  def build_controller(self):
+    """RL optimization interface.
+
+    Returns:
+      ops: A dictionary holding handles of the model used for training.
+    """
+
+    self._global_step = training_util.get_or_create_global_step()
+    ops = {}
+    ops["loss"] = 0
+
+    failing_signal = self.compute_reward(self.hparams.failing_signal)
+
+    ctr = {}
+
+    with tf_ops.name_scope("controller_{}".format(self.ctrl_id)):
+      with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)):
+        ctr["reward"] = {"value": [], "ph": [], "update": []}
+        ctr["ready"] = {"value": [], "ph": [], "update": []}
+        ctr["best_reward"] = {"value": [], "update": []}
+        for i in range(self.hparams.num_children):
+          reward_value = variable_scope.get_local_variable(
+              "reward_{}".format(i),
+              initializer=0.0,
+              dtype=dtypes.float32,
+              trainable=False)
+          reward_ph = array_ops.placeholder(
+              dtypes.float32, shape=(), name="reward_ph_{}".format(i))
+          reward_update = state_ops.assign(
+              reward_value, reward_ph, use_locking=True)
+          ctr["reward"]["value"].append(reward_value)
+          ctr["reward"]["ph"].append(reward_ph)
+          ctr["reward"]["update"].append(reward_update)
+          best_reward = variable_scope.get_local_variable(
+              "best_reward_{}".format(i),
+              initializer=failing_signal,
+              dtype=dtypes.float32,
+              trainable=False)
+          ctr["best_reward"]["value"].append(best_reward)
+          ctr["best_reward"]["update"].append(
+              state_ops.assign(best_reward,
+                               math_ops.minimum(best_reward, reward_update)))
+
+          ready_value = variable_scope.get_local_variable(
+              "ready_{}".format(i),
+              initializer=True,
+              dtype=dtypes.bool,
+              trainable=False)
+          ready_ph = array_ops.placeholder(
+              dtypes.bool, shape=(), name="ready_ph_{}".format(i))
+          ready_update = state_ops.assign(
+              ready_value, ready_ph, use_locking=True)
+          ctr["ready"]["value"].append(ready_value)
+          ctr["ready"]["ph"].append(ready_ph)
+          ctr["ready"]["update"].append(ready_update)
+
+      ctr["grouping_y_preds"], ctr["grouping_log_probs"] = self.get_groupings()
+      summary.histogram(
+          "grouping_actions",
+          array_ops.slice(ctr["grouping_y_preds"]["sample"], [0, 0],
+                          [1, array_ops.shape(self.op_embeddings)[0]]))
+
+      with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)):
+        ctr["baseline"] = variable_scope.get_local_variable(
+            "baseline",
+            initializer=failing_signal
+            if self.hparams.start_with_failing_signal else 0.0,
+            dtype=dtypes.float32,
+            trainable=False)
+
+      new_baseline = self.hparams.bl_dec * ctr["baseline"] + (
+          1 - self.hparams.bl_dec) * math_ops.reduce_mean(
+              ctr["reward"]["value"])
+      if not self.hparams.always_update_baseline:
+        baseline_mask = math_ops.less(ctr["reward"]["value"], failing_signal)
+        selected_reward = array_ops.boolean_mask(ctr["reward"]["value"],
+                                                 baseline_mask)
+        selected_baseline = control_flow_ops.cond(
+            math_ops.reduce_any(baseline_mask),
+            lambda: math_ops.reduce_mean(selected_reward),
+            lambda: constant_op.constant(0, dtype=dtypes.float32))
+        ctr["pos_reward"] = selected_baseline
+        pos_ = math_ops.less(
+            constant_op.constant(0, dtype=dtypes.float32), selected_baseline)
+        selected_baseline = self.hparams.bl_dec * ctr["baseline"] + (
+            1 - self.hparams.bl_dec) * selected_baseline
+        selected_baseline = control_flow_ops.cond(
+            pos_, lambda: selected_baseline, lambda: ctr["baseline"])
+        new_baseline = control_flow_ops.cond(
+            math_ops.less(self.global_step,
+                          self.hparams.stop_updating_after_steps),
+            lambda: new_baseline, lambda: selected_baseline)
+      ctr["baseline_update"] = state_ops.assign(
+          ctr["baseline"], new_baseline, use_locking=True)
+
+      ctr["y_preds"], ctr["log_probs"] = self.get_placements()
+      summary.histogram("actions", ctr["y_preds"]["sample"])
+      mask = math_ops.less(ctr["reward"]["value"], failing_signal)
+      ctr["loss"] = ctr["reward"]["value"] - ctr["baseline"]
+      ctr["loss"] *= (
+          ctr["log_probs"]["sample"] + ctr["grouping_log_probs"]["sample"])
+
+      selected_loss = array_ops.boolean_mask(ctr["loss"], mask)
+      selected_loss = control_flow_ops.cond(
+          math_ops.reduce_any(mask),
+          lambda: math_ops.reduce_mean(-selected_loss),
+          lambda: constant_op.constant(0, dtype=dtypes.float32))
+
+      ctr["loss"] = control_flow_ops.cond(
+          math_ops.less(self.global_step,
+                        self.hparams.stop_updating_after_steps),
+          lambda: math_ops.reduce_mean(-ctr["loss"]), lambda: selected_loss)
+
+      ctr["reward_s"] = math_ops.reduce_mean(ctr["reward"]["value"])
+      summary.scalar("loss", ctr["loss"])
+      summary.scalar("avg_reward", ctr["reward_s"])
+      summary.scalar("best_reward_so_far", best_reward)
+      summary.scalar(
+          "advantage",
+          math_ops.reduce_mean(ctr["reward"]["value"] - ctr["baseline"]))
+
+    with variable_scope.variable_scope(
+        "optimizer", reuse=variable_scope.AUTO_REUSE):
+      (ctr["train_op"], ctr["lr"], ctr["grad_norm"],
+       ctr["grad_norms"]) = self._get_train_ops(
+           ctr["loss"],
+           tf_ops.get_collection(tf_ops.GraphKeys.TRAINABLE_VARIABLES),
+           self.global_step,
+           grad_bound=self.hparams.grad_bound,
+           lr_init=self.hparams.lr,
+           lr_dec=self.hparams.lr_dec,
+           start_decay_step=self.hparams.start_decay_step,
+           decay_steps=self.hparams.decay_steps,
+           optimizer_type=self.hparams.optimizer_type)
+
+    summary.scalar("gradnorm", ctr["grad_norm"])
+    summary.scalar("lr", ctr["lr"])
+    ctr["summary"] = summary.merge_all()
+    ops["controller"] = ctr
+
+    self.ops = ops
+    return ops
+
+  @property
+  def global_step(self):
+    return self._global_step
+
+  def create_op_embeddings(self, verbose=False):
+    if verbose:
+      print("process input graph for op embeddings")
+    self.num_ops = len(self.important_ops)
+    # topological sort of important nodes
+    topo_order = [op.name for op in self.important_ops]
+
+    # create index to name for topologicaly sorted important nodes
+    name_to_topo_order_index = {}
+    for idx, x in enumerate(topo_order):
+      name_to_topo_order_index[x] = idx
+    self.name_to_topo_order_index = name_to_topo_order_index
+
+    # create adj matrix
+    adj_dict = {}
+    for idx, op in enumerate(self.important_ops):
+      for output_op in self.get_node_fanout(op):
+        output_op_name = output_op.name
+        if output_op_name in self.important_op_names:
+          if name_to_topo_order_index[op.name] not in adj_dict:
+            adj_dict[name_to_topo_order_index[op.name]] = []
+          adj_dict[name_to_topo_order_index[op.name]].extend(
+              [name_to_topo_order_index[output_op_name], 1])
+          if output_op_name not in adj_dict:
+            adj_dict[name_to_topo_order_index[output_op_name]] = []
+          adj_dict[name_to_topo_order_index[output_op_name]].extend(
+              [name_to_topo_order_index[op.name], -1])
+
+    # get op_type op_output_shape, and adj info
+    output_embed_dim = (self.hparams.max_num_outputs *
+                        self.hparams.max_output_size)
+
+    # TODO(bsteiner): don't filter based on used ops so that we can generalize
+    # to models that use other types of ops.
+    used_ops = set()
+    for node in self.important_ops:
+      op_type = str(node.op)
+      used_ops.add(op_type)
+
+    self.type_dict = {}
+    for op_type in self.cluster.ListAvailableOps():
+      if op_type in used_ops:
+        self.type_dict[op_type] = len(self.type_dict)
+
+    op_types = np.zeros([self.num_ops], dtype=np.int32)
+    op_output_shapes = np.full(
+        [self.num_ops, output_embed_dim], -1.0, dtype=np.float32)
+    for idx, node in enumerate(self.important_ops):
+      op_types[idx] = self.type_dict[node.op]
+      # output shape
+      op_name = node.name
+      for i, output_prop in enumerate(self.node_properties[op_name]):
+        if output_prop.shape.__str__() == "<unknown>":
+          continue
+        shape = output_prop.shape
+        for j, dim in enumerate(shape.dim):
+          if dim.size >= 0:
+            if i * self.hparams.max_output_size + j >= output_embed_dim:
+              break
+            op_output_shapes[idx,
+                             i * self.hparams.max_output_size + j] = dim.size
+    # adj for padding
+    op_adj = np.full(
+        [self.num_ops, self.hparams.adj_embed_dim], 0, dtype=np.float32)
+    for idx in adj_dict:
+      neighbors = adj_dict[int(idx)]
+      min_dim = min(self.hparams.adj_embed_dim, len(neighbors))
+      padding_size = self.hparams.adj_embed_dim - min_dim
+      neighbors = neighbors[:min_dim] + [0] * padding_size
+      op_adj[int(idx)] = neighbors
+
+    # op_embedding   starts here
+    op_embeddings = np.zeros(
+        [
+            self.num_ops,
+            1 + self.hparams.max_num_outputs * self.hparams.max_output_size +
+            self.hparams.adj_embed_dim
+        ],
+        dtype=np.float32)
+    for idx, op_name in enumerate(topo_order):
+      op_embeddings[idx] = np.concatenate(
+          (np.array([op_types[idx]]), op_output_shapes[idx], op_adj[int(idx)]))
+    self.op_embeddings = constant_op.constant(
+        op_embeddings, dtype=dtypes.float32)
+    if verbose:
+      print("num_ops = {}".format(self.num_ops))
+      print("num_types = {}".format(len(self.type_dict)))
+
+  def get_groupings(self, *args, **kwargs):
+    num_children = self.hparams.num_children
+    with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)):
+      grouping_actions_cache = variable_scope.get_local_variable(
+          "grouping_actions_cache",
+          initializer=init_ops.zeros_initializer,
+          dtype=dtypes.int32,
+          shape=[num_children, self.num_ops],
+          trainable=False)
+    input_layer = self.op_embeddings
+    input_layer = array_ops.expand_dims(input_layer, 0)
+    feed_ff_input_layer = array_ops.tile(input_layer, [num_children, 1, 1])
+    grouping_actions, grouping_log_probs = {}, {}
+    grouping_actions["sample"], grouping_log_probs[
+        "sample"] = self.make_grouping_predictions(feed_ff_input_layer)
+
+    grouping_actions["sample"] = state_ops.assign(grouping_actions_cache,
+                                                  grouping_actions["sample"])
+    self.grouping_actions_cache = grouping_actions_cache
+
+    return grouping_actions, grouping_log_probs
+
+  def make_grouping_predictions(self, input_layer, reuse=None):
+    """model that predicts grouping (grouping_actions).
+
+    Args:
+      input_layer: group_input_layer
+      reuse: reuse
+
+    Returns:
+       grouping_actions: actions
+       grouping_log_probs: log probabilities corresponding to actions
+    """
+    with variable_scope.variable_scope(self.hparams.name, reuse=True):
+      # input_layer: tensor of size [1, num_ops, hidden_size]
+      w_grouping_ff = variable_scope.get_variable("w_grouping_ff")
+      w_grouping_softmax = variable_scope.get_variable("w_grouping_softmax")
+
+    batch_size = array_ops.shape(input_layer)[0]
+    embedding_dim = array_ops.shape(input_layer)[2]
+
+    reshaped = array_ops.reshape(input_layer,
+                                 [batch_size * self.num_ops, embedding_dim])
+    ff_output = math_ops.matmul(reshaped, w_grouping_ff)
+    logits = math_ops.matmul(ff_output, w_grouping_softmax)
+    if self.hparams.logits_std_noise > 0:
+      num_in_logits = math_ops.cast(
+          array_ops.size(logits), dtype=dtypes.float32)
+      avg_norm = math_ops.divide(
+          linalg_ops.norm(logits), math_ops.sqrt(num_in_logits))
+      logits_noise = random_ops.random_normal(
+          array_ops.shape(logits),
+          stddev=self.hparams.logits_std_noise * avg_norm)
+      logits = control_flow_ops.cond(
+          self.global_step > self.hparams.stop_noise_step, lambda: logits,
+          lambda: logits + logits_noise)
+    logits = array_ops.reshape(logits,
+                               [batch_size * self.num_ops, self.num_groups])
+    actions = random_ops.multinomial(logits, 1, seed=self.hparams.seed)
+    actions = math_ops.to_int32(actions)
+    actions = array_ops.reshape(actions, [batch_size, self.num_ops])
+    action_label = array_ops.reshape(actions, [-1])
+    log_probs = nn_ops.sparse_softmax_cross_entropy_with_logits(
+        logits=logits, labels=action_label)
+    log_probs = array_ops.reshape(log_probs, [batch_size, -1])
+    log_probs = math_ops.reduce_sum(log_probs, 1)
+    grouping_actions = actions
+    grouping_log_probs = log_probs
+    return grouping_actions, grouping_log_probs
+
+  def create_group_embeddings(self, grouping_actions, verbose=False):
+    """Approximating the blocks of a TF graph from a graph_def.
+
+    Args:
+      grouping_actions: grouping predictions
+      verbose: print stuffs.
+
+    Returns:
+      groups: list of groups.
+    """
+    if verbose:
+      print("Processing input_graph")
+
+    # TODO(azalia): Build inter-adjacencies dag matrix.
+    # record dag_matrix
+    dag_matrix = np.zeros([self.num_groups, self.num_groups], dtype=np.float32)
+    for op in self.important_ops:
+      topo_op_index = self.name_to_topo_order_index[op.name]
+      # TODO(agoldie) child_id
+      group_index = grouping_actions[0][topo_op_index]
+      for output_op in self.get_node_fanout(op):
+        if output_op.name not in self.important_op_names:
+          continue
+        output_group_index = grouping_actions[0][self.name_to_topo_order_index[
+            output_op.name]]
+        dag_matrix[group_index, output_group_index] += 1.0
+    num_connections = np.sum(dag_matrix)
+    num_intra_group_connections = dag_matrix.trace()
+    num_inter_group_connections = num_connections - num_intra_group_connections
+    if verbose:
+      print("grouping evaluation metric")
+      print("num_connections={} num_intra_group_connections={} "
+            "num_inter_group_connections={}").format(
+                num_connections, num_intra_group_connections,
+                num_inter_group_connections)
+    self.dag_matrix = dag_matrix
+
+    # output_shape
+    op_output_shapes = np.zeros(
+        [
+            len(self.important_ops),
+            self.hparams.max_num_outputs * self.hparams.max_output_size
+        ],
+        dtype=np.float32)
+
+    for idx, op in enumerate(self.important_ops):
+      for i, output_properties in enumerate(self.node_properties[op.name]):
+        if output_properties.shape.__str__() == "<unknown>":
+          continue
+        if i > self.hparams.max_num_outputs:
+          break
+        shape = output_properties.shape
+        for j, dim in enumerate(shape.dim):
+          if dim.size > 0:
+            k = i * self.hparams.max_output_size + j
+            if k >= self.hparams.max_num_outputs * self.hparams.max_output_size:
+              break
+            op_output_shapes[idx, k] = dim.size
+
+    # group_embedding
+    group_embedding = np.zeros(
+        [
+            self.num_groups, len(self.type_dict) +
+            self.hparams.max_num_outputs * self.hparams.max_output_size
+        ],
+        dtype=np.float32)
+    for op_index, op in enumerate(self.important_ops):
+      group_index = grouping_actions[0][self.name_to_topo_order_index[op.name]]
+      type_name = str(op.op)
+      type_index = self.type_dict[type_name]
+      group_embedding[group_index, type_index] += 1
+      group_embedding[group_index, :self.hparams.max_num_outputs * self.hparams.
+                      max_output_size] += (
+                          op_output_shapes[op_index])
+    grouping_adjacencies = np.concatenate(
+        [dag_matrix, np.transpose(dag_matrix)], axis=1)
+    group_embedding = np.concatenate(
+        [grouping_adjacencies, group_embedding], axis=1)
+    group_normalizer = np.amax(group_embedding, axis=1, keepdims=True)
+    group_embedding /= (group_normalizer + 1.0)
+    if verbose:
+      print("Finished Processing Input Graph")
+    return group_embedding
+
+  def get_placements(self, *args, **kwargs):
+    num_children = self.hparams.num_children
+    with variable_scope.variable_scope("controller_{}".format(self.ctrl_id)):
+      actions_cache = variable_scope.get_local_variable(
+          "actions_cache",
+          initializer=init_ops.zeros_initializer,
+          dtype=dtypes.int32,
+          shape=[num_children, self.num_groups],
+          trainable=False)
+
+    x = array_ops.tile(self.seq2seq_input_layer, [num_children, 1, 1])
+    last_c, last_h, attn_mem = self.encode(x)
+    actions, log_probs = {}, {}
+    actions["sample"], log_probs["sample"] = (
+        self.decode(
+            x, last_c, last_h, attn_mem, mode="sample"))
+    actions["target"], log_probs["target"] = (
+        self.decode(
+            x,
+            last_c,
+            last_h,
+            attn_mem,
+            mode="target",
+            y=actions_cache))
+    actions["greedy"], log_probs["greedy"] = (
+        self.decode(
+            x, last_c, last_h, attn_mem, mode="greedy"))
+    actions["sample"] = control_flow_ops.cond(
+        self.global_step < self.hparams.stop_sampling,
+        lambda: state_ops.assign(actions_cache, actions["sample"]),
+        lambda: state_ops.assign(actions_cache, actions["target"]))
+    self.actions_cache = actions_cache
+
+    return actions, log_probs
+
+  def encode(self, x):
+    """Encoder using LSTM.
+
+    Args:
+      x: tensor of size [num_children, num_groups, embedding_size]
+
+    Returns:
+      last_c, last_h: tensors of size [num_children, hidden_size], the final
+        LSTM states
+      attn_mem: tensor of size [num_children, num_groups, hidden_size], the
+      attention
+        memory, i.e. concatenation of all hidden states, linearly transformed by
+        an attention matrix attn_w_1
+    """
+    if self.hparams.bi_lstm:
+      with variable_scope.variable_scope(self.hparams.name, reuse=True):
+        w_lstm_forward = variable_scope.get_variable("encoder_lstm_forward")
+        w_lstm_backward = variable_scope.get_variable("encoder_lstm_backward")
+        forget_bias = variable_scope.get_variable("encoder_forget_bias")
+        attn_w_1 = variable_scope.get_variable("attn_w_1")
+    else:
+      with variable_scope.variable_scope(self.hparams.name, reuse=True):
+        w_lstm = variable_scope.get_variable("encoder_lstm")
+        forget_bias = variable_scope.get_variable("encoder_forget_bias")
+        attn_w_1 = variable_scope.get_variable("attn_w_1")
+
+    embedding_size = array_ops.shape(x)[2]
+
+    signals = array_ops.split(x, self.num_groups, axis=1)
+    for i in range(len(signals)):
+      signals[i] = array_ops.reshape(
+          signals[i], [self.hparams.num_children, embedding_size])
+
+    if self.hparams.bi_lstm:
+
+      def body(i, prev_c_forward, prev_h_forward, prev_c_backward,
+               prev_h_backward):
+        """while loop for LSTM."""
+        signal_forward = signals[i]
+        next_c_forward, next_h_forward = lstm(signal_forward, prev_c_forward,
+                                              prev_h_forward, w_lstm_forward,
+                                              forget_bias)
+
+        signal_backward = signals[self.num_groups - 1 - i]
+        next_c_backward, next_h_backward = lstm(
+            signal_backward, prev_c_backward, prev_h_backward, w_lstm_backward,
+            forget_bias)
+
+        next_h = array_ops.concat([next_h_forward, next_h_backward], axis=1)
+        all_h.append(next_h)
+
+        return (next_c_forward, next_h_forward, next_c_backward,
+                next_h_backward)
+
+      c_forward = array_ops.zeros(
+          [self.hparams.num_children, self.hparams.hidden_size / 2],
+          dtype=dtypes.float32)
+      h_forward = array_ops.zeros(
+          [self.hparams.num_children, self.hparams.hidden_size / 2],
+          dtype=dtypes.float32)
+
+      c_backward = array_ops.zeros(
+          [self.hparams.num_children, self.hparams.hidden_size / 2],
+          dtype=dtypes.float32)
+      h_backward = array_ops.zeros(
+          [self.hparams.num_children, self.hparams.hidden_size / 2],
+          dtype=dtypes.float32)
+      all_h = []
+
+      for i in range(0, self.num_groups):
+        c_forward, h_forward, c_backward, h_backward = body(
+            i, c_forward, h_forward, c_backward, h_backward)
+
+      last_c = array_ops.concat([c_forward, c_backward], axis=1)
+      last_h = array_ops.concat([h_forward, h_backward], axis=1)
+      attn_mem = array_ops.stack(all_h)
+
+    else:
+
+      def body(i, prev_c, prev_h):
+        signal = signals[i]
+        next_c, next_h = lstm(signal, prev_c, prev_h, w_lstm, forget_bias)
+        all_h.append(next_h)
+        return next_c, next_h
+
+      c = array_ops.zeros(
+          [self.hparams.num_children, self.hparams.hidden_size],
+          dtype=dtypes.float32)
+      h = array_ops.zeros(
+          [self.hparams.num_children, self.hparams.hidden_size],
+          dtype=dtypes.float32)
+      all_h = []
+
+      for i in range(0, self.num_groups):
+        c, h = body(i, c, h)
+
+      last_c = c
+      last_h = h
+      attn_mem = array_ops.stack(all_h)
+
+    attn_mem = array_ops.transpose(attn_mem, [1, 0, 2])
+    attn_mem = array_ops.reshape(
+        attn_mem,
+        [self.hparams.num_children * self.num_groups, self.hparams.hidden_size])
+    attn_mem = math_ops.matmul(attn_mem, attn_w_1)
+    attn_mem = array_ops.reshape(
+        attn_mem,
+        [self.hparams.num_children, self.num_groups, self.hparams.hidden_size])
+
+    return last_c, last_h, attn_mem
+
+  def decode(self,
+             x,
+             last_c,
+             last_h,
+             attn_mem,
+             mode="target",
+             y=None):
+    """Decoder using LSTM.
+
+    Args:
+      x: tensor of size [num_children, num_groups, embedding_size].
+      last_c: tensor of size [num_children, hidden_size], the final LSTM states
+          computed by self.encoder.
+      last_h: same as last_c.
+      attn_mem: tensor of size [num_children, num_groups, hidden_size].
+      mode: "target" or "sample".
+      y: tensor of size [num_children, num_groups], the device placements.
+
+    Returns:
+      actions: tensor of size [num_children, num_groups], the placements of
+          devices
+    """
+    with variable_scope.variable_scope(self.hparams.name, reuse=True):
+      w_lstm = variable_scope.get_variable("decoder_lstm")
+      forget_bias = variable_scope.get_variable("decoder_forget_bias")
+      device_embeddings = variable_scope.get_variable("device_embeddings")
+      device_softmax = variable_scope.get_variable("device_softmax")
+      device_go_embedding = variable_scope.get_variable("device_go_embedding")
+      attn_w_2 = variable_scope.get_variable("attn_w_2")
+      attn_v = variable_scope.get_variable("attn_v")
+
+    actions = tensor_array_ops.TensorArray(
+        dtypes.int32,
+        size=self.num_groups,
+        infer_shape=False,
+        clear_after_read=False)
+
+    # pylint: disable=unused-argument
+    def condition(i, *args):
+      return math_ops.less(i, self.num_groups)
+
+    # pylint: disable=missing-docstring
+    def body(i, prev_c, prev_h, actions, log_probs):
+      # pylint: disable=g-long-lambda
+      signal = control_flow_ops.cond(
+          math_ops.equal(i, 0),
+          lambda: array_ops.tile(device_go_embedding,
+                                 [self.hparams.num_children, 1]),
+          lambda: embedding_ops.embedding_lookup(device_embeddings,
+                                                 actions.read(i - 1))
+      )
+      if self.hparams.keep_prob is not None:
+        signal = nn_ops.dropout(signal, self.hparams.keep_prob)
+      next_c, next_h = lstm(signal, prev_c, prev_h, w_lstm, forget_bias)
+      query = math_ops.matmul(next_h, attn_w_2)
+      query = array_ops.reshape(
+          query, [self.hparams.num_children, 1, self.hparams.hidden_size])
+      query = math_ops.tanh(query + attn_mem)
+      query = array_ops.reshape(query, [
+          self.hparams.num_children * self.num_groups, self.hparams.hidden_size
+      ])
+      query = math_ops.matmul(query, attn_v)
+      query = array_ops.reshape(query,
+                                [self.hparams.num_children, self.num_groups])
+      query = nn_ops.softmax(query)
+      query = array_ops.reshape(query,
+                                [self.hparams.num_children, self.num_groups, 1])
+      query = math_ops.reduce_sum(attn_mem * query, axis=1)
+      query = array_ops.concat([next_h, query], axis=1)
+      logits = math_ops.matmul(query, device_softmax)
+      logits /= self.hparams.temperature
+      if self.hparams.tanh_constant > 0:
+        logits = math_ops.tanh(logits) * self.hparams.tanh_constant
+      if self.hparams.logits_std_noise > 0:
+        num_in_logits = math_ops.cast(
+            array_ops.size(logits), dtype=dtypes.float32)
+        avg_norm = math_ops.divide(
+            linalg_ops.norm(logits), math_ops.sqrt(num_in_logits))
+        logits_noise = random_ops.random_normal(
+            array_ops.shape(logits),
+            stddev=self.hparams.logits_std_noise * avg_norm)
+        logits = control_flow_ops.cond(
+            self.global_step > self.hparams.stop_noise_step, lambda: logits,
+            lambda: logits + logits_noise)
+
+      if mode == "sample":
+        next_y = random_ops.multinomial(logits, 1, seed=self.hparams.seed)
+      elif mode == "greedy":
+        next_y = math_ops.argmax(logits, 1)
+      elif mode == "target":
+        next_y = array_ops.slice(y, [0, i], [-1, 1])
+      else:
+        raise NotImplementedError
+      next_y = math_ops.to_int32(next_y)
+      next_y = array_ops.reshape(next_y, [self.hparams.num_children])
+      actions = actions.write(i, next_y)
+      log_probs += nn_ops.sparse_softmax_cross_entropy_with_logits(
+          logits=logits, labels=next_y)
+      return i + 1, next_c, next_h, actions, log_probs
+
+    loop_vars = [
+        constant_op.constant(0, dtype=dtypes.int32), last_c, last_h, actions,
+        array_ops.zeros([self.hparams.num_children], dtype=dtypes.float32)
+    ]
+    loop_outputs = control_flow_ops.while_loop(condition, body, loop_vars)
+
+    last_c = loop_outputs[-4]
+    last_h = loop_outputs[-3]
+    actions = loop_outputs[-2].stack()
+    actions = array_ops.transpose(actions, [1, 0])
+    log_probs = loop_outputs[-1]
+    return actions, log_probs
+
+  def eval_placement(self,
+                     sess,
+                     child_id=0,
+                     verbose=False):
+    grouping_actions, actions = sess.run([
+        self.grouping_actions_cache,
+        self.actions_cache
+    ])
+    grouping_actions = grouping_actions[child_id]
+    actions = actions[child_id]
+    if verbose:
+      global_step = sess.run(self.global_step)
+      if global_step % 100 == 0:
+        log_string = "op group assignments: "
+        for a in grouping_actions:
+          log_string += "{} ".format(a)
+        print(log_string[:-1])
+        log_string = "group device assignments: "
+        for a in actions:
+          log_string += "{} ".format(a)
+        print(log_string[:-1])
+
+    for op in self.important_ops:
+      topo_order_index = self.name_to_topo_order_index[op.name]
+      group_index = grouping_actions[topo_order_index]
+      op.device = self.devices[actions[group_index]].name
+    try:
+      _, run_time, _ = self.cluster.MeasureCosts(self.item)
+    except errors.ResourceExhaustedError:
+      run_time = self.hparams.failing_signal
+    return run_time
+
+  def update_reward(self,
+                    sess,
+                    run_time,
+                    child_id=0,
+                    verbose=False):
+    reward = self.compute_reward(run_time)
+    controller_ops = self.ops["controller"]
+    _, best_reward = sess.run(
+        [
+            controller_ops["reward"]["update"][child_id],
+            controller_ops["best_reward"]["update"][child_id]
+        ],
+        feed_dict={
+            controller_ops["reward"]["ph"][child_id]: reward,
+        })
+    if verbose:
+      print("run_time={:<.5f} reward={:<.5f} "
+            "best_reward={:<.5f}").format(run_time, reward, best_reward)
+
+    # Reward is a double, best_reward a float: allow for some slack in the
+    # comparison.
+    updated = abs(best_reward - reward) < 1e-6
+    return updated
+
+  def generate_grouping(self, sess):
+    controller_ops = self.ops["controller"]
+    grouping_actions = sess.run(controller_ops["grouping_y_preds"]["sample"])
+    return grouping_actions
+
+  def generate_placement(self, grouping, sess):
+    controller_ops = self.ops["controller"]
+    feed_seq2seq_input_dict = {}
+    feed_seq2seq_input_dict[self.seq2seq_input_layer] = np.expand_dims(
+        grouping, axis=0)
+    sess.run(
+        controller_ops["y_preds"]["sample"], feed_dict=feed_seq2seq_input_dict)
+
+  def process_reward(self, sess):
+    controller_ops = self.ops["controller"]
+    run_ops = [
+        controller_ops["loss"], controller_ops["lr"],
+        controller_ops["grad_norm"], controller_ops["grad_norms"],
+        controller_ops["train_op"]
+    ]
+    sess.run(run_ops)
+    sess.run(controller_ops["baseline_update"])
+
+  def _get_train_ops(self,
+                     loss,
+                     tf_variables,
+                     global_step,
+                     grad_bound=1.25,
+                     lr_init=1e-3,
+                     lr_dec=0.9,
+                     start_decay_step=10000,
+                     decay_steps=100,
+                     optimizer_type="adam"):
+    """Loss optimizer.
+
+    Args:
+      loss: scalar tf tensor
+      tf_variables: list of training variables, typically
+        tf.trainable_variables()
+      global_step: global_step
+      grad_bound: max gradient norm
+      lr_init: initial learning rate
+      lr_dec: leaning rate decay coefficient
+      start_decay_step: start decaying learning rate after this many steps
+      decay_steps: apply decay rate factor at this step intervals
+      optimizer_type: optimizer type should be either adam or sgd
+
+    Returns:
+      train_op: training op
+      learning_rate: scalar learning rate tensor
+      grad_norm: l2 norm of the gradient vector
+      all_grad_norms: l2 norm of each component
+    """
+    lr_gstep = global_step - start_decay_step
+
+    def f1():
+      return constant_op.constant(lr_init)
+
+    def f2():
+      return learning_rate_decay.exponential_decay(lr_init, lr_gstep,
+                                                   decay_steps, lr_dec, True)
+
+    learning_rate = control_flow_ops.cond(
+        math_ops.less(global_step, start_decay_step),
+        f1,
+        f2,
+        name="learning_rate")
+
+    if optimizer_type == "adam":
+      opt = adam.AdamOptimizer(learning_rate)
+    elif optimizer_type == "sgd":
+      opt = gradient_descent.GradientDescentOptimizer(learning_rate)
+    grads_and_vars = opt.compute_gradients(loss, tf_variables)
+    grad_norm = clip_ops.global_norm([g for g, v in grads_and_vars])
+    all_grad_norms = {}
+    clipped_grads = []
+    clipped_rate = math_ops.maximum(grad_norm / grad_bound, 1.0)
+    for g, v in grads_and_vars:
+      if g is not None:
+        if isinstance(g, tf_ops.IndexedSlices):
+          clipped = g.values / clipped_rate
+          norm_square = math_ops.reduce_sum(clipped * clipped)
+          clipped = tf_ops.IndexedSlices(clipped, g.indices)
+        else:
+          clipped = g / clipped_rate
+          norm_square = math_ops.reduce_sum(clipped * clipped)
+        all_grad_norms[v.name] = math_ops.sqrt(norm_square)
+        clipped_grads.append((clipped, v))
+
+    train_op = opt.apply_gradients(clipped_grads, global_step)
+    return train_op, learning_rate, grad_norm, all_grad_norms
+
+
+def lstm(x, prev_c, prev_h, w_lstm, forget_bias):
+  """LSTM cell.
+
+  Args:
+    x: tensors of size [num_children, hidden_size].
+    prev_c: tensors of size [num_children, hidden_size].
+    prev_h: same as prev_c.
+    w_lstm: .
+    forget_bias: .
+
+  Returns:
+    next_c:
+    next_h:
+  """
+  ifog = math_ops.matmul(array_ops.concat([x, prev_h], axis=1), w_lstm)
+  i, f, o, g = array_ops.split(ifog, 4, axis=1)
+  i = math_ops.sigmoid(i)
+  f = math_ops.sigmoid(f + forget_bias)
+  o = math_ops.sigmoid(o)
+  g = math_ops.tanh(g)
+  next_c = i * g + f * prev_c
+  next_h = o * math_ops.tanh(next_c)
+  return next_c, next_h
diff --git a/tensorflow/python/grappler/item.i b/tensorflow/python/grappler/item.i
index d0fc1a04f2..9a84c60b04 100644
--- a/tensorflow/python/grappler/item.i
+++ b/tensorflow/python/grappler/item.i
@@ -96,10 +96,10 @@ static GItem TF_NewItem(
   return GItem(item.release());
 }
 
-static std::vector<string> TF_IdentifyImportantOps(GItem item, bool sort_topologically,
+static PyObject* TF_IdentifyImportantOps(GItem item, bool sort_topologically,
                                                    TF_Status* status) {
   if (item.is_none()) {
-    return {};
+    Py_RETURN_NONE;
   }
 
   std::vector<const tensorflow::NodeDef*> main_ops = item->MainOpsFanin();
@@ -132,7 +132,13 @@ static std::vector<string> TF_IdentifyImportantOps(GItem item, bool sort_topolog
     }
   }
 
-  return ops;
+  PyGILState_STATE gstate = PyGILState_Ensure();
+  PyObject* result = PyList_New(ops.size());
+  for (int i = 0; i < ops.size(); ++i) {
+    PyList_SetItem(result, i, PyString_FromString(ops[i].c_str()));
+  }
+  PyGILState_Release(gstate);
+  return result;
 }
 
 static PyObject* TF_GetOpProperties(GItem item) {
@@ -305,7 +311,7 @@ static PyObject* TF_GetColocationGroups(GItem item) {
 static GItem TF_NewItem(
     const tensorflow::MetaGraphDef& meta_graph, bool ignore_colocation,
     bool ignore_user_placement, TF_Status* out_status);
-static std::vector<string> TF_IdentifyImportantOps(GItem item, bool sort_topologically,
-                                                   TF_Status* status);
+static PyObject* TF_IdentifyImportantOps(GItem item, bool sort_topologically,
+                                         TF_Status* status);
 static PyObject* TF_GetOpProperties(GItem item);
 static PyObject* TF_GetColocationGroups(GItem item);
diff --git a/tensorflow/python/grappler/item_test.py b/tensorflow/python/grappler/item_test.py
index cd70e2fdec..7c3efd6249 100644
--- a/tensorflow/python/grappler/item_test.py
+++ b/tensorflow/python/grappler/item_test.py
@@ -56,7 +56,7 @@ class ItemTest(test.TestCase):
       mg = meta_graph.create_meta_graph_def(graph=g)
       grappler_item = item.Item(mg)
       op_list = grappler_item.IdentifyImportantOps()
-      self.assertItemsEqual([b'Const', b'Const_1', b'add'], op_list)
+      self.assertItemsEqual(['Const', 'Const_1', 'add'], op_list)
 
   def testOpProperties(self):
     with ops.Graph().as_default() as g:
-- 
GitLab


From 6e7df7bd8c0d5aaf70b3fc5c2b180b6bcb53629a Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Wed, 21 Feb 2018 21:06:54 -0800
Subject: [PATCH 181/575] Disable flaky moving_average test.

PiperOrigin-RevId: 186563841
---
 tensorflow/contrib/opt/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 827279bd47..52e88348c1 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -70,6 +70,9 @@ py_test(
     srcs = ["python/training/moving_average_optimizer_test.py"],
     srcs_version = "PY2AND3",
     tags = [
+        "manual",
+        "no_oss",
+        "notap",
         "notsan",  # b/31055119
     ],
     deps = [
-- 
GitLab


From c9a09c55a45b8b44c137f85fb8236043a636a67d Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Wed, 21 Feb 2018 22:02:54 -0800
Subject: [PATCH 182/575] Avoid creating large constants since protocol buffers
 are limited to 2GB in size.

PiperOrigin-RevId: 186567461
---
 .../optimizers/arithmetic_optimizer.cc        |  9 +++--
 .../grappler/optimizers/constant_folding.cc   | 36 ++++++++++++-------
 .../grappler/optimizers/constant_folding.h    |  3 +-
 3 files changed, 33 insertions(+), 15 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index c455f28a5b..fbb3e5aaee 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -870,8 +870,13 @@ string ArithmeticOptimizer::TrySimplifyAndReplaceUses(
       }
       TensorValue value(&t);
       NodeDef* new_const_node = AddNode(*node, "const", /*copy_node=*/false);
-      *new_const_node =
-          ConstantFolding::CreateNodeDef(new_const_node->name(), value);
+      status = ConstantFolding::CreateNodeDef(new_const_node->name(), value,
+                                              new_const_node);
+      if (!status.ok()) {
+        LOG(WARNING) << "Failed to create const node: "
+                     << status.error_message();
+        return "";
+      }
       new_const_node->set_device(node->device());
       nodes_to_simplify->PushBack(new_const_node);
 
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 95eaa31a46..064cb8b5ae 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -529,7 +529,8 @@ Status ConstantFolding::MaterializeBroadcastGradientArgs(
     out[j] = node_map_->GetNode(const_name);
     if (out[j] == nullptr) {
       out[j] = graph_->add_node();
-      *out[j] = CreateNodeDef(const_name, TensorValue(&value));
+      TF_RETURN_IF_ERROR(
+          CreateNodeDef(const_name, TensorValue(&value), out[j]));
       out[j]->set_device(node.device());
       node_map_->AddNode(const_name, out[j]);
       string ctrl_dep =
@@ -637,7 +638,8 @@ Status ConstantFolding::MaterializeReductionIndices(
       value.vec<int64>()(i) = i;
     }
   }
-  *reduction_indices = CreateNodeDef(const_name, TensorValue(&value));
+  TF_RETURN_IF_ERROR(
+      CreateNodeDef(const_name, TensorValue(&value), reduction_indices));
   reduction_indices->set_device(node->device());
   string ctrl_dep =
       AddControlDependency(node->input(1), graph_, node_map_.get());
@@ -792,19 +794,20 @@ Status CreateConstantTensorAttrValue(DataType type, double value,
 }  // namespace
 
 // static
-NodeDef ConstantFolding::CreateNodeDef(const string& name,
-                                       const TensorValue& tensor) {
-  NodeDef node;
-  node.set_name(name);
-  node.set_op("Const");
+Status ConstantFolding::CreateNodeDef(const string& name,
+                                      const TensorValue& tensor,
+                                      NodeDef* node) {
+  node->set_name(name);
+  node->set_op("Const");
 
   AttrValue attr_type;
   attr_type.set_type(tensor->dtype());
-  node.mutable_attr()->insert({"dtype", attr_type});
+  node->mutable_attr()->insert({"dtype", attr_type});
 
   AttrValue attr_tensor;
   TensorProto* t = attr_tensor.mutable_tensor();
   bool optimized = false;
+  size_t encoded_size;
   // Use the packed representation whenever possible to avoid generating large
   // graphdefs. Moreover, avoid repeating the last values if they're equal.
   if (tensor->NumElements() > 4) {
@@ -821,6 +824,7 @@ NodeDef ConstantFolding::CreateNodeDef(const string& name,
   }                                                                 \
   if (last_index < kint32max) {                                     \
     optimized = true;                                               \
+    encoded_size = (last_index + 1) * sizeof(NAME);                 \
     t->mutable_##NAME##_val()->Reserve(last_index + 1);             \
     t->mutable_##NAME##_val()->AddNAlreadyReserved(last_index + 1); \
     val_ptr = tensor->flat<TYPE>().data();                          \
@@ -853,9 +857,15 @@ NodeDef ConstantFolding::CreateNodeDef(const string& name,
     tensor->shape().AsProto(t->mutable_tensor_shape());
   } else {
     tensor->AsProtoTensorContent(t);
+    encoded_size = t->tensor_content().size();
+  }
+  node->mutable_attr()->insert({"value", attr_tensor});
+
+  if (encoded_size < 10 * 1024 * 1024) {
+    return Status::OK();
   }
-  node.mutable_attr()->insert({"value", attr_tensor});
-  return node;
+  return errors::InvalidArgument(
+      strings::StrCat("Can't fold ", name, ", its size would be too large"));
 }
 
 Status ConstantFolding::EvaluateNode(const NodeDef& node,
@@ -929,17 +939,19 @@ Status ConstantFolding::EvaluateOneFoldable(const NodeDef& node,
     return Status(error::INVALID_ARGUMENT, "Expected at least one output.");
   }
 
+  outputs->resize(output_tensors.size());
   for (size_t i = 0; i < output_tensors.size(); i++) {
     string node_name = OptimizedNodeName(node, "-folded");
     if (output_tensors.size() > 1) {
       node_name = strings::StrCat(node_name, "-", i);
     }
     if (output_tensors[i].tensor) {
-      outputs->push_back(CreateNodeDef(node_name, output_tensors[i]));
+      TF_RETURN_IF_ERROR(
+          CreateNodeDef(node_name, output_tensors[i], &outputs->at(i)));
     } else {
       // Create an empty NodeDef to identify dead outputs (e.g. the output of a
       // switch that's not selected by the switch predicate).
-      outputs->push_back(NodeDef());
+      outputs->at(i) = NodeDef();
     }
   }
   return Status::OK();
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index e4078514af..232b2f9fa0 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -33,7 +33,8 @@ const char kConstantFoldingCtrl[] = "ConstantFoldingCtrl";
 // Constant folding optimization for a graph.
 class ConstantFolding : public GraphOptimizer {
  public:
-  static NodeDef CreateNodeDef(const string& name, const TensorValue& tensor);
+  static Status CreateNodeDef(const string& name, const TensorValue& tensor,
+                              NodeDef* node);
   static string AddControlDependency(const string& input_name, GraphDef* graph,
                                      NodeMap* node_map);
 
-- 
GitLab


From 1b00edea4688111396683b967f0ad5f5848d2ece Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Wed, 21 Feb 2018 23:33:44 -0800
Subject: [PATCH 183/575] Disable flaky keras:metrics_test.

PiperOrigin-RevId: 186573303
---
 tensorflow/python/keras/BUILD | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index 1956478f39..bb6d6cf425 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -256,6 +256,11 @@ py_test(
     size = "small",
     srcs = ["_impl/keras/metrics_test.py"],
     srcs_version = "PY2AND3",
+    tags = [
+        "manual",
+        "no_oss",
+        "notap",
+    ],
     deps = [
         ":keras",
         "//tensorflow/python:client_testlib",
-- 
GitLab


From 37a5b16eb44e547d5122090ae1388e3ae60a2170 Mon Sep 17 00:00:00 2001
From: Ilya Biryukov <ibiryukov@google.com>
Date: Thu, 22 Feb 2018 03:11:14 -0800
Subject: [PATCH 184/575] Fix compile errors by patching eigen locally.

PiperOrigin-RevId: 186592198
---
 tensorflow/workspace.bzl                     |  1 +
 third_party/eigen_fix_cuda_compilation.patch | 38 ++++++++++++++++++++
 2 files changed, 39 insertions(+)
 create mode 100644 third_party/eigen_fix_cuda_compilation.patch

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 0ca19b769f..6eee41bfa1 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -126,6 +126,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "0cadb31a35b514bf2dfd6b5d38205da94ef326ec6908fc3fd7c269948467214f",
       strip_prefix = "eigen-eigen-2355b229ea4c",
       build_file = str(Label("//third_party:eigen.BUILD")),
+      patch_file = str(Label("//third_party:eigen_fix_cuda_compilation.patch"))
   )
 
   tf_http_archive(
diff --git a/third_party/eigen_fix_cuda_compilation.patch b/third_party/eigen_fix_cuda_compilation.patch
new file mode 100644
index 0000000000..b921a7c31d
--- /dev/null
+++ b/third_party/eigen_fix_cuda_compilation.patch
@@ -0,0 +1,38 @@
+diff --git a/Eigen/src/Core/ProductEvaluators.h b/Eigen/src/Core/ProductEvaluators.h
+--- a/Eigen/src/Core/ProductEvaluators.h
++++ b/Eigen/src/Core/ProductEvaluators.h
+@@ -137,7 +137,7 @@ struct Assignment<DstXprType, Product<Lh
+   typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
+ {
+   typedef Product<Lhs,Rhs,Options> SrcXprType;
+-  static EIGEN_STRONG_INLINE
++  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+   void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
+   {
+     Index dstRows = src.rows();
+@@ -390,7 +390,7 @@ struct generic_product_impl<Lhs,Rhs,Dens
+   typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+   
+   template<typename Dst>
+-  static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
++  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+   {
+     // Same as: dst.noalias() = lhs.lazyProduct(rhs);
+     // but easier on the compiler side
+@@ -398,14 +398,14 @@ struct generic_product_impl<Lhs,Rhs,Dens
+   }
+   
+   template<typename Dst>
+-  static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
++  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+   {
+     // dst.noalias() += lhs.lazyProduct(rhs);
+     call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar,Scalar>());
+   }
+   
+   template<typename Dst>
+-  static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
++  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+   {
+     // dst.noalias() -= lhs.lazyProduct(rhs);
+     call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());
-- 
GitLab


From 440f5daa8adac95b734aa23d4a5b7e438a51ce8c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 05:30:42 -0800
Subject: [PATCH 185/575] Adapt TensorFlow to LLVM API change from r325725

PiperOrigin-RevId: 186604023
---
 .../xla/service/cpu/compiler_functor.cc       | 25 +++----------------
 .../xla/service/cpu/compiler_functor.h        |  2 +-
 .../compiler/xla/service/cpu/cpu_compiler.cc  |  7 +++---
 .../compiler/xla/service/cpu/simple_orc_jit.h |  4 +--
 4 files changed, 8 insertions(+), 30 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
index ed290fcdf8..61b2da7a7d 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.cc
@@ -93,8 +93,8 @@ class FilteredPassManager : public llvm::legacy::PassManager {
 };
 }  // anonymous namespace
 
-llvm::object::OwningBinary<llvm::object::ObjectFile> CompilerFunctor::
-operator()(llvm::Module& module) const {
+std::unique_ptr<llvm::MemoryBuffer> CompilerFunctor::operator()(
+    llvm::Module& module) const {
   FilteredPassManager module_passes(disable_expensive_passes_);
   FilteredFunctionPassManager function_passes(&module,
                                               disable_expensive_passes_);
@@ -157,27 +157,8 @@ operator()(llvm::Module& module) const {
   codegen_passes.run(module);
 
   // Construct ObjectFile from machine code buffer.
-  std::unique_ptr<llvm::MemoryBuffer> memory_buffer(
+  return std::unique_ptr<llvm::MemoryBuffer>(
       new llvm::ObjectMemoryBuffer(std::move(stream_buffer)));
-  llvm::Expected<std::unique_ptr<llvm::object::ObjectFile>>
-      object_file_or_error = llvm::object::ObjectFile::createObjectFile(
-          memory_buffer->getMemBufferRef());
-  CHECK(object_file_or_error);
-
-  std::unique_ptr<llvm::object::ObjectFile> object_file =
-      std::move(object_file_or_error.get());
-  if (VLOG_IS_ON(2)) {
-    StatusOr<DisassemblerResult> disassembly_status =
-        disassembler_->DisassembleObjectFile(*object_file);
-    if (disassembly_status.ok()) {
-      auto result = disassembly_status.ValueOrDie();
-      XLA_VLOG_LINES(2, result.text);
-      VLOG(2) << "compiled code size: " << result.code_size_bytes << " bytes";
-    }
-  }
-
-  return llvm::object::OwningBinary<llvm::object::ObjectFile>(
-      std::move(object_file), std::move(memory_buffer));
 }
 
 static std::vector<llvm::VecDesc> VectorFunctionsForTargetLibraryInfoImpl() {
diff --git a/tensorflow/compiler/xla/service/cpu/compiler_functor.h b/tensorflow/compiler/xla/service/cpu/compiler_functor.h
index 1a8283a702..c38b896c50 100644
--- a/tensorflow/compiler/xla/service/cpu/compiler_functor.h
+++ b/tensorflow/compiler/xla/service/cpu/compiler_functor.h
@@ -47,7 +47,7 @@ class CompilerFunctor {
         post_optimization_hook_(post_optimization_hook) {}
 
   // Compile a Module to an ObjectFile.
-  llvm::object::OwningBinary<llvm::object::ObjectFile> operator()(
+  std::unique_ptr<llvm::MemoryBuffer> operator()(
       llvm::Module& module) const;  // NOLINT
 
  private:
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index f9cc965184..387806e24a 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -889,11 +889,10 @@ CpuCompiler::CompileAheadOfTime(std::vector<std::unique_ptr<HloModule>> modules,
         module->config().debug_options().xla_enable_fast_math(),
         module->config().debug_options().xla_llvm_disable_expensive_passes(),
         pre_optimization_ir_dump_hook, post_optimization_ir_dump_hook);
-    llvm::object::OwningBinary<llvm::object::ObjectFile> object_file =
+    std::unique_ptr<llvm::MemoryBuffer> object_file =
         compiler_functor(llvm_module);
-    llvm::StringRef object_file_data_ref = object_file.getBinary()->getData();
-    ObjectFileData object_file_data(object_file_data_ref.begin(),
-                                    object_file_data_ref.end());
+    ObjectFileData object_file_data(object_file->getBufferStart(),
+                                    object_file->getBufferEnd());
 
     BufferSizes buffer_sizes;
     for (const BufferAllocation& allocation : assignment->Allocations()) {
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
index d0011e0a18..aaeff2de87 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.h
@@ -46,9 +46,7 @@ namespace cpu {
 class SimpleOrcJIT {
  public:
   using ObjLayerT = llvm::orc::RTDyldObjectLinkingLayer;
-  using CompileFtor =
-      std::function<llvm::object::OwningBinary<llvm::object::ObjectFile>(
-          llvm::Module&)>;
+  using CompileFtor = std::function<ObjLayerT::ObjectPtr(llvm::Module&)>;
   using CompileLayerT = llvm::orc::IRCompileLayer<ObjLayerT, CompileFtor>;
   using VModuleKeyT = llvm::orc::VModuleKey;
 
-- 
GitLab


From 9279fc532d12b0630cc9b5b6752a085586ea0c1f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 06:48:27 -0800
Subject: [PATCH 186/575] Optionally have persistent make_vjp

PiperOrigin-RevId: 186610572
---
 tensorflow/python/eager/backprop.py      |  6 ++++--
 tensorflow/python/eager/backprop_test.py | 13 ++++++++++++-
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index eebdc5813d..14bcc60006 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -502,7 +502,7 @@ def val_and_grad_function(f, params=None):
   return decorated
 
 
-def make_vjp(f, params=None):
+def make_vjp(f, params=None, persistent=True):
   """Returns a function that computes f and is vjp w.r.t. params.
 
   The term "vjp" here is an abbreviation for vector-jacobian product.
@@ -511,6 +511,8 @@ def make_vjp(f, params=None):
     f: the function to be differentiated.
     params: the parameters (numbers or names) to differentiate with respect to.
        A value of None will differentiate with respect to all parameters.
+    persistent: Boolean controlling whether the VJP function can be re-used.
+      Must be True or False.
 
   Returns:
     A function, which when called, returns a tuple (value, vjp), where:
@@ -538,7 +540,7 @@ def make_vjp(f, params=None):
     """Computes the value and gradient of the decorated function."""
     parameter_positions = _get_arg_spec(f, params, args)
     assert not kwds, "The gradient function can't take keyword arguments."
-    this_tape = tape.push_new_tape()
+    this_tape = tape.push_new_tape(persistent=persistent)
     try:
       sources = []
       args = [
diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index a12113893a..734558dee2 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -205,11 +205,22 @@ class BackpropTest(test.TestCase):
     def f(x):
       return x * x
 
-    wrapped_fn = backprop.make_vjp(f)
+    wrapped_fn = backprop.make_vjp(f, persistent=False)
     result, vjp = wrapped_fn(constant_op.constant(3.0))
     self.assertAllEqual(result, 9.0)
     self.assertAllEqual(vjp(2.0)[0], 12.0)
 
+  def testPersistentMakeVJP(self):
+
+    def f(x):
+      return x * x
+
+    wrapped_fn = backprop.make_vjp(f, persistent=True)
+    _, vjp = wrapped_fn(constant_op.constant(3.0))
+    vjp_result1 = vjp(2.0)[0]
+    vjp_result2 = vjp(2.0)[0]
+    self.assertAllEqual(vjp_result1, vjp_result2, 12.0)
+
   @test_util.assert_no_new_tensors
   def testGradGrad(self):
 
-- 
GitLab


From ee40d87af8e6c24e6e84ff64e4932c38d6ccfcf7 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 22 Feb 2018 07:25:41 -0800
Subject: [PATCH 187/575] [tf.data] Add experimental ability to override the
 function threadpool.

The purpose of this feature is to enable experimentation with differentiating
the CPU resources available to different stages of a `tf.data` pipeline. As a
concrete example, we might use the new feature to move all input-related work
from the inter-op threadpool onto a separate threadpool, leaving the inter-op
threadpool free to execute higher priority work (such as dispatching ops that
send tensors to an accelerator). The current implementation only allows users
to create fixed-size `tensorflow::ThreadPool` resources, but we could imagine
opening up this API to allow custom threadpools as well.

PiperOrigin-RevId: 186614315
---
 .../contrib/cmake/tf_core_kernels.cmake       |   1 +
 tensorflow/contrib/data/kernels/BUILD         |  13 ++
 .../data/kernels/threadpool_dataset_op.cc     | 197 ++++++++++++++++++
 tensorflow/contrib/data/ops/dataset_ops.cc    |  29 +++
 .../contrib/data/python/kernel_tests/BUILD    |  14 ++
 .../threadpool_dataset_ops_test.py            |  77 +++++++
 tensorflow/contrib/data/python/ops/BUILD      |   2 +
 .../contrib/data/python/ops/threadpool.py     | 102 +++++++++
 tensorflow/contrib/eager/python/BUILD         |   1 +
 .../contrib/eager/python/datasets_test.py     |  35 ++++
 10 files changed, 471 insertions(+)
 create mode 100644 tensorflow/contrib/data/kernels/threadpool_dataset_op.cc
 create mode 100644 tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py
 create mode 100644 tensorflow/contrib/data/python/ops/threadpool.py

diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 7cae0afe43..998f99ecc1 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -71,6 +71,7 @@ if(tensorflow_BUILD_CONTRIB_KERNELS)
       "${tensorflow_source_dir}/tensorflow/contrib/cudnn_rnn/ops/cudnn_rnn_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/prefetching_kernels.cc"
+      "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/kernels/unique_dataset_op.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/data/ops/dataset_ops.cc"
       "${tensorflow_source_dir}/tensorflow/contrib/factorization/kernels/clustering_ops.cc"
diff --git a/tensorflow/contrib/data/kernels/BUILD b/tensorflow/contrib/data/kernels/BUILD
index 8b0556330e..9bd6a42da2 100644
--- a/tensorflow/contrib/data/kernels/BUILD
+++ b/tensorflow/contrib/data/kernels/BUILD
@@ -28,6 +28,16 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_library(
+    name = "threadpool_dataset_op",
+    srcs = ["threadpool_dataset_op.cc"],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
+        "@protobuf_archive//:protobuf_headers",
+    ],
+)
+
 cc_library(
     name = "unique_dataset_op",
     srcs = ["unique_dataset_op.cc"],
@@ -43,7 +53,10 @@ cc_library(
     deps = [
         ":ignore_errors_dataset_op",
         ":prefetching_kernels",
+        ":threadpool_dataset_op",
         ":unique_dataset_op",
+        "//tensorflow/core:framework_headers_lib",
+        "//third_party/eigen3",
         "@protobuf_archive//:protobuf_headers",
     ],
 )
diff --git a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc
new file mode 100644
index 0000000000..4b3edde85f
--- /dev/null
+++ b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc
@@ -0,0 +1,197 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/dataset.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+
+namespace tensorflow {
+namespace {
+
+class ThreadPoolResource : public ResourceBase {
+ public:
+  ThreadPoolResource(Env* env, const ThreadOptions& thread_options,
+                     const string& name, int num_threads, bool low_latency_hint)
+      : thread_pool_(env, thread_options, name, num_threads, low_latency_hint) {
+  }
+
+  // Schedules fn() for execution in the pool of threads.
+  void Schedule(std::function<void()> fn) {
+    thread_pool_.Schedule(std::move(fn));
+  }
+
+  string DebugString() override { return "ThreadPoolResource"; }
+
+ private:
+  thread::ThreadPool thread_pool_;
+};
+
+// Creates a handle to a ThreadPool resource. Note that we don't use
+// ResourceOpKernel here because the ThreadPoolResource constructor requires
+// access to `OpKernelContext::env()`, which isn't provided by
+// `ResourceOpKernel<T>::CreateResource()`.
+class ThreadPoolHandleOp : public OpKernel {
+ public:
+  explicit ThreadPoolHandleOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("display_name", &display_name_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("num_threads", &num_threads_));
+    OP_REQUIRES(
+        ctx, num_threads_ > 0,
+        errors::InvalidArgument("`num_threads` must be greater than zero."));
+  }
+
+  // The resource is deleted from the resource manager only when it is private
+  // to kernel. Ideally the resource should be deleted when it is no longer held
+  // by anyone, but it would break backward compatibility.
+  ~ThreadPoolHandleOp() override {
+    if (cinfo_.resource_is_private_to_kernel()) {
+      if (!cinfo_.resource_manager()
+               ->Delete<ThreadPoolResource>(cinfo_.container(), cinfo_.name())
+               .ok()) {
+        // Do nothing; the resource can have been deleted by session resets.
+      }
+    }
+  }
+
+  void Compute(OpKernelContext* ctx) override LOCKS_EXCLUDED(mu_) {
+    mutex_lock l(mu_);
+    if (!initialized_) {
+      ResourceMgr* mgr = ctx->resource_manager();
+      OP_REQUIRES_OK(ctx, cinfo_.Init(mgr, def()));
+      ThreadPoolResource* resource;
+      OP_REQUIRES_OK(ctx, mgr->LookupOrCreate<ThreadPoolResource>(
+                              cinfo_.container(), cinfo_.name(), &resource,
+                              [this, ctx](ThreadPoolResource** ret)
+                                  EXCLUSIVE_LOCKS_REQUIRED(mu_) {
+                                    *ret = new ThreadPoolResource(
+                                        ctx->env(), {}, display_name_,
+                                        num_threads_,
+                                        false /* low_latency_hint */);
+                                    return Status::OK();
+                                  }));
+      initialized_ = true;
+    }
+    OP_REQUIRES_OK(ctx, MakeResourceHandleToOutput(
+                            ctx, 0, cinfo_.container(), cinfo_.name(),
+                            MakeTypeIndex<ThreadPoolResource>()));
+  }
+
+ private:
+  mutex mu_;
+  ContainerInfo cinfo_ GUARDED_BY(mu_);
+  bool initialized_ GUARDED_BY(mu_) = false;
+  string display_name_;
+  int num_threads_;
+};
+
+class ThreadPoolDatasetOp : public UnaryDatasetOpKernel {
+ public:
+  explicit ThreadPoolDatasetOp(OpKernelConstruction* ctx)
+      : UnaryDatasetOpKernel(ctx) {}
+
+  void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
+                   DatasetBase** output) override {
+    ThreadPoolResource* threadpool_resource;
+    OP_REQUIRES_OK(ctx, LookupResource(ctx, HandleFromInput(ctx, 1),
+                                       &threadpool_resource));
+    core::ScopedUnref unref_iterator(threadpool_resource);
+
+    *output = new Dataset(ctx, input, threadpool_resource);
+  }
+
+ private:
+  class Dataset : public GraphDatasetBase {
+   public:
+    Dataset(OpKernelContext* ctx, const DatasetBase* input,
+            ThreadPoolResource* threadpool)
+        : GraphDatasetBase(ctx), input_(input), threadpool_(threadpool) {
+      input_->Ref();
+      threadpool_->Ref();
+    }
+
+    ~Dataset() override {
+      input_->Unref();
+      threadpool_->Unref();
+    }
+
+    std::unique_ptr<IteratorBase> MakeIterator(
+        const string& prefix) const override {
+      return std::unique_ptr<IteratorBase>(
+          new Iterator({this, strings::StrCat(prefix, "::ThreadPool")}));
+    }
+
+    const DataTypeVector& output_dtypes() const override {
+      return input_->output_dtypes();
+    }
+    const std::vector<PartialTensorShape>& output_shapes() const override {
+      return input_->output_shapes();
+    }
+
+    string DebugString() override { return "ThreadPoolDatasetOp::Dataset"; }
+
+   protected:
+    Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b,
+                              Node** output) const override {
+      return errors::Unimplemented(
+          "Cannot currently serialize the thread pool for a "
+          "ThreadPoolDataset.");
+    }
+
+   private:
+    class Iterator : public DatasetIterator<Dataset> {
+     public:
+      explicit Iterator(const Params& params)
+          : DatasetIterator<Dataset>(params),
+            input_impl_(params.dataset->input_->MakeIterator(params.prefix)) {}
+
+      Status GetNextInternal(IteratorContext* ctx,
+                             std::vector<Tensor>* out_tensors,
+                             bool* end_of_sequence) override {
+        ThreadPoolResource* pool = dataset()->threadpool_;
+        IteratorContext::Params params;
+        params.env = ctx->env();
+        params.runner = [pool](std::function<void()> c) {
+          pool->Schedule(std::move(c));
+        };
+        params.stats_aggregator_getter = [ctx]() {
+          return ctx->stats_aggregator();
+        };
+        params.lib = ctx->lib();
+        params.function_library = ctx->function_library();
+        params.allocator_getter = [ctx](AllocatorAttributes attrs) {
+          return ctx->allocator(attrs);
+        };
+        IteratorContext threadpool_ctx(params);
+        return input_impl_->GetNext(&threadpool_ctx, out_tensors,
+                                    end_of_sequence);
+      }
+
+     private:
+      std::unique_ptr<IteratorBase> input_impl_;
+    };
+
+    const DatasetBase* const input_;
+    ThreadPoolResource* const threadpool_;
+  };
+};
+
+REGISTER_KERNEL_BUILDER(Name("ThreadPoolHandle").Device(DEVICE_CPU),
+                        ThreadPoolHandleOp);
+REGISTER_KERNEL_BUILDER(Name("ThreadPoolDataset").Device(DEVICE_CPU),
+                        ThreadPoolDatasetOp);
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/data/ops/dataset_ops.cc b/tensorflow/contrib/data/ops/dataset_ops.cc
index d97a2a6589..a4c1212da1 100644
--- a/tensorflow/contrib/data/ops/dataset_ops.cc
+++ b/tensorflow/contrib/data/ops/dataset_ops.cc
@@ -75,4 +75,33 @@ output: A list of return values.
 output_types: The type list for the return values.
 )doc");
 
+REGISTER_OP("ThreadPoolDataset")
+    .Input("input_dataset: variant")
+    .Input("thread_pool: resource")
+    .Output("handle: variant")
+    .Attr("output_types: list(type) >= 1")
+    .Attr("output_shapes: list(shape) >= 1")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Doc(R"doc(
+Creates a dataset that uses a custom thread pool to compute `input_dataset`.
+
+handle: A resource produced by the ThreadPoolHandle op.
+)doc");
+
+REGISTER_OP("ThreadPoolHandle")
+    .Output("handle: resource")
+    .SetShapeFn(shape_inference::ScalarShape)
+    .Attr("num_threads: int")
+    .Attr("display_name: string")
+    .Attr("container: string = ''")
+    .Attr("shared_name: string = ''")
+    .Doc(R"doc(
+Creates a custom thread pool with the given number of threads.
+
+handle: A resource that can be consumed by one or more ThreadPoolDataset ops.
+num_threads: The number of threads in the thread pool.
+display_name: A human-readable name for the threads that may be visible in
+  some visualizations.
+)doc");
+
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index e51d57cc89..82cd276ce8 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -419,6 +419,20 @@ py_test(
     ],
 )
 
+py_test(
+    name = "threadpool_dataset_ops_test",
+    size = "small",
+    srcs = ["threadpool_dataset_ops_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        "//tensorflow/contrib/data/python/ops:dataset_ops",
+        "//tensorflow/contrib/data/python/ops:transformation_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:errors",
+    ],
+)
+
 py_test(
     name = "unique_dataset_op_test",
     size = "small",
diff --git a/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py
new file mode 100644
index 0000000000..9167cb3379
--- /dev/null
+++ b/tensorflow/contrib/data/python/kernel_tests/threadpool_dataset_ops_test.py
@@ -0,0 +1,77 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the experimental input pipeline statistics gathering ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import threading
+
+import numpy as np
+
+from tensorflow.contrib.data.python.ops import threadpool
+from tensorflow.contrib.data.python.ops import unique
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.ops import script_ops
+from tensorflow.python.platform import test
+
+
+class OverrideThreadpoolDatasetTest(test.TestCase):
+
+  def testNumThreads(self):
+
+    def get_thread_id(_):
+      # Python creates a dummy thread object to represent the current
+      # thread when called from an "alien" thread (such as a
+      # `PrivateThreadPool` thread in this case). It does not include
+      # the TensorFlow-given display name, but it has a unique
+      # identifier that maps one-to-one with the underlying OS thread.
+      return np.array(threading.current_thread().ident).astype(np.int64)
+
+    for num_threads in [1, 2, 4, 8, 16]:
+
+      dataset = (
+          dataset_ops.Dataset.range(1000).map(
+              lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
+              num_parallel_calls=32).apply(unique.unique()))
+
+      dataset = threadpool.override_threadpool(
+          dataset,
+          threadpool.PrivateThreadPool(
+              num_threads, display_name="private_thread_pool_%d" % num_threads))
+
+      iterator = dataset.make_initializable_iterator()
+      next_element = iterator.get_next()
+
+      with self.test_session() as sess:
+        sess.run(iterator.initializer)
+        thread_ids = []
+        try:
+          while True:
+            thread_ids.append(sess.run(next_element))
+        except errors.OutOfRangeError:
+          pass
+        self.assertEqual(len(thread_ids), len(set(thread_ids)))
+        self.assertGreater(len(thread_ids), 0)
+        # NOTE(mrry): We don't control the thread pool scheduling, and
+        # so cannot guarantee that all of the threads in the pool will
+        # perform work.
+        self.assertLessEqual(len(thread_ids), num_threads)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD
index b488357f22..789cb9c99a 100644
--- a/tensorflow/contrib/data/python/ops/BUILD
+++ b/tensorflow/contrib/data/python/ops/BUILD
@@ -105,6 +105,7 @@ py_library(
         "resampling.py",
         "scan_ops.py",
         "stats_ops.py",
+        "threadpool.py",
         "unique.py",
     ],
     srcs_version = "PY2AND3",
@@ -120,6 +121,7 @@ py_library(
         "//tensorflow/python:logging_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:random_ops",
+        "//tensorflow/python:resource_variable_ops",
         "//tensorflow/python:tensor_shape",
         "//tensorflow/python:tensor_util",
         "//tensorflow/python:util",
diff --git a/tensorflow/contrib/data/python/ops/threadpool.py b/tensorflow/contrib/data/python/ops/threadpool.py
new file mode 100644
index 0000000000..3f85aa84cd
--- /dev/null
+++ b/tensorflow/contrib/data/python/ops/threadpool.py
@@ -0,0 +1,102 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Experimental API for controlling threading in `tf.data` pipelines."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import threading
+
+from tensorflow.contrib.data.python.ops import contrib_op_loader  # pylint: disable=unused-import
+from tensorflow.contrib.data.python.ops import gen_dataset_ops
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.util import nest
+from tensorflow.python.data.util import sparse
+from tensorflow.python.eager import context
+from tensorflow.python.ops import resource_variable_ops
+
+_uid_counter = 0
+_uid_lock = threading.Lock()
+
+
+def _generate_shared_name(prefix):
+  with _uid_lock:
+    global _uid_counter
+    uid = _uid_counter
+    _uid_counter += 1
+  return "{}{}".format(prefix, uid)
+
+
+class PrivateThreadPool(object):
+  """A stateful resource that represents a private thread pool."""
+
+  def __init__(self, num_threads, display_name=None):
+    """Creates a `PrivateThreadPool` with the given number of threads."""
+    if context.in_eager_mode():
+      shared_name = _generate_shared_name("privatethreadpool")
+      self._resource = gen_dataset_ops.thread_pool_handle(
+          num_threads=num_threads,
+          display_name=display_name,
+          shared_name=shared_name)
+      self._resource_deleter = resource_variable_ops.EagerResourceDeleter(
+          handle=self._resource, handle_device=context.context().device_name)
+    else:
+      self._resource = gen_dataset_ops.thread_pool_handle(
+          num_threads=num_threads, display_name=display_name)
+
+
+class _ThreadPoolDataset(dataset_ops.Dataset):
+  """A `Dataset` that acts as an identity, and sets a custom threadpool."""
+
+  def __init__(self, input_dataset, thread_pool):
+    super(_ThreadPoolDataset, self).__init__()
+    self._input_dataset = input_dataset
+    self._thread_pool = thread_pool
+
+  def _as_variant_tensor(self):
+    return gen_dataset_ops.thread_pool_dataset(
+        self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
+        self._thread_pool._resource,  # pylint: disable=protected-access
+        output_shapes=nest.flatten(
+            sparse.as_dense_shapes(self.output_shapes, self.output_classes)),
+        output_types=nest.flatten(
+            sparse.as_dense_types(self.output_types, self.output_classes)))
+
+  @property
+  def output_shapes(self):
+    return self._input_dataset.output_shapes
+
+  @property
+  def output_types(self):
+    return self._input_dataset.output_types
+
+  @property
+  def output_classes(self):
+    return self._input_dataset.output_classes
+
+
+def override_threadpool(dataset, thread_pool):
+  """Returns a new dataset that uses the given thread pool for its operations.
+
+  Args:
+    dataset: A `tf.data.Dataset` object.
+    thread_pool: A `PrivateThreadPool` object.
+
+  Returns:
+    A dataset containing the same values as `dataset`, but which uses
+    `thread_pool` to compute any of its parallel operations (such as
+    @{tf.data.Dataset.map}).
+  """
+  return _ThreadPoolDataset(dataset, thread_pool)
diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD
index ad40e55cb4..a26ec8513f 100644
--- a/tensorflow/contrib/eager/python/BUILD
+++ b/tensorflow/contrib/eager/python/BUILD
@@ -69,6 +69,7 @@ cuda_py_test(
     srcs = ["datasets_test.py"],
     additional_deps = [
         ":datasets",
+        "//tensorflow/contrib/data/python/ops:transformation_ops",
         "//tensorflow/contrib/lookup:lookup_py",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
diff --git a/tensorflow/contrib/eager/python/datasets_test.py b/tensorflow/contrib/eager/python/datasets_test.py
index a1611e92b1..35c3c5d3fa 100644
--- a/tensorflow/contrib/eager/python/datasets_test.py
+++ b/tensorflow/contrib/eager/python/datasets_test.py
@@ -16,11 +16,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import threading
 import time
 
 import numpy as np
 
 from tensorflow.contrib import lookup
+from tensorflow.contrib.data.python.ops import threadpool
+from tensorflow.contrib.data.python.ops import unique
 from tensorflow.contrib.eager.python import datasets
 from tensorflow.python.data import Dataset
 from tensorflow.python.eager import test
@@ -165,6 +168,38 @@ class IteratorTest(test.TestCase):
       x = math_ops.add(x, x)
     self.assertAllEqual([0., 2.], x.numpy())
 
+  def testOverrideThreadPool(self):
+
+    def get_thread_id(_):
+      # Python creates a dummy thread object to represent the current
+      # thread when called from an "alien" thread (such as a
+      # `PrivateThreadPool` thread in this case). It does not include
+      # the TensorFlow-given display name, but it has a unique
+      # identifier that maps one-to-one with the underlying OS thread.
+      return np.array(threading.current_thread().ident).astype(np.int64)
+
+    for num_threads in [1, 2, 4, 8, 16]:
+
+      dataset = (
+          Dataset.range(1000).map(
+              lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
+              num_parallel_calls=32).apply(unique.unique()))
+
+      dataset = threadpool.override_threadpool(
+          dataset,
+          threadpool.PrivateThreadPool(
+              num_threads, display_name='private_thread_pool_%d' % num_threads))
+
+      thread_ids = []
+      for next_element in datasets.Iterator(dataset):
+        thread_ids.append(next_element)
+      self.assertEqual(len(thread_ids), len(set(thread_ids)))
+      self.assertGreater(len(thread_ids), 0)
+      # NOTE(mrry): We don't control the thread pool scheduling, and
+      # so cannot guarantee that all of the threads in the pool will
+      # perform work.
+      self.assertLessEqual(len(thread_ids), num_threads)
+
 
 class DatasetConstructorBenchmark(test.Benchmark):
 
-- 
GitLab


From 3c7ab56ef709d862ad450c51dae129f01454fd91 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Thu, 22 Feb 2018 07:53:38 -0800
Subject: [PATCH 188/575] Add S64 add/subtract test and convert tests.

* Fixed bugs in convert from U32 to S64.
END_PUBLIC

*** Original change description ***

BEGIN_PUBLIC
Automated g4 rollback of changelist 186494344

PiperOrigin-RevId: 186616875
---
 .../xla/service/elemental_ir_emitter.cc       |   2 +-
 .../xla/tests/array_elementwise_ops_test.cc   | 107 ++++++++++++++++++
 tensorflow/compiler/xla/tests/convert_test.cc | 105 ++++++++++++++++-
 .../xla/tests/scalar_computations_test.cc     |   2 +-
 4 files changed, 210 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
index 12b35b2f96..c732974995 100644
--- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc
@@ -226,7 +226,7 @@ StatusOr<llvm::Value*> ElementalIrEmitter::EmitIntegerUnaryOp(
       if (primitive_util::IsIntegralType(to_type)) {
         return ir_builder_->CreateIntCast(
             operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_),
-            primitive_util::IsSignedIntegralType(to_type));
+            primitive_util::IsSignedIntegralType(from_type));
       }
       if (primitive_util::IsFloatingPointType(to_type)) {
         if (to_type == BF16) {
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 739d201fad..8b35259013 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -101,6 +101,33 @@ XLA_TEST_F(ArrayElementwiseOpTest, NegConstantC64) {
       {}, error_spec_);
 }
 
+XLA_TEST_F(ArrayElementwiseOpTest, NegConstantS64) {
+  ComputationBuilder builder(client_, TestName());
+  auto a = builder.ConstantR1<int64>({
+      -1,
+      1,
+      0,
+      0x12345678,
+      static_cast<int64>(0xffffffff12345678l),
+      static_cast<int64>(0x8000000000000000LL),
+      static_cast<int64>(0x8000000000000001LL),
+  });
+  auto result = builder.Neg(a);
+  LOG(INFO) << -static_cast<int64>(0x7FFFFFFFFFFFFFFFLL);
+
+  ComputeAndCompareR1<int64>(&builder,
+                             {
+                                 1,
+                                 -1,
+                                 0,
+                                 -0x12345678,
+                                 0xedcba988,
+                                 static_cast<int64>(0x8000000000000000LL),
+                                 -static_cast<int64>(0x8000000000000001LL),
+                             },
+                             {});
+}
+
 XLA_TEST_F(ArrayElementwiseOpTest, IsFiniteZeroElementF32s) {
   ComputationBuilder builder(client_, TestName());
   auto a = builder.ConstantR1<float>({});
@@ -186,6 +213,86 @@ XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantZeroElementC64s) {
   ComputeAndCompareR1<complex64>(&builder, {}, {}, error_spec_);
 }
 
+XLA_TEST_F(ArrayElementwiseOpTest, AddTwoConstantU64s) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<uint64> lhs{0xFFFFFFFF,
+                          static_cast<uint64>(-1),
+                          0,
+                          0,
+                          0x7FFFFFFFFFFFFFFFLL,
+                          0x7FFFFFFFFFFFFFFLL,
+                          0x8000000000000000LL,
+                          0x8000000000000000LL,
+                          1};
+  std::unique_ptr<Literal> lhs_literal = Literal::CreateR1<uint64>({lhs});
+  auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param");
+  std::unique_ptr<GlobalData> lhs_data =
+      client_->TransferToServer(*lhs_literal).ConsumeValueOrDie();
+
+  std::vector<uint64> rhs{1,
+                          0x7FFFFFFFFFFFFFFLL,
+                          0x7FFFFFFFFFFFFFFFLL,
+                          0x8000000000000000LL,
+                          0,
+                          static_cast<uint64>(-1),
+                          0,
+                          1,
+                          0x8000000000000000LL};
+  std::unique_ptr<Literal> rhs_literal = Literal::CreateR1<uint64>({rhs});
+  auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param");
+  std::unique_ptr<GlobalData> rhs_data =
+      client_->TransferToServer(*rhs_literal).ConsumeValueOrDie();
+
+  auto add = b.Add(lhs_param, rhs_param);
+
+  std::vector<uint64> expected(lhs.size());
+  for (int64 i = 0; i < lhs.size(); ++i) {
+    expected[i] = lhs[i] + rhs[i];
+  }
+
+  ComputeAndCompareR1<uint64>(&b, expected, {lhs_data.get(), rhs_data.get()});
+}
+
+XLA_TEST_F(ArrayElementwiseOpTest, SubTwoConstantS64s) {
+  ComputationBuilder b(client_, TestName());
+
+  std::vector<int64> lhs{static_cast<int64>(0x8000000000000000LL),
+                         static_cast<int64>(0x8000000000000000LL),
+                         -1,
+                         0x7FFFFFFFFFFFFFFLL,
+                         0x7FFFFFFFFFFFFFFFLL,
+                         1,
+                         0,
+                         -1};
+  std::unique_ptr<Literal> lhs_literal = Literal::CreateR1<int64>({lhs});
+  auto lhs_param = b.Parameter(0, lhs_literal->shape(), "lhs_param");
+  std::unique_ptr<GlobalData> lhs_data =
+      client_->TransferToServer(*lhs_literal).ConsumeValueOrDie();
+
+  std::vector<int64> rhs{-1,
+                         0,
+                         static_cast<int64>(0x8000000000000000LL),
+                         1,
+                         0,
+                         0x7FFFFFFFFFFFFFFLL,
+                         0x7FFFFFFFFFFFFFFFLL,
+                         0x7FFFFFFFFFFFFFFFLL};
+  std::unique_ptr<Literal> rhs_literal = Literal::CreateR1<int64>({rhs});
+  auto rhs_param = b.Parameter(1, rhs_literal->shape(), "rhs_param");
+  std::unique_ptr<GlobalData> rhs_data =
+      client_->TransferToServer(*rhs_literal).ConsumeValueOrDie();
+
+  auto sub = b.Sub(lhs_param, rhs_param);
+
+  std::vector<int64> expected(lhs.size());
+  for (int64 i = 0; i < lhs.size(); ++i) {
+    expected[i] = lhs[i] - rhs[i];
+  }
+
+  ComputeAndCompareR1<int64>(&b, expected, {lhs_data.get(), rhs_data.get()});
+}
+
 TEST_P(ArrayElementwiseOpTestParamCount, AddManyValues) {
   const int count = GetParam();
   ComputationBuilder builder(client_, TestName());
diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc
index 1c6e7859a2..59d6d7a415 100644
--- a/tensorflow/compiler/xla/tests/convert_test.cc
+++ b/tensorflow/compiler/xla/tests/convert_test.cc
@@ -107,11 +107,108 @@ TEST_F(ConvertTest, ConvertR1F32ToR1S32) {
 
 XLA_TEST_F(ConvertTest, ConvertR1S64ToR1F32) {
   ComputationBuilder builder(client_, TestName());
-  auto a = builder.ConstantR1<int64>({32, 64});
-  builder.ConvertElementType(a, F32);
+  std::vector<int64> arg{
+      -9223371216516022272,
+      -2,
+      -1,
+      -0x7FFFFFFF,
+      -0x80000000,
+      0,
+      1,
+      2,
+      1073742145,
+      1073742656,
+      0x7FFFFFFF,
+      0x80000000,
+      826720496944058148,
+      4296062029846194332,
+      0x0007FB72E4000000LL,
+      0x0007FB72E4000001LL,
+      0x0007FB72E6000000LL,
+      0x0007FB72E7000000LL,
+      0x0007FB72E7FFFFFFLL,
+      0x0007FB72E8000000LL,
+      0x0007FB72E8000001LL,
+      0x0007FB72EA000000LL,
+      0x0007FB72EB000000LL,
+      0x0007FB72EBFFFFFFLL,
+      0x0007FB72EC000000LL,
+      0x7FFFFF0000000000LL,
+      0x7FFFFF8000000000LL,
+      0x7FFFFFFFFFFFFF00,
+      static_cast<int64>(0xFFFFFFFFFFFFFFFF),
+      static_cast<int64>(0x0000f234e67e0001LL),
+      static_cast<int64>(0x8000000000000000),
+      static_cast<int64>(0x8000000000000000LL),
+      static_cast<int64>(0x8000000000000001LL),
+      static_cast<int64>(0x8000008000000000LL),
+      static_cast<int64>(0x8000010000000000LL),
+  };
+  std::unique_ptr<Literal> arg_literal = Literal::CreateR1<int64>({arg});
+  auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param");
+  std::unique_ptr<GlobalData> arg_data =
+      client_->TransferToServer(*arg_literal).ConsumeValueOrDie();
+
+  builder.ConvertElementType(arg_param, F32);
+
+  std::vector<float> expected(arg.size());
+  for (int64 i = 0; i < arg.size(); ++i) {
+    expected[i] = static_cast<float>(arg[i]);
+  }
+  ComputeAndCompareR1<float>(&builder, expected, {arg_data.get()});
+}
 
-  std::vector<float> expected = {32.0, 64.0};
-  ComputeAndCompareR1<float>(&builder, expected, {});
+XLA_TEST_F(ConvertTest, ConvertR1U32ToR1F32) {
+  ComputationBuilder builder(client_, TestName());
+  std::vector<uint32> arg{0,          1,          0x1000,     0x7fffffff,
+                          0x80000000, 0x80000001, 0x80000002, 0x80000003,
+                          0x80000080, 0x80000081, 0x80000082, 0xFFFFFFFF};
+  std::unique_ptr<Literal> arg_literal = Literal::CreateR1<uint32>({arg});
+  auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param");
+  std::unique_ptr<GlobalData> arg_data =
+      client_->TransferToServer(*arg_literal).ConsumeValueOrDie();
+
+  builder.ConvertElementType(arg_param, F32);
+
+  std::vector<float> expected(arg.size());
+  for (int64 i = 0; i < arg.size(); ++i) {
+    expected[i] = static_cast<float>(arg[i]);
+  }
+  ComputeAndCompareR1<float>(&builder, expected, {arg_data.get()});
+}
+
+XLA_TEST_F(ConvertTest, ConvertR1U32ToR1S64) {
+  ComputationBuilder builder(client_, TestName());
+  std::vector<uint32> arg{0, 1, 0x1000, 0x7fffffff, 0x80000082, 0xFFFFFFFF};
+  std::unique_ptr<Literal> arg_literal = Literal::CreateR1<uint32>({arg});
+  auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param");
+  std::unique_ptr<GlobalData> arg_data =
+      client_->TransferToServer(*arg_literal).ConsumeValueOrDie();
+
+  builder.ConvertElementType(arg_param, S64);
+
+  std::vector<int64> expected(arg.size());
+  for (int64 i = 0; i < arg.size(); ++i) {
+    expected[i] = static_cast<int64>(arg[i]);
+  }
+  ComputeAndCompareR1<int64>(&builder, expected, {arg_data.get()});
+}
+
+XLA_TEST_F(ConvertTest, ConvertR1S32ToR1S64) {
+  ComputationBuilder builder(client_, TestName());
+  std::vector<int32> arg{0, 1, 0x1000, -1, -0x1000};
+  std::unique_ptr<Literal> arg_literal = Literal::CreateR1<int32>({arg});
+  auto arg_param = builder.Parameter(0, arg_literal->shape(), "arg_param");
+  std::unique_ptr<GlobalData> arg_data =
+      client_->TransferToServer(*arg_literal).ConsumeValueOrDie();
+
+  builder.ConvertElementType(arg_param, S64);
+
+  std::vector<int64> expected(arg.size());
+  for (int64 i = 0; i < arg.size(); ++i) {
+    expected[i] = static_cast<int64>(arg[i]);
+  }
+  ComputeAndCompareR1<int64>(&builder, expected, {arg_data.get()});
 }
 
 XLA_TEST_F(ConvertTest, ConvertR1U8ToR1F32) {
diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
index 4da6ee9160..d7bda77e87 100644
--- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc
+++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
@@ -163,7 +163,7 @@ XLA_TEST_F(ScalarComputationsTest, CastS64ToF32) {
   auto a = builder.Parameter(0, ShapeUtil::MakeShape(S64, {}), "a");
   builder.ConvertElementType(a, F32);
 
-  int64 value = 3LL << 32;
+  int64 value = 3LL << 35;
   std::unique_ptr<Literal> a_literal = Literal::CreateR0<int64>(value);
   std::unique_ptr<GlobalData> a_data =
       client_->TransferToServer(*a_literal).ConsumeValueOrDie();
-- 
GitLab


From 027b74e9ccf84adcf92abc0981a28a70b47bdc09 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 08:52:42 -0800
Subject: [PATCH 189/575] Add node name to placer
 device-placement/kernel-assignment error messages .

PiperOrigin-RevId: 186622923
---
 tensorflow/core/common_runtime/placer.cc | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tensorflow/core/common_runtime/placer.cc b/tensorflow/core/common_runtime/placer.cc
index a913f20751..e128b9257f 100644
--- a/tensorflow/core/common_runtime/placer.cc
+++ b/tensorflow/core/common_runtime/placer.cc
@@ -464,6 +464,7 @@ class ColocationGraph {
     // the user can see why an unsatisfiable placement occurred.
 
     std::unordered_map<string, string> type_to_devices;
+    std::vector<const Node*> colocation_nodes;
     int num_nodes_found = 0;
 
     for (const Node* node : graph_->nodes()) {
@@ -475,6 +476,7 @@ class ColocationGraph {
         continue;
       }
       ++num_nodes_found;
+      colocation_nodes.push_back(node);
       const string& op_type = node->type_string();
       string devices_registered;
       for (const auto& device_type : members_[id].supported_device_types) {
@@ -488,6 +490,13 @@ class ColocationGraph {
     for (const auto& td : type_to_devices) {
       strings::StrAppend(&text, "\n", td.first, ": ", td.second);
     }
+    strings::StrAppend(&text,
+                       "\n\nColocation members and user-requested devices:");
+    for (const Node* node : colocation_nodes) {
+      strings::StrAppend(&text, "\n  ", node->name(), " (", node->type_string(),
+                         ") ", node->requested_device());
+    }
+    strings::StrAppend(&text, "\n");
 
     if (num_nodes_found <= 1) {
       text.clear();
-- 
GitLab


From 825f6811586119dcb0cb4c49ef2f6ac8751e1b84 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 09:03:00 -0800
Subject: [PATCH 190/575] Update LLVM for API changes in r325725

PiperOrigin-RevId: 186624266
---
 tensorflow/workspace.bzl | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 6eee41bfa1..b3ef7d0edb 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -5,6 +5,7 @@ load("//third_party/tensorrt:tensorrt_configure.bzl", "tensorrt_configure")
 load("//third_party/mkl:build_defs.bzl", "mkl_repository")
 load("//third_party/git:git_configure.bzl", "git_configure")
 load("//third_party/py:python_configure.bzl", "python_configure")
+
 load("//third_party/sycl:sycl_configure.bzl", "sycl_configure")
 load("//third_party/toolchains/clang6:repo.bzl", "clang6_configure")
 load("//third_party/toolchains/cpus/arm:arm_compiler_configure.bzl", "arm_compiler_configure")
@@ -474,11 +475,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/cd1a39550da51f57a87e2701f09451860dd1d98d.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/cd1a39550da51f57a87e2701f09451860dd1d98d.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14.tar.gz",
       ],
-      sha256 = "62507d597053f36592725a515992668e7050b0259db2d4771661a0bd7a47882a",
-      strip_prefix = "llvm-cd1a39550da51f57a87e2701f09451860dd1d98d",
+      sha256 = "f5721d9cc18a9109c9e9f847f48e69b710b961cee83e6691227e310cb3b5da58",
+      strip_prefix = "llvm-fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
   )
 
-- 
GitLab


From e06e0764e33dd38403b087e2cf7edf53b7fec779 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 10:32:59 -0800
Subject: [PATCH 191/575] Change warning message for case where Python detects
 a colocation that conflicts with a device assignment.

PiperOrigin-RevId: 186637887
---
 tensorflow/python/framework/ops.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 013a4dfd94..5a14ea4176 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -3361,9 +3361,9 @@ class Graph(object):
           if (op.device and pydev.canonical_name(op.device) !=
               pydev.canonical_name(colocation_op.device)):
             logging.warning("Tried to colocate %s with an op %s that had "
-                            "a different device: %s vs %s. "
-                            "Ignoring colocation property.", op.name,
-                            colocation_op.name, op.device,
+                            "a different device: %s vs %s. Postponing "
+                            "error-checking until all devices are assigned.",
+                            op.name, colocation_op.name, op.device,
                             colocation_op.device)
           else:
             op._set_device(colocation_op.device)  # pylint: disable=protected-access
-- 
GitLab


From b3998ecba3bf600b7e2d4cb5454df5bc6c0ffe04 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 22 Feb 2018 10:44:19 -0800
Subject: [PATCH 192/575] Added a regression test to make sure we deal with
 large constants properly

PiperOrigin-RevId: 186639709
---
 .../optimizers/constant_folding_test.cc       | 38 +++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 3afc176402..2048692c22 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -1456,6 +1456,44 @@ TEST_F(ConstantFoldingTest, MaterializeReductionIndices) {
   EXPECT_EQ(3, found);
 }
 
+TEST_F(ConstantFoldingTest, LargeConstant) {
+  tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
+  // Generate a 4k by 4k constant matrix.
+  Output mat_diag =
+      ops::Const(scope.WithOpName("mat_diag"), 3.14f, TensorShape({1024 * 4}));
+  Output mat = ops::Diag(scope.WithOpName("mat"), mat_diag);
+  Output out = ops::Identity(scope.WithOpName("out"), mat);
+
+  GrapplerItem item;
+  TF_CHECK_OK(scope.ToGraphDef(&item.graph));
+  item.fetch.push_back("out");
+
+  ConstantFolding fold(nullptr /* cpu_device */);
+  GraphDef output;
+  Status status = fold.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  // Make sure the diag node hasn't been folded, since it would use too much
+  // memory to encode the corresponding constant.
+  int found = 0;
+  for (const NodeDef& node : output.node()) {
+    if (node.name() == "out") {
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("mat", node.input(0));
+      ++found;
+    } else if (node.name() == "mat") {
+      EXPECT_EQ("Diag", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("mat_diag", node.input(0));
+      ++found;
+    }
+  }
+  EXPECT_EQ(2, found);
+
+  EXPECT_GT(1024 * 1024, output.ByteSizeLong());
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From 90d2dede60a28e25e9873fb135ea08af89d35317 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 10:59:57 -0800
Subject: [PATCH 193/575] Change the MomentumOptimzer lambda so it has the same
 named argument (learning_rate) as the MomentumOptimzer constructor.

PiperOrigin-RevId: 186642325
---
 tensorflow/contrib/layers/python/layers/optimizers.py      | 2 +-
 tensorflow/contrib/layers/python/layers/optimizers_test.py | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/optimizers.py b/tensorflow/contrib/layers/python/layers/optimizers.py
index cdceea6fee..69d927e1b3 100644
--- a/tensorflow/contrib/layers/python/layers/optimizers.py
+++ b/tensorflow/contrib/layers/python/layers/optimizers.py
@@ -41,7 +41,7 @@ OPTIMIZER_CLS_NAMES = {
     "Adagrad": train.AdagradOptimizer,
     "Adam": train.AdamOptimizer,
     "Ftrl": train.FtrlOptimizer,
-    "Momentum": lambda lr: train.MomentumOptimizer(lr, momentum=0.9),
+    "Momentum": lambda learning_rate: train.MomentumOptimizer(learning_rate, momentum=0.9),  # pylint: disable=line-too-long
     "RMSProp": train.RMSPropOptimizer,
     "SGD": train.GradientDescentOptimizer,
 }
diff --git a/tensorflow/contrib/layers/python/layers/optimizers_test.py b/tensorflow/contrib/layers/python/layers/optimizers_test.py
index 1ea25bd1a5..a4461a20e5 100644
--- a/tensorflow/contrib/layers/python/layers/optimizers_test.py
+++ b/tensorflow/contrib/layers/python/layers/optimizers_test.py
@@ -61,7 +61,8 @@ class OptimizersTest(test.TestCase):
     optimizers = [
         "SGD", gradient_descent.GradientDescentOptimizer,
         gradient_descent.GradientDescentOptimizer(learning_rate=0.1),
-        lambda lr: gradient_descent.GradientDescentOptimizer(learning_rate=lr)
+        lambda lr: gradient_descent.GradientDescentOptimizer(learning_rate=lr),
+        "Momentum"
     ]
     for optimizer in optimizers:
       with ops.Graph().as_default() as g:
-- 
GitLab


From c4dc5b7af75b773e7bea2295cc7cbfa194b01947 Mon Sep 17 00:00:00 2001
From: Guangda Lai <laigd@google.com>
Date: Thu, 22 Feb 2018 11:25:24 -0800
Subject: [PATCH 194/575] Add a test only method to reset ProcessState.

PiperOrigin-RevId: 186647005
---
 .../common_runtime/gpu/gpu_device_test.cc     | 75 ++++++++++---------
 .../core/common_runtime/gpu/process_state.cc  | 14 ++++
 .../core/common_runtime/gpu/process_state.h   |  6 ++
 3 files changed, 60 insertions(+), 35 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device_test.cc b/tensorflow/core/common_runtime/gpu/gpu_device_test.cc
index b56823204a..f3935f6ba2 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device_test.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device_test.cc
@@ -18,42 +18,48 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/gpu/gpu_device.h"
 
 #include "tensorflow/core/common_runtime/gpu/gpu_init.h"
+#include "tensorflow/core/common_runtime/gpu/process_state.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
-namespace {
 const char* kDeviceNamePrefix = "/job:localhost/replica:0/task:0";
 
-static SessionOptions MakeSessionOptions(
-    const string& visible_device_list = "",
-    double per_process_gpu_memory_fraction = 0, int gpu_device_count = 1,
-    const std::vector<std::vector<float>>& memory_limit_mb = {}) {
-  SessionOptions options;
-  ConfigProto* config = &options.config;
-  (*config->mutable_device_count())["GPU"] = gpu_device_count;
-  GPUOptions* gpu_options = config->mutable_gpu_options();
-  gpu_options->set_visible_device_list(visible_device_list);
-  gpu_options->set_per_process_gpu_memory_fraction(
-      per_process_gpu_memory_fraction);
-  for (const auto& v : memory_limit_mb) {
-    auto virtual_devices =
-        gpu_options->mutable_experimental()->add_virtual_devices();
-    for (float mb : v) {
-      virtual_devices->add_memory_limit_mb(mb);
+class GPUDeviceTest : public ::testing::Test {
+ public:
+  void TearDown() { ProcessState::singleton()->TestOnlyReset(); }
+
+ protected:
+  static SessionOptions MakeSessionOptions(
+      const string& visible_device_list = "",
+      double per_process_gpu_memory_fraction = 0, int gpu_device_count = 1,
+      const std::vector<std::vector<float>>& memory_limit_mb = {}) {
+    SessionOptions options;
+    ConfigProto* config = &options.config;
+    (*config->mutable_device_count())["GPU"] = gpu_device_count;
+    GPUOptions* gpu_options = config->mutable_gpu_options();
+    gpu_options->set_visible_device_list(visible_device_list);
+    gpu_options->set_per_process_gpu_memory_fraction(
+        per_process_gpu_memory_fraction);
+    for (const auto& v : memory_limit_mb) {
+      auto virtual_devices =
+          gpu_options->mutable_experimental()->add_virtual_devices();
+      for (float mb : v) {
+        virtual_devices->add_memory_limit_mb(mb);
+      }
     }
+    return options;
   }
-  return options;
-}
 
-static bool StartsWith(const string& lhs, const string& rhs) {
-  if (rhs.length() > lhs.length()) return false;
-  return lhs.substr(0, rhs.length()) == rhs;
-}
+  static bool StartsWith(const string& lhs, const string& rhs) {
+    if (rhs.length() > lhs.length()) return false;
+    return lhs.substr(0, rhs.length()) == rhs;
+  }
+};
 
-TEST(GPUDeviceTest, FailedToParseVisibleDeviceList) {
+TEST_F(GPUDeviceTest, FailedToParseVisibleDeviceList) {
   SessionOptions opts = MakeSessionOptions("0,abc");
   std::vector<tensorflow::Device*> devices;
   Status status = DeviceFactory::GetFactory("GPU")->CreateDevices(
@@ -63,7 +69,7 @@ TEST(GPUDeviceTest, FailedToParseVisibleDeviceList) {
       << status;
 }
 
-TEST(GPUDeviceTest, InvalidGpuId) {
+TEST_F(GPUDeviceTest, InvalidGpuId) {
   SessionOptions opts = MakeSessionOptions("100");
   std::vector<tensorflow::Device*> devices;
   Status status = DeviceFactory::GetFactory("GPU")->CreateDevices(
@@ -74,7 +80,7 @@ TEST(GPUDeviceTest, InvalidGpuId) {
       << status;
 }
 
-TEST(GPUDeviceTest, DuplicateEntryInVisibleDeviceList) {
+TEST_F(GPUDeviceTest, DuplicateEntryInVisibleDeviceList) {
   SessionOptions opts = MakeSessionOptions("0,0");
   std::vector<tensorflow::Device*> devices;
   Status status = DeviceFactory::GetFactory("GPU")->CreateDevices(
@@ -85,7 +91,7 @@ TEST(GPUDeviceTest, DuplicateEntryInVisibleDeviceList) {
       << status;
 }
 
-TEST(GPUDeviceTest, VirtualDeviceConfigConflictsWithMemoryFractionSettings) {
+TEST_F(GPUDeviceTest, VirtualDeviceConfigConflictsWithMemoryFractionSettings) {
   SessionOptions opts = MakeSessionOptions("0", 0.1, 1, {{}});
   std::vector<tensorflow::Device*> devices;
   Status status = DeviceFactory::GetFactory("GPU")->CreateDevices(
@@ -96,7 +102,7 @@ TEST(GPUDeviceTest, VirtualDeviceConfigConflictsWithMemoryFractionSettings) {
       << status;
 }
 
-TEST(GPUDeviceTest, GpuDeviceCountTooSmall) {
+TEST_F(GPUDeviceTest, GpuDeviceCountTooSmall) {
   // device_count is 0, but with one entry in visible_device_list and one
   // (empty) VirtualDevices messages.
   SessionOptions opts = MakeSessionOptions("0", 0, 0, {{}});
@@ -109,7 +115,7 @@ TEST(GPUDeviceTest, GpuDeviceCountTooSmall) {
       << status;
 }
 
-TEST(GPUDeviceTest, NotEnoughGpuInVisibleDeviceList) {
+TEST_F(GPUDeviceTest, NotEnoughGpuInVisibleDeviceList) {
   // Single entry in visible_device_list with two (empty) VirtualDevices
   // messages.
   SessionOptions opts = MakeSessionOptions("0", 0, 8, {{}, {}});
@@ -122,7 +128,7 @@ TEST(GPUDeviceTest, NotEnoughGpuInVisibleDeviceList) {
       << status;
 }
 
-TEST(GPUDeviceTest, VirtualDeviceConfigConflictsWithVisibleDeviceList) {
+TEST_F(GPUDeviceTest, VirtualDeviceConfigConflictsWithVisibleDeviceList) {
   // This test requires at least two visible GPU hardware.
   if (GPUMachineManager()->VisibleDeviceCount() < 2) return;
   // Three entries in visible_device_list with two (empty) VirtualDevices
@@ -139,7 +145,7 @@ TEST(GPUDeviceTest, VirtualDeviceConfigConflictsWithVisibleDeviceList) {
       << status;
 }
 
-TEST(GPUDeviceTest, EmptyVirtualDeviceConfig) {
+TEST_F(GPUDeviceTest, EmptyVirtualDeviceConfig) {
   // It'll create single virtual device when the virtual device config is empty.
   SessionOptions opts = MakeSessionOptions("0");
   std::vector<tensorflow::Device*> devices;
@@ -150,7 +156,7 @@ TEST(GPUDeviceTest, EmptyVirtualDeviceConfig) {
   for (auto d : devices) delete d;
 }
 
-TEST(GPUDeviceTest, SingleVirtualDeviceWithNoMemoryLimit) {
+TEST_F(GPUDeviceTest, SingleVirtualDeviceWithNoMemoryLimit) {
   // It'll create single virtual device for the gpu in question when
   // memory_limit_mb is unset.
   SessionOptions opts = MakeSessionOptions("0", 0, 1, {{}});
@@ -162,7 +168,7 @@ TEST(GPUDeviceTest, SingleVirtualDeviceWithNoMemoryLimit) {
   for (auto d : devices) delete d;
 }
 
-TEST(GPUDeviceTest, SingleVirtualDeviceWithMemoryLimit) {
+TEST_F(GPUDeviceTest, SingleVirtualDeviceWithMemoryLimit) {
   SessionOptions opts = MakeSessionOptions("0", 0, 1, {{123}});
   std::vector<tensorflow::Device*> devices;
   TF_CHECK_OK(DeviceFactory::GetFactory("GPU")->CreateDevices(
@@ -172,7 +178,7 @@ TEST(GPUDeviceTest, SingleVirtualDeviceWithMemoryLimit) {
   for (auto d : devices) delete d;
 }
 
-TEST(GPUDeviceTest, MultipleVirtualDevices) {
+TEST_F(GPUDeviceTest, MultipleVirtualDevices) {
   SessionOptions opts = MakeSessionOptions("0", 0, 1, {{123, 456}});
   std::vector<tensorflow::Device*> devices;
   TF_CHECK_OK(DeviceFactory::GetFactory("GPU")->CreateDevices(
@@ -195,7 +201,6 @@ TEST(GPUDeviceTest, MultipleVirtualDevices) {
   for (auto d : devices) delete d;
 }
 
-}  // namespace
 }  // namespace tensorflow
 
 #endif
diff --git a/tensorflow/core/common_runtime/gpu/process_state.cc b/tensorflow/core/common_runtime/gpu/process_state.cc
index 61013bd1ac..866a03d046 100644
--- a/tensorflow/core/common_runtime/gpu/process_state.cc
+++ b/tensorflow/core/common_runtime/gpu/process_state.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/framework/allocator.h"
 #include "tensorflow/core/framework/log_memory.h"
 #include "tensorflow/core/framework/tracking_allocator.h"
+#include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -318,4 +319,17 @@ void ProcessState::AddGPUAllocVisitor(int bus_id, AllocVisitor visitor) {
 #endif  // GOOGLE_CUDA
 }
 
+void ProcessState::TestOnlyReset() {
+  mutex_lock lock(mu_);
+  gpu_device_enabled_ = false;
+  gpu_visitors_.clear();
+  mem_desc_map_.clear();
+  gtl::STLDeleteElements(&cpu_allocators_);
+  gtl::STLDeleteElements(&gpu_allocators_);
+  gtl::STLDeleteElements(&cuda_host_allocators_);
+  gtl::STLDeleteElements(&cpu_al_);
+  gtl::STLDeleteElements(&gpu_al_);
+  gtl::STLDeleteElements(&cuda_al_);
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/gpu/process_state.h b/tensorflow/core/common_runtime/gpu/process_state.h
index f6e2349673..bc2c4182d7 100644
--- a/tensorflow/core/common_runtime/gpu/process_state.h
+++ b/tensorflow/core/common_runtime/gpu/process_state.h
@@ -114,6 +114,10 @@ class ProcessState {
  protected:
   ProcessState();
 
+  // Helper method for unit tests to reset the ProcessState singleton by
+  // cleaning up everything. Never use in production.
+  virtual void TestOnlyReset();
+
   static ProcessState* instance_;
   bool gpu_device_enabled_;
 
@@ -132,6 +136,8 @@ class ProcessState {
   std::vector<Allocator*> cpu_al_ GUARDED_BY(mu_);
   std::vector<Allocator*> gpu_al_ GUARDED_BY(mu_);
   std::vector<Allocator*> cuda_al_ GUARDED_BY(mu_);
+
+  friend class GPUDeviceTest;
 };
 
 namespace internal {
-- 
GitLab


From 49eb5240c2270502d2ff4426b0ce80de91ab27f0 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 22 Feb 2018 11:34:16 -0800
Subject: [PATCH 195/575] [TF:XLA] Improve readability of HLO graphs when
 rendered via Tensorboard. Add operator metadata around the computation
 arguments and retvals, so they are grouped together. Teach the batchnorm
 expander pass to propagate the operator metadata from the original batch norm
 operators.

PiperOrigin-RevId: 186648547
---
 tensorflow/compiler/tf2xla/xla_compiler.cc     | 18 ++++++++++++++++++
 .../compiler/xla/service/batchnorm_expander.cc |  3 +++
 2 files changed, 21 insertions(+)

diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc
index 15bba46ac6..5ec05c4121 100644
--- a/tensorflow/compiler/tf2xla/xla_compiler.cc
+++ b/tensorflow/compiler/tf2xla/xla_compiler.cc
@@ -365,6 +365,13 @@ Status BuildComputation(
               return a->arg_num() < b->arg_num();
             });
 
+  // Attach a common operator name as metadata. This has no semantic effect — it
+  // merely makes the HLO graph more readable when visualized via TensorBoard,
+  // since TensorBoard forms groups out of operators with similar names.
+  xla::OpMetadata retval_metadata;
+  retval_metadata.set_op_name("XLA_Retvals");
+  builder->SetOpMetadata(retval_metadata);
+
   for (const XlaResource* resource : arg_resources) {
     const XlaCompiler::Argument& arg = args[resource->arg_num()];
     const int core = arg_cores[resource->arg_num()];
@@ -412,6 +419,8 @@ Status BuildComputation(
 
   // Builds the XLA computation.
   builder->Tuple(elems);
+  builder->ClearOpMetadata();
+
   xla::StatusOr<xla::Computation> computation_status = builder->Build();
   if (!computation_status.ok()) {
     return computation_status.status();
@@ -514,6 +523,13 @@ Status XlaCompiler::BuildArguments(
     }
   }
 
+  // Attach a common operator name as metadata. This has no semantic effect — it
+  // merely makes the HLO graph more readable when visualized via TensorBoard,
+  // since TensorBoard forms groups out of operators with similar names.
+  xla::OpMetadata arg_metadata;
+  arg_metadata.set_op_name("XLA_Args");
+  builder->SetOpMetadata(arg_metadata);
+
   // Build parameter handles for non-constant arguments.
   std::vector<xla::ComputationDataHandle> arg_handles(input_mapping->size());
   if (use_tuple_arg) {
@@ -552,6 +568,8 @@ Status XlaCompiler::BuildArguments(
     }
   }
 
+  builder->ClearOpMetadata();
+
   // Fill in the handles in non-constant arguments.
   VLOG(2) << "XLA computation inputs:";
   for (std::vector<int>::size_type i = 0; i < input_mapping->size(); ++i) {
diff --git a/tensorflow/compiler/xla/service/batchnorm_expander.cc b/tensorflow/compiler/xla/service/batchnorm_expander.cc
index 27ddfd47aa..84c9db3293 100644
--- a/tensorflow/compiler/xla/service/batchnorm_expander.cc
+++ b/tensorflow/compiler/xla/service/batchnorm_expander.cc
@@ -153,6 +153,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormTraining(
   std::vector<HloInstruction*> added_instructions;
   auto add = [&](std::unique_ptr<HloInstruction> inst) {
     HloInstruction* added_inst = computation_->AddInstruction(std::move(inst));
+    added_inst->set_metadata(batch_norm->metadata());
     added_instructions.push_back(added_inst);
     return added_inst;
   };
@@ -334,6 +335,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormInference(
   std::vector<HloInstruction*> added_instructions;
   auto add = [&](std::unique_ptr<HloInstruction> inst) {
     HloInstruction* added_inst = computation_->AddInstruction(std::move(inst));
+    added_inst->set_metadata(batch_norm->metadata());
     added_instructions.push_back(added_inst);
     return added_inst;
   };
@@ -419,6 +421,7 @@ Status BatchNormExpanderVisitor::HandleBatchNormGrad(
   std::vector<HloInstruction*> added_instructions;
   auto add = [&](std::unique_ptr<HloInstruction> inst) {
     HloInstruction* added_inst = computation_->AddInstruction(std::move(inst));
+    added_inst->set_metadata(batch_norm->metadata());
     added_instructions.push_back(added_inst);
     return added_inst;
   };
-- 
GitLab


From da66104f3d5e3b5ba5e79d0beccd1b91b9578bee Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 11:45:57 -0800
Subject: [PATCH 196/575] Add basic support for quantized unfused LSTMs.

PiperOrigin-RevId: 186650338
---
 tensorflow/contrib/lite/toco/args.h           |  1 +
 .../graph_transformations/hardcode_min_max.cc | 25 +++++++++++++++++++
 .../contrib/lite/toco/toco_cmdline_flags.cc   |  5 ++++
 tensorflow/contrib/lite/toco/toco_flags.proto |  7 +++++-
 tensorflow/contrib/lite/toco/toco_tooling.cc  |  4 ++-
 5 files changed, 40 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/args.h b/tensorflow/contrib/lite/toco/args.h
index b97a4720a7..59a6115920 100644
--- a/tensorflow/contrib/lite/toco/args.h
+++ b/tensorflow/contrib/lite/toco/args.h
@@ -229,6 +229,7 @@ struct ParsedTocoFlags {
   // Deprecated flags
   Arg<string> input_type;
   Arg<string> input_types;
+  Arg<bool> debug_disable_recurrent_cell_fusion = Arg<bool>(false);
   Arg<bool> drop_control_dependency = Arg<bool>(false);
 };
 
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc
index 1b0be85810..938d76386d 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc
@@ -125,6 +125,27 @@ bool HardcodeMinMaxForConcatenation(Model* model, Operator* op) {
   return changed;
 }
 
+bool HardcodeMinMaxForSplit(Model* model, Operator* op) {
+  for (const auto& output : op->outputs) {
+    if (model->GetArray(output).minmax) {
+      LOG(WARNING) << "Skipping min-max setting for " << LogName(*op)
+                   << " because output " << output << " already has min-max.";
+      return false;
+    }
+  }
+  // Data is in second input.
+  auto& input_array = model->GetArray(op->inputs[1]);
+  if (!input_array.minmax) {
+    return false;
+  } else {
+    for (const auto& output : op->outputs) {
+      auto& array = model->GetArray(output);
+      array.GetOrCreateMinMax() = *input_array.minmax;
+    }
+    return true;
+  }
+}
+
 // The output of average or max pooling is within the same range as its input.
 bool HardcodeMinMaxForAverageOrMaxPool(Model* model, Operator* op) {
   auto& output_array = model->GetArray(op->outputs[0]);
@@ -296,6 +317,10 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) {
       changed = HardcodeMinMaxForConcatenation(model, op);
       break;
 
+    case OperatorType::kTensorFlowSplit:
+      changed = HardcodeMinMaxForSplit(model, op);
+      break;
+
     case OperatorType::kAveragePool:
     case OperatorType::kMaxPool:
       changed = HardcodeMinMaxForAverageOrMaxPool(model, op);
diff --git a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc
index c5a62fdb62..0f67c2de72 100644
--- a/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc
+++ b/tensorflow/contrib/lite/toco/toco_cmdline_flags.cc
@@ -112,6 +112,11 @@ bool ParseTocoFlagsFromCommandLineFlags(
           "If true, ignore control dependency requirements in input TensorFlow "
           "GraphDef. Otherwise an error will be raised upon control dependency "
           "inputs."),
+      Flag("debug_disable_recurrent_cell_fusion",
+           parsed_flags.debug_disable_recurrent_cell_fusion.bind(),
+           parsed_flags.debug_disable_recurrent_cell_fusion.default_value(),
+           "If true, disable fusion of known identifiable cell subgraphs into "
+           "cells. This includes, for example, specific forms of LSTM cell."),
   };
   bool asked_for_help =
       *argc == 2 && (!strcmp(argv[1], "--help") || !strcmp(argv[1], "-help"));
diff --git a/tensorflow/contrib/lite/toco/toco_flags.proto b/tensorflow/contrib/lite/toco/toco_flags.proto
index 3b9d7e2257..3237147a73 100644
--- a/tensorflow/contrib/lite/toco/toco_flags.proto
+++ b/tensorflow/contrib/lite/toco/toco_flags.proto
@@ -36,7 +36,8 @@ enum FileFormat {
 // are not normally encoded in model files and in general may not be thought
 // of as properties of models, instead describing how models are to be
 // processed in the context of the present tooling job.
-// Next Id: 13
+//
+// Next ID to use: 14.
 message TocoFlags {
   // Input file format
   optional FileFormat input_format = 1;
@@ -136,4 +137,8 @@ message TocoFlags {
   //    - Default to false if the output format is TENSORFLOW_GRAPHDEF.
   //    - Default to true in all other cases.
   optional bool drop_control_dependency = 12;
+
+  // Disables transformations that fuse subgraphs such as known LSTMs (not all
+  // LSTMs are identified).
+  optional bool debug_disable_recurrent_cell_fusion = 13;
 }
diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc
index 1b836fbc15..6fcaa957cf 100644
--- a/tensorflow/contrib/lite/toco/toco_tooling.cc
+++ b/tensorflow/contrib/lite/toco/toco_tooling.cc
@@ -234,7 +234,9 @@ void Transform(const TocoFlags& toco_flags, Model* model) {
   }
   transformations.Add(new ConvertPureConvToDepthwise);
   if (SupportsLstmCell(output_format)) {
-    transformations.Add(new IdentifyLstmCell);
+    if (!toco_flags.debug_disable_recurrent_cell_fusion()) {
+      transformations.Add(new IdentifyLstmCell);
+    }
     if (output_format == TFLITE) {
       transformations.Add(new toco::SplitLstmCellInputs);
     } else {
-- 
GitLab


From 725d049caea2b75eae373760127ed9b55138f7dc Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Thu, 22 Feb 2018 11:46:05 -0800
Subject: [PATCH 197/575] Update bazel toolchains dependency.

PiperOrigin-RevId: 186650360
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index b3ef7d0edb..167942cefd 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -692,11 +692,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "bazel_toolchains",
       urls = [
-          "https://mirror.bazel.build/github.com/bazelbuild/bazel-toolchains/archive/f3b09700fae5d7b6e659d7cefe0dcc6e8498504c.tar.gz",
-          "https://github.com/bazelbuild/bazel-toolchains/archive/f3b09700fae5d7b6e659d7cefe0dcc6e8498504c.tar.gz",
+          "https://mirror.bazel.build/github.com/bazelbuild/bazel-toolchains/archive/44200e0c026d86c53470d107b3697a3e46469c43.tar.gz",
+          "https://github.com/bazelbuild/bazel-toolchains/archive/44200e0c026d86c53470d107b3697a3e46469c43.tar.gz",
       ],
-      sha256 = "ed829b5eea8af1f405f4cc3d6ecfc3b1365bb7843171036030a31b5127002311",
-      strip_prefix = "bazel-toolchains-f3b09700fae5d7b6e659d7cefe0dcc6e8498504c",
+      strip_prefix = "bazel-toolchains-44200e0c026d86c53470d107b3697a3e46469c43",
+      sha256 = "699b55a6916c687f4b7dc092dbbf5f64672cde0dc965f79717735ec4e5416556",
   )
 
   tf_http_archive(
-- 
GitLab


From a4de23973ddddfa8dc26d846dc0a902942347b11 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Thu, 22 Feb 2018 12:01:57 -0800
Subject: [PATCH 198/575] Checkpointable: Re-use the Saver's SaveableObject
 infrastructure to create restore ops, cache them.

The basic infrastructure is usable after this CL in graph and eager, but I still need to make a bunch of objects Checkpointable and make some other usability fixes.

Also sets some of the groundwork for feeding and fetching Python values during save/restore (as in, save has a feed dict now; gathering feeds and placeholders from Checkpointable objects is still to do but should be relatively straightforward).

PiperOrigin-RevId: 186652696
---
 .../eager/python/checkpointable_utils.py      | 395 ++++++++++++------
 .../eager/python/checkpointable_utils_test.py | 143 +++----
 tensorflow/python/BUILD                       |   1 -
 tensorflow/python/ops/variables.py            |  12 +-
 tensorflow/python/training/checkpointable.py  | 178 ++++----
 .../python/training/checkpointable_utils.py   |  78 ++++
 6 files changed, 506 insertions(+), 301 deletions(-)
 create mode 100644 tensorflow/python/training/checkpointable_utils.py

diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py
index 0506af391c..d9648ffb03 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils.py
@@ -18,8 +18,11 @@ from __future__ import division
 from __future__ import print_function
 
 import collections
+import weakref
 
 from tensorflow.contrib.eager.proto import checkpointable_object_graph_pb2
+from tensorflow.python import pywrap_tensorflow
+from tensorflow.python.client import session as session_lib
 from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -31,6 +34,7 @@ from tensorflow.python.ops import io_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.training import checkpointable as core_checkpointable
+from tensorflow.python.training import checkpointable_utils as core_checkpointable_utils
 from tensorflow.python.training import optimizer as optimizer_lib
 from tensorflow.python.training import saver as saver_lib
 
@@ -214,7 +218,7 @@ def _serialize_checkpointables(
     object_proto.slot_variables.extend(slot_variables.get(checkpointable, ()))
     object_name = object_names[checkpointable]
     for name, saveable in (
-        checkpointable._gather_tensors_for_checkpoint().items()):  # pylint: disable=protected-access
+        checkpointable._gather_saveables_for_checkpoint().items()):  # pylint: disable=protected-access
       attribute = object_proto.attributes.add()
       attribute.name = name
       attribute.checkpoint_key = "%s/%s/%s" % (
@@ -284,58 +288,39 @@ class _NoRestoreSaveable(saver_lib.BaseSaverBuilder.SaveableObject):
     return control_flow_ops.no_op()
 
 
-def save(file_prefix, root_checkpointable, checkpoint_number=None,
-         session=None):
-  """Save a training checkpoint.
-
-  Args:
-    file_prefix: A prefix to use for the checkpoint filenames
-      (/path/to/directory/and_a_prefix). Names are generated based on this
-      prefix and the global step, if provided.
-    root_checkpointable: A Checkpointable object to save. The checkpoint
-      includes variables created by this object and any Checkpointable objects
-      it depends on.
-    checkpoint_number: An integer variable or Tensor, used to number
-      checkpoints. Typically this value is saved along with other variables in
-      training checkpoints, which will happen automatically if it was created by
-      `root_checkpointable` or one of its dependencies (via
-      `Checkpointable._add_variable`).
-    session: The session to evaluate variables in. Ignored when executing
-      eagerly. If not provided when graph building, the default session is used.
+class CheckpointLoadStatus(object):
+  """Checks the status of checkpoint loading and manages restore ops.
 
-  Returns:
-    The full path to the checkpoint.
-  """
-  named_variables, serialized_graph = _serialize_object_graph(
-      root_checkpointable)
-  if context.in_graph_mode():
-    if session is None:
-      session = ops.get_default_session()
-  else:
-    session = None
-  assert _OBJECT_GRAPH_PROTO_KEY not in named_variables
-  # TODO(allenl): Feed rather than embedding a constant.
-  named_variables[_OBJECT_GRAPH_PROTO_KEY] = _NoRestoreSaveable(
-      tensor=constant_op.constant(
-          serialized_graph.SerializeToString(), dtype=dtypes.string),
-      name=_OBJECT_GRAPH_PROTO_KEY)
-  with ops.device("/device:CPU:0"):
-    save_path = saver_lib.Saver(var_list=named_variables).save(
-        sess=session,
-        save_path=file_prefix,
-        write_meta_graph=False,
-        global_step=checkpoint_number)
-  return save_path
+  Returned from `Saver.restore`. Since `restore` may defer the loading of values
+  in the checkpoint which don't yet have corresponding Python objects,
+  `CheckpointLoadStatus` provides a callback to verify that checkpoint loading
+  is complete (`assert_consumed`).
 
+  When graph building, `restore` does not run restore ops itself since their
+  creation may be deferred. The `run_restore_ops` method must be called once all
+  Python objects with values to restore have been created and added to the
+  dependency graph (this does not necessarily have to be the whole checkpoint;
+  calling `run_restore_ops` while `assert_consumed` fails is supported and will
+  partially restore the checkpoint).
 
-class CheckpointLoadStatus(object):
-  """Checks the status of checkpoint loading."""
+  See `Saver.restore` for usage examples.
+  """
 
-  def __init__(self, checkpoint):
+  def __init__(self, checkpoint, feed_dict):
     self._checkpoint = checkpoint
+    self._feed_dict = feed_dict
 
   def assert_consumed(self):
-    """Asserts that all objects in the checkpoint have been created/matched."""
+    """Asserts that all objects in the checkpoint have been created/matched.
+
+    Returns:
+      `self` for chaining.
+    Raises:
+      AssertionError: If there are any Python objects in the dependency graph
+        which have not been restored from this checkpoint or a later `restore`,
+        or if there are any checkpointed values which have not been matched to
+        Python objects.
+    """
     for node_id, node in enumerate(self._checkpoint.object_graph_proto.nodes):
       checkpointable = self._checkpoint.object_by_proto_id.get(node_id, None)
       if checkpointable is None:
@@ -348,88 +333,256 @@ class CheckpointLoadStatus(object):
       # restored.
       raise AssertionError("Unresolved slot restorations: %s" % (
           self._checkpoint.slot_restorations,))
+    if self._checkpoint.unused_attributes:
+      raise AssertionError(
+          ("Unused attributes in these objects (the attributes exist in the "
+           "checkpoint but not in the objects): %s") % (
+               self._checkpoint.unused_attributes.items(),))
     return self
 
-  @property
-  def restore_ops(self):
-    """Operations to restore objects in the dependency graph."""
-    return self._checkpoint.restore_ops
-
-
-def restore(save_path, root_checkpointable, session=None):
-  """Restore a training checkpoint.
-
-  Restores the values of variables created with `Checkpointable._add_variable`
-  in `root_checkpointable` and any objects that it tracks (transitive). Either
-  assigns values immediately if variables to restore have been created already,
-  or defers restoration until the variables are created. Dependencies added to
-  `root_checkpointable` after this call will be matched if they have a
-  corresponding object in the checkpoint.
+  def run_restore_ops(self, session=None):
+    """Run operations to restore objects in the dependency graph."""
+    if context.in_eager_mode():
+      return  # Run eagerly
+    if session is None:
+      session = ops.get_default_session()
+    session.run(self._checkpoint.restore_ops, feed_dict=self._feed_dict)
 
-  When building a graph, restorations are added to the graph but not run. A
-  session is required to retrieve checkpoint metadata.
 
-  To disallow deferred loading, assert immediately that all checkpointed
-  variables have been matched to variable objects:
+class _SessionWithFeedDictAdditions(session_lib.SessionInterface):
+  """Pretends to be a session, inserts extra feeds on run()."""
 
-  ```python
-  restore(path, root).assert_consumed()
-  ```
+  def __init__(self, session, feed_additions):
+    self._wrapped_session = session
+    self._feed_additions = feed_additions
 
-  An exception will be raised unless every object was matched and its variables
-  already exist.
+  def run(self, fetches, feed_dict=None, **kwargs):
+    if feed_dict is None:
+      feed_dict = {}
+    else:
+      feed_dict = feed_dict.copy()
+    feed_dict.update(self._feed_additions)
+    return self._wrapped_session.run(
+        fetches=fetches, feed_dict=feed_dict, **kwargs)
+
+
+class Saver(object):
+  """Saves and restores a `Checkpointable` object and its dependencies.
+
+  See `Checkpointable` for details of dependency management. `Saver` wraps
+  `tf.train.Saver` for saving, including extra information about the graph of
+  dependencies between Python objects. When restoring, it uses this information
+  about the save-time dependency graph to more robustly match objects with their
+  checkpointed values. When executing eagerly, it supports restoring variables
+  on object creation (see `Saver.restore`).
+
+  Values in a checkpoint are mapped to `Checkpointable` Python objects
+  (`Variable`s, `Optimizer`s, `Layer`s) based on the names provided when the
+  checkpoint was written. To avoid breaking existing checkpoints when modifying
+  a class, dependency names (the names of attributes to which `Checkpointable`
+  objects are assigned) may not change. These names are local to objects, in
+  contrast to the `Variable.name`-based save/restore from `tf.train.Saver`, and
+  so allow additional program transformations.
+  """
 
-  When graph building, `assert_consumed()` indicates that all of the restore ops
-  which will be created for this checkpoint have been created. They are
-  available in the `restore_ops` property of the status object:
+  def __init__(self, root_checkpointable):
+    """Configure saving.
+
+    Args:
+      root_checkpointable: The root of the object graph to save/restore. This
+        object and all of its dependencies are saved in the checkpoint. When
+        restoring, objects are matched and restored starting from this root.
+    """
+    # Allow passing in a weak reference to avoid reference cycles when
+    # `Checkpointable` objects save themselves.
+    self._root_checkpointable_ref = root_checkpointable
+    if context.in_graph_mode():
+      self._file_prefix_placeholder = constant_op.constant("model")
+    else:
+      self._file_prefix_placeholder = None
 
-  ```python
-  session.run(restore(path, root).assert_consumed().restore_ops)
-  ```
+    # Op caching for save
+    self._object_graph_feed_tensor = None
+    self._last_save_object_graph = None
+    self._last_save_saver = None
 
-  If the checkpoint has not been consumed completely, then the list of
-  `restore_ops` will grow as more objects are added to the dependency graph.
+    # Op caching for restore
+    self._object_graph_restore_tensor = None
+    self._last_restore_object_graph = None
+    self._last_restore_checkpoint = None
 
-  Args:
-    save_path: The path to the checkpoint, as returned by `save` or
-      `tf.train.latest_checkpoint`. If None (as when there is no latest
-      checkpoint for `tf.train.latest_checkpoint` to return), does nothing.
-    root_checkpointable: The root of the object graph to restore. Variables to
-      restore need not have been created yet, but all dependencies on other
-      `Checkpointable` objects should already be declared. Objects in the
-      dependency graph are matched to objects in the checkpointed graph, and
-      matching objects have their variables restored (or the checkpointed values
-      saved for eventual restoration when the variable is created).
-    session: The session to retrieve metadata with. Ignored when executing
-      eagerly. If not provided when graph building, the default session is used.
-  Returns:
-    A `CheckpointLoadStatus` object, which can be used to make assertions about
-    the status of checkpoint restoration and fetch restore ops.
-  """
-  if save_path is None:
-    return
-  if context.in_graph_mode():
-    if session is None:
-      session = ops.get_default_session()
-  else:
-    session = None
-  object_graph_string, = io_ops.restore_v2(
-      prefix=save_path,
-      tensor_names=[_OBJECT_GRAPH_PROTO_KEY],
-      shape_and_slices=[""],
-      dtypes=[dtypes.string],
-      name="object_graph_proto_read")
-  if session is not None:
-    object_graph_string = session.run(object_graph_string)
-  else:
-    object_graph_string = object_graph_string.numpy()
-  object_graph_proto = (
-      checkpointable_object_graph_pb2.CheckpointableObjectGraph())
-  object_graph_proto.ParseFromString(object_graph_string)
-  checkpoint = core_checkpointable._Checkpoint(  # pylint: disable=protected-access
-      object_graph_proto=object_graph_proto,
-      save_path=save_path)
-  core_checkpointable._CheckpointPosition(  # pylint: disable=protected-access
-      checkpoint=checkpoint, proto_id=0).restore(root_checkpointable)
-  load_status = CheckpointLoadStatus(checkpoint)
-  return load_status
+  @property
+  def _root_checkpointable(self):
+    if isinstance(self._root_checkpointable_ref, weakref.ref):
+      derefed = self._root_checkpointable_ref()
+      assert derefed is not None
+      return derefed
+    else:
+      return self._root_checkpointable_ref
+
+  def save(self, file_prefix, checkpoint_number=None, session=None):
+    """Save a training checkpoint.
+
+    The saved checkpoint includes variables created by this object and any
+    Checkpointable objects it depends on at the time `Saver.save()` is called.
+
+    Args:
+      file_prefix: A prefix to use for the checkpoint filenames
+        (/path/to/directory/and_a_prefix). Names are generated based on this
+        prefix and the global step, if provided.
+      checkpoint_number: An integer variable or Tensor, used to number
+        checkpoints. Typically this value is saved along with other variables in
+        training checkpoints, which will happen automatically if it was created
+        by `root_checkpointable` or one of its dependencies (via
+        `Checkpointable._add_variable`).
+      session: The session to evaluate variables in. Ignored when executing
+        eagerly. If not provided when graph building, the default session is
+        used.
+
+    Returns:
+      The full path to the checkpoint.
+    """
+    named_variables, graph_proto = _serialize_object_graph(
+        self._root_checkpointable)
+    in_graph_mode = context.in_graph_mode()
+    if in_graph_mode:
+      if session is None:
+        session = ops.get_default_session()
+      if self._object_graph_feed_tensor is None:
+        self._object_graph_feed_tensor = constant_op.constant(
+            "", dtype=dtypes.string)
+      object_graph_tensor = self._object_graph_feed_tensor
+      feed_additions = {object_graph_tensor: graph_proto.SerializeToString()}
+    else:
+      session = None
+      object_graph_tensor = constant_op.constant(
+          graph_proto.SerializeToString(), dtype=dtypes.string)
+      feed_additions = None
+    assert _OBJECT_GRAPH_PROTO_KEY not in named_variables
+    named_variables[_OBJECT_GRAPH_PROTO_KEY] = _NoRestoreSaveable(
+        tensor=object_graph_tensor,
+        name=_OBJECT_GRAPH_PROTO_KEY)
+    if not in_graph_mode or self._last_save_object_graph != graph_proto:
+      if self._last_save_object_graph is not None and in_graph_mode:
+        raise NotImplementedError(
+            "Using a single Saver to save a mutated object graph is not "
+            "currently supported when graph building. Use a different Saver "
+            "when the object graph changes (save ops will be duplicated), or "
+            "file a feature request if this limitation bothers you.")
+      saver = saver_lib.Saver(var_list=named_variables)
+      if in_graph_mode:
+        self._last_save_saver = saver
+        self._last_save_object_graph = graph_proto
+    else:
+      saver = self._last_save_saver
+    save_path = saver.save(
+        sess=_SessionWithFeedDictAdditions(
+            session=session, feed_additions=feed_additions),
+        save_path=file_prefix,
+        write_meta_graph=False,
+        global_step=checkpoint_number)
+    return save_path
+
+  def restore(self, save_path, session=None):
+    """Restore a training checkpoint.
+
+    Restores `root_checkpointable` and any objects that it tracks
+    (transitive). Either assigns values immediately if variables to restore have
+    been created already, or defers restoration until the variables are
+    created. Dependencies added to the `root_checkpointable` passed to the
+    constructor after this call will be matched if they have a corresponding
+    object in the checkpoint.
+
+    When building a graph, restorations are added to the graph but not run. A
+    session is required to retrieve checkpoint metadata.
+
+    To disallow deferred loading, assert immediately that all checkpointed
+    variables have been matched to variable objects:
+
+    ```python
+    saver = Saver(root)
+    saver.restore(path).assert_consumed()
+    ```
+
+    An exception will be raised unless every object was matched and its
+    variables already exist.
+
+    When graph building, `assert_consumed()` indicates that all of the restore
+    ops which will be created for this checkpoint have been created. They can be
+    run via the `run_restore_ops()` function of the status object:
+
+    ```python
+    saver.restore(path).assert_consumed().run_restore_ops()
+    ```
+
+    If the checkpoint has not been consumed completely, then the list of restore
+    ops will grow as more objects are added to the dependency graph.
+
+    Args:
+      save_path: The path to the checkpoint, as returned by `save` or
+        `tf.train.latest_checkpoint`. If None (as when there is no latest
+        checkpoint for `tf.train.latest_checkpoint` to return), does nothing.
+      session: The session to retrieve metadata with. Ignored when executing
+        eagerly. If not provided when graph building, the default session is
+        used.
+
+    Returns:
+      A `CheckpointLoadStatus` object, which can be used to make assertions
+      about the status of checkpoint restoration and run restore ops.
+    """
+    if save_path is None:
+      return
+    in_graph_mode = context.in_graph_mode()
+    if in_graph_mode:
+      if session is None:
+        session = ops.get_default_session()
+      file_prefix_tensor = self._file_prefix_placeholder
+      file_prefix_feed_dict = {self._file_prefix_placeholder: save_path}
+    else:
+      session = None
+      file_prefix_tensor = constant_op.constant(save_path)
+      file_prefix_feed_dict = None
+    if not in_graph_mode or self._object_graph_restore_tensor is None:
+      object_graph_string, = io_ops.restore_v2(
+          prefix=file_prefix_tensor,
+          tensor_names=[_OBJECT_GRAPH_PROTO_KEY],
+          shape_and_slices=[""],
+          dtypes=[dtypes.string],
+          name="object_graph_proto_read")
+      if in_graph_mode:
+        self._object_graph_restore_tensor = object_graph_string
+    if in_graph_mode:
+      object_graph_string = session.run(
+          self._object_graph_restore_tensor,
+          feed_dict=file_prefix_feed_dict)
+    else:
+      object_graph_string = object_graph_string.numpy()
+    object_graph_proto = (
+        checkpointable_object_graph_pb2.CheckpointableObjectGraph())
+    object_graph_proto.ParseFromString(object_graph_string)
+    if in_graph_mode and object_graph_proto == self._last_restore_object_graph:
+      checkpoint = self._last_restore_checkpoint
+    else:
+      if in_graph_mode:
+        dtype_map = None
+      else:
+        reader = pywrap_tensorflow.NewCheckpointReader(save_path)
+        dtype_map = reader.get_variable_to_dtype_map()
+      checkpoint = core_checkpointable_utils._Checkpoint(  # pylint: disable=protected-access
+          object_graph_proto=object_graph_proto,
+          save_path=file_prefix_tensor,
+          dtype_map=dtype_map)
+      if in_graph_mode:
+        if self._last_restore_object_graph is not None:
+          raise NotImplementedError(
+              "Using a single Saver to restore different object graphs is not "
+              "currently supported when graph building. Use a different Saver "
+              "for each object graph (restore ops will be duplicated), or "
+              "file a feature request if this limitation bothers you.")
+        self._last_restore_checkpoint = checkpoint
+        self._last_restore_object_graph = object_graph_proto
+    core_checkpointable._CheckpointPosition(  # pylint: disable=protected-access
+        checkpoint=checkpoint, proto_id=0).restore(self._root_checkpointable)
+    load_status = CheckpointLoadStatus(
+        checkpoint, feed_dict=file_prefix_feed_dict)
+    return load_status
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index 21ba6adc6a..b7554defde 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -18,7 +18,7 @@ from __future__ import print_function
 
 import functools
 import os
-import unittest
+import weakref
 
 import six
 
@@ -141,6 +141,7 @@ class Checkpoint(checkpointable.Checkpointable):
     for k, v in sorted(kwargs.items(), key=lambda item: item[0]):
       setattr(self, k, v)
     self._save_counter = None
+    self._saver = checkpointable_utils.Saver(weakref.ref(self))
 
   @property
   def save_counter(self):
@@ -163,16 +164,14 @@ class Checkpoint(checkpointable.Checkpointable):
       if session is None:
         session = ops.get_default_session()
       session.run(assign_op)
-    return checkpointable_utils.save(
+    return self._saver.save(
         file_prefix=file_prefix,
-        root_checkpointable=self,
         checkpoint_number=self.save_counter,
         session=session)
 
   def restore(self, save_path):
-    return checkpointable_utils.restore(
-        save_path=save_path,
-        root_checkpointable=self)
+    return self._saver.restore(
+        save_path=save_path)
 
 
 class InterfaceTests(test.TestCase):
@@ -399,7 +398,7 @@ class CheckpointingTests(test.TestCase):
     self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
     # Immediate restoration
     status = root_checkpointable.restore(save_path=save_path).assert_consumed()
-    self.evaluate(status.restore_ops)
+    status.run_restore_ops()
     self.assertAllEqual([42.], self.evaluate(network._named_dense.variables[1]))
     self.assertAllEqual(1, self.evaluate(root_checkpointable.save_counter))
     self.assertAllEqual([1.5], self.evaluate(m_bias_slot))
@@ -480,11 +479,8 @@ class CheckpointingTests(test.TestCase):
               # if no checkpoint is being loaded. This would make deferred
               # loading a bit more useful with graph execution.
             else:
-              status = checkpointable_utils.restore(
-                  save_path=checkpoint_path,
-                  root_checkpointable=root,
-                  session=session).assert_consumed()
-              session.run(status.restore_ops)
+              status = root.restore(save_path=checkpoint_path).assert_consumed()
+              status.run_restore_ops()
             for _ in range(num_training_steps):
               session.run(train_op)
             root.save(file_prefix=checkpoint_prefix,
@@ -555,14 +551,14 @@ class CheckpointingTests(test.TestCase):
     self.evaluate(state_ops.assign(original.dep.var, 123.))
     checkpoint_directory = self.get_temp_dir()
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
-    save_path = checkpointable_utils.save(checkpoint_prefix, original)
+    save_path = checkpointable_utils.Saver(original).save(checkpoint_prefix)
     load_into = LateDependencies()
-    status = checkpointable_utils.restore(save_path, load_into)
+    status = checkpointable_utils.Saver(load_into).restore(save_path)
     with self.assertRaises(AssertionError):
       status.assert_consumed()
     load_into.add_dep()
     status.assert_consumed()
-    self.evaluate(status.restore_ops)
+    status.run_restore_ops()
     self.assertEqual(123., self.evaluate(load_into.dep.var))
 
   @test_util.run_in_graph_and_eager_modes()
@@ -586,15 +582,14 @@ class CheckpointingTests(test.TestCase):
     self.evaluate(state_ops.assign(dep_after_var.dep.var, -14.))
     checkpoint_directory = self.get_temp_dir()
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
-    save_path = checkpointable_utils.save(
-        checkpoint_prefix, dep_after_var)
+    save_path = checkpointable_utils.Saver(dep_after_var).save(
+        checkpoint_prefix)
 
     loaded_dep_after_var = DepAfterVar()
-    status = checkpointable_utils.restore(
-        save_path, loaded_dep_after_var)
+    status = checkpointable_utils.Saver(loaded_dep_after_var).restore(save_path)
     loaded_dep_after_var.add_dep()
     status.assert_consumed()
-    self.evaluate(status.restore_ops)
+    status.run_restore_ops()
     self.assertEqual(-14., self.evaluate(loaded_dep_after_var.dep.var))
 
   @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
@@ -612,27 +607,25 @@ class CheckpointingTests(test.TestCase):
     else:
       optimizer.minimize(root.var.read_value)
     self.evaluate(state_ops.assign(root.var, 12.))
-    no_slots_path = checkpointable_utils.save(
-        os.path.join(checkpoint_directory, "no_slots"), root)
+    no_slots_path = checkpointable_utils.Saver(root).save(
+        os.path.join(checkpoint_directory, "no_slots"))
     root.optimizer = optimizer
     self.evaluate(state_ops.assign(root.var, 13.))
     self.evaluate(state_ops.assign(optimizer.get_slot(name="m", var=root.var),
                                    14.))
-    slots_path = checkpointable_utils.save(
-        os.path.join(checkpoint_directory, "with_slots"), root)
+    slots_path = checkpointable_utils.Saver(root).save(
+        os.path.join(checkpoint_directory, "with_slots"))
     new_root = checkpointable.Checkpointable()
     # Load the slot-containing checkpoint (deferred), then immediately overwrite
     # the non-slot variable (also deferred).
-    slot_status = checkpointable_utils.restore(
-        slots_path, new_root)
-    no_slot_status = checkpointable_utils.restore(
-        no_slots_path, new_root)
+    slot_status = checkpointable_utils.Saver(new_root).restore(slots_path)
+    no_slot_status = checkpointable_utils.Saver(new_root).restore(no_slots_path)
     with self.assertRaises(AssertionError):
       no_slot_status.assert_consumed()
     new_root.var = checkpointable_utils.add_variable(
         new_root, name="var", shape=[])
     no_slot_status.assert_consumed()
-    self.evaluate(no_slot_status.restore_ops)
+    no_slot_status.run_restore_ops()
     self.assertEqual(12., self.evaluate(new_root.var))
     new_root.optimizer = CheckpointableAdam(0.1)
     with self.assertRaisesRegexp(AssertionError, "beta1_power"):
@@ -650,7 +643,7 @@ class CheckpointingTests(test.TestCase):
       train_op = new_root.optimizer.minimize(new_root.var)
       # The slot variable now exists; restore() didn't create it, but we should
       # now have a restore op for it.
-      self.evaluate(slot_status.restore_ops)
+      slot_status.run_restore_ops()
       self.assertEqual(14., self.evaluate(
           new_root.optimizer.get_slot(name="m", var=new_root.var)))
       self.evaluate(train_op)
@@ -666,50 +659,43 @@ class CheckpointingTests(test.TestCase):
     save_root.dep.var = checkpointable_utils.add_variable(
         save_root.dep, name="var", initializer=0.)
     self.evaluate(state_ops.assign(save_root.dep.var, 12.))
-    first_path = checkpointable_utils.save(
-        os.path.join(checkpoint_directory, "first"), save_root)
+    saver = checkpointable_utils.Saver(save_root)
+    first_path = saver.save(os.path.join(checkpoint_directory, "first"))
     self.evaluate(state_ops.assign(save_root.dep.var, 13.))
-    second_path = checkpointable_utils.save(
-        os.path.join(checkpoint_directory, "second"), save_root)
+    second_path = saver.save(os.path.join(checkpoint_directory, "second"))
 
     first_root = checkpointable.Checkpointable()
     second_root = checkpointable.Checkpointable()
-    first_status = checkpointable_utils.restore(
-        first_path, first_root)
-    second_status = checkpointable_utils.restore(
-        second_path, second_root)
+    first_status = checkpointable_utils.Saver(first_root).restore(first_path)
+    second_status = checkpointable_utils.Saver(second_root).restore(second_path)
     load_dep = checkpointable.Checkpointable()
     load_dep.var = checkpointable_utils.add_variable(
         load_dep, name="var", shape=[])
     first_root.dep = load_dep
     first_status.assert_consumed()
-    self.evaluate(first_status.restore_ops)
-    self.assertEqual([], second_status.restore_ops)
+    first_status.run_restore_ops()
     self.assertEqual(12., self.evaluate(load_dep.var))
     second_root.dep = load_dep
     second_status.assert_consumed()
-    self.evaluate(second_status.restore_ops)
+    second_status.run_restore_ops()
     self.assertEqual(13., self.evaluate(load_dep.var))
 
     # Try again with the order of the restore() reversed. The last restore
     # determines the final value.
     first_root = checkpointable.Checkpointable()
     second_root = checkpointable.Checkpointable()
-    second_status = checkpointable_utils.restore(
-        second_path, second_root)
-    first_status = checkpointable_utils.restore(
-        first_path, first_root)
+    second_status = checkpointable_utils.Saver(second_root).restore(second_path)
+    first_status = checkpointable_utils.Saver(first_root).restore(first_path)
     load_dep = checkpointable.Checkpointable()
     load_dep.var = checkpointable_utils.add_variable(
         load_dep, name="var", shape=[])
     first_root.dep = load_dep
     first_status.assert_consumed()
-    self.assertEqual([], second_status.restore_ops)
-    self.evaluate(first_status.restore_ops)
+    first_status.run_restore_ops()
     self.assertEqual(12., self.evaluate(load_dep.var))
     second_root.dep = load_dep
     second_status.assert_consumed()
-    self.evaluate(second_status.restore_ops)
+    second_status.run_restore_ops()
     self.assertEqual(12., self.evaluate(load_dep.var))
 
   @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
@@ -724,10 +710,10 @@ class CheckpointingTests(test.TestCase):
     save_root.dep_two.dep_three = dep_three
     checkpointable_utils.add_variable(dep_three, name="var", initializer=0.)
     self.evaluate(variables.global_variables_initializer())
-    save_path = checkpointable_utils.save(
-        os.path.join(checkpoint_directory, "ckpt"), save_root)
+    save_path = checkpointable_utils.Saver(save_root).save(
+        os.path.join(checkpoint_directory, "ckpt"))
     load_root = checkpointable.Checkpointable()
-    checkpointable_utils.restore(save_path, load_root)
+    checkpointable_utils.Saver(load_root).restore(save_path)
     load_root.dep_one = checkpointable.Checkpointable()
     load_root.dep_two = checkpointable.Checkpointable()
     load_root.dep_one.dep_three = checkpointable.Checkpointable()
@@ -747,8 +733,8 @@ class CheckpointingTests(test.TestCase):
     checkpointable_utils.add_variable(
         save_root.dep_two, name="var2", initializer=64., dtype=dtypes.float64)
     self.evaluate(variables.global_variables_initializer())
-    save_path = checkpointable_utils.save(
-        os.path.join(checkpoint_directory, "ckpt"), save_root)
+    save_path = checkpointable_utils.Saver(save_root).save(
+        os.path.join(checkpoint_directory, "ckpt"))
     load_root = checkpointable.Checkpointable()
     load_root.dep_one = checkpointable.Checkpointable()
     load_root.dep_two = load_root.dep_one
@@ -756,9 +742,9 @@ class CheckpointingTests(test.TestCase):
         load_root.dep_one, name="var1", shape=[], dtype=dtypes.float64)
     v2 = checkpointable_utils.add_variable(
         load_root.dep_one, name="var2", shape=[], dtype=dtypes.float64)
-    status = checkpointable_utils.restore(
-        save_path, load_root).assert_consumed()
-    self.evaluate(status.restore_ops)
+    status = checkpointable_utils.Saver(load_root).restore(
+        save_path).assert_consumed()
+    status.run_restore_ops()
     self.assertEqual(32., self.evaluate(v1))
     self.assertEqual(64., self.evaluate(v2))
 
@@ -776,12 +762,12 @@ class CheckpointingTests(test.TestCase):
         second, "v2", initializer=[1., 1., 2., 3.])
     self.evaluate(variables.global_variables_initializer())
     checkpoint_directory = self.get_temp_dir()
-    save_path = checkpointable_utils.save(
-        os.path.join(checkpoint_directory, "ckpt"), first)
+    save_path = checkpointable_utils.Saver(first).save(
+        os.path.join(checkpoint_directory, "ckpt"))
 
     # Test deferred loading
     first_load = checkpointable.Checkpointable()
-    status = checkpointable_utils.restore(save_path, first_load)
+    status = checkpointable_utils.Saver(first_load).restore(save_path)
     second_load = checkpointable.Checkpointable()
     first_load.second = second_load
     second_load.first = first_load
@@ -792,7 +778,7 @@ class CheckpointingTests(test.TestCase):
     second_load.v = checkpointable_utils.add_variable(
         second_load, "v2", shape=[4])
     status.assert_consumed()
-    self.evaluate(status.restore_ops)
+    status.run_restore_ops()
     self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v))
     self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v))
 
@@ -801,9 +787,9 @@ class CheckpointingTests(test.TestCase):
     self.assertAllEqual([2., 7., 1.], self.evaluate(first_load.v))
     self.evaluate(second_load.v.assign([2., 7., 1., 8.]))
     self.assertAllEqual([2., 7., 1., 8.], self.evaluate(second_load.v))
-    status = checkpointable_utils.restore(
-        save_path, first_load).assert_consumed()
-    self.evaluate(status.restore_ops)
+    status = checkpointable_utils.Saver(first_load).restore(
+        save_path).assert_consumed()
+    status.run_restore_ops()
     self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v))
     self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v))
 
@@ -820,27 +806,24 @@ class CheckpointingTests(test.TestCase):
           name="blah", initializer=0.)
       self.evaluate(first.var1.assign(4.))
       self.evaluate(first.var2.assign(8.))
-      save_path = checkpointable_utils.save(
-          checkpoint_prefix, root_checkpointable=first)
+      save_path = checkpointable_utils.Saver(first).save(
+          checkpoint_prefix)
     restore_graph = ops.Graph()
     with restore_graph.as_default(), self.test_session(restore_graph):
       second = checkpointable.Checkpointable()
       second.var2 = variable_scope.get_variable(
           name="blah", initializer=0.)
-      status = checkpointable_utils.restore(
-          save_path, root_checkpointable=second)
+      status = checkpointable_utils.Saver(second).restore(save_path)
       recreated_var1 = variable_scope.get_variable(
           name="outside_var", initializer=0.)
-      self.evaluate(status.restore_ops)
+      status.run_restore_ops()
       self.assertEqual(8., self.evaluate(second.var2))
       self.evaluate(recreated_var1.assign(-2.))
       self.assertEqual(-2., self.evaluate(recreated_var1))
       second.var1 = recreated_var1
-      self.evaluate(status.restore_ops)
+      status.run_restore_ops()
       self.assertEqual(4., self.evaluate(recreated_var1))
 
-  # TODO(allenl): Saver class that doesn't pollute the graph with constants.
-  @unittest.skip("todo")
   def testManySavesGraph(self):
     """Saves after the first should not modify the graph."""
     with context.graph_mode():
@@ -853,14 +836,12 @@ class CheckpointingTests(test.TestCase):
         obj.opt = CheckpointableAdam(0.1)
         obj.opt.minimize(obj.var.read_value())
         self.evaluate(variables.global_variables_initializer())
-        checkpointable_utils.save(
-            checkpoint_prefix, root_checkpointable=obj)
+        saver = checkpointable_utils.Saver(obj)
+        saver.save(checkpoint_prefix)
         before_ops = graph.get_operations()
-        checkpointable_utils.save(
-            checkpoint_prefix, root_checkpointable=obj)
+        saver.save(checkpoint_prefix)
         self.assertEqual(before_ops, graph.get_operations())
 
-  @unittest.skip("todo")
   def testManyRestoresGraph(self):
     """Restores after the first should not modify the graph."""
     with context.graph_mode():
@@ -873,13 +854,11 @@ class CheckpointingTests(test.TestCase):
         obj.opt = CheckpointableAdam(0.1)
         obj.opt.minimize(obj.var.read_value())
         self.evaluate(variables.global_variables_initializer())
-        save_path = checkpointable_utils.save(
-            checkpoint_prefix, root_checkpointable=obj)
-        checkpointable_utils.restore(
-            save_path, root_checkpointable=obj)
+        saver = checkpointable_utils.Saver(obj)
+        save_path = saver.save(checkpoint_prefix)
+        saver.restore(save_path)
         before_ops = graph.get_operations()
-        checkpointable_utils.restore(
-            save_path, root_checkpointable=obj)
+        saver.restore(save_path)
         self.assertEqual(before_ops, graph.get_operations())
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 6a7ece457d..4c8c73548c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2860,7 +2860,6 @@ py_library(
         ":dtypes",
         ":io_ops_gen",
         ":ops",
-        ":pywrap_tensorflow",
         ":util",
         "//tensorflow/python/eager:context",
     ],
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index b785d0ede7..d382683858 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -792,17 +792,7 @@ class Variable(checkpointable.CheckpointableBase):
 
     setattr(Variable, operator, _run_op)
 
-  def _scatter_tensors_from_checkpoint(self, attributes):
-    """For implementing `Checkpointable`. Return an assignment op to run."""
-    if (len(attributes) != 1
-        or checkpointable.VARIABLE_VALUE_KEY not in attributes):
-      raise ValueError(
-          ("The variable %s was restored with unexpected values (expected one "
-           "with key %s, got %s)") % (
-               self, checkpointable.VARIABLE_VALUE_KEY, attributes))
-    return self.assign(attributes[checkpointable.VARIABLE_VALUE_KEY])
-
-  def _gather_tensors_for_checkpoint(self):
+  def _gather_saveables_for_checkpoint(self):
     """For implementing `Checkpointable`. This object is saveable on its own."""
     return {checkpointable.VARIABLE_VALUE_KEY: self}
 
diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py
index 9d62c5ff91..11caa761ae 100644
--- a/tensorflow/python/training/checkpointable.py
+++ b/tensorflow/python/training/checkpointable.py
@@ -18,9 +18,7 @@ from __future__ import division
 from __future__ import print_function
 
 import collections
-import weakref
 
-from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import context
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -28,7 +26,7 @@ from tensorflow.python.ops import gen_io_ops as io_ops
 from tensorflow.python.util import nest
 
 # A key indicating a variable's value in an object's checkpointed Tensors
-# (Checkpointable._gather_tensors_for_checkpoint). If this is the only key and
+# (Checkpointable._gather_saveables_for_checkpoint). If this is the only key and
 # the object has no dependencies, then its value may be restored on object
 # creation (avoiding double assignment when executing eagerly).
 VARIABLE_VALUE_KEY = "VARIABLE_VALUE"
@@ -57,7 +55,7 @@ class CheckpointInitialValue(ops.Tensor):
   """
 
   def __init__(self, checkpoint_position, shape=None):
-    self.wrapped_value = checkpoint_position.restore_ops()[
+    self.wrapped_value = checkpoint_position.value_tensors()[
         VARIABLE_VALUE_KEY]
     if shape:
       # We need to set the static shape information on the initializer if
@@ -168,22 +166,86 @@ class _CheckpointPosition(object):
             and attributes[0].name == VARIABLE_VALUE_KEY
             and not self.object_proto.children)
 
-  def restore_ops(self):
-    """Create restore ops for this object's attributes."""
-    restore_tensors = {}
+  def value_tensors(self):
+    """Create value `Tensor`s for this object's attributes.
+
+    Does not require that the Python object has been created. Used for
+    restore-on-create when executing eagerly.
+
+    Returns:
+      A dictionary mapping from object attribute names to `Tensor`s.
+    """
+    value_tensors = {}
     for serialized_tensor in self.object_proto.attributes:
       checkpoint_key = serialized_tensor.checkpoint_key
       dtype = self._checkpoint.dtype_map[checkpoint_key]
       base_type = dtype.base_dtype
       with ops.init_scope():
-        restore, = io_ops.restore_v2(
+        value, = io_ops.restore_v2(
             prefix=self._checkpoint.save_path,
             tensor_names=[checkpoint_key],
             shape_and_slices=[""],
             dtypes=[base_type],
             name="%s_checkpoint_read" % (serialized_tensor.name,))
-        restore_tensors[serialized_tensor.name] = restore
-      return restore_tensors
+        value_tensors[serialized_tensor.name] = value
+      return value_tensors
+
+  def restore_ops(self):
+    """Create or fetch restore ops for this object's attributes.
+
+    Requires that the `Checkpointable` Python object has been bound to an object
+    ID in the checkpoint.
+
+    Returns:
+      A list of operations when graph building, or an empty list when executing
+      eagerly.
+    """
+    saveables = self.checkpointable._gather_saveables_for_checkpoint()  # pylint: disable=protected-access
+    # Name saveables based on the name this object had when it was checkpointed.
+    named_saveables = {}
+    restore_ops = []
+    in_graph_mode = context.in_graph_mode()
+    for serialized_tensor in self.object_proto.attributes:
+      saveable_object = saveables.get(serialized_tensor.name, None)
+      if saveable_object is None:
+        # Purposefully does not throw an exception if attributes have been added
+        # or deleted. Stores unused attributes so an exception can be raised if
+        # the user decides to check that everything in the checkpoint was
+        # loaded.
+        self._checkpoint.unused_attributes.setdefault(
+            self.checkpointable, []).append(serialized_tensor.name)
+        continue
+      if in_graph_mode:
+        existing_ops = self._checkpoint.restore_ops_by_name.get(
+            serialized_tensor.name, None)
+      else:
+        existing_ops = None
+      if existing_ops is None:
+        named_saveables[serialized_tensor.checkpoint_key] = saveable_object
+    if named_saveables:
+      validated_saveables = (
+          self._checkpoint.builder._ValidateAndSliceInputs(named_saveables))  # pylint: disable=protected-access
+      validated_names = set(saveable.name for saveable in validated_saveables)
+      if set(named_saveables.keys()) != validated_names:
+        raise AssertionError(
+            ("Saveable keys changed when validating. Got back %s, was "
+             "expecting %s") % (named_saveables.keys(), validated_names))
+      all_tensors = self._checkpoint.builder.bulk_restore(
+          filename_tensor=self._checkpoint.save_path,
+          saveables=validated_saveables, preferred_shard=-1,
+          restore_sequentially=False)
+      saveable_index = 0
+      for saveable in validated_saveables:
+        num_specs = len(saveable.specs)
+        saveable_tensors = all_tensors[
+            saveable_index:saveable_index + num_specs]
+        saveable_index += num_specs
+        restore_op = saveable.restore(saveable_tensors, restored_shapes=None)
+        if in_graph_mode:
+          assert saveable.name not in self._checkpoint.restore_ops_by_name
+          self._checkpoint.restore_ops_by_name[saveable.name] = restore_op
+          restore_ops.append(restore_op)
+    return restore_ops
 
   @property
   def checkpoint(self):
@@ -225,54 +287,6 @@ _SlotVariableRestoration = collections.namedtuple(
     ])
 
 
-class _Checkpoint(object):
-  """Holds the status of an object-based checkpoint load."""
-
-  def __init__(self, object_graph_proto, save_path):
-    """Specify the checkpoint being loaded.
-
-    Args:
-      object_graph_proto: The CheckpointableObjectGraph protocol buffer
-        associated with this checkpoint.
-      save_path: The path to the checkpoint, as returned by
-        `tf.train.latest_checkpoint`.
-    """
-    self.object_graph_proto = object_graph_proto
-    self.restore_uid = ops.uid()
-    # Dictionary mapping from an id in the protocol buffer flat array to
-    # Checkpointable Python objects. This mapping may be deferred if a
-    # checkpoint is restored before all dependencies have been tracked. Uses
-    # weak references so that partial restorations don't create reference cycles
-    # (as objects with deferred dependencies will generally have references to
-    # this object).
-    self.object_by_proto_id = weakref.WeakValueDictionary()
-    self.save_path = save_path
-    reader = pywrap_tensorflow.NewCheckpointReader(save_path)
-    self.dtype_map = reader.get_variable_to_dtype_map()
-    # When graph building, contains a list of ops to run to restore objects from
-    # this checkpoint.
-    self.restore_ops = []
-    # A mapping from optimizer proto ids to lists of slot variables to be
-    # restored when the optimizer is tracked. Only includes slot variables whose
-    # regular variables have already been created, and only for optimizer
-    # objects which have not yet been created/tracked.
-    self.deferred_slot_restorations = {}
-    # A mapping from variable proto ids to lists of slot variables to be
-    # restored when the variable is created/tracked. These get shifted over to
-    # deferred_slot_restorations if the optimizer hasn't been created when that
-    # happens.
-    self.slot_restorations = {}
-    for node_index, node in enumerate(self.object_graph_proto.nodes):
-      for slot_reference in node.slot_variables:
-        # `node` refers to an `Optimizer`, since only these have slot variables.
-        self.slot_restorations.setdefault(
-            slot_reference.original_variable_node_id, []).append(
-                _SlotVariableRestoration(
-                    optimizer_id=node_index,
-                    slot_variable_id=slot_reference.slot_variable_node_id,
-                    slot_name=slot_reference.slot_name))
-
-
 class CheckpointableBase(object):
   """Base class for `Checkpointable` objects without automatic dependencies.
 
@@ -415,13 +429,10 @@ class CheckpointableBase(object):
     Indicates that checkpoints for this object should include variables from
     `checkpointable`.
 
-    Variables in a checkpoint are mapped to `Checkpointable`s based on names if
-    provided when the checkpoint was written, but otherwise use the order those
-    `Checkpointable`s were declared as dependencies.
-
-    To avoid breaking existing checkpoints when modifying a class, neither
-    variable names nor dependency names (the names passed to
-    `track_checkpointable`) may change.
+    Variables in a checkpoint are mapped to `Checkpointable`s based on the names
+    provided when the checkpoint was written. To avoid breaking existing
+    checkpoints when modifying a class, neither variable names nor dependency
+    names (the names passed to `_track_checkpointable`) may change.
 
     Args:
       checkpointable: A `Checkpointable` which this object depends on.
@@ -493,11 +504,11 @@ class CheckpointableBase(object):
     # need to actually restore the object. However, we should pass the
     # restoration on to our dependencies.
     if checkpoint.restore_uid > self._update_uid:
-      restore_op = self._scatter_tensors_from_checkpoint(
-          checkpoint_position.restore_ops())
+      restore_ops = checkpoint_position.restore_ops()
+      # TODO(allenl): Get a list of feeds for saving Python state
       self._update_uid = checkpoint.restore_uid
     else:
-      restore_op = ()
+      restore_ops = ()
     for child in checkpoint_position.object_proto.children:
       child_position = _CheckpointPosition(
           checkpoint=checkpoint,
@@ -515,25 +526,21 @@ class CheckpointableBase(object):
           # resolution order (shallowest paths first). The caller is responsible
           # for emptying visit_queue.
           visit_queue.append(child_position)
-    return restore_op
+    return restore_ops
 
-  def _scatter_tensors_from_checkpoint(self, attributes):
-    """Restores this object from a checkpoint.
+  def _gather_saveables_for_checkpoint(self):
+    """Returns a dictionary of values to checkpoint with this object.
 
-    Args:
-      attributes: A dictionary of Tensors, with key corresponding to those
-        returned from _gather_tensors_for_checkpoint.
-    Returns:
-      A restore op to run (if graph building).
-    """
-    if attributes:
-      raise AssertionError(
-          ("A Checkpointable object which was not expecting any data received "
-           "some from a checkpoint. (Got %s)") % (attributes,))
-    return ()  # No restore ops
+    Keys in the returned dictionary are local to this object and in a separate
+    namespace from dependencies. Values may either be `SaveableObject`s or
+    variables easily converted to `SaveableObject`s (as in `tf.train.Saver`'s
+    `var_list` constructor argument).
 
-  def _gather_tensors_for_checkpoint(self):
-    """Returns a dictionary of Tensors to save with this object."""
+    Returned values must be saved only by this object; if any value may be
+    shared, it should instead be a dependency. For example, variable objects
+    save their own values with the key `VARIABLE_VALUE_KEY`, but objects which
+    reference variables simply add a dependency.
+    """
     return {}
 
 
@@ -562,8 +569,7 @@ class Checkpointable(CheckpointableBase):
   `Checkpointable` objects may specify `Tensor`s to be saved and restored
   directly (e.g. a `Variable` indicating how to save itself) rather than through
   dependencies on other objects. See
-  `Checkpointable._scatter_tensors_from_checkpoint` and
-  `Checkpointable._gather_tensors_for_checkpoint` for details.
+  `Checkpointable._gather_saveables_for_checkpoint` for details.
   """
 
   def __setattr__(self, name, value):
diff --git a/tensorflow/python/training/checkpointable_utils.py b/tensorflow/python/training/checkpointable_utils.py
new file mode 100644
index 0000000000..32123f87ef
--- /dev/null
+++ b/tensorflow/python/training/checkpointable_utils.py
@@ -0,0 +1,78 @@
+"""Utilities for saving/loading Checkpointable objects."""
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import weakref
+
+from tensorflow.python.framework import ops
+from tensorflow.python.training import checkpointable
+from tensorflow.python.training import saver as saver_lib
+
+
+class _Checkpoint(object):
+  """Holds the status of an object-based checkpoint load."""
+
+  def __init__(self, object_graph_proto, save_path, dtype_map=None):
+    """Specify the checkpoint being loaded.
+
+    Args:
+      object_graph_proto: The CheckpointableObjectGraph protocol buffer
+        associated with this checkpoint.
+      save_path: A string `Tensor`. The path to the checkpoint, as returned by
+        `tf.train.latest_checkpoint`.
+      dtype_map: When executing eagerly, specifies dtypes for creating slot
+        variables. None when graph building.
+    """
+    self.builder = saver_lib.BulkSaverBuilder()
+    self.object_graph_proto = object_graph_proto
+    self.restore_uid = ops.uid()
+    # Maps from objects to lists of attributes which were in the checkpoint but
+    # not loaded into any object, for error checking.
+    self.unused_attributes = weakref.WeakKeyDictionary()
+    # Dictionary mapping from an id in the protocol buffer flat array to
+    # Checkpointable Python objects. This mapping may be deferred if a
+    # checkpoint is restored before all dependencies have been tracked. Uses
+    # weak references so that partial restorations don't create reference cycles
+    # (as objects with deferred dependencies will generally have references to
+    # this object).
+    self.object_by_proto_id = weakref.WeakValueDictionary()
+    self.save_path = save_path
+    self.dtype_map = dtype_map
+    # When graph building, contains a list of ops to run to restore objects from
+    # this checkpoint.
+    self.restore_ops = []
+    self.restore_ops_by_name = {}
+    # A mapping from optimizer proto ids to lists of slot variables to be
+    # restored when the optimizer is tracked. Only includes slot variables whose
+    # regular variables have already been created, and only for optimizer
+    # objects which have not yet been created/tracked.
+    self.deferred_slot_restorations = {}
+    # A mapping from variable proto ids to lists of slot variables to be
+    # restored when the variable is created/tracked. These get shifted over to
+    # deferred_slot_restorations if the optimizer hasn't been created when that
+    # happens.
+    self.slot_restorations = {}
+    for node_index, node in enumerate(self.object_graph_proto.nodes):
+      for slot_reference in node.slot_variables:
+        # `node` refers to an `Optimizer`, since only these have slot variables.
+        self.slot_restorations.setdefault(
+            slot_reference.original_variable_node_id, []).append(
+                checkpointable._SlotVariableRestoration(  # pylint: disable=protected-access
+                    optimizer_id=node_index,
+                    slot_variable_id=slot_reference.slot_variable_node_id,
+                    slot_name=slot_reference.slot_name))
-- 
GitLab


From e2a9276d485ab3c3b5b0ebfbc92fc105cfa7419f Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Thu, 22 Feb 2018 12:16:09 -0800
Subject: [PATCH 199/575] Add eager support for unit tests for most Keras
 layers. A few minor layers were left out: - noise layers (apparent issue with
 tf.random_normal) - bidirectional wrapper - conv recurrent layers (impending
 refactor)

PiperOrigin-RevId: 186654795
---
 .../python/keras/_impl/keras/backend.py       |   3 +-
 .../_impl/keras/engine/training_eager.py      |  17 +-
 .../_impl/keras/engine/training_eager_test.py |  19 +-
 .../_impl/keras/layers/convolutional_test.py  |  68 ++++-
 .../keras/_impl/keras/layers/core_test.py     | 244 ++++++++----------
 .../_impl/keras/layers/embeddings_test.py     |  72 +++---
 .../keras/_impl/keras/layers/gru_test.py      |  62 ++---
 .../keras/_impl/keras/layers/local_test.py    |  48 ++--
 .../keras/_impl/keras/layers/lstm_test.py     |  61 ++---
 .../keras/_impl/keras/layers/merge_test.py    | 212 +++++++--------
 .../keras/_impl/keras/layers/noise_test.py    |  11 +-
 .../keras/_impl/keras/layers/pooling_test.py  | 230 +++++++++--------
 .../keras/_impl/keras/layers/recurrent.py     | 136 ++++------
 .../_impl/keras/layers/simplernn_test.py      |  62 ++---
 .../keras/_impl/keras/layers/wrappers_test.py |  56 ++--
 .../python/keras/_impl/keras/optimizers.py    |   4 +-
 .../python/keras/_impl/keras/testing_utils.py |   7 +-
 17 files changed, 660 insertions(+), 652 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py
index a238a3f748..a2db05f6cf 100644
--- a/tensorflow/python/keras/_impl/keras/backend.py
+++ b/tensorflow/python/keras/_impl/keras/backend.py
@@ -3087,7 +3087,8 @@ def rnn(step_function,
   outputs_shape[1] = inputs_shape[1]
   outputs.set_shape(outputs_shape)
 
-  last_output._uses_learning_phase = uses_learning_phase
+  if not context.in_eager_mode():
+    last_output._uses_learning_phase = uses_learning_phase
   return last_output, outputs, new_states
 
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 3507f36e14..282dd0dc0d 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -29,6 +29,7 @@ from tensorflow.python.keras._impl.keras import metrics as metrics_module
 from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches
 from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
+from tensorflow.python.platform import tf_logging as logging
 
 
 def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None):
@@ -196,8 +197,7 @@ def _process_single_batch(eager_model_inputs, eager_model_outputs, model,
       output of the model, total loss and the loss associated with each output.
 
   Raises:
-      ValueError: If the model loss is 0 or if the trainable weights list is
-                  empty when the trainable parameter is set to True.
+      ValueError: If the model has no loss to optimize.
   """
   K.set_learning_phase(training)
   with GradientTape() as tape:
@@ -209,12 +209,13 @@ def _process_single_batch(eager_model_inputs, eager_model_outputs, model,
                        'because it has no loss to optimize.')
   if training:
     if not model._collected_trainable_weights:
-      raise ValueError('The list of trainable weights is empty. Make sure that '
-                       'you are not setting model.trainable to False before '
-                       'compiling the model.')
-    grads = tape.gradient(loss, model._collected_trainable_weights)
-    model.optimizer.apply_gradients(zip(grads,
-                                        model._collected_trainable_weights))
+      logging.warning('The list of trainable weights is empty. Make sure that '
+                      'you are not setting model.trainable to False before '
+                      'compiling the model.')
+    else:
+      grads = tape.gradient(loss, model._collected_trainable_weights)
+      model.optimizer.apply_gradients(zip(grads,
+                                          model._collected_trainable_weights))
   return outs, loss, loss_metrics
 
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
index 45601f964a..3d94b7537f 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
@@ -26,6 +26,7 @@ from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
 from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 
@@ -397,17 +398,13 @@ class LossWeightingTest(test.TestCase):
                   optimizer=RMSPropOptimizer(learning_rate=0.001))
 
     np.random.seed(43)
-    (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
+    (x_train, y_train), _ = testing_utils.get_test_data(
         train_samples=train_samples,
         test_samples=test_samples,
         input_shape=(input_dim,),
         num_classes=num_classes)
-    int_y_test = y_test.copy()
     int_y_train = y_train.copy()
-    # convert class vectors to binary class matrices
     y_train = keras.utils.to_categorical(y_train, num_classes)
-    y_test = keras.utils.to_categorical(y_test, num_classes)
-    test_ids = np.where(int_y_test == np.array(weighted_class))[0]
 
     class_weight = dict([(i, 1.) for i in range(num_classes)])
     class_weight[weighted_class] = 2.
@@ -549,8 +546,10 @@ class TestDynamicTrainability(test.TestCase):
     model.trainable = False
     model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
     model.trainable = True
-    with self.assertRaises(ValueError):
+    with test.mock.patch.object(logging, 'warning') as mock_log:
       model.train_on_batch(x, y)
+      self.assertRegexpMatches(str(mock_log.call_args),
+                               'trainable weights is empty')
 
   def test_trainable_argument(self):
     x = np.random.random((5, 3))
@@ -560,8 +559,10 @@ class TestDynamicTrainability(test.TestCase):
     model.add(keras.layers.Dense(2, input_dim=3, trainable=False))
     model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
     out = model.predict(x)
-    with self.assertRaises(ValueError):
+    with test.mock.patch.object(logging, 'warning') as mock_log:
       model.train_on_batch(x, y)
+      self.assertRegexpMatches(str(mock_log.call_args),
+                               'trainable weights is empty')
     out_2 = model.predict(x)
     self.assertAllClose(out, out_2)
 
@@ -571,8 +572,10 @@ class TestDynamicTrainability(test.TestCase):
     model = keras.models.Model(inputs, output)
     model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
     out = model.predict(x)
-    with self.assertRaises(ValueError):
+    with test.mock.patch.object(logging, 'warning') as mock_log:
       model.train_on_batch(x, y)
+      self.assertRegexpMatches(str(mock_log.call_args),
+                               'trainable weights is empty')
     out_2 = model.predict(x)
     self.assertAllClose(out, out_2)
 
diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py
index 4a6228121b..c612e97a9d 100644
--- a/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_test.py
@@ -22,6 +22,8 @@ import copy
 
 import numpy as np
 
+from tensorflow.python.eager import context
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.platform import test
@@ -43,6 +45,7 @@ class Convolution1DTest(test.TestCase):
             kwargs=test_kwargs,
             input_shape=(num_samples, length, stack_size))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_conv1d(self):
     kwargs = {
         'filters': 2,
@@ -114,6 +117,7 @@ class Conv2DTest(test.TestCase):
             kwargs=test_kwargs,
             input_shape=(num_samples, num_row, num_col, stack_size))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_conv2d(self):
     kwargs = {
         'filters': 2,
@@ -188,6 +192,7 @@ class Conv2DTransposeTest(test.TestCase):
             kwargs=test_kwargs,
             input_shape=(num_samples, num_row, num_col, stack_size))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_conv2dtranspose(self):
     kwargs = {
         'filters': 2,
@@ -253,6 +258,7 @@ class Conv3DTransposeTest(test.TestCase):
             kwargs=test_kwargs,
             input_shape=(num_samples, depth, num_row, num_col, stack_size))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_conv3dtranspose(self):
     kwargs = {
         'filters': 2,
@@ -316,6 +322,7 @@ class SeparableConv1DTest(test.TestCase):
             kwargs=test_kwargs,
             input_shape=(num_samples, length, stack_size))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_separable_conv1d(self):
     kwargs = {
         'filters': 2,
@@ -391,6 +398,7 @@ class SeparableConv2DTest(test.TestCase):
             kwargs=test_kwargs,
             input_shape=(num_samples, num_row, num_col, stack_size))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_separable_conv2d(self):
     kwargs = {
         'filters': 2,
@@ -469,6 +477,7 @@ class Conv3DTest(test.TestCase):
             kwargs=test_kwargs,
             input_shape=(num_samples, depth, num_row, num_col, stack_size))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_conv3d(self):
     kwargs = {
         'filters': 2,
@@ -520,6 +529,7 @@ class Conv3DTest(test.TestCase):
 
 class ZeroPaddingTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_zero_padding_1d(self):
     num_samples = 2
     input_dim = 2
@@ -543,7 +553,10 @@ class ZeroPaddingTest(test.TestCase):
       layer = keras.layers.ZeroPadding1D(padding=2)
       layer.build(shape)
       output = layer(keras.backend.variable(inputs))
-      np_output = keras.backend.eval(output)
+      if context.in_eager_mode():
+        np_output = output.numpy()
+      else:
+        np_output = keras.backend.eval(output)
       for offset in [0, 1, -1, -2]:
         np.testing.assert_allclose(np_output[:, offset, :], 0.)
       np.testing.assert_allclose(np_output[:, 2:-2, :], 1.)
@@ -551,7 +564,10 @@ class ZeroPaddingTest(test.TestCase):
       layer = keras.layers.ZeroPadding1D(padding=(1, 2))
       layer.build(shape)
       output = layer(keras.backend.variable(inputs))
-      np_output = keras.backend.eval(output)
+      if context.in_eager_mode():
+        np_output = output.numpy()
+      else:
+        np_output = keras.backend.eval(output)
       for left_offset in [0]:
         np.testing.assert_allclose(np_output[:, left_offset, :], 0.)
       for right_offset in [-1, -2]:
@@ -565,6 +581,7 @@ class ZeroPaddingTest(test.TestCase):
     with self.assertRaises(ValueError):
       keras.layers.ZeroPadding1D(padding=None)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_zero_padding_2d(self):
     num_samples = 2
     stack_size = 2
@@ -593,7 +610,10 @@ class ZeroPaddingTest(test.TestCase):
             padding=(2, 2), data_format=data_format)
         layer.build(inputs.shape)
         output = layer(keras.backend.variable(inputs))
-        np_output = keras.backend.eval(output)
+        if context.in_eager_mode():
+          np_output = output.numpy()
+        else:
+          np_output = keras.backend.eval(output)
         if data_format == 'channels_last':
           for offset in [0, 1, -1, -2]:
             np.testing.assert_allclose(np_output[:, offset, :, :], 0.)
@@ -609,7 +629,10 @@ class ZeroPaddingTest(test.TestCase):
             padding=((1, 2), (3, 4)), data_format=data_format)
         layer.build(inputs.shape)
         output = layer(keras.backend.variable(inputs))
-        np_output = keras.backend.eval(output)
+        if context.in_eager_mode():
+          np_output = output.numpy()
+        else:
+          np_output = keras.backend.eval(output)
         if data_format == 'channels_last':
           for top_offset in [0]:
             np.testing.assert_allclose(np_output[:, top_offset, :, :], 0.)
@@ -637,6 +660,7 @@ class ZeroPaddingTest(test.TestCase):
       with self.assertRaises(ValueError):
         keras.layers.ZeroPadding2D(padding=None)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_zero_padding_3d(self):
     num_samples = 2
     stack_size = 2
@@ -659,7 +683,10 @@ class ZeroPaddingTest(test.TestCase):
       layer = keras.layers.ZeroPadding3D(padding=(2, 2, 2))
       layer.build(inputs.shape)
       output = layer(keras.backend.variable(inputs))
-      np_output = keras.backend.eval(output)
+      if context.in_eager_mode():
+        np_output = output.numpy()
+      else:
+        np_output = keras.backend.eval(output)
       for offset in [0, 1, -1, -2]:
         np.testing.assert_allclose(np_output[:, offset, :, :, :], 0.)
         np.testing.assert_allclose(np_output[:, :, offset, :, :], 0.)
@@ -675,11 +702,13 @@ class ZeroPaddingTest(test.TestCase):
 
 class UpSamplingTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_upsampling_1d(self):
     with self.test_session(use_gpu=True):
       testing_utils.layer_test(
           keras.layers.UpSampling1D, kwargs={'size': 2}, input_shape=(3, 5, 4))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_upsampling_2d(self):
     num_samples = 2
     stack_size = 2
@@ -708,7 +737,10 @@ class UpSamplingTest(test.TestCase):
                 size=(length_row, length_col), data_format=data_format)
             layer.build(inputs.shape)
             output = layer(keras.backend.variable(inputs))
-            np_output = keras.backend.eval(output)
+            if context.in_eager_mode():
+              np_output = output.numpy()
+            else:
+              np_output = keras.backend.eval(output)
             if data_format == 'channels_first':
               assert np_output.shape[2] == length_row * input_num_row
               assert np_output.shape[3] == length_col * input_num_col
@@ -726,6 +758,7 @@ class UpSamplingTest(test.TestCase):
 
             np.testing.assert_allclose(np_output, expected_out)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_upsampling_3d(self):
     num_samples = 2
     stack_size = 2
@@ -757,7 +790,10 @@ class UpSamplingTest(test.TestCase):
                   data_format=data_format)
               layer.build(inputs.shape)
               output = layer(keras.backend.variable(inputs))
-              np_output = keras.backend.eval(output)
+              if context.in_eager_mode():
+                np_output = output.numpy()
+              else:
+                np_output = keras.backend.eval(output)
               if data_format == 'channels_first':
                 assert np_output.shape[2] == length_dim1 * input_len_dim1
                 assert np_output.shape[3] == length_dim2 * input_len_dim2
@@ -782,6 +818,7 @@ class UpSamplingTest(test.TestCase):
 
 class CroppingTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_cropping_1d(self):
     num_samples = 2
     time_length = 4
@@ -800,6 +837,7 @@ class CroppingTest(test.TestCase):
     with self.assertRaises(ValueError):
       keras.layers.Cropping1D(cropping=None)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_cropping_2d(self):
     num_samples = 2
     stack_size = 2
@@ -827,7 +865,10 @@ class CroppingTest(test.TestCase):
             cropping=cropping, data_format=data_format)
         layer.build(inputs.shape)
         output = layer(keras.backend.variable(inputs))
-        np_output = keras.backend.eval(output)
+        if context.in_eager_mode():
+          np_output = output.numpy()
+        else:
+          np_output = keras.backend.eval(output)
         # compare with numpy
         if data_format == 'channels_first':
           expected_out = inputs[:, :, cropping[0][0]:-cropping[0][1], cropping[
@@ -851,7 +892,10 @@ class CroppingTest(test.TestCase):
             cropping=cropping, data_format=data_format)
         layer.build(inputs.shape)
         output = layer(keras.backend.variable(inputs))
-        np_output = keras.backend.eval(output)
+        if context.in_eager_mode():
+          np_output = output.numpy()
+        else:
+          np_output = keras.backend.eval(output)
         # compare with input
         np.testing.assert_allclose(np_output, inputs)
 
@@ -861,6 +905,7 @@ class CroppingTest(test.TestCase):
     with self.assertRaises(ValueError):
       keras.layers.Cropping2D(cropping=None)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_cropping_3d(self):
     num_samples = 2
     stack_size = 2
@@ -892,7 +937,10 @@ class CroppingTest(test.TestCase):
                 cropping=cropping, data_format=data_format)
             layer.build(inputs.shape)
             output = layer(keras.backend.variable(inputs))
-            np_output = keras.backend.eval(output)
+            if context.in_eager_mode():
+              np_output = output.numpy()
+            else:
+              np_output = keras.backend.eval(output)
             # compare with numpy
             if data_format == 'channels_first':
               expected_out = inputs[:, :,
diff --git a/tensorflow/python/keras/_impl/keras/layers/core_test.py b/tensorflow/python/keras/_impl/keras/layers/core_test.py
index bdb99c91c2..2ca816adbd 100644
--- a/tensorflow/python/keras/_impl/keras/layers/core_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/core_test.py
@@ -20,11 +20,9 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.python.eager import context
-from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
-from tensorflow.python.ops import init_ops
 from tensorflow.python.platform import test
 
 
@@ -52,146 +50,134 @@ class CoreLayersTest(test.TestCase):
       dropout = keras.layers.Dropout(0.5)
       self.assertEqual(True, dropout.supports_masking)
 
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.SpatialDropout1D,
-          kwargs={'rate': 0.5},
-          input_shape=(2, 3, 4))
-
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.SpatialDropout2D,
-          kwargs={'rate': 0.5},
-          input_shape=(2, 3, 4, 5))
-
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.SpatialDropout2D,
-          kwargs={'rate': 0.5, 'data_format': 'channels_first'},
-          input_shape=(2, 3, 4, 5))
-
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.SpatialDropout3D,
-          kwargs={'rate': 0.5},
-          input_shape=(2, 3, 4, 4, 5))
-
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.SpatialDropout3D,
-          kwargs={'rate': 0.5, 'data_format': 'channels_first'},
-          input_shape=(2, 3, 4, 4, 5))
-
+  @tf_test_util.run_in_graph_and_eager_modes()
+  def test_spatial_dropout(self):
+    testing_utils.layer_test(
+        keras.layers.SpatialDropout1D,
+        kwargs={'rate': 0.5},
+        input_shape=(2, 3, 4))
+
+    testing_utils.layer_test(
+        keras.layers.SpatialDropout2D,
+        kwargs={'rate': 0.5},
+        input_shape=(2, 3, 4, 5))
+
+    testing_utils.layer_test(
+        keras.layers.SpatialDropout2D,
+        kwargs={'rate': 0.5, 'data_format': 'channels_first'},
+        input_shape=(2, 3, 4, 5))
+
+    testing_utils.layer_test(
+        keras.layers.SpatialDropout3D,
+        kwargs={'rate': 0.5},
+        input_shape=(2, 3, 4, 4, 5))
+
+    testing_utils.layer_test(
+        keras.layers.SpatialDropout3D,
+        kwargs={'rate': 0.5, 'data_format': 'channels_first'},
+        input_shape=(2, 3, 4, 4, 5))
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_activation(self):
     # with string argument
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Activation,
-          kwargs={'activation': 'relu'},
-          input_shape=(3, 2))
+    testing_utils.layer_test(
+        keras.layers.Activation,
+        kwargs={'activation': 'relu'},
+        input_shape=(3, 2))
 
     # with function argument
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Activation,
-          kwargs={'activation': keras.backend.relu},
-          input_shape=(3, 2))
+    testing_utils.layer_test(
+        keras.layers.Activation,
+        kwargs={'activation': keras.backend.relu},
+        input_shape=(3, 2))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_reshape(self):
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Reshape,
-          kwargs={'target_shape': (8, 1)},
-          input_shape=(3, 2, 4))
-
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Reshape,
-          kwargs={'target_shape': (-1, 1)},
-          input_shape=(3, 2, 4))
-
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Reshape,
-          kwargs={'target_shape': (1, -1)},
-          input_shape=(3, 2, 4))
-
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Reshape,
-          kwargs={'target_shape': (-1, 1)},
-          input_shape=(None, None, 2))
-
+    testing_utils.layer_test(
+        keras.layers.Reshape,
+        kwargs={'target_shape': (8, 1)},
+        input_shape=(3, 2, 4))
+
+    testing_utils.layer_test(
+        keras.layers.Reshape,
+        kwargs={'target_shape': (-1, 1)},
+        input_shape=(3, 2, 4))
+
+    testing_utils.layer_test(
+        keras.layers.Reshape,
+        kwargs={'target_shape': (1, -1)},
+        input_shape=(3, 2, 4))
+
+    testing_utils.layer_test(
+        keras.layers.Reshape,
+        kwargs={'target_shape': (-1, 1)},
+        input_shape=(None, None, 2))
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_permute(self):
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4))
+    testing_utils.layer_test(
+        keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_flatten(self):
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4))
+    testing_utils.layer_test(
+        keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_repeat_vector(self):
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.RepeatVector, kwargs={'n': 3}, input_shape=(3, 2))
+    testing_utils.layer_test(
+        keras.layers.RepeatVector, kwargs={'n': 3}, input_shape=(3, 2))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_lambda(self):
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Lambda,
-          kwargs={'function': lambda x: x + 1},
-          input_shape=(3, 2))
-
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Lambda,
-          kwargs={
-              'function': lambda x, a, b: x * a + b,
-              'arguments': {
-                  'a': 0.6,
-                  'b': 0.4
-              }
-          },
-          input_shape=(3, 2))
-
-    with self.test_session():
-      # test serialization with function
-      def f(x):
-        return x + 1
-
-      ld = keras.layers.Lambda(f)
-      config = ld.get_config()
-      ld = keras.layers.deserialize({
-          'class_name': 'Lambda',
-          'config': config
-      })
-
-      # test with lambda
-      ld = keras.layers.Lambda(
-          lambda x: keras.backend.concatenate([keras.backend.square(x), x]))
-      config = ld.get_config()
-      ld = keras.layers.Lambda.from_config(config)
-
+    testing_utils.layer_test(
+        keras.layers.Lambda,
+        kwargs={'function': lambda x: x + 1},
+        input_shape=(3, 2))
+
+    testing_utils.layer_test(
+        keras.layers.Lambda,
+        kwargs={
+            'function': lambda x, a, b: x * a + b,
+            'arguments': {
+                'a': 0.6,
+                'b': 0.4
+            }
+        },
+        input_shape=(3, 2))
+
+    # test serialization with function
+    def f(x):
+      return x + 1
+
+    ld = keras.layers.Lambda(f)
+    config = ld.get_config()
+    ld = keras.layers.deserialize({
+        'class_name': 'Lambda',
+        'config': config
+    })
+
+    # test with lambda
+    ld = keras.layers.Lambda(
+        lambda x: keras.backend.concatenate([keras.backend.square(x), x]))
+    config = ld.get_config()
+    ld = keras.layers.Lambda.from_config(config)
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_dense(self):
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 2))
+    testing_utils.layer_test(
+        keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 2))
 
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 2))
+    testing_utils.layer_test(
+        keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 2))
 
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Dense, kwargs={'units': 3}, input_shape=(None, None, 2))
+    testing_utils.layer_test(
+        keras.layers.Dense, kwargs={'units': 3}, input_shape=(None, None, 2))
 
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 5, 2))
+    testing_utils.layer_test(
+        keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 4, 5, 2))
 
-    # Test regularization
+  def test_dense_regularization(self):
     with self.test_session():
       layer = keras.layers.Dense(
           3,
@@ -202,7 +188,7 @@ class CoreLayersTest(test.TestCase):
       layer(keras.backend.variable(np.ones((2, 4))))
       self.assertEqual(3, len(layer.losses))
 
-    # Test constraints
+  def test_dense_constraints(self):
     with self.test_session():
       k_constraint = keras.constraints.max_norm(0.01)
       b_constraint = keras.constraints.max_norm(0.01)
@@ -212,12 +198,6 @@ class CoreLayersTest(test.TestCase):
       self.assertEqual(layer.kernel.constraint, k_constraint)
       self.assertEqual(layer.bias.constraint, b_constraint)
 
-  def test_eager_dense(self):
-    with context.eager_mode():
-      l = keras.layers.Dense(units=3,
-                             kernel_initializer=init_ops.zeros_initializer())
-      self.assertAllEqual(l(constant_op.constant([[1.0]])), [[0., 0., 0.]])
-
   def test_activity_regularization(self):
     with self.test_session():
       layer = keras.layers.ActivityRegularization(l1=0.1)
diff --git a/tensorflow/python/keras/_impl/keras/layers/embeddings_test.py b/tensorflow/python/keras/_impl/keras/layers/embeddings_test.py
index 1712111b87..26fd1f1c11 100644
--- a/tensorflow/python/keras/_impl/keras/layers/embeddings_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/embeddings_test.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.platform import test
@@ -25,47 +26,44 @@ from tensorflow.python.platform import test
 
 class EmbeddingTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_embedding(self):
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Embedding,
-          kwargs={'output_dim': 4,
-                  'input_dim': 10,
-                  'input_length': 2},
-          input_shape=(3, 2),
-          input_dtype='int32',
-          expected_output_dtype='float32')
+    testing_utils.layer_test(
+        keras.layers.Embedding,
+        kwargs={'output_dim': 4,
+                'input_dim': 10,
+                'input_length': 2},
+        input_shape=(3, 2),
+        input_dtype='int32',
+        expected_output_dtype='float32')
 
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Embedding,
-          kwargs={'output_dim': 4,
-                  'input_dim': 10,
-                  'mask_zero': True},
-          input_shape=(3, 2),
-          input_dtype='int32',
-          expected_output_dtype='float32')
+    testing_utils.layer_test(
+        keras.layers.Embedding,
+        kwargs={'output_dim': 4,
+                'input_dim': 10,
+                'mask_zero': True},
+        input_shape=(3, 2),
+        input_dtype='int32',
+        expected_output_dtype='float32')
 
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Embedding,
-          kwargs={'output_dim': 4,
-                  'input_dim': 10,
-                  'mask_zero': True},
-          input_shape=(3, 4, 2),
-          input_dtype='int32',
-          expected_output_dtype='float32')
+    testing_utils.layer_test(
+        keras.layers.Embedding,
+        kwargs={'output_dim': 4,
+                'input_dim': 10,
+                'mask_zero': True},
+        input_shape=(3, 4, 2),
+        input_dtype='int32',
+        expected_output_dtype='float32')
 
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.Embedding,
-          kwargs={'output_dim': 4,
-                  'input_dim': 10,
-                  'mask_zero': True,
-                  'input_length': (None, 2)},
-          input_shape=(3, 4, 2),
-          input_dtype='int32',
-          expected_output_dtype='float32')
+    testing_utils.layer_test(
+        keras.layers.Embedding,
+        kwargs={'output_dim': 4,
+                'input_dim': 10,
+                'mask_zero': True,
+                'input_length': (None, 2)},
+        input_shape=(3, 4, 2),
+        input_dtype='int32',
+        expected_output_dtype='float32')
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/keras/_impl/keras/layers/gru_test.py b/tensorflow/python/keras/_impl/keras/layers/gru_test.py
index c57fbac41c..48e7e14f5a 100644
--- a/tensorflow/python/keras/_impl/keras/layers/gru_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/gru_test.py
@@ -20,64 +20,66 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.platform import test
+from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 
 class GRULayerTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_return_sequences_GRU(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.GRU,
-          kwargs={'units': units,
-                  'return_sequences': True},
-          input_shape=(num_samples, timesteps, embedding_dim))
+    testing_utils.layer_test(
+        keras.layers.GRU,
+        kwargs={'units': units,
+                'return_sequences': True},
+        input_shape=(num_samples, timesteps, embedding_dim))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_dynamic_behavior_GRU(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      layer = keras.layers.GRU(units, input_shape=(None, embedding_dim))
-      model = keras.models.Sequential()
-      model.add(layer)
-      model.compile('sgd', 'mse')
-      x = np.random.random((num_samples, timesteps, embedding_dim))
-      y = np.random.random((num_samples, units))
-      model.train_on_batch(x, y)
-
+    layer = keras.layers.GRU(units, input_shape=(None, embedding_dim))
+    model = keras.models.Sequential()
+    model.add(layer)
+    model.compile(RMSPropOptimizer(0.01), 'mse')
+    x = np.random.random((num_samples, timesteps, embedding_dim))
+    y = np.random.random((num_samples, units))
+    model.train_on_batch(x, y)
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_dropout_GRU(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.GRU,
-          kwargs={'units': units,
-                  'dropout': 0.1,
-                  'recurrent_dropout': 0.1},
-          input_shape=(num_samples, timesteps, embedding_dim))
-
+    testing_utils.layer_test(
+        keras.layers.GRU,
+        kwargs={'units': units,
+                'dropout': 0.1,
+                'recurrent_dropout': 0.1},
+        input_shape=(num_samples, timesteps, embedding_dim))
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_implementation_mode_GRU(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      for mode in [0, 1, 2]:
-        testing_utils.layer_test(
-            keras.layers.GRU,
-            kwargs={'units': units,
-                    'implementation': mode},
-            input_shape=(num_samples, timesteps, embedding_dim))
+    for mode in [0, 1, 2]:
+      testing_utils.layer_test(
+          keras.layers.GRU,
+          kwargs={'units': units,
+                  'implementation': mode},
+          input_shape=(num_samples, timesteps, embedding_dim))
 
   def test_statefulness_GRU(self):
     num_samples = 2
diff --git a/tensorflow/python/keras/_impl/keras/layers/local_test.py b/tensorflow/python/keras/_impl/keras/layers/local_test.py
index a815a0fadc..93741d24b9 100644
--- a/tensorflow/python/keras/_impl/keras/layers/local_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/local_test.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.platform import test
@@ -27,6 +28,7 @@ from tensorflow.python.platform import test
 
 class LocallyConnectedLayersTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_locallyconnected_1d(self):
     num_samples = 2
     num_steps = 8
@@ -39,16 +41,15 @@ class LocallyConnectedLayersTest(test.TestCase):
         if padding == 'same' and strides != 1:
           continue
 
-        with self.test_session():
-          testing_utils.layer_test(
-              keras.layers.LocallyConnected1D,
-              kwargs={
-                  'filters': filters,
-                  'kernel_size': filter_length,
-                  'padding': padding,
-                  'strides': strides
-              },
-              input_shape=(num_samples, num_steps, input_dim))
+        testing_utils.layer_test(
+            keras.layers.LocallyConnected1D,
+            kwargs={
+                'filters': filters,
+                'kernel_size': filter_length,
+                'padding': padding,
+                'strides': strides
+            },
+            input_shape=(num_samples, num_steps, input_dim))
 
   def test_locallyconnected_1d_regularization(self):
     num_samples = 2
@@ -86,6 +87,7 @@ class LocallyConnectedLayersTest(test.TestCase):
       self.assertEqual(layer.kernel.constraint, k_constraint)
       self.assertEqual(layer.bias.constraint, b_constraint)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_locallyconnected_2d(self):
     num_samples = 8
     filters = 3
@@ -98,20 +100,18 @@ class LocallyConnectedLayersTest(test.TestCase):
         if padding == 'same' and strides != (1, 1):
           continue
 
-        with self.test_session():
-          testing_utils.layer_test(
-              keras.layers.LocallyConnected2D,
-              kwargs={
-                  'filters': filters,
-                  'kernel_size': 3,
-                  'padding': padding,
-                  'kernel_regularizer': 'l2',
-                  'bias_regularizer': 'l2',
-                  'activity_regularizer': 'l2',
-                  'strides': strides,
-                  'data_format': 'channels_last'
-              },
-              input_shape=(num_samples, num_row, num_col, stack_size))
+        testing_utils.layer_test(
+            keras.layers.LocallyConnected2D,
+            kwargs={
+                'filters': filters,
+                'kernel_size': 3,
+                'padding': padding,
+                'kernel_regularizer': 'l2',
+                'bias_regularizer': 'l2',
+                'strides': strides,
+                'data_format': 'channels_last'
+            },
+            input_shape=(num_samples, num_row, num_col, stack_size))
 
   def test_locallyconnected_2d_channels_first(self):
     num_samples = 8
diff --git a/tensorflow/python/keras/_impl/keras/layers/lstm_test.py b/tensorflow/python/keras/_impl/keras/layers/lstm_test.py
index 1de5485179..74548d05c8 100644
--- a/tensorflow/python/keras/_impl/keras/layers/lstm_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/lstm_test.py
@@ -20,28 +20,29 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.platform import test
+from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 
 class LSTMLayerTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_return_sequences_LSTM(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.LSTM,
-          kwargs={'units': units,
-                  'return_sequences': True},
-          input_shape=(num_samples, timesteps, embedding_dim))
+    testing_utils.layer_test(
+        keras.layers.LSTM,
+        kwargs={'units': units,
+                'return_sequences': True},
+        input_shape=(num_samples, timesteps, embedding_dim))
 
   def test_static_shape_inference_LSTM(self):
     # Github issue: 15165
-    num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
@@ -55,45 +56,45 @@ class LSTMLayerTest(test.TestCase):
     outputs = model.layers[-1].output
     self.assertEquals(outputs.get_shape().as_list(), [None, timesteps, units])
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_dynamic_behavior_LSTM(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      layer = keras.layers.LSTM(units, input_shape=(None, embedding_dim))
-      model = keras.models.Sequential()
-      model.add(layer)
-      model.compile('sgd', 'mse')
-      x = np.random.random((num_samples, timesteps, embedding_dim))
-      y = np.random.random((num_samples, units))
-      model.train_on_batch(x, y)
+    layer = keras.layers.LSTM(units, input_shape=(None, embedding_dim))
+    model = keras.models.Sequential()
+    model.add(layer)
+    model.compile(RMSPropOptimizer(0.001), 'mse')
+    x = np.random.random((num_samples, timesteps, embedding_dim))
+    y = np.random.random((num_samples, units))
+    model.train_on_batch(x, y)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_dropout_LSTM(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.LSTM,
-          kwargs={'units': units,
-                  'dropout': 0.1,
-                  'recurrent_dropout': 0.1},
-          input_shape=(num_samples, timesteps, embedding_dim))
-
+    testing_utils.layer_test(
+        keras.layers.LSTM,
+        kwargs={'units': units,
+                'dropout': 0.1,
+                'recurrent_dropout': 0.1},
+        input_shape=(num_samples, timesteps, embedding_dim))
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_implementation_mode_LSTM(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      for mode in [0, 1, 2]:
-        testing_utils.layer_test(
-            keras.layers.LSTM,
-            kwargs={'units': units,
-                    'implementation': mode},
-            input_shape=(num_samples, timesteps, embedding_dim))
+    for mode in [0, 1, 2]:
+      testing_utils.layer_test(
+          keras.layers.LSTM,
+          kwargs={'units': units,
+                  'implementation': mode},
+          input_shape=(num_samples, timesteps, embedding_dim))
 
   def test_statefulness_LSTM(self):
     num_samples = 2
diff --git a/tensorflow/python/keras/_impl/keras/layers/merge_test.py b/tensorflow/python/keras/_impl/keras/layers/merge_test.py
index bb03dda1fc..b2fe06f93e 100644
--- a/tensorflow/python/keras/_impl/keras/layers/merge_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/merge_test.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import test
@@ -27,24 +28,25 @@ from tensorflow.python.platform import test
 
 class MergeLayersTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_merge_add(self):
-    with self.test_session():
-      i1 = keras.layers.Input(shape=(4, 5))
-      i2 = keras.layers.Input(shape=(4, 5))
-      i3 = keras.layers.Input(shape=(4, 5))
+    i1 = keras.layers.Input(shape=(4, 5))
+    i2 = keras.layers.Input(shape=(4, 5))
+    i3 = keras.layers.Input(shape=(4, 5))
 
-      o = keras.layers.add([i1, i2, i3])
-      self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
-      model = keras.models.Model([i1, i2, i3], o)
+    o = keras.layers.add([i1, i2, i3])
+    self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
+    model = keras.models.Model([i1, i2, i3], o)
 
-      x1 = np.random.random((2, 4, 5))
-      x2 = np.random.random((2, 4, 5))
-      x3 = np.random.random((2, 4, 5))
-      out = model.predict([x1, x2, x3])
-      self.assertEqual(out.shape, (2, 4, 5))
-      self.assertAllClose(out, x1 + x2 + x3, atol=1e-4)
+    x1 = np.random.random((2, 4, 5))
+    x2 = np.random.random((2, 4, 5))
+    x3 = np.random.random((2, 4, 5))
+    out = model.predict([x1, x2, x3])
+    self.assertEqual(out.shape, (2, 4, 5))
+    self.assertAllClose(out, x1 + x2 + x3, atol=1e-4)
 
-      # test masking
+  def test_merge_add_masking(self):
+    with self.test_session():
       i1 = keras.layers.Input(shape=(4, 5))
       i2 = keras.layers.Input(shape=(4, 5))
       m1 = keras.layers.Masking()(i1)
@@ -54,11 +56,13 @@ class MergeLayersTest(test.TestCase):
       mask = layer.output_mask
       self.assertListEqual(mask.get_shape().as_list(), [None, 4])
 
-      # test missing shape
+  def test_merge_add_dynamic_shape(self):
+    with self.test_session():
       i1 = array_ops.placeholder(shape=(4, None), dtype='float32')
       i2 = array_ops.placeholder(shape=(4, 5), dtype='float32')
       layer = keras.layers.Add()
       o = layer([i1, i2])
+      self.assertListEqual(o.get_shape().as_list(), [4, 5])
 
   def test_merge_elementwise_errors(self):
     i1 = keras.layers.Input(shape=(4, 5))
@@ -72,79 +76,82 @@ class MergeLayersTest(test.TestCase):
     with self.assertRaises(ValueError):
       keras.layers.add([i1])
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_merge_multiply(self):
-    with self.test_session():
-      i1 = keras.layers.Input(shape=(4, 5))
-      i2 = keras.layers.Input(shape=(4, 5))
-      i3 = keras.layers.Input(shape=(4, 5))
-      o = keras.layers.multiply([i1, i2, i3])
-      self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
-      model = keras.models.Model([i1, i2, i3], o)
-
-      x1 = np.random.random((2, 4, 5))
-      x2 = np.random.random((2, 4, 5))
-      x3 = np.random.random((2, 4, 5))
-      out = model.predict([x1, x2, x3])
-      self.assertEqual(out.shape, (2, 4, 5))
-      self.assertAllClose(out, x1 * x2 * x3, atol=1e-4)
-
+    i1 = keras.layers.Input(shape=(4, 5))
+    i2 = keras.layers.Input(shape=(4, 5))
+    i3 = keras.layers.Input(shape=(4, 5))
+    o = keras.layers.multiply([i1, i2, i3])
+    self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
+    model = keras.models.Model([i1, i2, i3], o)
+
+    x1 = np.random.random((2, 4, 5))
+    x2 = np.random.random((2, 4, 5))
+    x3 = np.random.random((2, 4, 5))
+    out = model.predict([x1, x2, x3])
+    self.assertEqual(out.shape, (2, 4, 5))
+    self.assertAllClose(out, x1 * x2 * x3, atol=1e-4)
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_merge_average(self):
-    with self.test_session():
-      i1 = keras.layers.Input(shape=(4, 5))
-      i2 = keras.layers.Input(shape=(4, 5))
-      o = keras.layers.average([i1, i2])
-      self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
-      model = keras.models.Model([i1, i2], o)
+    i1 = keras.layers.Input(shape=(4, 5))
+    i2 = keras.layers.Input(shape=(4, 5))
+    o = keras.layers.average([i1, i2])
+    self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
+    model = keras.models.Model([i1, i2], o)
 
-      x1 = np.random.random((2, 4, 5))
-      x2 = np.random.random((2, 4, 5))
-      out = model.predict([x1, x2])
-      self.assertEqual(out.shape, (2, 4, 5))
-      self.assertAllClose(out, 0.5 * (x1 + x2), atol=1e-4)
+    x1 = np.random.random((2, 4, 5))
+    x2 = np.random.random((2, 4, 5))
+    out = model.predict([x1, x2])
+    self.assertEqual(out.shape, (2, 4, 5))
+    self.assertAllClose(out, 0.5 * (x1 + x2), atol=1e-4)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_merge_maximum(self):
-    with self.test_session():
-      i1 = keras.layers.Input(shape=(4, 5))
-      i2 = keras.layers.Input(shape=(4, 5))
-      o = keras.layers.maximum([i1, i2])
-      self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
-      model = keras.models.Model([i1, i2], o)
+    i1 = keras.layers.Input(shape=(4, 5))
+    i2 = keras.layers.Input(shape=(4, 5))
+    o = keras.layers.maximum([i1, i2])
+    self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
+    model = keras.models.Model([i1, i2], o)
 
-      x1 = np.random.random((2, 4, 5))
-      x2 = np.random.random((2, 4, 5))
-      out = model.predict([x1, x2])
-      self.assertEqual(out.shape, (2, 4, 5))
-      self.assertAllClose(out, np.maximum(x1, x2), atol=1e-4)
+    x1 = np.random.random((2, 4, 5))
+    x2 = np.random.random((2, 4, 5))
+    out = model.predict([x1, x2])
+    self.assertEqual(out.shape, (2, 4, 5))
+    self.assertAllClose(out, np.maximum(x1, x2), atol=1e-4)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_merge_minimum(self):
-    with self.test_session():
-      i1 = keras.layers.Input(shape=(4, 5))
-      i2 = keras.layers.Input(shape=(4, 5))
-      o = keras.layers.minimum([i1, i2])
-      self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
-      model = keras.models.Model([i1, i2], o)
+    i1 = keras.layers.Input(shape=(4, 5))
+    i2 = keras.layers.Input(shape=(4, 5))
+    o = keras.layers.minimum([i1, i2])
+    self.assertListEqual(o.get_shape().as_list(), [None, 4, 5])
+    model = keras.models.Model([i1, i2], o)
 
-      x1 = np.random.random((2, 4, 5))
-      x2 = np.random.random((2, 4, 5))
-      out = model.predict([x1, x2])
-      self.assertEqual(out.shape, (2, 4, 5))
-      self.assertAllClose(out, np.minimum(x1, x2), atol=1e-4)
+    x1 = np.random.random((2, 4, 5))
+    x2 = np.random.random((2, 4, 5))
+    out = model.predict([x1, x2])
+    self.assertEqual(out.shape, (2, 4, 5))
+    self.assertAllClose(out, np.minimum(x1, x2), atol=1e-4)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_merge_concatenate(self):
+    i1 = keras.layers.Input(shape=(4, 5))
+    i2 = keras.layers.Input(shape=(4, 5))
+    o = keras.layers.concatenate([i1, i2], axis=1)
+    self.assertListEqual(o.get_shape().as_list(), [None, 8, 5])
+    model = keras.models.Model([i1, i2], o)
+
+    x1 = np.random.random((2, 4, 5))
+    x2 = np.random.random((2, 4, 5))
+    out = model.predict([x1, x2])
+    self.assertEqual(out.shape, (2, 8, 5))
+    self.assertAllClose(out, np.concatenate([x1, x2], axis=1), atol=1e-4)
+
+  def test_merge_concatenate_masking(self):
     with self.test_session():
       i1 = keras.layers.Input(shape=(4, 5))
       i2 = keras.layers.Input(shape=(4, 5))
-      o = keras.layers.concatenate([i1, i2], axis=1)
-      self.assertListEqual(o.get_shape().as_list(), [None, 8, 5])
-      model = keras.models.Model([i1, i2], o)
-
-      x1 = np.random.random((2, 4, 5))
-      x2 = np.random.random((2, 4, 5))
-      out = model.predict([x1, x2])
-      self.assertEqual(out.shape, (2, 8, 5))
-      self.assertAllClose(out, np.concatenate([x1, x2], axis=1), atol=1e-4)
-
-      # test masking
       m1 = keras.layers.Masking()(i1)
       layer = keras.layers.Concatenate()
       o = layer([m1, i2])
@@ -162,35 +169,35 @@ class MergeLayersTest(test.TestCase):
     with self.assertRaisesRegexp(ValueError, 'called on a list'):
       keras.layers.concatenate([i1], axis=-1)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_merge_dot(self):
-    with self.test_session():
-      i1 = keras.layers.Input(shape=(4,))
-      i2 = keras.layers.Input(shape=(4,))
-      o = keras.layers.dot([i1, i2], axes=1)
-      self.assertListEqual(o.get_shape().as_list(), [None, 1])
-      model = keras.models.Model([i1, i2], o)
-      _ = keras.layers.Dot(axes=1).get_config()
-
-      x1 = np.random.random((2, 4))
-      x2 = np.random.random((2, 4))
-      out = model.predict([x1, x2])
-      self.assertEqual(out.shape, (2, 1))
-      expected = np.zeros((2, 1))
-      expected[0, 0] = np.dot(x1[0], x2[0])
-      expected[1, 0] = np.dot(x1[1], x2[1])
-      self.assertAllClose(out, expected, atol=1e-4)
-
-      # Test with negative tuple of axes.
-      o = keras.layers.dot([i1, i2], axes=(-1, -1))
-      self.assertListEqual(o.get_shape().as_list(), [None, 1])
-      model = keras.models.Model([i1, i2], o)
-      out = model.predict([x1, x2])
-      self.assertEqual(out.shape, (2, 1))
-      self.assertAllClose(out, expected, atol=1e-4)
-
-      # test compute_output_shape
-      layer = keras.layers.Dot(axes=-1)
-      self.assertEqual(layer.compute_output_shape([(4, 5), (4, 5)]), (4, 1))
+    i1 = keras.layers.Input(shape=(4,))
+    i2 = keras.layers.Input(shape=(4,))
+    o = keras.layers.dot([i1, i2], axes=1)
+    self.assertListEqual(o.get_shape().as_list(), [None, 1])
+    model = keras.models.Model([i1, i2], o)
+    _ = keras.layers.Dot(axes=1).get_config()
+
+    x1 = np.random.random((2, 4))
+    x2 = np.random.random((2, 4))
+    out = model.predict([x1, x2])
+    self.assertEqual(out.shape, (2, 1))
+    expected = np.zeros((2, 1))
+    expected[0, 0] = np.dot(x1[0], x2[0])
+    expected[1, 0] = np.dot(x1[1], x2[1])
+    self.assertAllClose(out, expected, atol=1e-4)
+
+    # Test with negative tuple of axes.
+    o = keras.layers.dot([i1, i2], axes=(-1, -1))
+    self.assertListEqual(o.get_shape().as_list(), [None, 1])
+    model = keras.models.Model([i1, i2], o)
+    out = model.predict([x1, x2])
+    self.assertEqual(out.shape, (2, 1))
+    self.assertAllClose(out, expected, atol=1e-4)
+
+    # test compute_output_shape
+    layer = keras.layers.Dot(axes=-1)
+    self.assertEqual(layer.compute_output_shape([(4, 5), (4, 5)]), (4, 1))
 
   def test_dot_errors(self):
     i1 = keras.layers.Input(shape=(4, 5))
@@ -208,6 +215,7 @@ class MergeLayersTest(test.TestCase):
       dot = keras.layers.Dot(1)
       dot.compute_output_shape(1)
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_merge_subtract(self):
     i1 = keras.layers.Input(shape=(4, 5))
     i2 = keras.layers.Input(shape=(4, 5))
diff --git a/tensorflow/python/keras/_impl/keras/layers/noise_test.py b/tensorflow/python/keras/_impl/keras/layers/noise_test.py
index f9b4d9cd09..af4f031ec9 100644
--- a/tensorflow/python/keras/_impl/keras/layers/noise_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/noise_test.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.platform import test
@@ -39,12 +40,12 @@ class NoiseLayersTest(test.TestCase):
           kwargs={'rate': 0.5},
           input_shape=(3, 2, 3))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_AlphaDropout(self):
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.AlphaDropout,
-          kwargs={'rate': 0.2},
-          input_shape=(3, 2, 3))
+    testing_utils.layer_test(
+        keras.layers.AlphaDropout,
+        kwargs={'rate': 0.2},
+        input_shape=(3, 2, 3))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/keras/_impl/keras/layers/pooling_test.py b/tensorflow/python/keras/_impl/keras/layers/pooling_test.py
index ec0a5ae560..70049f0976 100644
--- a/tensorflow/python/keras/_impl/keras/layers/pooling_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/pooling_test.py
@@ -18,6 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.python.eager import context
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.platform import test
@@ -25,81 +27,85 @@ from tensorflow.python.platform import test
 
 class GlobalPoolingTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
   def test_globalpooling_1d(self):
-    with self.test_session(use_gpu=True):
-      testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D,
-                               input_shape=(3, 4, 5))
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
+    testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D,
+                             input_shape=(3, 4, 5))
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
 
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
   def test_globalpooling_2d(self):
-    with self.test_session(use_gpu=True):
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalMaxPooling2D,
-          kwargs={'data_format': 'channels_first'},
-          input_shape=(3, 4, 5, 6))
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalMaxPooling2D,
-          kwargs={'data_format': 'channels_last'},
-          input_shape=(3, 5, 6, 4))
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalAveragePooling2D,
-          kwargs={'data_format': 'channels_first'},
-          input_shape=(3, 4, 5, 6))
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalAveragePooling2D,
-          kwargs={'data_format': 'channels_last'},
-          input_shape=(3, 5, 6, 4))
-
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalMaxPooling2D,
+        kwargs={'data_format': 'channels_first'},
+        input_shape=(3, 4, 5, 6))
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalMaxPooling2D,
+        kwargs={'data_format': 'channels_last'},
+        input_shape=(3, 5, 6, 4))
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalAveragePooling2D,
+        kwargs={'data_format': 'channels_first'},
+        input_shape=(3, 4, 5, 6))
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalAveragePooling2D,
+        kwargs={'data_format': 'channels_last'},
+        input_shape=(3, 5, 6, 4))
+
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
   def test_globalpooling_3d(self):
-    with self.test_session(use_gpu=True):
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalMaxPooling3D,
-          kwargs={'data_format': 'channels_first'},
-          input_shape=(3, 4, 3, 4, 3))
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalMaxPooling3D,
-          kwargs={'data_format': 'channels_last'},
-          input_shape=(3, 4, 3, 4, 3))
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalAveragePooling3D,
-          kwargs={'data_format': 'channels_first'},
-          input_shape=(3, 4, 3, 4, 3))
-      testing_utils.layer_test(
-          keras.layers.pooling.GlobalAveragePooling3D,
-          kwargs={'data_format': 'channels_last'},
-          input_shape=(3, 4, 3, 4, 3))
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalMaxPooling3D,
+        kwargs={'data_format': 'channels_first'},
+        input_shape=(3, 4, 3, 4, 3))
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalMaxPooling3D,
+        kwargs={'data_format': 'channels_last'},
+        input_shape=(3, 4, 3, 4, 3))
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalAveragePooling3D,
+        kwargs={'data_format': 'channels_first'},
+        input_shape=(3, 4, 3, 4, 3))
+    testing_utils.layer_test(
+        keras.layers.pooling.GlobalAveragePooling3D,
+        kwargs={'data_format': 'channels_last'},
+        input_shape=(3, 4, 3, 4, 3))
 
 
 class Pooling2DTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
   def test_maxpooling_2d(self):
     pool_size = (3, 3)
-    with self.test_session(use_gpu=True):
-      for strides in [(1, 1), (2, 2)]:
-        testing_utils.layer_test(
-            keras.layers.MaxPooling2D,
-            kwargs={
-                'strides': strides,
-                'padding': 'valid',
-                'pool_size': pool_size
-            },
-            input_shape=(3, 5, 6, 4))
-
-  def test_averagepooling_2d(self):
-    with self.test_session(use_gpu=True):
+    for strides in [(1, 1), (2, 2)]:
       testing_utils.layer_test(
-          keras.layers.AveragePooling2D,
-          kwargs={'strides': (2, 2),
-                  'padding': 'same',
-                  'pool_size': (2, 2)},
-          input_shape=(3, 5, 6, 4))
-      testing_utils.layer_test(
-          keras.layers.AveragePooling2D,
-          kwargs={'strides': (2, 2),
-                  'padding': 'valid',
-                  'pool_size': (3, 3)},
+          keras.layers.MaxPooling2D,
+          kwargs={
+              'strides': strides,
+              'padding': 'valid',
+              'pool_size': pool_size
+          },
           input_shape=(3, 5, 6, 4))
+
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
+  def test_averagepooling_2d(self):
+    testing_utils.layer_test(
+        keras.layers.AveragePooling2D,
+        kwargs={'strides': (2, 2),
+                'padding': 'same',
+                'pool_size': (2, 2)},
+        input_shape=(3, 5, 6, 4))
+    testing_utils.layer_test(
+        keras.layers.AveragePooling2D,
+        kwargs={'strides': (2, 2),
+                'padding': 'valid',
+                'pool_size': (3, 3)},
+        input_shape=(3, 5, 6, 4))
+
+    # This part of the test can only run on GPU but doesn't appear
+    # to be properly assigned to a GPU when running in eager mode.
+    if not context.in_eager_mode():
       # Only runs on GPU with CUDA, channels_first is not supported on CPU.
       # TODO(b/62340061): Support channels_first on CPU.
       if test.is_gpu_available(cuda_only=True):
@@ -116,66 +122,66 @@ class Pooling2DTest(test.TestCase):
 
 class Pooling3DTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
   def test_maxpooling_3d(self):
     pool_size = (3, 3, 3)
-    with self.test_session(use_gpu=True):
-      testing_utils.layer_test(
-          keras.layers.MaxPooling3D,
-          kwargs={'strides': 2,
-                  'padding': 'valid',
-                  'pool_size': pool_size},
-          input_shape=(3, 11, 12, 10, 4))
-      testing_utils.layer_test(
-          keras.layers.MaxPooling3D,
-          kwargs={
-              'strides': 3,
-              'padding': 'valid',
-              'data_format': 'channels_first',
-              'pool_size': pool_size
-          },
-          input_shape=(3, 4, 11, 12, 10))
-
+    testing_utils.layer_test(
+        keras.layers.MaxPooling3D,
+        kwargs={'strides': 2,
+                'padding': 'valid',
+                'pool_size': pool_size},
+        input_shape=(3, 11, 12, 10, 4))
+    testing_utils.layer_test(
+        keras.layers.MaxPooling3D,
+        kwargs={
+            'strides': 3,
+            'padding': 'valid',
+            'data_format': 'channels_first',
+            'pool_size': pool_size
+        },
+        input_shape=(3, 4, 11, 12, 10))
+
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
   def test_averagepooling_3d(self):
     pool_size = (3, 3, 3)
-    with self.test_session(use_gpu=True):
-      testing_utils.layer_test(
-          keras.layers.AveragePooling3D,
-          kwargs={'strides': 2,
-                  'padding': 'valid',
-                  'pool_size': pool_size},
-          input_shape=(3, 11, 12, 10, 4))
-      testing_utils.layer_test(
-          keras.layers.AveragePooling3D,
-          kwargs={
-              'strides': 3,
-              'padding': 'valid',
-              'data_format': 'channels_first',
-              'pool_size': pool_size
-          },
-          input_shape=(3, 4, 11, 12, 10))
+    testing_utils.layer_test(
+        keras.layers.AveragePooling3D,
+        kwargs={'strides': 2,
+                'padding': 'valid',
+                'pool_size': pool_size},
+        input_shape=(3, 11, 12, 10, 4))
+    testing_utils.layer_test(
+        keras.layers.AveragePooling3D,
+        kwargs={
+            'strides': 3,
+            'padding': 'valid',
+            'data_format': 'channels_first',
+            'pool_size': pool_size
+        },
+        input_shape=(3, 4, 11, 12, 10))
 
 
 class Pooling1DTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
   def test_maxpooling_1d(self):
-    with self.test_session(use_gpu=True):
-      for padding in ['valid', 'same']:
-        for stride in [1, 2]:
-          testing_utils.layer_test(
-              keras.layers.MaxPooling1D,
-              kwargs={'strides': stride,
-                      'padding': padding},
-              input_shape=(3, 5, 4))
+    for padding in ['valid', 'same']:
+      for stride in [1, 2]:
+        testing_utils.layer_test(
+            keras.layers.MaxPooling1D,
+            kwargs={'strides': stride,
+                    'padding': padding},
+            input_shape=(3, 5, 4))
 
+  @tf_test_util.run_in_graph_and_eager_modes(use_gpu=True)
   def test_averagepooling_1d(self):
-    with self.test_session(use_gpu=True):
-      for padding in ['valid', 'same']:
-        for stride in [1, 2]:
-          testing_utils.layer_test(
-              keras.layers.AveragePooling1D,
-              kwargs={'strides': stride,
-                      'padding': padding},
-              input_shape=(3, 5, 4))
+    for padding in ['valid', 'same']:
+      for stride in [1, 2]:
+        testing_utils.layer_test(
+            keras.layers.AveragePooling1D,
+            kwargs={'strides': stride,
+                    'padding': padding},
+            input_shape=(3, 5, 4))
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent.py b/tensorflow/python/keras/_impl/keras/layers/recurrent.py
index 2e9003f52d..a81971d9ee 100644
--- a/tensorflow/python/keras/_impl/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/_impl/keras/layers/recurrent.py
@@ -22,6 +22,7 @@ from __future__ import print_function
 import numbers
 import numpy as np
 
+from tensorflow.python.eager import context
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras._impl.keras import activations
 from tensorflow.python.keras._impl.keras import backend as K
@@ -935,7 +936,9 @@ class SimpleRNNCell(Layer):
 
     # Properly set learning phase on output tensor.
     if 0 < self.dropout + self.recurrent_dropout:
-      if training is None:
+      if training is None and not context.in_eager_mode():
+        # This would be harmless to set in eager mode, but eager tensors
+        # disallow setting arbitrary attributes.
         output._uses_learning_phase = True
     return output, [output]
 
@@ -1299,23 +1302,6 @@ class GRUCell(Layer):
           constraint=self.bias_constraint)
     else:
       self.bias = None
-
-    self.kernel_z = self.kernel[:, :self.units]
-    self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units]
-    self.kernel_r = self.kernel[:, self.units:self.units * 2]
-    self.recurrent_kernel_r = self.recurrent_kernel[:, self.units:
-                                                    self.units * 2]
-    self.kernel_h = self.kernel[:, self.units * 2:]
-    self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:]
-
-    if self.use_bias:
-      self.bias_z = self.bias[:self.units]
-      self.bias_r = self.bias[self.units:self.units * 2]
-      self.bias_h = self.bias[self.units * 2:]
-    else:
-      self.bias_z = None
-      self.bias_r = None
-      self.bias_h = None
     self.built = True
 
   def call(self, inputs, states, training=None):
@@ -1350,13 +1336,13 @@ class GRUCell(Layer):
         inputs_z = inputs
         inputs_r = inputs
         inputs_h = inputs
-      x_z = K.dot(inputs_z, self.kernel_z)
-      x_r = K.dot(inputs_r, self.kernel_r)
-      x_h = K.dot(inputs_h, self.kernel_h)
+      x_z = K.dot(inputs_z, self.kernel[:, :self.units])
+      x_r = K.dot(inputs_r, self.kernel[:, self.units:self.units * 2])
+      x_h = K.dot(inputs_h, self.kernel[:, self.units * 2:])
       if self.use_bias:
-        x_z = K.bias_add(x_z, self.bias_z)
-        x_r = K.bias_add(x_r, self.bias_r)
-        x_h = K.bias_add(x_h, self.bias_h)
+        x_z = K.bias_add(x_z, self.bias[:self.units])
+        x_r = K.bias_add(x_r, self.bias[self.units:self.units * 2])
+        x_h = K.bias_add(x_h, self.bias[self.units * 2:])
 
       if 0. < self.recurrent_dropout < 1.:
         h_tm1_z = h_tm1 * rec_dp_mask[0]
@@ -1367,11 +1353,14 @@ class GRUCell(Layer):
         h_tm1_r = h_tm1
         h_tm1_h = h_tm1
       z = self.recurrent_activation(
-          x_z + K.dot(h_tm1_z, self.recurrent_kernel_z))
+          x_z + K.dot(h_tm1_z, self.recurrent_kernel[:, :self.units]))
       r = self.recurrent_activation(
-          x_r + K.dot(h_tm1_r, self.recurrent_kernel_r))
+          x_r + K.dot(h_tm1_r, self.recurrent_kernel[:, self.units:
+                                                     self.units * 2]))
 
-      hh = self.activation(x_h + K.dot(r * h_tm1_h, self.recurrent_kernel_h))
+      hh = self.activation(x_h + K.dot(r * h_tm1_h,
+                                       self.recurrent_kernel[:,
+                                                             self.units * 2:]))
     else:
       if 0. < self.dropout < 1.:
         inputs *= dp_mask[0]
@@ -1395,44 +1384,34 @@ class GRUCell(Layer):
       hh = self.activation(x_h + recurrent_h)
     h = z * h_tm1 + (1 - z) * hh
     if 0 < self.dropout + self.recurrent_dropout:
-      if training is None:
+      if training is None and not context.in_eager_mode():
+        # This would be harmless to set in eager mode, but eager tensors
+        # disallow setting arbitrary attributes.
         h._uses_learning_phase = True
     return h, [h]
 
   def get_config(self):
     config = {
-        'units':
-            self.units,
-        'activation':
-            activations.serialize(self.activation),
+        'units': self.units,
+        'activation': activations.serialize(self.activation),
         'recurrent_activation':
             activations.serialize(self.recurrent_activation),
-        'use_bias':
-            self.use_bias,
-        'kernel_initializer':
-            initializers.serialize(self.kernel_initializer),
+        'use_bias': self.use_bias,
+        'kernel_initializer': initializers.serialize(self.kernel_initializer),
         'recurrent_initializer':
             initializers.serialize(self.recurrent_initializer),
-        'bias_initializer':
-            initializers.serialize(self.bias_initializer),
-        'kernel_regularizer':
-            regularizers.serialize(self.kernel_regularizer),
+        'bias_initializer': initializers.serialize(self.bias_initializer),
+        'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
         'recurrent_regularizer':
             regularizers.serialize(self.recurrent_regularizer),
-        'bias_regularizer':
-            regularizers.serialize(self.bias_regularizer),
-        'kernel_constraint':
-            constraints.serialize(self.kernel_constraint),
+        'bias_regularizer': regularizers.serialize(self.bias_regularizer),
+        'kernel_constraint': constraints.serialize(self.kernel_constraint),
         'recurrent_constraint':
             constraints.serialize(self.recurrent_constraint),
-        'bias_constraint':
-            constraints.serialize(self.bias_constraint),
-        'dropout':
-            self.dropout,
-        'recurrent_dropout':
-            self.recurrent_dropout,
-        'implementation':
-            self.implementation
+        'bias_constraint': constraints.serialize(self.bias_constraint),
+        'dropout': self.dropout,
+        'recurrent_dropout': self.recurrent_dropout,
+        'implementation': self.implementation
     }
     base_config = super(GRUCell, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
@@ -1809,29 +1788,6 @@ class LSTMCell(Layer):
           constraint=self.bias_constraint)
     else:
       self.bias = None
-
-    self.kernel_i = self.kernel[:, :self.units]
-    self.kernel_f = self.kernel[:, self.units:self.units * 2]
-    self.kernel_c = self.kernel[:, self.units * 2:self.units * 3]
-    self.kernel_o = self.kernel[:, self.units * 3:]
-
-    self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units]
-    self.recurrent_kernel_f = self.recurrent_kernel[:, self.units:
-                                                    self.units * 2]
-    self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2:
-                                                    self.units * 3]
-    self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:]
-
-    if self.use_bias:
-      self.bias_i = self.bias[:self.units]
-      self.bias_f = self.bias[self.units:self.units * 2]
-      self.bias_c = self.bias[self.units * 2:self.units * 3]
-      self.bias_o = self.bias[self.units * 3:]
-    else:
-      self.bias_i = None
-      self.bias_f = None
-      self.bias_c = None
-      self.bias_o = None
     self.built = True
 
   def call(self, inputs, states, training=None):
@@ -1869,15 +1825,15 @@ class LSTMCell(Layer):
         inputs_f = inputs
         inputs_c = inputs
         inputs_o = inputs
-      x_i = K.dot(inputs_i, self.kernel_i)
-      x_f = K.dot(inputs_f, self.kernel_f)
-      x_c = K.dot(inputs_c, self.kernel_c)
-      x_o = K.dot(inputs_o, self.kernel_o)
+      x_i = K.dot(inputs_i, self.kernel[:, :self.units])
+      x_f = K.dot(inputs_f, self.kernel[:, self.units:self.units * 2])
+      x_c = K.dot(inputs_c, self.kernel[:, self.units * 2:self.units * 3])
+      x_o = K.dot(inputs_o, self.kernel[:, self.units * 3:])
       if self.use_bias:
-        x_i = K.bias_add(x_i, self.bias_i)
-        x_f = K.bias_add(x_f, self.bias_f)
-        x_c = K.bias_add(x_c, self.bias_c)
-        x_o = K.bias_add(x_o, self.bias_o)
+        x_i = K.bias_add(x_i, self.bias[:self.units])
+        x_f = K.bias_add(x_f, self.bias[self.units:self.units * 2])
+        x_c = K.bias_add(x_c, self.bias[self.units * 2:self.units * 3])
+        x_o = K.bias_add(x_o, self.bias[self.units * 3:])
 
       if 0 < self.recurrent_dropout < 1.:
         h_tm1_i = h_tm1 * rec_dp_mask[0]
@@ -1890,13 +1846,15 @@ class LSTMCell(Layer):
         h_tm1_c = h_tm1
         h_tm1_o = h_tm1
       i = self.recurrent_activation(
-          x_i + K.dot(h_tm1_i, self.recurrent_kernel_i))
+          x_i + K.dot(h_tm1_i, self.recurrent_kernel[:, :self.units]))
       f = self.recurrent_activation(
-          x_f + K.dot(h_tm1_f, self.recurrent_kernel_f))
+          x_f + K.dot(h_tm1_f,
+                      self.recurrent_kernel[:, self.units: self.units * 2]))
       c = f * c_tm1 + i * self.activation(
-          x_c + K.dot(h_tm1_c, self.recurrent_kernel_c))
+          x_c + K.dot(h_tm1_c,
+                      self.recurrent_kernel[:, self.units * 2: self.units * 3]))
       o = self.recurrent_activation(
-          x_o + K.dot(h_tm1_o, self.recurrent_kernel_o))
+          x_o + K.dot(h_tm1_o, self.recurrent_kernel[:, self.units * 3:]))
     else:
       if 0. < self.dropout < 1.:
         inputs *= dp_mask[0]
@@ -1919,7 +1877,9 @@ class LSTMCell(Layer):
 
     h = o * self.activation(c)
     if 0 < self.dropout + self.recurrent_dropout:
-      if training is None:
+      if training is None and not context.in_eager_mode():
+        # This would be harmless to set in eager mode, but eager tensors
+        # disallow setting arbitrary attributes.
         h._uses_learning_phase = True
     return h, [h, c]
 
diff --git a/tensorflow/python/keras/_impl/keras/layers/simplernn_test.py b/tensorflow/python/keras/_impl/keras/layers/simplernn_test.py
index 7edebdacd0..8c7189cd47 100644
--- a/tensorflow/python/keras/_impl/keras/layers/simplernn_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/simplernn_test.py
@@ -20,64 +20,66 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
 from tensorflow.python.platform import test
+from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 
 class SimpleRNNLayerTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_return_sequences_SimpleRNN(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.SimpleRNN,
-          kwargs={'units': units,
-                  'return_sequences': True},
-          input_shape=(num_samples, timesteps, embedding_dim))
+    testing_utils.layer_test(
+        keras.layers.SimpleRNN,
+        kwargs={'units': units,
+                'return_sequences': True},
+        input_shape=(num_samples, timesteps, embedding_dim))
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_dynamic_behavior_SimpleRNN(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      layer = keras.layers.SimpleRNN(units, input_shape=(None, embedding_dim))
-      model = keras.models.Sequential()
-      model.add(layer)
-      model.compile('sgd', 'mse')
-      x = np.random.random((num_samples, timesteps, embedding_dim))
-      y = np.random.random((num_samples, units))
-      model.train_on_batch(x, y)
-
+    layer = keras.layers.SimpleRNN(units, input_shape=(None, embedding_dim))
+    model = keras.models.Sequential()
+    model.add(layer)
+    model.compile(RMSPropOptimizer(0.01), 'mse')
+    x = np.random.random((num_samples, timesteps, embedding_dim))
+    y = np.random.random((num_samples, units))
+    model.train_on_batch(x, y)
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_dropout_SimpleRNN(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      testing_utils.layer_test(
-          keras.layers.SimpleRNN,
-          kwargs={'units': units,
-                  'dropout': 0.1,
-                  'recurrent_dropout': 0.1},
-          input_shape=(num_samples, timesteps, embedding_dim))
-
+    testing_utils.layer_test(
+        keras.layers.SimpleRNN,
+        kwargs={'units': units,
+                'dropout': 0.1,
+                'recurrent_dropout': 0.1},
+        input_shape=(num_samples, timesteps, embedding_dim))
+
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_implementation_mode_SimpleRNN(self):
     num_samples = 2
     timesteps = 3
     embedding_dim = 4
     units = 2
-    with self.test_session():
-      for mode in [0, 1, 2]:
-        testing_utils.layer_test(
-            keras.layers.SimpleRNN,
-            kwargs={'units': units,
-                    'implementation': mode},
-            input_shape=(num_samples, timesteps, embedding_dim))
+    for mode in [0, 1, 2]:
+      testing_utils.layer_test(
+          keras.layers.SimpleRNN,
+          kwargs={'units': units,
+                  'implementation': mode},
+          input_shape=(num_samples, timesteps, embedding_dim))
 
   def test_statefulness_SimpleRNN(self):
     num_samples = 2
diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py b/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py
index c81d6b883c..8fcf66e90f 100644
--- a/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py
+++ b/tensorflow/python/keras/_impl/keras/layers/wrappers_test.py
@@ -20,44 +20,43 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.framework import test_util as tf_test_util
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.platform import test
+from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 
 class TimeDistributedTest(test.TestCase):
 
+  @tf_test_util.run_in_graph_and_eager_modes()
   def test_timedistributed_dense(self):
-    # first, test with Dense layer
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(
-          keras.layers.TimeDistributed(
-              keras.layers.Dense(2), input_shape=(3, 4)))
-      model.compile(optimizer='rmsprop', loss='mse')
-      model.fit(
-          np.random.random((10, 3, 4)),
-          np.random.random((10, 3, 2)),
-          epochs=1,
-          batch_size=10)
-
-      # test config
-      model.get_config()
+    model = keras.models.Sequential()
+    model.add(
+        keras.layers.TimeDistributed(
+            keras.layers.Dense(2), input_shape=(3, 4)))
+    model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
+    model.fit(
+        np.random.random((10, 3, 4)),
+        np.random.random((10, 3, 2)),
+        epochs=1,
+        batch_size=10)
+
+    # test config
+    model.get_config()
 
   def test_timedistributed_static_batch_size(self):
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(
-          keras.layers.TimeDistributed(
-              keras.layers.Dense(2), input_shape=(3, 4), batch_size=10))
-      model.compile(optimizer='rmsprop', loss='mse')
-      model.fit(
-          np.random.random((10, 3, 4)),
-          np.random.random((10, 3, 2)),
-          epochs=1,
-          batch_size=10)
+    model = keras.models.Sequential()
+    model.add(
+        keras.layers.TimeDistributed(
+            keras.layers.Dense(2), input_shape=(3, 4), batch_size=10))
+    model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
+    model.fit(
+        np.random.random((10, 3, 4)),
+        np.random.random((10, 3, 2)),
+        epochs=1,
+        batch_size=10)
 
   def test_timedistributed_conv2d(self):
-    # test with Conv2D
     with self.test_session():
       model = keras.models.Sequential()
       model.add(
@@ -73,7 +72,6 @@ class TimeDistributedTest(test.TestCase):
       model.summary()
 
   def test_timedistributed_stacked(self):
-    # test stacked layers
     with self.test_session():
       model = keras.models.Sequential()
       model.add(
@@ -167,7 +165,7 @@ class BidirectionalTest(test.TestCase):
         model.add(
             keras.layers.Bidirectional(
                 rnn(output_dim), merge_mode=mode, input_shape=(timesteps, dim)))
-        model.compile(loss='mse', optimizer='sgd')
+        model.compile(optimizer=RMSPropOptimizer(0.01), loss='mse')
         model.fit(x, y, epochs=1, batch_size=1)
 
         # test compute output shape
diff --git a/tensorflow/python/keras/_impl/keras/optimizers.py b/tensorflow/python/keras/_impl/keras/optimizers.py
index 76a97156ed..6520128c5b 100644
--- a/tensorflow/python/keras/_impl/keras/optimizers.py
+++ b/tensorflow/python/keras/_impl/keras/optimizers.py
@@ -704,8 +704,10 @@ class TFOptimizer(Optimizer):
     return self.optimizer.compute_gradients(loss, params)
 
   def get_updates(self, loss, params):
-    grads = self.optimizer.compute_gradients(loss, params)
     self.updates = [K.update_add(self.iterations, 1)]
+    if not params:
+      return self.updates
+    grads = self.optimizer.compute_gradients(loss, params)
     opt_update = self.optimizer.apply_gradients(
         grads, global_step=self.iterations)
     self.updates.append(opt_update)
diff --git a/tensorflow/python/keras/_impl/keras/testing_utils.py b/tensorflow/python/keras/_impl/keras/testing_utils.py
index fa1ee2fa3d..60799ee1e0 100644
--- a/tensorflow/python/keras/_impl/keras/testing_utils.py
+++ b/tensorflow/python/keras/_impl/keras/testing_utils.py
@@ -22,6 +22,7 @@ import numpy as np
 
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras._impl import keras
+from tensorflow.python.training.rmsprop import RMSPropOptimizer
 from tensorflow.python.util import tf_inspect
 
 
@@ -145,7 +146,7 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
     np.testing.assert_allclose(output, actual_output, rtol=1e-3)
 
   # test training mode (e.g. useful for dropout tests)
-  model.compile('rmsprop', 'mse')
+  model.compile(RMSPropOptimizer(0.01), 'mse')
   model.train_on_batch(input_data, actual_output)
 
   # test as first layer in Sequential API
@@ -181,9 +182,5 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
     output = recovered_model.predict(input_data)
     np.testing.assert_allclose(output, actual_output, rtol=1e-3)
 
-  # test training mode (e.g. useful for dropout tests)
-  model.compile('rmsprop', 'mse')
-  model.train_on_batch(input_data, actual_output)
-
   # for further checks in the caller function
   return actual_output
-- 
GitLab


From 51b334875125b1e76545d02d2e8e18c7ff2be0af Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 22 Feb 2018 12:18:39 -0800
Subject: [PATCH 200/575] Measure the performance of the original placement to
 ensure that we preserve it in case the placer isn't given enough time to find
 a better solution.

PiperOrigin-RevId: 186655094
---
 tensorflow/python/grappler/graph_placer.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/grappler/graph_placer.py b/tensorflow/python/grappler/graph_placer.py
index 2cc3536792..1cd51df4d9 100644
--- a/tensorflow/python/grappler/graph_placer.py
+++ b/tensorflow/python/grappler/graph_placer.py
@@ -68,6 +68,16 @@ def PlaceGraph(metagraph,
 
   item = gitem.Item(optimized_metagraph)
 
+  # Measure the runtime achievable with the original placement.
+  try:
+    _, original_run_time, _ = cluster.MeasureCosts(item)
+    if verbose:
+      print("Runtime for original placement: " + str(original_run_time))
+  except errors.OpError as e:
+    if verbose:
+      print("Original placement isn't feasible: " + str(e))
+    original_run_time = hparams.failing_signal
+
   if hparams is None:
     hparams = hierarchical_controller.hierarchical_controller_hparams()
   # We run with a single child
@@ -98,7 +108,7 @@ def PlaceGraph(metagraph,
               print("Failed to run graph:" + str(e))
             run_time = hparams.failing_signal
           updated = model.update_reward(sess, run_time, verbose=verbose)
-          if updated:
+          if updated and run_time < original_run_time:
             if verbose:
               print("Found better placement, with runtime " + str(run_time))
             model.export_placement(metagraph)
-- 
GitLab


From 16625e97c5fa041dc40f29c1f57a0e92047123ba Mon Sep 17 00:00:00 2001
From: Seo Sanghyeon <sanxiyn@gmail.com>
Date: Fri, 23 Feb 2018 05:27:40 +0900
Subject: [PATCH 201/575] Fix typo (#17193)

---
 configure.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/configure.py b/configure.py
index f77a048d86..b5436dba20 100644
--- a/configure.py
+++ b/configure.py
@@ -1217,7 +1217,7 @@ def set_host_c_compiler(environ_cp):
       environ_cp,
       var_name='HOST_C_COMPILER',
       var_default=default_c_host_compiler,
-      ask_for_var=('Please specify which C compiler should be used as the host'
+      ask_for_var=('Please specify which C compiler should be used as the host '
                    'C compiler.'),
       check_success=os.path.exists,
       error_msg='Invalid C compiler path. %s cannot be found.',
-- 
GitLab


From 2e707494c4b1058e1186c67b1030f635bdf52dac Mon Sep 17 00:00:00 2001
From: Guangda Lai <laigd@google.com>
Date: Thu, 22 Feb 2018 12:24:22 -0800
Subject: [PATCH 202/575] Fix BaseGPUDevice, let it report the actual memory
 limit of the allocator. Also added a helper method to reset ProcessState.

PiperOrigin-RevId: 186655996
---
 .../core/common_runtime/gpu/gpu_device.cc     | 29 ++++++++++++++-----
 1 file changed, 21 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index 15ff15fd5a..8357cc5a72 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -1013,21 +1013,34 @@ Status BaseGPUDeviceFactory::CreateGPUDevice(const SessionOptions& options,
   GpuIdUtil::CheckValidTfGpuId(tf_gpu_id);
   CudaGpuId cuda_gpu_id = GpuIdManager::TfToCudaGpuId(tf_gpu_id);
   int numa_node = dev_locality.numa_node();
-  Bytes allocated_bytes = static_cast<Bytes>(memory_limit);
 
   gpu::StreamExecutor* se =
       GpuIdUtil::ExecutorForCudaGpuId(cuda_gpu_id).ValueOrDie();
   const gpu::DeviceDescription& desc = se->GetDeviceDescription();
-  LOG(INFO) << "Creating TensorFlow device (" << device_name << " with "
-            << (memory_limit >> 20) << " MB memory) -> physical GPU ("
-            << GetShortDeviceDescription(cuda_gpu_id, desc) << ")";
   ProcessState* process_state = ProcessState::singleton();
+  Allocator* gpu_allocator = process_state->GetGPUAllocator(
+      options.config.gpu_options(), tf_gpu_id, memory_limit);
+  if (gpu_allocator == nullptr) {
+    return errors::Internal("Failed to get memory allocator for TF GPU ",
+                            tf_gpu_id.value(), " with ", memory_limit,
+                            " bytes of memory.");
+  }
+  AllocatorStats stats;
+  gpu_allocator->GetStats(&stats);
+  // 'memory_limit' is the required memory size, but if the allocator with given
+  // tf_gpu_id was created before, we'll use it instead of creating a new one
+  // (as TF gpu device is a shared resource), in which case the actual memory
+  // limit represented by 'stats.bytes_limit' used by that allocator may be
+  // different (which should be an error).
+  //
+  // TODO(laigd): report error if memory_limit doesn't match stats.bytes_limit.
   BaseGPUDevice* gpu_device = CreateGPUDevice(
-      options, device_name, allocated_bytes, dev_locality, tf_gpu_id,
-      GetShortDeviceDescription(cuda_gpu_id, desc),
-      process_state->GetGPUAllocator(options.config.gpu_options(), tf_gpu_id,
-                                     memory_limit),
+      options, device_name, static_cast<Bytes>(stats.bytes_limit), dev_locality,
+      tf_gpu_id, GetShortDeviceDescription(cuda_gpu_id, desc), gpu_allocator,
       process_state->GetCPUAllocator(numa_node));
+  LOG(INFO) << "Created TensorFlow device (" << device_name << " with "
+            << (stats.bytes_limit >> 20) << " MB memory) -> physical GPU ("
+            << GetShortDeviceDescription(cuda_gpu_id, desc) << ")";
   TF_RETURN_IF_ERROR(gpu_device->Init(options));
   devices->push_back(gpu_device);
 
-- 
GitLab


From 78916e73383da9860ccdf07018892acb558249d7 Mon Sep 17 00:00:00 2001
From: Zhixian Yan <zhixianyan@google.com>
Date: Thu, 22 Feb 2018 12:26:22 -0800
Subject: [PATCH 203/575] Generate example for basic lstm cell in tflite

PiperOrigin-RevId: 186656247
---
 tensorflow/contrib/lite/testing/BUILD         |   1 +
 .../contrib/lite/testing/generate_examples.py | 168 +++++++++++++-----
 .../testing/generated_examples_zip_test.cc    |   1 +
 .../contrib/lite/testing/parse_testdata.cc    |  24 ++-
 4 files changed, 136 insertions(+), 58 deletions(-)

diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index 1ccf7d4d0e..b5960d6f8d 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -34,6 +34,7 @@ gen_zipped_test_files(
         "l2norm.zip",
         "local_response_norm.zip",
         "log_softmax.zip",
+        "lstm.zip",
         "max_pool.zip",
         "mean.zip",
         "mul.zip",
diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 2cbac7caa6..2481add769 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -46,6 +46,7 @@ from google.protobuf import text_format
 # TODO(aselle): switch to TensorFlow's resource_loader
 from tensorflow.contrib.lite.testing import generate_examples_report as report_lib
 from tensorflow.python.framework import graph_util as tf_graph_util
+from tensorflow.python.ops import rnn
 
 parser = argparse.ArgumentParser(description="Script to generate TFLite tests.")
 parser.add_argument("output_path",
@@ -108,11 +109,23 @@ KNOWN_BUGS = {
 }
 
 
+class ExtraTocoOptions(object):
+  """Additonal toco options besides input, output, shape."""
+
+  def __init__(self):
+    # Whether to ignore control dependency nodes.
+    self.drop_control_dependency = False
+    # Allow custom ops in the toco conversion.
+    self.allow_custom_ops = False
+    # Rnn states that are used to support rnn / lstm cells.
+    self.rnn_states = None
+
+
 def toco_options(data_types,
                  input_arrays,
                  output_arrays,
                  shapes,
-                 drop_control_dependency):
+                 extra_toco_options=ExtraTocoOptions()):
   """Create TOCO options to process a model.
 
   Args:
@@ -120,8 +133,7 @@ def toco_options(data_types,
     input_arrays: names of the input tensors
     output_arrays: name of the output tensors
     shapes: shapes of the input tensors
-    drop_control_dependency: whether to ignore control dependency nodes.
-
+    extra_toco_options: additional toco options
   Returns:
     the options in a string.
   """
@@ -137,37 +149,15 @@ def toco_options(data_types,
        " --input_arrays=%s" % ",".join(input_arrays) +
        " --input_shapes=%s" % shape_str +
        " --output_arrays=%s" % ",".join(output_arrays))
-  if drop_control_dependency:
+  if extra_toco_options.drop_control_dependency:
     s += " --drop_control_dependency"
+  if extra_toco_options.allow_custom_ops:
+    s += " --allow_custom_ops"
+  if extra_toco_options.rnn_states:
+    s += (" --rnn_states='" + extra_toco_options.rnn_states + "'")
   return s
 
 
-def write_toco_options(filename,
-                       data_types,
-                       input_arrays,
-                       output_arrays,
-                       shapes,
-                       drop_control_dependency=False):
-  """Create TOCO options to process a model.
-
-  Args:
-    filename: Filename to write the options to.
-    data_types: input and inference types used by TOCO.
-    input_arrays: names of the input tensors
-    output_arrays: names of the output tensors
-    shapes: shapes of the input tensors
-    drop_control_dependency: whether to ignore control dependency nodes.
-  """
-  with open(filename, "w") as fp:
-    fp.write(
-        toco_options(
-            data_types=data_types,
-            input_arrays=input_arrays,
-            output_arrays=output_arrays,
-            shapes=shapes,
-            drop_control_dependency=drop_control_dependency))
-
-
 def write_examples(fp, examples):
   """Given a list `examples`, write a text format representation.
 
@@ -285,12 +275,14 @@ def make_control_dep_tests(zip_path):
     return [input_values], sess.run(
         outputs, feed_dict=dict(zip(inputs, [input_values])))
 
+  extra_toco_options = ExtraTocoOptions()
+  extra_toco_options.drop_control_dependency = True
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs,
-                    drop_control_dependency=True)
+                    extra_toco_options)
 
 
 def toco_convert(graph_def_str, input_tensors, output_tensors,
-                 drop_control_dependency=False):
+                 extra_toco_options):
   """Convert a model's graph def into a tflite model.
 
   NOTE: this currently shells out to the toco binary, but we would like
@@ -298,9 +290,9 @@ def toco_convert(graph_def_str, input_tensors, output_tensors,
 
   Args:
     graph_def_str: Graph def proto in serialized string format.
-    input_tensors: List of input tensor tuples `(name, shape, type)`
-    output_tensors: List of output tensors (names)
-    drop_control_dependency: whether to ignore control dependency nodes.
+    input_tensors: List of input tensor tuples `(name, shape, type)`.
+    output_tensors: List of output tensors (names).
+    extra_toco_options: Additional toco options.
 
   Returns:
     output tflite model, log_txt from conversion
@@ -312,7 +304,7 @@ def toco_convert(graph_def_str, input_tensors, output_tensors,
       input_arrays=[x[0] for x in input_tensors],
       shapes=[x[1] for x in input_tensors],
       output_arrays=output_tensors,
-      drop_control_dependency=drop_control_dependency)
+      extra_toco_options=extra_toco_options)
 
   with tempfile.NamedTemporaryFile() as graphdef_file, \
        tempfile.NamedTemporaryFile() as output_file, \
@@ -341,7 +333,8 @@ def make_zip_of_tests(zip_path,
                       test_parameters,
                       make_graph,
                       make_test_inputs,
-                      drop_control_dependency=False):
+                      extra_toco_options=ExtraTocoOptions(),
+                      use_frozen_graph=False):
   """Helper to make a zip file of a bunch of TensorFlow models.
 
   This does a cartestian product of the dictionary of test_parameters and
@@ -359,7 +352,9 @@ def make_zip_of_tests(zip_path,
       `[input1, input2, ...], [output1, output2, ...]`
     make_test_inputs: function taking `curr_params`, `session`, `input_tensors`,
       `output_tensors` and returns tuple `(input_values, output_values)`.
-    drop_control_dependency: whether to ignore control dependency nodes.
+    extra_toco_options: Additional toco options.
+    use_frozen_graph: Whether or not freeze graph before toco converter.
+
   Raises:
     RuntimeError: if there are toco errors that can't be ignored.
   """
@@ -419,21 +414,25 @@ def make_zip_of_tests(zip_path,
           return None, report
         report["toco"] = report_lib.FAILED
         report["tf"] = report_lib.SUCCESS
-
         # Convert graph to toco
+        input_tensors = [(input_tensor.name.split(":")[0],
+                          input_tensor.get_shape(), input_tensor.dtype)
+                         for input_tensor in inputs]
+        output_tensors = [normalize_output_name(out.name) for out in outputs]
+        graph_def = freeze_graph(
+            sess,
+            tf.global_variables() + inputs +
+            outputs) if use_frozen_graph else sess.graph_def
         tflite_model_binary, toco_log = toco_convert(
-            sess.graph_def.SerializeToString(),
-            [(input_tensor.name.split(":")[0], input_tensor.get_shape(),
-              input_tensor.dtype) for input_tensor in inputs],
-            [normalize_output_name(out.name) for out in outputs],
-            drop_control_dependency)
+            graph_def.SerializeToString(), input_tensors, output_tensors,
+            extra_toco_options)
         report["toco"] = (report_lib.SUCCESS if tflite_model_binary is not None
                           else report_lib.FAILED)
         report["toco_log"] = toco_log
 
         if FLAGS.save_graphdefs:
           archive.writestr(label + ".pb",
-                           text_format.MessageToString(sess.graph_def),
+                           text_format.MessageToString(graph_def),
                            zipfile.ZIP_DEFLATED)
 
         if tflite_model_binary:
@@ -1761,6 +1760,84 @@ def make_strided_slice_tests(zip_path):
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)
 
 
+def make_lstm_tests(zip_path):
+  """Make a set of tests to do basic Lstm cell."""
+
+  test_parameters = [
+      {
+          "dtype": [tf.float32],
+          "num_batchs": [1],
+          "time_step_size": [1],
+          "input_vec_size": [3],
+          "num_cells": [4],
+      },
+  ]
+
+  def build_graph(parameters):
+    """Build a simple graph with BasicLSTMCell."""
+
+    num_batchs = parameters["num_batchs"]
+    time_step_size = parameters["time_step_size"]
+    input_vec_size = parameters["input_vec_size"]
+    num_cells = parameters["num_cells"]
+    inputs_after_split = []
+    for i in xrange(time_step_size):
+      one_timestamp_input = tf.placeholder(
+          dtype=parameters["dtype"],
+          name="split_{}".format(i),
+          shape=[num_batchs, input_vec_size])
+      inputs_after_split.append(one_timestamp_input)
+    # Currently lstm identifier has a few limitations: only supports
+    # forget_bias == 0, inner state activiation == tanh.
+    # TODO(zhixianyan): Add another test with forget_bias == 1.
+    # TODO(zhixianyan): Add another test with relu as activation.
+    lstm_cell = tf.contrib.rnn.BasicLSTMCell(
+        num_cells, forget_bias=0.0, state_is_tuple=True)
+    cell_outputs, _ = rnn.static_rnn(
+        lstm_cell, inputs_after_split, dtype=tf.float32)
+    out = cell_outputs[-1]
+    return inputs_after_split, [out]
+
+  def build_inputs(parameters, sess, inputs, outputs):
+    """Feed inputs, assign vairables, and freeze graph."""
+
+    with tf.variable_scope("", reuse=True):
+      kernel = tf.get_variable("rnn/basic_lstm_cell/kernel")
+      bias = tf.get_variable("rnn/basic_lstm_cell/bias")
+      kernel_values = create_tensor_data(
+          parameters["dtype"], [kernel.shape[0], kernel.shape[1]], -1, 1)
+      bias_values = create_tensor_data(parameters["dtype"], [bias.shape[0]], 0,
+                                       1)
+      sess.run(tf.group(kernel.assign(kernel_values), bias.assign(bias_values)))
+
+    num_batchs = parameters["num_batchs"]
+    time_step_size = parameters["time_step_size"]
+    input_vec_size = parameters["input_vec_size"]
+    input_values = []
+    for _ in xrange(time_step_size):
+      tensor_data = create_tensor_data(parameters["dtype"],
+                                       [num_batchs, input_vec_size], 0, 1)
+      input_values.append(tensor_data)
+    out = sess.run(outputs, feed_dict=dict(zip(inputs, input_values)))
+    return input_values, out
+
+  # TODO(zhixianyan): Automatically generate rnn_states for lstm cell.
+  extra_toco_options = ExtraTocoOptions()
+  extra_toco_options.rnn_states = (
+      "{state_array:rnn/BasicLSTMCellZeroState/zeros,"
+      "back_edge_source_array:rnn/basic_lstm_cell/Add_1,size:4},"
+      "{state_array:rnn/BasicLSTMCellZeroState/zeros_1,"
+      "back_edge_source_array:rnn/basic_lstm_cell/Mul_2,size:4}")
+
+  make_zip_of_tests(
+      zip_path,
+      test_parameters,
+      build_graph,
+      build_inputs,
+      extra_toco_options,
+      use_frozen_graph=True)
+
+
 def make_l2_pool(input_tensor, ksize, strides, padding, data_format):
   """Given an input perform a sequence of TensorFlow ops to produce l2pool."""
   return tf.sqrt(tf.nn.avg_pool(
@@ -1850,6 +1927,7 @@ def main(unused_args):
         "strided_slice.zip": make_strided_slice_tests,
         "exp.zip": make_exp_tests,
         "log_softmax.zip": make_log_softmax_tests,
+        "lstm.zip": make_lstm_tests,
     }
     out = FLAGS.zip_to_output
     bin_path = FLAGS.toco
diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 89a5841371..976363fd44 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -266,6 +266,7 @@ INSTANTIATE_TESTS(sub)
 INSTANTIATE_TESTS(split)
 INSTANTIATE_TESTS(div)
 INSTANTIATE_TESTS(transpose)
+INSTANTIATE_TESTS(lstm)
 INSTANTIATE_TESTS(mean)
 INSTANTIATE_TESTS(squeeze)
 INSTANTIATE_TESTS(strided_slice)
diff --git a/tensorflow/contrib/lite/testing/parse_testdata.cc b/tensorflow/contrib/lite/testing/parse_testdata.cc
index c8f2e49f93..389688d552 100644
--- a/tensorflow/contrib/lite/testing/parse_testdata.cc
+++ b/tensorflow/contrib/lite/testing/parse_testdata.cc
@@ -192,27 +192,25 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter,
   int model_outputs = interpreter->outputs().size();
   TF_LITE_ENSURE_EQ(context, model_outputs, example.outputs.size());
   for (size_t i = 0; i < interpreter->outputs().size(); i++) {
+    bool tensors_differ = false;
     int output_index = interpreter->outputs()[i];
     if (const float* data = interpreter->typed_tensor<float>(output_index)) {
       for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) {
         float computed = data[idx];
         float reference = example.outputs[0].flat_data[idx];
         float diff = std::abs(computed - reference);
-        bool error_is_large = false;
         // For very small numbers, try absolute error, otherwise go with
         // relative.
-        if (std::abs(reference) < kRelativeThreshold) {
-          error_is_large = (diff > kAbsoluteThreshold);
-        } else {
-          error_is_large = (diff > kRelativeThreshold * std::abs(reference));
-        }
-        if (error_is_large) {
+        bool local_tensors_differ =
+            std::abs(reference) < kRelativeThreshold
+                ? diff > kAbsoluteThreshold
+                : diff > kRelativeThreshold * std::abs(reference);
+        if (local_tensors_differ) {
           fprintf(stdout, "output[%zu][%zu] did not match %f vs reference %f\n",
                   i, idx, data[idx], reference);
-          return kTfLiteError;
+          tensors_differ = local_tensors_differ;
         }
       }
-      fprintf(stderr, "\n");
     } else if (const int32_t* data =
                    interpreter->typed_tensor<int32_t>(output_index)) {
       for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) {
@@ -221,10 +219,9 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter,
         if (std::abs(computed - reference) > 0) {
           fprintf(stderr, "output[%zu][%zu] did not match %d vs reference %d\n",
                   i, idx, computed, reference);
-          return kTfLiteError;
+          tensors_differ = true;
         }
       }
-      fprintf(stderr, "\n");
     } else if (const int64_t* data =
                    interpreter->typed_tensor<int64_t>(output_index)) {
       for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) {
@@ -235,14 +232,15 @@ TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter,
                   "output[%zu][%zu] did not match %" PRId64
                   " vs reference %" PRId64 "\n",
                   i, idx, computed, reference);
-          return kTfLiteError;
+          tensors_differ = true;
         }
       }
-      fprintf(stderr, "\n");
     } else {
       fprintf(stderr, "output[%zu] was not float or int data\n", i);
       return kTfLiteError;
     }
+    fprintf(stderr, "\n");
+    if (tensors_differ) return kTfLiteError;
   }
   return kTfLiteOk;
 }
-- 
GitLab


From 30727a6b673ff64ea8b5ad8754dee598b829a4aa Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu <yuanzx@google.com>
Date: Thu, 22 Feb 2018 12:27:37 -0800
Subject: [PATCH 204/575] [XLA] HLO BF16 propagation pass.

Using BFloat16Support provided by the backend to determine what precision is needed for
each HloInstruction. If the implementation of some HLOs already reduces input precision to BF16, this pass can enable BF16 on more ops without affecting the result.

PiperOrigin-RevId: 186656378
---
 tensorflow/compiler/xla/service/BUILD         |  32 ++
 .../xla/service/bfloat16_propagation.cc       | 334 +++++++++++++++++
 .../xla/service/bfloat16_propagation.h        | 119 +++++++
 .../xla/service/bfloat16_propagation_test.cc  | 335 ++++++++++++++++++
 .../compiler/xla/service/bfloat16_support.h   |   2 +-
 5 files changed, 821 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/compiler/xla/service/bfloat16_propagation.cc
 create mode 100644 tensorflow/compiler/xla/service/bfloat16_propagation.h
 create mode 100644 tensorflow/compiler/xla/service/bfloat16_propagation_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 4a076ac090..37ca1b893a 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -118,6 +118,38 @@ tf_cc_test(
     ],
 )
 
+cc_library(
+    name = "bfloat16_propagation",
+    srcs = ["bfloat16_propagation.cc"],
+    hdrs = ["bfloat16_propagation.h"],
+    deps = [
+        ":bfloat16_support",
+        ":hlo",
+        ":hlo_dataflow_analysis",
+        ":hlo_pass",
+        "//tensorflow/compiler/xla:shape_tree",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
+tf_cc_test(
+    name = "bfloat16_propagation_test",
+    srcs = ["bfloat16_propagation_test.cc"],
+    deps = [
+        ":bfloat16_propagation",
+        ":bfloat16_support",
+        ":hlo",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla:test_helpers",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/compiler/xla/tests:xla_internal_test_main",  # fixdeps: keep
+    ],
+)
+
 cc_library(
     name = "shape_inference",
     srcs = ["shape_inference.cc"],
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc
new file mode 100644
index 0000000000..9246cb25d2
--- /dev/null
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc
@@ -0,0 +1,334 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/bfloat16_propagation.h"
+
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/shape_tree.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/core/lib/gtl/cleanup.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace xla {
+
+BFloat16Propagation::BFloat16Propagation(
+    const BFloat16Support* bfloat16_support)
+    : bfloat16_support_(bfloat16_support) {}
+
+void BFloat16Propagation::DetermineAndMutateFusionComputationPrecision(
+    HloInstruction* fusion) {
+  CHECK_EQ(fusion->opcode(), HloOpcode::kFusion);
+  if (!bfloat16_support_->SupportsMixedPrecisions(*fusion)) {
+    return;
+  }
+
+  // We are depending on the fusion node itself having already been analyzed
+  // for whether it can output BF16 and this has been adjusted in the output
+  // shape, and now we're looking to update the interior of the fusion node to
+  // match the new output shape, as well as recursively process the whole fusion
+  // node even if the output shape was not modified.
+  auto root = fusion->fused_instructions_computation()->root_instruction();
+
+  // Adjust root's element types according to the fusion's output shape.
+  ShapeUtil::ForEachMutableSubshape(
+      root->mutable_shape(), [&](Shape* subshape, const ShapeIndex& index) {
+        if (subshape->element_type() != F32) {
+          return;
+        }
+        if (ShapeUtil::GetSubshape(fusion->shape(), index).element_type() ==
+            BF16) {
+          subshape->set_element_type(BF16);
+          changed_ = true;
+          VLOG(2) << "Fused root " << root->ToString() << " at shape index "
+                  << index << " changed to BF16 precision for fusion "
+                  << fusion->ToString();
+        }
+      });
+
+  // Propagate BF16 in the fusion computation.
+  auto insts =
+      fusion->fused_instructions_computation()->MakeInstructionPostOrder();
+  for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
+    DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false);
+  }
+}
+
+void BFloat16Propagation::AdjustFusionParameters(HloInstruction* fusion) {
+  CHECK_EQ(fusion->fused_parameters().size(), fusion->operand_count());
+  for (int64 i = 0; i < fusion->operand_count(); ++i) {
+    auto parameter = fusion->fused_parameter(i);
+    ShapeUtil::ForEachMutableSubshape(
+        parameter->mutable_shape(),
+        [&](Shape* subshape, const ShapeIndex& index) {
+          if (!ShapeUtil::IsLeafIndex(parameter->shape(), index)) {
+            return;
+          }
+          PrimitiveType operand_type =
+              ShapeUtil::GetSubshape(fusion->operand(i)->shape(), index)
+                  .element_type();
+          if (subshape->element_type() == operand_type) {
+            return;
+          }
+          CHECK(operand_type == F32 || operand_type == BF16);
+          subshape->set_element_type(operand_type);
+          changed_ = true;
+          VLOG(2) << "Fused parameter " << parameter->ToString()
+                  << " at shape index " << index
+                  << " adjusted to match operand in fusion "
+                  << fusion->ToString();
+        });
+  }
+}
+
+bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo,
+                                              const ShapeIndex& index) const {
+  auto value_set = dataflow_->GetValueSet(&hlo, index);
+  for (const HloValue* value : value_set.values()) {
+    if (ContainsKey(values_that_must_be_kept_as_f32_, value)) {
+      return false;
+    }
+    if (value->shape().element_type() == BF16) {
+      continue;
+    }
+    for (const HloUse& use : value->uses()) {
+      if (use.instruction->opcode() == HloOpcode::kFusion) {
+        auto fused_parameter =
+            use.instruction->fused_parameter(use.operand_number);
+        if (ShapeUtil::GetSubshape(fused_parameter->shape(), use.operand_index)
+                .element_type() != BF16) {
+          return false;
+        }
+        continue;
+      }
+      if (bfloat16_support_->EffectiveOperandPrecisionIsBF16(
+              *use.instruction, use.operand_number)) {
+        continue;
+      }
+      // If the op propagates precision and it outputs a BF16, then it's OK to
+      // supply BF16 also as the input. In the backward mutation pass, the users
+      // shapes should have already been processed.
+      PrimitiveType user_output_type = PRIMITIVE_TYPE_INVALID;
+      if (use.instruction->opcode() == HloOpcode::kTuple ||
+          (use.instruction->opcode() == HloOpcode::kCrossReplicaSum &&
+           ShapeUtil::IsTuple(use.instruction->shape()))) {
+        user_output_type = ShapeUtil::GetSubshape(
+                               ShapeUtil::GetSubshape(use.instruction->shape(),
+                                                      {use.operand_number}),
+                               use.operand_index)
+                               .element_type();
+      } else {
+        user_output_type = use.instruction->shape().element_type();
+      }
+      if (bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(
+              *use.instruction, use.operand_number) &&
+          user_output_type == BF16) {
+        continue;
+      }
+      return false;
+    }
+  }
+  return true;
+}
+
+void BFloat16Propagation::DetermineAndMutateInstructionPrecision(
+    HloInstruction* hlo, bool skip_parameters) {
+  // We handle any fusion computation after the instruction is handled, because
+  // we need to know a fusion's output shape before propagating inside its fused
+  // computation.
+  auto cleaner = tensorflow::gtl::MakeCleanup([this, hlo] {
+    if (hlo->opcode() == HloOpcode::kFusion) {
+      DetermineAndMutateFusionComputationPrecision(hlo);
+    }
+  });
+
+  // Do not change precision for instructions related to entry and exit of a
+  // computation, and control flow, because this pass might break the interfaces
+  // or assumptions for them.
+  if (hlo->opcode() == HloOpcode::kInfeed ||       //
+      hlo->opcode() == HloOpcode::kOutfeed ||      //
+      hlo->opcode() == HloOpcode::kConstant ||     //
+      hlo->opcode() == HloOpcode::kCustomCall ||   //
+      hlo->opcode() == HloOpcode::kCall ||         //
+      hlo->opcode() == HloOpcode::kWhile ||        //
+      hlo->opcode() == HloOpcode::kConditional ||  //
+      (hlo->opcode() == HloOpcode::kParameter && skip_parameters)) {
+    return;
+  }
+
+  // Prevent root instructions from having their output modified by recording
+  // all F32 output values as needing to stay as F32.
+  CHECK(hlo->parent() != nullptr);
+  if (hlo == hlo->parent()->root_instruction()) {
+    if (!hlo->parent()->IsFusionComputation()) {
+      ShapeUtil::ForEachSubshape(hlo->shape(), [&](const Shape& subshape,
+                                                   const ShapeIndex& index) {
+        if (subshape.element_type() != F32) {
+          return;
+        }
+        for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) {
+          // Since we use HloValues from the dataflow analysis, this can also
+          // affect HLO instructions beyond the root, e.g., if the root is a
+          // Tuple HLO, then its operands are also affected.
+          values_that_must_be_kept_as_f32_.insert(value);
+        }
+      });
+    }
+    return;
+  }
+
+  if (!ContainsKey(consider_using_bfloat16_, hlo)) {
+    return;
+  }
+
+  if (!bfloat16_support_->SupportsBF16Output(*hlo)) {
+    return;
+  }
+
+  ShapeUtil::ForEachMutableSubshape(
+      hlo->mutable_shape(),
+      [hlo, this](Shape* subshape, const ShapeIndex& index) {
+        if (subshape->element_type() == F32 &&
+            AllUsersConsumeBF16(*hlo, index)) {
+          subshape->set_element_type(BF16);
+          changed_ = true;
+          VLOG(2) << "HloInstruction output at shape index " << index
+                  << " changed to BF16 precision: " << hlo->ToString();
+        }
+      });
+}
+
+bool BFloat16Propagation::InstructionIsCandidateForBF16Output(
+    HloInstruction* hlo) {
+  if (!bfloat16_support_->SupportsMixedPrecisions(*hlo) &&
+      hlo->opcode() != HloOpcode::kTuple &&
+      hlo->opcode() != HloOpcode::kGetTupleElement &&
+      hlo->shape().element_type() != BF16) {
+    for (int64 i = 0; i < hlo->operand_count(); ++i) {
+      if (!bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo,
+                                                                         i) ||
+          !ContainsKey(consider_using_bfloat16_, hlo->operand(i))) {
+        return false;
+      }
+    }
+  }
+  return true;
+}
+
+// The algorithm first does a forward pass (parameters to root) to determine a
+// set of instructions to consider using bfloat16, then does a backward pass to
+// determine the precisions of those instructions according to the need of
+// their users.
+StatusOr<bool> BFloat16Propagation::Run(HloModule* module) {
+  TF_ASSIGN_OR_RETURN(dataflow_, HloDataflowAnalysis::Run(*module));
+
+  std::list<HloComputation*> computations_topological_order =
+      module->MakeComputationPostOrder();
+  // The first step is a forward pass (parameters to root), where we determine
+  // the potential candidate instructions to use bfloat16 in the outputs that
+  // are not likely to cause overhead from extra explicit conversions. This is
+  // done forwardly because we determine whether an HLO is a candidate partially
+  // based on whether its operands are candidates.
+  for (auto computation : computations_topological_order) {
+    for (auto inst : computation->MakeInstructionPostOrder()) {
+      if (InstructionIsCandidateForBF16Output(inst)) {
+        consider_using_bfloat16_.insert(inst);
+      }
+    }
+  }
+
+  // The second step is a backward pass (root to parameters), where we modify
+  // the precisions of the instructions identified in the first step when
+  // feasible. This is done backwardly because we determine the precision of an
+  // HLO's output based on how it is later used.
+  //
+  // The precision of an instruction is determined by its users, so we do the
+  // propagation in reverse topological order.
+  for (auto comp_it = computations_topological_order.rbegin();
+       comp_it != computations_topological_order.rend(); ++comp_it) {
+    if ((*comp_it)->IsFusionComputation()) {
+      // Fusion computations are handled when visiting the fusion instruction.
+      continue;
+    }
+    auto insts = (*comp_it)->MakeInstructionPostOrder();
+    for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
+      DetermineAndMutateInstructionPrecision(*inst_it,
+                                             /*skip_parameters=*/true);
+    }
+  }
+
+  if (!changed_) {
+    return false;
+  }
+
+  // It's possible that an instruction does not define a buffer, but the
+  // defining instruction's shape has changed. So we need to adjust the output
+  // shapes of instructions according to the HLO values they refer to.
+  for (auto comp_it = computations_topological_order.rbegin();
+       comp_it != computations_topological_order.rend(); ++comp_it) {
+    auto insts = (*comp_it)->MakeInstructionPostOrder();
+    // Do the adjustment on each instruction in the computation in reverse
+    // topological order.
+    for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
+      auto hlo = *inst_it;
+      auto adjust_buffer = [this, hlo](Shape* subshape,
+                                       const ShapeIndex& index) {
+        if (subshape->element_type() != F32 &&
+            subshape->element_type() != BF16) {
+          return;
+        }
+        PrimitiveType type = BF16;
+        for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) {
+          if (value->shape().element_type() == BF16) {
+            continue;
+          }
+          CHECK_EQ(value->shape().element_type(), F32);
+          type = F32;
+          break;
+        }
+        // It's possible that a user has been changed from BF16 to F32
+        // during this final adjustment pass, so we need to check
+        // AllUsersConsumeBF16() again.
+        if (type == BF16 && !AllUsersConsumeBF16(*hlo, index)) {
+          type = F32;
+        }
+        if (type == F32) {
+          for (const auto* value :
+               dataflow_->GetValueSet(hlo, index).values()) {
+            // We rely on the fact that this adjustment works in reverse
+            // topological order. Adding the value to
+            // values_that_must_be_kept_as_f32_ will ensure the correctness
+            // of the adjustment for HLOs that will be processed later.
+            values_that_must_be_kept_as_f32_.insert(value);
+          }
+        }
+        subshape->set_element_type(type);
+      };
+      ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), adjust_buffer);
+    }
+    // Now adjust parameters of fusions inside this computation.
+    for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
+      auto hlo = *inst_it;
+      if (hlo->opcode() == HloOpcode::kFusion) {
+        AdjustFusionParameters(hlo);
+      }
+    }
+  }
+  return true;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.h b/tensorflow/compiler/xla/service/bfloat16_propagation.h
new file mode 100644
index 0000000000..aa81dde3b0
--- /dev/null
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation.h
@@ -0,0 +1,119 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_PROPAGATION_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_PROPAGATION_H_
+
+#include <memory>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/bfloat16_support.h"
+#include "tensorflow/compiler/xla/service/hlo_dataflow_analysis.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+
+namespace xla {
+
+// HLO pass which reduces the precision of some HLO instructions to BF16
+// according to the backend-specific BFloat16Support rule provided by the
+// caller.
+//
+// This pass can be used to reduce instruction precision without affecting the
+// numerical accuracy of the module, i.e., the final output of the module would
+// be bitwise identical to that without this pass; this is possible if the
+// backend already reduces precision to BF16 on some HLO instructions.
+//
+// This pass will not modify the signature of any non-fusion computation.
+//
+// !!! WARNING !!! This pass can introduce mixed precision in individual HLOs,
+// which has two issues:
+//
+// 1) It does not guarantee to respect the passed-in BFloat16Support
+// specification in terms of mixed precision, so the backend may not support an
+// HLO that has mixed precision produced by this pass. To address this issue,
+// run BFloat16Normalization with the same BFloat16Support after this pass.
+//
+// 2) In general, mixed precision may break the assumptions of some other HLO
+// passes even if the specific backend supports the individual HLOs. Such
+// assumptions include that there are no HLOs using mixed precision, or that the
+// precision of an HLO's output is determined by its inputs. It should be used
+// at the end of the HLO optimization pipeline but before
+// BFloat16ConversionFolding. If other passes are needed after this pass, run
+// BFloat16MixedPrecisionRemoval first to undo some of the changes made by this
+// pass.
+class BFloat16Propagation : public HloPassInterface {
+ public:
+  explicit BFloat16Propagation(const BFloat16Support* bfloat16_support);
+
+  ~BFloat16Propagation() override = default;
+
+  tensorflow::StringPiece name() const override {
+    return "bfloat16-propagation";
+  }
+
+  // Runs the pass on the given module. Returns whether the module was changed
+  // (precision reductions were added).
+  StatusOr<bool> Run(HloModule* module) override;
+
+ private:
+  // ***************************
+  // Function called and state produced by the forward analysis pass (from
+  // parameters to root) that determines the candidate HLOs to use BF16 outputs.
+
+  // Determines whether we should consider changing the precision of the given
+  // instruction in the forward pass.
+  bool InstructionIsCandidateForBF16Output(HloInstruction* hlo);
+
+  // The set of instructions to consider using bfloat16, computed in the forward
+  // pass.
+  tensorflow::gtl::FlatSet<const HloInstruction*> consider_using_bfloat16_;
+
+  // ***************************
+  // Functions called and state produced by the backward mutation pass (from
+  // root to parameters).
+
+  // Determines the precision for the given instruction in the mutation pass.
+  void DetermineAndMutateInstructionPrecision(HloInstruction* hlo,
+                                              bool skip_parameters);
+
+  // Special handling in the mutation pass for fusion computations.
+  void DetermineAndMutateFusionComputationPrecision(HloInstruction* fusion);
+
+  // Makes the fusion parameters match the precision of the actual parameters
+  // passed to the fusion node.
+  void AdjustFusionParameters(HloInstruction* fusion);
+
+  // Returns whether all uses of the given HloInstruction can consume BF16
+  // input.
+  bool AllUsersConsumeBF16(const HloInstruction& hlo,
+                           const ShapeIndex& index) const;
+
+  // The set of F32 HLO values that must be kept in F32.
+  tensorflow::gtl::FlatSet<const HloValue*> values_that_must_be_kept_as_f32_;
+
+  // ***************************
+  // State used by both passes.
+  const BFloat16Support* bfloat16_support_;
+  std::unique_ptr<HloDataflowAnalysis> dataflow_;
+
+  bool changed_ = false;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_BFLOAT16_PROPAGATION_H_
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
new file mode 100644
index 0000000000..4c86c6b26e
--- /dev/null
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
@@ -0,0 +1,335 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/bfloat16_propagation.h"
+#include "tensorflow/compiler/xla/service/bfloat16_support.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/test_helpers.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+
+namespace xla {
+
+// A class specifying the BF16 support used to test the propagation pass. It
+// specifies that BF16 and mixed precision are supported in all HloInstructions,
+// and that kDot reduces its operands precision to BF16.
+class TestBFloat16Support : public BFloat16Support {
+ public:
+  TestBFloat16Support() {}
+  ~TestBFloat16Support() override {}
+
+  bool SupportsBF16Operand(const HloInstruction& hlo,
+                           int64 operand_index) const override {
+    return true;
+  }
+
+  bool SupportsBF16Output(const HloInstruction& hlo) const override {
+    return true;
+  }
+
+  bool SupportsMixedPrecisions(const HloInstruction& hlo) const override {
+    return true;
+  }
+
+  bool EffectiveOperandPrecisionIsBF16(const HloInstruction& hlo,
+                                       int64 operand_index) const override {
+    return hlo.opcode() == HloOpcode::kDot;
+  }
+};
+
+class BFloat16PropagationTest : public HloTestBase {
+ protected:
+  // Runs the propagation pass on the given module, and returns whether the
+  // module is changed after this pass.
+  bool PropagatePrecision(HloModule* module) {
+    TestBFloat16Support bfloat16_support;
+    BFloat16Propagation propagation(&bfloat16_support);
+    StatusOr<bool> result = propagation.Run(module);
+    EXPECT_IS_OK(result.status());
+    return result.ValueOrDie();
+  }
+
+  // Returns whether the given HloInstruction's output element type is BF16 or
+  // the only use of it is converting to BF16.
+  bool OutputsBF16(HloInstruction* inst) {
+    if (inst->shape().element_type() == BF16) {
+      return true;
+    }
+    return inst->user_count() == 1 &&
+           inst->users()[0]->opcode() == HloOpcode::kConvert &&
+           inst->users()[0]->shape().element_type() == BF16;
+  }
+};
+
+// Tests that BF16 can propagate through select over non-tuple buffers, but not
+// through add where reducing operand precision can affect the result.
+TEST_F(BFloat16PropagationTest, PropagateThroughSelectButNotAdd) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {2, 4});
+
+  HloInstruction* a =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b"));
+  HloInstruction* c =
+      builder.AddInstruction(HloInstruction::CreateParameter(2, shape, "c"));
+  HloInstruction* add0 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, b));
+  HloInstruction* add1 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, add0, b));
+  HloInstruction* pred = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kEq, a, b));
+  HloInstruction* sel = builder.AddInstruction(
+      HloInstruction::CreateTernary(shape, HloOpcode::kSelect, pred, c, add1));
+  HloInstruction* xpose =
+      builder.AddInstruction(HloInstruction::CreateTranspose(
+          ShapeUtil::MakeShape(F32, {4, 2}), sel, {1, 0}));
+  HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, xpose, a));
+  HloInstruction* root = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, dot, dot));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), root);
+  EXPECT_TRUE(OutputsBF16(xpose));
+  EXPECT_TRUE(OutputsBF16(sel));
+  EXPECT_TRUE(OutputsBF16(add1));
+  EXPECT_FALSE(OutputsBF16(add0));
+  EXPECT_FALSE(OutputsBF16(a));
+  EXPECT_FALSE(OutputsBF16(b));
+  EXPECT_FALSE(OutputsBF16(c));
+}
+
+// Tests that BF16 can be propagated through nested tuples.
+TEST_F(BFloat16PropagationTest, PropagateThroughTuples) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {2, 4});
+
+  HloInstruction* a =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b"));
+  HloInstruction* add0 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, b));
+  HloInstruction* add1 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, a));
+  HloInstruction* add2 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, b, b));
+  HloInstruction* xpose =
+      builder.AddInstruction(HloInstruction::CreateTranspose(
+          ShapeUtil::MakeShape(F32, {4, 2}), add1, {1, 0}));
+
+  HloInstruction* tuple0 =
+      builder.AddInstruction(HloInstruction::CreateTuple({add0, add1, add2}));
+  HloInstruction* tuple1 =
+      builder.AddInstruction(HloInstruction::CreateTuple({tuple0, xpose}));
+
+  HloInstruction* lhs = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(xpose->shape(), tuple1, 1));
+  HloInstruction* rhs =
+      builder.AddInstruction(HloInstruction::CreateGetTupleElement(
+          add0->shape(),
+          builder.AddInstruction(HloInstruction::CreateGetTupleElement(
+              tuple0->shape(), tuple1, 0)),
+          0));
+  HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, lhs, rhs));
+
+  HloInstruction* output_tuple =
+      builder.AddInstruction(HloInstruction::CreateTuple({dot, add2}));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), output_tuple);
+  EXPECT_TRUE(OutputsBF16(xpose));
+  EXPECT_TRUE(OutputsBF16(add0));
+  EXPECT_TRUE(OutputsBF16(add1));
+  EXPECT_FALSE(OutputsBF16(add2));
+}
+
+// Tests that even if an instruction does not define a buffer in its output, its
+// shape must match the defining instruction.
+TEST_F(BFloat16PropagationTest, SameValueReferencedTwice) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {2, 4});
+
+  HloInstruction* a =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b"));
+  HloInstruction* add0 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, b));
+  HloInstruction* add1 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, a));
+
+  HloInstruction* lhs = builder.AddInstruction(HloInstruction::CreateTranspose(
+      ShapeUtil::MakeShape(F32, {4, 2}), add1, {1, 0}));
+
+  HloInstruction* tuple =
+      builder.AddInstruction(HloInstruction::CreateTuple({add0, add1}));
+  HloInstruction* rhs = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(add1->shape(), tuple, 1));
+
+  // lhs is the transpose of add1, and rhs is a get-tuple-element aliasing add1.
+  HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, lhs, rhs));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), dot);
+  EXPECT_TRUE(OutputsBF16(add0));
+  EXPECT_TRUE(OutputsBF16(add1));
+  EXPECT_TRUE(OutputsBF16(lhs));
+  // rhs is a get-tuple-element, which does not define a buffer, but its shape
+  // should also be adjusted accordingly.
+  EXPECT_TRUE(OutputsBF16(rhs));
+}
+
+// Tests that a non-fusion computation's root should not be changed.
+TEST_F(BFloat16PropagationTest, DoNotChangeComputationRoot) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {2, 4});
+
+  HloInstruction* a =
+      builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b =
+      builder.AddInstruction(HloInstruction::CreateParameter(1, shape, "b"));
+  HloInstruction* add = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a, b));
+
+  HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, add, add));
+
+  HloInstruction* tuple =
+      builder.AddInstruction(HloInstruction::CreateTuple({add, dot}));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_FALSE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), tuple);
+  EXPECT_FALSE(OutputsBF16(add));
+}
+
+// Tests that BF16 is propagated properly through fused computations.
+TEST_F(BFloat16PropagationTest, PropagateThroughFusion) {
+  auto module = CreateNewModule();
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {2, 4});
+
+  HloInstruction* param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, shape, "param"));
+  HloInstruction* add = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param));
+
+  auto builder_f0 = HloComputation::Builder("fusion0");
+  HloInstruction* a_f0 =
+      builder_f0.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b_f0 =
+      builder_f0.AddInstruction(HloInstruction::CreateParameter(1, shape, "b"));
+  HloInstruction* tuple_f0 =
+      builder_f0.AddInstruction(HloInstruction::CreateTuple({a_f0, b_f0}));
+  auto comp_f0 = module->AddEmbeddedComputation(builder_f0.Build());
+  auto fusion0 = builder.AddInstruction(HloInstruction::CreateFusion(
+      tuple_f0->shape(), HloInstruction::FusionKind::kCustom, {add, add},
+      comp_f0));
+
+  auto builder_f1 = HloComputation::Builder("fusion1");
+  HloInstruction* p_f1 = builder_f1.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple_f0->shape(), "param"));
+  HloInstruction* a_f1 = builder_f1.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, p_f1, 0));
+  HloInstruction* b_f1 = builder_f1.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, p_f1, 1));
+  HloInstruction* dot = builder_f1.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, a_f1, b_f1));
+  auto comp_f1 = module->AddEmbeddedComputation(builder_f1.Build());
+  auto fusion1 = builder.AddInstruction(HloInstruction::CreateFusion(
+      dot->shape(), HloInstruction::FusionKind::kCustom, {fusion0}, comp_f1));
+
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), fusion1);
+  EXPECT_TRUE(OutputsBF16(add));
+  EXPECT_TRUE(OutputsBF16(a_f0));
+  EXPECT_TRUE(OutputsBF16(b_f0));
+  EXPECT_TRUE(OutputsBF16(a_f1));
+  EXPECT_TRUE(OutputsBF16(b_f1));
+}
+
+// A select over tuples does not define the leaf buffers, so the types in
+// on_true and on_false must match, so that as long as one of them is F32, the
+// other must be F32 as well.
+TEST_F(BFloat16PropagationTest, SelectOverTuples) {
+  auto module = CreateNewModule();
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {2, 4});
+
+  HloInstruction* param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, shape, "param"));
+  HloInstruction* pred = builder.AddInstruction(HloInstruction::CreateParameter(
+      1, ShapeUtil::MakeShape(PRED, {}), "pred"));
+
+  HloInstruction* add0 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param));
+  HloInstruction* add1 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, add0, param));
+  HloInstruction* tuple0 =
+      builder.AddInstruction(HloInstruction::CreateTuple({param, add0}));
+  HloInstruction* tuple1 =
+      builder.AddInstruction(HloInstruction::CreateTuple({param, add1}));
+  HloInstruction* sel = builder.AddInstruction(HloInstruction::CreateTernary(
+      tuple0->shape(), HloOpcode::kSelect, pred, tuple0, tuple1));
+  HloInstruction* gte0 = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, sel, 0));
+  HloInstruction* gte1 = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, sel, 1));
+  HloInstruction* xpose =
+      builder.AddInstruction(HloInstruction::CreateTranspose(
+          ShapeUtil::MakeShape(F32, {4, 2}), gte0, {1, 0}));
+  HloInstruction* dot = builder.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(F32, {4, 4}), HloOpcode::kDot, xpose, gte1));
+
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), dot);
+  EXPECT_FALSE(OutputsBF16(add0));
+  EXPECT_FALSE(OutputsBF16(add1));
+  EXPECT_FALSE(OutputsBF16(gte0));
+  EXPECT_FALSE(OutputsBF16(gte1));
+  EXPECT_TRUE(OutputsBF16(xpose));
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/bfloat16_support.h b/tensorflow/compiler/xla/service/bfloat16_support.h
index 29f662d22b..82c2745f44 100644
--- a/tensorflow/compiler/xla/service/bfloat16_support.h
+++ b/tensorflow/compiler/xla/service/bfloat16_support.h
@@ -39,7 +39,7 @@ class BFloat16Support {
   // precisions (BF16 and F32).
   virtual bool SupportsMixedPrecisions(const HloInstruction& hlo) const;
 
-  // Returns whether the given HLO inherits its BF16 operand precision at the
+  // Returns whether the given HLO preserves its BF16 operand precision at the
   // given index, so even if the output is F32, elements in the output that
   // depend on the BF16 operand will still have BF16 effective precision even if
   // they have F32 format. Similarly, this also means if the output is BF16 then
-- 
GitLab


From 2f4dc33c7ebbf10290aaaea512895f021fc61e71 Mon Sep 17 00:00:00 2001
From: Jeremy Lau <lauj@google.com>
Date: Thu, 22 Feb 2018 12:45:38 -0800
Subject: [PATCH 205/575] Internal change.

PiperOrigin-RevId: 186658974
---
 tensorflow/contrib/estimator/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index 6cdbed5b89..ddccfce3c0 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -138,6 +138,7 @@ py_test(
     size = "medium",
     srcs = ["python/estimator/extenders_test.py"],
     srcs_version = "PY2AND3",
+    tags = ["notsan"],  # b/62863147
     deps = [
         ":extenders",
         "//tensorflow/contrib/data/python/ops:dataset_ops",
-- 
GitLab


From 2dfd7da39aef63c6139b4a033099b8699359fa29 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Thu, 22 Feb 2018 12:53:25 -0800
Subject: [PATCH 206/575] Remove a bit of misleading documentation (we no
 longer do Graph containers)

PiperOrigin-RevId: 186660057
---
 tensorflow/python/ops/resource_variable_ops.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index 09d349fc2d..2d6d0672e0 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -117,8 +117,7 @@ class EagerResourceDeleter(object):
 
   def __del__(self):
     # Resources follow object-identity when executing eagerly, so it is safe to
-    # delete the resource we have a handle to. Each Graph has a unique container
-    # name, which prevents resource sharing.
+    # delete the resource we have a handle to.
     try:
       # This resource was created in eager mode. However, this destructor may be
       # running in graph mode (especially during unit tests). To clean up
-- 
GitLab


From be04bbc441b6a9c03d162ce5cdc0cf4ceed4a5a5 Mon Sep 17 00:00:00 2001
From: Max Galkin <maxgalkin@google.com>
Date: Thu, 22 Feb 2018 13:04:13 -0800
Subject: [PATCH 207/575] Relax one of the error conditions to allow modeling
 graphs without explicit set of feed nodes.

PiperOrigin-RevId: 186661729
---
 .../core/grappler/costs/virtual_scheduler.cc       | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc
index b9a80fbff2..3ac3ae0f8f 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc
@@ -325,7 +325,7 @@ Status VirtualScheduler::Init() {
 
   // Get the nodes that would run to output fetch_nodes.
   bool ill_formed = false;
-  std::vector<const NodeDef*> nodes =
+  const std::vector<const NodeDef*> fetch_fanin_nodes =
       ComputeTransitiveFanin(graph, fetch_nodes, &ill_formed);
   if (ill_formed) {
     return errors::InvalidArgument(
@@ -339,7 +339,7 @@ Status VirtualScheduler::Init() {
   // exactly the same as those executed for real. One possible discrepancy could
   // be the control flow nodes, where tf only executes one path.
   std::unordered_map<string, const NodeDef*> name_to_node;
-  for (const auto& node : nodes) {
+  for (const auto& node : fetch_fanin_nodes) {
     name_to_node[node->name()] = node;
   }
 
@@ -360,7 +360,7 @@ Status VirtualScheduler::Init() {
 
   // Build node_map; for each node, create its NodeState and connect its inputs
   // and outputs.
-  for (const auto* curr_node : nodes) {
+  for (const auto* curr_node : fetch_fanin_nodes) {
     auto& curr_node_state = GetNodeStateOrCreateIt(curr_node);
     const string curr_node_device = DeviceName(curr_node);
     std::vector<string> inputs;
@@ -461,9 +461,11 @@ Status VirtualScheduler::Init() {
   }
 
   if (!feed_nodes.empty()) {
-    return errors::InvalidArgument(
-        strings::StrCat("Some feed nodes were not found in the graph: ",
-                        str_util::Join(feed_nodes, ",")));
+    // This isn't always a bug: when the caller hasn't specified the exact list
+    // of feed and fetch nodes, by default we consider all placeholders as feed
+    // nodes, but some of them may not be needed for the default fetch node.
+    VLOG(1) << "Some feed nodes were not consumed by the fetch fanin: "
+            << str_util::Join(feed_nodes, ",");
   }
   initialized_ = true;
   return Status::OK();
-- 
GitLab


From 4d6f80b1eb374192d4c83d44ce49f54f50435790 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 13:09:53 -0800
Subject: [PATCH 208/575] Add min_check_interval_secs with 5s as default to
 ValidationMonitor to avoid checking for existance of a new checkpoint overly
 frequent that can lead to severe performance issues on remote filesystems.

PiperOrigin-RevId: 186662441
---
 .../contrib/learn/python/learn/experiment.py  | 32 +++++++++++--------
 .../learn/python/learn/experiment_test.py     | 26 ---------------
 .../contrib/learn/python/learn/monitors.py    | 18 ++++++++++-
 .../learn/python/learn/monitors_test.py       |  6 +++-
 4 files changed, 40 insertions(+), 42 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index bec976afd2..331bc11549 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -152,7 +152,8 @@ class Experiment(object):
                export_strategies=None,
                train_steps_per_iteration=None,
                checkpoint_and_export=False,
-               saving_listeners=None):
+               saving_listeners=None,
+               check_interval_secs=5):
     """Constructor for `Experiment`.
 
     Creates an Experiment instance. None of the functions passed to this
@@ -190,8 +191,9 @@ class Experiment(object):
         number of steps between evaluations. Of course, evaluation does not
         occur if no new snapshot is available, hence, this is the minimum.
         If 0, the evaluation will only happen after training.
-        If None, defaults to 1, unless model_dir is on GCS, in which case the
-        default is 1000.
+        If None, defaults to 1. To avoid checking for new checkpoints too
+        frequent, the interval is further limited to be at least
+        check_interval_secs between checks.
       delay_workers_by_global_step: if `True` delays training workers
         based on global step instead of time.
       export_strategies: Iterable of `ExportStrategy`s, or a single one, or
@@ -215,7 +217,10 @@ class Experiment(object):
       saving_listeners: list of `CheckpointSaverListener` objects. Used by
         tf.estimator.Estimator for callbacks that run immediately before or
         after checkpoint savings.
-
+      check_interval_secs:
+        Minimum time between subsequent checks for a new checkpoint. This
+        mostly applies if both min_eval_frequency and the time spent per
+        training step is low.
     Raises:
       ValueError: if `estimator` does not implement Estimator interface,
         or if export_strategies has the wrong type.
@@ -261,13 +266,9 @@ class Experiment(object):
     self._continuous_eval_throttle_secs = continuous_eval_throttle_secs
     self._checkpoint_and_export = checkpoint_and_export
     self._saving_listeners = saving_listeners
-    # Using 1 on a non-cached file system requires a lot of overhead to
-    # read the checkpoint state file. This is particular bad on GCS, so
-    # we use a different default. This is a temporary band-aid, to be
-    # fixed holistically later (b/36498507).
-    default_min_eval_frequency = 1000 if _is_gcs(estimator.model_dir) else 1
     self._min_eval_frequency = min_eval_frequency if (
-        min_eval_frequency is not None) else default_min_eval_frequency
+        min_eval_frequency is not None) else 1
+    self._check_interval_secs = check_interval_secs
     self._delay_workers_by_global_step = delay_workers_by_global_step
     self._train_monitors = train_monitors[:] if train_monitors else []
     self._eval_hooks = eval_hooks[:] if eval_hooks else []
@@ -646,12 +647,19 @@ class Experiment(object):
         self._train_monitors += [saver_hook]
       else:
         if self._min_eval_frequency:
+          # Using low min_eval_frequency (default is 1) on a non-cached file
+          # system requires a lot of overhead to read the checkpoint state file.
+          # This is particular bad on GCS and CNS. See also b/36498507 for
+          # context. `check_interval_secs = 5` avoids polling a remote
+          # fileystem too often.
+
           self._train_monitors += [
               monitors.ValidationMonitor(
                   input_fn=self._eval_input_fn,
                   eval_steps=self._eval_steps,
                   metrics=self._eval_metrics,
                   every_n_steps=self._min_eval_frequency,
+                  check_interval_secs=self._check_interval_secs,
                   name=eval_dir_suffix,
                   hooks=self._eval_hooks)
           ]
@@ -928,7 +936,3 @@ def _new_attr_context(obj, attr):
     yield
   finally:
     setattr(obj, attr, saved)
-
-
-def _is_gcs(model_dir):
-  return model_dir and model_dir.startswith("gs://")
diff --git a/tensorflow/contrib/learn/python/learn/experiment_test.py b/tensorflow/contrib/learn/python/learn/experiment_test.py
index 545d7d8924..d10927a0cd 100644
--- a/tensorflow/contrib/learn/python/learn/experiment_test.py
+++ b/tensorflow/contrib/learn/python/learn/experiment_test.py
@@ -674,37 +674,11 @@ class ExperimentTest(test.TestCase):
   def test_min_eval_frequency_defaults(self):
     def dummy_model_fn(features, labels):  # pylint: disable=unused-argument
       pass
-
-    # The default value when model_dir is on GCS is 1000
-    estimator = core_estimator.Estimator(dummy_model_fn, 'gs://dummy_bucket')
-    ex = experiment.Experiment(
-        estimator, train_input_fn=None, eval_input_fn=None)
-    self.assertEquals(ex._min_eval_frequency, 1000)
-
-    # The default value when model_dir is not on GCS is 1
     estimator = core_estimator.Estimator(dummy_model_fn, '/tmp/dummy')
     ex = experiment.Experiment(
         estimator, train_input_fn=None, eval_input_fn=None)
     self.assertEquals(ex._min_eval_frequency, 1)
 
-    # Make sure default not used when explicitly set
-    estimator = core_estimator.Estimator(dummy_model_fn, 'gs://dummy_bucket')
-    ex = experiment.Experiment(
-        estimator,
-        min_eval_frequency=123,
-        train_input_fn=None,
-        eval_input_fn=None)
-    self.assertEquals(ex._min_eval_frequency, 123)
-
-    # Make sure default not used when explicitly set as 0
-    estimator = core_estimator.Estimator(dummy_model_fn, 'gs://dummy_bucket')
-    ex = experiment.Experiment(
-        estimator,
-        min_eval_frequency=0,
-        train_input_fn=None,
-        eval_input_fn=None)
-    self.assertEquals(ex._min_eval_frequency, 0)
-
   def test_continuous_train_and_eval(self):
     for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
       if isinstance(est, core_estimator.Estimator):
diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py
index 51381a7427..9457a73ecf 100644
--- a/tensorflow/contrib/learn/python/learn/monitors.py
+++ b/tensorflow/contrib/learn/python/learn/monitors.py
@@ -573,7 +573,8 @@ class ValidationMonitor(EveryN):
                early_stopping_rounds=None,
                early_stopping_metric="loss",
                early_stopping_metric_minimize=True,
-               name=None):
+               name=None,
+               check_interval_secs=5):
     """Initializes a ValidationMonitor.
 
     Args:
@@ -600,6 +601,9 @@ class ValidationMonitor(EveryN):
           loss metrics like mean squared error, and False for performance
           metrics like accuracy.
       name: See `BaseEstimator.evaluate`.
+      check_interval_secs: Only check for new checkpoint if at least
+          `check_interval_secs` have passed. Ignore if None. Default is 5 secs.
+
 
     Raises:
       ValueError: If both x and input_fn are provided.
@@ -626,6 +630,8 @@ class ValidationMonitor(EveryN):
     self._early_stopped = False
     self._latest_path = None
     self._latest_path_step = None
+    self._last_checkpoint_check_time = None
+    self._check_interval_secs = check_interval_secs
 
   @property
   def early_stopped(self):
@@ -690,6 +696,16 @@ class ValidationMonitor(EveryN):
     # that's what is being evaluated.
     if self._estimator is None:
       raise ValueError("Missing call to set_estimator.")
+    current_time = time.time()
+    if (self._check_interval_secs is not None and
+        self._last_checkpoint_check_time is not None and
+        current_time - self._last_checkpoint_check_time <=
+        self._check_interval_secs):
+      logging.debug(
+          "Skipping evaluation since less than %d seconds have passed since "
+          "last check for a new checkpoint.", self._check_interval_secs)
+      return False
+    self._last_checkpoint_check_time = current_time
     # Check that we are not running evaluation on the same checkpoint.
     latest_path = saver_lib.latest_checkpoint(self._estimator.model_dir)
     if latest_path is None:
diff --git a/tensorflow/contrib/learn/python/learn/monitors_test.py b/tensorflow/contrib/learn/python/learn/monitors_test.py
index b2b24776c6..5c34d0ddb0 100644
--- a/tensorflow/contrib/learn/python/learn/monitors_test.py
+++ b/tensorflow/contrib/learn/python/learn/monitors_test.py
@@ -385,7 +385,11 @@ class MonitorsTest(test.TestCase):
     estimator.evaluate.return_value = validation_outputs
 
     monitor = learn.monitors.ValidationMonitor(
-        x=constant_op.constant(2.0), every_n_steps=0, early_stopping_rounds=2)
+        x=constant_op.constant(2.0),
+        every_n_steps=0,
+        early_stopping_rounds=2,
+        check_interval_secs=None)
+
     self._assert_validation_monitor(monitor)
     monitor.set_estimator(estimator)
     with ops.Graph().as_default() as g, self.test_session(g):
-- 
GitLab


From 83a35a8c3b05cc5eb2b1bc1b7ed3499834e1c7e5 Mon Sep 17 00:00:00 2001
From: Bixia Zheng <bixia@google.com>
Date: Thu, 22 Feb 2018 13:23:47 -0800
Subject: [PATCH 209/575] [XLA] Enable F16 convolution test for CPU.

Remove TODO(b/72509305) as the issue has been fixed.

PiperOrigin-RevId: 186664459
---
 tensorflow/compiler/xla/tests/convolution_test.cc | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc
index 1385b437fc..1ea7d84141 100644
--- a/tensorflow/compiler/xla/tests/convolution_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_test.cc
@@ -53,8 +53,7 @@ class ConvolutionTest : public ClientLibraryTestBase {
 #endif
 };
 
-// TODO(b/72509305): Enable half data type tests for CPU
-#if (XLA_TEST_BACKEND_GPU)
+#if (XLA_TEST_BACKEND_GPU || XLA_TEST_BACKEND_CPU)
 using TestTypes = ::testing::Types<float, Eigen::half>;
 #else
 using TestTypes = ::testing::Types<float>;
@@ -700,8 +699,7 @@ INSTANTIATE_TEST_CASE_P(
 class Convolve1D1WindowTestHalf : public Convolve1D1WindowTestBase {};
 
 // TODO(b/72509305): Enable half data type tests for CPU.
-XLA_TEST_P(Convolve1D1WindowTestHalf,
-           DISABLED_ON_CPU_PARALLEL(DISABLED_ON_CPU(Convolve1D1Window))) {
+XLA_TEST_P(Convolve1D1WindowTestHalf, Convolve1D1Window) {
   TestImpl<Eigen::half>();
 }
 
@@ -719,14 +717,16 @@ INSTANTIATE_TEST_CASE_P(
                       Convolve1DTestParam{130, 1, 1, 1, 3},
                       Convolve1DTestParam{64, 1, 1, 1, 1},
                       Convolve1DTestParam{128, 1, 1, 1, 1},
-                      // TODO(b/72566306): the following three tests fail on CPU
-                      // backend due to result miscompare.
+// TODO(b/72566306): the following five tests fail on CPU
+// backend due to result miscompare.
+#if XLA_TEST_BACKEND_GPU
                       Convolve1DTestParam{139, 1, 1, 128, 1},
                       Convolve1DTestParam{640, 3, 3, 128, 1},
                       Convolve1DTestParam{900, 1, 1, 10, 1},
                       Convolve1DTestParam{1, 10, 10, 1, 10},
-                      Convolve1DTestParam{1, 10, 130, 1, 2},
                       Convolve1DTestParam{1, 10, 130, 1, 1},
+#endif
+                      Convolve1DTestParam{1, 10, 130, 1, 2},
                       Convolve1DTestParam{1, 64, 64, 1, 10},
                       Convolve1DTestParam{1, 65, 65, 1, 1},
                       Convolve1DTestParam{1, 128, 128, 1, 1},
-- 
GitLab


From 30310e4aa106b662ac3c3f98ad3199d0ef768657 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Thu, 22 Feb 2018 13:43:57 -0800
Subject: [PATCH 210/575] Enable int64 outfeed.

PiperOrigin-RevId: 186667574
---
 tensorflow/contrib/tpu/python/ops/tpu_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/tpu/python/ops/tpu_ops.py b/tensorflow/contrib/tpu/python/ops/tpu_ops.py
index 9787621679..14c63a7976 100644
--- a/tensorflow/contrib/tpu/python/ops/tpu_ops.py
+++ b/tensorflow/contrib/tpu/python/ops/tpu_ops.py
@@ -47,7 +47,7 @@ if platform.system() != "Windows":
   # types are supported.
 
   _SUPPORTED_INFEED_DTYPES = set([
-      dtypes.bool, dtypes.int32, dtypes.bfloat16, dtypes.float32,
+      dtypes.bool, dtypes.int32, dtypes.int64, dtypes.bfloat16, dtypes.float32,
       dtypes.complex64
   ])
 
-- 
GitLab


From 963b9beb803b0b1c62f33d79275cf837726c6e58 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 13:54:25 -0800
Subject: [PATCH 211/575] Adds support for identifying dilated convolution
 emulation.

Dilated convolution can be emulated by a chain of SpaceToBatchND -> Conv2D -> BatchtoSpaceND. This change adds a graph transformation that identifies this pattern, and variations of it, and substitutes a true dilated convolution instead.

PiperOrigin-RevId: 186669260
---
 tensorflow/contrib/lite/toco/BUILD            |   1 +
 .../graph_transformations.h                   |   1 +
 .../identify_dilated_conv.cc                  | 213 ++++++++++++++++++
 tensorflow/contrib/lite/toco/toco_tooling.cc  |   1 +
 4 files changed, 216 insertions(+)
 create mode 100644 tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc

diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD
index e2879fad32..17407f3db2 100644
--- a/tensorflow/contrib/lite/toco/BUILD
+++ b/tensorflow/contrib/lite/toco/BUILD
@@ -186,6 +186,7 @@ cc_library(
         "graph_transformations/fuse_binary_into_preceding_affine.cc",
         "graph_transformations/graph_transformations.cc",
         "graph_transformations/hardcode_min_max.cc",
+        "graph_transformations/identify_dilated_conv.cc",
         "graph_transformations/identify_l2_normalization.cc",
         "graph_transformations/identify_l2_pool.cc",
         "graph_transformations/identify_lstm.cc",
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
index 616bdac268..f2c81ebc81 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
+++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
@@ -128,6 +128,7 @@ DECLARE_GRAPH_TRANSFORMATION(IdentifyLstmCell)
 DECLARE_GRAPH_TRANSFORMATION(SplitLstmCellInputs)
 DECLARE_GRAPH_TRANSFORMATION(MergeLstmCellInputs)
 DECLARE_GRAPH_TRANSFORMATION(IdentifyRelu1)
+DECLARE_GRAPH_TRANSFORMATION(IdentifyDilatedConv)
 DECLARE_GRAPH_TRANSFORMATION(MakeInitialDequantizeOperator)
 DECLARE_GRAPH_TRANSFORMATION(PropagateArrayDataTypes)
 DECLARE_GRAPH_TRANSFORMATION(PropagateFixedSizes)
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc b/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc
new file mode 100644
index 0000000000..ae3301f467
--- /dev/null
+++ b/tensorflow/contrib/lite/toco/graph_transformations/identify_dilated_conv.cc
@@ -0,0 +1,213 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h"
+#include "tensorflow/contrib/lite/toco/model.h"
+#include "tensorflow/contrib/lite/toco/tooling_util.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace toco {
+
+// A dilated convolution can be emulated with a regular convolution by chaining
+// SpaceToBatch and BatchToSpace ops before and after it:
+//
+//     SpaceToBatchND -> Conv2D -> BatchToSpaceND
+//
+// This method was common before Conv2D fully supported dilated convolution in
+// TensorFlow. This transformation detects this "emulation", and replaces it
+// with a true dilated convolution, eliminating the SpaceToBatch and
+// BatchtoSpace ops.
+//
+// Detecting this alone would be relatively easy. However, in practice some
+// extra ops are used, so we detect the following patterns:
+//
+//
+//   SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BatchToSpaceND -> BiasAdd
+//
+//   SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> Pad -> BatchToSpaceND ->
+//   BiasAdd
+//
+//   SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BiasAdd -> BatchToSpaceND
+//
+//   SpaceToBatchND -> Conv2D -> Pad -> BatchToSpaceND -> BiasAdd
+//
+//   SpaceToBatchND -> Conv2D -> BatchToSpaceND -> BiasAdd
+//
+//
+// The Expand/Squeeze combination is used to adapt a 3D array (such as in
+// WaveNet) to the 4D arrays that Conv2D requires. Padding and BiasAdd are
+// thrown in just for the extra headache. Padding adapts non-conforming input
+// sizes, and can be discarded. The bias is necessary, so is kept.
+
+bool IdentifyDilatedConv::Run(Model* model, std::size_t op_index) {
+  const auto it = model->operators.begin() + op_index;
+  auto* stb_op = it->get();
+
+  // 1. IDENTIFY OPERATORS
+  // ***************************************************************************
+  // SpaceToBatch Op.
+  if (stb_op->type != OperatorType::kSpaceToBatchND) {
+    return false;
+  }
+  if (stb_op->inputs.size() != 3) {
+    return false;
+  }
+  CHECK_EQ(stb_op->outputs.size(), 1);
+  // Extract the dilation factor from Input[1] of SpaceToBatch
+  // TODO(mjmatthews): Support 2D dilation factors.
+  const auto& block_shape_array = model->GetArray(stb_op->inputs[1]);
+  if (!block_shape_array.buffer) {
+    return false;
+  }
+  CHECK_EQ(block_shape_array.shape().dimensions_count(), 1);
+  int dilation_factor =
+      block_shape_array.Array::GetBuffer<ArrayDataType::kInt32>().data[0];
+
+  // Expand Op
+  auto* post_stb_op = GetOpWithInput(*model, stb_op->outputs[0]);
+  if (!post_stb_op) {
+    return false;
+  }
+  bool has_expand_op = false;
+  if (post_stb_op->type == OperatorType::kExpandDims) {
+    has_expand_op = true;
+    CHECK_EQ(post_stb_op->inputs.size(), 2);
+    CHECK_EQ(post_stb_op->outputs.size(), 1);
+  }
+
+  // Conv Op
+  ConvOperator* conv_op = dynamic_cast<ConvOperator*>(
+      has_expand_op ? GetOpWithInput(*model, post_stb_op->outputs[0])
+                    : GetOpWithInput(*model, stb_op->outputs[0]));
+  if (!conv_op || conv_op->type != OperatorType::kConv) {
+    return false;
+  }
+  if (conv_op->inputs.size() != 2) {
+    // The conv op must only have weights, no bias.
+    return false;
+  }
+  CHECK_EQ(conv_op->outputs.size(), 1);
+
+  // Squeeze Op
+  auto* post_conv_op = GetOpWithInput(*model, conv_op->outputs[0]);
+  if (!post_conv_op) {
+    return false;
+  }
+  if (has_expand_op) {
+    if (post_conv_op->type != OperatorType::kSqueeze) {
+      // If an expand op was used, the post-conv op must be a squeeze op
+      return false;
+    }
+    CHECK_EQ(post_conv_op->inputs.size(), 1);
+    CHECK_EQ(post_conv_op->outputs.size(), 1);
+  }
+
+  // Pad Op
+  const auto* pad_op = has_expand_op
+                           ? GetOpWithInput(*model, post_conv_op->outputs[0])
+                           : GetOpWithInput(*model, conv_op->outputs[0]);
+  bool has_pad_op = false;
+  if (pad_op->type == OperatorType::kPad) {
+    has_pad_op = true;
+    CHECK_EQ(pad_op->inputs.size(), 2);
+    CHECK_EQ(pad_op->outputs.size(), 1);
+  }
+  // TODO(mjmatthews): Perform validity checking on padding dimensions.
+
+  // Pre-BatchToSpace Bias Op
+  auto* next_op = has_pad_op
+                      ? GetOpWithInput(*model, pad_op->outputs[0])
+                      : has_expand_op
+                            ? GetOpWithInput(*model, post_conv_op->outputs[0])
+                            : GetOpWithInput(*model, conv_op->outputs[0]);
+  bool has_bias_before_bts = false;
+  if (next_op->type == OperatorType::kAdd) {
+    has_bias_before_bts = true;
+  }
+  auto final_op = GetOpWithInput(*model, next_op->outputs[0]);
+
+  // BatchToSpace Op
+  const auto* bts_op = has_bias_before_bts ? final_op : next_op;
+  if (bts_op->type != OperatorType::kBatchToSpaceND) {
+    return false;
+  }
+  CHECK_EQ(bts_op->inputs.size(), 3);
+  CHECK_EQ(bts_op->outputs.size(), 1);
+
+  // Post-BatchToSpace Bias Op
+  Operator* bias_add_op = !has_bias_before_bts ? final_op : next_op;
+  if (bias_add_op->type != OperatorType::kAdd) {
+    // Bias op is required before or after BatchToSpace
+    return false;
+  }
+  CHECK_EQ(bias_add_op->inputs.size(), 2);
+  CHECK_EQ(bias_add_op->outputs.size(), 1);
+
+  LOG(INFO) << "Identified sub-network emulating dilated convolution.";
+
+  // 2. RE-WIRE OPERATORS
+  // ***************************************************************************
+  // Re-use the existing Conv2D op.
+  conv_op->dilation_width_factor = dilation_factor;
+  conv_op->dilation_height_factor = dilation_factor;
+  conv_op->padding.type = PaddingType::kSame;
+
+  // Rewire the ops to bypass SpaceToBatch, BatchToSpace, and Pad.
+  bias_add_op->outputs[0] = final_op->outputs[0];
+  if (has_expand_op) {
+    bias_add_op->inputs[0] = post_conv_op->outputs[0];
+    post_conv_op->inputs[0] = conv_op->outputs[0];
+    conv_op->inputs[0] = post_stb_op->outputs[0];
+    post_stb_op->inputs[0] = stb_op->inputs[0];
+  } else {
+    bias_add_op->inputs[0] = conv_op->outputs[0];
+    conv_op->inputs[0] = stb_op->inputs[0];
+  }
+  // TODO(mjmatthews): Connect bias directly into the Conv2D?
+
+  // 3. DELETE LEFTOVER OPERATORS
+  // ***************************************************************************
+  // Order is important. Delete the output array first, then the op, then it's
+  // redundant inputs.
+  // BatchToSpace Op
+  DeleteArrayIfUnused(bts_op->outputs[0], model);
+  std::vector<string> bts_op_inputs = bts_op->inputs;
+  model->operators.erase(FindOp(*model, bts_op));
+  DeleteArrayIfUnused(bts_op_inputs[1], model);
+  DeleteArrayIfUnused(bts_op_inputs[2], model);
+
+  // Pad Op if present
+  if (has_pad_op) {
+    DeleteArrayIfUnused(pad_op->outputs[0], model);
+    std::vector<string> pad_op_inputs = pad_op->inputs;
+    model->operators.erase(FindOp(*model, pad_op));
+    DeleteArrayIfUnused(pad_op_inputs[1], model);
+  }
+
+  // SpaceToBatch Op
+  DeleteArrayIfUnused(stb_op->outputs[0], model);
+  std::vector<string> stb_op_inputs = stb_op->inputs;
+  model->operators.erase(FindOp(*model, stb_op));
+  DeleteArrayIfUnused(stb_op_inputs[1], model);
+  DeleteArrayIfUnused(stb_op_inputs[2], model);
+
+  LOG(INFO) << "Replaced with Dilated Conv2D op outputting \""
+            << conv_op->outputs[0] << "\".";
+  return true;
+}
+
+}  // namespace toco
diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc
index 6fcaa957cf..2153bab096 100644
--- a/tensorflow/contrib/lite/toco/toco_tooling.cc
+++ b/tensorflow/contrib/lite/toco/toco_tooling.cc
@@ -87,6 +87,7 @@ void MakeGeneralGraphTransformationsSet(
   transformations->Add(new ResolveTensorFlowTile);
   transformations->Add(new ResolveTensorFlowConcat);
   transformations->Add(new ResolveMultiplyByZero);
+  transformations->Add(new IdentifyDilatedConv);
   transformations->Add(new IdentifyL2Normalization);
   transformations->Add(new IdentifyL2Pool);
   transformations->Add(new IdentifyRelu1);
-- 
GitLab


From cb7e1963c625fd9713e7475d85621f95be6762f1 Mon Sep 17 00:00:00 2001
From: Jeremy Lau <lauj@google.com>
Date: Thu, 22 Feb 2018 14:20:35 -0800
Subject: [PATCH 212/575] Internal change.

PiperOrigin-RevId: 186673561
---
 tensorflow/python/keras/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index bb6d6cf425..16738066ce 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -784,7 +784,7 @@ py_test(
 
 py_test(
     name = "estimator_test",
-    size = "medium",
+    size = "large",
     srcs = ["_impl/keras/estimator_test.py"],
     srcs_version = "PY2AND3",
     tags = ["notsan"],
-- 
GitLab


From dce9a49c19f406ba45919e8c94474e55dc5ccd54 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Thu, 22 Feb 2018 14:24:57 -0800
Subject: [PATCH 213/575] Merge changes from github.

PiperOrigin-RevId: 186674197
---
 RELEASE.md                                    |   27 +-
 configure.py                                  |   25 +-
 tensorflow/c/c_api_test.cc                    |    2 +-
 tensorflow/compiler/xla/literal_util.cc       |    3 +-
 .../compiler/xla/service/hlo_instruction.h    |    9 +-
 tensorflow/contrib/BUILD                      |    5 +-
 tensorflow/contrib/android/README.md          |    5 +
 .../boosted_trees/python/utils/losses.py      |    4 +-
 tensorflow/contrib/cmake/CMakeLists.txt       |   22 +-
 tensorflow/contrib/cmake/external/zlib.cmake  |  108 +-
 tensorflow/contrib/cmake/python_modules.txt   |    3 +
 tensorflow/contrib/crf/python/ops/crf.py      |   12 +-
 .../python/ops/relaxed_onehot_categorical.py  |    2 +-
 .../contrib/eager/python/g3doc/guide.md       |    9 +-
 .../python/ops/clustering_ops.py              |    4 +-
 tensorflow/contrib/framework/__init__.py      |    6 +-
 .../framework/python/framework/graph_util.py  |   12 +
 .../image/kernels/bipartite_match_op.cc       |    2 +-
 .../contrib/layers/python/layers/layers.py    |   12 +-
 tensorflow/contrib/lite/README.md             |   17 +-
 .../src/main/assets/labels_imagenet_slim.txt  | 1001 +++++++++++
 .../assets/labels_mobilenet_quant_v1_224.txt  | 1001 +++++++++++
 .../Camera2BasicFragment.java                 |    6 +-
 .../tflitecamerademo/ImageClassifier.java     |  137 +-
 .../ImageClassifierFloatInception.java        |  103 ++
 .../ImageClassifierQuantizedMobileNet.java    |   94 +
 .../python/metric_learning/metric_loss_ops.py |   48 +-
 tensorflow/contrib/makefile/README.md         |   99 +
 .../build_and_run_inception_hexagon.sh        |    6 +-
 tensorflow/contrib/rnn/python/ops/lstm_ops.py |    5 +-
 tensorflow/contrib/rnn/python/ops/rnn_cell.py |    5 +-
 .../seq2seq/kernels/beam_search_ops.cc        |    8 +-
 .../contrib/signal/python/ops/spectral_ops.py |    2 +-
 .../slim/python/slim/evaluation_test.py       |    3 +-
 tensorflow/contrib/tensor_forest/BUILD        |    1 +
 tensorflow/contrib/tensorrt/BUILD             |  204 ++-
 tensorflow/contrib/tensorrt/README.md         |   40 +
 tensorflow/contrib/tensorrt/__init__.py       |   23 +
 .../contrib/tensorrt/convert/convert_graph.cc |  273 +++
 .../contrib/tensorrt/convert/convert_graph.h  |   47 +
 .../contrib/tensorrt/convert/convert_nodes.cc | 1601 +++++++++++++++++
 .../contrib/tensorrt/convert/convert_nodes.h  |   52 +
 .../contrib/tensorrt/kernels/trt_engine_op.cc |  140 ++
 .../contrib/tensorrt/kernels/trt_engine_op.h  |   62 +
 tensorflow/contrib/tensorrt/log/trt_logger.cc |   57 +
 tensorflow/contrib/tensorrt/log/trt_logger.h  |   42 +
 .../contrib/tensorrt/ops/trt_engine_op.cc     |   43 +
 .../contrib/tensorrt/python/__init__.py       |   24 +
 .../tensorrt/python/ops/trt_engine_op.py      |   34 +
 .../contrib/tensorrt/python/trt_convert.py    |  103 ++
 .../contrib/tensorrt/segment/segment.cc       |  253 +++
 tensorflow/contrib/tensorrt/segment/segment.h |   56 +
 .../contrib/tensorrt/segment/segment_test.cc  |  367 ++++
 .../contrib/tensorrt/segment/union_find.h     |   79 +
 .../contrib/tensorrt/shape_fn/trt_shfn.cc     |   89 +
 .../contrib/tensorrt/shape_fn/trt_shfn.h      |   33 +
 .../contrib/tensorrt/test/test_tftrt.py       |   88 +
 tensorflow/contrib/tensorrt/trt_conversion.i  |  131 ++
 .../contrib/tpu/profiler/pip_package/setup.py |    2 +-
 tensorflow/core/common_runtime/gpu/gpu_id.h   |    2 +-
 .../core/common_runtime/mkl_cpu_allocator.h   |    1 -
 tensorflow/core/framework/tensor_shape.h      |    3 -
 .../core/graph/mkl_tfconversion_pass.cc       |    2 +-
 tensorflow/core/kernels/colorspace_op.cc      |    2 +-
 .../core/kernels/depthwise_conv_op_gpu.cu.cc  |    8 +-
 .../core/kernels/mkl_batch_matmul_op.cc       |   28 +-
 .../core/kernels/mkl_input_conversion_op.cc   |    4 +-
 tensorflow/core/kernels/mkl_matmul_op.cc      |   28 +-
 tensorflow/core/kernels/mkl_tfconv_op.h       |    2 +-
 tensorflow/core/kernels/mkl_transpose_op.cc   |   34 +-
 .../core/kernels/non_max_suppression_op.cc    |    5 +-
 ...arameterized_truncated_normal_op_gpu.cu.cc |    2 +-
 .../kernels/quantized_resize_bilinear_op.cc   |    4 +-
 tensorflow/core/kernels/random_crop_op.cc     |    4 +-
 tensorflow/core/kernels/resize_area_op.cc     |    5 +-
 tensorflow/core/kernels/resize_bicubic_op.cc  |   10 +-
 tensorflow/core/kernels/resize_bilinear_op.cc |   10 +-
 .../kernels/resize_nearest_neighbor_op.cc     |    8 +-
 .../sample_distorted_bounding_box_op.cc       |    6 +-
 tensorflow/core/kernels/slice_op.cc           |   10 +-
 tensorflow/core/kernels/substr_op.cc          |   20 +-
 tensorflow/core/kernels/xsmm_conv2d.cc        |   12 -
 tensorflow/core/lib/io/record_writer.cc       |    2 +-
 tensorflow/core/ops/image_ops.cc              |    8 +
 tensorflow/core/platform/platform.h           |    7 +-
 tensorflow/core/protobuf/config.proto         |    2 +-
 tensorflow/core/public/version.h              |    2 +-
 tensorflow/core/util/mkl_util.h               |   10 +-
 tensorflow/docs_src/about/roadmap.md          |  101 +-
 tensorflow/docs_src/about/uses.md             |    8 +
 tensorflow/docs_src/deploy/index.md           |    2 +
 tensorflow/docs_src/deploy/leftnav_files      |    1 +
 tensorflow/docs_src/deploy/s3.md              |   40 +
 tensorflow/docs_src/extend/add_filesys.md     |    2 +
 tensorflow/docs_src/install/install_c.md      |    2 +-
 tensorflow/docs_src/install/install_go.md     |    2 +-
 tensorflow/docs_src/install/install_java.md   |   22 +-
 tensorflow/docs_src/install/install_linux.md  |   23 +-
 tensorflow/docs_src/install/install_mac.md    |   10 +-
 .../docs_src/install/install_sources.md       |   16 +-
 .../docs_src/install/install_windows.md       |    4 +-
 tensorflow/docs_src/mobile/mobile_intro.md    |    2 +-
 .../programmers_guide/low_level_intro.md      |    6 +-
 tensorflow/docs_src/tutorials/layers.md       |    2 +-
 .../android/res/animator/color_animation.xml  |   30 +
 .../org/tensorflow/demo/SpeechActivity.java   |   21 +-
 .../get_started/regression/imports85.py       |   11 +-
 .../examples/image_retraining/retrain.py      |   55 +-
 .../examples/speech_commands/label_wav_dir.py |  136 ++
 tensorflow/examples/speech_commands/train.py  |    6 +-
 tensorflow/examples/udacity/5_word2vec.ipynb  |    2 +-
 tensorflow/python/estimator/estimator.py      |    2 +-
 tensorflow/python/estimator/estimator_test.py |   15 +-
 tensorflow/python/framework/common_shapes.py  |    2 +-
 tensorflow/python/framework/function_test.py  |    2 +-
 .../python/kernel_tests/reduction_ops_test.py |   88 +-
 .../kernel_tests/reduction_ops_test_big.py    |   18 +-
 tensorflow/python/layers/core.py              |    9 +-
 tensorflow/python/layers/normalization.py     |    8 +-
 tensorflow/python/layers/utils.py             |   83 +-
 tensorflow/python/ops/clip_ops.py             |    2 +-
 tensorflow/python/ops/control_flow_ops.py     |   93 +
 .../python/ops/control_flow_ops_test.py       |   36 +
 tensorflow/python/ops/data_flow_ops.py        |    2 +-
 .../python/ops/distributions/multinomial.py   |    2 +-
 tensorflow/python/ops/distributions/util.py   |   11 +-
 tensorflow/python/ops/image_ops_impl.py       |  183 +-
 tensorflow/python/ops/image_ops_test.py       |  165 +-
 tensorflow/python/ops/losses/losses_impl.py   |   25 +-
 tensorflow/python/ops/math_ops_test.py        |    2 +-
 tensorflow/python/ops/nn_grad.py              |   14 +-
 tensorflow/python/ops/nn_ops.py               |   30 +-
 tensorflow/python/ops/nn_test.py              |   25 +
 tensorflow/python/profiler/option_builder.py  |    2 +-
 tensorflow/python/tools/freeze_graph.py       |   20 +-
 tensorflow/python/training/saver.py           |   12 +-
 tensorflow/tensorflow.bzl                     |   63 +-
 .../tools/ci_build/install/install_bazel.sh   |    2 +-
 tensorflow/tools/graph_transforms/BUILD       |    1 +
 tensorflow/tools/graph_transforms/README.md   |    7 +
 .../remove_control_dependencies.cc            |   47 +
 .../tools/graph_transforms/remove_nodes.cc    |   12 +-
 tensorflow/tools/pip_package/BUILD            |    5 +-
 tensorflow/tools/pip_package/setup.py         |    8 +-
 third_party/gpus/cuda_configure.bzl           |   19 +-
 third_party/tensorrt/BUILD.tpl                |   34 +-
 third_party/tensorrt/LICENSE                  |  203 +++
 third_party/tensorrt/tensorrt_configure.bzl   |    7 +-
 148 files changed, 7965 insertions(+), 690 deletions(-)
 create mode 100644 tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_imagenet_slim.txt
 create mode 100644 tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt
 create mode 100644 tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatInception.java
 create mode 100644 tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java
 create mode 100644 tensorflow/contrib/tensorrt/README.md
 create mode 100644 tensorflow/contrib/tensorrt/__init__.py
 create mode 100644 tensorflow/contrib/tensorrt/convert/convert_graph.cc
 create mode 100644 tensorflow/contrib/tensorrt/convert/convert_graph.h
 create mode 100644 tensorflow/contrib/tensorrt/convert/convert_nodes.cc
 create mode 100644 tensorflow/contrib/tensorrt/convert/convert_nodes.h
 create mode 100644 tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
 create mode 100644 tensorflow/contrib/tensorrt/kernels/trt_engine_op.h
 create mode 100644 tensorflow/contrib/tensorrt/log/trt_logger.cc
 create mode 100644 tensorflow/contrib/tensorrt/log/trt_logger.h
 create mode 100644 tensorflow/contrib/tensorrt/ops/trt_engine_op.cc
 create mode 100644 tensorflow/contrib/tensorrt/python/__init__.py
 create mode 100644 tensorflow/contrib/tensorrt/python/ops/trt_engine_op.py
 create mode 100644 tensorflow/contrib/tensorrt/python/trt_convert.py
 create mode 100644 tensorflow/contrib/tensorrt/segment/segment.cc
 create mode 100644 tensorflow/contrib/tensorrt/segment/segment.h
 create mode 100644 tensorflow/contrib/tensorrt/segment/segment_test.cc
 create mode 100644 tensorflow/contrib/tensorrt/segment/union_find.h
 create mode 100644 tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc
 create mode 100644 tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h
 create mode 100644 tensorflow/contrib/tensorrt/test/test_tftrt.py
 create mode 100644 tensorflow/contrib/tensorrt/trt_conversion.i
 create mode 100644 tensorflow/docs_src/deploy/s3.md
 create mode 100644 tensorflow/examples/android/res/animator/color_animation.xml
 create mode 100644 tensorflow/examples/speech_commands/label_wav_dir.py
 create mode 100644 tensorflow/tools/graph_transforms/remove_control_dependencies.cc
 create mode 100644 third_party/tensorrt/LICENSE

diff --git a/RELEASE.md b/RELEASE.md
index 0720a8c639..6f54dee58f 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -21,7 +21,7 @@ newcomers.
 * Other:
   * Add `tf.contrib.distributions.Kumaraswamy`.
   * `RetryingFileSystem::FlushCaches()` calls the base FileSystem's `FlushCaches()`.
-  * Add auto_correlation to distributions.
+  * Add `auto_correlation` to distributions.
   * Add `tf.contrib.distributions.Autoregressive`.
   * Add SeparableConv1D layer.
   * Add convolutional Flipout layers.
@@ -31,12 +31,12 @@ newcomers.
   * Output variance over trees predictions for classifications tasks.
   * For `pt` and `eval` commands, allow writing tensor values to filesystem as numpy files.
   * gRPC: Propagate truncated errors (instead of returning gRPC internal error).
-  * Augment parallel_interleave to support 2 kinds of prefetching.
+  * Augment `parallel_interleave` to support 2 kinds of prefetching.
   * Improved XLA support for C64-related ops log, pow, atan2, tanh.
   * Add probabilistic convolutional layers.
 
 ## API Changes
-* Introducing prepare_variance boolean with default setting to False for backward compatibility.
+* Introducing `prepare_variance` boolean with default setting to False for backward compatibility.
 * Move `layers_dense_variational_impl.py` to `layers_dense_variational.py`.
 
 ## Known Bugs
@@ -96,27 +96,6 @@ Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田
 * Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
   This may break TF on older CPUs.
 
-## Known Bugs
-* Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
-  `CUDA_ILLEGAL_ADDRESS` failures.
-
-  Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
-  and CUDA 9.1 sometimes does not properly compute the carry bit when
-  decomposing 64-bit address calculations with large offsets (e.g. `load [x +
-  large_constant]`) into 32-bit arithmetic in SASS.
-
-  As a result, these versions of `ptxas` miscompile most XLA programs which use
-  more than 4GB of temp memory.  This results in garbage results and/or
-  `CUDA_ERROR_ILLEGAL_ADDRESS` failures.
-
-  A fix in CUDA 9.1.121 is expected in late February 2018.  We do not expect a
-  fix for CUDA 9.0.x.  Until the fix is available, the only workaround is to
-  [downgrade](https://developer.nvidia.com/cuda-toolkit-archive) to CUDA 8.0.x
-  or disable XLA:GPU.
-
-  TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
-  CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.
-
 ## Major Features And Improvements
 * [Eager execution](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager)
   preview version is now available.
diff --git a/configure.py b/configure.py
index 6b1fa7f1a8..9744f6ac81 100644
--- a/configure.py
+++ b/configure.py
@@ -445,7 +445,7 @@ def convert_version_to_int(version):
 
 
 def check_bazel_version(min_version):
-  """Check installed bezel version is at least min_version.
+  """Check installed bazel version is at least min_version.
 
   Args:
     min_version: string for minimum bazel version.
@@ -1078,12 +1078,22 @@ def set_tf_tensorrt_install_path(environ_cp):
           break
 
     # Reset and Retry
-    print('Invalid path to TensorRT. None of the following files can be found:')
-    print(trt_install_path)
-    print(os.path.join(trt_install_path, 'lib'))
-    print(os.path.join(trt_install_path, 'lib64'))
-    if search_result:
-      print(libnvinfer_path_from_ldconfig)
+    if possible_files:
+      print('TensorRT libraries found in one the following directories',
+            'are not compatible with selected cuda and cudnn installations')
+      print(trt_install_path)
+      print(os.path.join(trt_install_path, 'lib'))
+      print(os.path.join(trt_install_path, 'lib64'))
+      if search_result:
+        print(libnvinfer_path_from_ldconfig)
+    else:
+      print(
+          'Invalid path to TensorRT. None of the following files can be found:')
+      print(trt_install_path)
+      print(os.path.join(trt_install_path, 'lib'))
+      print(os.path.join(trt_install_path, 'lib64'))
+      if search_result:
+        print(libnvinfer_path_from_ldconfig)
 
   else:
     raise UserInputError('Invalid TF_TENSORRT setting was provided %d '
@@ -1481,7 +1491,6 @@ def main():
         'more details.')
   config_info_line('mkl', 'Build with MKL support.')
   config_info_line('monolithic', 'Config for mostly static monolithic build.')
-  config_info_line('tensorrt', 'Build with TensorRT support.')
 
 if __name__ == '__main__':
   main()
diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index 69fe5bec51..028f146be3 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -2081,7 +2081,7 @@ TEST_F(CApiAttributesTest, Tensor) {
 }
 
 TEST_F(CApiAttributesTest, StringTensor) {
-  // Create the string-Tensor "atttribute" value.
+  // Create the string-Tensor "attribute" value.
   char encoded[] = {
       0,   0, 0, 0, 0, 0, 0, 0,  // array[uint64] offsets
       1,                         // varint encoded string length
diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index ed9d2a187a..823da43b5a 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -234,7 +234,8 @@ Status Literal::CopySliceFromInternal(
       int64 src_index = linear_index(src_literal.shape(), src_indexes);
       int64 dest_index = linear_index(shape(), dest_indexes);
 
-      StridedCopy(data<NativeT>(), dest_index, stride_config.dest_stride,
+      // `this->` is needed to workaround MSVC bug: #16882
+      StridedCopy(this->data<NativeT>(), dest_index, stride_config.dest_stride,
                   src_literal.data<NativeT>(), src_index,
                   stride_config.source_stride, stride_config.minor_loop_size);
       return true;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index 1762d227be..c4fe132d1d 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -589,12 +589,9 @@ class HloInstruction {
     if (opcode() != other.opcode()) {
       return false;
     }
-    auto eq_shapes = layout_sensitive
-                         ? [](const Shape& a,
-                              const Shape& b) { return ShapeUtil::Equal(a, b); }
-                         : [](const Shape& a, const Shape& b) {
-                             return ShapeUtil::Compatible(a, b);
-                           };
+    using EqShapeFuncType = bool (*)(const Shape&, const Shape&);
+    EqShapeFuncType eq_shapes =
+        layout_sensitive ? ShapeUtil::Equal : ShapeUtil::Compatible;
     if (!eq_shapes(shape(), other.shape())) {
       return false;
     }
diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index 6b3343bb2f..bab37e8906 100644
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -7,6 +7,7 @@ package(default_visibility = ["//tensorflow:__subpackages__"])
 
 load("//third_party/mpi:mpi.bzl", "if_mpi")
 load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
+load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt")
 
 py_library(
     name = "contrib_py",
@@ -107,7 +108,9 @@ py_library(
         "//tensorflow/contrib/training:training_py",
         "//tensorflow/contrib/util:util_py",
         "//tensorflow/python:util",
-    ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]),
+    ] + if_mpi(["//tensorflow/contrib/mpi_collectives:mpi_collectives_py"]) + if_tensorrt([
+        "//tensorflow/contrib/tensorrt:init_py",
+    ]),
 )
 
 cc_library(
diff --git a/tensorflow/contrib/android/README.md b/tensorflow/contrib/android/README.md
index b8d73bf24c..db37bcf73d 100644
--- a/tensorflow/contrib/android/README.md
+++ b/tensorflow/contrib/android/README.md
@@ -81,6 +81,11 @@ For documentation on building a self-contained AAR file with cmake, see
 [tensorflow/contrib/android/cmake](cmake).
 
 
+### Makefile
+
+For documentation on building native TF libraries with make, including a CUDA-enabled variant for devices like the Nvidia Shield TV, see [tensorflow/contrib/makefile/README.md](../makefile/README.md)
+
+
 ## AssetManagerFileSystem
 
 This directory also contains a TensorFlow filesystem supporting the Android
diff --git a/tensorflow/contrib/boosted_trees/python/utils/losses.py b/tensorflow/contrib/boosted_trees/python/utils/losses.py
index 1e8b3ac08a..ab7ac2aba6 100644
--- a/tensorflow/contrib/boosted_trees/python/utils/losses.py
+++ b/tensorflow/contrib/boosted_trees/python/utils/losses.py
@@ -78,7 +78,7 @@ def per_example_maxent_loss(labels, weights, logits, num_classes, eps=1e-15):
 
   # Calculate softmax probabilities for each class.
   unnormalized_probs = math_ops.exp(logits)
-  normalizers = math_ops.reduce_sum(unnormalized_probs, 1, keep_dims=True)
+  normalizers = math_ops.reduce_sum(unnormalized_probs, 1, keepdims=True)
   softmax_predictions = math_ops.divide(unnormalized_probs,
                                         math_ops.add(normalizers, eps))
 
@@ -120,7 +120,7 @@ def per_example_squared_loss(labels, weights, predictions):
     update_op: An update operation to update the loss's internal state.
   """
   unweighted_loss = math_ops.reduce_sum(
-      math_ops.square(predictions - labels), 1, keep_dims=True)
+      math_ops.square(predictions - labels), 1, keepdims=True)
 
   return unweighted_loss * weights, control_flow_ops.no_op()
 
diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index 524946a9a5..23b31ae1dc 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -52,6 +52,7 @@ if (NOT WIN32)
   # for targets that link ${CMAKE_THREAD_LIBS_INIT}.
   find_package (Threads)
 
+  # Options for linking CUDA/CUDNN libraries
   option(tensorflow_PATH_STATIC_LIB "Additional library search path for libcudnn_static.a, libnccl_static.a, libculibos.a" /usr/local/cuda/lib64/)
   option(tensorflow_CUDNN_INCLUDE "cudnn.h header install path" /usr/include/)
   if (NOT tensorflow_CUDNN_INCLUDE)
@@ -73,6 +74,14 @@ if (NOT WIN32)
     # option's default value is OFF. Fill it with real default values
     set(tensorflow_CUDA_LIBRARY_PATH /usr/local/cuda/lib64)
   endif (NOT tensorflow_CUDA_LIBRARY_PATH)
+
+  # Options for linking other libraries
+  option(systemlib_ZLIB "Use the system installed library as shared objects instead of downloading ZLIB and statically linking to it: ZLIB" OFF)
+
+  option(systemlib_ALL "Turn on every possible systemlib_* options" OFF)
+  if (systemlib_ALL)
+    set (systmelib_ZLIB ON)
+  endif (systemlib_ALL)
 endif()
 
 if (WIN32)
@@ -188,8 +197,10 @@ if (tensorflow_BUILD_CC_TESTS)
   include(googletest)
 endif()
 
+add_definitions(${ADD_CFLAGS})
+link_directories(${ADD_LINK_DIRECTORY})
+
 set(tensorflow_EXTERNAL_LIBRARIES
-    ${zlib_STATIC_LIBRARIES}
     ${gif_STATIC_LIBRARIES}
     ${png_STATIC_LIBRARIES}
     ${jpeg_STATIC_LIBRARIES}
@@ -203,6 +214,15 @@ set(tensorflow_EXTERNAL_LIBRARIES
     ${re2_STATIC_LIBRARIES}
     ${sqlite_STATIC_LIBRARIES}
 )
+
+if (systemlib_ZLIB)
+  set(tensorflow_EXTERNAL_LIBRARIES ${tensorflow_EXTERNAL_LIBRARIES}
+      ${ZLIB_LIBRARIES})
+else (systemlib_ZLIB)
+  set(tensorflow_EXTERNAL_LIBRARIES ${tensorflow_EXTERNAL_LIBRARIES}
+    ${zlib_STATIC_LIBRARIES})
+endif (systemlib_ZLIB)
+
 set(tensorflow_EXTERNAL_DEPENDENCIES
     zlib_copy_headers_to_destination
     gif_copy_headers_to_destination
diff --git a/tensorflow/contrib/cmake/external/zlib.cmake b/tensorflow/contrib/cmake/external/zlib.cmake
index c5eb0cbcc7..116d423093 100644
--- a/tensorflow/contrib/cmake/external/zlib.cmake
+++ b/tensorflow/contrib/cmake/external/zlib.cmake
@@ -12,61 +12,75 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-include (ExternalProject)
+if (systemlib_ZLIB)
+  find_package(PkgConfig)
+  pkg_search_module(ZLIB REQUIRED zlib)
+  set(zlib_INCLUDE_DIR ${ZLIB_INCLUDE_DIRS})
+  set(ADD_LINK_DIRECTORY ${ADD_LINK_DIRECTORY} ${ZLIB_LIBRARY_DIRS})
+  set(ADD_CFLAGS ${ADD_CFLAGS} ${ZLIB_CFLAGS_OTHER})
 
-set(zlib_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/zlib_archive)
-set(ZLIB_URL https://github.com/madler/zlib)
-set(ZLIB_BUILD ${CMAKE_CURRENT_BINARY_DIR}/zlib/src/zlib)
-set(ZLIB_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/zlib/install)
-set(ZLIB_TAG 50893291621658f355bc5b4d450a8d06a563053d)
+  # To meet DEPENDS zlib from other projects.
+  # If we hit this line, zlib is already built and installed to the system.
+  add_custom_target(zlib)
+  add_custom_target(zlib_copy_headers_to_destination)
 
-if(WIN32)
-  if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
-    set(zlib_STATIC_LIBRARIES
-        debug ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib
-        optimized ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib)
-  else()
-    if(CMAKE_BUILD_TYPE EQUAL Debug)
+else (systemlib_ZLIB)
+  include (ExternalProject)
+
+  set(zlib_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/zlib_archive)
+  set(ZLIB_URL https://github.com/madler/zlib)
+  set(ZLIB_BUILD ${CMAKE_CURRENT_BINARY_DIR}/zlib/src/zlib)
+  set(ZLIB_INSTALL ${CMAKE_CURRENT_BINARY_DIR}/zlib/install)
+  set(ZLIB_TAG 50893291621658f355bc5b4d450a8d06a563053d)
+
+  if(WIN32)
+    if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*")
       set(zlib_STATIC_LIBRARIES
-          ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib)
+          debug ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib
+          optimized ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib)
     else()
-      set(zlib_STATIC_LIBRARIES
-          ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib)
+      if(CMAKE_BUILD_TYPE EQUAL Debug)
+        set(zlib_STATIC_LIBRARIES
+            ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstaticd.lib)
+      else()
+        set(zlib_STATIC_LIBRARIES
+            ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/zlibstatic.lib)
+      endif()
     endif()
+  else()
+    set(zlib_STATIC_LIBRARIES
+        ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/libz.a)
   endif()
-else()
-  set(zlib_STATIC_LIBRARIES
-      ${CMAKE_CURRENT_BINARY_DIR}/zlib/install/lib/libz.a)
-endif()
 
-set(ZLIB_HEADERS
-    "${ZLIB_INSTALL}/include/zconf.h"
-    "${ZLIB_INSTALL}/include/zlib.h"
-)
+  set(ZLIB_HEADERS
+      "${ZLIB_INSTALL}/include/zconf.h"
+      "${ZLIB_INSTALL}/include/zlib.h"
+  )
 
-ExternalProject_Add(zlib
-    PREFIX zlib
-    GIT_REPOSITORY ${ZLIB_URL}
-    GIT_TAG ${ZLIB_TAG}
-    INSTALL_DIR ${ZLIB_INSTALL}
-    BUILD_IN_SOURCE 1
-    BUILD_BYPRODUCTS ${zlib_STATIC_LIBRARIES}
-    DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
-    CMAKE_CACHE_ARGS
-        -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE}
-        -DCMAKE_BUILD_TYPE:STRING=Release
-        -DCMAKE_INSTALL_PREFIX:STRING=${ZLIB_INSTALL}
-)
+  ExternalProject_Add(zlib
+      PREFIX zlib
+      GIT_REPOSITORY ${ZLIB_URL}
+      GIT_TAG ${ZLIB_TAG}
+      INSTALL_DIR ${ZLIB_INSTALL}
+      BUILD_IN_SOURCE 1
+      BUILD_BYPRODUCTS ${zlib_STATIC_LIBRARIES}
+      DOWNLOAD_DIR "${DOWNLOAD_LOCATION}"
+      CMAKE_CACHE_ARGS
+          -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=${tensorflow_ENABLE_POSITION_INDEPENDENT_CODE}
+          -DCMAKE_BUILD_TYPE:STRING=Release
+          -DCMAKE_INSTALL_PREFIX:STRING=${ZLIB_INSTALL}
+  )
 
-# put zlib includes in the directory where they are expected
-add_custom_target(zlib_create_destination_dir
-    COMMAND ${CMAKE_COMMAND} -E make_directory ${zlib_INCLUDE_DIR}
-    DEPENDS zlib)
+  # put zlib includes in the directory where they are expected
+  add_custom_target(zlib_create_destination_dir
+      COMMAND ${CMAKE_COMMAND} -E make_directory ${zlib_INCLUDE_DIR}
+      DEPENDS zlib)
 
-add_custom_target(zlib_copy_headers_to_destination
-    DEPENDS zlib_create_destination_dir)
+  add_custom_target(zlib_copy_headers_to_destination
+      DEPENDS zlib_create_destination_dir)
 
-foreach(header_file ${ZLIB_HEADERS})
-    add_custom_command(TARGET zlib_copy_headers_to_destination PRE_BUILD
-    COMMAND ${CMAKE_COMMAND} -E copy_if_different ${header_file} ${zlib_INCLUDE_DIR})
-endforeach()
+  foreach(header_file ${ZLIB_HEADERS})
+      add_custom_command(TARGET zlib_copy_headers_to_destination PRE_BUILD
+      COMMAND ${CMAKE_COMMAND} -E copy_if_different ${header_file} ${zlib_INCLUDE_DIR})
+  endforeach()
+endif (systemlib_ZLIB)
diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt
index f55043c93d..bfe53c01b3 100644
--- a/tensorflow/contrib/cmake/python_modules.txt
+++ b/tensorflow/contrib/cmake/python_modules.txt
@@ -413,6 +413,9 @@ tensorflow/contrib/tensorboard
 tensorflow/contrib/tensorboard/plugins
 tensorflow/contrib/tensorboard/plugins/projector
 tensorflow/contrib/tensorboard/plugins/trace
+# TODO(sami): Add cmake implementations.
+# tensorflow/contrib/tensorrt/python
+# tensorflow/contrib/tensorrt/python/ops
 tensorflow/contrib/tensor_forest
 tensorflow/contrib/tensor_forest/client
 tensorflow/contrib/tensor_forest/hybrid
diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py
index faa78769b9..1233c8f251 100644
--- a/tensorflow/contrib/crf/python/ops/crf.py
+++ b/tensorflow/contrib/crf/python/ops/crf.py
@@ -105,8 +105,8 @@ def crf_sequence_score(inputs, tag_indices, sequence_lengths,
   return utils.smart_cond(
       pred=math_ops.equal(inputs.shape[1].value or array_ops.shape(inputs)[1],
                           1),
-      fn1=_single_seq_fn,
-      fn2=_multi_seq_fn)
+      true_fn=_single_seq_fn,
+      false_fn=_multi_seq_fn)
 
 
 def crf_log_norm(inputs, sequence_lengths, transition_params):
@@ -511,7 +511,7 @@ def crf_decode(potentials, transition_params, sequence_length):
     return decode_tags, best_score
 
   return utils.smart_cond(
-      pred=math_ops.equal(
-          potentials.shape[1].value or array_ops.shape(potentials)[1], 1),
-      fn1=_single_seq_fn,
-      fn2=_multi_seq_fn)
+      pred=math_ops.equal(potentials.shape[1].value or
+                          array_ops.shape(potentials)[1], 1),
+      true_fn=_single_seq_fn,
+      false_fn=_multi_seq_fn)
diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
index b6becfa9fc..2aa771a71e 100644
--- a/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
+++ b/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py
@@ -278,7 +278,7 @@ class ExpRelaxedOneHotCategorical(distribution.Distribution):
                       * math_ops.log(self.temperature))
     # compute the unnormalized density
     log_softmax = nn_ops.log_softmax(logits_2d - x_2d * self._temperature_2d)
-    log_unnorm_prob = math_ops.reduce_sum(log_softmax, [-1], keep_dims=False)
+    log_unnorm_prob = math_ops.reduce_sum(log_softmax, [-1], keepdims=False)
     # combine unnormalized density with normalization constant
     log_prob = log_norm_const + log_unnorm_prob
     # Reshapes log_prob to be consistent with shape of user-supplied logits
diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md
index 4724aa4aee..ebb05051f2 100644
--- a/tensorflow/contrib/eager/python/g3doc/guide.md
+++ b/tensorflow/contrib/eager/python/g3doc/guide.md
@@ -22,11 +22,10 @@ to models defined without using eager execution.
 Eager execution is included in TensorFlow versions 1.5 and above.
 Installation instructions at https://www.tensorflow.org/install/
 
-The contents of this guide are compatible with TensorFlow 1.5.
-However, if you run into bugs that are fixed in source but not the
-release, you may want to either either [building from
-source](https://www.tensorflow.org/install/install_sources)
-or the try latest nightly builds. The nightly builds are available as:
+The contents of this guide are compatible with TensorFlow 1.5. However, if you
+run into bugs that are fixed in source but not the release, you may want to
+either [build from source](https://www.tensorflow.org/install/install_sources)
+or try a nightly build. The nightly builds are available as:
 
 - [`pip` packages](https://github.com/tensorflow/tensorflow/blob/master/README.md#installation) and
 
diff --git a/tensorflow/contrib/factorization/python/ops/clustering_ops.py b/tensorflow/contrib/factorization/python/ops/clustering_ops.py
index 6d3acb2750..23137e0a97 100644
--- a/tensorflow/contrib/factorization/python/ops/clustering_ops.py
+++ b/tensorflow/contrib/factorization/python/ops/clustering_ops.py
@@ -192,11 +192,11 @@ class KMeans(object):
         # Computes Euclidean distance. Note the first and third terms are
         # broadcast additions.
         squared_distance = (
-            math_ops.reduce_sum(math_ops.square(inp), 1, keep_dims=True) -
+            math_ops.reduce_sum(math_ops.square(inp), 1, keepdims=True) -
             2 * math_ops.matmul(inp, clusters, transpose_b=True) +
             array_ops.transpose(
                 math_ops.reduce_sum(
-                    math_ops.square(clusters), 1, keep_dims=True)))
+                    math_ops.square(clusters), 1, keepdims=True)))
         output.append(squared_distance)
 
     return output
diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py
index fb101c3653..deeb5bec79 100644
--- a/tensorflow/contrib/framework/__init__.py
+++ b/tensorflow/contrib/framework/__init__.py
@@ -85,6 +85,8 @@ See the @{$python/contrib.framework} guide.
 @@py_func
 @@sort
 
+@@get_placeholders
+
 @@CriticalSection
 
 @@BoundedTensorSpec
@@ -102,10 +104,10 @@ from tensorflow.contrib.framework.python.ops import *
 
 from tensorflow.python.framework.ops import prepend_name_scope
 from tensorflow.python.framework.ops import strip_name_scope
-
 from tensorflow.python.framework.tensor_spec import BoundedTensorSpec
 from tensorflow.python.framework.tensor_spec import TensorSpec
-
+from tensorflow.python.ops.control_flow_ops import smart_cond
+from tensorflow.python.ops.control_flow_ops import smart_constant_value
 from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = ['nest']
diff --git a/tensorflow/contrib/framework/python/framework/graph_util.py b/tensorflow/contrib/framework/python/framework/graph_util.py
index a18ff2320d..49eec3a3f1 100644
--- a/tensorflow/contrib/framework/python/framework/graph_util.py
+++ b/tensorflow/contrib/framework/python/framework/graph_util.py
@@ -133,6 +133,18 @@ def fuse_op(graph_def, input_nodes, output_nodes, output_dtypes,
 def get_placeholders(graph):
   """Get placeholders of a graph.
 
+  For example:
+
+  ```python
+  a = tf.placeholder(dtype=tf.float32, shape=[2, 2], name='a')
+  a = tf.placeholder(dtype=tf.int32, shape=[3, 2], name='b')
+
+  tf.contrib.framework.get_placeholders(tf.get_default_graph())
+  # Returns:
+  #  [<tf.Tensor 'a:0' shape=(2, 2) dtype=float32>,
+  #   <tf.Tensor 'b:0' shape=(3, 2) dtype=int32>]
+  ```
+
   Args:
     graph: A tf.Graph.
   Returns:
diff --git a/tensorflow/contrib/image/kernels/bipartite_match_op.cc b/tensorflow/contrib/image/kernels/bipartite_match_op.cc
index 7d207c388b..726adb0777 100644
--- a/tensorflow/contrib/image/kernels/bipartite_match_op.cc
+++ b/tensorflow/contrib/image/kernels/bipartite_match_op.cc
@@ -85,7 +85,7 @@ class BipartiteMatchOp : public OpKernel {
                    context->allocate_output(1, TensorShape({num_input_columns}),
                                             &column_to_row_match_indices));
 
-    typename TTypes<float, 2>::ConstTensor distance_mat =
+    TTypes<float, 2>::ConstTensor distance_mat =
         input_distance_mat.shaped<float, 2>(
             {num_input_rows, num_input_columns});
 
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 45ddfbfc9f..b2ea75c7e1 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -517,8 +517,8 @@ def batch_norm(inputs,
       then the batch normalization uses weighted mean and
       variance. (This can be used to correct for bias in training
       example selection.)
-    fused: if `True`, use a faster, fused implementation if possible.
-      If `None`, use the system recommended implementation.
+    fused: if `None` or `True`, use a faster, fused implementation if possible.
+      If `False`, use the system recommended implementation.
     data_format: A string. `NHWC` (default) and `NCHW` are supported.
     zero_debias_moving_mean: Use zero_debias for moving_mean. It creates a new
       pair of variables 'moving_mean/biased' and 'moving_mean/local_step'.
@@ -778,7 +778,7 @@ def batch_norm(inputs,
       else:
         if data_format == DATA_FORMAT_NCHW:
           mean, variance = nn.weighted_moments(
-              inputs, moments_axes, batch_weights, keep_dims=True)
+              inputs, moments_axes, batch_weights, keepdims=True)
           mean = array_ops.reshape(mean, [-1])
           variance = array_ops.reshape(variance, [-1])
         else:
@@ -2836,9 +2836,9 @@ def spatial_softmax(features,
 
       softmax_attention = nn.softmax(features / temperature)
       expected_x = math_ops.reduce_sum(
-          pos_x * softmax_attention, [1], keep_dims=True)
+          pos_x * softmax_attention, [1], keepdims=True)
       expected_y = math_ops.reduce_sum(
-          pos_y * softmax_attention, [1], keep_dims=True)
+          pos_y * softmax_attention, [1], keepdims=True)
       expected_xy = array_ops.concat([expected_x, expected_y], 1)
       feature_keypoints = array_ops.reshape(expected_xy,
                                             [-1, num_channels.value * 2])
@@ -3018,7 +3018,7 @@ def poincare_normalize(x, axis=1, epsilon=1e-5, name=None):
   """
   with ops.name_scope(name, 'poincare_normalize', [x]) as name:
     x = ops.convert_to_tensor(x, name='x')
-    square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keep_dims=True)
+    square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keepdims=True)
     x_inv_norm = math_ops.rsqrt(square_sum)
     x_inv_norm = math_ops.minimum((1. - epsilon) * x_inv_norm, 1.)
     return math_ops.multiply(x, x_inv_norm, name=name)
diff --git a/tensorflow/contrib/lite/README.md b/tensorflow/contrib/lite/README.md
index 3e55d2a496..00e93d2c4f 100644
--- a/tensorflow/contrib/lite/README.md
+++ b/tensorflow/contrib/lite/README.md
@@ -6,7 +6,7 @@ TensorFlow Lite uses many techniques for achieving low latency like optimizing t
 ![image](g3doc/TFLite-Architecture.jpg)
 # Getting Started with an Android Demo App
 
-This section contains an example application using TensorFlow Lite for Android devices. The demo is a sample camera app that classifies images continuously using a quantized Mobilenet model. A device running Android 5.0 ( API 21) or higher is required to run the demo.
+This section contains an example application using TensorFlow Lite for Android devices. The demo is a sample camera app that classifies images continuously using either a quantized Mobilenet model or a floating point Inception-v3 model. A device running Android 5.0 ( API 21) or higher is required to run the demo.
 
 There are 3 ways to get the demo app to your device
  - Download the prebuilt binary or
@@ -29,9 +29,16 @@ The simplest way to compile the demo app, and try out changes to the project cod
  - Make sure the Android SDK version is greater than 26 and NDK version is greater than 14 (in the Android Studio Settings).
  - Import the `tensorflow/contrib/lite/java/demo` directory as a new Android Studio project.
  - Click through installing all the Gradle extensions it requests.
- - Download the quantized Mobilenet TensorFlow Lite model from [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip)
-     - unzip and copy mobilenet_quant_v1_224.tflite to the assets directory:
-       `tensorflow/contrib/lite/java/demo/app/src/main/assets/`
+ - Either
+     - Download the quantized Mobilenet TensorFlow Lite model from [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip)
+         - unzip and copy mobilenet_quant_v1_224.tflite to the assets directory:
+           `tensorflow/contrib/lite/java/demo/app/src/main/assets/`
+     - Or download the floating point Inception-v3 model from [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip)
+         - unzip and copy inceptionv3_non_slim_2015.tflite to the assets directory
+         - change the chosen classifier in [Camera2BasicFragment.java](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java) from
+         `classifier = new ImageClassifierQuantizedMobileNet(getActivity());`
+         to
+         `classifier = new ImageClassifierFloatInception(getActivity());`
  - Build and run the demo app
 
 ## Building TensorFlow Lite and the demo app from source
@@ -84,7 +91,7 @@ Currently, we only support building the Android demo app within a Python 2
 environment (due to a Bazel bug).
 
 ### More about the demo
-The demo is resizing each camera image frame to (224 width * 224 height) to match the  quantized Mobilenet model being used. The resized image is converted into a ByteBuffer row by row of size 1 * 224 * 224 * 3 bytes, where 1 is the number of images in a batch 224 * 224 is the width and height of the image 3 bytes represents three colors of a pixel. This demo uses the TensorFlow Lite Java inference API for models which take a single input and provide a single output. This outputs a two-dimensional array, with the first dimension being the category index and the second dimension being the confidence of classification. The Mobilenet model has 1001 unique categories and the app sorts the probabilities of all the categories and displays the top three. The Mobilenet quantized model is bundled within the assets directory of the app.
+The demo is resizing each camera image frame to (224 width * 224 height) to match the quantized Mobilenet model being used (229 * 229 for Inception-v3). The resized image is converted into a ByteBuffer row by row of size 1 * 224 * 224 * 3 bytes, where 1 is the number of images in a batch. 224 * 224 (299 * 299) is the width and height of the image. 3 bytes represents three colors of a pixel. This demo uses the TensorFlow Lite Java inference API for models which take a single input and provide a single output. This outputs a two-dimensional array, with the first dimension being the category index and the second dimension being the confidence of classification. Both models have 1001 unique categories and the app sorts the probabilities of all the categories and displays the top three. The model file must be downloaded and bundled within the assets directory of the app.
 
 # iOS Demo App
 
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_imagenet_slim.txt b/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_imagenet_slim.txt
new file mode 100644
index 0000000000..572eccf900
--- /dev/null
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_imagenet_slim.txt
@@ -0,0 +1,1001 @@
+dummy
+tench
+goldfish
+great white shark
+tiger shark
+hammerhead
+electric ray
+stingray
+cock
+hen
+ostrich
+brambling
+goldfinch
+house finch
+junco
+indigo bunting
+robin
+bulbul
+jay
+magpie
+chickadee
+water ouzel
+kite
+bald eagle
+vulture
+great grey owl
+European fire salamander
+common newt
+eft
+spotted salamander
+axolotl
+bullfrog
+tree frog
+tailed frog
+loggerhead
+leatherback turtle
+mud turtle
+terrapin
+box turtle
+banded gecko
+common iguana
+American chameleon
+whiptail
+agama
+frilled lizard
+alligator lizard
+Gila monster
+green lizard
+African chameleon
+Komodo dragon
+African crocodile
+American alligator
+triceratops
+thunder snake
+ringneck snake
+hognose snake
+green snake
+king snake
+garter snake
+water snake
+vine snake
+night snake
+boa constrictor
+rock python
+Indian cobra
+green mamba
+sea snake
+horned viper
+diamondback
+sidewinder
+trilobite
+harvestman
+scorpion
+black and gold garden spider
+barn spider
+garden spider
+black widow
+tarantula
+wolf spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse
+prairie chicken
+peacock
+quail
+partridge
+African grey
+macaw
+sulphur-crested cockatoo
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser
+goose
+black swan
+tusker
+echidna
+platypus
+wallaby
+koala
+wombat
+jellyfish
+sea anemone
+brain coral
+flatworm
+nematode
+conch
+snail
+slug
+sea slug
+chiton
+chambered nautilus
+Dungeness crab
+rock crab
+fiddler crab
+king crab
+American lobster
+spiny lobster
+crayfish
+hermit crab
+isopod
+white stork
+black stork
+spoonbill
+flamingo
+little blue heron
+American egret
+bittern
+crane
+limpkin
+European gallinule
+American coot
+bustard
+ruddy turnstone
+red-backed sandpiper
+redshank
+dowitcher
+oystercatcher
+pelican
+king penguin
+albatross
+grey whale
+killer whale
+dugong
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog
+Pekinese
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound
+basset
+beagle
+bloodhound
+bluetick
+black-and-tan coonhound
+Walker hound
+English foxhound
+redbone
+borzoi
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound
+Norwegian elkhound
+otterhound
+Saluki
+Scottish deerhound
+Weimaraner
+Staffordshire bullterrier
+American Staffordshire terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier
+Airedale
+cairn
+Australian terrier
+Dandie Dinmont
+Boston bull
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier
+Tibetan terrier
+silky terrier
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla
+English setter
+Irish setter
+Gordon setter
+Brittany spaniel
+clumber
+English springer
+Welsh springer spaniel
+cocker spaniel
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog
+Shetland sheepdog
+collie
+Border collie
+Bouvier des Flandres
+Rottweiler
+German shepherd
+Doberman
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard
+Eskimo dog
+malamute
+Siberian husky
+dalmatian
+affenpinscher
+basenji
+pug
+Leonberg
+Newfoundland
+Great Pyrenees
+Samoyed
+Pomeranian
+chow
+keeshond
+Brabancon griffon
+Pembroke
+Cardigan
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf
+white wolf
+red wolf
+coyote
+dingo
+dhole
+African hunting dog
+hyena
+red fox
+kit fox
+Arctic fox
+grey fox
+tabby
+tiger cat
+Persian cat
+Siamese cat
+Egyptian cat
+cougar
+lynx
+leopard
+snow leopard
+jaguar
+lion
+tiger
+cheetah
+brown bear
+American black bear
+ice bear
+sloth bear
+mongoose
+meerkat
+tiger beetle
+ladybug
+ground beetle
+long-horned beetle
+leaf beetle
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant
+grasshopper
+cricket
+walking stick
+cockroach
+mantis
+cicada
+leafhopper
+lacewing
+dragonfly
+damselfly
+admiral
+ringlet
+monarch
+cabbage butterfly
+sulphur butterfly
+lycaenid
+starfish
+sea urchin
+sea cucumber
+wood rabbit
+hare
+Angora
+hamster
+porcupine
+fox squirrel
+marmot
+beaver
+guinea pig
+sorrel
+zebra
+hog
+wild boar
+warthog
+hippopotamus
+ox
+water buffalo
+bison
+ram
+bighorn
+ibex
+hartebeest
+impala
+gazelle
+Arabian camel
+llama
+weasel
+mink
+polecat
+black-footed ferret
+otter
+skunk
+badger
+armadillo
+three-toed sloth
+orangutan
+gorilla
+chimpanzee
+gibbon
+siamang
+guenon
+patas
+baboon
+macaque
+langur
+colobus
+proboscis monkey
+marmoset
+capuchin
+howler monkey
+titi
+spider monkey
+squirrel monkey
+Madagascar cat
+indri
+Indian elephant
+African elephant
+lesser panda
+giant panda
+barracouta
+eel
+coho
+rock beauty
+anemone fish
+sturgeon
+gar
+lionfish
+puffer
+abacus
+abaya
+academic gown
+accordion
+acoustic guitar
+aircraft carrier
+airliner
+airship
+altar
+ambulance
+amphibian
+analog clock
+apiary
+apron
+ashcan
+assault rifle
+backpack
+bakery
+balance beam
+balloon
+ballpoint
+Band Aid
+banjo
+bannister
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel
+barrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap
+bath towel
+bathtub
+beach wagon
+beacon
+beaker
+bearskin
+beer bottle
+beer glass
+bell cote
+bib
+bicycle-built-for-two
+bikini
+binder
+binoculars
+birdhouse
+boathouse
+bobsled
+bolo tie
+bonnet
+bookcase
+bookshop
+bottlecap
+bow
+bow tie
+brass
+brassiere
+breakwater
+breastplate
+broom
+bucket
+buckle
+bulletproof vest
+bullet train
+butcher shop
+cab
+caldron
+candle
+cannon
+canoe
+can opener
+cardigan
+car mirror
+carousel
+carpenter's kit
+carton
+car wheel
+cash machine
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello
+cellular telephone
+chain
+chainlink fence
+chain mail
+chain saw
+chest
+chiffonier
+chime
+china cabinet
+Christmas stocking
+church
+cinema
+cleaver
+cliff dwelling
+cloak
+clog
+cocktail shaker
+coffee mug
+coffeepot
+coil
+combination lock
+computer keyboard
+confectionery
+container ship
+convertible
+corkscrew
+cornet
+cowboy boot
+cowboy hat
+cradle
+crane
+crash helmet
+crate
+crib
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam
+desk
+desktop computer
+dial telephone
+diaper
+digital clock
+digital watch
+dining table
+dishrag
+dishwasher
+disk brake
+dock
+dogsled
+dome
+doormat
+drilling platform
+drum
+drumstick
+dumbbell
+Dutch oven
+electric fan
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa
+file
+fireboat
+fire engine
+fire screen
+flagpole
+flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn
+frying pan
+fur coat
+garbage truck
+gasmask
+gas pump
+goblet
+go-kart
+golf ball
+golfcart
+gondola
+gong
+gown
+grand piano
+greenhouse
+grille
+grocery store
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower
+hand-held computer
+handkerchief
+hard disc
+harmonica
+harp
+harvester
+hatchet
+holster
+home theater
+honeycomb
+hook
+hoopskirt
+horizontal bar
+horse cart
+hourglass
+iPod
+iron
+jack-o'-lantern
+jean
+jeep
+jersey
+jigsaw puzzle
+jinrikisha
+joystick
+kimono
+knee pad
+knot
+lab coat
+ladle
+lampshade
+laptop
+lawn mower
+lens cap
+letter opener
+library
+lifeboat
+lighter
+limousine
+liner
+lipstick
+Loafer
+lotion
+loudspeaker
+loupe
+lumbermill
+magnetic compass
+mailbag
+mailbox
+maillot
+maillot
+manhole cover
+maraca
+marimba
+mask
+matchstick
+maypole
+maze
+measuring cup
+medicine chest
+megalith
+microphone
+microwave
+military uniform
+milk can
+minibus
+miniskirt
+minivan
+missile
+mitten
+mixing bowl
+mobile home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter
+mountain bike
+mountain tent
+mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook
+obelisk
+oboe
+ocarina
+odometer
+oil filter
+organ
+oscilloscope
+overskirt
+oxcart
+oxygen mask
+packet
+paddle
+paddlewheel
+padlock
+paintbrush
+pajama
+palace
+panpipe
+paper towel
+parachute
+parallel bars
+park bench
+parking meter
+passenger car
+patio
+pay-phone
+pedestal
+pencil box
+pencil sharpener
+perfume
+Petri dish
+photocopier
+pick
+pickelhaube
+picket fence
+pickup
+pier
+piggy bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate
+pitcher
+plane
+planetarium
+plastic bag
+plate rack
+plow
+plunger
+Polaroid camera
+pole
+police van
+poncho
+pool table
+pop bottle
+pot
+potter's wheel
+power drill
+prayer rug
+printer
+prison
+projectile
+projector
+puck
+punching bag
+purse
+quill
+quilt
+racer
+racket
+radiator
+radio
+radio telescope
+rain barrel
+recreational vehicle
+reel
+reflex camera
+refrigerator
+remote control
+restaurant
+revolver
+rifle
+rocking chair
+rotisserie
+rubber eraser
+rugby ball
+rule
+running shoe
+safe
+safety pin
+saltshaker
+sandal
+sarong
+sax
+scabbard
+scale
+school bus
+schooner
+scoreboard
+screen
+screw
+screwdriver
+seat belt
+sewing machine
+shield
+shoe shop
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule
+sliding door
+slot
+snorkel
+snowmobile
+snowplow
+soap dispenser
+soccer ball
+sock
+solar dish
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web
+spindle
+sports car
+spotlight
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch
+stove
+strainer
+streetcar
+stretcher
+studio couch
+stupa
+submarine
+suit
+sundial
+sunglass
+sunglasses
+sunscreen
+suspension bridge
+swab
+sweatshirt
+swimming trunks
+swing
+switch
+syringe
+table lamp
+tank
+tape player
+teapot
+teddy
+television
+tennis ball
+thatch
+theater curtain
+thimble
+thresher
+throne
+tile roof
+toaster
+tobacco shop
+toilet seat
+torch
+totem pole
+tow truck
+toyshop
+tractor
+trailer truck
+tray
+trench coat
+tricycle
+trimaran
+tripod
+triumphal arch
+trolleybus
+trombone
+tub
+turnstile
+typewriter keyboard
+umbrella
+unicycle
+upright
+vacuum
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin
+volleyball
+waffle iron
+wall clock
+wallet
+wardrobe
+warplane
+washbasin
+washer
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool
+worm fence
+wreck
+yawl
+yurt
+web site
+comic book
+crossword puzzle
+street sign
+traffic light
+book jacket
+menu
+plate
+guacamole
+consomme
+hot pot
+trifle
+ice cream
+ice lolly
+French loaf
+bagel
+pretzel
+cheeseburger
+hotdog
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini
+spaghetti squash
+acorn squash
+butternut squash
+cucumber
+artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple
+banana
+jackfruit
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce
+dough
+meat loaf
+pizza
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff
+coral reef
+geyser
+lakeside
+promontory
+sandbar
+seashore
+valley
+volcano
+ballplayer
+groom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper
+corn
+acorn
+hip
+buckeye
+coral fungus
+agaric
+gyromitra
+stinkhorn
+earthstar
+hen-of-the-woods
+bolete
+ear
+toilet tissue
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt b/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt
new file mode 100644
index 0000000000..fe811239d8
--- /dev/null
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt
@@ -0,0 +1,1001 @@
+background
+tench
+goldfish
+great white shark
+tiger shark
+hammerhead
+electric ray
+stingray
+cock
+hen
+ostrich
+brambling
+goldfinch
+house finch
+junco
+indigo bunting
+robin
+bulbul
+jay
+magpie
+chickadee
+water ouzel
+kite
+bald eagle
+vulture
+great grey owl
+European fire salamander
+common newt
+eft
+spotted salamander
+axolotl
+bullfrog
+tree frog
+tailed frog
+loggerhead
+leatherback turtle
+mud turtle
+terrapin
+box turtle
+banded gecko
+common iguana
+American chameleon
+whiptail
+agama
+frilled lizard
+alligator lizard
+Gila monster
+green lizard
+African chameleon
+Komodo dragon
+African crocodile
+American alligator
+triceratops
+thunder snake
+ringneck snake
+hognose snake
+green snake
+king snake
+garter snake
+water snake
+vine snake
+night snake
+boa constrictor
+rock python
+Indian cobra
+green mamba
+sea snake
+horned viper
+diamondback
+sidewinder
+trilobite
+harvestman
+scorpion
+black and gold garden spider
+barn spider
+garden spider
+black widow
+tarantula
+wolf spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse
+prairie chicken
+peacock
+quail
+partridge
+African grey
+macaw
+sulphur-crested cockatoo
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser
+goose
+black swan
+tusker
+echidna
+platypus
+wallaby
+koala
+wombat
+jellyfish
+sea anemone
+brain coral
+flatworm
+nematode
+conch
+snail
+slug
+sea slug
+chiton
+chambered nautilus
+Dungeness crab
+rock crab
+fiddler crab
+king crab
+American lobster
+spiny lobster
+crayfish
+hermit crab
+isopod
+white stork
+black stork
+spoonbill
+flamingo
+little blue heron
+American egret
+bittern
+crane
+limpkin
+European gallinule
+American coot
+bustard
+ruddy turnstone
+red-backed sandpiper
+redshank
+dowitcher
+oystercatcher
+pelican
+king penguin
+albatross
+grey whale
+killer whale
+dugong
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog
+Pekinese
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound
+basset
+beagle
+bloodhound
+bluetick
+black-and-tan coonhound
+Walker hound
+English foxhound
+redbone
+borzoi
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound
+Norwegian elkhound
+otterhound
+Saluki
+Scottish deerhound
+Weimaraner
+Staffordshire bullterrier
+American Staffordshire terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier
+Airedale
+cairn
+Australian terrier
+Dandie Dinmont
+Boston bull
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier
+Tibetan terrier
+silky terrier
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla
+English setter
+Irish setter
+Gordon setter
+Brittany spaniel
+clumber
+English springer
+Welsh springer spaniel
+cocker spaniel
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog
+Shetland sheepdog
+collie
+Border collie
+Bouvier des Flandres
+Rottweiler
+German shepherd
+Doberman
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard
+Eskimo dog
+malamute
+Siberian husky
+dalmatian
+affenpinscher
+basenji
+pug
+Leonberg
+Newfoundland
+Great Pyrenees
+Samoyed
+Pomeranian
+chow
+keeshond
+Brabancon griffon
+Pembroke
+Cardigan
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf
+white wolf
+red wolf
+coyote
+dingo
+dhole
+African hunting dog
+hyena
+red fox
+kit fox
+Arctic fox
+grey fox
+tabby
+tiger cat
+Persian cat
+Siamese cat
+Egyptian cat
+cougar
+lynx
+leopard
+snow leopard
+jaguar
+lion
+tiger
+cheetah
+brown bear
+American black bear
+ice bear
+sloth bear
+mongoose
+meerkat
+tiger beetle
+ladybug
+ground beetle
+long-horned beetle
+leaf beetle
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant
+grasshopper
+cricket
+walking stick
+cockroach
+mantis
+cicada
+leafhopper
+lacewing
+dragonfly
+damselfly
+admiral
+ringlet
+monarch
+cabbage butterfly
+sulphur butterfly
+lycaenid
+starfish
+sea urchin
+sea cucumber
+wood rabbit
+hare
+Angora
+hamster
+porcupine
+fox squirrel
+marmot
+beaver
+guinea pig
+sorrel
+zebra
+hog
+wild boar
+warthog
+hippopotamus
+ox
+water buffalo
+bison
+ram
+bighorn
+ibex
+hartebeest
+impala
+gazelle
+Arabian camel
+llama
+weasel
+mink
+polecat
+black-footed ferret
+otter
+skunk
+badger
+armadillo
+three-toed sloth
+orangutan
+gorilla
+chimpanzee
+gibbon
+siamang
+guenon
+patas
+baboon
+macaque
+langur
+colobus
+proboscis monkey
+marmoset
+capuchin
+howler monkey
+titi
+spider monkey
+squirrel monkey
+Madagascar cat
+indri
+Indian elephant
+African elephant
+lesser panda
+giant panda
+barracouta
+eel
+coho
+rock beauty
+anemone fish
+sturgeon
+gar
+lionfish
+puffer
+abacus
+abaya
+academic gown
+accordion
+acoustic guitar
+aircraft carrier
+airliner
+airship
+altar
+ambulance
+amphibian
+analog clock
+apiary
+apron
+ashcan
+assault rifle
+backpack
+bakery
+balance beam
+balloon
+ballpoint
+Band Aid
+banjo
+bannister
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel
+barrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap
+bath towel
+bathtub
+beach wagon
+beacon
+beaker
+bearskin
+beer bottle
+beer glass
+bell cote
+bib
+bicycle-built-for-two
+bikini
+binder
+binoculars
+birdhouse
+boathouse
+bobsled
+bolo tie
+bonnet
+bookcase
+bookshop
+bottlecap
+bow
+bow tie
+brass
+brassiere
+breakwater
+breastplate
+broom
+bucket
+buckle
+bulletproof vest
+bullet train
+butcher shop
+cab
+caldron
+candle
+cannon
+canoe
+can opener
+cardigan
+car mirror
+carousel
+carpenter's kit
+carton
+car wheel
+cash machine
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello
+cellular telephone
+chain
+chainlink fence
+chain mail
+chain saw
+chest
+chiffonier
+chime
+china cabinet
+Christmas stocking
+church
+cinema
+cleaver
+cliff dwelling
+cloak
+clog
+cocktail shaker
+coffee mug
+coffeepot
+coil
+combination lock
+computer keyboard
+confectionery
+container ship
+convertible
+corkscrew
+cornet
+cowboy boot
+cowboy hat
+cradle
+crane
+crash helmet
+crate
+crib
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam
+desk
+desktop computer
+dial telephone
+diaper
+digital clock
+digital watch
+dining table
+dishrag
+dishwasher
+disk brake
+dock
+dogsled
+dome
+doormat
+drilling platform
+drum
+drumstick
+dumbbell
+Dutch oven
+electric fan
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa
+file
+fireboat
+fire engine
+fire screen
+flagpole
+flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn
+frying pan
+fur coat
+garbage truck
+gasmask
+gas pump
+goblet
+go-kart
+golf ball
+golfcart
+gondola
+gong
+gown
+grand piano
+greenhouse
+grille
+grocery store
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower
+hand-held computer
+handkerchief
+hard disc
+harmonica
+harp
+harvester
+hatchet
+holster
+home theater
+honeycomb
+hook
+hoopskirt
+horizontal bar
+horse cart
+hourglass
+iPod
+iron
+jack-o'-lantern
+jean
+jeep
+jersey
+jigsaw puzzle
+jinrikisha
+joystick
+kimono
+knee pad
+knot
+lab coat
+ladle
+lampshade
+laptop
+lawn mower
+lens cap
+letter opener
+library
+lifeboat
+lighter
+limousine
+liner
+lipstick
+Loafer
+lotion
+loudspeaker
+loupe
+lumbermill
+magnetic compass
+mailbag
+mailbox
+maillot
+maillot
+manhole cover
+maraca
+marimba
+mask
+matchstick
+maypole
+maze
+measuring cup
+medicine chest
+megalith
+microphone
+microwave
+military uniform
+milk can
+minibus
+miniskirt
+minivan
+missile
+mitten
+mixing bowl
+mobile home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter
+mountain bike
+mountain tent
+mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook
+obelisk
+oboe
+ocarina
+odometer
+oil filter
+organ
+oscilloscope
+overskirt
+oxcart
+oxygen mask
+packet
+paddle
+paddlewheel
+padlock
+paintbrush
+pajama
+palace
+panpipe
+paper towel
+parachute
+parallel bars
+park bench
+parking meter
+passenger car
+patio
+pay-phone
+pedestal
+pencil box
+pencil sharpener
+perfume
+Petri dish
+photocopier
+pick
+pickelhaube
+picket fence
+pickup
+pier
+piggy bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate
+pitcher
+plane
+planetarium
+plastic bag
+plate rack
+plow
+plunger
+Polaroid camera
+pole
+police van
+poncho
+pool table
+pop bottle
+pot
+potter's wheel
+power drill
+prayer rug
+printer
+prison
+projectile
+projector
+puck
+punching bag
+purse
+quill
+quilt
+racer
+racket
+radiator
+radio
+radio telescope
+rain barrel
+recreational vehicle
+reel
+reflex camera
+refrigerator
+remote control
+restaurant
+revolver
+rifle
+rocking chair
+rotisserie
+rubber eraser
+rugby ball
+rule
+running shoe
+safe
+safety pin
+saltshaker
+sandal
+sarong
+sax
+scabbard
+scale
+school bus
+schooner
+scoreboard
+screen
+screw
+screwdriver
+seat belt
+sewing machine
+shield
+shoe shop
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule
+sliding door
+slot
+snorkel
+snowmobile
+snowplow
+soap dispenser
+soccer ball
+sock
+solar dish
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web
+spindle
+sports car
+spotlight
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch
+stove
+strainer
+streetcar
+stretcher
+studio couch
+stupa
+submarine
+suit
+sundial
+sunglass
+sunglasses
+sunscreen
+suspension bridge
+swab
+sweatshirt
+swimming trunks
+swing
+switch
+syringe
+table lamp
+tank
+tape player
+teapot
+teddy
+television
+tennis ball
+thatch
+theater curtain
+thimble
+thresher
+throne
+tile roof
+toaster
+tobacco shop
+toilet seat
+torch
+totem pole
+tow truck
+toyshop
+tractor
+trailer truck
+tray
+trench coat
+tricycle
+trimaran
+tripod
+triumphal arch
+trolleybus
+trombone
+tub
+turnstile
+typewriter keyboard
+umbrella
+unicycle
+upright
+vacuum
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin
+volleyball
+waffle iron
+wall clock
+wallet
+wardrobe
+warplane
+washbasin
+washer
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool
+worm fence
+wreck
+yawl
+yurt
+web site
+comic book
+crossword puzzle
+street sign
+traffic light
+book jacket
+menu
+plate
+guacamole
+consomme
+hot pot
+trifle
+ice cream
+ice lolly
+French loaf
+bagel
+pretzel
+cheeseburger
+hotdog
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini
+spaghetti squash
+acorn squash
+butternut squash
+cucumber
+artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple
+banana
+jackfruit
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce
+dough
+meat loaf
+pizza
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff
+coral reef
+geyser
+lakeside
+promontory
+sandbar
+seashore
+valley
+volcano
+ballplayer
+groom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper
+corn
+acorn
+hip
+buckeye
+coral fungus
+agaric
+gyromitra
+stinkhorn
+earthstar
+hen-of-the-woods
+bolete
+ear
+toilet tissue
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java
index 74737a8b88..9b9fdffab5 100644
--- a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java
@@ -296,7 +296,8 @@ public class Camera2BasicFragment extends Fragment
   public void onActivityCreated(Bundle savedInstanceState) {
     super.onActivityCreated(savedInstanceState);
     try {
-      classifier = new ImageClassifier(getActivity());
+      // create either a new ImageClassifierQuantizedMobileNet or an ImageClassifierFloatInception
+      classifier = new ImageClassifierQuantizedMobileNet(getActivity());
     } catch (IOException e) {
       Log.e(TAG, "Failed to initialize an image classifier.");
     }
@@ -658,8 +659,7 @@ public class Camera2BasicFragment extends Fragment
       showToast("Uninitialized Classifier or invalid context.");
       return;
     }
-    Bitmap bitmap =
-        textureView.getBitmap(ImageClassifier.DIM_IMG_SIZE_X, ImageClassifier.DIM_IMG_SIZE_Y);
+    Bitmap bitmap = textureView.getBitmap(classifier.getImageSizeX(), classifier.getImageSizeY());
     String textToShow = classifier.classifyFrame(bitmap);
     bitmap.recycle();
     showToast(textToShow);
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java
index e44c5ae6b4..2c91be9d62 100644
--- a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java
@@ -37,17 +37,11 @@ import java.util.PriorityQueue;
 import org.tensorflow.lite.Interpreter;
 
 /** Classifies images with Tensorflow Lite. */
-public class ImageClassifier {
+public abstract class ImageClassifier {
 
   /** Tag for the {@link Log}. */
   private static final String TAG = "TfLiteCameraDemo";
 
-  /** Name of the model file stored in Assets. */
-  private static final String MODEL_PATH = "mobilenet_quant_v1_224.tflite";
-
-  /** Name of the label file stored in Assets. */
-  private static final String LABEL_PATH = "labels.txt";
-
   /** Number of results to show in the UI. */
   private static final int RESULTS_TO_SHOW = 3;
 
@@ -56,23 +50,18 @@ public class ImageClassifier {
 
   private static final int DIM_PIXEL_SIZE = 3;
 
-  static final int DIM_IMG_SIZE_X = 224;
-  static final int DIM_IMG_SIZE_Y = 224;
-
   /* Preallocated buffers for storing image data in. */
-  private int[] intValues = new int[DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y];
+  private int[] intValues = new int[getImageSizeX() * getImageSizeY()];
 
   /** An instance of the driver class to run model inference with Tensorflow Lite. */
-  private Interpreter tflite;
+  protected Interpreter tflite;
 
   /** Labels corresponding to the output of the vision model. */
   private List<String> labelList;
 
   /** A ByteBuffer to hold image data, to be feed into Tensorflow Lite as inputs. */
-  private ByteBuffer imgData = null;
+  protected ByteBuffer imgData = null;
 
-  /** An array to hold inference results, to be feed into Tensorflow Lite as outputs. */
-  private byte[][] labelProbArray = null;
   /** multi-stage low pass filter * */
   private float[][] filterLabelProbArray = null;
 
@@ -95,10 +84,13 @@ public class ImageClassifier {
     labelList = loadLabelList(activity);
     imgData =
         ByteBuffer.allocateDirect(
-            DIM_BATCH_SIZE * DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y * DIM_PIXEL_SIZE);
+            DIM_BATCH_SIZE
+                * getImageSizeX()
+                * getImageSizeY()
+                * DIM_PIXEL_SIZE
+                * getNumBytesPerChannel());
     imgData.order(ByteOrder.nativeOrder());
-    labelProbArray = new byte[1][labelList.size()];
-    filterLabelProbArray = new float[FILTER_STAGES][labelList.size()];
+    filterLabelProbArray = new float[FILTER_STAGES][getNumLabels()];
     Log.d(TAG, "Created a Tensorflow Lite Image Classifier.");
   }
 
@@ -111,7 +103,7 @@ public class ImageClassifier {
     convertBitmapToByteBuffer(bitmap);
     // Here's where the magic happens!!!
     long startTime = SystemClock.uptimeMillis();
-    tflite.run(imgData, labelProbArray);
+    runInference();
     long endTime = SystemClock.uptimeMillis();
     Log.d(TAG, "Timecost to run model inference: " + Long.toString(endTime - startTime));
 
@@ -125,12 +117,12 @@ public class ImageClassifier {
   }
 
   void applyFilter() {
-    int numLabels = labelList.size();
+    int numLabels = getNumLabels();
 
     // Low pass filter `labelProbArray` into the first stage of the filter.
     for (int j = 0; j < numLabels; ++j) {
       filterLabelProbArray[0][j] +=
-          FILTER_FACTOR * (labelProbArray[0][j] - filterLabelProbArray[0][j]);
+          FILTER_FACTOR * (getProbability(j) - filterLabelProbArray[0][j]);
     }
     // Low pass filter each stage into the next.
     for (int i = 1; i < FILTER_STAGES; ++i) {
@@ -142,7 +134,7 @@ public class ImageClassifier {
 
     // Copy the last stage filter output back to `labelProbArray`.
     for (int j = 0; j < numLabels; ++j) {
-      labelProbArray[0][j] = (byte)filterLabelProbArray[FILTER_STAGES - 1][j];
+      setProbability(j, filterLabelProbArray[FILTER_STAGES - 1][j]);
     }
   }
 
@@ -156,7 +148,7 @@ public class ImageClassifier {
   private List<String> loadLabelList(Activity activity) throws IOException {
     List<String> labelList = new ArrayList<String>();
     BufferedReader reader =
-        new BufferedReader(new InputStreamReader(activity.getAssets().open(LABEL_PATH)));
+        new BufferedReader(new InputStreamReader(activity.getAssets().open(getLabelPath())));
     String line;
     while ((line = reader.readLine()) != null) {
       labelList.add(line);
@@ -167,7 +159,7 @@ public class ImageClassifier {
 
   /** Memory-map the model file in Assets. */
   private MappedByteBuffer loadModelFile(Activity activity) throws IOException {
-    AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(MODEL_PATH);
+    AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(getModelPath());
     FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
     FileChannel fileChannel = inputStream.getChannel();
     long startOffset = fileDescriptor.getStartOffset();
@@ -185,12 +177,10 @@ public class ImageClassifier {
     // Convert the image to floating point.
     int pixel = 0;
     long startTime = SystemClock.uptimeMillis();
-    for (int i = 0; i < DIM_IMG_SIZE_X; ++i) {
-      for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) {
+    for (int i = 0; i < getImageSizeX(); ++i) {
+      for (int j = 0; j < getImageSizeY(); ++j) {
         final int val = intValues[pixel++];
-        imgData.put((byte) ((val >> 16) & 0xFF));
-        imgData.put((byte) ((val >> 8) & 0xFF));
-        imgData.put((byte) (val & 0xFF));
+        addPixelValue(val);
       }
     }
     long endTime = SystemClock.uptimeMillis();
@@ -199,9 +189,9 @@ public class ImageClassifier {
 
   /** Prints top-K labels, to be shown in UI as the results. */
   private String printTopKLabels() {
-    for (int i = 0; i < labelList.size(); ++i) {
+    for (int i = 0; i < getNumLabels(); ++i) {
       sortedLabels.add(
-          new AbstractMap.SimpleEntry<>(labelList.get(i), (labelProbArray[0][i] & 0xff) / 255.0f));
+          new AbstractMap.SimpleEntry<>(labelList.get(i), getNormalizedProbability(i)));
       if (sortedLabels.size() > RESULTS_TO_SHOW) {
         sortedLabels.poll();
       }
@@ -214,4 +204,89 @@ public class ImageClassifier {
     }
     return textToShow;
   }
+
+  /**
+   * Get the name of the model file stored in Assets.
+   *
+   * @return
+   */
+  protected abstract String getModelPath();
+
+  /**
+   * Get the name of the label file stored in Assets.
+   *
+   * @return
+   */
+  protected abstract String getLabelPath();
+
+  /**
+   * Get the image size along the x axis.
+   *
+   * @return
+   */
+  protected abstract int getImageSizeX();
+
+  /**
+   * Get the image size along the y axis.
+   *
+   * @return
+   */
+  protected abstract int getImageSizeY();
+
+  /**
+   * Get the number of bytes that is used to store a single color channel value.
+   *
+   * @return
+   */
+  protected abstract int getNumBytesPerChannel();
+
+  /**
+   * Add pixelValue to byteBuffer.
+   *
+   * @param pixelValue
+   */
+  protected abstract void addPixelValue(int pixelValue);
+
+  /**
+   * Read the probability value for the specified label This is either the original value as it was
+   * read from the net's output or the updated value after the filter was applied.
+   *
+   * @param labelIndex
+   * @return
+   */
+  protected abstract float getProbability(int labelIndex);
+
+  /**
+   * Set the probability value for the specified label.
+   *
+   * @param labelIndex
+   * @param value
+   */
+  protected abstract void setProbability(int labelIndex, Number value);
+
+  /**
+   * Get the normalized probability value for the specified label. This is the final value as it
+   * will be shown to the user.
+   *
+   * @return
+   */
+  protected abstract float getNormalizedProbability(int labelIndex);
+
+  /**
+   * Run inference using the prepared input in {@link #imgData}. Afterwards, the result will be
+   * provided by getProbability().
+   *
+   * <p>This additional method is necessary, because we don't have a common base for different
+   * primitive data types.
+   */
+  protected abstract void runInference();
+
+  /**
+   * Get the total number of labels.
+   *
+   * @return
+   */
+  protected int getNumLabels() {
+    return labelList.size();
+  }
 }
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatInception.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatInception.java
new file mode 100644
index 0000000000..3108422952
--- /dev/null
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatInception.java
@@ -0,0 +1,103 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+import java.io.IOException;
+
+/**
+ * This classifier works with the Inception-v3 slim model. It applies floating point inference
+ * rather than using a quantized model.
+ */
+public class ImageClassifierFloatInception extends ImageClassifier {
+
+  /** The inception net requires additional normalization of the used input. */
+  private static final int IMAGE_MEAN = 128;
+
+  private static final float IMAGE_STD = 128.0f;
+
+  /**
+   * An array to hold inference results, to be feed into Tensorflow Lite as outputs. This isn't part
+   * of the super class, because we need a primitive array here.
+   */
+  private float[][] labelProbArray = null;
+
+  /**
+   * Initializes an {@code ImageClassifier}.
+   *
+   * @param activity
+   */
+  ImageClassifierFloatInception(Activity activity) throws IOException {
+    super(activity);
+    labelProbArray = new float[1][getNumLabels()];
+  }
+
+  @Override
+  protected String getModelPath() {
+    // you can download this file from
+    // https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip
+    return "inceptionv3_slim_2016.tflite";
+  }
+
+  @Override
+  protected String getLabelPath() {
+    return "labels_imagenet_slim.txt";
+  }
+
+  @Override
+  protected int getImageSizeX() {
+    return 299;
+  }
+
+  @Override
+  protected int getImageSizeY() {
+    return 299;
+  }
+
+  @Override
+  protected int getNumBytesPerChannel() {
+    // a 32bit float value requires 4 bytes
+    return 4;
+  }
+
+  @Override
+  protected void addPixelValue(int pixelValue) {
+    imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+    imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+    imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+  }
+
+  @Override
+  protected float getProbability(int labelIndex) {
+    return labelProbArray[0][labelIndex];
+  }
+
+  @Override
+  protected void setProbability(int labelIndex, Number value) {
+    labelProbArray[0][labelIndex] = value.floatValue();
+  }
+
+  @Override
+  protected float getNormalizedProbability(int labelIndex) {
+    // TODO the following value isn't in [0,1] yet, but may be greater. Why?
+    return getProbability(labelIndex);
+  }
+
+  @Override
+  protected void runInference() {
+    tflite.run(imgData, labelProbArray);
+  }
+}
diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java
new file mode 100644
index 0000000000..5f341f0f5b
--- /dev/null
+++ b/tensorflow/contrib/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java
@@ -0,0 +1,94 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+package com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+import java.io.IOException;
+
+/** This classifier works with the quantized MobileNet model. */
+public class ImageClassifierQuantizedMobileNet extends ImageClassifier {
+
+  /**
+   * An array to hold inference results, to be feed into Tensorflow Lite as outputs. This isn't part
+   * of the super class, because we need a primitive array here.
+   */
+  private byte[][] labelProbArray = null;
+
+  /**
+   * Initializes an {@code ImageClassifier}.
+   *
+   * @param activity
+   */
+  ImageClassifierQuantizedMobileNet(Activity activity) throws IOException {
+    super(activity);
+    labelProbArray = new byte[1][getNumLabels()];
+  }
+
+  @Override
+  protected String getModelPath() {
+    // you can download this file from
+    // https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip
+    return "mobilenet_quant_v1_224.tflite";
+  }
+
+  @Override
+  protected String getLabelPath() {
+    return "labels_mobilenet_quant_v1_224.txt";
+  }
+
+  @Override
+  protected int getImageSizeX() {
+    return 224;
+  }
+
+  @Override
+  protected int getImageSizeY() {
+    return 224;
+  }
+
+  @Override
+  protected int getNumBytesPerChannel() {
+    // the quantized model uses a single byte only
+    return 1;
+  }
+
+  @Override
+  protected void addPixelValue(int pixelValue) {
+    imgData.put((byte) ((pixelValue >> 16) & 0xFF));
+    imgData.put((byte) ((pixelValue >> 8) & 0xFF));
+    imgData.put((byte) (pixelValue & 0xFF));
+  }
+
+  @Override
+  protected float getProbability(int labelIndex) {
+    return labelProbArray[0][labelIndex];
+  }
+
+  @Override
+  protected void setProbability(int labelIndex, Number value) {
+    labelProbArray[0][labelIndex] = value.byteValue();
+  }
+
+  @Override
+  protected float getNormalizedProbability(int labelIndex) {
+    return (labelProbArray[0][labelIndex] & 0xff) / 255.0f;
+  }
+
+  @Override
+  protected void runInference() {
+    tflite.run(imgData, labelProbArray);
+  }
+}
diff --git a/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py b/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py
index c3a57ba51b..2b9eee4ef7 100644
--- a/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py
+++ b/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py
@@ -50,16 +50,12 @@ def pairwise_distance(feature, squared=False):
     pairwise_distances: 2-D Tensor of size [number of data, number of data].
   """
   pairwise_distances_squared = math_ops.add(
+      math_ops.reduce_sum(math_ops.square(feature), axis=[1], keepdims=True),
       math_ops.reduce_sum(
-          math_ops.square(feature),
-          axis=[1],
-          keep_dims=True),
-      math_ops.reduce_sum(
-          math_ops.square(
-              array_ops.transpose(feature)),
+          math_ops.square(array_ops.transpose(feature)),
           axis=[0],
-          keep_dims=True)) - 2.0 * math_ops.matmul(
-              feature, array_ops.transpose(feature))
+          keepdims=True)) - 2.0 * math_ops.matmul(feature,
+                                                  array_ops.transpose(feature))
 
   # Deal with numerical inaccuracies. Set small negatives to zero.
   pairwise_distances_squared = math_ops.maximum(pairwise_distances_squared, 0.0)
@@ -132,10 +128,10 @@ def masked_maximum(data, mask, dim=1):
     masked_maximums: N-D `Tensor`.
       The maximized dimension is of size 1 after the operation.
   """
-  axis_minimums = math_ops.reduce_min(data, dim, keep_dims=True)
+  axis_minimums = math_ops.reduce_min(data, dim, keepdims=True)
   masked_maximums = math_ops.reduce_max(
-      math_ops.multiply(
-          data - axis_minimums, mask), dim, keep_dims=True) + axis_minimums
+      math_ops.multiply(data - axis_minimums, mask), dim,
+      keepdims=True) + axis_minimums
   return masked_maximums
 
 
@@ -151,10 +147,10 @@ def masked_minimum(data, mask, dim=1):
     masked_minimums: N-D `Tensor`.
       The minimized dimension is of size 1 after the operation.
   """
-  axis_maximums = math_ops.reduce_max(data, dim, keep_dims=True)
+  axis_maximums = math_ops.reduce_max(data, dim, keepdims=True)
   masked_minimums = math_ops.reduce_min(
-      math_ops.multiply(
-          data - axis_maximums, mask), dim, keep_dims=True) + axis_maximums
+      math_ops.multiply(data - axis_maximums, mask), dim,
+      keepdims=True) + axis_maximums
   return masked_minimums
 
 
@@ -202,8 +198,7 @@ def triplet_semihard_loss(labels, embeddings, margin=1.0):
   mask_final = array_ops.reshape(
       math_ops.greater(
           math_ops.reduce_sum(
-              math_ops.cast(
-                  mask, dtype=dtypes.float32), 1, keep_dims=True),
+              math_ops.cast(mask, dtype=dtypes.float32), 1, keepdims=True),
           0.0), [batch_size, batch_size])
   mask_final = array_ops.transpose(mask_final)
 
@@ -290,7 +285,7 @@ def npairs_loss(labels, embeddings_anchor, embeddings_positive,
 
   labels_remapped = math_ops.to_float(
       math_ops.equal(labels, array_ops.transpose(labels)))
-  labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keep_dims=True)
+  labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keepdims=True)
 
   # Add the softmax loss.
   xent_loss = nn.softmax_cross_entropy_with_logits(
@@ -395,7 +390,7 @@ def npairs_loss_multilabel(sparse_labels, embeddings_anchor,
 
     multilabel_adjacency_matrix = _build_multilabel_adjacency(sparse_labels)
     labels_remapped = math_ops.to_float(multilabel_adjacency_matrix)
-    labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keep_dims=True)
+    labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keepdims=True)
 
     # Add the softmax loss.
     xent_loss = nn.softmax_cross_entropy_with_logits(
@@ -448,10 +443,10 @@ def lifted_struct_loss(labels, embeddings, margin=1.0):
   # Safe maximum: Temporarily shift negative distances
   #   above zero before taking max.
   #     this is to take the max only among negatives.
-  row_minimums = math_ops.reduce_min(diff, 1, keep_dims=True)
+  row_minimums = math_ops.reduce_min(diff, 1, keepdims=True)
   row_negative_maximums = math_ops.reduce_max(
-      math_ops.multiply(
-          diff - row_minimums, mask), 1, keep_dims=True) + row_minimums
+      math_ops.multiply(diff - row_minimums, mask), 1,
+      keepdims=True) + row_minimums
 
   # Compute the loss.
   # Keep track of matrix of maximums where M_ij = max(m_i, m_j)
@@ -467,10 +462,11 @@ def lifted_struct_loss(labels, embeddings, margin=1.0):
       array_ops.transpose(max_elements), [-1, 1])
 
   loss_exp_left = array_ops.reshape(
-      math_ops.reduce_sum(math_ops.multiply(
-          math_ops.exp(
-              diff_tiled - max_elements_vect),
-          mask_tiled), 1, keep_dims=True), [batch_size, batch_size])
+      math_ops.reduce_sum(
+          math_ops.multiply(
+              math_ops.exp(diff_tiled - max_elements_vect), mask_tiled),
+          1,
+          keepdims=True), [batch_size, batch_size])
 
   loss_mat = max_elements + math_ops.log(
       loss_exp_left + array_ops.transpose(loss_exp_left))
@@ -686,7 +682,7 @@ def _find_loss_augmented_facility_idx(pairwise_distances, labels, chosen_ids,
                   array_ops.reshape(pairwise_distances_candidate, [1, -1])
               ], 0),
               axis=0,
-              keep_dims=True), [num_candidates, -1]),
+              keepdims=True), [num_candidates, -1]),
       axis=1)
 
   nmi_scores = array_ops.zeros([num_candidates])
diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md
index 6959ca344f..995230dfa8 100644
--- a/tensorflow/contrib/makefile/README.md
+++ b/tensorflow/contrib/makefile/README.md
@@ -130,6 +130,105 @@ adb shell '/data/local/tmp/benchmark \
 
 For more details, see the [benchmark documentation](../../tools/benchmark).
 
+## CUDA support for Tegra devices running Android (Nvidia Shield TV, etc)
+
+With the release of TF 1.6 and JetPack for Android 3.2 (currently pending), you can now build a version of TensorFlow for compatible devices according to the following instructions which will receive the full benefits of GPU acceleration.
+
+#### Environment setup:
+
+First, download and install JetPack for Android version 3.2 or greater from [Nvidia](https://developers.nvidia.com). Note that as of the TF 1.6 release the JetPack for Android 3.2 release is still pending, and regular JetPack for L4T will not work.
+
+```bash
+git clone https://github.com/tensorflow/tensorflow.git
+cd tensorflow
+JETPACK=$HOME/JetPack_Android_3.2
+TEGRA_LIBS="$JETPACK/cuDNN/aarch64/cuda/lib64/libcudnn.so  $JETPACK/cuda-9.0/extras/CUPTI/lib64/libcupti.so $JETPACK/cuda/targets/aarch64-linux-androideabi/lib64/libcufft.so"
+```
+
+#### Building all CUDA-enabled native binaries:
+This will build CUDA-enabled versions of libtensorflow_inference.so and the benchmark binary. (libtensorflow_demo.so will also be built incidentally, but it does not support CUDA)
+
+```bash
+NDK_ROOT=$JETPACK/android-ndk-r13b
+CC_PREFIX=ccache tensorflow/contrib/makefile/build_all_android.sh -s tensorflow/contrib/makefile/sub_makefiles/android/Makefile.in -t "libtensorflow_inference.so libtensorflow_demo.so all" -a tegra
+```
+(add -T on subsequent builds to skip protobuf downloading/building)
+
+
+#### Testing the CUDA-enabled benchmark via adb:
+Build binaries first as above, then run:
+
+```bash
+adb shell mkdir -p /data/local/tmp/lib64
+adb push $TEGRA_LIBS /data/local/tmp/lib64
+adb push tensorflow/contrib/makefile/gen/bin/android_arm64-v8a/benchmark /data/local/tmp
+wget  https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk
+unzip tensorflow_demo.apk -d /tmp/tensorflow_demo
+adb push /tmp/tensorflow_demo/assets/*.pb /data/local/tmp
+adb shell "LD_LIBRARY_PATH=/data/local/tmp/lib64 /data/local/tmp/benchmark --graph=/data/local/tmp/tensorflow_inception_graph.pb"
+```
+
+#### Building the CUDA-enabled TensorFlow AAR with Bazel:
+Build the native binaries first as above. Then, build the aar and package the native libs by executing the following:
+```bash
+mkdir -p /tmp/tf/jni/arm64-v8a
+cp tensorflow/contrib/makefile/gen/lib/android_tegra/libtensorflow_*.so /tmp/tf/jni/arm64-v8a/
+cp $TEGRA_LIBS /tmp/tf/jni/arm64-v8a
+bazel build //tensorflow/contrib/android:android_tensorflow_inference_java.aar
+cp bazel-bin/tensorflow/contrib/android/android_tensorflow_inference_java.aar /tmp/tf/tensorflow.aar
+cd /tmp/tf
+chmod +w tensorflow.aar
+zip -ur tensorflow.aar $(find jni -name *.so)
+```
+
+#### Building the CUDA-enabled TensorFlow Android demo with Bazel:
+Build binaries first as above, then edit tensorflow/examples/android/BUILD and replace: 
+```
+    srcs = [
+       ":libtensorflow_demo.so",
+       "//tensorflow/contrib/android:libtensorflow_inference.so",
+    ],
+```
+with:
+```
+srcs = glob(["libs/arm64-v8a/*.so"]),
+```
+
+Then run:
+```bash
+# Create dir for native libs
+mkdir -p tensorflow/examples/android/libs/arm64-v8a
+
+# Copy JetPack libs
+cp $TEGRA_LIBS  tensorflow/examples/android/libs/arm64-v8a
+
+# Copy native TensorFlow libraries
+cp tensorflow/contrib/makefile/gen/lib/android_arm64-v8a/libtensorflow_*.so tensorflow/examples/android/libs/arm64-v8a/
+
+# Build APK
+bazel build -c opt --fat_apk_cpu=arm64-v8a tensorflow/android:tensorflow_demo
+
+# Install
+adb install -r -f bazel-bin/tensorflow/examples/android/tensorflow_demo.apk 
+```
+
+#### Building the CUDA-enabled Android demo with gradle/Android Studio:
+
+Add tensorflow/examples/android as an Android project in Android Studio as normal.
+
+Edit build.gradle and:
+* set nativeBuildSystem = 'makefile'
+* set cpuType = 'arm64-v8a'
+* in "buildNativeMake", replace cpuType with 'tegra' (optional speedups like -T and ccache also work) 
+* set the environment "NDK_ROOT" var to $JETPACK/android-ndk-r13b
+
+Click "build apk" to build.
+
+Install:
+```bash
+adb install -r -f tensorflow/examples/android/gradleBuild/outputs/apk/debug/android-debug.apk
+```
+
 ## iOS
 
 _Note: To use this library in an iOS application, see related instructions in
diff --git a/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh b/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh
index 203ff4f890..421ddd210f 100755
--- a/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh
+++ b/tensorflow/contrib/makefile/samples/build_and_run_inception_hexagon.sh
@@ -36,7 +36,7 @@ while getopts "bc:Eps" opt_name; do
     b) BUILD_ONLY="true";;
     c) TEST_COUNT="${OPTARG}";;
     E) ENABLE_EXPERIMENTAL_HEXNN_OPS="true";;
-    p) USE_PREBUILT_HEXAOGON_BINARIES="true";;
+    p) USE_PREBUILT_HEXAGON_BINARIES="true";;
     s) SKIP_DOWNLOAD_IF_EXIST="true";;
     *) usage;;
   esac
@@ -49,7 +49,7 @@ if [[ -z "${NDK_ROOT}" ]]; then
     exit 1
 fi
 
-if [[ "${USE_PREBUILT_HEXAOGON_BINARIES}" != "true" &&
+if [[ "${USE_PREBUILT_HEXAGON_BINARIES}" != "true" &&
       -z "${QUALCOMM_SDK}" ]]; then
     echo "QUALCOMM_SDK is empty" 1>&2
     usage
@@ -84,7 +84,7 @@ rm -rf "${GEN_DIR}"
 mkdir -p "${GEN_LIBS_DIR}"
 mkdir -p "${GEN_DOWNLOAD_DIR}"
 
-if [[ "${USE_PREBUILT_HEXAOGON_BINARIES}" == "true" ]]; then
+if [[ "${USE_PREBUILT_HEXAGON_BINARIES}" == "true" ]]; then
     echo "Download prebuilt hexagon binaries"
     if [[ "${BUILD_ONLY}" != "true" ]]; then
         CONTROLLER_PUSH_DEST="/data/local/tmp"
diff --git a/tensorflow/contrib/rnn/python/ops/lstm_ops.py b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
index f700717394..4eb4fbcd92 100644
--- a/tensorflow/contrib/rnn/python/ops/lstm_ops.py
+++ b/tensorflow/contrib/rnn/python/ops/lstm_ops.py
@@ -572,9 +572,8 @@ class LSTMBlockWrapper(base_layer.Layer):
 
   def _gather_states(self, data, indices, batch_size):
     """Produce `out`, s.t. out(i, j) = data(indices(i), i, j)."""
-    mod_indices = indices * batch_size + math_ops.range(batch_size)
-    return array_ops.gather(
-        array_ops.reshape(data, [-1, self.num_units]), mod_indices)
+    return array_ops.gather_nd(
+        data, array_ops.stack([indices, math_ops.range(batch_size)], axis=1))
 
 
 class LSTMBlockFusedCell(LSTMBlockWrapper):
diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index dce71c393a..a6c2d9cdbb 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -424,8 +424,9 @@ class TimeFreqLSTMCell(rnn_cell_impl.RNNCell):
           "W_O_diag", shape=[self._num_units], dtype=dtype)
 
     # initialize the first freq state to be zero
-    m_prev_freq = array_ops.zeros([int(inputs.get_shape()[0]), self._num_units],
-                                  dtype)
+    m_prev_freq = array_ops.zeros(
+        [inputs.shape[0].value or inputs.get_shape()[0], self._num_units],
+        dtype)
     for fq in range(len(freq_inputs)):
       c_prev = array_ops.slice(state, [0, 2 * fq * self._num_units],
                                [-1, self._num_units])
diff --git a/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc b/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc
index 64973ccccd..dfa12e873a 100644
--- a/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc
+++ b/tensorflow/contrib/seq2seq/kernels/beam_search_ops.cc
@@ -80,12 +80,12 @@ class GatherTreeOp : public OpKernel {
                                 max_sequence_lengths.shape().DebugString()));
     Tensor* beams;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, step_ids_shape, &beams));
-    typename TTypes<T, 3>::ConstTensor step_ids_t = step_ids.tensor<T, 3>();
-    typename TTypes<T, 3>::ConstTensor parent_ids_t = parent_ids.tensor<T, 3>();
+    typename TTypes<T, 3>::ConstTensor step_ids_t(step_ids.tensor<T, 3>());
+    typename TTypes<T, 3>::ConstTensor parent_ids_t(parent_ids.tensor<T, 3>());
     typename TTypes<int32>::ConstVec max_seq_lens_t =
         max_sequence_lengths.vec<int32>();
-    typename TTypes<T>::ConstScalar end_token_t = end_token.scalar<T>();
-    typename TTypes<T, 3>::Tensor beams_t = beams->tensor<T, 3>();
+    typename TTypes<T>::ConstScalar end_token_t(end_token.scalar<T>());
+    typename TTypes<T, 3>::Tensor beams_t(beams->tensor<T, 3>());
     const T end_token_value = end_token_t();
     functor::GatherTree<Device, T>()(ctx, device, step_ids_t, parent_ids_t,
                                      max_seq_lens_t, end_token_value, beams_t);
diff --git a/tensorflow/contrib/signal/python/ops/spectral_ops.py b/tensorflow/contrib/signal/python/ops/spectral_ops.py
index bca2e01d7b..a8b5deff6c 100644
--- a/tensorflow/contrib/signal/python/ops/spectral_ops.py
+++ b/tensorflow/contrib/signal/python/ops/spectral_ops.py
@@ -144,7 +144,7 @@ def inverse_stft_window_fn(frame_step,
       overlaps = -(-frame_length // frame_step)  # Ceiling division.
       denom = array_ops.pad(denom, [(0, overlaps * frame_step - frame_length)])
       denom = array_ops.reshape(denom, [overlaps, frame_step])
-      denom = math_ops.reduce_sum(denom, 0, keep_dims=True)
+      denom = math_ops.reduce_sum(denom, 0, keepdims=True)
       denom = array_ops.tile(denom, [overlaps, 1])
       denom = array_ops.reshape(denom, [overlaps * frame_step])
 
diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py
index 7ab6805fac..c24bd04851 100644
--- a/tensorflow/contrib/slim/python/slim/evaluation_test.py
+++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py
@@ -29,6 +29,7 @@ from tensorflow.contrib.framework.python.ops import variables as variables_lib
 from tensorflow.contrib.metrics.python.ops import metric_ops
 from tensorflow.contrib.slim.python.slim import evaluation
 from tensorflow.contrib.training.python.training import evaluation as evaluation_lib
+from tensorflow.core.protobuf import saver_pb2
 from tensorflow.python.debug.lib import debug_data
 from tensorflow.python.debug.wrappers import hooks
 from tensorflow.python.framework import constant_op
@@ -235,7 +236,7 @@ class SingleEvaluationTest(test.TestCase):
   def _prepareCheckpoint(self, checkpoint_path):
     init_op = control_flow_ops.group(variables.global_variables_initializer(),
                                      variables.local_variables_initializer())
-    saver = saver_lib.Saver()
+    saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
     with self.test_session() as sess:
       sess.run(init_op)
       saver.save(sess, checkpoint_path)
diff --git a/tensorflow/contrib/tensor_forest/BUILD b/tensorflow/contrib/tensor_forest/BUILD
index 58a7fa095d..1e4cc3f095 100644
--- a/tensorflow/contrib/tensor_forest/BUILD
+++ b/tensorflow/contrib/tensor_forest/BUILD
@@ -497,6 +497,7 @@ py_library(
         ":tensor_forest_v4_ops_py",
         "//tensorflow/contrib/decision_trees/proto:generic_tree_model_py",
         "//tensorflow/contrib/framework:framework_py",
+        "//tensorflow/contrib/tensor_forest/proto:fertile_stats_proto_py",
         "//tensorflow/contrib/tensor_forest/proto:tensor_forest_params_proto_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD
index 28f571e1f0..65a0e903a7 100644
--- a/tensorflow/contrib/tensorrt/BUILD
+++ b/tensorflow/contrib/tensorrt/BUILD
@@ -1,5 +1,6 @@
 # Description:
-#   Wrap NVIDIA TensorRT (http://developer.nvidia.com/tensorrt) with tensorflow.
+#   Wrap NVIDIA TensorRT (http://developer.nvidia.com/tensorrt) with tensorflow
+#   and provide TensorRT operators and converter package.
 #   APIs are meant to change over time.
 
 package(default_visibility = ["//tensorflow:__subpackages__"])
@@ -8,7 +9,19 @@ licenses(["notice"])  # Apache 2.0
 
 exports_files(["LICENSE"])
 
+load(
+    "//tensorflow:tensorflow.bzl",
+    "tf_cc_test",
+    "tf_copts",
+    "tf_cuda_library",
+    "tf_custom_op_library",
+    "tf_custom_op_library_additional_deps",
+    "tf_gen_op_libs",
+    "tf_gen_op_wrapper_py",
+)
 load("//tensorflow:tensorflow.bzl", "tf_cuda_cc_test")
+load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library")
+load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc")
 load(
     "@local_config_tensorrt//:build_defs.bzl",
     "if_tensorrt",
@@ -32,6 +45,195 @@ tf_cuda_cc_test(
     ]),
 )
 
+tf_custom_op_library(
+    name = "python/ops/_trt_engine_op.so",
+    srcs = ["ops/trt_engine_op.cc"],
+    deps = [
+        ":trt_engine_op_kernel",
+        ":trt_shape_function",
+        "//tensorflow/core:lib_proto_parsing",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]),
+)
+
+tf_cuda_library(
+    name = "trt_shape_function",
+    srcs = ["shape_fn/trt_shfn.cc"],
+    hdrs = ["shape_fn/trt_shfn.h"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":trt_logging",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]) + tf_custom_op_library_additional_deps(),
+)
+
+cc_library(
+    name = "trt_engine_op_kernel",
+    srcs = ["kernels/trt_engine_op.cc"],
+    hdrs = ["kernels/trt_engine_op.h"],
+    copts = tf_copts(),
+    deps = [
+        ":trt_logging",
+        "//tensorflow/core:gpu_headers_lib",
+        "//tensorflow/core:lib_proto_parsing",
+        "//tensorflow/core:stream_executor_headers_lib",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]) + tf_custom_op_library_additional_deps(),
+    # TODO(laigd)
+    alwayslink = 1,  # buildozer: disable=alwayslink-with-hdrs
+)
+
+tf_gen_op_libs(
+    op_lib_names = ["trt_engine_op"],
+    deps = if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]),
+)
+
+tf_cuda_library(
+    name = "trt_logging",
+    srcs = ["log/trt_logger.cc"],
+    hdrs = ["log/trt_logger.h"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/core:lib_proto_parsing",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]),
+)
+
+tf_gen_op_wrapper_py(
+    name = "trt_engine_op",
+    deps = [
+        ":trt_engine_op_op_lib",
+        ":trt_logging",
+        ":trt_shape_function",
+    ],
+)
+
+tf_custom_op_py_library(
+    name = "trt_engine_op_loader",
+    srcs = ["python/ops/trt_engine_op.py"],
+    dso = [
+        ":python/ops/_trt_engine_op.so",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]),
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:resources",
+    ],
+)
+
+py_library(
+    name = "init_py",
+    srcs = [
+        "__init__.py",
+        "python/__init__.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":trt_convert_py",
+        ":trt_ops_py",
+    ],
+)
+
+py_library(
+    name = "trt_ops_py",
+    srcs_version = "PY2AND3",
+    deps = [
+        ":trt_engine_op",
+        ":trt_engine_op_loader",
+    ],
+)
+
+py_library(
+    name = "trt_convert_py",
+    srcs = ["python/trt_convert.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":wrap_conversion",
+    ],
+)
+
+tf_py_wrap_cc(
+    name = "wrap_conversion",
+    srcs = ["trt_conversion.i"],
+    copts = tf_copts(),
+    deps = [
+        ":trt_conversion",
+        "//tensorflow/core:framework_lite",
+        "//util/python:python_headers",
+    ],
+)
+
+# Library for the node-level conversion portion of TensorRT operation creation
+tf_cuda_library(
+    name = "trt_conversion",
+    srcs = [
+        "convert/convert_graph.cc",
+        "convert/convert_nodes.cc",
+    ],
+    hdrs = [
+        "convert/convert_graph.h",
+        "convert/convert_nodes.h",
+    ],
+    deps = [
+        ":segment",
+        ":trt_logging",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:utils",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_lite",
+        "//tensorflow/core:graph",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:devices",
+        "//tensorflow/core/grappler/clusters:virtual_cluster",
+        "//tensorflow/core/grappler/costs:graph_properties",
+        "//tensorflow/core/grappler/optimizers:constant_folding",
+        "//tensorflow/core/grappler/optimizers:layout_optimizer",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]) + tf_custom_op_library_additional_deps(),
+)
+
+# Library for the segmenting portion of TensorRT operation creation
+cc_library(
+    name = "segment",
+    srcs = ["segment/segment.cc"],
+    hdrs = [
+        "segment/segment.h",
+        "segment/union_find.h",
+    ],
+    linkstatic = 1,
+    deps = [
+        "//tensorflow/core:graph",
+        "//tensorflow/core:lib_proto_parsing",
+        "//tensorflow/core:protos_all_cc",
+        "@protobuf_archive//:protobuf_headers",
+    ],
+)
+
+tf_cc_test(
+    name = "segment_test",
+    size = "small",
+    srcs = ["segment/segment_test.cc"],
+    deps = [
+        ":segment",
+        "//tensorflow/c:c_api",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md
new file mode 100644
index 0000000000..dfcce0fd00
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/README.md
@@ -0,0 +1,40 @@
+Using TensorRT in TensorFlow
+============================
+
+This module provides necessary bindings and introduces TRT_engine_op
+operator that wraps a subgraph in TensorRT.
+
+Compilation
+-----------
+
+In order to compile the module, you need to have a local TensorRT
+installation (libnvinfer.so and respective include files). During the
+configuration step, TensorRT should be enabled and installation path
+should be set. If installed through package managers (deb,rpm),
+configure script should find the necessary components from the system
+automatically. If installed from tar packages, user has to set path to
+location where the library is installed during configuration.
+
+
+```
+bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
+bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
+```
+
+After the installation of tensorflow package, TensorRT transformation
+will be available. An example use is shown below.
+
+```python
+import tensorflow as tf
+import tensorflow.contrib.tensorrt as trt
+#... create and train or load model
+gdef = sess.graph.as_graph_def()
+trt_gdef = trt.create_inference_graph(
+    gdef, #original graph_def
+    ["output"], #name of output node(s)
+    max_batch_size, #maximum batch size to run the inference
+    max_workspace_size_bytes) # max memory for TensorRT to use
+tf.reset_default_graph()
+tf.import_graph_def(graph_def=trt_gdef)
+#...... run inference
+```
diff --git a/tensorflow/contrib/tensorrt/__init__.py b/tensorflow/contrib/tensorrt/__init__.py
new file mode 100644
index 0000000000..fd551d70b4
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/__init__.py
@@ -0,0 +1,23 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Exposes the python wrapper for TensorRT graph transforms."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.tensorrt.python import *
+# pylint: enable=unused-import,wildcard-import
diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc
new file mode 100644
index 0000000000..970f810473
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc
@@ -0,0 +1,273 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/convert/convert_graph.h"
+
+#include <map>
+#include <set>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/contrib/tensorrt/convert/convert_nodes.h"
+#include "tensorflow/contrib/tensorrt/segment/segment.h"
+#include "tensorflow/core/graph/algorithm.h"
+#include "tensorflow/core/graph/graph.h"
+#include "tensorflow/core/graph/graph_constructor.h"
+#include "tensorflow/core/grappler/clusters/virtual_cluster.h"
+#include "tensorflow/core/grappler/costs/graph_properties.h"
+#include "tensorflow/core/grappler/devices.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/optimizers/constant_folding.h"
+#include "tensorflow/core/grappler/optimizers/layout_optimizer.h"
+#include "tensorflow/core/grappler/utils.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/protobuf/device_properties.pb.h"  // NOLINT
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "tensorrt/include/NvInfer.h"
+
+namespace tensorflow {
+namespace tensorrt {
+namespace convert {
+namespace {
+
+static bool IsTensorRTCandidate(const tensorflow::NodeDef& node_def) {
+  // LINT.IfChange
+  // TODO(jie): Segmentation shouldn't associated with op name.
+  //            Split it into a registration for each kernel.
+  static const std::set<string> candidate_ops = {
+      "Identity", "Const", "Conv2D", "MaxPool", "BiasAdd", "Relu",
+      "Add",      "Mul",   "Sub",    "Rsqrt",   "Pad"  // "Placeholder" ,"Mean"
+  };
+  // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.h)
+  return candidate_ops.count(node_def.op());
+}
+
+void GetSubGraphIncomingEdges(const tensorflow::Graph& graph,
+                              const std::set<int>& subgraph_node_ids,
+                              tensorflow::EdgeSet* incoming_edges) {
+  for (int node_id : subgraph_node_ids) {
+    const tensorflow::Node* node = graph.FindNodeId(node_id);
+    for (const tensorflow::Edge* edge : node->in_edges()) {
+      if (!subgraph_node_ids.count(edge->src()->id()) &&
+          !edge->src()->IsSource()) {
+        incoming_edges->insert(edge);
+      }
+    }
+  }
+}
+
+void GetSubGraphOutgoingEdges(const tensorflow::Graph& graph,
+                              const std::set<int>& subgraph_node_ids,
+                              tensorflow::EdgeSet* outgoing_edges) {
+  for (int node_id : subgraph_node_ids) {
+    const tensorflow::Node* node = graph.FindNodeId(node_id);
+    for (const tensorflow::Edge* edge : node->out_edges()) {
+      if (!subgraph_node_ids.count(edge->dst()->id()) &&
+          !edge->dst()->IsSink()) {
+        outgoing_edges->insert(edge);
+      }
+    }
+  }
+}
+
+std::pair<string, int> ParseTensorName(string name, int default_idx = 0) {
+  int idx = default_idx;
+  size_t sep = name.find_last_of(':');
+  if (sep != string::npos) {
+    name = name.substr(0, sep);
+    idx = std::stoi(name.substr(sep + 1));
+  }
+  return std::make_pair(name, idx);
+}
+
+std::unordered_map<string, std::vector<int>> BuildTensorNameMap(
+    const std::vector<string>& tensor_names) {
+  std::unordered_map<string, std::vector<int>> result;
+  for (string const& tensor_name : tensor_names) {
+    string node_name;
+    int index;
+    std::tie(node_name, index) = ParseTensorName(tensor_name);
+    result[node_name].push_back(index);
+  }
+  return result;
+}
+
+tensorflow::Status ConvertSubGraphToTensorRT(
+    const std::vector<string>& output_names,
+    const std::set<int>& subgraph_node_ids,
+    size_t max_batch_size,  // Max batch size that engine will be created for
+    // Max amount of memory that engine will be allowed to consume, in bytes
+    size_t max_workspace_size_bytes,
+    const tensorflow::grappler::GraphProperties& graph_properties,
+    tensorflow::Graph* graph) {
+  tensorflow::EdgeSet subgraph_incoming_edges;
+  GetSubGraphIncomingEdges(*graph, subgraph_node_ids, &subgraph_incoming_edges);
+
+  std::vector<std::pair<int, int>> subgraph_inputs;
+
+  // Collect inputs by looking for incoming edges
+  for (const tensorflow::Edge* edge : subgraph_incoming_edges) {
+    subgraph_inputs.push_back({edge->src()->id(), edge->src_output()});
+  }
+  std::set<std::pair<int, int>> subgraph_outputs_set;
+  // Collect outputs referenced from output_names
+  auto output_name_to_index_map = BuildTensorNameMap(output_names);
+  for (int node_id : subgraph_node_ids) {
+    tensorflow::Node* node = graph->FindNodeId(node_id);
+    if (output_name_to_index_map.count(node->name())) {
+      for (int index : output_name_to_index_map.at(node->name())) {
+        subgraph_outputs_set.insert({node_id, index});
+      }
+    }
+  }
+  // Collect outputs referenced from outgoing edges
+  tensorflow::EdgeSet subgraph_outgoing_edges;
+  GetSubGraphOutgoingEdges(*graph, subgraph_node_ids, &subgraph_outgoing_edges);
+  for (const tensorflow::Edge* edge : subgraph_outgoing_edges) {
+    subgraph_outputs_set.insert({edge->src()->id(), edge->src_output()});
+  }
+  // Impose an ordering on the outputs
+  std::vector<std::pair<int, int>> subgraph_outputs(
+      subgraph_outputs_set.begin(), subgraph_outputs_set.end());
+  // Build TensorRT node and add it to the graph
+  tensorflow::NodeDef trt_node_def;
+  TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRTNodeDef(
+      *graph, subgraph_node_ids, subgraph_inputs, subgraph_outputs,
+      max_batch_size, max_workspace_size_bytes, graph_properties,
+      &trt_node_def));
+  tensorflow::Status status;
+  tensorflow::Node* trt_node = graph->AddNode(trt_node_def, &status);
+  TF_RETURN_IF_ERROR(status);
+
+  // Re-map outgoing edges to use the new TRT node instead of the orig subgraph
+  std::map<std::pair<int, int>, int> subgraph_edge_to_output_map;
+  for (size_t i = 0; i < subgraph_outputs.size(); ++i) {
+    subgraph_edge_to_output_map.insert({subgraph_outputs.at(i), i});
+  }
+  TF_RETURN_IF_ERROR(status);
+  for (const tensorflow::Edge* edge : subgraph_outgoing_edges) {
+    std::pair<int, int> old_src = {edge->src()->id(), edge->src_output()};
+    int new_src_output = subgraph_edge_to_output_map.at(old_src);
+    TF_RETURN_IF_ERROR(graph->UpdateEdge(trt_node, new_src_output, edge->dst(),
+                                         edge->dst_input()));
+  }
+  // Remove the original subgraph
+  for (int node_id : subgraph_node_ids) {
+    tensorflow::Node* node = graph->FindNodeId(node_id);
+    // Don't remove the input placeholders
+    if (node->type_string() == "Placeholder") {
+      continue;
+    }
+    graph->RemoveNode(node);
+  }
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status BuildNodeMap(
+    const tensorflow::Graph& graph,
+    std::unordered_map<string, tensorflow::Node*>* node_map) {
+  for (auto* node : graph.op_nodes()) {
+    if (!node_map->insert({node->name(), node}).second) {
+      return tensorflow::errors::AlreadyExists(
+          "Node name is not unique in graph: " + node->name());
+    }
+  }
+  return tensorflow::Status::OK();
+}
+
+}  // namespace
+
+tensorflow::Status ConvertGraphDefToTensorRT(
+    const tensorflow::GraphDef& graph_def,
+    const std::vector<string>& output_names, size_t max_batch_size,
+    size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def) {
+  // Optimization pass
+  tensorflow::grappler::GrapplerItem item;
+  item.fetch = output_names;
+  tensorflow::GraphDef gdef;
+
+  // Layout optimization
+  item.graph = graph_def;
+  tensorflow::grappler::LayoutOptimizer optimizer;
+  tensorflow::grappler::Cluster* cluster;
+
+  // Virtual cluster
+  tensorflow::DeviceProperties device_properties;
+  device_properties.set_type("GPU");
+  device_properties.mutable_environment()->insert({"architecture", "6"});
+  cluster =
+      new tensorflow::grappler::VirtualCluster({{"/GPU:0", device_properties}});
+
+  TF_RETURN_IF_ERROR(optimizer.Optimize(cluster, item, &gdef));
+
+  // Constant folding
+  item.graph = gdef;
+  tensorflow::grappler::ConstantFolding fold(nullptr);
+  TF_RETURN_IF_ERROR(fold.Optimize(nullptr, item, &gdef));
+
+  // AJ refactoring shape inference through grappler/GraphProperties.
+  tensorflow::grappler::GraphProperties static_graph_properties(item);
+  TF_RETURN_IF_ERROR(static_graph_properties.InferStatically(false));
+
+  // Build full graph
+  tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(),
+                                             gdef.library());
+  tensorflow::Graph graph(flib);
+  TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph(
+      tensorflow::GraphConstructorOptions(), gdef, &graph));
+
+  // Segment the graph into subgraphs that can be converted to TensorRT
+  tensorflow::tensorrt::segment::SegmentOptions segment_options;
+
+  // TODO(ben,jie,sami): exclude output nodes (DISCUSS IT)
+  for (auto node : output_names) {
+    segment_options.exclude_node_list.insert(node);
+  }
+
+  // TODO(sami): this should be passed as a knob!!!!
+  segment_options.minimum_segment_size = 2;
+  tensorflow::tensorrt::segment::SegmentNodesVector segments;
+  TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph(
+      gdef, IsTensorRTCandidate, segment_options, &segments));
+  if (segments.size() > 1) {
+    VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << segments.size();
+  }
+  std::unordered_map<string, tensorflow::Node*> node_map;
+  TF_RETURN_IF_ERROR(BuildNodeMap(graph, &node_map));
+  for (const std::set<string>& subgraph_node_names : segments) {
+    std::set<int> subgraph_node_ids;
+    for (const string& node_name : subgraph_node_names) {
+      subgraph_node_ids.insert(node_map.at(node_name)->id());
+    }
+    TF_RETURN_IF_ERROR(ConvertSubGraphToTensorRT(
+        output_names, subgraph_node_ids, max_batch_size,
+        max_workspace_size_bytes, static_graph_properties, &graph));
+  }
+  graph.ToGraphDef(new_graph_def);
+  return tensorflow::Status::OK();
+}
+
+}  // namespace convert
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.h b/tensorflow/contrib/tensorrt/convert/convert_graph.h
new file mode 100644
index 0000000000..154ad3f2e8
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/convert/convert_graph.h
@@ -0,0 +1,47 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_GRAPH_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_GRAPH_H_
+
+#include <vector>
+
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/types.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+
+namespace tensorflow {
+namespace tensorrt {
+namespace convert {
+
+// max_batch_size: maximum batch size which can be used for inference for
+//                 optimization targets inference run with max batch size.
+// max_workspace_size_bytes: The upper bound of memory allowence for
+//                 engine building.
+tensorflow::Status ConvertGraphDefToTensorRT(
+    const tensorflow::GraphDef& graph_def,
+    const std::vector<string>& output_names, size_t max_batch_size,
+    size_t max_workspace_size_bytes, tensorflow::GraphDef* new_graph_def);
+
+}  // namespace convert
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
+
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_GRAPH_H_
diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
new file mode 100644
index 0000000000..4003ba056d
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc
@@ -0,0 +1,1601 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/convert/convert_nodes.h"
+
+#include <algorithm>
+#include <list>
+#include <map>
+#include <memory>
+#include <set>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"  // NOLINT
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/graph/algorithm.h"
+#include "tensorflow/core/graph/graph.h"
+#include "tensorflow/core/graph/graph_constructor.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/tensor_coding.h"
+#include "tensorflow/core/platform/types.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "tensorflow/contrib/tensorrt/log/trt_logger.h"
+#include "tensorrt/include/NvInfer.h"
+
+//  Check if the types are equal. Cast to int first so that failure log message
+//  would work!
+#define CHECK_EQ_TYPE(val1, val2) CHECK_EQ((int)val1, (int)val2)
+
+namespace tensorflow {
+namespace tensorrt {
+namespace convert {
+
+namespace {
+
+inline tensorflow::Status ConvertDType(tensorflow::DataType tf_dtype,
+                                       nvinfer1::DataType* trt_dtype) {
+  switch (tf_dtype) {
+    case tensorflow::DataType::DT_FLOAT:
+      *trt_dtype = nvinfer1::DataType::kFLOAT;
+      break;
+    case tensorflow::DataType::DT_INT8:
+      *trt_dtype = nvinfer1::DataType::kINT8;
+      break;
+    case tensorflow::DataType::DT_HALF:
+      *trt_dtype = nvinfer1::DataType::kHALF;
+      break;
+    default:
+      return tensorflow::errors::InvalidArgument("Unsupported data type");
+  }
+  return tensorflow::Status::OK();
+}
+
+inline nvinfer1::Dims GetTensorShape(const tensorflow::Tensor& tensor) {
+  nvinfer1::Dims dims;
+  dims.nbDims = tensor.dims();
+  for (int i = 0; i < dims.nbDims; i++) {
+    dims.d[i] = tensor.dim_size(i);
+  }
+  return dims;
+}
+
+inline int64_t GetShapeSize(nvinfer1::Dims shape) {
+  // Returns total number of elements in shape
+  int64_t count = 1;
+  for (int d = 0; d < shape.nbDims; ++d) {
+    count *= shape.d[d];
+  }
+  return count;
+}
+
+static std::vector<std::pair<int, int>> CreateSamePadding(
+    const nvinfer1::DimsHW& stride, const nvinfer1::DimsHW& kernel,
+    const std::vector<int64_t>& input_dims) {
+  std::vector<std::pair<int, int>> padding(input_dims.size());
+  CHECK_EQ((size_t)stride.nbDims, input_dims.size());  // TODO(jie): N+C? NC+?
+
+  for (size_t i = 0; i < input_dims.size(); ++i) {
+    // Formula to calculate the padding
+    int p = ((input_dims[i] - 1) / stride.d[i]) * stride.d[i] + kernel.d[i] -
+            input_dims[i];
+    p = (p > 0) ? p : 0;
+
+    // Right precedence padding, like in TensorFlow
+    int left = p / 2;
+    int right = p - left;
+
+    VLOG(2) << "PADDING_" << i << " pre: " << left << ", post: " << right
+            << "paras: " << input_dims[i] << ", " << stride.d[i] << ", "
+            << "kernel: " << kernel.d[i];
+    padding[i] = {left, right};
+  }
+  return padding;
+}
+
+class TRT_ShapedWeights {
+ public:
+  TRT_ShapedWeights(tensorflow::DataType type, const void* values,
+                    nvinfer1::Dims shape)
+      : shape_(shape), type_(type), values_(values), empty_weight_flag_(false) {
+    // Note: this->shape.type[] is not used
+  }
+
+  explicit TRT_ShapedWeights(tensorflow::DataType type)
+      : shape_(), type_(type), values_(nullptr), empty_weight_flag_(true) {}
+
+  TRT_ShapedWeights(const TRT_ShapedWeights& rhs)
+      : shape_(rhs.shape_),
+        type_(rhs.type_),
+        values_(rhs.values_),
+        empty_weight_flag_(rhs.empty_weight_flag_) {}
+
+  int64_t count() const {
+    int64_t c = 1;
+    for (int i = 0; i < shape_.nbDims; i++) c *= shape_.d[i];
+    return c;
+  }
+
+  nvinfer1::Weights GetWeightsForTRT() const {
+    nvinfer1::DataType trt_type(nvinfer1::DataType::kFLOAT);
+    TF_CHECK_OK(ConvertDType(type_, &trt_type));
+    if (empty_weight_flag_) return nvinfer1::Weights{trt_type, nullptr, 0};
+
+    // Note: this->shape.type[] is not used
+    return nvinfer1::Weights{trt_type, GetValues(), GetShapeSize(shape_)};
+  }
+
+  const void* GetValues() const { return values_; }
+
+  void SetValues(const void* values) { values_ = values; }
+
+  size_t size_bytes() const {
+    int type_size = tensorflow::DataTypeSize(this->type_);
+    return this->count() * type_size;
+  }
+
+  // Default converter
+  operator nvinfer1::Weights() const { return GetWeightsForTRT(); }
+
+  nvinfer1::Dims shape_;
+  tensorflow::DataType type_;
+
+ private:
+  const void* values_;
+  bool empty_weight_flag_;
+};
+
+class TRT_TensorOrWeights {
+ public:
+  explicit TRT_TensorOrWeights(nvinfer1::ITensor* tensor)
+      : tensor_(tensor), weights_(DT_FLOAT), variant_(TRT_NODE_TENSOR) {}
+  explicit TRT_TensorOrWeights(const TRT_ShapedWeights& weights)
+      : tensor_(nullptr), weights_(weights), variant_(TRT_NODE_WEIGHTS) {}
+  TRT_TensorOrWeights(const TRT_TensorOrWeights& rhs)
+      : tensor_(rhs.tensor_), weights_(rhs.weights_), variant_(rhs.variant_) {}
+  ~TRT_TensorOrWeights() {}
+
+  bool is_tensor() const { return variant_ == TRT_NODE_TENSOR; }
+  bool is_weights() const { return variant_ == TRT_NODE_WEIGHTS; }
+
+  nvinfer1::ITensor* tensor() {
+    CHECK_EQ(is_tensor(), true);
+    return tensor_;
+  }
+  const nvinfer1::ITensor* tensor() const {
+    CHECK_EQ(is_tensor(), true);
+    return tensor_;
+  }
+  TRT_ShapedWeights& weights() {
+    CHECK_EQ(is_weights(), true);
+    return weights_;
+  }
+  const TRT_ShapedWeights& weights() const {
+    CHECK_EQ(is_weights(), true);
+    return weights_;
+  }
+  nvinfer1::Dims shape() const {
+    if (is_tensor()) {
+      return tensor()->getDimensions();
+    } else {
+      return weights().shape_;
+    }
+  }
+
+ private:
+  nvinfer1::ITensor* tensor_;
+  TRT_ShapedWeights weights_;
+  enum { TRT_NODE_TENSOR, TRT_NODE_WEIGHTS } variant_;
+};
+
+class TFAttrs {
+ public:
+  explicit TFAttrs(const tensorflow::NodeDef& tf_node) {
+    for (const auto& attr : tf_node.attr()) {
+      attrs_.insert({attr.first, &attr.second});
+    }
+  }
+  bool count(string key) const { return attrs_.count(key); }
+  tensorflow::AttrValue const* at(string key) const {
+    if (!attrs_.count(key)) {
+      LOG(FATAL) << "Attribute not found: " << key;
+    }
+    return attrs_.at(key);
+  }
+  template <typename T>
+  T get(string key) const;
+  template <typename T>
+  T get(string key, const T& default_value) const {
+    return attrs_.count(key) ? this->get<T>(key) : default_value;
+  }
+
+ private:
+  typedef std::map<string, tensorflow::AttrValue const*> AttrMap;
+  AttrMap attrs_;
+};
+
+template <>
+string TFAttrs::get<string>(string key) const {
+  return this->at(key)->s();
+}
+
+template <>
+std::vector<int> TFAttrs::get<std::vector<int>>(string key) const {
+  auto attr = this->at(key)->list().i();
+  return std::vector<int>(attr.begin(), attr.end());
+}
+
+template <>
+nvinfer1::Dims TFAttrs::get<nvinfer1::Dims>(string key) const {
+  auto values = this->get<std::vector<int>>(key);
+  nvinfer1::Dims dims;
+  dims.nbDims = values.size();
+  std::copy(values.begin(), values.end(), dims.d);
+  // Note: No dimension type information is included
+  return dims;
+}
+
+template <>
+nvinfer1::DataType TFAttrs::get<nvinfer1::DataType>(string key) const {
+  nvinfer1::DataType trt_dtype(nvinfer1::DataType::kFLOAT);
+  TF_CHECK_OK(ConvertDType(this->at(key)->type(), &trt_dtype));
+  return trt_dtype;
+}
+
+template <>
+tensorflow::DataType TFAttrs::get<tensorflow::DataType>(string key) const {
+  return this->at(key)->type();
+}
+
+template <typename T>
+void Reorder4(nvinfer1::DimsNCHW shape, const T* idata,
+              nvinfer1::DimsNCHW istrides, T* odata,
+              nvinfer1::DimsNCHW ostrides) {
+  for (int n = 0; n < shape.n(); ++n) {
+    for (int c = 0; c < shape.c(); ++c) {
+      for (int h = 0; h < shape.h(); ++h) {
+        for (int w = 0; w < shape.w(); ++w) {
+          odata[n * ostrides.n() + c * ostrides.c() + h * ostrides.h() +
+                w * ostrides.w()] = idata[n * istrides.n() + c * istrides.c() +
+                                          h * istrides.h() + w * istrides.w()];
+        }
+      }
+    }
+  }
+}
+
+void ReorderRSCKToKCRS(const TRT_ShapedWeights& iweights,
+                       TRT_ShapedWeights* oweights) {
+  CHECK_EQ(iweights.type_, oweights->type_);
+  CHECK_EQ(iweights.size_bytes(), oweights->size_bytes());
+  int r = iweights.shape_.d[0];
+  int s = iweights.shape_.d[1];
+  int c = iweights.shape_.d[2];
+  int k = iweights.shape_.d[3];
+  oweights->shape_.d[0] = k;
+  oweights->shape_.d[1] = c;
+  oweights->shape_.d[2] = r;
+  oweights->shape_.d[3] = s;
+  nvinfer1::DimsNCHW istrides = {1, k, s * k * c, c * k};
+  nvinfer1::DimsNCHW ostrides = {c * r * s, r * s, s, 1};
+  switch (iweights.type_) {
+    case tensorflow::DataType::DT_FLOAT:
+      Reorder4({k, c, r, s}, static_cast<float const*>(iweights.GetValues()),
+               istrides,
+               static_cast<float*>(const_cast<void*>(oweights->GetValues())),
+               ostrides);
+      break;
+    default:
+      LOG(FATAL) << "!!!!!!!!!!!!!!!!!!!!!!!!broke!!!!!!!!!!!!";
+  }
+}
+
+struct InferDeleter {
+  template <typename T>
+  void operator()(T* obj) const {
+    if (obj) {
+      obj->destroy();
+    }
+  }
+};
+
+template <typename T>
+inline std::shared_ptr<T> infer_object(T* obj) {
+  return std::shared_ptr<T>(obj, InferDeleter());
+}
+
+// Logger for GIE info/warning/errors
+class Converter;
+
+using OpConverter =
+    std::function<tensorflow::Status(Converter&, const tensorflow::NodeDef&,
+                                     std::vector<TRT_TensorOrWeights> const&,
+                                     std::vector<TRT_TensorOrWeights>*)>;
+
+class Converter {
+  std::unordered_map<string, TRT_TensorOrWeights> trt_tensors_;
+  std::unordered_map<string, OpConverter> op_registry_;
+  nvinfer1::INetworkDefinition* trt_network_;
+  std::list<std::vector<uint8_t>> temp_bufs_;
+
+  void register_op_converters();
+
+  std::vector<TRT_TensorOrWeights> get_inputs(
+      const tensorflow::NodeDef& node_def) {
+    std::vector<TRT_TensorOrWeights> inputs;
+    for (const auto& input_name : node_def.input()) {
+      VLOG(2) << "Retrieve input: " << input_name;
+      inputs.push_back(trt_tensors_.at(input_name));
+    }
+    return inputs;
+  }
+
+ public:
+  explicit Converter(nvinfer1::INetworkDefinition* trt_network)
+      : trt_network_(trt_network) {
+    this->register_op_converters();
+  }
+
+  TRT_ShapedWeights get_temp_weights(tensorflow::DataType type,
+                                     nvinfer1::Dims shape) {
+    TRT_ShapedWeights weights(type, nullptr, shape);
+    // TODO(jie): check weights size_bytes. 0 means type error
+    temp_bufs_.push_back(std::vector<uint8_t>(weights.size_bytes()));
+    weights.SetValues(temp_bufs_.back().data());
+    return weights;
+  }
+
+  TRT_ShapedWeights get_temp_weights_like(const TRT_ShapedWeights& weights) {
+    return this->get_temp_weights(weights.type_, weights.shape_);
+  }
+
+  tensorflow::Status convert_node(const tensorflow::NodeDef& node_def) {
+    std::vector<TRT_TensorOrWeights> inputs = this->get_inputs(node_def);
+    string op = node_def.op();
+    if (!op_registry_.count(op)) {
+      return tensorflow::errors::Unimplemented(
+          "No converter registered for op: " + op);
+    }
+    OpConverter op_converter = op_registry_.at(op);
+    std::vector<TRT_TensorOrWeights> outputs;
+    TF_RETURN_IF_ERROR(op_converter(*this, node_def, inputs, &outputs));
+    for (size_t i = 0; i < outputs.size(); ++i) {
+      TRT_TensorOrWeights output = outputs.at(i);
+      // TODO(jie): tf protobuf seems to be omitting the :0 suffix
+      string output_name = node_def.name();
+      if (i != 0) output_name = output_name + ":" + std::to_string(i);
+      if (output.is_tensor()) {
+        output.tensor()->setName(output_name.c_str());
+      }
+      VLOG(2) << "Write out tensor: " << output_name;
+      if (!trt_tensors_.insert({output_name, output}).second) {
+        return tensorflow::errors::AlreadyExists(
+            "Output tensor already exists for op: " + op);
+      }
+    }
+    return tensorflow::Status::OK();
+  }
+
+  nvinfer1::INetworkDefinition* network() { return trt_network_; }
+
+  TRT_TensorOrWeights get_tensor(string name) {
+    if (!trt_tensors_.count(name)) {
+      return TRT_TensorOrWeights(nullptr);
+    }
+    return trt_tensors_.at(name);
+  }
+
+  bool insert_input_tensor(string name, nvinfer1::ITensor* tensor) {
+    return trt_tensors_.insert({name, TRT_TensorOrWeights(tensor)}).second;
+  }
+
+  nvinfer1::ITensor* TransposeTensor(nvinfer1::ITensor* input_tensor,
+                                     std::vector<int> order) {
+    auto dims = input_tensor->getDimensions();
+
+    // TODO(jie): change the return to status and properly exit
+    if (order.size() - 1 != size_t(dims.nbDims))
+      LOG(ERROR) << "Dimension does not match, fail gracefully";
+
+    nvinfer1::IShuffleLayer* layer = this->network()->addShuffle(*input_tensor);
+    nvinfer1::Permutation permutation;
+    for (int32_t i = 0; i < dims.nbDims; ++i) {
+      permutation.order[i] = order[i + 1] - 1;
+    }
+    layer->setFirstTranspose(permutation);
+
+    nvinfer1::Dims reshape_dims;
+    reshape_dims.nbDims = dims.nbDims;
+    for (int32_t i = 0; i < reshape_dims.nbDims; ++i) {
+      reshape_dims.d[i] = 0;
+      reshape_dims.type[i] = dims.type[i];
+    }
+    layer->setReshapeDimensions(reshape_dims);
+    return layer->getOutput(0);
+  }
+};
+
+// ****************************************************************************
+// Constant folding functions
+// TODO(jie): once optimizer kicks in, we should have done constant folding
+// there.
+//*****************************************************************************/
+struct LambdaFactory {
+  enum class OP_CATEGORY : int { RSQRT = 0, NEG, ADD, MUL, SUB };
+  OP_CATEGORY op;
+
+  template <typename T>
+  std::function<T(T)> unary() {
+    switch (op) {
+      case OP_CATEGORY::RSQRT: {
+        VLOG(2) << "RSQRT GETS DONE";
+        return [](T t) -> T { return 1.0 / std::sqrt(t); };
+      }
+      case OP_CATEGORY::NEG:
+        return [](T t) -> T { return -t; };
+      default:
+        VLOG(2) << "Not supported op for unary: " << static_cast<int>(op);
+        return nullptr;
+    }
+  }
+
+  template <typename T>
+  std::function<T(T, T)> binary() {
+    switch (op) {
+      case OP_CATEGORY::ADD:
+        return [](T l, T r) -> T { return l + r; };
+      case OP_CATEGORY::SUB:
+        return [](T l, T r) -> T { return l - r; };
+      case OP_CATEGORY::MUL:
+        return [](T l, T r) -> T { return l * r; };
+      default:
+        LOG(WARNING) << "Not supported op for binary: " << static_cast<int>(op);
+    }
+    return [](T l, T r) -> T {
+      LOG(FATAL) << "Unsupported op type ";
+      return l;
+    };
+  }
+
+  template <typename T>
+  std::function<T(T)> broadcast_r(T val) {
+    VLOG(2) << "LAMBDA VAL : " << val;
+    switch (op) {
+      case OP_CATEGORY::ADD:
+        return [val](T l) -> T {
+          VLOG(2) << "LAMBDA VAL : " << val;
+          return l + val;
+        };
+      // Return [val](T l)-> T {return l+val;};
+      case OP_CATEGORY::SUB:
+        return [val](T l) -> T {
+          VLOG(2) << "LAMBDA VAL : " << val;
+          return l - val;
+        };
+      case OP_CATEGORY::MUL:
+        return [val](T l) -> T {
+          VLOG(2) << "LAMBDA VAL : " << val;
+          return l * val;
+        };
+      default:
+        LOG(WARNING) << "Not supported op for binary: " << static_cast<int>(op);
+    }
+    return [val](T l) -> T {
+      LOG(FATAL) << "Unsupported op type ";
+      return l;
+    };
+  }
+
+  template <typename T>
+  std::function<T(T)> broadcast_l(T val) {
+    VLOG(2) << "LAMBDA VAL : " << val;
+    switch (op) {
+      case OP_CATEGORY::ADD:
+        return [val](T l) -> T {
+          VLOG(2) << "LAMBDA VAL : " << val;
+          return val + l;
+        };
+      case OP_CATEGORY::SUB:
+        return [val](T l) -> T {
+          VLOG(2) << "LAMBDA VAL : " << val;
+          return val - l;
+        };
+      case OP_CATEGORY::MUL:
+        return [val](T l) -> T {
+          VLOG(2) << "LAMBDA VAL : " << val;
+          return val * l;
+        };
+      default:
+        LOG(ERROR) << "Not supported op for binary: " << static_cast<int>(op);
+    }
+    return [val](T l) -> T {
+      LOG(FATAL) << "Unsupported op type ";
+      return l;
+    };
+  }
+};
+
+tensorflow::Status UnaryCompute(const TRT_ShapedWeights& iweights,
+                                TRT_ShapedWeights* oweights,
+                                LambdaFactory unary_op) {
+  CHECK_EQ(iweights.type_, oweights->type_);
+  switch (iweights.type_) {
+    case tensorflow::DataType::DT_FLOAT: {
+      auto inp = static_cast<float const*>(iweights.GetValues());
+      auto oup = static_cast<float*>(const_cast<void*>(oweights->GetValues()));
+      std::transform(inp, inp + iweights.count(), oup, unary_op.unary<float>());
+      break;
+    }
+    default:
+      return tensorflow::errors::Unimplemented(
+          "Data type not supported: " +
+          tensorflow::DataTypeString(iweights.type_));
+  }
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status BinaryCompute(const TRT_ShapedWeights& iweights_l,
+                                 const TRT_ShapedWeights& iweights_r,
+                                 TRT_ShapedWeights* oweights,
+                                 LambdaFactory binary_op) {
+  // Assume iweights_l.type == iweight_r.type
+  CHECK_EQ(iweights_l.type_, oweights->type_);
+  CHECK_EQ(iweights_r.type_, oweights->type_);
+  VLOG(2) << "SANITY CHECK!";
+
+  switch (iweights_l.type_) {
+    case tensorflow::DataType::DT_FLOAT: {
+      auto inp_l = static_cast<const float*>(iweights_l.GetValues());
+      auto inp_r = static_cast<const float*>(iweights_r.GetValues());
+      auto oup = static_cast<float*>(const_cast<void*>(oweights->GetValues()));
+
+      if (iweights_l.count() != iweights_r.count()) {
+        // We only supports broadcast of RankZero
+        if (iweights_l.count() == 1) {
+          VLOG(2) << "I bet it is not working!" << (*inp_l);
+          std::transform(inp_r, inp_r + iweights_r.count(), oup,
+                         binary_op.broadcast_l<float>(*inp_l));
+        } else if (iweights_r.count() == 1) {
+          VLOG(2) << "I bet it is not working!" << (*inp_r);
+          std::transform(inp_l, inp_l + iweights_l.count(), oup,
+                         binary_op.broadcast_r<float>(*inp_r));
+        } else {
+          return tensorflow::errors::Unimplemented(
+              "Binary op with non-rankZero broadcast not supported");
+        }
+      } else {
+        std::transform(inp_l, inp_l + iweights_l.count(), inp_r, oup,
+                       binary_op.binary<float>());
+      }
+      break;
+    }
+    default:
+      return tensorflow::errors::Unimplemented(
+          "Data type not supported: " +
+          tensorflow::DataTypeString(iweights_l.type_));
+  }
+
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConstantFoldUnary(
+    Converter& ctx, const tensorflow::NodeDef& node_def,
+    std::vector<TRT_TensorOrWeights> const& inputs,
+    std::vector<TRT_TensorOrWeights>* outputs) {
+  TRT_ShapedWeights weights_input = inputs.at(0).weights();
+
+  // Allocate output weights
+  TRT_ShapedWeights weights_output = ctx.get_temp_weights_like(weights_input);
+
+  // FIXME assume type matches input weights
+  // Get trt type & shape
+  // Maybe this part has to be moved into the block of rsqrt later
+  // Check type consistency
+  CHECK_EQ(weights_input.type_,
+           TFAttrs(node_def).get<tensorflow::DataType>("T"));
+
+  // Maybe I should do a switch
+  LambdaFactory unary_op;
+  if (node_def.op() == "Rsqrt") {
+    // Compute rsqrt
+    unary_op.op = LambdaFactory::OP_CATEGORY::RSQRT;
+    auto ret = UnaryCompute(weights_input, &weights_output, unary_op);
+    // PAss the output
+    if (ret == tensorflow::Status::OK()) {
+      outputs->push_back(TRT_TensorOrWeights(weights_output));
+    }
+    return ret;
+  } else {
+    return tensorflow::errors::Unimplemented("Binary op not supported: " +
+                                             node_def.op());
+  }
+}
+
+// TODO(jie,ben) broadcast is needed yet not implemented
+// Let's get the simple stuff working first. Maybe we should fall bakc to TF
+//   approach for constant folding
+tensorflow::Status ConstantFoldBinary(
+    Converter& ctx, const tensorflow::NodeDef& node_def,
+    std::vector<TRT_TensorOrWeights> const& inputs,
+    std::vector<TRT_TensorOrWeights>* outputs) {
+  TRT_ShapedWeights weights_input_l = inputs.at(0).weights();
+  TRT_ShapedWeights weights_input_r = inputs.at(1).weights();
+
+  // Check type consistency
+  CHECK_EQ(weights_input_l.type_, weights_input_r.type_);
+
+  if (weights_input_l.shape_.nbDims != weights_input_r.shape_.nbDims)
+    return tensorflow::errors::Unimplemented(
+        "Binary op implicit broadcast not supported: " + node_def.op());
+
+  // TODO(jie): constant fold should really fall back to TF.
+  int nb_dims = weights_input_l.shape_.nbDims;
+  nvinfer1::Dims output_shape;
+  output_shape.nbDims = nb_dims;
+  VLOG(2) << "nb_dims: " << nb_dims
+          << ", the other: " << weights_input_r.shape_.nbDims;
+  for (int i = 0; i < nb_dims; i++) {
+    if (weights_input_l.shape_.d[i] == weights_input_r.shape_.d[i]) {
+      output_shape.d[i] = weights_input_l.shape_.d[i];
+    } else if (weights_input_l.shape_.d[i] == 1 ||
+               weights_input_r.shape_.d[i] == 1) {
+      output_shape.d[i] =
+          std::max(weights_input_l.shape_.d[i], weights_input_r.shape_.d[i]);
+    } else {
+      return tensorflow::errors::Unimplemented(
+          "Binary op with incompatible shape at, " + node_def.op());
+    }
+    VLOG(2) << "left: " << weights_input_l.shape_.d[i]
+            << "right: " << weights_input_r.shape_.d[i]
+            << "output: " << output_shape.d[i];
+  }
+
+  // FIXME assume type matches input weights
+  // Get trt type & shape
+  TFAttrs attrs(node_def);
+  // Maybe this part has to be moved into the block of rsqrt later
+  tensorflow::DataType dtype = attrs.get<tensorflow::DataType>("T");
+
+  // Allocate output weights
+  TRT_ShapedWeights weights_output = ctx.get_temp_weights(dtype, output_shape);
+
+  // Maybe I should do a switch
+  LambdaFactory binary_op;
+  if (node_def.op() == "Sub") {
+    binary_op.op = LambdaFactory::OP_CATEGORY::SUB;
+  } else if (node_def.op() == "Mul") {
+    binary_op.op = LambdaFactory::OP_CATEGORY::MUL;
+  } else if (node_def.op() == "Add") {
+    binary_op.op = LambdaFactory::OP_CATEGORY::ADD;
+  } else {
+    return tensorflow::errors::Unimplemented("Binary op not supported: " +
+                                             node_def.op());
+  }
+  auto ret = BinaryCompute(weights_input_l, weights_input_r, &weights_output,
+                           binary_op);
+
+  // Pass the output
+  if (ret == tensorflow::Status::OK()) {
+    outputs->push_back(TRT_TensorOrWeights(weights_output));
+  }
+
+  return ret;
+}
+
+// TODO(jie): broadcast is needed yet not implemented.
+// Only implemented channel wise for the time being
+tensorflow::Status BinaryTensorOpWeight(
+    Converter& ctx, const tensorflow::NodeDef& node_def,
+    const nvinfer1::ITensor* tensor, TRT_ShapedWeights weights,
+    std::vector<TRT_TensorOrWeights>* outputs) {
+  // FIXME assume type matches input weights
+  // Get trt type & shape
+  // Maybe this part has to be moved into the block of rsqrt later
+
+  // Check type consistency
+  auto dtype = TFAttrs(node_def).get<nvinfer1::DataType>("T");
+  CHECK_EQ_TYPE(tensor->getType(), dtype);  // Cast to int for error messages
+  nvinfer1::DataType ttype;
+  TF_CHECK_OK(ConvertDType(weights.type_, &ttype));
+  CHECK_EQ_TYPE(ttype, dtype);  // Cast to int for error message
+
+  // Check scale mode
+  auto dims_w = weights.shape_;
+  auto dims_t = tensor->getDimensions();
+
+  // Default to channel-wise
+  auto scale_mode = nvinfer1::ScaleMode::kELEMENTWISE;
+
+  if (weights.count() == 1) {
+    VLOG(2) << "UNIFORM";
+    scale_mode = nvinfer1::ScaleMode::kUNIFORM;
+  } else {
+    // No broadcasting on Batch dimension;
+    assert(dims_w.d[0] == 1);
+
+    // Broadcasting on Channel dimension only allowed in kUNIFORM
+    assert(dims_w.d[1] == dims_t.d[0]);
+    assert(dims_w.nbDims == dims_t.nbDims);
+
+    // Default is element;
+    for (int i = 2; i < dims_w.nbDims; i++) {
+      if (dims_w.d[i] != dims_t.d[i - 1]) {
+        scale_mode = nvinfer1::ScaleMode::kCHANNEL;
+        break;
+      }
+    }
+    if (scale_mode == nvinfer1::ScaleMode::kELEMENTWISE) {
+      scale_mode = nvinfer1::ScaleMode::kELEMENTWISE;
+      for (int i = 2; i < dims_w.nbDims; i++) {
+        if (dims_w.d[i] != 1)
+          return tensorflow::errors::InvalidArgument(
+              "Weight shape not compatible at, " + node_def.name());
+      }
+    }
+  }
+
+  // Prepare weights
+  TRT_ShapedWeights shift_weights(weights.type_);
+  TRT_ShapedWeights scale_weights(weights.type_);
+  TRT_ShapedWeights power_weights(weights.type_);
+
+  // Maybe I should do a switch
+  if (node_def.op() == "Sub") {
+    TRT_ShapedWeights neg_weights = ctx.get_temp_weights_like(weights);
+    LambdaFactory unary_op;
+    unary_op.op = LambdaFactory::OP_CATEGORY::NEG;
+    TF_RETURN_IF_ERROR(UnaryCompute(weights, &neg_weights, unary_op));
+    shift_weights = neg_weights;
+  } else if (node_def.op() == "Mul") {
+    scale_weights = weights;
+  } else if (node_def.op() == "Add") {
+    shift_weights = weights;
+  } else {
+    return tensorflow::errors::Unimplemented("Binary op not supported: " +
+                                             node_def.op());
+  }
+
+  nvinfer1::IScaleLayer* layer = ctx.network()->addScale(
+      *const_cast<nvinfer1::ITensor*>(tensor), scale_mode, shift_weights,
+      scale_weights, power_weights);
+
+  nvinfer1::ITensor* output_tensor = layer->getOutput(0);
+
+  // Pass the output
+  outputs->push_back(TRT_TensorOrWeights(output_tensor));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status BinaryTensorOpTensor(
+    Converter& ctx, const tensorflow::NodeDef& node_def,
+    const nvinfer1::ITensor* tensor_l, const nvinfer1::ITensor* tensor_r,
+    std::vector<TRT_TensorOrWeights>* outputs) {
+  static const std::unordered_map<string, nvinfer1::ElementWiseOperation> ops{
+      {"Add", nvinfer1::ElementWiseOperation::kSUM},
+      {"Mul", nvinfer1::ElementWiseOperation::kPROD},
+      // {"max", nvinfer1::ElementWiseOperation::kMAX},
+      // {"min", nvinfer1::ElementWiseOperation::kMIN},
+      {"Sub", nvinfer1::ElementWiseOperation::kSUB},
+      {"Div", nvinfer1::ElementWiseOperation::kDIV},
+  };
+
+  // FIXME assume type matches input weights
+  // Get trt type & shape
+  TFAttrs attrs(node_def);
+  // Maybe this part has to be moved into the block of rsqrt later
+  nvinfer1::DataType dtype = attrs.get<nvinfer1::DataType>("T");
+
+  // Check type consistency
+  CHECK_EQ_TYPE(tensor_l->getType(), dtype);
+  CHECK_EQ_TYPE(tensor_r->getType(), dtype);
+  auto op_pair = ops.find(node_def.op());
+  if (op_pair == ops.end())
+    return tensorflow::errors::Unimplemented(
+        "binary op: " + node_def.op() +
+        " not supported at: " + node_def.name());
+
+  nvinfer1::IElementWiseLayer* layer = ctx.network()->addElementWise(
+      *const_cast<nvinfer1::ITensor*>(tensor_l),
+      *const_cast<nvinfer1::ITensor*>(tensor_r), op_pair->second);
+
+  nvinfer1::ITensor* output_tensor = layer->getOutput(0);
+
+  // Pass the output
+  outputs->push_back(TRT_TensorOrWeights(output_tensor));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertPlaceholder(
+    Converter& ctx, const tensorflow::NodeDef& node_def,
+    std::vector<TRT_TensorOrWeights> const& inputs,
+    std::vector<TRT_TensorOrWeights>* outputs) {
+  VLOG(2) << "Placeholder should have been replace already";
+  return tensorflow::errors::Unimplemented(", cannot convert Placeholder op");
+  // OK this make sense since we are supposed to replace it with input
+  TFAttrs attrs(node_def);
+  nvinfer1::DataType dtype = attrs.get<nvinfer1::DataType>("dtype");
+  nvinfer1::Dims dims = attrs.get<nvinfer1::Dims>("shape");
+
+  dims.nbDims--;
+  for (int i = 0; i < dims.nbDims; i++) dims.d[i] = dims.d[i + 1];
+
+  nvinfer1::ITensor* output =
+      ctx.network()->addInput(node_def.name().c_str(), dtype, dims);
+  if (!output) {
+    return tensorflow::errors::InvalidArgument("Failed to create Input layer");
+  }
+  outputs->push_back(TRT_TensorOrWeights(output));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertConv2D(Converter& ctx,
+                                 const tensorflow::NodeDef& node_def,
+                                 const std::vector<TRT_TensorOrWeights>& inputs,
+                                 std::vector<TRT_TensorOrWeights>* outputs) {
+  nvinfer1::ITensor const* tensor = inputs.at(0).tensor();
+  // TODO(jie): handle NHWC/NCHW transpose;
+  TRT_ShapedWeights weights_rsck = inputs.at(1).weights();
+  TRT_ShapedWeights weights = ctx.get_temp_weights_like(weights_rsck);
+  ReorderRSCKToKCRS(weights_rsck, &weights);
+  TRT_ShapedWeights biases(weights.type_);
+  int noutput = weights.shape_.d[0];
+  nvinfer1::DimsHW kernel_size;
+  kernel_size.h() = weights.shape_.d[2];
+  kernel_size.w() = weights.shape_.d[3];
+  TFAttrs attrs(node_def);
+
+  int h_index = 2;
+  int w_index = 3;
+  auto data_format = attrs.get<string>("data_format");
+  if (data_format == "NHWC") {
+    tensor = ctx.TransposeTensor(const_cast<nvinfer1::ITensor*>(tensor),
+                                 {0, 3, 1, 2});
+    h_index = 1;
+    w_index = 2;
+    // TODO(jie): transpose it
+  }
+
+  // TODO(jie): stride. (NHWC/NCHW)
+  auto tf_stride = attrs.get<std::vector<int>>("strides");
+  nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]);
+
+  auto tensor_dim = tensor->getDimensions();
+  std::vector<std::pair<int, int>> padding;
+  // TODO(jie): padding.
+  if (attrs.get<string>("padding") == "SAME") {
+    // This is NCHW tensor with no batch dimension.
+    //  1 -> h
+    //  2 -> w
+    padding = CreateSamePadding(
+        stride, kernel_size,
+        {static_cast<int>(tensor_dim.d[1]), static_cast<int>(tensor_dim.d[2])});
+  } else {
+    padding = {{0, 0}, {0, 0}};
+  }
+
+  if (padding[0].first != padding[0].second ||
+      padding[1].first != padding[1].second) {
+    // TODO(jie): handle asymmetric padding
+    VLOG(2) << "Padding!!!: " << padding[0].first << padding[0].second
+            << padding[1].first << padding[1].second;
+
+    auto dim_before = tensor->getDimensions();
+    VLOG(2) << "TENSOR before: " << dim_before.d[0] << ", " << dim_before.d[1]
+            << dim_before.d[2] << ", " << dim_before.d[3];
+    auto pad_layer = ctx.network()->addPadding(
+        *const_cast<nvinfer1::ITensor*>(tensor),
+        nvinfer1::DimsHW(padding[0].first, padding[1].first),
+        nvinfer1::DimsHW(padding[0].second, padding[1].second));
+    padding = {{0, 0}, {0, 0}};
+    tensor = pad_layer->getOutput(0);
+    auto dim_after = tensor->getDimensions();
+    VLOG(2) << "TENSOR after: " << dim_after.d[0] << ", " << dim_after.d[1]
+            << dim_after.d[2] << ", " << dim_after.d[3];
+  }
+
+  nvinfer1::IConvolutionLayer* layer =
+      ctx.network()->addConvolution(*const_cast<nvinfer1::ITensor*>(tensor),
+                                    noutput, kernel_size, weights, biases);
+
+  layer->setStride(stride);
+  layer->setPadding({padding[0].first, padding[1].first});
+  layer->setName(node_def.name().c_str());
+  nvinfer1::ITensor* output_tensor = layer->getOutput(0);
+
+  auto dim_after = output_tensor->getDimensions();
+  VLOG(2) << "TENSOR out: " << dim_after.d[0] << ", " << dim_after.d[1]
+          << dim_after.d[2] << ", " << dim_after.d[3];
+
+  if (data_format == "NHWC") {
+    // TODO(jie): transpose it back!
+    output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1});
+  } else {
+    VLOG(2) << "NCHW !!!!";
+  }
+  outputs->push_back(TRT_TensorOrWeights(output_tensor));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertPool(Converter& ctx,
+                               const tensorflow::NodeDef& node_def,
+                               std::vector<TRT_TensorOrWeights> const& inputs,
+                               std::vector<TRT_TensorOrWeights>* outputs) {
+  nvinfer1::ITensor const* tensor = inputs.at(0).tensor();
+  TFAttrs attrs(node_def);
+
+  int h_index = 2;
+  int w_index = 3;
+  auto data_format = attrs.get<string>("data_format");
+  if (data_format == "NHWC") {
+    h_index = 1;
+    w_index = 2;
+    tensor = ctx.TransposeTensor(const_cast<nvinfer1::ITensor*>(tensor),
+                                 {0, 3, 1, 2});
+  } else {
+    VLOG(2) << "NCHW !!!!";
+  }
+  nvinfer1::PoolingType type;
+  // TODO(jie): support other pooling type
+  if (node_def.op() == "MaxPool")
+    type = nvinfer1::PoolingType::kMAX;
+  else
+    return tensorflow::errors::Unimplemented("Only supports Max pool");
+
+  // TODO(jie): NCHW
+  auto tf_stride = attrs.get<std::vector<int>>("strides");
+  nvinfer1::DimsHW stride(tf_stride[h_index], tf_stride[w_index]);
+
+  auto tf_kernel = attrs.get<std::vector<int>>("ksize");
+  nvinfer1::DimsHW ksize(tf_kernel[h_index], tf_kernel[w_index]);
+
+  auto tensor_dim = tensor->getDimensions();
+  std::vector<std::pair<int, int>> padding;
+  // TODO(jie): padding.
+  if (attrs.get<string>("padding") == "SAME") {
+    // This is NCHW tensor with no batch dimension.
+    //  1 -> h
+    //  2 -> w
+    padding = CreateSamePadding(
+        stride, ksize,
+        {static_cast<int>(tensor_dim.d[1]), static_cast<int>(tensor_dim.d[2])});
+  } else if (attrs.get<string>("padding") == "VALID") {
+    // No padding for valid padding here
+    VLOG(2) << "No padding added for VALID padding in pool" << node_def.name();
+    padding = {{0, 0}, {0, 0}};
+  } else {
+    return tensorflow::errors::Unimplemented(
+        "Current MaxPool cannot support padding other than SAME");
+  }
+
+  if (padding[0].first != padding[0].second ||
+      padding[1].first != padding[1].second) {
+    // TODO(jie): handle asymmetric padding
+    VLOG(2) << "Padding!!!: " << padding[0].first << padding[0].second
+            << padding[1].first << padding[1].second;
+    auto pad_layer = ctx.network()->addPadding(
+        *const_cast<nvinfer1::ITensor*>(tensor),
+        nvinfer1::DimsHW(padding[0].first, padding[1].first),
+        nvinfer1::DimsHW(padding[0].second, padding[1].second));
+    padding = {{0, 0}, {0, 0}};
+    tensor = pad_layer->getOutput(0);
+  }
+
+  nvinfer1::IPoolingLayer* layer = ctx.network()->addPooling(
+      *const_cast<nvinfer1::ITensor*>(tensor), type, ksize);
+
+  layer->setStride(stride);
+  layer->setPadding({padding[0].first, padding[1].first});
+  layer->setName(node_def.name().c_str());
+  nvinfer1::ITensor* output_tensor = layer->getOutput(0);
+
+  if (data_format == "NHWC") {
+    // TODO(jie): transpose it back!
+    output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1});
+  } else {
+    VLOG(2) << "NCHW !!!!";
+  }
+  outputs->push_back(TRT_TensorOrWeights(output_tensor));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertActivation(
+    Converter& ctx, const tensorflow::NodeDef& node_def,
+    std::vector<TRT_TensorOrWeights> const& inputs,
+    std::vector<TRT_TensorOrWeights>* outputs) {
+  nvinfer1::ITensor const* tensor = inputs.at(0).tensor();
+  nvinfer1::IActivationLayer* layer = ctx.network()->addActivation(
+      *const_cast<nvinfer1::ITensor*>(tensor), nvinfer1::ActivationType::kRELU);
+  nvinfer1::ITensor* output_tensor = layer->getOutput(0);
+  outputs->push_back(TRT_TensorOrWeights(output_tensor));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertScale(Converter& ctx,
+                                const tensorflow::NodeDef& node_def,
+                                std::vector<TRT_TensorOrWeights> const& inputs,
+                                std::vector<TRT_TensorOrWeights>* outputs) {
+  if (inputs.size() != 2 || !inputs.at(0).is_tensor() ||
+      !inputs.at(1).is_weights())
+    return tensorflow::errors::Unimplemented(
+        "Only supports tensor op weight for now, at " + node_def.name());
+  // Implement tensor binaryOp weight [channel wise] for now;
+  nvinfer1::ITensor const* tensor = inputs.at(0).tensor();
+
+  // TODO(jie): handle NHWC/NCHW transpose;
+  TRT_ShapedWeights weights = inputs.at(1).weights();
+  TRT_ShapedWeights empty_weights(weights.type_);
+
+  TFAttrs attrs(node_def);
+
+  // Transpose NHWC
+  auto data_format = attrs.get<string>("data_format");
+  if (data_format == "NHWC") {
+    tensor = ctx.TransposeTensor(const_cast<nvinfer1::ITensor*>(tensor),
+                                 {0, 3, 1, 2});
+    // TODO(jie): transpose it
+  } else {
+    VLOG(2) << "NCHW !!!!";
+  }
+  nvinfer1::IScaleLayer* layer = ctx.network()->addScale(
+      *const_cast<nvinfer1::ITensor*>(tensor), nvinfer1::ScaleMode::kCHANNEL,
+      weights, empty_weights, empty_weights);
+
+  nvinfer1::ITensor* output_tensor = layer->getOutput(0);
+  if (data_format == "NHWC") {
+    // TODO(jie): transpose it back!
+    output_tensor = ctx.TransposeTensor(output_tensor, {0, 2, 3, 1});
+  } else {
+    VLOG(2) << "NCHW !!!!";
+  }
+  outputs->push_back(TRT_TensorOrWeights(output_tensor));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertConst(Converter& ctx,
+                                const tensorflow::NodeDef& node_def,
+                                std::vector<TRT_TensorOrWeights> const& inputs,
+                                std::vector<TRT_TensorOrWeights>* outputs) {
+  const auto& weights_tensor = node_def.attr().at("value").tensor();
+
+  // Get trt type & shape
+  TFAttrs attrs(node_def);
+  const tensorflow::DataType dtype = attrs.get<tensorflow::DataType>("dtype");
+
+  // Create shaped weights as output
+  tensorflow::Tensor tensor;
+  if (!tensor.FromProto(weights_tensor))
+    return tensorflow::errors::Internal("Cannot parse weight tensor proto: " +
+                                        node_def.name());
+
+  TRT_ShapedWeights weights(dtype);
+  if (!weights_tensor.float_val().empty()) {
+    VLOG(2) << "SCALAR!!!" << node_def.name();
+    nvinfer1::Dims scalar_shape;
+    if (tensor.dims() > 0) {
+      VLOG(2) << "Dimensions: " << tensor.dims();
+      weights = TRT_ShapedWeights(dtype, weights_tensor.float_val().data(),
+                                  GetTensorShape(tensor));
+    } else {
+      VLOG(2) << "Dimensions: " << tensor.dims();
+      scalar_shape.nbDims = 1;
+      scalar_shape.d[0] = 1;
+      scalar_shape.type[0] = nvinfer1::DimensionType::kSPATIAL;
+      for (int i = 1; i < nvinfer1::Dims::MAX_DIMS; i++) {
+        scalar_shape.d[i] = 0;
+        scalar_shape.type[i] = nvinfer1::DimensionType::kSPATIAL;
+      }
+      weights = TRT_ShapedWeights(dtype, weights_tensor.float_val().data(),
+                                  scalar_shape);
+    }
+  } else if (!weights_tensor.tensor_content().empty()) {
+    VLOG(2) << "TENSOR!!!" << node_def.name();
+    const auto& content = weights_tensor.tensor_content();
+
+    weights = ctx.get_temp_weights(dtype, GetTensorShape(tensor));
+    if (content.size() > 0) {
+      const int dtype_size = tensorflow::DataTypeSize(dtype);
+      CHECK_EQ(0, content.size() % dtype_size)
+          << "Tensor content size (" << content.size()
+          << ") is not a multiple of " << dtype_size;
+      port::CopyToArray(
+          content, static_cast<char*>(const_cast<void*>(weights.GetValues())));
+    }
+  } else {
+    return tensorflow::errors::Unimplemented(
+        "Not supported constant type, at " + node_def.name());
+  }
+  // Pass the output
+  outputs->push_back(TRT_TensorOrWeights(weights));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertIdentity(
+    Converter& ctx, const tensorflow::NodeDef& node_def,
+    std::vector<TRT_TensorOrWeights> const& inputs,
+    std::vector<TRT_TensorOrWeights>* outputs) {
+  outputs->push_back(inputs.at(0));
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertBinary(Converter& ctx,
+                                 const tensorflow::NodeDef& node_def,
+                                 std::vector<TRT_TensorOrWeights> const& inputs,
+                                 std::vector<TRT_TensorOrWeights>* outputs) {
+  if (inputs.size() != 2)
+    return tensorflow::errors::FailedPrecondition(
+        "Binary ops require two tensor input, at " + node_def.name());
+
+  if (inputs.at(0).is_weights() && inputs.at(1).is_weights())
+    return ConstantFoldBinary(ctx, node_def, inputs, outputs);
+
+  if (inputs.at(0).is_tensor() && inputs.at(1).is_weights())
+    return BinaryTensorOpWeight(ctx, node_def, inputs.at(0).tensor(),
+                                inputs.at(1).weights(), outputs);
+
+  if (inputs.at(0).is_weights() && inputs.at(1).is_tensor())
+    return BinaryTensorOpWeight(ctx, node_def, inputs.at(1).tensor(),
+                                inputs.at(0).weights(), outputs);
+
+  if (inputs.at(0).is_tensor() && inputs.at(1).is_tensor())
+    return BinaryTensorOpTensor(ctx, node_def, inputs.at(0).tensor(),
+                                inputs.at(1).tensor(), outputs);
+
+  return tensorflow::errors::Unknown("Binary op input error, at " +
+                                     node_def.name());
+}
+
+tensorflow::Status ConvertUnary(Converter& ctx,
+                                const tensorflow::NodeDef& node_def,
+                                std::vector<TRT_TensorOrWeights> const& inputs,
+                                std::vector<TRT_TensorOrWeights>* outputs) {
+  if (inputs.size() != 1)
+    return tensorflow::errors::FailedPrecondition(
+        "Unary ops require single tensor input, at " + node_def.name());
+
+  if (inputs.at(0).is_weights())
+    return ConstantFoldUnary(ctx, node_def, inputs, outputs);
+  else if (inputs.at(0).is_tensor())
+    return tensorflow::errors::Unimplemented(
+        "Unary op for tensor not supported, at " + node_def.name());
+
+  return tensorflow::errors::Unknown("Binary op input error, at " +
+                                     node_def.name());
+}
+
+tensorflow::Status ConvertReduce(Converter& ctx,
+                                 const tensorflow::NodeDef& node_def,
+                                 std::vector<TRT_TensorOrWeights> const& inputs,
+                                 std::vector<TRT_TensorOrWeights>* outputs) {
+  if (inputs.size() != 2 || !inputs.at(0).is_tensor() ||
+      !inputs.at(1).is_weights())
+    return tensorflow::errors::InvalidArgument(
+        "Input expects tensor and weights, at" + node_def.name());
+
+  // Implement tensor binaryOp weight [channel wise] for now;
+  nvinfer1::ITensor const* tensor = inputs.at(0).tensor();
+  auto dims = tensor->getDimensions();
+  // Restore implicit batch dimension
+  int nb_dims = dims.nbDims + 1;
+
+  TRT_ShapedWeights index_list = inputs.at(1).weights();
+
+  TFAttrs attrs(node_def);
+  // TODO(jie): handle data type.
+  // Index type here is done through TF type, so I can leverage their
+  // EnumToDataType for my cast
+  auto index_type = attrs.get<tensorflow::DataType>("Tidx");
+
+  // Only expect to handle INT32 as attributes for now
+  if (index_type != tensorflow::DataType::DT_INT32)
+    return tensorflow::errors::Unimplemented("Tidx supports only DT_INT32");
+  auto index_list_data =
+      static_cast<int*>(const_cast<void*>(index_list.GetValues()));
+
+  // Hack warning: have to fall back to pool layer since reduce is not in public
+  // TRT yet.
+  if (nb_dims != 4)
+    return tensorflow::errors::InvalidArgument(
+        "TRT only support reduce on 4 dimensional tensors, at" +
+        node_def.name());
+  if (index_list.count() > 2)
+    return tensorflow::errors::InvalidArgument(
+        "TRT cannot support reduce on more than 2 dimensions, at" +
+        node_def.name());
+
+  std::set<int> idx_set;
+  // We cannot operate on Channel. permutation flag used to transpose tensor
+  int permuted_index = -1;
+  for (int i = 0; i < index_list.count(); i++) {
+    if (index_list_data[i] == 0)
+      return tensorflow::errors::InvalidArgument("TRT cannot reduce at 0, at" +
+                                                 node_def.name());
+    if (index_list_data[i] == 1) permuted_index = 1;
+    idx_set.emplace(index_list_data[i]);
+  }
+
+  std::vector<int> permutation_order(nb_dims);
+  nvinfer1::DimsHW pool_kernel;
+  if (permuted_index == 1) {
+    for (int i = 2; i < nb_dims; i++) {
+      if (idx_set.count(i)) {
+        permuted_index = i;
+        break;
+      }
+    }
+    for (int i = 0; i < nb_dims; i++) permutation_order[i] = i;
+
+    permutation_order[permuted_index] = 1;
+    permutation_order[1] = permuted_index;
+
+    // Apply permutation before extracting dimension for pool_kernel
+    tensor = ctx.TransposeTensor(const_cast<nvinfer1::ITensor*>(tensor),
+                                 permutation_order);
+  }
+
+  // Apply permutation before extracting dimension for pool_kernel
+  pool_kernel.d[0] = (idx_set.count(2) || permuted_index == 2) ? dims.d[1] : 1;
+  pool_kernel.d[1] = (idx_set.count(3) || permuted_index == 3) ? dims.d[2] : 1;
+
+  nvinfer1::ITensor* output_tensor;
+
+  if (node_def.op() == "Mean") {
+    nvinfer1::IPoolingLayer* layer =
+        ctx.network()->addPooling(*const_cast<nvinfer1::ITensor*>(tensor),
+                                  nvinfer1::PoolingType::kAVERAGE, pool_kernel);
+    output_tensor = layer->getOutput(0);
+  } else {
+    return tensorflow::errors::Unimplemented(
+        "Op not supported " + node_def.op() + " , at " + node_def.name());
+  }
+  if (permuted_index != -1) {
+    // Apply permutation before extracting dimension for pool_kernel
+    output_tensor = ctx.TransposeTensor(
+        const_cast<nvinfer1::ITensor*>(output_tensor), permutation_order);
+  }
+  return tensorflow::Status::OK();
+}
+
+tensorflow::Status ConvertPad(Converter& ctx,
+                              const tensorflow::NodeDef& node_def,
+                              std::vector<TRT_TensorOrWeights> const& inputs,
+                              std::vector<TRT_TensorOrWeights>* outputs) {
+  if (inputs.size() != 2 || !inputs.at(0).is_tensor() ||
+      !inputs.at(1).is_weights())
+    return tensorflow::errors::InvalidArgument(
+        "Input expects tensor and weights, at" + node_def.name());
+
+  // Implement tensor binaryOp weight [channel wise] for now;
+  nvinfer1::ITensor const* tensor = inputs.at(0).tensor();
+  auto dims = tensor->getDimensions();
+  // Restore implicit batch dimension
+  int nb_dims = dims.nbDims + 1;
+
+  TRT_ShapedWeights pads = inputs.at(1).weights();
+
+  TFAttrs attrs(node_def);
+  // Padding type here is done through TF type
+  //   so I can leverage their EnumToDataType for my cast
+  auto padding_type = attrs.get<tensorflow::DataType>("Tpaddings");
+  // TODO(jie): handle data type conversion for TRT?
+
+  if (pads.shape_.d[0] != nb_dims || pads.shape_.d[1] != 2)
+    return tensorflow::errors::InvalidArgument(
+        "Pad only supports explicit padding on 4 dimensional tensor, at " +
+        node_def.name());
+
+  // Only expect to handle INT32 as attributes for now
+  if (padding_type != tensorflow::DataType::DT_INT32)
+    return tensorflow::errors::Unimplemented(
+        "Tpaddings supports only DT_INT32");
+  auto pad_data = static_cast<int*>(const_cast<void*>(pads.GetValues()));
+
+  std::vector<int32_t> pad_index;
+  for (int i = 0; i < nb_dims; i++) {
+    if (pad_data[2 * i] != 0 || pad_data[2 * i + 1] != 0)
+      pad_index.push_back(i);
+  }
+
+  // No padding at all, we should exit
+  if (pad_index.size() == 0) {
+    outputs->push_back(inputs.at(0));
+    return tensorflow::Status::OK();
+  }
+
+  // Only supports padding on less than 2 axis GIE-2579
+  if (pad_index.size() > 2)
+    return tensorflow::errors::InvalidArgument(
+        "Padding layer does not support padding on > 2");
+
+  // Padding on batch dimension is not supported
+  if (pad_index[0] == 0)
+    return tensorflow::errors::InvalidArgument(
+        "Padding layer does not support padding on batch dimension");
+
+  // Not doing the legit thing here. ignoring padding on dim 1 and 3;
+  // TODO(jie): implement pad as uff parser
+  if (pad_index.size() == 2 && pad_index[0] == 0 && pad_index[1] == 3)
+    return tensorflow::errors::Unimplemented(
+        "Padding layer does not support padding on dimension 1 and 3 yet");
+
+  bool legit_pad = true;
+  nvinfer1::DimsHW pre_padding(0, 0);
+  nvinfer1::DimsHW post_padding(0, 0);
+
+  std::vector<int32_t> permuted_pad_index(pad_index);
+  if (pad_index[0] == 1) {
+    legit_pad = false;
+    tensor = ctx.TransposeTensor(const_cast<nvinfer1::ITensor*>(tensor),
+                                 {0, 3, 2, 1});
+    permuted_pad_index[0] = 3;
+  }
+
+  for (size_t i = 0; i < pad_index.size(); i++) {
+    int index = pad_index[i];
+    if (permuted_pad_index[i] == 2) {
+      pre_padding.h() = pad_data[index * 2];
+      post_padding.h() = pad_data[index * 2 + 1];
+    } else if (permuted_pad_index[i] == 3) {
+      pre_padding.w() = pad_data[index * 2];
+      post_padding.w() = pad_data[index * 2 + 1];
+    }
+  }
+
+  nvinfer1::IPaddingLayer* layer = ctx.network()->addPadding(
+      *const_cast<nvinfer1::ITensor*>(tensor), pre_padding, post_padding);
+  nvinfer1::ITensor* output_tensor = layer->getOutput(0);
+
+  if (!legit_pad)
+    output_tensor = ctx.TransposeTensor(
+        const_cast<nvinfer1::ITensor*>(output_tensor), {0, 3, 2, 1});
+
+  outputs->push_back(TRT_TensorOrWeights(output_tensor));
+  return tensorflow::Status::OK();
+}
+
+void Converter::register_op_converters() {
+  // vgg_16 slim implementation
+  op_registry_["Placeholder"] = ConvertPlaceholder;
+  op_registry_["Conv2D"] = ConvertConv2D;
+  op_registry_["Relu"] = ConvertActivation;
+  op_registry_["MaxPool"] = ConvertPool;
+  // This could be really handled as ConvertBinary
+  op_registry_["BiasAdd"] = ConvertScale;
+  op_registry_["Const"] = ConvertConst;
+  // op_registry_["MatMul"] = ConvertFullyConnected;  // Not used in vgg
+  // TODO(ben,jie): this is a temp hack.
+  op_registry_["Identity"] = ConvertIdentity;  // Identity should be removed
+  // op_registry_["AvgPool"] = ConvertPool;
+
+  // resnet_50_v1 slim implementation
+  op_registry_["Add"] = ConvertBinary;
+  op_registry_["Mul"] = ConvertBinary;
+  op_registry_["Sub"] = ConvertBinary;
+  op_registry_["Rsqrt"] = ConvertUnary;
+  op_registry_["Mean"] = ConvertReduce;
+  op_registry_["Pad"] = ConvertPad;
+  // TODO(ben,jie): Add more ops
+}
+
+}  // namespace
+
+tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
+    const tensorflow::Graph& graph, const std::set<int>& subgraph_node_ids,
+    const std::vector<std::pair<int, int>>& input_inds,
+    const std::vector<std::pair<int, int>>& output_inds, size_t max_batch_size,
+    size_t max_workspace_size_bytes,
+    const tensorflow::grappler::GraphProperties& graph_properties,
+    tensorflow::NodeDef* trt_node) {
+  // Visit nodes in reverse topological order and construct the TRT network.
+
+  // Toposort
+  std::vector<tensorflow::Node*> order_vec;
+  tensorflow::GetPostOrder(graph, &order_vec);
+  // Select just the subgraph
+  std::list<tensorflow::Node*> order;
+  for (tensorflow::Node* node : order_vec) {
+    if (subgraph_node_ids.count(node->id())) {
+      // We want topological order to contstruct the
+      // network layer by layer
+      order.push_front(node);
+    }
+  }
+  // Topological order is needed to build TRT network
+
+  tensorflow::tensorrt::Logger trt_logger;
+
+  auto trt_builder = infer_object(nvinfer1::createInferBuilder(trt_logger));
+  if (!trt_builder) {
+    return tensorflow::errors::Internal(
+        "Failed to create TensorRT builder object");
+  }
+
+  auto trt_network = infer_object(trt_builder->createNetwork());
+  if (!trt_network) {
+    return tensorflow::errors::Internal(
+        "Failed to create TensorRT network object");
+  }
+
+  // Build the network
+  Converter converter(trt_network.get());
+
+  std::vector<string> input_names;
+  std::vector<tensorflow::DataType> input_dtypes;
+  for (std::pair<int, int> const& input : input_inds) {
+    int node_id = input.first;
+    int output_idx = input.second;
+    tensorflow::Node* node = graph.FindNodeId(node_id);
+    auto node_name = node->name();
+    input_names.push_back(node_name);  // Insert original node name without port
+    // TODO(jie): alternative :)
+    if (!graph_properties.HasOutputProperties(node_name))
+      return tensorflow::errors::Internal("Failed to find input node: " +
+                                          node_name);
+
+    auto op_info_vec = graph_properties.GetOutputProperties(node_name);
+    if (static_cast<int>(op_info_vec.size()) < output_idx)
+      return tensorflow::errors::Internal(
+          "Accessing output index of: " + std::to_string(output_idx) +
+          ", at node: " + node_name + " with output entry from shape_map: " +
+          std::to_string(op_info_vec.size()));
+
+    auto op_info = op_info_vec.at(output_idx);
+
+    tensorflow::DataType tf_dtype = op_info.dtype();
+    input_dtypes.push_back(tf_dtype);
+
+    nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT);
+    TF_CHECK_OK(ConvertDType(tf_dtype, &dtype));
+
+    VLOG(2) << "Accessing output index of: " << std::to_string(output_idx)
+            << ", at node: " << node_name
+            << " with output entry from shape_map: "
+            << std::to_string(op_info_vec.size());
+
+    // TODO(ben,jie): update TRT input format/dimension
+    nvinfer1::DimsCHW input_dim_pseudo_chw;
+    for (int i = 0; i < 3; i++) input_dim_pseudo_chw.d[i] = 1;
+
+    for (int i = 1; i < op_info.shape().dim_size(); i++) {
+      VLOG(2) << "dimension: " << i
+              << " , size: " << op_info.shape().dim(i).size();
+      input_dim_pseudo_chw.d[i - 1] = op_info.shape().dim(i).size();
+    }
+
+    // TODO(ben,jie): proper way to restore input tensor name?
+    auto input_tensor_name = node_name;
+    if (output_idx != 0)
+      input_tensor_name = node_name + ":" + std::to_string(output_idx);
+
+    nvinfer1::ITensor* input_tensor = converter.network()->addInput(
+        input_tensor_name.c_str(), dtype, input_dim_pseudo_chw);
+
+    if (!input_tensor)
+      return tensorflow::errors::InvalidArgument(
+          "Failed to create Input layer");
+    VLOG(2) << "Input tensor name :" << input_tensor_name;
+
+    if (!converter.insert_input_tensor(input_tensor_name, input_tensor))
+      return tensorflow::errors::AlreadyExists(
+          "Output tensor already exists for op: " + input_tensor_name);
+  }
+
+  VLOG(2) << "Finished sorting";
+
+  for (const tensorflow::Node* node : order) {
+    const tensorflow::NodeDef& node_def = node->def();
+    VLOG(2) << "Converting node: " << node_def.name() << " , " << node_def.op();
+    TF_RETURN_IF_ERROR(converter.convert_node(node_def));
+  }
+
+  VLOG(2) << "Finished conversion";
+
+  // Gather output metadata
+  std::vector<string> output_names;
+  std::vector<tensorflow::DataType> output_dtypes;
+  for (std::pair<int, int> const& output : output_inds) {
+    int node_id = output.first;
+    int output_idx = output.second;
+    tensorflow::Node* node = graph.FindNodeId(node_id);
+    string op_name = node->name();
+    string tensor_name = op_name;
+    if (output_idx != 0)
+      tensor_name = tensor_name + ":" + std::to_string(output_idx);
+    VLOG(2) << "Output tensor name: " << tensor_name;
+    output_names.push_back(tensor_name);
+    auto tensor_or_weights = converter.get_tensor(tensor_name);
+    if (!tensor_or_weights.is_tensor()) {
+      return tensorflow::errors::InvalidArgument(
+          "Output node is weights not tensor");
+    }
+    nvinfer1::ITensor* tensor = tensor_or_weights.tensor();
+    if (!tensor) {
+      return tensorflow::errors::NotFound("Output tensor not found: " +
+                                          tensor_name);
+    }
+    converter.network()->markOutput(*tensor);
+    tensorflow::DataType tf_dtype = node->output_type(output_idx);
+    output_dtypes.push_back(tf_dtype);
+    nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT;
+    TF_RETURN_IF_ERROR(ConvertDType(tf_dtype, &trt_dtype));
+    tensor->setType(trt_dtype);
+  }
+
+  VLOG(2) << "Finished output";
+  // TODO(jie): static_id is not thread safe.
+  static int static_id = 0;
+
+  // Build the engine
+  trt_builder->setMaxBatchSize(max_batch_size);
+  trt_builder->setMaxWorkspaceSize(max_workspace_size_bytes);
+  VLOG(0) << "Starting build engine " << static_id;
+  // TODO(ben,jie): half2 and int8 mode support
+  string engine_plan_string;
+  {
+    auto trt_engine =
+        infer_object(trt_builder->buildCudaEngine(*converter.network()));
+    VLOG(0) << "Built network";
+    auto engine_plan = infer_object(trt_engine->serialize());
+    VLOG(0) << "Serialized engine";
+    const char* engine_plan_data =
+        static_cast<const char*>(engine_plan->data());
+    engine_plan_string =
+        string(engine_plan_data, engine_plan_data + engine_plan->size());
+  }
+
+  VLOG(0) << "Finished engine";
+
+  // Build the TRT op
+  // TODO(sami,ben,jie): proper naming!
+  tensorflow::NodeDefBuilder op_builder(
+      tensorflow::strings::StrCat("my_trt_op", static_id++), "TRTEngineOp");
+  std::vector<tensorflow::NodeDefBuilder::NodeOut> income_edges;
+  for (size_t i = 0; i < input_names.size(); ++i) {
+    int output_idx = input_inds.at(i).second;
+    // We wired up the input here already, it is redundant to do it again in
+    // ConvertSubGraphToTensorRT(convert_graph.cc)
+    auto incoming_edge = tensorflow::NodeDefBuilder::NodeOut(
+        input_names.at(i), output_idx, input_dtypes.at(i));
+    income_edges.push_back(incoming_edge);
+  }
+  tensorflow::gtl::ArraySlice<tensorflow::NodeDefBuilder::NodeOut> input_list(
+      income_edges);
+  op_builder.Input(input_list);
+
+  VLOG(0) << "Finished op preparation";
+
+  auto status = op_builder.Attr("serialized_engine", engine_plan_string)
+                    .Attr("input_nodes", input_names)
+                    .Attr("output_nodes", output_names)
+                    .Attr("OutT", output_dtypes)
+                    .Finalize(trt_node);
+
+  VLOG(0) << status.ToString() << " finished op building";
+
+  return tensorflow::Status::OK();
+}
+
+}  // namespace convert
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h
new file mode 100644
index 0000000000..2e7fd19566
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h
@@ -0,0 +1,52 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_
+
+#include <set>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/graph/graph.h"
+#include "tensorflow/core/grappler/costs/graph_properties.h"
+#include "tensorflow/core/lib/core/status.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+
+namespace tensorflow {
+namespace tensorrt {
+namespace convert {
+
+tensorflow::Status ConvertSubGraphToTensorRTNodeDef(
+    const tensorflow::Graph& graph, const std::set<int>& subgraph_node_ids,
+    const std::vector<std::pair<int, int>>&
+        input_inds,  // {node_id, output_idx}
+    const std::vector<std::pair<int, int>>&
+        output_inds,  // {node_id, output_idx}
+    size_t max_batch_size, size_t max_workspace_size_bytes,
+    const tensorflow::grappler::GraphProperties& graph_prop,
+    tensorflow::NodeDef* trt_node);
+
+}  // namespace convert
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
+
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_
diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
new file mode 100644
index 0000000000..8efdf63ebe
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc
@@ -0,0 +1,140 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h"
+
+#include "tensorflow/contrib/tensorrt/log/trt_logger.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor.h"
+#include "tensorflow/core/platform/types.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "cuda/include/cuda_runtime_api.h"
+
+namespace tensorflow {
+namespace tensorrt {
+static ::tensorflow::tensorrt::Logger logger;
+
+TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) : OpKernel(context) {
+  // read serialized_engine
+  string serialized_engine;
+  OP_REQUIRES_OK(context,
+                 context->GetAttr("serialized_engine", &serialized_engine));
+
+  // register input output node name in trt_sub_graph
+  OP_REQUIRES_OK(context, context->GetAttr("input_nodes", &input_nodes_));
+  OP_REQUIRES_OK(context, context->GetAttr("output_nodes", &output_nodes_));
+
+  // TODO(samikama) runtime should be taken from a resourcemanager as well.
+  // Only engine should be in the op and context and runtime should be taken
+  // from resourcemanager
+  nvinfer1::IRuntime* infer = nvinfer1::createInferRuntime(logger);
+  trt_engine_ptr_.reset(infer->deserializeCudaEngine(
+      serialized_engine.c_str(), serialized_engine.size(), nullptr));
+
+  trt_execution_context_ptr_.reset(trt_engine_ptr_->createExecutionContext());
+  // Runtime is safe to delete after engine creation
+  infer->destroy();
+}
+
+void TRTEngineOp::Compute(OpKernelContext* context) {
+  int num_binding = context->num_inputs() + context->num_outputs();
+  std::vector<void*> buffers(num_binding);
+
+  size_t binding_index;
+  int num_batch = 0;
+  bool valid = true;
+  for (int i = 0; i < context->num_inputs(); i++) {
+    // Grab the input tensor
+    binding_index = trt_engine_ptr_->getBindingIndex(input_nodes_[i].c_str());
+
+    const Tensor& input_tensor = context->input(i);
+    const TensorShape& input_shape = input_tensor.shape();
+    if (i == 0) {
+      num_batch = input_shape.dim_size(0);
+    } else if (num_batch != input_shape.dim_size(0)) {
+      valid = false;
+      break;
+    }
+    switch (trt_engine_ptr_->getBindingDataType(binding_index)) {
+      case nvinfer1::DataType::kFLOAT:
+        buffers[binding_index] = (void*)(input_tensor.flat<float>().data());
+        break;
+      case nvinfer1::DataType::kHALF:
+        LOG(FATAL) << "half size is not supported yet!";
+        break;
+      case nvinfer1::DataType::kINT8:
+        LOG(FATAL) << "int8 is not supported yet!";
+        break;
+    }
+  }
+
+  // Might want a different way to inform the user of batch size inconsistency
+  if (!valid) LOG(WARNING) << "input data inconsistent batch size";
+
+  for (int i = 0; i < static_cast<int>(output_nodes_.size()); i++) {
+    // This is bad that we have to reallocate output buffer every run.
+    // Create an output tensor
+    binding_index = trt_engine_ptr_->getBindingIndex(output_nodes_[i].c_str());
+    Tensor* output_tensor = nullptr;
+
+    TensorShape output_shape;
+    if (binding_index != -1) {
+      auto dims = trt_engine_ptr_->getBindingDimensions(binding_index);
+      std::vector<int> trt_shape(dims.nbDims + 1);
+      trt_shape[0] = num_batch;
+      for (int j = 0; j < dims.nbDims; j++) trt_shape[j + 1] = dims.d[j];
+      OP_REQUIRES_OK(context,
+                     TensorShapeUtils::MakeShape(
+                         trt_shape.data(), trt_shape.size(), &output_shape));
+    } else {
+      LOG(FATAL) << "output node not found, at " << output_nodes_[i];
+      break;
+    }
+
+    OP_REQUIRES_OK(context,
+                   context->allocate_output(i, output_shape, &output_tensor));
+    switch (trt_engine_ptr_->getBindingDataType(binding_index)) {
+      case nvinfer1::DataType::kFLOAT:
+        buffers[binding_index] =
+            reinterpret_cast<void*>(output_tensor->flat<float>().data());
+        break;
+      case nvinfer1::DataType::kHALF:
+        LOG(FATAL) << "half size is not supported yet!";
+        break;
+      case nvinfer1::DataType::kINT8:
+        LOG(FATAL) << "int8 is not supported yet!";
+        break;
+    }
+  }
+  // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files
+  const cudaStream_t* stream = CHECK_NOTNULL(
+      reinterpret_cast<const cudaStream_t*>(context->op_device_context()
+                                                ->stream()
+                                                ->implementation()
+                                                ->CudaStreamMemberHack()));
+
+  // execution handled by TF since we are getting stream from TF.
+  // it is safe for CPU pointer array (buffers) to go out of scope after enqueue
+  trt_execution_context_ptr_->enqueue(num_batch, &buffers[0], *stream, nullptr);
+}
+
+REGISTER_KERNEL_BUILDER(Name("TRTEngineOp").Device(DEVICE_GPU), TRTEngineOp);
+
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h
new file mode 100644
index 0000000000..0964b4b18a
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h
@@ -0,0 +1,62 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_ENGINE_OP_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_ENGINE_OP_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "cuda/include/cuda_runtime_api.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorrt/include/NvInfer.h"
+
+namespace tensorflow {
+namespace tensorrt {
+class Logger;
+
+class TRTEngineOp : public OpKernel {
+ public:
+  explicit TRTEngineOp(OpKernelConstruction* context);
+
+  void Compute(OpKernelContext* context) override;
+
+ private:
+  template <typename T>
+  struct Destroyer {
+    void operator()(T* d) { d->destroy(); }
+  };
+
+  template <typename T>
+  using destroyed_ptr = std::unique_ptr<T, Destroyer<T>>;
+  destroyed_ptr<nvinfer1::ICudaEngine> trt_engine_ptr_;
+  // TODO(samikama): context should go to a resource manager!
+  destroyed_ptr<nvinfer1::IExecutionContext> trt_execution_context_ptr_;
+
+  std::vector<string> input_nodes_;
+  std::vector<string> output_nodes_;
+};
+
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
+
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_ENGINE_OP_H_
diff --git a/tensorflow/contrib/tensorrt/log/trt_logger.cc b/tensorflow/contrib/tensorrt/log/trt_logger.cc
new file mode 100644
index 0000000000..7add8cb8b3
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/log/trt_logger.cc
@@ -0,0 +1,57 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/log/trt_logger.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "tensorflow/core/platform/logging.h"
+
+namespace tensorflow {
+namespace tensorrt {
+
+// Use TF logging for TensorRT informations
+void Logger::log(Severity severity, const char* msg) {
+  // Suppress info-level messages
+  switch (severity) {
+    case Severity::kINFO: {  // Mark TRT info messages as debug!
+      VLOG(2) << msg;
+      break;
+    }
+    case Severity::kWARNING: {
+      LOG(WARNING) << msg;
+      break;
+    }
+    case Severity::kERROR: {
+      LOG(ERROR) << msg;
+      break;
+    }
+    case Severity::kINTERNAL_ERROR: {
+      LOG(FATAL) << msg;
+      break;
+    }
+    // This is useless for now. But would catch it in future if enum changes. It
+    // is always good to have default case!
+    default: {
+      LOG(FATAL) << name_ << "Got unknown severity level from TRT " << msg;
+      break;
+    }
+  }
+}
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // GOOGLE_CUDA
+#endif  // GOOGLE_TENSORRT
diff --git a/tensorflow/contrib/tensorrt/log/trt_logger.h b/tensorflow/contrib/tensorrt/log/trt_logger.h
new file mode 100644
index 0000000000..d71f66b933
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/log/trt_logger.h
@@ -0,0 +1,42 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_LOG_TRT_LOGGER_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_LOG_TRT_LOGGER_H_
+
+#include "tensorflow/core/platform/types.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "tensorrt/include/NvInfer.h"
+
+namespace tensorflow {
+namespace tensorrt {
+
+// Logger for GIE info/warning/errors
+class Logger : public nvinfer1::ILogger {
+ private:
+  void log(nvinfer1::ILogger::Severity severity, const char* msg) override;
+
+  string name_;
+};
+
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
+
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_LOG_TRT_LOGGER_H_
diff --git a/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc b/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc
new file mode 100644
index 0000000000..079d73f7be
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/ops/trt_engine_op.cc
@@ -0,0 +1,43 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+
+namespace tensorflow {
+
+namespace shape_inference {
+extern Status TRTEngineOpShapeInference(InferenceContext* c);
+}
+
+REGISTER_OP("TRTEngineOp")
+    .Attr("serialized_engine: string")
+    .Attr("input_nodes: list(string)")
+    .Attr("output_nodes: list(string)")
+    .Attr("InT: list({float32})")
+    .Attr("OutT: list({float32})")
+    .Input("in_tensor: InT")
+    .Output("out_tensor: OutT")
+    .SetShapeFn(shape_inference::TRTEngineOpShapeInference);
+
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/contrib/tensorrt/python/__init__.py b/tensorflow/contrib/tensorrt/python/__init__.py
new file mode 100644
index 0000000000..7e050a768c
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/python/__init__.py
@@ -0,0 +1,24 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Exposes the python wrapper for TensorRT graph transforms."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,line-too-long
+from tensorflow.contrib.tensorrt.python.ops import trt_engine_op
+from tensorflow.contrib.tensorrt.python.trt_convert import create_inference_graph
+# pylint: enable=unused-import,line-too-long
diff --git a/tensorflow/contrib/tensorrt/python/ops/trt_engine_op.py b/tensorflow/contrib/tensorrt/python/ops/trt_engine_op.py
new file mode 100644
index 0000000000..31a313182b
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/python/ops/trt_engine_op.py
@@ -0,0 +1,34 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Exposes the Python wrapper of TRTEngineOp."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import platform
+
+if platform.system() != "Windows":
+  # pylint: disable=wildcard-import,unused-import,g-import-not-at-top
+  from tensorflow.contrib.tensorrt.ops.gen_trt_engine_op import *
+
+  from tensorflow.contrib.util import loader
+  from tensorflow.python.platform import resource_loader
+  # pylint: enable=wildcard-import,unused-import,g-import-not-at-top
+
+  _trt_engine_op = loader.load_op_library(
+      resource_loader.get_path_to_datafile("_trt_engine_op.so"))
+else:
+  raise RuntimeError("Windows platforms are not supported")
diff --git a/tensorflow/contrib/tensorrt/python/trt_convert.py b/tensorflow/contrib/tensorrt/python/trt_convert.py
new file mode 100644
index 0000000000..9454862f85
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/python/trt_convert.py
@@ -0,0 +1,103 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+"""Exposes the Python wrapper conversion to trt_graph."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# pylint: disable=unused-import,line-too-long
+import six as _six
+from tensorflow.contrib.tensorrt.wrap_conversion import trt_convert
+from tensorflow.core.framework import graph_pb2
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import errors_impl as _impl
+from tensorflow.python.framework import ops
+
+
+# TODO(skama): get outputs from session when implemented as c++
+# optimization pass
+def create_inference_graph(input_graph_def,
+                           outputs,
+                           max_batch_size=1,
+                           max_workspace_size_bytes=2 << 20):
+  """Python wrapper for the TRT transormation.
+
+
+  Args:
+    input_graph_def: GraphDef object containing a model to be transformed.
+    outputs: List of tensors or node names for the model outputs.
+    max_batch_size: max size for the input batch
+    max_workspace_size_bytes: parameter to control memory allocation (in Bytes)
+
+  Returns:
+    New GraphDef with TRTEngineOps placed in graph replacing subgraphs.
+
+  Raises:
+    RuntimeError: if the returned status message is malformed.
+  """
+
+  def py2bytes(inp):
+    return inp
+
+  def py3bytes(inp):
+    return inp.encode("utf-8", errors="surrogateescape")
+
+  def py2string(inp):
+    return inp
+
+  def py3string(inp):
+    return inp.decode("utf-8")
+
+  if _six.PY2:
+    to_bytes = py2bytes
+    to_string = py2string
+  else:
+    to_bytes = py3bytes
+    to_string = py3string
+
+  out_names = []
+  for i in outputs:
+    if isinstance(i, ops.Tensor):
+      out_names.append(to_bytes(i.name))
+    else:
+      out_names.append(to_bytes(i))
+
+  input_graph_def_str = input_graph_def.SerializeToString()
+
+  # TODO(sami): Fix this when we can return status from C++ library
+  # There is a problem with the TF internal library setup that doesn't
+  # allow us to return a status object from C++.  Thus we return a
+  # pair or strings where first one is encoded status and the second
+  # one is the transformed graphs protobuf string.
+  out = trt_convert(input_graph_def_str, out_names, max_batch_size,
+                    max_workspace_size_bytes)
+  status = to_string(out[0])
+  output_graph_def_string = out[1]
+  del input_graph_def_str  # Save some memory
+  if len(status) < 2:
+    raise _impl.UnknownError(None, None, status)
+  if status[:2] != "OK":
+    msg = status.split(";")
+    if len(msg) == 1:
+      raise RuntimeError("Status message is malformed {}".format(status))
+    # pylint: disable=protected-access
+    raise _impl._make_specific_exception(None, None, ";".join(msg[1:]),
+                                         int(msg[0]))
+    # pylint: enable=protected-access
+  output_graph_def = graph_pb2.GraphDef()
+  output_graph_def.ParseFromString(output_graph_def_string)
+  del output_graph_def_string  # Save some memory
+  return output_graph_def
diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc
new file mode 100644
index 0000000000..6193f0b0a1
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/segment/segment.cc
@@ -0,0 +1,253 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/segment/segment.h"
+
+#include <set>
+#include <unordered_map>
+#include <vector>
+
+#include "tensorflow/contrib/tensorrt/segment/union_find.h"
+#include "tensorflow/core/graph/algorithm.h"
+#include "tensorflow/core/graph/graph.h"
+#include "tensorflow/core/graph/graph_constructor.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace tensorrt {
+namespace segment {
+
+namespace {
+
+bool CanContractEdge(const tensorflow::Edge* edge,
+                     const tensorflow::Graph& graph) {
+  const tensorflow::Node* src = edge->src();
+  const tensorflow::Node* dst = edge->dst();
+
+  // Can't contract edge if doing so would cause a cycle in the
+  // graph. So, if there is a directed path from 'src' to 'dst', other
+  // than 'edge' (or any other direct edge from 'src' to 'dst'), then
+  // combining 'src' and 'dst' will cause a cycle along that path.
+  //
+  // In practice, to avoid modifying the graph and to take advantage
+  // of existing graph functions, we perform an equivalent.
+  //   1. Get all nodes incoming to 'dst', excluding 'src'
+  //   2. Reverse DFS from those nodes
+  //   3. If reverse DFS reaches 'src' then we have a cycle
+  std::vector<tensorflow::Node*> dfs_start_nodes;
+  for (tensorflow::Node* node : dst->in_nodes()) {
+    if (node != src) {
+      dfs_start_nodes.push_back(node);
+    }
+  }
+
+  bool is_cycle = false;
+  if (!dfs_start_nodes.empty()) {
+    tensorflow::ReverseDFSFrom(graph, dfs_start_nodes, {},
+                               [&is_cycle, src](tensorflow::Node* node) {
+                                 if (node == src) {
+                                   is_cycle = true;
+                                 }
+                               });
+  }
+
+  return !is_cycle;
+}
+
+void ContractEdge(tensorflow::Edge* edge, tensorflow::Graph* graph,
+                  std::vector<const tensorflow::Edge*>* remove_edges) {
+  // Transfer all inputs and outputs of 'dst' to 'src' except edges
+  // connecting the two.
+  tensorflow::Node* src = edge->src();
+  tensorflow::Node* dst = edge->dst();
+
+  // We can use '0' for input/output index because we don't need them
+  // to be accurate for the way we are using the graph.
+  std::vector<const tensorflow::Edge*> in_edges(dst->in_edges().begin(),
+                                                dst->in_edges().end());
+  for (const tensorflow::Edge* in_edge : in_edges) {
+    if (in_edge->src() != src) {
+      tensorflow::Edge* e = const_cast<tensorflow::Edge*>(in_edge);
+      if (e->src() == graph->source_node()) {
+        graph->AddEdge(e->src(), e->src_output(), src,
+                       tensorflow::Graph::kControlSlot);
+      } else {
+        graph->AddEdge(e->src(), e->src_output(), src, 0 /* input index */);
+      }
+    }
+  }
+
+  std::vector<const tensorflow::Edge*> out_edges(dst->out_edges().begin(),
+                                                 dst->out_edges().end());
+  for (const tensorflow::Edge* out_edge : out_edges) {
+    tensorflow::Edge* e = const_cast<tensorflow::Edge*>(out_edge);
+    if (e->dst() == graph->sink_node()) {
+      graph->AddEdge(src, tensorflow::Graph::kControlSlot, e->dst(),
+                     e->dst_input());
+    } else {
+      graph->AddEdge(src, 0 /* output index */, e->dst(), e->dst_input());
+    }
+  }
+
+  // Return the edges that must be removed to disconnect 'dst' from
+  // the graph. We don't actually remove 'dst' since the caller holds
+  // references to all the nodes.
+  for (const auto& in_edge : dst->in_edges()) {
+    remove_edges->push_back(in_edge);
+  }
+  for (const auto& out_edge : dst->out_edges()) {
+    remove_edges->push_back(out_edge);
+  }
+}
+
+}  // namespace
+
+tensorflow::Status SegmentGraph(
+    const tensorflow::GraphDef& gdef,
+    const std::function<bool(const tensorflow::NodeDef&)>& candidate_fn,
+    const SegmentOptions& options, SegmentNodesVector* segments) {
+  // Create a Graph representation of the GraphDef.
+  tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(),
+                                             gdef.library());
+  tensorflow::Graph graph(flib);
+  TF_RETURN_IF_ERROR(tensorflow::ConvertGraphDefToGraph(
+      tensorflow::GraphConstructorOptions(), gdef, &graph));
+
+  // tensorflow::DumpGraph("Pre-Segment", &graph);
+
+  // Use a union-find to collect the nodes that belong to the same
+  // segment. A node value of nullptr indicates that the node is not a
+  // candidate for TRT.
+  std::vector<UnionFind<tensorflow::Node*>> node_segments;
+  for (int i = 0; i < graph.num_node_ids(); ++i) {
+    tensorflow::Node* node = graph.FindNodeId(i);
+    if (options.exclude_node_list.count(node->name()) != 0 ||
+        !candidate_fn(node->def())) {
+      node = nullptr;
+    }
+    node_segments.emplace_back(node);
+  }
+
+  // The segmentation algorithm below visits nodes in reverse
+  // topological order and attempts to merge nodes along output
+  // edges. That means that subgraphs grow from the output-side of the
+  // network towards the inputs. In general this is not guaranteed to
+  // produce a globally optimal segmentation. In the future if we have
+  // a measure of how beneficial it is to include a given node in a
+  // TRT subgraph then we can revisit this algorithm to take advantage
+  // of that information.
+  std::vector<tensorflow::Node*> order;
+  tensorflow::GetPostOrder(graph, &order);
+
+  for (const tensorflow::Node* node : order) {
+    // All output nodes of 'node' have been visited...
+    VLOG(2) << "Trying node " << node->name();
+
+    // 'node' must be a TRT candidate...
+    if (node_segments[node->id()].Value() == nullptr) {
+      VLOG(2) << "... not a TRT candidate";
+      continue;
+    }
+
+    // Contract output edges to combine 'node' with output
+    // nodes. Iterate since combining two nodes may unblock other
+    // combining.
+    while (true) {
+      std::set<const tensorflow::Edge*> contract_edges;
+      for (const tensorflow::Edge* out_edge : node->out_edges()) {
+        VLOG(2) << "... out node " << out_edge->dst()->name();
+
+        // Out node must be TRT candidate...
+        if (node_segments[out_edge->dst()->id()].Value() == nullptr) {
+          VLOG(2) << "... ... not a TRT candidate";
+          continue;
+        }
+
+        if (CanContractEdge(out_edge, graph)) {
+          VLOG(2) << "... ... can contract";
+          contract_edges.insert(out_edge);
+        } else {
+          VLOG(2) << "... ... cannot contract, would form cycle";
+        }
+      }
+
+      if (contract_edges.empty()) {
+        break;
+      }
+
+      // Contract edges and collect the adjacent nodes into the same
+      // segment/subgraph.
+      while (!contract_edges.empty()) {
+        const tensorflow::Edge* contract_edge = *contract_edges.begin();
+        const tensorflow::Node* src = contract_edge->src();
+        const tensorflow::Node* dst = contract_edge->dst();
+
+        VLOG(2) << "Merge " << src->name() << " <- " << dst->name();
+        node_segments[src->id()].Merge(&node_segments[dst->id()]);
+
+        // Contracting the edge leaves disconnected graph edges.
+        // Remove these from the graph and from 'contract_edges' so we
+        // don't visit them again.
+        tensorflow::Edge* e = const_cast<tensorflow::Edge*>(contract_edge);
+        std::vector<const tensorflow::Edge*> remove_edges;
+        ContractEdge(e, &graph, &remove_edges);
+
+        for (const tensorflow::Edge* r : remove_edges) {
+          contract_edges.erase(r);
+          graph.RemoveEdge(r);
+        }
+      }
+    }
+  }
+
+  // Collect the segments/subgraphs. Each subgraph is represented by a
+  // set of the names of the nodes in that subgraph.
+  std::unordered_map<string, std::set<string>> sg_map;
+  for (auto& u : node_segments) {
+    if ((u.Value() != nullptr) && (u.ParentValue() != nullptr)) {
+      sg_map[u.ParentValue()->name()].insert(u.Value()->name());
+    }
+  }
+
+  // Convert the segments into the expected return format
+  for (const auto& itr : sg_map) {
+    const auto& segment_node_names = itr.second;
+    if (VLOG_IS_ON(1)) {
+      string s;
+      for (const auto& name : segment_node_names) {
+        s += " " + name;
+      }
+      VLOG(1) << "Segment " << segments->size() << ":" << s;
+    }
+
+    // Don't use small segments.
+    if (static_cast<int>(segment_node_names.size()) <
+        options.minimum_segment_size) {
+      VLOG(1) << "Segment " << segments->size() << " has only "
+              << segment_node_names.size() << " nodes, dropping";
+      continue;
+    }
+
+    segments->emplace_back(segment_node_names);
+  }
+
+  return tensorflow::Status::OK();
+}
+
+}  // namespace segment
+}  // namespace tensorrt
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h
new file mode 100644
index 0000000000..ee6e2b3ed2
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/segment/segment.h
@@ -0,0 +1,56 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_SEGMENT_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_SEGMENT_H_
+
+#include <set>
+#include <vector>
+
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace tensorrt {
+namespace segment {
+
+using SegmentNodesVector = std::vector<std::set<string>>;
+
+struct SegmentOptions {
+  // Segment must contain at least this many nodes.
+  int minimum_segment_size = 2;
+  std::set<string> exclude_node_list;
+};
+
+// Get the subgraphs of a graph that can be handled by TensorRT.
+//
+// @param gdef The GraphDef describing the network
+// @param candidate_fn A function that returns true for a NodeDef if
+// that node can be handled by TensorRT.
+// @param segments Returns the TensorRT segments/subgraphs. Each entry
+// in the vector describes a subgraph by giving a set of the names of
+// all the NodeDefs in that subgraph.
+// @return the status.
+tensorflow::Status SegmentGraph(
+    const tensorflow::GraphDef& gdef,
+    const std::function<bool(const tensorflow::NodeDef&)>& candidate_fn,
+    const SegmentOptions& options, SegmentNodesVector* segments);
+
+}  // namespace segment
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_SEGMENT_H_
diff --git a/tensorflow/contrib/tensorrt/segment/segment_test.cc b/tensorflow/contrib/tensorrt/segment/segment_test.cc
new file mode 100644
index 0000000000..74cbc5f2b3
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/segment/segment_test.cc
@@ -0,0 +1,367 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/segment/segment.h"
+#include "tensorflow/c/c_api.h"
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace tensorrt {
+namespace segment {
+namespace test {
+
+class SegmentTest : public ::testing::Test {
+ public:
+  bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def);
+
+  TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name);
+  TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
+                    TF_Status* s, const char* name);
+
+  std::function<bool(const NodeDef&)> MakeCandidateFn(
+      const std::set<string>& node_names);
+
+ protected:
+  void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name,
+                         TF_Operation** op);
+  void AddHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
+                 TF_Status* s, const char* name, TF_Operation** op, bool check);
+
+  SegmentOptions default_options_;
+};
+
+bool SegmentTest::GetGraphDef(TF_Graph* graph,
+                              tensorflow::GraphDef* graph_def) {
+  TF_Status* s = TF_NewStatus();
+  TF_Buffer* buffer = TF_NewBuffer();
+  TF_GraphToGraphDef(graph, buffer, s);
+  bool ret = TF_GetCode(s) == TF_OK;
+  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  if (ret) ret = graph_def->ParseFromArray(buffer->data, buffer->length);
+  TF_DeleteBuffer(buffer);
+  TF_DeleteStatus(s);
+  return ret;
+}
+
+std::function<bool(const NodeDef&)> SegmentTest::MakeCandidateFn(
+    const std::set<string>& node_names) {
+  return [node_names](const NodeDef& node) -> bool {
+    return node_names.find(node.name()) != node_names.end();
+  };
+}
+
+void SegmentTest::PlaceholderHelper(TF_Graph* graph, TF_Status* s,
+                                    const char* name, TF_Operation** op) {
+  TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
+  TF_SetAttrType(desc, "dtype", TF_INT32);
+  *op = TF_FinishOperation(desc, s);
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  ASSERT_NE(*op, nullptr);
+}
+
+TF_Operation* SegmentTest::Placeholder(TF_Graph* graph, TF_Status* s,
+                                       const char* name) {
+  TF_Operation* op;
+  PlaceholderHelper(graph, s, name, &op);
+  return op;
+}
+
+void SegmentTest::AddHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
+                            TF_Status* s, const char* name, TF_Operation** op,
+                            bool check) {
+  TF_OperationDescription* desc = TF_NewOperation(graph, "AddN", name);
+  TF_Output add_inputs[2] = {{l, 0}, {r, 0}};
+  TF_AddInputList(desc, add_inputs, 2);
+  *op = TF_FinishOperation(desc, s);
+  if (check) {
+    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+    ASSERT_NE(*op, nullptr);
+  }
+}
+
+TF_Operation* SegmentTest::Add(TF_Operation* l, TF_Operation* r,
+                               TF_Graph* graph, TF_Status* s,
+                               const char* name) {
+  TF_Operation* op;
+  AddHelper(l, r, graph, s, name, &op, true);
+  return op;
+}
+
+TEST_F(SegmentTest, Empty) {
+  TF_Graph* graph = TF_NewGraph();
+
+  GraphDef graph_def;
+  ASSERT_TRUE(GetGraphDef(graph, &graph_def));
+
+  SegmentNodesVector segments;
+  ASSERT_EQ(
+      SegmentGraph(graph_def, MakeCandidateFn({}), default_options_, &segments),
+      tensorflow::Status::OK());
+
+  // Expect no segments/subgraphs.
+  EXPECT_TRUE(segments.empty());
+  TF_DeleteGraph(graph);
+}
+
+TEST_F(SegmentTest, Simple) {
+  TF_Status* s = TF_NewStatus();
+  TF_Graph* graph = TF_NewGraph();
+
+  //           feed
+  //         //    ||
+  //       add0    add1
+  //        | |    /
+  //        |  add2
+  //        |  /  ||
+  //       add3    add4
+  //           |  /
+  //          <sink>
+  //
+  TF_Operation* feed = Placeholder(graph, s, "feed");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
+
+  TF_Operation* add0 = Add(feed, feed, graph, s, "add0");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add1 = Add(feed, feed, graph, s, "add1");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add2 = Add(add0, add1, graph, s, "add2");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add3 = Add(add0, add2, graph, s, "add3");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("add3"), string(TF_OperationName(add3)));
+  TF_Operation* add4 = Add(add2, add2, graph, s, "add4");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("add4"), string(TF_OperationName(add4)));
+
+  GraphDef graph_def;
+  ASSERT_TRUE(GetGraphDef(graph, &graph_def));
+
+  SegmentNodesVector segments;
+  ASSERT_EQ(
+      SegmentGraph(graph_def,
+                   MakeCandidateFn({"add0", "add1", "add2", "add3", "add4"}),
+                   default_options_, &segments),
+      tensorflow::Status::OK());
+
+  // Expect all Add operations to be collapsed into a single segment
+  ASSERT_EQ(segments.size(), 1);
+  std::vector<string> expected{"add0", "add1", "add2", "add3", "add4"};
+  for (const auto& ex : expected) {
+    EXPECT_TRUE(segments[0].find(ex) != segments[0].end())
+        << "Missing expected node " << ex;
+  }
+  TF_DeleteGraph(graph);
+  TF_DeleteStatus(s);
+}
+
+TEST_F(SegmentTest, AvoidCycle) {
+  TF_Status* s = TF_NewStatus();
+  TF_Graph* graph = TF_NewGraph();
+
+  // add2 is not a TRT candidate so add0/add3 cannot be formed as a
+  // subgraph
+  //
+  //           feed
+  //         //    ||
+  //       add0    add1
+  //        | |    /
+  //        |  add2
+  //        |  /  ||
+  //       add3    add4
+  //           |  /
+  //          <sink>
+  //
+  TF_Operation* feed = Placeholder(graph, s, "feed");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
+
+  TF_Operation* add0 = Add(feed, feed, graph, s, "add0");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add1 = Add(feed, feed, graph, s, "add1");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add2 = Add(add0, add1, graph, s, "add2");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add3 = Add(add0, add2, graph, s, "add3");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("add3"), string(TF_OperationName(add3)));
+  TF_Operation* add4 = Add(add2, add2, graph, s, "add4");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("add4"), string(TF_OperationName(add4)));
+
+  GraphDef graph_def;
+  ASSERT_TRUE(GetGraphDef(graph, &graph_def));
+
+  SegmentNodesVector segments;
+  ASSERT_EQ(
+      SegmentGraph(graph_def, MakeCandidateFn({"add0", "add1", "add3", "add4"}),
+                   default_options_, &segments),
+      tensorflow::Status::OK());
+
+  // Expect no subgraphs
+  EXPECT_EQ(segments.size(), 0);
+  TF_DeleteGraph(graph);
+  TF_DeleteStatus(s);
+}
+
+TEST_F(SegmentTest, Multiple) {
+  TF_Status* s = TF_NewStatus();
+  TF_Graph* graph = TF_NewGraph();
+
+  // add5 is not a TRT candidate so two subgraphs should be formed
+  //
+  //                feed
+  //         //      ||     ||
+  //       add0    add1      add7
+  //        | |    /        /   ||
+  //        |  add2-----add5    add8
+  //        |  /  |    |  |    |
+  //       add3   add4     add6
+  //           |     |     /
+  //               <sink>
+  //
+  TF_Operation* feed = Placeholder(graph, s, "feed");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
+
+  TF_Operation* add0 = Add(feed, feed, graph, s, "add0");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add1 = Add(feed, feed, graph, s, "add1");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add7 = Add(feed, feed, graph, s, "add7");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add2 = Add(add0, add1, graph, s, "add2");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add5 = Add(add2, add7, graph, s, "add5");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add8 = Add(add7, add7, graph, s, "add8");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add3 = Add(add0, add2, graph, s, "add3");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("add3"), string(TF_OperationName(add3)));
+  TF_Operation* add4 = Add(add2, add5, graph, s, "add4");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("add4"), string(TF_OperationName(add4)));
+  TF_Operation* add6 = Add(add5, add8, graph, s, "add6");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("add6"), string(TF_OperationName(add6)));
+
+  GraphDef graph_def;
+  ASSERT_TRUE(GetGraphDef(graph, &graph_def));
+
+  SegmentNodesVector segments;
+  ASSERT_EQ(SegmentGraph(graph_def,
+                         MakeCandidateFn({"add0", "add1", "add2", "add3",
+                                          "add4", "add6", "add7", "add8"}),
+                         default_options_, &segments),
+            tensorflow::Status::OK());
+
+  // Expect two subgraphs
+  EXPECT_EQ(segments.size(), 2);
+
+  std::vector<string> expected0{"add0", "add1", "add2", "add3"};
+  for (const auto& ex : expected0) {
+    EXPECT_TRUE(segments[0].find(ex) != segments[0].end())
+        << "Missing expected node " << ex;
+  }
+
+  std::vector<string> expected1{"add6", "add8"};
+  for (const auto& ex : expected1) {
+    EXPECT_TRUE(segments[1].find(ex) != segments[1].end())
+        << "Missing expected node " << ex;
+  }
+  TF_DeleteGraph(graph);
+  TF_DeleteStatus(s);
+}
+
+TEST_F(SegmentTest, BigIfElse) {
+  TF_Status* s = TF_NewStatus();
+  TF_Graph* graph = TF_NewGraph();
+
+  // add2 is not a TRT candidate
+  //
+  //           feed
+  //            ||
+  //           add0
+  //         //    ||
+  //       add1    add4
+  //        ||      ||
+  //       add2    add5
+  //        ||      ||
+  //       add3    add6
+  //         ||    //
+  //           add7
+  //            ||
+  //          <sink>
+  //
+  TF_Operation* feed = Placeholder(graph, s, "feed");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
+
+  TF_Operation* add0 = Add(feed, feed, graph, s, "add0");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add1 = Add(add0, add0, graph, s, "add1");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add2 = Add(add1, add1, graph, s, "add2");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add3 = Add(add2, add2, graph, s, "add3");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add4 = Add(add0, add0, graph, s, "add4");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add5 = Add(add4, add4, graph, s, "add5");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add6 = Add(add5, add5, graph, s, "add6");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  TF_Operation* add7 = Add(add3, add6, graph, s, "add7");
+  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
+  EXPECT_EQ(string("add7"), string(TF_OperationName(add7)));
+
+  GraphDef graph_def;
+  ASSERT_TRUE(GetGraphDef(graph, &graph_def));
+
+  SegmentNodesVector segments;
+  ASSERT_EQ(SegmentGraph(graph_def,
+                         MakeCandidateFn({"add0", "add1", "add3", "add4",
+                                          "add5", "add6", "add7"}),
+                         default_options_, &segments),
+            tensorflow::Status::OK());
+
+  // Expect 2 subgraphs
+  EXPECT_EQ(segments.size(), 2);
+
+  std::vector<string> expected0{"add3", "add4", "add5", "add6", "add7"};
+  for (const auto& ex : expected0) {
+    EXPECT_TRUE(segments[0].find(ex) != segments[0].end())
+        << "Missing expected node " << ex;
+  }
+
+  std::vector<string> expected1{"add0", "add1"};
+  for (const auto& ex : expected1) {
+    EXPECT_TRUE(segments[1].find(ex) != segments[1].end())
+        << "Missing expected node " << ex;
+  }
+  TF_DeleteGraph(graph);
+  TF_DeleteStatus(s);
+}
+
+}  // namespace test
+}  // namespace segment
+}  // namespace tensorrt
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensorrt/segment/union_find.h b/tensorflow/contrib/tensorrt/segment/union_find.h
new file mode 100644
index 0000000000..1c64ebbb0a
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/segment/union_find.h
@@ -0,0 +1,79 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_UNION_FIND_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_UNION_FIND_H_
+
+namespace tensorflow {
+namespace tensorrt {
+namespace segment {
+
+// Union-Find data structure.
+// Each cluster has an associated value; when merging clusters we can control
+// which value becomes the representative of the merged clusters. Values must be
+// copyable.
+template <typename T>
+class UnionFind {
+ public:
+  UnionFind() : size_(1), parent_(nullptr) {}
+  explicit UnionFind(const T& v) : size_(1), parent_(nullptr), value_(v) {}
+
+  // Returns the number of elements in a cluster.
+  int Size() { return FindRoot()->size_; }
+
+  // Merges this cluster with 'other'. This cluster's value becomes
+  // the value of the merged cluster; the value of 'other' is ignored.
+  void Merge(UnionFind* other);
+
+  // Each cluster has an associated value. Retrieves the value associated
+  // with this cluster.
+  T& ParentValue() { return FindRoot()->value_; }
+
+  // Get the original value of this node.
+  T& Value() { return value_; }
+
+ private:
+  // Finds the root element of the cluster. Performs path compression.
+  UnionFind* FindRoot();
+
+  int size_;
+  UnionFind* parent_;
+  T value_;
+};
+
+template <typename T>
+void UnionFind<T>::Merge(UnionFind* other) {
+  UnionFind<T>* a = FindRoot();
+  UnionFind<T>* b = other->FindRoot();
+  if (a == b) return;
+
+  b->parent_ = a;
+  a->size_ += b->size_;
+}
+
+template <typename T>
+UnionFind<T>* UnionFind<T>::FindRoot() {
+  if (!parent_) return this;
+  // Path compression: update intermediate nodes to point to the root of the
+  // equivalence class.
+  parent_ = parent_->FindRoot();
+  return parent_;
+}
+
+}  // namespace segment
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_SEGMENT_UNION_FIND_H_
diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc
new file mode 100644
index 0000000000..8b475177bc
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.cc
@@ -0,0 +1,89 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h"
+
+#include <string>
+#include <vector>
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "tensorflow/contrib/tensorrt/log/trt_logger.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorrt/include/NvInfer.h"
+
+namespace tensorflow {
+namespace shape_inference {
+
+tensorflow::Status TRTEngineOpShapeInference(InferenceContext* context) {
+  tensorflow::tensorrt::Logger logger;
+  string serialized_engine;
+  TF_RETURN_IF_ERROR(context->GetAttr("serialized_engine", &serialized_engine));
+  nvinfer1::IRuntime* infer = nvinfer1::createInferRuntime(logger);
+  nvinfer1::ICudaEngine* trt_engine = infer->deserializeCudaEngine(
+      serialized_engine.c_str(), serialized_engine.size(), nullptr);
+
+  int num_batch = -1;
+  std::vector<::tensorflow::DataType> input_type;
+  TF_RETURN_IF_ERROR(context->GetAttr("InT", &input_type));
+  for (size_t i = 0; i < context->num_inputs(); i++) {
+    // Check if input shape is legit
+    auto input_shape = context->input(i);
+    for (int j = 0; j < context->Rank(input_shape); j++) {
+      auto dim_handler = context->Dim(input_shape, j);
+      if (j == 0) {
+        if (i == 0) {
+          num_batch = context->Value(dim_handler);
+        } else if (num_batch != context->Value(dim_handler)) {
+          // TODO(jie): TensorRT engine requires consistent batch between inputs
+          //            tensors. Segmenter should be aware of this.
+          LOG(FATAL) << "TensorRT engine requires consistent batch size";
+        }
+      }
+    }
+  }
+
+  // Arrange input here
+  std::vector<string> input_nodes;
+  TF_RETURN_IF_ERROR(context->GetAttr("input_nodes", &input_nodes));
+
+  // Arrange output here
+  std::vector<string> output_nodes;
+  TF_RETURN_IF_ERROR(context->GetAttr("output_nodes", &output_nodes));
+  for (size_t i = 0; i < output_nodes.size(); i++) {
+    int binding_index = trt_engine->getBindingIndex(output_nodes[i].c_str());
+    ShapeHandle output_shape;
+    std::vector<DimensionHandle> dim_vec;
+    dim_vec.emplace_back(context->MakeDim(num_batch));
+    if (binding_index != -1) {
+      auto dims = trt_engine->getBindingDimensions(binding_index);
+      for (int j = 0; j < dims.nbDims; j++) {
+        dim_vec.emplace_back(context->MakeDim(dims.d[j]));
+      }
+    } else {
+      LOG(FATAL) << "TensorRT engine cannot find binding: " << output_nodes[i];
+    }
+    output_shape = context->MakeShape(dim_vec);
+    context->set_output(i, output_shape);
+  }
+
+  return Status::OK();
+}
+
+}  // namespace shape_inference
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h
new file mode 100644
index 0000000000..4b50f66699
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/shape_fn/trt_shfn.h
@@ -0,0 +1,33 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_SHAPE_FN_TRT_SHFN_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_SHAPE_FN_TRT_SHFN_H_
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+namespace shape_inference {
+Status TRTEngineOpShapeInference(InferenceContext* c);
+}  // namespace shape_inference
+}  // namespace tensorflow
+
+#endif  // GOOGLE_TENSORRT
+#endif  // GOOGLE_CUDA
+
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_SHAPE_FN_TRT_SHFN_H_
diff --git a/tensorflow/contrib/tensorrt/test/test_tftrt.py b/tensorflow/contrib/tensorrt/test/test_tftrt.py
new file mode 100644
index 0000000000..c78f6f2224
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/test/test_tftrt.py
@@ -0,0 +1,88 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Script to test TF-TensorRT integration."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+# normally we should do import tensorflow as tf and then
+# tf.placeholder, tf.constant, tf.nn.conv2d etc but
+# it looks like internal builds don't like it so
+# importing every module individually
+
+from tensorflow.contrib import tensorrt as trt
+from tensorflow.core.protobuf import config_pb2 as cpb2
+from tensorflow.python.client import session as csess
+from tensorflow.python.framework import constant_op as cop
+from tensorflow.python.framework import dtypes as dtypes
+from tensorflow.python.framework import importer as importer
+from tensorflow.python.framework import ops as ops
+from tensorflow.python.ops import array_ops as aops
+from tensorflow.python.ops import nn as nn
+from tensorflow.python.ops import nn_ops as nn_ops
+
+
+def get_simple_graph_def():
+  """Create a simple graph and return its graph_def."""
+  g = ops.Graph()
+  with g.as_default():
+    a = aops.placeholder(
+        dtype=dtypes.float32, shape=(None, 24, 24, 2), name="input")
+    e = cop.constant(
+        [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]],
+        name="weights",
+        dtype=dtypes.float32)
+    conv = nn.conv2d(
+        input=a, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv")
+    b = cop.constant(
+        [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtypes.float32)
+    t = nn.bias_add(conv, b, name="biasAdd")
+    relu = nn.relu(t, "relu")
+    idty = aops.identity(relu, "ID")
+    v = nn_ops.max_pool(
+        idty, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool")
+    aops.squeeze(v, name="output")
+  return g.as_graph_def()
+
+
+def run_graph(gdef, dumm_inp):
+  gpu_options = cpb2.GPUOptions(per_process_gpu_memory_fraction=0.50)
+  ops.reset_default_graph()
+  g = ops.Graph()
+  with g.as_default():
+    inp, out = importer.import_graph_def(
+        graph_def=gdef, return_elements=["input", "output"])
+    inp = inp.outputs[0]
+    out = out.outputs[0]
+  with csess.Session(
+      config=cpb2.ConfigProto(gpu_options=gpu_options), graph=g) as sess:
+    val = sess.run(out, {inp: dumm_inp})
+  return val
+
+
+if "__main__" in __name__:
+  inp_dims = (100, 24, 24, 2)
+  dummy_input = np.random.random_sample(inp_dims)
+  gdef = get_simple_graph_def()
+  # Get optimized graph
+  trt_graph = trt.create_inference_graph(gdef, ["output"], inp_dims[0])
+  o1 = run_graph(gdef, dummy_input)
+  o2 = run_graph(trt_graph, dummy_input)
+  o3 = run_graph(trt_graph, dummy_input)
+  assert np.array_equal(o1, o2)
+  assert np.array_equal(o3, o2)  # sanity check
+  print("Pass")
diff --git a/tensorflow/contrib/tensorrt/trt_conversion.i b/tensorflow/contrib/tensorrt/trt_conversion.i
new file mode 100644
index 0000000000..d679945d56
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/trt_conversion.i
@@ -0,0 +1,131 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/* Wrap trt_conversion */
+%{
+#define SWIG_FILE_WITH_INIT
+%}
+%include "std_pair.i"
+%include "tensorflow/python/platform/base.i"
+
+%{
+PyObject* pair_helper(std::pair<string, string>* in) {
+  PyObject *first(nullptr), *second(nullptr), *tuple(nullptr);
+  first = PyBytes_FromStringAndSize(in->first.data(), in->first.length());
+  if (!first) {
+    if (!PyErr_Occurred()) {
+      PyErr_SetString(PyExc_TypeError, "Pair conversion first argument failed");
+    }
+    return NULL;
+  }
+  second = PyBytes_FromStringAndSize(in->second.data(), in->second.length());
+  if (!second) {
+    if (!PyErr_Occurred()) {
+      PyErr_SetString(PyExc_TypeError,
+                      "Pair conversion second argument failed");
+    }
+    return NULL;
+  }
+  tuple = Py_BuildValue("(OO)", first, second);
+  if (!tuple) {
+    if (!PyErr_Occurred()) {
+      PyErr_SetString(PyExc_TypeError,
+                      "Tuple creation from pair<string,string> failed!");
+    }
+    return NULL;
+  }
+  return tuple;
+}
+%}
+%typemap(out) std::pair<string, string> {
+  PyObject *tuple = pair_helper(&$1);
+  if (!tuple) SWIG_fail;
+  $result = tuple;
+}
+%{
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/util/stat_summarizer.h"
+#include "tensorflow/contrib/tensorrt/convert/convert_graph.h"
+%}
+
+%ignoreall
+%unignore tensorflow;
+%unignore trt_convert;
+
+%{
+std::pair<string, string> trt_convert(
+    string graph_def_string,  // The serialized GraphDef string.
+    std::vector<string> output_names,
+    size_t max_batch_size,
+    size_t max_workspace_size_bytes
+    // Unfortunately we can't use TF_Status here since it
+    // is in c/c_api and brings in a lot of other libraries
+    // which in turn declare ops. These ops are included
+    // statically in our library and cause an abort when
+    // module is loaded due to double registration
+    // until Tensorflow properly exposes these headers
+    // we have to work around this by returning a string
+    // and converting it to exception on python side.
+    //,TF_Status* out_status) {
+) {
+#if GOOGLE_CUDA && GOOGLE_TENSORRT
+  string out_status;
+
+  tensorflow::GraphDef graph_def;
+  if (!graph_def.ParseFromString(graph_def_string)) {
+    out_status = "InvalidArgument;Couldn't interpret input as a GraphDef";
+    return std::pair<string, string>{out_status, ""};
+  }
+
+  if (!output_names.size()) {
+    out_status = "InvalidArgument;Size of the output_names vector is 0";
+    return std::pair<string, string>{out_status, ""};
+    // return "";
+  }
+  tensorflow::GraphDef outGraph;
+  tensorflow::Status conversion_status =
+      tensorflow::tensorrt::convert::ConvertGraphDefToTensorRT(
+          graph_def, output_names, max_batch_size, max_workspace_size_bytes,
+          &outGraph);
+  if (!conversion_status.ok()) {
+    auto retCode = (int)conversion_status.code();
+    char buff[2000];
+    snprintf(buff, 2000, "%d;%s", retCode,
+             conversion_status.error_message().c_str());
+    out_status = buff;
+    return std::pair<string, string>{out_status, ""};
+  }
+  string result;
+  if (!outGraph.SerializeToString(&result)) {
+    out_status = "InvalidArgument;Couldn't serialize output as a GraphDef";
+    return std::pair<string, string>{out_status, ""};
+  }
+  out_status = "OK;All good!";
+  return std::pair<string, string>{out_status, result};
+#else
+  // Returns FAILED_PRECONDITION.
+  return std::pair<string, string>{"9;TensorRT is not enabled!", ""};
+#endif  // GOOGLE_CUDA && GOOGLE_TENSORRT
+}
+%}
+
+std::pair<string, string> trt_convert(string graph_def_string,
+                                      std::vector<string> output_names,
+                                      size_t max_batch_size,
+                                      size_t max_workspace_size_bytes);
+
+
+%unignoreall
diff --git a/tensorflow/contrib/tpu/profiler/pip_package/setup.py b/tensorflow/contrib/tpu/profiler/pip_package/setup.py
index 76f1dd2a56..8d99835b64 100644
--- a/tensorflow/contrib/tpu/profiler/pip_package/setup.py
+++ b/tensorflow/contrib/tpu/profiler/pip_package/setup.py
@@ -20,7 +20,7 @@ from __future__ import print_function
 
 from setuptools import setup
 
-_VERSION = '1.6.0-rc0'
+_VERSION = '1.6.0-rc1'
 
 CONSOLE_SCRIPTS = [
     'capture_tpu_profile=cloud_tpu_profiler.main:run_main',
diff --git a/tensorflow/core/common_runtime/gpu/gpu_id.h b/tensorflow/core/common_runtime/gpu/gpu_id.h
index 4e9c4abce1..2a6caea296 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_id.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_id.h
@@ -40,7 +40,7 @@ namespace tensorflow {
 //   a BaseGPUDevice. Note that the configuration allows us to create multiple
 //   BaseGPUDevice per GPU hardware in order to use multi CUDA streams on the
 //   hardware, so the mapping between TF GPU id and CUDA GPU id is not a 1:1
-//   mappping, see the example below.
+//   mapping, see the example below.
 //
 // For example, assuming that in the machine we have GPU device with index 0, 1,
 // 2 and 3 (physical GPU id). Setting "CUDA_VISIBLE_DEVICES=1,2,3" will create
diff --git a/tensorflow/core/common_runtime/mkl_cpu_allocator.h b/tensorflow/core/common_runtime/mkl_cpu_allocator.h
index 77eeb56b19..fb092424bf 100644
--- a/tensorflow/core/common_runtime/mkl_cpu_allocator.h
+++ b/tensorflow/core/common_runtime/mkl_cpu_allocator.h
@@ -21,7 +21,6 @@ limitations under the License.
 
 #ifdef INTEL_MKL
 
-#include <unistd.h>
 #include <cstdlib>
 #include <string>
 #include "tensorflow/core/common_runtime/bfc_allocator.h"
diff --git a/tensorflow/core/framework/tensor_shape.h b/tensorflow/core/framework/tensor_shape.h
index adb41b81c6..fe2ba375aa 100644
--- a/tensorflow/core/framework/tensor_shape.h
+++ b/tensorflow/core/framework/tensor_shape.h
@@ -191,9 +191,6 @@ class TensorShapeBase : public TensorShapeRep {
   /// Appends all the dimensions from `shape`.
   void AppendShape(const TensorShapeBase& shape);
 
-  // Maximum number of dimensions in a tensor.
-  static constexpr int MaxDimensions() { return 254; }
-
   /// \brief Insert a dimension somewhere in the `TensorShape`.
   /// REQUIRES: `0 <= d <= dims()`
   /// REQUIRES: `size >= 0`
diff --git a/tensorflow/core/graph/mkl_tfconversion_pass.cc b/tensorflow/core/graph/mkl_tfconversion_pass.cc
index 5343e6802d..e9ced4d2b6 100644
--- a/tensorflow/core/graph/mkl_tfconversion_pass.cc
+++ b/tensorflow/core/graph/mkl_tfconversion_pass.cc
@@ -222,7 +222,7 @@ Status MklToTfConversionPass::InsertInputConversionNode(
            BaseType(n->input_type(0)));
 
   // Check ordering of edges
-  for (uint i = 0; i < 4; i++) {
+  for (uint32 i = 0; i < 4; i++) {
     CHECK_EQ((edges[i]->dst_input() == i), true);
   }
 
diff --git a/tensorflow/core/kernels/colorspace_op.cc b/tensorflow/core/kernels/colorspace_op.cc
index 9cc2e67bbe..f4402a245d 100644
--- a/tensorflow/core/kernels/colorspace_op.cc
+++ b/tensorflow/core/kernels/colorspace_op.cc
@@ -71,7 +71,7 @@ class RGBToHSVOp : public OpKernel {
                                         TensorShape({input_data.dimension(0)}),
                                         &trange));
 
-    typename TTypes<T, 1>::Tensor range = trange.tensor<T, 1>();
+    typename TTypes<T, 1>::Tensor range(trange.tensor<T, 1>());
 
     functor::RGBToHSV<Device, T>()(context->eigen_device<Device>(), input_data,
                                    range, output_data);
diff --git a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
index 2b3b7184dc..94989089ec 100644
--- a/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc
@@ -24,12 +24,12 @@ limitations under the License.
 #include "tensorflow/core/util/cuda_kernel_helper.h"
 #include "tensorflow/core/util/tensor_format.h"
 
-#if !defined(_MSC_VER)
-#define UNROLL _Pragma("unroll")
-#define NOUNROLL _Pragma("nounroll")
-#else
+#if defined(_MSC_VER) && !defined(__clang__)
 #define UNROLL
 #define NOUNROLL
+#else
+#define UNROLL _Pragma("unroll")
+#define NOUNROLL _Pragma("nounroll")
 #endif
 
 namespace tensorflow {
diff --git a/tensorflow/core/kernels/mkl_batch_matmul_op.cc b/tensorflow/core/kernels/mkl_batch_matmul_op.cc
index d9713075be..723b445a75 100644
--- a/tensorflow/core/kernels/mkl_batch_matmul_op.cc
+++ b/tensorflow/core/kernels/mkl_batch_matmul_op.cc
@@ -29,7 +29,6 @@ limitations under the License.
 #include <vector>
 #include "mkl_cblas.h"
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/core/framework/numeric_types.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/register_types.h"
@@ -41,9 +40,6 @@ limitations under the License.
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 
-#define MKL_Complex8 tensorflow::complex64
-#define MKL_Complex16 tensorflow::complex128
-
 namespace tensorflow {
 
 typedef Eigen::ThreadPoolDevice CPUDevice;
@@ -180,16 +176,16 @@ class BatchMatMulMkl : public OpKernel {
   void MklCblasGemmBatch(const CBLAS_LAYOUT Layout, const bool TransA,
                          const bool TransB, const MKL_INT *M_Array,
                          const MKL_INT *N_Array, const MKL_INT *K_Array,
-                         const MKL_Complex8 **A_Array, const MKL_INT *lda_Array,
-                         const MKL_Complex8 **B_Array, const MKL_INT *ldb_Array,
-                         MKL_Complex8 **C_Array, const MKL_INT *ldc_Array,
+                         const complex64 **A_Array, const MKL_INT *lda_Array,
+                         const complex64 **B_Array, const MKL_INT *ldb_Array,
+                         complex64 **C_Array, const MKL_INT *ldc_Array,
                          const MKL_INT group_count, const MKL_INT *group_size) {
     std::vector<CBLAS_TRANSPOSE> TransA_array(
         group_size[0], TransA ? CblasConjTrans : CblasNoTrans);
     std::vector<CBLAS_TRANSPOSE> TransB_array(
         group_size[0], TransB ? CblasConjTrans : CblasNoTrans);
-    std::vector<MKL_Complex8> alpha_Array(group_size[0], {1.0f, 0.0f});
-    std::vector<MKL_Complex8> beta_Array(group_size[0], {0.0f, 0.0f});
+    std::vector<complex64> alpha_Array(group_size[0], {1.0f, 0.0f});
+    std::vector<complex64> beta_Array(group_size[0], {0.0f, 0.0f});
     cblas_cgemm_batch(
         Layout, &TransA_array[0], &TransB_array[0], M_Array, N_Array, K_Array,
         static_cast<const void *>(&alpha_Array[0]),
@@ -202,18 +198,16 @@ class BatchMatMulMkl : public OpKernel {
   void MklCblasGemmBatch(const CBLAS_LAYOUT Layout, const bool TransA,
                          const bool TransB, const MKL_INT *M_Array,
                          const MKL_INT *N_Array, const MKL_INT *K_Array,
-                         const MKL_Complex16 **A_Array,
-                         const MKL_INT *lda_Array,
-                         const MKL_Complex16 **B_Array,
-                         const MKL_INT *ldb_Array, MKL_Complex16 **C_Array,
-                         const MKL_INT *ldc_Array, const MKL_INT group_count,
-                         const MKL_INT *group_size) {
+                         const complex128 **A_Array, const MKL_INT *lda_Array,
+                         const complex128 **B_Array, const MKL_INT *ldb_Array,
+                         complex128 **C_Array, const MKL_INT *ldc_Array,
+                         const MKL_INT group_count, const MKL_INT *group_size) {
     std::vector<CBLAS_TRANSPOSE> TransA_array(
         group_size[0], TransA ? CblasConjTrans : CblasNoTrans);
     std::vector<CBLAS_TRANSPOSE> TransB_array(
         group_size[0], TransB ? CblasConjTrans : CblasNoTrans);
-    std::vector<MKL_Complex16> alpha_Array(group_size[0], {1.0f, 0.0f});
-    std::vector<MKL_Complex16> beta_Array(group_size[0], {0.0f, 0.0f});
+    std::vector<complex128> alpha_Array(group_size[0], {1.0f, 0.0f});
+    std::vector<complex128> beta_Array(group_size[0], {0.0f, 0.0f});
     cblas_zgemm_batch(
         Layout, &TransA_array[0], &TransB_array[0], M_Array, N_Array, K_Array,
         static_cast<const void *>(&alpha_Array[0]),
diff --git a/tensorflow/core/kernels/mkl_input_conversion_op.cc b/tensorflow/core/kernels/mkl_input_conversion_op.cc
index 5a8799ae93..e9a2376b54 100644
--- a/tensorflow/core/kernels/mkl_input_conversion_op.cc
+++ b/tensorflow/core/kernels/mkl_input_conversion_op.cc
@@ -145,8 +145,8 @@ class MklInputConversionOp : public OpKernel {
     const MklShape* mkl_shape;
     const Tensor* tf_tensor;
     MklShape* tf_mkl_shape;
-    uint mkl_tensor_index;
-    uint tf_tensor_index;
+    uint32 mkl_tensor_index;
+    uint32 tf_tensor_index;
     if (input_shape_0.IsMklTensor() && !input_shape_1.IsMklTensor()) {
       mkl_tensor = &input_tensor_0;
       mkl_shape = &input_shape_0;
diff --git a/tensorflow/core/kernels/mkl_matmul_op.cc b/tensorflow/core/kernels/mkl_matmul_op.cc
index 47598f443f..dfa6cecc9b 100644
--- a/tensorflow/core/kernels/mkl_matmul_op.cc
+++ b/tensorflow/core/kernels/mkl_matmul_op.cc
@@ -170,32 +170,32 @@ class MklMatMulOp : public OpKernel {
   // Matrix-Matrix Multiplication with Complex64 (std::complex<float>) tensors.
   // For detailed info about parameters, look at FP32 function description.
   void MklBlasGemm(bool transa, bool transb, const int m, const int n,
-                   const int k, const std::complex<float>* a, const int lda,
-                   const std::complex<float>* b, const int ldb,
-                   std::complex<float>* c, int const ldc) {
+                   const int k, const complex64* a, const int lda,
+                   const complex64* b, const int ldb, complex64* c,
+                   int const ldc) {
     const MKL_Complex8 alpha = {1.0f, 0.0f};
     const MKL_Complex8 beta = {0.0f, 0.0f};
     cblas_cgemm(CblasRowMajor, transa ? CblasTrans : CblasNoTrans,
-                transb ? CblasTrans : CblasNoTrans, m, n, k,
-                static_cast<const void*>(&alpha), static_cast<const void*>(a),
-                lda, static_cast<const void*>(b), ldb,
-                static_cast<const void*>(&beta), static_cast<void*>(c), ldc);
+                transb ? CblasTrans : CblasNoTrans, m, n, k, &alpha,
+                reinterpret_cast<const MKL_Complex8*>(a), lda,
+                reinterpret_cast<const MKL_Complex8*>(b), ldb, &beta,
+                reinterpret_cast<MKL_Complex8*>(c), ldc);
   }
 
   // Matrix-Matrix Multiplication with Complex128 (std::complex<double>)
   // tensors. For detailed info about parameters, look at FP32 function
   // description.
   void MklBlasGemm(bool transa, bool transb, const int m, const int n,
-                   const int k, const std::complex<double>* a, const int lda,
-                   const std::complex<double>* b, const int ldb,
-                   std::complex<double>* c, const int ldc) {
+                   const int k, const complex128* a, const int lda,
+                   const complex128* b, const int ldb, complex128* c,
+                   const int ldc) {
     const MKL_Complex16 alpha = {1.0, 0.0};
     const MKL_Complex16 beta = {0.0, 0.0};
     cblas_zgemm(CblasRowMajor, transa ? CblasTrans : CblasNoTrans,
-                transb ? CblasTrans : CblasNoTrans, m, n, k,
-                static_cast<const void*>(&alpha), static_cast<const void*>(a),
-                lda, static_cast<const void*>(b), ldb,
-                static_cast<const void*>(&beta), static_cast<void*>(c), ldc);
+                transb ? CblasTrans : CblasNoTrans, m, n, k, &alpha,
+                reinterpret_cast<const MKL_Complex16*>(a), lda,
+                reinterpret_cast<const MKL_Complex16*>(b), ldb, &beta,
+                reinterpret_cast<MKL_Complex16*>(c), ldc);
   }
 };
 
diff --git a/tensorflow/core/kernels/mkl_tfconv_op.h b/tensorflow/core/kernels/mkl_tfconv_op.h
index 5fafa14b5d..ddea9e281b 100644
--- a/tensorflow/core/kernels/mkl_tfconv_op.h
+++ b/tensorflow/core/kernels/mkl_tfconv_op.h
@@ -128,7 +128,7 @@ class MklToTfOp : public OpKernel {
 #else
   static void ConvertMklToTf(OpKernel* op_kernel, OpKernelContext* context,
                              string data_format_str, DataType op_data_type,
-                             bool has_avx512f, uint input_number) {
+                             bool has_avx512f, uint32 input_number) {
     // Check that input tensor is in MKL format.
     const Tensor& input_tensor = MklGetInput(context, input_number);
     MklShape input_shape;
diff --git a/tensorflow/core/kernels/mkl_transpose_op.cc b/tensorflow/core/kernels/mkl_transpose_op.cc
index 764d4c9400..3f07b317c4 100644
--- a/tensorflow/core/kernels/mkl_transpose_op.cc
+++ b/tensorflow/core/kernels/mkl_transpose_op.cc
@@ -18,9 +18,6 @@ limitations under the License.
 #ifdef INTEL_MKL
 #define EIGEN_USE_THREADS
 
-#include "tensorflow/core/framework/numeric_types.h"
-#define MKL_Complex8 tensorflow::complex64
-#define MKL_Complex16 tensorflow::complex128
 #include "mkl_trans.h"
 #include "tensorflow/core/kernels/transpose_functor.h"
 #include "tensorflow/core/kernels/transpose_op.h"
@@ -62,10 +59,37 @@ Status MKLTranspose2D(const char trans, const Tensor& in, Tensor* out);
 
 INSTANTIATE(float, s)
 INSTANTIATE(double, d)
-INSTANTIATE(complex64, c)
-INSTANTIATE(complex128, z)
+
 #undef INSTANTIATE
 
+template <>
+Status MKLTranspose2D<complex64>(const char trans, const Tensor& in,
+                                 Tensor* out) {
+  const MKL_Complex8 alpha = {1.0f, 0.0f};
+  mkl_comatcopy(
+      'R', trans, in.dim_size(0), in.dim_size(1), alpha,
+      reinterpret_cast<const MKL_Complex8*>(in.flat<complex64>().data()),
+      in.dim_size(1),
+      reinterpret_cast<MKL_Complex8*>(
+          const_cast<complex64*>(out->flat<complex64>().data())),
+      in.dim_size(0));
+  return Status::OK();
+}
+
+template <>
+Status MKLTranspose2D<complex128>(const char trans, const Tensor& in,
+                                  Tensor* out) {
+  const MKL_Complex16 alpha = {1.0, 0.0};
+  mkl_zomatcopy(
+      'R', trans, in.dim_size(0), in.dim_size(1), alpha,
+      reinterpret_cast<const MKL_Complex16*>(in.flat<complex128>().data()),
+      in.dim_size(1),
+      reinterpret_cast<MKL_Complex16*>(
+          const_cast<complex128*>(out->flat<complex128>().data())),
+      in.dim_size(0));
+  return Status::OK();
+}
+
 static const char kMKLTranspose = 'T';
 static const char kMKLConjugateTranspose = 'C';
 
diff --git a/tensorflow/core/kernels/non_max_suppression_op.cc b/tensorflow/core/kernels/non_max_suppression_op.cc
index 5d28b87e6b..903b898d0a 100644
--- a/tensorflow/core/kernels/non_max_suppression_op.cc
+++ b/tensorflow/core/kernels/non_max_suppression_op.cc
@@ -105,7 +105,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
   }
 
   const int output_size = std::min(max_output_size.scalar<int>()(), num_boxes);
-  typename TTypes<float, 2>::ConstTensor boxes_data = boxes.tensor<float, 2>();
+  TTypes<float, 2>::ConstTensor boxes_data = boxes.tensor<float, 2>();
 
   std::vector<float> scores_data(num_boxes);
   std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
@@ -138,8 +138,7 @@ void DoNonMaxSuppressionOp(OpKernelContext* context, const Tensor& boxes,
   Tensor* output = nullptr;
   TensorShape output_shape({static_cast<int>(selected.size())});
   OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
-  typename TTypes<int, 1>::Tensor selected_indices_data =
-      output->tensor<int, 1>();
+  TTypes<int, 1>::Tensor selected_indices_data = output->tensor<int, 1>();
   std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
 }
 
diff --git a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
index ddfeb1bb79..661d47d925 100644
--- a/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/parameterized_truncated_normal_op_gpu.cu.cc
@@ -29,7 +29,7 @@ limitations under the License.
 #include "tensorflow/core/lib/random/random_distributions.h"
 #include "tensorflow/core/util/cuda_kernel_helper.h"
 
-#ifdef COMPILER_MSVC
+#if defined(_MSC_VER) && !defined(__clang__)
 // msvc does not support unroll. One could try the loop pragma but we need to
 // take a closer look if this generates better code in this case. For now let
 // the compiler take care of it.
diff --git a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc b/tensorflow/core/kernels/quantized_resize_bilinear_op.cc
index fb2faede2f..9a1dcd0d49 100644
--- a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc
+++ b/tensorflow/core/kernels/quantized_resize_bilinear_op.cc
@@ -697,8 +697,8 @@ class QuantizedResizeBilinearOp : public OpKernel {
     // Return if the output is empty.
     if (st.output->NumElements() == 0) return;
 
-    typename TTypes<T, 4>::ConstTensor image_data = input.tensor<T, 4>();
-    typename TTypes<T, 4>::Tensor output_data = st.output->tensor<T, 4>();
+    typename TTypes<T, 4>::ConstTensor image_data(input.tensor<T, 4>());
+    typename TTypes<T, 4>::Tensor output_data(st.output->tensor<T, 4>());
 
     ResizeBilinear<T>(image_data, st.height_scale, st.width_scale, in_min,
                       in_max, &output_data);
diff --git a/tensorflow/core/kernels/random_crop_op.cc b/tensorflow/core/kernels/random_crop_op.cc
index 554909760a..b89bda4769 100644
--- a/tensorflow/core/kernels/random_crop_op.cc
+++ b/tensorflow/core/kernels/random_crop_op.cc
@@ -92,8 +92,8 @@ class RandomCropOp : public OpKernel {
 
     // TODO(shlens): Do this more efficiently with memcpy once padding is
     // available for smaller images.
-    typename TTypes<T, 3>::ConstTensor input_data = input.tensor<T, 3>();
-    typename TTypes<T, 3>::Tensor output_data = output->tensor<T, 3>();
+    typename TTypes<T, 3>::ConstTensor input_data(input.tensor<T, 3>());
+    typename TTypes<T, 3>::Tensor output_data(output->tensor<T, 3>());
 
     for (int y = 0; y < target_height; ++y) {
       for (int x = 0; x < target_width; ++x) {
diff --git a/tensorflow/core/kernels/resize_area_op.cc b/tensorflow/core/kernels/resize_area_op.cc
index ada50dfb70..98b8a0df28 100644
--- a/tensorflow/core/kernels/resize_area_op.cc
+++ b/tensorflow/core/kernels/resize_area_op.cc
@@ -149,7 +149,7 @@ class ResizeAreaOp : public OpKernel {
 
     if (!context->status().ok()) return;
 
-    typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
+    typename TTypes<T, 4>::ConstTensor input_data(input.tensor<T, 4>());
 
     // Precompute values used when iterating over x coordinates within a row.
     // Note that it may be useful to cache x_interps for a given
@@ -190,8 +190,7 @@ class ResizeAreaOp : public OpKernel {
   void ComputeLoop(const ImageResizerState& st,
                    const std::vector<CachedInterpolation>& x_interps,
                    typename TTypes<T, 4>::ConstTensor input_data) {
-    typename TTypes<float, 4>::Tensor output_data =
-        st.output->tensor<float, 4>();
+    TTypes<float, 4>::Tensor output_data = st.output->tensor<float, 4>();
 
     // When using this algorithm for downsizing, the target pixel value is the
     // weighted average of all the source pixels. The weight is determined by
diff --git a/tensorflow/core/kernels/resize_bicubic_op.cc b/tensorflow/core/kernels/resize_bicubic_op.cc
index 86e61bbcef..65014b6c44 100644
--- a/tensorflow/core/kernels/resize_bicubic_op.cc
+++ b/tensorflow/core/kernels/resize_bicubic_op.cc
@@ -480,9 +480,8 @@ class ResizeBicubicOp : public OpKernel {
 
     if (!context->status().ok()) return;
 
-    typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
-    typename TTypes<float, 4>::Tensor output_data =
-        st.output->tensor<float, 4>();
+    typename TTypes<T, 4>::ConstTensor input_data(input.tensor<T, 4>());
+    TTypes<float, 4>::Tensor output_data = st.output->tensor<float, 4>();
 
     interpolate_with_caching<T>(input_data, st, output_data);
   }
@@ -510,9 +509,8 @@ class ResizeBicubicOpGrad : public OpKernel {
 
     if (!context->status().ok()) return;
 
-    typename TTypes<float, 4>::ConstTensor input_grad =
-        input.tensor<float, 4>();
-    typename TTypes<T, 4>::Tensor output_grad = st.output->tensor<T, 4>();
+    TTypes<float, 4>::ConstTensor input_grad = input.tensor<float, 4>();
+    typename TTypes<T, 4>::Tensor output_grad(st.output->tensor<T, 4>());
 
     ResizeBicubicGrad<T>(input_grad, st, output_grad);
   }
diff --git a/tensorflow/core/kernels/resize_bilinear_op.cc b/tensorflow/core/kernels/resize_bilinear_op.cc
index d9cb993a4b..dde59e8e74 100644
--- a/tensorflow/core/kernels/resize_bilinear_op.cc
+++ b/tensorflow/core/kernels/resize_bilinear_op.cc
@@ -51,9 +51,8 @@ class ResizeBilinearOp : public OpKernel {
     // Return if the output is empty.
     if (st.output->NumElements() == 0) return;
 
-    typename TTypes<T, 4>::ConstTensor image_data = input.tensor<T, 4>();
-    typename TTypes<float, 4>::Tensor output_data =
-        st.output->tensor<float, 4>();
+    typename TTypes<T, 4>::ConstTensor image_data(input.tensor<T, 4>());
+    TTypes<float, 4>::Tensor output_data = st.output->tensor<float, 4>();
 
     functor::ResizeBilinear<Device, T>()(context->eigen_device<Device>(),
                                          image_data, st.height_scale,
@@ -258,9 +257,8 @@ class ResizeBilinearOpGrad : public OpKernel {
 
     if (!context->status().ok()) return;
 
-    typename TTypes<float, 4>::ConstTensor input_grad =
-        input.tensor<float, 4>();
-    typename TTypes<T, 4>::Tensor output_grad = st.output->tensor<T, 4>();
+    TTypes<float, 4>::ConstTensor input_grad = input.tensor<float, 4>();
+    typename TTypes<T, 4>::Tensor output_grad(st.output->tensor<T, 4>());
 
     functor::ResizeBilinearGrad<Device, T>()(context->eigen_device<Device>(),
                                              input_grad, st.height_scale,
diff --git a/tensorflow/core/kernels/resize_nearest_neighbor_op.cc b/tensorflow/core/kernels/resize_nearest_neighbor_op.cc
index bfd29b7ec8..8ec526c2b2 100644
--- a/tensorflow/core/kernels/resize_nearest_neighbor_op.cc
+++ b/tensorflow/core/kernels/resize_nearest_neighbor_op.cc
@@ -56,8 +56,8 @@ class ResizeNearestNeighborOp : public OpKernel {
     // Return if the output is empty.
     if (st.output->NumElements() == 0) return;
 
-    typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
-    typename TTypes<T, 4>::Tensor output_data = st.output->tensor<T, 4>();
+    typename TTypes<T, 4>::ConstTensor input_data(input.tensor<T, 4>());
+    typename TTypes<T, 4>::Tensor output_data(st.output->tensor<T, 4>());
 
     bool status;
     if (align_corners_) {
@@ -162,8 +162,8 @@ class ResizeNearestNeighborOpGrad : public OpKernel {
     // Return if the output is empty.
     if (output->NumElements() == 0) return;
 
-    typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
-    typename TTypes<T, 4>::Tensor output_data = output->tensor<T, 4>();
+    typename TTypes<T, 4>::ConstTensor input_data(input.tensor<T, 4>());
+    typename TTypes<T, 4>::Tensor output_data(output->tensor<T, 4>());
 
     const float height_scale =
         CalculateResizeScale(out_height, in_height, align_corners_);
diff --git a/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc b/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
index 44a817a5c7..c0fde8042e 100644
--- a/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
+++ b/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc
@@ -387,9 +387,9 @@ class SampleDistortedBoundingBoxV2Op : public OpKernel {
     OP_REQUIRES_OK(
         context, context->allocate_output(2, TensorShape({1, 1, 4}), &bboxes));
 
-    typename TTypes<T, 1>::Tensor begin_data = begin->tensor<T, 1>();
-    typename TTypes<T, 1>::Tensor size_data = size->tensor<T, 1>();
-    typename TTypes<float, 3>::Tensor bboxes_data = bboxes->tensor<float, 3>();
+    typename TTypes<T, 1>::Tensor begin_data(begin->tensor<T, 1>());
+    typename TTypes<T, 1>::Tensor size_data(size->tensor<T, 1>());
+    TTypes<float, 3>::Tensor bboxes_data = bboxes->tensor<float, 3>();
 
     begin_data(0) = T(offset_height);
     size_data(0) = T(target_height);
diff --git a/tensorflow/core/kernels/slice_op.cc b/tensorflow/core/kernels/slice_op.cc
index 79369fd4a9..77594479cb 100644
--- a/tensorflow/core/kernels/slice_op.cc
+++ b/tensorflow/core/kernels/slice_op.cc
@@ -358,11 +358,11 @@ class MklSliceOp : public OpKernel {
         /* data format = NCHW */
 
 #pragma omp parallel for
-        for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
+        for (ssize_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
           T* ip = in_buf + (d0 * in_strides[0]);
           T* op = op_buf + ((d0 - begin[0]) * out_strides[0]);
 #pragma omp parallel for
-          for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
+          for (ssize_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
             T* ip1 = ip + (d1 * in_strides[1]);
             T* op1 = op + ((d1 - begin[1]) * out_strides[1]);
             // For NCHW, H and W will be contiguous. So we can copy
@@ -376,15 +376,15 @@ class MklSliceOp : public OpKernel {
         /* data_format = NHWC */
 
 #pragma omp parallel for
-        for (size_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
+        for (ssize_t d0 = begin[0]; d0 < begin[0] + size[0]; d0++) {
           T* ip = in_buf + (d0 * in_strides[0]);
           T* op = op_buf + ((d0 - begin[0]) * out_strides[0]);
 #pragma omp parallel for
-          for (size_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
+          for (ssize_t d1 = begin[1]; d1 < begin[1] + size[1]; d1++) {
             T* ip1 = ip + (d1 * in_strides[1]);
             T* op1 = op + ((d1 - begin[1]) * out_strides[1]);
 #pragma omp parallel for
-            for (size_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) {
+            for (ssize_t d2 = begin[2]; d2 < begin[2] + size[2]; d2++) {
               T* ip2 = ip1 + (d2 * in_strides[2]);
               T* ip3 = ip2 + begin[3];
               T* op2 = op1 + ((d2 - begin[2]) * out_strides[2]);
diff --git a/tensorflow/core/kernels/substr_op.cc b/tensorflow/core/kernels/substr_op.cc
index e29f67297f..22e45918a0 100644
--- a/tensorflow/core/kernels/substr_op.cc
+++ b/tensorflow/core/kernels/substr_op.cc
@@ -115,7 +115,7 @@ class SubstrOp : public OpKernel {
           Tensor input_buffer;
           OP_REQUIRES_OK(context, context->allocate_temp(
                                       DT_STRING, output_shape, &input_buffer));
-          typename TTypes<string, 1>::Tensor input_bcast =
+          TTypes<string, 1>::Tensor input_bcast =
               input_buffer.shaped<string, 1>(bcast.result_shape());
           input_bcast =
               input.broadcast(BCast::ToIndexArray<1>(bcast.x_bcast()));
@@ -125,8 +125,8 @@ class SubstrOp : public OpKernel {
           OP_REQUIRES_OK(context,
                          context->allocate_temp(DataTypeToEnum<T>::v(),
                                                 output_shape, &pos_buffer));
-          typename TTypes<T, 1>::Tensor pos_bcast =
-              pos_buffer.shaped<T, 1>(bcast.result_shape());
+          typename TTypes<T, 1>::Tensor pos_bcast(
+              pos_buffer.shaped<T, 1>(bcast.result_shape()));
           pos_bcast =
               pos_shaped.broadcast(BCast::ToIndexArray<1>(bcast.y_bcast()));
 
@@ -135,8 +135,8 @@ class SubstrOp : public OpKernel {
           OP_REQUIRES_OK(context,
                          context->allocate_temp(DataTypeToEnum<T>::v(),
                                                 output_shape, &len_buffer));
-          typename TTypes<T, 1>::Tensor len_bcast =
-              len_buffer.shaped<T, 1>(bcast.result_shape());
+          typename TTypes<T, 1>::Tensor len_bcast(
+              len_buffer.shaped<T, 1>(bcast.result_shape()));
           len_bcast =
               len_shaped.broadcast(BCast::ToIndexArray<1>(bcast.y_bcast()));
 
@@ -164,7 +164,7 @@ class SubstrOp : public OpKernel {
           Tensor input_buffer;
           OP_REQUIRES_OK(context, context->allocate_temp(
                                       DT_STRING, output_shape, &input_buffer));
-          typename TTypes<string, 2>::Tensor input_bcast =
+          TTypes<string, 2>::Tensor input_bcast =
               input_buffer.shaped<string, 2>(bcast.result_shape());
           input_bcast =
               input.broadcast(BCast::ToIndexArray<2>(bcast.x_bcast()));
@@ -174,8 +174,8 @@ class SubstrOp : public OpKernel {
           OP_REQUIRES_OK(context,
                          context->allocate_temp(DataTypeToEnum<T>::v(),
                                                 output_shape, &pos_buffer));
-          typename TTypes<T, 2>::Tensor pos_bcast =
-              pos_buffer.shaped<T, 2>(bcast.result_shape());
+          typename TTypes<T, 2>::Tensor pos_bcast(
+              pos_buffer.shaped<T, 2>(bcast.result_shape()));
           pos_bcast =
               pos_shaped.broadcast(BCast::ToIndexArray<2>(bcast.y_bcast()));
 
@@ -184,8 +184,8 @@ class SubstrOp : public OpKernel {
           OP_REQUIRES_OK(context,
                          context->allocate_temp(DataTypeToEnum<T>::v(),
                                                 output_shape, &len_buffer));
-          typename TTypes<T, 2>::Tensor len_bcast =
-              len_buffer.shaped<T, 2>(bcast.result_shape());
+          typename TTypes<T, 2>::Tensor len_bcast(
+              len_buffer.shaped<T, 2>(bcast.result_shape()));
           len_bcast =
               len_shaped.broadcast(BCast::ToIndexArray<2>(bcast.y_bcast()));
 
diff --git a/tensorflow/core/kernels/xsmm_conv2d.cc b/tensorflow/core/kernels/xsmm_conv2d.cc
index 601704c8a7..ba03357cc6 100644
--- a/tensorflow/core/kernels/xsmm_conv2d.cc
+++ b/tensorflow/core/kernels/xsmm_conv2d.cc
@@ -27,9 +27,6 @@ void dummy_xsmm_conv2d_ensure_file_is_not_empty();
 
 #include <stdlib.h>
 #include <cstring>
-#if 0
-#include <omp.h>
-#endif
 
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/lib/core/blocking_counter.h"
@@ -360,7 +357,6 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
   l_tick6 = libxsmm_timer_tick();
 #endif
 
-#if 1
   BlockingCounter counter(num_threads);
 
   for (int i = 0; i < num_threads; ++i) {
@@ -371,14 +367,6 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
     });
   }
   counter.Wait();
-#else
-#pragma omp parallel
-  {
-    chk_libxsmm_err(
-        libxsmm_dnn_execute_st(libxsmm_handle, kind, 0, omp_get_thread_num()),
-        "Worker");
-  }
-#endif
 
 #if defined(LIBXSMM_DETAILED_TIMING)
   l_tick7 = libxsmm_timer_tick();
diff --git a/tensorflow/core/lib/io/record_writer.cc b/tensorflow/core/lib/io/record_writer.cc
index 3657243c5d..ebc5648269 100644
--- a/tensorflow/core/lib/io/record_writer.cc
+++ b/tensorflow/core/lib/io/record_writer.cc
@@ -49,7 +49,7 @@ RecordWriterOptions RecordWriterOptions::CreateRecordWriterOptions(
 #endif  // IS_SLIM_BUILD
   } else if (compression_type != compression::kNone) {
     LOG(ERROR) << "Unsupported compression_type:" << compression_type
-               << ". No comprression will be used.";
+               << ". No compression will be used.";
   }
   return options;
 }
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index 4c05b274fe..c3b08e067a 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -619,6 +619,10 @@ REGISTER_OP("NonMaxSuppression")
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &max_output_size));
       // The boxes is a 2-D float Tensor of shape [num_boxes, 4].
       DimensionHandle unused;
+      // The boxes[0] and scores[0] are both num_boxes.
+      TF_RETURN_IF_ERROR(
+          c->Merge(c->Dim(boxes, 0), c->Dim(scores, 0), &unused));
+      // The boxes[1] is 4.
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 1), 4, &unused));
 
       c->set_output(0, c->Vector(c->UnknownDim()));
@@ -643,6 +647,10 @@ REGISTER_OP("NonMaxSuppressionV2")
       TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &iou_threshold));
       // The boxes is a 2-D float Tensor of shape [num_boxes, 4].
       DimensionHandle unused;
+      // The boxes[0] and scores[0] are both num_boxes.
+      TF_RETURN_IF_ERROR(
+          c->Merge(c->Dim(boxes, 0), c->Dim(scores, 0), &unused));
+      // The boxes[1] is 4.
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 1), 4, &unused));
 
       c->set_output(0, c->Vector(c->UnknownDim()));
diff --git a/tensorflow/core/platform/platform.h b/tensorflow/core/platform/platform.h
index 12120c4ab9..0481b36871 100644
--- a/tensorflow/core/platform/platform.h
+++ b/tensorflow/core/platform/platform.h
@@ -43,10 +43,11 @@ limitations under the License.
 #elif defined(__arm__)
 #define PLATFORM_POSIX
 
-// Require an outside macro to tell us if we're building for Raspberry Pi.
-#if !defined(RASPBERRY_PI)
+// Require an outside macro to tell us if we're building for Raspberry Pi or
+// another ARM device that's not a mobile platform.
+#if !defined(RASPBERRY_PI) && !defined(ARM_NON_MOBILE)
 #define IS_MOBILE_PLATFORM
-#endif  // !defined(RASPBERRY_PI)
+#endif  // !defined(RASPBERRY_PI) && !defined(ARM_NON_MOBILE)
 
 #else
 // If no platform specified, use:
diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index ccab69b9c0..3606c5f127 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -387,7 +387,7 @@ message RunOptions {
   // EXPERIMENTAL.  Options used to initialize DebuggerState, if enabled.
   DebugOptions debug_options = 6;
 
-  // When enabled, causes tensor alllocation information to be included in
+  // When enabled, causes tensor allocation information to be included in
   // the error message when the Run() call fails because the allocator ran
   // out of memory (OOM).
   //
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 50bfa91267..7405e01e14 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -24,7 +24,7 @@ limitations under the License.
 
 // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
 // "-beta", "-rc", "-rc.1")
-#define TF_VERSION_SUFFIX "-rc0"
+#define TF_VERSION_SUFFIX "-rc1"
 
 #define TF_STR_HELPER(x) #x
 #define TF_STR(x) TF_STR_HELPER(x)
diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h
index db4c5c35e3..34db96075d 100644
--- a/tensorflow/core/util/mkl_util.h
+++ b/tensorflow/core/util/mkl_util.h
@@ -1112,9 +1112,11 @@ inline void ForwardMklTensorInToOutWithMklShape(OpKernelContext* context,
 // Forward the MKL shape ONLY (used in elementwise and other ops where
 // we call the eigen implementation and MKL shape is not used)
 inline void ForwardMklMetaDataInToOut(OpKernelContext* context,
-                                      uint idx_data_in, uint idx_data_out) {
-  uint idx_meta_in = GetTensorMetaDataIndex(idx_data_in, context->num_inputs());
-  uint idx_meta_out =
+                                      uint32 idx_data_in,
+                                      uint32_t idx_data_out) {
+  uint32 idx_meta_in =
+      GetTensorMetaDataIndex(idx_data_in, context->num_inputs());
+  uint32 idx_meta_out =
       GetTensorMetaDataIndex(idx_data_out, context->num_outputs());
 
   if (IsRefType(context->input_dtype(idx_data_in))) {
@@ -1126,7 +1128,7 @@ inline void ForwardMklMetaDataInToOut(OpKernelContext* context,
 
 // Set a dummy MKL shape (called when the output is in TF format)
 inline void SetDummyMklShapeOutput(OpKernelContext* context,
-                                   uint idx_data_out) {
+                                   uint32 idx_data_out) {
   MklShape mkl_shape_output;
   mkl_shape_output.SetMklTensor(false);
   AllocateOutputSetMklShape(context, idx_data_out, mkl_shape_output);
diff --git a/tensorflow/docs_src/about/roadmap.md b/tensorflow/docs_src/about/roadmap.md
index 3ee825ed40..1f934acab6 100644
--- a/tensorflow/docs_src/about/roadmap.md
+++ b/tensorflow/docs_src/about/roadmap.md
@@ -1,37 +1,86 @@
 # Roadmap
-**Last updated: January 23, 2017**
+**Last updated: Feb 15, 2018**
 
-TensorFlow is a fast moving project. In order for the community to better
-understand what the near future will bring, this document shares what we are
-working on internally. Many of these features were requested by the community,
-and we welcome
-[contributions](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome).
+TensorFlow is a rapidly moving, community supported project. This document is intended 
+to provide guidance about priorities and focus areas of the core set of TensorFlow 
+developers and about functionality that can be expected in the upcoming releases of 
+TensorFlow. Many of these areas are driven by  community use cases, and we welcome 
+further 
+[contributions](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md) 
+to TensorFlow.
 
-The features on this list are targeted for the next few months. At this point,
-we do not have timelines for these features.
+The features below do not have concrete release dates. However, the majority can be 
+expected in the next one to two releases. 
 
-### Improve non-Python language support
+### APIs
+#### High Level APIs:
+* Easy multi-GPU utilization with Estimators
+* Easy-to-use high-level pre-made estimators for Gradient Boosted Trees, Time Series, and other models
 
-* Support for adding gradient computation for graphs constructed in other
-  languages (C++, Java, Go etc.)
+#### Eager Execution:
+* Efficient utilization of multiple GPUs
+* Distributed training (multi-machine)
+* Performance improvements
+* Simpler export to a GraphDef/SavedModel 
 
-### Making TensorFlow easier to use
-* High-level APIs
-* Well-maintained models showing best practices
+#### Keras API:
+* Better integration with tf.data (ability to call `model.fit` with data tensors)
+* Full support for Eager Execution (both Eager support for the regular Keras API, and ability 
+to create Keras models Eager- style via Model subclassing)
+* Better distribution/multi-GPU support and TPU support (including a smoother model-to-estimator workflow)
 
-### Performance
-* Speed and memory benchmarks
-* Distributed full model benchmarks
-* Performance and memory usage improvements
+#### Official Models:
+* A set of 
+[reference models](https://github.com/tensorflow/models/tree/master/official) 
+across image recognition, speech, object detection, and 
+  translation that demonstrate best practices and serve as a starting point for 
+  high-performance model development.
+
+#### Contrib:
+* Deprecation notices added to parts of tf.contrib where preferred implementations exist outside of tf.contrib.
+* As much as possible, large projects inside tf.contrib moved to separate repositories.
+* The tf.contrib module will eventually be discontinued in its current form, experimental development will in future happen in other repositories.
 
-### Core Features
-* Automatic op placement ([#2126](https://github.com/tensorflow/tensorflow/issues/2126))
-* Support for graph-level functions
+
+#### Probabilistic Reasoning and Statistical Analysis:
+* Rich set of tools for probabilistic and statistical analysis in tf.distributions 
+  and tf.probability. These include new samplers, layers, optimizers, losses, and structured models
+* Statistical tools for hypothesis testing, convergence diagnostics, and sample statistics
+* Edward 2.0: High-level API for probabilistic programming
 
 ### Platforms
-* OpenCL support ([#22](https://github.com/tensorflow/tensorflow/issues/22))
+#### TensorFlow Lite:
+* Increased coverage of supported ops in TensorFlow Lite
+* Easier conversion of a trained TensorFlow graph for use on TensorFlow Lite
+* Support for GPU acceleration in TensorFlow Lite (iOS and Android)
+* Support for hardware accelerators via Android NeuralNets API 
+* Improved CPU performance by quantization and other network optimizations (eg. pruning, distillation)
+* Increased support for devices beyond Android and iOS (eg. RPi, Cortex-M)
+
+### Performance
+#### Distributed TensorFlow:
+* Multi-GPU support optimized for a variety of GPU topologies
+* Improved mechanisms for distributing computations on several machines
+
+#### Optimizations:
+* Mixed precision training support with initial example model and guide
+* Native TensorRT support
+* Int8 support for SkyLake via MKL
+* Dynamic loading of SIMD-optimized kernels
+
+### Documentation and Usability:
+* Updated documentation, tutorials and Getting Started guides
+* Process to enable external contributions to tutorials, documentation, and blogs showcasing best practice use-cases of TensorFlow and high-impact applications
+
+### Community and Partner Engagement
+#### Special Interest Groups: 
+* Mobilizing the community to work together in focused domains
+* [tf-distribute](https://groups.google.com/a/tensorflow.org/forum/#!forum/tf-distribute)
+: build and packaging of TensorFlow
+* More to be identified and launched
 
-### Community
-* More educational resources
-* Better integration of TensorFlow into the opensource big data ecosystem (e.g.
-[#2655](https://github.com/tensorflow/tensorflow/issues/2655))
+#### Community:
+* Incorporate public feedback on significant design decisions via a Request-for-Comment (RFC) process
+* Formalize process for external contributions to land in TensorFlow and associated projects 
+* Grow global TensorFlow communities and user groups
+* Collaborate with partners to co-develop and publish research papers
diff --git a/tensorflow/docs_src/about/uses.md b/tensorflow/docs_src/about/uses.md
index 8818177a28..d646880bd3 100644
--- a/tensorflow/docs_src/about/uses.md
+++ b/tensorflow/docs_src/about/uses.md
@@ -22,6 +22,14 @@ This section describes some of the current uses of the TensorFlow system.
 > TensorFlow, or even better, send us a pull request to add an entry to this
 > file.
 
+* **Deep Speech**
+<ul>
+   <li>**Organization**: Mozilla</li>
+   <li> **Domain**: Speech Recognition</li>
+   <li> **Description**:  A TensorFlow implementation motivated by Baidu's Deep Speech architecture.</li>
+   <li> **More info**: [GitHub Repo](https://github.com/mozilla/deepspeech)</li>
+</ul>
+
 * **RankBrain**
 <ul>
    <li>**Organization**: Google</li>
diff --git a/tensorflow/docs_src/deploy/index.md b/tensorflow/docs_src/deploy/index.md
index 5831960b4f..07b1bc9257 100644
--- a/tensorflow/docs_src/deploy/index.md
+++ b/tensorflow/docs_src/deploy/index.md
@@ -7,6 +7,8 @@ the following documents:
     a cluster of TensorFlow servers.
   * @{$hadoop$How to run TensorFlow on Hadoop}, which has a highly
     self-explanatory title.
+  * @{$s3$How to run TensorFlow with the S3 filesystem}, which explains how
+    to run TensorFlow with the S3 file system.
   * The entire document set for [TensorFlow serving](/serving), an open-source,
     flexible, high-performance serving system for machine-learned models
     designed for production environments. TensorFlow Serving provides
diff --git a/tensorflow/docs_src/deploy/leftnav_files b/tensorflow/docs_src/deploy/leftnav_files
index f8f8d578e6..c682e7add1 100644
--- a/tensorflow/docs_src/deploy/leftnav_files
+++ b/tensorflow/docs_src/deploy/leftnav_files
@@ -1,3 +1,4 @@
 index.md
 distributed.md
 hadoop.md
+s3.md
diff --git a/tensorflow/docs_src/deploy/s3.md b/tensorflow/docs_src/deploy/s3.md
new file mode 100644
index 0000000000..38f8428634
--- /dev/null
+++ b/tensorflow/docs_src/deploy/s3.md
@@ -0,0 +1,40 @@
+# How to run TensorFlow on S3
+
+This document describes how to run TensorFlow on S3 file system.
+
+## S3
+
+We assume that you are familiar with @{$reading_data$reading data}.
+
+To use S3 with TensorFlow, change the file paths you use to read and write
+data to an S3 path. For example:
+
+```python
+filenames = ["s3://bucketname/path/to/file1.tfrecord",
+             "s3://bucketname/path/to/file2.tfrecord"]
+dataset = tf.data.TFRecordDataset(filenames)
+```
+
+When reading or writing data on S3 with your TensorFlow program, the behavior
+could be controlled by various environmental variables:
+
+*   **AWS_REGION**: By default, regional endpoint is used for S3, with region
+    controlled by `AWS_REGION`. If `AWS_REGION` is not specified, then
+    `us-east-1` is used.
+*   **S3_ENDPOINT**: The endpoint could be overridden explicitly with
+    `S3_ENDPOINT` specified.
+*   **S3_USE_HTTPS**: HTTPS is used to access S3 by default, unless
+    `S3_USE_HTTPS=0`.
+*   **S3_VERIFY_SSL**: If HTTPS is used, SSL verification could be disabled
+    with `S3_VERIFY_SSL=0`.
+
+To read or write objects in a bucket that is no publicly accessible,
+AWS credentials must be provided through one of the following methods:
+
+*   Set credentials in the AWS credentials profile file on the local system,
+    located at: `~/.aws/credentials` on Linux, macOS, or Unix, or
+    `C:\Users\USERNAME\.aws\credentials` on Windows.
+*   Set the `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` environment
+    variables.
+*   If TensorFlow is deployed on an EC2 instance, specify an IAM role and then
+    give the EC2 instance access to that role.
diff --git a/tensorflow/docs_src/extend/add_filesys.md b/tensorflow/docs_src/extend/add_filesys.md
index f0591b7b7d..06f11de4eb 100644
--- a/tensorflow/docs_src/extend/add_filesys.md
+++ b/tensorflow/docs_src/extend/add_filesys.md
@@ -81,6 +81,8 @@ filesystem implementations call their existing libraries. Examples include:
     plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/hadoop/hadoop_file_system.h)
 *   [GCS
     plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/cloud/gcs_file_system.h)
+*   [S3
+    plugin](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/platform/s3/s3_file_system.h)
 
 #### The File interfaces
 
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 9563eb5017..818798555a 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -38,7 +38,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for macOS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.6.0-rc1.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index f4207debe0..4c6dfa8daf 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0-rc0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.6.0-rc1.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 9a80c18aa5..527884863e 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.6.0-rc0</version>
+  <version>1.6.0-rc1</version>
 </dependency>
 ```
 
@@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.6.0-rc0</version>
+                 <version>1.6.0-rc1</version>
                </dependency>
              </dependencies>
          </project>
@@ -123,12 +123,12 @@ instead:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow</artifactId>
-  <version>1.6.0-rc0</version>
+  <version>1.6.0-rc1</version>
 </dependency>
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>libtensorflow_jni_gpu</artifactId>
-  <version>1.6.0-rc0</version>
+  <version>1.6.0-rc1</version>
 </dependency>
 ```
 
@@ -147,7 +147,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or macOS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc1.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -166,7 +166,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0-rc0.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.6.0-rc1.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -174,10 +174,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc0.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.6.0-rc1.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0-rc0.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.6.0-rc1.zip).
   3. Extract this .zip file.
 
 
@@ -225,7 +225,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.6.0-rc0.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.6.0-rc1.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -239,11 +239,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and macOS X:
 
-<pre><b>java -cp libtensorflow-1.6.0-rc0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.6.0-rc1.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.6.0-rc0.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.6.0-rc1.jar;. -Djava.library.path=jni HelloTF</b></pre>d
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 105b225177..e3e115d9f6 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -188,7 +188,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      Virtualenv environment:
 
      <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -293,7 +293,7 @@ take the following steps:
 
      <pre>
      $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -480,8 +480,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre>
-
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl</b></pre>
 
 <a name="ValidateYourInstallation"></a>
 ## Validate your installation
@@ -648,14 +647,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -667,14 +666,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -686,14 +685,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -705,14 +704,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.6.0rc1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0rc1-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index d6df27f8c8..5be38ae1ef 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv:
      TensorFlow in the active Virtualenv is as follows:
 
      <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -242,7 +242,7 @@ take the following steps:
      issue the following command:
 
      <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl</b> </pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -351,7 +351,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (<i>targetDirectory</i>)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -524,7 +524,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl
 </pre>
 
 
@@ -532,5 +532,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py2-none-a
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl
 </pre>
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 90031b4b5e..8d83e9f119 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -359,10 +359,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.6.0rc0 on Linux:
+for TensorFlow 1.6.0rc1 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc0-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc1-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
@@ -393,7 +393,7 @@ TensorFlow programs:
 
 <pre>Hello, TensorFlow!</pre>
 
-If you are new to TensorFlow, see @{$get_started$Getting Started with
+If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with
 TensorFlow}.
 
 If the system outputs an error message instead of a greeting, see [Common
@@ -460,8 +460,8 @@ Stack Overflow and specify the `tensorflow` tag.
 **Linux**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
-<tr><td>tensorflow-1.6.0rc0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>N/A</td><td>N/A</td></tr>
-<tr><td>tensorflow_gpu-1.6.0rc0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>7</td><td>9</td></tr>
+<tr><td>tensorflow-1.6.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow_gpu-1.6.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.9.0</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.8.0</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>GCC 4.8</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr>
@@ -479,7 +479,7 @@ Stack Overflow and specify the `tensorflow` tag.
 **Mac**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
-<tr><td>tensorflow-1.6.0rc0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow-1.6.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.5.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.8.1</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.5.4</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow-1.3.0</td><td>CPU</td><td>2.7, 3.3-3.6</td><td>Clang from xcode</td><td>Bazel 0.4.5</td><td>N/A</td><td>N/A</td></tr>
@@ -493,8 +493,8 @@ Stack Overflow and specify the `tensorflow` tag.
 **Windows**
 <table>
 <tr><th>Version:</th><th>CPU/GPU:</th><th>Python Version:</th><th>Compiler:</th><th>Build Tools:</th><th>cuDNN:</th><th>CUDA:</th></tr>
-<tr><td>tensorflow-1.6.0rc0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
-<tr><td>tensorflow_gpu-1.6.0rc0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
+<tr><td>tensorflow-1.6.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
+<tr><td>tensorflow_gpu-1.6.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.5.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
 <tr><td>tensorflow_gpu-1.5.0</td><td>GPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>7</td><td>9</td></tr>
 <tr><td>tensorflow-1.4.0</td><td>CPU</td><td>3.5-3.6</td><td>MSVC 2015 update 3</td><td>Cmake v3.6.3</td><td>N/A</td><td>N/A</td></tr>
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index e020451c04..dedf485f93 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -47,7 +47,7 @@ installed on your system:
 
 If you have a different version of one of the preceding packages, please
 change to the specified versions.  In particular, the cuDNN version
-must match exactly: TensorFlow will not load if it cannot find `cudnn64_7.dll`.
+must match exactly: TensorFlow will not load if it cannot find `cuDNN64_7.dll`.
 To use a different version of cuDNN, you must build from source.
 
 ## Determine how to install TensorFlow
@@ -153,7 +153,7 @@ TensorFlow programs:
 
 <pre>Hello, TensorFlow!</pre>
 
-If you are new to TensorFlow, see @{$get_started$Getting Started with
+If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with
 TensorFlow}.
 
 If the system outputs an error message instead of a greeting, see [Common
diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md
index 17dbf1c3e6..69b63ae7d2 100644
--- a/tensorflow/docs_src/mobile/mobile_intro.md
+++ b/tensorflow/docs_src/mobile/mobile_intro.md
@@ -235,7 +235,7 @@ TensorFlow [on Github](https://github.com/tensorflow/models) that you can look
 through. Lean towards the simplest model you can find, and try to get started as
 soon as you have even a small amount of labelled data, since you’ll get the best
 results when you’re able to iterate quickly. The shorter the time it takes to
-try training a model and running it in s real application, the better overall
+try training a model and running it in its real application, the better overall
 results you’ll see. It’s common for an algorithm to get great training accuracy
 numbers but then fail to be useful within a real application because there’s a
 mismatch between the dataset and real usage. Prototype end-to-end usage as soon
diff --git a/tensorflow/docs_src/programmers_guide/low_level_intro.md b/tensorflow/docs_src/programmers_guide/low_level_intro.md
index a8cc0feae3..05709ad10a 100644
--- a/tensorflow/docs_src/programmers_guide/low_level_intro.md
+++ b/tensorflow/docs_src/programmers_guide/low_level_intro.md
@@ -312,7 +312,7 @@ the same input.  @{tf.layers$Layers} are the preferred way to add trainable
 parameters to a graph.
 
 Layers package together both the variables and the operations that act
-on them, . For example a
+on them. For example a
 [densely-connected layer](https://developers.google.com/machine-learning/glossary/#fully_connected_layer)
 performs a weighted sum across all inputs
 for each output and applies an optional
@@ -495,7 +495,7 @@ good. Here's what we got; your own output will almost certainly differ:
  [ 0.10527515]]
 ```
 
-### loss
+### Loss
 
 To optimize a model, you first need to define the loss. We'll use the mean
 square error, a standard loss for regression problems.
@@ -521,7 +521,7 @@ TensorFlow provides
 [**optimizers**](https://developers.google.com/machine-learning/glossary/#optimizer)
 implementing standard optimization algorithms. These are implemented as
 sub-classes of @{tf.train.Optimizer}. They incrementally change each
-variable in order to minimizethe loss. The simplest optimization algorithm is
+variable in order to minimize the loss. The simplest optimization algorithm is
 [**gradient descent**](https://developers.google.com/machine-learning/glossary/#gradient_descent),
 implemented by @{tf.train.GradientDescentOptimizer}. It modifies each
 variable according to the magnitude of the derivative of loss with respect to
diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md
index b898cbe29c..5111b16247 100644
--- a/tensorflow/docs_src/tutorials/layers.md
+++ b/tensorflow/docs_src/tutorials/layers.md
@@ -635,7 +635,7 @@ should be logged after every 50 steps of training.
 ### Train the Model
 
 Now we're ready to train our model, which we can do by creating `train_input_fn`
-ans calling `train()` on `mnist_classifier`. Add the following to `main()`:
+and calling `train()` on `mnist_classifier`. Add the following to `main()`:
 
 ```python
 # Train the model
diff --git a/tensorflow/examples/android/res/animator/color_animation.xml b/tensorflow/examples/android/res/animator/color_animation.xml
new file mode 100644
index 0000000000..891d8cc1d4
--- /dev/null
+++ b/tensorflow/examples/android/res/animator/color_animation.xml
@@ -0,0 +1,30 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+ Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+<set xmlns:android="http://schemas.android.com/apk/res/android"
+  android:ordering="sequentially">
+  <objectAnimator
+    android:propertyName="backgroundColor"
+    android:duration="375"
+    android:valueFrom="0x00b3ccff"
+    android:valueTo="0xffb3ccff"
+    android:valueType="colorType"/>
+  <objectAnimator
+    android:propertyName="backgroundColor"
+    android:duration="375"
+    android:valueFrom="0xffb3ccff"
+    android:valueTo="0x00b3ccff"
+    android:valueType="colorType"/>
+</set>
diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java b/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java
index 184df1bdb4..1cddf3dc55 100644
--- a/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java
+++ b/tensorflow/examples/android/src/org/tensorflow/demo/SpeechActivity.java
@@ -31,7 +31,8 @@ the RecognizeCommands helper class.
 
 package org.tensorflow.demo;
 
-import android.animation.ValueAnimator;
+import android.animation.AnimatorInflater;
+import android.animation.AnimatorSet;
 import android.app.Activity;
 import android.content.pm.PackageManager;
 import android.media.AudioFormat;
@@ -329,17 +330,13 @@ public class SpeechActivity extends Activity {
                     labelIndex = i;
                   }
                 }
-                final View labelView = (View) labelsListView.getChildAt(labelIndex - 2);
-                ValueAnimator colorAnimation =
-                     ValueAnimator.ofArgb(0x00b3ccff, 0xffb3ccff, 0x00b3ccff);
-                colorAnimation.setDuration(750);
-                colorAnimation.addUpdateListener(
-                    new ValueAnimator.AnimatorUpdateListener() {
-                      @Override
-                      public void onAnimationUpdate(ValueAnimator animator) {
-                        labelView.setBackgroundColor((int) animator.getAnimatedValue());
-                      }
-                    });
+                final View labelView = labelsListView.getChildAt(labelIndex - 2);
+
+                AnimatorSet colorAnimation =
+                    (AnimatorSet)
+                        AnimatorInflater.loadAnimator(
+                            SpeechActivity.this, R.animator.color_animation);
+                colorAnimation.setTarget(labelView);
                 colorAnimation.start();
               }
             }
diff --git a/tensorflow/examples/get_started/regression/imports85.py b/tensorflow/examples/get_started/regression/imports85.py
index 6bee556eb8..4fdaceea9a 100644
--- a/tensorflow/examples/get_started/regression/imports85.py
+++ b/tensorflow/examples/get_started/regression/imports85.py
@@ -131,11 +131,12 @@ def dataset(y_name="price", train_fraction=0.7):
     # booleans but we are dealing with symbolic tensors.
     return ~in_training_set(line)
 
-  base_dataset = (tf.contrib.data
-                  # Get the lines from the file.
-                  .TextLineDataset(path)
-                  # drop lines with question marks.
-                  .filter(has_no_question_marks))
+  base_dataset = (
+      tf.data
+      # Get the lines from the file.
+      .TextLineDataset(path)
+      # drop lines with question marks.
+      .filter(has_no_question_marks))
 
   train = (base_dataset
            # Take only the training-set lines.
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 868310cbc0..25e09fecbf 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -41,7 +41,6 @@ The subfolder names are important, since they define what label is applied to
 each image, but the filenames themselves don't matter. Once your images are
 prepared, you can run the training with a command like this:
 
-
 ```bash
 bazel build tensorflow/examples/image_retraining:retrain && \
 bazel-bin/tensorflow/examples/image_retraining/retrain \
@@ -70,12 +69,14 @@ on resource-limited platforms, you can try the `--architecture` flag with a
 Mobilenet model. For example:
 
 Run floating-point version of mobilenet:
+
 ```bash
 python tensorflow/examples/image_retraining/retrain.py \
     --image_dir ~/flower_photos --architecture mobilenet_1.0_224
 ```
 
 Run quantized version of mobilenet:
+
 ```bash
 python tensorflow/examples/image_retraining/retrain.py \
     --image_dir ~/flower_photos/   --architecture mobilenet_1.0_224_quantized
@@ -96,6 +97,12 @@ Visualize the summaries with this command:
 
 tensorboard --logdir /tmp/retrain_logs
 
+To use with Tensorflow Serving:
+
+```bash
+tensorflow_model_server --port=9000 --model_name=inception \
+    --model_base_path=/tmp/saved_models/
+```
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -1004,6 +1011,45 @@ def add_jpeg_decoding(input_width, input_height, input_depth, input_mean,
   return jpeg_data, mul_image
 
 
+def export_model(sess, architecture, saved_model_dir):
+  """Exports model for serving.
+
+  Args:
+    sess: Current active TensorFlow Session.
+    architecture: Model architecture.
+    saved_model_dir: Directory in which to save exported model and variables.
+  """
+  if architecture == 'inception_v3':
+    input_tensor = 'DecodeJpeg/contents:0'
+  elif architecture.startswith('mobilenet_'):
+    input_tensor = 'input:0'
+  else:
+    raise ValueError('Unknown architecture', architecture)
+  in_image = sess.graph.get_tensor_by_name(input_tensor)
+  inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}
+
+  out_classes = sess.graph.get_tensor_by_name('final_result:0')
+  outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_classes)}
+
+  signature = tf.saved_model.signature_def_utils.build_signature_def(
+      inputs=inputs,
+      outputs=outputs,
+      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
+
+  legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
+
+  # Save out the SavedModel.
+  builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
+  builder.add_meta_graph_and_variables(
+      sess, [tf.saved_model.tag_constants.SERVING],
+      signature_def_map={
+          tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+              signature
+      },
+      legacy_init_op=legacy_init_op)
+  builder.save()
+
+
 def main(_):
   # Needed to make sure the logging output is visible.
   # See https://github.com/tensorflow/tensorflow/issues/3047
@@ -1179,6 +1225,8 @@ def main(_):
     with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
       f.write('\n'.join(image_lists.keys()) + '\n')
 
+    export_model(sess, FLAGS.architecture, FLAGS.saved_model_dir)
+
 
 if __name__ == '__main__':
   parser = argparse.ArgumentParser()
@@ -1362,5 +1410,10 @@ if __name__ == '__main__':
       takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
       for more information on Mobilenet.\
       """)
+  parser.add_argument(
+      '--saved_model_dir',
+      type=str,
+      default='/tmp/saved_models/1/',
+      help='Where to save the exported graph.')
   FLAGS, unparsed = parser.parse_known_args()
   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tensorflow/examples/speech_commands/label_wav_dir.py b/tensorflow/examples/speech_commands/label_wav_dir.py
new file mode 100644
index 0000000000..a34db512dd
--- /dev/null
+++ b/tensorflow/examples/speech_commands/label_wav_dir.py
@@ -0,0 +1,136 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Runs a trained audio graph against WAVE files and reports the results.
+
+The model, labels and .wav files specified in the arguments will be loaded, and
+then the predictions from running the model against the audio data will be
+printed to the console. This is a useful script for sanity checking trained
+models, and as an example of how to use an audio model from Python.
+
+Here's an example of running it:
+
+python tensorflow/examples/speech_commands/label_wav_dir.py \
+--graph=/tmp/my_frozen_graph.pb \
+--labels=/tmp/speech_commands_train/conv_labels.txt \
+--wav_dir=/tmp/speech_dataset/left
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import glob
+import sys
+
+import tensorflow as tf
+
+# pylint: disable=unused-import
+from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
+# pylint: enable=unused-import
+
+FLAGS = None
+
+
+def load_graph(filename):
+  """Unpersists graph from file as default graph."""
+  with tf.gfile.FastGFile(filename, 'rb') as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+    tf.import_graph_def(graph_def, name='')
+
+
+def load_labels(filename):
+  """Read in labels, one label per line."""
+  return [line.rstrip() for line in tf.gfile.GFile(filename)]
+
+
+def run_graph(wav_dir, labels, input_layer_name, output_layer_name,
+              num_top_predictions):
+  """Runs the audio data through the graph and prints predictions."""
+  with tf.Session() as sess:
+    # Feed the audio data as input to the graph.
+    #   predictions  will contain a two-dimensional array, where one
+    #   dimension represents the input image count, and the other has
+    #   predictions per class
+    for wav_path in glob.glob(wav_dir + '/*.wav'):
+      if not wav_path or not tf.gfile.Exists(wav_path):
+        tf.logging.fatal('Audio file does not exist %s', wav_path)
+
+      with open(wav_path, 'rb') as wav_file:
+        wav_data = wav_file.read()
+
+      softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
+      predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data})
+
+      # Sort to show labels in order of confidence
+      print('\n%s' % (wav_path.split('/')[-1]))
+      top_k = predictions.argsort()[-num_top_predictions:][::-1]
+      for node_id in top_k:
+        human_string = labels[node_id]
+        score = predictions[node_id]
+        print('%s (score = %.5f)' % (human_string, score))
+
+    return 0
+
+
+def label_wav(wav_dir, labels, graph, input_name, output_name, how_many_labels):
+  """Loads the model and labels, and runs the inference to print predictions."""
+  if not labels or not tf.gfile.Exists(labels):
+    tf.logging.fatal('Labels file does not exist %s', labels)
+
+  if not graph or not tf.gfile.Exists(graph):
+    tf.logging.fatal('Graph file does not exist %s', graph)
+
+  labels_list = load_labels(labels)
+
+  # load graph, which is stored in the default session
+  load_graph(graph)
+
+  run_graph(wav_dir, labels_list, input_name, output_name, how_many_labels)
+
+
+def main(_):
+  """Entry point for script, converts flags to arguments."""
+  label_wav(FLAGS.wav_dir, FLAGS.labels, FLAGS.graph, FLAGS.input_name,
+            FLAGS.output_name, FLAGS.how_many_labels)
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument(
+      '--wav_dir', type=str, default='', help='Audio file to be identified.')
+  parser.add_argument(
+      '--graph', type=str, default='', help='Model to use for identification.')
+  parser.add_argument(
+      '--labels', type=str, default='', help='Path to file containing labels.')
+  parser.add_argument(
+      '--input_name',
+      type=str,
+      default='wav_data:0',
+      help='Name of WAVE data input node in model.')
+  parser.add_argument(
+      '--output_name',
+      type=str,
+      default='labels_softmax:0',
+      help='Name of node outputting a prediction in the model.')
+  parser.add_argument(
+      '--how_many_labels',
+      type=int,
+      default=3,
+      help='Number of results to show.')
+
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/tensorflow/examples/speech_commands/train.py b/tensorflow/examples/speech_commands/train.py
index a4e80041f8..07c1919347 100644
--- a/tensorflow/examples/speech_commands/train.py
+++ b/tensorflow/examples/speech_commands/train.py
@@ -357,12 +357,14 @@ if __name__ == '__main__':
       '--window_size_ms',
       type=float,
       default=30.0,
-      help='How long each spectrogram timeslice is',)
+      help='How long each spectrogram timeslice is.',
+  )
   parser.add_argument(
       '--window_stride_ms',
       type=float,
       default=10.0,
-      help='How long each spectrogram timeslice is',)
+      help='How far to move in time between spectogram timeslices.',
+  )
   parser.add_argument(
       '--dct_coefficient_count',
       type=int,
diff --git a/tensorflow/examples/udacity/5_word2vec.ipynb b/tensorflow/examples/udacity/5_word2vec.ipynb
index 18c456cad7..3b43d1fb55 100644
--- a/tensorflow/examples/udacity/5_word2vec.ipynb
+++ b/tensorflow/examples/udacity/5_word2vec.ipynb
@@ -455,7 +455,7 @@
         "  \n",
         "  # Compute the similarity between minibatch examples and all embeddings.\n",
         "  # We use the cosine distance:\n",
-        "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
+        "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n",
         "  normalized_embeddings = embeddings / norm\n",
         "  valid_embeddings = tf.nn.embedding_lookup(\n",
         "    normalized_embeddings, valid_dataset)\n",
diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index e269b71f2e..1167b3834e 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -1114,7 +1114,7 @@ def _write_dict_to_summary(output_dir,
           isinstance(dictionary[key], np.int32) or
           isinstance(dictionary[key], int)):
       summary_proto.value.add(tag=key, simple_value=int(dictionary[key]))
-    elif isinstance(dictionary[key], six.string_types):
+    elif isinstance(dictionary[key], six.binary_type):
       try:
         summ = summary_pb2.Summary.FromString(dictionary[key])
         for i, _ in enumerate(summ.value):
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 7c7d913c32..7a0745b1d0 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -80,18 +80,18 @@ def dummy_model_fn(features, labels, params):
   _, _, _ = features, labels, params
 
 
-def check_eventfile_for_keyword(keyword, est):
+def check_eventfile_for_keyword(keyword, dir_):
   """Checks event files for the keyword."""
 
   writer_cache.FileWriterCache.clear()
 
   # Get last Event written.
-  event_paths = glob.glob(os.path.join(est.model_dir, 'events*'))
+  event_paths = glob.glob(os.path.join(dir_, 'events*'))
   last_event = None
   for last_event in summary_iterator.summary_iterator(event_paths[-1]):
     if last_event.summary is not None:
-      if last_event.summary.value:
-        if keyword in last_event.summary.value[0].tag:
+      for value in last_event.summary.value:
+        if keyword in value.tag:
           return True
 
   return False
@@ -610,7 +610,7 @@ class EstimatorTrainTest(test.TestCase):
     # Make sure nothing is stuck in limbo.
     writer_cache.FileWriterCache.clear()
 
-    if check_eventfile_for_keyword('loss', est):
+    if check_eventfile_for_keyword('loss', est.model_dir):
       return
     self.fail('{} should be part of reported summaries.'.format('loss'))
 
@@ -1290,8 +1290,9 @@ class EstimatorEvaluateTest(test.TestCase):
     # Make sure nothing is stuck in limbo.
     writer_cache.FileWriterCache.clear()
 
-    # Get last Event written.
-    if check_eventfile_for_keyword('image', est):
+    # Get last evaluation Event written.
+    if check_eventfile_for_keyword('image', os.path.join(est.model_dir,
+                                                         'eval')):
       return
     self.fail('{} should be part of reported summaries.'.format('image'))
 
diff --git a/tensorflow/python/framework/common_shapes.py b/tensorflow/python/framework/common_shapes.py
index 3b1092f923..3c5aebbce8 100644
--- a/tensorflow/python/framework/common_shapes.py
+++ b/tensorflow/python/framework/common_shapes.py
@@ -34,7 +34,7 @@ def scalar_shape(unused_op):
 
 
 def unchanged_shape(op):
-  """Shape function for ops that output an tensor like their first input."""
+  """Shape function for ops that output a tensor like their first input."""
   return [op.inputs[0].get_shape()]
 
 
diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py
index b35cee0111..301a7f682d 100644
--- a/tensorflow/python/framework/function_test.py
+++ b/tensorflow/python/framework/function_test.py
@@ -1458,7 +1458,7 @@ class FunctionInlineControlTest(test.TestCase):
       def Cell(v):
         # If v is a vector [n, 1], x is a big square matrix.
         x = math_ops.tanh(v + array_ops.transpose(v, [1, 0]))
-        return math_ops.reduce_sum(x, 1, keep_dims=True)
+        return math_ops.reduce_sum(x, 1, keepdims=True)
 
       @function.Defun(dtype)
       def Forward(x):
diff --git a/tensorflow/python/kernel_tests/reduction_ops_test.py b/tensorflow/python/kernel_tests/reduction_ops_test.py
index 4231a79b2d..d306d1b8d6 100644
--- a/tensorflow/python/kernel_tests/reduction_ops_test.py
+++ b/tensorflow/python/kernel_tests/reduction_ops_test.py
@@ -110,10 +110,10 @@ class ReductionUnknownShape(test.TestCase):
 
 class BaseReductionTest(test.TestCase):
 
-  def _tf_reduce(self, x, reduction_axes, keep_dims):
+  def _tf_reduce(self, x, reduction_axes, keepdims):
     raise NotImplementedError()
 
-  def _np_reduce(self, x, reduction_axes, keep_dims):
+  def _np_reduce(self, x, reduction_axes, keepdims):
     raise NotImplementedError()
 
   def _makeIncremental(self, shape, dtype):
@@ -128,10 +128,10 @@ class BaseReductionTest(test.TestCase):
       data -= 2j * data
     return data
 
-  def _compare(self, x, reduction_axes, keep_dims, feed_dict=None):
-    np_ans = self._np_reduce(x, reduction_axes, keep_dims)
+  def _compare(self, x, reduction_axes, keepdims, feed_dict=None):
+    np_ans = self._np_reduce(x, reduction_axes, keepdims)
     with self.test_session(use_gpu=True) as sess:
-      tf_ans = self._tf_reduce(x, reduction_axes, keep_dims)
+      tf_ans = self._tf_reduce(x, reduction_axes, keepdims)
       out = sess.run(tf_ans, feed_dict)
     self.assertAllClose(np_ans, out)
     self.assertShapeEqual(np_ans, tf_ans)
@@ -140,8 +140,8 @@ class BaseReductionTest(test.TestCase):
     if reduction_axes is not None and np.shape(reduction_axes) == (1,):
       # Test scalar reduction_axes argument
       self._compareAll(x, reduction_axes[0])
-    self._compare(x, reduction_axes, keep_dims=False, feed_dict=feed_dict)
-    self._compare(x, reduction_axes, keep_dims=True, feed_dict=feed_dict)
+    self._compare(x, reduction_axes, keepdims=False, feed_dict=feed_dict)
+    self._compare(x, reduction_axes, keepdims=True, feed_dict=feed_dict)
 
   def _compareAllAxes(self, x, feed_dict=None):
     self._compareAll(x, None)
@@ -171,14 +171,14 @@ class BaseReductionTest(test.TestCase):
 
 class SumReductionTest(BaseReductionTest):
 
-  def _tf_reduce(self, x, reduction_axes, keep_dims):
-    return math_ops.reduce_sum(x, reduction_axes, keep_dims)
+  def _tf_reduce(self, x, reduction_axes, keepdims):
+    return math_ops.reduce_sum(x, reduction_axes, keepdims)
 
-  def _np_reduce(self, x, reduction_axes, keep_dims):
+  def _np_reduce(self, x, reduction_axes, keepdims):
     if isinstance(reduction_axes, list) or isinstance(reduction_axes,
                                                       np.ndarray):
       reduction_axes = tuple(reduction_axes)
-    return np.sum(x, axis=reduction_axes, keepdims=keep_dims)
+    return np.sum(x, axis=reduction_axes, keepdims=keepdims)
 
   def testAxesType(self):
     for dtype in [dtypes.int64, dtypes.int32]:
@@ -298,7 +298,7 @@ class SumReductionTest(BaseReductionTest):
     c_known_rank = array_ops.placeholder(dtypes.float32)
     c_known_rank.set_shape(tensor_shape.unknown_shape(ndims=3))
     s_known_rank = math_ops.reduce_sum(
-        c_known_rank, reduction_axes, keep_dims=True)
+        c_known_rank, reduction_axes, keepdims=True)
     self.assertEqual(3, s_known_rank.get_shape().ndims)
 
     np_input = np.random.randn(3, 3, 3)
@@ -308,11 +308,11 @@ class SumReductionTest(BaseReductionTest):
     unknown_indices = array_ops.placeholder(dtypes.int32)
     c_unknown_indices = constant_op.constant([[10.0], [20.0]])
     s_unknown_indices = math_ops.reduce_sum(
-        c_unknown_indices, unknown_indices, keep_dims=False)
+        c_unknown_indices, unknown_indices, keepdims=False)
     self.assertEqual(tensor_shape.unknown_shape(),
                      s_unknown_indices.get_shape())
     s_unknown_indices_keep = math_ops.reduce_sum(
-        c_unknown_indices, unknown_indices, keep_dims=True)
+        c_unknown_indices, unknown_indices, keepdims=True)
     self.assertEqual(2, s_unknown_indices_keep.get_shape().ndims)
 
   def testWrongShapeForReductionIndices(self):
@@ -372,10 +372,10 @@ class SumReductionTest(BaseReductionTest):
 
 class MeanReductionTest(BaseReductionTest):
 
-  def _tf_reduce(self, x, reduction_axes, keep_dims):
-    return math_ops.reduce_mean(x, reduction_axes, keep_dims)
+  def _tf_reduce(self, x, reduction_axes, keepdims):
+    return math_ops.reduce_mean(x, reduction_axes, keepdims)
 
-  def _np_reduce(self, x, reduction_axes, keep_dims):
+  def _np_reduce(self, x, reduction_axes, keepdims):
     if isinstance(reduction_axes, list) or isinstance(reduction_axes,
                                                       np.ndarray):
       reduction_axes = tuple(reduction_axes)
@@ -389,7 +389,7 @@ class MeanReductionTest(BaseReductionTest):
     # np.mean automatically converts integer inputs to float, while TensorFlow's
     # reduce_mean does not. For integer inputs, we emulate TensorFlow's behavior
     # using np.sum and truncating division.
-    np_sum = np.sum(x, axis=reduction_axes, keepdims=keep_dims)
+    np_sum = np.sum(x, axis=reduction_axes, keepdims=keepdims)
     if np.issubdtype(x.dtype, np.integer):
       return np_sum // count
     return np_sum / count
@@ -458,14 +458,14 @@ class MeanReductionTest(BaseReductionTest):
 
 class ProdReductionTest(BaseReductionTest):
 
-  def _tf_reduce(self, x, reduction_axes, keep_dims):
-    return math_ops.reduce_prod(x, reduction_axes, keep_dims)
+  def _tf_reduce(self, x, reduction_axes, keepdims):
+    return math_ops.reduce_prod(x, reduction_axes, keepdims)
 
-  def _np_reduce(self, x, reduction_axes, keep_dims):
+  def _np_reduce(self, x, reduction_axes, keepdims):
     if isinstance(reduction_axes, list) or isinstance(reduction_axes,
                                                       np.ndarray):
       reduction_axes = tuple(reduction_axes)
-    return np.prod(x, axis=reduction_axes, keepdims=keep_dims)
+    return np.prod(x, axis=reduction_axes, keepdims=keepdims)
 
   def testAxesType(self):
     for dtype in [dtypes.int64, dtypes.int32]:
@@ -549,17 +549,17 @@ class ProdReductionTest(BaseReductionTest):
 
 class MinReductionTest(test.TestCase):
 
-  def _compare(self, x, reduction_axes, keep_dims, use_gpu=False):
+  def _compare(self, x, reduction_axes, keepdims, use_gpu=False):
     np_ans = x
     if reduction_axes is None:
-      np_ans = np.amin(np_ans, keepdims=keep_dims)
+      np_ans = np.amin(np_ans, keepdims=keepdims)
     else:
       for ra in reduction_axes[::-1]:
-        np_ans = np.amin(np_ans, axis=ra, keepdims=keep_dims)
+        np_ans = np.amin(np_ans, axis=ra, keepdims=keepdims)
     with self.test_session(use_gpu=use_gpu):
       if reduction_axes is not None:
         reduction_axes = np.array(reduction_axes).astype(np.int32)
-      tf_ans = math_ops.reduce_min(x, reduction_axes, keep_dims)
+      tf_ans = math_ops.reduce_min(x, reduction_axes, keepdims)
       out = tf_ans.eval()
     self.assertAllClose(np_ans, out)
     self.assertShapeEqual(np_ans, tf_ans)
@@ -662,17 +662,17 @@ class MinReductionTest(test.TestCase):
 
 class MaxReductionTest(test.TestCase):
 
-  def _compare(self, x, reduction_axes, keep_dims, use_gpu=False):
+  def _compare(self, x, reduction_axes, keepdims, use_gpu=False):
     np_ans = x
     if reduction_axes is None:
-      np_ans = np.amax(np_ans, keepdims=keep_dims)
+      np_ans = np.amax(np_ans, keepdims=keepdims)
     else:
       for ra in reduction_axes[::-1]:
-        np_ans = np.amax(np_ans, axis=ra, keepdims=keep_dims)
+        np_ans = np.amax(np_ans, axis=ra, keepdims=keepdims)
     with self.test_session(use_gpu=use_gpu):
       if reduction_axes is not None:
         reduction_axes = np.array(reduction_axes).astype(np.int32)
-      tf_ans = math_ops.reduce_max(x, reduction_axes, keep_dims)
+      tf_ans = math_ops.reduce_max(x, reduction_axes, keepdims)
       out = tf_ans.eval()
     self.assertAllClose(np_ans, out)
     self.assertShapeEqual(np_ans, tf_ans)
@@ -789,17 +789,17 @@ class MaxReductionTest(test.TestCase):
 
 class AllReductionTest(test.TestCase):
 
-  def _compare(self, x, reduction_axes, keep_dims, use_gpu=False):
+  def _compare(self, x, reduction_axes, keepdims, use_gpu=False):
     np_ans = x
     if reduction_axes is None:
-      np_ans = np.all(np_ans, keepdims=keep_dims)
+      np_ans = np.all(np_ans, keepdims=keepdims)
     else:
       for ra in reduction_axes[::-1]:
-        np_ans = np.all(np_ans, axis=ra, keepdims=keep_dims)
+        np_ans = np.all(np_ans, axis=ra, keepdims=keepdims)
     with self.test_session(use_gpu=use_gpu):
       if reduction_axes is not None:
         reduction_axes = np.array(reduction_axes).astype(np.int32)
-      tf_ans = math_ops.reduce_all(x, reduction_axes, keep_dims)
+      tf_ans = math_ops.reduce_all(x, reduction_axes, keepdims)
       out = tf_ans.eval()
     self.assertAllEqual(np_ans, out)
     self.assertShapeEqual(np_ans, tf_ans)
@@ -838,17 +838,17 @@ class AllReductionTest(test.TestCase):
 
 class AnyReductionTest(test.TestCase):
 
-  def _compare(self, x, reduction_axes, keep_dims, use_gpu=False):
+  def _compare(self, x, reduction_axes, keepdims, use_gpu=False):
     np_ans = x
     if reduction_axes is None:
-      np_ans = np.any(np_ans, keepdims=keep_dims)
+      np_ans = np.any(np_ans, keepdims=keepdims)
     else:
       for ra in reduction_axes[::-1]:
-        np_ans = np.any(np_ans, axis=ra, keepdims=keep_dims)
+        np_ans = np.any(np_ans, axis=ra, keepdims=keepdims)
     with self.test_session(use_gpu=use_gpu):
       if reduction_axes is not None:
         reduction_axes = np.array(reduction_axes).astype(np.int32)
-      tf_ans = math_ops.reduce_any(x, reduction_axes, keep_dims)
+      tf_ans = math_ops.reduce_any(x, reduction_axes, keepdims)
       out = tf_ans.eval()
     self.assertAllEqual(np_ans, out)
     self.assertShapeEqual(np_ans, tf_ans)
@@ -887,21 +887,17 @@ class AnyReductionTest(test.TestCase):
 
 class CountNonzeroReductionTest(test.TestCase):
 
-  def _compare(self,
-               x,
-               reduction_axes,
-               keep_dims,
-               use_gpu=False,
+  def _compare(self, x, reduction_axes, keepdims, use_gpu=False,
                feed_dict=None):
     np_ans = (x != 0).astype(np.int32)
     if reduction_axes is None:
-      np_ans = np.sum(np_ans, keepdims=keep_dims)
+      np_ans = np.sum(np_ans, keepdims=keepdims)
     else:
       reduction_axes = np.array(reduction_axes).astype(np.int32)
       for ra in reduction_axes.ravel()[::-1]:
-        np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
+        np_ans = np.sum(np_ans, axis=ra, keepdims=keepdims)
     with self.test_session(use_gpu=use_gpu) as sess:
-      tf_ans = math_ops.count_nonzero(x, reduction_axes, keep_dims)
+      tf_ans = math_ops.count_nonzero(x, reduction_axes, keepdims)
       out = sess.run(tf_ans, feed_dict)
     self.assertAllClose(np_ans, out)
     self.assertShapeEqual(np_ans, tf_ans)
diff --git a/tensorflow/python/kernel_tests/reduction_ops_test_big.py b/tensorflow/python/kernel_tests/reduction_ops_test_big.py
index 0959adb026..d70360775a 100644
--- a/tensorflow/python/kernel_tests/reduction_ops_test_big.py
+++ b/tensorflow/python/kernel_tests/reduction_ops_test_big.py
@@ -27,24 +27,24 @@ from tensorflow.python.platform import test
 
 class BaseReductionTest(test.TestCase):
 
-  def _tf_reduce(self, x, reduction_axes, keep_dims):
+  def _tf_reduce(self, x, reduction_axes, keepdims):
     raise NotImplementedError()
 
 
 class BigReductionTest(BaseReductionTest):
   """Test reductions for sum and boolean all over a wide range of shapes."""
 
-  def _tf_reduce_max(self, x, reduction_axes, keep_dims):
-    return math_ops.reduce_max(x, reduction_axes, keep_dims)
+  def _tf_reduce_max(self, x, reduction_axes, keepdims):
+    return math_ops.reduce_max(x, reduction_axes, keepdims)
 
-  def _tf_reduce_all(self, x, reduction_axes, keep_dims):
-    return math_ops.reduce_all(x, reduction_axes, keep_dims)
+  def _tf_reduce_all(self, x, reduction_axes, keepdims):
+    return math_ops.reduce_all(x, reduction_axes, keepdims)
 
-  def _tf_reduce_mean(self, x, reduction_axes, keep_dims):
-    return math_ops.reduce_mean(x, reduction_axes, keep_dims)
+  def _tf_reduce_mean(self, x, reduction_axes, keepdims):
+    return math_ops.reduce_mean(x, reduction_axes, keepdims)
 
-  def _tf_reduce_sum(self, x, reduction_axes, keep_dims):
-    return math_ops.reduce_sum(x, reduction_axes, keep_dims)
+  def _tf_reduce_sum(self, x, reduction_axes, keepdims):
+    return math_ops.reduce_sum(x, reduction_axes, keepdims)
 
   def testFloat32Sum(self):
     # make sure we test all possible kernel invocations
diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py
index ec4fca78f0..6970bf9234 100644
--- a/tensorflow/python/layers/core.py
+++ b/tensorflow/python/layers/core.py
@@ -36,6 +36,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
+from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import standard_ops
 from tensorflow.python.util.tf_export import tf_export
 
@@ -291,13 +292,7 @@ class Dropout(base.Layer):
     # shapes with dynamically sized inputs.
     if self.noise_shape is None:
       return self.noise_shape
-
-    symbolic_shape = array_ops.shape(inputs)
-    noise_shape = [
-        symbolic_shape[axis] if shape is None else shape
-        for axis, shape in enumerate(self.noise_shape)
-    ]
-    return noise_shape
+    return nn_ops._get_noise_shape(inputs, self.noise_shape)
 
   def call(self, inputs, training=False):
 
diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py
index 323a9f8ee3..d83292b809 100644
--- a/tensorflow/python/layers/normalization.py
+++ b/tensorflow/python/layers/normalization.py
@@ -94,8 +94,8 @@ class BatchNormalization(base.Layer):
       and should be neither too small (which would add noise) nor too large
       (which would give stale estimates). Note that `momentum` is still applied
       to get the means and variances for inference.
-    fused: if `True`, use a faster, fused implementation if possible.
-      If `None`, use the system recommended implementation.
+    fused: if `None` or `True`, use a faster, fused implementation if possible.
+      If `False`, use the system recommended implementation.
     trainable: Boolean, if `True` also add variables to the graph collection
       `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
     virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
@@ -729,8 +729,8 @@ def batch_normalization(inputs,
       and should be neither too small (which would add noise) nor too large
       (which would give stale estimates). Note that `momentum` is still applied
       to get the means and variances for inference.
-    fused: if `True`, use a faster, fused implementation if possible.
-      If `None`, use the system recommended implementation.
+    fused: if `None` or `True`, use a faster, fused implementation if possible.
+      If `False`, use the system recommended implementation.
     virtual_batch_size: An `int`. By default, `virtual_batch_size` is `None`,
       which means batch normalization is performed across the whole batch. When
       `virtual_batch_size` is not `None`, instead perform "Ghost Batch
diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py
index 1195284024..484c6fc466 100644
--- a/tensorflow/python/layers/utils.py
+++ b/tensorflow/python/layers/utils.py
@@ -179,73 +179,56 @@ def deconv_output_length(input_length, filter_size, padding, stride):
   return input_length
 
 
-def smart_cond(pred, fn1, fn2, name=None):
-  """Return either `fn1()` or `fn2()` based on the boolean predicate `pred`.
+def smart_cond(pred, true_fn=None, false_fn=None, name=None):
+  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
 
-  If `pred` is a bool or has a constant value, we return either `fn1()`
-  or `fn2()`, otherwise we use `tf.cond` to dynamically route to both.
+  If `pred` is a bool or has a constant value, we return either `true_fn()`
+  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
 
   Arguments:
-    pred: A scalar determining whether to return the result of `fn1` or `fn2`.
-    fn1: The callable to be performed if pred is true.
-    fn2: The callable to be performed if pred is false.
+    pred: A scalar determining whether to return the result of `true_fn` or
+      `false_fn`.
+    true_fn: The callable to be performed if pred is true.
+    false_fn: The callable to be performed if pred is false.
     name: Optional name prefix when using `tf.cond`.
 
   Returns:
-    Tensors returned by the call to either `fn1` or `fn2`.
+    Tensors returned by the call to either `true_fn` or `false_fn`.
 
   Raises:
-    TypeError: If `fn1` or `fn2` is not callable.
+    TypeError: If `true_fn` or `false_fn` is not callable.
   """
-  if not callable(fn1):
-    raise TypeError('`fn1` must be callable.')
-  if not callable(fn2):
-    raise TypeError('`fn2` must be callable.')
-
-  if context.in_eager_mode():
-    if pred:
-      return fn1()
-    else:
-      return fn2()
-
-  pred_value = constant_value(pred)
-  if pred_value is not None:
-    if pred_value:
-      return fn1()
-    else:
-      return fn2()
-  else:
-    return control_flow_ops.cond(pred, true_fn=fn1, false_fn=fn2, name=name)
+  if isinstance(pred, variables.Variable):
+    return control_flow_ops.cond(
+        pred, true_fn=true_fn, false_fn=false_fn, name=name)
+  return control_flow_ops.smart_cond(
+      pred, true_fn=true_fn, false_fn=false_fn, name=name)
 
 
 def constant_value(pred):
   """Return the bool value for `pred`, or None if `pred` had a dynamic value.
 
-  Arguments:
-    pred: A scalar, either a Python bool or a TensorFlow boolean variable
-      or tensor, or the Python integer 1 or 0.
+    Arguments:
+      pred: A scalar, either a Python bool or a TensorFlow boolean variable
+        or tensor, or the Python integer 1 or 0.
 
-  Returns:
-    True or False if `pred` has a constant boolean value, None otherwise.
+    Returns:
+      True or False if `pred` has a constant boolean value, None otherwise.
 
-  Raises:
-    TypeError: If `pred` is not a Variable, Tensor or bool.
-  """
+    Raises:
+      TypeError: If `pred` is not a Variable, Tensor or bool, or Python
+        interger 1 or 0.
+    """
   # Allow integer booleans.
-  if pred == 0:
-    pred = False
-  elif pred == 1:
-    pred = True
-
-  if isinstance(pred, bool):
-    pred_value = pred
-  elif isinstance(pred, variables.Variable):
-    pred_value = None
-  elif isinstance(pred, ops.Tensor):
-    pred_value = tensor_util.constant_value(pred)
-  else:
-    raise TypeError('`pred` must be a Tensor, a Variable, or a Python bool.')
-  return pred_value
+  if isinstance(pred, int):
+    if pred == 1:
+      pred = True
+    elif pred == 0:
+      pred = False
+
+  if isinstance(pred, variables.Variable):
+    return None
+  return control_flow_ops.smart_constant_value(pred)
 
 
 def object_list_uid(object_list):
diff --git a/tensorflow/python/ops/clip_ops.py b/tensorflow/python/ops/clip_ops.py
index dd8c33247c..49f8c66531 100644
--- a/tensorflow/python/ops/clip_ops.py
+++ b/tensorflow/python/ops/clip_ops.py
@@ -110,7 +110,7 @@ def clip_by_norm(t, clip_norm, axes=None, name=None):
     t = ops.convert_to_tensor(t, name="t")
 
     # Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
-    l2norm = math_ops.sqrt(math_ops.reduce_sum(t * t, axes, keep_dims=True))
+    l2norm = math_ops.sqrt(math_ops.reduce_sum(t * t, axes, keepdims=True))
     intermediate = t * clip_norm
     # Assert that the shape is compatible with the initial shape,
     # to prevent unintentional broadcasting.
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index a2d605532a..b4bfc0fe47 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -23,6 +23,7 @@ See the @{$python/control_flow_ops} guide.
 @@no_op
 @@count_up_to
 @@cond
+@@smart_cond
 @@case
 @@while_loop
 @@logical_and
@@ -2129,6 +2130,61 @@ def cond(pred,
 # pylint: enable=redefined-outer-name
 
 
+def smart_cond(pred, true_fn=None, false_fn=None, name=None):
+  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
+
+  If `pred` is a bool or has a constant value, we return either `true_fn()`
+  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
+
+  Arguments:
+    pred: A scalar determining whether to return the result of `true_fn` or
+      `false_fn`.
+    true_fn: The callable to be performed if pred is true.
+    false_fn: The callable to be performed if pred is false.
+    name: Optional name prefix when using `tf.cond`.
+
+  Returns:
+    Tensors returned by the call to either `true_fn` or `false_fn`.
+
+  Raises:
+    TypeError: If `true_fn` or `false_fn` is not callable.
+  """
+  if not callable(true_fn):
+    raise TypeError("`true_fn` must be callable.")
+  if not callable(false_fn):
+    raise TypeError("`false_fn` must be callable.")
+
+  pred_value = smart_constant_value(pred)
+  if pred_value is not None:
+    if pred_value:
+      return true_fn()
+    else:
+      return false_fn()
+  else:
+    return cond(pred, true_fn=true_fn, false_fn=false_fn, name=name)
+
+
+def smart_constant_value(pred):
+  """Return the bool value for `pred`, or None if `pred` had a dynamic value.
+
+  Arguments:
+    pred: A scalar, either a Python bool or tensor.
+
+  Returns:
+    True or False if `pred` has a constant boolean value, None otherwise.
+
+  Raises:
+    TypeError: If `pred` is not a Tensor or bool.
+  """
+  if isinstance(pred, bool):
+    pred_value = pred
+  elif isinstance(pred, ops.Tensor):
+    pred_value = tensor_util.constant_value(pred)
+  else:
+    raise TypeError("`pred` must be a Tensor or a Python bool.")
+  return pred_value
+
+
 def _resource_safe_shape(t):
   """Returns the shape of t or the variable it points to."""
   if t.dtype == dtypes.resource:
@@ -3126,6 +3182,43 @@ def while_loop(cond,
       shape_invariants=[i0.get_shape(), tf.TensorShape([None, 2])])
   ```
 
+  Example which demonstrates non-strict semantics: In the following
+  example, the final value of the counter `i` does not depend on `x`. So
+  the `while_loop` can increment the counter parallel to updates of `x`.
+  However, because the loop counter at one loop iteration depends
+  on the value at the previous iteration, the loop counter itself cannot
+  be incremented in parallel. Hence if we just want the final value of the
+  counter (which we print on the line `print(sess.run(i))`), then
+  `x` will never be incremented, but the counter will be updated on a
+  single thread. Conversely, if we want the value of the output (which we
+  print on the line `print(sess.run(out).shape)`), then the counter may be
+  incremented on its own thread, while `x` can be incremented in
+  parallel on a separate thread. In the extreme case, it is conceivable
+  that the thread incrementing the counter runs until completion before
+  `x` is incremented even a single time. The only thing that can never
+  happen is that the thread updating `x` can never get ahead of the
+  counter thread because the thread incrementing `x` depends on the value
+  of the counter.
+  ```python
+  import tensorflow as tf
+
+  n = 10000
+  x = tf.constant(list(range(n)))
+  c = lambda i, x: i < n
+  b = lambda i, x: (tf.Print(i + 1, [i]), tf.Print(x + 1, [i], "x:"))
+  i, out = tf.while_loop(c, b, (0, x))
+  with tf.Session() as sess:
+      print(sess.run(i))  # prints [0] ... [9999]
+
+      # The following line may increment the counter and x in parallel.
+      # The counter thread may get ahead of the other thread, but not the
+      # other way around. So you may see things like
+      # [9996] x:[9987]
+      # meaning that the counter thread is on iteration 9996,
+      # while the other thread is on iteration 9987
+      print(sess.run(out).shape)
+  ```
+
   """
   with ops.name_scope(name, "while", loop_vars):
     if not loop_vars:
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index f22f3059d1..adc8c51e11 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -349,6 +349,42 @@ class SwitchTestCase(test_util.TensorFlowTestCase):
       self.assertEquals(grad_x_false.eval(), 0.)
 
 
+@test_util.with_c_api
+class SmartCondTest(test_util.TensorFlowTestCase):
+
+  def testSmartCondTrue(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(2)
+        y = constant_op.constant(5)
+        z = control_flow_ops.smart_cond(True, lambda: math_ops.multiply(x, 16),
+                                        lambda: math_ops.multiply(y, 5))
+        self.assertEqual(z.eval(), 32)
+
+  def testSmartCondFalse(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(4)
+        y = constant_op.constant(3)
+        z = control_flow_ops.smart_cond(False, lambda: math_ops.multiply(x, 16),
+                                        lambda: math_ops.multiply(y, 3))
+        self.assertEqual(z.eval(), 9)
+
+  def testSmartCondMissingArg1(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(1)
+        with self.assertRaises(TypeError):
+          control_flow_ops.smart_cond(True, false_fn=lambda: x)
+
+  def testSmartCondMissingArg2(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(1)
+        with self.assertRaises(TypeError):
+          control_flow_ops.smart_cond(True, lambda: x)
+
+
 @test_util.with_c_api
 class CondTest(test_util.TensorFlowTestCase):
 
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 95e45bff06..03ed537cfc 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -474,7 +474,7 @@ class QueueBase(object):
       name: A name for the operation (optional).
 
     Returns:
-      The tuple of concatenated tensors that was dequeued.
+      The list of concatenated tensors that was dequeued.
     """
     if name is None:
       name = "%s_DequeueMany" % self._name
diff --git a/tensorflow/python/ops/distributions/multinomial.py b/tensorflow/python/ops/distributions/multinomial.py
index 26b5c5aef9..4ae67a009b 100644
--- a/tensorflow/python/ops/distributions/multinomial.py
+++ b/tensorflow/python/ops/distributions/multinomial.py
@@ -238,7 +238,7 @@ class Multinomial(distribution.Distribution):
     n_draws = math_ops.cast(self.total_count, dtype=dtypes.int32)
     k = self.event_shape_tensor()[0]
 
-    # boardcast the total_count and logits to same shape
+    # broadcast the total_count and logits to same shape
     n_draws = array_ops.ones_like(
         self.logits[..., 0], dtype=n_draws.dtype) * n_draws
     logits = array_ops.ones_like(
diff --git a/tensorflow/python/ops/distributions/util.py b/tensorflow/python/ops/distributions/util.py
index 5bc25128a8..0fe6aa30f9 100644
--- a/tensorflow/python/ops/distributions/util.py
+++ b/tensorflow/python/ops/distributions/util.py
@@ -1041,14 +1041,14 @@ def reduce_weighted_logsumexp(
   with ops.name_scope(name, "reduce_weighted_logsumexp", [logx, w]):
     logx = ops.convert_to_tensor(logx, name="logx")
     if w is None:
-      lswe = math_ops.reduce_logsumexp(logx, axis=axis, keep_dims=keep_dims)
+      lswe = math_ops.reduce_logsumexp(logx, axis=axis, keepdims=keep_dims)
       if return_sign:
         sgn = array_ops.ones_like(lswe)
         return lswe, sgn
       return lswe
     w = ops.convert_to_tensor(w, dtype=logx.dtype, name="w")
     log_absw_x = logx + math_ops.log(math_ops.abs(w))
-    max_log_absw_x = math_ops.reduce_max(log_absw_x, axis=axis, keep_dims=True)
+    max_log_absw_x = math_ops.reduce_max(log_absw_x, axis=axis, keepdims=True)
     # If the largest element is `-inf` or `inf` then we don't bother subtracting
     # off the max. We do this because otherwise we'd get `inf - inf = NaN`. That
     # this is ok follows from the fact that we're actually free to subtract any
@@ -1060,9 +1060,7 @@ def reduce_weighted_logsumexp(
     wx_over_max_absw_x = (
         math_ops.sign(w) * math_ops.exp(log_absw_x - max_log_absw_x))
     sum_wx_over_max_absw_x = math_ops.reduce_sum(
-        wx_over_max_absw_x,
-        axis=axis,
-        keep_dims=keep_dims)
+        wx_over_max_absw_x, axis=axis, keepdims=keep_dims)
     if not keep_dims:
       max_log_absw_x = array_ops.squeeze(max_log_absw_x, axis)
     sgn = math_ops.sign(sum_wx_over_max_absw_x)
@@ -1180,8 +1178,7 @@ def process_quadrature_grid_and_probs(
     grid = ops.convert_to_tensor(grid, name="grid", dtype=dtype)
     probs = ops.convert_to_tensor(probs, name="unnormalized_probs",
                                   dtype=dtype)
-    probs /= linalg_ops.norm(probs, ord=1, axis=-1, keep_dims=True,
-                             name="probs")
+    probs /= linalg_ops.norm(probs, ord=1, axis=-1, keepdims=True, name="probs")
 
     def _static_event_size(x):
       """Returns the static size of a specific dimension or `None`."""
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index bcd9e5683a..53bd108c44 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -167,6 +167,28 @@ def _Assert3DImage(image):
       _Check3DImage(image, require_static=False), image)
 
 
+def _AssertAtLeast3DImage(image):
+  """Assert that we are working with a properly shaped image.
+
+    Performs the check statically if possible (i.e. if the shape
+    is statically known). Otherwise adds a control dependency
+    to an assert op that checks the dynamic shape.
+
+    Args:
+      image: >= 3-D Tensor of size [*, height, width, depth]
+
+    Raises:
+      ValueError: if image.shape is not a [>= 3] vector.
+
+    Returns:
+      If the shape of `image` could be verified statically, `image` is
+      returned unchanged, otherwise there will be a control dependency
+      added that asserts the correct dynamic shape.
+  """
+  return control_flow_ops.with_dependencies(
+      _CheckAtLeast3DImage(image, require_static=False), image)
+
+
 def _CheckAtLeast3DImage(image, require_static=True):
   """Assert that we are working with properly shaped image.
 
@@ -292,108 +314,187 @@ def random_flip_left_right(image, seed=None):
 def flip_left_right(image):
   """Flip an image horizontally (left to right).
 
-  Outputs the contents of `image` flipped along the second dimension, which is
-  `width`.
+  Outputs the contents of `image` flipped along the width dimension.
 
   See also `reverse()`.
 
   Args:
-    image: A 3-D tensor of shape `[height, width, channels].`
+    image: 4-D Tensor of shape `[batch, height, width, channels]` or
+           3-D Tensor of shape `[height, width, channels]`.
 
   Returns:
-    A 3-D tensor of the same type and shape as `image`.
+    A tensor of the same type and shape as `image`.
 
   Raises:
     ValueError: if the shape of `image` not supported.
   """
-  with ops.name_scope(None, 'flip_left_right', [image]) as scope:
+  with ops.name_scope(None, 'flip_left_right', [image]):
     image = ops.convert_to_tensor(image, name='image')
-    image = _Assert3DImage(image)
-    return fix_image_flip_shape(image, array_ops.reverse(
-        image, [1], name=scope))
+    image = _AssertAtLeast3DImage(image)
+    shape = image.get_shape()
+    if shape.ndims == 3 or shape.ndims is None:
+      return fix_image_flip_shape(image, array_ops.reverse(image, [1]))
+    elif shape.ndims == 4:
+      return array_ops.reverse(image, [2])
+    else:
+      raise ValueError('\'image\' must have either 3 or 4 dimensions.')
 
 
 @tf_export('image.flip_up_down')
 def flip_up_down(image):
   """Flip an image vertically (upside down).
 
-  Outputs the contents of `image` flipped along the first dimension, which is
-  `height`.
+  Outputs the contents of `image` flipped along the height dimension.
 
   See also `reverse()`.
 
   Args:
-    image: A 3-D tensor of shape `[height, width, channels].`
+    image: 4-D Tensor of shape `[batch, height, width, channels]` or
+           3-D Tensor of shape `[height, width, channels]`.
 
   Returns:
-    A 3-D tensor of the same type and shape as `image`.
+    A tensor of the same type and shape as `image`.
 
   Raises:
     ValueError: if the shape of `image` not supported.
   """
-  with ops.name_scope(None, 'flip_up_down', [image]) as scope:
+  with ops.name_scope(None, 'flip_up_down', [image]):
     image = ops.convert_to_tensor(image, name='image')
-    image = _Assert3DImage(image)
-    return fix_image_flip_shape(image, array_ops.reverse(
-        image, [0], name=scope))
+    image = _AssertAtLeast3DImage(image)
+    shape = image.get_shape()
+    if shape.ndims == 3 or shape.ndims is None:
+      return fix_image_flip_shape(image, array_ops.reverse(image, [0]))
+    elif shape.ndims == 4:
+      return array_ops.reverse(image, [1])
+    else:
+      raise ValueError('\'image\' must have either 3 or 4 dimensions.')
 
 
 @tf_export('image.rot90')
 def rot90(image, k=1, name=None):
-  """Rotate an image counter-clockwise by 90 degrees.
+  """Rotate image(s) counter-clockwise by 90 degrees.
 
   Args:
-    image: A 3-D tensor of shape `[height, width, channels]`.
+    image: 4-D Tensor of shape `[batch, height, width, channels]` or
+           3-D Tensor of shape `[height, width, channels]`.
     k: A scalar integer. The number of times the image is rotated by 90 degrees.
     name: A name for this operation (optional).
 
   Returns:
-    A rotated 3-D tensor of the same type and shape as `image`.
+    A rotated tensor of the same type and shape as `image`.
+
+  Raises:
+    ValueError: if the shape of `image` not supported.
   """
   with ops.name_scope(name, 'rot90', [image, k]) as scope:
     image = ops.convert_to_tensor(image, name='image')
-    image = _Assert3DImage(image)
+    image = _AssertAtLeast3DImage(image)
     k = ops.convert_to_tensor(k, dtype=dtypes.int32, name='k')
     k.get_shape().assert_has_rank(0)
     k = math_ops.mod(k, 4)
 
-    def _rot90():
-      return array_ops.transpose(array_ops.reverse_v2(image, [1]), [1, 0, 2])
+    shape = image.get_shape()
+    if shape.ndims == 3 or shape.ndims is None:
+      return _rot90_3D(image, k, scope)
+    elif shape.ndims == 4:
+      return _rot90_4D(image, k, scope)
+    else:
+      raise ValueError('\'image\' must have either 3 or 4 dimensions.')
+
+
+def _rot90_3D(image, k, name_scope):
+  """Rotate image counter-clockwise by 90 degrees `k` times.
+
+  Args:
+    image: 3-D Tensor of shape `[height, width, channels]`.
+    k: A scalar integer. The number of times the image is rotated by 90 degrees.
+    name_scope: A valid TensorFlow name scope.
+
+  Returns:
+    A 3-D tensor of the same type and shape as `image`.
+
+  """
+
+  def _rot90():
+    return array_ops.transpose(array_ops.reverse_v2(image, [1]), [1, 0, 2])
+
+  def _rot180():
+    return array_ops.reverse_v2(image, [0, 1])
+
+  def _rot270():
+    return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]), [1])
+
+  cases = [(math_ops.equal(k, 1), _rot90), (math_ops.equal(k, 2), _rot180),
+           (math_ops.equal(k, 3), _rot270)]
+
+  result = control_flow_ops.case(
+      cases, default=lambda: image, exclusive=True, name=name_scope)
+  result.set_shape([None, None, image.get_shape()[2]])
+  return result
+
+
+def _rot90_4D(images, k, name_scope):
+  """Rotate batch of images counter-clockwise by 90 degrees `k` times.
+
+  Args:
+    images: 4-D Tensor of shape `[height, width, channels]`.
+    k: A scalar integer. The number of times the images are rotated by 90
+      degrees.
+    name_scope: A valid TensorFlow name scope.
+
+  Returns:
+    A 4-D tensor of the same type and shape as `images`.
+
+  """
 
-    def _rot180():
-      return array_ops.reverse_v2(image, [0, 1])
+  def _rot90():
+    return array_ops.transpose(array_ops.reverse_v2(images, [2]), [0, 2, 1, 3])
 
-    def _rot270():
-      return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]), [1])
+  def _rot180():
+    return array_ops.reverse_v2(images, [1, 2])
 
-    cases = [(math_ops.equal(k, 1), _rot90), (math_ops.equal(k, 2), _rot180),
-             (math_ops.equal(k, 3), _rot270)]
+  def _rot270():
+    return array_ops.reverse_v2(array_ops.transpose(images, [0, 2, 1, 3]), [2])
 
-    ret = control_flow_ops.case(
-        cases, default=lambda: image, exclusive=True, name=scope)
-    ret.set_shape([None, None, image.get_shape()[2]])
-    return ret
+  cases = [(math_ops.equal(k, 1), _rot90), (math_ops.equal(k, 2), _rot180),
+           (math_ops.equal(k, 3), _rot270)]
+
+  result = control_flow_ops.case(
+      cases, default=lambda: images, exclusive=True, name=name_scope)
+  shape = result.get_shape()
+  result.set_shape([shape[0], None, None, shape[3]])
+  return result
 
 
 @tf_export('image.transpose_image')
 def transpose_image(image):
-  """Transpose an image by swapping the first and second dimension.
+  """Transpose image(s) by swapping the height and width dimension.
 
   See also `transpose()`.
 
   Args:
-    image: 3-D tensor of shape `[height, width, channels]`
+    image: 4-D Tensor of shape `[batch, height, width, channels]` or
+           3-D Tensor of shape `[height, width, channels]`.
 
   Returns:
-    A 3-D tensor of shape `[width, height, channels]`
+    If `image` was 4-D, a 4-D float Tensor of shape
+   `[batch, width, height, channels]`
+    If `image` was 3-D, a 3-D float Tensor of shape
+   `[width, height, channels]`
 
   Raises:
     ValueError: if the shape of `image` not supported.
   """
-  with ops.name_scope(None, 'transpose_image', [image]) as scope:
+  with ops.name_scope(None, 'transpose_image', [image]):
     image = ops.convert_to_tensor(image, name='image')
-    image = _Assert3DImage(image)
-    return array_ops.transpose(image, [1, 0, 2], name=scope)
+    image = _AssertAtLeast3DImage(image)
+    shape = image.get_shape()
+    if shape.ndims == 3 or shape.ndims is None:
+      return array_ops.transpose(image, [1, 0, 2], name='transpose_image')
+    elif shape.ndims == 4:
+      return array_ops.transpose(image, [0, 2, 1, 3], name='transpose_image')
+    else:
+      raise ValueError('\'image\' must have either 3 or 4 dimensions.')
 
 
 @tf_export('image.central_crop')
@@ -1026,9 +1127,9 @@ def adjust_contrast(images, contrast_factor):
 def adjust_gamma(image, gamma=1, gain=1):
   """Performs Gamma Correction on the input image.
 
-    Also known as Power Law Transform. This function transforms the
-    input image pixelwise according to the equation Out = In**gamma
-    after scaling each pixel to the range 0 to 1.
+  Also known as Power Law Transform. This function transforms the
+  input image pixelwise according to the equation `Out = In**gamma`
+  after scaling each pixel to the range 0 to 1.
 
   Args:
     image : A Tensor.
diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py
index 18625293e0..b67e7cc558 100644
--- a/tensorflow/python/ops/image_ops_test.py
+++ b/tensorflow/python/ops/image_ops_test.py
@@ -934,7 +934,7 @@ class AdjustSaturationTest(test_util.TensorFlowTestCase):
 
 class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
 
-  def testIdempotentLeftRight(self):
+  def testInvolutionLeftRight(self):
     x_np = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1])
     with self.test_session(use_gpu=True):
       x_tf = constant_op.constant(x_np, shape=x_np.shape)
@@ -942,6 +942,16 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
       y_tf = y.eval()
       self.assertAllEqual(y_tf, x_np)
 
+  def testInvolutionLeftRightWithBatch(self):
+    x_np = np.array(
+        [[[1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3]]],
+        dtype=np.uint8).reshape([2, 2, 3, 1])
+    with self.test_session(use_gpu=True):
+      x_tf = constant_op.constant(x_np, shape=x_np.shape)
+      y = image_ops.flip_left_right(image_ops.flip_left_right(x_tf))
+      y_tf = y.eval()
+      self.assertAllEqual(y_tf, x_np)
+
   def testLeftRight(self):
     x_np = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1])
     y_np = np.array([[3, 2, 1], [3, 2, 1]], dtype=np.uint8).reshape([2, 3, 1])
@@ -953,9 +963,24 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
       y_tf = y.eval()
       self.assertAllEqual(y_tf, y_np)
 
+  def testLeftRightWithBatch(self):
+    x_np = np.array(
+        [[[1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3]]],
+        dtype=np.uint8).reshape([2, 2, 3, 1])
+    y_np = np.array(
+        [[[3, 2, 1], [3, 2, 1]], [[3, 2, 1], [3, 2, 1]]],
+        dtype=np.uint8).reshape([2, 2, 3, 1])
+
+    with self.test_session(use_gpu=True):
+      x_tf = constant_op.constant(x_np, shape=x_np.shape)
+      y = image_ops.flip_left_right(x_tf)
+      y_tf = y.eval()
+      self.assertAllEqual(y_tf, y_np)
+
   def testRandomFlipLeftRight(self):
     x_np = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1])
     y_np = np.array([[3, 2, 1], [3, 2, 1]], dtype=np.uint8).reshape([2, 3, 1])
+    seed = 42
 
     with self.test_session(use_gpu=True):
       x_tf = constant_op.constant(x_np, shape=x_np.shape)
@@ -964,7 +989,7 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
 
       count_flipped = 0
       count_unflipped = 0
-      for _ in range(50):
+      for _ in range(100):
         y_tf = y.eval()
         if y_tf[0][0] == 1:
           self.assertAllEqual(y_tf, x_np)
@@ -972,10 +997,15 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
         else:
           self.assertAllEqual(y_tf, y_np)
           count_flipped += 1
-      self.assertGreaterEqual(count_flipped, 1)
-      self.assertGreaterEqual(count_unflipped, 1)
 
-  def testIdempotentUpDown(self):
+      # 100 trials
+      # Mean: 50
+      # Std Dev: ~5
+      # Six Sigma: 50 - (5 * 6) = 20
+      self.assertGreaterEqual(count_flipped, 20)
+      self.assertGreaterEqual(count_unflipped, 20)
+
+  def testInvolutionUpDown(self):
     x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1])
 
     with self.test_session(use_gpu=True):
@@ -984,6 +1014,17 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
       y_tf = y.eval()
       self.assertAllEqual(y_tf, x_np)
 
+  def testInvolutionUpDownWithBatch(self):
+    x_np = np.array(
+        [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]],
+        dtype=np.uint8).reshape([2, 2, 3, 1])
+
+    with self.test_session(use_gpu=True):
+      x_tf = constant_op.constant(x_np, shape=x_np.shape)
+      y = image_ops.flip_up_down(image_ops.flip_up_down(x_tf))
+      y_tf = y.eval()
+      self.assertAllEqual(y_tf, x_np)
+
   def testUpDown(self):
     x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1])
     y_np = np.array([[4, 5, 6], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1])
@@ -995,17 +1036,31 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
       y_tf = y.eval()
       self.assertAllEqual(y_tf, y_np)
 
+  def testUpDownWithBatch(self):
+    x_np = np.array(
+        [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]],
+        dtype=np.uint8).reshape([2, 2, 3, 1])
+    y_np = np.array(
+        [[[4, 5, 6], [1, 2, 3]], [[10, 11, 12], [7, 8, 9]]],
+        dtype=np.uint8).reshape([2, 2, 3, 1])
+
+    with self.test_session(use_gpu=True):
+      x_tf = constant_op.constant(x_np, shape=x_np.shape)
+      y = image_ops.flip_up_down(x_tf)
+      y_tf = y.eval()
+      self.assertAllEqual(y_tf, y_np)
+
   def testRandomFlipUpDown(self):
     x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1])
     y_np = np.array([[4, 5, 6], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1])
 
     with self.test_session(use_gpu=True):
       x_tf = constant_op.constant(x_np, shape=x_np.shape)
-      y = image_ops.random_flip_up_down(x_tf)
+      y = image_ops.random_flip_up_down(x_tf, seed=42)
       self.assertTrue(y.op.name.startswith("random_flip_up_down"))
       count_flipped = 0
       count_unflipped = 0
-      for _ in range(50):
+      for _ in range(100):
         y_tf = y.eval()
         if y_tf[0][0] == 1:
           self.assertAllEqual(y_tf, x_np)
@@ -1013,10 +1068,15 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
         else:
           self.assertAllEqual(y_tf, y_np)
           count_flipped += 1
-      self.assertGreaterEqual(count_flipped, 1)
-      self.assertGreaterEqual(count_unflipped, 1)
 
-  def testIdempotentTranspose(self):
+      # 100 trials
+      # Mean: 50
+      # Std Dev: ~5
+      # Six Sigma: 50 - (5 * 6) = 20
+      self.assertGreaterEqual(count_flipped, 20)
+      self.assertGreaterEqual(count_unflipped, 20)
+
+  def testInvolutionTranspose(self):
     x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1])
 
     with self.test_session(use_gpu=True):
@@ -1025,6 +1085,17 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
       y_tf = y.eval()
       self.assertAllEqual(y_tf, x_np)
 
+  def testInvolutionTransposeWithBatch(self):
+    x_np = np.array(
+        [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]],
+        dtype=np.uint8).reshape([2, 2, 3, 1])
+
+    with self.test_session(use_gpu=True):
+      x_tf = constant_op.constant(x_np, shape=x_np.shape)
+      y = image_ops.transpose_image(image_ops.transpose_image(x_tf))
+      y_tf = y.eval()
+      self.assertAllEqual(y_tf, x_np)
+
   def testTranspose(self):
     x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1])
     y_np = np.array([[1, 4], [2, 5], [3, 6]], dtype=np.uint8).reshape([3, 2, 1])
@@ -1036,15 +1107,34 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
       y_tf = y.eval()
       self.assertAllEqual(y_tf, y_np)
 
+  def testTransposeWithBatch(self):
+    x_np = np.array(
+        [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]],
+        dtype=np.uint8).reshape([2, 2, 3, 1])
+
+    y_np = np.array(
+        [[[1, 4], [2, 5], [3, 6]], [[7, 10], [8, 11], [9, 12]]],
+        dtype=np.uint8).reshape([2, 3, 2, 1])
+
+    with self.test_session(use_gpu=True):
+      x_tf = constant_op.constant(x_np, shape=x_np.shape)
+      y = image_ops.transpose_image(x_tf)
+      y_tf = y.eval()
+      self.assertAllEqual(y_tf, y_np)
+
   def testPartialShapes(self):
     p_unknown_rank = array_ops.placeholder(dtypes.uint8)
-    p_unknown_dims = array_ops.placeholder(
+    p_unknown_dims_3 = array_ops.placeholder(
         dtypes.uint8, shape=[None, None, None])
+    p_unknown_dims_4 = array_ops.placeholder(
+        dtypes.uint8, shape=[None, None, None, None])
     p_unknown_width = array_ops.placeholder(dtypes.uint8, shape=[64, None, 3])
-
+    p_unknown_batch = array_ops.placeholder(
+        dtypes.uint8, shape=[None, 64, 64, 3])
     p_wrong_rank = array_ops.placeholder(dtypes.uint8, shape=[None, None])
     p_zero_dim = array_ops.placeholder(dtypes.uint8, shape=[64, 0, 3])
 
+    #Ops that support 3D input
     for op in [
         image_ops.flip_left_right, image_ops.flip_up_down,
         image_ops.random_flip_left_right, image_ops.random_flip_up_down,
@@ -1052,16 +1142,34 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
     ]:
       transformed_unknown_rank = op(p_unknown_rank)
       self.assertEqual(3, transformed_unknown_rank.get_shape().ndims)
-      transformed_unknown_dims = op(p_unknown_dims)
-      self.assertEqual(3, transformed_unknown_dims.get_shape().ndims)
+      transformed_unknown_dims_3 = op(p_unknown_dims_3)
+      self.assertEqual(3, transformed_unknown_dims_3.get_shape().ndims)
       transformed_unknown_width = op(p_unknown_width)
       self.assertEqual(3, transformed_unknown_width.get_shape().ndims)
 
-      with self.assertRaisesRegexp(ValueError, "must be three-dimensional"):
-        op(p_wrong_rank)
       with self.assertRaisesRegexp(ValueError, "must be > 0"):
         op(p_zero_dim)
 
+    #Ops that support 4D input
+    for op in [
+        image_ops.flip_left_right, image_ops.flip_up_down,
+        image_ops.transpose_image, image_ops.rot90
+    ]:
+      transformed_unknown_dims_4 = op(p_unknown_dims_4)
+      self.assertEqual(4, transformed_unknown_dims_4.get_shape().ndims)
+      transformed_unknown_batch = op(p_unknown_batch)
+      self.assertEqual(4, transformed_unknown_batch.get_shape().ndims)
+      with self.assertRaisesRegexp(ValueError,
+                                   "must be at least three-dimensional"):
+        op(p_wrong_rank)
+
+    for op in [
+        image_ops.random_flip_left_right,
+        image_ops.random_flip_up_down,
+    ]:
+      with self.assertRaisesRegexp(ValueError, "must be three-dimensional"):
+        op(p_wrong_rank)
+
   def testRot90GroupOrder(self):
     image = np.arange(24, dtype=np.uint8).reshape([2, 4, 3])
     with self.test_session(use_gpu=True):
@@ -1070,6 +1178,14 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
         rotated = image_ops.rot90(rotated)
       self.assertAllEqual(image, rotated.eval())
 
+  def testRot90GroupOrderWithBatch(self):
+    image = np.arange(48, dtype=np.uint8).reshape([2, 2, 4, 3])
+    with self.test_session(use_gpu=True):
+      rotated = image
+      for _ in xrange(4):
+        rotated = image_ops.rot90(rotated)
+      self.assertAllEqual(image, rotated.eval())
+
   def testRot90NumpyEquivalence(self):
     image = np.arange(24, dtype=np.uint8).reshape([2, 4, 3])
     with self.test_session(use_gpu=True):
@@ -1079,6 +1195,15 @@ class FlipTransposeRotateTest(test_util.TensorFlowTestCase):
         y_np = np.rot90(image, k=k)
         self.assertAllEqual(y_np, y_tf.eval({k_placeholder: k}))
 
+  def testRot90NumpyEquivalenceWithBatch(self):
+    image = np.arange(48, dtype=np.uint8).reshape([2, 2, 4, 3])
+    with self.test_session(use_gpu=True):
+      k_placeholder = array_ops.placeholder(dtypes.int32, shape=[])
+      y_tf = image_ops.rot90(image, k_placeholder)
+      for k in xrange(4):
+        y_np = np.rot90(image, k=k, axes=(1, 2))
+        self.assertAllEqual(y_np, y_tf.eval({k_placeholder: k}))
+
 
 class RandomFlipTest(test_util.TensorFlowTestCase):
 
@@ -3173,6 +3298,14 @@ class NonMaxSuppressionTest(test_util.TensorFlowTestCase):
       scores = constant_op.constant([0.9])
       image_ops.non_max_suppression(boxes, scores, 3, 0.5)
 
+    # The boxes is of shape [num_boxes, 4], and the scores is
+    # of shape [num_boxes]. So an error will thrown.
+    with self.assertRaisesRegexp(ValueError,
+                                 "Dimensions must be equal, but are 1 and 2"):
+      boxes = constant_op.constant([[0.0, 0.0, 1.0, 1.0]])
+      scores = constant_op.constant([0.9, 0.75])
+      selected_indices = image_ops.non_max_suppression(boxes, scores, 3, 0.5)
+
     # The scores should be 1D of shape [num_boxes].
     with self.assertRaisesRegexp(ValueError,
                                  "Shape must be rank 1 but is rank 2"):
diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index c86cc92321..a39417139e 100644
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -156,8 +156,10 @@ def _num_present(losses, weights, per_batch=False):
     present = weights_broadcast_ops.broadcast_weights(present, losses)
     if per_batch:
       return math_ops.reduce_sum(
-          present, axis=math_ops.range(1, array_ops.rank(present)),
-          keep_dims=True, name=scope)
+          present,
+          axis=math_ops.range(1, array_ops.rank(present)),
+          keepdims=True,
+          name=scope)
     return math_ops.reduce_sum(present, name=scope)
 
 
@@ -324,7 +326,7 @@ def cosine_distance(
     predictions.get_shape().assert_is_compatible_with(labels.get_shape())
 
     radial_diffs = math_ops.multiply(predictions, labels)
-    losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(axis,), keep_dims=True)
+    losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(axis,), keepdims=True)
     return compute_weighted_loss(
         losses, weights, scope, loss_collection, reduction=reduction)
 
@@ -390,7 +392,7 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None,
 
   `weights` acts as a coefficient for the loss. If a scalar is provided, then
   the loss is simply scaled by the given value. If `weights` is a tensor of size
-  [batch_size], then the total loss for each sample of the batch is rescaled
+  `[batch_size]`, then the total loss for each sample of the batch is rescaled
   by the corresponding element in the `weights` vector. If the shape of
   `weights` matches the shape of `predictions`, then the loss of each
   measurable element of `predictions` is scaled by the corresponding value of
@@ -452,7 +454,7 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None,
 
   `weights` acts as a coefficient for the loss. If a scalar is provided, then
   the loss is simply scaled by the given value. If `weights` is a tensor of size
-  [batch_size], then the total loss for each sample of the batch is rescaled
+  `[batch_size]`, then the total loss for each sample of the batch is rescaled
   by the corresponding element in the `weights` vector. If the shape of
   `weights` matches the shape of `predictions`, then the loss of each
   measurable element of `predictions` is scaled by the corresponding value of
@@ -519,7 +521,7 @@ def mean_pairwise_squared_error(
 
   `weights` acts as a coefficient for the loss. If a scalar is provided, then
   the loss is simply scaled by the given value. If `weights` is a tensor of size
-  [batch_size], then the total loss for each sample of the batch is rescaled
+  `[batch_size]`, then the total loss for each sample of the batch is rescaled
   by the corresponding element in the `weights` vector.
 
   Args:
@@ -559,15 +561,16 @@ def mean_pairwise_squared_error(
       reduction_indices = math_ops.range(1, array_ops.rank(diffs))
 
       sum_squares_diff_per_batch = math_ops.reduce_sum(
-          math_ops.square(diffs), reduction_indices=reduction_indices,
-          keep_dims=True)
+          math_ops.square(diffs),
+          reduction_indices=reduction_indices,
+          keepdims=True)
       num_present_per_batch = _num_present(diffs, weights, per_batch=True)
 
       term1 = 2.0 * _safe_div(sum_squares_diff_per_batch,
                               num_present_per_batch - 1)
 
       sum_diff = math_ops.reduce_sum(
-          diffs, reduction_indices=reduction_indices, keep_dims=True)
+          diffs, reduction_indices=reduction_indices, keepdims=True)
       term2 = 2.0 * _safe_div(
           math_ops.square(sum_diff),
           math_ops.multiply(num_present_per_batch, num_present_per_batch - 1))
@@ -593,7 +596,7 @@ def mean_squared_error(
 
   `weights` acts as a coefficient for the loss. If a scalar is provided, then
   the loss is simply scaled by the given value. If `weights` is a tensor of size
-  [batch_size], then the total loss for each sample of the batch is rescaled
+  `[batch_size]`, then the total loss for each sample of the batch is rescaled
   by the corresponding element in the `weights` vector. If the shape of
   `weights` matches the shape of `predictions`, then the loss of each
   measurable element of `predictions` is scaled by the corresponding value of
@@ -812,7 +815,7 @@ def sparse_softmax_cross_entropy(
 
   `weights` acts as a coefficient for the loss. If a scalar is provided,
   then the loss is simply scaled by the given value. If `weights` is a
-  tensor of shape [`batch_size`], then the loss weights apply to each
+  tensor of shape `[batch_size]`, then the loss weights apply to each
   corresponding sample.
 
   Args:
diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py
index bd26ff6696..d314124ccd 100644
--- a/tensorflow/python/ops/math_ops_test.py
+++ b/tensorflow/python/ops/math_ops_test.py
@@ -105,7 +105,7 @@ class LogSumExpTest(test_util.TensorFlowTestCase):
     for dtype in [np.float16, np.float32, np.double]:
       x_np = np.random.rand(5, 5).astype(dtype)
       with self.test_session(use_gpu=True):
-        y_tf_np = math_ops.reduce_logsumexp(x_np, keep_dims=True).eval()
+        y_tf_np = math_ops.reduce_logsumexp(x_np, keepdims=True).eval()
         self.assertEqual(y_tf_np.ndim, x_np.ndim)
         y_np = log(np.sum(exp(x_np), keepdims=True))
         self.assertAllClose(y_tf_np, y_np)
diff --git a/tensorflow/python/ops/nn_grad.py b/tensorflow/python/ops/nn_grad.py
index 2a883eb0d5..dc24b821a5 100644
--- a/tensorflow/python/ops/nn_grad.py
+++ b/tensorflow/python/ops/nn_grad.py
@@ -863,27 +863,27 @@ def _BatchNormGrad(grad_y,
     grad_y = math_ops.cast(grad_y, dtypes.float32)
   if is_training:
     if data_format == b"NHWC":
-      keep_dims = False
+      keepdims = False
       reduce_axis = [0, 1, 2]
     else:
-      keep_dims = True
+      keepdims = True
       reduce_axis = [0, 2, 3]
       shape = [1, array_ops.size(scale), 1, 1]
       scale = array_ops.reshape(scale, shape)
-    mean_grad_y = math_ops.reduce_mean(grad_y, reduce_axis, keep_dims=keep_dims)
-    mean_x = math_ops.reduce_mean(x, reduce_axis, keep_dims=keep_dims)
+    mean_grad_y = math_ops.reduce_mean(grad_y, reduce_axis, keepdims=keepdims)
+    mean_x = math_ops.reduce_mean(x, reduce_axis, keepdims=keepdims)
     var_x = math_ops.reduce_mean(
         math_ops.squared_difference(x, array_ops.stop_gradient(mean_x)),
         reduce_axis,
-        keep_dims=keep_dims)
+        keepdims=keepdims)
     grad_y_offset = grad_y - mean_grad_y
     x_offset = x - mean_x
     mean = math_ops.reduce_mean(
-        grad_y * x_offset, axis=reduce_axis, keep_dims=keep_dims)
+        grad_y * x_offset, axis=reduce_axis, keepdims=keepdims)
     grad_x = scale * math_ops.rsqrt(var_x + epsilon) * (
         grad_y_offset - math_ops.reciprocal(var_x + epsilon) * mean * x_offset)
     grad_scale = math_ops.rsqrt(var_x + epsilon) * math_ops.reduce_sum(
-        grad_y * x_offset, axis=reduce_axis, keep_dims=keep_dims)
+        grad_y * x_offset, axis=reduce_axis, keepdims=keepdims)
     if data_format == b"NCHW":
       grad_scale = array_ops.squeeze(grad_scale)
     grad_offset = math_ops.reduce_sum(grad_y, axis=reduce_axis)
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index 47f48a7e16..8fbe698914 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -2215,6 +2215,31 @@ def xw_plus_b_v1(x, weights, biases, name=None):  # pylint: disable=invalid-name
     return bias_add_v1(mm, biases, name=name)
 
 
+def _get_noise_shape(x, noise_shape):
+  # If noise_shape is none return immediately.
+  if noise_shape is None:
+    return array_ops.shape(x)
+
+  try:
+    # Best effort to figure out the intended shape.
+    # If not possible, let the op to handle it.
+    # In eager mode exception will show up.
+    noise_shape_ = tensor_shape.as_shape(noise_shape)
+  except (TypeError, ValueError):
+    return noise_shape
+
+  if x.shape.dims is not None and len(x.shape.dims) == len(noise_shape_.dims):
+    new_dims = []
+    for i, dim in enumerate(x.shape.dims):
+      if noise_shape_.dims[i].value is None and dim.value is not None:
+        new_dims.append(dim.value)
+      else:
+        new_dims.append(noise_shape_.dims[i].value)
+    return tensor_shape.TensorShape(new_dims)
+
+  return noise_shape
+
+
 @tf_export("nn.dropout")
 def dropout(x, keep_prob, noise_shape=None, seed=None, name=None):  # pylint: disable=invalid-name
   """Computes dropout.
@@ -2265,7 +2290,8 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None):  # pylint: di
     if tensor_util.constant_value(keep_prob) == 1:
       return x
 
-    noise_shape = noise_shape if noise_shape is not None else array_ops.shape(x)
+    noise_shape = _get_noise_shape(x, noise_shape)
+
     # uniform [keep_prob, 1.0 + keep_prob)
     random_tensor = keep_prob
     random_tensor += random_ops.random_uniform(
@@ -2380,7 +2406,7 @@ def conv1d(value,
 
   Args:
     value: A 3D `Tensor`.  Must be of type `float16` or `float32`.
-    filters: A 3D `Tensor`.  Must have the same type as `input`.
+    filters: A 3D `Tensor`.  Must have the same type as `value`.
     stride: An `integer`.  The number of entries by which
       the filter is moved right at each step.
     padding: 'SAME' or 'VALID'
diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index 5a45bdc1e5..21eea3db25 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -383,6 +383,31 @@ class DropoutTest(test_lib.TestCase):
         x, keep_prob, noise_shape=array_ops.placeholder(dtypes.int32))
     self.assertEqual(x.get_shape(), dropout_x.get_shape())
 
+  def testPartialShapedDropout(self):
+    x_dim = 40 * 30
+    y_dim = 3
+    num_iter = 10
+    for keep_prob in [0.1, 0.5, 0.8]:
+      with self.test_session():
+        t = constant_op.constant(
+            1.0, shape=[x_dim, y_dim], dtype=dtypes.float32)
+        # Set noise_shape=[None, 1] which means [x_dim, 1].
+        dropout = nn_ops.dropout(t, keep_prob, noise_shape=[None, 1])
+        self.assertEqual([x_dim, y_dim], dropout.get_shape())
+        final_count = 0
+        for _ in xrange(0, num_iter):
+          value = dropout.eval()
+          final_count += np.count_nonzero(value)
+          # Verifies that there are only two values: 0 and 1/keep_prob.
+          sorted_value = np.unique(np.sort(value))
+          self.assertEqual(0, sorted_value[0])
+          self.assertAllClose(1 / keep_prob, sorted_value[1])
+      # Check that we are in the 15% error range
+      expected_count = x_dim * y_dim * keep_prob * num_iter
+      rel_error = math.fabs(final_count - expected_count) / expected_count
+      print(rel_error)
+      self.assertTrue(rel_error < 0.15)
+
   def testInvalidKeepProb(self):
     x_dim = 40
     y_dim = 30
diff --git a/tensorflow/python/profiler/option_builder.py b/tensorflow/python/profiler/option_builder.py
index 957ebe6ddd..2ad7adf769 100644
--- a/tensorflow/python/profiler/option_builder.py
+++ b/tensorflow/python/profiler/option_builder.py
@@ -300,7 +300,7 @@ class ProfileOptionBuilder(object):
     # pylint: disable=line-too-long
     """Only show profiler nodes consuming no less than 'min_float_ops'.
 
-    Please see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profilerg3doc/profile_model_architecture.md
+    Please see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler/g3doc/profile_model_architecture.md
     on the caveats of calculating float operations.
 
     Args:
diff --git a/tensorflow/python/tools/freeze_graph.py b/tensorflow/python/tools/freeze_graph.py
index 074b8e7132..a52f325ddb 100644
--- a/tensorflow/python/tools/freeze_graph.py
+++ b/tensorflow/python/tools/freeze_graph.py
@@ -109,7 +109,7 @@ def freeze_graph_with_def_protos(input_graph_def,
           input_meta_graph_def, clear_devices=True)
       restorer.restore(sess, input_checkpoint)
       if initializer_nodes:
-        sess.run(initializer_nodes.split(","))
+        sess.run(initializer_nodes.replace(" ", "").split(","))
     elif input_saved_model_dir:
       if saved_model_tags is None:
         saved_model_tags = []
@@ -130,25 +130,27 @@ def freeze_graph_with_def_protos(input_graph_def,
           var_list=var_list, write_version=checkpoint_version)
       saver.restore(sess, input_checkpoint)
       if initializer_nodes:
-        sess.run(initializer_nodes.split(","))
+        sess.run(initializer_nodes.replace(" ", "").split(","))
 
-    variable_names_whitelist = (variable_names_whitelist.split(",")
-                                if variable_names_whitelist else None)
-    variable_names_blacklist = (variable_names_blacklist.split(",")
-                                if variable_names_blacklist else None)
+    variable_names_whitelist = (
+        variable_names_whitelist.replace(" ", "").split(",")
+        if variable_names_whitelist else None)
+    variable_names_blacklist = (
+        variable_names_blacklist.replace(" ", "").split(",")
+        if variable_names_blacklist else None)
 
     if input_meta_graph_def:
       output_graph_def = graph_util.convert_variables_to_constants(
           sess,
           input_meta_graph_def.graph_def,
-          output_node_names.split(","),
+          output_node_names.replace(" ", "").split(","),
           variable_names_whitelist=variable_names_whitelist,
           variable_names_blacklist=variable_names_blacklist)
     else:
       output_graph_def = graph_util.convert_variables_to_constants(
           sess,
           input_graph_def,
-          output_node_names.split(","),
+          output_node_names.replace(" ", "").split(","),
           variable_names_whitelist=variable_names_whitelist,
           variable_names_blacklist=variable_names_blacklist)
 
@@ -250,7 +252,7 @@ def freeze_graph(input_graph,
       variable_names_blacklist,
       input_meta_graph_def,
       input_saved_model_dir,
-      saved_model_tags.split(","),
+      saved_model_tags.replace(" ", "").split(","),
       checkpoint_version=checkpoint_version)
 
 
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 0c1c8e664b..3888e9bba4 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -1597,9 +1597,9 @@ class Saver(object):
         [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
 
     Returns:
-      A string: path prefix used for the checkpoint files. If checkpoint
-        format is V1 and the saver is sharded, this string ends with:
-         '-?????-of-nnnnn' where 'nnnnn' is the number of shards created.
+      A string: path prefix used for the checkpoint files.  If the saver is
+        sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn'
+        is the number of shards created.
       If the saver is empty, returns None.
 
     Raises:
@@ -1749,12 +1749,6 @@ class Saver(object):
       return
     if save_path is None:
       raise ValueError("Can't load save_path when it is None.")
-    if (os.path.isfile(save_path) and
-        self._write_version not in (
-            saver_pb2.SaverDef.V1, saver_pb2.SaverDef.LEGACY)):
-      raise ValueError("The specified path: %s is a file."
-                       " Please specify only the path prefix"
-                       " to the checkpoint files." % save_path)
     logging.info("Restoring parameters from %s", save_path)
     if context.in_graph_mode():
       sess.run(self.saver_def.restore_op_name,
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 82142fa21d..818d67f7b5 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -618,7 +618,7 @@ def tf_cc_test(name,
       srcs=srcs + tf_binary_additional_srcs(),
       copts=tf_copts() + extra_copts,
       linkopts=select({
-        "//tensorflow:android": [
+        clean_dep("//tensorflow:android"): [
             "-pie",
           ],
         clean_dep("//tensorflow:windows"): [],
@@ -1312,6 +1312,46 @@ def tf_extension_linkopts():
 def tf_extension_copts():
   return []  # No extension c opts
 
+# In tf_py_wrap_cc generated libraries
+# module init functions are not exported unless
+# they contain one of the keywords in the version file
+# this prevents custom python modules.
+# This function attempts to append init_module_name to list of
+# exported functions in version script
+def _append_init_to_versionscript_impl(ctx):
+  mod_name = ctx.attr.module_name
+  if ctx.attr.is_version_script:
+    ctx.actions.expand_template(
+      template=ctx.file.template_file,
+      output=ctx.outputs.versionscript,
+      substitutions={
+        "global:":"global:\n     init_%s;\n     PyInit_*;"%(mod_name),
+      },
+      is_executable=False,
+    )
+  else:
+    ctx.actions.expand_template(
+      template=ctx.file.template_file,
+      output=ctx.outputs.versionscript,
+      substitutions={
+        "*tensorflow*":"*tensorflow*\ninit_%s\nPyInit_*\n"%(mod_name),
+      },
+      is_executable=False,
+    )
+
+
+_append_init_to_versionscript= rule(
+  implementation=_append_init_to_versionscript_impl,
+  attrs={
+    "module_name":attr.string(mandatory=True),
+    "template_file":attr.label(allow_files=True,single_file=True,mandatory=True),
+    "is_version_script":attr.bool(default=True,
+      doc='whether target is a ld version script or exported symbol list',
+      mandatory=False),
+  },
+  outputs={"versionscript":"%{name}.lds"},
+)
+
 def tf_py_wrap_cc(name,
                              srcs,
                              swig_includes=[],
@@ -1333,26 +1373,39 @@ def tf_py_wrap_cc(name,
       toolchain_deps=["//tools/defaults:crosstool"],
       module_name=module_name,
       py_module_name=name)
+  vscriptname=name+"_versionscript"
+  _append_init_to_versionscript(
+      name=vscriptname,
+      module_name=module_name,
+      is_version_script=select({
+          "@local_config_cuda//cuda:darwin":False,
+          "//conditions:default":True,
+          }),
+      template_file=select({
+          "@local_config_cuda//cuda:darwin":clean_dep("//tensorflow:tf_exported_symbols.lds"),
+          "//conditions:default":clean_dep("//tensorflow:tf_version_script.lds")
+      })
+  )
   extra_linkopts = select({
       "@local_config_cuda//cuda:darwin": [
           "-Wl,-exported_symbols_list",
-          clean_dep("//tensorflow:tf_exported_symbols.lds")
+          "%s.lds"%vscriptname,
       ],
       clean_dep("//tensorflow:windows"): [],
       clean_dep("//tensorflow:windows_msvc"): [],
       "//conditions:default": [
           "-Wl,--version-script",
-          clean_dep("//tensorflow:tf_version_script.lds")
+          "%s.lds"%vscriptname,
       ]
   })
   extra_deps += select({
       "@local_config_cuda//cuda:darwin": [
-          clean_dep("//tensorflow:tf_exported_symbols.lds")
+          "%s.lds"%vscriptname,
       ],
       clean_dep("//tensorflow:windows"): [],
       clean_dep("//tensorflow:windows_msvc"): [],
       "//conditions:default": [
-          clean_dep("//tensorflow:tf_version_script.lds")
+          "%s.lds"%vscriptname,
       ]
   })
 
diff --git a/tensorflow/tools/ci_build/install/install_bazel.sh b/tensorflow/tools/ci_build/install/install_bazel.sh
index cf8737c2d8..1df6a84d7c 100755
--- a/tensorflow/tools/ci_build/install/install_bazel.sh
+++ b/tensorflow/tools/ci_build/install/install_bazel.sh
@@ -15,7 +15,7 @@
 # ==============================================================================
 
 # Select bazel version.
-BAZEL_VERSION="0.8.0"
+BAZEL_VERSION="0.10.0"
 
 set +e
 local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}')
diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD
index 8601b3d0f1..ad3668fa02 100644
--- a/tensorflow/tools/graph_transforms/BUILD
+++ b/tensorflow/tools/graph_transforms/BUILD
@@ -103,6 +103,7 @@ cc_library(
         "quantize_nodes.cc",
         "quantize_weights.cc",
         "remove_attribute.cc",
+        "remove_control_dependencies.cc",
         "remove_device.cc",
         "remove_ema.cc",
         "remove_nodes.cc",
diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md
index 345d9eadb8..67badb4869 100644
--- a/tensorflow/tools/graph_transforms/README.md
+++ b/tensorflow/tools/graph_transforms/README.md
@@ -639,6 +639,13 @@ specified devices may not be available. In order to work with graphs like these,
 you can run this transform to wipe the slate clean and delete the device
 specifier from all ops.
 
+### remove_control_dependencies
+
+Args: None \
+Prerequisites: None
+
+Removes all control dependencies from the graph.
+
 ### remove_nodes
 
 Args:
diff --git a/tensorflow/tools/graph_transforms/remove_control_dependencies.cc b/tensorflow/tools/graph_transforms/remove_control_dependencies.cc
new file mode 100644
index 0000000000..a900ee65b0
--- /dev/null
+++ b/tensorflow/tools/graph_transforms/remove_control_dependencies.cc
@@ -0,0 +1,47 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/graph/graph_constructor.h"
+#include "tensorflow/core/graph/node_builder.h"
+#include "tensorflow/tools/graph_transforms/transform_utils.h"
+
+namespace tensorflow {
+namespace graph_transforms {
+
+// Remove control depdencies in preparation for inference.
+// In the tensorflow graph, control dependencies are represented as extra
+// inputs which are referenced with "^tensor_name".
+// See node_def.proto for more details.
+Status RemoveControlDependencies(const GraphDef& input_graph_def,
+                                 const TransformFuncContext& context,
+                                 GraphDef* output_graph_def) {
+  output_graph_def->Clear();
+  for (const NodeDef& node : input_graph_def.node()) {
+    NodeDef* new_node = output_graph_def->mutable_node()->Add();
+    *new_node = node;
+    new_node->clear_input();
+    for (const auto& input : node.input()) {
+      if (input[0] != '^') {
+        new_node->add_input(input);
+      }
+    }
+  }
+  return Status::OK();
+}
+
+REGISTER_GRAPH_TRANSFORM("remove_control_dependencies",
+                         RemoveControlDependencies);
+
+}  // namespace graph_transforms
+}  // namespace tensorflow
diff --git a/tensorflow/tools/graph_transforms/remove_nodes.cc b/tensorflow/tools/graph_transforms/remove_nodes.cc
index 119b44d6a4..05f036a86a 100644
--- a/tensorflow/tools/graph_transforms/remove_nodes.cc
+++ b/tensorflow/tools/graph_transforms/remove_nodes.cc
@@ -81,7 +81,17 @@ Status RemoveNodes(const GraphDef& input_graph_def,
                 return Status::OK();
               }
               const NodeDef& input_node = match.inputs[0].node;
-              inputs_to_rename[replace_node.name()] = input_node.name();
+              string target_name = input_node.name();
+              for (const string& input : replace_node.input()) {
+                if (!input.compare(0, target_name.size(), target_name)) {
+                  if (input.size() == target_name.size() ||
+                      input[target_name.size()] == ':') {
+                    target_name = input;
+                    break;
+                  }
+                }
+              }
+              inputs_to_rename[replace_node.name()] = target_name;
               inputs_to_rename["^" + replace_node.name()] =
                   "^" + input_node.name();
               new_nodes->push_back(input_node);
diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD
index 791016e8b7..fb6eaa4faa 100644
--- a/tensorflow/tools/pip_package/BUILD
+++ b/tensorflow/tools/pip_package/BUILD
@@ -11,6 +11,7 @@ load(
 )
 load("//third_party/mkl:build_defs.bzl", "if_mkl")
 load("//tensorflow:tensorflow.bzl", "if_cuda")
+load("@local_config_tensorrt//:build_defs.bzl", "if_tensorrt")
 load("//tensorflow/core:platform/default/build_config_root.bzl", "tf_additional_license_deps")
 
 # This returns a list of headers of all public header libraries (e.g.,
@@ -191,7 +192,9 @@ sh_binary(
             "//tensorflow/python:test_ops",
             "//tensorflow/tools/dist_test/server:grpc_tensorflow_server",
         ],
-    }) + if_mkl(["//third_party/mkl:intel_binary_blob"]),
+    }) + if_mkl(["//third_party/mkl:intel_binary_blob"]) + if_tensorrt([
+        "//tensorflow/contrib/tensorrt:init_py",
+    ]),
 )
 
 # A genrule for generating a marker file for the pip package on Windows
diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py
index 0e6b32bb49..4b6f123daa 100644
--- a/tensorflow/tools/pip_package/setup.py
+++ b/tensorflow/tools/pip_package/setup.py
@@ -29,17 +29,17 @@ from setuptools.dist import Distribution
 # This version string is semver compatible, but incompatible with pip.
 # For pip, we will remove all '-' characters from this string, and use the
 # result for pip.
-_VERSION = '1.6.0-rc0'
+_VERSION = '1.6.0-rc1'
 
 REQUIRED_PACKAGES = [
     'absl-py >= 0.1.6',
     'astor >= 0.6.0',
     'gast >= 0.2.0',
     'grpcio >= 1.8.6',
-    'numpy >= 1.12.1',
+    'numpy >= 1.13.3',
     'six >= 1.10.0',
     'protobuf >= 3.4.0',
-    'tensorflow-tensorboard >= 1.5.0, < 1.6.0',
+    'tensorboard >= 1.6.0, < 1.7.0',
     'termcolor >= 1.1.0',
 ]
 
@@ -62,7 +62,7 @@ else:
 if 'tf_nightly' in project_name:
   for i, pkg in enumerate(REQUIRED_PACKAGES):
     if 'tensorboard' in pkg:
-      REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.5.0a0, < 1.6.0a0'
+      REQUIRED_PACKAGES[i] = 'tb-nightly >= 1.7.0a0, < 1.8.0a0'
       break
 
 # weakref.finalize and enum were introduced in Python 3.4
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 255ae01190..b7c47a19dd 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -367,11 +367,20 @@ def find_cuda_define(repository_ctx, header_dir, header_file, define):
   if result.stdout.find(define) == -1:
     auto_configure_fail("Cannot find line containing '%s' in %s" %
                         (define, h_path))
-  version = result.stdout
-  # Remove the new line and '\' character if any.
-  version = version.replace("\\", " ")
-  version = version.replace("\n", " ")
-  version = version.replace(define, "").lstrip()
+  # Split results to lines
+  lines = result.stdout.split('\n')
+  num_lines = len(lines)
+  for l in range(num_lines):
+    line = lines[l]
+    if define in line:  # Find the line with define
+      version = line
+      if l != num_lines-1 and line[-1] == '\\':  # Add next line, if multiline
+        version = version[:-1] + lines[l+1]
+      break
+  # Remove any comments
+  version = version.split("//")[0]
+  # Remove define name
+  version = version.replace(define, "").strip()
   # Remove the code after the version number.
   version_end = version.find(" ")
   if version_end != -1:
diff --git a/third_party/tensorrt/BUILD.tpl b/third_party/tensorrt/BUILD.tpl
index feaeb0bea6..57682e8735 100644
--- a/third_party/tensorrt/BUILD.tpl
+++ b/third_party/tensorrt/BUILD.tpl
@@ -3,6 +3,8 @@
 
 licenses(["notice"])
 
+exports_files(["LICENSE"])
+
 load("@local_config_cuda//cuda:build_defs.bzl", "cuda_default_copts")
 
 package(default_visibility = ["//visibility:public"])
@@ -32,36 +34,6 @@ cc_library(
     visibility = ["//visibility:public"],
 )
 
-cc_library(
-    name = "nv_infer_plugin",
-    srcs = [%{nv_infer_plugin}],
-    data = [%{nv_infer_plugin}],
-    includes = [
-        "include",
-    ],
-    copts= cuda_default_copts(),
-    deps = [
-        "@local_config_cuda//cuda:cuda",
-        ":nv_infer",
-        ":tensorrt_headers",
-    ],
-    linkstatic = 1,
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "nv_parsers",
-    srcs = [%{nv_parsers}],
-    data = [%{nv_parsers}],
-    includes = [
-        "include",
-    ],
-    copts= cuda_default_copts(),
-    deps = [
-        ":tensorrt_headers",
-    ],
-    linkstatic = 1,
-    visibility = ["//visibility:public"],
-)
 
 %{tensorrt_genrules}
+
diff --git a/third_party/tensorrt/LICENSE b/third_party/tensorrt/LICENSE
new file mode 100644
index 0000000000..146d9b765c
--- /dev/null
+++ b/third_party/tensorrt/LICENSE
@@ -0,0 +1,203 @@
+Copyright 2018 The TensorFlow Authors.  All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
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+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+   9. Accepting Warranty or Additional Liability. While redistributing
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+      defend, and hold each Contributor harmless for any liability
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+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
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+
+   Copyright 2018, The TensorFlow Authors.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/third_party/tensorrt/tensorrt_configure.bzl b/third_party/tensorrt/tensorrt_configure.bzl
index 8aa0f28f39..8e76e5d02a 100644
--- a/third_party/tensorrt/tensorrt_configure.bzl
+++ b/third_party/tensorrt/tensorrt_configure.bzl
@@ -19,11 +19,8 @@ load(
 _TENSORRT_INSTALL_PATH = "TENSORRT_INSTALL_PATH"
 _TF_TENSORRT_VERSION = "TF_TENSORRT_VERSION"
 
-_TF_TENSORRT_LIBS = ["nvinfer", "nvinfer_plugin", "nvparsers"]
-_TF_TENSORRT_HEADERS = [
-    "NvInfer.h", "NvInferPlugin.h", "NvCaffeParser.h", "NvUffParser.h",
-    "NvUtils.h"
-]
+_TF_TENSORRT_LIBS = ["nvinfer"]
+_TF_TENSORRT_HEADERS = ["NvInfer.h", "NvUtils.h"]
 
 _DEFINE_TENSORRT_SONAME_MAJOR = "#define NV_TENSORRT_SONAME_MAJOR"
 _DEFINE_TENSORRT_SONAME_MINOR = "#define NV_TENSORRT_SONAME_MINOR"
-- 
GitLab


From 6006f46dd7531b112360b831aa61de6c46618166 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 22 Feb 2018 15:06:45 -0800
Subject: [PATCH 214/575] [tf.data] Handle a function-raised OutOfRange error
 correctly in ParallelMapDataset.

PiperOrigin-RevId: 186680982
---
 .../kernels/data/parallel_map_dataset_op.cc   |  9 +++++++-
 .../data/kernel_tests/map_dataset_op_test.py  | 22 +++++++++++++++++++
 2 files changed, 30 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/data/parallel_map_dataset_op.cc b/tensorflow/core/kernels/data/parallel_map_dataset_op.cc
index bc4426a9fd..33053b1bd9 100644
--- a/tensorflow/core/kernels/data/parallel_map_dataset_op.cc
+++ b/tensorflow/core/kernels/data/parallel_map_dataset_op.cc
@@ -199,7 +199,14 @@ class ParallelMapDatasetOp : public UnaryDatasetOpKernel {
           }
         }
         ++num_outputs_consumed_;
-        return result->status;
+        if (errors::IsOutOfRange(result->status)) {
+          // `f` may deliberately raise `errors::OutOfRange` to indicate
+          // that we should terminate the iteration early.
+          *end_of_sequence = true;
+          return Status::OK();
+        } else {
+          return result->status;
+        }
       }
 
      protected:
diff --git a/tensorflow/python/data/kernel_tests/map_dataset_op_test.py b/tensorflow/python/data/kernel_tests/map_dataset_op_test.py
index 04d1abdb25..0791c614fa 100644
--- a/tensorflow/python/data/kernel_tests/map_dataset_op_test.py
+++ b/tensorflow/python/data/kernel_tests/map_dataset_op_test.py
@@ -602,6 +602,28 @@ class MapDatasetTest(test.TestCase):
       with self.assertRaises(errors.OutOfRangeError):
         sess.run(get_next)
 
+  def testParallelMapOutOfRangeError(self):
+    def raising_py_func(i):
+      if i == 100:
+        raise StopIteration()
+      else:
+        return i
+
+    iterator = (
+        dataset_ops.Dataset.range(105)
+        .map(lambda x: script_ops.py_func(raising_py_func, [x], dtypes.int64),
+             num_parallel_calls=2)
+        .make_initializable_iterator())
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      for i in range(100):
+        self.assertEqual(i, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
 
 class MapDatasetBenchmark(test.Benchmark):
 
-- 
GitLab


From 848c53fb11cab2631695cdb6c38bbdfeee972a75 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 15:34:50 -0800
Subject: [PATCH 215/575] Implement the logic to parse TensorProto (the tensor
 value for input or filter shape info) in op_level_cost_estimator.

PiperOrigin-RevId: 186685409
---
 .../grappler/costs/op_level_cost_estimator.cc |  86 ++++++++++----
 .../grappler/costs/op_level_cost_estimator.h  |   3 +
 .../costs/op_level_cost_estimator_test.cc     | 105 ++++++++++++++++++
 3 files changed, 172 insertions(+), 22 deletions(-)

diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index a57cfdd989..983b6891f1 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -718,6 +718,56 @@ int64 OpLevelCostEstimator::CountBatchMatMulOperations(
   return ops;
 }
 
+bool GetTensorShapeProtoFromTensorProto(const TensorProto& tensor_proto,
+                                        TensorShapeProto* tensor_shape_proto) {
+  tensor_shape_proto->Clear();
+  // First convert TensorProto into Tensor class so that it correctly parses
+  // data values within TensorProto (whether it's in int_val, int64_val,
+  // tensor_content, or anything.
+  Tensor tensor(tensor_proto.dtype());
+  if (!tensor.FromProto(tensor_proto)) {
+    LOG(WARNING) << "GetTensorShapeProtoFromTensorProto() -- "
+                 << "failed to parse TensorProto: "
+                 << tensor_proto.DebugString();
+    return false;
+  }
+  if (tensor.dims() != 1) {
+    LOG(WARNING) << "GetTensorShapeProtoFromTensorProto() -- "
+                 << "tensor is not 1D: " << tensor.dims();
+    return false;
+  }
+  // Then, convert it back to TensorProto using AsProtoField, which makes sure
+  // the data is in int_val, int64_val, or such repeated data fields, not in
+  // tensor_content.
+  TensorProto temp_tensor;
+  tensor.AsProtoField(&temp_tensor);
+
+#define TENSOR_VALUES_TO_TENSOR_SHAPE_PROTO(type)        \
+  do {                                                   \
+    for (const auto& value : temp_tensor.type##_val()) { \
+      tensor_shape_proto->add_dim()->set_size(value);    \
+    }                                                    \
+  } while (0)
+
+  if (tensor.dtype() == DT_INT32 || tensor.dtype() == DT_INT16 ||
+      tensor.dtype() == DT_INT8 || tensor.dtype() == DT_UINT8) {
+    TENSOR_VALUES_TO_TENSOR_SHAPE_PROTO(int);
+  } else if (tensor.dtype() == DT_INT64) {
+    TENSOR_VALUES_TO_TENSOR_SHAPE_PROTO(int64);
+  } else if (tensor.dtype() == DT_UINT32) {
+    TENSOR_VALUES_TO_TENSOR_SHAPE_PROTO(uint32);
+  } else if (tensor.dtype() == DT_UINT64) {
+    TENSOR_VALUES_TO_TENSOR_SHAPE_PROTO(uint64);
+  } else {
+    LOG(WARNING) << "GetTensorShapeProtoFromTensorProto() -- "
+                 << "Unsupported dtype: " << tensor.dtype();
+    return false;
+  }
+#undef TENSOR_VALUES_TO_TENSOR_SHAPE_PROTO
+
+  return true;
+}
+
 // TODO(cliffy): Dedup this method and CountConv2DBackpropFilterOperations.
 int64 OpLevelCostEstimator::CountConv2DBackpropInputOperations(
     const OpInfo& op_features, ConvolutionDimensions* returned_conv_dims,
@@ -732,20 +782,16 @@ int64 OpLevelCostEstimator::CountConv2DBackpropInputOperations(
   }
 
   TensorShapeProto input_shape;
+  bool shape_found = false;
   if (op_features.inputs(0).has_value()) {
     const TensorProto& value = op_features.inputs(0).value();
-    if (value.int64_val_size() > 0) {
-      for (int i = 0; i < value.int64_val_size(); ++i) {
-        input_shape.add_dim()->set_size(value.int64_val(i));
-      }
-    } else {
-      for (int i = 0; i < value.int_val_size(); ++i) {
-        input_shape.add_dim()->set_size(value.int_val(i));
-      }
-    }
-  } else if (op_features.outputs_size() == 1) {
+    shape_found = GetTensorShapeProtoFromTensorProto(value, &input_shape);
+  }
+  if (!shape_found && op_features.outputs_size() == 1) {
     input_shape = op_features.outputs(0).shape();
-  } else {
+    shape_found = true;
+  }
+  if (!shape_found) {
     // Set the minimum filter size that's feasible.
     for (int i = 0; i < 4; ++i) {
       input_shape.add_dim()->set_size(1);
@@ -778,20 +824,16 @@ int64 OpLevelCostEstimator::CountConv2DBackpropFilterOperations(
   DCHECK_EQ(kConv2dBackpropFilter, op_features.op());
 
   TensorShapeProto filter_shape;
+  bool shape_found = false;
   if (op_features.inputs_size() >= 2 && op_features.inputs(1).has_value()) {
     const TensorProto& value = op_features.inputs(1).value();
-    if (value.int64_val_size() > 0) {
-      for (int i = 0; i < value.int64_val_size(); ++i) {
-        filter_shape.add_dim()->set_size(value.int64_val(i));
-      }
-    } else {
-      for (int i = 0; i < value.int_val_size(); ++i) {
-        filter_shape.add_dim()->set_size(value.int_val(i));
-      }
-    }
-  } else if (op_features.outputs_size() == 1) {
+    shape_found = GetTensorShapeProtoFromTensorProto(value, &filter_shape);
+  }
+  if (!shape_found && op_features.outputs_size() == 1) {
     filter_shape = op_features.outputs(0).shape();
-  } else {
+    shape_found = true;
+  }
+  if (!shape_found) {
     // Set the minimum filter size that's feasible.
     for (int i = 0; i < 4; ++i) {
       filter_shape.add_dim()->set_size(1);
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.h b/tensorflow/core/grappler/costs/op_level_cost_estimator.h
index a292e5e97f..7bb530fe31 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.h
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.h
@@ -28,6 +28,9 @@ limitations under the License.
 namespace tensorflow {
 namespace grappler {
 
+bool GetTensorShapeProtoFromTensorProto(const TensorProto& tensor_proto,
+                                        TensorShapeProto* tensor_shape_proto);
+
 class OpLevelCostEstimator {
  public:
   OpLevelCostEstimator();
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
index 60fc783472..583d2619b2 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
@@ -14,6 +14,8 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/grappler/costs/op_level_cost_estimator.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/platform/test.h"
@@ -247,5 +249,108 @@ TEST_F(OpLevelCostEstimatorTest, BatchMatMul) {
   EXPECT_NE(matmul_inaccurate, batch_matmul_inaccurate);
 }
 
+// Helper functions for testing GetTensorShapeProtoFromTensorProto().
+void GetTensorProto(const DataType dtype, const std::vector<int64>& shape,
+                    const std::vector<int64> values, const bool tensor_content,
+                    TensorProto* tensor_proto) {
+  tensor_proto->Clear();
+  TensorProto temp_tensor_proto;
+  temp_tensor_proto.set_dtype(dtype);
+  for (const auto& x : shape) {
+    temp_tensor_proto.mutable_tensor_shape()->add_dim()->set_size(x);
+  }
+  for (const auto& x : values) {
+    if (dtype == DT_INT64) {
+      temp_tensor_proto.add_int64_val(x);
+    } else if (dtype == DT_INT32 || dtype == DT_INT16 || dtype == DT_INT8 ||
+               dtype == DT_UINT8) {
+      temp_tensor_proto.add_int_val(x);
+    } else if (dtype == DT_UINT32) {
+      temp_tensor_proto.add_uint32_val(x);
+    } else if (dtype == DT_UINT64) {
+      temp_tensor_proto.add_uint64_val(x);
+    } else {
+      CHECK(false) << "Unsupported dtype: " << dtype;
+    }
+  }
+  Tensor tensor(dtype);
+  CHECK(tensor.FromProto(temp_tensor_proto));
+  if (tensor_content) {
+    tensor.AsProtoTensorContent(tensor_proto);
+  } else {
+    tensor.AsProtoField(tensor_proto);
+  }
+}
+
+void ExpectTensorShape(const std::vector<int64>& expected,
+                       const TensorShapeProto& tensor_shape_proto) {
+  TensorShape tensor_shape_expected(expected);
+  TensorShape tensor_shape(tensor_shape_proto);
+
+  LOG(INFO) << "Expected: " << tensor_shape_expected.DebugString();
+  LOG(INFO) << "TensorShape: " << tensor_shape.DebugString();
+  EXPECT_TRUE(tensor_shape_expected == tensor_shape);
+}
+
+TEST_F(OpLevelCostEstimatorTest, GetTensorShapeProtoFromTensorProto) {
+  TensorProto tensor_proto;
+  TensorShapeProto tensor_shape_proto;
+
+  // Dimention larger than max value; should fail while converting to Tensor
+  // class.
+  tensor_proto.mutable_tensor_shape()->add_dim()->set_size(255);
+  EXPECT_FALSE(
+      GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto));
+
+  tensor_proto.Clear();
+  // Expect only 1D shape.
+  tensor_proto.mutable_tensor_shape()->add_dim()->set_size(1);
+  tensor_proto.mutable_tensor_shape()->add_dim()->set_size(2);
+  EXPECT_FALSE(
+      GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto));
+
+  // Expect only handle integer data types.
+  GetTensorProto(DT_FLOAT, {}, {}, /*tensor_content=*/false, &tensor_proto);
+  EXPECT_FALSE(
+      GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto));
+
+  // Check GetTensorShapeProtoFromTensorProto() resturns correct values.
+  {
+    std::vector<int64> shape_expected = {10, 20, 30, 40};
+    GetTensorProto(DT_INT32, {4}, shape_expected, /*tensor_content=*/false,
+                   &tensor_proto);
+    EXPECT_TRUE(
+        GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto));
+    ExpectTensorShape(shape_expected, tensor_shape_proto);
+  }
+
+  {
+    std::vector<int64> shape_expected = {40, 20, 90, 40};
+    GetTensorProto(DT_INT64, {4}, shape_expected, /*tensor_content=*/false,
+                   &tensor_proto);
+    EXPECT_TRUE(
+        GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto));
+    ExpectTensorShape(shape_expected, tensor_shape_proto);
+  }
+
+  {
+    std::vector<int64> shape_expected = {10, 20, 30, 40};
+    GetTensorProto(DT_INT32, {4}, shape_expected, /*tensor_content=*/true,
+                   &tensor_proto);
+    EXPECT_TRUE(
+        GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto));
+    ExpectTensorShape(shape_expected, tensor_shape_proto);
+  }
+
+  {
+    std::vector<int64> shape_expected = {40, 20, 90, 40};
+    GetTensorProto(DT_INT64, {4}, shape_expected, /*tensor_content=*/true,
+                   &tensor_proto);
+    EXPECT_TRUE(
+        GetTensorShapeProtoFromTensorProto(tensor_proto, &tensor_shape_proto));
+    ExpectTensorShape(shape_expected, tensor_shape_proto);
+  }
+}
+
 }  // end namespace grappler
 }  // end namespace tensorflow
-- 
GitLab


From c50e3515e44020b22a20c5b2363b4119a6026497 Mon Sep 17 00:00:00 2001
From: Max Galkin <maxgalkin@google.com>
Date: Thu, 22 Feb 2018 16:13:00 -0800
Subject: [PATCH 216/575] Add a regression test for virtual_scheduler.

PiperOrigin-RevId: 186691392
---
 .../grappler/costs/virtual_scheduler_test.cc  | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
index d44b83d035..f9154e42f9 100644
--- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
+++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc
@@ -205,6 +205,25 @@ class VirtualSchedulerTest : public ::testing::Test {
     dependency_["out"] = {"x", "y", "z", "w"};
   }
 
+  // Graph with some placeholder feed nodes that are not in the fetch fan-in.
+  void CreateGrapplerItemWithUnnecessaryPlaceholderNodes() {
+    Scope s = Scope::NewRootScope().WithDevice(kCPU0);
+    auto unnecessary = ops::Placeholder(s.WithOpName("unnecessary"), DT_FLOAT);
+    auto x = ops::Placeholder(s.WithOpName("x"), DT_FLOAT);
+
+    GraphDef def;
+    TF_CHECK_OK(s.ToGraphDef(&def));
+
+    grappler_item_.reset(new GrapplerItem);
+    grappler_item_->id = "test_extra_placeholders";
+    grappler_item_->graph = def;
+    grappler_item_->fetch = {"x"};
+
+    // Grappler Item Builder puts all placeholder nodes into the feed
+    // list by default.
+    grappler_item_->feed = {{"x", Tensor()}, {"unnecessary", Tensor()}};
+  }
+
   // NoOp that takes 7 NoOps as control dependency.
   void CreateGrapplerItemWithControlDependency() {
     Scope s = Scope::NewRootScope().WithDevice(kCPU0);
@@ -1757,6 +1776,16 @@ TEST_F(VirtualSchedulerTest, MemoryUsage) {
                               cpu_state.mem_usage_snapshot_at_peak);
 }
 
+TEST_F(VirtualSchedulerTest, UnnecessaryFeedNodes) {
+  CreateGrapplerItemWithUnnecessaryPlaceholderNodes();
+  InitScheduler();
+
+  // Test that scheduler can run graphs with extra unnecessary feed nodes.
+  auto ops_executed = RunScheduler("");
+  ASSERT_EQ(1, ops_executed.size());
+  ASSERT_EQ(ops_executed.count("x"), 1);
+}
+
 TEST_F(VirtualSchedulerTest, ControlDependency) {
   // Init.
   CreateGrapplerItemWithControlDependency();
-- 
GitLab


From 810ddac312ca6e7b2d3569dab311b1092b84bae4 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 22 Feb 2018 16:18:34 -0800
Subject: [PATCH 217/575] New Mutex operations for a distributed-happy and
 Function-less CriticalSection.

Original idea by Alex Passos; impl and cancellation handling by Eugene Brevdo with help from Alex.

PiperOrigin-RevId: 186692306
---
 tensorflow/contrib/framework/BUILD            |   3 +
 .../python/ops/critical_section_ops.py        | 139 ++++++----
 .../python/ops/critical_section_test.py       | 112 +++++---
 .../base_api/api_def_ConsumeMutexLock.pbtxt   |  19 ++
 .../base_api/api_def_CriticalSectionOp.pbtxt  |  16 --
 .../api_def_ExecuteInCriticalSection.pbtxt    |  49 ----
 .../api_def/base_api/api_def_MutexLock.pbtxt  |  58 ++++
 .../api_def/base_api/api_def_MutexV2.pbtxt    |  24 ++
 tensorflow/core/kernels/BUILD                 |  10 +-
 tensorflow/core/kernels/critical_section.cc   | 246 -----------------
 tensorflow/core/kernels/mutex_ops.cc          | 249 ++++++++++++++++++
 .../core/ops/compat/ops_history.v1.pbtxt      |  94 -------
 tensorflow/core/ops/resource_variable_ops.cc  |  28 +-
 tensorflow/python/eager/function.py           |  90 +++++--
 tensorflow/python/ops/control_flow_ops.py     |  12 +-
 15 files changed, 608 insertions(+), 541 deletions(-)
 create mode 100644 tensorflow/core/api_def/base_api/api_def_ConsumeMutexLock.pbtxt
 delete mode 100644 tensorflow/core/api_def/base_api/api_def_CriticalSectionOp.pbtxt
 delete mode 100644 tensorflow/core/api_def/base_api/api_def_ExecuteInCriticalSection.pbtxt
 create mode 100644 tensorflow/core/api_def/base_api/api_def_MutexLock.pbtxt
 create mode 100644 tensorflow/core/api_def/base_api/api_def_MutexV2.pbtxt
 delete mode 100644 tensorflow/core/kernels/critical_section.cc
 create mode 100644 tensorflow/core/kernels/mutex_ops.cc

diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD
index 9e5f54f097..dbdb5cfaac 100644
--- a/tensorflow/contrib/framework/BUILD
+++ b/tensorflow/contrib/framework/BUILD
@@ -185,11 +185,14 @@ cuda_py_test(
     additional_deps = [
         "//tensorflow/python:client_testlib",
         ":framework_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:control_flow_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:gradients",
         "//tensorflow/python:platform_test",
         "//tensorflow/python:resource_variable_ops",
+        "//tensorflow/python:tensor_array_ops",
     ],
 )
 
diff --git a/tensorflow/contrib/framework/python/ops/critical_section_ops.py b/tensorflow/contrib/framework/python/ops/critical_section_ops.py
index 182fec924f..3c5c55ed65 100644
--- a/tensorflow/contrib/framework/python/ops/critical_section_ops.py
+++ b/tensorflow/contrib/framework/python/ops/critical_section_ops.py
@@ -27,7 +27,11 @@ from tensorflow.python.eager import context
 from tensorflow.python.eager import function
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gen_resource_variable_ops
+from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.util import nest
 
 
@@ -38,7 +42,8 @@ CRITICAL_SECTION_EXECUTIONS = "critical_section_executions"
 
 class _ExecutionSignature(
     collections.namedtuple("_ExecutionSignature",
-                           ("op", "exclusive_resource_access"))):
+                           ("op", "handle",
+                            "resources", "exclusive_resource_access"))):
   """A class storing an `ExecuteInCriticalResource` op and associated attrs."""
   pass
 
@@ -112,16 +117,18 @@ class CriticalSection(object):
   ```
   """
 
-  def __init__(self, name=None, critical_section_def=None, import_scope=None):
+  def __init__(self, name=None, shared_name=None,
+               critical_section_def=None, import_scope=None):
     """Creates a critical section."""
     if critical_section_def and name is not None:
-      raise ValueError("critical_section_def and name are mutually exclusive.")
+      raise ValueError("critical_section_def and shared_name are "
+                       "mutually exclusive.")
     if critical_section_def:
       self._init_from_proto(critical_section_def, import_scope=import_scope)
     else:
-      self._init_from_args(name)
+      self._init_from_args(name, shared_name)
 
-  def _init_from_proto(self, critical_section_def, import_scope):
+  def _init_from_proto(self, critical_section_def, import_scope):  # pylint: disable=invalid-name
     raise NotImplementedError("Not yet implemented")
     # TODO(ebrevdo): Re-enable once CriticalSection is in core.
     # assert isinstance(
@@ -133,18 +140,20 @@ class CriticalSection(object):
     #         critical_section_def.critical_section_name,
     #         import_scope=import_scope))
 
-  def _init_from_args(self, name):
+  def _init_from_args(self, name, shared_name):  # pylint: disable=invalid-name
     """Initialize the CriticalSection from constructor arguments."""
     with ops.name_scope(name, "CriticalSection", []) as name:
       with ops.control_dependencies(None):
         # pylint: disable=protected-access
-        handle_name = ops._name_from_scope_name(name)
         container = ops.get_default_graph()._container
         # pylint: enable=protected-access
+        if shared_name is None:
+          shared_name = name
         if container is None:
           container = ""
-        self._handle = gen_resource_variable_ops.critical_section_op(
-            shared_name=handle_name, name=name)
+        self._handle = gen_resource_variable_ops.mutex_v2(
+            shared_name=shared_name, container=container, name=name)
+
     if context.in_graph_mode():
       ops.add_to_collections(CRITICAL_SECTIONS, self)
 
@@ -183,68 +192,96 @@ class CriticalSection(object):
     name = kwargs.pop("name", None)
     exclusive_resource_access = kwargs.pop("exclusive_resource_access", True)
 
-    args = nest.map_structure(ops.convert_to_tensor, args)
     with ops.name_scope(name, "critical_section_execute", []):
-      fn_op = function.make_defun_op(fn, *args, **kwargs)
-      flat_dtypes = nest.flatten(fn_op.output_dtypes)
-      flat_shapes = nest.flatten(fn_op.output_shapes)
-      all_inputs = nest.flatten(args) + fn_op.captured_inputs
-      if self._handle in all_inputs:
+      lock = gen_resource_variable_ops.mutex_lock(self._handle)
+
+      with ops.control_dependencies([lock]):
+        c_known_ops = set()
+        c_captured_tensors = set()
+
+        def add_op_internal(op):
+          c_known_ops.add(op)
+          for i in op.inputs:
+            if i.op not in c_known_ops:
+              c_captured_tensors.add(i)
+
+        c = function.HelperContext(add_op_internal)
+        with c:
+          r = fn(*args, **kwargs)
+
+        resource_inputs = set([
+            x for x in
+            list(nest.flatten(args)) + nest.flatten(kwargs.values()) +
+            list(c_captured_tensors)
+            if tensor_util.is_tensor(x) and x.dtype == dtypes.resource])
+
+      if self._handle in resource_inputs:
         raise ValueError("The function fn attempts to access the "
-                         "CriticalSection in which it would be running.  This "
-                         "is illegal and would cause deadlocks.  "
+                         "CriticalSection in which it would be running.  "
+                         "This is illegal and would cause deadlocks.  "
                          "CriticalSection: %s." % self._handle)
 
       if context.in_graph_mode():
         # Collections and op introspection does not work in eager
         # mode.  This is generally ok; since eager mode (as of
         # writing) executes sequentially anyway.
-        all_input_resources = [
-            x for x in all_inputs if x.dtype == dtypes.resource]
         for sg in ops.get_collection(CRITICAL_SECTION_EXECUTIONS):
-          if sg.op.inputs[0].name == self._handle.name:
+          if sg.handle.name == self._handle.name:
             # Other executions in the same critical section are allowed.
             continue
           if not (exclusive_resource_access or sg.exclusive_resource_access):
             # Neither execution requested exclusive access.
             continue
-          sg_input_names = [y.name for y in sg.op.inputs[1:]]
-          for res in all_input_resources:
-            if res.name in sg_input_names:
-              raise ValueError(
-                  "This execution would access resource %s; but either this "
-                  "execution (CriticalSection: %s) or Execution '%s' "
-                  "(CriticalSection: %s) requested exclusive resource access "
-                  "of this resource for their critical section.  Did you mean "
-                  "to call execute with keyword argument "
-                  "exclusive_resource_access=False?"
-                  % (res.name,
-                     self.name,
-                     sg.op.name,
-                     sg.op.inputs[0].op.name))
-
-      flat_outputs = gen_resource_variable_ops.execute_in_critical_section(
-          critical_section=self._handle,
-          arguments=all_inputs,
-          f=fn_op,
-          output_types=flat_dtypes,
-          output_shapes=flat_shapes)
+          resource_intersection = resource_inputs.intersection(sg.resources)
+          if resource_intersection:
+            raise ValueError(
+                "This execution would access resources: %s.  Either this "
+                "lock (CriticalSection: %s) or lock '%s' "
+                "(CriticalSection: %s) requested exclusive resource access "
+                "of this resource.  Did you mean to call execute with keyword "
+                "argument exclusive_resource_access=False?" %
+                (list(resource_intersection), self._handle.name,
+                 sg.op.name, sg.handle.name))
+
+      def identity(x):  # pylint: disable=invalid-name
+        if isinstance(x, tensor_array_ops.TensorArray):
+          return x.identity()
+        elif isinstance(x, ops.Operation):
+          return control_flow_ops.group(x)
+        elif context.in_eager_mode() and x is None:
+          return None
+        else:
+          return array_ops.identity(x)
+
+      r_flat = [identity(x) for x in nest.flatten(r)]
+
+      with ops.control_dependencies(r_flat):
+        # The identity must run on the same machine as self._handle
+        with ops.colocate_with(self._handle):
+          # Do not use array_ops.identity as there are special
+          # optimizations within TensorFlow which seem to elide it
+          # even when optimizations are disabled(!).
+          ensure_lock_exists = gen_resource_variable_ops.consume_mutex_lock(
+              lock)
+
+        # Make sure that if any element of r is accessed, all of
+        # them are executed together.
+        r = nest.pack_sequence_as(
+            r, control_flow_ops.tuple(nest.flatten(r)))
+
+      with ops.control_dependencies([ensure_lock_exists]):
+        outputs = nest.map_structure(identity, r)
 
       if context.in_graph_mode():
-        if isinstance(flat_outputs, ops.Operation):
-          flat_outputs = [flat_outputs]
-        op = (flat_outputs[0].op if isinstance(flat_outputs[0], ops.Tensor)
-              else flat_outputs[0])
         signature = _ExecutionSignature(
-            op=op,
+            op=lock.op,
+            handle=self._handle,
+            resources=list(resource_inputs),
             exclusive_resource_access=exclusive_resource_access)
         ops.add_to_collections(
             CRITICAL_SECTION_EXECUTIONS, signature)
 
-      return (flat_outputs[0]
-              if (len(flat_outputs) == 1
-                  and isinstance(flat_outputs[0], ops.Operation))
-              else nest.pack_sequence_as(fn_op.output_dtypes, flat_outputs))
+      return outputs
 
   # TODO(ebrevdo): Re-enable once CriticalSection is in core.
 
@@ -276,6 +313,7 @@ class CriticalSection(object):
 
 # def _execution_to_proto_fn(execution_signature, export_scope=None):
 #   """Converts `_ExecutionSignature` to a `CriticalSectionExecutionDef`.
+#   # TODO(ebrevdo): Update for _ExecutionSignature storing resource list.
 
 #   Args:
 #     execution_signature: Instance of `_ExecutionSignature`.
@@ -298,6 +336,7 @@ class CriticalSection(object):
 
 # def _execution_from_proto_fn(op_def, import_scope=None):
 #   """Converts a `CriticalSectionExecutionDef` to a `_ExecutionSignature`."""
+#   # TODO(ebrevdo): Update for _ExecutionSignature storing resource list.
 #   assert isinstance(
 #       op_def, critical_section_pb2.CriticalSectionExecutionDef)
 
diff --git a/tensorflow/contrib/framework/python/ops/critical_section_test.py b/tensorflow/contrib/framework/python/ops/critical_section_test.py
index a416724d3b..c916592ce1 100644
--- a/tensorflow/contrib/framework/python/ops/critical_section_test.py
+++ b/tensorflow/contrib/framework/python/ops/critical_section_test.py
@@ -19,12 +19,10 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.framework.python.ops import critical_section_ops
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import test
 # TODO(ebrevdo): Re-enable once CriticalSection is in core.
@@ -35,7 +33,7 @@ class CriticalSectionTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testCreateCriticalSection(self):
-    cs = critical_section_ops.CriticalSection(name="cs")
+    cs = critical_section_ops.CriticalSection(shared_name="cs")
     v = resource_variable_ops.ResourceVariable(0.0, name="v")
 
     def fn(a, b):
@@ -45,16 +43,72 @@ class CriticalSectionTest(test.TestCase):
         with ops.control_dependencies([nv]):
           return array_ops.identity(c)
 
-    num_concurrent = 1000
+    num_concurrent = 100
     r = [cs.execute(fn, 1.0, 2.0) for _ in range(num_concurrent)]
     self.evaluate(v.initializer)
     r_value = self.evaluate(r)
     self.assertAllClose([2.0 * i for i in range(num_concurrent)],
                         sorted(r_value))
 
+  @test_util.run_in_graph_and_eager_modes()
+  def testCriticalSectionWithControlFlow(self):
+    for outer_cond in [False, True]:
+      for inner_cond in [False, True]:
+        cs = critical_section_ops.CriticalSection(shared_name="cs")
+        v = resource_variable_ops.ResourceVariable(0.0, name="v")
+        num_concurrent = 100
+
+        # pylint: disable=cell-var-from-loop
+        def fn(a, b):
+          c = v.read_value()
+          def true_fn():
+            with ops.control_dependencies([c]):
+              nv = v.assign_add(a * b)
+              with ops.control_dependencies([nv]):
+                return array_ops.identity(c)
+          return control_flow_ops.cond(
+              array_ops.identity(inner_cond), true_fn, lambda: c)
+
+        def execute():
+          return cs.execute(fn, 1.0, 2.0)
+
+        r = [
+            control_flow_ops.cond(array_ops.identity(outer_cond),
+                                  execute,
+                                  v.read_value)
+            for _ in range(num_concurrent)
+        ]
+        # pylint: enable=cell-var-from-loop
+
+        self.evaluate(v.initializer)
+        r_value = self.evaluate(r)
+        if inner_cond and outer_cond:
+          self.assertAllClose([2.0 * i for i in range(num_concurrent)],
+                              sorted(r_value))
+        else:
+          self.assertAllClose([0] * num_concurrent, r_value)
+
+  def testCriticalSectionInParallelDoesntDeadlockOnError(self):
+    # No eager mode execution of this test because eager does not
+    # run fn() in parallel, which is where the deadlock could
+    # potentially occur (in graph mode).
+    cs = critical_section_ops.CriticalSection(shared_name="cs")
+    v = resource_variable_ops.ResourceVariable(0.0, name="v")
+
+    def fn(i):
+      error = control_flow_ops.Assert((i % 2) == 1, ["Error"])
+      with ops.control_dependencies([error]):
+        return v.read_value()
+    num_concurrent = 2
+    r = [cs.execute(fn, i) for i in range(num_concurrent)]
+    self.evaluate(v.initializer)
+    for _ in range(100):
+      with self.assertRaisesOpError("Error"):
+        self.evaluate(r)
+
   @test_util.run_in_graph_and_eager_modes()
   def testCreateCriticalSectionFnReturnsOp(self):
-    cs = critical_section_ops.CriticalSection(name="cs")
+    cs = critical_section_ops.CriticalSection(shared_name="cs")
     v = resource_variable_ops.ResourceVariable(0.0, name="v")
 
     def fn_return_op(a, b):
@@ -62,7 +116,7 @@ class CriticalSectionTest(test.TestCase):
       with ops.control_dependencies([c]):
         nv = v.assign_add(a * b)
         with ops.control_dependencies([nv]):
-          return ()
+          return control_flow_ops.no_op()
 
     num_concurrent = 100
     r = [cs.execute(fn_return_op, 1.0, 2.0) for _ in range(num_concurrent)]
@@ -71,47 +125,25 @@ class CriticalSectionTest(test.TestCase):
     final_v = self.evaluate(v)
     self.assertAllClose(2.0 * num_concurrent, final_v)
 
-  def testCreateCriticalSectionRaw(self):
-    cs = critical_section_ops.CriticalSection(name="cs")
-    v = resource_variable_ops.ResourceVariable(0.0, name="v")
-
-    @function.Defun(dtypes.float32, dtypes.float32)
-    def fn(a, b):
-      c = v.read_value()
-      with ops.control_dependencies([c]):
-        nv = v.assign_add(a * b)
-        with ops.control_dependencies([nv]):
-          return array_ops.identity(c)
-
-    def execute(fn, *args):
-      output_args = fn.definition.signature.output_arg
-      return resource_variable_ops.execute_in_critical_section(
-          critical_section=cs._handle,
-          arguments=list(args) + fn.captured_inputs,
-          f=fn,
-          output_types=[out.type for out in output_args],
-          output_shapes=[tensor_shape.TensorShape(None) for _ in output_args])
-
-    num_concurrent = 1000
-    r = [execute(fn, 1.0, 2.0)[0] for _ in range(num_concurrent)]
-    self.evaluate(v.initializer)
-    r_value = self.evaluate(r)
-    self.assertAllClose([2.0 * i for i in range(num_concurrent)],
-                        sorted(r_value))
-
   def testCollection(self):
-    cs = critical_section_ops.CriticalSection(name="cs")
+    cs = critical_section_ops.CriticalSection(shared_name="cs")
     self.assertIn(
         cs, ops.get_collection(critical_section_ops.CRITICAL_SECTIONS))
-    execute_op = cs.execute(lambda x: x + 1, 1.0).op
+    execute = cs.execute(lambda x: x + 1, 1.0, name="my_execute")
+    execute_op = [
+        x for x in execute.graph.get_operations()
+        if "my_execute" in x.name and "MutexLock" in x.type
+    ][0]
     self.assertIn(
         execute_op,
         [signature.op for signature in
          ops.get_collection(critical_section_ops.CRITICAL_SECTION_EXECUTIONS)])
 
-  @test_util.run_in_graph_and_eager_modes()
   def testRecursiveCriticalSectionAccessIsIllegal(self):
-    cs = critical_section_ops.CriticalSection(name="cs")
+    # This does not work properly in eager mode.  Eager users will
+    # just hit a deadlock if they do this.  But at least it'll be easier
+    # to debug.
+    cs = critical_section_ops.CriticalSection(shared_name="cs")
     def fn(x):
       return cs.execute(lambda x: x+1, x)
     with self.assertRaisesRegexp(
@@ -167,7 +199,7 @@ class CriticalSectionTest(test.TestCase):
   #     self.assertEqual(restored_exec[0].op.name, "imported/%s" % r.op.name)
 
   # def testToProto(self):
-  #   cs = critical_section_ops.CriticalSection(name="cs")
+  #   cs = critical_section_ops.CriticalSection(shared_name="cs")
   #   proto = cs.to_proto()
   #   self.assertEqual(proto.critical_section_name, cs._handle.name)
   #   cs_copy = critical_section_ops.CriticalSection.from_proto(proto)
diff --git a/tensorflow/core/api_def/base_api/api_def_ConsumeMutexLock.pbtxt b/tensorflow/core/api_def/base_api/api_def_ConsumeMutexLock.pbtxt
new file mode 100644
index 0000000000..b9db8274de
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_ConsumeMutexLock.pbtxt
@@ -0,0 +1,19 @@
+op {
+  graph_op_name: "ConsumeMutexLock"
+  in_arg {
+    name: "mutex_lock"
+    description: <<END
+A tensor returned by `MutexLock`.
+END
+  }
+  summary: "This op consumes a lock created by `MutexLock`."
+  description: <<END
+This op exists to consume a tensor created by `MutexLock` (other than
+direct control dependencies).  It should be the only that consumes the tensor,
+and will raise an error if it is not.  Its only purpose is to keep the
+mutex lock tensor alive until it is consumed by this op.
+
+**NOTE**: This operation must run on the same device as its input.  This may
+be enforced via the `colocate_with` mechanism.
+END
+}
diff --git a/tensorflow/core/api_def/base_api/api_def_CriticalSectionOp.pbtxt b/tensorflow/core/api_def/base_api/api_def_CriticalSectionOp.pbtxt
deleted file mode 100644
index 5027fa861e..0000000000
--- a/tensorflow/core/api_def/base_api/api_def_CriticalSectionOp.pbtxt
+++ /dev/null
@@ -1,16 +0,0 @@
-op {
-  graph_op_name: "CriticalSectionOp"
-  attr {
-    name: "container"
-    description: <<END
-the container this critical section is placed in.
-END
-  }
-  attr {
-    name: "shared_name"
-    description: <<END
-the name by which this critical section is referred to.
-END
-  }
-  summary: "Creates a handle to a CriticalSection resource."
-}
diff --git a/tensorflow/core/api_def/base_api/api_def_ExecuteInCriticalSection.pbtxt b/tensorflow/core/api_def/base_api/api_def_ExecuteInCriticalSection.pbtxt
deleted file mode 100644
index cd5fc84a74..0000000000
--- a/tensorflow/core/api_def/base_api/api_def_ExecuteInCriticalSection.pbtxt
+++ /dev/null
@@ -1,49 +0,0 @@
-op {
-  graph_op_name: "ExecuteInCriticalSection"
-  in_arg {
-    name: "critical_section"
-    description: <<END
-The handle of the `critical_section`.
-END
-  }
-  in_arg {
-    name: "arguments"
-    description: <<END
-Arguments for `f`, including any captured inputs appended at the end.
-END
-  }
-  out_arg {
-    name: "outputs"
-    description: <<END
-The outputs of `f`.
-END
-  }
-  attr {
-    name: "f"
-    description: <<END
-The `Function` to execute.
-END
-  }
-  summary: "Executes function `f` within critical section `critical_section`."
-  description: <<END
-While `f` is running in `critical_section`, no other functions which wish to
-use this critical section may run.
-
-Often the use case is that two executions of the same graph, in parallel,
-wish to run `f`; and we wish to ensure that only one of them executes
-at a time.  This is especially important if `f` modifies one or more
-variables at a time.
-
-It is also useful if two separate functions must share a resource, but we
-wish to ensure the usage is exclusive.
-
-The signature of `f` is expected to be:
-
-```
-  outputs <- F(arguments)
-```
-Typically, but this is not required, `arguments` contain resources.  The
-primary purpose of this op is to limit access to these resources to one
-execution of `F` at a time.
-END
-}
diff --git a/tensorflow/core/api_def/base_api/api_def_MutexLock.pbtxt b/tensorflow/core/api_def/base_api/api_def_MutexLock.pbtxt
new file mode 100644
index 0000000000..cd3eb43486
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_MutexLock.pbtxt
@@ -0,0 +1,58 @@
+op {
+  graph_op_name: "MutexLock"
+  in_arg {
+    name: "mutex"
+    description: <<END
+The mutex resource to lock.
+END
+  }
+  out_arg {
+    name: "mutex_lock"
+    description: <<END
+A tensor that keeps a shared pointer to a lock on the mutex;
+when the Tensor is destroyed, the use count on the shared pointer is decreased
+by 1.  When it reaches 0, the lock is released.
+END
+  }
+  summary: "Locks a mutex resource.  The output is the lock.  So long as the lock tensor"
+  description: <<END
+is alive, any other request to use `MutexLock` with this mutex will wait.
+
+This is particularly useful for creating a critical section when used in
+conjunction with `MutexLockIdentity`:
+
+```python
+
+mutex = mutex_v2(
+  shared_name=handle_name, container=container, name=name)
+
+def execute_in_critical_section(fn, *args, **kwargs):
+  lock = gen_resource_variable_ops.mutex_lock(mutex)
+
+  with ops.control_dependencies([lock]):
+    r = fn(*args, **kwargs)
+
+  with ops.control_dependencies(nest.flatten(r)):
+    with ops.colocate_with(mutex):
+      ensure_lock_exists = mutex_lock_identity(lock)
+
+    # Make sure that if any element of r is accessed, all of
+    # them are executed together.
+    r = nest.map_structure(tf.identity, r)
+
+  with ops.control_dependencies([ensure_lock_exists]):
+    return nest.map_structure(tf.identity, r)
+```
+
+While `fn` is running in the critical section, no other functions which wish to
+use this critical section may run.
+
+Often the use case is that two executions of the same graph, in parallel,
+wish to run `fn`; and we wish to ensure that only one of them executes
+at a time.  This is especially important if `fn` modifies one or more
+variables at a time.
+
+It is also useful if two separate functions must share a resource, but we
+wish to ensure the usage is exclusive.
+END
+}
diff --git a/tensorflow/core/api_def/base_api/api_def_MutexV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_MutexV2.pbtxt
new file mode 100644
index 0000000000..22295ecbd8
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_MutexV2.pbtxt
@@ -0,0 +1,24 @@
+op {
+  graph_op_name: "MutexV2"
+  out_arg {
+    name: "resource"
+    description: <<END
+The mutex resource.
+END
+  }
+  attr {
+    name: "container"
+    description: <<END
+If non-empty, this variable is placed in the given container.
+Otherwise, a default container is used.
+END
+  }
+  attr {
+    name: "shared_name"
+    description: <<END
+If non-empty, this variable is named in the given bucket
+with this shared_name. Otherwise, the node name is used instead.
+END
+  }
+  summary: "Creates a Mutex resource that can be locked by `MutexLock`."
+}
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index dc93c76eae..dd0de7829f 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -1891,9 +1891,9 @@ tf_kernel_library(
     srcs = ["resource_variable_ops.cc"],
     deps = [
         ":bounds_check",
-        ":critical_section",
         ":dense_update_functor",
         ":gather_functor",
+        ":mutex_ops",
         ":scatter_functor",
         ":state",
         ":training_op_helpers",
@@ -4094,9 +4094,9 @@ tf_kernel_library(
 )
 
 tf_kernel_library(
-    name = "critical_section",
-    prefix = "critical_section",
-    deps = STATE_DEPS + [":captured_function"],
+    name = "mutex_ops",
+    prefix = "mutex_ops",
+    deps = STATE_DEPS + [":ops_util"],
 )
 
 tf_cc_test(
@@ -5048,7 +5048,7 @@ filegroup(
             # Excluded due to experimental status:
             "debug_ops.*",
             "scatter_nd_op*",
-            "critical_section.*",
+            "mutex_ops.*",
             "batch_kernels.*",
         ],
     ),
diff --git a/tensorflow/core/kernels/critical_section.cc b/tensorflow/core/kernels/critical_section.cc
deleted file mode 100644
index 30a9abf4ee..0000000000
--- a/tensorflow/core/kernels/critical_section.cc
+++ /dev/null
@@ -1,246 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#define EIGEN_USE_THREADS
-
-#include <deque>
-#include <utility>
-
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-#include "tensorflow/core/framework/resource_mgr.h"
-#include "tensorflow/core/kernels/captured_function.h"
-#include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/mutex.h"
-#include "tensorflow/core/platform/types.h"
-
-namespace tensorflow {
-
-class CriticalSection : public ResourceBase {
- public:
-  explicit CriticalSection() : is_locked_(false) {}
-  ~CriticalSection() override {
-    // Wait for all closures to finish running.
-    mutex_lock lock(mu_);
-    while (!closures_.empty()) {
-      queue_empty_cv_.wait(lock);
-    }
-  }
-
- private:
-  friend class ExecuteInCriticalSectionOp;
-
-  void Acquire(std::function<void()> closure) {
-    std::function<void()> next;
-    {
-      mutex_lock ml(mu_);
-      if (is_locked_) {
-        closures_.push_back(std::move(closure));
-      } else {
-        // This branch is the common case.  Avoid the queue.
-        is_locked_ = true;
-        next = std::move(closure);
-      }
-    }
-    if (next) {
-      next();
-    }
-  }
-
-  void Release() {
-    std::function<void()> next;
-    {
-      mutex_lock ml(mu_);
-      CHECK(is_locked_);
-      if (!closures_.empty()) {
-        // if queue is not empty, start the next entry off the queue.
-        std::swap(next, closures_.front());
-        closures_.pop_front();
-      } else {
-        is_locked_ = false;
-        queue_empty_cv_.notify_all();
-      }
-    }
-    if (next) {
-      next();
-    }
-  }
-
-  string DebugString() override {
-    tf_shared_lock ml(mu_);
-    return strings::StrCat("CriticalSection(locked: ", is_locked_,
-                           " queue_size: ", closures_.size(), ")");
-  }
-
- private:
-  mutex mu_;
-  std::deque<std::function<void()>> closures_ GUARDED_BY(mu_);
-  bool is_locked_ GUARDED_BY(mu_);
-  condition_variable queue_empty_cv_ GUARDED_BY(mu_);
-};
-
-class ExecuteInCriticalSectionOp : public AsyncOpKernel {
- public:
-  explicit ExecuteInCriticalSectionOp(OpKernelConstruction* c)
-      : AsyncOpKernel(c) {
-    OP_REQUIRES_OK(c, c->GetAttr("f", &func_));
-  }
-
- public:
-  void ComputeAsync(OpKernelContext* c, DoneCallback done) override {
-    CriticalSection* critical_section = nullptr;
-    OP_REQUIRES_OK_ASYNC(c,
-                         LookupOrCreateResource<CriticalSection>(
-                             c, HandleFromInput(c, 0), &critical_section,
-                             [this, c](CriticalSection** ptr) {
-                               *ptr = new CriticalSection;
-                               return Status::OK();
-                             }),
-                         done);
-    // No need to Unref critical_section; the Closure below will take
-    // care of the Unref associated with this execution.
-
-    auto* execution = new Closure{std::move(done), c, critical_section, &func_};
-    execution->Start();
-  }
-
- private:
-  class Closure {
-   public:
-    AsyncOpKernel::DoneCallback done_;
-    OpKernelContext* ctx_;
-    CriticalSection* cs_;
-    FunctionLibraryRuntime::Handle handle_;
-    FunctionLibraryRuntime::Options opts_;
-    std::vector<Tensor> arguments_t_;
-    std::vector<Tensor> output_t_;
-    NameAttrList* func_;
-
-    explicit Closure(AsyncOpKernel::DoneCallback done, OpKernelContext* ctx,
-                     CriticalSection* critical_section, NameAttrList* func)
-        : done_(std::move(done)),
-          ctx_(ctx),
-          cs_(critical_section),
-          handle_(-1),
-          func_(func) {}
-
-    ~Closure();
-
-    void Start() {
-      // Perform ExecuteFunction isnide a separate thread to avoid
-      // having lightweight Functions be inlined in this thread.
-      // That inlining would in turn inline DoneAndDelete inside the
-      // same thread.  Since DoneAndDelete can call the next
-      // ExecuteFunction in the CriticalSection, this can cause a
-      // stack overflow.
-      cs_->Acquire(
-          [this]() { (*ctx_->runner())([this]() { ExecuteFunction(); }); });
-    }
-
-   private:
-    void ExecuteFunction();
-    void DoneAndDelete(const Status& status);
-  };
-
-  NameAttrList func_;
-};
-
-void ExecuteInCriticalSectionOp::Closure::ExecuteFunction() {
-  // Arguments to a Function are in the order:
-  //   concat(<formal arguments>, <captured arguments>)
-  OpInputList arguments;
-  Status s = ctx_->input_list("arguments", &arguments);
-  if (!s.ok()) {
-    DoneAndDelete(s);
-    return;
-  }
-
-  arguments_t_.reserve(arguments.size());
-  for (const Tensor& t : arguments) {
-    arguments_t_.push_back(t);
-  }
-
-  auto* function_library = ctx_->function_library();
-  s = function_library->Instantiate(func_->name(), AttrSlice(&func_->attr()),
-                                    &handle_);
-  if (!s.ok()) {
-    DoneAndDelete(s);
-    return;
-  }
-
-  opts_.step_id = CapturedFunction::generate_step_id();
-  auto* step_container =
-      new ScopedStepContainer(opts_.step_id, [this](const string& name) {
-        ctx_->resource_manager()->Cleanup(name).IgnoreError();
-      });
-  opts_.cancellation_manager = ctx_->cancellation_manager();
-  opts_.step_container = step_container;
-  opts_.runner = ctx_->runner();
-
-  function_library->Run(opts_, handle_, arguments_t_, &output_t_,
-                        [this](const Status& s) { DoneAndDelete(s); });
-}
-
-void ExecuteInCriticalSectionOp::Closure::DoneAndDelete(const Status& status) {
-  cs_->Release();
-
-  if (!status.ok()) {
-    ctx_->SetStatus(status);
-  } else {
-    OpOutputList output;
-    const Status s = ctx_->output_list("outputs", &output);
-    if (!s.ok()) {
-      ctx_->SetStatus(s);
-    } else if (output_t_.size() != output.size()) {
-      ctx_->SetStatus(errors::Internal(
-          "Could not set all outputs.  Expected output size is ", output.size(),
-          " but function set ", output_t_.size(), " output values."));
-    } else {
-      for (int i = 0; i < output_t_.size(); ++i) {
-        output.set(i, output_t_[i]);
-      }
-    }
-  }
-
-  delete opts_.step_container;
-  opts_.step_container = nullptr;
-  done_();
-  cs_->Unref();
-  delete this;
-}
-
-ExecuteInCriticalSectionOp::Closure::~Closure() {
-  CHECK(!opts_.step_container)
-      << "Initialized closure destroyed without calling Done";
-}
-
-REGISTER_KERNEL_BUILDER(Name("ExecuteInCriticalSection").Device(DEVICE_CPU),
-                        ExecuteInCriticalSectionOp);
-
-REGISTER_KERNEL_BUILDER(Name("CriticalSectionOp").Device(DEVICE_CPU),
-                        ResourceHandleOp<CriticalSection>);
-
-// TODO(ebrevdo): Re-enable once the cross-device function execution works.
-#if GOOGLE_CUDA
-REGISTER_KERNEL_BUILDER(Name("ExecuteInCriticalSection")
-                            .Device(DEVICE_GPU)
-                            .HostMemory("critical_section"),
-                        ExecuteInCriticalSectionOp);
-REGISTER_KERNEL_BUILDER(
-    Name("CriticalSectionOp").Device(DEVICE_GPU).HostMemory("resource"),
-    ResourceHandleOp<CriticalSection>);
-#endif  // GOOGLE_CUDA
-
-}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/mutex_ops.cc b/tensorflow/core/kernels/mutex_ops.cc
new file mode 100644
index 0000000000..b8b1fc7679
--- /dev/null
+++ b/tensorflow/core/kernels/mutex_ops.cc
@@ -0,0 +1,249 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#include <deque>
+#include <utility>
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/variant.h"
+#include "tensorflow/core/framework/variant_encode_decode.h"
+#include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+namespace {
+
+class Mutex : public ResourceBase {
+ public:
+  explicit Mutex(OpKernelContext* c, const string& name)
+      : locked_(false),
+        thread_pool_(new thread::ThreadPool(
+            c->env(), ThreadOptions(),
+            strings::StrCat("mutex_lock_thread_", SanitizeThreadSuffix(name)),
+            1 /* num_threads */, false /* low_latency_hint */)),
+        name_(name) {
+    VLOG(2) << "Creating mutex with name " << name << ": " << this;
+  }
+
+  string DebugString() override { return strings::StrCat("Mutex ", name_); }
+
+  class LockReleaser {
+   public:
+    explicit LockReleaser(Mutex* mutex) : mutex_(mutex) {}
+
+    LockReleaser(const LockReleaser&) = delete;
+    LockReleaser& operator=(const LockReleaser&) = delete;
+
+    virtual ~LockReleaser() {
+      VLOG(3) << "Destroying LockReleaser " << this << " for mutex: " << mutex_;
+      if (mutex_) {
+        mutex_lock lock(mutex_->mu_);
+        mutex_->locked_ = false;
+        mutex_->cv_.notify_all();
+        VLOG(3) << "Destroying LockReleaser " << this
+                << ": sent notifications.";
+      }
+    }
+
+   private:
+    Mutex* mutex_;
+  };
+
+  struct SharedLockReleaser {
+    std::shared_ptr<LockReleaser> shared_lock;
+
+    explicit SharedLockReleaser(std::shared_ptr<LockReleaser>&& lock)
+        : shared_lock(std::forward<decltype(lock)>(lock)) {
+      VLOG(3) << "Creating shared_ptr of " << shared_lock.get()
+              << " count is: " << shared_lock.use_count();
+    }
+
+    SharedLockReleaser(SharedLockReleaser&& rhs)
+        : shared_lock(std::move(rhs.shared_lock)) {
+      VLOG(3) << "Moving SharedLockReleaser of " << shared_lock.get()
+              << " count is: " << shared_lock.use_count();
+    }
+
+    SharedLockReleaser(const SharedLockReleaser& rhs)
+        : shared_lock(rhs.shared_lock) {
+      VLOG(3) << "Copying SharedLockReleaser of " << shared_lock.get()
+              << " count is: " << shared_lock.use_count();
+    }
+
+    ~SharedLockReleaser() {
+      VLOG(3) << "Destroying SharedLockReleaser of " << shared_lock.get()
+              << " count is: " << shared_lock.use_count();
+    }
+
+    void Encode(VariantTensorData*) const {
+      // Not supported.
+    }
+
+    bool Decode(const VariantTensorData&) {
+      return false;  // Not supported.
+    }
+  };
+
+  void AcquireAsync(
+      OpKernelContext* c,
+      std::function<void(const Status& s, SharedLockReleaser lock)> fn) {
+    CancellationManager* cm = c->cancellation_manager();
+    CancellationToken token{};
+    bool* cancelled = nullptr;
+    if (cm) {
+      cancelled = new bool(false);  // GUARDED_BY(mu_);
+      token = cm->get_cancellation_token();
+      const bool already_cancelled =
+          !cm->RegisterCallback(token, [this, cancelled]() {
+            mutex_lock lock(mu_);
+            *cancelled = true;
+            cv_.notify_all();
+          });
+      if (already_cancelled) {
+        delete cancelled;
+        fn(errors::Cancelled("Lock acquisition cancelled."),
+           SharedLockReleaser{nullptr});
+        return;
+      }
+    }
+    thread_pool_->Schedule(std::bind(
+        [this, c, cm, cancelled,
+         token](std::function<void(const Status& s, SharedLockReleaser&& lock)>
+                    fn_) {
+          bool local_locked;
+          {
+            mutex_lock lock(mu_);
+            while (locked_ && !(cancelled && *cancelled)) {
+              cv_.wait(lock);
+            }
+            local_locked = locked_ = !(cancelled && *cancelled);
+          }
+          if (cm) {
+            cm->DeregisterCallback(token);
+            delete cancelled;
+          }
+          if (local_locked) {  // Not cancelled.
+            fn_(Status::OK(),
+                SharedLockReleaser{std::make_shared<LockReleaser>(this)});
+          } else {
+            fn_(errors::Cancelled("Lock acqusition cancelled."),
+                SharedLockReleaser{nullptr});
+          }
+        },
+        std::move(fn)));
+  }
+
+ private:
+  mutex mu_;
+  condition_variable cv_ GUARDED_BY(mu_);
+  bool locked_ GUARDED_BY(mu_);
+  std::unique_ptr<thread::ThreadPool> thread_pool_;
+  string name_;
+};
+
+}  // namespace
+
+class MutexLockOp : public AsyncOpKernel {
+ public:
+  explicit MutexLockOp(OpKernelConstruction* c) : AsyncOpKernel(c) {}
+
+ public:
+  void ComputeAsync(OpKernelContext* c, DoneCallback done) override {
+    Mutex* mutex = nullptr;
+    OP_REQUIRES_OK_ASYNC(
+        c,
+        LookupOrCreateResource<Mutex>(c, HandleFromInput(c, 0), &mutex,
+                                      [this, c](Mutex** ptr) {
+                                        *ptr = new Mutex(
+                                            c, HandleFromInput(c, 0).name());
+                                        return Status::OK();
+                                      }),
+        done);
+
+    Tensor* variant;
+    OP_REQUIRES_OK_ASYNC(c, c->allocate_output(0, TensorShape({}), &variant),
+                         done);
+
+    mutex->AcquireAsync(
+        c, std::bind(
+               [this, c, variant, mutex](DoneCallback done_,
+                                         // End of bound arguments.
+                                         const Status& s,
+                                         Mutex::SharedLockReleaser&& lock) {
+                 core::ScopedUnref unref(mutex);
+                 VLOG(2) << "Finished locking mutex " << mutex
+                         << " with lock: " << lock.shared_lock.get()
+                         << " status: " << s.ToString();
+                 if (s.ok()) {
+                   variant->scalar<Variant>()() = std::move(lock);
+                 } else {
+                   c->SetStatus(s);
+                 }
+                 done_();
+               },
+               std::move(done), std::placeholders::_1, std::placeholders::_2));
+  }
+};
+
+class ConsumeMutexLockOp : public OpKernel {
+ public:
+  explicit ConsumeMutexLockOp(OpKernelConstruction* context)
+      : OpKernel(context) {}
+
+  void Compute(OpKernelContext* c) override {
+    VLOG(2) << "Executing ConsumeMutexLockOp";
+    const Tensor& lock_t = c->input(0);
+    OP_REQUIRES(
+        c, lock_t.dims() == 0,
+        errors::InvalidArgument("Expected input to be a scalar, saw shape: ",
+                                lock_t.shape().DebugString()));
+    OP_REQUIRES(
+        c, lock_t.dtype() == DT_VARIANT,
+        errors::InvalidArgument("Expected input to be a variant, saw type: ",
+                                DataTypeString(lock_t.dtype())));
+    const auto* lock =
+        lock_t.scalar<Variant>()().get<Mutex::SharedLockReleaser>();
+    OP_REQUIRES(c, lock,
+                errors::InvalidArgument(
+                    "Expected input to contain a SharedLockReleaser "
+                    "object, but saw variant: '",
+                    lock_t.scalar<Variant>()().DebugString(), "'"));
+    const int use_count = lock->shared_lock.use_count();
+    OP_REQUIRES(
+        c, use_count == 1,
+        errors::InvalidArgument("Expected use count of lock to be 1, but saw: ",
+                                use_count));
+  }
+
+  bool IsExpensive() override { return false; }
+};
+
+REGISTER_KERNEL_BUILDER(Name("MutexLock").Device(DEVICE_CPU), MutexLockOp);
+
+REGISTER_KERNEL_BUILDER(Name("MutexV2").Device(DEVICE_CPU),
+                        ResourceHandleOp<Mutex>);
+
+REGISTER_KERNEL_BUILDER(Name("ConsumeMutexLock").Device(DEVICE_CPU),
+                        ConsumeMutexLockOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 7da2365f62..3fb17d92d2 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -12814,28 +12814,6 @@ op {
     }
   }
 }
-op {
-  name: "CriticalSectionOp"
-  output_arg {
-    name: "resource"
-    type: DT_RESOURCE
-  }
-  attr {
-    name: "container"
-    type: "string"
-    default_value {
-      s: ""
-    }
-  }
-  attr {
-    name: "shared_name"
-    type: "string"
-    default_value {
-      s: ""
-    }
-  }
-  is_stateful: true
-}
 op {
   name: "CropAndResize"
   input_arg {
@@ -17433,78 +17411,6 @@ op {
     }
   }
 }
-op {
-  name: "ExecuteInCriticalSection"
-  input_arg {
-    name: "critical_section"
-    type: DT_RESOURCE
-  }
-  input_arg {
-    name: "arguments"
-    type_list_attr: "Targuments"
-  }
-  output_arg {
-    name: "outputs"
-    type_list_attr: "output_types"
-  }
-  attr {
-    name: "f"
-    type: "func"
-  }
-  attr {
-    name: "Targuments"
-    type: "list(type)"
-    has_minimum: true
-  }
-  attr {
-    name: "output_types"
-    type: "list(type)"
-    has_minimum: true
-    minimum: 1
-  }
-  attr {
-    name: "output_shapes"
-    type: "list(shape)"
-    has_minimum: true
-    minimum: 1
-  }
-  is_stateful: true
-}
-op {
-  name: "ExecuteInCriticalSection"
-  input_arg {
-    name: "critical_section"
-    type: DT_RESOURCE
-  }
-  input_arg {
-    name: "arguments"
-    type_list_attr: "Targuments"
-  }
-  output_arg {
-    name: "outputs"
-    type_list_attr: "output_types"
-  }
-  attr {
-    name: "f"
-    type: "func"
-  }
-  attr {
-    name: "Targuments"
-    type: "list(type)"
-    has_minimum: true
-  }
-  attr {
-    name: "output_types"
-    type: "list(type)"
-    has_minimum: true
-  }
-  attr {
-    name: "output_shapes"
-    type: "list(shape)"
-    has_minimum: true
-  }
-  is_stateful: true
-}
 op {
   name: "Exit"
   input_arg {
diff --git a/tensorflow/core/ops/resource_variable_ops.cc b/tensorflow/core/ops/resource_variable_ops.cc
index 8dae7e1ff5..0d8cf78cc2 100644
--- a/tensorflow/core/ops/resource_variable_ops.cc
+++ b/tensorflow/core/ops/resource_variable_ops.cc
@@ -211,7 +211,7 @@ REGISTER_OP("ResourceScatterUpdate")
       return Status::OK();
     });
 
-REGISTER_OP("CriticalSectionOp")
+REGISTER_OP("MutexV2")
     .Attr("container: string = ''")
     .Attr("shared_name: string = ''")
     .Output("resource: resource")
@@ -221,24 +221,18 @@ REGISTER_OP("CriticalSectionOp")
       return Status::OK();
     });
 
-REGISTER_OP("ExecuteInCriticalSection")
-    .Input("critical_section: resource")
-    .Input("arguments: Targuments")
-    .Output("outputs: output_types")
-    .Attr("f: func")
-    .Attr("Targuments: list(type) >= 0")
-    .Attr("output_types: list(type) >= 0")
-    .Attr("output_shapes: list(shape) >= 0")
+REGISTER_OP("MutexLock")
+    .Input("mutex: resource")
+    .Output("mutex_lock: variant")
+    .SetIsStateful()
     .SetShapeFn([](InferenceContext* c) {
-      std::vector<PartialTensorShape> output_shapes;
-      TF_RETURN_IF_ERROR(c->GetAttr("output_shapes", &output_shapes));
-      for (int i = 0; i < output_shapes.size(); ++i) {
-        ShapeHandle s;
-        TF_RETURN_IF_ERROR(
-            c->MakeShapeFromPartialTensorShape(output_shapes[i], &s));
-        c->set_output(i, s);
-      }
+      c->set_output(0, c->Scalar());
       return Status::OK();
     });
 
+REGISTER_OP("ConsumeMutexLock")
+    .Input("mutex_lock: variant")
+    .SetIsStateful()
+    .SetShapeFn([](InferenceContext* c) { return Status::OK(); });
+
 }  // namespace tensorflow
diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index 28f5289ffc..b3317bd323 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -196,33 +196,66 @@ ops.register_tensor_conversion_function(
     ops.EagerTensor, _convert_to_graph_tensor, priority=-1)
 
 
-class _CapturingContext(object):
-  """Tracks references to Tensors outside this context while it is active."""
+# pylint: disable=invalid-name
+class HelperContext(object):
+  """ControlFlowContext with a customizable AddOp method."""
 
-  def __init__(self):
-    # known_ops are ops which are created while this context is active
-    self.known_ops = set()
+  def __init__(self, add_op_internal):
+    self._add_op_internal = add_op_internal
+    self._values = set()  # control flow code sometimes updates this.
+
+  def _AddOpInternal(self, op):
+    self._add_op_internal(op)
+
+  @property
+  def outer_context(self):
+    return self._outer_context
+
+  def GetWhileContext(self):
+    if self._outer_context:
+      return self._outer_context.GetWhileContext()
+
+  def IsWhileContext(self):
+    return False
+
+  def IsCondContext(self):
+    return False
 
-    # captured_tensors are all tensors referenced to by ops in this context but
-    # not produced in it
-    self.captured_tensors = set()
+  def IsXLAContext(self):
+    return False
 
   def AddOp(self, op):  # pylint: disable=invalid-name
-    if op.type in ["Variable", "VariableV2", "VarHandleOp"]:
-      raise ValueError("tfe.defun cannot capture variables created without "
-                       "using tf.get_variable. Op: %s" % op)
-    self.known_ops.add(op)
-    for i in op.inputs:
-      if i.op not in self.known_ops:
-        self.captured_tensors.add(i)
+    self._AddOpInternal(op)
+    if self._outer_context:
+      self._outer_context.AddOp(op)
+
+  def AddName(self, _):
+    pass
+
+  def AddInnerOp(self, op):
+    self._AddOpInternal(op)
+    if self._outer_context:
+      self._outer_context.AddInnerOp(op)
+
+  def AddValue(self, val):
+    if self._outer_context:
+      return self._outer_context.AddValue(val)
+    else:
+      return val
 
   def __enter__(self):
+    # pylint: disable=protected-access
     self._g = ops.get_default_graph()
-    self._old = self._g._get_control_flow_context()  # pylint: disable=protected-access
-    self._g._set_control_flow_context(self)  # pylint: disable=protected-access
+    self._outer_context = self._g._get_control_flow_context()
+    self._g._set_control_flow_context(self)
+    self._nested_contexts = (
+        self._outer_context._nested_contexts
+        if self._outer_context is not None else None)
+    # pylint: enable=protected-access
 
-  def __exit__(self, _, __, ___):  # pylint: disable=invalid-name
-    self._g._set_control_flow_context(self._old)  # pylint: disable=protected-access
+  def __exit__(self, *_):
+    self._g._set_control_flow_context(self._outer_context)  # pylint: disable=protected-access
+# pylint: enable=invalid-name
 
 
 def _forward_name(n):
@@ -368,7 +401,20 @@ class GraphModeFunction(object):
   def _construct_backprop_function(self):
     """Constructs the backprop function object for this function."""
     with self._graph.as_default(), context.graph_mode():
-      c = _CapturingContext()
+      c_known_ops = set()
+      c_captured_tensors = set()
+
+      def add_op_internal(op):
+        if op.type in ["Variable", "VariableV2", "VarHandleOp"]:
+          raise ValueError("tfe.defun cannot capture variables created without "
+                           "using tf.get_variable. Op: %s" % op)
+        c_known_ops.add(op)
+        for i in op.inputs:
+          if i.op not in c_known_ops:
+            c_captured_tensors.add(i)
+
+      c = HelperContext(add_op_internal)
+
       with c:
         filtered_outputs = [x for x in self._returns if x is not None]
         self._out_grad_placeholders = [
@@ -382,7 +428,7 @@ class GraphModeFunction(object):
         grad for grad in _flatten(in_gradients) if grad is not None)
     output_shapes = tuple(grad.shape for grad in backward_outputs)
 
-    captures = list(sorted(c.captured_tensors, key=lambda x: x.name))
+    captures = list(sorted(c_captured_tensors, key=lambda x: x.name))
     forward_name = _forward_name(self._func_name)
     self._forward_fdef = _EagerDefinedFunction(
         forward_name, self._graph, self._ops, self._input_placeholders,
@@ -395,7 +441,7 @@ class GraphModeFunction(object):
     # means rerunning the function-defining code will always define the same
     # function, which is useful if we serialize this etc.
     function_def_ops = tuple(x
-                             for x in sorted(c.known_ops, key=lambda x: x.name)
+                             for x in sorted(c_known_ops, key=lambda x: x.name)
                              if x not in all_ignored_ops)
     bname = _backward_name(self._func_name)
     self._backward_function = GraphModeFunction(
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index b4bfc0fe47..c78a5aa8c2 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -3477,7 +3477,12 @@ def tuple(tensors, name=None, control_inputs=None):  # pylint: disable=redefined
   if context.in_eager_mode():
     return tensors
   with ops.name_scope(name, "tuple", tensors) as name:
-    gating_ops = [t.op for t in tensors if t is not None]
+    tensors = [t if (isinstance(t, ops.Operation)
+                     or tensor_util.is_tensor(t)
+                     or t is None)
+               else ops.convert_to_tensor(t) for t in tensors]
+    gating_ops = [t if isinstance(t, ops.Operation) else t.op for t in tensors
+                  if t is not None]
     if control_inputs:
       for c in control_inputs:
         if isinstance(c, ops.Tensor):
@@ -3493,8 +3498,11 @@ def tuple(tensors, name=None, control_inputs=None):  # pylint: disable=redefined
     gate = group(*gating_ops)
     tpl = []
     for t in tensors:
-      if t is not None:
+      if tensor_util.is_tensor(t):
         tpl.append(with_dependencies([gate], t))
+      elif isinstance(t, ops.Operation):
+        with ops.control_dependencies([gate]):
+          tpl.append(group(t))
       else:
         tpl.append(None)
     return tpl
-- 
GitLab


From 911400575dc791face3e157ff2de8db420239c49 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 16:34:47 -0800
Subject: [PATCH 218/575] Add integration tests for remote build execution.

PiperOrigin-RevId: 186694734
---
 .../gcs_smoke_test/BUILD.bazel                |  56 ++++
 .../gcs_smoke_test/gcs_smoke.py               | 253 ++++++++++++++++++
 .../integration_tests/gcs_smoke_test/setup.sh |  20 ++
 .../gcs_smoke_test/teardown.sh                |  26 ++
 .../gcs_smoke_test/test_wrapper.sh            |  21 ++
 tensorflow/workspace.bzl                      |  10 +
 6 files changed, 386 insertions(+)
 create mode 100755 tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel
 create mode 100755 tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py
 create mode 100755 tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh
 create mode 100755 tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh
 create mode 100755 tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh

diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel b/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel
new file mode 100755
index 0000000000..439d86c5d2
--- /dev/null
+++ b/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel
@@ -0,0 +1,56 @@
+package(default_visibility = ["//visibility:public"])
+
+load("@rbe_integration_test//skylark:integration_tests.bzl", "sut_component", "integration_test")
+load("@rbe_integration_test//skylark:toolchains.bzl", "toolchain_container_images")
+
+sut_component(
+    name = "gcs",
+    docker_image = toolchain_container_images()["tensorflow"],
+    setups = [{
+        "program": "setup.sh",
+        "args": [
+            "gs://tensorflow-test-bucket/tf-gcs-test",
+        ],
+        "output_properties": ["gcs_path"],
+        "timeout_seconds": 100,
+    }],
+    teardowns = [{
+        "program": "teardown.sh",
+        "args": ["{gcs_path}"],
+        "timeout_seconds": 100,
+    }],
+)
+
+py_binary(
+    name = "gcs_smoke",
+    srcs = ["gcs_smoke.py"],
+)
+
+sh_binary(
+    name = "test_wrapper",
+    srcs = ["test_wrapper.sh"],
+    data = [
+        "gcs_smoke",
+    ],
+)
+
+integration_test(
+    name = "gcs_smoke_test",
+    sut_deps = {
+        ":gcs": "gcs",
+    },
+    tags = [
+        "manual",
+        "notap",
+    ],
+    test = {
+        "program": ":test_wrapper",
+        "args": [
+            "--gcs_bucket_url={gcs#gcs_path}",
+            "--num_examples=20",
+        ],
+        "timeout_seconds": 250,
+    },
+    test_docker_image = toolchain_container_images()["tensorflow"],
+    test_type = "MultiMachine",
+)
diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py b/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py
new file mode 100755
index 0000000000..8438c2156c
--- /dev/null
+++ b/tensorflow/tools/integration_tests/gcs_smoke_test/gcs_smoke.py
@@ -0,0 +1,253 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Smoke test for reading records from GCS to TensorFlow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+import time
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.core.example import example_pb2
+from tensorflow.python.lib.io import file_io
+
+flags = tf.app.flags
+flags.DEFINE_string("gcs_bucket_url", "",
+                    "The URL to the GCS bucket in which the temporary "
+                    "tfrecord file is to be written and read, e.g., "
+                    "gs://my-gcs-bucket/test-directory")
+flags.DEFINE_integer("num_examples", 10, "Number of examples to generate")
+
+FLAGS = flags.FLAGS
+
+
+def create_examples(num_examples, input_mean):
+  """Create ExampleProto's containing data."""
+  ids = np.arange(num_examples).reshape([num_examples, 1])
+  inputs = np.random.randn(num_examples, 1) + input_mean
+  target = inputs - input_mean
+  examples = []
+  for row in range(num_examples):
+    ex = example_pb2.Example()
+    ex.features.feature["id"].bytes_list.value.append(str(ids[row, 0]))
+    ex.features.feature["target"].float_list.value.append(target[row, 0])
+    ex.features.feature["inputs"].float_list.value.append(inputs[row, 0])
+    examples.append(ex)
+  return examples
+
+
+def create_dir_test():
+  """Verifies file_io directory handling methods."""
+
+  # Test directory creation.
+  starttime_ms = int(round(time.time() * 1000))
+  dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime_ms)
+  print("Creating dir %s" % dir_name)
+  file_io.create_dir(dir_name)
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  print("Created directory in: %d milliseconds" % elapsed_ms)
+
+  # Check that the directory exists.
+  dir_exists = file_io.is_directory(dir_name)
+  assert dir_exists
+  print("%s directory exists: %s" % (dir_name, dir_exists))
+
+  # Test recursive directory creation.
+  starttime_ms = int(round(time.time() * 1000))
+  recursive_dir_name = "%s/%s/%s" % (dir_name,
+                                     "nested_dir1",
+                                     "nested_dir2")
+  print("Creating recursive dir %s" % recursive_dir_name)
+  file_io.recursive_create_dir(recursive_dir_name)
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  print("Created directory recursively in: %d milliseconds" % elapsed_ms)
+
+  # Check that the directory exists.
+  recursive_dir_exists = file_io.is_directory(recursive_dir_name)
+  assert recursive_dir_exists
+  print("%s directory exists: %s" % (recursive_dir_name, recursive_dir_exists))
+
+  # Create some contents in the just created directory and list the contents.
+  num_files = 10
+  files_to_create = ["file_%d.txt" % n for n in range(num_files)]
+  for file_num in files_to_create:
+    file_name = "%s/%s" % (dir_name, file_num)
+    print("Creating file %s." % file_name)
+    file_io.write_string_to_file(file_name, "test file.")
+
+  print("Listing directory %s." % dir_name)
+  starttime_ms = int(round(time.time() * 1000))
+  directory_contents = file_io.list_directory(dir_name)
+  print(directory_contents)
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  print("Listed directory %s in %s milliseconds" % (dir_name, elapsed_ms))
+  assert set(directory_contents) == set(files_to_create + ["nested_dir1/"])
+
+  # Test directory renaming.
+  dir_to_rename = "%s/old_dir" % dir_name
+  new_dir_name = "%s/new_dir" % dir_name
+  file_io.create_dir(dir_to_rename)
+  assert file_io.is_directory(dir_to_rename)
+  assert not file_io.is_directory(new_dir_name)
+
+  starttime_ms = int(round(time.time() * 1000))
+  print("Will try renaming directory %s to %s" % (dir_to_rename, new_dir_name))
+  file_io.rename(dir_to_rename, new_dir_name)
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  print("Renamed directory %s to %s in %s milliseconds" % (
+      dir_to_rename, new_dir_name, elapsed_ms))
+  assert not file_io.is_directory(dir_to_rename)
+  assert file_io.is_directory(new_dir_name)
+
+  # Test Delete directory recursively.
+  print("Deleting directory recursively %s." % dir_name)
+  starttime_ms = int(round(time.time() * 1000))
+  file_io.delete_recursively(dir_name)
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  dir_exists = file_io.is_directory(dir_name)
+  assert not dir_exists
+  print("Deleted directory recursively %s in %s milliseconds" % (
+      dir_name, elapsed_ms))
+
+
+def create_object_test():
+  """Verifies file_io's object manipulation methods ."""
+  starttime_ms = int(round(time.time() * 1000))
+  dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime_ms)
+  print("Creating dir %s." % dir_name)
+  file_io.create_dir(dir_name)
+
+  num_files = 5
+  # Create files of 2 different patterns in this directory.
+  files_pattern_1 = ["%s/test_file_%d.txt" % (dir_name, n)
+                     for n in range(num_files)]
+  files_pattern_2 = ["%s/testfile%d.txt" % (dir_name, n)
+                     for n in range(num_files)]
+
+  starttime_ms = int(round(time.time() * 1000))
+  files_to_create = files_pattern_1 + files_pattern_2
+  for file_name in files_to_create:
+    print("Creating file %s." % file_name)
+    file_io.write_string_to_file(file_name, "test file creation.")
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  print("Created %d files in %s milliseconds" %
+        (len(files_to_create), elapsed_ms))
+
+  # Listing files of pattern1.
+  list_files_pattern = "%s/test_file*.txt" % dir_name
+  print("Getting files matching pattern %s." % list_files_pattern)
+  starttime_ms = int(round(time.time() * 1000))
+  files_list = file_io.get_matching_files(list_files_pattern)
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  print("Listed files in %s milliseconds" % elapsed_ms)
+  print(files_list)
+  assert set(files_list) == set(files_pattern_1)
+
+  # Listing files of pattern2.
+  list_files_pattern = "%s/testfile*.txt" % dir_name
+  print("Getting files matching pattern %s." % list_files_pattern)
+  starttime_ms = int(round(time.time() * 1000))
+  files_list = file_io.get_matching_files(list_files_pattern)
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  print("Listed files in %s milliseconds" % elapsed_ms)
+  print(files_list)
+  assert set(files_list) == set(files_pattern_2)
+
+  # Test renaming file.
+  file_to_rename = "%s/oldname.txt" % dir_name
+  file_new_name = "%s/newname.txt" % dir_name
+  file_io.write_string_to_file(file_to_rename, "test file.")
+  assert file_io.file_exists(file_to_rename)
+  assert not file_io.file_exists(file_new_name)
+
+  print("Will try renaming file %s to %s" % (file_to_rename, file_new_name))
+  starttime_ms = int(round(time.time() * 1000))
+  file_io.rename(file_to_rename, file_new_name)
+  elapsed_ms = int(round(time.time() * 1000)) - starttime_ms
+  print("File %s renamed to %s in %s milliseconds" % (
+      file_to_rename, file_new_name, elapsed_ms))
+  assert not file_io.file_exists(file_to_rename)
+  assert file_io.file_exists(file_new_name)
+
+  # Delete directory.
+  print("Deleting directory %s." % dir_name)
+  file_io.delete_recursively(dir_name)
+
+
+def main(argv):
+  del argv  # Unused.
+  # Sanity check on the GCS bucket URL.
+  if not FLAGS.gcs_bucket_url or not FLAGS.gcs_bucket_url.startswith("gs://"):
+    print("ERROR: Invalid GCS bucket URL: \"%s\"" % FLAGS.gcs_bucket_url)
+    sys.exit(1)
+
+  # Verify that writing to the records file in GCS works.
+  print("\n=== Testing writing and reading of GCS record file... ===")
+  example_data = create_examples(FLAGS.num_examples, 5)
+  with tf.python_io.TFRecordWriter(FLAGS.gcs_bucket_url) as hf:
+    for e in example_data:
+      hf.write(e.SerializeToString())
+
+    print("Data written to: %s" % FLAGS.gcs_bucket_url)
+
+  # Verify that reading from the tfrecord file works and that
+  # tf_record_iterator works.
+  record_iter = tf.python_io.tf_record_iterator(FLAGS.gcs_bucket_url)
+  read_count = 0
+  for _ in record_iter:
+    read_count += 1
+  print("Read %d records using tf_record_iterator" % read_count)
+
+  if read_count != FLAGS.num_examples:
+    print("FAIL: The number of records read from tf_record_iterator (%d) "
+          "differs from the expected number (%d)" % (read_count,
+                                                     FLAGS.num_examples))
+    sys.exit(1)
+
+  # Verify that running the read op in a session works.
+  print("\n=== Testing TFRecordReader.read op in a session... ===")
+  with tf.Graph().as_default() as _:
+    filename_queue = tf.train.string_input_producer([FLAGS.gcs_bucket_url],
+                                                    num_epochs=1)
+    reader = tf.TFRecordReader()
+    _, serialized_example = reader.read(filename_queue)
+
+    with tf.Session() as sess:
+      sess.run(tf.global_variables_initializer())
+      sess.run(tf.local_variables_initializer())
+      tf.train.start_queue_runners()
+      index = 0
+      for _ in range(FLAGS.num_examples):
+        print("Read record: %d" % index)
+        sess.run(serialized_example)
+        index += 1
+
+      # Reading one more record should trigger an exception.
+      try:
+        sess.run(serialized_example)
+        print("FAIL: Failed to catch the expected OutOfRangeError while "
+              "reading one more record than is available")
+        sys.exit(1)
+      except tf.errors.OutOfRangeError:
+        print("Successfully caught the expected OutOfRangeError while "
+              "reading one more record than is available")
+
+  create_dir_test()
+  create_object_test()
+
+if __name__ == "__main__":
+  tf.app.run(main)
diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh b/tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh
new file mode 100755
index 0000000000..6553ba5e30
--- /dev/null
+++ b/tensorflow/tools/integration_tests/gcs_smoke_test/setup.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+GCS_NUMBER=$(cat /dev/urandom | tr -dc 'A-F0-9' | fold -w 8 | head -n 1)
+GCS_PATH="$1"/"$GCS_NUMBER".tfrecord
+
+echo "gcs_path=$GCS_PATH" > "$_SETUP_OUTPUT"
+touch "$_SETUP_DONE"
diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh b/tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh
new file mode 100755
index 0000000000..852486d167
--- /dev/null
+++ b/tensorflow/tools/integration_tests/gcs_smoke_test/teardown.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+GSUTIL_BIN="/var/gcloud/google-cloud-sdk/bin/gsutil"
+
+echo "Got teardown argument $1"
+
+if "${GSUTIL_BIN}" rm "$1"
+then
+  echo "Cleaned up new tfrecord file in GCS: '$1'"
+else
+  echo "FAIL: Unable to clean up new tfrecord file in GCS: '$1'"
+  exit 1
+fi
diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh b/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh
new file mode 100755
index 0000000000..ef29dee346
--- /dev/null
+++ b/tensorflow/tools/integration_tests/gcs_smoke_test/test_wrapper.sh
@@ -0,0 +1,21 @@
+# This is a python2 only test.
+#!/bin/bash
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# Test Tensorflow package installation.
+/usr/local/bin/pip install --user tf-nightly
+
+# Test Tensorflow interaction with GCS.
+python tensorflow/tools/integration_test/gcs_smoke_test/gcs_smoke.py "$@"
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 167942cefd..2b370ffbac 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -699,6 +699,16 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       sha256 = "699b55a6916c687f4b7dc092dbbf5f64672cde0dc965f79717735ec4e5416556",
   )
 
+  tf_http_archive(
+      name = "rbe_integration_test",
+      urls = [
+          "http://mirror.bazel.build/github.com/google/rbe-integration-test/archive/78a6194c7dda200b9522cf07707e3bc695804d1e.tar.gz",
+          "https://github.com/google/rbe-integration-test/archive/78a6194c7dda200b9522cf07707e3bc695804d1e.tar.gz",
+      ],
+      sha256 = "66d93b3919a165d486c31f5290d312abe9fda2685242f812c110653c124e1db4",
+      strip_prefix = "rbe-integration-test-78a6194c7dda200b9522cf07707e3bc695804d1e",
+   )
+
   tf_http_archive(
       name = "arm_neon_2_x86_sse",
       sha256 = "c8d90aa4357f8079d427e87a6f4c493da1fa4140aee926c05902d7ec1533d9a5",
-- 
GitLab


From cdb0dd685836d96696ad5c8e5152f1c36c906f10 Mon Sep 17 00:00:00 2001
From: Bixia Zheng <bixia@google.com>
Date: Thu, 22 Feb 2018 16:37:30 -0800
Subject: [PATCH 219/575] Add to disabled tests the date they were last ran and
 failed.

This is to address the comments in cl/186664459.

PiperOrigin-RevId: 186695081
---
 tensorflow/compiler/xla/tests/convolution_test.cc | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc
index 1ea7d84141..e2b5c91653 100644
--- a/tensorflow/compiler/xla/tests/convolution_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_test.cc
@@ -698,7 +698,6 @@ INSTANTIATE_TEST_CASE_P(
 #if (XLA_TEST_BACKEND_GPU || XLA_TEST_BACKEND_CPU)
 class Convolve1D1WindowTestHalf : public Convolve1D1WindowTestBase {};
 
-// TODO(b/72509305): Enable half data type tests for CPU.
 XLA_TEST_P(Convolve1D1WindowTestHalf, Convolve1D1Window) {
   TestImpl<Eigen::half>();
 }
@@ -717,8 +716,8 @@ INSTANTIATE_TEST_CASE_P(
                       Convolve1DTestParam{130, 1, 1, 1, 3},
                       Convolve1DTestParam{64, 1, 1, 1, 1},
                       Convolve1DTestParam{128, 1, 1, 1, 1},
-// TODO(b/72566306): the following five tests fail on CPU
-// backend due to result miscompare.
+// TODO(b/72566306): The following five tests failed on CPU with unreasonable
+// relative errors.  Last ran on 2018-02-22.
 #if XLA_TEST_BACKEND_GPU
                       Convolve1DTestParam{139, 1, 1, 128, 1},
                       Convolve1DTestParam{640, 3, 3, 128, 1},
-- 
GitLab


From db0db838c0086fab0d24ab52a062ee2994e7e644 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Thu, 22 Feb 2018 16:40:19 -0800
Subject: [PATCH 220/575] [XLA] Enable most xla/tests for interpreter.

PiperOrigin-RevId: 186695486
---
 tensorflow/compiler/xla/tests/BUILD | 84 +++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index f955d54c64..1958e5abf6 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -271,6 +271,9 @@ cc_library(
 xla_test(
     name = "bad_rng_shape_validation_test",
     srcs = ["bad_rng_shape_validation_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:test",
@@ -290,6 +293,9 @@ xla_test(
 xla_test(
     name = "check_execution_arity_test",
     srcs = ["check_execution_arity_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
@@ -309,6 +315,9 @@ xla_test(
 xla_test(
     name = "query_inferred_shape_test",
     srcs = ["query_inferred_shape_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:statusor",
@@ -366,6 +375,9 @@ xla_test(
 xla_test(
     name = "axpy_simple_test",
     srcs = ["axpy_simple_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
@@ -430,6 +442,9 @@ xla_test(
 xla_test(
     name = "pred_test",
     srcs = ["pred_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla/client:computation_builder",
@@ -444,6 +459,9 @@ xla_test(
 xla_test(
     name = "select_test",
     srcs = ["select_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
@@ -678,6 +696,9 @@ xla_test(
 xla_test(
     name = "transpose_test",
     srcs = ["transpose_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:reference_util",
@@ -696,6 +717,9 @@ xla_test(
 xla_test(
     name = "constants_test",
     srcs = ["constants_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -880,6 +904,9 @@ xla_test(
     name = "slice_test",
     srcs = ["slice_test.cc"],
     shard_count = 40,
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:reference_util",
@@ -896,6 +923,9 @@ xla_test(
 xla_test(
     name = "multidimensional_slice_test",
     srcs = ["multidimensional_slice_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -958,6 +988,9 @@ xla_test(
 xla_test(
     name = "vector_ops_reduce_test",
     srcs = ["vector_ops_reduce_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -976,6 +1009,9 @@ xla_test(
     name = "reduce_test",
     srcs = ["reduce_test.cc"],
     shard_count = 40,
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
@@ -1069,6 +1105,9 @@ xla_test(
 xla_test(
     name = "copy_test",
     srcs = ["copy_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         ":client_library_test_base",
         "//tensorflow/compiler/xla:array2d",
@@ -1087,6 +1126,9 @@ xla_test(
 xla_test(
     name = "reduce_hlo_test",
     srcs = ["reduce_hlo_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         ":client_library_test_base",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
@@ -1137,6 +1179,9 @@ xla_test(
 xla_test(
     name = "binop_scaling_test",
     srcs = ["binop_scaling_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
@@ -1153,6 +1198,9 @@ xla_test(
 xla_test(
     name = "broadcast_simple_test",
     srcs = ["broadcast_simple_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
@@ -1170,6 +1218,9 @@ xla_test(
 xla_test(
     name = "pad_test",
     srcs = ["pad_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
@@ -1190,6 +1241,9 @@ xla_test(
 xla_test(
     name = "fmax_test",
     srcs = ["fmax_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
@@ -1203,6 +1257,9 @@ xla_test(
 xla_test(
     name = "log_test",
     srcs = ["log_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
@@ -1216,6 +1273,9 @@ xla_test(
 xla_test(
     name = "matrix_ops_simple_test",
     srcs = ["matrix_ops_simple_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:literal_util",
@@ -1258,6 +1318,9 @@ xla_test(
     name = "reshape_test",
     srcs = ["reshape_test.cc"],
     shard_count = 30,
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
@@ -1284,6 +1347,9 @@ xla_test(
 xla_test(
     name = "reverse_test",
     srcs = ["reverse_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array4d",
@@ -1300,6 +1366,9 @@ xla_test(
 xla_test(
     name = "vector_ops_simple_test",
     srcs = ["vector_ops_simple_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array4d",
         "//tensorflow/compiler/xla:shape_util",
@@ -1323,6 +1392,9 @@ xla_test(
 xla_test(
     name = "concat_test",
     srcs = ["concat_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
@@ -1343,6 +1415,9 @@ xla_test(
 xla_test(
     name = "convert_test",
     srcs = ["convert_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:types",
@@ -1400,6 +1475,9 @@ xla_test(
 xla_test(
     name = "floor_ceil_test",
     srcs = ["floor_ceil_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla/client:computation_builder",
         "//tensorflow/compiler/xla/client:local_client",
@@ -1483,6 +1561,9 @@ xla_test(
 xla_test(
     name = "replay_test",
     srcs = ["replay_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:protobuf_util",
@@ -1505,6 +1586,9 @@ xla_test(
 xla_test(
     name = "broadcast_test",
     srcs = ["broadcast_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
-- 
GitLab


From d4bf5b2a6f081e8c6e0dbbdd2da1b55e25695232 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 16:40:41 -0800
Subject: [PATCH 221/575] Internal change.

PiperOrigin-RevId: 186695534
---
 tensorflow/contrib/distributions/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 4f413e5512..35dd2ee439 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -984,7 +984,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "reshape_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/bijectors/reshape_test.py"],
     additional_deps = [
         ":bijectors_py",
-- 
GitLab


From 29859199de4b15b94e4e94d8fe632aeeb34c4991 Mon Sep 17 00:00:00 2001
From: Igor Ganichev <iga@google.com>
Date: Thu, 22 Feb 2018 16:42:55 -0800
Subject: [PATCH 222/575] Support degenerate strided slicing under XLA.

For example, when start, end, and stride are all positive, but `start` is
greater than `end`, tf2xla used to raise an error instead of returning a
tensor with that dimension size being 0. Latter is the regular
tensorflow and python behavior. This change makes XLA behave the same
way.

PiperOrigin-RevId: 186695842
---
 tensorflow/compiler/tests/slice_ops_test.py   | 29 +++++++++++++++++++
 .../tf2xla/kernels/strided_slice_op.cc        |  5 ++--
 2 files changed, 32 insertions(+), 2 deletions(-)

diff --git a/tensorflow/compiler/tests/slice_ops_test.py b/tensorflow/compiler/tests/slice_ops_test.py
index a7cbfb0400..305ca0c6b7 100644
--- a/tensorflow/compiler/tests/slice_ops_test.py
+++ b/tensorflow/compiler/tests/slice_ops_test.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 from tensorflow.compiler.tests.xla_test import XLATestCase
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import googletest
 
@@ -137,6 +138,34 @@ class StridedSliceTest(XLATestCase):
 
         self.assertAllEqual([6, 4], result)
 
+  def test2DDegenerate(self):
+    for dtype in self.numeric_types:
+      with self.test_session():
+        i = array_ops.placeholder(dtype, shape=[2, 3])
+        with self.test_scope():
+          o = array_ops.strided_slice(i, [-1, 0], [0, 3])
+        params = {
+            i: [[0, 1, 2],
+                [3, 4, 5]]
+        }
+        result = o.eval(feed_dict=params)
+
+        self.assertEqual(tensor_shape.TensorShape((0, 3)), result.shape)
+
+  def test2DDegenerateNegativeStride(self):
+    for dtype in self.numeric_types:
+      with self.test_session():
+        i = array_ops.placeholder(dtype, shape=[2, 3])
+        with self.test_scope():
+          o = array_ops.strided_slice(i, [0, 0], [-1, 3], [-1, 1])
+        params = {
+            i: [[0, 1, 2],
+                [3, 4, 5]]
+        }
+        result = o.eval(feed_dict=params)
+
+        self.assertEqual(tensor_shape.TensorShape((0, 3)), result.shape)
+
   def test3D(self):
     for dtype in self.numeric_types:
       with self.test_session():
diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
index 91c169428c..6204aa4e27 100644
--- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc
@@ -77,13 +77,14 @@ class StridedSliceOp : public XlaOpKernel {
     for (int i = 0; i < begin.size(); ++i) {
       if (strides[i] > 0) {
         slice_begin.push_back(begin[i]);
-        slice_end.push_back(end[i]);
+        slice_end.push_back(std::max(end[i], begin[i]));
         slice_strides.push_back(strides[i]);
       } else {
         // Negative stride: swap begin and end, add 1 because the interval
         // is semi-open, and mark the dimension to be reversed.
         slice_begin.push_back(input_shape.dim_size(i) - begin[i] - 1);
-        slice_end.push_back(input_shape.dim_size(i) - end[i] - 1);
+        slice_end.push_back(std::max(input_shape.dim_size(i) - end[i] - 1,
+                                     input_shape.dim_size(i) - begin[i] - 1));
         slice_strides.push_back(-strides[i]);
         dimensions_to_reverse.push_back(i);
       }
-- 
GitLab


From 1ae8533e1df634e6d1201c6aeb9646379cc53a65 Mon Sep 17 00:00:00 2001
From: Andrew Selle <aselle@google.com>
Date: Thu, 22 Feb 2018 16:49:48 -0800
Subject: [PATCH 223/575] Clarify ownership story of TfLiteIntArray*
 nodes_to_replace

PiperOrigin-RevId: 186696787
---
 tensorflow/contrib/lite/context.h | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h
index c604cbc39e..ed7f4515fa 100644
--- a/tensorflow/contrib/lite/context.h
+++ b/tensorflow/contrib/lite/context.h
@@ -283,7 +283,8 @@ typedef struct TfLiteContext {
                                          TfLiteNode** node,
                                          TfLiteRegistration** registration);
 
-  // Replace ops with delegate.
+  // Replace ops with one or more stub delegate operations. This function
+  // does not take ownership of `nodes_to_replace`.
   TfLiteStatus (*ReplaceSubgraphsWithDelegateKernels)(
       struct TfLiteContext*, TfLiteRegistration registration,
       const TfLiteIntArray* nodes_to_replace);
-- 
GitLab


From f68f5e465461e0e8331c24deff97e93a8079f365 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 16:50:27 -0800
Subject: [PATCH 224/575] Go: Update generated wrapper functions for TensorFlow
 ops. PiperOrigin-RevId: 186696903

---
 tensorflow/go/op/wrappers.go | 631 ++++++++++++++++-------------------
 1 file changed, 295 insertions(+), 336 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 04c20511ba..d9e684a661 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -329,73 +329,59 @@ func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Ou
 	return op.Output(0)
 }
 
-// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
-//
-// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
-// becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
-// are placed in `outputs[i]` in lexicographic order of `js`, and the first
-// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
-// In detail,
-//
-// ```python
-//     outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
-//
-//     outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
-// ```
-//
-// `data.shape` must start with `partitions.shape`.
-//
-// For example:
+// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient.
+type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr)
+
+// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value.
 //
-// ```python
-//     # Scalar partitions.
-//     partitions = 1
-//     num_partitions = 2
-//     data = [10, 20]
-//     outputs[0] = []  # Empty with shape [0, 2]
-//     outputs[1] = [[10, 20]]
+// value: The bitwidth of the quantization; between 2 and 8, inclusive.
+// If not specified, defaults to 8
+func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr {
+	return func(m optionalAttr) {
+		m["num_bits"] = value
+	}
+}
+
+// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value.
 //
-//     # Vector partitions.
-//     partitions = [0, 0, 1, 1, 0]
-//     num_partitions = 2
-//     data = [10, 20, 30, 40, 50]
-//     outputs[0] = [10, 20, 50]
-//     outputs[1] = [30, 40]
-// ```
+// value: Whether to quantize into 2^num_bits - 1 distinct values.
+// If not specified, defaults to false
+func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr {
+	return func(m optionalAttr) {
+		m["narrow_range"] = value
+	}
+}
+
+// Compute gradients for a FakeQuantWithMinMaxVars operation.
 //
-// See `dynamic_stitch` for an example on how to merge partitions back.
+// Arguments:
+//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
+//	inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation.
+// min, max: Quantization interval, scalar floats.
 //
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicPartition.png" alt>
-// </div>
 //
-// Arguments:
 //
-//	partitions: Any shape.  Indices in the range `[0, num_partitions)`.
-//	num_partitions: The number of partitions to output.
-func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) {
+// Returns Backpropagated gradients w.r.t. inputs:
+// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter:
+// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter:
+// `sum(gradients * (inputs > max))`.
+func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"num_partitions": num_partitions}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "DynamicPartition",
+		Type: "FakeQuantWithMinMaxVarsGradient",
 		Input: []tf.Input{
-			data, partitions,
+			gradients, inputs, min, max,
 		},
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	if scope.Err() != nil {
-		return
-	}
-	var idx int
-	var err error
-	if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
-		scope.UpdateErr("DynamicPartition", err)
-		return
-	}
-	return outputs
+	return op.Output(0), op.Output(1), op.Output(2)
 }
 
 // MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2.
@@ -1695,61 +1681,6 @@ func Igammac(scope *Scope, a tf.Output, x tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
-// FakeQuantWithMinMaxVarsGradientAttr is an optional argument to FakeQuantWithMinMaxVarsGradient.
-type FakeQuantWithMinMaxVarsGradientAttr func(optionalAttr)
-
-// FakeQuantWithMinMaxVarsGradientNumBits sets the optional num_bits attribute to value.
-//
-// value: The bitwidth of the quantization; between 2 and 8, inclusive.
-// If not specified, defaults to 8
-func FakeQuantWithMinMaxVarsGradientNumBits(value int64) FakeQuantWithMinMaxVarsGradientAttr {
-	return func(m optionalAttr) {
-		m["num_bits"] = value
-	}
-}
-
-// FakeQuantWithMinMaxVarsGradientNarrowRange sets the optional narrow_range attribute to value.
-//
-// value: Whether to quantize into 2^num_bits - 1 distinct values.
-// If not specified, defaults to false
-func FakeQuantWithMinMaxVarsGradientNarrowRange(value bool) FakeQuantWithMinMaxVarsGradientAttr {
-	return func(m optionalAttr) {
-		m["narrow_range"] = value
-	}
-}
-
-// Compute gradients for a FakeQuantWithMinMaxVars operation.
-//
-// Arguments:
-//	gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
-//	inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation.
-// min, max: Quantization interval, scalar floats.
-//
-//
-//
-// Returns Backpropagated gradients w.r.t. inputs:
-// `gradients * (inputs >= min && inputs <= max)`.Backpropagated gradients w.r.t. min parameter:
-// `sum(gradients * (inputs < min))`.Backpropagated gradients w.r.t. max parameter:
-// `sum(gradients * (inputs > max))`.
-func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsGradientAttr) (backprops_wrt_input tf.Output, backprop_wrt_min tf.Output, backprop_wrt_max tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "FakeQuantWithMinMaxVarsGradient",
-		Input: []tf.Input{
-			gradients, inputs, min, max,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1), op.Output(2)
-}
-
 // LogUniformCandidateSamplerAttr is an optional argument to LogUniformCandidateSampler.
 type LogUniformCandidateSamplerAttr func(optionalAttr)
 
@@ -4487,34 +4418,6 @@ func MaxPoolGradGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output
 	return op.Output(0)
 }
 
-// Fast Fourier transform.
-//
-// Computes the 1-dimensional discrete Fourier transform over the inner-most
-// dimension of `input`.
-//
-// Arguments:
-//	input: A complex64 tensor.
-//
-// Returns A complex64 tensor of the same shape as `input`. The inner-most
-//   dimension of `input` is replaced with its 1D Fourier transform.
-//
-// @compatibility(numpy)
-// Equivalent to np.fft.fft
-// @end_compatibility
-func FFT(scope *Scope, input tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "FFT",
-		Input: []tf.Input{
-			input,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // MaxPoolAttr is an optional argument to MaxPool.
 type MaxPoolAttr func(optionalAttr)
 
@@ -4628,47 +4531,6 @@ func MaxPoolGradWithArgmax(scope *Scope, input tf.Output, grad tf.Output, argmax
 	return op.Output(0)
 }
 
-// CriticalSectionOpAttr is an optional argument to CriticalSectionOp.
-type CriticalSectionOpAttr func(optionalAttr)
-
-// CriticalSectionOpContainer sets the optional container attribute to value.
-//
-// value: the container this critical section is placed in.
-// If not specified, defaults to ""
-func CriticalSectionOpContainer(value string) CriticalSectionOpAttr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// CriticalSectionOpSharedName sets the optional shared_name attribute to value.
-//
-// value: the name by which this critical section is referred to.
-// If not specified, defaults to ""
-func CriticalSectionOpSharedName(value string) CriticalSectionOpAttr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// Creates a handle to a CriticalSection resource.
-func CriticalSectionOp(scope *Scope, optional ...CriticalSectionOpAttr) (resource tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "CriticalSectionOp",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // FakeQuantWithMinMaxArgsGradientAttr is an optional argument to FakeQuantWithMinMaxArgsGradient.
 type FakeQuantWithMinMaxArgsGradientAttr func(optionalAttr)
 
@@ -5036,6 +4898,78 @@ func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, stri
 	return op.Output(0)
 }
 
+// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2.
+type MaxPoolGradV2Attr func(optionalAttr)
+
+// MaxPoolGradV2DataFormat sets the optional data_format attribute to value.
+//
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+//     [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+//     [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr {
+	return func(m optionalAttr) {
+		m["data_format"] = value
+	}
+}
+
+// Computes gradients of the maxpooling function.
+//
+// Arguments:
+//	orig_input: The original input tensor.
+//	orig_output: The original output tensor.
+//	grad: 4-D.  Gradients w.r.t. the output of `max_pool`.
+//	ksize: The size of the window for each dimension of the input tensor.
+//	strides: The stride of the sliding window for each dimension of the
+// input tensor.
+//	padding: The type of padding algorithm to use.
+//
+// Returns Gradients w.r.t. the input to `max_pool`.
+func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"padding": padding}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "MaxPoolGradV2",
+		Input: []tf.Input{
+			orig_input, orig_output, grad, ksize, strides,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Restore a reader to a previously saved state.
+//
+// Not all Readers support being restored, so this can produce an
+// Unimplemented error.
+//
+// Arguments:
+//	reader_handle: Handle to a Reader.
+//	state: Result of a ReaderSerializeState of a Reader with type
+// matching reader_handle.
+//
+// Returns the created operation.
+func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ReaderRestoreStateV2",
+		Input: []tf.Input{
+			reader_handle, state,
+		},
+	}
+	return scope.AddOperation(opspec)
+}
+
 // TensorArrayGatherV3Attr is an optional argument to TensorArrayGatherV3.
 type TensorArrayGatherV3Attr func(optionalAttr)
 
@@ -5792,138 +5726,66 @@ func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segm
 // Hence, the `SparseTensor` result has exactly the same non-zero indices and
 // shape.
 //
-// Arguments:
-//	sp_indices: 2-D.  `NNZ x R` matrix with the indices of non-empty values in a
-// SparseTensor, in canonical ordering.
-//	sp_values: 1-D.  `NNZ` non-empty values corresponding to `sp_indices`.
-//	sp_shape: 1-D.  Shape of the input SparseTensor.
-//
-// Returns 1-D.  The `NNZ` values for the result `SparseTensor`.
-func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SparseSoftmax",
-		Input: []tf.Input{
-			sp_indices, sp_values, sp_shape,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// RandomPoissonAttr is an optional argument to RandomPoisson.
-type RandomPoissonAttr func(optionalAttr)
-
-// RandomPoissonSeed sets the optional seed attribute to value.
-// If not specified, defaults to 0
-func RandomPoissonSeed(value int64) RandomPoissonAttr {
-	return func(m optionalAttr) {
-		m["seed"] = value
-	}
-}
-
-// RandomPoissonSeed2 sets the optional seed2 attribute to value.
-// If not specified, defaults to 0
-func RandomPoissonSeed2(value int64) RandomPoissonAttr {
-	return func(m optionalAttr) {
-		m["seed2"] = value
-	}
-}
-
-// Use RandomPoissonV2 instead.
-//
-// DEPRECATED at GraphDef version 25: Replaced by RandomPoissonV2
-func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "RandomPoisson",
-		Input: []tf.Input{
-			shape, rate,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// MaxPoolGradV2Attr is an optional argument to MaxPoolGradV2.
-type MaxPoolGradV2Attr func(optionalAttr)
-
-// MaxPoolGradV2DataFormat sets the optional data_format attribute to value.
-//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-//     [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-//     [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func MaxPoolGradV2DataFormat(value string) MaxPoolGradV2Attr {
-	return func(m optionalAttr) {
-		m["data_format"] = value
-	}
-}
-
-// Computes gradients of the maxpooling function.
-//
-// Arguments:
-//	orig_input: The original input tensor.
-//	orig_output: The original output tensor.
-//	grad: 4-D.  Gradients w.r.t. the output of `max_pool`.
-//	ksize: The size of the window for each dimension of the input tensor.
-//	strides: The stride of the sliding window for each dimension of the
-// input tensor.
-//	padding: The type of padding algorithm to use.
+// Arguments:
+//	sp_indices: 2-D.  `NNZ x R` matrix with the indices of non-empty values in a
+// SparseTensor, in canonical ordering.
+//	sp_values: 1-D.  `NNZ` non-empty values corresponding to `sp_indices`.
+//	sp_shape: 1-D.  Shape of the input SparseTensor.
 //
-// Returns Gradients w.r.t. the input to `max_pool`.
-func MaxPoolGradV2(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize tf.Output, strides tf.Output, padding string, optional ...MaxPoolGradV2Attr) (output tf.Output) {
+// Returns 1-D.  The `NNZ` values for the result `SparseTensor`.
+func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"padding": padding}
-	for _, a := range optional {
-		a(attrs)
-	}
 	opspec := tf.OpSpec{
-		Type: "MaxPoolGradV2",
+		Type: "SparseSoftmax",
 		Input: []tf.Input{
-			orig_input, orig_output, grad, ksize, strides,
+			sp_indices, sp_values, sp_shape,
 		},
-		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Restore a reader to a previously saved state.
-//
-// Not all Readers support being restored, so this can produce an
-// Unimplemented error.
-//
-// Arguments:
-//	reader_handle: Handle to a Reader.
-//	state: Result of a ReaderSerializeState of a Reader with type
-// matching reader_handle.
+// RandomPoissonAttr is an optional argument to RandomPoisson.
+type RandomPoissonAttr func(optionalAttr)
+
+// RandomPoissonSeed sets the optional seed attribute to value.
+// If not specified, defaults to 0
+func RandomPoissonSeed(value int64) RandomPoissonAttr {
+	return func(m optionalAttr) {
+		m["seed"] = value
+	}
+}
+
+// RandomPoissonSeed2 sets the optional seed2 attribute to value.
+// If not specified, defaults to 0
+func RandomPoissonSeed2(value int64) RandomPoissonAttr {
+	return func(m optionalAttr) {
+		m["seed2"] = value
+	}
+}
+
+// Use RandomPoissonV2 instead.
 //
-// Returns the created operation.
-func ReaderRestoreStateV2(scope *Scope, reader_handle tf.Output, state tf.Output) (o *tf.Operation) {
+// DEPRECATED at GraphDef version 25: Replaced by RandomPoissonV2
+func RandomPoisson(scope *Scope, shape tf.Output, rate tf.Output, optional ...RandomPoissonAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
 	opspec := tf.OpSpec{
-		Type: "ReaderRestoreStateV2",
+		Type: "RandomPoisson",
 		Input: []tf.Input{
-			reader_handle, state,
+			shape, rate,
 		},
+		Attrs: attrs,
 	}
-	return scope.AddOperation(opspec)
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
 // ResourceSparseApplyFtrlV2Attr is an optional argument to ResourceSparseApplyFtrlV2.
@@ -8843,6 +8705,75 @@ func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Outp
 	return op.Output(0)
 }
 
+// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
+//
+// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
+// becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
+// are placed in `outputs[i]` in lexicographic order of `js`, and the first
+// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
+// In detail,
+//
+// ```python
+//     outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
+//
+//     outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
+// ```
+//
+// `data.shape` must start with `partitions.shape`.
+//
+// For example:
+//
+// ```python
+//     # Scalar partitions.
+//     partitions = 1
+//     num_partitions = 2
+//     data = [10, 20]
+//     outputs[0] = []  # Empty with shape [0, 2]
+//     outputs[1] = [[10, 20]]
+//
+//     # Vector partitions.
+//     partitions = [0, 0, 1, 1, 0]
+//     num_partitions = 2
+//     data = [10, 20, 30, 40, 50]
+//     outputs[0] = [10, 20, 50]
+//     outputs[1] = [30, 40]
+// ```
+//
+// See `dynamic_stitch` for an example on how to merge partitions back.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicPartition.png" alt>
+// </div>
+//
+// Arguments:
+//
+//	partitions: Any shape.  Indices in the range `[0, num_partitions)`.
+//	num_partitions: The number of partitions to output.
+func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"num_partitions": num_partitions}
+	opspec := tf.OpSpec{
+		Type: "DynamicPartition",
+		Input: []tf.Input{
+			data, partitions,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	if scope.Err() != nil {
+		return
+	}
+	var idx int
+	var err error
+	if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
+		scope.UpdateErr("DynamicPartition", err)
+		return
+	}
+	return outputs
+}
+
 // ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad.
 type ResourceApplyAdagradAttr func(optionalAttr)
 
@@ -9332,6 +9263,34 @@ func SparseSoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, label
 	return op.Output(0), op.Output(1)
 }
 
+// Fast Fourier transform.
+//
+// Computes the 1-dimensional discrete Fourier transform over the inner-most
+// dimension of `input`.
+//
+// Arguments:
+//	input: A complex64 tensor.
+//
+// Returns A complex64 tensor of the same shape as `input`. The inner-most
+//   dimension of `input` is replaced with its 1D Fourier transform.
+//
+// @compatibility(numpy)
+// Equivalent to np.fft.fft
+// @end_compatibility
+func FFT(scope *Scope, input tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "FFT",
+		Input: []tf.Input{
+			input,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // ResourceSparseApplyAdagradDAAttr is an optional argument to ResourceSparseApplyAdagradDA.
 type ResourceSparseApplyAdagradDAAttr func(optionalAttr)
 
@@ -11468,6 +11427,54 @@ func ResourceApplyMomentum(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.
 	return scope.AddOperation(opspec)
 }
 
+// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad.
+type MaxPoolGradGradAttr func(optionalAttr)
+
+// MaxPoolGradGradDataFormat sets the optional data_format attribute to value.
+//
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+//     [batch, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+//     [batch, in_channels, in_height, in_width].
+// If not specified, defaults to "NHWC"
+func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr {
+	return func(m optionalAttr) {
+		m["data_format"] = value
+	}
+}
+
+// Computes second-order gradients of the maxpooling function.
+//
+// Arguments:
+//	orig_input: The original input tensor.
+//	orig_output: The original output tensor.
+//	grad: 4-D.  Gradients of gradients w.r.t. the input of `max_pool`.
+//	ksize: The size of the window for each dimension of the input tensor.
+//	strides: The stride of the sliding window for each dimension of the
+// input tensor.
+//	padding: The type of padding algorithm to use.
+//
+// Returns Gradients of gradients w.r.t. the input to `max_pool`.
+func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "MaxPoolGradGrad",
+		Input: []tf.Input{
+			orig_input, orig_output, grad,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Returns the truth value of (x >= y) element-wise.
 //
 // *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting
@@ -15025,54 +15032,6 @@ func TensorArrayCloseV3(scope *Scope, handle tf.Output) (o *tf.Operation) {
 	return scope.AddOperation(opspec)
 }
 
-// MaxPoolGradGradAttr is an optional argument to MaxPoolGradGrad.
-type MaxPoolGradGradAttr func(optionalAttr)
-
-// MaxPoolGradGradDataFormat sets the optional data_format attribute to value.
-//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-//     [batch, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-//     [batch, in_channels, in_height, in_width].
-// If not specified, defaults to "NHWC"
-func MaxPoolGradGradDataFormat(value string) MaxPoolGradGradAttr {
-	return func(m optionalAttr) {
-		m["data_format"] = value
-	}
-}
-
-// Computes second-order gradients of the maxpooling function.
-//
-// Arguments:
-//	orig_input: The original input tensor.
-//	orig_output: The original output tensor.
-//	grad: 4-D.  Gradients of gradients w.r.t. the input of `max_pool`.
-//	ksize: The size of the window for each dimension of the input tensor.
-//	strides: The stride of the sliding window for each dimension of the
-// input tensor.
-//	padding: The type of padding algorithm to use.
-//
-// Returns Gradients of gradients w.r.t. the input to `max_pool`.
-func MaxPoolGradGrad(scope *Scope, orig_input tf.Output, orig_output tf.Output, grad tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPoolGradGradAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "MaxPoolGradGrad",
-		Input: []tf.Input{
-			orig_input, orig_output, grad,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // RandomUniformIntAttr is an optional argument to RandomUniformInt.
 type RandomUniformIntAttr func(optionalAttr)
 
-- 
GitLab


From 33b6cc7b4a049ae87bf104e7afb571ae42207d15 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 16:53:40 -0800
Subject: [PATCH 225/575] LSTM support: Quantized types, quantization params
 for 16-bit unfused LSTMs.

PiperOrigin-RevId: 186697357
---
 tensorflow/contrib/lite/toco/dump_graphviz.cc |   4 +-
 .../toco/graph_transformations/quantize.cc    | 128 ++++++++++++++----
 .../contrib/lite/toco/model_flags.proto       |   2 +
 tensorflow/contrib/lite/toco/toco_tooling.cc  |   3 +-
 tensorflow/contrib/lite/toco/tooling_util.cc  |  16 ++-
 tensorflow/contrib/lite/toco/types.proto      |   3 +
 6 files changed, 123 insertions(+), 33 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/dump_graphviz.cc b/tensorflow/contrib/lite/toco/dump_graphviz.cc
index 2184e8f607..c8352741b4 100644
--- a/tensorflow/contrib/lite/toco/dump_graphviz.cc
+++ b/tensorflow/contrib/lite/toco/dump_graphviz.cc
@@ -193,12 +193,12 @@ NodeProperties GetPropertiesForArray(const Model& model,
   }
 
   if (array.minmax) {
-    AppendF(&node_properties.label, "\\nMinMax: [%.3g, %.3g]",
+    AppendF(&node_properties.label, "\\nMinMax: [%.7g, %.7g]",
             array.minmax->min, array.minmax->max);
   }
 
   if (array.quantization_params) {
-    AppendF(&node_properties.label, "\\nQuantization: %.3g * (x - %d)",
+    AppendF(&node_properties.label, "\\nQuantization: %7g * (x - %d)",
             array.quantization_params->scale,
             array.quantization_params->zero_point);
   }
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
index d7f804ee43..77316751bc 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc
@@ -100,7 +100,13 @@ void QuantizeArray(GraphTransformation* transformation, Model* model,
 void QuantizeArray(GraphTransformation* transformation, Model* model,
                    const string& name, ArrayDataType quantized_data_type,
                    const QuantizationParams& quantization_params) {
-  switch (quantized_data_type) {
+  ArrayDataType adjusted_data_type = quantized_data_type;
+  auto& array = model->GetArray(name);
+  if (array.final_data_type == ArrayDataType::kInt16) {
+    adjusted_data_type = array.final_data_type;
+  }
+
+  switch (adjusted_data_type) {
     case ArrayDataType::kUint8:
       return QuantizeArray<ArrayDataType::kUint8>(transformation, model, name,
                                                   quantization_params);
@@ -166,6 +172,60 @@ const MinMax& GetOrComputeMinMax(Model* model, const string& array_name) {
                 "proceed with quantization.";
 }
 
+struct QuantizationPoints {
+  int64 min_value;
+  int64 max_value;
+  int64 central_value;
+};
+
+template <ArrayDataType A>
+QuantizationPoints GetQuantizationPoints() {
+  QuantizationPoints qp;
+  using Integer = DataType<A>;
+  qp.min_value = std::numeric_limits<Integer>::min();
+  qp.max_value = std::numeric_limits<Integer>::max();
+  // eg [-128,127]...
+  qp.central_value = (qp.min_value / 2 +        // -128 -> -64.
+                      (qp.max_value - 1) / 2 +  // 127 -> 63.
+                      1);
+  return qp;
+}
+
+QuantizationPoints GetQuantizationPoints(ArrayDataType data_type) {
+  switch (data_type) {
+    case ArrayDataType::kUint8:
+      return GetQuantizationPoints<ArrayDataType::kUint8>();
+    case ArrayDataType::kInt16:
+      return GetQuantizationPoints<ArrayDataType::kInt16>();
+    case ArrayDataType::kInt32:
+      return GetQuantizationPoints<ArrayDataType::kInt32>();
+    default:
+      LOG(FATAL) << "Unhandled case.";
+  }
+}
+
+ArrayDataType GetQuantizedDataType(const Array& array,
+                                   ArrayDataType default_type) {
+  switch (array.final_data_type) {
+    case ArrayDataType::kInt8:
+    case ArrayDataType::kUint8:
+    case ArrayDataType::kInt16:
+    case ArrayDataType::kUint16:
+    case ArrayDataType::kInt32:
+    case ArrayDataType::kUint32:
+    case ArrayDataType::kInt64:
+    case ArrayDataType::kUint64:
+      return array.final_data_type;
+    case ArrayDataType::kFloat:
+    case ArrayDataType::kNone:
+      return default_type;
+    default:
+      LOG(FATAL) << "Unhandled final quantization type "
+                 << static_cast<int>(array.final_data_type);
+      return default_type;
+  }
+}
+
 bool ChooseQuantizationForOperatorInput(
     GraphTransformation* transformation, Model* model, const Operator& op,
     std::size_t input_index, ArrayDataType* quantized_data_type,
@@ -212,7 +272,7 @@ bool ChooseQuantizationForOperatorInput(
     const auto input_weights_scale = input_weights.quantization_params->scale;
     quantization_params->scale = input_activations_scale * input_weights_scale;
     quantization_params->zero_point = 0;
-    *quantized_data_type = ArrayDataType::kInt32;
+    *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kInt32);
     transformation->AddMessageF(
         "Input array %s is a bias vector. Choosing quantization params "
         "accordingly.",
@@ -233,14 +293,14 @@ bool ChooseQuantizationForOperatorInput(
 
   GetQuantizationParamsFromMinMax<ArrayDataType::kUint8>(model->flags, minmax,
                                                          quantization_params);
+  *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kUint8);
   transformation->AddMessageF(
       "For input array %s with min=%g"
       ", max=%g"
-      ", chose to quantize as uint8 with zero_point=%d"
+      ", chose to quantize as %s with zero_point=%d"
       ", scale=%g",
-      input, minmax.min, minmax.max, quantization_params->zero_point,
-      quantization_params->scale);
-  *quantized_data_type = ArrayDataType::kUint8;
+      input, minmax.min, minmax.max, ArrayDataTypeName(*quantized_data_type),
+      quantization_params->zero_point, quantization_params->scale);
   return true;
 }
 
@@ -262,16 +322,18 @@ bool IsExactlyRepresentable(double real_value, ArrayDataType data_type,
   return true;
 }
 
+// Quantized data type is preset to the type of the input before this function.
 bool ChooseHardcodedQuantizationForOperatorOutput(
-    const Operator& op, ArrayDataType* quantized_data_type,
+    const Operator& op, const Array& array, ArrayDataType* quantized_data_type,
     QuantizationParams* quantization_params) {
   if (op.type == OperatorType::kL2Normalization) {
     // L2Normalization has range: [-1, 1].
     // 0 should be exactly representable, as values will typically be centered
     // around 0, with many values near 0.
-    *quantized_data_type = ArrayDataType::kUint8;
-    quantization_params->zero_point = 128;
-    quantization_params->scale = 1. / 128.;
+    *quantized_data_type = GetQuantizedDataType(array, *quantized_data_type);
+    const QuantizationPoints qp = GetQuantizationPoints(*quantized_data_type);
+    quantization_params->zero_point = qp.central_value;
+    quantization_params->scale = 1. / (qp.central_value - qp.min_value);
     CHECK(
         IsExactlyRepresentable(0., *quantized_data_type, *quantization_params));
     return true;
@@ -284,18 +346,20 @@ bool ChooseHardcodedQuantizationForOperatorOutput(
     // will typically exploit the symmetry logistic(-x) = 1 - logistic(x), and
     // the glueing of the two halves of the graph will only be seamless if we
     // are accurately representing logistic(0) == 0.5.
-    *quantized_data_type = ArrayDataType::kUint8;
+    *quantized_data_type = GetQuantizedDataType(array, *quantized_data_type);
+    const QuantizationPoints qp = GetQuantizationPoints(*quantized_data_type);
     quantization_params->zero_point = 0;
-    quantization_params->scale = 1. / 256.;
+    quantization_params->scale = 1. / (qp.max_value + 1);
     CHECK(IsExactlyRepresentable(0.5, *quantized_data_type,
                                  *quantization_params));
     return true;
   }
   if (op.type == OperatorType::kTanh) {
     // Tanh has the range: [-1, 1].
-    *quantized_data_type = ArrayDataType::kUint8;
-    quantization_params->zero_point = 128;
-    quantization_params->scale = 1. / 128.;
+    *quantized_data_type = GetQuantizedDataType(array, *quantized_data_type);
+    const QuantizationPoints qp = GetQuantizationPoints(*quantized_data_type);
+    quantization_params->zero_point = qp.central_value;
+    quantization_params->scale = 1. / (qp.central_value - qp.min_value);
     // 0 should be exactly representable, as values will typically be centered
     // around 0, with many values near 0.
     CHECK(
@@ -314,8 +378,9 @@ bool ChooseQuantizationForOperatorOutput(
   if (array.data_type != ArrayDataType::kFloat) {
     return false;
   }
-  if (ChooseHardcodedQuantizationForOperatorOutput(op, quantized_data_type,
-                                                   quantization_params)) {
+  *quantized_data_type = model->GetArray(op.inputs[0]).data_type;
+  if (ChooseHardcodedQuantizationForOperatorOutput(
+          op, array, quantized_data_type, quantization_params)) {
     transformation->AddMessageF(
         "Output array %s is produced by a %s operator. Choosing fixed "
         "quantization params accordingly.",
@@ -323,12 +388,21 @@ bool ChooseQuantizationForOperatorOutput(
     return true;
   }
   if ((op.type == OperatorType::kDepthToSpace) ||
-      (op.type == OperatorType::kSpaceToDepth)) {
-    // DepthToSpace and SpaceToDepth should preserve the quantization parameters
-    // of the input array, as these are simple reshape operations.
-    const auto& input_quantization_params =
-        model->GetArray(op.inputs[0]).GetQuantizationParams();
-    *quantized_data_type = ArrayDataType::kUint8;
+      (op.type == OperatorType::kSpaceToDepth) ||
+      (op.type == OperatorType::kTensorFlowReshape) ||
+      (op.type == OperatorType::kTensorFlowSplit) ||
+      (op.type == OperatorType::kConcatenation)) {
+    int data_input_index = 0;
+    if (op.type == OperatorType::kTensorFlowSplit) {
+      data_input_index = 1;
+    }
+    // Copying and rearrangement ops should preserve the quantization parameters
+    // of the input array.
+    const auto& input_array = model->GetArray(op.inputs[data_input_index]);
+    const auto& input_quantization_params = input_array.GetQuantizationParams();
+    *quantized_data_type =
+        GetQuantizedDataType(input_array, ArrayDataType::kUint8);
+    *quantized_data_type = GetQuantizedDataType(array, *quantized_data_type);
     quantization_params->zero_point = input_quantization_params.zero_point;
     quantization_params->scale = input_quantization_params.scale;
 
@@ -350,13 +424,13 @@ bool ChooseQuantizationForOperatorOutput(
   }
   GetQuantizationParamsFromMinMax<ArrayDataType::kUint8>(model->flags, minmax,
                                                          quantization_params);
-  *quantized_data_type = ArrayDataType::kUint8;
+  *quantized_data_type = GetQuantizedDataType(array, ArrayDataType::kUint8);
   transformation->AddMessageF(
       "For output array %s with min=%g, max=%g"
-      ", chose to quantize as uint8 with zero_point=%d"
+      ", chose to quantize as %s with zero_point=%d"
       ", scale=%g",
-      output, minmax.min, minmax.max, quantization_params->zero_point,
-      quantization_params->scale);
+      output, minmax.min, minmax.max, ArrayDataTypeName(*quantized_data_type),
+      quantization_params->zero_point, quantization_params->scale);
 
   return true;
 }
diff --git a/tensorflow/contrib/lite/toco/model_flags.proto b/tensorflow/contrib/lite/toco/model_flags.proto
index e4b39b34e8..867b86f31d 100644
--- a/tensorflow/contrib/lite/toco/model_flags.proto
+++ b/tensorflow/contrib/lite/toco/model_flags.proto
@@ -96,9 +96,11 @@ message RnnState {
 // model that does not already contain such MinMax information.
 message ArraysExtraInfo {
   message Entry {
+    // Next ID to use: 5.
     optional string name = 1;
     optional float min = 2;
     optional float max = 3;
+    optional IODataType data_type = 4;
   }
   repeated Entry entries = 1;
 }
diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc
index 2153bab096..a09a3c4ef5 100644
--- a/tensorflow/contrib/lite/toco/toco_tooling.cc
+++ b/tensorflow/contrib/lite/toco/toco_tooling.cc
@@ -199,7 +199,8 @@ void Transform(const TocoFlags& toco_flags, Model* model) {
   const IODataType inference_type = toco_flags.inference_type();
 
   const bool quantize_output =
-      SupportsQuantization(output_format) && inference_type == QUANTIZED_UINT8;
+      SupportsQuantization(output_format) &&
+      (inference_type == QUANTIZED_UINT8 || inference_type == QUANTIZED_INT16);
 
   if (quantize_output) {
     QCHECK_NE(toco_flags.inference_input_type(), FLOAT)
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index eec35b7b59..9e72582238 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -1801,6 +1801,8 @@ ArrayDataType ConvertIODataTypeToArrayDataType(IODataType type) {
       return ArrayDataType::kFloat;
     case QUANTIZED_UINT8:
       return ArrayDataType::kUint8;
+    case QUANTIZED_INT16:
+      return ArrayDataType::kInt16;
     case INT32:
       return ArrayDataType::kInt32;
     case INT64:
@@ -1832,9 +1834,17 @@ void UseArraysExtraInfo(Model* model) {
     QCHECK(model->HasArray(entry.name()))
         << "ArraysExtraInfo refers to non-existent array name: "
         << entry.name();
-    auto& minmax = model->GetArray(entry.name()).GetOrCreateMinMax();
-    minmax.min = entry.min();
-    minmax.max = entry.max();
+    auto& array = model->GetArray(entry.name());
+    auto& minmax = array.GetOrCreateMinMax();
+    if (entry.has_min() || entry.has_max()) {
+      CHECK_EQ(entry.has_min(), entry.has_max());
+      minmax.min = entry.min();
+      minmax.max = entry.max();
+    }
+    if (entry.has_data_type()) {
+      array.final_data_type =
+          ConvertIODataTypeToArrayDataType(entry.data_type());
+    }
   }
 }
 
diff --git a/tensorflow/contrib/lite/toco/types.proto b/tensorflow/contrib/lite/toco/types.proto
index 318fd4b7b2..03bd6150bc 100644
--- a/tensorflow/contrib/lite/toco/types.proto
+++ b/tensorflow/contrib/lite/toco/types.proto
@@ -34,4 +34,7 @@ enum IODataType {
 
   // String, not quantized
   STRING = 5;
+
+  // Int16, quantized
+  QUANTIZED_INT16 = 6;
 }
-- 
GitLab


From 8722aeebdf823763596869a71eb6a7077bff7ccf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 17:19:49 -0800
Subject: [PATCH 226/575] Moved LIBXSMM convolutions to a separate --define
 flag so that they are disabled by default.

PiperOrigin-RevId: 186700936
---
 tensorflow/core/kernels/BUILD                 | 47 ++++++++++++-------
 .../core/kernels/conv_grad_filter_ops.cc      | 10 ++--
 .../core/kernels/conv_grad_input_ops.cc       | 10 ++--
 tensorflow/core/kernels/conv_ops.cc           |  6 +--
 tensorflow/core/kernels/xsmm_conv2d.cc        | 22 +++++++--
 5 files changed, 62 insertions(+), 33 deletions(-)

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index dd0de7829f..3426cf6e40 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -56,8 +56,8 @@ load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
 
 config_setting(
     # Add "--define tensorflow_xsmm=1" to your build command to use libxsmm for
-    # convolutions (and possibly more in the future). You will also need
-    # appropriate -mavx*, as required by specific op you use.
+    # sparse matrix multiplications. You will also need appropriate -mavx*
+    # options, as required by specific op you use.
     name = "xsmm",
     values = {
         "define": "tensorflow_xsmm=1",
@@ -65,12 +65,23 @@ config_setting(
 )
 
 config_setting(
-    # Add "--define tensorflow_xsmm_backward=1" to your build command to use
-    # libxsmm for backward convolutions (and possibly more in the future). You
-    # will also need appropriate -mavx*, as required by specific op you use.
-    name = "xsmm_backward",
+    # Add "--define tensorflow_xsmm_convolutions=1" to your build command to
+    # use libxsmm for forward convolutions. You will also need appropriate
+    # -mavx* # options, as required by specific op you use.
+    name = "xsmm_convolutions",
     values = {
-        "define": "tensorflow_xsmm_backward=1",
+        "define": "tensorflow_xsmm_convolutions=1",
+    },
+)
+
+config_setting(
+    # Add "--define tensorflow_xsmm_convolutions=1 --define
+    # tensorflow_xsmm_backward_convolutions=1" to your build command to use libxsmm for
+    # backward convolutions (and possibly more in the future). You will also
+    # need appropriate -mavx* options, as required by specific op you use.
+    name = "xsmm_backward_convolutions",
+    values = {
+        "define": "tensorflow_xsmm_backward_convolutions=1",
     },
 )
 
@@ -1017,7 +1028,7 @@ tf_cc_test(
     name = "xsmm_conv2d_test",
     size = "small",
     srcs = select({
-        ":xsmm": ["xsmm_conv2d_test.cc"],
+        ":xsmm_convolutions": ["xsmm_conv2d_test.cc"],
         "//conditions:default": [],
     }),
     deps = [
@@ -1032,7 +1043,7 @@ tf_cc_test(
         "//tensorflow/core:test_main",
         "//tensorflow/core:testlib",
     ] + select({
-        ":xsmm": [
+        ":xsmm_convolutions": [
             "@libxsmm_archive//:xsmm_avx",
         ],
         "//conditions:default": [],
@@ -3138,7 +3149,7 @@ tf_kernel_library(
         "conv_grad_ops_3d.cc",
         "deep_conv2d.cc",
     ] + select({
-        ":xsmm": ["xsmm_conv2d.cc"],
+        ":xsmm_convolutions": ["xsmm_conv2d.cc"],
         "//conditions:default": [],
     }),
     hdrs = [
@@ -3148,7 +3159,7 @@ tf_kernel_library(
         "gemm_functors.h",
         "winograd_transform.h",
     ] + select({
-        ":xsmm": ["xsmm_conv2d.h"],
+        ":xsmm_convolutions": ["xsmm_conv2d.h"],
         "//conditions:default": [],
     }),
     # Override EIGEN_STRONG_INLINE to inline when --define=override_eigen_strong_inline=true,
@@ -3156,13 +3167,15 @@ tf_kernel_library(
     # on Windows. See https://github.com/tensorflow/tensorflow/issues/10521
     copts = if_override_eigen_strong_inline(["/DEIGEN_STRONG_INLINE=inline"]),
     defines = select({
-        ":xsmm": [
-            "TENSORFLOW_USE_LIBXSMM",
-            "EIGEN_USE_LIBXSMM",
+        ":xsmm_convolutions": [
+            "TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS",
         ],
         "//conditions:default": [],
     }) + select({
-        ":xsmm_backward": ["TENSORFLOW_USE_LIBXSMM_BACKWARD"],
+        ":xsmm": ["EIGEN_USE_LIBXSMM"],
+        "//conditions:default": [],
+    }) + select({
+        ":xsmm_backward_convolutions": ["TENSORFLOW_USE_LIBXSMM_BACKWARD_CONVOLUTIONS"],
         "//conditions:default": [],
     }),
     prefix = "conv_ops",
@@ -3179,7 +3192,7 @@ tf_kernel_library(
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:nn_ops_op_lib",
     ] + select({
-        ":xsmm": [
+        ":xsmm_convolutions": [
             "@libxsmm_archive//:xsmm_avx",
         ],
         "//conditions:default": [],
@@ -4868,7 +4881,7 @@ filegroup(
         "winograd_transform.h",
         ":android_extended_ops_headers",
     ] + select({
-        ":xsmm": [
+        ":xsmm_convolutions": [
             "xsmm_conv2d.h",
             "xsmm_conv2d.cc",
         ],
diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc
index b8a5ae6a08..e6ae595291 100644
--- a/tensorflow/core/kernels/conv_grad_filter_ops.cc
+++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc
@@ -31,7 +31,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_slice.h"
 #include "tensorflow/core/kernels/conv_2d.h"
 #include "tensorflow/core/kernels/fill_functor.h"
-#ifdef TENSORFLOW_USE_LIBXSMM
+#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 #include "tensorflow/core/kernels/xsmm_conv2d.h"
 #endif
 #include "tensorflow/core/kernels/ops_util.h"
@@ -106,7 +106,7 @@ struct LaunchConv2DBackpropFilterOp<CPUDevice, T> {
   }
 };
 
-#ifdef TENSORFLOW_USE_LIBXSMM
+#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 template <typename Device, class T>
 struct LaunchXsmmBackwardFilter {
   bool operator()(OpKernelContext* context, const Device& d,
@@ -243,7 +243,8 @@ class Conv2DFastBackpropFilterOp : public OpKernel {
       return;
     }
 
-#if defined TENSORFLOW_USE_LIBXSMM && defined TENSORFLOW_USE_LIBXSMM_BACKWARD
+#if defined TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS && \
+    defined TENSORFLOW_USE_LIBXSMM_BACKWARD_CONVOLUTIONS
     int64 pad_top, pad_bottom;
     int64 pad_left, pad_right;
     OP_REQUIRES_OK(
@@ -371,7 +372,8 @@ class Conv2DCustomBackpropFilterOp : public OpKernel {
             dims.spatial_dims[1].input_size, dims.spatial_dims[1].filter_size,
             dims.spatial_dims[1].stride, padding_,
             &dims.spatial_dims[1].output_size, &pad_left, &pad_right));
-#if defined TENSORFLOW_USE_LIBXSMM && defined TENSORFLOW_USE_LIBXSMM_BACKWARD
+#if defined TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS && \
+    defined TENSORFLOW_USE_LIBXSMM_BACKWARD_CONVOLUTIONS
     if (pad_left == pad_right && pad_top == pad_bottom) {
       if (LaunchXsmmBackwardFilter<Device, T>()(
               context, context->eigen_device<Device>(), input.tensor<T, 4>(),
diff --git a/tensorflow/core/kernels/conv_grad_input_ops.cc b/tensorflow/core/kernels/conv_grad_input_ops.cc
index b87c7899c0..15c55e4d99 100644
--- a/tensorflow/core/kernels/conv_grad_input_ops.cc
+++ b/tensorflow/core/kernels/conv_grad_input_ops.cc
@@ -30,7 +30,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_slice.h"
 #include "tensorflow/core/kernels/conv_2d.h"
-#ifdef TENSORFLOW_USE_LIBXSMM
+#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 #include "tensorflow/core/kernels/xsmm_conv2d.h"
 #endif
 #include "tensorflow/core/kernels/ops_util.h"
@@ -111,7 +111,7 @@ struct LaunchConv2DBackpropInputOp<CPUDevice, T> {
   }
 };
 
-#ifdef TENSORFLOW_USE_LIBXSMM
+#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 template <typename Device, class T>
 struct LaunchXsmmBackwardInputConvolution {
   bool operator()(OpKernelContext* context, const Device& d,
@@ -246,7 +246,8 @@ class Conv2DFastBackpropInputOp : public OpKernel {
       return;
     }
 
-#if defined TENSORFLOW_USE_LIBXSMM && defined TENSORFLOW_USE_LIBXSMM_BACKWARD
+#if defined TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS && \
+    defined TENSORFLOW_USE_LIBXSMM_BACKWARD_CONVOLUTIONS
     int64 pad_top, pad_bottom;
     int64 pad_left, pad_right;
     OP_REQUIRES_OK(
@@ -363,7 +364,8 @@ class Conv2DCustomBackpropInputOp : public OpKernel {
 
 // TODO(andydavis) Consider moving code shared with
 // Conv2DCustomBackpropFilterOp into a shared helper function.
-#if defined TENSORFLOW_USE_LIBXSMM && defined TENSORFLOW_USE_LIBXSMM_BACKWARD
+#if defined TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS && \
+    defined TENSORFLOW_USE_LIBXSMM_BACKWARD_CONVOLUTIONS
     int64 pad_top, pad_bottom;
     int64 pad_left, pad_right;
     OP_REQUIRES_OK(
diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc
index 2b81e14f95..47f6907c04 100644
--- a/tensorflow/core/kernels/conv_ops.cc
+++ b/tensorflow/core/kernels/conv_ops.cc
@@ -32,7 +32,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/conv_2d.h"
 #include "tensorflow/core/kernels/deep_conv2d.h"
 #include "tensorflow/core/kernels/ops_util.h"
-#ifdef TENSORFLOW_USE_LIBXSMM
+#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 #include "tensorflow/core/kernels/xsmm_conv2d.h"
 #endif
 #include "tensorflow/core/lib/core/errors.h"
@@ -185,7 +185,7 @@ class LaunchDeepConvOp<CPUDevice, float> {
   }
 };
 
-#ifdef TENSORFLOW_USE_LIBXSMM
+#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 template <typename Device, typename T>
 class LaunchXsmmConvOp {
  public:
@@ -401,7 +401,7 @@ class Conv2DOp : public BinaryOp<T> {
       return;
     }
 
-#ifdef TENSORFLOW_USE_LIBXSMM
+#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
     if (LaunchXsmmConvOp<Device, T>::Run(
             context, input, filter, batch, input_rows, input_cols, in_depth,
             filter_rows, filter_cols, pad_rows, pad_cols, out_rows, out_cols,
diff --git a/tensorflow/core/kernels/xsmm_conv2d.cc b/tensorflow/core/kernels/xsmm_conv2d.cc
index ba03357cc6..f8c06988cb 100644
--- a/tensorflow/core/kernels/xsmm_conv2d.cc
+++ b/tensorflow/core/kernels/xsmm_conv2d.cc
@@ -16,7 +16,7 @@ limitations under the License.
 // Make this file empty (or nearly empty) so that it can be compiled even when
 // libxsmm is not available.
 
-#ifndef TENSORFLOW_USE_LIBXSMM
+#ifndef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 void dummy_xsmm_conv2d_ensure_file_is_not_empty();
 #else
 
@@ -32,9 +32,9 @@ void dummy_xsmm_conv2d_ensure_file_is_not_empty();
 #include "tensorflow/core/lib/core/blocking_counter.h"
 #include "tensorflow/core/lib/core/threadpool.h"
 
-#include "libxsmm_main.h"  // TODO(bsteiner): API to avoid incl. header from src/
 #include "include/libxsmm_cpuid.h"
 #include "include/libxsmm_malloc.h"
+#include "third_party/libxsmm/src/libxsmm_main.h"  // TODO(bsteiner): API to avoid incl. header from src/
 
 namespace tensorflow {
 
@@ -173,8 +173,16 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
                                    InputPtr input, FilterPtr filter,
                                    OutputPtr output) {
 #if defined(LIBXSMM_DETAILED_TIMING)
-  unsigned long long l_tick1, l_tick2, l_tick3, l_tick4, l_tick5, l_tick6,
-      l_tick7, l_tick8, l_tick9, l_tick10;
+  uint64 l_tick1;
+  uint64 l_tick2;
+  uint64 l_tick3;
+  uint64 l_tick4;
+  uint64 l_tick5;
+  uint64 l_tick6;
+  uint64 l_tick7;
+  uint64 l_tick8;
+  uint64 l_tick9;
+  uint64 l_tick10;
   l_tick1 = libxsmm_timer_tick();
 #endif
   // setup scoped allocator, which adopts the allocator from the context
@@ -453,6 +461,7 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
   return true;  // Succeeded
 }
 
+#ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 template <typename T>
 struct XsmmFwdConv2D<CPUDevice, T> {
   bool operator()(OpKernelContext* ctx, const libxsmm_dnn_conv_desc& desc,
@@ -461,7 +470,9 @@ struct XsmmFwdConv2D<CPUDevice, T> {
                                   input, filter, output);
   }
 };
+#endif
 
+#ifdef TENSORFLOW_USE_LIBXSMM_BACKWARD_CONVOLUTIONS
 template <typename T>
 struct XsmmBkwInputConv2D<CPUDevice, T> {
   bool operator()(OpKernelContext* ctx, const libxsmm_dnn_conv_desc& desc,
@@ -479,6 +490,7 @@ struct XsmmBkwFilterConv2D<CPUDevice, T> {
                                   input, filter, output);
   }
 };
+#endif
 
 }  // namespace functor
 
@@ -488,4 +500,4 @@ template struct functor::XsmmBkwFilterConv2D<CPUDevice, float>;
 
 }  // namespace tensorflow
 
-#endif  // TENSORFLOW_USE_LIBXSMM
+#endif  // TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
-- 
GitLab


From 57ee22dd44d61f18d75399398c3c33fa21079f71 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 17:52:58 -0800
Subject: [PATCH 227/575] Turn on strip_default_attrs by default during custom
 export.

PiperOrigin-RevId: 186704976
---
 .../boosted_trees/estimator_batch/custom_export_strategy.py   | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py
index 31f5c44481..23ba76210b 100644
--- a/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py
+++ b/tensorflow/contrib/boosted_trees/estimator_batch/custom_export_strategy.py
@@ -93,7 +93,9 @@ def make_custom_export_strategy(name,
                          "w") as f:
           f.write("\n".join("%s, %f" % (k, v) for k, v in sorted_by_importance))
     return result_dir
-  return export_strategy.ExportStrategy(name, export_fn)
+
+  return export_strategy.ExportStrategy(
+      name, export_fn, strip_default_attrs=True)
 
 
 def convert_to_universal_format(dtec, sorted_feature_names,
-- 
GitLab


From 8852be3ed15e11071d6807b61294d36168be693c Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 22 Feb 2018 18:01:16 -0800
Subject: [PATCH 228/575] Fix fix Defun logic when returning a value captured
 from an outer scope.

Previously, the following would fail:

```python
c = tf.constant(...)
@Defun(...)
def Foo(...):
  return c
```

The fix involves ensuring that every output of the function is a member of the function graph, and invoking the capturing logic if it is not.

PiperOrigin-RevId: 186705800
---
 tensorflow/python/framework/function.py      | 50 ++++++++++++--------
 tensorflow/python/framework/function_test.py |  7 +++
 2 files changed, 36 insertions(+), 21 deletions(-)

diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py
index cba225e749..caa604999c 100644
--- a/tensorflow/python/framework/function.py
+++ b/tensorflow/python/framework/function.py
@@ -353,8 +353,10 @@ class _DefinedFunction(object):
           outputs = (outputs,)
         if any([_ is None for _ in outputs]):
           raise ValueError("Function can not return None.")
-      # Ensures each output is a Tensor.
-      outputs = [ops.convert_to_tensor(_) for _ in outputs]
+      # Ensures each output is a Tensor in the function graph.
+      outputs = [ops.convert_to_tensor(t) for t in outputs]
+      outputs = [temp_graph.capture(t) if t.graph is not temp_graph else t
+                 for t in outputs]
     self._extra_inputs = temp_graph.extra_inputs
     inputs.extend(temp_graph.extra_args)
     # pylint: disable=protected-access
@@ -683,28 +685,34 @@ class _FuncGraph(ops.Graph):
   def create_op(self, op_type, inputs, data_types, **kwargs):
     for i, x in enumerate(inputs):
       if isinstance(x, ops.EagerTensor) or x.graph is not self:
-        # Referring to a tensor from other graph.
-        if x in self._captured:
-          # Captured already.
-          inputs[i] = self._captured[x]
-        elif self._capture_by_value:
-          inputs[i] = self._add_tensor_and_parents(x)
-        else:
-          # Substitute with a placeholder.
-          self.extra_inputs.append(x)
-          # Hoist the new input placeholder out of any control flow context
-          # we're currently in.
-          with ops.control_dependencies(None):
-            ph = array_ops.placeholder(x.dtype, shape=x.get_shape())
-          # pylint: disable=protected-access
-          ph._handle_data = x._handle_data
-          # pylint: enable=protected-access
-          inputs[i] = ph
-          self._captured[x] = ph
-          self.extra_args.append(ph)
+        inputs[i] = self.capture(x)
     return super(_FuncGraph, self).create_op(op_type, inputs, data_types,
                                              **kwargs)
 
+  def capture(self, tensor):
+    """Adds the given tensor to this graph and returns the captured tensor."""
+    if tensor in self._captured:
+      # Captured already.
+      return self._captured[tensor]
+    elif self._capture_by_value:
+      return self._add_tensor_and_parents(tensor)
+    else:
+      return self._capture_tensor_as_extra_input(tensor)
+
+  def _capture_tensor_as_extra_input(self, tensor):
+    # Substitute with a placeholder.
+    self.extra_inputs.append(tensor)
+    # Hoist the new input placeholder out of any control flow context
+    # we're currently in.
+    with ops.control_dependencies(None):
+      ph = array_ops.placeholder(tensor.dtype, shape=tensor.get_shape())
+    # pylint: disable=protected-access
+    ph._handle_data = tensor._handle_data
+    # pylint: enable=protected-access
+    self._captured[tensor] = ph
+    self.extra_args.append(ph)
+    return ph
+
   def _add_tensor_and_parents(self, tensor):
     op = self._add_op_and_parents(tensor.op)
     return op.outputs[tensor.value_index]
diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py
index 301a7f682d..52052ba77d 100644
--- a/tensorflow/python/framework/function_test.py
+++ b/tensorflow/python/framework/function_test.py
@@ -725,9 +725,16 @@ class FunctionTest(test.TestCase):
 
       y = Foo(constant_op.constant([[10.]]))
 
+      @function.Defun()
+      def Bar():
+        return w
+
+      z = Bar()
+
     with self.test_session(graph=g):
       variables.global_variables_initializer().run()
       self.assertAllEqual(y.eval(), [[12.0]])
+      self.assertAllEqual(z.eval(), [[1.0]])
 
   def testCaptureControls(self):
     g = ops.Graph()
-- 
GitLab


From 491fb62d90f080d4daf32b5539ec9b4a2de71c6c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 18:02:04 -0800
Subject: [PATCH 229/575] Add cost estimator tests for the BiasAdd, ReLU, and
 Conv2D operations.

PiperOrigin-RevId: 186705930
---
 .../grappler/costs/op_level_cost_estimator.cc |   2 +
 .../costs/op_level_cost_estimator_test.cc     | 102 ++++++++++++++----
 2 files changed, 82 insertions(+), 22 deletions(-)

diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
index 983b6891f1..29ef317e46 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc
@@ -245,6 +245,8 @@ OpLevelCostEstimator::OpLevelCostEstimator() {
       {"Add", Eigen::internal::functor_traits<
                   Eigen::internal::scalar_sum_op<float>>::Cost},
       {"ApproximateEqual", 1},
+      {"BiasAdd", Eigen::internal::functor_traits<
+                      Eigen::internal::scalar_sum_op<float>>::Cost},
       {"Div", Eigen::internal::functor_traits<
                   Eigen::internal::scalar_quotient_op<float>>::Cost},
       {"Equal", 1},
diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
index 583d2619b2..4790b9bab2 100644
--- a/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
+++ b/tensorflow/core/grappler/costs/op_level_cost_estimator_test.cc
@@ -99,47 +99,81 @@ OpContext DescribeBatchMatMul(const std::vector<int>& dims_a,
 // Wrangles the minimum number of proto fields to set up a 4D Tensor for cost
 // estimation purposes.
 void DescribeTensor4D(int dim0, int dim1, int dim2, int dim3,
-                      OpInfo* op_features) {
-  auto input = op_features->add_inputs();
-  auto shape = input->mutable_shape();
+                      OpInfo::TensorProperties* tensor) {
+  auto shape = tensor->mutable_shape();
   shape->add_dim()->set_size(dim0);
   shape->add_dim()->set_size(dim1);
   shape->add_dim()->set_size(dim2);
   shape->add_dim()->set_size(dim3);
-  input->set_dtype(DT_FLOAT);
+  tensor->set_dtype(DT_FLOAT);
 }
 
-// Returns an OpInfo for Conv2D with the minimum set of fields set up.
+// DescribeConvolution constructs an OpContext for a Conv2D applied to an input
+// tensor with shape (batch, ix, iy, iz1) and a kernel tensor with shape
+// (kx, ky, iz2, oz).
 OpContext DescribeConvolution(int batch, int ix, int iy, int iz1, int iz2,
                               int kx, int ky, int oz) {
   OpContext op_context;
   SetCpuDevice(&op_context.op_info);
   op_context.op_info.set_op("Conv2D");
 
-  DescribeTensor4D(batch, ix, iy, iz1, &op_context.op_info);
-  DescribeTensor4D(kx, ky, iz2, oz, &op_context.op_info);
+  DescribeTensor4D(batch, ix, iy, iz1, op_context.op_info.add_inputs());
+  DescribeTensor4D(kx, ky, iz2, oz, op_context.op_info.add_inputs());
+
+  return op_context;
+}
+
+// DescribeUnaryOp constructs an OpContext for the given operation applied to
+// a 4-tensor with shape (size1, 1, 1, 1).
+OpContext DescribeUnaryOp(const string& op, int size1) {
+  OpContext op_context;
+  SetCpuDevice(&op_context.op_info);
+  op_context.op_info.set_op(op);
+
+  DescribeTensor4D(size1, 1, 1, 1, op_context.op_info.add_inputs());
+  DescribeTensor4D(size1, 1, 1, 1, op_context.op_info.add_outputs());
+
   return op_context;
 }
 
-OpContext DescribeOp(const string& op, int size1, int size2) {
+// DescribeBinaryOp constructs an OpContext for the given operation applied to
+// a 4-tensor with dimensions (size1, 1, 1, 1) and a 4-tensor with dimensions
+// (2 * size1, size2, 1, 1).
+//
+// The choice of dimension here is arbitrary, and is used strictly to test the
+// cost model for applying elementwise operations to tensors with unequal
+// dimension values.
+OpContext DescribeBinaryOp(const string& op, int size1, int size2) {
   OpContext op_context;
   SetCpuDevice(&op_context.op_info);
   op_context.op_info.set_op(op);
 
-  DescribeTensor4D(size1, 1, 1, 1, &op_context.op_info);
-  DescribeTensor4D(2 * size1, size2, 1, 1, &op_context.op_info);
+  DescribeTensor4D(size1, 1, 1, 1, op_context.op_info.add_inputs());
+  DescribeTensor4D(2 * size1, size2, 1, 1, op_context.op_info.add_inputs());
+  DescribeTensor4D(2 * size1, size2, 1, 1, op_context.op_info.add_outputs());
 
-  auto output = op_context.op_info.add_outputs();
-  auto shape = output->mutable_shape();
-  shape->add_dim()->set_size(2 * size1);
-  shape->add_dim()->set_size(size2);
-  shape->add_dim()->set_size(1);
-  shape->add_dim()->set_size(1);
-  output->set_dtype(DT_FLOAT);
+  return op_context;
+}
 
+// DescribeBiasAdd constructs an OpContext for a BiasAdd applied to a 4-tensor
+// with dimensions (1, 1, size2, size1) and a bias with dimension (size1),
+// according to the constraint that the bias must be 1D with size equal to that
+// of the last dimension of the input value.
+OpContext DescribeBiasAdd(int size1, int size2) {
+  OpContext op_context;
   SetCpuDevice(&op_context.op_info);
+  op_context.op_info.set_op("BiasAdd");
+
+  DescribeTensor4D(1, 1, size2, size1, op_context.op_info.add_inputs());
+  DescribeTensor4D(1, 1, size2, size1, op_context.op_info.add_outputs());
+
+  auto bias = op_context.op_info.add_inputs();
+  bias->mutable_shape()->add_dim()->set_size(size1);
+  bias->set_dtype(DT_FLOAT);
+
   return op_context;
 }
+
 }  // namespace
 
 class OpLevelCostEstimatorTest : public ::testing::Test {
@@ -166,8 +200,24 @@ class OpLevelCostEstimatorTest : public ::testing::Test {
   OpLevelCostEstimator estimator_;
 };
 
+TEST_F(OpLevelCostEstimatorTest, BiasAddExecutionTime) {
+  auto cost = PredictCosts(DescribeBiasAdd(1000, 10));
+  EXPECT_EQ(Costs::Duration(8400), cost.memory_time);
+  EXPECT_EQ(Costs::Duration(1000), cost.compute_time);
+  EXPECT_EQ(Costs::Duration(9400), cost.execution_time);
+  EXPECT_FALSE(cost.inaccurate);
+}
+
+TEST_F(OpLevelCostEstimatorTest, Conv2DExecutionTime) {
+  auto cost = PredictCosts(DescribeConvolution(16, 19, 19, 48, 48, 5, 5, 256));
+  EXPECT_EQ(Costs::Duration(233780), cost.memory_time);
+  EXPECT_EQ(Costs::Duration(354877440), cost.compute_time);
+  EXPECT_EQ(Costs::Duration(355111220), cost.execution_time);
+  EXPECT_FALSE(cost.inaccurate);
+}
+
 TEST_F(OpLevelCostEstimatorTest, DummyExecutionTime) {
-  auto cost = PredictCosts(DescribeOp("Dummy", 1000, 1));
+  auto cost = PredictCosts(DescribeBinaryOp("Dummy", 1000, 1));
   EXPECT_EQ(Costs::Duration(2000), cost.memory_time);
   EXPECT_EQ(Costs::Duration(0), cost.compute_time);
   EXPECT_EQ(Costs::Duration(2000), cost.execution_time);
@@ -176,7 +226,7 @@ TEST_F(OpLevelCostEstimatorTest, DummyExecutionTime) {
 
 TEST_F(OpLevelCostEstimatorTest, ExecutionTimeSumOrMax) {
   SetComputeMemoryOverlap(true);
-  auto cost = PredictCosts(DescribeOp("Dummy", 1000, 1));
+  auto cost = PredictCosts(DescribeBinaryOp("Dummy", 1000, 1));
   EXPECT_EQ(Costs::Duration(2000), cost.memory_time);
   EXPECT_EQ(Costs::Duration(0), cost.compute_time);
   EXPECT_EQ(Costs::Duration(2000), cost.execution_time);  // max(2000, 200)
@@ -185,7 +235,7 @@ TEST_F(OpLevelCostEstimatorTest, ExecutionTimeSumOrMax) {
 }
 
 TEST_F(OpLevelCostEstimatorTest, MulExecutionTime) {
-  auto cost = PredictCosts(DescribeOp("Mul", 1000, 1));
+  auto cost = PredictCosts(DescribeBinaryOp("Mul", 1000, 1));
   EXPECT_EQ(Costs::Duration(2000), cost.memory_time);
   EXPECT_EQ(Costs::Duration(200), cost.compute_time);
   EXPECT_EQ(Costs::Duration(2200), cost.execution_time);
@@ -193,7 +243,7 @@ TEST_F(OpLevelCostEstimatorTest, MulExecutionTime) {
 }
 
 TEST_F(OpLevelCostEstimatorTest, MulBroadcastExecutionTime) {
-  auto cost = PredictCosts(DescribeOp("Mul", 1000, 2));
+  auto cost = PredictCosts(DescribeBinaryOp("Mul", 1000, 2));
   EXPECT_EQ(Costs::Duration(3600), cost.memory_time);
   EXPECT_EQ(Costs::Duration(400), cost.compute_time);
   EXPECT_EQ(Costs::Duration(4000), cost.execution_time);
@@ -201,13 +251,21 @@ TEST_F(OpLevelCostEstimatorTest, MulBroadcastExecutionTime) {
 }
 
 TEST_F(OpLevelCostEstimatorTest, ModExecutionTime) {
-  auto cost = PredictCosts(DescribeOp("Mod", 1000, 1));
+  auto cost = PredictCosts(DescribeBinaryOp("Mod", 1000, 1));
   EXPECT_EQ(Costs::Duration(2000), cost.memory_time);
   EXPECT_EQ(Costs::Duration(1600), cost.compute_time);
   EXPECT_EQ(Costs::Duration(3600), cost.execution_time);
   EXPECT_FALSE(cost.inaccurate);
 }
 
+TEST_F(OpLevelCostEstimatorTest, ReluExecutionTime) {
+  auto cost = PredictCosts(DescribeUnaryOp("Relu", 1000));
+  EXPECT_EQ(Costs::Duration(800), cost.memory_time);
+  EXPECT_EQ(Costs::Duration(100), cost.compute_time);
+  EXPECT_EQ(Costs::Duration(900), cost.execution_time);
+  EXPECT_FALSE(cost.inaccurate);
+}
+
 TEST_F(OpLevelCostEstimatorTest, UnknownOrPartialShape) {
   EXPECT_FALSE(PredictCosts(DescribeMatMul(2, 4, 7, 7)).inaccurate);
   EXPECT_TRUE(PredictCosts(DescribeMatMul(-1, 4, 7, 7)).inaccurate);
-- 
GitLab


From 0f18c8eff518ef7d449eb7447ca44d691ef94701 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 18:13:40 -0800
Subject: [PATCH 230/575] [XLA] Add SliceInDim to the local Python XLA client.
 [XLA] Revise a comment regarding how the two-argument version of Reshape
 flattens dimensions (it's first-to-last, not major-to-minor since those terms
 apply to memory layout rather than logical dimensions, and the comment wasn't
 consistent about major-to-minor or minor-to-major).

PiperOrigin-RevId: 186707393
---
 .../compiler/xla/client/computation_builder.h |  5 +--
 .../xla/python/local_computation_builder.cc   |  6 +++
 .../xla/python/local_computation_builder.h    |  4 ++
 .../xla/python/local_computation_builder.i    |  1 +
 tensorflow/compiler/xla/python/xla_client.py  | 39 ++++++++++++++++---
 .../compiler/xla/python/xla_client_test.py    | 17 ++++++++
 6 files changed, 64 insertions(+), 8 deletions(-)

diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index e3facb3f25..377b671639 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -198,9 +198,8 @@ class ComputationBuilder {
                                 tensorflow::gtl::ArraySlice<int64> new_sizes);
 
   // Enqueues an operation onto the computation that collapses the operand, from
-  // minor to major order, then reshapes it into the shape with the given
-  // dimension sizes, also from major to minor. Conceptually, this is a limited
-  // form of "shape casting".
+  // first to last dimension (C order), then reshapes it to the given dimension
+  // sizes. Conceptually, this is a limited form of "shape casting".
   ComputationDataHandle Reshape(const ComputationDataHandle& operand,
                                 tensorflow::gtl::ArraySlice<int64> new_sizes);
 
diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc
index cb7bb21e09..b21ab3044f 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.cc
+++ b/tensorflow/compiler/xla/python/local_computation_builder.cc
@@ -368,6 +368,12 @@ ComputationDataHandle LocalComputationBuilder::Slice(
   return builder_.Slice(operand, start_indices, limit_indices, strides);
 }
 
+ComputationDataHandle LocalComputationBuilder::SliceInDim(
+    const ComputationDataHandle& operand, int64 start_index, int64 limit_index,
+    int64 stride, int64 dimno) {
+  return builder_.SliceInDim(operand, start_index, limit_index, stride, dimno);
+}
+
 ComputationDataHandle LocalComputationBuilder::DynamicSlice(
     const ComputationDataHandle& operand,
     const ComputationDataHandle& start_indices,
diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h
index d3e9503ea1..a7375c8965 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.h
+++ b/tensorflow/compiler/xla/python/local_computation_builder.h
@@ -170,6 +170,10 @@ class LocalComputationBuilder {
                               tensorflow::gtl::ArraySlice<int64> limit_indices,
                               tensorflow::gtl::ArraySlice<int64> strides);
 
+  ComputationDataHandle SliceInDim(const ComputationDataHandle& operand,
+                                   int64 start_index, int64 limit_index,
+                                   int64 stride, int64 dimno);
+
   ComputationDataHandle DynamicSlice(
       const ComputationDataHandle& operand,
       const ComputationDataHandle& start_indices,
diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i
index 456e341f87..b5354131c9 100644
--- a/tensorflow/compiler/xla/python/local_computation_builder.i
+++ b/tensorflow/compiler/xla/python/local_computation_builder.i
@@ -886,6 +886,7 @@ tensorflow::ImportNumpy();
 %unignore xla::swig::LocalComputationBuilder::Collapse;
 %unignore xla::swig::LocalComputationBuilder::CrossReplicaSum;
 %unignore xla::swig::LocalComputationBuilder::Slice;
+%unignore xla::swig::LocalComputationBuilder::SliceInDim;
 %unignore xla::swig::LocalComputationBuilder::DynamicSlice;
 %unignore xla::swig::LocalComputationBuilder::DynamicUpdateSlice;
 %unignore xla::swig::LocalComputationBuilder::ConcatInDim;
diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py
index 9bda9d0929..3b8ec851d5 100644
--- a/tensorflow/compiler/xla/python/xla_client.py
+++ b/tensorflow/compiler/xla/python/xla_client.py
@@ -656,7 +656,7 @@ class ComputationBuilder(object):
         representing the configuration of the padding operation.
 
     Returns:
-      A ComputationDataHandle representing the added pad op.
+      A ComputationDataHandle representing the added Pad op.
     """
     if not isinstance(padding_config, xla_data_pb2.PaddingConfig):
       padding_config = GetPaddingConfigFromTriples(padding_config)
@@ -666,7 +666,20 @@ class ComputationBuilder(object):
                          padding_config))
 
   def Reshape(self, operand, dimensions, new_sizes):
-    """Reshape op."""
+    """Enqueues a reshape op onto the computation.
+
+    Args:
+      operand: ComputationDataHandle representing the array to be reshaped.
+      dimensions: sequence of integers encoding the order in which dimensions
+        are collapsed or None, in which case dimensions are flattened in order.
+      new_sizes: sequence of integers encoding the new dimension sizes (shape).
+
+    Returns:
+      A ComputationDataHandle representing the added Reshape op.
+    """
+    if dimensions is None:
+      ndim = len(self.GetShape(operand).dimensions())
+      dimensions = tuple(range(ndim))
     return _wrap_data_handle(
         self._client.Reshape(
             _unwrap_data_handle(operand), dimensions, new_sizes))
@@ -772,11 +785,27 @@ class ComputationBuilder(object):
       strides = [1] * len(start_indices)
     return _wrap_data_handle(
         self._client.Slice(
-            _unwrap_data_handle(operand),
-            start_indices,
-            limit_indices,
+            _unwrap_data_handle(operand), start_indices, limit_indices,
             strides))
 
+  def SliceInDim(self, operand, start_index, limit_index, stride, dimno):
+    """Enqueues a slice-in-dimension operation onto the computation.
+
+    Args:
+      operand: ComputationDataHandle for the N dimensional array to be sliced.
+      start_index: an integer containing the start index of the slice.
+      limit_index: an integer containing the end index of the slice.
+      stride: an integer containing the stride size for the slice.
+      dimno: an integer indicating the dimension along which to slice.
+
+    Returns:
+      A ComputationDataHandle representing the added Slice op.
+    """
+    return _wrap_data_handle(
+        self._client.SliceInDim(
+            _unwrap_data_handle(operand), start_index, limit_index, stride,
+            dimno))
+
   def DynamicSlice(self, operand, start_indices, slice_sizes):
     """Enqueues a slice op with dynamic start indices onto the computation.
 
diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py
index c9d09cd5d5..4c16c1f8b0 100644
--- a/tensorflow/compiler/xla/python/xla_client_test.py
+++ b/tensorflow/compiler/xla/python/xla_client_test.py
@@ -762,6 +762,23 @@ class SingleOpTest(LocalComputationTest):
         [3, 2])
     self._ExecuteAndCompareExact(c, expected=[[4, 5], [7, 8]])
 
+  def testSliceInDim(self):
+    c = self._NewComputation()
+    c.SliceInDim(
+        c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
+        start_index=1,
+        limit_index=2,
+        stride=1,
+        dimno=1)
+    self._ExecuteAndCompareExact(c, expected=[[2], [5], [8]])
+    c.SliceInDim(
+        c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
+        start_index=0,
+        limit_index=3,
+        stride=2,
+        dimno=0)
+    self._ExecuteAndCompareExact(c, expected=[[1, 2, 3], [7, 8, 9]])
+
   def testDynamicSlice(self):
     c = self._NewComputation()
     c.DynamicSlice(
-- 
GitLab


From 917a4ac01f1ddeb56584eb4c22de146d08c73589 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 18:18:20 -0800
Subject: [PATCH 231/575] Update ops-related pbtxt files.

PiperOrigin-RevId: 186707935
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 42 ++++++++
 tensorflow/core/ops/ops.pbtxt                 | 99 ++++++++-----------
 2 files changed, 84 insertions(+), 57 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 3fb17d92d2..dddde1624a 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -11460,6 +11460,14 @@ op {
     type: "type"
   }
 }
+op {
+  name: "ConsumeMutexLock"
+  input_arg {
+    name: "mutex_lock"
+    type: DT_VARIANT
+  }
+  is_stateful: true
+}
 op {
   name: "ControlTrigger"
 }
@@ -30077,6 +30085,40 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "MutexLock"
+  input_arg {
+    name: "mutex"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "mutex_lock"
+    type: DT_VARIANT
+  }
+  is_stateful: true
+}
+op {
+  name: "MutexV2"
+  output_arg {
+    name: "resource"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  is_stateful: true
+}
 op {
   name: "Neg"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 14d8598aa1..55be0519a7 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -4773,6 +4773,14 @@ op {
     type: "type"
   }
 }
+op {
+  name: "ConsumeMutexLock"
+  input_arg {
+    name: "mutex_lock"
+    type: DT_VARIANT
+  }
+  is_stateful: true
+}
 op {
   name: "ControlTrigger"
 }
@@ -5465,28 +5473,6 @@ op {
     }
   }
 }
-op {
-  name: "CriticalSectionOp"
-  output_arg {
-    name: "resource"
-    type: DT_RESOURCE
-  }
-  attr {
-    name: "container"
-    type: "string"
-    default_value {
-      s: ""
-    }
-  }
-  attr {
-    name: "shared_name"
-    type: "string"
-    default_value {
-      s: ""
-    }
-  }
-  is_stateful: true
-}
 op {
   name: "CropAndResize"
   input_arg {
@@ -7788,41 +7774,6 @@ op {
     }
   }
 }
-op {
-  name: "ExecuteInCriticalSection"
-  input_arg {
-    name: "critical_section"
-    type: DT_RESOURCE
-  }
-  input_arg {
-    name: "arguments"
-    type_list_attr: "Targuments"
-  }
-  output_arg {
-    name: "outputs"
-    type_list_attr: "output_types"
-  }
-  attr {
-    name: "f"
-    type: "func"
-  }
-  attr {
-    name: "Targuments"
-    type: "list(type)"
-    has_minimum: true
-  }
-  attr {
-    name: "output_types"
-    type: "list(type)"
-    has_minimum: true
-  }
-  attr {
-    name: "output_shapes"
-    type: "list(shape)"
-    has_minimum: true
-  }
-  is_stateful: true
-}
 op {
   name: "Exit"
   input_arg {
@@ -14367,6 +14318,40 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "MutexLock"
+  input_arg {
+    name: "mutex"
+    type: DT_RESOURCE
+  }
+  output_arg {
+    name: "mutex_lock"
+    type: DT_VARIANT
+  }
+  is_stateful: true
+}
+op {
+  name: "MutexV2"
+  output_arg {
+    name: "resource"
+    type: DT_RESOURCE
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  is_stateful: true
+}
 op {
   name: "Neg"
   input_arg {
-- 
GitLab


From 1dcd464f249ddcdc9c3864aff0a881c0948d08fb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 18:34:32 -0800
Subject: [PATCH 232/575] Pass 'mode' argument to input_fn if appropriate in
 TPUEstimator.

PiperOrigin-RevId: 186709373
---
 tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
index ff53fe4f5d..1b2eda1caa 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py
@@ -1763,6 +1763,9 @@ class TPUEstimator(estimator_lib.Estimator):
     if 'config' in input_fn_args:
       kwargs['config'] = config
 
+    if 'mode' in input_fn_args:
+      kwargs['mode'] = mode
+
     with self._ctx.with_mode(mode) as ctx:
       # Setting the batch size in params first. This helps user to have same
       # input_fn for use_tpu=True/False.
-- 
GitLab


From befd8234e1c209b26457eb5df37d2952004bdeaf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 22 Feb 2018 19:47:03 -0800
Subject: [PATCH 233/575] Enable constant propagation across Switch(x,x) by
 rewriting the two outputs as Const(false), Const(true) with appropriate
 control dependencies. This is a fairly common pattern when the graph contains
 assertions.

By rewriting the graph a bit, we can propagate the constants down the two output branches, and just use control dependencies to trigger the selected one at runtime. For example,
         +------+
     x-->|Switch|-->a
     x-->|      |-->b
         +------+

Is rewritten as

         +------+
     x-->|Switch|-->Identity--^>Const(false)-->a
     x-->|      |-->Identity--^>Const(true)-->b
         +------+

(In practice there may be multiple consumers of each output branch.)

PiperOrigin-RevId: 186714991
---
 .../optimizers/arithmetic_optimizer.cc        |  39 ----
 .../grappler/optimizers/constant_folding.cc   | 200 ++++++++++++++----
 .../optimizers/constant_folding_test.cc       |  66 +++++-
 tensorflow/core/grappler/utils.cc             |  50 +++++
 tensorflow/core/grappler/utils.h              |   3 +
 5 files changed, 267 insertions(+), 91 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
index fbb3e5aaee..709a434e40 100644
--- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc
@@ -45,45 +45,6 @@ namespace tensorflow {
 namespace grappler {
 namespace {
 
-template <typename T>
-bool SafeSetTensorValue(double value, Tensor* tensor) {
-  using RealType = typename Eigen::NumTraits<T>::Real;
-  if (value > std::numeric_limits<RealType>::max() ||
-      value < std::numeric_limits<RealType>::min()) {
-    return false;
-  }
-  tensor->flat<T>()(0) = static_cast<T>(value);
-  return true;
-}
-
-#define HANDLE_CASE(DTYPE)                                          \
-  case DTYPE:                                                       \
-    if (!SafeSetTensorValue<EnumToDataType<DTYPE>::Type>(           \
-            static_cast<double>(value), tensor)) {                  \
-      return errors::InvalidArgument("Cannot store value ", value,  \
-                                     " in tensor of type " #DTYPE); \
-    }                                                               \
-    break
-
-Status SetTensorValue(DataType dtype, int value, Tensor* tensor) {
-  switch (dtype) {
-    //    HANDLE_CASE(DT_HALF);
-    HANDLE_CASE(DT_FLOAT);
-    HANDLE_CASE(DT_DOUBLE);
-    HANDLE_CASE(DT_UINT8);
-    HANDLE_CASE(DT_INT8);
-    HANDLE_CASE(DT_UINT16);
-    HANDLE_CASE(DT_INT16);
-    HANDLE_CASE(DT_INT32);
-    HANDLE_CASE(DT_INT64);
-    HANDLE_CASE(DT_COMPLEX64);
-    HANDLE_CASE(DT_COMPLEX128);
-    default:
-      return errors::InvalidArgument("Unexpected type ", DataTypeString(dtype));
-  }
-  return Status::OK();
-}
-
 template <typename T>
 bool AreInversePermutations(const std::vector<T>& a, const std::vector<T>& b) {
   if (a.size() != b.size()) {
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 064cb8b5ae..182e03f04e 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -811,44 +811,51 @@ Status ConstantFolding::CreateNodeDef(const string& name,
   // Use the packed representation whenever possible to avoid generating large
   // graphdefs. Moreover, avoid repeating the last values if they're equal.
   if (tensor->NumElements() > 4) {
-#define POPULATE_TENSOR_PROTO(tensor, t, TYPE, NAME)                \
-  const TYPE* val_ptr = tensor->flat<TYPE>().data();                \
-  TYPE last = *val_ptr;                                             \
-  int64 last_index = 0;                                             \
-  for (int64 i = 0; i < tensor->NumElements(); ++i) {               \
-    TYPE cur = *val_ptr++;                                          \
-    if (cur != last) {                                              \
-      last = cur;                                                   \
-      last_index = i;                                               \
-    }                                                               \
-  }                                                                 \
-  if (last_index < kint32max) {                                     \
-    optimized = true;                                               \
-    encoded_size = (last_index + 1) * sizeof(NAME);                 \
-    t->mutable_##NAME##_val()->Reserve(last_index + 1);             \
-    t->mutable_##NAME##_val()->AddNAlreadyReserved(last_index + 1); \
-    val_ptr = tensor->flat<TYPE>().data();                          \
-    for (int64 i = 0; i <= last_index; ++i) {                       \
-      t->set_##NAME##_val(i, *val_ptr++);                           \
-    }                                                               \
-  }
-
-    if (tensor->dtype() == DT_FLOAT) {
-      POPULATE_TENSOR_PROTO(tensor, t, float, float)
-    } else if (tensor->dtype() == DT_DOUBLE) {
-      POPULATE_TENSOR_PROTO(tensor, t, double, double)
-    } else if (tensor->dtype() == DT_INT64) {
-      POPULATE_TENSOR_PROTO(tensor, t, int64, int64)
-    } else if (tensor->dtype() == DT_INT32) {
-      POPULATE_TENSOR_PROTO(tensor, t, int32, int)
-    } else if (tensor->dtype() == DT_INT16) {
-      POPULATE_TENSOR_PROTO(tensor, t, int16, int)
-    } else if (tensor->dtype() == DT_INT8) {
-      POPULATE_TENSOR_PROTO(tensor, t, int8, int)
-    } else if (tensor->dtype() == DT_UINT8) {
-      POPULATE_TENSOR_PROTO(tensor, t, uint8, int)
-    } else if (tensor->dtype() == DT_BOOL) {
-      POPULATE_TENSOR_PROTO(tensor, t, bool, bool)
+#define POPULATE_TENSOR_PROTO(tensor, t, TYPE, NAME)                  \
+  {                                                                   \
+    const TYPE* val_ptr = tensor->flat<TYPE>().data();                \
+    TYPE last = *val_ptr;                                             \
+    int64 last_index = 0;                                             \
+    for (int64 i = 0; i < tensor->NumElements(); ++i) {               \
+      TYPE cur = *val_ptr++;                                          \
+      if (cur != last) {                                              \
+        last = cur;                                                   \
+        last_index = i;                                               \
+      }                                                               \
+    }                                                                 \
+    if (last_index < kint32max) {                                     \
+      optimized = true;                                               \
+      encoded_size = (last_index + 1) * sizeof(NAME);                 \
+      t->mutable_##NAME##_val()->Reserve(last_index + 1);             \
+      t->mutable_##NAME##_val()->AddNAlreadyReserved(last_index + 1); \
+      val_ptr = tensor->flat<TYPE>().data();                          \
+      for (int64 i = 0; i <= last_index; ++i) {                       \
+        t->set_##NAME##_val(i, *val_ptr++);                           \
+      }                                                               \
+    }                                                                 \
+  }                                                                   \
+  break
+
+    switch (tensor->dtype()) {
+      case DT_FLOAT:
+        POPULATE_TENSOR_PROTO(tensor, t, float, float);
+      case DT_DOUBLE:
+        POPULATE_TENSOR_PROTO(tensor, t, double, double);
+      case DT_INT64:
+        POPULATE_TENSOR_PROTO(tensor, t, int64, int64);
+      case DT_INT32:
+        POPULATE_TENSOR_PROTO(tensor, t, int32, int);
+      case DT_INT16:
+        POPULATE_TENSOR_PROTO(tensor, t, int16, int);
+      case DT_INT8:
+        POPULATE_TENSOR_PROTO(tensor, t, int8, int);
+      case DT_UINT8:
+        POPULATE_TENSOR_PROTO(tensor, t, uint8, int);
+      case DT_BOOL:
+        POPULATE_TENSOR_PROTO(tensor, t, bool, bool);
+      default:
+        /* Do nothing. */
+        break;
     }
   }
   if (optimized) {
@@ -1469,9 +1476,111 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
       for (int j = 0; j < shape.dim_size(); ++j) {
         replaceable &= shape.dim(j).size() == 1;
       }
-      if (replaceable) ReplaceOperationWithIdentity(0, node, output);
+      if (replaceable) {
+        ReplaceOperationWithIdentity(0, node, output);
+      }
     }
 
+    // Switch(x, x) will always feed false to its false branch and true to
+    // its true branch. By rewriting the graph a bit, we can propagate these
+    // constants down the two output branches, and just use control dependencies
+    // to trigger the selected one at runtime. For example,
+    //
+    //     +------+
+    // x-->|Switch|-->a  (in practice there may be multiple consumers of each
+    // x-->|      |-->b   output branch.)
+    //     +------+
+    //
+    // Is rewritten as
+    //
+    //     +------+
+    // x-->|Switch|-->Identity--^>Const(false)-->a
+    // x-->|      |-->Identity--^>Const(true)-->b
+    //     +------+
+    if (node->op() == "Switch" && node->input(0) == node->input(1) &&
+        !OptimizedNodeExists(*node, "_const_false") &&
+        !OptimizedNodeExists(*node, "_const_true")) {
+      bool already_optimized = true;
+      // If the optimization was already applied, the switch would have exactly
+      // one Identity node consuming each of its outputs, each without any
+      // non-control outputs.
+      auto fanouts = node_map_->GetOutputs(node->name());
+      if (fanouts.size() == 2) {
+        for (NodeDef* fanout : fanouts) {
+          if (!IsIdentity(*fanout) ||
+              NumNonControlOutputs(*fanout, *node_map_) > 0) {
+            already_optimized = false;
+            break;
+          }
+        }
+      }
+      Tensor false_t(DT_BOOL, TensorShape({}));
+      Tensor true_t(DT_BOOL, TensorShape({}));
+      // Make sure we don't proceed if this switch node was already optimized.
+      if (!already_optimized && SetTensorValue(DT_BOOL, true, &true_t).ok() &&
+          SetTensorValue(DT_BOOL, false, &false_t).ok()) {
+        // Copy the set of consumers of the switch as they will be manipulated
+        // below.
+        const std::set<NodeDef*>& consumer_set =
+            node_map_->GetOutputs(node->name());
+        std::vector<NodeDef*> consumers(consumer_set.begin(),
+                                        consumer_set.end());
+        std::sort(consumers.begin(), consumers.end(),
+                  [](const NodeDef* n1, const NodeDef* n2) {
+                    return n1->name() < n2->name();
+                  });
+        // Create constant false & true nodes.
+        NodeDef* false_node = output->add_node();
+        false_node->set_name(OptimizedNodeName(*node, "_const_false"));
+        if (!CreateNodeDef(false_node->name(), TensorValue(&false_t),
+                           false_node)
+                 .ok()) {
+          continue;
+        }
+        false_node->set_device(node->device());
+
+        NodeDef* true_node = output->add_node();
+        true_node->set_name(OptimizedNodeName(*node, "_const_true"));
+        if (!CreateNodeDef(true_node->name(), TensorValue(&true_t), true_node)
+                 .ok()) {
+          continue;
+        }
+        true_node->set_device(node->device());
+
+        // Add controls from the switch ports to the constants, and connect the
+        // constants to the original switch outputs.
+        const string false_port = node->name();
+        const string true_port = strings::StrCat(node->name(), ":1");
+        const string false_ctrl_dep =
+            AddControlDependency(false_port, output, node_map_.get());
+        false_node->add_input(false_ctrl_dep);
+        const string true_ctrl_dep =
+            AddControlDependency(true_port, output, node_map_.get());
+        true_node->add_input(true_ctrl_dep);
+
+        node_map_->AddNode(false_node->name(), false_node);
+        node_map_->AddNode(true_node->name(), true_node);
+        node_map_->AddOutput(NodeName(false_ctrl_dep), false_node->name());
+        node_map_->AddOutput(NodeName(true_ctrl_dep), true_node->name());
+
+        for (NodeDef* consumer : consumers) {
+          for (int i = 0; i < consumer->input_size(); ++i) {
+            const string& input = consumer->input(i);
+            if (input == false_port) {
+              consumer->set_input(i, false_node->name());
+              node_map_->UpdateInput(consumer->name(), false_port,
+                                     false_node->name());
+            } else if (input == true_port) {
+              consumer->set_input(i, true_node->name());
+              node_map_->UpdateInput(consumer->name(), true_port,
+                                     true_node->name());
+            }
+          }
+        }
+        graph_modified_ = true;
+        continue;
+      }
+    }
     if (IsSimplifiableReduction(*node)) {
       // Replace the reduction node with an identity node, that can be further
       // optimized by the model pruner.
@@ -1547,9 +1656,8 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
       const bool y_is_zero = IsZeros(*y);
       const bool y_is_one = IsOnes(*y);
       const bool x_matches_output_shape = ShapesEqual(output_shape, x_shape);
-      if (x_matches_output_shape &&
-          (((is_mul || is_any_div) && y_is_one) ||
-           ((is_add || is_sub) && y_is_zero))) {
+      if (x_matches_output_shape && (((is_mul || is_any_div) && y_is_one) ||
+                                     ((is_add || is_sub) && y_is_zero))) {
         // x * 1 = x or x / 1 = x or x +/- 0 = x
         ReplaceOperationWithSnapshot(0, node, output);
         continue;
@@ -1601,8 +1709,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
       }
       // Insert new reciprocal op and change node from Div to Mul.
       NodeDef* reciprocal_node = output->add_node();
-      reciprocal_node->set_name(AddPrefixToNodeName(
-          strings::StrCat(node->name(), "_recip"), kConstantFoldingConst));
+      reciprocal_node->set_name(OptimizedNodeName(*node, "_recip"));
       reciprocal_node->set_op("Reciprocal");
       reciprocal_node->set_device(node->device());
       node->set_op("Mul");
@@ -1701,6 +1808,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
       graph_modified_ = true;
     }
   }
+
   return Status::OK();
 }
 
@@ -1779,5 +1887,5 @@ void ConstantFolding::Feedback(Cluster* cluster, const GrapplerItem& item,
   // Nothing to do for ConstantFolding.
 }
 
-}  // end namespace grappler
-}  // end namespace tensorflow
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 2048692c22..219f3bd5ec 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -469,7 +469,6 @@ TEST_F(ConstantFoldingTest, NeutralElement_PartialShape_KnownOutputShape) {
   GraphDef output;
   Status status = optimizer.Optimize(nullptr, item, &output);
   TF_EXPECT_OK(status);
-  LOG(INFO) << output.DebugString();
 
   EXPECT_EQ(10, output.node_size());
   for (int i = 0; i < output.node_size(); ++i) {
@@ -991,8 +990,10 @@ TEST_F(ConstantFoldingTest, SwitchNodesEmptyFetch) {
   EXPECT_EQ(present_nodes.size(), output.node_size());
   int found = 0;
   for (const auto& node : output.node()) {
-    EXPECT_TRUE(present_nodes.find(node.name()) != present_nodes.end());
-    EXPECT_TRUE(not_present_nodes.find(node.name()) == not_present_nodes.end());
+    EXPECT_TRUE(present_nodes.find(node.name()) != present_nodes.end())
+        << node.name();
+    EXPECT_TRUE(not_present_nodes.find(node.name()) == not_present_nodes.end())
+        << node.name();
     present_nodes.erase(node.name());
     not_present_nodes.erase(node.name());
     if (node.name() == "rank") {
@@ -1212,7 +1213,8 @@ TEST_F(ConstantFoldingTest, ShuffleReverseOnScalarRemoval) {
 }
 
 TEST_F(ConstantFoldingTest, NoOpReduction) {
-  // Build a simple graph with a reduction that can be reduced to the identity.
+  // Build a simple graph with a reduction that can be reduced to the
+  // identity.
   tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
 
   Output v = ops::Variable(scope.WithOpName("v"), {3, 5, 7}, DT_FLOAT);
@@ -1338,8 +1340,8 @@ TEST_F(ConstantFoldingTest, Packing) {
   TF_EXPECT_OK(status);
 
   // Make sure that the representation of the folded constant is space
-  // efficient: in particular, the whole message should be smaller than 8k (the
-  // size needed to naively encode 1000 floats folded twice).
+  // efficient: in particular, the whole message should be smaller than 8k
+  // (the size needed to naively encode 1000 floats folded twice).
   EXPECT_GT(8000, output.ByteSizeLong());
 }
 
@@ -1494,6 +1496,58 @@ TEST_F(ConstantFoldingTest, LargeConstant) {
   EXPECT_GT(1024 * 1024, output.ByteSizeLong());
 }
 
+TEST_F(ConstantFoldingTest, SwitchIdenticalInputs) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  Output x = ops::Placeholder(s.WithOpName("x"), DT_BOOL,
+                              ops::Placeholder::Shape(TensorShape({})));
+  ops::Switch sw = ops::Switch(s.WithOpName("switch"), x, x);
+  Output id_false = ops::LogicalNot(s.WithOpName("id_false"), sw.output_false);
+  Output id_true = ops::LogicalNot(s.WithOpName("id_true"), sw.output_true);
+
+  GrapplerItem item;
+  item.fetch.push_back("id_false");
+  item.fetch.push_back("id_true");
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  ConstantFolding fold(nullptr /* cpu_device */);
+  GraphDef output;
+  Status status = fold.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  EXPECT_EQ(6, output.node_size());
+  int found = 0;
+  for (const auto& node : output.node()) {
+    if (node.name() == "switch" || node.name() == "x") {
+      ++found;
+    }
+    if (node.name() == "id_false") {
+      EXPECT_EQ("Const", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("^ConstantFoldingCtrl/switch_0", node.input(0));
+      ++found;
+    }
+    if (node.name() == "id_true") {
+      EXPECT_EQ("Const", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("^ConstantFoldingCtrl/switch_1", node.input(0));
+      ++found;
+    }
+    if (node.name() == "ConstantFoldingCtrl/switch_0") {
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("switch", node.input(0));
+      ++found;
+    }
+    if (node.name() == "ConstantFoldingCtrl/switch_1") {
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("switch:1", node.input(0));
+      ++found;
+    }
+  }
+  EXPECT_EQ(6, found);
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/utils.cc b/tensorflow/core/grappler/utils.cc
index eb5a2c48dc..81bb5e6c3b 100644
--- a/tensorflow/core/grappler/utils.cc
+++ b/tensorflow/core/grappler/utils.cc
@@ -29,6 +29,18 @@ limitations under the License.
 
 namespace tensorflow {
 namespace grappler {
+namespace {
+template <typename T>
+bool SafeSetScalarTensorValue(double value, Tensor* tensor) {
+  using RealType = typename Eigen::NumTraits<T>::Real;
+  if (value > std::numeric_limits<RealType>::max() ||
+      value < std::numeric_limits<RealType>::min()) {
+    return false;
+  }
+  tensor->flat<T>()(0) = static_cast<T>(value);
+  return true;
+}
+}  // namespace
 
 NodeMap::NodeMap(GraphDef* graph) {
   CHECK(graph != nullptr);
@@ -402,5 +414,43 @@ string SimpleGraphView::PrintToString() const {
   return str;
 }
 
+#define HANDLE_CASE(DTYPE)                                          \
+  case DTYPE:                                                       \
+    if (!SafeSetScalarTensorValue<EnumToDataType<DTYPE>::Type>(     \
+            static_cast<double>(value), tensor)) {                  \
+      return errors::InvalidArgument("Cannot store value ", value,  \
+                                     " in tensor of type " #DTYPE); \
+    }                                                               \
+    break
+
+Status SetTensorValue(DataType dtype, int value, Tensor* tensor) {
+  // TODO(rmlarsen): Support more general shapes.
+  if (tensor->NumElements() != 1) {
+    return errors::InvalidArgument(
+        "Expected scalar tensor, got num_elements = ", tensor->NumElements());
+  }
+  switch (dtype) {
+    // TODO(rmlarsen): Handle DT_HALF.
+    //    HANDLE_CASE(DT_HALF);
+    HANDLE_CASE(DT_BOOL);
+    HANDLE_CASE(DT_FLOAT);
+    HANDLE_CASE(DT_DOUBLE);
+    HANDLE_CASE(DT_UINT8);
+    HANDLE_CASE(DT_INT8);
+    HANDLE_CASE(DT_UINT16);
+    HANDLE_CASE(DT_INT16);
+    HANDLE_CASE(DT_INT32);
+    HANDLE_CASE(DT_INT64);
+    HANDLE_CASE(DT_COMPLEX64);
+    HANDLE_CASE(DT_COMPLEX128);
+    default:
+      return errors::InvalidArgument("Unsupported type ",
+                                     DataTypeString(dtype));
+  }
+  return Status::OK();
+}
+
+#undef HANDLE_CASE
+
 }  // end namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h
index 4ecb28f681..255319693a 100644
--- a/tensorflow/core/grappler/utils.h
+++ b/tensorflow/core/grappler/utils.h
@@ -23,6 +23,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/threadpool.h"
@@ -167,6 +168,8 @@ NodeDef* GetTailOfChain(const NodeDef& source, const NodeMap& node_map,
 void PermuteNodesInPlace(GraphDef* graph, std::vector<int>* permutation,
                          bool invert_permutation);
 
+Status SetTensorValue(DataType dtype, int value, Tensor* tensor);
+
 class SimpleGraphView {
  public:
   Status Initialize(const GraphDef& graph) {
-- 
GitLab


From bff1648a179aa522fb13e2eb1b26f8464da26af6 Mon Sep 17 00:00:00 2001
From: Dustin Tran <trandustin@google.com>
Date: Thu, 22 Feb 2018 20:02:33 -0800
Subject: [PATCH 234/575] Unify metropolis_hastings interface with HMC kernel.

PiperOrigin-RevId: 186716023
---
 .../bayesflow/python/kernel_tests/hmc_test.py |  70 +--
 .../kernel_tests/metropolis_hastings_test.py  | 199 ++++++-
 .../contrib/bayesflow/python/ops/hmc_impl.py  |  70 ++-
 .../python/ops/metropolis_hastings.py         |   5 +-
 .../python/ops/metropolis_hastings_impl.py    | 511 +++++++++++-------
 5 files changed, 558 insertions(+), 297 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py
index 5bd834e562..819095a060 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/hmc_test.py
@@ -224,12 +224,13 @@ class HMCTest(test.TestCase):
 
     expected_exp_x = self._shape_param / self._rate_param
 
-    acceptance_probs_, samples_, expected_x_ = sess.run(
-        [kernel_results.acceptance_probs, samples, expected_x],
+    log_accept_ratio_, samples_, expected_x_ = sess.run(
+        [kernel_results.log_accept_ratio, samples, expected_x],
         feed_dict)
 
     actual_x = samples_.mean()
     actual_exp_x = np.exp(samples_).mean()
+    acceptance_probs = np.exp(np.minimum(log_accept_ratio_, 0.))
 
     logging_ops.vlog(1, "True      E[x, exp(x)]: {}\t{}".format(
         expected_x_, expected_exp_x))
@@ -237,10 +238,10 @@ class HMCTest(test.TestCase):
         actual_x, actual_exp_x))
     self.assertNear(actual_x, expected_x_, 2e-2)
     self.assertNear(actual_exp_x, expected_exp_x, 2e-2)
-    self.assertAllEqual(np.ones_like(acceptance_probs_, np.bool),
-                        acceptance_probs_ > 0.5)
-    self.assertAllEqual(np.ones_like(acceptance_probs_, np.bool),
-                        acceptance_probs_ <= 1.)
+    self.assertAllEqual(np.ones_like(acceptance_probs, np.bool),
+                        acceptance_probs > 0.5)
+    self.assertAllEqual(np.ones_like(acceptance_probs, np.bool),
+                        acceptance_probs <= 1.)
 
   def _chain_gets_correct_expectations_wrapper(self, independent_chain_ndims):
     with self.test_session(graph=ops.Graph()) as sess:
@@ -265,7 +266,7 @@ class HMCTest(test.TestCase):
           -x - x**2,  # Non-constant gradient.
           array_ops.fill(x.shape, math_ops.cast(-np.inf, x.dtype)))
     # This log_prob has the property that it is likely to attract
-    # the HMC flow toward, and below, zero...but for x <=0,
+    # the flow toward, and below, zero...but for x <=0,
     # log_prob(x) = -inf, which should result in rejection, as well
     # as a non-finite log_prob.  Thus, this distribution gives us an opportunity
     # to test out the kernel results ability to correctly capture rejections due
@@ -305,11 +306,10 @@ class HMCTest(test.TestCase):
       self.assertLess(0, neg_inf_mask.sum())
       # We better have some rejections due to something other than -inf.
       self.assertLess(neg_inf_mask.sum(), (~kernel_results_.is_accepted).sum())
-      # We better have been accepted a decent amount, even near the end of the
-      # chain, or else this HMC run just got stuck at some point.
+      # We better have accepted a decent amount, even near end of the chain.
       self.assertLess(
           0.1, kernel_results_.is_accepted[int(0.9 * num_results):].mean())
-      # We better not have any NaNs in proposed state or log_prob.
+      # We better not have any NaNs in states or log_prob.
       # We may have some NaN in grads, which involve multiplication/addition due
       # to gradient rules.  This is the known "NaN grad issue with tf.where."
       self.assertAllEqual(np.zeros_like(states_),
@@ -333,9 +333,11 @@ class HMCTest(test.TestCase):
       np.testing.assert_array_less(0., pstates_[~neg_inf_mask])
 
       # Acceptance probs are zero whenever proposed state is negative.
+      acceptance_probs = np.exp(np.minimum(
+          kernel_results_.log_accept_ratio, 0.))
       self.assertAllEqual(
           np.zeros_like(pstates_[neg_inf_mask]),
-          kernel_results_.acceptance_probs[neg_inf_mask])
+          acceptance_probs[neg_inf_mask])
 
       # The move is accepted ==> state = proposed state.
       self.assertAllEqual(
@@ -383,26 +385,28 @@ class HMCTest(test.TestCase):
         seed=44)
 
     [
-        acceptance_probs_,
-        bad_acceptance_probs_,
+        log_accept_ratio_,
+        bad_log_accept_ratio_,
         initial_draws_,
         updated_draws_,
         fake_draws_,
     ] = sess.run([
-        kernel_results.acceptance_probs,
-        bad_kernel_results.acceptance_probs,
+        kernel_results.log_accept_ratio,
+        bad_kernel_results.log_accept_ratio,
         initial_draws,
         sample,
         bad_sample,
     ], feed_dict)
 
     # Confirm step size is small enough that we usually accept.
-    self.assertGreater(acceptance_probs_.mean(), 0.5)
-    self.assertGreater(bad_acceptance_probs_.mean(), 0.5)
+    acceptance_probs = np.exp(np.minimum(log_accept_ratio_, 0.))
+    bad_acceptance_probs = np.exp(np.minimum(bad_log_accept_ratio_, 0.))
+    self.assertGreater(acceptance_probs.mean(), 0.5)
+    self.assertGreater(bad_acceptance_probs.mean(), 0.5)
 
     # Confirm step size is large enough that we sometimes reject.
-    self.assertLess(acceptance_probs_.mean(), 0.99)
-    self.assertLess(bad_acceptance_probs_.mean(), 0.99)
+    self.assertLess(acceptance_probs.mean(), 0.99)
+    self.assertLess(bad_acceptance_probs.mean(), 0.99)
 
     _, ks_p_value_true = stats.ks_2samp(initial_draws_.flatten(),
                                         updated_draws_.flatten())
@@ -410,9 +414,9 @@ class HMCTest(test.TestCase):
                                         fake_draws_.flatten())
 
     logging_ops.vlog(1, "acceptance rate for true target: {}".format(
-        acceptance_probs_.mean()))
+        acceptance_probs.mean()))
     logging_ops.vlog(1, "acceptance rate for fake target: {}".format(
-        bad_acceptance_probs_.mean()))
+        bad_acceptance_probs.mean()))
     logging_ops.vlog(1, "K-S p-value for true target: {}".format(
         ks_p_value_true))
     logging_ops.vlog(1, "K-S p-value for fake target: {}".format(
@@ -615,15 +619,16 @@ class HMCTest(test.TestCase):
           step_size=2.,
           num_leapfrog_steps=5,
           seed=46)
-      initial_x_, updated_x_, acceptance_probs_ = sess.run(
-          [initial_x, updated_x, kernel_results.acceptance_probs])
+      initial_x_, updated_x_, log_accept_ratio_ = sess.run(
+          [initial_x, updated_x, kernel_results.log_accept_ratio])
+      acceptance_probs = np.exp(np.minimum(log_accept_ratio_, 0.))
 
       logging_ops.vlog(1, "initial_x = {}".format(initial_x_))
       logging_ops.vlog(1, "updated_x = {}".format(updated_x_))
-      logging_ops.vlog(1, "acceptance_probs = {}".format(acceptance_probs_))
+      logging_ops.vlog(1, "log_accept_ratio = {}".format(log_accept_ratio_))
 
       self.assertAllEqual(initial_x_, updated_x_)
-      self.assertEqual(acceptance_probs_, 0.)
+      self.assertEqual(acceptance_probs, 0.)
 
   def testNanFromGradsDontPropagate(self):
     """Test that update with NaN gradients does not cause NaN in results."""
@@ -638,15 +643,16 @@ class HMCTest(test.TestCase):
           step_size=2.,
           num_leapfrog_steps=5,
           seed=47)
-      initial_x_, updated_x_, acceptance_probs_ = sess.run(
-          [initial_x, updated_x, kernel_results.acceptance_probs])
+      initial_x_, updated_x_, log_accept_ratio_ = sess.run(
+          [initial_x, updated_x, kernel_results.log_accept_ratio])
+      acceptance_probs = np.exp(np.minimum(log_accept_ratio_, 0.))
 
       logging_ops.vlog(1, "initial_x = {}".format(initial_x_))
       logging_ops.vlog(1, "updated_x = {}".format(updated_x_))
-      logging_ops.vlog(1, "acceptance_probs = {}".format(acceptance_probs_))
+      logging_ops.vlog(1, "log_accept_ratio = {}".format(log_accept_ratio_))
 
       self.assertAllEqual(initial_x_, updated_x_)
-      self.assertEqual(acceptance_probs_, 0.)
+      self.assertEqual(acceptance_probs, 0.)
 
       self.assertAllFinite(
           gradients_ops.gradients(updated_x, initial_x)[0].eval())
@@ -671,10 +677,10 @@ class HMCTest(test.TestCase):
           step_size=0.01,
           num_leapfrog_steps=10,
           seed=48)
-      states_, acceptance_probs_ = sess.run(
-          [states, kernel_results.acceptance_probs])
+      states_, log_accept_ratio_ = sess.run(
+          [states, kernel_results.log_accept_ratio])
       self.assertEqual(dtype, states_.dtype)
-      self.assertEqual(dtype, acceptance_probs_.dtype)
+      self.assertEqual(dtype, log_accept_ratio_.dtype)
 
   def testChainWorksIn64Bit(self):
     self._testChainWorksDtype(np.float64)
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/metropolis_hastings_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/metropolis_hastings_test.py
index 63d93fad64..f508e5b114 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/metropolis_hastings_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/metropolis_hastings_test.py
@@ -12,34 +12,195 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for metropolis_hastings.py."""
+"""Tests for Metropolis-Hastings."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+
 from tensorflow.contrib.bayesflow.python.ops import metropolis_hastings_impl as mh
+from tensorflow.contrib.distributions.python.ops import mvn_tril as mvn_tril_lib
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
+from tensorflow.python.ops.distributions import normal as normal_lib
 from tensorflow.python.platform import test
 
 
-class McmcStepTest(test.TestCase):
+class MetropolisHastingsTest(test.TestCase):
+
+  def testKernelStateTensor(self):
+    """Test that transition kernel works with tensor input to `state`."""
+    loc = variable_scope.get_variable("loc", initializer=0.)
+
+    def target_log_prob_fn(loc):
+      return normal_lib.Normal(loc=0.0, scale=0.1).log_prob(loc)
+
+    new_state, _ = mh.kernel(
+        target_log_prob_fn=target_log_prob_fn,
+        proposal_fn=mh.proposal_normal(scale=0.05),
+        current_state=loc,
+        seed=231251)
+    loc_update = loc.assign(new_state)
+
+    init = variables.initialize_all_variables()
+    with self.test_session() as sess:
+      sess.run(init)
+      loc_samples = []
+      for _ in range(2500):
+        loc_sample = sess.run(loc_update)
+        loc_samples.append(loc_sample)
+    loc_samples = loc_samples[500:]  # drop samples for burn-in
+
+    self.assertAllClose(np.mean(loc_samples), 0.0, rtol=1e-5, atol=1e-1)
+    self.assertAllClose(np.std(loc_samples), 0.1, rtol=1e-5, atol=1e-1)
+
+  def testKernelStateList(self):
+    """Test that transition kernel works with list input to `state`."""
+    num_chains = 2
+    loc_one = variable_scope.get_variable(
+        "loc_one", [num_chains],
+        initializer=init_ops.zeros_initializer())
+    loc_two = variable_scope.get_variable(
+        "loc_two", [num_chains], initializer=init_ops.zeros_initializer())
+
+    def target_log_prob_fn(loc_one, loc_two):
+      loc = array_ops.stack([loc_one, loc_two])
+      log_prob = mvn_tril_lib.MultivariateNormalTriL(
+          loc=constant_op.constant([0., 0.]),
+          scale_tril=constant_op.constant([[0.1, 0.1], [0.0, 0.1]])).log_prob(
+              loc)
+      return math_ops.reduce_sum(log_prob, 0)
+
+    def proposal_fn(loc_one, loc_two):
+      loc_one_proposal = mh.proposal_normal(scale=0.05)
+      loc_two_proposal = mh.proposal_normal(scale=0.05)
+      loc_one_sample, _ = loc_one_proposal(loc_one)
+      loc_two_sample, _ = loc_two_proposal(loc_two)
+      return [loc_one_sample, loc_two_sample], None
+
+    new_state, _ = mh.kernel(
+        target_log_prob_fn=target_log_prob_fn,
+        proposal_fn=proposal_fn,
+        current_state=[loc_one, loc_two],
+        seed=12415)
+    loc_one_update = loc_one.assign(new_state[0])
+    loc_two_update = loc_two.assign(new_state[1])
+
+    init = variables.initialize_all_variables()
+    with self.test_session() as sess:
+      sess.run(init)
+      loc_one_samples = []
+      loc_two_samples = []
+      for _ in range(10000):
+        loc_one_sample, loc_two_sample = sess.run(
+            [loc_one_update, loc_two_update])
+        loc_one_samples.append(loc_one_sample)
+        loc_two_samples.append(loc_two_sample)
+
+    loc_one_samples = np.array(loc_one_samples)
+    loc_two_samples = np.array(loc_two_samples)
+    loc_one_samples = loc_one_samples[1000:]  # drop samples for burn-in
+    loc_two_samples = loc_two_samples[1000:]  # drop samples for burn-in
+
+    self.assertAllClose(np.mean(loc_one_samples, 0),
+                        np.array([0.] * num_chains),
+                        rtol=1e-5, atol=1e-1)
+    self.assertAllClose(np.mean(loc_two_samples, 0),
+                        np.array([0.] * num_chains),
+                        rtol=1e-5, atol=1e-1)
+    self.assertAllClose(np.std(loc_one_samples, 0),
+                        np.array([0.1] * num_chains),
+                        rtol=1e-5, atol=1e-1)
+    self.assertAllClose(np.std(loc_two_samples, 0),
+                        np.array([0.1] * num_chains),
+                        rtol=1e-5, atol=1e-1)
+
+  def testKernelResultsUsingTruncatedDistribution(self):
+    def log_prob(x):
+      return array_ops.where(
+          x >= 0.,
+          -x - x**2,
+          array_ops.fill(x.shape, math_ops.cast(-np.inf, x.dtype)))
+    # The truncated distribution has the property that it is likely to attract
+    # the flow toward, and below, zero...but for x <=0,
+    # log_prob(x) = -inf, which should result in rejection, as well
+    # as a non-finite log_prob.  Thus, this distribution gives us an opportunity
+    # to test out the kernel results ability to correctly capture rejections due
+    # to finite AND non-finite reasons.
+
+    num_results = 1000
+    # Large step size, will give rejections due to going into a region of
+    # log_prob = -inf.
+    step_size = 0.3
+    num_chains = 2
+
+    with self.test_session(graph=ops.Graph()) as sess:
+
+      # Start multiple independent chains.
+      initial_state = ops.convert_to_tensor([0.1] * num_chains)
 
-  def test_density_increasing_step_accepted(self):
+      states = []
+      is_accepted = []
+      proposed_states = []
+      current_state = initial_state
+      for _ in range(num_results):
+        current_state, kernel_results = mh.kernel(
+            target_log_prob_fn=log_prob,
+            proposal_fn=mh.proposal_uniform(step_size=step_size),
+            current_state=current_state,
+            seed=42)
+        states.append(current_state)
+        proposed_states.append(kernel_results.proposed_state)
+        is_accepted.append(kernel_results.is_accepted)
+
+      states = array_ops.stack(states)
+      proposed_states = array_ops.stack(proposed_states)
+      is_accepted = array_ops.stack(is_accepted)
+      states_, pstates_, is_accepted_ = sess.run(
+          [states, proposed_states, is_accepted])
+
+      # We better have accepted a decent amount, even near end of the chain.
+      self.assertLess(
+          0.1, is_accepted_[int(0.9 * num_results):].mean())
+      # We better not have any NaNs in states.
+      self.assertAllEqual(np.zeros_like(states_),
+                          np.isnan(states_))
+      # We better not have any +inf in states.
+      self.assertAllEqual(np.zeros_like(states_),
+                          np.isposinf(states_))
+
+      # The move is accepted ==> state = proposed state.
+      self.assertAllEqual(
+          states_[is_accepted_],
+          pstates_[is_accepted_],
+      )
+
+      # The move was rejected <==> state[t] == state[t - 1].
+      for t in range(1, num_results):
+        for i in range(num_chains):
+          if is_accepted_[t, i]:
+            self.assertNotEqual(states_[t, i], states_[t - 1, i])
+          else:
+            self.assertEqual(states_[t, i], states_[t - 1, i])
+
+  def testDensityIncreasingStepAccepted(self):
     """Tests that if a transition increases density, it is always accepted."""
     target_log_density = lambda x: - x * x
-    state = variable_scope.get_variable('state', initializer=10.)
+    state = variable_scope.get_variable("state", initializer=10.)
     state_log_density = variable_scope.get_variable(
-        'state_log_density',
+        "state_log_density",
         initializer=target_log_density(state.initialized_value()))
     log_accept_ratio = variable_scope.get_variable(
-        'log_accept_ratio', initializer=0.)
+        "log_accept_ratio", initializer=0.)
 
     get_next_proposal = lambda x: (x - 1., None)
     step = mh.evolve(state, state_log_density, log_accept_ratio,
@@ -54,7 +215,7 @@ class McmcStepTest(test.TestCase):
         self.assertAlmostEqual(sample, 9 - j)
         self.assertAlmostEqual(sample_log_density, - (9 - j) * (9 - j))
 
-  def test_sample_properties(self):
+  def testSampleProperties(self):
     """Tests that the samples converge to the target distribution."""
 
     def target_log_density(x):
@@ -62,16 +223,16 @@ class McmcStepTest(test.TestCase):
       return - (x - 2.0) * (x - 2.0) * 0.5
 
     # Use the uniform random walker to generate proposals.
-    proposal_fn = mh.uniform_random_proposal(
+    proposal_fn = mh.proposal_uniform(
         step_size=1.0, seed=1234)
 
-    state = variable_scope.get_variable('state', initializer=0.0)
+    state = variable_scope.get_variable("state", initializer=0.0)
     state_log_density = variable_scope.get_variable(
-        'state_log_density',
+        "state_log_density",
         initializer=target_log_density(state.initialized_value()))
-
     log_accept_ratio = variable_scope.get_variable(
-        'log_accept_ratio', initializer=0.)
+        "log_accept_ratio", initializer=0.)
+
     # Random walk MCMC converges slowly so need to put in enough iterations.
     num_iterations = 5000
     step = mh.evolve(state, state_log_density, log_accept_ratio,
@@ -98,11 +259,11 @@ class McmcStepTest(test.TestCase):
     self.assertAlmostEqual(sample_mean, 2.0, delta=0.1)
     self.assertAlmostEqual(sample_variance, 1.0, delta=0.1)
 
-  def test_normal_proposals(self):
+  def testProposalNormal(self):
     """Tests that the normal proposals are correctly distributed."""
 
     initial_points = array_ops.ones([10000], dtype=dtypes.float32)
-    proposal_fn = mh.normal_random_proposal(
+    proposal_fn = mh.proposal_normal(
         scale=2.0, seed=1234)
     proposal_points, _ = proposal_fn(initial_points)
 
@@ -115,7 +276,7 @@ class McmcStepTest(test.TestCase):
     self.assertAlmostEqual(np.mean(sample), 1.0, delta=0.1)
     self.assertAlmostEqual(np.std(sample), 2.0, delta=0.1)
 
-  def test_docstring_example(self):
+  def testDocstringExample(self):
     """Tests the simplified docstring example with multiple chains."""
 
     n = 2  # dimension of the problem
@@ -123,7 +284,7 @@ class McmcStepTest(test.TestCase):
     # Generate 300 initial values randomly. Each of these would be an
     # independent starting point for a Markov chain.
     state = variable_scope.get_variable(
-        'state', initializer=random_ops.random_normal(
+        "state", initializer=random_ops.random_normal(
             [300, n], mean=3.0, dtype=dtypes.float32, seed=42))
 
     # Computes the log(p(x)) for the unit normal density and ignores the
@@ -133,12 +294,12 @@ class McmcStepTest(test.TestCase):
 
     # Initial log-density value
     state_log_density = variable_scope.get_variable(
-        'state_log_density',
+        "state_log_density",
         initializer=log_density(state.initialized_value()))
 
     # A variable to store the log_acceptance_ratio:
     log_acceptance_ratio = variable_scope.get_variable(
-        'log_acceptance_ratio',
+        "log_acceptance_ratio",
         initializer=array_ops.zeros([300], dtype=dtypes.float32))
 
     # Generates random proposals by moving each coordinate uniformly and
@@ -175,5 +336,5 @@ class McmcStepTest(test.TestCase):
                         - np.reshape(covariance, [n**2]))), 0,
           delta=0.2)
 
-if __name__ == '__main__':
+if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py b/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py
index 9e45c19411..82693c2b7b 100644
--- a/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/hmc_impl.py
@@ -46,15 +46,13 @@ __all__ = [
 KernelResults = collections.namedtuple(
     "KernelResults",
     [
-        "acceptance_probs",
+        "log_accept_ratio",
         "current_grads_target_log_prob",  # "Current result" means "accepted".
         "current_target_log_prob",  # "Current result" means "accepted".
-        "energy_change",
         "is_accepted",
         "proposed_grads_target_log_prob",
         "proposed_state",
         "proposed_target_log_prob",
-        "random_positive",
     ])
 
 
@@ -63,15 +61,13 @@ def _make_dummy_kernel_results(
     dummy_target_log_prob,
     dummy_grads_target_log_prob):
   return KernelResults(
-      acceptance_probs=dummy_target_log_prob,
+      log_accept_ratio=dummy_target_log_prob,
       current_grads_target_log_prob=dummy_grads_target_log_prob,
       current_target_log_prob=dummy_target_log_prob,
-      energy_change=dummy_target_log_prob,
       is_accepted=array_ops.ones_like(dummy_target_log_prob, dtypes.bool),
       proposed_grads_target_log_prob=dummy_grads_target_log_prob,
       proposed_state=dummy_state,
       proposed_target_log_prob=dummy_target_log_prob,
-      random_positive=dummy_target_log_prob,
   )
 
 
@@ -244,7 +240,7 @@ def sample_chain(
       Default value: `None` (i.e., "hmc_sample_chain").
 
   Returns:
-    accepted_states: Tensor or Python list of `Tensor`s representing the
+    next_states: Tensor or Python list of `Tensor`s representing the
       state(s) of the Markov chain(s) at each result step. Has same shape as
       input `current_state` but with a prepended `num_results`-size dimension.
     kernel_results: `collections.namedtuple` of internal calculations used to
@@ -470,7 +466,7 @@ def sample_annealed_importance_chain(
       Default value: `None` (i.e., "hmc_sample_annealed_importance_chain").
 
   Returns:
-    accepted_state: `Tensor` or Python list of `Tensor`s representing the
+    next_state: `Tensor` or Python list of `Tensor`s representing the
       state(s) of the Markov chain(s) at the final iteration. Has same shape as
       input `current_state`.
     ais_weights: Tensor with the estimated weight(s). Has shape matching
@@ -591,18 +587,19 @@ def kernel(target_log_prob_fn,
 
   target = tfd.Normal(loc=dtype(0), scale=dtype(1))
 
-  new_x, other_results = hmc.kernel(
+  next_x, other_results = hmc.kernel(
       target_log_prob_fn=target.log_prob,
       current_state=x,
       step_size=step_size,
       num_leapfrog_steps=3)[:4]
 
-  x_update = x.assign(new_x)
+  x_update = x.assign(next_x)
 
   step_size_update = step_size.assign_add(
       step_size * tf.where(
-        other_results.acceptance_probs > target_accept_rate,
-        0.01, -0.01))
+          tf.exp(tf.minimum(other_results.log_accept_ratio), 0.) >
+              target_accept_rate,
+          0.01, -0.01))
 
   warmup = tf.group([x_update, step_size_update])
 
@@ -753,7 +750,7 @@ def kernel(target_log_prob_fn,
       Default value: `None` (i.e., "hmc_kernel").
 
   Returns:
-    accepted_state: Tensor or Python list of `Tensor`s representing the state(s)
+    next_state: Tensor or Python list of `Tensor`s representing the state(s)
       of the Markov chain(s) at each result step. Has same shape as
       `current_state`.
     kernel_results: `collections.namedtuple` of internal calculations used to
@@ -806,30 +803,27 @@ def kernel(target_log_prob_fn,
                                            proposed_target_log_prob,
                                            proposed_momentums,
                                            independent_chain_ndims)
+    log_accept_ratio = -energy_change
 
-    # u < exp(min(-energy, 0)),  where u~Uniform[0,1)
-    # ==> -log(u) >= max(e, 0)
-    # ==> -log(u) >= e
-    # (Perhaps surprisingly, we don't have a better way to obtain a random
-    # uniform from positive reals, i.e., `tf.random_uniform(minval=0,
-    # maxval=np.inf)` won't work.)
-    random_uniform = random_ops.random_uniform(
+    # u < exp(log_accept_ratio),  where u~Uniform[0,1)
+    # ==> log(u) < log_accept_ratio
+    random_value = random_ops.random_uniform(
         shape=array_ops.shape(energy_change),
         dtype=energy_change.dtype,
         seed=seed)
-    random_positive = -math_ops.log(random_uniform)
-    is_accepted = random_positive >= energy_change
+    random_negative = math_ops.log(random_value)
+    is_accepted = random_negative < log_accept_ratio
 
     accepted_target_log_prob = array_ops.where(is_accepted,
                                                proposed_target_log_prob,
                                                current_target_log_prob)
 
-    accepted_state_parts = [_choose(is_accepted,
-                                    proposed_state_part,
-                                    current_state_part,
-                                    independent_chain_ndims)
-                            for current_state_part, proposed_state_part
-                            in zip(current_state_parts, proposed_state_parts)]
+    next_state_parts = [_choose(is_accepted,
+                                proposed_state_part,
+                                current_state_part,
+                                independent_chain_ndims)
+                        for current_state_part, proposed_state_part
+                        in zip(current_state_parts, proposed_state_parts)]
 
     accepted_grads_target_log_prob = [
         _choose(is_accepted,
@@ -841,17 +835,15 @@ def kernel(target_log_prob_fn,
 
     maybe_flatten = lambda x: x if _is_list_like(current_state) else x[0]
     return [
-        maybe_flatten(accepted_state_parts),
+        maybe_flatten(next_state_parts),
         KernelResults(
-            acceptance_probs=math_ops.exp(math_ops.minimum(-energy_change, 0.)),
+            log_accept_ratio=log_accept_ratio,
             current_grads_target_log_prob=accepted_grads_target_log_prob,
             current_target_log_prob=accepted_target_log_prob,
-            energy_change=energy_change,
             is_accepted=is_accepted,
             proposed_grads_target_log_prob=proposed_grads_target_log_prob,
             proposed_state=maybe_flatten(proposed_state_parts),
             proposed_target_log_prob=proposed_target_log_prob,
-            random_positive=random_positive,
         ),
     ]
 
@@ -883,8 +875,8 @@ def _leapfrog_integrator(current_momentums,
   momentum = tf.placeholder(np.float32)
 
   [
-      new_momentums,
-      new_positions,
+      next_momentums,
+      next_positions,
   ] = hmc._leapfrog_integrator(
       current_momentums=[momentum],
       target_log_prob_fn=tfd.MultivariateNormalDiag(
@@ -901,7 +893,7 @@ def _leapfrog_integrator(current_momentums,
   positions = np.zeros([num_iter, dims], dtype)
   for i in xrange(num_iter):
     position_, momentum_ = sess.run(
-        [new_momentums[0], new_position[0]],
+        [next_momentums[0], next_position[0]],
         feed_dict={position: position_, momentum: momentum_})
     positions[i] = position_
 
@@ -944,9 +936,9 @@ def _leapfrog_integrator(current_momentums,
       state(s) of the Markov chain(s) at each result step. Has same shape as
       input `current_state_parts`.
     proposed_target_log_prob: `Tensor` representing the value of
-      `target_log_prob_fn` at `accepted_state`.
+      `target_log_prob_fn` at `next_state`.
     proposed_grads_target_log_prob: Gradient of `proposed_target_log_prob` wrt
-      `accepted_state`.
+      `next_state`.
 
   Raises:
     ValueError: if `len(momentums) != len(state_parts)`.
@@ -1066,8 +1058,8 @@ def _compute_energy_change(current_target_log_prob,
                                                   axis=-1)
     lk1 = -np.log(2.) + math_ops.reduce_logsumexp(array_ops.stack(lk1, axis=-1),
                                                   axis=-1)
-    lp0 = -current_target_log_prob   # log_potential
-    lp1 = -proposed_target_log_prob  # proposed_log_potential
+    lp0 = -current_target_log_prob   # potential
+    lp1 = -proposed_target_log_prob  # proposed_potential
     x = array_ops.stack([lp1, math_ops.exp(lk1), -lp0, -math_ops.exp(lk0)],
                         axis=-1)
 
diff --git a/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings.py b/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings.py
index 7bdeaa862d..e7fcbc65ef 100644
--- a/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings.py
+++ b/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings.py
@@ -25,9 +25,10 @@ from tensorflow.contrib.bayesflow.python.ops.metropolis_hastings_impl import *
 from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = [
+    'kernel',
     'evolve',
-    'uniform_random_proposal',
-    'normal_random_proposal',
+    'proposal_uniform',
+    'proposal_normal',
 ]
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings_impl.py b/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings_impl.py
index dc1ac68ce0..05aa134ed5 100644
--- a/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings_impl.py
+++ b/tensorflow/contrib/bayesflow/python/ops/metropolis_hastings_impl.py
@@ -12,17 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Functions to create a Markov Chain Monte Carlo Metropolis step.
+"""Metropolis-Hastings and proposal distributions.
 
+@@kernel
 @@evolve
-@@uniform_random_proposal
-@@normal_random_proposal
+@@proposal_uniform
+@@proposal_normal
 """
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
+
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
@@ -31,123 +34,198 @@ from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import state_ops
 
 __all__ = [
-    'evolve',
-    'uniform_random_proposal',
-    'normal_random_proposal',
+    "kernel",
+    "evolve",
+    "proposal_uniform",
+    "proposal_normal",
 ]
 
 
-def _single_iteration(current_state, current_log_density,
-                      log_unnormalized_prob_fn, proposal_fn, seed=None,
-                      name='None'):
-  """Performs a single Metropolis-Hastings step.
+KernelResults = collections.namedtuple(
+    "KernelResults",
+    [
+        "log_accept_ratio",
+        "current_target_log_prob",  # "Current result" means "accepted".
+        "is_accepted",
+        "proposed_state",
+    ])
+
+
+def kernel(target_log_prob_fn,
+           proposal_fn,
+           current_state,
+           seed=None,
+           current_target_log_prob=None,
+           name=None):
+  """Runs the Metropolis-Hastings transition kernel.
+
+  This function can update multiple chains in parallel. It assumes that all
+  leftmost dimensions of `current_state` index independent chain states (and are
+  therefore updated independently). The output of `target_log_prob_fn()` should
+  sum log-probabilities across all event dimensions. Slices along the rightmost
+  dimensions may have different target distributions; for example,
+  `current_state[0, :]` could have a different target distribution from
+  `current_state[1, :]`. This is up to `target_log_prob_fn()`. (The number of
+  independent chains is `tf.size(target_log_prob_fn(*current_state))`.)
 
   Args:
-    current_state: Float-like `Tensor` (i.e., `dtype` is either
-      `tf.float16`, `tf.float32` or `tf.float64`) of any shape that can
-      be consumed by the `log_unnormalized_prob_fn` and `proposal_fn`
-      callables.
-    current_log_density: Float-like `Tensor` with `dtype` and shape equivalent
-      to `log_unnormalized_prob_fn(current_state)`, i.e., matching the result of
-      `log_unnormalized_prob_fn` invoked at `current_state`.
-    log_unnormalized_prob_fn: A Python callable evaluated at
-      `current_state` and returning a float-like `Tensor` of log target-density
-      up to a normalizing constant. In other words,
-      `log_unnormalized_prob_fn(x) = log(g(x))`, where
-      `target_density = g(x)/Z` for some constant `A`. The shape of the input
-      tensor is the same as the shape of the `current_state`. The shape of the
-      output tensor is either
-        (a). Same as the input shape if the density being sampled is one
-          dimensional, or
-        (b). If the density is defined for `events` of shape
-          `event_shape = [E1, E2, ... Ee]`, then the input tensor should be of
-          shape `batch_shape + event_shape`, where `batch_shape = [B1, ..., Bb]`
-          and the result must be of shape [B1, ..., Bb]. For example, if the
-          distribution that is being sampled is a 10 dimensional normal,
-          then the input tensor may be of shape [100, 10] or [30, 20, 10]. The
-          last dimension will then be 'consumed' by `log_unnormalized_prob_fn`
-          and it should return tensors of shape [100] and [30, 20] respectively.
-    proposal_fn: A callable accepting a real valued `Tensor` of current sample
-      points and returning a tuple of two `Tensors`. The first element of the
-      pair is a `Tensor` containing the proposal state and should have
-      the same shape as the input `Tensor`. The second element of the pair gives
-      the log of the ratio of the probability of transitioning from the
-      proposal points to the input points and the probability of transitioning
-      from the input points to the proposal points. If the proposal is
-      symmetric (e.g., random walk, where the proposal is either
-      normal or uniform centered at `current_state`), i.e.,
-      Probability(Proposal -> Current) = Probability(Current -> Proposal)
-      the second value should be set to `None` instead of explicitly supplying a
-      tensor of zeros. In addition to being convenient, this also leads to a
-      more efficient graph.
-    seed: `int` or None. The random seed for this `Op`. If `None`, no seed is
-      applied.
-    name: Python `str` name prefix for ops managed by this function.
+    target_log_prob_fn: Python callable which takes an argument like
+      `current_state` (or `*current_state` if it's a list) and returns its
+      (possibly unnormalized) log-density under the target distribution.
+    proposal_fn: Python callable which takes an argument like `current_state`
+      (or `*current_state` if it's a list) and returns a tuple of proposed
+      states of same shape as `state`, and a log ratio `Tensor` of same shape
+      as `current_target_log_prob`. The log ratio is the log-probability of
+      `state` given proposed states minus the log-probability of proposed
+      states given `state`. If the proposal is symmetric, set the second value
+      to `None`: this enables more efficient computation than explicitly
+      supplying a tensor of zeros.
+    current_state: `Tensor` or Python `list` of `Tensor`s representing the
+      current state(s) of the Markov chain(s). The first `r` dimensions index
+      independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`.
+    seed: Python integer to seed the random number generator.
+    current_target_log_prob: (Optional) `Tensor` representing the value of
+      `target_log_prob_fn` at the `current_state`. The only reason to
+      specify this argument is to reduce TF graph size.
+      Default value: `None` (i.e., compute as needed).
+    name: A name of the operation (optional).
 
   Returns:
-    next_state: `Tensor` with `dtype` and shape matching `current_state`.
-      Created by propagating the chain by one step, starting from
+    next_state: Tensor or Python list of `Tensor`s representing the state(s)
+      of the Markov chain(s) at each result step. Has same shape as
       `current_state`.
-    next_log_density: `Tensor` with `dtype` and shape matching
-      `current_log_density`, which is equal to the value of the unnormalized
-      `log_unnormalized_prob_fn` computed at `next_state`.
-    log_accept_ratio: `Tensor` with `dtype` and shape matching
-      `current_log_density`. Stands for the log of Metropolis-Hastings
-      acceptance ratio used in generating the `next_state`.
-  """
+    kernel_results: `collections.namedtuple` of internal calculations used to
+      advance the chain.
 
-  with ops.name_scope(name, 'single_iteration', [current_state]):
-    # The proposed state and the log of the corresponding Hastings ratio.
-    proposal_state, log_transit_ratio = proposal_fn(current_state)
-
-    # If the log ratio is None, assume that the transitions are symmetric,
-    # i.e., Prob(Current -> Proposed) = Prob(Proposed -> Current).
-    if log_transit_ratio is None:
-      log_transit_ratio = 0.
-
-    # Log-density of the proposal state.
-    proposal_log_density = log_unnormalized_prob_fn(proposal_state)
-
-    # Ops to compute the log of the acceptance ratio. Recall that the
-    # acceptance ratio is: [Prob(Proposed) / Prob(Current)] *
-    # [Prob(Proposed -> Current) / Prob(Current -> Proposed)]. The log of the
-    # second term is the log_transit_ratio.
-    with ops.name_scope('accept_reject'):
-      # The log of the acceptance ratio.
-      log_accept_ratio = (proposal_log_density - current_log_density
-                          + log_transit_ratio)
-
-      # A proposal is accepted or rejected depending on the acceptance ratio.
-      # If the acceptance ratio is greater than 1 then it is always accepted.
-      # If the acceptance ratio is less than 1 then the proposal is accepted
-      # with probability = acceptance ratio. As we are working in log space to
-      # prevent over/underflows, this logic is expressed in log terms below.
-      # If a proposal is accepted we place a True in the acceptance state
-      # tensor and if it is to be rejected we place a False.
-      # The log_draws below have to be compared to the log_accept_ratio so we
-      # make sure that they have the same data type.
-      log_draws = math_ops.log(random_ops.random_uniform(
-          array_ops.shape(current_log_density), seed=seed,
-          dtype=log_accept_ratio.dtype))
-      is_proposal_accepted = log_draws < log_accept_ratio
-
-    # The acceptance state decides which elements of the current state are to
-    # be replaced with the corresponding elements in the proposal state.
-    with ops.name_scope(name, 'metropolis_single_step',
-                        [current_state, current_log_density]):
-      next_log_density = array_ops.where(is_proposal_accepted,
-                                         proposal_log_density,
-                                         current_log_density)
-      next_state = array_ops.where(is_proposal_accepted, proposal_state,
-                                   current_state)
-
-    return next_state, next_log_density, log_accept_ratio
+  #### Examples
+
+  We illustrate Metropolis-Hastings on a Normal likelihood with
+  unknown mean.
+
+  ```python
+  tfd = tf.contrib.distributions
+  tfp = tf.contrib.bayesflow
+
+  loc = tf.get_variable("loc", initializer=1.)
+  x = tf.constant([0.0] * 50)
+
+  def make_target_log_prob_fn(x):
+    def target_log_prob_fn(loc):
+      prior = tfd.Normal(loc=0., scale=1.)
+      likelihood = tfd.Independent(
+        tfd.Normal(loc=loc, scale=0.1),
+        reinterpreted_batch_ndims=1)
+      return prior.log_prob(loc) + likelihood.log_prob(x)
+    return target_log_prob_fn
+
+  next_state, kernel_results = tfp.metropolis_hastings.kernel(
+      target_log_prob_fn=make_target_log_prob_fn(x),
+      proposal_fn=tfp.metropolis_hastings.proposal_normal(),
+      current_state=loc)
+  loc_update = loc.assign(next_state)
+  ```
+
+  We illustrate Metropolis-Hastings on a Normal likelihood with
+  unknown mean and variance. We apply 4 chains.
+
+  ```python
+  tfd = tf.contrib.distributions
+  tfp = tf.contrib.bayesflow
+
+  num_chains = 4
+  loc = tf.get_variable("loc", shape=[num_chains],
+                        initializer=tf.random_normal_initializer())
+  scale = tf.get_variable("scale", shape=[num_chains],
+                          initializer=tf.ones_initializer())
+  x = tf.constant([0.0] * 50)
+
+  def make_target_log_prob_fn(x):
+    data = tf.reshape(x, shape=[-1, 1])
+    def target_log_prob_fn(loc, scale):
+      prior_loc = tfd.Normal(loc=0., scale=1.)
+      prior_scale = tfd.InverseGamma(concentration=1., rate=1.)
+      likelihood = tfd.Independent(
+        tfd.Normal(loc=loc, scale=scale),
+        reinterpreted_batch_ndims=1)
+      return (prior_loc.log_prob(loc) +
+              prior_scale.log_prob(scale) +
+              likelihood.log_prob(data))
+    return target_log_prob_fn
+
+  def proposal_fn(loc, scale):
+    loc_proposal = tfp.metropolis_hastings.proposal_normal()
+    scale_proposal = tfp.metropolis_hastings.proposal_uniform(minval=-1.)
+    proposed_loc, _ = loc_proposal(loc)
+    proposed_scale, _ = scale_proposal(scale)
+    proposed_scale = tf.maximum(proposed_scale, 0.01)
+    return [proposed_loc, proposed_scale], None
+
+  next_state, kernel_results = tfp.metropolis_hastings.kernel(
+      target_log_prob_fn=make_target_log_prob_fn(x),
+      proposal_fn=proposal_fn,
+      current_state=[loc, scale])
+  train_op = tf.group(loc.assign(next_state[0]),
+                      scale.assign(next_state[1]))
+  ```
+
+  """
+  with ops.name_scope(
+      name, "metropolis_hastings_kernel",
+      [current_state, seed, current_target_log_prob]):
+    with ops.name_scope("initialize"):
+      maybe_expand = lambda x: list(x) if _is_list_like(x) else [x]
+      current_state_parts = maybe_expand(current_state)
+      if current_target_log_prob is None:
+        current_target_log_prob = target_log_prob_fn(*current_state_parts)
+
+    proposed_state, log_transit_ratio = proposal_fn(*current_state_parts)
+    proposed_state_parts = maybe_expand(proposed_state)
+
+    proposed_target_log_prob = target_log_prob_fn(*proposed_state_parts)
+
+    with ops.name_scope(
+        "accept_reject",
+        [current_state_parts, proposed_state_parts,
+         current_target_log_prob, proposed_target_log_prob]):
+      log_accept_ratio = proposed_target_log_prob - current_target_log_prob
+      if log_transit_ratio is not None:
+        # If the log_transit_ratio is None, then assume the proposal is
+        # symmetric, i.e.,
+        #   log p(old | new) - log p(new | old) = 0.
+        log_accept_ratio += log_transit_ratio
+
+      # u < exp(log_accept_ratio),  where u~Uniform[0,1)
+      # ==> log(u) < log_accept_ratio
+      random_value = random_ops.random_uniform(
+          array_ops.shape(log_accept_ratio),
+          dtype=log_accept_ratio.dtype,
+          seed=seed)
+      random_negative = math_ops.log(random_value)
+      is_accepted = random_negative < log_accept_ratio
+      next_state_parts = [array_ops.where(is_accepted,
+                                          proposed_state_part,
+                                          current_state_part)
+                          for proposed_state_part, current_state_part in
+                          zip(proposed_state_parts, current_state_parts)]
+      accepted_log_prob = array_ops.where(is_accepted,
+                                          proposed_target_log_prob,
+                                          current_target_log_prob)
+    maybe_flatten = lambda x: x if _is_list_like(current_state) else x[0]
+    return [
+        maybe_flatten(next_state_parts),
+        KernelResults(
+            log_accept_ratio=log_accept_ratio,
+            current_target_log_prob=accepted_log_prob,
+            is_accepted=is_accepted,
+            proposed_state=maybe_flatten(proposed_state_parts),
+        ),
+    ]
 
 
 def evolve(initial_sample,
            initial_log_density,
            initial_log_accept_ratio,
-           log_unnormalized_prob_fn,
+           target_log_prob_fn,
            proposal_fn,
            n_steps=1,
            seed=None,
@@ -162,9 +240,11 @@ def evolve(initial_sample,
 
   The probability distribution may have an unknown normalization constan.
   We parameterize the probability density as follows:
-    ```
-      f(x) = exp(L(x) + constant)
-    ```
+
+  ```none
+  f(x) = exp(L(x) + constant)
+  ```
+
   Here `L(x)` is any continuous function with an (possibly unknown but finite)
   upper bound, i.e. there exists a number beta such that
   `L(x)< beta < infinity` for all x. The constant is the normalization needed
@@ -188,72 +268,77 @@ def evolve(initial_sample,
 
   The following example, demonstrates the use to generate a 1000 uniform random
   walk Metropolis samplers run in parallel for the normal target distribution.
+
   ```python
-    n = 3  # dimension of the problem
-
-    # Generate 1000 initial values randomly. Each of these would be an
-    # independent starting point for a Markov chain.
-    state = tf.get_variable(
-        'state',initializer=tf.random_normal([1000, n], mean=3.0,
-                                             dtype=tf.float64, seed=42))
-
-    # Computes the log(p(x)) for the unit normal density and ignores the
-    # normalization constant.
-    def log_density(x):
-      return  - tf.reduce_sum(x * x, reduction_indices=-1) / 2.0
-
-    # Initial log-density value
-    state_log_density = tf.get_variable(
-        'state_log_density', initializer=log_density(state.initialized_value()))
-
-    # A variable to store the log_acceptance_ratio:
-    log_acceptance_ratio = tf.get_variable(
-        'log_acceptance_ratio', initializer=tf.zeros([1000], dtype=tf.float64))
-
-    # Generates random proposals by moving each coordinate uniformly and
-    # independently in a box of size 2 centered around the current value.
-    # Returns the new point and also the log of the Hastings ratio (the
-    # ratio of the probability of going from the proposal to origin and the
-    # probability of the reverse transition). When this ratio is 1, the value
-    # may be omitted and replaced by None.
-    def random_proposal(x):
-      return (x + tf.random_uniform(tf.shape(x), minval=-1, maxval=1,
-                                    dtype=x.dtype, seed=12)), None
-
-    #  Create the op to propagate the chain for 100 steps.
-    stepper = mh.evolve(
-        state, state_log_density, log_acceptance_ratio,
-        log_density, random_proposal, n_steps=100, seed=123)
-    init = tf.initialize_all_variables()
-    with tf.Session() as sess:
-      sess.run(init)
-      # Run the chains for a total of 1000 steps and print out the mean across
-      # the chains every 100 iterations.
-      for n_iter in range(10):
-        # Executing the stepper advances the chain to the next state.
-        sess.run(stepper)
-        # Print out the current value of the mean(sample) for every dimension.
-        print(np.mean(sess.run(state), 0))
-      # Estimated covariance matrix
-      samples = sess.run(state)
-      print('')
-      print(np.cov(samples, rowvar=False))
+  n = 3  # dimension of the problem
+
+  # Generate 1000 initial values randomly. Each of these would be an
+  # independent starting point for a Markov chain.
+  state = tf.get_variable(
+      "state",
+      initializer=tf.random_normal([1000, n],
+                                   mean=3.0,
+                                   dtype=tf.float64,
+                                   seed=42))
+
+  # Computes the log(p(x)) for the unit normal density and ignores the
+  # normalization constant.
+  def log_density(x):
+    return -tf.reduce_sum(x * x, reduction_indices=-1) / 2.0
+
+  # Initial log-density value
+  state_log_density = tf.get_variable(
+      "state_log_density",
+      initializer=log_density(state.initialized_value()))
+
+  # A variable to store the log_acceptance_ratio:
+  log_acceptance_ratio = tf.get_variable(
+      "log_acceptance_ratio",
+      initializer=tf.zeros([1000], dtype=tf.float64))
+
+  # Generates random proposals by moving each coordinate uniformly and
+  # independently in a box of size 2 centered around the current value.
+  # Returns the new point and also the log of the Hastings ratio (the
+  # ratio of the probability of going from the proposal to origin and the
+  # probability of the reverse transition). When this ratio is 1, the value
+  # may be omitted and replaced by None.
+  def random_proposal(x):
+    return (x + tf.random_uniform(tf.shape(x), minval=-1, maxval=1,
+                                  dtype=x.dtype, seed=12)), None
+
+  #  Create the op to propagate the chain for 100 steps.
+  stepper = mh.evolve(
+      state, state_log_density, log_acceptance_ratio,
+      log_density, random_proposal, n_steps=100, seed=123)
+  init = tf.initialize_all_variables()
+  with tf.Session() as sess:
+    sess.run(init)
+    # Run the chains for a total of 1000 steps and print out the mean across
+    # the chains every 100 iterations.
+    for n_iter in range(10):
+      # Executing the stepper advances the chain to the next state.
+      sess.run(stepper)
+      # Print out the current value of the mean(sample) for every dimension.
+      print(np.mean(sess.run(state), 0))
+    # Estimated covariance matrix
+    samples = sess.run(state)
+    print(np.cov(samples, rowvar=False))
   ```
 
   Args:
     initial_sample: A float-like `tf.Variable` of any shape that can
-      be consumed by the `log_unnormalized_prob_fn` and `proposal_fn`
+      be consumed by the `target_log_prob_fn` and `proposal_fn`
       callables.
     initial_log_density: Float-like `tf.Variable` with `dtype` and shape
-      equivalent  to `log_unnormalized_prob_fn(initial_sample)`, i.e., matching
-        the result of `log_unnormalized_prob_fn` invoked at `current_state`.
+      equivalent  to `target_log_prob_fn(initial_sample)`, i.e., matching
+        the result of `target_log_prob_fn` invoked at `current_state`.
     initial_log_accept_ratio: A `tf.Variable` with `dtype` and shape matching
       `initial_log_density`. Stands for the log of Metropolis-Hastings
       acceptance ratio after propagating the chain for `n_steps`.
-    log_unnormalized_prob_fn: A Python callable evaluated at
+    target_log_prob_fn: A Python callable evaluated at
       `current_state` and returning a float-like `Tensor` of log target-density
       up to a normalizing constant. In other words,
-      `log_unnormalized_prob_fn(x) = log(g(x))`, where
+      `target_log_prob_fn(x) = log(g(x))`, where
       `target_density = g(x)/Z` for some constant `A`. The shape of the input
       tensor is the same as the shape of the `current_state`. The shape of the
       output tensor is either
@@ -265,7 +350,7 @@ def evolve(initial_sample,
           and the result must be of shape [B1, ..., Bb]. For example, if the
           distribution that is being sampled is a 10 dimensional normal,
           then the input tensor may be of shape [100, 10] or [30, 20, 10]. The
-          last dimension will then be 'consumed' by `log_unnormalized_prob_fn`
+          last dimension will then be 'consumed' by `target_log_prob_fn`
           and it should return tensors of shape [100] and [30, 20] respectively.
     proposal_fn: A callable accepting a real valued `Tensor` of current sample
       points and returning a tuple of two `Tensors`. The first element of the
@@ -289,42 +374,48 @@ def evolve(initial_sample,
     forward_step: an `Op` to step the Markov chain forward for `n_steps`.
   """
 
-  with ops.name_scope(name, 'metropolis_hastings', [initial_sample]):
+  with ops.name_scope(name, "metropolis_hastings", [initial_sample]):
     current_state = initial_sample
-    current_log_density = initial_log_density
+    current_target_log_prob = initial_log_density
     log_accept_ratio = initial_log_accept_ratio
 
-    # Stop condition for the while_loop
-    def stop_condition(i, _):
-      return i < n_steps
-
-    def step(i, loop_vars):
-      """Wrap `_single_iteration` for `while_loop`."""
-      state = loop_vars[0]
-      state_log_density = loop_vars[1]
-      return i + 1, list(_single_iteration(state, state_log_density,
-                                           log_unnormalized_prob_fn,
-                                           proposal_fn, seed=seed))
-
-    loop_vars = [current_state, current_log_density, log_accept_ratio]
-    # Build an `Op` to evolve the Markov chain for `n_steps`
-    (_, [end_state, end_log_density, end_log_acceptance]) = (
+    def step(i, current_state, current_target_log_prob, log_accept_ratio):
+      """Wrap single Markov chain iteration in `while_loop`."""
+      next_state, kernel_results = kernel(
+          target_log_prob_fn=target_log_prob_fn,
+          proposal_fn=proposal_fn,
+          current_state=current_state,
+          current_target_log_prob=current_target_log_prob,
+          seed=seed)
+      accepted_log_prob = kernel_results.current_target_log_prob
+      log_accept_ratio = kernel_results.log_accept_ratio
+      return i + 1, next_state, accepted_log_prob, log_accept_ratio
+
+    (_, accepted_state, accepted_target_log_prob, accepted_log_accept_ratio) = (
         control_flow_ops.while_loop(
-            stop_condition, step,
-            (0, loop_vars),
-            parallel_iterations=1, swap_memory=1))
+            cond=lambda i, *ignored_args: i < n_steps,
+            body=step,
+            loop_vars=[
+                0,  # i
+                current_state,
+                current_target_log_prob,
+                log_accept_ratio,
+            ],
+            parallel_iterations=1 if seed is not None else 10,
+            # TODO(b/73775595): Confirm optimal setting of swap_memory.
+            swap_memory=1))
 
     forward_step = control_flow_ops.group(
-        state_ops.assign(current_log_density, end_log_density),
-        state_ops.assign(current_state, end_state),
-        state_ops.assign(log_accept_ratio, end_log_acceptance))
+        state_ops.assign(current_target_log_prob, accepted_target_log_prob),
+        state_ops.assign(current_state, accepted_state),
+        state_ops.assign(log_accept_ratio, accepted_log_accept_ratio))
 
     return forward_step
 
 
-def uniform_random_proposal(step_size=1.,
-                            seed=None,
-                            name=None):
+def proposal_uniform(step_size=1.,
+                     seed=None,
+                     name=None):
   """Returns a callable that adds a random uniform tensor to the input.
 
   This function returns a callable that accepts one `Tensor` argument of any
@@ -346,11 +437,13 @@ def uniform_random_proposal(step_size=1.,
 
   Returns:
     proposal_fn:  A callable accepting one float-like `Tensor` and returning a
-    2-tuple. The first value in the tuple is a `Tensor` of the same shape and
-    dtype as the input argument and the second element of the tuple is None.
+      2-tuple. The first value in the tuple is a `Tensor` of the same shape and
+      dtype as the input argument and the second element of the tuple is None.
   """
 
-  with ops.name_scope(name, 'uniform_random_proposal', [step_size]):
+  with ops.name_scope(name, "proposal_uniform", [step_size]):
+    step_size = ops.convert_to_tensor(step_size, name="step_size")
+
     def proposal_fn(input_state, name=None):
       """Adds a uniform perturbation to the input state.
 
@@ -359,12 +452,12 @@ def uniform_random_proposal(step_size=1.,
         name: A string that sets the name for this `Op`.
 
       Returns:
-        proposal_state:  A float-like `Tensot` with `dtype` and shape matching
+        proposal_state:  A float-like `Tensor` with `dtype` and shape matching
           `input_state`.
         log_transit_ratio: `None`. Proposal is symmetric.
       """
-      with ops.name_scope(name, 'proposer', [input_state]):
-        input_state = ops.convert_to_tensor(input_state, name='input_state')
+      with ops.name_scope(name, "proposer", [input_state]):
+        input_state = ops.convert_to_tensor(input_state, name="input_state")
         return input_state + random_ops.random_uniform(
             array_ops.shape(input_state),
             minval=-step_size,
@@ -373,9 +466,9 @@ def uniform_random_proposal(step_size=1.,
     return proposal_fn
 
 
-def normal_random_proposal(scale=1.,
-                           seed=None,
-                           name=None):
+def proposal_normal(scale=1.,
+                    seed=None,
+                    name=None):
   """Returns a callable that adds a random normal tensor to the input.
 
   This function returns a callable that accepts one `Tensor` argument of any
@@ -398,11 +491,13 @@ def normal_random_proposal(scale=1.,
 
   Returns:
     proposal_fn: A callable accepting one float-like `Tensor` and returning a
-    2-tuple. The first value in the tuple is a `Tensor` of the same shape and
-    dtype as the input argument and the second element of the tuple is None.
+      2-tuple. The first value in the tuple is a `Tensor` of the same shape and
+      dtype as the input argument and the second element of the tuple is None.
   """
 
-  with ops.name_scope(name, 'normal_random_proposal', [scale]):
+  with ops.name_scope(name, "proposal_normal", [scale]):
+    scale = ops.convert_to_tensor(scale, name="scale")
+
     def proposal_fn(input_state, name=None):
       """Adds a normal perturbation to the input state.
 
@@ -411,16 +506,22 @@ def normal_random_proposal(scale=1.,
         name: A string that sets the name for this `Op`.
 
       Returns:
-        proposal_state:  A float-like `Tensot` with `dtype` and shape matching
+        proposal_state:  A float-like `Tensor` with `dtype` and shape matching
           `input_state`.
         log_transit_ratio: `None`. Proposal is symmetric.
       """
 
-      with ops.name_scope(name, 'proposer', [input_state]):
-        input_state = ops.convert_to_tensor(input_state, name='input_state')
+      with ops.name_scope(name, "proposer", [input_state]):
+        input_state = ops.convert_to_tensor(input_state, name="input_state")
         return input_state + random_ops.random_normal(
             array_ops.shape(input_state),
             mean=0.,
             stddev=scale,
+            dtype=scale.dtype,
             seed=seed), None
     return proposal_fn
+
+
+def _is_list_like(x):
+  """Helper which returns `True` if input is `list`-like."""
+  return isinstance(x, (tuple, list))
-- 
GitLab


From 1acc02f4689f0a5ac5ecd5bc1a1fa3b5236fd56c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 02:16:32 -0800
Subject: [PATCH 235/575] Let variables initialized from checkpoints answer
 ".initialized_value()" correctly.

PiperOrigin-RevId: 186741832
---
 .../python/training/checkpoint_utils.py       |  2 ++
 .../python/training/checkpoint_utils_test.py  | 30 +++++++++++++++++++
 2 files changed, 32 insertions(+)

diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py
index fa3de6fad2..0af1cdecfa 100644
--- a/tensorflow/python/training/checkpoint_utils.py
+++ b/tensorflow/python/training/checkpoint_utils.py
@@ -293,6 +293,8 @@ def _set_checkpoint_initializer(variable,
     restore_op = io_ops.restore_v2(
         ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
     variable._initializer_op = state_ops.assign(variable, restore_op)  # pylint:disable=protected-access
+    restore_op.set_shape(variable.shape)
+    variable._initial_value = restore_op  # pylint:disable=protected-access
 
 
 def _set_variable_or_list_initializer(variable_or_list, ckpt_file,
diff --git a/tensorflow/python/training/checkpoint_utils_test.py b/tensorflow/python/training/checkpoint_utils_test.py
index cd17faa040..a461b24cbb 100644
--- a/tensorflow/python/training/checkpoint_utils_test.py
+++ b/tensorflow/python/training/checkpoint_utils_test.py
@@ -145,6 +145,36 @@ class CheckpointsTest(test.TestCase):
         # Check that tensors are not explicitly in the graph.
         self.assertLess(len(str(session.graph.as_graph_def())), 29000)
 
+  def testInitialValueComesFromCheckpoint(self):
+    checkpoint_dir = self.get_temp_dir()
+    with self.test_session() as session:
+      v1, _, _, _ = _create_checkpoints(session, checkpoint_dir)
+
+    # New graph and session.
+    with ops.Graph().as_default() as g:
+      with self.test_session(graph=g) as session:
+        with variable_scope.variable_scope(
+            "some_scope", initializer=init_ops.zeros_initializer()):
+          my1 = variable_scope.get_variable("my1", [1, 10])
+
+        # At this point, my1.initialized_value() will add ops that reference
+        # the zeros initializer of my1.
+        before = variables.Variable(my1.initialized_value(), name="before")
+
+        checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"var1": my1})
+
+        # At this point, my1.initialized_value() will add ops that reference
+        # the newly set initializer of my1.
+        after = variables.Variable(my1.initialized_value(), name="after")
+
+        session.run(variables.global_variables_initializer())
+        self.assertAllEqual(session.run(my1), v1)
+        self.assertAllEqual(session.run(my1.initialized_value()), v1)
+        self.assertAllClose(session.run(before), [[0.0] * 10])
+        self.assertAllClose(session.run(after), v1)
+        with self.assertRaises(AssertionError):
+          self.assertAllClose(session.run(before), session.run(after))
+
   def testInitWithScopeDoesNotCaptureSuffixes(self):
     checkpoint_dir = self.get_temp_dir()
     with self.test_session() as session:
-- 
GitLab


From 7d095f1bccc9d923fe64420e552268d220160488 Mon Sep 17 00:00:00 2001
From: KB Sriram <kbsriram@gmail.com>
Date: Thu, 22 Feb 2018 07:21:39 -0800
Subject: [PATCH 236/575] C++ gradients for MaxPool3D, AvgPool and AvgPool3D

Resolves tensorflow/tensorflow#17195
---
 tensorflow/cc/gradients/nn_grad.cc      | 64 +++++++++++++++++++++++++
 tensorflow/cc/gradients/nn_grad_test.cc | 44 +++++++++++++++--
 2 files changed, 105 insertions(+), 3 deletions(-)

diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc
index 13a3bba5e6..63a67f09f6 100644
--- a/tensorflow/cc/gradients/nn_grad.cc
+++ b/tensorflow/cc/gradients/nn_grad.cc
@@ -196,6 +196,70 @@ Status MaxPoolGradV2Helper(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("MaxPoolV2", MaxPoolGradV2Helper);
 
+Status MaxPool3DGradHelper(const Scope& scope, const Operation& op,
+                           const std::vector<Output>& grad_inputs,
+                           std::vector<Output>* grad_outputs) {
+  std::vector<int32> ksize;
+  std::vector<int32> strides;
+  string padding;
+  string data_format;
+  auto attrs = op.output(0).node()->attrs();
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
+  MaxPool3DGrad::Attrs grad_attrs;
+  grad_attrs.DataFormat(data_format);
+  auto dx = MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0],
+                          ksize, strides, padding, grad_attrs);
+  grad_outputs->push_back(dx);
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("MaxPool3D", MaxPool3DGradHelper);
+
+Status AvgPoolGradHelper(const Scope& scope, const Operation& op,
+                         const std::vector<Output>& grad_inputs,
+                         std::vector<Output>* grad_outputs) {
+  std::vector<int32> ksize;
+  std::vector<int32> strides;
+  string padding;
+  string data_format;
+  auto attrs = op.output(0).node()->attrs();
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
+  internal::AvgPoolGrad::Attrs grad_attrs;
+  grad_attrs.DataFormat(data_format);
+  auto dx =
+      internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), grad_inputs[0],
+                            ksize, strides, padding, grad_attrs);
+  grad_outputs->push_back(dx);
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("AvgPool", AvgPoolGradHelper);
+
+Status AvgPool3DGradHelper(const Scope& scope, const Operation& op,
+                           const std::vector<Output>& grad_inputs,
+                           std::vector<Output>* grad_outputs) {
+  std::vector<int32> ksize;
+  std::vector<int32> strides;
+  string padding;
+  string data_format;
+  auto attrs = op.output(0).node()->attrs();
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
+  AvgPool3DGrad::Attrs grad_attrs;
+  grad_attrs.DataFormat(data_format);
+  auto dx = AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0],
+                          ksize, strides, padding, grad_attrs);
+  grad_outputs->push_back(dx);
+  return scope.status();
+}
+REGISTER_GRADIENT_OP("AvgPool3D", AvgPool3DGradHelper);
+
 Status LRNGradHelper(const Scope& scope, const Operation& op,
                      const std::vector<Output>& grad_inputs,
                      std::vector<Output>* grad_outputs){
diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc
index 0cfe5f6e3c..c4eba7ecb0 100644
--- a/tensorflow/cc/gradients/nn_grad_test.cc
+++ b/tensorflow/cc/gradients/nn_grad_test.cc
@@ -31,8 +31,11 @@ using ops::Elu;
 using ops::L2Loss;
 using ops::LogSoftmax;
 using ops::LRN;
+using ops::AvgPool;
+using ops::AvgPool3D;
 using ops::MaxPool;
 using ops::MaxPoolV2;
+using ops::MaxPool3D;
 using ops::Placeholder;
 using ops::Relu;
 using ops::Relu6;
@@ -70,9 +73,9 @@ class NNGradTest : public ::testing::Test {
 
   // Sets tensor with random values, ensuring that the max value is largest by
   // a reasonable amount.
-  // This is an issue for MaxPool and MaxPoolV2, in which perturbations by the
-  // numeric gradient computation in the gradient checker can change the max
-  // value if values are too close together.
+  // This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which
+  // perturbations by the numeric gradient computation in the gradient checker
+  // can change the max value if values are too close together.
   template <typename T>
   void SetRandomValuesWithBumpedMax(Tensor* tensor) {
     auto tensor_flat = tensor->flat<T>();
@@ -203,6 +206,41 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) {
   RunTest(x, x_init_value, y, y_shape);
 }
 
+TEST_F(NNGradTest, MaxPool3DGradHelper) {
+  TensorShape x_shape({1, 3, 3, 3, 1});
+  TensorShape y_shape({1, 1, 1, 1, 1});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
+  // Setup window and strides so that we only do one MaxPool3D.
+  const std::vector<int> ksize{1, 3, 3, 3, 1};
+  const std::vector<int> strides{1, 3, 3, 3, 1};
+  auto y = MaxPool3D(scope_, x, ksize, strides, "VALID");
+  Tensor x_init_value = Tensor(DT_FLOAT, x_shape);
+  SetRandomValuesWithBumpedMax<float>(&x_init_value);
+  RunTest(x, x_init_value, y, y_shape);
+}
+
+TEST_F(NNGradTest, AvgPoolGradHelper) {
+  TensorShape x_shape({1, 2, 2, 1});
+  TensorShape y_shape({1, 1, 1, 1});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
+  // Setup window and strides so that we only do one AvgPool.
+  const std::vector<int> ksize{1, 2, 2, 1};
+  const std::vector<int> strides{1, 2, 2, 1};
+  auto y = AvgPool(scope_, x, ksize, strides, "SAME");
+  RunTest(x, x_shape, y, y_shape);
+}
+
+TEST_F(NNGradTest, AvgPool3DGradHelper) {
+  TensorShape x_shape({1, 3, 3, 3, 1});
+  TensorShape y_shape({1, 1, 1, 1, 1});
+  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
+  // Setup window and strides so that we only do one AvgPool3D.
+  const std::vector<int> ksize{1, 3, 3, 3, 1};
+  const std::vector<int> strides{1, 3, 3, 3, 1};
+  auto y = AvgPool3D(scope_, x, ksize, strides, "SAME");
+  RunTest(x, x_shape, y, y_shape);
+}
+
 TEST_F(NNGradTest, LRN){
   TensorShape x_shape({1, 1, 2, 1});
   auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
-- 
GitLab


From cef8364617d417c1a8388c346c67f17a850c7f54 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 09:15:58 -0800
Subject: [PATCH 237/575] Allow setting of OpMetadata for Send HLOs.

PiperOrigin-RevId: 186777369
---
 tensorflow/compiler/xla/service/hlo_instruction.h   |  6 ++++++
 tensorflow/compiler/xla/service/hlo_module_config.h | 12 ++++++++++++
 tensorflow/compiler/xla/service/hlo_sharding.cc     |  6 +++++-
 tensorflow/compiler/xla/service/service.cc          |  6 ++++--
 tensorflow/compiler/xla/service/user_computation.cc |  5 +++--
 tensorflow/compiler/xla/service/user_computation.h  |  3 ++-
 6 files changed, 32 insertions(+), 6 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index c4fe132d1d..e4d22e5703 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -824,6 +824,12 @@ class HloInstruction {
   // Precondition: opcode() == HloOpcode::kSend or HloOpcode::kRecv
   int64 channel_id() const { return channel_id_; }
 
+  // Returns the channel name associated with the instruction. The name is
+  // used to identify host Send/Recv operations.
+  //
+  // Precondition: opcode() == HloOpcode::kHostCompute
+  string channel_name() const { return channel_name_; }
+
   // Returns feature_index field associated with the instruction. The index
   // represents the index of the feature dimension.
   //
diff --git a/tensorflow/compiler/xla/service/hlo_module_config.h b/tensorflow/compiler/xla/service/hlo_module_config.h
index a5ee895e48..d3c1fae592 100644
--- a/tensorflow/compiler/xla/service/hlo_module_config.h
+++ b/tensorflow/compiler/xla/service/hlo_module_config.h
@@ -67,6 +67,15 @@ class HloModuleConfig {
   bool hlo_profiling_enabled() const { return hlo_profiling_enabled_; }
   void enable_hlo_profiling(bool enabled) { hlo_profiling_enabled_ = enabled; }
 
+  // Sets/returns whether this is a "host module".  Host modules are used to
+  // record the data- and control-flow dependencies of host side computation
+  // that communicates with compiled code.  They are used for analysis and
+  // scheduling purposes, but no code is generated.
+  bool is_host_module() const { return is_host_module_; }
+  void set_is_host_module(bool is_host_module) {
+    is_host_module_ = is_host_module;
+  }
+
   // Sets/returns the module seed set during execution.
   void set_seed(uint64 seed) { seed_ = seed; }
   uint64 seed() const { return seed_; }
@@ -104,6 +113,9 @@ class HloModuleConfig {
   // Whether to enable HLO-level profiling.
   bool hlo_profiling_enabled_ = false;
 
+  // Whether this is a 'host module'.
+  bool is_host_module_ = false;
+
   // Module/graph-level seed handle.
   uint64 seed_ = 0;
 
diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc
index 447c244666..afe79c9f17 100644
--- a/tensorflow/compiler/xla/service/hlo_sharding.cc
+++ b/tensorflow/compiler/xla/service/hlo_sharding.cc
@@ -183,6 +183,10 @@ Status HloSharding::ValidateTuple(const Shape& shape, int64 num_devices) const {
   // shape tree.
   ShapeTree<HloSharding> shape_tree = GetAsShapeTree(shape);
   for (const auto& index_to_sharding : shape_tree.leaves()) {
+    if (index_to_sharding.first.empty()) {
+      // An empty tuple has a ShapeTree with a single leaf at the empty index.
+      continue;
+    }
     Status status = index_to_sharding.second.ValidateNonTuple(
         ShapeUtil::GetSubshape(shape, index_to_sharding.first), num_devices);
     if (!status.ok()) {
@@ -222,7 +226,7 @@ Status HloSharding::ValidateNonTuple(const Shape& shape,
   Status status = Status::OK();
   std::set<int64> seen_cores;
   tile_assignment_.Each(
-      [&](tensorflow::gtl::ArraySlice<int64> indices, uint32 core) {
+      [&](tensorflow::gtl::ArraySlice<int64> indices, int32 core) {
         // Don't overwrite a bad status, so we report the first error.
         if (status.ok()) {
           if (core >= num_devices) {
diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index e278eab690..43d0f60598 100644
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -1556,8 +1556,10 @@ tensorflow::Status Service::Op(const OpRequest* arg, OpResponse* result) {
     case OpRequest::kSendRequest: {
       TF_RETURN_IF_ERROR(
           channel_tracker_.RegisterSend(arg->send_request().channel_handle()));
-      TF_RETURN_IF_ERROR(computation->AddSendInstruction(arg->send_request()));
-      return tensorflow::Status::OK();
+      // Send does not return a value, but we need a handle to be able to
+      // set OpMetadata and OpSharding (device assignment).
+      handle_status = computation->AddSendInstruction(arg->send_request());
+      break;
     }
     case OpRequest::kRecvRequest: {
       TF_RETURN_IF_ERROR(
diff --git a/tensorflow/compiler/xla/service/user_computation.cc b/tensorflow/compiler/xla/service/user_computation.cc
index 4a55e4095a..06735e9442 100644
--- a/tensorflow/compiler/xla/service/user_computation.cc
+++ b/tensorflow/compiler/xla/service/user_computation.cc
@@ -226,7 +226,8 @@ StatusOr<ComputationDataHandle> UserComputation::AddParameterInstruction(
   return handle;
 }
 
-Status UserComputation::AddSendInstruction(const SendRequest& send_request) {
+StatusOr<ComputationDataHandle> UserComputation::AddSendInstruction(
+    const SendRequest& send_request) {
   tensorflow::mutex_lock lock(mutex_);
 
   // Check if the operand of the instruction is valid.
@@ -244,7 +245,7 @@ Status UserComputation::AddSendInstruction(const SendRequest& send_request) {
   VLOG(1) << "AddSendInstruction (" << GetVersionedHandleInternal()
           << "), data handle " << handle.handle() << ": "
           << send_request.ShortDebugString();
-  return Status::OK();
+  return handle;
 }
 
 StatusOr<ComputationDataHandle> UserComputation::AddRecvInstruction(
diff --git a/tensorflow/compiler/xla/service/user_computation.h b/tensorflow/compiler/xla/service/user_computation.h
index fd5a2ace9b..5544c868fe 100644
--- a/tensorflow/compiler/xla/service/user_computation.h
+++ b/tensorflow/compiler/xla/service/user_computation.h
@@ -236,7 +236,8 @@ class UserComputation {
       const UserComputation& false_computation);
 
   // Enqueues a Send instruction onto this user computation.
-  Status AddSendInstruction(const SendRequest& send_request);
+  StatusOr<ComputationDataHandle> AddSendInstruction(
+      const SendRequest& send_request);
 
   // Enqueues a Recv instruction onto this user computation.
   StatusOr<ComputationDataHandle> AddRecvInstruction(
-- 
GitLab


From 9f76540a85370bc8aad610d80d50dbd7f0abb391 Mon Sep 17 00:00:00 2001
From: cclauss <cclauss@bluewin.ch>
Date: Fri, 23 Feb 2018 18:51:28 +0100
Subject: [PATCH 238/575] from six.moves import xrange for Python 3

Lines 1785 and 1818 contain calls to the Python 2-only builtin function __xrange()__ which was removed in Python 3 in favor of __range()__.  This PR adds the line [__from six.moves import xrange__](https://pythonhosted.org/six/#module-six.moves) for compatibility with both Python 2 and Python 3.
---
 tensorflow/contrib/lite/testing/generate_examples.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 2481add769..5488b71fcf 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -36,6 +36,7 @@ import traceback
 import zipfile
 import numpy as np
 from six import StringIO
+from six.moves import xrange
 
 # TODO(aselle): Disable GPU for now
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-- 
GitLab


From 45de35b19a1d1edc8e04ca9603f12df5d7924d26 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 10:29:58 -0800
Subject: [PATCH 239/575] Remove redundant line which is almost a duplicate of
 one a few lines below in datasets_quickstart document.

PiperOrigin-RevId: 186788306
---
 tensorflow/docs_src/get_started/datasets_quickstart.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tensorflow/docs_src/get_started/datasets_quickstart.md b/tensorflow/docs_src/get_started/datasets_quickstart.md
index bc69773d21..c972e5e555 100644
--- a/tensorflow/docs_src/get_started/datasets_quickstart.md
+++ b/tensorflow/docs_src/get_started/datasets_quickstart.md
@@ -265,9 +265,6 @@ ds = tf.data.TextLineDataset(train_path).skip(1)
 
 ### Build a csv line parser
 
-Ultimately we will need to parse each of the lines in the dataset, to
-produce the necessary `(features, label)` pairs.
-
 We will start by building a function to parse a single line.
 
 The following `iris_data.parse_line` function accomplishes this task using the
-- 
GitLab


From db8c7e976f2cf259b87a9441db1cf371586046b6 Mon Sep 17 00:00:00 2001
From: Yifei Feng <yifeif@google.com>
Date: Fri, 23 Feb 2018 11:03:14 -0800
Subject: [PATCH 240/575] Remove repeated defination due to auto-merge.

---
 tensorflow/python/ops/nn_ops.py | 24 ------------------------
 1 file changed, 24 deletions(-)

diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index bf20035f53..8fbe698914 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -2214,30 +2214,6 @@ def xw_plus_b_v1(x, weights, biases, name=None):  # pylint: disable=invalid-name
     mm = math_ops.matmul(x, weights)
     return bias_add_v1(mm, biases, name=name)
 
-def _get_noise_shape(x, noise_shape):
-  # If noise_shape is none return immediately.
-  if noise_shape is None:
-    return array_ops.shape(x)
-
-  try:
-    # Best effort to figure out the intended shape.
-    # If not possible, let the op to handle it.
-    # In eager mode exception will show up.
-    noise_shape_ = tensor_shape.as_shape(noise_shape)
-  except (TypeError, ValueError):
-    return noise_shape
-
-  if (x.shape.dims is not None and
-      len(x.shape.dims) == len(noise_shape_.dims)):
-    new_dims = []
-    for i, dim in enumerate(x.shape.dims):
-      if noise_shape_.dims[i].value is None and dim.value is not None:
-        new_dims.append(dim.value)
-      else:
-        new_dims.append(noise_shape_.dims[i].value)
-    return tensor_shape.TensorShape(new_dims)
-
-  return noise_shape
 
 def _get_noise_shape(x, noise_shape):
   # If noise_shape is none return immediately.
-- 
GitLab


From 95fa8b31cc98bac0e9ce84721e4e8535befb1193 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Fri, 23 Feb 2018 11:58:10 -0800
Subject: [PATCH 241/575] [XLA] Internal change.

PiperOrigin-RevId: 186802115
---
 tensorflow/compiler/xla/tests/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 1958e5abf6..97abf217d7 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1011,6 +1011,7 @@ xla_test(
     shard_count = 40,
     tags = [
         "enable_for_xla_interpreter",
+        "optonly",
     ],
     deps = [
         "//tensorflow/compiler/xla:array2d",
-- 
GitLab


From f412e5c71781003d2408c1082220dee6b140f632 Mon Sep 17 00:00:00 2001
From: Akshay Agrawal <akshayka@google.com>
Date: Fri, 23 Feb 2018 12:18:31 -0800
Subject: [PATCH 242/575] Cleanup for graph functions.

(1) Define constants for the names of Arg and Retval ops, and use them in various places.
(2) Change the signature and documentation for `BuildControlFlow` to reflect the fact that the supplied Graph is not mutated.
(3) Expose the FunctionLibraryRuntime's DeviceMgr, in preparation for multi-device functions.

PiperOrigin-RevId: 186804968
---
 tensorflow/core/common_runtime/function.cc | 11 ++-----
 tensorflow/core/framework/function.cc      |  8 ++---
 tensorflow/core/framework/function.h       | 10 +++++++
 tensorflow/core/graph/control_flow.cc      | 11 +++----
 tensorflow/core/graph/control_flow.h       | 16 +++++-----
 tensorflow/core/kernels/function_ops.cc    | 34 ++++++++++++----------
 6 files changed, 49 insertions(+), 41 deletions(-)

diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index b941819838..3e937ceb64 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -42,11 +42,8 @@ limitations under the License.
 namespace tensorflow {
 
 // A few string constant used throughout this module.
-//
-// TODO(zhifengc): Dedup some of these constants into
-// framework/function.h
-static constexpr const char* const kArgOp = "_Arg";
-static constexpr const char* const kRetOp = "_Retval";
+static constexpr const char* const kArgOp = FunctionLibraryDefinition::kArgOp;
+static constexpr const char* const kRetOp = FunctionLibraryDefinition::kRetOp;
 static constexpr const char* const kGradientOp =
     FunctionLibraryDefinition::kGradientOp;
 static constexpr const char* const kNodeLabel = "Func";
@@ -177,6 +174,7 @@ class FunctionLibraryRuntimeImpl : public FunctionLibraryRuntime {
   }
 
   Device* device() override { return device_; }
+  const DeviceMgr* device_mgr() const override { return device_mgr_; }
   Env* env() override { return env_; }
   int graph_def_version() override { return graph_def_version_; }
 
@@ -1580,9 +1578,6 @@ Status FunctionDefToBodyHelper(
 
   // Call BuildControlFlowInfo to validate that this function body has
   // well-formed control flow.
-  // NOTE(skyewm): this is usually done in Partition(), but we don't partition
-  // function bodies. This should be removed if function bodies ever go through
-  // the Partition() path.
   std::vector<ControlFlowInfo> dummy;
   TF_RETURN_IF_ERROR(BuildControlFlowInfo(graph.get(), &dummy));
 
diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc
index eae8e6c3c1..3e7b89d4eb 100644
--- a/tensorflow/core/framework/function.cc
+++ b/tensorflow/core/framework/function.cc
@@ -168,7 +168,7 @@ class FunctionInstantiationHelper {
         strings::StrAppend(&name, "_", i);
       }
       NodeDef* gnode = AddNode(name);
-      gnode->set_op("_Arg");
+      gnode->set_op(FunctionLibraryDefinition::kArgOp);
       AddAttr("T", dtypes[i], gnode);
       AddAttr("index", arg_index, gnode);
       result_.arg_types.push_back(dtypes[i]);
@@ -328,7 +328,7 @@ class FunctionInstantiationHelper {
         strings::StrAppend(&name, "_", i);
       }
       NodeDef* gnode = AddNode(name);
-      gnode->set_op("_Retval");
+      gnode->set_op(FunctionLibraryDefinition::kRetOp);
       AddInput(nodes_.size() - 1, item->nid, item->idx + i);
       AddAttr("T", dtypes[i], gnode);
       AddAttr("index", (*ret_index)++, gnode);
@@ -558,9 +558,9 @@ string Print(gtl::ArraySlice<const NodeDef*> nodes) {
   std::vector<const NodeDef*> ret;
   std::vector<const NodeDef*> body;
   for (const NodeDef* n : nodes) {
-    if (n->op() == "_Arg") {
+    if (n->op() == FunctionLibraryDefinition::kArgOp) {
       arg.push_back(n);
-    } else if (n->op() == "_Retval") {
+    } else if (n->op() == FunctionLibraryDefinition::kRetOp) {
       ret.push_back(n);
     } else {
       body.push_back(n);
diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h
index e27001133b..e00399f97d 100644
--- a/tensorflow/core/framework/function.h
+++ b/tensorflow/core/framework/function.h
@@ -344,6 +344,11 @@ class FunctionLibraryDefinition : public OpRegistryInterface {
   Status LookUp(const string& op_type_name,
                 const OpRegistrationData** op_reg_data) const override;
 
+  // Ops created for function arguments bear the name given by `kArgOp`; those
+  // created for return values bear the name given by `kRetOp`.
+  static constexpr const char* const kArgOp = "_Arg";
+  static constexpr const char* const kRetOp = "_Retval";
+
   static constexpr const char* const kGradientOp = "SymbolicGradient";
   static constexpr const char* const kFuncAttr = "f";
 
@@ -404,6 +409,8 @@ struct FunctionBody;
 
 // Forward declare. Defined in common_runtime/device.h
 class Device;
+// Forward declare. Defined in common_runtime/device_mgr.h
+class DeviceMgr;
 
 class FunctionLibraryRuntime {
  public:
@@ -518,6 +525,9 @@ class FunctionLibraryRuntime {
   // Returns the device on which the function executes.
   virtual Device* device() = 0;
 
+  // Get the DeviceMgr from which the device was obtained.
+  virtual const DeviceMgr* device_mgr() const = 0;
+
   // Returns the function library definition that backs this runtime.
   // NOTE(mrry): The returned library definition is the default function library
   // for this runtime. The runtime may instantiate functions from separate
diff --git a/tensorflow/core/graph/control_flow.cc b/tensorflow/core/graph/control_flow.cc
index db6683d1e7..30ff19cd7e 100644
--- a/tensorflow/core/graph/control_flow.cc
+++ b/tensorflow/core/graph/control_flow.cc
@@ -24,23 +24,24 @@ limitations under the License.
 
 namespace tensorflow {
 
-Status BuildControlFlowInfo(Graph* g, std::vector<ControlFlowInfo>* info) {
+Status BuildControlFlowInfo(const Graph* g,
+                            std::vector<ControlFlowInfo>* info) {
   info->clear();
   info->resize(g->num_node_ids());
 
   std::vector<const Node*> parent_nodes;
   parent_nodes.resize(g->num_node_ids());
 
-  Node* src_node = g->source_node();
+  const Node* src_node = g->source_node();
   ControlFlowInfo& src_info = (*info)[src_node->id()];
   src_info.frame = src_node;
   src_info.parent_frame = src_node;
 
   string frame_name;
-  std::deque<Node*> ready;
+  std::deque<const Node*> ready;
   ready.push_back(src_node);
   while (!ready.empty()) {
-    Node* curr_node = ready.front();
+    const Node* curr_node = ready.front();
     ready.pop_front();
     const ControlFlowInfo& curr_info = (*info)[curr_node->id()];
     const Node* frame = curr_info.frame;
@@ -56,7 +57,7 @@ Status BuildControlFlowInfo(Graph* g, std::vector<ControlFlowInfo>* info) {
     }
 
     for (const Edge* out_edge : curr_node->out_edges()) {
-      Node* out = out_edge->dst();
+      const Node* out = out_edge->dst();
       int out_id = out->id();
       ControlFlowInfo* out_info = &(*info)[out_id];
       const Node* out_parent = out_info->parent_frame;
diff --git a/tensorflow/core/graph/control_flow.h b/tensorflow/core/graph/control_flow.h
index 372044f538..79e2be0d4b 100644
--- a/tensorflow/core/graph/control_flow.h
+++ b/tensorflow/core/graph/control_flow.h
@@ -30,14 +30,14 @@ struct ControlFlowInfo {
   string frame_name;                   // frame name of a node
 };
 
-// Assign to each node the name of the frame and the level it belongs to.
-// We check the well-formedness of the graph: All inputs to a node must
-// come from the same frame and have the same "static" iteration level.
-// `info` is cleared and populated by this function.
-// NOTE(yuanbyu): For now, we require all sends/recvs have iteration level
-// 0. This essentially means there can't be multiple serial Nexts in
-// an iteration, which all sane front-ends should satisfy.
-Status BuildControlFlowInfo(Graph* g, std::vector<ControlFlowInfo>* info);
+// Clear and populate `info` with each node's frame and the level it belongs to.
+// We check the well-formedness of the graph: All inputs to a node must come
+// from the same frame and have the same "static" iteration level.
+//
+// NOTE(yuanbyu): For now, we require all sends/recvs have iteration level 0.
+// This essentially means there can't be multiple serial Nexts in an iteration,
+// which all sane front-ends should satisfy.
+Status BuildControlFlowInfo(const Graph* g, std::vector<ControlFlowInfo>* info);
 
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/kernels/function_ops.cc b/tensorflow/core/kernels/function_ops.cc
index 9d4bc35ba8..a094ebe5e2 100644
--- a/tensorflow/core/kernels/function_ops.cc
+++ b/tensorflow/core/kernels/function_ops.cc
@@ -32,7 +32,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-static const char* const kGradientOp = "SymbolicGradient";
+static const char* const kArgOp = FunctionLibraryDefinition::kArgOp;
+static const char* const kRetOp = FunctionLibraryDefinition::kRetOp;
+static const char* const kGradientOp = FunctionLibraryDefinition::kGradientOp;
 
 class ArgOp : public OpKernel {
  public:
@@ -89,26 +91,25 @@ class RetvalOp : public OpKernel {
   TF_DISALLOW_COPY_AND_ASSIGN(RetvalOp);
 };
 
-REGISTER_SYSTEM_KERNEL_BUILDER(Name("_Arg").Device(DEVICE_CPU), ArgOp);
-REGISTER_SYSTEM_KERNEL_BUILDER(Name("_Retval").Device(DEVICE_CPU), RetvalOp);
+REGISTER_SYSTEM_KERNEL_BUILDER(Name(kArgOp).Device(DEVICE_CPU), ArgOp);
+REGISTER_SYSTEM_KERNEL_BUILDER(Name(kRetOp).Device(DEVICE_CPU), RetvalOp);
 
 #if TENSORFLOW_USE_SYCL
 #define REGISTER(type)     \
   REGISTER_KERNEL_BUILDER( \
-      Name("_Arg").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ArgOp);
+      Name(kArgOp).Device(DEVICE_SYCL).TypeConstraint<type>("T"), ArgOp);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER)
-TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Arg")
+TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name(kArgOp)
                                                    .Device(DEVICE_SYCL)
                                                    .HostMemory("output")
                                                    .TypeConstraint<int32>("T"),
                                                ArgOp);
 #undef REGISTER
-#define REGISTER(type)                                               \
-  REGISTER_KERNEL_BUILDER(                                           \
-      Name("_Retval").Device(DEVICE_SYCL).TypeConstraint<type>("T"), \
-      RetvalOp);
+#define REGISTER(type)     \
+  REGISTER_KERNEL_BUILDER( \
+      Name(kRetOp).Device(DEVICE_SYCL).TypeConstraint<type>("T"), RetvalOp);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER)
-TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Retval")
+TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name(kRetOp)
                                                    .Device(DEVICE_SYCL)
                                                    .HostMemory("input")
                                                    .TypeConstraint<int32>("T"),
@@ -118,16 +119,16 @@ TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Retval")
 
 #define REGISTER(type)     \
   REGISTER_KERNEL_BUILDER( \
-      Name("_Arg").Device(DEVICE_GPU).TypeConstraint<type>("T"), ArgOp);
+      Name(kArgOp).Device(DEVICE_GPU).TypeConstraint<type>("T"), ArgOp);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER)
-TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Arg")
+TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name(kArgOp)
                                                    .Device(DEVICE_GPU)
                                                    .HostMemory("output")
                                                    .TypeConstraint<int32>("T"),
                                                ArgOp);
 #undef REGISTER
 
-REGISTER_KERNEL_BUILDER(Name("_Arg")
+REGISTER_KERNEL_BUILDER(Name(kArgOp)
                             .Device(DEVICE_GPU)
                             .HostMemory("output")
                             .TypeConstraint<ResourceHandle>("T"),
@@ -135,9 +136,9 @@ REGISTER_KERNEL_BUILDER(Name("_Arg")
 
 #define REGISTER(type)     \
   REGISTER_KERNEL_BUILDER( \
-      Name("_Retval").Device(DEVICE_GPU).TypeConstraint<type>("T"), RetvalOp);
+      Name(kRetOp).Device(DEVICE_GPU).TypeConstraint<type>("T"), RetvalOp);
 TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER)
-TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Retval")
+TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name(kRetOp)
                                                    .Device(DEVICE_GPU)
                                                    .HostMemory("input")
                                                    .TypeConstraint<int32>("T"),
@@ -287,7 +288,8 @@ REGISTER_KERNEL_BUILDER(Name(kGradientOp).Device(DEVICE_SYCL),
 class RemoteCallOp : public AsyncOpKernel {
  public:
   explicit RemoteCallOp(OpKernelConstruction* ctx) : AsyncOpKernel(ctx) {
-    OP_REQUIRES_OK(ctx, ctx->GetAttr("f", &func_));
+    OP_REQUIRES_OK(ctx,
+                   ctx->GetAttr(FunctionLibraryDefinition::kFuncAttr, &func_));
   }
 
   ~RemoteCallOp() override {}
-- 
GitLab


From ee333be5d16ae39029f9c58a989a84089ffadb5d Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Fri, 23 Feb 2018 12:24:19 -0800
Subject: [PATCH 243/575] [TF:XLA] Fix a bug where executor's device_ordinal
 should be passed to AllocateShapedBuffer. Also enable C64 type for
 interpreter device.

PiperOrigin-RevId: 186805709
---
 tensorflow/compiler/jit/BUILD                             | 7 ++++++-
 tensorflow/compiler/jit/xla_interpreter_device.cc         | 4 ++--
 tensorflow/compiler/xla/service/interpreter/executable.cc | 2 +-
 3 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index a711319607..af259e0564 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -102,12 +102,17 @@ cc_library(
 cc_library(
     name = "xla_interpreter_device",
     srcs = ["xla_interpreter_device.cc"],
+    visibility = [":friends"],
     deps = [
+        ":jit_compilation_passes",
         ":xla_device",
         "//tensorflow/compiler/jit/kernels:xla_launch_op",
         "//tensorflow/compiler/tf2xla:xla_compiler",
+        "//tensorflow/compiler/tf2xla/kernels:xla_ops",
+        "//tensorflow/compiler/xla/service:interpreter_plugin",  # buildcleaner: keep
+        "//tensorflow/core:lib",
     ],
-    alwayslink = True,
+    alwayslink = 1,
 )
 
 cc_library(
diff --git a/tensorflow/compiler/jit/xla_interpreter_device.cc b/tensorflow/compiler/jit/xla_interpreter_device.cc
index 2614deefd8..a329451b14 100644
--- a/tensorflow/compiler/jit/xla_interpreter_device.cc
+++ b/tensorflow/compiler/jit/xla_interpreter_device.cc
@@ -25,8 +25,8 @@ namespace tensorflow {
 const char* const DEVICE_XLA_INTERPRETER = "XLA_INTERPRETER";
 const char* const DEVICE_INTERPRETER_XLA_JIT = "XLA_INTERPRETER_JIT";
 
-constexpr std::array<DataType, 5> kExecAllTypes = {
-    {DT_INT32, DT_FLOAT, DT_BOOL, DT_DOUBLE, DT_INT64}};
+constexpr std::array<DataType, 6> kExecAllTypes = {
+    {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE, DT_COMPLEX64, DT_BOOL}};
 
 class XlaInterpreterDeviceFactory : public DeviceFactory {
  public:
diff --git a/tensorflow/compiler/xla/service/interpreter/executable.cc b/tensorflow/compiler/xla/service/interpreter/executable.cc
index 0cb9b5d810..883063d0f0 100644
--- a/tensorflow/compiler/xla/service/interpreter/executable.cc
+++ b/tensorflow/compiler/xla/service/interpreter/executable.cc
@@ -93,7 +93,7 @@ StatusOr<std::unique_ptr<ShapedBuffer>> InterpreterExecutable::ExecuteOnStream(
   TF_ASSIGN_OR_RETURN(std::unique_ptr<ShapedBuffer> result,
                       transfer_manager->AllocateShapedBuffer(
                           result_literal->shape(), run_options->allocator(),
-                          run_options->device_ordinal()));
+                          executor->device_ordinal()));
   TF_RETURN_IF_ERROR(transfer_manager->TransferLiteralToDevice(
       executor, *result_literal, *result));
 
-- 
GitLab


From d0aaeae4ce79c0982c8a8894d3f87d3adae06683 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 12:24:53 -0800
Subject: [PATCH 244/575] Add usage example to KMeans Estimator documentation.

PiperOrigin-RevId: 186805772
---
 .../factorization/python/ops/kmeans.py        | 61 +++++++++++++++++--
 1 file changed, 57 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py
index c861cfff54..7319eaa7de 100644
--- a/tensorflow/contrib/factorization/python/ops/kmeans.py
+++ b/tensorflow/contrib/factorization/python/ops/kmeans.py
@@ -61,8 +61,8 @@ class _LossRelativeChangeHook(session_run_hook.SessionRunHook):
     loss = run_values.results
     assert loss is not None
     if self._prev_loss:
-      relative_change = (abs(loss - self._prev_loss) /
-                         (1 + abs(self._prev_loss)))
+      relative_change = (
+          abs(loss - self._prev_loss) / (1 + abs(self._prev_loss)))
       if relative_change < self._tolerance:
         run_context.request_stop()
     self._prev_loss = loss
@@ -233,7 +233,57 @@ class _ModelFn(object):
 
 # TODO(agarwal,ands): support sharded input.
 class KMeansClustering(estimator.Estimator):
-  """An Estimator for K-Means clustering."""
+  """An Estimator for K-Means clustering.
+
+  Example:
+  ```
+  import numpy as np
+  import tensorflow as tf
+
+  num_points = 100
+  dimensions = 2
+  points = np.random.uniform(0, 1000, [num_points, dimensions])
+
+  def input_fn():
+    return tf.train.limit_epochs(
+        tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1)
+
+  num_clusters = 5
+  kmeans = tf.contrib.factorization.KMeansClustering(
+      num_clusters=num_clusters, use_mini_batch=False)
+
+  # train
+  num_iterations = 10
+  previous_centers = None
+  for _ in xrange(num_iterations):
+    kmeans.train(input_fn)
+    cluster_centers = kmeans.cluster_centers()
+    if previous_centers is not None:
+      print 'delta:', cluster_centers - previous_centers
+    previous_centers = cluster_centers
+    print 'score:', kmeans.score(input_fn)
+  print 'cluster centers:', cluster_centers
+
+  # map the input points to their clusters
+  cluster_indices = list(kmeans.predict_cluster_index(input_fn))
+  for i, point in enumerate(points):
+    cluster_index = cluster_indices[i]
+    center = cluster_centers[cluster_index]
+    print 'point:', point, 'is in cluster', cluster_index, 'centered at', center
+  ```
+
+  The `SavedModel` saved by the `export_savedmodel` method does not include the
+  cluster centers. However, the cluster centers may be retrieved by the
+  latest checkpoint saved during training. Specifically,
+  ```
+  kmeans.cluster_centers()
+  ```
+  is equivalent to
+  ```
+  tf.train.load_variable(
+      kmeans.model_dir, KMeansClustering.CLUSTER_CENTERS_VAR_NAME)
+  ```
+  """
 
   # Valid values for the distance_metric constructor argument.
   SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE
@@ -253,6 +303,9 @@ class KMeansClustering(estimator.Estimator):
   CLUSTER_INDEX = 'cluster_index'
   ALL_DISTANCES = 'all_distances'
 
+  # Variable name used by cluster_centers().
+  CLUSTER_CENTERS_VAR_NAME = clustering_ops.CLUSTERS_VAR_NAME
+
   def __init__(self,
                num_clusters,
                model_dir=None,
@@ -406,4 +459,4 @@ class KMeansClustering(estimator.Estimator):
 
   def cluster_centers(self):
     """Returns the cluster centers."""
-    return self.get_variable_value(clustering_ops.CLUSTERS_VAR_NAME)
+    return self.get_variable_value(KMeansClustering.CLUSTER_CENTERS_VAR_NAME)
-- 
GitLab


From 75af2e0afeb30325e2e0d37e30054e67fde43707 Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu <yuanzx@google.com>
Date: Fri, 23 Feb 2018 13:13:25 -0800
Subject: [PATCH 245/575] [XLA] Fix BF16 propagation pass to produce matching
 fusion root and output.

Previously, the propagation pass might produce different procision in the fused
computation's root than the fusion itself, when the fused root doesn't define a buffer.

Add explicit converts at such fusion roots.

PiperOrigin-RevId: 186812368
---
 tensorflow/compiler/xla/service/BUILD         |   2 +
 .../xla/service/bfloat16_propagation.cc       | 207 ++++++++++++++----
 .../xla/service/bfloat16_propagation.h        |  11 +
 .../xla/service/bfloat16_propagation_test.cc  |  60 ++++-
 tensorflow/compiler/xla/service/hlo_dce.cc    |   2 +-
 5 files changed, 233 insertions(+), 49 deletions(-)

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index 37ca1b893a..e6a6e54927 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -126,7 +126,9 @@ cc_library(
         ":bfloat16_support",
         ":hlo",
         ":hlo_dataflow_analysis",
+        ":hlo_dce",
         ":hlo_pass",
+        ":tuple_simplifier",
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc
index 9246cb25d2..6145c690b9 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc
@@ -17,8 +17,10 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_dce.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/tuple_simplifier.h"
 #include "tensorflow/compiler/xla/shape_tree.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/core/lib/gtl/cleanup.h"
@@ -229,55 +231,10 @@ bool BFloat16Propagation::InstructionIsCandidateForBF16Output(
   return true;
 }
 
-// The algorithm first does a forward pass (parameters to root) to determine a
-// set of instructions to consider using bfloat16, then does a backward pass to
-// determine the precisions of those instructions according to the need of
-// their users.
-StatusOr<bool> BFloat16Propagation::Run(HloModule* module) {
-  TF_ASSIGN_OR_RETURN(dataflow_, HloDataflowAnalysis::Run(*module));
-
+Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
+    HloModule* module) {
   std::list<HloComputation*> computations_topological_order =
       module->MakeComputationPostOrder();
-  // The first step is a forward pass (parameters to root), where we determine
-  // the potential candidate instructions to use bfloat16 in the outputs that
-  // are not likely to cause overhead from extra explicit conversions. This is
-  // done forwardly because we determine whether an HLO is a candidate partially
-  // based on whether its operands are candidates.
-  for (auto computation : computations_topological_order) {
-    for (auto inst : computation->MakeInstructionPostOrder()) {
-      if (InstructionIsCandidateForBF16Output(inst)) {
-        consider_using_bfloat16_.insert(inst);
-      }
-    }
-  }
-
-  // The second step is a backward pass (root to parameters), where we modify
-  // the precisions of the instructions identified in the first step when
-  // feasible. This is done backwardly because we determine the precision of an
-  // HLO's output based on how it is later used.
-  //
-  // The precision of an instruction is determined by its users, so we do the
-  // propagation in reverse topological order.
-  for (auto comp_it = computations_topological_order.rbegin();
-       comp_it != computations_topological_order.rend(); ++comp_it) {
-    if ((*comp_it)->IsFusionComputation()) {
-      // Fusion computations are handled when visiting the fusion instruction.
-      continue;
-    }
-    auto insts = (*comp_it)->MakeInstructionPostOrder();
-    for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
-      DetermineAndMutateInstructionPrecision(*inst_it,
-                                             /*skip_parameters=*/true);
-    }
-  }
-
-  if (!changed_) {
-    return false;
-  }
-
-  // It's possible that an instruction does not define a buffer, but the
-  // defining instruction's shape has changed. So we need to adjust the output
-  // shapes of instructions according to the HLO values they refer to.
   for (auto comp_it = computations_topological_order.rbegin();
        comp_it != computations_topological_order.rend(); ++comp_it) {
     auto insts = (*comp_it)->MakeInstructionPostOrder();
@@ -328,6 +285,162 @@ StatusOr<bool> BFloat16Propagation::Run(HloModule* module) {
       }
     }
   }
+
+  // We could have changed a fusion computation's root shape to have a different
+  // precision than the fusion node's output, if the fusion root does not
+  // define a buffer (e.g., a tuple). Now we add conversions after such fusion
+  // roots to make them match the fusion output. If the fusion output is a
+  // (possibly nested) tuple, we first create get-tuple-elements, then convert
+  // the unmatching leaf nodes, and finally create a new tuple as the fusion
+  // computation's root. If tuples and get-tuple-elements are created, we will
+  // run tuple simplifier and dead code elimination at the end (dead code is not
+  // allowed in fusion computation). E.g.,
+  //
+  // (1)             (2)             (3)
+  // a  b            a  b            a  b
+  // |\ |            |\ |            |\ |
+  // \ add   ->      |add    ->      | add
+  //  \ |            \ |        convert |
+  //  tuple         tuple             \ |
+  //                 / \              tuple
+  //               gte gte
+  //                |   |
+  //           convert  |
+  //                 \  /
+  //                 tuple
+  // (1) a is F32 but tuple is BF16
+  // (2) after adding conversion
+  // (3) after tuple simplifier and DCE.
+  bool needs_tuple_simplifier = false;
+  for (auto computation : computations_topological_order) {
+    auto insts = computation->MakeInstructionPostOrder();
+    for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
+      auto hlo = *inst_it;
+      if (hlo->opcode() != HloOpcode::kFusion) {
+        continue;
+      }
+      auto fusion_computation = hlo->fused_instructions_computation();
+      auto fusion_root = fusion_computation->root_instruction();
+      if (ShapeUtil::Compatible(fusion_root->shape(), hlo->shape())) {
+        continue;
+      }
+      ShapeTree<HloInstruction*> converted_outputs(hlo->shape());
+      // Iterate through nodes in the shape tree in pre-order and initialize
+      // each non-root node with a corresponding get-tuple-element. For a leaf
+      // node, if its shape does not match the fusion output, create a
+      // conversion node to overwrite the node value.
+      for (auto it = converted_outputs.begin(); it != converted_outputs.end();
+           ++it) {
+        ShapeIndex output_index = it->first;
+        HloInstruction*& output = it->second;
+        const Shape subshape =
+            ShapeUtil::GetSubshape(hlo->shape(), output_index);
+        if (output_index.empty()) {
+          output = fusion_root;
+        } else {
+          ShapeIndex parent_index = output_index;
+          parent_index.pop_back();
+          output = fusion_computation->AddInstruction(
+              HloInstruction::CreateGetTupleElement(
+                  subshape, converted_outputs.element(parent_index),
+                  output_index.back()));
+        }
+        if (ShapeUtil::IsTuple(subshape)) {
+          continue;
+        }
+        if (!ShapeUtil::Compatible(
+                subshape,
+                ShapeUtil::GetSubshape(fusion_root->shape(), output_index))) {
+          output = fusion_computation->AddInstruction(
+              HloInstruction::CreateConvert(subshape, output));
+        }
+      }
+      // Iterate through nodes in the shape tree in reverse pre-order and create
+      // a tuple instruction for each non-leaf node where the elements are the
+      // values of its child nodes.
+      for (auto it = converted_outputs.rbegin(); it != converted_outputs.rend();
+           ++it) {
+        ShapeIndex output_index = it->first;
+        HloInstruction*& output = it->second;
+        const Shape& subshape =
+            ShapeUtil::GetSubshape(hlo->shape(), output_index);
+        if (!ShapeUtil::IsTuple(subshape)) {
+          continue;
+        }
+        std::vector<HloInstruction*> elements(
+            ShapeUtil::TupleElementCount(subshape));
+        ShapeIndex child_index = output_index;
+        for (int64 i = 0; i < elements.size(); ++i) {
+          child_index.push_back(i);
+          elements[i] = converted_outputs.element(child_index);
+          child_index.pop_back();
+        }
+        output = fusion_computation->AddInstruction(
+            HloInstruction::CreateTuple(elements));
+      }
+      fusion_computation->set_root_instruction(converted_outputs.element({}));
+      needs_tuple_simplifier |= ShapeUtil::IsTuple(hlo->shape());
+    }
+  }
+  if (needs_tuple_simplifier) {
+    TupleSimplifier tuple_simplifier;
+    TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status());
+    HloDCE dce;
+    TF_RETURN_IF_ERROR(dce.Run(module).status());
+  }
+  return Status::OK();
+}
+
+// The algorithm first does a forward pass (parameters to root) to determine a
+// set of instructions to consider using bfloat16, then does a backward pass to
+// determine the precisions of those instructions according to the need of
+// their users.
+StatusOr<bool> BFloat16Propagation::Run(HloModule* module) {
+  TF_ASSIGN_OR_RETURN(dataflow_, HloDataflowAnalysis::Run(*module));
+
+  std::list<HloComputation*> computations_topological_order =
+      module->MakeComputationPostOrder();
+  // The first step is a forward pass (parameters to root), where we determine
+  // the potential candidate instructions to use bfloat16 in the outputs that
+  // are not likely to cause overhead from extra explicit conversions. This is
+  // done forwardly because we determine whether an HLO is a candidate partially
+  // based on whether its operands are candidates.
+  for (auto computation : computations_topological_order) {
+    for (auto inst : computation->MakeInstructionPostOrder()) {
+      if (InstructionIsCandidateForBF16Output(inst)) {
+        consider_using_bfloat16_.insert(inst);
+      }
+    }
+  }
+
+  // The second step is a backward pass (root to parameters), where we modify
+  // the precisions of the instructions identified in the first step when
+  // feasible. This is done backwardly because we determine the precision of an
+  // HLO's output based on how it is later used.
+  //
+  // The precision of an instruction is determined by its users, so we do the
+  // propagation in reverse topological order.
+  for (auto comp_it = computations_topological_order.rbegin();
+       comp_it != computations_topological_order.rend(); ++comp_it) {
+    if ((*comp_it)->IsFusionComputation()) {
+      // Fusion computations are handled when visiting the fusion instruction.
+      continue;
+    }
+    auto insts = (*comp_it)->MakeInstructionPostOrder();
+    for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
+      DetermineAndMutateInstructionPrecision(*inst_it,
+                                             /*skip_parameters=*/true);
+    }
+  }
+
+  if (!changed_) {
+    return false;
+  }
+
+  // It's possible that an instruction does not define a buffer, but the
+  // defining instruction's shape has changed. So we need to adjust the output
+  // shapes of instructions according to the HLO values they refer to.
+  TF_RETURN_IF_ERROR(ResolveInconsistencyOfAliasingBuffers(module));
   return true;
 }
 
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.h b/tensorflow/compiler/xla/service/bfloat16_propagation.h
index aa81dde3b0..ccf77d7b4e 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation.h
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation.h
@@ -94,10 +94,21 @@ class BFloat16Propagation : public HloPassInterface {
   // Special handling in the mutation pass for fusion computations.
   void DetermineAndMutateFusionComputationPrecision(HloInstruction* fusion);
 
+  // ***************************
+  // Functions called by the final inconsistency resolving pass.
+
+  // Adjusts the output shapes of HloInstructions such that if two
+  // HloInstructions have aliasing buffers in their outputs, they must have the
+  // same precision.
+  Status ResolveInconsistencyOfAliasingBuffers(HloModule* module);
+
   // Makes the fusion parameters match the precision of the actual parameters
   // passed to the fusion node.
   void AdjustFusionParameters(HloInstruction* fusion);
 
+  // ***************************
+  // Functions called and state used by two or more passes.
+
   // Returns whether all uses of the given HloInstruction can consume BF16
   // input.
   bool AllUsersConsumeBF16(const HloInstruction& hlo,
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
index 4c86c6b26e..2047e2053a 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
@@ -68,7 +68,7 @@ class BFloat16PropagationTest : public HloTestBase {
 
   // Returns whether the given HloInstruction's output element type is BF16 or
   // the only use of it is converting to BF16.
-  bool OutputsBF16(HloInstruction* inst) {
+  bool OutputsBF16(const HloInstruction* inst) {
     if (inst->shape().element_type() == BF16) {
       return true;
     }
@@ -287,6 +287,64 @@ TEST_F(BFloat16PropagationTest, PropagateThroughFusion) {
   EXPECT_TRUE(OutputsBF16(b_f1));
 }
 
+// Tests that if 1) the root instruction of a fusion is a tuple, 2) the fusion
+// outputs are only used by a dot, and 3) one element of the tuple is used by
+// an add in the fusion computation, then the propagation pass should create a
+// convert in the fusion computation to keep the add's operand in F32 but change
+// the fusion output to BF16. E.g., the following fusion computation
+//   (F32, F32) fusion_computation(F32 a, F32 b)
+//     = tuple(F32 a, F32 add(F32 a, F32 b))
+// will be changed to
+//   (BF16, BF16) fusion_computation(F32 a, F32 b)
+//     = tuple(BF16 convert(a), BF16 add(F32 a, F32 b))
+TEST_F(BFloat16PropagationTest, ConvertTupleFusionElementIfUsedByAdd) {
+  auto module = CreateNewModule();
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {4, 4});
+
+  HloInstruction* param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, shape, "param"));
+  HloInstruction* add = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param));
+
+  auto builder_f = HloComputation::Builder("fusion0");
+  HloInstruction* a_f =
+      builder_f.AddInstruction(HloInstruction::CreateParameter(0, shape, "a"));
+  HloInstruction* b_f =
+      builder_f.AddInstruction(HloInstruction::CreateParameter(1, shape, "b"));
+  HloInstruction* add_f = builder_f.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, a_f, b_f));
+  HloInstruction* tuple_f =
+      builder_f.AddInstruction(HloInstruction::CreateTuple({a_f, add_f}));
+  auto comp_f = module->AddEmbeddedComputation(builder_f.Build());
+  auto fusion = builder.AddInstruction(HloInstruction::CreateFusion(
+      tuple_f->shape(), HloInstruction::FusionKind::kCustom, {add, add},
+      comp_f));
+
+  HloInstruction* gte0 = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, fusion, 0));
+  HloInstruction* gte1 = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, fusion, 1));
+  HloInstruction* dot = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kDot, gte0, gte1));
+
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), dot);
+  EXPECT_TRUE(OutputsBF16(gte0));
+  EXPECT_TRUE(OutputsBF16(gte1));
+  EXPECT_FALSE(OutputsBF16(a_f));
+  EXPECT_FALSE(OutputsBF16(b_f));
+  EXPECT_TRUE(OutputsBF16(add_f));
+  auto new_fusion_root = comp_f->root_instruction();
+  EXPECT_EQ(new_fusion_root->opcode(), HloOpcode::kTuple);
+  EXPECT_EQ(new_fusion_root->operand(1), add_f);
+  EXPECT_EQ(new_fusion_root->operand(0)->opcode(), HloOpcode::kConvert);
+  EXPECT_TRUE(OutputsBF16(new_fusion_root->operand(0)));
+}
+
 // A select over tuples does not define the leaf buffers, so the types in
 // on_true and on_false must match, so that as long as one of them is F32, the
 // other must be F32 as well.
diff --git a/tensorflow/compiler/xla/service/hlo_dce.cc b/tensorflow/compiler/xla/service/hlo_dce.cc
index 1e5f0f797a..fcd723af14 100644
--- a/tensorflow/compiler/xla/service/hlo_dce.cc
+++ b/tensorflow/compiler/xla/service/hlo_dce.cc
@@ -40,7 +40,7 @@ StatusOr<bool> HloDCE::Run(HloModule* module) {
   VLOG(2) << "Before dce:";
   XLA_VLOG_LINES(2, module->ToString());
 
-  for (auto* computation : module->MakeNonfusionComputations()) {
+  for (auto* computation : module->MakeComputationPostOrder()) {
     std::unordered_set<HloInstruction*> live_instructions;
     TF_RETURN_IF_ERROR(computation->root_instruction()->Accept(
         [&live_instructions](HloInstruction* instruction) {
-- 
GitLab


From 0b4fdf183a020ea3daf9a54501434038082c198b Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Fri, 23 Feb 2018 14:06:49 -0800
Subject: [PATCH 246/575] Respects some form of log_device_placement in eager.

PiperOrigin-RevId: 186820292
---
 tensorflow/c/eager/c_api.cc         | 4 ++++
 tensorflow/c/eager/c_api_internal.h | 6 +++++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index 98ef6f0d0a..cc318c3878 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -802,6 +802,10 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
   }
   if (kernel == nullptr) {
     const tensorflow::NodeDef& ndef = op->attrs.BuildNodeDef();
+    if (ctx->log_device_placement) {
+      LOG(INFO) << "Executing op " << ndef.op() << " in device "
+                << device->name();
+    }
     kernel = new tensorflow::KernelAndDevice(ctx->rendezvous);
     // Knowledge of the implementation of Init (and in-turn
     // FunctionLibraryRuntime::CreateKernel) tells us that ctx->func_lib_def
diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h
index 7b9f1db02e..3356054cd0 100644
--- a/tensorflow/c/eager/c_api_internal.h
+++ b/tensorflow/c/eager/c_api_internal.h
@@ -50,7 +50,9 @@ struct TFE_Context {
         rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)),
         pflr(new tensorflow::ProcessFunctionLibraryRuntime(
             session->device_mgr, opts.session_options.options.env,
-            TF_GRAPH_DEF_VERSION, &func_lib_def, {})) {}
+            TF_GRAPH_DEF_VERSION, &func_lib_def, {})),
+        log_device_placement(
+            opts.session_options.options.config.log_device_placement()) {}
 
   const TFE_ContextDevicePlacementPolicy policy;
 
@@ -88,6 +90,8 @@ struct TFE_Context {
   std::atomic<bool> should_store_metadata{false};
   tensorflow::mutex metadata_mu;
   tensorflow::RunMetadata run_metadata GUARDED_BY(metadata_mu);
+
+  const bool log_device_placement;
 };
 
 struct TFE_TensorHandle {
-- 
GitLab


From eba67dea5a5e83e2bc49a40202233823b7ea9973 Mon Sep 17 00:00:00 2001
From: Noah Eisen <ncteisen@google.com>
Date: Fri, 23 Feb 2018 14:08:57 -0800
Subject: [PATCH 247/575] Automated g4 rollback of changelist 185688704

PiperOrigin-RevId: 186820593
---
 tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc   | 2 +-
 tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
index 2ed07e3669..bb14e0197b 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
@@ -34,7 +34,7 @@ namespace {
 class GrpcWorkerCache : public WorkerCachePartial {
  public:
   // TODO(ncteisen): consider adding a config var or flag for this
-  static constexpr const size_t kGrpcWorkerCacheThreadCount = 2;
+  static constexpr const size_t kGrpcWorkerCacheThreadCount = 8;
 
   explicit GrpcWorkerCache(GrpcChannelCache* channel_cache,
                            WorkerInterface* local_worker,
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
index 1beb198732..b20e744a97 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
@@ -52,7 +52,7 @@ namespace {
 
 class GrpcWorkerService : public AsyncServiceInterface {
   // TODO(ncteisen): consider adding a config var or flag for this
-  static constexpr const size_t kGrpcWorkerServiceThreadCount = 2;
+  static constexpr const size_t kGrpcWorkerServiceThreadCount = 8;
 
  public:
   GrpcWorkerService(GrpcWorker* worker, ::grpc::ServerBuilder* builder)
-- 
GitLab


From a8213e7d032e676b3135f1ac8ec019f86f7fcd18 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 23 Feb 2018 14:22:06 -0800
Subject: [PATCH 248/575] Preserve user placement as much as possible when
 optimizing the graph

PiperOrigin-RevId: 186822511
---
 tensorflow/python/grappler/tf_optimizer.i | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/grappler/tf_optimizer.i b/tensorflow/python/grappler/tf_optimizer.i
index 1b657983a4..de9326ccfc 100644
--- a/tensorflow/python/grappler/tf_optimizer.i
+++ b/tensorflow/python/grappler/tf_optimizer.i
@@ -100,6 +100,7 @@ PyObject* TF_OptimizeGraph(
     tensorflow::grappler::ItemConfig item_config;
     item_config.inline_functions = false;
     item_config.apply_optimizations = false;
+    item_config.ignore_user_placement = false;
     std::unique_ptr<tensorflow::grappler::GrapplerItem> grappler_item =
         tensorflow::grappler::GrapplerItemFromMetaGraphDef(graph_id, metagraph, item_config);
 
-- 
GitLab


From bca04a3181d23211b6646021dd971932317bc962 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 14:22:53 -0800
Subject: [PATCH 249/575] * CUB updated to 1.8.0 * updated ShuffleIndex because
 of API change

PiperOrigin-RevId: 186822637
---
 .../core/kernels/reduction_gpu_kernels.cu.h   |  4 ++--
 tensorflow/workspace.bzl                      | 11 ++++-----
 third_party/cub/BUILD                         |  0
 .../cub/fix_compilation_in_clang.patch        | 23 -------------------
 4 files changed, 6 insertions(+), 32 deletions(-)
 delete mode 100644 third_party/cub/BUILD
 delete mode 100644 third_party/cub/fix_compilation_in_clang.patch

diff --git a/tensorflow/core/kernels/reduction_gpu_kernels.cu.h b/tensorflow/core/kernels/reduction_gpu_kernels.cu.h
index 15ae4c1fc5..9237fa51d8 100644
--- a/tensorflow/core/kernels/reduction_gpu_kernels.cu.h
+++ b/tensorflow/core/kernels/reduction_gpu_kernels.cu.h
@@ -280,8 +280,8 @@ __global__ void ColumnReduceMax16ColumnsKernel(
   const int rows_in_this_warp = min(rows_per_warp, num_rows - start_row_warp);
   // not the most efficient way to do this sum
   for (int i = 1; i < rows_in_this_warp; ++i) {
-    value_type tmp =
-        cub::ShuffleIndex(sum, threadIdx.x + i * num_cols, 32, 0xffffffff);
+    value_type tmp = cub::ShuffleIndex<32, value_type>(
+        sum, static_cast<int>(threadIdx.x + i * num_cols), 0xffffffff);
     if (lane < num_cols) sum = op(sum, tmp);
   }
 
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 2b370ffbac..d6ac7be8b5 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -666,15 +666,12 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "cub_archive",
       urls = [
-          "https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.7.4.zip",
-          "https://github.com/NVlabs/cub/archive/1.7.4.zip",
+          "https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.8.0.zip",
+          "https://github.com/NVlabs/cub/archive/1.8.0.zip",
       ],
-      sha256 = "20a1a39fd97e5da7f40f5f2e7fd73fd2ea59f9dc4bb8a6c5f228aa543e727e31",
-      strip_prefix = "cub-1.7.4",
+      sha256 = "6bfa06ab52a650ae7ee6963143a0bbc667d6504822cbd9670369b598f18c58c3",
+      strip_prefix = "cub-1.8.0",
       build_file = str(Label("//third_party:cub.BUILD")),
-      # TODO: remove the patch when upstream fix is accepted and released.
-      #       PR with a fix: https://github.com/NVlabs/cub/pull/125
-      patch_file = str(Label("//third_party/cub:fix_compilation_in_clang.patch")),
   )
 
   tf_http_archive(
diff --git a/third_party/cub/BUILD b/third_party/cub/BUILD
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/third_party/cub/fix_compilation_in_clang.patch b/third_party/cub/fix_compilation_in_clang.patch
deleted file mode 100644
index 384e674f20..0000000000
--- a/third_party/cub/fix_compilation_in_clang.patch
+++ /dev/null
@@ -1,23 +0,0 @@
-From 565b77f7c82048871a4d5e3e506dc663d53cd469 Mon Sep 17 00:00:00 2001
-From: Ilya Biryukov <ibiryukov@google.com>
-Date: Fri, 26 Jan 2018 18:46:06 +0100
-Subject: [PATCH] Added missing 'template' keyword.
-
-To unbreak compilation with clang.
----
- cub/device/dispatch/dispatch_radix_sort.cuh | 2 +-
- 1 file changed, 1 insertion(+), 1 deletion(-)
-
-diff --git a/cub/device/dispatch/dispatch_radix_sort.cuh b/cub/device/dispatch/dispatch_radix_sort.cuh
-index 7fbc621f..f622e212 100644
---- a/cub/device/dispatch/dispatch_radix_sort.cuh
-+++ b/cub/device/dispatch/dispatch_radix_sort.cuh
-@@ -104,7 +104,7 @@ __global__ void DeviceRadixSortUpsweepKernel(
-     CTA_SYNC();
- 
-     // Write out digit counts (striped)
--    upsweep.ExtractCounts<IS_DESCENDING>(d_spine, gridDim.x, blockIdx.x);
-+    upsweep.template ExtractCounts<IS_DESCENDING>(d_spine, gridDim.x, blockIdx.x);
- }
- 
- 
-- 
GitLab


From ab6da3024642429367302d6d2623d57beba9b20b Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 23 Feb 2018 14:29:41 -0800
Subject: [PATCH 250/575] Make it easier to debug @assert_no_garbage_created
 unit test failures

Prints a bunch of information about each object.

PiperOrigin-RevId: 186823593
---
 tensorflow/python/framework/test_util.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index ad9b1291f0..e1c37a52c6 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -506,6 +506,30 @@ def assert_no_garbage_created(f):
     previous_garbage = len(gc.garbage)
     f(self, **kwargs)
     gc.collect()
+    if len(gc.garbage) > previous_garbage:
+      logging.error(
+          "The decorated test created work for Python's garbage collector, "
+          "likely due to a reference cycle. New objects in cycle(s):")
+      for i, obj in enumerate(gc.garbage[previous_garbage:]):
+        try:
+          logging.error(
+              "Object %d of %d" % (i, len(gc.garbage) - previous_garbage))
+          def _safe_object_str(obj):
+            return "<%s %d>" % (obj.__class__.__name__, id(obj))
+          logging.error("  Object type: %s" % (_safe_object_str(obj),))
+          logging.error("  Referrer types: %s" % (
+              ', '.join([_safe_object_str(ref)
+                         for ref in gc.get_referrers(obj)]),))
+          logging.error("  Referent types: %s" % (
+              ', '.join([_safe_object_str(ref)
+                         for ref in gc.get_referents(obj)]),))
+          logging.error("  Object attribute names: %s" % (dir(obj),))
+          logging.error("  Object __str__:")
+          logging.error(obj)
+          logging.error("  Object __repr__:")
+          logging.error(repr(obj))
+        except Exception:
+          logging.error("(Exception while printing object)")
     # This will fail if any garbage has been created, typically because of a
     # reference cycle.
     self.assertEqual(previous_garbage, len(gc.garbage))
-- 
GitLab


From 7134e84a3dcf2e18e98e4ccc1498e4b4f41de014 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Fri, 23 Feb 2018 14:38:37 -0800
Subject: [PATCH 251/575] Make tf.size() with optimize=True encode 0 if any
 dimension is 0.

PiperOrigin-RevId: 186824964
---
 tensorflow/python/ops/array_ops.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 08db8a17b5..b3020efc9a 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -401,8 +401,11 @@ def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
     else:
       input_tensor = ops.convert_to_tensor(input)
       input_shape = input_tensor.get_shape()
-      if optimize and input_shape.is_fully_defined():
-        return constant(input_shape.num_elements(), out_type, name=name)
+      if optimize:
+        if input_shape.is_fully_defined():
+          return constant(input_shape.num_elements(), out_type, name=name)
+        if input_shape.dims and any(dim == 0 for dim in input_shape.dims):
+          return constant(0, out_type, name=name)
       return gen_array_ops.size(input, name=name, out_type=out_type)
 
 
-- 
GitLab


From 9d2499fd757120a8d23d800b8fcd00a30a3d7420 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 15:06:46 -0800
Subject: [PATCH 252/575] Eager/C: Add a TF_Status argument to a couple of
 functions.

PiperOrigin-RevId: 186829318
---
 tensorflow/c/eager/c_api.cc              | 12 +++-
 tensorflow/c/eager/c_api.h               |  8 ++-
 tensorflow/c/eager/c_api_test.cc         |  6 +-
 tensorflow/python/eager/pywrap_tensor.cc | 89 +++++++++++++++++-------
 4 files changed, 81 insertions(+), 34 deletions(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index cc318c3878..f615e3f11d 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -154,16 +154,22 @@ TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h) {
   return static_cast<TF_DataType>(h->t.dtype());
 }
 
-int TFE_TensorHandleNumDims(TFE_TensorHandle* h) { return h->t.dims(); }
+int TFE_TensorHandleNumDims(TFE_TensorHandle* h, TF_Status* status) {
+  status->status = tensorflow::Status::OK();
+  return h->t.dims();
+}
 
-int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index) {
+int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index,
+                            TF_Status* status) {
+  status->status = tensorflow::Status::OK();
   return h->t.dim_size(dim_index);
 }
 
-const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h) {
+const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h, TF_Status* status) {
   // TODO(apassos) this will be potentially incorrect in the distributed case as
   // our local device will have a name which depends on the ClusterSpec and
   // hence will require the context to resolve.
+  status->status = tensorflow::Status::OK();
   return (h->d == nullptr) ? "/job:localhost/replica:0/task:0/device:CPU:0"
                            : h->d->name().c_str();
 }
diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h
index 7a321b54da..90cfb7500e 100644
--- a/tensorflow/c/eager/c_api.h
+++ b/tensorflow/c/eager/c_api.h
@@ -119,11 +119,13 @@ TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t,
                                                             TF_Status* status);
 TF_CAPI_EXPORT extern void TFE_DeleteTensorHandle(TFE_TensorHandle* h);
 TF_CAPI_EXPORT extern TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h);
-TF_CAPI_EXPORT extern int TFE_TensorHandleNumDims(TFE_TensorHandle* h);
+TF_CAPI_EXPORT extern int TFE_TensorHandleNumDims(TFE_TensorHandle* h,
+                                                  TF_Status* status);
 TF_CAPI_EXPORT extern int64_t TFE_TensorHandleDim(TFE_TensorHandle* h,
-                                                  int dim_index);
+                                                  int dim_index,
+                                                  TF_Status* status);
 TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(
-    TFE_TensorHandle* h);
+    TFE_TensorHandle* h, TF_Status* status);
 TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,
                                                          TF_Status* status);
 
diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc
index 4a3ecbc0ab..00fb7e68d0 100644
--- a/tensorflow/c/eager/c_api_test.cc
+++ b/tensorflow/c/eager/c_api_test.cc
@@ -932,7 +932,8 @@ TEST(CAPI, Variables) {
   ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
   ASSERT_EQ(1, num_retvals);
   EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(value_handle));
-  EXPECT_EQ(0, TFE_TensorHandleNumDims(value_handle));
+  EXPECT_EQ(0, TFE_TensorHandleNumDims(value_handle, status));
+  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
   float value = 0.0f;
   TF_Tensor* t = TFE_TensorHandleResolve(value_handle, status);
   ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
@@ -974,7 +975,8 @@ void BM_ReadVariable(int iters) {
     CHECK_EQ(1, num_retvals);
     CHECK(h);
     CHECK_EQ(TF_FLOAT, TFE_TensorHandleDataType(h));
-    CHECK_EQ(0, TFE_TensorHandleNumDims(h));
+    CHECK_EQ(0, TFE_TensorHandleNumDims(h, status));
+    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
     h = nullptr;
   }
   tensorflow::testing::StopTiming();
diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc
index 6fa076507d..3ec2109d32 100644
--- a/tensorflow/python/eager/pywrap_tensor.cc
+++ b/tensorflow/python/eager/pywrap_tensor.cc
@@ -185,6 +185,12 @@ typedef struct EagerTensor {
 
   // This stores `_keras_mask` object and is set by Tensorflow layers.
   PyObject* keras_mask;
+
+  // We store a status object here as an optimization to avoid allocating a new
+  // Status objects on different functions that operate on EagerTensor and need
+  // to use a TF_Status object. However note that accesses to `status` are not
+  // thread-safe.
+  TF_Status* status;
 } EagerTensor;
 
 // tp_init for EagerTensor.
@@ -195,6 +201,7 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) {
   self->handle_data = Py_None;
   Py_INCREF(Py_None);
   self->keras_mask = Py_None;
+  self->status = TF_NewStatus();
   PyObject* value;
   PyObject* context = nullptr;
   PyObject* device = nullptr;
@@ -269,17 +276,17 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) {
   }
   TF_DataType handle_dtype = TFE_TensorHandleDataType(handle.get());
   if (desired_dtype >= 0 && desired_dtype != handle_dtype) {
-    auto out_status = tensorflow::make_safe(TF_NewStatus());
     handle = tensorflow::make_safe(
         EagerCast(GetContext(context), handle.get(), handle_dtype,
-                  static_cast<TF_DataType>(desired_dtype), out_status.get()));
-    if (TF_GetCode(out_status.get()) != TF_OK) {
-      PyErr_SetString(
-          PyExc_ValueError,
-          tensorflow::strings::StrCat("Error while casting from DataType ",
-                                      handle_dtype, " to ", desired_dtype, ". ",
-                                      TF_Message(out_status.get()))
-              .c_str());
+                  static_cast<TF_DataType>(desired_dtype), self->status));
+    if (TF_GetCode(self->status) != TF_OK) {
+      PyErr_SetString(PyExc_ValueError,
+                      tensorflow::strings::StrCat(
+                          "Error while casting from DataType ", handle_dtype,
+                          " to ", desired_dtype, ". ", TF_Message(self->status))
+                          .c_str());
+      // Cleanup self->status before returning.
+      TF_SetStatus(self->status, TF_OK, "");
       return -1;
     }
     handle_dtype = TFE_TensorHandleDataType(handle.get());
@@ -323,6 +330,7 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) {
 
 // tp_dealloc for EagerTensor.
 void EagerTensor_dealloc(EagerTensor* self) {
+  TF_DeleteStatus(self->status);
   Py_DECREF(self->handle_data);
   Py_DECREF(self->keras_mask);
   TFE_DeleteTensorHandle(self->handle);
@@ -348,12 +356,21 @@ static PyObject* EagerTensor_datatype_enum(EagerTensor* self) {
 // Getter for `_shape_tuple`.
 static PyObject* EagerTensor_shape_tuple(EagerTensor* self) {
   auto handle = self->handle;
-  int n = TFE_TensorHandleNumDims(handle);
+  int n = TFE_TensorHandleNumDims(handle, self->status);
+  if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) {
+    // Cleanup self->status before returning.
+    TF_SetStatus(self->status, TF_OK, "");
+    return nullptr;
+  }
   PyObject* shape = PyTuple_New(n);
   if (PyErr_Occurred()) return nullptr;
   for (int i = 0; i < n; ++i) {
-    PyObject* dim = PyLong_FromLongLong(TFE_TensorHandleDim(handle, i));
-    if (dim == nullptr || PyTuple_SetItem(shape, i, dim) != 0) {
+    PyObject* dim =
+        PyLong_FromLongLong(TFE_TensorHandleDim(handle, i, self->status));
+    if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError) ||
+        dim == nullptr || PyTuple_SetItem(shape, i, dim) != 0) {
+      // Cleanup self->status before returning.
+      TF_SetStatus(self->status, TF_OK, "");
       Py_DECREF(shape);
       if (dim != nullptr) Py_DECREF(dim);
       PyErr_SetString(PyExc_RuntimeError, "Error while creating shape");
@@ -365,10 +382,16 @@ static PyObject* EagerTensor_shape_tuple(EagerTensor* self) {
 
 // Getter for `_rank`.
 static PyObject* EagerTensor_rank(EagerTensor* self) {
+  int num_dims = TFE_TensorHandleNumDims(self->handle, self->status);
+  if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) {
+    // Cleanup self->status before returning.
+    TF_SetStatus(self->status, TF_OK, "");
+    return nullptr;
+  }
 #if PY_MAJOR_VERSION < 3
-  return PyInt_FromLong(TFE_TensorHandleNumDims(self->handle));
+  return PyInt_FromLong(num_dims);
 #else
-  return PyLong_FromLong(TFE_TensorHandleNumDims(self->handle));
+  return PyLong_FromLong(num_dims);
 #endif
 }
 
@@ -437,10 +460,16 @@ static PyObject* EagerTensor_numpy(EagerTensor* self) {
 
 // Getter `device`.
 static PyObject* EagerTensor_device(EagerTensor* self) {
+  const char* device = TFE_TensorHandleDeviceName(self->handle, self->status);
+  if (MaybeRaiseExceptionFromTFStatus(self->status, PyExc_ValueError)) {
+    // Cleanup self->status before returning.
+    TF_SetStatus(self->status, TF_OK, "");
+    return nullptr;
+  }
 #if PY_MAJOR_VERSION >= 3
-  return PyUnicode_FromString(TFE_TensorHandleDeviceName(self->handle));
+  return PyUnicode_FromString(device);
 #else
-  return PyBytes_FromString(TFE_TensorHandleDeviceName(self->handle));
+  return PyBytes_FromString(device);
 #endif
 }
 
@@ -576,6 +605,7 @@ PyObject* EagerTensorFromHandle(TFE_TensorHandle* handle) {
     Py_INCREF(Py_None);
     t->keras_mask = Py_None;
     t->handle = handle;
+    t->status = TF_NewStatus();
   }
   return reinterpret_cast<PyObject*>(t);
 }
@@ -673,6 +703,7 @@ PyObject* TFE_Py_TensorShapeSlice(PyObject* tensor_list, int slice_dim) {
   auto tensor = tensorflow::make_safe(TF_AllocateTensor(
       TF_INT32, &num_tensors_int, /*num_dims=*/1, /*len=*/4 * num_tensors_int));
   int32_t* data = reinterpret_cast<int32_t*>(TF_TensorData(tensor.get()));
+  auto status = tensorflow::make_safe(TF_NewStatus());
   for (Py_ssize_t i = 0; i < num_tensors; ++i) {
     PyObject* tensor_obj = PyList_GET_ITEM(tensor_list, i);
     if (!EagerTensor_CheckExact(tensor_obj)) {
@@ -687,21 +718,27 @@ PyObject* TFE_Py_TensorShapeSlice(PyObject* tensor_list, int slice_dim) {
 
     EagerTensor* t = reinterpret_cast<EagerTensor*>(tensor_obj);
     TFE_TensorHandle* handle = t->handle;
-    if (slice_dim >= TFE_TensorHandleNumDims(handle)) {
-      PyErr_SetString(PyExc_IndexError,
-                      tensorflow::strings::StrCat(
-                          "Slice dimension (", slice_dim,
-                          ") must be smaller than rank of all "
-                          "tensors, but tensor at index ",
-                          i, " has rank ", TFE_TensorHandleNumDims(handle))
-                          .c_str());
+    int num_dims = TFE_TensorHandleNumDims(handle, status.get());
+    if (MaybeRaiseExceptionFromTFStatus(status.get(), PyExc_ValueError)) {
+      return nullptr;
+    }
+    if (slice_dim >= num_dims) {
+      PyErr_SetString(
+          PyExc_IndexError,
+          tensorflow::strings::StrCat("Slice dimension (", slice_dim,
+                                      ") must be smaller than rank of all "
+                                      "tensors, but tensor at index ",
+                                      i, " has rank ", num_dims)
+              .c_str());
+      return nullptr;
+    }
+    int64_t dim = TFE_TensorHandleDim(handle, slice_dim, status.get());
+    if (MaybeRaiseExceptionFromTFStatus(status.get(), PyExc_ValueError)) {
       return nullptr;
     }
-    int64_t dim = TFE_TensorHandleDim(handle, slice_dim);
     data[i] = dim;
   }
 
-  auto status = tensorflow::make_safe(TF_NewStatus());
   TFE_TensorHandle* handle = TFE_NewTensorHandle(tensor.get(), status.get());
   if (TF_GetCode(status.get()) != TF_OK) {
     PyErr_SetString(
-- 
GitLab


From eb5f3afcb8717ac6bd737ee78997562f67657fd0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 15:33:22 -0800
Subject: [PATCH 253/575] Adds unit tests for mean op with uint8_t input data.

PiperOrigin-RevId: 186833364
---
 .../internal/reference/reference_ops.h        |  8 ++-
 tensorflow/contrib/lite/kernels/mean_test.cc  | 72 +++++++++++++++++--
 2 files changed, 73 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
index f5290a14d3..53de21697b 100644
--- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
+++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h
@@ -2899,9 +2899,11 @@ inline void Mean(T* input_data, const int* input_dims, const int input_num_dims,
   for (int idx = 0; idx < num_resolved_axis; ++idx) {
     num_elements_in_axis *= static_cast<size_t>(input_dims[resolved_axis[idx]]);
   }
-  for (size_t idx = 0; idx < num_outputs; ++idx) {
-    output_data[idx] = static_cast<T>(static_cast<float>(output_data[idx]) /
-                                      num_elements_in_axis);
+  if (num_elements_in_axis > 0) {
+    for (size_t idx = 0; idx < num_outputs; ++idx) {
+      output_data[idx] = static_cast<T>(static_cast<float>(output_data[idx]) /
+                                        num_elements_in_axis);
+    }
   }
 }
 
diff --git a/tensorflow/contrib/lite/kernels/mean_test.cc b/tensorflow/contrib/lite/kernels/mean_test.cc
index c4c53c2ded..2d6d4bc2da 100644
--- a/tensorflow/contrib/lite/kernels/mean_test.cc
+++ b/tensorflow/contrib/lite/kernels/mean_test.cc
@@ -74,7 +74,7 @@ class MeanOpDynamicModel : public BaseMeanOpModel {
   }
 };
 
-TEST(ConstMeanOpTest, NotKeepDims) {
+TEST(ConstFloatMeanOpTest, NotKeepDims) {
   std::initializer_list<float> data = {
       1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0,  10.0, 11.0, 12.0,
       13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
@@ -86,7 +86,7 @@ TEST(ConstMeanOpTest, NotKeepDims) {
   EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({12, 13})));
 }
 
-TEST(ConstMeanOpTest, KeepDims) {
+TEST(ConstFloatMeanOpTest, KeepDims) {
   std::initializer_list<float> data = {
       1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0,  10.0, 11.0, 12.0,
       13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
@@ -99,7 +99,7 @@ TEST(ConstMeanOpTest, KeepDims) {
               ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5})));
 }
 
-TEST(DynamicMeanOpTest, NotKeepDims) {
+TEST(DynamicFloatMeanOpTest, NotKeepDims) {
   std::initializer_list<float> data = {
       1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0,  10.0, 11.0, 12.0,
       13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
@@ -114,7 +114,7 @@ TEST(DynamicMeanOpTest, NotKeepDims) {
   EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({12, 13})));
 }
 
-TEST(DynamicMeanOpTest, KeepDims) {
+TEST(DynamicFloatMeanOpTest, KeepDims) {
   std::initializer_list<float> data = {
       1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0,  10.0, 11.0, 12.0,
       13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
@@ -130,6 +130,70 @@ TEST(DynamicMeanOpTest, KeepDims) {
               ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5})));
 }
 
+TEST(DynamicFloatMeanOpTest, Scale) {
+  std::initializer_list<float> data = {9.527};
+  MeanOpDynamicModel m({TensorType_FLOAT32, {1}}, {TensorType_FLOAT32, {1}},
+                       {TensorType_INT32, {1}}, true);
+  std::initializer_list<int> axis = {0};
+  m.SetAxis(axis);
+  m.SetInput(data);
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1}));
+  EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({9.527})));
+}
+
+TEST(ConstUint8MeanOpTest, NotKeepDims) {
+  std::initializer_list<uint8_t> data = {1,  2,  3,  4,  5,  6,  7,  8,
+                                         9,  10, 11, 12, 13, 14, 15, 16,
+                                         17, 18, 19, 20, 21, 22, 23, 24};
+  MeanOpConstModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {2}},
+                     {4}, {1, 0, -3, -3}, false);
+  m.SetInput(data);
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
+  EXPECT_THAT(m.GetOutput<uint8_t>(), ElementsAreArray({12, 13}));
+}
+
+TEST(ConstUint8MeanOpTest, KeepDims) {
+  std::initializer_list<uint8_t> data = {1,  2,  3,  4,  5,  6,  7,  8,
+                                         9,  10, 11, 12, 13, 14, 15, 16,
+                                         17, 18, 19, 20, 21, 22, 23, 24};
+  MeanOpConstModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {3}},
+                     {2}, {0, 2}, true);
+  m.SetInput(data);
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
+  EXPECT_THAT(m.GetOutput<uint8_t>(), ElementsAreArray({10, 12, 14}));
+}
+
+TEST(DynamicUint8MeanOpTest, NotKeepDims) {
+  std::initializer_list<uint8_t> data = {1,  2,  3,  4,  5,  6,  7,  8,
+                                         9,  10, 11, 12, 13, 14, 15, 16,
+                                         17, 18, 19, 20, 21, 22, 23, 24};
+  MeanOpDynamicModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {2}},
+                       {TensorType_INT32, {4}}, false);
+  std::initializer_list<int> axis = {1, 0, -3, -3};
+  m.SetAxis(axis);
+  m.SetInput(data);
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
+  EXPECT_THAT(m.GetOutput<uint8_t>(), ElementsAreArray({12, 13}));
+}
+
+TEST(DynamicUint8MeanOpTest, KeepDims) {
+  std::initializer_list<uint8_t> data = {1,  2,  3,  4,  5,  6,  7,  8,
+                                         9,  10, 11, 12, 13, 14, 15, 16,
+                                         17, 18, 19, 20, 21, 22, 23, 24};
+  MeanOpDynamicModel m({TensorType_UINT8, {4, 3, 2}}, {TensorType_UINT8, {3}},
+                       {TensorType_INT32, {2}}, true);
+  std::initializer_list<int> axis = {0, 2};
+  m.SetAxis(axis);
+  m.SetInput(data);
+  m.Invoke();
+  EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
+  EXPECT_THAT(m.GetOutput<uint8_t>(), ElementsAreArray({10, 12, 14}));
+}
+
 }  // namespace
 }  // namespace tflite
 
-- 
GitLab


From fedca2059d52d4cb753c46d4e65884877b5b4f38 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Fri, 23 Feb 2018 15:35:35 -0800
Subject: [PATCH 254/575] Improvement to the eager device placement heuristic.

PiperOrigin-RevId: 186833677
---
 tensorflow/python/eager/context.py   |  3 +--
 tensorflow/python/eager/core_test.py | 16 ++++++++++++++--
 tensorflow/python/ops/array_ops.py   |  5 ++++-
 tensorflow/python/training/saver.py  |  4 ++--
 4 files changed, 21 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py
index 07652d3e02..0e9c21b221 100644
--- a/tensorflow/python/eager/context.py
+++ b/tensorflow/python/eager/context.py
@@ -60,8 +60,7 @@ class _EagerContext(threading.local):
 
   def __init__(self):
     super(_EagerContext, self).__init__()
-    self.device_spec = pydev.DeviceSpec.from_string(
-        "/job:localhost/replica:0/task:0/device:CPU:0")
+    self.device_spec = pydev.DeviceSpec.from_string("")
     self.device_name = self.device_spec.to_string()
     self.mode = _default_mode
     self.scope_name = ""
diff --git a/tensorflow/python/eager/core_test.py b/tensorflow/python/eager/core_test.py
index c68e2f422e..0e40d8a5c0 100644
--- a/tensorflow/python/eager/core_test.py
+++ b/tensorflow/python/eager/core_test.py
@@ -33,6 +33,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import nn_ops
 
 
@@ -65,8 +66,7 @@ class TFETest(test_util.TensorFlowTestCase):
     ctx.summary_writer_resource = 'mock'
     self.assertEqual('mock', ctx.summary_writer_resource)
 
-    self.assertEqual('/job:localhost/replica:0/task:0/device:CPU:0',
-                     ctx.device_name)
+    self.assertEqual('', ctx.device_name)
     self.assertEqual(ctx.device_name, ctx.device_spec.to_string())
     with ctx.device('GPU:0'):
       self.assertEqual('/job:localhost/replica:0/task:0/device:GPU:0',
@@ -100,6 +100,18 @@ class TFETest(test_util.TensorFlowTestCase):
     self.assertEqual(len(cpu_stats.node_stats), 1)
     self.assertEqual(cpu_stats.node_stats[0].node_name, 'Add')
 
+  def testShouldCopy(self):
+    if not context.context().num_gpus():
+      self.skipTest('No devices other than CPUs found')
+    with ops.device('gpu:0'):
+      x = constant_op.constant(1.0)
+    y = array_ops.identity(x)
+    # The value we're testing y.device against will depend on what the behavior
+    # of not explicitly specifying a device in the context is.  This behavior is
+    # subject to change (for example, in the future we may want to use GPUs, if
+    # available, when no device is explicitly provided)
+    self.assertEqual(y.device, '/job:localhost/replica:0/task:0/device:CPU:0')
+
   def testContextStackContainsEagerMode(self):
     # Eager execution has been enabled, and no other context
     # switch has occurred, so `context_stack` should contain
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index b3020efc9a..cdfb955f54 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -134,7 +134,10 @@ def identity(input, name=None):  # pylint: disable=redefined-builtin
     input = ops.convert_to_tensor(input)
     in_device = input.device
     # TODO(ashankar): Does 'identity' need to invoke execution callbacks?
-    if context.context().device_name != in_device:
+    context_device = context.context().device_name
+    if not context_device:
+      context_device = "/job:localhost/replica:0/task:0/device:CPU:0"
+    if context_device != in_device:
       return input._copy()  # pylint: disable=protected-access
     return input
 
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 3888e9bba4..83e848d598 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -196,8 +196,8 @@ class BaseSaverBuilder(object):
       # Copy the restored tensor to the variable's device.
       with ops.device(self._var_device):
         restored_tensor = array_ops.identity(restored_tensor)
-      return resource_variable_ops.shape_safe_assign_variable_handle(
-          self.handle_op, self._var_shape, restored_tensor)
+        return resource_variable_ops.shape_safe_assign_variable_handle(
+            self.handle_op, self._var_shape, restored_tensor)
 
   def __init__(self, write_version=saver_pb2.SaverDef.V2):
     self._write_version = write_version
-- 
GitLab


From cc171bb7371590ee45e361b6a50a018d026412f6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 15:43:09 -0800
Subject: [PATCH 255/575] Add test for bug in CUB that caused dynamic partition
 to fail on the GPU.

PiperOrigin-RevId: 186834668
---
 .../python/kernel_tests/dynamic_partition_op_test.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/tensorflow/python/kernel_tests/dynamic_partition_op_test.py b/tensorflow/python/kernel_tests/dynamic_partition_op_test.py
index fedbf9e696..5e8937ad2c 100644
--- a/tensorflow/python/kernel_tests/dynamic_partition_op_test.py
+++ b/tensorflow/python/kernel_tests/dynamic_partition_op_test.py
@@ -326,6 +326,18 @@ class DynamicPartitionTest(test.TestCase):
     with self.assertRaises(ValueError):
       data_flow_ops.dynamic_partition(data, indices, num_partitions=4)
 
+  #  see https://github.com/tensorflow/tensorflow/issues/17106
+  def testCUBBug(self):
+    x = constant_op.constant(np.random.randn(3072))
+    inds = [0]*189 + [1]*184 + [2]*184 + [3]*191 + [4]*192 + [5]*195 + [6]*195
+    inds += [7]*195 + [8]*188 + [9]*195 + [10]*188 + [11]*202 + [12]*194
+    inds += [13]*194 + [14]*194 + [15]*192
+    self.assertEqual(len(inds), x.shape[0])
+    partitioned = data_flow_ops.dynamic_partition(x, inds, 16)
+    with self.test_session() as sess:
+      res = sess.run(partitioned)
+    self.assertEqual(res[-1].shape[0], 192)
+
 
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 9a84277be2cb8233c5c14270db6fcdff31ab4d93 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Fri, 23 Feb 2018 15:45:02 -0800
Subject: [PATCH 256/575] eager: Change various examples to use tf.keras.Model
 instead of tfe.Network.

PiperOrigin-RevId: 186834891
---
 .../eager/python/examples/gan/mnist.py        |  99 ++++++------
 .../linear_regression/linear_regression.py    |  16 +-
 .../python/examples/resnet50/resnet50.py      | 153 ++++++++----------
 .../examples/resnet50/resnet50_graph_test.py  |   4 +-
 .../python/examples/resnet50/resnet50_test.py |   6 +-
 5 files changed, 122 insertions(+), 156 deletions(-)

diff --git a/tensorflow/contrib/eager/python/examples/gan/mnist.py b/tensorflow/contrib/eager/python/examples/gan/mnist.py
index b9ac79f46c..5f51d52622 100644
--- a/tensorflow/contrib/eager/python/examples/gan/mnist.py
+++ b/tensorflow/contrib/eager/python/examples/gan/mnist.py
@@ -35,7 +35,7 @@ from tensorflow.examples.tutorials.mnist import input_data
 FLAGS = None
 
 
-class Discriminator(tfe.Network):
+class Discriminator(tf.keras.Model):
   """GAN Discriminator.
 
   A network to differentiate between generated and real handwritten digits.
@@ -56,19 +56,15 @@ class Discriminator(tfe.Network):
     else:
       assert data_format == 'channels_last'
       self._input_shape = [-1, 28, 28, 1]
-    self.conv1 = self.track_layer(tf.layers.Conv2D(64, 5, padding='SAME',
-                                                   data_format=data_format,
-                                                   activation=tf.tanh))
-    self.pool1 = self.track_layer(
-        tf.layers.AveragePooling2D(2, 2, data_format=data_format))
-    self.conv2 = self.track_layer(tf.layers.Conv2D(128, 5,
-                                                   data_format=data_format,
-                                                   activation=tf.tanh))
-    self.pool2 = self.track_layer(
-        tf.layers.AveragePooling2D(2, 2, data_format=data_format))
-    self.flatten = self.track_layer(tf.layers.Flatten())
-    self.fc1 = self.track_layer(tf.layers.Dense(1024, activation=tf.tanh))
-    self.fc2 = self.track_layer(tf.layers.Dense(1, activation=None))
+    self.conv1 = tf.layers.Conv2D(
+        64, 5, padding='SAME', data_format=data_format, activation=tf.tanh)
+    self.pool1 = tf.layers.AveragePooling2D(2, 2, data_format=data_format)
+    self.conv2 = tf.layers.Conv2D(
+        128, 5, data_format=data_format, activation=tf.tanh)
+    self.pool2 = tf.layers.AveragePooling2D(2, 2, data_format=data_format)
+    self.flatten = tf.layers.Flatten()
+    self.fc1 = tf.layers.Dense(1024, activation=tf.tanh)
+    self.fc2 = tf.layers.Dense(1, activation=None)
 
   def call(self, inputs):
     """Return two logits per image estimating input authenticity.
@@ -95,7 +91,7 @@ class Discriminator(tfe.Network):
     return x
 
 
-class Generator(tfe.Network):
+class Generator(tf.keras.Model):
   """Generator of handwritten digits similar to the ones in the MNIST dataset.
   """
 
@@ -116,18 +112,17 @@ class Generator(tfe.Network):
     else:
       assert data_format == 'channels_last'
       self._pre_conv_shape = [-1, 6, 6, 128]
-    self.fc1 = self.track_layer(tf.layers.Dense(6 * 6 * 128,
-                                                activation=tf.tanh))
+    self.fc1 = tf.layers.Dense(6 * 6 * 128, activation=tf.tanh)
 
     # In call(), we reshape the output of fc1 to _pre_conv_shape
 
     # Deconvolution layer. Resulting image shape: (batch, 14, 14, 64)
-    self.conv1 = self.track_layer(tf.layers.Conv2DTranspose(
-        64, 4, strides=2, activation=None, data_format=data_format))
+    self.conv1 = tf.layers.Conv2DTranspose(
+        64, 4, strides=2, activation=None, data_format=data_format)
 
     # Deconvolution layer. Resulting image shape: (batch, 28, 28, 1)
-    self.conv2 = self.track_layer(tf.layers.Conv2DTranspose(
-        1, 2, strides=2, activation=tf.nn.sigmoid, data_format=data_format))
+    self.conv2 = tf.layers.Conv2DTranspose(
+        1, 2, strides=2, activation=tf.nn.sigmoid, data_format=data_format)
 
   def call(self, inputs):
     """Return a batch of generated images.
@@ -168,7 +163,8 @@ def discriminator_loss(discriminator_real_outputs, discriminator_gen_outputs):
   """
 
   loss_on_real = tf.losses.sigmoid_cross_entropy(
-      tf.ones_like(discriminator_real_outputs), discriminator_real_outputs,
+      tf.ones_like(discriminator_real_outputs),
+      discriminator_real_outputs,
       label_smoothing=0.25)
   loss_on_generated = tf.losses.sigmoid_cross_entropy(
       tf.zeros_like(discriminator_gen_outputs), discriminator_gen_outputs)
@@ -198,9 +194,8 @@ def generator_loss(discriminator_gen_outputs):
   return loss
 
 
-def train_one_epoch(generator, discriminator,
-                    generator_optimizer, discriminator_optimizer,
-                    dataset, log_interval, noise_dim):
+def train_one_epoch(generator, discriminator, generator_optimizer,
+                    discriminator_optimizer, dataset, log_interval, noise_dim):
   """Trains `generator` and `discriminator` models on `dataset`.
 
   Args:
@@ -222,14 +217,18 @@ def train_one_epoch(generator, discriminator,
 
     with tf.contrib.summary.record_summaries_every_n_global_steps(log_interval):
       current_batch_size = images.shape[0]
-      noise = tf.random_uniform(shape=[current_batch_size, noise_dim],
-                                minval=-1., maxval=1., seed=batch_index)
+      noise = tf.random_uniform(
+          shape=[current_batch_size, noise_dim],
+          minval=-1.,
+          maxval=1.,
+          seed=batch_index)
 
       with tfe.GradientTape(persistent=True) as g:
         generated_images = generator(noise)
-        tf.contrib.summary.image('generated_images',
-                                 tf.reshape(generated_images, [-1, 28, 28, 1]),
-                                 max_images=10)
+        tf.contrib.summary.image(
+            'generated_images',
+            tf.reshape(generated_images, [-1, 28, 28, 1]),
+            max_images=10)
 
         discriminator_gen_outputs = discriminator(generated_images)
         discriminator_real_outputs = discriminator(images)
@@ -245,17 +244,17 @@ def train_one_epoch(generator, discriminator,
                                       discriminator.variables)
 
       with tf.variable_scope('generator'):
-        generator_optimizer.apply_gradients(zip(generator_grad,
-                                                generator.variables))
+        generator_optimizer.apply_gradients(
+            zip(generator_grad, generator.variables))
       with tf.variable_scope('discriminator'):
-        discriminator_optimizer.apply_gradients(zip(discriminator_grad,
-                                                    discriminator.variables))
+        discriminator_optimizer.apply_gradients(
+            zip(discriminator_grad, discriminator.variables))
 
       if log_interval and batch_index > 0 and batch_index % log_interval == 0:
         print('Batch #%d\tAverage Generator Loss: %.6f\t'
-              'Average Discriminator Loss: %.6f' % (
-                  batch_index, total_generator_loss/batch_index,
-                  total_discriminator_loss/batch_index))
+              'Average Discriminator Loss: %.6f' %
+              (batch_index, total_generator_loss / batch_index,
+               total_discriminator_loss / batch_index))
 
 
 def main(_):
@@ -266,10 +265,9 @@ def main(_):
 
   # Load the datasets
   data = input_data.read_data_sets(FLAGS.data_dir)
-  dataset = (tf.data.Dataset
-             .from_tensor_slices(data.train.images)
-             .shuffle(60000)
-             .batch(FLAGS.batch_size))
+  dataset = (
+      tf.data.Dataset.from_tensor_slices(data.train.images).shuffle(60000)
+      .batch(FLAGS.batch_size))
 
   # Create the models and optimizers
   generator = Generator(data_format)
@@ -294,20 +292,17 @@ def main(_):
         start = time.time()
         with summary_writer.as_default():
           train_one_epoch(generator, discriminator, generator_optimizer,
-                          discriminator_optimizer,
-                          dataset, FLAGS.log_interval, FLAGS.noise)
+                          discriminator_optimizer, dataset, FLAGS.log_interval,
+                          FLAGS.noise)
         end = time.time()
-        print('\nTrain time for epoch #%d (global step %d): %f' % (
-            epoch, global_step.numpy(), end - start))
+        print('\nTrain time for epoch #%d (global step %d): %f' %
+              (epoch, global_step.numpy(), end - start))
 
       all_variables = (
-          generator.variables
-          + discriminator.variables
-          + generator_optimizer.variables()
-          + discriminator_optimizer.variables()
-          + [global_step])
-      tfe.Saver(all_variables).save(
-          checkpoint_prefix, global_step=global_step)
+          generator.variables + discriminator.variables +
+          generator_optimizer.variables() +
+          discriminator_optimizer.variables() + [global_step])
+      tfe.Saver(all_variables).save(checkpoint_prefix, global_step=global_step)
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py b/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py
index 6ce4de6ee0..157a6360ea 100644
--- a/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py
+++ b/tensorflow/contrib/eager/python/examples/linear_regression/linear_regression.py
@@ -33,23 +33,13 @@ import tensorflow as tf
 import tensorflow.contrib.eager as tfe
 
 
-class LinearModel(tfe.Network):
-  """A TensorFlow linear regression model.
-
-  Uses TensorFlow's eager execution.
-
-  For those familiar with TensorFlow graphs, notice the absence of
-  `tf.Session`. The `forward()` method here immediately executes and
-  returns output values. The `loss()` method immediately compares the
-  output of `forward()` with the target and returns the MSE loss value.
-  The `fit()` performs gradient-descent training on the model's weights
-  and bias.
-  """
+class LinearModel(tf.keras.Model):
+  """A TensorFlow linear regression model."""
 
   def __init__(self):
     """Constructs a LinearModel object."""
     super(LinearModel, self).__init__()
-    self._hidden_layer = self.track_layer(tf.layers.Dense(1))
+    self._hidden_layer = tf.layers.Dense(1)
 
   def call(self, xs):
     """Invoke the linear model.
diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py
index 9982fdb07e..6b59413141 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50.py
@@ -27,10 +27,9 @@ from __future__ import print_function
 import functools
 
 import tensorflow as tf
-import tensorflow.contrib.eager as tfe
 
 
-class _IdentityBlock(tfe.Network):
+class _IdentityBlock(tf.keras.Model):
   """_IdentityBlock is the block that has no conv layer at shortcut.
 
   Args:
@@ -50,31 +49,24 @@ class _IdentityBlock(tfe.Network):
     bn_name_base = 'bn' + str(stage) + block + '_branch'
     bn_axis = 1 if data_format == 'channels_first' else 3
 
-    self.conv2a = self.track_layer(
-        tf.layers.Conv2D(
-            filters1, (1, 1),
-            name=conv_name_base + '2a',
-            data_format=data_format))
-    self.bn2a = self.track_layer(
-        tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a'))
-
-    self.conv2b = self.track_layer(
-        tf.layers.Conv2D(
-            filters2,
-            kernel_size,
-            padding='same',
-            data_format=data_format,
-            name=conv_name_base + '2b'))
-    self.bn2b = self.track_layer(
-        tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b'))
-
-    self.conv2c = self.track_layer(
-        tf.layers.Conv2D(
-            filters3, (1, 1),
-            name=conv_name_base + '2c',
-            data_format=data_format))
-    self.bn2c = self.track_layer(
-        tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c'))
+    self.conv2a = tf.layers.Conv2D(
+        filters1, (1, 1), name=conv_name_base + '2a', data_format=data_format)
+    self.bn2a = tf.layers.BatchNormalization(
+        axis=bn_axis, name=bn_name_base + '2a')
+
+    self.conv2b = tf.layers.Conv2D(
+        filters2,
+        kernel_size,
+        padding='same',
+        data_format=data_format,
+        name=conv_name_base + '2b')
+    self.bn2b = tf.layers.BatchNormalization(
+        axis=bn_axis, name=bn_name_base + '2b')
+
+    self.conv2c = tf.layers.Conv2D(
+        filters3, (1, 1), name=conv_name_base + '2c', data_format=data_format)
+    self.bn2c = tf.layers.BatchNormalization(
+        axis=bn_axis, name=bn_name_base + '2c')
 
   def call(self, input_tensor, training=False):
     x = self.conv2a(input_tensor)
@@ -92,7 +84,7 @@ class _IdentityBlock(tfe.Network):
     return tf.nn.relu(x)
 
 
-class _ConvBlock(tfe.Network):
+class _ConvBlock(tf.keras.Model):
   """_ConvBlock is the block that has a conv layer at shortcut.
 
   Args:
@@ -121,41 +113,35 @@ class _ConvBlock(tfe.Network):
     bn_name_base = 'bn' + str(stage) + block + '_branch'
     bn_axis = 1 if data_format == 'channels_first' else 3
 
-    self.conv2a = self.track_layer(
-        tf.layers.Conv2D(
-            filters1, (1, 1),
-            strides=strides,
-            name=conv_name_base + '2a',
-            data_format=data_format))
-    self.bn2a = self.track_layer(
-        tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a'))
-
-    self.conv2b = self.track_layer(
-        tf.layers.Conv2D(
-            filters2,
-            kernel_size,
-            padding='same',
-            name=conv_name_base + '2b',
-            data_format=data_format))
-    self.bn2b = self.track_layer(
-        tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b'))
-
-    self.conv2c = self.track_layer(
-        tf.layers.Conv2D(
-            filters3, (1, 1),
-            name=conv_name_base + '2c',
-            data_format=data_format))
-    self.bn2c = self.track_layer(
-        tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c'))
-
-    self.conv_shortcut = self.track_layer(
-        tf.layers.Conv2D(
-            filters3, (1, 1),
-            strides=strides,
-            name=conv_name_base + '1',
-            data_format=data_format))
-    self.bn_shortcut = self.track_layer(
-        tf.layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '1'))
+    self.conv2a = tf.layers.Conv2D(
+        filters1, (1, 1),
+        strides=strides,
+        name=conv_name_base + '2a',
+        data_format=data_format)
+    self.bn2a = tf.layers.BatchNormalization(
+        axis=bn_axis, name=bn_name_base + '2a')
+
+    self.conv2b = tf.layers.Conv2D(
+        filters2,
+        kernel_size,
+        padding='same',
+        name=conv_name_base + '2b',
+        data_format=data_format)
+    self.bn2b = tf.layers.BatchNormalization(
+        axis=bn_axis, name=bn_name_base + '2b')
+
+    self.conv2c = tf.layers.Conv2D(
+        filters3, (1, 1), name=conv_name_base + '2c', data_format=data_format)
+    self.bn2c = tf.layers.BatchNormalization(
+        axis=bn_axis, name=bn_name_base + '2c')
+
+    self.conv_shortcut = tf.layers.Conv2D(
+        filters3, (1, 1),
+        strides=strides,
+        name=conv_name_base + '1',
+        data_format=data_format)
+    self.bn_shortcut = tf.layers.BatchNormalization(
+        axis=bn_axis, name=bn_name_base + '1')
 
   def call(self, input_tensor, training=False):
     x = self.conv2a(input_tensor)
@@ -176,7 +162,8 @@ class _ConvBlock(tfe.Network):
     return tf.nn.relu(x)
 
 
-class ResNet50(tfe.Network):
+# pylint: disable=not-callable
+class ResNet50(tf.keras.Model):
   """Instantiates the ResNet50 architecture.
 
   Args:
@@ -220,32 +207,28 @@ class ResNet50(tfe.Network):
     self.include_top = include_top
 
     def conv_block(filters, stage, block, strides=(2, 2)):
-      l = _ConvBlock(
+      return _ConvBlock(
           3,
           filters,
           stage=stage,
           block=block,
           data_format=data_format,
           strides=strides)
-      return self.track_layer(l)
 
     def id_block(filters, stage, block):
-      l = _IdentityBlock(
+      return _IdentityBlock(
           3, filters, stage=stage, block=block, data_format=data_format)
-      return self.track_layer(l)
-
-    self.conv1 = self.track_layer(
-        tf.layers.Conv2D(
-            64, (7, 7),
-            strides=(2, 2),
-            data_format=data_format,
-            padding='same',
-            name='conv1'))
+
+    self.conv1 = tf.layers.Conv2D(
+        64, (7, 7),
+        strides=(2, 2),
+        data_format=data_format,
+        padding='same',
+        name='conv1')
     bn_axis = 1 if data_format == 'channels_first' else 3
-    self.bn_conv1 = self.track_layer(
-        tf.layers.BatchNormalization(axis=bn_axis, name='bn_conv1'))
-    self.max_pool = self.track_layer(
-        tf.layers.MaxPooling2D((3, 3), strides=(2, 2), data_format=data_format))
+    self.bn_conv1 = tf.layers.BatchNormalization(axis=bn_axis, name='bn_conv1')
+    self.max_pool = tf.layers.MaxPooling2D(
+        (3, 3), strides=(2, 2), data_format=data_format)
 
     self.l2a = conv_block([64, 64, 256], stage=2, block='a', strides=(1, 1))
     self.l2b = id_block([64, 64, 256], stage=2, block='b')
@@ -267,13 +250,11 @@ class ResNet50(tfe.Network):
     self.l5b = id_block([512, 512, 2048], stage=5, block='b')
     self.l5c = id_block([512, 512, 2048], stage=5, block='c')
 
-    self.avg_pool = self.track_layer(
-        tf.layers.AveragePooling2D(
-            (7, 7), strides=(7, 7), data_format=data_format))
+    self.avg_pool = tf.layers.AveragePooling2D(
+        (7, 7), strides=(7, 7), data_format=data_format)
 
     if self.include_top:
-      self.fc1000 = self.track_layer(
-          tf.layers.Dense(classes, name='fc1000'))
+      self.fc1000 = tf.layers.Dense(classes, name='fc1000')
     else:
       reduction_indices = [1, 2] if data_format == 'channels_last' else [2, 3]
       reduction_indices = tf.constant(reduction_indices)
@@ -288,7 +269,7 @@ class ResNet50(tfe.Network):
       else:
         self.global_pooling = None
 
-  def call(self, input_tensor, training=False):
+  def call(self, input_tensor, training):
     x = self.conv1(input_tensor)
     x = self.bn_conv1(x, training=training)
     x = tf.nn.relu(x)
diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py
index 23317886e7..551c76b0df 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_graph_test.py
@@ -55,7 +55,7 @@ class ResNet50GraphTest(tf.test.TestCase):
     with tf.Graph().as_default():
       images = tf.placeholder(tf.float32, image_shape(None))
       model = resnet50.ResNet50(data_format())
-      predictions = model(images)
+      predictions = model(images, training=False)
 
       init = tf.global_variables_initializer()
 
@@ -114,7 +114,7 @@ class ResNet50Benchmarks(tf.test.Benchmark):
     with tf.Graph().as_default():
       images = tf.placeholder(tf.float32, image_shape(None))
       model = resnet50.ResNet50(data_format())
-      predictions = model(images)
+      predictions = model(images, training=False)
 
       init = tf.global_variables_initializer()
 
diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
index 0ff8746884..c106ab0a06 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
@@ -71,7 +71,7 @@ class ResNet50Test(tf.test.TestCase):
       model.call = tfe.defun(model.call)
     with tf.device(device):
       images, _ = random_batch(2)
-      output = model(images)
+      output = model(images, training=False)
     self.assertEqual((2, 1000), output.shape)
 
   def test_apply(self):
@@ -85,7 +85,7 @@ class ResNet50Test(tf.test.TestCase):
     model = resnet50.ResNet50(data_format, include_top=False)
     with tf.device(device):
       images, _ = random_batch(2)
-      output = model(images)
+      output = model(images, training=False)
     output_shape = ((2, 2048, 1, 1)
                     if data_format == 'channels_first' else (2, 1, 1, 2048))
     self.assertEqual(output_shape, output.shape)
@@ -95,7 +95,7 @@ class ResNet50Test(tf.test.TestCase):
     model = resnet50.ResNet50(data_format, include_top=False, pooling='avg')
     with tf.device(device):
       images, _ = random_batch(2)
-      output = model(images)
+      output = model(images, training=False)
     self.assertEqual((2, 2048), output.shape)
 
   def test_train(self):
-- 
GitLab


From bd946a5bd7b59be8bb276fdd93e0a97653dedbfd Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 23 Feb 2018 15:51:23 -0800
Subject: [PATCH 257/575] Checkpointable: Utility to gather initialization ops

A bit safer, since only variables which will be saved get initialized. Graph
building then raises an error when you've used one which won't be saved. Reduces
the need for the global collection.

Makes it a bit easier to deal with initialization when writing graph/eager
agnostic programs.

PiperOrigin-RevId: 186835744
---
 .../eager/python/checkpointable_utils.py      | 128 +++++++++++++++++-
 .../eager/python/checkpointable_utils_test.py |  86 ++++++++----
 tensorflow/python/framework/test_util.py      |   1 +
 3 files changed, 186 insertions(+), 29 deletions(-)

diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py
index d9648ffb03..e26ecc774a 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils.py
@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import abc
 import collections
 import weakref
 
@@ -278,6 +279,37 @@ def _serialize_object_graph(root_checkpointable):
       slot_variables=slot_variables)
 
 
+def gather_initializers(root_checkpointable):
+  """Traverse the object graph and find initialization ops.
+
+  Looks for `Checkpointable` objects which are dependencies of
+  `root_checkpointable` and which have an `initializer` property. Includes
+  initializers for slot variables only if the variable they are slotting for and
+  the optimizer are dependencies of `root_checkpointable` (i.e. if they would be
+  saved with a checkpoint).
+
+  Args:
+    root_checkpointable: A `Checkpointable` object to gather initializers for.
+  Returns:
+    A list of initialization ops.
+  """
+  # TODO(allenl): Extract out gathering logic so the naming logic doesn't have
+  # to run.
+  checkpointable_objects, path_to_root = (
+      _breadth_first_checkpointable_traversal(root_checkpointable))
+  object_names = {
+      obj: _object_prefix_from_path(path)
+      for obj, path in path_to_root.items()}
+  node_ids = {node: node_id for node_id, node
+              in enumerate(checkpointable_objects)}
+  _serialize_slot_variables(
+      checkpointable_objects=checkpointable_objects,
+      node_ids=node_ids,
+      object_names=object_names)
+  return [c.initializer for c in checkpointable_objects
+          if hasattr(c, "initializer") and c.initializer is not None]
+
+
 class _NoRestoreSaveable(saver_lib.BaseSaverBuilder.SaveableObject):
 
   def __init__(self, tensor, name):
@@ -288,7 +320,26 @@ class _NoRestoreSaveable(saver_lib.BaseSaverBuilder.SaveableObject):
     return control_flow_ops.no_op()
 
 
-class CheckpointLoadStatus(object):
+class _LoadStatus(object):
+  """Abstract base for load status callbacks."""
+
+  @abc.abstractmethod
+  def assert_consumed(self):
+    """Raises an exception unless a non-trivial restoration has completed."""
+    pass
+
+  @abc.abstractmethod
+  def run_restore_ops(self, session=None):
+    """Runs restore ops from the checkpoint. Requires a valid checkpoint."""
+    pass
+
+  @abc.abstractmethod
+  def initialize_or_restore(self, session=None):
+    """Runs restore ops from the checkpoint, or initializes variables."""
+    pass
+
+
+class CheckpointLoadStatus(_LoadStatus):
   """Checks the status of checkpoint loading and manages restore ops.
 
   Returned from `Saver.restore`. Since `restore` may defer the loading of values
@@ -348,6 +399,70 @@ class CheckpointLoadStatus(object):
       session = ops.get_default_session()
     session.run(self._checkpoint.restore_ops, feed_dict=self._feed_dict)
 
+  def initialize_or_restore(self, session=None):
+    """Alias for `run_restore_ops`.
+
+    This method has a sibling in `InitializationOnlyStatus` which instead
+    initializes variables. That type is returned if no checkpoint is specified
+    in `Saver.restore`.
+
+    Args:
+      session: The session to run restore ops in. If `None`, uses the default
+        session.
+    """
+    self.run_restore_ops(session=session)
+
+
+class InitializationOnlyStatus(_LoadStatus):
+  """Returned from `Saver.restore` when no checkpoint has been specified.
+
+  Objects of this type have the same `assert_consumed` method as
+  `CheckpointLoadStatus`, but it always fails. However,
+  `initialize_or_restore` works on objects of both types, and will
+  initialize variables in `InitializationOnlyStatus` objects or restore them
+  otherwise.
+  """
+
+  def __init__(self, root_checkpointable):
+    self._root_checkpointable = root_checkpointable
+
+  def assert_consumed(self):
+    """Assertion for consistency with `CheckpointLoadStatus`. Always fails."""
+    raise AssertionError(
+        "No checkpoint specified (save_path=None); nothing is being restored.")
+
+  def run_restore_ops(self, session=None):
+    """For consistency with `CheckpointLoadStatus`.
+
+    Use `initialize_or_restore` for initializing if no checkpoint was passed
+    to `Saver.restore` and restoring otherwise.
+
+    Args:
+      session: Not used.
+    """
+    raise AssertionError(
+        "No checkpoint specified, so no restore ops are available "
+        "(save_path=None to Saver.restore).")
+
+  def initialize_or_restore(self, session=None):
+    """Runs initialization ops for variables.
+
+    Only objects which would be saved by `Saver.save` will be initialized. See
+    `gather_initializers` for details.
+
+    This method does nothing when executing eagerly (initializers get run
+    eagerly).
+
+    Args:
+      session: The session to run initialization ops in. If `None`, uses the
+        default session.
+    """
+    if context.in_eager_mode():
+      return  # run eagerly
+    if session is None:
+      session = ops.get_default_session()
+    session.run(gather_initializers(self._root_checkpointable))
+
 
 class _SessionWithFeedDictAdditions(session_lib.SessionInterface):
   """Pretends to be a session, inserts extra feeds on run()."""
@@ -521,17 +636,20 @@ class Saver(object):
     Args:
       save_path: The path to the checkpoint, as returned by `save` or
         `tf.train.latest_checkpoint`. If None (as when there is no latest
-        checkpoint for `tf.train.latest_checkpoint` to return), does nothing.
+        checkpoint for `tf.train.latest_checkpoint` to return), returns an
+        object which may run initializers for objects in the dependency graph.
       session: The session to retrieve metadata with. Ignored when executing
         eagerly. If not provided when graph building, the default session is
         used.
 
     Returns:
-      A `CheckpointLoadStatus` object, which can be used to make assertions
-      about the status of checkpoint restoration and run restore ops.
+      A load status object, which can be used to make assertions about the
+      status of checkpoint restoration and run initialization/restore ops
+      (of type `CheckpointLoadStatus`, or `InitializationOnlyStatus` if
+      `save_path` is `None`).
     """
     if save_path is None:
-      return
+      return InitializationOnlyStatus(self._root_checkpointable)
     in_graph_mode = context.in_graph_mode()
     if in_graph_mode:
       if session is None:
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index b7554defde..6b86d41bdb 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -36,7 +36,6 @@ from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope
-from tensorflow.python.ops import variables
 from tensorflow.python.training import adam
 from tensorflow.python.training import checkpointable
 from tensorflow.python.training import saver as core_saver
@@ -140,7 +139,7 @@ class Checkpoint(checkpointable.Checkpointable):
     super(Checkpoint, self).__init__()
     for k, v in sorted(kwargs.items(), key=lambda item: item[0]):
       setattr(self, k, v)
-    self._save_counter = None
+    self._save_counter = None  # Created lazily for restore-on-create.
     self._saver = checkpointable_utils.Saver(weakref.ref(self))
 
   @property
@@ -170,8 +169,12 @@ class Checkpoint(checkpointable.Checkpointable):
         session=session)
 
   def restore(self, save_path):
-    return self._saver.restore(
-        save_path=save_path)
+    status = self._saver.restore(save_path=save_path)
+    # Create the save counter now so it gets initialized with other variables
+    # when graph building. Creating it earlier would lead to double
+    # initialization when executing eagerly.
+    self.save_counter  # pylint: disable=pointless-statement
+    return status
 
 
 class InterfaceTests(test.TestCase):
@@ -206,8 +209,7 @@ class InterfaceTests(test.TestCase):
     with self.assertRaisesRegexp(ValueError, "'duplicate' already exists"):
       checkpointable_utils.add_variable(obj, name="duplicate", shape=[])
 
-    if context.in_graph_mode():
-      self.evaluate(variables.global_variables_initializer())
+    self.evaluate(checkpointable_utils.gather_initializers(obj))
     self.assertEqual("constant_initializer:0", constant_initializer.name)
     self.assertEqual(1, self.evaluate(constant_initializer))
     self.assertEqual("some_variable_scope/ones_initializer:0",
@@ -287,7 +289,8 @@ class CheckpointingTests(test.TestCase):
       optimizer.minimize(
           other_network(input_value),
           global_step=optimizer_step)
-      self.evaluate(variables.global_variables_initializer())
+      self.evaluate(checkpointable_utils.gather_initializers(
+          root_checkpointable))
       self.evaluate(train_op)
     named_variables, serialized_graph = (
         checkpointable_utils._serialize_object_graph(root_checkpointable))
@@ -385,7 +388,8 @@ class CheckpointingTests(test.TestCase):
       train_op = optimizer.minimize(network(input_value))
       # TODO(allenl): Make initialization more pleasant when graph building.
       root_checkpointable.save_counter  # pylint: disable=pointless-statement
-      self.evaluate(variables.global_variables_initializer())
+      self.evaluate(checkpointable_utils.gather_initializers(
+          root_checkpointable))
       self.evaluate(train_op)
     prefix = os.path.join(self.get_temp_dir(), "ckpt")
     self.evaluate(state_ops.assign(network._named_dense.variables[1], [42.]))
@@ -429,6 +433,7 @@ class CheckpointingTests(test.TestCase):
     self.assertAllEqual(optimizer_variables[0], self.evaluate(beta1_power))
     self.assertAllEqual(optimizer_variables[1], self.evaluate(beta2_power))
 
+  # TODO(allenl): Debug garbage created by this test in python3.
   def testDeferredRestorationUsageEager(self):
     """An idiomatic eager execution example."""
     num_training_steps = 10
@@ -468,28 +473,57 @@ class CheckpointingTests(test.TestCase):
           train_op = optimizer.minimize(
               network(input_value),
               global_step=root.global_step)
-          root.save_counter  # pylint: disable=pointless-statement
-          init_op = variables.global_variables_initializer()
           checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory)
           with self.test_session(graph=ops.get_default_graph()) as session:
+            status = root.restore(save_path=checkpoint_path)
+            status.initialize_or_restore(session=session)
             if checkpoint_path is None:
               self.assertEqual(0, training_continuation)
-              session.run(init_op)
-              # Another alternative would be to run initializers automatically
-              # if no checkpoint is being loaded. This would make deferred
-              # loading a bit more useful with graph execution.
+              with self.assertRaises(AssertionError):
+                status.assert_consumed()
             else:
-              status = root.restore(save_path=checkpoint_path).assert_consumed()
-              status.run_restore_ops()
+              status.assert_consumed()
             for _ in range(num_training_steps):
               session.run(train_op)
-            root.save(file_prefix=checkpoint_prefix,
-                      session=session)
+            root.save(file_prefix=checkpoint_prefix, session=session)
             self.assertEqual((training_continuation + 1) * num_training_steps,
                              session.run(root.global_step))
             self.assertEqual(training_continuation + 1,
                              session.run(root.save_counter))
 
+  @test_util.run_in_graph_and_eager_modes()
+  def testAgnosticUsage(self):
+    """Graph/eager agnostic usage."""
+    # Does create garbage when executing eagerly due to ops.Graph() creation.
+    num_training_steps = 10
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    for training_continuation in range(3):
+      with ops.Graph().as_default(), self.test_session(
+          graph=ops.get_default_graph()):
+        network = MyNetwork()
+        optimizer = CheckpointableAdam(0.001)
+        root = Checkpoint(
+            optimizer=optimizer, network=network,
+            global_step=training_util.get_or_create_global_step())
+        checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory)
+        status = root.restore(save_path=checkpoint_path)
+        input_value = constant_op.constant([[3.]])
+        train_fn = functools.partial(
+            optimizer.minimize,
+            functools.partial(network, input_value),
+            global_step=root.global_step)
+        if context.in_graph_mode():
+          train_fn = functools.partial(self.evaluate, train_fn())
+        status.initialize_or_restore()
+        for _ in range(num_training_steps):
+          train_fn()
+        root.save(file_prefix=checkpoint_prefix)
+        self.assertEqual((training_continuation + 1) * num_training_steps,
+                         self.evaluate(root.global_step))
+        self.assertEqual(training_continuation + 1,
+                         self.evaluate(root.save_counter))
+
   def _get_checkpoint_name(self, name):
     root = checkpointable.Checkpointable()
     checkpointable_utils.add_variable(
@@ -602,7 +636,11 @@ class CheckpointingTests(test.TestCase):
     optimizer = CheckpointableAdam(0.1)
     if context.in_graph_mode():
       train_op = optimizer.minimize(root.var)
-      self.evaluate(variables.global_variables_initializer())
+      # Note that `optimizer` has not been added as a dependency of
+      # `root`. Create a one-off grouping so that slot variables for `root.var`
+      # get initialized too.
+      self.evaluate(checkpointable_utils.gather_initializers(
+          Checkpoint(root=root, optimizer=optimizer)))
       self.evaluate(train_op)
     else:
       optimizer.minimize(root.var.read_value)
@@ -709,7 +747,7 @@ class CheckpointingTests(test.TestCase):
     save_root.dep_one.dep_three = dep_three
     save_root.dep_two.dep_three = dep_three
     checkpointable_utils.add_variable(dep_three, name="var", initializer=0.)
-    self.evaluate(variables.global_variables_initializer())
+    self.evaluate(checkpointable_utils.gather_initializers(save_root))
     save_path = checkpointable_utils.Saver(save_root).save(
         os.path.join(checkpoint_directory, "ckpt"))
     load_root = checkpointable.Checkpointable()
@@ -732,7 +770,7 @@ class CheckpointingTests(test.TestCase):
         save_root.dep_one, name="var1", initializer=32., dtype=dtypes.float64)
     checkpointable_utils.add_variable(
         save_root.dep_two, name="var2", initializer=64., dtype=dtypes.float64)
-    self.evaluate(variables.global_variables_initializer())
+    self.evaluate(checkpointable_utils.gather_initializers(save_root))
     save_path = checkpointable_utils.Saver(save_root).save(
         os.path.join(checkpoint_directory, "ckpt"))
     load_root = checkpointable.Checkpointable()
@@ -760,7 +798,7 @@ class CheckpointingTests(test.TestCase):
         first, "v1", initializer=[3., 1., 4.])
     second.v = checkpointable_utils.add_variable(
         second, "v2", initializer=[1., 1., 2., 3.])
-    self.evaluate(variables.global_variables_initializer())
+    self.evaluate(checkpointable_utils.gather_initializers(first))
     checkpoint_directory = self.get_temp_dir()
     save_path = checkpointable_utils.Saver(first).save(
         os.path.join(checkpoint_directory, "ckpt"))
@@ -835,7 +873,7 @@ class CheckpointingTests(test.TestCase):
         obj.var = variable_scope.get_variable(name="v", initializer=0.)
         obj.opt = CheckpointableAdam(0.1)
         obj.opt.minimize(obj.var.read_value())
-        self.evaluate(variables.global_variables_initializer())
+        self.evaluate(checkpointable_utils.gather_initializers(obj))
         saver = checkpointable_utils.Saver(obj)
         saver.save(checkpoint_prefix)
         before_ops = graph.get_operations()
@@ -853,7 +891,7 @@ class CheckpointingTests(test.TestCase):
         obj.var = variable_scope.get_variable(name="v", initializer=0.)
         obj.opt = CheckpointableAdam(0.1)
         obj.opt.minimize(obj.var.read_value())
-        self.evaluate(variables.global_variables_initializer())
+        self.evaluate(checkpointable_utils.gather_initializers(obj))
         saver = checkpointable_utils.Saver(obj)
         save_path = saver.save(checkpoint_prefix)
         saver.restore(save_path)
diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py
index e1c37a52c6..aabf89a234 100644
--- a/tensorflow/python/framework/test_util.py
+++ b/tensorflow/python/framework/test_util.py
@@ -588,6 +588,7 @@ def run_in_graph_and_eager_modes(__unused__=None,
         # This decorator runs the wrapped test twice.
         # Reset the test environment between runs.
         self.tearDown()
+        self._tempdir = None
         self.setUp()
 
       def run_eager_mode(self, **kwargs):
-- 
GitLab


From beed05217cf8c3d90784a66cec7c97e042ff5258 Mon Sep 17 00:00:00 2001
From: Patrick Nguyen <drpng@google.com>
Date: Fri, 23 Feb 2018 16:04:38 -0800
Subject: [PATCH 258/575] Add custom registered graph optimizers run by
 MetaOptimizer.

PiperOrigin-RevId: 186837828
---
 tensorflow/core/grappler/optimizers/BUILD     | 56 ++++++++++++
 .../optimizers/custom_graph_optimizer.h       | 35 ++++++++
 .../custom_graph_optimizer_registry.cc        | 61 +++++++++++++
 .../custom_graph_optimizer_registry.h         | 65 ++++++++++++++
 .../custom_graph_optimizer_registry_test.cc   | 87 +++++++++++++++++++
 .../grappler/optimizers/meta_optimizer.cc     | 21 ++++-
 .../optimizers/meta_optimizer_test.cc         | 77 ++++++++++++++++
 .../core/protobuf/rewriter_config.proto       |  3 +
 8 files changed, 401 insertions(+), 4 deletions(-)
 create mode 100644 tensorflow/core/grappler/optimizers/custom_graph_optimizer.h
 create mode 100644 tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc
 create mode 100644 tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h
 create mode 100644 tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry_test.cc
 create mode 100644 tensorflow/core/grappler/optimizers/meta_optimizer_test.cc

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index e839630605..50ba48ea7a 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -157,6 +157,18 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "custom_graph_optimizer",
+    hdrs = [
+        "custom_graph_optimizer.h",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":graph_optimizer",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_library(
     name = "arithmetic_optimizer",
     srcs = ["arithmetic_optimizer.cc"],
@@ -368,6 +380,8 @@ cc_library(
         ":arithmetic_optimizer",
         ":auto_parallel",
         ":constant_folding",
+        ":custom_graph_optimizer",
+        ":custom_graph_optimizer_registry",
         ":dependency_optimizer",
         ":graph_optimizer",
         ":layout_optimizer",
@@ -382,6 +396,48 @@ cc_library(
     ],
 )
 
+tf_cc_test(
+    name = "meta_optimizer_test",
+    srcs = ["meta_optimizer_test.cc"],
+    deps = [
+        ":custom_graph_optimizer",
+        ":custom_graph_optimizer_registry",
+        ":meta_optimizer",
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:tensorflow",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:utils",
+        "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder",
+    ],
+)
+
+cc_library(
+    name = "custom_graph_optimizer_registry",
+    srcs = ["custom_graph_optimizer_registry.cc"],
+    hdrs = ["custom_graph_optimizer_registry.h"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":custom_graph_optimizer",
+        "//tensorflow/core:lib",
+    ],
+)
+
+tf_cc_test(
+    name = "custom_graph_optimizer_registry_test",
+    size = "small",
+    srcs = ["custom_graph_optimizer_registry_test.cc"],
+    deps = [
+        ":custom_graph_optimizer",
+        ":custom_graph_optimizer_registry",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
 cc_library(
     name = "loop_optimizer",
     srcs = ["loop_optimizer.cc"],
diff --git a/tensorflow/core/grappler/optimizers/custom_graph_optimizer.h b/tensorflow/core/grappler/optimizers/custom_graph_optimizer.h
new file mode 100644
index 0000000000..a80d46f416
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/custom_graph_optimizer.h
@@ -0,0 +1,35 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_
+#define TENSORFLOW_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_
+
+#include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+namespace grappler {
+
+// A custom optimizer that can be registered.
+class CustomGraphOptimizer : public GraphOptimizer {
+ public:
+  virtual ~CustomGraphOptimizer() {}
+  virtual Status Init() = 0;
+};
+
+}  // end namespace grappler
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_H_
diff --git a/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc b/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc
new file mode 100644
index 0000000000..6eed43c2b1
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc
@@ -0,0 +1,61 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h"
+
+#include <string>
+#include <unordered_map>
+
+#include "tensorflow/core/platform/logging.h"
+
+namespace tensorflow {
+namespace grappler {
+
+namespace {
+typedef std::unordered_map<string, CustomGraphOptimizerRegistry::Creator>
+    RegistrationMap;
+RegistrationMap* registered_optimizers = nullptr;
+RegistrationMap* GetRegistrationMap() {
+  if (registered_optimizers == nullptr)
+    registered_optimizers = new RegistrationMap;
+  return registered_optimizers;
+}
+}  // namespace
+
+std::unique_ptr<CustomGraphOptimizer>
+CustomGraphOptimizerRegistry::CreateByNameOrNull(const string& name) {
+  const auto it = GetRegistrationMap()->find(name);
+  if (it == GetRegistrationMap()->end()) return nullptr;
+  return std::unique_ptr<CustomGraphOptimizer>(it->second());
+}
+
+std::vector<string> CustomGraphOptimizerRegistry::GetRegisteredOptimizers() {
+  std::vector<string> optimizer_names;
+  optimizer_names.reserve(GetRegistrationMap()->size());
+  for (const auto& opt : *GetRegistrationMap())
+    optimizer_names.emplace_back(opt.first);
+  return optimizer_names;
+}
+
+void CustomGraphOptimizerRegistry::RegisterOptimizerOrDie(
+    const Creator& optimizer_creator, const string& name) {
+  const auto it = GetRegistrationMap()->find(name);
+  if (it != GetRegistrationMap()->end()) {
+    LOG(FATAL) << "CustomGraphOptimizer is registered twice: " << name;
+  }
+  GetRegistrationMap()->insert({name, optimizer_creator});
+}
+
+}  // end namespace grappler
+}  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h b/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h
new file mode 100644
index 0000000000..796da91373
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h
@@ -0,0 +1,65 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_REGISTRY_H_
+#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_REGISTRY_H_
+
+#include <functional>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer.h"
+
+namespace tensorflow {
+namespace grappler {
+
+class CustomGraphOptimizerRegistry {
+ public:
+  static std::unique_ptr<CustomGraphOptimizer> CreateByNameOrNull(
+      const string& name);
+
+  static std::vector<string> GetRegisteredOptimizers();
+
+  typedef std::function<CustomGraphOptimizer*()> Creator;
+  // Regsiter graph optimizer which can be called during program initialization.
+  // This class is not thread-safe.
+  static void RegisterOptimizerOrDie(const Creator& optimizer_creator,
+                                     const string& name);
+};
+
+class CustomGraphOptimizerRegistrar {
+ public:
+  explicit CustomGraphOptimizerRegistrar(
+      const CustomGraphOptimizerRegistry::Creator& creator,
+      const string& name) {
+    CustomGraphOptimizerRegistry::RegisterOptimizerOrDie(creator, name);
+  }
+};
+
+#define REGISTER_GRAPH_OPTIMIZER_AS(MyCustomGraphOptimizerClass, name) \
+  namespace {                                                          \
+  static CustomGraphOptimizerRegistrar                                 \
+      MyCustomGraphOptimizerClass##_registrar(                         \
+          []() { return new MyCustomGraphOptimizerClass; }, (name));   \
+  }  // namespace
+
+#define REGISTER_GRAPH_OPTIMIZER(MyCustomGraphOptimizerClass) \
+  REGISTER_GRAPH_OPTIMIZER_AS(MyCustomGraphOptimizerClass,    \
+                              #MyCustomGraphOptimizerClass)
+
+}  // end namespace grappler
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_CUSTOM_GRAPH_OPTIMIZER_REGISTRY_H_
diff --git a/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry_test.cc b/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry_test.cc
new file mode 100644
index 0000000000..629f5e83c1
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry_test.cc
@@ -0,0 +1,87 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h"
+
+#include <algorithm>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace grappler {
+namespace {
+
+static const char* kTestOptimizerName = "Test";
+
+class TestGraphOptimizer : public CustomGraphOptimizer {
+ public:
+  Status Init() override { return Status::OK(); }
+  string name() const override { return kTestOptimizerName; }
+  Status Optimize(Cluster* cluster, const GrapplerItem& item,
+                  GraphDef* optimized_graph) override {
+    return Status::OK();
+  }
+  void Feedback(Cluster* cluster, const GrapplerItem& item,
+                const GraphDef& optimized_graph, double result) override {}
+};
+
+REGISTER_GRAPH_OPTIMIZER_AS(TestGraphOptimizer, "StaticRegister");
+
+TEST(CustomGraphOptimizerRegistryTest, DynamicRegistration) {
+  std::vector<string> optimizers =
+      CustomGraphOptimizerRegistry::GetRegisteredOptimizers();
+  std::unique_ptr<const CustomGraphOptimizer> test_optimizer;
+  ASSERT_EQ(
+      0, std::count(optimizers.begin(), optimizers.end(), "DynamicRegister"));
+  test_optimizer =
+      CustomGraphOptimizerRegistry::CreateByNameOrNull("DynamicRegister");
+  EXPECT_EQ(nullptr, test_optimizer);
+  CustomGraphOptimizerRegistry::RegisterOptimizerOrDie(
+      []() { return new TestGraphOptimizer; }, "DynamicRegister");
+  optimizers = CustomGraphOptimizerRegistry::GetRegisteredOptimizers();
+  ASSERT_EQ(
+      1, std::count(optimizers.begin(), optimizers.end(), "DynamicRegister"));
+  test_optimizer =
+      CustomGraphOptimizerRegistry::CreateByNameOrNull("DynamicRegister");
+  ASSERT_NE(nullptr, test_optimizer);
+  EXPECT_EQ(kTestOptimizerName, test_optimizer->name());
+}
+
+TEST(CustomGraphOptimizerRegistryTest, StaticRegistration) {
+  const std::vector<string> optimizers =
+      CustomGraphOptimizerRegistry::GetRegisteredOptimizers();
+  EXPECT_EQ(1,
+            std::count(optimizers.begin(), optimizers.end(), "StaticRegister"));
+  std::unique_ptr<const CustomGraphOptimizer> test_optimizer =
+      CustomGraphOptimizerRegistry::CreateByNameOrNull("StaticRegister");
+  ASSERT_NE(nullptr, test_optimizer);
+  EXPECT_EQ(kTestOptimizerName, test_optimizer->name());
+}
+
+TEST(GraphOptimizerRegistryTest, CrashesOnDuplicateRegistration) {
+  const auto creator = []() { return new TestGraphOptimizer; };
+  EXPECT_DEATH(CustomGraphOptimizerRegistry::RegisterOptimizerOrDie(
+                   creator, "StaticRegister"),
+               "twice");
+}
+
+}  // namespace
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index e27b9df620..7ae77207af 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -19,6 +19,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/optimizers/arithmetic_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/auto_parallel.h"
 #include "tensorflow/core/grappler/optimizers/constant_folding.h"
+#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h"
 #include "tensorflow/core/grappler/optimizers/dependency_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/layout_optimizer.h"
@@ -126,14 +127,26 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
           new AutoParallel(cfg_.auto_parallel().num_replicas())));
     }
   } else {
-    std::set<string> available_optimizers = {
+    const std::set<string> available_optimizers = {
         "pruning",      "constfold",  "layout",     "memory",
         "autoparallel", "arithmetic", "dependency", "loop"};
-    for (const auto& optimizer : cfg_.optimizers()) {
-      if (available_optimizers.find(optimizer) != available_optimizers.end()) {
-        optimizers.push_back(NewOptimizer(optimizer));
+    std::vector<string> custom_optimizer_names;
+    for (const auto& optimizer_name : cfg_.optimizers()) {
+      if (available_optimizers.find(optimizer_name) !=
+          available_optimizers.end()) {
+        optimizers.push_back(NewOptimizer(optimizer_name));
+      } else {
+        custom_optimizer_names.push_back(optimizer_name);
       }
     }
+    // Now run the custom optimizers.
+    for (const auto& optimizer_name : custom_optimizer_names) {
+      std::unique_ptr<CustomGraphOptimizer> opt =
+          CustomGraphOptimizerRegistry::CreateByNameOrNull(optimizer_name);
+      if (opt == nullptr) continue;
+      TF_RETURN_IF_ERROR(opt->Init());
+      optimizers.push_back(std::move(opt));
+    }
   }
 
   if (optimizers.empty()) {
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc b/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc
new file mode 100644
index 0000000000..536347d834
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer_test.cc
@@ -0,0 +1,77 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/optimizers/meta_optimizer.h"
+
+#include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
+#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer.h"
+#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h"
+#include "tensorflow/core/grappler/utils.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace grappler {
+namespace {
+
+class TestOptimizer : public CustomGraphOptimizer {
+ public:
+  static void SetOptimized(const bool flag_value) { optimized_ = flag_value; }
+  static bool IsOptimized() { return optimized_; }
+
+  TestOptimizer() {}
+  string name() const override { return "test_optimizer"; }
+
+  Status Init() override { return Status::OK(); }
+
+  Status Optimize(Cluster* cluster, const GrapplerItem& item,
+                  GraphDef* optimized_graph) override {
+    optimized_ = true;
+    *optimized_graph = item.graph;
+    return Status::OK();
+  }
+
+  void Feedback(Cluster* cluster, const GrapplerItem& item,
+                const GraphDef& optimized_graph, double result) override {}
+
+ private:
+  static bool optimized_;
+};
+
+bool TestOptimizer::optimized_;
+
+REGISTER_GRAPH_OPTIMIZER(TestOptimizer);
+
+TEST(MetaOptimizerTest, RunsCustomOptimizer) {
+  TrivialTestGraphInputYielder fake_input(4, 1, 10, false, {"CPU:0"});
+  GrapplerItem item;
+  CHECK(fake_input.NextItem(&item));
+
+  TestOptimizer::SetOptimized(false);
+  RewriterConfig rewriter_config;
+  rewriter_config.add_optimizers("TestOptimizer");
+
+  MetaOptimizer optimizer(nullptr, rewriter_config);
+  GraphDef output;
+  const Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+  EXPECT_TRUE(TestOptimizer::IsOptimized());
+}
+
+}  // namespace
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index a61eecaa29..504ed5d819 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -87,5 +87,8 @@ message RewriterConfig {
   // ("autoparallel"). Memory optimization passes ("memory") invoked here are
   // not configurable (in contrast to memory optimization passes through the
   // meta-optimizer) and act only on manual op annotations.
+  //
+  // Custom registered optimizers will be run after the base optimizers, in
+  // the order that they are specified.
   repeated string optimizers = 100;
 }
-- 
GitLab


From 73b14e0c9b9ed70e7b44b5ea95ad2cef9feb7102 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 16:05:57 -0800
Subject: [PATCH 259/575] Add Kumaraswamy Bijector, and let Kumaraswamy
 distribution depend on it.

PiperOrigin-RevId: 186838045
---
 tensorflow/contrib/distributions/BUILD        |  34 ++++
 .../bijectors/kumaraswamy_bijector_test.py    |  80 +++++++++
 .../python/kernel_tests/kumaraswamy_test.py   |   8 +-
 .../python/ops/bijectors/__init__.py          |   2 +
 .../python/ops/bijectors/kumaraswamy.py       | 153 ++++++++++++++++++
 .../distributions/python/ops/kumaraswamy.py   |  89 ++++------
 6 files changed, 305 insertions(+), 61 deletions(-)
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index 35dd2ee439..ed79ef70f8 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -251,6 +251,21 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "kumaraswamy_test",
+    srcs = ["python/kernel_tests/kumaraswamy_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "moving_stats_test",
     size = "small",
@@ -915,6 +930,25 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "kumaraswamy_bijector_test",
+    size = "small",
+    srcs = ["python/kernel_tests/bijectors/kumaraswamy_bijector_test.py"],
+    additional_deps = [
+        ":bijectors_py",
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "@six_archive//:six",
+        "//tensorflow/contrib/linalg:linalg_py",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "masked_autoregressive_test",
     size = "small",
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py
new file mode 100644
index 0000000000..ad11d9f248
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/kumaraswamy_bijector_test.py
@@ -0,0 +1,80 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Kumaraswamy Bijector."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.distributions.python.ops.bijectors.kumaraswamy import Kumaraswamy
+from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite
+from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency
+from tensorflow.python.platform import test
+
+
+class KumaraswamyBijectorTest(test.TestCase):
+  """Tests correctness of the Kumaraswamy bijector."""
+
+  def testBijector(self):
+    with self.test_session():
+      a = 2.
+      b = 0.3
+      bijector = Kumaraswamy(
+          concentration1=a, concentration0=b,
+          event_ndims=0, validate_args=True)
+      self.assertEqual("kumaraswamy", bijector.name)
+      x = np.array([[[0.1], [0.2], [0.3], [0.4], [0.5]]], dtype=np.float32)
+      # Kumaraswamy cdf. This is the same as inverse(x).
+      y = 1. - (1. - x ** a) ** b
+      self.assertAllClose(y, bijector.inverse(x).eval())
+      self.assertAllClose(x, bijector.forward(y).eval())
+      kumaraswamy_log_pdf = (np.log(a) + np.log(b) + (a - 1) * np.log(x) +
+                             (b - 1) * np.log1p(-x ** a))
+
+      self.assertAllClose(
+          # We should lose a dimension from calculating the determinant of the
+          # jacobian.
+          kumaraswamy_log_pdf,
+          bijector.inverse_log_det_jacobian(x).eval())
+      self.assertAllClose(
+          -bijector.inverse_log_det_jacobian(x).eval(),
+          bijector.forward_log_det_jacobian(y).eval(),
+          rtol=1e-4,
+          atol=0.)
+
+  def testScalarCongruency(self):
+    with self.test_session():
+      assert_scalar_congruency(
+          Kumaraswamy(concentration1=0.5, concentration0=1.1),
+          lower_x=0., upper_x=1., n=int(10e3), rtol=0.02)
+
+  def testBijectiveAndFinite(self):
+    with self.test_session():
+      concentration1 = 1.2
+      concentration0 = 2.
+      bijector = Kumaraswamy(
+          concentration1=concentration1,
+          concentration0=concentration0, validate_args=True)
+      # Omitting the endpoints 0 and 1, since idlj will be inifinity at these
+      # endpoints.
+      y = np.linspace(.01, 0.99, num=10).astype(np.float32)
+      x = 1 - (1 - y ** concentration1) ** concentration0
+      assert_bijective_and_finite(bijector, x, y, rtol=1e-3)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py b/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py
index ea3c86b5c0..2980e2bfe9 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/kumaraswamy_test.py
@@ -130,10 +130,8 @@ class KumaraswamyTest(test.TestCase):
       dist.prob([.1, .3, .6]).eval()
       dist.prob([.2, .3, .5]).eval()
       # Either condition can trigger.
-      with self.assertRaisesOpError("sample must be positive"):
+      with self.assertRaisesOpError("sample must be non-negative"):
         dist.prob([-1., 0.1, 0.5]).eval()
-      with self.assertRaisesOpError("sample must be positive"):
-        dist.prob([0., 0.1, 0.5]).eval()
       with self.assertRaisesOpError("sample must be no larger than `1`"):
         dist.prob([.1, .2, 1.2]).eval()
 
@@ -249,13 +247,13 @@ class KumaraswamyTest(test.TestCase):
       a = np.array([1., 2, 3])
       b = np.array([2., 4, 1.2])
       dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False)
-      with self.assertRaisesOpError("Condition x < y.*"):
+      with self.assertRaisesOpError("Mode undefined for concentration1 <= 1."):
         dist.mode().eval()
 
       a = np.array([2., 2, 3])
       b = np.array([1., 4, 1.2])
       dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False)
-      with self.assertRaisesOpError("Condition x < y.*"):
+      with self.assertRaisesOpError("Mode undefined for concentration0 <= 1."):
         dist.mode().eval()
 
   def testKumaraswamyModeEnableAllowNanStats(self):
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
index 93923c3f08..9437f56b1e 100644
--- a/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/__init__.py
@@ -26,6 +26,7 @@
 @@Identity
 @@Inline
 @@Invert
+@@Kumaraswamy
 @@MaskedAutoregressiveFlow
 @@Permute
 @@PowerTransform
@@ -59,6 +60,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.exp import *
 from tensorflow.contrib.distributions.python.ops.bijectors.gumbel import *
 from tensorflow.contrib.distributions.python.ops.bijectors.inline import *
 from tensorflow.contrib.distributions.python.ops.bijectors.invert import *
+from tensorflow.contrib.distributions.python.ops.bijectors.kumaraswamy import *
 from tensorflow.contrib.distributions.python.ops.bijectors.masked_autoregressive import *
 from tensorflow.contrib.distributions.python.ops.bijectors.permute import *
 from tensorflow.contrib.distributions.python.ops.bijectors.power_transform import *
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py
new file mode 100644
index 0000000000..f5de052c9e
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/kumaraswamy.py
@@ -0,0 +1,153 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Kumaraswamy bijector."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops.distributions import bijector
+
+__all__ = [
+    "Kumaraswamy",
+]
+
+
+class Kumaraswamy(bijector.Bijector):
+  """Compute `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a), X in [0, 1]`.
+
+  This bijector maps inputs from `[0, 1]` to [0, 1]`. The inverse of the
+  bijector applied to a uniform random variable `X ~ U(0, 1) gives back a
+  random variable with the [Kumaraswamy distribution](
+  https://en.wikipedia.org/wiki/Kumaraswamy_distribution):
+
+  ```none
+  Y ~ Kumaraswamy(a, b)
+  pdf(y; a, b, 0 <= y <= 1) = a * b * y ** (a - 1) * (1 - y**a) ** (b - 1)
+  ```
+  """
+
+  def __init__(self,
+               concentration1=None,
+               concentration0=None,
+               event_ndims=0,
+               validate_args=False,
+               name="kumaraswamy"):
+    """Instantiates the `Kumaraswamy` bijector.
+
+    Args:
+      concentration1: Python `float` scalar indicating the transform power,
+        i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `a` is
+        `concentration1`.
+      concentration0: Python `float` scalar indicating the transform power,
+        i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `b` is
+        `concentration0`.
+      event_ndims: Python scalar indicating the number of dimensions associated
+        with a particular draw from the distribution. Currently only zero is
+        supported.
+      validate_args: Python `bool` indicating whether arguments should be
+        checked for correctness.
+      name: Python `str` name given to ops managed by this object.
+
+    Raises:
+      ValueError:  If `event_ndims` is not zero.
+    """
+    self._graph_parents = []
+    self._name = name
+    self._validate_args = validate_args
+
+    event_ndims = ops.convert_to_tensor(event_ndims, name="event_ndims")
+    event_ndims_const = tensor_util.constant_value(event_ndims)
+    if event_ndims_const is not None and event_ndims_const not in (0,):
+      raise ValueError("event_ndims(%s) was not 0" % event_ndims_const)
+    else:
+      if validate_args:
+        event_ndims = control_flow_ops.with_dependencies(
+            [check_ops.assert_equal(
+                event_ndims, 0, message="event_ndims was not 0")],
+            event_ndims)
+
+    with self._name_scope("init", values=[concentration1, concentration0]):
+      concentration1 = self._maybe_assert_valid_concentration(
+          ops.convert_to_tensor(concentration1, name="concentration1"),
+          validate_args=validate_args)
+      concentration0 = self._maybe_assert_valid_concentration(
+          ops.convert_to_tensor(concentration0, name="concentration0"),
+          validate_args=validate_args)
+
+    self._concentration1 = concentration1
+    self._concentration0 = concentration0
+    super(Kumaraswamy, self).__init__(
+        event_ndims=0,
+        validate_args=validate_args,
+        name=name)
+
+  @property
+  def concentration1(self):
+    """The `a` in: `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)`."""
+    return self._concentration1
+
+  @property
+  def concentration0(self):
+    """The `b` in: `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)`."""
+    return self._concentration0
+
+  def _forward(self, x):
+    x = self._maybe_assert_valid(x)
+    return math_ops.exp(
+        math_ops.log1p(-math_ops.exp(math_ops.log1p(-x) / self.concentration0))
+        / self.concentration1)
+
+  def _inverse(self, y):
+    y = self._maybe_assert_valid(y)
+    return math_ops.exp(math_ops.log1p(
+        -(1 - y**self.concentration1)**self.concentration0))
+
+  def _inverse_log_det_jacobian(self, y):
+    y = self._maybe_assert_valid(y)
+    event_dims = self._event_dims_tensor(y)
+    return math_ops.reduce_sum(
+        math_ops.log(self.concentration1) + math_ops.log(self.concentration0) +
+        (self.concentration1 - 1) * math_ops.log(y) +
+        (self.concentration0 - 1) * math_ops.log1p(-y**self.concentration1),
+        axis=event_dims)
+
+  def _maybe_assert_valid_concentration(self, concentration, validate_args):
+    """Checks the validity of a concentration parameter."""
+    if not validate_args:
+      return concentration
+    return control_flow_ops.with_dependencies([
+        check_ops.assert_positive(
+            concentration,
+            message="Concentration parameter must be positive."),
+    ], concentration)
+
+  def _maybe_assert_valid(self, x):
+    if not self.validate_args:
+      return x
+    return control_flow_ops.with_dependencies([
+        check_ops.assert_non_negative(
+            x,
+            message="sample must be non-negative"),
+        check_ops.assert_less_equal(
+            x, array_ops.ones([], self.concentration0.dtype),
+            message="sample must be no larger than `1`."),
+    ], x)
diff --git a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py
index 74d5d8773c..120b38db3c 100644
--- a/tensorflow/contrib/distributions/python/ops/kumaraswamy.py
+++ b/tensorflow/contrib/distributions/python/ops/kumaraswamy.py
@@ -20,15 +20,17 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.contrib.distributions.python.ops import bijectors
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import special_math_ops
-from tensorflow.python.ops.distributions import beta
 from tensorflow.python.ops.distributions import distribution
-from tensorflow.python.ops.distributions import util as distribution_util
+from tensorflow.python.ops.distributions import transformed_distribution
+from tensorflow.python.ops.distributions import uniform
 from tensorflow.python.util.tf_export import tf_export
 
 __all__ = [
@@ -60,7 +62,7 @@ def _harmonic_number(x):
 
 
 @tf_export("distributions.Kumaraswamy")
-class Kumaraswamy(beta.Beta):
+class Kumaraswamy(transformed_distribution.TransformedDistribution):
   """Kumaraswamy distribution.
 
   The Kumaraswamy distribution is defined over the `(0, 1)` interval using
@@ -151,59 +153,32 @@ class Kumaraswamy(beta.Beta):
         more of the statistic's batch members are undefined.
       name: Python `str` name prefixed to Ops created by this class.
     """
+    concentration1 = ops.convert_to_tensor(
+        concentration1, name="concentration1")
+    concentration0 = ops.convert_to_tensor(
+        concentration0, name="concentration0")
     super(Kumaraswamy, self).__init__(
-        concentration1=concentration1,
-        concentration0=concentration0,
-        validate_args=validate_args,
-        allow_nan_stats=allow_nan_stats,
+        distribution=uniform.Uniform(
+            low=array_ops.zeros([], dtype=concentration1.dtype),
+            high=array_ops.ones([], dtype=concentration1.dtype),
+            allow_nan_stats=allow_nan_stats),
+        bijector=bijectors.Kumaraswamy(
+            concentration1=concentration1, concentration0=concentration0,
+            validate_args=validate_args),
+        batch_shape=distribution_util.get_broadcast_shape(
+            concentration1, concentration0),
         name=name)
     self._reparameterization_type = distribution.FULLY_REPARAMETERIZED
 
-  def _sample_n(self, n, seed=None):
-    expanded_concentration1 = array_ops.ones_like(
-        self.total_concentration, dtype=self.dtype) * self.concentration1
-    expanded_concentration0 = array_ops.ones_like(
-        self.total_concentration, dtype=self.dtype) * self.concentration0
-    shape = array_ops.concat([[n], self.batch_shape_tensor()], 0)
-    uniform_sample = random_ops.random_uniform(
-        shape=shape, minval=0.0, maxval=1.0, dtype=self.dtype, seed=seed)
-
-    kumaraswamy_sample = (1 - uniform_sample**(1. / expanded_concentration0))**(
-        1. / expanded_concentration1)
-    return kumaraswamy_sample
-
-  @distribution_util.AppendDocstring(_kumaraswamy_sample_note)
-  def _log_cdf(self, x):
-    a = self.concentration1
-    b = self.concentration0
-    return math_ops.log1p(-(1 - x**a)**b)
+  @property
+  def concentration1(self):
+    """Concentration parameter associated with a `1` outcome."""
+    return self.bijector.concentration1
 
-  @distribution_util.AppendDocstring(_kumaraswamy_sample_note)
-  def _cdf(self, x):
-    a = self.concentration1
-    b = self.concentration0
-    return 1 - (1 - x**a)**b
-
-  def _survival_function(self, x):
-    a = self.concentration1
-    b = self.concentration0
-    return (1 - x**a)**b
-
-  def _log_survival_function(self, x):
-    a = self.concentration1
-    b = self.concentration0
-    return b * math_ops.log1p(-x**a)
-
-  def _log_unnormalized_prob(self, x):
-    x = self._maybe_assert_valid_sample(x)
-    a = self.concentration1
-    b = self.concentration0
-    return (a - 1) * math_ops.log(x) + (b - 1) * math_ops.log1p(-x**a)
-
-  def _log_normalization(self):
-    a = self.concentration1
-    b = self.concentration0
-    return -(math_ops.log(a) + math_ops.log(b))
+  @property
+  def concentration0(self):
+    """Concentration parameter associated with a `0` outcome."""
+    return self.bijector.concentration0
 
   def _entropy(self):
     a = self.concentration1
@@ -213,10 +188,11 @@ class Kumaraswamy(beta.Beta):
 
   def _moment(self, n):
     """Compute the n'th (uncentered) moment."""
+    total_concentration = self.concentration1 + self.concentration0
     expanded_concentration1 = array_ops.ones_like(
-        self.total_concentration, dtype=self.dtype) * self.concentration1
+        total_concentration, dtype=self.dtype) * self.concentration1
     expanded_concentration0 = array_ops.ones_like(
-        self.total_concentration, dtype=self.dtype) * self.concentration0
+        total_concentration, dtype=self.dtype) * self.concentration0
     beta_arg0 = 1 + n / expanded_concentration1
     beta_arg = array_ops.stack([beta_arg0, expanded_concentration0], -1)
     log_moment = math_ops.log(expanded_concentration0) + special_math_ops.lbeta(
@@ -246,13 +222,14 @@ class Kumaraswamy(beta.Beta):
           name="nan")
       is_defined = (self.concentration1 > 1.) & (self.concentration0 > 1.)
       return array_ops.where(is_defined, mode, nan)
+
     return control_flow_ops.with_dependencies([
         check_ops.assert_less(
-            array_ops.ones([], dtype=self.dtype),
+            array_ops.ones([], dtype=self.concentration1.dtype),
             self.concentration1,
             message="Mode undefined for concentration1 <= 1."),
         check_ops.assert_less(
-            array_ops.ones([], dtype=self.dtype),
+            array_ops.ones([], dtype=self.concentration0.dtype),
             self.concentration0,
             message="Mode undefined for concentration0 <= 1.")
     ], mode)
-- 
GitLab


From aed54c857802cc191293e0c4df8bbc9a0a15dca9 Mon Sep 17 00:00:00 2001
From: Martin Wicke <577277+martinwicke@users.noreply.github.com>
Date: Fri, 23 Feb 2018 16:26:54 -0800
Subject: [PATCH 260/575] Add nasm mirror

---
 tensorflow/workspace.bzl | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 2b370ffbac..d5c61baa8b 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -226,6 +226,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       urls = [
           "https://mirror.bazel.build/github.com/libjpeg-turbo/libjpeg-turbo/archive/1.5.1.tar.gz",
           "https://github.com/libjpeg-turbo/libjpeg-turbo/archive/1.5.1.tar.gz",
+          "http://www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2",
       ],
       sha256 = "c15a9607892113946379ccea3ca8b85018301b200754f209453ab21674268e77",
       strip_prefix = "libjpeg-turbo-1.5.1",
-- 
GitLab


From f230f639c53c3e9b54ba4b2c3f7650ba2daae307 Mon Sep 17 00:00:00 2001
From: Jeremy Lau <lauj@google.com>
Date: Fri, 23 Feb 2018 16:48:56 -0800
Subject: [PATCH 261/575] Internal change.

PiperOrigin-RevId: 186843326
---
 tensorflow/contrib/bayesflow/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index d7beb26e1b..08b29fb6bc 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -39,7 +39,7 @@ py_library(
 
 cuda_py_test(
     name = "metropolis_hastings_test",
-    size = "medium",
+    size = "large",
     srcs = ["python/kernel_tests/metropolis_hastings_test.py"],
     additional_deps = [
         ":bayesflow_py",
-- 
GitLab


From f3d2c3dc6f32d63309b683a258bd9a3f19004ac2 Mon Sep 17 00:00:00 2001
From: Zhixian Yan <zhixianyan@google.com>
Date: Fri, 23 Feb 2018 16:51:24 -0800
Subject: [PATCH 262/575] Internal change.

PiperOrigin-RevId: 186843632
---
 tensorflow/contrib/lite/testing/generated_examples_zip_test.cc | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
index 976363fd44..86606d1239 100644
--- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
+++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc
@@ -92,6 +92,9 @@ std::map<string, string> kBrokenTests = {
 
     // Transpose only supports 1D-4D input tensors.
     {R"(^\/transpose.*input_shape=\[.,.,.,.,.\])", "71545879"},
+
+    // Lstm kernel gets different results on tsan, asan, msan.
+    {R"(^\/lstmdtype=tf.float32.*)", "73830845"},
 };
 
 // Allows test data to be unzipped into a temporary directory and makes
-- 
GitLab


From 18bab99ac33f31192d400aebcfb7670a121655bd Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Fri, 23 Feb 2018 16:51:50 -0800
Subject: [PATCH 263/575] Registers None gradients for ArgMax

PiperOrigin-RevId: 186843686
---
 tensorflow/python/ops/math_grad.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index 9d5289f23d..bf28f74153 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -35,6 +35,12 @@ def _safe_shape_div(x, y):
   return x // math_ops.maximum(y, 1)
 
 
+@ops.RegisterGradient("ArgMax")
+def _ArgMaxGrad(op, grad):
+  del op, grad
+  return [None, None]
+
+
 @ops.RegisterGradient("Sum")
 def _SumGrad(op, grad):
   """Gradient for Sum."""
-- 
GitLab


From 4f983f23e05da691868a1e20c56e900bb4afbadd Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 23 Feb 2018 16:53:21 -0800
Subject: [PATCH 264/575] Checkpointable: allow using Checkpointable objects in
 a tf.train.Saver()

Checkpointable objects in a Saver's var_list will be unpacked into their
SaveableObjects, possibly running some Python logic along the way.

This should help keep the transition from name-based saving smooth: to save
either way, just override CheckpointableBase._gather_saveables_for_checkpoint.

PiperOrigin-RevId: 186843857
---
 tensorflow/python/training/saver.py      |  6 ++
 tensorflow/python/training/saver_test.py | 88 ++++++++++++++++++++++++
 2 files changed, 94 insertions(+)

diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 83e848d598..9afd1e6643 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -50,6 +50,7 @@ from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import checkpointable
 from tensorflow.python.training import training_util
 from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState
 from tensorflow.python.util import compat
@@ -577,6 +578,11 @@ class BaseSaverBuilder(object):
           names_to_saveables[name].append(var)
         else:
           names_to_saveables[name] = [var]
+      elif (isinstance(var, checkpointable.CheckpointableBase)
+            and not isinstance(var, variables.Variable)):
+        names_to_saveables.update(
+            BaseSaverBuilder.OpListToDict(
+                list(var._gather_saveables_for_checkpoint().values())))
       else:
         if context.in_graph_mode():
           if convert_variable_to_tensor:
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index c5a6f49df5..f00f98db00 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -66,6 +66,7 @@ from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 from tensorflow.python.summary import summary
 from tensorflow.python.training import adam
+from tensorflow.python.training import checkpointable
 from tensorflow.python.training import gradient_descent
 from tensorflow.python.training import queue_runner_impl
 from tensorflow.python.training import saver as saver_module
@@ -2660,5 +2661,92 @@ class ScopedGraphTest(test.TestCase):
       self.assertEqual(2.0, var_dict2["variable2:0"].eval())
 
 
+class _OwnsAVariableSimple(checkpointable.CheckpointableBase):
+  """A Checkpointable object which can be saved using a tf.train.Saver."""
+
+  def __init__(self):
+    self.non_dep_variable = variable_scope.get_variable(
+        name="non_dep_variable", initializer=6., use_resource=True)
+
+  def _gather_saveables_for_checkpoint(self):
+    return {checkpointable.VARIABLE_VALUE_KEY: self.non_dep_variable}
+
+  # The Saver sorts by name before parsing, so we need a name property.
+  @property
+  def name(self):
+    return self.non_dep_variable.name
+
+
+class _MirroringSaveable(
+    saver_module.BaseSaverBuilder.ResourceVariableSaveable):
+
+  def __init__(self, primary_variable, mirrored_variable):
+    self._primary_variable = primary_variable
+    self._mirrored_variable = mirrored_variable
+    super(_MirroringSaveable, self).__init__(
+        self._primary_variable, "", self._primary_variable.name)
+
+  def restore(self, restored_tensors, restored_shapes):
+    """Restore the same value into both variables."""
+    tensor, = restored_tensors
+    return control_flow_ops.group(
+        self._primary_variable.assign(tensor),
+        self._mirrored_variable.assign(tensor))
+
+
+class _OwnsMirroredVariables(checkpointable.CheckpointableBase):
+  """A Checkpointable object which returns a more complex SaveableObject."""
+
+  def __init__(self):
+    self.non_dep_variable = variable_scope.get_variable(
+        name="non_dep_variable", initializer=6., use_resource=True)
+    self.mirrored = variable_scope.get_variable(
+        name="mirrored", initializer=15., use_resource=True)
+
+  def _gather_saveables_for_checkpoint(self):
+    saveable = _MirroringSaveable(
+        primary_variable=self.non_dep_variable,
+        mirrored_variable=self.mirrored)
+    return {checkpointable.VARIABLE_VALUE_KEY: saveable}
+
+  # The Saver sorts by name before parsing, so we need a name property.
+  @property
+  def name(self):
+    return self.non_dep_variable.name
+
+
+@test_util.with_c_api
+class CheckpointableCompatibilityTests(test.TestCase):
+
+  # TODO(allenl): Track down python3 reference cycles in these tests.
+  @test_util.run_in_graph_and_eager_modes()
+  def testNotSaveableButIsCheckpointable(self):
+    v = _OwnsAVariableSimple()
+    saver = saver_module.Saver(var_list=[v])
+    test_dir = self.get_temp_dir()
+    prefix = os.path.join(test_dir, "ckpt")
+    self.evaluate(v.non_dep_variable.assign(42.))
+    with self.test_session() as sess:
+      save_path = saver.save(sess, prefix)
+      self.evaluate(v.non_dep_variable.assign(43.))
+      saver.restore(sess, save_path)
+      self.assertEqual(42., self.evaluate(v.non_dep_variable))
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testMoreComplexSaveableReturned(self):
+    v = _OwnsMirroredVariables()
+    saver = saver_module.Saver(var_list=[v])
+    test_dir = self.get_temp_dir()
+    prefix = os.path.join(test_dir, "ckpt")
+    self.evaluate(v.non_dep_variable.assign(42.))
+    with self.test_session() as sess:
+      save_path = saver.save(sess, prefix)
+      self.evaluate(v.non_dep_variable.assign(43.))
+      self.evaluate(v.mirrored.assign(44.))
+      saver.restore(sess, save_path)
+      self.assertEqual(42., self.evaluate(v.non_dep_variable))
+      self.assertEqual(42., self.evaluate(v.mirrored))
+
+
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From ce4ae5bed9b47f49b085d9d8287cee2fcc5d42ac Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 23 Feb 2018 16:59:01 -0800
Subject: [PATCH 265/575] Checkpointable: compatibility mode with name-based
 saving

Allows loading a name-based checkpoint using the object-based API. When
graph building it's quite seamless. There's no restore-on-create for
eager, so it would require program changes to do much useful there (i.e.
is not seamless).

Adds several tests for checkpoint compatibility (name->object in eager/graph,
and eager->graph/graph->eager for object-based saving)

PiperOrigin-RevId: 186844431
---
 .../eager/python/checkpointable_utils.py      |  98 +++++++++++++---
 .../eager/python/checkpointable_utils_test.py | 110 ++++++++++++++++++
 2 files changed, 192 insertions(+), 16 deletions(-)

diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py
index e26ecc774a..e57093bdbc 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils.py
@@ -27,6 +27,7 @@ from tensorflow.python.client import session as session_lib
 from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import control_flow_ops
@@ -38,6 +39,7 @@ from tensorflow.python.training import checkpointable as core_checkpointable
 from tensorflow.python.training import checkpointable_utils as core_checkpointable_utils
 from tensorflow.python.training import optimizer as optimizer_lib
 from tensorflow.python.training import saver as saver_lib
+from tensorflow.python.util import deprecation
 
 
 _ESCAPE_CHAR = "."  # For avoiding conflicts with user-specified names.
@@ -464,6 +466,41 @@ class InitializationOnlyStatus(_LoadStatus):
     session.run(gather_initializers(self._root_checkpointable))
 
 
+_DEPRECATED_RESTORE_INSTRUCTIONS = (
+    "Restoring a name-based tf.train.Saver checkpoint using the object-based "
+    "restore API. This mode uses global names to match variables, and so is "
+    "somewhat fragile. It also adds new restore ops to the graph each time it "
+    "is called. Prefer re-encoding training checkpoints in the object-based "
+    "format: run save() on the object-based saver (the same one this message "
+    "is coming from) and use that checkpoint in the future.")
+
+
+class NameBasedSaverStatus(_LoadStatus):
+  """Status for loading a name-based training checkpoint."""
+
+  def __init__(self, object_saver, save_path):
+    self._object_saver = object_saver
+    self._save_path = save_path
+
+  def assert_consumed(self):
+    """Assertion for consistency with `CheckpointLoadStatus`. Always fails."""
+    raise AssertionError(
+        "Restoring a name-based checkpoint. No load status is available.")
+
+  @deprecation.deprecated(
+      date=None, instructions=_DEPRECATED_RESTORE_INSTRUCTIONS)
+  def run_restore_ops(self, session=None):
+    """Load the name-based training checkpoint using a new `tf.train.Saver`."""
+    if session is None and context.in_graph_mode():
+      session = ops.get_default_session()
+    saver_lib.Saver(self._object_saver._global_variable_names()).restore(  # pylint: disable=protected-access
+        sess=session, save_path=self._save_path)
+
+  def initialize_or_restore(self, session=None):
+    """Alias for `run_restore_ops`."""
+    self.run_restore_ops(session=session)
+
+
 class _SessionWithFeedDictAdditions(session_lib.SessionInterface):
   """Pretends to be a session, inserts extra feeds on run()."""
 
@@ -544,7 +581,7 @@ class Saver(object):
     Args:
       file_prefix: A prefix to use for the checkpoint filenames
         (/path/to/directory/and_a_prefix). Names are generated based on this
-        prefix and the global step, if provided.
+        prefix and `checkpoint_number`, if provided.
       checkpoint_number: An integer variable or Tensor, used to number
         checkpoints. Typically this value is saved along with other variables in
         training checkpoints, which will happen automatically if it was created
@@ -598,6 +635,17 @@ class Saver(object):
         global_step=checkpoint_number)
     return save_path
 
+  def _global_variable_names(self):
+    """Generate a `tf.train.Saver`-style `var_list` using `variable.name`s."""
+    named_saveables, graph_proto = _serialize_object_graph(
+        self._root_checkpointable)
+    saver_names = {}
+    for object_proto in graph_proto.nodes:
+      for attribute_proto in object_proto.attributes:
+        saver_names[attribute_proto.full_name] = named_saveables[
+            attribute_proto.checkpoint_key]
+    return saver_names
+
   def restore(self, save_path, session=None):
     """Restore a training checkpoint.
 
@@ -633,11 +681,20 @@ class Saver(object):
     If the checkpoint has not been consumed completely, then the list of restore
     ops will grow as more objects are added to the dependency graph.
 
+    Name-based `tf.train.Saver` checkpoints can be loaded using this
+    method. There is no deferred loading, and names are used to match
+    variables. No restore ops are created/run until `run_restore_ops()` or
+    `initialize_or_restore()` are called on the returned status object, even
+    when executing eagerly. Re-encode name-based checkpoints using this
+    object-based `Saver.save` as soon as possible.
+
     Args:
       save_path: The path to the checkpoint, as returned by `save` or
         `tf.train.latest_checkpoint`. If None (as when there is no latest
         checkpoint for `tf.train.latest_checkpoint` to return), returns an
-        object which may run initializers for objects in the dependency graph.
+        object which may run initializers for objects in the dependency
+        graph. If the checkpoint was written by the name-based `tf.train.Saver`,
+        names are used to match variables.
       session: The session to retrieve metadata with. Ignored when executing
         eagerly. If not provided when graph building, the default session is
         used.
@@ -647,6 +704,9 @@ class Saver(object):
       status of checkpoint restoration and run initialization/restore ops
       (of type `CheckpointLoadStatus`, or `InitializationOnlyStatus` if
       `save_path` is `None`).
+
+      If `save_path` points to a name-based checkpoint, a `NameBasedSaverStatus`
+      object is returned which runs restore ops from a name-based saver.
     """
     if save_path is None:
       return InitializationOnlyStatus(self._root_checkpointable)
@@ -660,21 +720,27 @@ class Saver(object):
       session = None
       file_prefix_tensor = constant_op.constant(save_path)
       file_prefix_feed_dict = None
-    if not in_graph_mode or self._object_graph_restore_tensor is None:
-      object_graph_string, = io_ops.restore_v2(
-          prefix=file_prefix_tensor,
-          tensor_names=[_OBJECT_GRAPH_PROTO_KEY],
-          shape_and_slices=[""],
-          dtypes=[dtypes.string],
-          name="object_graph_proto_read")
+    try:
+      if not in_graph_mode or self._object_graph_restore_tensor is None:
+        object_graph_string, = io_ops.restore_v2(
+            prefix=file_prefix_tensor,
+            tensor_names=[_OBJECT_GRAPH_PROTO_KEY],
+            shape_and_slices=[""],
+            dtypes=[dtypes.string],
+            name="object_graph_proto_read")
+        if in_graph_mode:
+          self._object_graph_restore_tensor = object_graph_string
       if in_graph_mode:
-        self._object_graph_restore_tensor = object_graph_string
-    if in_graph_mode:
-      object_graph_string = session.run(
-          self._object_graph_restore_tensor,
-          feed_dict=file_prefix_feed_dict)
-    else:
-      object_graph_string = object_graph_string.numpy()
+        object_graph_string = session.run(
+            self._object_graph_restore_tensor,
+            feed_dict=file_prefix_feed_dict)
+      else:
+        object_graph_string = object_graph_string.numpy()
+    except errors_impl.NotFoundError:
+      # The object graph proto does not exist in this checkpoint. Try again with
+      # name-based saving.
+      return NameBasedSaverStatus(self, save_path)
+
     object_graph_proto = (
         checkpointable_object_graph_pb2.CheckpointableObjectGraph())
     object_graph_proto.ParseFromString(object_graph_string)
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index 6b86d41bdb..3d6a200276 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -899,5 +899,115 @@ class CheckpointingTests(test.TestCase):
         saver.restore(save_path)
         self.assertEqual(before_ops, graph.get_operations())
 
+
+class CheckpointCompatibilityTests(test.TestCase):
+
+  def _initialized_model(self):
+    input_value = constant_op.constant([[3.]])
+    network = MyNetwork()
+    optimizer = CheckpointableAdam(0.001)
+    optimizer_step = training_util.get_or_create_global_step()
+    root_checkpointable = Checkpoint(
+        optimizer=optimizer, network=network, optimizer_step=optimizer_step)
+    train_op = optimizer.minimize(
+        functools.partial(network, input_value),
+        global_step=optimizer_step)
+    self.evaluate(checkpointable_utils.gather_initializers(
+        root_checkpointable))
+    self.evaluate(train_op)
+    # A regular variable, a slot variable, and a non-slot Optimizer variable
+    # with known values to check when loading.
+    self.evaluate(network._named_dense.bias.assign([1.]))
+    self.evaluate(optimizer.get_slot(
+        var=network._named_dense.bias, name="m").assign([2.]))
+    beta1_power, _ = optimizer._get_beta_accumulators()
+    self.evaluate(beta1_power.assign(3.))
+    return root_checkpointable
+
+  def _set_sentinels(self, root_checkpointable):
+    self.evaluate(root_checkpointable.network._named_dense.bias.assign([101.]))
+    self.evaluate(
+        root_checkpointable.optimizer.get_slot(
+            var=root_checkpointable.network._named_dense.bias, name="m")
+        .assign([102.]))
+    beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators()
+    self.evaluate(beta1_power.assign(103.))
+
+  def _check_sentinels(self, root_checkpointable):
+    self.assertAllEqual(
+        [1.], self.evaluate(root_checkpointable.network._named_dense.bias))
+    self.assertAllEqual([2.], self.evaluate(
+        root_checkpointable.optimizer.get_slot(
+            var=root_checkpointable.network._named_dense.bias, name="m")))
+    beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators()
+    self.assertAllEqual(3., self.evaluate(beta1_power))
+
+  def _write_name_based_checkpoint(self):
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    with context.graph_mode():
+      save_graph = ops.Graph()
+      with save_graph.as_default(), self.test_session(
+          graph=save_graph) as session:
+        root = self._initialized_model()
+        name_saver = core_saver.Saver()
+        return name_saver.save(
+            sess=session, save_path=checkpoint_prefix,
+            global_step=root.optimizer_step)
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testLoadFromNameBasedSaver(self):
+    """Save a name-based checkpoint, load it using the object-based API."""
+    save_path = self._write_name_based_checkpoint()
+    root = self._initialized_model()
+    self._set_sentinels(root)
+    with self.assertRaises(AssertionError):
+      self._check_sentinels(root)
+    object_saver = checkpointable_utils.Saver(root)
+    status = object_saver.restore(save_path)
+    with self.assertRaises(AssertionError):
+      status.assert_consumed()
+    status.run_restore_ops()
+    self._check_sentinels(root)
+    self._set_sentinels(root)
+    status.initialize_or_restore()
+    self._check_sentinels(root)
+
+  # TODO(allenl): Test for the core name-based saver loading object-based
+  # checkpoints once object-based checkpointing is in core.
+
+  def testSaveGraphLoadEager(self):
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    with context.graph_mode():
+      save_graph = ops.Graph()
+      with save_graph.as_default(), self.test_session(
+          graph=save_graph) as session:
+        root = self._initialized_model()
+        object_saver = checkpointable_utils.Saver(root)
+        save_path = object_saver.save(
+            session=session, file_prefix=checkpoint_prefix)
+    with context.eager_mode():
+      root = self._initialized_model()
+      self._set_sentinels(root)
+      root.restore(save_path).assert_consumed()
+      self._check_sentinels(root)
+
+  def testSaveEagerLoadGraph(self):
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    with context.eager_mode():
+      root = self._initialized_model()
+      object_saver = checkpointable_utils.Saver(root)
+      save_path = object_saver.save(file_prefix=checkpoint_prefix)
+    with context.graph_mode():
+      save_graph = ops.Graph()
+      with save_graph.as_default(), self.test_session(
+          graph=save_graph):
+        root = self._initialized_model()
+        self._set_sentinels(root)
+        root.restore(save_path).assert_consumed().run_restore_ops()
+        self._check_sentinels(root)
+
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From b1cc57604cadb4251efeb764074c9138d4e24521 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Fri, 23 Feb 2018 17:19:00 -0800
Subject: [PATCH 266/575] Dropped from previous change.

PiperOrigin-RevId: 186846681
---
 tensorflow/c/eager/c_api.cc | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index f615e3f11d..c27a7129fa 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -303,11 +303,9 @@ void TFE_OpSetXLACompilation(TFE_Op* op, unsigned char enable) {
 
 void TFE_OpAddInput(TFE_Op* op, TFE_TensorHandle* h, TF_Status* status) {
   // Questionable heuristic ...
-  //
-  // Motivation: After an 'op' is placed on GPU because some of its earlier
-  // inputs are on GPU, we want to keep the 'op' there, even if some later
-  // inputs of it are not on GPU.
-  if (IsCPU(op->device) && !IsCPU(h->d)) {
+  // - If a device was explicitly set on the op, always use that.
+  // - If not, place on the first non-host device seen.
+  if (op->device == nullptr && !IsCPU(h->d)) {
     op->device = h->d;
   }
   if (!status->status.ok()) return;
-- 
GitLab


From 44bec5d15f656d054df5c61e3eb70d5fbe8bb77a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 23 Feb 2018 17:22:37 -0800
Subject: [PATCH 267/575] Add another utility that captures a function's
 namespace as a mapping from symbol names to actual values. Update
 getmethodclass with a hopefully more robust method.

PiperOrigin-RevId: 186847003
---
 .../contrib/py2tf/pyct/inspect_utils.py       | 128 +++++++++++++-----
 .../contrib/py2tf/pyct/inspect_utils_test.py  |  91 +++++++++----
 2 files changed, 158 insertions(+), 61 deletions(-)

diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils.py b/tensorflow/contrib/py2tf/pyct/inspect_utils.py
index 86cf52afd5..c1af95e2ab 100644
--- a/tensorflow/contrib/py2tf/pyct/inspect_utils.py
+++ b/tensorflow/contrib/py2tf/pyct/inspect_utils.py
@@ -21,22 +21,53 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import itertools
+
 import six
 
 from tensorflow.python.util import tf_inspect
 
 
+def getnamespace(f):
+  """Returns the complete namespace of a function.
+
+  Namespace is defined here as the mapping of all non-local variables to values.
+  This includes the globals and the closure variables. Note that this captures
+  the entire globals collection of the function, and may contain extra symbols
+  that it does not actually use.
+
+  Args:
+    f: User defined function.
+  Returns:
+    A dict mapping symbol names to values.
+  """
+  namespace = dict(six.get_function_globals(f))
+  closure = six.get_function_closure(f)
+  freevars = six.get_function_code(f).co_freevars
+  if freevars and closure:
+    for name, cell in zip(freevars, closure):
+      namespace[name] = cell.cell_contents
+  return namespace
+
+
 def getcallargs(c, *args, **kwargs):
   """Extension of getcallargs to non-function callables."""
-  if tf_inspect.isfunction(c):
+  if tf_inspect.isfunction(c) or tf_inspect.ismethod(c):
     # The traditional getcallargs
     return tf_inspect.getcallargs(c, *args, **kwargs)
 
   if tf_inspect.isclass(c):
-    # Constructors: pass a fake None for self, then remove it.
-    arg_map = tf_inspect.getcallargs(c.__init__, None, *args, **kwargs)
-    assert 'self' in arg_map, 'no "self" argument, is this not a constructor?'
-    del arg_map['self']
+    # Constructors: use a sentinel to remove the self argument.
+    self_sentinel = object()
+    arg_map = tf_inspect.getcallargs(
+        c.__init__, self_sentinel, *args, **kwargs)
+    # Find and remove the self arg. We cannot assume it's called 'self'.
+    self_arg_name = None
+    for name, value in arg_map.items():
+      if value is self_sentinel:
+        self_arg_name = name
+        break
+    del arg_map[self_arg_name]
     return arg_map
 
   if hasattr(c, '__call__'):
@@ -46,8 +77,29 @@ def getcallargs(c, *args, **kwargs):
   raise NotImplementedError('unknown callable "%s"' % type(c))
 
 
-def getmethodclass(m, namespace):
-  """Resolves a function's owner, e.g. a method's class."""
+def getmethodclass(m):
+  """Resolves a function's owner, e.g. a method's class.
+
+  Note that this returns the object that the function was retrieved from, not
+  necessarily the class where it was defined.
+
+  This function relies on Python stack frame support in the interpreter, and
+  has the same limitations that inspect.currentframe.
+
+  Limitations. This function will only work correctly if the owned class is
+  visible in the caller's global or local variables.
+
+  Args:
+    m: A user defined function
+
+  Returns:
+    The class that this function was retrieved from, or None if the function
+    is not an object or class method, or the class that owns the object or
+    method is not visible to m.
+
+  Raises:
+    ValueError: if the class could not be resolved for any unexpected reason.
+  """
 
   # Instance method and class methods: should be bound to a non-null "self".
   # If self is a class, then it's a class method.
@@ -57,34 +109,38 @@ def getmethodclass(m, namespace):
         return m.__self__
       return type(m.__self__)
 
-  # Class and static methods: platform specific.
-  if hasattr(m, 'im_class'):  # Python 2
-    return m.im_class
-
-  if hasattr(m, '__qualname__'):  # Python 3
-    qn = m.__qualname__.split('.')
-    if len(qn) < 2:
-      return None
-    owner_name, func_name = qn[-2:]
-    assert func_name == m.__name__, (
-        'inconsistent names detected '
-        '(__qualname__[1] = "%s", __name__ = "%s") for %s.' % (func_name,
-                                                               m.__name__, m))
-    if owner_name == '<locals>':
-      return None
-    if owner_name not in namespace:
-      raise ValueError(
-          'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' %
-          (owner_name, m, namespace))
-    return namespace[owner_name]
-
-  if six.PY2:
-    # In Python 2 it's impossible, to our knowledge, to detect the class of a
-    # static function. So we're forced to walk all the objects in the
-    # namespace and see if they own it. If any reader finds a better solution,
-    # please let us know.
-    for _, v in namespace.items():
-      if hasattr(v, m.__name__) and getattr(v, m.__name__) is m:
-        return v
+  # Class, static and unbound methods: search all defined classes in any
+  # namespace. This is inefficient but more robust method.
+  owners = []
+  caller_frame = tf_inspect.currentframe().f_back
+  try:
+    # TODO(mdan): This doesn't consider cell variables.
+    # TODO(mdan): This won't work if the owner is hidden inside a container.
+    # Cell variables may be pulled using co_freevars and the closure.
+    for v in itertools.chain(caller_frame.f_locals.values(),
+                             caller_frame.f_globals.values()):
+      if hasattr(v, m.__name__):
+        candidate = getattr(v, m.__name__)
+        # Py2 methods may be bound or unbound, extract im_func to get the
+        # underlying function.
+        if hasattr(candidate, 'im_func'):
+          candidate = candidate.im_func
+        if hasattr(m, 'im_func'):
+          m = m.im_func
+        if candidate is m:
+          owners.append(v)
+  finally:
+    del caller_frame
+
+  if owners:
+    if len(owners) == 1:
+      return owners[0]
+
+    # If multiple owners are found, and are not subclasses, raise an error.
+    owner_types = tuple(o if tf_inspect.isclass(o) else type(o) for o in owners)
+    for o in owner_types:
+      if tf_inspect.isclass(o) and issubclass(o, tuple(owner_types)):
+        return o
+    raise ValueError('Found too many owners of %s: %s' % (m, owners))
 
   return None
diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
index 5d92e75b18..d96c3df547 100644
--- a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
+++ b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
@@ -20,6 +20,8 @@ from __future__ import print_function
 
 from functools import wraps
 
+import six
+
 from tensorflow.contrib.py2tf.pyct import inspect_utils
 from tensorflow.python.platform import test
 
@@ -76,6 +78,10 @@ def free_function():
   pass
 
 
+def factory():
+  return free_function
+
+
 def free_factory():
   def local_function():
     pass
@@ -84,6 +90,43 @@ def free_factory():
 
 class InspectUtilsTest(test.TestCase):
 
+  def test_getnamespace_globals(self):
+    ns = inspect_utils.getnamespace(factory)
+    self.assertEqual(ns['free_function'], free_function)
+
+  def test_getnamespace_hermetic(self):
+
+    # Intentionally hiding the global function to make sure we don't overwrite
+    # it in the global namespace.
+    free_function = object()  # pylint:disable=redefined-outer-name
+
+    def test_fn():
+      return free_function
+
+    ns = inspect_utils.getnamespace(test_fn)
+    globs = six.get_function_globals(test_fn)
+    self.assertTrue(ns['free_function'] is free_function)
+    self.assertFalse(globs['free_function'] is free_function)
+
+  def test_getnamespace_locals(self):
+
+    def called_fn():
+      return 0
+
+    closed_over_list = []
+    closed_over_primitive = 1
+
+    def local_fn():
+      closed_over_list.append(1)
+      local_var = 1
+      return called_fn() + local_var + closed_over_primitive
+
+    ns = inspect_utils.getnamespace(local_fn)
+    self.assertEqual(ns['called_fn'], called_fn)
+    self.assertEqual(ns['closed_over_list'], closed_over_list)
+    self.assertEqual(ns['closed_over_primitive'], closed_over_primitive)
+    self.assertTrue('local_var' not in ns)
+
   def test_getcallargs_constructor(self):
 
     class TestSuperclass(object):
@@ -123,48 +166,47 @@ class InspectUtilsTest(test.TestCase):
   def test_getmethodclass(self):
 
     self.assertEqual(
-        inspect_utils.getmethodclass(free_function, {}), None)
+        inspect_utils.getmethodclass(free_function), None)
     self.assertEqual(
-        inspect_utils.getmethodclass(free_factory(), {}), None)
+        inspect_utils.getmethodclass(free_factory()), None)
 
-    ns = {'TestClass': TestClass}
     self.assertEqual(
-        inspect_utils.getmethodclass(TestClass.member_function, ns),
+        inspect_utils.getmethodclass(TestClass.member_function),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(TestClass.decorated_member, ns),
+        inspect_utils.getmethodclass(TestClass.decorated_member),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(TestClass.fn_decorated_member, ns),
+        inspect_utils.getmethodclass(TestClass.fn_decorated_member),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(TestClass.wrap_decorated_member, ns),
+        inspect_utils.getmethodclass(TestClass.wrap_decorated_member),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(TestClass.static_method, ns),
+        inspect_utils.getmethodclass(TestClass.static_method),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(TestClass.class_method, ns),
+        inspect_utils.getmethodclass(TestClass.class_method),
         TestClass)
 
     test_obj = TestClass()
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.member_function, ns),
+        inspect_utils.getmethodclass(test_obj.member_function),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.decorated_member, ns),
+        inspect_utils.getmethodclass(test_obj.decorated_member),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.fn_decorated_member, ns),
+        inspect_utils.getmethodclass(test_obj.fn_decorated_member),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.wrap_decorated_member, ns),
+        inspect_utils.getmethodclass(test_obj.wrap_decorated_member),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.static_method, ns),
+        inspect_utils.getmethodclass(test_obj.static_method),
         TestClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.class_method, ns),
+        inspect_utils.getmethodclass(test_obj.class_method),
         TestClass)
 
   def test_getmethodclass_locals(self):
@@ -190,34 +232,33 @@ class InspectUtilsTest(test.TestCase):
         pass
 
     self.assertEqual(
-        inspect_utils.getmethodclass(local_function, {}), None)
+        inspect_utils.getmethodclass(local_function), None)
 
-    ns = {'LocalClass': LocalClass}
     self.assertEqual(
-        inspect_utils.getmethodclass(LocalClass.member_function, ns),
+        inspect_utils.getmethodclass(LocalClass.member_function),
         LocalClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(LocalClass.decorated_member, ns),
+        inspect_utils.getmethodclass(LocalClass.decorated_member),
         LocalClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(LocalClass.fn_decorated_member, ns),
+        inspect_utils.getmethodclass(LocalClass.fn_decorated_member),
         LocalClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(LocalClass.wrap_decorated_member, ns),
+        inspect_utils.getmethodclass(LocalClass.wrap_decorated_member),
         LocalClass)
 
     test_obj = LocalClass()
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.member_function, ns),
+        inspect_utils.getmethodclass(test_obj.member_function),
         LocalClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.decorated_member, ns),
+        inspect_utils.getmethodclass(test_obj.decorated_member),
         LocalClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.fn_decorated_member, ns),
+        inspect_utils.getmethodclass(test_obj.fn_decorated_member),
         LocalClass)
     self.assertEqual(
-        inspect_utils.getmethodclass(test_obj.wrap_decorated_member, ns),
+        inspect_utils.getmethodclass(test_obj.wrap_decorated_member),
         LocalClass)
 
 
-- 
GitLab


From ca8cb9e928b622d202008c12046a4fb0b7ba9c09 Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Fri, 23 Feb 2018 17:32:14 -0800
Subject: [PATCH 268/575] Refactor Keras engine by splitting it into short,
 specialized files. The purpose of this change is to make the codebase more
 maintainable and readable.

Before:
engine/topology.py
models.py

After:
engine/base_layer.py
engine/input_layer.py
engine/network.py
engine/sequential.py
engine/saving.py

This is a large change but it only moves code around with no change in logic or API.

New files are all under 1000 lines of logic (network.py is 1500 lines, but under 1000 if you remove imports and docstrings), and often under 500.

PiperOrigin-RevId: 186847895
---
 tensorflow/python/keras/BUILD                 |   32 +-
 .../_impl/keras/applications/densenet.py      |    2 +-
 .../keras/applications/inception_resnet_v2.py |    2 +-
 .../_impl/keras/applications/inception_v3.py  |    2 +-
 .../_impl/keras/applications/mobilenet.py     |    4 +-
 .../keras/_impl/keras/applications/nasnet.py  |    2 +-
 .../_impl/keras/applications/resnet50.py      |    2 +-
 .../keras/_impl/keras/applications/vgg16.py   |    2 +-
 .../keras/_impl/keras/applications/vgg19.py   |    2 +-
 .../_impl/keras/applications/xception.py      |    2 +-
 .../keras/_impl/keras/engine/__init__.py      |   15 +-
 .../keras/_impl/keras/engine/base_layer.py    |  504 +++++++
 .../keras/_impl/keras/engine/input_layer.py   |  230 +++
 .../keras/engine/{topology.py => network.py}  | 1059 +------------
 .../python/keras/_impl/keras/engine/saving.py |  671 +++++++++
 .../keras/_impl/keras/engine/saving_test.py   |  375 +++++
 .../keras/_impl/keras/engine/sequential.py    |  997 +++++++++++++
 .../_impl/keras/engine/sequential_test.py     |  152 ++
 .../keras/_impl/keras/engine/topology_test.py |  169 +--
 .../keras/_impl/keras/engine/training.py      |    4 +-
 .../keras/layers/advanced_activations.py      |    2 +-
 .../keras/layers/convolutional_recurrent.py   |    2 +-
 .../keras/_impl/keras/layers/embeddings.py    |    2 +-
 .../python/keras/_impl/keras/layers/local.py  |    2 +-
 .../python/keras/_impl/keras/layers/merge.py  |    4 +-
 .../python/keras/_impl/keras/layers/noise.py  |    2 +-
 .../keras/_impl/keras/layers/recurrent.py     |    2 +-
 .../keras/_impl/keras/layers/wrappers.py      |    2 +-
 tensorflow/python/keras/_impl/keras/models.py | 1325 +----------------
 .../python/keras/_impl/keras/models_test.py   |  348 +----
 .../keras/_impl/keras/utils/generic_utils.py  |   17 +
 .../api/golden/tensorflow.keras.-model.pbtxt  |    4 +-
 .../golden/tensorflow.keras.-sequential.pbtxt |    6 +-
 .../tensorflow.keras.layers.-activation.pbtxt |    2 +-
 ...eras.layers.-activity-regularization.pbtxt |    2 +-
 .../golden/tensorflow.keras.layers.-add.pbtxt |    2 +-
 ...nsorflow.keras.layers.-alpha-dropout.pbtxt |    2 +-
 ...low.keras.layers.-average-pooling1-d.pbtxt |    2 +-
 ...low.keras.layers.-average-pooling2-d.pbtxt |    2 +-
 ...low.keras.layers.-average-pooling3-d.pbtxt |    2 +-
 .../tensorflow.keras.layers.-average.pbtxt    |    2 +-
 ...tensorflow.keras.layers.-avg-pool1-d.pbtxt |    2 +-
 ...tensorflow.keras.layers.-avg-pool2-d.pbtxt |    2 +-
 ...tensorflow.keras.layers.-avg-pool3-d.pbtxt |    2 +-
 ...ow.keras.layers.-batch-normalization.pbtxt |    2 +-
 ...nsorflow.keras.layers.-bidirectional.pbtxt |    2 +-
 ...tensorflow.keras.layers.-concatenate.pbtxt |    2 +-
 ...orflow.keras.layers.-conv-l-s-t-m2-d.pbtxt |    2 +-
 .../tensorflow.keras.layers.-conv1-d.pbtxt    |    2 +-
 ...flow.keras.layers.-conv2-d-transpose.pbtxt |    2 +-
 .../tensorflow.keras.layers.-conv2-d.pbtxt    |    2 +-
 ...flow.keras.layers.-conv3-d-transpose.pbtxt |    2 +-
 .../tensorflow.keras.layers.-conv3-d.pbtxt    |    2 +-
 ...sorflow.keras.layers.-convolution1-d.pbtxt |    2 +-
 ...ras.layers.-convolution2-d-transpose.pbtxt |    2 +-
 ...sorflow.keras.layers.-convolution2-d.pbtxt |    2 +-
 ...ras.layers.-convolution3-d-transpose.pbtxt |    2 +-
 ...sorflow.keras.layers.-convolution3-d.pbtxt |    2 +-
 ...tensorflow.keras.layers.-cropping1-d.pbtxt |    2 +-
 ...tensorflow.keras.layers.-cropping2-d.pbtxt |    2 +-
 ...tensorflow.keras.layers.-cropping3-d.pbtxt |    2 +-
 .../tensorflow.keras.layers.-dense.pbtxt      |    2 +-
 .../golden/tensorflow.keras.layers.-dot.pbtxt |    2 +-
 .../tensorflow.keras.layers.-dropout.pbtxt    |    2 +-
 .../tensorflow.keras.layers.-e-l-u.pbtxt      |    2 +-
 .../tensorflow.keras.layers.-embedding.pbtxt  |    2 +-
 .../tensorflow.keras.layers.-flatten.pbtxt    |    2 +-
 .../tensorflow.keras.layers.-g-r-u-cell.pbtxt |    2 +-
 .../tensorflow.keras.layers.-g-r-u.pbtxt      |    2 +-
 ...rflow.keras.layers.-gaussian-dropout.pbtxt |    2 +-
 ...sorflow.keras.layers.-gaussian-noise.pbtxt |    2 +-
 ...as.layers.-global-average-pooling1-d.pbtxt |    2 +-
 ...as.layers.-global-average-pooling2-d.pbtxt |    2 +-
 ...as.layers.-global-average-pooling3-d.pbtxt |    2 +-
 ...low.keras.layers.-global-avg-pool1-d.pbtxt |    2 +-
 ...low.keras.layers.-global-avg-pool2-d.pbtxt |    2 +-
 ...low.keras.layers.-global-avg-pool3-d.pbtxt |    2 +-
 ...low.keras.layers.-global-max-pool1-d.pbtxt |    2 +-
 ...low.keras.layers.-global-max-pool2-d.pbtxt |    2 +-
 ...low.keras.layers.-global-max-pool3-d.pbtxt |    2 +-
 ....keras.layers.-global-max-pooling1-d.pbtxt |    2 +-
 ....keras.layers.-global-max-pooling2-d.pbtxt |    2 +-
 ....keras.layers.-global-max-pooling3-d.pbtxt |    2 +-
 ...tensorflow.keras.layers.-input-layer.pbtxt |    4 +-
 ...ensorflow.keras.layers.-l-s-t-m-cell.pbtxt |    2 +-
 .../tensorflow.keras.layers.-l-s-t-m.pbtxt    |    2 +-
 .../tensorflow.keras.layers.-lambda.pbtxt     |    2 +-
 .../tensorflow.keras.layers.-layer.pbtxt      |    2 +-
 ...ensorflow.keras.layers.-leaky-re-l-u.pbtxt |    2 +-
 ...w.keras.layers.-locally-connected1-d.pbtxt |    2 +-
 ...w.keras.layers.-locally-connected2-d.pbtxt |    2 +-
 .../tensorflow.keras.layers.-masking.pbtxt    |    2 +-
 ...tensorflow.keras.layers.-max-pool1-d.pbtxt |    2 +-
 ...tensorflow.keras.layers.-max-pool2-d.pbtxt |    2 +-
 ...tensorflow.keras.layers.-max-pool3-d.pbtxt |    2 +-
 ...sorflow.keras.layers.-max-pooling1-d.pbtxt |    2 +-
 ...sorflow.keras.layers.-max-pooling2-d.pbtxt |    2 +-
 ...sorflow.keras.layers.-max-pooling3-d.pbtxt |    2 +-
 .../tensorflow.keras.layers.-maximum.pbtxt    |    2 +-
 .../tensorflow.keras.layers.-multiply.pbtxt   |    2 +-
 .../tensorflow.keras.layers.-p-re-l-u.pbtxt   |    2 +-
 .../tensorflow.keras.layers.-permute.pbtxt    |    2 +-
 .../tensorflow.keras.layers.-r-n-n.pbtxt      |    2 +-
 ...nsorflow.keras.layers.-repeat-vector.pbtxt |    2 +-
 .../tensorflow.keras.layers.-reshape.pbtxt    |    2 +-
 ...flow.keras.layers.-separable-conv1-d.pbtxt |    2 +-
 ...flow.keras.layers.-separable-conv2-d.pbtxt |    2 +-
 ...ras.layers.-separable-convolution1-d.pbtxt |    2 +-
 ...ras.layers.-separable-convolution2-d.pbtxt |    2 +-
 ...flow.keras.layers.-simple-r-n-n-cell.pbtxt |    2 +-
 ...ensorflow.keras.layers.-simple-r-n-n.pbtxt |    2 +-
 .../tensorflow.keras.layers.-softmax.pbtxt    |    2 +-
 ...low.keras.layers.-spatial-dropout1-d.pbtxt |    2 +-
 ...low.keras.layers.-spatial-dropout2-d.pbtxt |    2 +-
 ...low.keras.layers.-spatial-dropout3-d.pbtxt |    2 +-
 ...ow.keras.layers.-stacked-r-n-n-cells.pbtxt |    2 +-
 ...low.keras.layers.-thresholded-re-l-u.pbtxt |    2 +-
 ...rflow.keras.layers.-time-distributed.pbtxt |    2 +-
 ...sorflow.keras.layers.-up-sampling1-d.pbtxt |    2 +-
 ...sorflow.keras.layers.-up-sampling2-d.pbtxt |    2 +-
 ...sorflow.keras.layers.-up-sampling3-d.pbtxt |    2 +-
 .../tensorflow.keras.layers.-wrapper.pbtxt    |    2 +-
 ...orflow.keras.layers.-zero-padding1-d.pbtxt |    2 +-
 ...orflow.keras.layers.-zero-padding2-d.pbtxt |    2 +-
 ...orflow.keras.layers.-zero-padding3-d.pbtxt |    2 +-
 .../tensorflow.keras.models.-model.pbtxt      |    4 +-
 .../tensorflow.keras.models.-sequential.pbtxt |    6 +-
 127 files changed, 3162 insertions(+), 2980 deletions(-)
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/base_layer.py
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/input_layer.py
 rename tensorflow/python/keras/_impl/keras/engine/{topology.py => network.py} (59%)
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/saving.py
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/saving_test.py
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/sequential.py
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/sequential_test.py

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index 16738066ce..a98d08f928 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -39,7 +39,11 @@ py_library(
         "_impl/keras/datasets/mnist.py",
         "_impl/keras/datasets/reuters.py",
         "_impl/keras/engine/__init__.py",
-        "_impl/keras/engine/topology.py",
+        "_impl/keras/engine/base_layer.py",
+        "_impl/keras/engine/input_layer.py",
+        "_impl/keras/engine/network.py",
+        "_impl/keras/engine/saving.py",
+        "_impl/keras/engine/sequential.py",
         "_impl/keras/engine/training.py",
         "_impl/keras/engine/training_eager.py",
         "_impl/keras/estimator.py",
@@ -761,9 +765,31 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":keras",
-        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python:dtypes",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "saving_test",
+    size = "small",
+    srcs = ["_impl/keras/engine/saving_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":keras",
+        "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
+    ],
+)
+
+py_test(
+    name = "sequential_test",
+    size = "small",
+    srcs = ["_impl/keras/engine/sequential_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":keras",
+        "//tensorflow/python:client_testlib",
         "//third_party/py/numpy",
     ],
 )
diff --git a/tensorflow/python/keras/_impl/keras/applications/densenet.py b/tensorflow/python/keras/_impl/keras/applications/densenet.py
index 6521f84104..ca83e86912 100644
--- a/tensorflow/python/keras/_impl/keras/applications/densenet.py
+++ b/tensorflow/python/keras/_impl/keras/applications/densenet.py
@@ -31,7 +31,7 @@ from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras.applications import imagenet_utils
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Activation
 from tensorflow.python.keras._impl.keras.layers import AveragePooling2D
 from tensorflow.python.keras._impl.keras.layers import BatchNormalization
diff --git a/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py
index bf3901fc54..17e407dd58 100644
--- a/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py
+++ b/tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2.py
@@ -31,7 +31,7 @@ from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras.applications import imagenet_utils
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Activation
 from tensorflow.python.keras._impl.keras.layers import AveragePooling2D
 from tensorflow.python.keras._impl.keras.layers import BatchNormalization
diff --git a/tensorflow/python/keras/_impl/keras/applications/inception_v3.py b/tensorflow/python/keras/_impl/keras/applications/inception_v3.py
index e268e97bc6..2897c6058e 100644
--- a/tensorflow/python/keras/_impl/keras/applications/inception_v3.py
+++ b/tensorflow/python/keras/_impl/keras/applications/inception_v3.py
@@ -37,7 +37,7 @@ from tensorflow.python.keras._impl.keras import layers
 from tensorflow.python.keras._impl.keras.applications import imagenet_utils
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Activation
 from tensorflow.python.keras._impl.keras.layers import AveragePooling2D
 from tensorflow.python.keras._impl.keras.layers import BatchNormalization
diff --git a/tensorflow/python/keras/_impl/keras/applications/mobilenet.py b/tensorflow/python/keras/_impl/keras/applications/mobilenet.py
index 1bbbedb85e..ad96b53a45 100644
--- a/tensorflow/python/keras/_impl/keras/applications/mobilenet.py
+++ b/tensorflow/python/keras/_impl/keras/applications/mobilenet.py
@@ -79,8 +79,8 @@ from tensorflow.python.keras._impl.keras.applications import imagenet_utils
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
 from tensorflow.python.keras._impl.keras.engine import InputSpec
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Activation
 from tensorflow.python.keras._impl.keras.layers import BatchNormalization
 from tensorflow.python.keras._impl.keras.layers import Conv2D
diff --git a/tensorflow/python/keras/_impl/keras/applications/nasnet.py b/tensorflow/python/keras/_impl/keras/applications/nasnet.py
index 08dae57f00..dd33230a7e 100644
--- a/tensorflow/python/keras/_impl/keras/applications/nasnet.py
+++ b/tensorflow/python/keras/_impl/keras/applications/nasnet.py
@@ -49,7 +49,7 @@ from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
 from tensorflow.python.keras._impl.keras.applications.inception_v3 import preprocess_input
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Activation
 from tensorflow.python.keras._impl.keras.layers import add
 from tensorflow.python.keras._impl.keras.layers import AveragePooling2D
diff --git a/tensorflow/python/keras/_impl/keras/applications/resnet50.py b/tensorflow/python/keras/_impl/keras/applications/resnet50.py
index a47dd657bb..46c0e63557 100644
--- a/tensorflow/python/keras/_impl/keras/applications/resnet50.py
+++ b/tensorflow/python/keras/_impl/keras/applications/resnet50.py
@@ -34,7 +34,7 @@ from tensorflow.python.keras._impl.keras import layers
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Activation
 from tensorflow.python.keras._impl.keras.layers import AveragePooling2D
 from tensorflow.python.keras._impl.keras.layers import BatchNormalization
diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg16.py b/tensorflow/python/keras/_impl/keras/applications/vgg16.py
index 9da74253ab..cefb25063e 100644
--- a/tensorflow/python/keras/_impl/keras/applications/vgg16.py
+++ b/tensorflow/python/keras/_impl/keras/applications/vgg16.py
@@ -32,7 +32,7 @@ from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Conv2D
 from tensorflow.python.keras._impl.keras.layers import Dense
 from tensorflow.python.keras._impl.keras.layers import Flatten
diff --git a/tensorflow/python/keras/_impl/keras/applications/vgg19.py b/tensorflow/python/keras/_impl/keras/applications/vgg19.py
index 961c1f9918..dadaf4fdf0 100644
--- a/tensorflow/python/keras/_impl/keras/applications/vgg19.py
+++ b/tensorflow/python/keras/_impl/keras/applications/vgg19.py
@@ -32,7 +32,7 @@ from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Conv2D
 from tensorflow.python.keras._impl.keras.layers import Dense
 from tensorflow.python.keras._impl.keras.layers import Flatten
diff --git a/tensorflow/python/keras/_impl/keras/applications/xception.py b/tensorflow/python/keras/_impl/keras/applications/xception.py
index 7e7ca5a18a..971063a16d 100644
--- a/tensorflow/python/keras/_impl/keras/applications/xception.py
+++ b/tensorflow/python/keras/_impl/keras/applications/xception.py
@@ -44,7 +44,7 @@ from tensorflow.python.keras._impl.keras import layers
 from tensorflow.python.keras._impl.keras.applications import imagenet_utils
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
 from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
 from tensorflow.python.keras._impl.keras.layers import Activation
 from tensorflow.python.keras._impl.keras.layers import BatchNormalization
 from tensorflow.python.keras._impl.keras.layers import Conv2D
diff --git a/tensorflow/python/keras/_impl/keras/engine/__init__.py b/tensorflow/python/keras/_impl/keras/engine/__init__.py
index 31f624f9af..1bc533ab8f 100644
--- a/tensorflow/python/keras/_impl/keras/engine/__init__.py
+++ b/tensorflow/python/keras/_impl/keras/engine/__init__.py
@@ -18,13 +18,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
-from tensorflow.python.keras._impl.keras.engine.topology import Input
-from tensorflow.python.keras._impl.keras.engine.topology import InputLayer
-from tensorflow.python.keras._impl.keras.engine.topology import InputSpec
-from tensorflow.python.keras._impl.keras.engine.topology import Layer
+from tensorflow.python.keras._impl.keras.engine.base_layer import InputSpec
+from tensorflow.python.keras._impl.keras.engine.base_layer import Layer
+from tensorflow.python.keras._impl.keras.engine.input_layer import Input
+from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer
+from tensorflow.python.keras._impl.keras.engine.network import get_source_inputs
+from tensorflow.python.keras._impl.keras.engine.network import Network
 from tensorflow.python.keras._impl.keras.engine.training import Model
-
-
-# Note: topology.Node is an internal class,
-# it isn't meant to be used by Keras users.
diff --git a/tensorflow/python/keras/_impl/keras/engine/base_layer.py b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
new file mode 100644
index 0000000000..142325041b
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
@@ -0,0 +1,504 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=protected-access
+"""Base layer code (`Layer`).
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from six.moves import zip  # pylint: disable=redefined-builtin
+
+from tensorflow.python.eager import context
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.keras._impl.keras import backend as K
+from tensorflow.python.keras._impl.keras import constraints
+from tensorflow.python.keras._impl.keras import initializers
+from tensorflow.python.keras._impl.keras import regularizers
+from tensorflow.python.keras._impl.keras.utils import generic_utils
+from tensorflow.python.layers import base as tf_base_layers
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util.tf_export import tf_export
+
+
+# pylint: disable=invalid-name
+InputSpec = tf_base_layers.InputSpec
+Node = tf_base_layers.Node
+TFBaseLayer = tf_base_layers.Layer
+# pylint: enable=invalid-name
+
+
+@tf_export('keras.layers.Layer')
+class Layer(tf_base_layers.Layer):
+  """Abstract base layer class.
+
+  # Properties
+      name: String, must be unique within a model.
+      input_spec: List of InputSpec class instances
+          each entry describes one required input:
+              - ndim
+              - dtype
+          A layer with `n` input tensors must have
+          an `input_spec` of length `n`.
+      trainable: Boolean, whether the layer weights
+          will be updated during training.
+      uses_learning_phase: Whether any operation
+          of the layer uses `K.in_training_phase()`
+          or `K.in_test_phase()`.
+      input_shape: Shape tuple. Provided for convenience,
+          but note that there may be cases in which this
+          attribute is ill-defined (e.g. a shared layer
+          with multiple input shapes), in which case
+          requesting `input_shape` will raise an Exception.
+          Prefer using `layer.get_input_shape_for(input_shape)`,
+          or `layer.get_input_shape_at(node_index)`.
+      output_shape: Shape tuple. See above.
+      inbound_nodes: List of nodes.
+      outbound_nodes: List of nodes.
+      input, output: Input/output tensor(s). Note that if the layer is used
+          more than once (shared layer), this is ill-defined
+          and will raise an exception. In such cases, use
+          `layer.get_input_at(node_index)`.
+      input_mask, output_mask: Same as above, for masks.
+      trainable_weights: List of variables.
+      non_trainable_weights: List of variables.
+      weights: The concatenation of the lists trainable_weights and
+          non_trainable_weights (in this order).
+
+  # Methods
+      call(x, mask=None): Where the layer's logic lives.
+      __call__(x, mask=None): Wrapper around the layer logic (`call`).
+          If x is a Keras tensor:
+              - Connect current layer with last layer from tensor:
+                  `self._add_inbound_node(last_layer)`
+              - Add layer to tensor history
+          If layer is not built:
+              - Build from inputs shape
+      get_weights()
+      set_weights(weights)
+      get_config()
+      count_params()
+      compute_output_shape(input_shape)
+      compute_mask(x, mask)
+      get_input_at(node_index)
+      get_output_at(node_index)
+      get_input_shape_at(node_index)
+      get_output_shape_at(node_index)
+      get_input_mask_at(node_index)
+      get_output_mask_at(node_index)
+
+  # Class Methods
+      from_config(config)
+
+  # Internal methods:
+      build(input_shape)
+      _add_inbound_node(layer, index=0)
+  """
+
+  def __init__(self, **kwargs):
+    # These properties should be set by the user via keyword arguments.
+    # note that 'dtype', 'input_shape' and 'batch_input_shape'
+    # are only applicable to input layers: do not pass these keywords
+    # to non-input layers.
+    allowed_kwargs = {
+        'activity_regularizer',
+        'input_shape',
+        'batch_input_shape',
+        'batch_size',
+        'dtype',
+        'name',
+        'trainable',
+        'weights',
+    }
+    # Validate optional keyword arguments.
+    for kwarg in kwargs:
+      if kwarg not in allowed_kwargs:
+        raise TypeError('Keyword argument not understood:', kwarg)
+
+    # Get layer name.
+    name = kwargs.get('name')
+
+    # Get `trainable` status.
+    trainable = kwargs.get('trainable', True)
+
+    # Get `dtype`.
+    dtype = kwargs.get('dtype')
+    if dtype is None:
+      dtype = K.floatx()
+
+    # Call super, which will set all properties common to Keras layers
+    # and core TF layers.
+    super(Layer, self).__init__(
+        name=name, dtype=dtype, trainable=trainable,
+        activity_regularizer=kwargs.get('activity_regularizer'))
+
+    # Add properties that are Keras-only for now.
+    self.supports_masking = False
+
+    # Manage input shape information if passed.
+    if 'input_shape' in kwargs or 'batch_input_shape' in kwargs:
+      # In this case we will later create an input layer
+      # to insert before the current layer
+      if 'batch_input_shape' in kwargs:
+        batch_input_shape = tuple(kwargs['batch_input_shape'])
+      elif 'input_shape' in kwargs:
+        if 'batch_size' in kwargs:
+          batch_size = kwargs['batch_size']
+        else:
+          batch_size = None
+        batch_input_shape = (batch_size,) + tuple(kwargs['input_shape'])
+      self._batch_input_shape = batch_input_shape
+
+    # Manage initial weight values if passed.
+    if 'weights' in kwargs:
+      self._initial_weights = kwargs['weights']
+    else:
+      self._initial_weights = None
+
+  def add_weight(self,
+                 name,
+                 shape,
+                 dtype=None,
+                 initializer=None,
+                 regularizer=None,
+                 trainable=True,
+                 constraint=None):
+    """Adds a weight variable to the layer.
+
+    Arguments:
+        name: String, the name for the weight variable.
+        shape: The shape tuple of the weight.
+        dtype: The dtype of the weight.
+        initializer: An Initializer instance (callable).
+        regularizer: An optional Regularizer instance.
+        trainable: A boolean, whether the weight should
+            be trained via backprop or not (assuming
+            that the layer itself is also trainable).
+        constraint: An optional Constraint instance.
+
+    Returns:
+        The created weight variable.
+    """
+    if dtype is None:
+      dtype = K.floatx()
+    weight = self.add_variable(name, shape,
+                               dtype=dtype,
+                               initializer=initializers.get(initializer),
+                               regularizer=regularizers.get(regularizer),
+                               constraint=constraints.get(constraint),
+                               trainable=trainable)
+    return weight
+
+  def call(self, inputs, **kwargs):  # pylint: disable=unused-argument
+    """This is where the layer's logic lives.
+
+    Arguments:
+        inputs: Input tensor, or list/tuple of input tensors.
+        **kwargs: Additional keyword arguments.
+
+    Returns:
+        A tensor or list/tuple of tensors.
+    """
+    return inputs
+
+  def __call__(self, inputs, **kwargs):
+    """Wrapper around self.call(), for handling internal references.
+
+    If a Keras tensor is passed:
+        - We call self._add_inbound_node().
+        - If necessary, we `build` the layer to match
+            the shape of the input(s).
+        - We update the _keras_history of the output tensor(s)
+            with the current layer.
+            This is done as part of _add_inbound_node().
+
+    Arguments:
+        inputs: Can be a tensor or list/tuple of tensors.
+        **kwargs: Additional keyword arguments to be passed to `call()`.
+
+    Returns:
+        Output of the layer's `call` method.
+
+    Raises:
+        ValueError: in case the layer is missing shape information
+            for its `build` call.
+    """
+    # Actually call the layer (optionally building it).
+    output = super(Layer, self).__call__(inputs, **kwargs)
+    if context.in_eager_mode():
+      return output
+
+    # Un-built subclassed network: build it
+    if hasattr(self, '_set_inputs') and not self.inputs:
+      self._set_inputs(inputs, training=kwargs.get('training'))
+
+    # Update learning phase info.
+    output_tensors = generic_utils.to_list(output)
+    uses_lp = any(
+        [getattr(x, '_uses_learning_phase', False)
+         for x in generic_utils.to_list(inputs)])
+    uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp
+    for i in range(len(output_tensors)):
+      output_tensors[i]._uses_learning_phase = getattr(
+          output_tensors[i], '_uses_learning_phase', False) or uses_lp
+
+    # Optionally load weight values that were specified at layer instantiation.
+    if hasattr(self, '_initial_weights') and self._initial_weights is not None:
+      self.set_weights(self._initial_weights)
+      del self._initial_weights
+    return output
+
+  def compute_output_shape(self, input_shape):
+    """Computes the output shape of the layer.
+
+    Assumes that the layer will be built
+    to match that input shape provided.
+
+    Arguments:
+        input_shape: Shape tuple (tuple of integers)
+            or list of shape tuples (one per output tensor of the layer).
+            Shape tuples can include None for free dimensions,
+            instead of an integer.
+
+    Returns:
+        An input shape tuple.
+    """
+    logging.warning(
+        'All custom layers should implement the '
+        '`compute_output_shape` method. This layer (' + self.name + ') '
+        'is relying on the base `Layer.compute_output_shape` implementation, '
+        'which will start raising a `NotImplementedError` '
+        'as of July 1st, 2018.')
+    return input_shape
+
+  def compute_mask(self, inputs, mask=None):  # pylint: disable=unused-argument
+    """Computes an output mask tensor.
+
+    Arguments:
+        inputs: Tensor or list of tensors.
+        mask: Tensor or list of tensors.
+
+    Returns:
+        None or a tensor (or list of tensors,
+            one per output tensor of the layer).
+    """
+    if not self.supports_masking:
+      if mask is not None:
+        if isinstance(mask, list):
+          if any(m is not None for m in mask):
+            raise TypeError('Layer ' + self.name + ' does not support masking, '
+                            'but was passed an input_mask: ' + str(mask))
+        else:
+          raise TypeError('Layer ' + self.name + ' does not support masking, '
+                          'but was passed an input_mask: ' + str(mask))
+      # masking not explicitly supported: return None as mask
+      return None
+    # if masking is explicitly supported, by default
+    # carry over the input mask
+    return mask
+
+  def get_input_mask_at(self, node_index):
+    """Retrieves the input mask tensor(s) of a layer at a given node.
+
+    Arguments:
+        node_index: Integer, index of the node
+            from which to retrieve the attribute.
+            E.g. `node_index=0` will correspond to the
+            first time the layer was called.
+
+    Returns:
+        A mask tensor
+        (or list of tensors if the layer has multiple inputs).
+    """
+    inputs = self.get_input_at(node_index)
+    if isinstance(inputs, list):
+      return [getattr(x, '_keras_mask', None) for x in inputs]
+    else:
+      return getattr(inputs, '_keras_mask', None)
+
+  def get_output_mask_at(self, node_index):
+    """Retrieves the output mask tensor(s) of a layer at a given node.
+
+    Arguments:
+        node_index: Integer, index of the node
+            from which to retrieve the attribute.
+            E.g. `node_index=0` will correspond to the
+            first time the layer was called.
+
+    Returns:
+        A mask tensor
+        (or list of tensors if the layer has multiple outputs).
+    """
+    output = self.get_output_at(node_index)
+    if isinstance(output, list):
+      return [getattr(x, '_keras_mask', None) for x in output]
+    else:
+      return getattr(output, '_keras_mask', None)
+
+  @property
+  def input_mask(self):
+    """Retrieves the input mask tensor(s) of a layer.
+
+    Only applicable if the layer has exactly one inbound node,
+    i.e. if it is connected to one incoming layer.
+
+    Returns:
+        Input mask tensor (potentially None) or list of input
+        mask tensors.
+
+    Raises:
+        AttributeError: if the layer is connected to
+        more than one incoming layers.
+    """
+    inputs = self.input
+    if isinstance(inputs, list):
+      return [getattr(x, '_keras_mask', None) for x in inputs]
+    else:
+      return getattr(inputs, '_keras_mask', None)
+
+  @property
+  def output_mask(self):
+    """Retrieves the output mask tensor(s) of a layer.
+
+    Only applicable if the layer has exactly one inbound node,
+    i.e. if it is connected to one incoming layer.
+
+    Returns:
+        Output mask tensor (potentially None) or list of output
+        mask tensors.
+
+    Raises:
+        AttributeError: if the layer is connected to
+        more than one incoming layers.
+    """
+    output = self.output
+    if isinstance(output, list):
+      return [getattr(x, '_keras_mask', None) for x in output]
+    else:
+      return getattr(output, '_keras_mask', None)
+
+  def set_weights(self, weights):
+    """Sets the weights of the layer, from Numpy arrays.
+
+    Arguments:
+        weights: a list of Numpy arrays. The number
+            of arrays and their shape must match
+            number of the dimensions of the weights
+            of the layer (i.e. it should match the
+            output of `get_weights`).
+
+    Raises:
+        ValueError: If the provided weights list does not match the
+            layer's specifications.
+    """
+    params = self.weights
+    if len(params) != len(weights):
+      raise ValueError('You called `set_weights(weights)` on layer "' +
+                       self.name + '" with a  weight list of length ' +
+                       str(len(weights)) + ', but the layer was expecting ' +
+                       str(len(params)) + ' weights. Provided weights: ' +
+                       str(weights)[:50] + '...')
+    if not params:
+      return
+    weight_value_tuples = []
+    param_values = K.batch_get_value(params)
+    for pv, p, w in zip(param_values, params, weights):
+      if pv.shape != w.shape:
+        raise ValueError('Layer weight shape ' + str(pv.shape) +
+                         ' not compatible with '
+                         'provided weight shape ' + str(w.shape))
+      weight_value_tuples.append((p, w))
+    K.batch_set_value(weight_value_tuples)
+
+  def get_weights(self):
+    """Returns the current weights of the layer.
+
+    Returns:
+        Weights values as a list of numpy arrays.
+    """
+    params = self.weights
+    return K.batch_get_value(params)
+
+  def get_config(self):
+    """Returns the config of the layer.
+
+    A layer config is a Python dictionary (serializable)
+    containing the configuration of a layer.
+    The same layer can be reinstantiated later
+    (without its trained weights) from this configuration.
+
+    The config of a layer does not include connectivity
+    information, nor the layer class name. These are handled
+    by `Network` (one layer of abstraction above).
+
+    Returns:
+        Python dictionary.
+    """
+    config = {'name': self.name, 'trainable': self.trainable}
+    if hasattr(self, '_batch_input_shape'):
+      config['batch_input_shape'] = self._batch_input_shape
+    if hasattr(self, 'dtype'):
+      config['dtype'] = self.dtype
+    return config
+
+  @classmethod
+  def from_config(cls, config):
+    """Creates a layer from its config.
+
+    This method is the reverse of `get_config`,
+    capable of instantiating the same layer from the config
+    dictionary. It does not handle layer connectivity
+    (handled by Network), nor weights (handled by `set_weights`).
+
+    Arguments:
+        config: A Python dictionary, typically the
+            output of get_config.
+
+    Returns:
+        A layer instance.
+    """
+    return cls(**config)
+
+  @tf_base_layers.Layer.activity_regularizer.setter
+  def activity_regularizer(self, activity_regularizer):
+    self._activity_regularizer = activity_regularizer
+
+
+def shape_type_conversion(fn):
+  """Decorator that handles tuple/TensorShape conversion.
+
+  Used in `compute_output_shape` and `build`.
+
+  Arguments:
+    fn: function to wrap.
+
+  Returns:
+    Wrapped function.
+  """
+
+  def wrapper(instance, input_shape):
+    if input_shape is not None:
+      if isinstance(input_shape, list):
+        input_shape = [
+            tuple(tensor_shape.TensorShape(x).as_list()) for x in input_shape]
+      else:
+        input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list())
+    output_shape = fn(instance, input_shape)
+    if output_shape is not None:
+      if isinstance(output_shape, list):
+        return [tensor_shape.TensorShape(x) for x in output_shape]
+      return tensor_shape.TensorShape(output_shape)
+
+  return wrapper
diff --git a/tensorflow/python/keras/_impl/keras/engine/input_layer.py b/tensorflow/python/keras/_impl/keras/engine/input_layer.py
new file mode 100644
index 0000000000..8f9ea6f7a4
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/input_layer.py
@@ -0,0 +1,230 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=protected-access
+"""Input layer code (`Input` and `InputLayer`).
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.eager import context
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.keras._impl.keras import backend as K
+from tensorflow.python.keras._impl.keras.engine import base_layer
+from tensorflow.python.layers import base as tf_base_layers
+from tensorflow.python.ops import array_ops
+from tensorflow.python.util.tf_export import tf_export
+
+
+class InputLayer(base_layer.Layer):
+  """Layer to be used as an entry point into a Network (a graph of layers).
+
+  It can either wrap an existing tensor (pass an `input_tensor` argument)
+  or create its a placeholder tensor (pass arguments `input_shape`, and
+  optionally, `dtype`).
+
+  It is generally recommend to use the functional layer API via `Input`,
+  (which creates an `InputLayer`) without directly using `InputLayer`.
+
+  Arguments:
+      input_shape: Shape tuple (not including the batch axis), or `TensorShape`
+        instance (not including the batch axis).
+      batch_size: Optional input batch size (integer or None).
+      dtype: Datatype of the input.
+      input_tensor: Optional tensor to use as layer input
+          instead of creating a placeholder.
+      sparse: Boolean, whether the placeholder created
+          is meant to be sparse.
+      name: Name of the layer (string).
+  """
+
+  def __init__(self,
+               input_shape=None,
+               batch_size=None,
+               dtype=None,
+               input_tensor=None,
+               sparse=False,
+               name=None,
+               **kwargs):
+    if 'batch_input_shape' in kwargs:
+      batch_input_shape = kwargs.pop('batch_input_shape')
+      if input_shape and batch_input_shape:
+        raise ValueError('Only provide the input_shape OR '
+                         'batch_input_shape argument to '
+                         'InputLayer, not both at the same time.')
+      batch_size = batch_input_shape[0]
+      input_shape = batch_input_shape[1:]
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments:', kwargs.keys())
+
+    if not name:
+      prefix = 'input'
+      name = prefix + '_' + str(K.get_uid(prefix))
+
+    if not dtype:
+      if input_tensor is None:
+        dtype = K.floatx()
+      else:
+        dtype = K.dtype(input_tensor)
+    super(InputLayer, self).__init__(dtype=dtype, name=name)
+    self.built = True
+    self.sparse = sparse
+    self.batch_size = batch_size
+
+    if isinstance(input_shape, tensor_shape.TensorShape):
+      input_shape = tuple(input_shape.as_list())
+
+    if input_tensor is None:
+      if input_shape is not None:
+        batch_input_shape = (batch_size,) + tuple(input_shape)
+      else:
+        batch_input_shape = None
+
+      if context.in_eager_mode():
+        # In eager mode, create a temporary placeholder to call the layer on.
+        input_tensor = tf_base_layers._DeferredTensor(  # pylint: disable=protected-access
+            shape=batch_input_shape,
+            dtype=dtype,
+            name=self.name)
+      else:
+        # In graph mode, create a graph placeholder to call the layer on.
+        if sparse:
+          input_tensor = array_ops.sparse_placeholder(
+              shape=batch_input_shape,
+              dtype=dtype,
+              name=self.name)
+        else:
+          input_tensor = array_ops.placeholder(
+              shape=batch_input_shape,
+              dtype=dtype,
+              name=self.name)
+
+      # For compatibility with Keras API.
+      self.is_placeholder = True
+      self._batch_input_shape = batch_input_shape
+    else:
+      # For compatibility with Keras API.
+      self.is_placeholder = False
+      self._batch_input_shape = tuple(input_tensor.get_shape().as_list())
+
+    # Create an input node to add to self.outbound_node
+    # and set output_tensors' _keras_history.
+    input_tensor._keras_history = (self, 0, 0)  # pylint: disable=protected-access
+    tf_base_layers.Node(
+        self,
+        inbound_layers=[],
+        node_indices=[],
+        tensor_indices=[],
+        input_tensors=[input_tensor],
+        output_tensors=[input_tensor])
+
+  def get_config(self):
+    config = {
+        'batch_input_shape': self._batch_input_shape,
+        'dtype': self.dtype,
+        'sparse': self.sparse,
+        'name': self.name
+    }
+    return config
+
+
+@tf_export('keras.layers.Input', 'keras.Input')
+def Input(  # pylint: disable=invalid-name
+    shape=None,
+    batch_size=None,
+    name=None,
+    dtype=None,
+    sparse=False,
+    tensor=None,
+    **kwargs):
+  """`Input()` is used to instantiate a Keras tensor.
+
+  A Keras tensor is a tensor object from the underlying backend
+  (Theano or TensorFlow), which we augment with certain
+  attributes that allow us to build a Keras model
+  just by knowing the inputs and outputs of the model.
+
+  For instance, if a, b and c are Keras tensors,
+  it becomes possible to do:
+  `model = Model(input=[a, b], output=c)`
+
+  The added Keras attribute is:
+      `_keras_history`: Last layer applied to the tensor.
+          the entire layer graph is retrievable from that layer,
+          recursively.
+
+  Arguments:
+      shape: A shape tuple (integers), not including the batch size.
+          For instance, `shape=(32,)` indicates that the expected input
+          will be batches of 32-dimensional vectors.
+      batch_size: optional static batch size (integer).
+      name: An optional name string for the layer.
+          Should be unique in a model (do not reuse the same name twice).
+          It will be autogenerated if it isn't provided.
+      dtype: The data type expected by the input, as a string
+          (`float32`, `float64`, `int32`...)
+      sparse: A boolean specifying whether the placeholder
+          to be created is sparse.
+      tensor: Optional existing tensor to wrap into the `Input` layer.
+          If set, the layer will not create a placeholder tensor.
+      **kwargs: deprecated arguments support.
+
+  Returns:
+      A tensor.
+
+  Example:
+
+      ```python
+      # this is a logistic regression in Keras
+      x = Input(shape=(32,))
+      y = Dense(16, activation='softmax')(x)
+      model = Model(x, y)
+      ```
+
+  Raises:
+    ValueError: in case of invalid arguments.
+  """
+  if 'batch_shape' in kwargs:
+    batch_shape = kwargs.pop('batch_shape')
+    if shape and batch_shape:
+      raise ValueError('Only provide the shape OR '
+                       'batch_shape argument to '
+                       'Input, not both at the same time.')
+    batch_size = batch_shape[0]
+    shape = batch_shape[1:]
+  if kwargs:
+    raise ValueError('Unrecognized keyword arguments:', kwargs.keys())
+
+  if dtype is None:
+    dtype = K.floatx()
+  if not shape and tensor is None:
+    raise ValueError('Please provide to Input either a `shape`'
+                     ' or a `tensor` argument. Note that '
+                     '`shape` does not include the batch '
+                     'dimension.')
+  input_layer = InputLayer(
+      input_shape=shape,
+      batch_size=batch_size,
+      name=name,
+      dtype=dtype,
+      sparse=sparse,
+      input_tensor=tensor)
+  # Return tensor including `_keras_history`.
+  # Note that in this case train_output and test_output are the same pointer.
+  outputs = input_layer._inbound_nodes[0].output_tensors
+  if len(outputs) == 1:
+    return outputs[0]
+  else:
+    return outputs
diff --git a/tensorflow/python/keras/_impl/keras/engine/topology.py b/tensorflow/python/keras/_impl/keras/engine/network.py
similarity index 59%
rename from tensorflow/python/keras/_impl/keras/engine/topology.py
rename to tensorflow/python/keras/_impl/keras/engine/network.py
index f562a19cf5..453cc8f8b7 100644
--- a/tensorflow/python/keras/_impl/keras/engine/topology.py
+++ b/tensorflow/python/keras/_impl/keras/engine/network.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 # pylint: disable=protected-access
-"""Base layer code and base model (Network) code.
+"""A `Network` is way to compose layers: the topological form of a `Model`.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -30,19 +30,16 @@ from tensorflow.python.eager import context
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras._impl.keras import backend as K
-from tensorflow.python.keras._impl.keras import constraints
-from tensorflow.python.keras._impl.keras import initializers
-from tensorflow.python.keras._impl.keras import regularizers
-from tensorflow.python.keras._impl.keras.utils import conv_utils
+from tensorflow.python.keras._impl.keras.engine import base_layer
+from tensorflow.python.keras._impl.keras.engine import saving
+from tensorflow.python.keras._impl.keras.utils import generic_utils
 from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite
 from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary as print_layer_summary
 from tensorflow.python.layers import base as tf_base_layers
 from tensorflow.python.layers import utils as tf_layers_util
-from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_inspect
-from tensorflow.python.util.tf_export import tf_export
 
 
 # pylint: disable=g-import-not-at-top
@@ -57,684 +54,12 @@ except ImportError:
   yaml = None
 # pylint: enable=g-import-not-at-top
 
-# pylint: disable=invalid-name
-InputSpec = tf_base_layers.InputSpec
-Node = tf_base_layers.Node
-TFBaseLayer = tf_base_layers.Layer
-# pylint: enable=invalid-name
-
-
-@tf_export('keras.layers.Layer')
-class Layer(tf_base_layers.Layer):
-  """Abstract base layer class.
-
-  # Properties
-      name: String, must be unique within a model.
-      input_spec: List of InputSpec class instances
-          each entry describes one required input:
-              - ndim
-              - dtype
-          A layer with `n` input tensors must have
-          an `input_spec` of length `n`.
-      trainable: Boolean, whether the layer weights
-          will be updated during training.
-      uses_learning_phase: Whether any operation
-          of the layer uses `K.in_training_phase()`
-          or `K.in_test_phase()`.
-      input_shape: Shape tuple. Provided for convenience,
-          but note that there may be cases in which this
-          attribute is ill-defined (e.g. a shared layer
-          with multiple input shapes), in which case
-          requesting `input_shape` will raise an Exception.
-          Prefer using `layer.get_input_shape_for(input_shape)`,
-          or `layer.get_input_shape_at(node_index)`.
-      output_shape: Shape tuple. See above.
-      inbound_nodes: List of nodes.
-      outbound_nodes: List of nodes.
-      input, output: Input/output tensor(s). Note that if the layer is used
-          more than once (shared layer), this is ill-defined
-          and will raise an exception. In such cases, use
-          `layer.get_input_at(node_index)`.
-      input_mask, output_mask: Same as above, for masks.
-      trainable_weights: List of variables.
-      non_trainable_weights: List of variables.
-      weights: The concatenation of the lists trainable_weights and
-          non_trainable_weights (in this order).
-
-  # Methods
-      call(x, mask=None): Where the layer's logic lives.
-      __call__(x, mask=None): Wrapper around the layer logic (`call`).
-          If x is a Keras tensor:
-              - Connect current layer with last layer from tensor:
-                  `self._add_inbound_node(last_layer)`
-              - Add layer to tensor history
-          If layer is not built:
-              - Build from inputs shape
-      get_weights()
-      set_weights(weights)
-      get_config()
-      count_params()
-      compute_output_shape(input_shape)
-      compute_mask(x, mask)
-      get_input_at(node_index)
-      get_output_at(node_index)
-      get_input_shape_at(node_index)
-      get_output_shape_at(node_index)
-      get_input_mask_at(node_index)
-      get_output_mask_at(node_index)
-
-  # Class Methods
-      from_config(config)
-
-  # Internal methods:
-      build(input_shape)
-      _add_inbound_node(layer, index=0)
-  """
-
-  def __init__(self, **kwargs):
-    # These properties should be set by the user via keyword arguments.
-    # note that 'dtype', 'input_shape' and 'batch_input_shape'
-    # are only applicable to input layers: do not pass these keywords
-    # to non-input layers.
-    allowed_kwargs = {
-        'activity_regularizer',
-        'input_shape',
-        'batch_input_shape',
-        'batch_size',
-        'dtype',
-        'name',
-        'trainable',
-        'weights',
-    }
-    # Validate optional keyword arguments.
-    for kwarg in kwargs:
-      if kwarg not in allowed_kwargs:
-        raise TypeError('Keyword argument not understood:', kwarg)
-
-    # Get layer name.
-    name = kwargs.get('name')
-
-    # Get `trainable` status.
-    trainable = kwargs.get('trainable', True)
-
-    # Get `dtype`.
-    dtype = kwargs.get('dtype')
-    if dtype is None:
-      dtype = K.floatx()
-
-    # Call super, which will set all properties common to Keras layers
-    # and core TF layers.
-    super(Layer, self).__init__(
-        name=name, dtype=dtype, trainable=trainable,
-        activity_regularizer=kwargs.get('activity_regularizer'))
-
-    # Add properties that are Keras-only for now.
-    self.supports_masking = False
-
-    # Manage input shape information if passed.
-    if 'input_shape' in kwargs or 'batch_input_shape' in kwargs:
-      # In this case we will later create an input layer
-      # to insert before the current layer
-      if 'batch_input_shape' in kwargs:
-        batch_input_shape = tuple(kwargs['batch_input_shape'])
-      elif 'input_shape' in kwargs:
-        if 'batch_size' in kwargs:
-          batch_size = kwargs['batch_size']
-        else:
-          batch_size = None
-        batch_input_shape = (batch_size,) + tuple(kwargs['input_shape'])
-      self._batch_input_shape = batch_input_shape
-
-    # Manage initial weight values if passed.
-    if 'weights' in kwargs:
-      self._initial_weights = kwargs['weights']
-    else:
-      self._initial_weights = None
-
-  def add_weight(self,
-                 name,
-                 shape,
-                 dtype=None,
-                 initializer=None,
-                 regularizer=None,
-                 trainable=True,
-                 constraint=None):
-    """Adds a weight variable to the layer.
-
-    Arguments:
-        name: String, the name for the weight variable.
-        shape: The shape tuple of the weight.
-        dtype: The dtype of the weight.
-        initializer: An Initializer instance (callable).
-        regularizer: An optional Regularizer instance.
-        trainable: A boolean, whether the weight should
-            be trained via backprop or not (assuming
-            that the layer itself is also trainable).
-        constraint: An optional Constraint instance.
-
-    Returns:
-        The created weight variable.
-    """
-    if dtype is None:
-      dtype = K.floatx()
-    weight = self.add_variable(name, shape,
-                               dtype=dtype,
-                               initializer=initializers.get(initializer),
-                               regularizer=regularizers.get(regularizer),
-                               constraint=constraints.get(constraint),
-                               trainable=trainable)
-    return weight
-
-  def call(self, inputs, **kwargs):  # pylint: disable=unused-argument
-    """This is where the layer's logic lives.
-
-    Arguments:
-        inputs: Input tensor, or list/tuple of input tensors.
-        **kwargs: Additional keyword arguments.
-
-    Returns:
-        A tensor or list/tuple of tensors.
-    """
-    return inputs
-
-  def __call__(self, inputs, **kwargs):
-    """Wrapper around self.call(), for handling internal references.
-
-    If a Keras tensor is passed:
-        - We call self._add_inbound_node().
-        - If necessary, we `build` the layer to match
-            the shape of the input(s).
-        - We update the _keras_history of the output tensor(s)
-            with the current layer.
-            This is done as part of _add_inbound_node().
-
-    Arguments:
-        inputs: Can be a tensor or list/tuple of tensors.
-        **kwargs: Additional keyword arguments to be passed to `call()`.
-
-    Returns:
-        Output of the layer's `call` method.
-
-    Raises:
-        ValueError: in case the layer is missing shape information
-            for its `build` call.
-    """
-    # Actually call the layer (optionally building it).
-    output = super(Layer, self).__call__(inputs, **kwargs)
-    if context.in_eager_mode():
-      return output
-
-    # Un-built subclassed network: build it
-    if isinstance(self, Network) and not self.inputs:
-      self._set_inputs(inputs, training=kwargs.get('training'))
-
-    # Update learning phase info.
-    output_tensors = to_list(output)
-    uses_lp = any(
-        [getattr(x, '_uses_learning_phase', False) for x in to_list(inputs)])
-    uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp
-    for i in range(len(output_tensors)):
-      output_tensors[i]._uses_learning_phase = getattr(
-          output_tensors[i], '_uses_learning_phase', False) or uses_lp
-
-    # Optionally load weight values that were specified at layer instantiation.
-    if hasattr(self, '_initial_weights') and self._initial_weights is not None:
-      self.set_weights(self._initial_weights)
-      del self._initial_weights
-    return output
-
-  def compute_output_shape(self, input_shape):
-    """Computes the output shape of the layer.
-
-    Assumes that the layer will be built
-    to match that input shape provided.
-
-    Arguments:
-        input_shape: Shape tuple (tuple of integers)
-            or list of shape tuples (one per output tensor of the layer).
-            Shape tuples can include None for free dimensions,
-            instead of an integer.
-
-    Returns:
-        An input shape tuple.
-    """
-    logging.warning(
-        'All custom layers should implement the '
-        '`compute_output_shape` method. This layer (' + self.name + ') '
-        'is relying on the base `Layer.compute_output_shape` implementation, '
-        'which will start raising a `NotImplementedError` '
-        'as of July 1st, 2018.')
-    return input_shape
-
-  def compute_mask(self, inputs, mask=None):  # pylint: disable=unused-argument
-    """Computes an output mask tensor.
-
-    Arguments:
-        inputs: Tensor or list of tensors.
-        mask: Tensor or list of tensors.
-
-    Returns:
-        None or a tensor (or list of tensors,
-            one per output tensor of the layer).
-    """
-    if not self.supports_masking:
-      if mask is not None:
-        if isinstance(mask, list):
-          if any(m is not None for m in mask):
-            raise TypeError('Layer ' + self.name + ' does not support masking, '
-                            'but was passed an input_mask: ' + str(mask))
-        else:
-          raise TypeError('Layer ' + self.name + ' does not support masking, '
-                          'but was passed an input_mask: ' + str(mask))
-      # masking not explicitly supported: return None as mask
-      return None
-    # if masking is explicitly supported, by default
-    # carry over the input mask
-    return mask
-
-  def get_input_mask_at(self, node_index):
-    """Retrieves the input mask tensor(s) of a layer at a given node.
-
-    Arguments:
-        node_index: Integer, index of the node
-            from which to retrieve the attribute.
-            E.g. `node_index=0` will correspond to the
-            first time the layer was called.
-
-    Returns:
-        A mask tensor
-        (or list of tensors if the layer has multiple inputs).
-    """
-    inputs = self.get_input_at(node_index)
-    if isinstance(inputs, list):
-      return [getattr(x, '_keras_mask', None) for x in inputs]
-    else:
-      return getattr(inputs, '_keras_mask', None)
-
-  def get_output_mask_at(self, node_index):
-    """Retrieves the output mask tensor(s) of a layer at a given node.
-
-    Arguments:
-        node_index: Integer, index of the node
-            from which to retrieve the attribute.
-            E.g. `node_index=0` will correspond to the
-            first time the layer was called.
-
-    Returns:
-        A mask tensor
-        (or list of tensors if the layer has multiple outputs).
-    """
-    output = self.get_output_at(node_index)
-    if isinstance(output, list):
-      return [getattr(x, '_keras_mask', None) for x in output]
-    else:
-      return getattr(output, '_keras_mask', None)
-
-  @property
-  def input_mask(self):
-    """Retrieves the input mask tensor(s) of a layer.
-
-    Only applicable if the layer has exactly one inbound node,
-    i.e. if it is connected to one incoming layer.
-
-    Returns:
-        Input mask tensor (potentially None) or list of input
-        mask tensors.
-
-    Raises:
-        AttributeError: if the layer is connected to
-        more than one incoming layers.
-    """
-    inputs = self.input
-    if isinstance(inputs, list):
-      return [getattr(x, '_keras_mask', None) for x in inputs]
-    else:
-      return getattr(inputs, '_keras_mask', None)
-
-  @property
-  def output_mask(self):
-    """Retrieves the output mask tensor(s) of a layer.
-
-    Only applicable if the layer has exactly one inbound node,
-    i.e. if it is connected to one incoming layer.
-
-    Returns:
-        Output mask tensor (potentially None) or list of output
-        mask tensors.
-
-    Raises:
-        AttributeError: if the layer is connected to
-        more than one incoming layers.
-    """
-    output = self.output
-    if isinstance(output, list):
-      return [getattr(x, '_keras_mask', None) for x in output]
-    else:
-      return getattr(output, '_keras_mask', None)
-
-  def set_weights(self, weights):
-    """Sets the weights of the layer, from Numpy arrays.
-
-    Arguments:
-        weights: a list of Numpy arrays. The number
-            of arrays and their shape must match
-            number of the dimensions of the weights
-            of the layer (i.e. it should match the
-            output of `get_weights`).
-
-    Raises:
-        ValueError: If the provided weights list does not match the
-            layer's specifications.
-    """
-    params = self.weights
-    if len(params) != len(weights):
-      raise ValueError('You called `set_weights(weights)` on layer "' +
-                       self.name + '" with a  weight list of length ' +
-                       str(len(weights)) + ', but the layer was expecting ' +
-                       str(len(params)) + ' weights. Provided weights: ' +
-                       str(weights)[:50] + '...')
-    if not params:
-      return
-    weight_value_tuples = []
-    param_values = K.batch_get_value(params)
-    for pv, p, w in zip(param_values, params, weights):
-      if pv.shape != w.shape:
-        raise ValueError('Layer weight shape ' + str(pv.shape) +
-                         ' not compatible with '
-                         'provided weight shape ' + str(w.shape))
-      weight_value_tuples.append((p, w))
-    K.batch_set_value(weight_value_tuples)
-
-  def get_weights(self):
-    """Returns the current weights of the layer.
-
-    Returns:
-        Weights values as a list of numpy arrays.
-    """
-    params = self.weights
-    return K.batch_get_value(params)
-
-  def get_config(self):
-    """Returns the config of the layer.
-
-    A layer config is a Python dictionary (serializable)
-    containing the configuration of a layer.
-    The same layer can be reinstantiated later
-    (without its trained weights) from this configuration.
-
-    The config of a layer does not include connectivity
-    information, nor the layer class name. These are handled
-    by `Network` (one layer of abstraction above).
-
-    Returns:
-        Python dictionary.
-    """
-    config = {'name': self.name, 'trainable': self.trainable}
-    if hasattr(self, '_batch_input_shape'):
-      config['batch_input_shape'] = self._batch_input_shape
-    if hasattr(self, 'dtype'):
-      config['dtype'] = self.dtype
-    return config
-
-  @classmethod
-  def from_config(cls, config):
-    """Creates a layer from its config.
-
-    This method is the reverse of `get_config`,
-    capable of instantiating the same layer from the config
-    dictionary. It does not handle layer connectivity
-    (handled by Network), nor weights (handled by `set_weights`).
-
-    Arguments:
-        config: A Python dictionary, typically the
-            output of get_config.
-
-    Returns:
-        A layer instance.
-    """
-    return cls(**config)
-
-  @tf_base_layers.Layer.activity_regularizer.setter
-  def activity_regularizer(self, activity_regularizer):
-    self._activity_regularizer = activity_regularizer
 
+class Network(base_layer.Layer):
+  """A `Network` is a composition of layers.
 
-class InputLayer(Layer):
-  """Layer to be used as an entry point into a Network (a graph of layers).
-
-  It can either wrap an existing tensor (pass an `input_tensor` argument)
-  or create its a placeholder tensor (pass arguments `input_shape`, and
-  optionally, `dtype`).
-
-  It is generally recommend to use the functional layer API via `Input`,
-  (which creates an `InputLayer`) without directly using `InputLayer`.
-
-  Arguments:
-      input_shape: Shape tuple (not including the batch axis), or `TensorShape`
-        instance (not including the batch axis).
-      batch_size: Optional input batch size (integer or None).
-      dtype: Datatype of the input.
-      input_tensor: Optional tensor to use as layer input
-          instead of creating a placeholder.
-      sparse: Boolean, whether the placeholder created
-          is meant to be sparse.
-      name: Name of the layer (string).
-  """
-
-  def __init__(self,
-               input_shape=None,
-               batch_size=None,
-               dtype=None,
-               input_tensor=None,
-               sparse=False,
-               name=None,
-               **kwargs):
-    if 'batch_input_shape' in kwargs:
-      batch_input_shape = kwargs.pop('batch_input_shape')
-      if input_shape and batch_input_shape:
-        raise ValueError('Only provide the input_shape OR '
-                         'batch_input_shape argument to '
-                         'InputLayer, not both at the same time.')
-      batch_size = batch_input_shape[0]
-      input_shape = batch_input_shape[1:]
-    if kwargs:
-      raise ValueError('Unrecognized keyword arguments:', kwargs.keys())
-
-    if not name:
-      prefix = 'input'
-      name = prefix + '_' + str(K.get_uid(prefix))
-
-    if not dtype:
-      if input_tensor is None:
-        dtype = K.floatx()
-      else:
-        dtype = K.dtype(input_tensor)
-    super(InputLayer, self).__init__(dtype=dtype, name=name)
-    self.built = True
-    self.sparse = sparse
-    self.batch_size = batch_size
-
-    if isinstance(input_shape, tensor_shape.TensorShape):
-      input_shape = tuple(input_shape.as_list())
-
-    if input_tensor is None:
-      if input_shape is not None:
-        batch_input_shape = (batch_size,) + tuple(input_shape)
-      else:
-        batch_input_shape = None
-
-      if context.in_eager_mode():
-        # In eager mode, create a temporary placeholder to call the layer on.
-        input_tensor = tf_base_layers._DeferredTensor(  # pylint: disable=protected-access
-            shape=batch_input_shape,
-            dtype=dtype,
-            name=self.name)
-      else:
-        # In graph mode, create a graph placeholder to call the layer on.
-        if sparse:
-          input_tensor = array_ops.sparse_placeholder(
-              shape=batch_input_shape,
-              dtype=dtype,
-              name=self.name)
-        else:
-          input_tensor = array_ops.placeholder(
-              shape=batch_input_shape,
-              dtype=dtype,
-              name=self.name)
-
-      # For compatibility with Keras API.
-      self.is_placeholder = True
-      self._batch_input_shape = batch_input_shape
-    else:
-      # For compatibility with Keras API.
-      self.is_placeholder = False
-      self._batch_input_shape = tuple(input_tensor.get_shape().as_list())
-
-    # Create an input node to add to self.outbound_node
-    # and set output_tensors' _keras_history.
-    input_tensor._keras_history = (self, 0, 0)  # pylint: disable=protected-access
-    tf_base_layers.Node(
-        self,
-        inbound_layers=[],
-        node_indices=[],
-        tensor_indices=[],
-        input_tensors=[input_tensor],
-        output_tensors=[input_tensor])
-
-  def get_config(self):
-    config = {
-        'batch_input_shape': self._batch_input_shape,
-        'dtype': self.dtype,
-        'sparse': self.sparse,
-        'name': self.name
-    }
-    return config
-
-
-@tf_export('keras.layers.Input', 'keras.Input')
-def Input(  # pylint: disable=invalid-name
-    shape=None,
-    batch_size=None,
-    name=None,
-    dtype=None,
-    sparse=False,
-    tensor=None,
-    **kwargs):
-  """`Input()` is used to instantiate a Keras tensor.
-
-  A Keras tensor is a tensor object from the underlying backend
-  (Theano or TensorFlow), which we augment with certain
-  attributes that allow us to build a Keras model
-  just by knowing the inputs and outputs of the model.
-
-  For instance, if a, b and c are Keras tensors,
-  it becomes possible to do:
-  `model = Model(input=[a, b], output=c)`
-
-  The added Keras attribute is:
-      `_keras_history`: Last layer applied to the tensor.
-          the entire layer graph is retrievable from that layer,
-          recursively.
-
-  Arguments:
-      shape: A shape tuple (integers), not including the batch size.
-          For instance, `shape=(32,)` indicates that the expected input
-          will be batches of 32-dimensional vectors.
-      batch_size: optional static batch size (integer).
-      name: An optional name string for the layer.
-          Should be unique in a model (do not reuse the same name twice).
-          It will be autogenerated if it isn't provided.
-      dtype: The data type expected by the input, as a string
-          (`float32`, `float64`, `int32`...)
-      sparse: A boolean specifying whether the placeholder
-          to be created is sparse.
-      tensor: Optional existing tensor to wrap into the `Input` layer.
-          If set, the layer will not create a placeholder tensor.
-      **kwargs: deprecated arguments support.
-
-  Returns:
-      A tensor.
-
-  Example:
-
-      ```python
-      # this is a logistic regression in Keras
-      x = Input(shape=(32,))
-      y = Dense(16, activation='softmax')(x)
-      model = Model(x, y)
-      ```
-
-  Raises:
-    ValueError: in case of invalid arguments.
-  """
-  if 'batch_shape' in kwargs:
-    batch_shape = kwargs.pop('batch_shape')
-    if shape and batch_shape:
-      raise ValueError('Only provide the shape OR '
-                       'batch_shape argument to '
-                       'Input, not both at the same time.')
-    batch_size = batch_shape[0]
-    shape = batch_shape[1:]
-  if kwargs:
-    raise ValueError('Unrecognized keyword arguments:', kwargs.keys())
-
-  if dtype is None:
-    dtype = K.floatx()
-  if not shape and tensor is None:
-    raise ValueError('Please provide to Input either a `shape`'
-                     ' or a `tensor` argument. Note that '
-                     '`shape` does not include the batch '
-                     'dimension.')
-  input_layer = InputLayer(
-      input_shape=shape,
-      batch_size=batch_size,
-      name=name,
-      dtype=dtype,
-      sparse=sparse,
-      input_tensor=tensor)
-  # Return tensor including `_keras_history`.
-  # Note that in this case train_output and test_output are the same pointer.
-  outputs = input_layer._inbound_nodes[0].output_tensors
-  if len(outputs) == 1:
-    return outputs[0]
-  else:
-    return outputs
-
-
-class Network(Layer):
-  """A Network is a directed acyclic graph of layers.
-
-  It is the topological form of a "model". A Model
-  is simply a Network with added training routines.
-
-  # Properties
-      name
-      inputs
-      outputs
-      input_layers
-      output_layers
-      input_spec (list of class instances)
-          each entry describes one required input:
-              - ndim
-              - dtype
-      trainable (boolean)
-      input_shape
-      output_shape
-      inbound_nodes: list of nodes
-      outbound_nodes: list of nodes
-      trainable_weights (list of variables)
-      non_trainable_weights (list of variables)
-
-  # Methods
-      summary
-      get_layer
-      get_weights
-      set_weights
-      get_config
-      compute_output_shape
-
-  # Class Methods
-      from_config
+  It is the topological form of a "model". A `Model`
+  is simply a `Network` with added training routines.
   """
 
   def __init__(self, *args, **kwargs):  # pylint: disable=super-init-not-called
@@ -1053,11 +378,11 @@ class Network(Layer):
     if not self._is_graph_network:
       return None
 
-    inputs = to_list(inputs)
+    inputs = generic_utils.to_list(inputs)
     if mask is None:
       masks = [None for _ in range(len(inputs))]
     else:
-      masks = to_list(mask)
+      masks = generic_utils.to_list(mask)
     cache_key = (tf_layers_util.object_list_uid(inputs)
                  + '_' + tf_layers_util.object_list_uid(masks))
     if cache_key in self._output_mask_cache:
@@ -1818,7 +1143,7 @@ class Network(Layer):
       if not proceed:
         return
     with h5py.File(filepath, 'w') as f:
-      save_weights_to_hdf5_group(f, self.layers)
+      saving.save_weights_to_hdf5_group(f, self.layers)
 
   def load_weights(self, filepath, by_name=False):
     """Loads all layer weights from a HDF5 save file.
@@ -1849,9 +1174,9 @@ class Network(Layer):
       if 'layer_names' not in f.attrs and 'model_weights' in f:
         f = f['model_weights']
       if by_name:
-        load_weights_from_hdf5_group_by_name(f, self.layers)
+        saving.load_weights_from_hdf5_group_by_name(f, self.layers)
       else:
-        load_weights_from_hdf5_group(f, self.layers)
+        saving.load_weights_from_hdf5_group(f, self.layers)
 
   def _updated_config(self):
     """Util hared between different serialization methods.
@@ -1989,364 +1314,6 @@ def get_source_inputs(tensor, layer=None, node_index=None):
       return source_tensors
 
 
-def to_list(x):
-  """Normalizes a list/tensor into a list.
-
-  If a tensor is passed, we return
-  a list of size 1 containing the tensor.
-
-  Arguments:
-      x: target object to be normalized.
-
-  Returns:
-      A list.
-  """
-  if isinstance(x, list):
-    return x
-  return [x]
-
-
-def save_weights_to_hdf5_group(f, layers):
-  from tensorflow.python.keras._impl.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top
-
-  f.attrs['layer_names'] = [layer.name.encode('utf8') for layer in layers]
-  f.attrs['backend'] = K.backend().encode('utf8')
-  f.attrs['keras_version'] = str(keras_version).encode('utf8')
-
-  for layer in layers:
-    g = f.create_group(layer.name)
-    symbolic_weights = layer.weights
-    weight_values = K.batch_get_value(symbolic_weights)
-    weight_names = []
-    for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
-      if hasattr(w, 'name') and w.name:
-        name = str(w.name)
-      else:
-        name = 'param_' + str(i)
-      weight_names.append(name.encode('utf8'))
-    g.attrs['weight_names'] = weight_names
-    for name, val in zip(weight_names, weight_values):
-      param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
-      if not val.shape:
-        # scalar
-        param_dset[()] = val
-      else:
-        param_dset[:] = val
-
-
-def preprocess_weights_for_loading(layer,
-                                   weights,
-                                   original_keras_version=None,
-                                   original_backend=None):
-  """Converts layers weights from Keras 1 format to Keras 2.
-
-  Arguments:
-      layer: Layer instance.
-      weights: List of weights values (Numpy arrays).
-      original_keras_version: Keras version for the weights, as a string.
-      original_backend: Keras backend the weights were trained with,
-          as a string.
-
-  Returns:
-      A list of weights values (Numpy arrays).
-  """
-  if layer.__class__.__name__ == 'Bidirectional':
-    num_weights_per_layer = len(weights) // 2
-    forward_weights = preprocess_weights_for_loading(
-        layer.forward_layer, weights[:num_weights_per_layer],
-        original_keras_version, original_backend)
-    backward_weights = preprocess_weights_for_loading(
-        layer.backward_layer, weights[num_weights_per_layer:],
-        original_keras_version, original_backend)
-    weights = forward_weights + backward_weights
-
-  if original_keras_version == '1':
-    if layer.__class__.__name__ == 'TimeDistributed':
-      weights = preprocess_weights_for_loading(
-          layer.layer, weights, original_keras_version, original_backend)
-
-    if layer.__class__.__name__ == 'Conv1D':
-      shape = weights[0].shape
-      # Handle Keras 1.1 format
-      if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters:
-        # Legacy shape:
-        # (filters, input_dim, filter_length, 1)
-        assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0],
-                                                           1)
-        weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
-      weights[0] = weights[0][:, 0, :, :]
-
-    if layer.__class__.__name__ == 'Conv2D':
-      if layer.data_format == 'channels_first':
-        # old: (filters, stack_size, kernel_rows, kernel_cols)
-        # new: (kernel_rows, kernel_cols, stack_size, filters)
-        weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
-
-    if layer.__class__.__name__ == 'Conv2DTranspose':
-      if layer.data_format == 'channels_last':
-        # old: (kernel_rows, kernel_cols, stack_size, filters)
-        # new: (kernel_rows, kernel_cols, filters, stack_size)
-        weights[0] = np.transpose(weights[0], (0, 1, 3, 2))
-      if layer.data_format == 'channels_first':
-        # old: (filters, stack_size, kernel_rows, kernel_cols)
-        # new: (kernel_rows, kernel_cols, filters, stack_size)
-        weights[0] = np.transpose(weights[0], (2, 3, 0, 1))
-
-    if layer.__class__.__name__ == 'Conv3D':
-      if layer.data_format == 'channels_first':
-        # old: (filters, stack_size, ...)
-        # new: (..., stack_size, filters)
-        weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0))
-
-    if layer.__class__.__name__ == 'GRU':
-      if len(weights) == 9:
-        kernel = np.concatenate([weights[0], weights[3], weights[6]], axis=-1)
-        recurrent_kernel = np.concatenate(
-            [weights[1], weights[4], weights[7]], axis=-1)
-        bias = np.concatenate([weights[2], weights[5], weights[8]], axis=-1)
-        weights = [kernel, recurrent_kernel, bias]
-
-    if layer.__class__.__name__ == 'LSTM':
-      if len(weights) == 12:
-        # old: i, c, f, o
-        # new: i, f, c, o
-        kernel = np.concatenate(
-            [weights[0], weights[6], weights[3], weights[9]], axis=-1)
-        recurrent_kernel = np.concatenate(
-            [weights[1], weights[7], weights[4], weights[10]], axis=-1)
-        bias = np.concatenate(
-            [weights[2], weights[8], weights[5], weights[11]], axis=-1)
-        weights = [kernel, recurrent_kernel, bias]
-
-    if layer.__class__.__name__ == 'ConvLSTM2D':
-      if len(weights) == 12:
-        kernel = np.concatenate(
-            [weights[0], weights[6], weights[3], weights[9]], axis=-1)
-        recurrent_kernel = np.concatenate(
-            [weights[1], weights[7], weights[4], weights[10]], axis=-1)
-        bias = np.concatenate(
-            [weights[2], weights[8], weights[5], weights[11]], axis=-1)
-        if layer.data_format == 'channels_first':
-          # old: (filters, stack_size, kernel_rows, kernel_cols)
-          # new: (kernel_rows, kernel_cols, stack_size, filters)
-          kernel = np.transpose(kernel, (2, 3, 1, 0))
-          recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0))
-        weights = [kernel, recurrent_kernel, bias]
-
-    if layer.__class__.__name__ in ['Model', 'Sequential']:
-      new_weights = []
-      # trainable weights
-      for sublayer in layer.layers:
-        num_weights = len(sublayer.trainable_weights)
-        if num_weights > 0:
-          new_weights.extend(
-              preprocess_weights_for_loading(
-                  layer=sublayer,
-                  weights=weights[:num_weights],
-                  original_keras_version=original_keras_version,
-                  original_backend=original_backend))
-          weights = weights[num_weights:]
-
-      # non-trainable weights
-      for sublayer in layer.layers:
-        num_weights = len([
-            l for l in sublayer.weights if l not in sublayer.trainable_weights
-        ])
-        if num_weights > 0:
-          new_weights.extend(
-              preprocess_weights_for_loading(
-                  layer=sublayer,
-                  weights=weights[:num_weights],
-                  original_keras_version=original_keras_version,
-                  original_backend=original_backend))
-          weights = weights[num_weights:]
-      weights = new_weights
-
-  conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D']
-  if layer.__class__.__name__ in conv_layers:
-    if original_backend == 'theano':
-      weights[0] = conv_utils.convert_kernel(weights[0])
-      if layer.__class__.__name__ == 'ConvLSTM2D':
-        weights[1] = conv_utils.convert_kernel(weights[1])
-    if K.int_shape(layer.weights[0]) != weights[0].shape:
-      weights[0] = np.transpose(weights[0], (3, 2, 0, 1))
-      if layer.__class__.__name__ == 'ConvLSTM2D':
-        weights[1] = np.transpose(weights[1], (3, 2, 0, 1))
-
-  # Convert the weights of CuDNNLSTM so that they could be loaded into LSTM
-  if layer.__class__.__name__ == 'LSTM' and len(weights) == 3:
-    # Determine if loading a CuDNNLSTM layer from the number of bias weights:
-    # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4)
-    # if there's no bias weight in the file, skip this conversion
-    units = weights[1].shape[0]
-    bias = weights[2]
-    if len(bias) == units * 8:
-      # reshape the kernels
-      kernels = np.split(weights[0], 4, axis=1)
-      kernels = [
-          kernel.reshape(-1).reshape(kernel.shape, order='F')
-          for kernel in kernels
-      ]
-      weights[0] = np.concatenate(kernels, axis=1)
-
-      # transpose the recurrent kernels
-      recurrent_kernels = np.split(weights[1], 4, axis=1)
-      recurrent_kernels = [kernel.T for kernel in recurrent_kernels]
-      weights[1] = np.concatenate(recurrent_kernels, axis=1)
-
-      # split the bias into half and merge
-      weights[2] = bias[:units * 4] + bias[units * 4:]
-
-  return weights
-
-
-def load_weights_from_hdf5_group(f, layers):
-  """Implements topological (order-based) weight loading.
-
-  Arguments:
-      f: A pointer to a HDF5 group.
-      layers: a list of target layers.
-
-  Raises:
-      ValueError: in case of mismatch between provided layers
-          and weights file.
-  """
-  if 'keras_version' in f.attrs:
-    original_keras_version = f.attrs['keras_version'].decode('utf8')
-  else:
-    original_keras_version = '1'
-  if 'backend' in f.attrs:
-    original_backend = f.attrs['backend'].decode('utf8')
-  else:
-    original_backend = None
-
-  filtered_layers = []
-  for layer in layers:
-    weights = layer.weights
-    if weights:
-      filtered_layers.append(layer)
-
-  layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
-  filtered_layer_names = []
-  for name in layer_names:
-    g = f[name]
-    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
-    if weight_names:
-      filtered_layer_names.append(name)
-  layer_names = filtered_layer_names
-  if len(layer_names) != len(filtered_layers):
-    raise ValueError('You are trying to load a weight file '
-                     'containing ' + str(len(layer_names)) +
-                     ' layers into a model with ' + str(len(filtered_layers)) +
-                     ' layers.')
-
-  # We batch weight value assignments in a single backend call
-  # which provides a speedup in TensorFlow.
-  weight_value_tuples = []
-  for k, name in enumerate(layer_names):
-    g = f[name]
-    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
-    weight_values = [g[weight_name] for weight_name in weight_names]
-    layer = filtered_layers[k]
-    symbolic_weights = layer.weights
-    weight_values = preprocess_weights_for_loading(
-        layer, weight_values, original_keras_version, original_backend)
-    if len(weight_values) != len(symbolic_weights):
-      raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
-                       '" in the current model) was found to '
-                       'correspond to layer ' + name + ' in the save file. '
-                       'However the new layer ' + layer.name + ' expects ' +
-                       str(len(symbolic_weights)) +
-                       ' weights, but the saved weights have ' +
-                       str(len(weight_values)) + ' elements.')
-    weight_value_tuples += zip(symbolic_weights, weight_values)
-  K.batch_set_value(weight_value_tuples)
-
-
-def load_weights_from_hdf5_group_by_name(f, layers):
-  """Implements name-based weight loading.
-
-  (instead of topological weight loading).
-
-  Layers that have no matching name are skipped.
-
-  Arguments:
-      f: A pointer to a HDF5 group.
-      layers: a list of target layers.
-
-  Raises:
-      ValueError: in case of mismatch between provided layers
-          and weights file.
-  """
-  if 'keras_version' in f.attrs:
-    original_keras_version = f.attrs['keras_version'].decode('utf8')
-  else:
-    original_keras_version = '1'
-  if 'backend' in f.attrs:
-    original_backend = f.attrs['backend'].decode('utf8')
-  else:
-    original_backend = None
-
-  # New file format.
-  layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
-
-  # Reverse index of layer name to list of layers with name.
-  index = {}
-  for layer in layers:
-    if layer.name:
-      index.setdefault(layer.name, []).append(layer)
-
-  # We batch weight value assignments in a single backend call
-  # which provides a speedup in TensorFlow.
-  weight_value_tuples = []
-  for k, name in enumerate(layer_names):
-    g = f[name]
-    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
-    weight_values = [g[weight_name] for weight_name in weight_names]
-
-    for layer in index.get(name, []):
-      symbolic_weights = layer.weights
-      weight_values = preprocess_weights_for_loading(
-          layer, weight_values, original_keras_version, original_backend)
-      if len(weight_values) != len(symbolic_weights):
-        raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
-                         '") expects ' + str(len(symbolic_weights)) +
-                         ' weight(s), but the saved weights' + ' have ' +
-                         str(len(weight_values)) + ' element(s).')
-      # Set values.
-      for i in range(len(weight_values)):
-        weight_value_tuples.append((symbolic_weights[i], weight_values[i]))
-  K.batch_set_value(weight_value_tuples)
-
-
-def shape_type_conversion(fn):
-  """Decorator that handles tuple/TensorShape conversion.
-
-  Used in `compute_output_shape` and `build`.
-
-  Arguments:
-    fn: function to wrap.
-
-  Returns:
-    Wrapped function.
-  """
-
-  def wrapper(instance, input_shape):
-    if input_shape is not None:
-      if isinstance(input_shape, list):
-        input_shape = [
-            tuple(tensor_shape.TensorShape(x).as_list()) for x in input_shape]
-      else:
-        input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list())
-    output_shape = fn(instance, input_shape)
-    if output_shape is not None:
-      if isinstance(output_shape, list):
-        return [tensor_shape.TensorShape(x) for x in output_shape]
-      return tensor_shape.TensorShape(output_shape)
-
-  return wrapper
-
-
 def _make_node_key(layer_name, node_index):
   return layer_name + '_ib-' + str(node_index)
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/saving.py b/tensorflow/python/keras/_impl/keras/engine/saving.py
new file mode 100644
index 0000000000..52522e6935
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/saving.py
@@ -0,0 +1,671 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=protected-access
+"""Model saving utilities.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+import os
+
+import numpy as np
+from six.moves import zip  # pylint: disable=redefined-builtin
+
+from tensorflow.python.keras._impl.keras import backend as K
+from tensorflow.python.keras._impl.keras import optimizers
+from tensorflow.python.keras._impl.keras.utils import conv_utils
+from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util.tf_export import tf_export
+
+# pylint: disable=g-import-not-at-top
+try:
+  import h5py
+except ImportError:
+  h5py = None
+
+try:
+  import yaml
+except ImportError:
+  yaml = None
+# pylint: enable=g-import-not-at-top
+
+
+@tf_export('keras.models.save_model')
+def save_model(model, filepath, overwrite=True, include_optimizer=True):
+  """Save a model to a HDF5 file.
+
+  The saved model contains:
+      - the model's configuration (topology)
+      - the model's weights
+      - the model's optimizer's state (if any)
+
+  Thus the saved model can be reinstantiated in
+  the exact same state, without any of the code
+  used for model definition or training.
+
+  Arguments:
+      model: Keras model instance to be saved.
+      filepath: String, path where to save the model.
+      overwrite: Whether we should overwrite any existing
+          model at the target location, or instead
+          ask the user with a manual prompt.
+      include_optimizer: If True, save optimizer's state together.
+
+  Raises:
+      ImportError: if h5py is not available.
+  """
+
+  if h5py is None:
+    raise ImportError('`save_model` requires h5py.')
+
+  def get_json_type(obj):
+    """Serialize any object to a JSON-serializable structure.
+
+    Arguments:
+        obj: the object to serialize
+
+    Returns:
+        JSON-serializable structure representing `obj`.
+
+    Raises:
+        TypeError: if `obj` cannot be serialized.
+    """
+    # if obj is a serializable Keras class instance
+    # e.g. optimizer, layer
+    if hasattr(obj, 'get_config'):
+      return {'class_name': obj.__class__.__name__, 'config': obj.get_config()}
+
+    # if obj is any numpy type
+    if type(obj).__module__ == np.__name__:
+      if isinstance(obj, np.ndarray):
+        return {'type': type(obj), 'value': obj.tolist()}
+      else:
+        return obj.item()
+
+    # misc functions (e.g. loss function)
+    if callable(obj):
+      return obj.__name__
+
+    # if obj is a python 'type'
+    if type(obj).__name__ == type.__name__:
+      return obj.__name__
+
+    raise TypeError('Not JSON Serializable:', obj)
+
+  from tensorflow.python.keras._impl.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top
+
+  # If file exists and should not be overwritten.
+  if not overwrite and os.path.isfile(filepath):
+    proceed = ask_to_proceed_with_overwrite(filepath)
+    if not proceed:
+      return
+
+  with h5py.File(filepath, mode='w') as f:
+    f.attrs['keras_version'] = str(keras_version).encode('utf8')
+    f.attrs['backend'] = K.backend().encode('utf8')
+    f.attrs['model_config'] = json.dumps(
+        {
+            'class_name': model.__class__.__name__,
+            'config': model.get_config()
+        },
+        default=get_json_type).encode('utf8')
+
+    model_weights_group = f.create_group('model_weights')
+    model_layers = model.layers
+    save_weights_to_hdf5_group(model_weights_group, model_layers)
+
+    if include_optimizer and hasattr(model, 'optimizer'):
+      if isinstance(model.optimizer, optimizers.TFOptimizer):
+        logging.warning(
+            'TensorFlow optimizers do not '
+            'make it possible to access '
+            'optimizer attributes or optimizer state '
+            'after instantiation. '
+            'As a result, we cannot save the optimizer '
+            'as part of the model save file.'
+            'You will have to compile your model again after loading it. '
+            'Prefer using a Keras optimizer instead '
+            '(see keras.io/optimizers).')
+      else:
+        f.attrs['training_config'] = json.dumps(
+            {
+                'optimizer_config': {
+                    'class_name': model.optimizer.__class__.__name__,
+                    'config': model.optimizer.get_config()
+                },
+                'loss': model.loss,
+                'metrics': model.metrics,
+                'sample_weight_mode': model.sample_weight_mode,
+                'loss_weights': model.loss_weights,
+            },
+            default=get_json_type).encode('utf8')
+
+        # Save optimizer weights.
+        symbolic_weights = getattr(model.optimizer, 'weights')
+        if symbolic_weights:
+          optimizer_weights_group = f.create_group('optimizer_weights')
+          weight_values = K.batch_get_value(symbolic_weights)
+          weight_names = []
+          for w, val in zip(symbolic_weights, weight_values):
+            name = str(w.name)
+            weight_names.append(name.encode('utf8'))
+          optimizer_weights_group.attrs['weight_names'] = weight_names
+          for name, val in zip(weight_names, weight_values):
+            param_dset = optimizer_weights_group.create_dataset(
+                name, val.shape, dtype=val.dtype)
+            if not val.shape:
+              # scalar
+              param_dset[()] = val
+            else:
+              param_dset[:] = val
+    f.flush()
+
+
+@tf_export('keras.models.load_model')
+def load_model(filepath, custom_objects=None, compile=True):  # pylint: disable=redefined-builtin
+  """Loads a model saved via `save_model`.
+
+  Arguments:
+      filepath: String, path to the saved model.
+      custom_objects: Optional dictionary mapping names
+          (strings) to custom classes or functions to be
+          considered during deserialization.
+      compile: Boolean, whether to compile the model
+          after loading.
+
+  Returns:
+      A Keras model instance. If an optimizer was found
+      as part of the saved model, the model is already
+      compiled. Otherwise, the model is uncompiled and
+      a warning will be displayed. When `compile` is set
+      to False, the compilation is omitted without any
+      warning.
+
+  Raises:
+      ImportError: if h5py is not available.
+      ValueError: In case of an invalid savefile.
+  """
+  if h5py is None:
+    raise ImportError('`load_model` requires h5py.')
+
+  if not custom_objects:
+    custom_objects = {}
+
+  def convert_custom_objects(obj):
+    """Handles custom object lookup.
+
+    Arguments:
+        obj: object, dict, or list.
+
+    Returns:
+        The same structure, where occurrences
+            of a custom object name have been replaced
+            with the custom object.
+    """
+    if isinstance(obj, list):
+      deserialized = []
+      for value in obj:
+        deserialized.append(convert_custom_objects(value))
+      return deserialized
+    if isinstance(obj, dict):
+      deserialized = {}
+      for key, value in obj.items():
+        deserialized[key] = convert_custom_objects(value)
+      return deserialized
+    if obj in custom_objects:
+      return custom_objects[obj]
+    return obj
+
+  with h5py.File(filepath, mode='r') as f:
+    # instantiate model
+    model_config = f.attrs.get('model_config')
+    if model_config is None:
+      raise ValueError('No model found in config file.')
+    model_config = json.loads(model_config.decode('utf-8'))
+    model = model_from_config(model_config, custom_objects=custom_objects)
+
+    # set weights
+    load_weights_from_hdf5_group(f['model_weights'], model.layers)
+
+    # Early return if compilation is not required.
+    if not compile:
+      return model
+
+    # instantiate optimizer
+    training_config = f.attrs.get('training_config')
+    if training_config is None:
+      logging.warning('No training configuration found in save file: '
+                      'the model was *not* compiled. Compile it manually.')
+      return model
+    training_config = json.loads(training_config.decode('utf-8'))
+    optimizer_config = training_config['optimizer_config']
+    optimizer = optimizers.deserialize(
+        optimizer_config, custom_objects=custom_objects)
+
+    # Recover loss functions and metrics.
+    loss = convert_custom_objects(training_config['loss'])
+    metrics = convert_custom_objects(training_config['metrics'])
+    sample_weight_mode = training_config['sample_weight_mode']
+    loss_weights = training_config['loss_weights']
+
+    # Compile model.
+    model.compile(
+        optimizer=optimizer,
+        loss=loss,
+        metrics=metrics,
+        loss_weights=loss_weights,
+        sample_weight_mode=sample_weight_mode)
+
+    # Set optimizer weights.
+    if 'optimizer_weights' in f:
+      # Build train function (to get weight updates).
+      model._make_train_function()
+      optimizer_weights_group = f['optimizer_weights']
+      optimizer_weight_names = [
+          n.decode('utf8')
+          for n in optimizer_weights_group.attrs['weight_names']
+      ]
+      optimizer_weight_values = [
+          optimizer_weights_group[n] for n in optimizer_weight_names
+      ]
+      try:
+        model.optimizer.set_weights(optimizer_weight_values)
+      except ValueError:
+        logging.warning('Error in loading the saved optimizer '
+                        'state. As a result, your model is '
+                        'starting with a freshly initialized '
+                        'optimizer.')
+  return model
+
+
+@tf_export('keras.models.model_from_config')
+def model_from_config(config, custom_objects=None):
+  """Instantiates a Keras model from its config.
+
+  Arguments:
+      config: Configuration dictionary.
+      custom_objects: Optional dictionary mapping names
+          (strings) to custom classes or functions to be
+          considered during deserialization.
+
+  Returns:
+      A Keras model instance (uncompiled).
+
+  Raises:
+      TypeError: if `config` is not a dictionary.
+  """
+  if isinstance(config, list):
+    raise TypeError('`model_from_config` expects a dictionary, not a list. '
+                    'Maybe you meant to use '
+                    '`Sequential.from_config(config)`?')
+  from tensorflow.python.keras._impl.keras.layers import deserialize  # pylint: disable=g-import-not-at-top
+  return deserialize(config, custom_objects=custom_objects)
+
+
+@tf_export('keras.models.model_from_yaml')
+def model_from_yaml(yaml_string, custom_objects=None):
+  """Parses a yaml model configuration file and returns a model instance.
+
+  Arguments:
+      yaml_string: YAML string encoding a model configuration.
+      custom_objects: Optional dictionary mapping names
+          (strings) to custom classes or functions to be
+          considered during deserialization.
+
+  Returns:
+      A Keras model instance (uncompiled).
+
+  Raises:
+      ImportError: if yaml module is not found.
+  """
+  if yaml is None:
+    raise ImportError('Requires yaml module installed.')
+  config = yaml.load(yaml_string)
+  from tensorflow.python.keras._impl.keras.layers import deserialize  # pylint: disable=g-import-not-at-top
+  return deserialize(config, custom_objects=custom_objects)
+
+
+@tf_export('keras.models.model_from_json')
+def model_from_json(json_string, custom_objects=None):
+  """Parses a JSON model configuration file and returns a model instance.
+
+  Arguments:
+      json_string: JSON string encoding a model configuration.
+      custom_objects: Optional dictionary mapping names
+          (strings) to custom classes or functions to be
+          considered during deserialization.
+
+  Returns:
+      A Keras model instance (uncompiled).
+  """
+  config = json.loads(json_string)
+  from tensorflow.python.keras._impl.keras.layers import deserialize  # pylint: disable=g-import-not-at-top
+  return deserialize(config, custom_objects=custom_objects)
+
+
+def save_weights_to_hdf5_group(f, layers):
+  from tensorflow.python.keras._impl.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top
+
+  f.attrs['layer_names'] = [layer.name.encode('utf8') for layer in layers]
+  f.attrs['backend'] = K.backend().encode('utf8')
+  f.attrs['keras_version'] = str(keras_version).encode('utf8')
+
+  for layer in layers:
+    g = f.create_group(layer.name)
+    symbolic_weights = layer.weights
+    weight_values = K.batch_get_value(symbolic_weights)
+    weight_names = []
+    for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
+      if hasattr(w, 'name') and w.name:
+        name = str(w.name)
+      else:
+        name = 'param_' + str(i)
+      weight_names.append(name.encode('utf8'))
+    g.attrs['weight_names'] = weight_names
+    for name, val in zip(weight_names, weight_values):
+      param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
+      if not val.shape:
+        # scalar
+        param_dset[()] = val
+      else:
+        param_dset[:] = val
+
+
+def preprocess_weights_for_loading(layer,
+                                   weights,
+                                   original_keras_version=None,
+                                   original_backend=None):
+  """Converts layers weights from Keras 1 format to Keras 2.
+
+  Arguments:
+      layer: Layer instance.
+      weights: List of weights values (Numpy arrays).
+      original_keras_version: Keras version for the weights, as a string.
+      original_backend: Keras backend the weights were trained with,
+          as a string.
+
+  Returns:
+      A list of weights values (Numpy arrays).
+  """
+  if layer.__class__.__name__ == 'Bidirectional':
+    num_weights_per_layer = len(weights) // 2
+    forward_weights = preprocess_weights_for_loading(
+        layer.forward_layer, weights[:num_weights_per_layer],
+        original_keras_version, original_backend)
+    backward_weights = preprocess_weights_for_loading(
+        layer.backward_layer, weights[num_weights_per_layer:],
+        original_keras_version, original_backend)
+    weights = forward_weights + backward_weights
+
+  if original_keras_version == '1':
+    if layer.__class__.__name__ == 'TimeDistributed':
+      weights = preprocess_weights_for_loading(
+          layer.layer, weights, original_keras_version, original_backend)
+
+    if layer.__class__.__name__ == 'Conv1D':
+      shape = weights[0].shape
+      # Handle Keras 1.1 format
+      if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters:
+        # Legacy shape:
+        # (filters, input_dim, filter_length, 1)
+        assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0],
+                                                           1)
+        weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
+      weights[0] = weights[0][:, 0, :, :]
+
+    if layer.__class__.__name__ == 'Conv2D':
+      if layer.data_format == 'channels_first':
+        # old: (filters, stack_size, kernel_rows, kernel_cols)
+        # new: (kernel_rows, kernel_cols, stack_size, filters)
+        weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
+
+    if layer.__class__.__name__ == 'Conv2DTranspose':
+      if layer.data_format == 'channels_last':
+        # old: (kernel_rows, kernel_cols, stack_size, filters)
+        # new: (kernel_rows, kernel_cols, filters, stack_size)
+        weights[0] = np.transpose(weights[0], (0, 1, 3, 2))
+      if layer.data_format == 'channels_first':
+        # old: (filters, stack_size, kernel_rows, kernel_cols)
+        # new: (kernel_rows, kernel_cols, filters, stack_size)
+        weights[0] = np.transpose(weights[0], (2, 3, 0, 1))
+
+    if layer.__class__.__name__ == 'Conv3D':
+      if layer.data_format == 'channels_first':
+        # old: (filters, stack_size, ...)
+        # new: (..., stack_size, filters)
+        weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0))
+
+    if layer.__class__.__name__ == 'GRU':
+      if len(weights) == 9:
+        kernel = np.concatenate([weights[0], weights[3], weights[6]], axis=-1)
+        recurrent_kernel = np.concatenate(
+            [weights[1], weights[4], weights[7]], axis=-1)
+        bias = np.concatenate([weights[2], weights[5], weights[8]], axis=-1)
+        weights = [kernel, recurrent_kernel, bias]
+
+    if layer.__class__.__name__ == 'LSTM':
+      if len(weights) == 12:
+        # old: i, c, f, o
+        # new: i, f, c, o
+        kernel = np.concatenate(
+            [weights[0], weights[6], weights[3], weights[9]], axis=-1)
+        recurrent_kernel = np.concatenate(
+            [weights[1], weights[7], weights[4], weights[10]], axis=-1)
+        bias = np.concatenate(
+            [weights[2], weights[8], weights[5], weights[11]], axis=-1)
+        weights = [kernel, recurrent_kernel, bias]
+
+    if layer.__class__.__name__ == 'ConvLSTM2D':
+      if len(weights) == 12:
+        kernel = np.concatenate(
+            [weights[0], weights[6], weights[3], weights[9]], axis=-1)
+        recurrent_kernel = np.concatenate(
+            [weights[1], weights[7], weights[4], weights[10]], axis=-1)
+        bias = np.concatenate(
+            [weights[2], weights[8], weights[5], weights[11]], axis=-1)
+        if layer.data_format == 'channels_first':
+          # old: (filters, stack_size, kernel_rows, kernel_cols)
+          # new: (kernel_rows, kernel_cols, stack_size, filters)
+          kernel = np.transpose(kernel, (2, 3, 1, 0))
+          recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0))
+        weights = [kernel, recurrent_kernel, bias]
+
+    if layer.__class__.__name__ in ['Model', 'Sequential']:
+      new_weights = []
+      # trainable weights
+      for sublayer in layer.layers:
+        num_weights = len(sublayer.trainable_weights)
+        if num_weights > 0:
+          new_weights.extend(
+              preprocess_weights_for_loading(
+                  layer=sublayer,
+                  weights=weights[:num_weights],
+                  original_keras_version=original_keras_version,
+                  original_backend=original_backend))
+          weights = weights[num_weights:]
+
+      # non-trainable weights
+      for sublayer in layer.layers:
+        num_weights = len([
+            l for l in sublayer.weights if l not in sublayer.trainable_weights
+        ])
+        if num_weights > 0:
+          new_weights.extend(
+              preprocess_weights_for_loading(
+                  layer=sublayer,
+                  weights=weights[:num_weights],
+                  original_keras_version=original_keras_version,
+                  original_backend=original_backend))
+          weights = weights[num_weights:]
+      weights = new_weights
+
+  conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D']
+  if layer.__class__.__name__ in conv_layers:
+    if original_backend == 'theano':
+      weights[0] = conv_utils.convert_kernel(weights[0])
+      if layer.__class__.__name__ == 'ConvLSTM2D':
+        weights[1] = conv_utils.convert_kernel(weights[1])
+    if K.int_shape(layer.weights[0]) != weights[0].shape:
+      weights[0] = np.transpose(weights[0], (3, 2, 0, 1))
+      if layer.__class__.__name__ == 'ConvLSTM2D':
+        weights[1] = np.transpose(weights[1], (3, 2, 0, 1))
+
+  # Convert the weights of CuDNNLSTM so that they could be loaded into LSTM
+  if layer.__class__.__name__ == 'LSTM' and len(weights) == 3:
+    # Determine if loading a CuDNNLSTM layer from the number of bias weights:
+    # CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4)
+    # if there's no bias weight in the file, skip this conversion
+    units = weights[1].shape[0]
+    bias = weights[2]
+    if len(bias) == units * 8:
+      # reshape the kernels
+      kernels = np.split(weights[0], 4, axis=1)
+      kernels = [
+          kernel.reshape(-1).reshape(kernel.shape, order='F')
+          for kernel in kernels
+      ]
+      weights[0] = np.concatenate(kernels, axis=1)
+
+      # transpose the recurrent kernels
+      recurrent_kernels = np.split(weights[1], 4, axis=1)
+      recurrent_kernels = [kernel.T for kernel in recurrent_kernels]
+      weights[1] = np.concatenate(recurrent_kernels, axis=1)
+
+      # split the bias into half and merge
+      weights[2] = bias[:units * 4] + bias[units * 4:]
+
+  return weights
+
+
+def load_weights_from_hdf5_group(f, layers):
+  """Implements topological (order-based) weight loading.
+
+  Arguments:
+      f: A pointer to a HDF5 group.
+      layers: a list of target layers.
+
+  Raises:
+      ValueError: in case of mismatch between provided layers
+          and weights file.
+  """
+  if 'keras_version' in f.attrs:
+    original_keras_version = f.attrs['keras_version'].decode('utf8')
+  else:
+    original_keras_version = '1'
+  if 'backend' in f.attrs:
+    original_backend = f.attrs['backend'].decode('utf8')
+  else:
+    original_backend = None
+
+  filtered_layers = []
+  for layer in layers:
+    weights = layer.weights
+    if weights:
+      filtered_layers.append(layer)
+
+  layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
+  filtered_layer_names = []
+  for name in layer_names:
+    g = f[name]
+    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
+    if weight_names:
+      filtered_layer_names.append(name)
+  layer_names = filtered_layer_names
+  if len(layer_names) != len(filtered_layers):
+    raise ValueError('You are trying to load a weight file '
+                     'containing ' + str(len(layer_names)) +
+                     ' layers into a model with ' + str(len(filtered_layers)) +
+                     ' layers.')
+
+  # We batch weight value assignments in a single backend call
+  # which provides a speedup in TensorFlow.
+  weight_value_tuples = []
+  for k, name in enumerate(layer_names):
+    g = f[name]
+    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
+    weight_values = [g[weight_name] for weight_name in weight_names]
+    layer = filtered_layers[k]
+    symbolic_weights = layer.weights
+    weight_values = preprocess_weights_for_loading(
+        layer, weight_values, original_keras_version, original_backend)
+    if len(weight_values) != len(symbolic_weights):
+      raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
+                       '" in the current model) was found to '
+                       'correspond to layer ' + name + ' in the save file. '
+                       'However the new layer ' + layer.name + ' expects ' +
+                       str(len(symbolic_weights)) +
+                       ' weights, but the saved weights have ' +
+                       str(len(weight_values)) + ' elements.')
+    weight_value_tuples += zip(symbolic_weights, weight_values)
+  K.batch_set_value(weight_value_tuples)
+
+
+def load_weights_from_hdf5_group_by_name(f, layers):
+  """Implements name-based weight loading.
+
+  (instead of topological weight loading).
+
+  Layers that have no matching name are skipped.
+
+  Arguments:
+      f: A pointer to a HDF5 group.
+      layers: a list of target layers.
+
+  Raises:
+      ValueError: in case of mismatch between provided layers
+          and weights file.
+  """
+  if 'keras_version' in f.attrs:
+    original_keras_version = f.attrs['keras_version'].decode('utf8')
+  else:
+    original_keras_version = '1'
+  if 'backend' in f.attrs:
+    original_backend = f.attrs['backend'].decode('utf8')
+  else:
+    original_backend = None
+
+  # New file format.
+  layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
+
+  # Reverse index of layer name to list of layers with name.
+  index = {}
+  for layer in layers:
+    if layer.name:
+      index.setdefault(layer.name, []).append(layer)
+
+  # We batch weight value assignments in a single backend call
+  # which provides a speedup in TensorFlow.
+  weight_value_tuples = []
+  for k, name in enumerate(layer_names):
+    g = f[name]
+    weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
+    weight_values = [g[weight_name] for weight_name in weight_names]
+
+    for layer in index.get(name, []):
+      symbolic_weights = layer.weights
+      weight_values = preprocess_weights_for_loading(
+          layer, weight_values, original_keras_version, original_backend)
+      if len(weight_values) != len(symbolic_weights):
+        raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
+                         '") expects ' + str(len(symbolic_weights)) +
+                         ' weight(s), but the saved weights' + ' have ' +
+                         str(len(weight_values)) + ' element(s).')
+      # Set values.
+      for i in range(len(weight_values)):
+        weight_value_tuples.append((symbolic_weights[i], weight_values[i]))
+  K.batch_set_value(weight_value_tuples)
diff --git a/tensorflow/python/keras/_impl/keras/engine/saving_test.py b/tensorflow/python/keras/_impl/keras/engine/saving_test.py
new file mode 100644
index 0000000000..bdb17641b0
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/saving_test.py
@@ -0,0 +1,375 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#,============================================================================
+"""Tests for model saving."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import shutil
+import tempfile
+
+import numpy as np
+
+from tensorflow.python.keras._impl import keras
+from tensorflow.python.platform import test
+from tensorflow.python.training import training as training_module
+
+try:
+  import h5py  # pylint:disable=g-import-not-at-top
+except ImportError:
+  h5py = None
+
+
+class TestWeightSavingAndLoading(test.TestCase):
+
+  def test_weight_loading(self):
+    with self.test_session():
+      a = keras.layers.Input(shape=(2,))
+      x = keras.layers.Dense(3)(a)
+      b = keras.layers.Dense(1)(x)
+      model = keras.models.Model(a, b)
+
+      x = np.random.random((3, 2))
+      ref_y = model.predict(x)
+      weights = model.get_weights()
+      model.set_weights(weights)
+      y = model.predict(x)
+      self.assertAllClose(ref_y, y)
+
+      with self.assertRaises(ValueError):
+        model.set_weights(weights[1:])
+      with self.assertRaises(ValueError):
+        model.set_weights(weights[::-1])
+
+      if h5py is None:
+        return  # Skip rest of test if H5py isn't available.
+
+      temp_dir = self.get_temp_dir()
+      self.addCleanup(shutil.rmtree, temp_dir)
+
+      h5_path = os.path.join(temp_dir, 'test.h5')
+      model.save_weights(h5_path)
+      model.load_weights(h5_path)
+      y = model.predict(x)
+      self.assertAllClose(ref_y, y)
+
+      model.load_weights(h5_path, by_name=True)
+      y = model.predict(x)
+      self.assertAllClose(ref_y, y)
+
+  def test_weight_preprocessing(self):
+    input_dim = 3
+    output_dim = 3
+    size = 2
+    cases = [
+        [
+            (keras.layers.Bidirectional(keras.layers.SimpleRNN(2))),
+            [np.random.random((2, 1)), np.random.random((2, 1))],
+            (None, 3, 2),
+        ],
+        [
+            (keras.layers.TimeDistributed(keras.layers.Dense(1))),
+            [np.random.random((2, 1)), np.random.random((1,))],
+            (None, 3, 2),
+        ],
+        [
+            (keras.layers.Conv1D(output_dim, size, use_bias=False)),
+            [np.random.random((output_dim, input_dim, size, 1))],
+            (None, 4, input_dim),
+        ],
+        [
+            (keras.layers.Conv2D(output_dim, size,
+                                 use_bias=False, data_format='channels_first')),
+            [np.random.random((output_dim, input_dim, size, size))],
+            (None, input_dim, 4, 4),
+        ],
+        [
+            (keras.layers.Conv2DTranspose(output_dim, size,
+                                          use_bias=False,
+                                          data_format='channels_first')),
+            [np.random.random((output_dim, input_dim, size, size))],
+            (None, input_dim, 4, 4),
+        ],
+        [
+            (keras.layers.Conv2DTranspose(output_dim, size,
+                                          use_bias=False,
+                                          data_format='channels_last')),
+            [np.random.random((size, size, input_dim, output_dim))],
+            (None, 4, 4, input_dim),
+        ],
+        [
+            (keras.layers.Conv3D(output_dim, size,
+                                 use_bias=False, data_format='channels_first')),
+            [np.random.random((output_dim, input_dim, size, size, size))],
+            (None, input_dim, 4, 4, 4),
+        ],
+        [
+            (keras.layers.GRU(output_dim)),
+            [np.random.random((input_dim, output_dim)),
+             np.random.random((output_dim, output_dim)),
+             np.random.random((output_dim,)),
+             np.random.random((input_dim, output_dim)),
+             np.random.random((output_dim, output_dim)),
+             np.random.random((output_dim,)),
+             np.random.random((input_dim, output_dim)),
+             np.random.random((output_dim, output_dim)),
+             np.random.random((output_dim,))],
+            (None, 4, input_dim),
+        ],
+        [
+            (keras.layers.LSTM(output_dim)),
+            [np.random.random((input_dim, output_dim)),
+             np.random.random((output_dim, output_dim)),
+             np.random.random((output_dim,)),
+             np.random.random((input_dim, output_dim)),
+             np.random.random((output_dim, output_dim)),
+             np.random.random((output_dim,)),
+             np.random.random((input_dim, output_dim)),
+             np.random.random((output_dim, output_dim)),
+             np.random.random((output_dim,)),
+             np.random.random((input_dim, output_dim)),
+             np.random.random((output_dim, output_dim)),
+             np.random.random((output_dim,))],
+            (None, 4, input_dim),
+        ],
+    ]
+    for layer, weights, input_shape in cases:
+      layer.build(input_shape)
+      _ = keras.engine.saving.preprocess_weights_for_loading(
+          layer, weights, original_keras_version='1')
+
+    model = keras.models.Sequential([keras.layers.Dense(2, input_dim=2)])
+    _ = keras.engine.saving.preprocess_weights_for_loading(
+        model, model.weights, original_keras_version='1')
+
+    x = keras.Input((2,))
+    y = keras.layers.Dense(2)(x)
+    model = keras.models.Model(x, y)
+    _ = keras.engine.saving.preprocess_weights_for_loading(
+        model, model.weights, original_keras_version='1')
+
+  def test_sequential_weight_loading(self):
+    if h5py is None:
+      return
+
+    temp_dir = self.get_temp_dir()
+    self.addCleanup(shutil.rmtree, temp_dir)
+    h5_path = os.path.join(temp_dir, 'test.h5')
+
+    num_hidden = 5
+    input_dim = 3
+    batch_size = 5
+    num_classes = 2
+
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
+      model.add(keras.layers.Dense(num_classes))
+
+      x = np.random.random((batch_size, input_dim))
+      ref_y = model.predict(x)
+
+      model.save_weights(h5_path)
+
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
+      model.add(keras.layers.Dense(num_classes))
+      model.load_weights(h5_path)
+      y = model.predict(x)
+
+      self.assertAllClose(y, ref_y)
+
+
+class TestWholeModelSaving(test.TestCase):
+
+  def test_sequential_model_saving(self):
+    if h5py is None:
+      return  # Skip test if models cannot be saved.
+
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_shape=(3,)))
+      model.add(keras.layers.RepeatVector(3))
+      model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
+      model.compile(loss=keras.losses.MSE,
+                    optimizer=keras.optimizers.RMSprop(lr=0.0001),
+                    metrics=[keras.metrics.categorical_accuracy],
+                    sample_weight_mode='temporal')
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3, 3))
+      model.train_on_batch(x, y)
+
+      out = model.predict(x)
+      fd, fname = tempfile.mkstemp('.h5')
+      keras.models.save_model(model, fname)
+
+      new_model = keras.models.load_model(fname)
+      os.close(fd)
+      os.remove(fname)
+
+      out2 = new_model.predict(x)
+      self.assertAllClose(out, out2, atol=1e-05)
+
+      # test that new updates are the same with both models
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3, 3))
+      model.train_on_batch(x, y)
+      new_model.train_on_batch(x, y)
+      out = model.predict(x)
+      out2 = new_model.predict(x)
+      self.assertAllClose(out, out2, atol=1e-05)
+
+  def test_sequential_model_saving_2(self):
+    if h5py is None:
+      return  # Skip test if models cannot be saved.
+
+    with self.test_session():
+      # test with custom optimizer, loss
+
+      class CustomOp(keras.optimizers.RMSprop):
+        pass
+
+      def custom_loss(y_true, y_pred):
+        return keras.losses.mse(y_true, y_pred)
+
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_shape=(3,)))
+      model.add(keras.layers.Dense(3))
+      model.compile(loss=custom_loss, optimizer=CustomOp(), metrics=['acc'])
+
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3))
+      model.train_on_batch(x, y)
+
+      out = model.predict(x)
+      fd, fname = tempfile.mkstemp('.h5')
+      keras.models.save_model(model, fname)
+
+      model = keras.models.load_model(
+          fname,
+          custom_objects={'CustomOp': CustomOp,
+                          'custom_loss': custom_loss})
+      os.close(fd)
+      os.remove(fname)
+
+      out2 = model.predict(x)
+      self.assertAllClose(out, out2, atol=1e-05)
+
+  def test_functional_model_saving(self):
+    if h5py is None:
+      return  # Skip test if models cannot be saved.
+
+    with self.test_session():
+      inputs = keras.layers.Input(shape=(3,))
+      x = keras.layers.Dense(2)(inputs)
+      output = keras.layers.Dense(3)(x)
+
+      model = keras.models.Model(inputs, output)
+      model.compile(loss=keras.losses.MSE,
+                    optimizer=keras.optimizers.RMSprop(lr=0.0001),
+                    metrics=[keras.metrics.categorical_accuracy])
+      x = np.random.random((1, 3))
+      y = np.random.random((1, 3))
+      model.train_on_batch(x, y)
+
+      out = model.predict(x)
+      fd, fname = tempfile.mkstemp('.h5')
+      keras.models.save_model(model, fname)
+
+      model = keras.models.load_model(fname)
+      os.close(fd)
+      os.remove(fname)
+
+      out2 = model.predict(x)
+      self.assertAllClose(out, out2, atol=1e-05)
+
+  def test_saving_without_compilation(self):
+    if h5py is None:
+      return  # Skip test if models cannot be saved.
+
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_shape=(3,)))
+      model.add(keras.layers.Dense(3))
+      model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
+
+      fd, fname = tempfile.mkstemp('.h5')
+      keras.models.save_model(model, fname)
+      model = keras.models.load_model(fname)
+      os.close(fd)
+      os.remove(fname)
+
+  def test_saving_with_tf_optimizer(self):
+    if h5py is None:
+      return  # Skip test if models cannot be saved.
+
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_shape=(3,)))
+      model.add(keras.layers.Dense(3))
+      model.compile(loss='mse',
+                    optimizer=training_module.AdadeltaOptimizer(0.1),
+                    metrics=['acc'])
+
+      fd, fname = tempfile.mkstemp('.h5')
+      keras.models.save_model(model, fname)
+      model = keras.models.load_model(fname)
+      os.close(fd)
+      os.remove(fname)
+
+  def test_saving_right_after_compilation(self):
+    if h5py is None:
+      return  # Skip test if models cannot be saved.
+
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(2, input_shape=(3,)))
+      model.add(keras.layers.Dense(3))
+      model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
+      model.model._make_train_function()
+
+      fd, fname = tempfile.mkstemp('.h5')
+      keras.models.save_model(model, fname)
+      model = keras.models.load_model(fname)
+      os.close(fd)
+      os.remove(fname)
+
+  def test_saving_lambda_numpy_array_arguments(self):
+    if h5py is None:
+      return  # Skip test if models cannot be saved.
+
+    mean = np.random.random((4, 2, 3))
+    std = np.abs(np.random.random((4, 2, 3))) + 1e-5
+    inputs = keras.layers.Input(shape=(4, 2, 3))
+    output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std,
+                                 arguments={'mu': mean, 'std': std})(inputs)
+    model = keras.models.Model(inputs, output)
+    model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
+
+    fd, fname = tempfile.mkstemp('.h5')
+    keras.models.save_model(model, fname)
+
+    model = keras.models.load_model(fname)
+    os.close(fd)
+    os.remove(fname)
+
+    self.assertAllClose(mean, model.layers[1].arguments['mu'])
+    self.assertAllClose(std, model.layers[1].arguments['std'])
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/python/keras/_impl/keras/engine/sequential.py b/tensorflow/python/keras/_impl/keras/engine/sequential.py
new file mode 100644
index 0000000000..db5e7754bc
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/sequential.py
@@ -0,0 +1,997 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=protected-access
+"""Home of the `Sequential` model.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import os
+
+from tensorflow.python.framework import ops
+from tensorflow.python.keras._impl.keras import backend as K
+from tensorflow.python.keras._impl.keras import layers as layer_module
+from tensorflow.python.keras._impl.keras.engine import base_layer
+from tensorflow.python.keras._impl.keras.engine import network
+from tensorflow.python.keras._impl.keras.engine import saving
+from tensorflow.python.keras._impl.keras.engine.input_layer import Input
+from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer
+from tensorflow.python.keras._impl.keras.engine.training import Model
+from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util.tf_export import tf_export
+
+try:
+  import h5py  # pylint: disable=g-import-not-at-top
+except ImportError:
+  h5py = None
+
+
+@tf_export('keras.models.Sequential', 'keras.Sequential')
+class Sequential(Model):
+  """Linear stack of layers.
+
+  Arguments:
+      layers: list of layers to add to the model.
+
+  # Note
+      The first layer passed to a Sequential model
+      should have a defined input shape. What that
+      means is that it should have received an `input_shape`
+      or `batch_input_shape` argument,
+      or for some type of layers (recurrent, Dense...)
+      an `input_dim` argument.
+
+  Example:
+
+      ```python
+          model = Sequential()
+          # first layer must have a defined input shape
+          model.add(Dense(32, input_dim=500))
+          # afterwards, Keras does automatic shape inference
+          model.add(Dense(32))
+
+          # also possible (equivalent to the above):
+          model = Sequential()
+          model.add(Dense(32, input_shape=(500,)))
+          model.add(Dense(32))
+
+          # also possible (equivalent to the above):
+          model = Sequential()
+          # here the batch dimension is None,
+          # which means any batch size will be accepted by the model.
+          model.add(Dense(32, batch_input_shape=(None, 500)))
+          model.add(Dense(32))
+      ```
+  """
+
+  def __init__(self, layers=None, name=None):
+    self._is_graph_network = True
+    self._is_compiled = False
+    self._layers = []  # Stack of layers.
+    self.model = None  # Internal Model instance.
+    self.inputs = []  # List of input tensors
+    self.outputs = []  # List of length 1: the output tensor (unique).
+    self._trainable = True
+    self._initial_weights = None
+    self._input_layers = []
+
+    # Model attributes.
+    self._inbound_nodes = []
+    self._outbound_nodes = []
+    self.built = False
+
+    # Set model name.
+    if not name:
+      prefix = 'sequential_'
+      name = prefix + str(K.get_uid(prefix))
+    self._name = name
+
+    # Used by Layer base class.
+    self._dtype = None
+    self._activity_regularizer = None
+
+    # The following properties are not actually used by Keras;
+    # they exist for compatibility with TF's variable scoping mechanism.
+    self._updates = []
+    self._losses = []
+    self._scope = None
+    self._reuse = None
+    self._base_name = name
+    self._graph = ops.get_default_graph()
+
+    # Add to the model any layers passed to the constructor.
+    if layers:
+      for layer in layers:
+        self.add(layer)
+
+  def add(self, layer):
+    """Adds a layer instance on top of the layer stack.
+
+    Arguments:
+        layer: layer instance.
+
+    Raises:
+        TypeError: If `layer` is not a layer instance.
+        ValueError: In case the `layer` argument does not
+            know its input shape.
+        ValueError: In case the `layer` argument has
+            multiple output tensors, or is already connected
+            somewhere else (forbidden in `Sequential` models).
+    """
+    if not isinstance(layer, (base_layer.Layer, base_layer.TFBaseLayer)):
+      raise TypeError('The added layer must be '
+                      'an instance of class Layer. '
+                      'Found: ' + str(layer))
+    if not self.outputs:
+      # First layer in model: check that it is an input layer.
+      if not isinstance(layer, InputLayer):
+        # Create an input layer.
+        # First, we need to infer its expected input shape and dtype.
+        if isinstance(layer, (Model, Sequential)):
+          # We were passed a model as first layer.
+          # This requires a specific way to figure out the
+          # input shape and dtype.
+          if not layer.layers:
+            raise ValueError('Cannot add an empty model '
+                             'to a `Sequential` model.')
+          # In case of nested models: recover the first layer
+          # of the deepest model to infer input shape and dtype.
+          first_layer = layer.layers[0]
+          while isinstance(first_layer, (Model, Sequential)):
+            first_layer = first_layer.layers[0]
+          batch_shape = first_layer._batch_input_shape
+          dtype = first_layer.dtype
+        else:
+          # We were passed a regular layer, and it should
+          # know about its input shape. Otherwise, that's an error.
+          if not hasattr(layer, '_batch_input_shape'):
+            raise ValueError('The first layer in a '
+                             'Sequential model must '
+                             'get an `input_shape` argument.')
+          batch_shape = layer._batch_input_shape
+          dtype = layer.dtype
+        # Instantiate the input layer.
+        x = Input(
+            batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
+        # This will build the current layer
+        # and create the node connecting the current layer
+        # to the input layer we just created.
+        layer(x)
+
+      if len(layer._inbound_nodes[-1].output_tensors) != 1:
+        raise ValueError('All layers in a Sequential model '
+                         'should have a single output tensor. '
+                         'For multi-output layers, '
+                         'use the functional API.')
+
+      self.outputs = [layer._inbound_nodes[-1].output_tensors[0]]
+      self.inputs = network.get_source_inputs(self.outputs[0])
+
+      # We create an input node, which we will keep updated
+      # as we add more layers
+      base_layer.Node(
+          outbound_layer=self,
+          inbound_layers=[],
+          node_indices=[],
+          tensor_indices=[],
+          input_tensors=self.inputs,
+          output_tensors=self.outputs)
+    else:
+      output_tensor = layer(self.outputs[0])
+      if isinstance(output_tensor, list):
+        raise TypeError('All layers in a Sequential model '
+                        'should have a single output tensor. '
+                        'For multi-output layers, '
+                        'use the functional API.')
+      self.outputs = [output_tensor]
+      # update self._inbound_nodes
+      self._inbound_nodes[0].output_tensors = self.outputs
+      self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])]
+
+    self._layers.append(layer)
+    self.built = False
+
+  def pop(self):
+    """Removes the last layer in the model.
+
+    Raises:
+        TypeError: if there are no layers in the model.
+    """
+    if not self.layers:
+      raise TypeError('There are no layers in the model.')
+
+    self.layers.pop()
+    if not self.layers:
+      self.outputs = []
+      self._inbound_nodes = []
+      self._outbound_nodes = []
+    else:
+      self.layers[-1]._outbound_nodes = []
+      self.outputs = [self.layers[-1].output]
+      # update self._inbound_nodes
+      self._inbound_nodes[0].output_tensors = self.outputs
+      self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])]
+    self.built = False
+
+  def get_layer(self, name=None, index=None):
+    """Retrieve a layer that is part of the model.
+
+    Returns a layer based on either its name (unique)
+    or its index in the graph. Indices are based on
+    order of horizontal graph traversal (bottom-up).
+
+    Arguments:
+        name: string, name of layer.
+        index: integer, index of layer.
+
+    Returns:
+        A layer instance.
+    """
+    if not self.built:
+      self.build()
+    return self.model.get_layer(name, index)
+
+  def call(self, inputs, **kwargs):
+    if not self.built:
+      self.build()
+    return self.model.call(inputs, **kwargs)
+
+  def build(self, input_shape=None):
+    if not self.inputs or not self.outputs:
+      raise TypeError('Sequential model cannot be built: model is empty.'
+                      ' Add some layers first.')
+    # actually create the model
+    self.model = Model(self.inputs, self.outputs[0], name=self.name + '_model')
+    self.model.trainable = self.trainable
+
+    # mirror model attributes
+    self.supports_masking = self.model.supports_masking
+    self._output_mask_cache = self.model._output_mask_cache
+    self._output_tensor_cache = self.model._output_tensor_cache
+    self._output_shape_cache = self.model._output_shape_cache
+    self._input_layers = self.model._input_layers
+    self._output_layers = self.model._output_layers
+    self._input_coordinates = self.model._input_coordinates
+    self._output_coordinates = self.model._output_coordinates
+    self._nodes_by_depth = self.model._nodes_by_depth
+    self._network_nodes = self.model._network_nodes
+    self.output_names = self.model.output_names
+    self.input_names = self.model.input_names
+    self._feed_input_names = self.model._feed_input_names
+    self._feed_inputs = self.model._feed_inputs
+
+    # Make sure child model callbacks
+    # will call the parent Sequential model.
+    self.model.callback_model = self
+
+    self.built = True
+
+  @property
+  def uses_learning_phase(self):
+    if not self.built:
+      self.build()
+    return self.model.uses_learning_phase
+
+  def _gather_list_attr(self, attr):
+    all_attrs = []
+    for layer in self.layers:
+      all_attrs += getattr(layer, attr, [])
+    return all_attrs
+
+  def _make_train_function(self):
+    self.model._make_train_function()
+
+  def _make_test_function(self):
+    self.model._make_test_function()
+
+  def _make_predict_function(self):
+    self.model._make_predict_function()
+
+  @property
+  def trainable(self):
+    return self._trainable
+
+  @trainable.setter
+  def trainable(self, value):
+    if self.model:
+      self.model.trainable = value
+    self._trainable = value
+
+  @property
+  def trainable_weights(self):
+    if not self.trainable:
+      return []
+    return self._gather_list_attr('trainable_weights')
+
+  @property
+  def non_trainable_weights(self):
+    weights = self._gather_list_attr('non_trainable_weights')
+    if not self.trainable:
+      trainable_weights = self._gather_list_attr('trainable_weights')
+      return trainable_weights + weights
+    return weights
+
+  @property
+  def regularizers(self):
+    if not self.built:
+      self.build()
+    return self.model.regularizers
+
+  def get_weights(self):
+    """Retrieves the weights of the model.
+
+    Returns:
+        A flat list of Numpy arrays
+        (one array per model weight).
+    """
+    if not self.built:
+      self.build()
+    return self.model.get_weights()
+
+  def set_weights(self, weights):
+    """Sets the weights of the model.
+
+    Arguments:
+        weights: Should be a list
+            of Numpy arrays with shapes and types matching
+            the output of `model.get_weights()`.
+    """
+    if not self.built:
+      self.build()
+    self.model.set_weights(weights)
+
+  def load_weights(self, filepath, by_name=False):
+    if h5py is None:
+      raise ImportError('`load_weights` requires h5py.')
+    f = h5py.File(filepath, mode='r')
+    if 'layer_names' not in f.attrs and 'model_weights' in f:
+      f = f['model_weights']
+    layers = self.layers
+    if by_name:
+      saving.load_weights_from_hdf5_group_by_name(f, layers)
+    else:
+      saving.load_weights_from_hdf5_group(f, layers)
+    if hasattr(f, 'close'):
+      f.close()
+
+  def save_weights(self, filepath, overwrite=True):
+    if h5py is None:
+      raise ImportError('`save_weights` requires h5py.')
+    # If file exists and should not be overwritten:
+    if not overwrite and os.path.isfile(filepath):
+      proceed = ask_to_proceed_with_overwrite(filepath)
+      if not proceed:
+        return
+    layers = self.layers
+    f = h5py.File(filepath, 'w')
+    saving.save_weights_to_hdf5_group(f, layers)
+    f.flush()
+    f.close()
+
+  def compile(self,
+              optimizer,
+              loss,
+              metrics=None,
+              sample_weight_mode=None,
+              weighted_metrics=None,
+              target_tensors=None,
+              **kwargs):
+    """Configures the model for training.
+
+    Arguments:
+        optimizer: String (name of optimizer) or optimizer object.
+            See [optimizers](/optimizers).
+        loss: String (name of objective function) or objective function.
+            See [losses](/losses).
+            If the model has multiple outputs, you can use a different loss
+            on each output by passing a dictionary or a list of losses.
+            The loss value that will be minimized by the model
+            will then be the sum of all individual losses.
+        metrics: List of metrics to be evaluated by the model
+            during training and testing.
+            Typically you will use `metrics=['accuracy']`.
+            To specify different metrics for different outputs of a
+            multi-output model, you could also pass a dictionary,
+            such as `metrics={'output_a': 'accuracy'}`.
+        sample_weight_mode: If you need to do timestep-wise
+            sample weighting (2D weights), set this to `"temporal"`.
+            `None` defaults to sample-wise weights (1D).
+            If the model has multiple outputs, you can use a different
+            `sample_weight_mode` on each output by passing a
+            dictionary or a list of modes.
+        weighted_metrics: list of metrics to be evaluated and weighted
+             by `sample_weight` or `class_weight` during training and testing.
+        target_tensors: By default, Keras will create a placeholder for the
+            model's target, which will be fed with the target data during
+            training. If instead you would like to use your own
+            target tensor (in turn, Keras will not expect external
+            Numpy data for these targets at training time), you
+            can specify them via the `target_tensors` argument.
+            It should be a single tensor
+            (for a single-output `Sequential` model).
+        **kwargs: These arguments are passed into `tf.Session.run`.
+
+    Example:
+        ```python
+            model = Sequential()
+            model.add(Dense(32, input_shape=(500,)))
+            model.add(Dense(10, activation='softmax'))
+            model.compile(optimizer='rmsprop',
+                          loss='categorical_crossentropy',
+                          metrics=['accuracy'])
+        ```
+    """
+    # create the underlying model
+    self.build()
+    # call compile method of Model class
+    self.model.compile(
+        optimizer,
+        loss,
+        metrics=metrics,
+        sample_weight_mode=sample_weight_mode,
+        weighted_metrics=weighted_metrics,
+        target_tensors=target_tensors,
+        **kwargs)
+    self.optimizer = self.model.optimizer
+    self.loss = self.model.loss
+    self.metrics = self.model.metrics
+    self.loss_weights = self.model.loss_weights
+    self.sample_weight_mode = self.model.sample_weight_mode
+    self.weighted_metrics = self.model.weighted_metrics
+    self.targets = self.model.targets
+    self.metrics_tensors = self.model.metrics_tensors
+    self.metrics_names = self.model.metrics_names
+    self.sample_weights = self.model.sample_weights
+    self.total_loss = self.model.total_loss
+
+  def fit(self,
+          x=None,
+          y=None,
+          batch_size=None,
+          epochs=1,
+          verbose=1,
+          callbacks=None,
+          validation_split=0.,
+          validation_data=None,
+          shuffle=True,
+          class_weight=None,
+          sample_weight=None,
+          initial_epoch=0,
+          steps_per_epoch=None,
+          validation_steps=None,
+          **kwargs):
+    """Trains the model for a fixed number of epochs.
+
+    Arguments:
+        x: Numpy array of training data.
+            If the input layer in the model is named, you can also pass a
+            dictionary mapping the input name to a Numpy array.
+            `x` can be `None` (default) if feeding from
+            TensorFlow data tensors.
+        y: Numpy array of target (label) data.
+            If the output layer in the model is named, you can also pass a
+            dictionary mapping the output name to a Numpy array.
+            `y` can be `None` (default) if feeding from
+            TensorFlow data tensors.
+        batch_size: Integer or `None`.
+            Number of samples per gradient update.
+            If unspecified, it will default to 32.
+        epochs: Integer. Number of epochs to train the model.
+            An epoch is an iteration over the entire `x` and `y`
+            data provided.
+            Note that in conjunction with `initial_epoch`,
+            `epochs` is to be understood as "final epoch".
+            The model is not trained for a number of iterations
+            given by `epochs`, but merely until the epoch
+            of index `epochs` is reached.
+        verbose: 0, 1, or 2. Verbosity mode.
+            0 = silent, 1 = progress bar, 2 = one line per epoch.
+        callbacks: List of `keras.callbacks.Callback` instances.
+            List of callbacks to apply during training.
+            See [callbacks](/callbacks).
+        validation_split: Float between 0 and 1:
+            Fraction of the training data to be used as validation data.
+            The model will set apart this fraction of the training data,
+            will not train on it, and will evaluate
+            the loss and any model metrics
+            on this data at the end of each epoch.
+            The validation data is selected from the last samples
+            in the `x` and `y` data provided, before shuffling.
+        validation_data: tuple `(x_val, y_val)` or tuple
+            `(x_val, y_val, val_sample_weights)` on which to evaluate
+            the loss and any model metrics at the end of each epoch.
+            The model will not be trained on this data.
+            This will override `validation_split`.
+        shuffle: Boolean (whether to shuffle the training data
+            before each epoch) or str (for 'batch').
+            'batch' is a special option for dealing with the
+            limitations of HDF5 data; it shuffles in batch-sized chunks.
+            Has no effect when `steps_per_epoch` is not `None`.
+        class_weight: Optional dictionary mapping class indices (integers)
+            to a weight (float) value, used for weighting the loss function
+            (during training only).
+            This can be useful to tell the model to
+            "pay more attention" to samples from
+            an under-represented class.
+        sample_weight: Optional Numpy array of weights for
+            the training samples, used for weighting the loss function
+            (during training only). You can either pass a flat (1D)
+            Numpy array with the same length as the input samples
+            (1:1 mapping between weights and samples),
+            or in the case of temporal data,
+            you can pass a 2D array with shape
+            `(samples, sequence_length)`,
+            to apply a different weight to every timestep of every sample.
+            In this case you should make sure to specify
+            `sample_weight_mode="temporal"` in `compile()`.
+        initial_epoch: Epoch at which to start training
+            (useful for resuming a previous training run).
+        steps_per_epoch: Total number of steps (batches of samples)
+            before declaring one epoch finished and starting the
+            next epoch. When training with input tensors such as
+            TensorFlow data tensors, the default `None` is equal to
+            the number of unique samples in your dataset divided by
+            the batch size, or 1 if that cannot be determined.
+        validation_steps: Only relevant if `steps_per_epoch`
+            is specified. Total number of steps (batches of samples)
+            to validate before stopping.
+        **kwargs: Used for backwards compatibility support.
+
+    Returns:
+        A `History` object. Its `History.history` attribute is
+        a record of training loss values and metrics values
+        at successive epochs, as well as validation loss values
+        and validation metrics values (if applicable).
+
+    Raises:
+        RuntimeError: If the model was never compiled.
+        ValueError: In case of mismatch between the provided input data
+            and what the model expects.
+    """
+    if not self.built:
+      raise RuntimeError('The model needs to be compiled before being used.')
+    return self.model.fit(
+        x,
+        y,
+        batch_size=batch_size,
+        epochs=epochs,
+        verbose=verbose,
+        callbacks=callbacks,
+        validation_split=validation_split,
+        validation_data=validation_data,
+        shuffle=shuffle,
+        class_weight=class_weight,
+        sample_weight=sample_weight,
+        initial_epoch=initial_epoch,
+        steps_per_epoch=steps_per_epoch,
+        validation_steps=validation_steps)
+
+  def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
+    """Computes the loss on some input data, batch by batch.
+
+    Arguments:
+        x: input data, as a Numpy array or list of Numpy arrays
+            (if the model has multiple inputs).
+        y: labels, as a Numpy array.
+        batch_size: integer. Number of samples per gradient update.
+        verbose: verbosity mode, 0 or 1.
+        sample_weight: sample weights, as a Numpy array.
+
+    Returns:
+        Scalar test loss (if the model has no metrics)
+        or list of scalars (if the model computes other metrics).
+        The attribute `model.metrics_names` will give you
+        the display labels for the scalar outputs.
+
+    Raises:
+        RuntimeError: if the model was never compiled.
+    """
+    if not self.built:
+      raise RuntimeError('The model needs to be compiled before being used.')
+    return self.model.evaluate(
+        x,
+        y,
+        batch_size=batch_size,
+        verbose=verbose,
+        sample_weight=sample_weight)
+
+  def predict(self, x, batch_size=32, verbose=0):
+    """Generates output predictions for the input samples.
+
+    The input samples are processed batch by batch.
+
+    Arguments:
+        x: the input data, as a Numpy array.
+        batch_size: integer.
+        verbose: verbosity mode, 0 or 1.
+
+    Returns:
+        A Numpy array of predictions.
+    """
+    if not self.built:
+      self.build()
+    return self.model.predict(x, batch_size=batch_size, verbose=verbose)
+
+  def predict_on_batch(self, x):
+    """Returns predictions for a single batch of samples.
+
+    Arguments:
+        x: input data, as a Numpy array or list of Numpy arrays
+            (if the model has multiple inputs).
+
+    Returns:
+        A Numpy array of predictions.
+    """
+    if not self.built:
+      self.build()
+    return self.model.predict_on_batch(x)
+
+  def train_on_batch(self, x, y, class_weight=None, sample_weight=None):
+    """Single gradient update over one batch of samples.
+
+    Arguments:
+        x: input data, as a Numpy array or list of Numpy arrays
+            (if the model has multiple inputs).
+        y: labels, as a Numpy array.
+        class_weight: dictionary mapping classes to a weight value,
+            used for scaling the loss function (during training only).
+        sample_weight: sample weights, as a Numpy array.
+
+    Returns:
+        Scalar training loss (if the model has no metrics)
+        or list of scalars (if the model computes other metrics).
+        The attribute `model.metrics_names` will give you
+        the display labels for the scalar outputs.
+
+    Raises:
+        RuntimeError: if the model was never compiled.
+    """
+    if not self.built:
+      raise RuntimeError('The model needs to be compiled before being used.')
+    return self.model.train_on_batch(
+        x, y, sample_weight=sample_weight, class_weight=class_weight)
+
+  def test_on_batch(self, x, y, sample_weight=None):
+    """Evaluates the model over a single batch of samples.
+
+    Arguments:
+        x: input data, as a Numpy array or list of Numpy arrays
+            (if the model has multiple inputs).
+        y: labels, as a Numpy array.
+        sample_weight: sample weights, as a Numpy array.
+
+    Returns:
+        Scalar test loss (if the model has no metrics)
+        or list of scalars (if the model computes other metrics).
+        The attribute `model.metrics_names` will give you
+        the display labels for the scalar outputs.
+
+    Raises:
+        RuntimeError: if the model was never compiled.
+    """
+    if not self.built:
+      raise RuntimeError('The model needs to be compiled before being used.')
+    return self.model.test_on_batch(x, y, sample_weight=sample_weight)
+
+  def predict_proba(self, x, batch_size=32, verbose=0):
+    """Generates class probability predictions for the input samples.
+
+    The input samples are processed batch by batch.
+
+    Arguments:
+        x: input data, as a Numpy array or list of Numpy arrays
+            (if the model has multiple inputs).
+        batch_size: integer.
+        verbose: verbosity mode, 0 or 1.
+
+    Returns:
+        A Numpy array of probability predictions.
+    """
+    preds = self.predict(x, batch_size, verbose)
+    if preds.min() < 0. or preds.max() > 1.:
+      logging.warning('Network returning invalid probability values. '
+                      'The last layer might not normalize predictions '
+                      'into probabilities '
+                      '(like softmax or sigmoid would).')
+    return preds
+
+  def predict_classes(self, x, batch_size=32, verbose=0):
+    """Generate class predictions for the input samples.
+
+    The input samples are processed batch by batch.
+
+    Arguments:
+        x: input data, as a Numpy array or list of Numpy arrays
+            (if the model has multiple inputs).
+        batch_size: integer.
+        verbose: verbosity mode, 0 or 1.
+
+    Returns:
+        A numpy array of class predictions.
+    """
+    proba = self.predict(x, batch_size=batch_size, verbose=verbose)
+    if proba.shape[-1] > 1:
+      return proba.argmax(axis=-1)
+    else:
+      return (proba > 0.5).astype('int32')
+
+  def fit_generator(self,
+                    generator,
+                    steps_per_epoch=None,
+                    epochs=1,
+                    verbose=1,
+                    callbacks=None,
+                    validation_data=None,
+                    validation_steps=None,
+                    class_weight=None,
+                    max_queue_size=10,
+                    workers=1,
+                    use_multiprocessing=False,
+                    shuffle=True,
+                    initial_epoch=0,
+                    **kwargs):
+    """Fits the model on data generated batch-by-batch by a Python generator.
+
+    The generator is run in parallel to the model, for efficiency.
+    For instance, this allows you to do real-time data augmentation
+    on images on CPU in parallel to training your model on GPU.
+
+    Arguments:
+        generator: A generator.
+            The output of the generator must be either
+            - a tuple (inputs, targets)
+            - a tuple (inputs, targets, sample_weights).
+            All arrays should contain the same number of samples.
+            The generator is expected to loop over its data
+            indefinitely. An epoch finishes when `steps_per_epoch`
+            batches have been seen by the model.
+        steps_per_epoch: Total number of steps (batches of samples)
+            to yield from `generator` before declaring one epoch
+            finished and starting the next epoch. It should typically
+            be equal to the number of samples of your dataset
+            divided by the batch size.
+            Optional for `Sequence`: if unspecified, will use
+            the `len(generator)` as a number of steps.
+        epochs: Integer, total number of iterations on the data.
+            Note that in conjunction with initial_epoch, the parameter
+            epochs is to be understood as "final epoch". The model is
+            not trained for n steps given by epochs, but until the
+            epoch epochs is reached.
+        verbose: Verbosity mode, 0, 1, or 2.
+        callbacks: List of callbacks to be called during training.
+        validation_data: This can be either
+            - A generator for the validation data
+            - A tuple (inputs, targets)
+            - A tuple (inputs, targets, sample_weights).
+        validation_steps: Only relevant if `validation_data`
+            is a generator.
+            Number of steps to yield from validation generator
+            at the end of every epoch. It should typically
+            be equal to the number of samples of your
+            validation dataset divided by the batch size.
+            Optional for `Sequence`: if unspecified, will use
+            the `len(validation_data)` as a number of steps.
+        class_weight: Dictionary mapping class indices to a weight
+            for the class.
+        max_queue_size: Maximum size for the generator queue
+        workers: Maximum number of processes to spin up
+        use_multiprocessing: If True, use process based threading.
+            Note that because
+            this implementation relies on multiprocessing,
+            you should not pass
+            non picklable arguments to the generator
+            as they can't be passed
+            easily to children processes.
+       shuffle: Whether to shuffle the order of the batches at
+              the beginning of each epoch. Only used with instances
+              of `Sequence` (keras.utils.Sequence).
+        initial_epoch: Epoch at which to start training
+            (useful for resuming a previous training run)
+        **kwargs: support for legacy arguments.
+
+    Returns:
+        A `History` object.
+
+    Raises:
+        RuntimeError: if the model was never compiled.
+        ValueError: In case the generator yields
+            data in an invalid format.
+
+    Example:
+
+    ```python
+        def generate_arrays_from_file(path):
+            while 1:
+                f = open(path)
+                for line in f:
+                    # create Numpy arrays of input data
+                    # and labels, from each line in the file
+                    x, y = process_line(line)
+                    yield (x, y)
+                    f.close()
+
+        model.fit_generator(generate_arrays_from_file('/my_file.txt'),
+                            steps_per_epoch=1000, epochs=10)
+    ```
+    """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
+    if not self.built:
+      raise RuntimeError('The model needs to be compiled before being used.')
+    return self.model.fit_generator(
+        generator,
+        steps_per_epoch,
+        epochs,
+        verbose=verbose,
+        callbacks=callbacks,
+        validation_data=validation_data,
+        validation_steps=validation_steps,
+        class_weight=class_weight,
+        max_queue_size=max_queue_size,
+        workers=workers,
+        use_multiprocessing=use_multiprocessing,
+        shuffle=shuffle,
+        initial_epoch=initial_epoch)
+
+  def evaluate_generator(self,
+                         generator,
+                         steps=None,
+                         max_queue_size=10,
+                         workers=1,
+                         use_multiprocessing=False,
+                         **kwargs):
+    """Evaluates the model on a data generator.
+
+    The generator should return the same kind of data
+    as accepted by `test_on_batch`.
+
+    Arguments:
+        generator: Generator yielding tuples (inputs, targets)
+            or (inputs, targets, sample_weights)
+        steps: Total number of steps (batches of samples)
+            to yield from `generator` before stopping.
+            Optional for `Sequence`: if unspecified, will use
+            the `len(generator)` as a number of steps.
+        max_queue_size: maximum size for the generator queue
+        workers: maximum number of processes to spin up
+        use_multiprocessing: if True, use process based threading.
+            Note that because this implementation
+            relies on multiprocessing, you should not pass
+            non picklable arguments to the generator
+            as they can't be passed easily to children processes.
+        **kwargs: support for legacy arguments.
+
+    Returns:
+        Scalar test loss (if the model has no metrics)
+        or list of scalars (if the model computes other metrics).
+        The attribute `model.metrics_names` will give you
+        the display labels for the scalar outputs.
+
+    Raises:
+        RuntimeError: if the model was never compiled.
+        ValueError: In case the generator yields
+            data in an invalid format.
+    """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
+    if not self.built:
+      raise RuntimeError('The model needs to be compiled before being used.')
+    return self.model.evaluate_generator(
+        generator,
+        steps,
+        max_queue_size=max_queue_size,
+        workers=workers,
+        use_multiprocessing=use_multiprocessing)
+
+  def predict_generator(self,
+                        generator,
+                        steps=None,
+                        max_queue_size=10,
+                        workers=1,
+                        use_multiprocessing=False,
+                        verbose=0,
+                        **kwargs):
+    """Generates predictions for the input samples from a data generator.
+
+    The generator should return the same kind of data as accepted by
+    `predict_on_batch`.
+
+    Arguments:
+        generator: generator yielding batches of input samples.
+        steps: Total number of steps (batches of samples)
+            to yield from `generator` before stopping.
+            Optional for `Sequence`: if unspecified, will use
+            the `len(generator)` as a number of steps.
+        max_queue_size: maximum size for the generator queue
+        workers: maximum number of processes to spin up
+        use_multiprocessing: if True, use process based threading.
+            Note that because this implementation
+            relies on multiprocessing, you should not pass
+            non picklable arguments to the generator
+            as they can't be passed easily to children processes.
+        verbose: verbosity mode, 0 or 1.
+        **kwargs: support for legacy arguments.
+
+    Returns:
+        A Numpy array of predictions.
+
+    Raises:
+        ValueError: In case the generator yields
+            data in an invalid format.
+    """
+    # Legacy support
+    if 'max_q_size' in kwargs:
+      max_queue_size = kwargs.pop('max_q_size')
+      logging.warning('The argument `max_q_size` has been renamed '
+                      '`max_queue_size`. Update your method calls accordingly.')
+    if 'pickle_safe' in kwargs:
+      use_multiprocessing = kwargs.pop('pickle_safe')
+      logging.warning('The argument `pickle_safe` has been renamed '
+                      '`use_multiprocessing`. '
+                      'Update your method calls accordingly.')
+    if kwargs:
+      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
+
+    if not self.built:
+      self.build()
+    return self.model.predict_generator(
+        generator,
+        steps,
+        max_queue_size=max_queue_size,
+        workers=workers,
+        use_multiprocessing=use_multiprocessing,
+        verbose=verbose)
+
+  def get_config(self):
+    config = []
+    for layer in self.layers:
+      config.append({
+          'class_name': layer.__class__.__name__,
+          'config': layer.get_config()
+      })
+    return copy.deepcopy(config)
+
+  @classmethod
+  def from_config(cls, config, custom_objects=None):
+    model = cls()
+    for conf in config:
+      layer = layer_module.deserialize(conf, custom_objects=custom_objects)
+      model.add(layer)
+    return model
diff --git a/tensorflow/python/keras/_impl/keras/engine/sequential_test.py b/tensorflow/python/keras/_impl/keras/engine/sequential_test.py
new file mode 100644
index 0000000000..166634bd82
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/sequential_test.py
@@ -0,0 +1,152 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests specific to `Sequential` model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.keras._impl import keras
+from tensorflow.python.platform import test
+
+
+class TestSequential(test.TestCase):
+  """Most Sequential model API tests are covered in `training_test.py`.
+  """
+
+  def test_basic_methods(self):
+    model = keras.models.Sequential()
+    model.add(keras.layers.Dense(1, input_dim=2))
+    model.add(keras.layers.Dropout(0.3, name='dp'))
+    model.add(keras.layers.Dense(2, kernel_regularizer='l2',
+                                 kernel_constraint='max_norm'))
+    model.build()
+    self.assertEqual(model.state_updates, model.model.state_updates)
+    self.assertEqual(model.get_layer(name='dp').name, 'dp')
+
+  def test_sequential_pop(self):
+    num_hidden = 5
+    input_dim = 3
+    batch_size = 5
+    num_classes = 2
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
+      model.add(keras.layers.Dense(num_classes))
+      model.compile(loss='mse', optimizer='sgd')
+      x = np.random.random((batch_size, input_dim))
+      y = np.random.random((batch_size, num_classes))
+      model.fit(x, y, epochs=1)
+      model.pop()
+      self.assertEqual(len(model.layers), 1)
+      self.assertEqual(model.output_shape, (None, num_hidden))
+      model.compile(loss='mse', optimizer='sgd')
+      y = np.random.random((batch_size, num_hidden))
+      model.fit(x, y, epochs=1)
+
+      # Test popping single-layer model
+      model = keras.models.Sequential()
+      model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
+      model.pop()
+      self.assertEqual(len(model.layers), 0)
+      self.assertEqual(len(model.outputs), 0)
+
+      # Invalid use case
+      model = keras.models.Sequential()
+      with self.assertRaises(TypeError):
+        model.pop()
+
+  def test_invalid_use_cases(self):
+    with self.test_session():
+      # Added objects must be layer instances
+      with self.assertRaises(TypeError):
+        model = keras.models.Sequential()
+        model.add(None)
+
+      # Added layers must have an inputs shape
+      with self.assertRaises(ValueError):
+        model = keras.models.Sequential()
+        model.add(keras.layers.Dense(1))
+
+      # Added layers cannot have multiple outputs
+      class MyLayer(keras.layers.Layer):
+
+        def call(self, inputs):
+          return [3 * inputs, 2 * inputs]
+
+        def compute_output_shape(self, input_shape):
+          return [input_shape, input_shape]
+
+      with self.assertRaises(ValueError):
+        model = keras.models.Sequential()
+        model.add(MyLayer(input_shape=(3,)))
+      with self.assertRaises(TypeError):
+        model = keras.models.Sequential()
+        model.add(keras.layers.Dense(1, input_dim=1))
+        model.add(MyLayer())
+
+      # Building empty model
+      model = keras.models.Sequential()
+      with self.assertRaises(TypeError):
+        model.build()
+
+  def test_nested_sequential_trainability(self):
+    input_dim = 20
+    num_units = 10
+    num_classes = 2
+
+    inner_model = keras.models.Sequential()
+    inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,)))
+
+    model = keras.models.Sequential()
+    model.add(inner_model)
+    model.add(keras.layers.Dense(num_classes))
+
+    self.assertEqual(len(model.trainable_weights), 4)
+    inner_model.trainable = False
+    self.assertEqual(len(model.trainable_weights), 2)
+    inner_model.trainable = True
+    self.assertEqual(len(model.trainable_weights), 4)
+
+  def test_sequential_update_disabling(self):
+    val_a = np.random.random((10, 4))
+    val_out = np.random.random((10, 4))
+
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(keras.layers.BatchNormalization(input_shape=(4,)))
+
+      model.trainable = False
+      assert not model.updates
+
+      model.compile('sgd', 'mse')
+      assert not model.updates
+      assert not model.model.updates
+
+      x1 = model.predict(val_a)
+      model.train_on_batch(val_a, val_out)
+      x2 = model.predict(val_a)
+      self.assertAllClose(x1, x2, atol=1e-7)
+
+      model.trainable = True
+      model.compile('sgd', 'mse')
+      assert model.updates
+      assert model.model.updates
+
+      model.train_on_batch(val_a, val_out)
+      x2 = model.predict(val_a)
+      assert np.abs(np.sum(x1 - x2)) > 1e-5
diff --git a/tensorflow/python/keras/_impl/keras/engine/topology_test.py b/tensorflow/python/keras/_impl/keras/engine/topology_test.py
index 139621db6d..04434323d6 100644
--- a/tensorflow/python/keras/_impl/keras/engine/topology_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/topology_test.py
@@ -18,9 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-import shutil
-
 import numpy as np
 
 from tensorflow.python.eager import context
@@ -28,7 +25,7 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras._impl import keras
-from tensorflow.python.layers import base as base_layers
+from tensorflow.python.layers import base as tf_base_layers
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
@@ -39,11 +36,6 @@ try:
 except ImportError:
   yaml = None
 
-try:
-  import h5py  # pylint:disable=g-import-not-at-top
-except ImportError:
-  h5py = None
-
 
 class TopologyConstructionTest(test.TestCase):
 
@@ -84,7 +76,7 @@ class TopologyConstructionTest(test.TestCase):
     self.assertEqual(len(layer.get_updates_for(x2)), 1)
     self.assertEqual(len(layer.get_updates_for(None)), 1)
 
-    network = keras.engine.topology.Network(x2, y2)
+    network = keras.engine.Network(x2, y2)
     self.assertEqual(len(network.updates), 2)
     self.assertEqual(len(network.get_updates_for(x1)), 0)
     self.assertEqual(len(network.get_updates_for(x2)), 1)
@@ -146,7 +138,7 @@ class TopologyConstructionTest(test.TestCase):
     self.assertEqual(len(layer.get_losses_for(x2)), 1)
     self.assertEqual(len(layer.get_losses_for(None)), 1)
 
-    network = keras.engine.topology.Network(x2, y2)
+    network = keras.engine.Network(x2, y2)
     self.assertEqual(len(network.losses), 2)
     self.assertEqual(len(network.get_losses_for(x1)), 0)
     self.assertEqual(len(network.get_losses_for(x2)), 1)
@@ -267,7 +259,7 @@ class TopologyConstructionTest(test.TestCase):
     x = keras.Input(shape=(32,))
     dense = keras.layers.Dense(2)
     y = dense(x)
-    network = keras.engine.topology.Network(x, y, name='dense_network')
+    network = keras.engine.Network(x, y, name='dense_network')
 
     # test basic attributes
     self.assertEqual(network.name, 'dense_network')
@@ -502,7 +494,7 @@ class TopologyConstructionTest(test.TestCase):
       self.assertListEqual([x.shape for x in fn_outputs], [(10, 64), (10, 5)])
 
       # test get_source_inputs
-      self.assertListEqual(keras.engine.topology.get_source_inputs(c), [a, b])
+      self.assertListEqual(keras.engine.network.get_source_inputs(c), [a, b])
 
       # serialization / deserialization
       json_config = model.to_json()
@@ -762,7 +754,7 @@ class TopologyConstructionTest(test.TestCase):
     if context.in_graph_mode():
       x = keras.Input(shape=(32,))
       y = MaskedLayer()(x)  # pylint: disable=not-callable
-      network = keras.engine.topology.Network(x, y)
+      network = keras.engine.Network(x, y)
 
       # test callability on Input
       x_2 = keras.Input(shape=(32,))
@@ -875,139 +867,12 @@ class TopologyConstructionTest(test.TestCase):
       self.assertEqual(np.min(preds), 0.)  # At least one unit was dropped.
 
 
-class TestSaving(test.TestCase):
-
-  def test_weight_loading(self):
-    with self.test_session():
-      a = keras.layers.Input(shape=(2,))
-      x = keras.layers.Dense(3)(a)
-      b = keras.layers.Dense(1)(x)
-      model = keras.models.Model(a, b)
-
-      x = np.random.random((3, 2))
-      ref_y = model.predict(x)
-      weights = model.get_weights()
-      model.set_weights(weights)
-      y = model.predict(x)
-      self.assertAllClose(ref_y, y)
-
-      with self.assertRaises(ValueError):
-        model.set_weights(weights[1:])
-      with self.assertRaises(ValueError):
-        model.set_weights(weights[::-1])
-
-      if h5py is None:
-        return  # Skip rest of test if H5py isn't available.
-
-      temp_dir = self.get_temp_dir()
-      self.addCleanup(shutil.rmtree, temp_dir)
-
-      h5_path = os.path.join(temp_dir, 'test.h5')
-      model.save_weights(h5_path)
-      model.load_weights(h5_path)
-      y = model.predict(x)
-      self.assertAllClose(ref_y, y)
-
-      model.load_weights(h5_path, by_name=True)
-      y = model.predict(x)
-      self.assertAllClose(ref_y, y)
-
-  def test_weight_preprocessing(self):
-    input_dim = 3
-    output_dim = 3
-    size = 2
-    cases = [
-        [
-            (keras.layers.Bidirectional(keras.layers.SimpleRNN(2))),
-            [np.random.random((2, 1)), np.random.random((2, 1))],
-            (None, 3, 2),
-        ],
-        [
-            (keras.layers.TimeDistributed(keras.layers.Dense(1))),
-            [np.random.random((2, 1)), np.random.random((1,))],
-            (None, 3, 2),
-        ],
-        [
-            (keras.layers.Conv1D(output_dim, size, use_bias=False)),
-            [np.random.random((output_dim, input_dim, size, 1))],
-            (None, 4, input_dim),
-        ],
-        [
-            (keras.layers.Conv2D(output_dim, size,
-                                 use_bias=False, data_format='channels_first')),
-            [np.random.random((output_dim, input_dim, size, size))],
-            (None, input_dim, 4, 4),
-        ],
-        [
-            (keras.layers.Conv2DTranspose(output_dim, size,
-                                          use_bias=False,
-                                          data_format='channels_first')),
-            [np.random.random((output_dim, input_dim, size, size))],
-            (None, input_dim, 4, 4),
-        ],
-        [
-            (keras.layers.Conv2DTranspose(output_dim, size,
-                                          use_bias=False,
-                                          data_format='channels_last')),
-            [np.random.random((size, size, input_dim, output_dim))],
-            (None, 4, 4, input_dim),
-        ],
-        [
-            (keras.layers.Conv3D(output_dim, size,
-                                 use_bias=False, data_format='channels_first')),
-            [np.random.random((output_dim, input_dim, size, size, size))],
-            (None, input_dim, 4, 4, 4),
-        ],
-        [
-            (keras.layers.GRU(output_dim)),
-            [np.random.random((input_dim, output_dim)),
-             np.random.random((output_dim, output_dim)),
-             np.random.random((output_dim,)),
-             np.random.random((input_dim, output_dim)),
-             np.random.random((output_dim, output_dim)),
-             np.random.random((output_dim,)),
-             np.random.random((input_dim, output_dim)),
-             np.random.random((output_dim, output_dim)),
-             np.random.random((output_dim,))],
-            (None, 4, input_dim),
-        ],
-        [
-            (keras.layers.LSTM(output_dim)),
-            [np.random.random((input_dim, output_dim)),
-             np.random.random((output_dim, output_dim)),
-             np.random.random((output_dim,)),
-             np.random.random((input_dim, output_dim)),
-             np.random.random((output_dim, output_dim)),
-             np.random.random((output_dim,)),
-             np.random.random((input_dim, output_dim)),
-             np.random.random((output_dim, output_dim)),
-             np.random.random((output_dim,)),
-             np.random.random((input_dim, output_dim)),
-             np.random.random((output_dim, output_dim)),
-             np.random.random((output_dim,))],
-            (None, 4, input_dim),
-        ],
-    ]
-    for layer, weights, input_shape in cases:
-      layer.build(input_shape)
-      _ = keras.engine.topology.preprocess_weights_for_loading(
-          layer, weights, original_keras_version='1')
-
-    model = keras.models.Sequential([keras.layers.Dense(2, input_dim=2)])
-    _ = keras.engine.topology.preprocess_weights_for_loading(
-        model, model.weights, original_keras_version='1')
-
-    x = keras.Input((2,))
-    y = keras.layers.Dense(2)(x)
-    model = keras.models.Model(x, y)
-    _ = keras.engine.topology.preprocess_weights_for_loading(
-        model, model.weights, original_keras_version='1')
-
-
 class DeferredModeTest(test.TestCase):
 
   def testDeferredTensorAttributes(self):
-    x = base_layers._DeferredTensor(shape=(None, 2), dtype='float32', name='x')
+    x = tf_base_layers._DeferredTensor(shape=(None, 2),
+                                       dtype='float32',
+                                       name='x')
     self.assertEqual(str(x),
                      'DeferredTensor(\'x\', shape=(?, 2), dtype=float32)')
     self.assertEqual(repr(x),
@@ -1015,21 +880,21 @@ class DeferredModeTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testSimpleNetworkBuilding(self):
-    inputs = keras.engine.topology.Input(shape=(32,))
+    inputs = keras.engine.Input(shape=(32,))
     if context.in_eager_mode():
-      self.assertIsInstance(inputs, base_layers._DeferredTensor)
+      self.assertIsInstance(inputs, tf_base_layers._DeferredTensor)
       self.assertEqual(inputs.dtype.name, 'float32')
       self.assertEqual(inputs.shape.as_list(), [None, 32])
 
     x = keras.layers.Dense(2)(inputs)
     if context.in_eager_mode():
-      self.assertIsInstance(x, base_layers._DeferredTensor)
+      self.assertIsInstance(x, tf_base_layers._DeferredTensor)
       self.assertEqual(x.dtype.name, 'float32')
       self.assertEqual(x.shape.as_list(), [None, 2])
 
     outputs = keras.layers.Dense(4)(x)
-    network = keras.engine.topology.Network(inputs, outputs)
-    self.assertIsInstance(network, keras.engine.topology.Network)
+    network = keras.engine.Network(inputs, outputs)
+    self.assertIsInstance(network, keras.engine.Network)
 
     if context.in_eager_mode():
       # It should be possible to call such a network on EagerTensors.
@@ -1040,8 +905,8 @@ class DeferredModeTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes()
   def testMultiIONetworkbuilding(self):
-    input_a = keras.engine.topology.Input(shape=(32,))
-    input_b = keras.engine.topology.Input(shape=(16,))
+    input_a = keras.engine.Input(shape=(32,))
+    input_b = keras.engine.Input(shape=(16,))
     a = keras.layers.Dense(16)(input_a)
 
     class AddLayer(keras.layers.Layer):
@@ -1055,7 +920,7 @@ class DeferredModeTest(test.TestCase):
     c = AddLayer()([a, input_b])  # pylint: disable=not-callable
     c = keras.layers.Dense(2)(c)
 
-    network = keras.engine.topology.Network([input_a, input_b], [a, c])
+    network = keras.engine.Network([input_a, input_b], [a, c])
     if context.in_eager_mode():
       a_val = constant_op.constant(
           np.random.random((10, 32)).astype('float32'))
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index d8ea2fe3db..57451ad470 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -31,8 +31,8 @@ from tensorflow.python.keras._impl.keras import losses
 from tensorflow.python.keras._impl.keras import metrics as metrics_module
 from tensorflow.python.keras._impl.keras import optimizers
 from tensorflow.python.keras._impl.keras.engine import training_eager
-from tensorflow.python.keras._impl.keras.engine.topology import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import Network
+from tensorflow.python.keras._impl.keras.engine.base_layer import Layer
+from tensorflow.python.keras._impl.keras.engine.network import Network
 from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer
 from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer
 from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence
diff --git a/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py b/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py
index 7cac17c51a..c40ee109aa 100644
--- a/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py
+++ b/tensorflow/python/keras/_impl/keras/layers/advanced_activations.py
@@ -25,7 +25,7 @@ from tensorflow.python.keras._impl.keras import initializers
 from tensorflow.python.keras._impl.keras import regularizers
 from tensorflow.python.keras._impl.keras.engine import InputSpec
 from tensorflow.python.keras._impl.keras.engine import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
 from tensorflow.python.util.tf_export import tf_export
 
 
diff --git a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
index d2792b9636..d95a094245 100644
--- a/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
+++ b/tensorflow/python/keras/_impl/keras/layers/convolutional_recurrent.py
@@ -26,7 +26,7 @@ from tensorflow.python.keras._impl.keras import constraints
 from tensorflow.python.keras._impl.keras import initializers
 from tensorflow.python.keras._impl.keras import regularizers
 from tensorflow.python.keras._impl.keras.engine import InputSpec
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
 from tensorflow.python.keras._impl.keras.layers.recurrent import Recurrent
 from tensorflow.python.keras._impl.keras.utils import conv_utils
 from tensorflow.python.util.tf_export import tf_export
diff --git a/tensorflow/python/keras/_impl/keras/layers/embeddings.py b/tensorflow/python/keras/_impl/keras/layers/embeddings.py
index ca92899a45..006ecd3135 100644
--- a/tensorflow/python/keras/_impl/keras/layers/embeddings.py
+++ b/tensorflow/python/keras/_impl/keras/layers/embeddings.py
@@ -23,7 +23,7 @@ from tensorflow.python.keras._impl.keras import constraints
 from tensorflow.python.keras._impl.keras import initializers
 from tensorflow.python.keras._impl.keras import regularizers
 from tensorflow.python.keras._impl.keras.engine import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
 from tensorflow.python.util.tf_export import tf_export
 
 
diff --git a/tensorflow/python/keras/_impl/keras/layers/local.py b/tensorflow/python/keras/_impl/keras/layers/local.py
index df0efe6b8b..13d96e9392 100644
--- a/tensorflow/python/keras/_impl/keras/layers/local.py
+++ b/tensorflow/python/keras/_impl/keras/layers/local.py
@@ -25,7 +25,7 @@ from tensorflow.python.keras._impl.keras import initializers
 from tensorflow.python.keras._impl.keras import regularizers
 from tensorflow.python.keras._impl.keras.engine import InputSpec
 from tensorflow.python.keras._impl.keras.engine import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
 from tensorflow.python.keras._impl.keras.utils import conv_utils
 from tensorflow.python.util.tf_export import tf_export
 
diff --git a/tensorflow/python/keras/_impl/keras/layers/merge.py b/tensorflow/python/keras/_impl/keras/layers/merge.py
index cdf2878e83..c660cbd449 100644
--- a/tensorflow/python/keras/_impl/keras/layers/merge.py
+++ b/tensorflow/python/keras/_impl/keras/layers/merge.py
@@ -21,8 +21,8 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.keras._impl.keras import backend as K
-from tensorflow.python.keras._impl.keras.engine.topology import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import Layer
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
 from tensorflow.python.util.tf_export import tf_export
 
 
diff --git a/tensorflow/python/keras/_impl/keras/layers/noise.py b/tensorflow/python/keras/_impl/keras/layers/noise.py
index 9010f49615..e309d160e5 100644
--- a/tensorflow/python/keras/_impl/keras/layers/noise.py
+++ b/tensorflow/python/keras/_impl/keras/layers/noise.py
@@ -22,7 +22,7 @@ import numpy as np
 
 from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras.engine import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
 from tensorflow.python.util.tf_export import tf_export
 
 
diff --git a/tensorflow/python/keras/_impl/keras/layers/recurrent.py b/tensorflow/python/keras/_impl/keras/layers/recurrent.py
index a81971d9ee..0264c7ae01 100644
--- a/tensorflow/python/keras/_impl/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/_impl/keras/layers/recurrent.py
@@ -31,7 +31,7 @@ from tensorflow.python.keras._impl.keras import initializers
 from tensorflow.python.keras._impl.keras import regularizers
 from tensorflow.python.keras._impl.keras.engine import InputSpec
 from tensorflow.python.keras._impl.keras.engine import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
 from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util.tf_export import tf_export
diff --git a/tensorflow/python/keras/_impl/keras/layers/wrappers.py b/tensorflow/python/keras/_impl/keras/layers/wrappers.py
index 61f1a758e4..76ddd9299d 100644
--- a/tensorflow/python/keras/_impl/keras/layers/wrappers.py
+++ b/tensorflow/python/keras/_impl/keras/layers/wrappers.py
@@ -25,7 +25,7 @@ from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras.engine import InputSpec
 from tensorflow.python.keras._impl.keras.engine import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion
+from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
 from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg
 from tensorflow.python.layers import utils as tf_layers_util
 from tensorflow.python.util.tf_export import tf_export
diff --git a/tensorflow/python/keras/_impl/keras/models.py b/tensorflow/python/keras/_impl/keras/models.py
index 8000eaabab..9602e7ba39 100644
--- a/tensorflow/python/keras/_impl/keras/models.py
+++ b/tensorflow/python/keras/_impl/keras/models.py
@@ -13,1305 +13,30 @@
 # limitations under the License.
 # ==============================================================================
 # pylint: disable=protected-access
-"""Home of the Sequential model, and the `save_model`/`load_model` functions.
+"""Code for model cloning, plus model-related API entries.
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import copy
-import json
-import os
-
-import numpy as np
-
-from tensorflow.python.framework import ops
 from tensorflow.python.keras._impl.keras import backend as K
-from tensorflow.python.keras._impl.keras import layers as layer_module
-from tensorflow.python.keras._impl.keras import optimizers
-from tensorflow.python.keras._impl.keras.engine import topology
-from tensorflow.python.keras._impl.keras.engine.topology import Input
-from tensorflow.python.keras._impl.keras.engine.topology import InputLayer
-from tensorflow.python.keras._impl.keras.engine.topology import Layer
-from tensorflow.python.keras._impl.keras.engine.topology import TFBaseLayer
-from tensorflow.python.keras._impl.keras.engine.training import Model
+from tensorflow.python.keras._impl.keras.engine import saving
+from tensorflow.python.keras._impl.keras.engine import sequential
+from tensorflow.python.keras._impl.keras.engine import training
+from tensorflow.python.keras._impl.keras.engine.input_layer import Input
+from tensorflow.python.keras._impl.keras.engine.input_layer import InputLayer
+from tensorflow.python.keras._impl.keras.utils import generic_utils
 from tensorflow.python.keras._impl.keras.utils.generic_utils import has_arg
-from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite
-from tensorflow.python.platform import tf_logging as logging
-from tensorflow.python.util.tf_export import tf_export
-
-
-# pylint: disable=g-import-not-at-top
-try:
-  import h5py
-except ImportError:
-  h5py = None
-
-try:
-  import yaml
-except ImportError:
-  yaml = None
-# pylint: enable=g-import-not-at-top
-
-
-@tf_export('keras.models.save_model')
-def save_model(model, filepath, overwrite=True, include_optimizer=True):
-  """Save a model to a HDF5 file.
-
-  The saved model contains:
-      - the model's configuration (topology)
-      - the model's weights
-      - the model's optimizer's state (if any)
-
-  Thus the saved model can be reinstantiated in
-  the exact same state, without any of the code
-  used for model definition or training.
-
-  Arguments:
-      model: Keras model instance to be saved.
-      filepath: String, path where to save the model.
-      overwrite: Whether we should overwrite any existing
-          model at the target location, or instead
-          ask the user with a manual prompt.
-      include_optimizer: If True, save optimizer's state together.
-
-  Raises:
-      ImportError: if h5py is not available.
-  """
-
-  if h5py is None:
-    raise ImportError('`save_model` requires h5py.')
-
-  def get_json_type(obj):
-    """Serialize any object to a JSON-serializable structure.
-
-    Arguments:
-        obj: the object to serialize
-
-    Returns:
-        JSON-serializable structure representing `obj`.
-
-    Raises:
-        TypeError: if `obj` cannot be serialized.
-    """
-    # if obj is a serializable Keras class instance
-    # e.g. optimizer, layer
-    if hasattr(obj, 'get_config'):
-      return {'class_name': obj.__class__.__name__, 'config': obj.get_config()}
-
-    # if obj is any numpy type
-    if type(obj).__module__ == np.__name__:
-      if isinstance(obj, np.ndarray):
-        return {'type': type(obj), 'value': obj.tolist()}
-      else:
-        return obj.item()
-
-    # misc functions (e.g. loss function)
-    if callable(obj):
-      return obj.__name__
-
-    # if obj is a python 'type'
-    if type(obj).__name__ == type.__name__:
-      return obj.__name__
-
-    raise TypeError('Not JSON Serializable:', obj)
-
-  from tensorflow.python.keras._impl.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top
-
-  # If file exists and should not be overwritten.
-  if not overwrite and os.path.isfile(filepath):
-    proceed = ask_to_proceed_with_overwrite(filepath)
-    if not proceed:
-      return
-
-  with h5py.File(filepath, mode='w') as f:
-    f.attrs['keras_version'] = str(keras_version).encode('utf8')
-    f.attrs['backend'] = K.backend().encode('utf8')
-    f.attrs['model_config'] = json.dumps(
-        {
-            'class_name': model.__class__.__name__,
-            'config': model.get_config()
-        },
-        default=get_json_type).encode('utf8')
-
-    model_weights_group = f.create_group('model_weights')
-    model_layers = model.layers
-    topology.save_weights_to_hdf5_group(model_weights_group, model_layers)
-
-    if include_optimizer and hasattr(model, 'optimizer'):
-      if isinstance(model.optimizer, optimizers.TFOptimizer):
-        logging.warning(
-            'TensorFlow optimizers do not '
-            'make it possible to access '
-            'optimizer attributes or optimizer state '
-            'after instantiation. '
-            'As a result, we cannot save the optimizer '
-            'as part of the model save file.'
-            'You will have to compile your model again after loading it. '
-            'Prefer using a Keras optimizer instead '
-            '(see keras.io/optimizers).')
-      else:
-        f.attrs['training_config'] = json.dumps(
-            {
-                'optimizer_config': {
-                    'class_name': model.optimizer.__class__.__name__,
-                    'config': model.optimizer.get_config()
-                },
-                'loss': model.loss,
-                'metrics': model.metrics,
-                'sample_weight_mode': model.sample_weight_mode,
-                'loss_weights': model.loss_weights,
-            },
-            default=get_json_type).encode('utf8')
-
-        # Save optimizer weights.
-        symbolic_weights = getattr(model.optimizer, 'weights')
-        if symbolic_weights:
-          optimizer_weights_group = f.create_group('optimizer_weights')
-          weight_values = K.batch_get_value(symbolic_weights)
-          weight_names = []
-          for w, val in zip(symbolic_weights, weight_values):
-            name = str(w.name)
-            weight_names.append(name.encode('utf8'))
-          optimizer_weights_group.attrs['weight_names'] = weight_names
-          for name, val in zip(weight_names, weight_values):
-            param_dset = optimizer_weights_group.create_dataset(
-                name, val.shape, dtype=val.dtype)
-            if not val.shape:
-              # scalar
-              param_dset[()] = val
-            else:
-              param_dset[:] = val
-    f.flush()
-
-
-@tf_export('keras.models.load_model')
-def load_model(filepath, custom_objects=None, compile=True):  # pylint: disable=redefined-builtin
-  """Loads a model saved via `save_model`.
-
-  Arguments:
-      filepath: String, path to the saved model.
-      custom_objects: Optional dictionary mapping names
-          (strings) to custom classes or functions to be
-          considered during deserialization.
-      compile: Boolean, whether to compile the model
-          after loading.
-
-  Returns:
-      A Keras model instance. If an optimizer was found
-      as part of the saved model, the model is already
-      compiled. Otherwise, the model is uncompiled and
-      a warning will be displayed. When `compile` is set
-      to False, the compilation is omitted without any
-      warning.
-
-  Raises:
-      ImportError: if h5py is not available.
-      ValueError: In case of an invalid savefile.
-  """
-  if h5py is None:
-    raise ImportError('`load_model` requires h5py.')
-
-  if not custom_objects:
-    custom_objects = {}
-
-  def convert_custom_objects(obj):
-    """Handles custom object lookup.
-
-    Arguments:
-        obj: object, dict, or list.
-
-    Returns:
-        The same structure, where occurrences
-            of a custom object name have been replaced
-            with the custom object.
-    """
-    if isinstance(obj, list):
-      deserialized = []
-      for value in obj:
-        deserialized.append(convert_custom_objects(value))
-      return deserialized
-    if isinstance(obj, dict):
-      deserialized = {}
-      for key, value in obj.items():
-        deserialized[key] = convert_custom_objects(value)
-      return deserialized
-    if obj in custom_objects:
-      return custom_objects[obj]
-    return obj
-
-  with h5py.File(filepath, mode='r') as f:
-    # instantiate model
-    model_config = f.attrs.get('model_config')
-    if model_config is None:
-      raise ValueError('No model found in config file.')
-    model_config = json.loads(model_config.decode('utf-8'))
-    model = model_from_config(model_config, custom_objects=custom_objects)
-
-    # set weights
-    topology.load_weights_from_hdf5_group(f['model_weights'], model.layers)
-
-    # Early return if compilation is not required.
-    if not compile:
-      return model
-
-    # instantiate optimizer
-    training_config = f.attrs.get('training_config')
-    if training_config is None:
-      logging.warning('No training configuration found in save file: '
-                      'the model was *not* compiled. Compile it manually.')
-      return model
-    training_config = json.loads(training_config.decode('utf-8'))
-    optimizer_config = training_config['optimizer_config']
-    optimizer = optimizers.deserialize(
-        optimizer_config, custom_objects=custom_objects)
-
-    # Recover loss functions and metrics.
-    loss = convert_custom_objects(training_config['loss'])
-    metrics = convert_custom_objects(training_config['metrics'])
-    sample_weight_mode = training_config['sample_weight_mode']
-    loss_weights = training_config['loss_weights']
-
-    # Compile model.
-    model.compile(
-        optimizer=optimizer,
-        loss=loss,
-        metrics=metrics,
-        loss_weights=loss_weights,
-        sample_weight_mode=sample_weight_mode)
-
-    # Set optimizer weights.
-    if 'optimizer_weights' in f:
-      # Build train function (to get weight updates).
-      if isinstance(model, Sequential):
-        model.model._make_train_function()
-      else:
-        model._make_train_function()
-      optimizer_weights_group = f['optimizer_weights']
-      optimizer_weight_names = [
-          n.decode('utf8')
-          for n in optimizer_weights_group.attrs['weight_names']
-      ]
-      optimizer_weight_values = [
-          optimizer_weights_group[n] for n in optimizer_weight_names
-      ]
-      try:
-        model.optimizer.set_weights(optimizer_weight_values)
-      except ValueError:
-        logging.warning('Error in loading the saved optimizer '
-                        'state. As a result, your model is '
-                        'starting with a freshly initialized '
-                        'optimizer.')
-  return model
-
-
-@tf_export('keras.models.model_from_config')
-def model_from_config(config, custom_objects=None):
-  """Instantiates a Keras model from its config.
-
-  Arguments:
-      config: Configuration dictionary.
-      custom_objects: Optional dictionary mapping names
-          (strings) to custom classes or functions to be
-          considered during deserialization.
-
-  Returns:
-      A Keras model instance (uncompiled).
-
-  Raises:
-      TypeError: if `config` is not a dictionary.
-  """
-  if isinstance(config, list):
-    raise TypeError('`model_from_config` expects a dictionary, not a list. '
-                    'Maybe you meant to use '
-                    '`Sequential.from_config(config)`?')
-  return layer_module.deserialize(config, custom_objects=custom_objects)
-
-
-@tf_export('keras.models.model_from_yaml')
-def model_from_yaml(yaml_string, custom_objects=None):
-  """Parses a yaml model configuration file and returns a model instance.
-
-  Arguments:
-      yaml_string: YAML string encoding a model configuration.
-      custom_objects: Optional dictionary mapping names
-          (strings) to custom classes or functions to be
-          considered during deserialization.
-
-  Returns:
-      A Keras model instance (uncompiled).
-
-  Raises:
-      ImportError: if yaml module is not found.
-  """
-  if yaml is None:
-    raise ImportError('Requires yaml module installed.')
-  config = yaml.load(yaml_string)
-  return layer_module.deserialize(config, custom_objects=custom_objects)
-
-
-@tf_export('keras.models.model_from_json')
-def model_from_json(json_string, custom_objects=None):
-  """Parses a JSON model configuration file and returns a model instance.
-
-  Arguments:
-      json_string: JSON string encoding a model configuration.
-      custom_objects: Optional dictionary mapping names
-          (strings) to custom classes or functions to be
-          considered during deserialization.
-
-  Returns:
-      A Keras model instance (uncompiled).
-  """
-  config = json.loads(json_string)
-  return layer_module.deserialize(config, custom_objects=custom_objects)
-
-
-@tf_export('keras.models.Sequential', 'keras.Sequential')
-class Sequential(Model):
-  """Linear stack of layers.
-
-  Arguments:
-      layers: list of layers to add to the model.
-
-  # Note
-      The first layer passed to a Sequential model
-      should have a defined input shape. What that
-      means is that it should have received an `input_shape`
-      or `batch_input_shape` argument,
-      or for some type of layers (recurrent, Dense...)
-      an `input_dim` argument.
-
-  Example:
-
-      ```python
-          model = Sequential()
-          # first layer must have a defined input shape
-          model.add(Dense(32, input_dim=500))
-          # afterwards, Keras does automatic shape inference
-          model.add(Dense(32))
-
-          # also possible (equivalent to the above):
-          model = Sequential()
-          model.add(Dense(32, input_shape=(500,)))
-          model.add(Dense(32))
-
-          # also possible (equivalent to the above):
-          model = Sequential()
-          # here the batch dimension is None,
-          # which means any batch size will be accepted by the model.
-          model.add(Dense(32, batch_input_shape=(None, 500)))
-          model.add(Dense(32))
-      ```
-  """
-
-  def __init__(self, layers=None, name=None):
-    self._is_graph_network = True
-    self._is_compiled = False
-    self._layers = []  # Stack of layers.
-    self.model = None  # Internal Model instance.
-    self.inputs = []  # List of input tensors
-    self.outputs = []  # List of length 1: the output tensor (unique).
-    self._trainable = True
-    self._initial_weights = None
-    self._input_layers = []
-
-    # Model attributes.
-    self._inbound_nodes = []
-    self._outbound_nodes = []
-    self.built = False
-
-    # Set model name.
-    if not name:
-      prefix = 'sequential_'
-      name = prefix + str(K.get_uid(prefix))
-    self._name = name
-
-    # Used by Layer base class.
-    self._dtype = None
-    self._activity_regularizer = None
-
-    # The following properties are not actually used by Keras;
-    # they exist for compatibility with TF's variable scoping mechanism.
-    self._updates = []
-    self._losses = []
-    self._scope = None
-    self._reuse = None
-    self._base_name = name
-    self._graph = ops.get_default_graph()
-
-    # Add to the model any layers passed to the constructor.
-    if layers:
-      for layer in layers:
-        self.add(layer)
-
-  def add(self, layer):
-    """Adds a layer instance on top of the layer stack.
-
-    Arguments:
-        layer: layer instance.
-
-    Raises:
-        TypeError: If `layer` is not a layer instance.
-        ValueError: In case the `layer` argument does not
-            know its input shape.
-        ValueError: In case the `layer` argument has
-            multiple output tensors, or is already connected
-            somewhere else (forbidden in `Sequential` models).
-    """
-    if not isinstance(layer, (Layer, TFBaseLayer)):
-      raise TypeError('The added layer must be '
-                      'an instance of class Layer. '
-                      'Found: ' + str(layer))
-    if not self.outputs:
-      # First layer in model: check that it is an input layer.
-      if not isinstance(layer, InputLayer):
-        # Create an input layer.
-        # First, we need to infer its expected input shape and dtype.
-        if isinstance(layer, (Model, Sequential)):
-          # We were passed a model as first layer.
-          # This requires a specific way to figure out the
-          # input shape and dtype.
-          if not layer.layers:
-            raise ValueError('Cannot add an empty model '
-                             'to a `Sequential` model.')
-          # In case of nested models: recover the first layer
-          # of the deepest model to infer input shape and dtype.
-          first_layer = layer.layers[0]
-          while isinstance(first_layer, (Model, Sequential)):
-            first_layer = first_layer.layers[0]
-          batch_shape = first_layer._batch_input_shape
-          dtype = first_layer.dtype
-        else:
-          # We were passed a regular layer, and it should
-          # know about its input shape. Otherwise, that's an error.
-          if not hasattr(layer, '_batch_input_shape'):
-            raise ValueError('The first layer in a '
-                             'Sequential model must '
-                             'get an `input_shape` argument.')
-          batch_shape = layer._batch_input_shape
-          dtype = layer.dtype
-        # Instantiate the input layer.
-        x = Input(
-            batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
-        # This will build the current layer
-        # and create the node connecting the current layer
-        # to the input layer we just created.
-        layer(x)
-
-      if len(layer._inbound_nodes[-1].output_tensors) != 1:
-        raise ValueError('All layers in a Sequential model '
-                         'should have a single output tensor. '
-                         'For multi-output layers, '
-                         'use the functional API.')
-
-      self.outputs = [layer._inbound_nodes[-1].output_tensors[0]]
-      self.inputs = topology.get_source_inputs(self.outputs[0])
-
-      # We create an input node, which we will keep updated
-      # as we add more layers
-      topology.Node(
-          outbound_layer=self,
-          inbound_layers=[],
-          node_indices=[],
-          tensor_indices=[],
-          input_tensors=self.inputs,
-          output_tensors=self.outputs)
-    else:
-      output_tensor = layer(self.outputs[0])
-      if isinstance(output_tensor, list):
-        raise TypeError('All layers in a Sequential model '
-                        'should have a single output tensor. '
-                        'For multi-output layers, '
-                        'use the functional API.')
-      self.outputs = [output_tensor]
-      # update self._inbound_nodes
-      self._inbound_nodes[0].output_tensors = self.outputs
-      self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])]
-
-    self._layers.append(layer)
-    self.built = False
-
-  def pop(self):
-    """Removes the last layer in the model.
-
-    Raises:
-        TypeError: if there are no layers in the model.
-    """
-    if not self.layers:
-      raise TypeError('There are no layers in the model.')
-
-    self.layers.pop()
-    if not self.layers:
-      self.outputs = []
-      self._inbound_nodes = []
-      self._outbound_nodes = []
-    else:
-      self.layers[-1]._outbound_nodes = []
-      self.outputs = [self.layers[-1].output]
-      # update self._inbound_nodes
-      self._inbound_nodes[0].output_tensors = self.outputs
-      self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])]
-    self.built = False
-
-  def get_layer(self, name=None, index=None):
-    """Retrieve a layer that is part of the model.
-
-    Returns a layer based on either its name (unique)
-    or its index in the graph. Indices are based on
-    order of horizontal graph traversal (bottom-up).
-
-    Arguments:
-        name: string, name of layer.
-        index: integer, index of layer.
-
-    Returns:
-        A layer instance.
-    """
-    if not self.built:
-      self.build()
-    return self.model.get_layer(name, index)
-
-  def call(self, inputs, **kwargs):
-    if not self.built:
-      self.build()
-    return self.model.call(inputs, **kwargs)
-
-  def build(self, input_shape=None):
-    if not self.inputs or not self.outputs:
-      raise TypeError('Sequential model cannot be built: model is empty.'
-                      ' Add some layers first.')
-    # actually create the model
-    self.model = Model(self.inputs, self.outputs[0], name=self.name + '_model')
-    self.model.trainable = self.trainable
-
-    # mirror model attributes
-    self.supports_masking = self.model.supports_masking
-    self._output_mask_cache = self.model._output_mask_cache
-    self._output_tensor_cache = self.model._output_tensor_cache
-    self._output_shape_cache = self.model._output_shape_cache
-    self._input_layers = self.model._input_layers
-    self._output_layers = self.model._output_layers
-    self._input_coordinates = self.model._input_coordinates
-    self._output_coordinates = self.model._output_coordinates
-    self._nodes_by_depth = self.model._nodes_by_depth
-    self._network_nodes = self.model._network_nodes
-    self.output_names = self.model.output_names
-    self.input_names = self.model.input_names
-    self._feed_input_names = self.model._feed_input_names
-    self._feed_inputs = self.model._feed_inputs
-
-    # Make sure child model callbacks
-    # will call the parent Sequential model.
-    self.model.callback_model = self
-
-    self.built = True
-
-  @property
-  def uses_learning_phase(self):
-    if not self.built:
-      self.build()
-    return self.model.uses_learning_phase
-
-  def _gather_list_attr(self, attr):
-    all_attrs = []
-    for layer in self.layers:
-      all_attrs += getattr(layer, attr, [])
-    return all_attrs
-
-  @property
-  def trainable(self):
-    return self._trainable
-
-  @trainable.setter
-  def trainable(self, value):
-    if self.model:
-      self.model.trainable = value
-    self._trainable = value
-
-  @property
-  def trainable_weights(self):
-    if not self.trainable:
-      return []
-    return self._gather_list_attr('trainable_weights')
-
-  @property
-  def non_trainable_weights(self):
-    weights = self._gather_list_attr('non_trainable_weights')
-    if not self.trainable:
-      trainable_weights = self._gather_list_attr('trainable_weights')
-      return trainable_weights + weights
-    return weights
-
-  @property
-  def regularizers(self):
-    if not self.built:
-      self.build()
-    return self.model.regularizers
-
-  def get_weights(self):
-    """Retrieves the weights of the model.
-
-    Returns:
-        A flat list of Numpy arrays
-        (one array per model weight).
-    """
-    if not self.built:
-      self.build()
-    return self.model.get_weights()
-
-  def set_weights(self, weights):
-    """Sets the weights of the model.
-
-    Arguments:
-        weights: Should be a list
-            of Numpy arrays with shapes and types matching
-            the output of `model.get_weights()`.
-    """
-    if not self.built:
-      self.build()
-    self.model.set_weights(weights)
-
-  def load_weights(self, filepath, by_name=False):
-    if h5py is None:
-      raise ImportError('`load_weights` requires h5py.')
-    f = h5py.File(filepath, mode='r')
-    if 'layer_names' not in f.attrs and 'model_weights' in f:
-      f = f['model_weights']
-    layers = self.layers
-    if by_name:
-      topology.load_weights_from_hdf5_group_by_name(f, layers)
-    else:
-      topology.load_weights_from_hdf5_group(f, layers)
-    if hasattr(f, 'close'):
-      f.close()
-
-  def save_weights(self, filepath, overwrite=True):
-    if h5py is None:
-      raise ImportError('`save_weights` requires h5py.')
-    # If file exists and should not be overwritten:
-    if not overwrite and os.path.isfile(filepath):
-      proceed = ask_to_proceed_with_overwrite(filepath)
-      if not proceed:
-        return
-    layers = self.layers
-    f = h5py.File(filepath, 'w')
-    topology.save_weights_to_hdf5_group(f, layers)
-    f.flush()
-    f.close()
-
-  def compile(self,
-              optimizer,
-              loss,
-              metrics=None,
-              sample_weight_mode=None,
-              weighted_metrics=None,
-              target_tensors=None,
-              **kwargs):
-    """Configures the model for training.
-
-    Arguments:
-        optimizer: String (name of optimizer) or optimizer object.
-            See [optimizers](/optimizers).
-        loss: String (name of objective function) or objective function.
-            See [losses](/losses).
-            If the model has multiple outputs, you can use a different loss
-            on each output by passing a dictionary or a list of losses.
-            The loss value that will be minimized by the model
-            will then be the sum of all individual losses.
-        metrics: List of metrics to be evaluated by the model
-            during training and testing.
-            Typically you will use `metrics=['accuracy']`.
-            To specify different metrics for different outputs of a
-            multi-output model, you could also pass a dictionary,
-            such as `metrics={'output_a': 'accuracy'}`.
-        sample_weight_mode: If you need to do timestep-wise
-            sample weighting (2D weights), set this to `"temporal"`.
-            `None` defaults to sample-wise weights (1D).
-            If the model has multiple outputs, you can use a different
-            `sample_weight_mode` on each output by passing a
-            dictionary or a list of modes.
-        weighted_metrics: list of metrics to be evaluated and weighted
-             by `sample_weight` or `class_weight` during training and testing.
-        target_tensors: By default, Keras will create a placeholder for the
-            model's target, which will be fed with the target data during
-            training. If instead you would like to use your own
-            target tensor (in turn, Keras will not expect external
-            Numpy data for these targets at training time), you
-            can specify them via the `target_tensors` argument.
-            It should be a single tensor
-            (for a single-output `Sequential` model).
-        **kwargs: These arguments are passed into `tf.Session.run`.
-
-    Example:
-        ```python
-            model = Sequential()
-            model.add(Dense(32, input_shape=(500,)))
-            model.add(Dense(10, activation='softmax'))
-            model.compile(optimizer='rmsprop',
-                          loss='categorical_crossentropy',
-                          metrics=['accuracy'])
-        ```
-    """
-    # create the underlying model
-    self.build()
-    # call compile method of Model class
-    self.model.compile(
-        optimizer,
-        loss,
-        metrics=metrics,
-        sample_weight_mode=sample_weight_mode,
-        weighted_metrics=weighted_metrics,
-        target_tensors=target_tensors,
-        **kwargs)
-    self.optimizer = self.model.optimizer
-    self.loss = self.model.loss
-    self.metrics = self.model.metrics
-    self.loss_weights = self.model.loss_weights
-    self.sample_weight_mode = self.model.sample_weight_mode
-    self.weighted_metrics = self.model.weighted_metrics
-    self.targets = self.model.targets
-    self.metrics_tensors = self.model.metrics_tensors
-    self.metrics_names = self.model.metrics_names
-    self.sample_weights = self.model.sample_weights
-    self.total_loss = self.model.total_loss
-
-  def fit(self,
-          x=None,
-          y=None,
-          batch_size=None,
-          epochs=1,
-          verbose=1,
-          callbacks=None,
-          validation_split=0.,
-          validation_data=None,
-          shuffle=True,
-          class_weight=None,
-          sample_weight=None,
-          initial_epoch=0,
-          steps_per_epoch=None,
-          validation_steps=None,
-          **kwargs):
-    """Trains the model for a fixed number of epochs.
-
-    Arguments:
-        x: Numpy array of training data.
-            If the input layer in the model is named, you can also pass a
-            dictionary mapping the input name to a Numpy array.
-            `x` can be `None` (default) if feeding from
-            TensorFlow data tensors.
-        y: Numpy array of target (label) data.
-            If the output layer in the model is named, you can also pass a
-            dictionary mapping the output name to a Numpy array.
-            `y` can be `None` (default) if feeding from
-            TensorFlow data tensors.
-        batch_size: Integer or `None`.
-            Number of samples per gradient update.
-            If unspecified, it will default to 32.
-        epochs: Integer. Number of epochs to train the model.
-            An epoch is an iteration over the entire `x` and `y`
-            data provided.
-            Note that in conjunction with `initial_epoch`,
-            `epochs` is to be understood as "final epoch".
-            The model is not trained for a number of iterations
-            given by `epochs`, but merely until the epoch
-            of index `epochs` is reached.
-        verbose: 0, 1, or 2. Verbosity mode.
-            0 = silent, 1 = progress bar, 2 = one line per epoch.
-        callbacks: List of `keras.callbacks.Callback` instances.
-            List of callbacks to apply during training.
-            See [callbacks](/callbacks).
-        validation_split: Float between 0 and 1:
-            Fraction of the training data to be used as validation data.
-            The model will set apart this fraction of the training data,
-            will not train on it, and will evaluate
-            the loss and any model metrics
-            on this data at the end of each epoch.
-            The validation data is selected from the last samples
-            in the `x` and `y` data provided, before shuffling.
-        validation_data: tuple `(x_val, y_val)` or tuple
-            `(x_val, y_val, val_sample_weights)` on which to evaluate
-            the loss and any model metrics at the end of each epoch.
-            The model will not be trained on this data.
-            This will override `validation_split`.
-        shuffle: Boolean (whether to shuffle the training data
-            before each epoch) or str (for 'batch').
-            'batch' is a special option for dealing with the
-            limitations of HDF5 data; it shuffles in batch-sized chunks.
-            Has no effect when `steps_per_epoch` is not `None`.
-        class_weight: Optional dictionary mapping class indices (integers)
-            to a weight (float) value, used for weighting the loss function
-            (during training only).
-            This can be useful to tell the model to
-            "pay more attention" to samples from
-            an under-represented class.
-        sample_weight: Optional Numpy array of weights for
-            the training samples, used for weighting the loss function
-            (during training only). You can either pass a flat (1D)
-            Numpy array with the same length as the input samples
-            (1:1 mapping between weights and samples),
-            or in the case of temporal data,
-            you can pass a 2D array with shape
-            `(samples, sequence_length)`,
-            to apply a different weight to every timestep of every sample.
-            In this case you should make sure to specify
-            `sample_weight_mode="temporal"` in `compile()`.
-        initial_epoch: Epoch at which to start training
-            (useful for resuming a previous training run).
-        steps_per_epoch: Total number of steps (batches of samples)
-            before declaring one epoch finished and starting the
-            next epoch. When training with input tensors such as
-            TensorFlow data tensors, the default `None` is equal to
-            the number of unique samples in your dataset divided by
-            the batch size, or 1 if that cannot be determined.
-        validation_steps: Only relevant if `steps_per_epoch`
-            is specified. Total number of steps (batches of samples)
-            to validate before stopping.
-        **kwargs: Used for backwards compatibility support.
-
-    Returns:
-        A `History` object. Its `History.history` attribute is
-        a record of training loss values and metrics values
-        at successive epochs, as well as validation loss values
-        and validation metrics values (if applicable).
-
-    Raises:
-        RuntimeError: If the model was never compiled.
-        ValueError: In case of mismatch between the provided input data
-            and what the model expects.
-    """
-    if not self.built:
-      raise RuntimeError('The model needs to be compiled before being used.')
-    return self.model.fit(
-        x,
-        y,
-        batch_size=batch_size,
-        epochs=epochs,
-        verbose=verbose,
-        callbacks=callbacks,
-        validation_split=validation_split,
-        validation_data=validation_data,
-        shuffle=shuffle,
-        class_weight=class_weight,
-        sample_weight=sample_weight,
-        initial_epoch=initial_epoch,
-        steps_per_epoch=steps_per_epoch,
-        validation_steps=validation_steps)
-
-  def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
-    """Computes the loss on some input data, batch by batch.
-
-    Arguments:
-        x: input data, as a Numpy array or list of Numpy arrays
-            (if the model has multiple inputs).
-        y: labels, as a Numpy array.
-        batch_size: integer. Number of samples per gradient update.
-        verbose: verbosity mode, 0 or 1.
-        sample_weight: sample weights, as a Numpy array.
-
-    Returns:
-        Scalar test loss (if the model has no metrics)
-        or list of scalars (if the model computes other metrics).
-        The attribute `model.metrics_names` will give you
-        the display labels for the scalar outputs.
-
-    Raises:
-        RuntimeError: if the model was never compiled.
-    """
-    if not self.built:
-      raise RuntimeError('The model needs to be compiled before being used.')
-    return self.model.evaluate(
-        x,
-        y,
-        batch_size=batch_size,
-        verbose=verbose,
-        sample_weight=sample_weight)
-
-  def predict(self, x, batch_size=32, verbose=0):
-    """Generates output predictions for the input samples.
-
-    The input samples are processed batch by batch.
-
-    Arguments:
-        x: the input data, as a Numpy array.
-        batch_size: integer.
-        verbose: verbosity mode, 0 or 1.
-
-    Returns:
-        A Numpy array of predictions.
-    """
-    if not self.built:
-      self.build()
-    return self.model.predict(x, batch_size=batch_size, verbose=verbose)
-
-  def predict_on_batch(self, x):
-    """Returns predictions for a single batch of samples.
-
-    Arguments:
-        x: input data, as a Numpy array or list of Numpy arrays
-            (if the model has multiple inputs).
-
-    Returns:
-        A Numpy array of predictions.
-    """
-    if not self.built:
-      self.build()
-    return self.model.predict_on_batch(x)
-
-  def train_on_batch(self, x, y, class_weight=None, sample_weight=None):
-    """Single gradient update over one batch of samples.
-
-    Arguments:
-        x: input data, as a Numpy array or list of Numpy arrays
-            (if the model has multiple inputs).
-        y: labels, as a Numpy array.
-        class_weight: dictionary mapping classes to a weight value,
-            used for scaling the loss function (during training only).
-        sample_weight: sample weights, as a Numpy array.
-
-    Returns:
-        Scalar training loss (if the model has no metrics)
-        or list of scalars (if the model computes other metrics).
-        The attribute `model.metrics_names` will give you
-        the display labels for the scalar outputs.
-
-    Raises:
-        RuntimeError: if the model was never compiled.
-    """
-    if not self.built:
-      raise RuntimeError('The model needs to be compiled before being used.')
-    return self.model.train_on_batch(
-        x, y, sample_weight=sample_weight, class_weight=class_weight)
-
-  def test_on_batch(self, x, y, sample_weight=None):
-    """Evaluates the model over a single batch of samples.
-
-    Arguments:
-        x: input data, as a Numpy array or list of Numpy arrays
-            (if the model has multiple inputs).
-        y: labels, as a Numpy array.
-        sample_weight: sample weights, as a Numpy array.
-
-    Returns:
-        Scalar test loss (if the model has no metrics)
-        or list of scalars (if the model computes other metrics).
-        The attribute `model.metrics_names` will give you
-        the display labels for the scalar outputs.
-
-    Raises:
-        RuntimeError: if the model was never compiled.
-    """
-    if not self.built:
-      raise RuntimeError('The model needs to be compiled before being used.')
-    return self.model.test_on_batch(x, y, sample_weight=sample_weight)
-
-  def predict_proba(self, x, batch_size=32, verbose=0):
-    """Generates class probability predictions for the input samples.
-
-    The input samples are processed batch by batch.
-
-    Arguments:
-        x: input data, as a Numpy array or list of Numpy arrays
-            (if the model has multiple inputs).
-        batch_size: integer.
-        verbose: verbosity mode, 0 or 1.
-
-    Returns:
-        A Numpy array of probability predictions.
-    """
-    preds = self.predict(x, batch_size, verbose)
-    if preds.min() < 0. or preds.max() > 1.:
-      logging.warning('Network returning invalid probability values. '
-                      'The last layer might not normalize predictions '
-                      'into probabilities '
-                      '(like softmax or sigmoid would).')
-    return preds
-
-  def predict_classes(self, x, batch_size=32, verbose=0):
-    """Generate class predictions for the input samples.
-
-    The input samples are processed batch by batch.
-
-    Arguments:
-        x: input data, as a Numpy array or list of Numpy arrays
-            (if the model has multiple inputs).
-        batch_size: integer.
-        verbose: verbosity mode, 0 or 1.
-
-    Returns:
-        A numpy array of class predictions.
-    """
-    proba = self.predict(x, batch_size=batch_size, verbose=verbose)
-    if proba.shape[-1] > 1:
-      return proba.argmax(axis=-1)
-    else:
-      return (proba > 0.5).astype('int32')
-
-  def fit_generator(self,
-                    generator,
-                    steps_per_epoch=None,
-                    epochs=1,
-                    verbose=1,
-                    callbacks=None,
-                    validation_data=None,
-                    validation_steps=None,
-                    class_weight=None,
-                    max_queue_size=10,
-                    workers=1,
-                    use_multiprocessing=False,
-                    shuffle=True,
-                    initial_epoch=0,
-                    **kwargs):
-    """Fits the model on data generated batch-by-batch by a Python generator.
-
-    The generator is run in parallel to the model, for efficiency.
-    For instance, this allows you to do real-time data augmentation
-    on images on CPU in parallel to training your model on GPU.
-
-    Arguments:
-        generator: A generator.
-            The output of the generator must be either
-            - a tuple (inputs, targets)
-            - a tuple (inputs, targets, sample_weights).
-            All arrays should contain the same number of samples.
-            The generator is expected to loop over its data
-            indefinitely. An epoch finishes when `steps_per_epoch`
-            batches have been seen by the model.
-        steps_per_epoch: Total number of steps (batches of samples)
-            to yield from `generator` before declaring one epoch
-            finished and starting the next epoch. It should typically
-            be equal to the number of samples of your dataset
-            divided by the batch size.
-            Optional for `Sequence`: if unspecified, will use
-            the `len(generator)` as a number of steps.
-        epochs: Integer, total number of iterations on the data.
-            Note that in conjunction with initial_epoch, the parameter
-            epochs is to be understood as "final epoch". The model is
-            not trained for n steps given by epochs, but until the
-            epoch epochs is reached.
-        verbose: Verbosity mode, 0, 1, or 2.
-        callbacks: List of callbacks to be called during training.
-        validation_data: This can be either
-            - A generator for the validation data
-            - A tuple (inputs, targets)
-            - A tuple (inputs, targets, sample_weights).
-        validation_steps: Only relevant if `validation_data`
-            is a generator.
-            Number of steps to yield from validation generator
-            at the end of every epoch. It should typically
-            be equal to the number of samples of your
-            validation dataset divided by the batch size.
-            Optional for `Sequence`: if unspecified, will use
-            the `len(validation_data)` as a number of steps.
-        class_weight: Dictionary mapping class indices to a weight
-            for the class.
-        max_queue_size: Maximum size for the generator queue
-        workers: Maximum number of processes to spin up
-        use_multiprocessing: If True, use process based threading.
-            Note that because
-            this implementation relies on multiprocessing,
-            you should not pass
-            non picklable arguments to the generator
-            as they can't be passed
-            easily to children processes.
-       shuffle: Whether to shuffle the order of the batches at
-              the beginning of each epoch. Only used with instances
-              of `Sequence` (keras.utils.Sequence).
-        initial_epoch: Epoch at which to start training
-            (useful for resuming a previous training run)
-        **kwargs: support for legacy arguments.
-
-    Returns:
-        A `History` object.
-
-    Raises:
-        RuntimeError: if the model was never compiled.
-        ValueError: In case the generator yields
-            data in an invalid format.
-
-    Example:
-
-    ```python
-        def generate_arrays_from_file(path):
-            while 1:
-                f = open(path)
-                for line in f:
-                    # create Numpy arrays of input data
-                    # and labels, from each line in the file
-                    x, y = process_line(line)
-                    yield (x, y)
-                    f.close()
-
-        model.fit_generator(generate_arrays_from_file('/my_file.txt'),
-                            steps_per_epoch=1000, epochs=10)
-    ```
-    """
-    # Legacy support
-    if 'max_q_size' in kwargs:
-      max_queue_size = kwargs.pop('max_q_size')
-      logging.warning('The argument `max_q_size` has been renamed '
-                      '`max_queue_size`. Update your method calls accordingly.')
-    if 'pickle_safe' in kwargs:
-      use_multiprocessing = kwargs.pop('pickle_safe')
-      logging.warning('The argument `pickle_safe` has been renamed '
-                      '`use_multiprocessing`. '
-                      'Update your method calls accordingly.')
-    if kwargs:
-      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
-
-    if not self.built:
-      raise RuntimeError('The model needs to be compiled before being used.')
-    return self.model.fit_generator(
-        generator,
-        steps_per_epoch,
-        epochs,
-        verbose=verbose,
-        callbacks=callbacks,
-        validation_data=validation_data,
-        validation_steps=validation_steps,
-        class_weight=class_weight,
-        max_queue_size=max_queue_size,
-        workers=workers,
-        use_multiprocessing=use_multiprocessing,
-        shuffle=shuffle,
-        initial_epoch=initial_epoch)
-
-  def evaluate_generator(self,
-                         generator,
-                         steps=None,
-                         max_queue_size=10,
-                         workers=1,
-                         use_multiprocessing=False,
-                         **kwargs):
-    """Evaluates the model on a data generator.
-
-    The generator should return the same kind of data
-    as accepted by `test_on_batch`.
-
-    Arguments:
-        generator: Generator yielding tuples (inputs, targets)
-            or (inputs, targets, sample_weights)
-        steps: Total number of steps (batches of samples)
-            to yield from `generator` before stopping.
-            Optional for `Sequence`: if unspecified, will use
-            the `len(generator)` as a number of steps.
-        max_queue_size: maximum size for the generator queue
-        workers: maximum number of processes to spin up
-        use_multiprocessing: if True, use process based threading.
-            Note that because this implementation
-            relies on multiprocessing, you should not pass
-            non picklable arguments to the generator
-            as they can't be passed easily to children processes.
-        **kwargs: support for legacy arguments.
-
-    Returns:
-        Scalar test loss (if the model has no metrics)
-        or list of scalars (if the model computes other metrics).
-        The attribute `model.metrics_names` will give you
-        the display labels for the scalar outputs.
-
-    Raises:
-        RuntimeError: if the model was never compiled.
-        ValueError: In case the generator yields
-            data in an invalid format.
-    """
-    # Legacy support
-    if 'max_q_size' in kwargs:
-      max_queue_size = kwargs.pop('max_q_size')
-      logging.warning('The argument `max_q_size` has been renamed '
-                      '`max_queue_size`. Update your method calls accordingly.')
-    if 'pickle_safe' in kwargs:
-      use_multiprocessing = kwargs.pop('pickle_safe')
-      logging.warning('The argument `pickle_safe` has been renamed '
-                      '`use_multiprocessing`. '
-                      'Update your method calls accordingly.')
-    if kwargs:
-      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
-
-    if not self.built:
-      raise RuntimeError('The model needs to be compiled before being used.')
-    return self.model.evaluate_generator(
-        generator,
-        steps,
-        max_queue_size=max_queue_size,
-        workers=workers,
-        use_multiprocessing=use_multiprocessing)
-
-  def predict_generator(self,
-                        generator,
-                        steps=None,
-                        max_queue_size=10,
-                        workers=1,
-                        use_multiprocessing=False,
-                        verbose=0,
-                        **kwargs):
-    """Generates predictions for the input samples from a data generator.
-
-    The generator should return the same kind of data as accepted by
-    `predict_on_batch`.
-
-    Arguments:
-        generator: generator yielding batches of input samples.
-        steps: Total number of steps (batches of samples)
-            to yield from `generator` before stopping.
-            Optional for `Sequence`: if unspecified, will use
-            the `len(generator)` as a number of steps.
-        max_queue_size: maximum size for the generator queue
-        workers: maximum number of processes to spin up
-        use_multiprocessing: if True, use process based threading.
-            Note that because this implementation
-            relies on multiprocessing, you should not pass
-            non picklable arguments to the generator
-            as they can't be passed easily to children processes.
-        verbose: verbosity mode, 0 or 1.
-        **kwargs: support for legacy arguments.
-
-    Returns:
-        A Numpy array of predictions.
-
-    Raises:
-        ValueError: In case the generator yields
-            data in an invalid format.
-    """
-    # Legacy support
-    if 'max_q_size' in kwargs:
-      max_queue_size = kwargs.pop('max_q_size')
-      logging.warning('The argument `max_q_size` has been renamed '
-                      '`max_queue_size`. Update your method calls accordingly.')
-    if 'pickle_safe' in kwargs:
-      use_multiprocessing = kwargs.pop('pickle_safe')
-      logging.warning('The argument `pickle_safe` has been renamed '
-                      '`use_multiprocessing`. '
-                      'Update your method calls accordingly.')
-    if kwargs:
-      raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
-
-    if not self.built:
-      self.build()
-    return self.model.predict_generator(
-        generator,
-        steps,
-        max_queue_size=max_queue_size,
-        workers=workers,
-        use_multiprocessing=use_multiprocessing,
-        verbose=verbose)
 
-  def get_config(self):
-    config = []
-    for layer in self.layers:
-      config.append({
-          'class_name': layer.__class__.__name__,
-          'config': layer.get_config()
-      })
-    return copy.deepcopy(config)
 
-  @classmethod
-  def from_config(cls, config, custom_objects=None):
-    model = cls()
-    for conf in config:
-      layer = layer_module.deserialize(conf, custom_objects=custom_objects)
-      model.add(layer)
-    return model
+# API entries importable from `keras.models`:
+Model = training.Model  # pylint: disable=invalid-name
+Sequential = sequential.Sequential  # pylint: disable=invalid-name
+save_model = saving.save_model
+load_model = saving.load_model
+model_from_config = saving.model_from_config
+model_from_yaml = saving.model_from_yaml
+model_from_json = saving.model_from_json
 
 
 def _clone_functional_model(model, input_tensors=None):
@@ -1365,7 +90,7 @@ def _clone_functional_model(model, input_tensors=None):
   else:
     # Make sure that all input tensors come from a Keras layer.
     # If tensor comes from an input layer: cache the input layer.
-    input_tensors = topology.to_list(input_tensors)
+    input_tensors = generic_utils.to_list(input_tensors)
     input_tensors_ = []
     for i, x in enumerate(input_tensors):
       if not K.is_keras_tensor(x):
@@ -1402,7 +127,7 @@ def _clone_functional_model(model, input_tensors=None):
         # Reuse previously cloned layer.
         layer = layer_map[layer]
         # Don't call InputLayer multiple times.
-        if isinstance(layer, topology.InputLayer):
+        if isinstance(layer, InputLayer):
           continue
 
       # Gather inputs to call the new layer.
@@ -1427,8 +152,9 @@ def _clone_functional_model(model, input_tensors=None):
           if has_arg(layer.call, 'mask'):
             if 'mask' not in kwargs:
               kwargs['mask'] = computed_mask
-          output_tensors = topology.to_list(layer(computed_tensor, **kwargs))
-          output_masks = topology.to_list(
+          output_tensors = generic_utils.to_list(layer(computed_tensor,
+                                                       **kwargs))
+          output_masks = generic_utils.to_list(
               layer.compute_mask(computed_tensor, computed_mask))
           computed_tensors = [computed_tensor]
           computed_masks = [computed_mask]
@@ -1438,8 +164,9 @@ def _clone_functional_model(model, input_tensors=None):
           if has_arg(layer.call, 'mask'):
             if 'mask' not in kwargs:
               kwargs['mask'] = computed_masks
-          output_tensors = topology.to_list(layer(computed_tensors, **kwargs))
-          output_masks = topology.to_list(
+          output_tensors = generic_utils.to_list(layer(computed_tensors,
+                                                       **kwargs))
+          output_masks = generic_utils.to_list(
               layer.compute_mask(computed_tensors, computed_masks))
         # Update tensor_map.
         for x, y, mask in zip(reference_output_tensors, output_tensors,
@@ -1489,14 +216,14 @@ def _clone_sequential_model(model, input_tensors=None):
   if input_tensors is None:
     return Sequential(layers=layers, name=model.name)
   else:
-    if len(topology.to_list(input_tensors)) != 1:
+    if len(generic_utils.to_list(input_tensors)) != 1:
       raise ValueError('To clone a `Sequential` model, we expect '
                        ' at most one tensor '
                        'as part of `input_tensors`.')
-    x = topology.to_list(input_tensors)[0]
+    x = generic_utils.to_list(input_tensors)[0]
     if K.is_keras_tensor(x):
       origin_layer = x._keras_history[0]
-      if isinstance(origin_layer, topology.InputLayer):
+      if isinstance(origin_layer, InputLayer):
         return Sequential(layers=[origin_layer] + layers, name=model.name)
       else:
         raise ValueError('Cannot clone a `Sequential` model on top '
diff --git a/tensorflow/python/keras/_impl/keras/models_test.py b/tensorflow/python/keras/_impl/keras/models_test.py
index 04017e4b28..5978ddd987 100644
--- a/tensorflow/python/keras/_impl/keras/models_test.py
+++ b/tensorflow/python/keras/_impl/keras/models_test.py
@@ -12,362 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for training routines."""
+"""Tests for `models.py` (model cloning, mainly)."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-import shutil
-import tempfile
-
 import numpy as np
 
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.platform import test
-from tensorflow.python.training import training as training_module
-
-try:
-  import h5py  # pylint:disable=g-import-not-at-top
-except ImportError:
-  h5py = None
-
-
-class TestModelSaving(test.TestCase):
-
-  def test_sequential_model_saving(self):
-    if h5py is None:
-      return  # Skip test if models cannot be saved.
-
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(2, input_shape=(3,)))
-      model.add(keras.layers.RepeatVector(3))
-      model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
-      model.compile(loss=keras.losses.MSE,
-                    optimizer=keras.optimizers.RMSprop(lr=0.0001),
-                    metrics=[keras.metrics.categorical_accuracy],
-                    sample_weight_mode='temporal')
-      x = np.random.random((1, 3))
-      y = np.random.random((1, 3, 3))
-      model.train_on_batch(x, y)
-
-      out = model.predict(x)
-      fd, fname = tempfile.mkstemp('.h5')
-      keras.models.save_model(model, fname)
-
-      new_model = keras.models.load_model(fname)
-      os.close(fd)
-      os.remove(fname)
-
-      out2 = new_model.predict(x)
-      self.assertAllClose(out, out2, atol=1e-05)
-
-      # test that new updates are the same with both models
-      x = np.random.random((1, 3))
-      y = np.random.random((1, 3, 3))
-      model.train_on_batch(x, y)
-      new_model.train_on_batch(x, y)
-      out = model.predict(x)
-      out2 = new_model.predict(x)
-      self.assertAllClose(out, out2, atol=1e-05)
-
-  def test_sequential_model_saving_2(self):
-    if h5py is None:
-      return  # Skip test if models cannot be saved.
-
-    with self.test_session():
-      # test with custom optimizer, loss
-
-      class CustomOp(keras.optimizers.RMSprop):
-        pass
-
-      def custom_loss(y_true, y_pred):
-        return keras.losses.mse(y_true, y_pred)
-
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(2, input_shape=(3,)))
-      model.add(keras.layers.Dense(3))
-      model.compile(loss=custom_loss, optimizer=CustomOp(), metrics=['acc'])
-
-      x = np.random.random((1, 3))
-      y = np.random.random((1, 3))
-      model.train_on_batch(x, y)
-
-      out = model.predict(x)
-      fd, fname = tempfile.mkstemp('.h5')
-      keras.models.save_model(model, fname)
-
-      model = keras.models.load_model(
-          fname,
-          custom_objects={'CustomOp': CustomOp,
-                          'custom_loss': custom_loss})
-      os.close(fd)
-      os.remove(fname)
-
-      out2 = model.predict(x)
-      self.assertAllClose(out, out2, atol=1e-05)
-
-  def test_functional_model_saving(self):
-    if h5py is None:
-      return  # Skip test if models cannot be saved.
-
-    with self.test_session():
-      inputs = keras.layers.Input(shape=(3,))
-      x = keras.layers.Dense(2)(inputs)
-      output = keras.layers.Dense(3)(x)
-
-      model = keras.models.Model(inputs, output)
-      model.compile(loss=keras.losses.MSE,
-                    optimizer=keras.optimizers.RMSprop(lr=0.0001),
-                    metrics=[keras.metrics.categorical_accuracy])
-      x = np.random.random((1, 3))
-      y = np.random.random((1, 3))
-      model.train_on_batch(x, y)
-
-      out = model.predict(x)
-      fd, fname = tempfile.mkstemp('.h5')
-      keras.models.save_model(model, fname)
-
-      model = keras.models.load_model(fname)
-      os.close(fd)
-      os.remove(fname)
-
-      out2 = model.predict(x)
-      self.assertAllClose(out, out2, atol=1e-05)
-
-  def test_saving_without_compilation(self):
-    if h5py is None:
-      return  # Skip test if models cannot be saved.
-
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(2, input_shape=(3,)))
-      model.add(keras.layers.Dense(3))
-      model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
-
-      fd, fname = tempfile.mkstemp('.h5')
-      keras.models.save_model(model, fname)
-      model = keras.models.load_model(fname)
-      os.close(fd)
-      os.remove(fname)
-
-  def test_saving_with_tf_optimizer(self):
-    if h5py is None:
-      return  # Skip test if models cannot be saved.
-
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(2, input_shape=(3,)))
-      model.add(keras.layers.Dense(3))
-      model.compile(loss='mse',
-                    optimizer=training_module.AdadeltaOptimizer(0.1),
-                    metrics=['acc'])
-
-      fd, fname = tempfile.mkstemp('.h5')
-      keras.models.save_model(model, fname)
-      model = keras.models.load_model(fname)
-      os.close(fd)
-      os.remove(fname)
-
-  def test_saving_right_after_compilation(self):
-    if h5py is None:
-      return  # Skip test if models cannot be saved.
-
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(2, input_shape=(3,)))
-      model.add(keras.layers.Dense(3))
-      model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
-      model.model._make_train_function()
-
-      fd, fname = tempfile.mkstemp('.h5')
-      keras.models.save_model(model, fname)
-      model = keras.models.load_model(fname)
-      os.close(fd)
-      os.remove(fname)
-
-  def test_saving_lambda_numpy_array_arguments(self):
-    if h5py is None:
-      return  # Skip test if models cannot be saved.
-
-    mean = np.random.random((4, 2, 3))
-    std = np.abs(np.random.random((4, 2, 3))) + 1e-5
-    inputs = keras.layers.Input(shape=(4, 2, 3))
-    output = keras.layers.Lambda(lambda image, mu, std: (image - mu) / std,
-                                 arguments={'mu': mean, 'std': std})(inputs)
-    model = keras.models.Model(inputs, output)
-    model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
-
-    fd, fname = tempfile.mkstemp('.h5')
-    keras.models.save_model(model, fname)
-
-    model = keras.models.load_model(fname)
-    os.close(fd)
-    os.remove(fname)
-
-    self.assertAllClose(mean, model.layers[1].arguments['mu'])
-    self.assertAllClose(std, model.layers[1].arguments['std'])
-
-
-class TestSequential(test.TestCase):
-  """Most Sequential model API tests are covered in `training_test.py`.
-  """
-
-  def test_basic_methods(self):
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(1, input_dim=2))
-    model.add(keras.layers.Dropout(0.3, name='dp'))
-    model.add(keras.layers.Dense(2, kernel_regularizer='l2',
-                                 kernel_constraint='max_norm'))
-    model.build()
-    self.assertEqual(model.state_updates, model.model.state_updates)
-    self.assertEqual(model.get_layer(name='dp').name, 'dp')
-
-  def test_sequential_pop(self):
-    num_hidden = 5
-    input_dim = 3
-    batch_size = 5
-    num_classes = 2
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
-      model.add(keras.layers.Dense(num_classes))
-      model.compile(loss='mse', optimizer='sgd')
-      x = np.random.random((batch_size, input_dim))
-      y = np.random.random((batch_size, num_classes))
-      model.fit(x, y, epochs=1)
-      model.pop()
-      self.assertEqual(len(model.layers), 1)
-      self.assertEqual(model.output_shape, (None, num_hidden))
-      model.compile(loss='mse', optimizer='sgd')
-      y = np.random.random((batch_size, num_hidden))
-      model.fit(x, y, epochs=1)
-
-      # Test popping single-layer model
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
-      model.pop()
-      self.assertEqual(len(model.layers), 0)
-      self.assertEqual(len(model.outputs), 0)
-
-      # Invalid use case
-      model = keras.models.Sequential()
-      with self.assertRaises(TypeError):
-        model.pop()
-
-  def test_sequential_weight_loading(self):
-    if h5py is None:
-      return
-
-    temp_dir = self.get_temp_dir()
-    self.addCleanup(shutil.rmtree, temp_dir)
-    h5_path = os.path.join(temp_dir, 'test.h5')
-
-    num_hidden = 5
-    input_dim = 3
-    batch_size = 5
-    num_classes = 2
-
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
-      model.add(keras.layers.Dense(num_classes))
-
-      x = np.random.random((batch_size, input_dim))
-      ref_y = model.predict(x)
-
-      model.save_weights(h5_path)
-
-      model = keras.models.Sequential()
-      model.add(keras.layers.Dense(num_hidden, input_dim=input_dim))
-      model.add(keras.layers.Dense(num_classes))
-      model.load_weights(h5_path)
-      y = model.predict(x)
-
-      self.assertAllClose(y, ref_y)
-
-  def test_invalid_use_cases(self):
-    with self.test_session():
-      # Added objects must be layer instances
-      with self.assertRaises(TypeError):
-        model = keras.models.Sequential()
-        model.add(None)
-
-      # Added layers must have an inputs shape
-      with self.assertRaises(ValueError):
-        model = keras.models.Sequential()
-        model.add(keras.layers.Dense(1))
-
-      # Added layers cannot have multiple outputs
-      class MyLayer(keras.layers.Layer):
-
-        def call(self, inputs):
-          return [3 * inputs, 2 * inputs]
-
-        def compute_output_shape(self, input_shape):
-          return [input_shape, input_shape]
-
-      with self.assertRaises(ValueError):
-        model = keras.models.Sequential()
-        model.add(MyLayer(input_shape=(3,)))
-      with self.assertRaises(TypeError):
-        model = keras.models.Sequential()
-        model.add(keras.layers.Dense(1, input_dim=1))
-        model.add(MyLayer())
-
-      # Building empty model
-      model = keras.models.Sequential()
-      with self.assertRaises(TypeError):
-        model.build()
-
-  def test_nested_sequential_trainability(self):
-    input_dim = 20
-    num_units = 10
-    num_classes = 2
-
-    inner_model = keras.models.Sequential()
-    inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,)))
-
-    model = keras.models.Sequential()
-    model.add(inner_model)
-    model.add(keras.layers.Dense(num_classes))
-
-    self.assertEqual(len(model.trainable_weights), 4)
-    inner_model.trainable = False
-    self.assertEqual(len(model.trainable_weights), 2)
-    inner_model.trainable = True
-    self.assertEqual(len(model.trainable_weights), 4)
-
-  def test_sequential_update_disabling(self):
-    val_a = np.random.random((10, 4))
-    val_out = np.random.random((10, 4))
-
-    with self.test_session():
-      model = keras.models.Sequential()
-      model.add(keras.layers.BatchNormalization(input_shape=(4,)))
-
-      model.trainable = False
-      assert not model.updates
-
-      model.compile('sgd', 'mse')
-      assert not model.updates
-      assert not model.model.updates
-
-      x1 = model.predict(val_a)
-      model.train_on_batch(val_a, val_out)
-      x2 = model.predict(val_a)
-      self.assertAllClose(x1, x2, atol=1e-7)
-
-      model.trainable = True
-      model.compile('sgd', 'mse')
-      assert model.updates
-      assert model.model.updates
-
-      model.train_on_batch(val_a, val_out)
-      x2 = model.predict(val_a)
-      assert np.abs(np.sum(x1 - x2)) > 1e-5
 
 
 class TestModelCloning(test.TestCase):
diff --git a/tensorflow/python/keras/_impl/keras/utils/generic_utils.py b/tensorflow/python/keras/_impl/keras/utils/generic_utils.py
index 462d600bf8..5196bf1740 100644
--- a/tensorflow/python/keras/_impl/keras/utils/generic_utils.py
+++ b/tensorflow/python/keras/_impl/keras/utils/generic_utils.py
@@ -509,3 +509,20 @@ def slice_arrays(arrays, start=None, stop=None):
       return arrays[start:stop]
     else:
       return [None]
+
+
+def to_list(x):
+  """Normalizes a list/tensor into a list.
+
+  If a tensor is passed, we return
+  a list of size 1 containing the tensor.
+
+  Arguments:
+      x: target object to be normalized.
+
+  Returns:
+      A list.
+  """
+  if isinstance(x, list):
+    return x
+  return [x]
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
index 04724e3a1a..241db8956a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
@@ -1,8 +1,8 @@
 path: "tensorflow.keras.Model"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.training.Model\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Network\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.network.Network\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
index c94bd2faa4..9673a508d6 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
@@ -1,9 +1,9 @@
 path: "tensorflow.keras.Sequential"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.models.Sequential\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.sequential.Sequential\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.training.Model\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Network\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.network.Network\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
index f4ab075959..041acf29ff 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Activation"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Activation\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
index eb558cddaf..48143b2cd6 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.ActivityRegularization"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.ActivityRegularization\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
index 770a107b66..11f78fed97 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Add"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge.Add\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
index 0ce42b706e..84eb825632 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.AlphaDropout"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.noise.AlphaDropout\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
index d6c98fa225..ab377a248f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.AveragePooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
index 754fd310c6..c2edd79f52 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.AveragePooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
index 9b62880c79..f3f37eed99 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.AveragePooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
index b371ad148c..31d1d1c049 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Average"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge.Average\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
index 3e2aba55fd..6582e1b18e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.AveragePooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
index fb37308cce..12f66095d2 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.AveragePooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
index 813470ffc7..3a45fa180e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.AveragePooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
index e251ac18e5..a0f272c178 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.BatchNormalization"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.normalization.BatchNormalization\'>"
   is_instance: "<class \'tensorflow.python.layers.normalization.BatchNormalization\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
index 699208a0b9..9c7d3154ad 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Bidirectional"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.wrappers.Bidirectional\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.wrappers.Wrapper\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
index ff08def0a0..949b225e54 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Concatenate"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge.Concatenate\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
index 6db22ca032..a736c84a10 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional_recurrent.ConvLSTM2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional_recurrent.ConvRecurrent2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.Recurrent\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
index 577f206e35..95f9afed28 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Conv1D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv1D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
index 72924c32b4..38ba15400a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
@@ -4,7 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2DTranspose\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
index 16be08d9b2..bc84e2a97e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Conv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
index 11e05f884d..0802578c22 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
@@ -4,7 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3DTranspose\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
index 72b72d6b3b..8ad4646c74 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Conv3D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
index ee93247f63..110e267b75 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Conv1D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv1D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
index e5023287e5..24cfc83af6 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
@@ -4,7 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2DTranspose\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
index ba38cb7121..c56e89187f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Conv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
index 58724a1e16..3674f2746c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
@@ -4,7 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3DTranspose\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
index 98d52c430c..5a8f9d7702 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Conv3D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
index 33b6ebe1af..caa748be81 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Cropping1D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Cropping1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
index 4b241ebb0f..97bd4a265a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Cropping2D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Cropping2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
index 1856a9ee21..20c43eeed1 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Cropping3D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Cropping3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
index a8c37af31f..256f0e4bdf 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Dense"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Dense\'>"
   is_instance: "<class \'tensorflow.python.layers.core.Dense\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
index 07d3f023e5..d1e53f900c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Dot"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge.Dot\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
index e2e21b5f12..b010ff6805 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Dropout"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
index 92b9760d53..fffd3854bb 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.ELU"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.ELU\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
index 83c528b401..1155fe03fc 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Embedding"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.embeddings.Embedding\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
index 7360975288..5e4bebb15b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Flatten"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Flatten\'>"
   is_instance: "<class \'tensorflow.python.layers.core.Flatten\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
index b329f1c46b..cb9bb3d821 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.GRUCell"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.GRUCell\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
index c741d4d6e6..9a36e80649 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GRU"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.GRU\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
index 57596badf1..eb32238e15 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.GaussianDropout"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.noise.GaussianDropout\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
index 3829353cc3..37fc8e29ae 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.GaussianNoise"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.noise.GaussianNoise\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
index e53e78a977..490816458b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalAveragePooling1D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalAveragePooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
index 48fcd1044e..ab49f67f33 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalAveragePooling2D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalAveragePooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
index 66c06ed472..3d7cb3ba49 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalAveragePooling3D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalAveragePooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
index 4f2420f74a..c99ddab4f3 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalAvgPool1D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalAveragePooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
index 7912a6d933..290d2eaebe 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalAvgPool2D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalAveragePooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
index d5b2d2c274..cf63069641 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalAvgPool3D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalAveragePooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
index d88ff17eb6..2dadc67c09 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalMaxPool1D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalMaxPooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
index c8cc5a0ddf..1a1a1dcf64 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalMaxPool2D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalMaxPooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
index 7956c5a340..44898e23ad 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalMaxPool3D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalMaxPooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
index 0a7e16413d..941d867d24 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalMaxPooling1D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalMaxPooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
index 6c8a58a996..9a5a6325f8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalMaxPooling2D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalMaxPooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
index 7678ce8aab..7a0c1932f6 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.GlobalMaxPooling3D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.GlobalMaxPooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
index 1e9370b02f..f679c1d006 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.InputLayer"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.InputLayer\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.input_layer.InputLayer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
index 3b171b137a..ad1e7f2cad 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.LSTMCell"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.LSTMCell\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
index 29d9cf78ab..6dad4b4897 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.LSTM"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.LSTM\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
index ca01449299..fa45d8c902 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Lambda"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Lambda\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
index c52ad72754..023d6c0d69 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
@@ -1,6 +1,6 @@
 path: "tensorflow.keras.layers.Layer"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
index 8134fb7386..e429fced77 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.LeakyReLU"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.LeakyReLU\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
index c5d4523009..462568124f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.LocallyConnected1D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.local.LocallyConnected1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
index bcbed9241b..11bf6a2b42 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.LocallyConnected2D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.local.LocallyConnected2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
index 244e79b4ff..a932448891 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Masking"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Masking\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
index 56cbf5df78..6ff2adddac 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.MaxPooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
index 33c2d30e86..2957673d4d 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.MaxPooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
index 94f91059b7..2191c10b73 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.MaxPooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
index 247230a6d6..af750ac1b6 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.MaxPooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
index 8d61b67e7c..9046061510 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.MaxPooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
index ad2e308020..a40666807b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling.MaxPooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
index ff0db15f19..65378cef42 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Maximum"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge.Maximum\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
index 1d3f33f045..b037559e02 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.Multiply"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge.Multiply\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
index c86bc49b22..b3a7f47fa5 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.PReLU"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.PReLU\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
index 2043e1a126..b2f22f7da3 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Permute"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Permute\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
index ad539a7c4c..792eacf90d 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.RNN"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
index 4b0e98520a..5b79a021ca 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.RepeatVector"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.RepeatVector\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
index 34bc71af8a..99c64505ee 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Reshape"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Reshape\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
index dd67b76523..d5873ccf76 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
@@ -4,7 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.SeparableConv1D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._SeparableConv\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
index 5d898fb2bd..76b4c10a46 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
@@ -4,7 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.SeparableConv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._SeparableConv\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
index bf62c095e7..40cd87de5f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
@@ -4,7 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.SeparableConv1D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._SeparableConv\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
index c758d87993..c44c0da148 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
@@ -4,7 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.SeparableConv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._SeparableConv\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
index 6e3cde3e3e..bd70c31c38 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.SimpleRNNCell"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.SimpleRNNCell\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
index 6fafc77b94..de717976cf 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.SimpleRNN"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.SimpleRNN\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
index ee4b2fa39e..a93b7b8f6e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Softmax"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.Softmax\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
index e4727072e3..4dc24b195e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.SpatialDropout1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
index c5ff704311..a3bb1cc414 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.SpatialDropout2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
index 476a7f362c..f9a78106fa 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
@@ -3,7 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.SpatialDropout3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
index 3dde1e5769..5aa21f4022 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.StackedRNNCells"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.StackedRNNCells\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
index ef31c5443e..88e8a46572 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.ThresholdedReLU"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.ThresholdedReLU\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
index 1e176d8d4b..f2a7673998 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
@@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.TimeDistributed"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.wrappers.TimeDistributed\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.wrappers.Wrapper\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
index a81b83be49..4db82ddfa9 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.UpSampling1D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.UpSampling1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
index 5403279d45..61e65ad56d 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.UpSampling2D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.UpSampling2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
index 96c337caf2..3d9402db4e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.UpSampling3D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.UpSampling3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
index ea3bb2f8f5..0223799ed4 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.Wrapper"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.wrappers.Wrapper\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
index b81a4b1c50..2e4429833a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.ZeroPadding1D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.ZeroPadding1D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
index 1a26f2f3c9..26cf7b9e49 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.ZeroPadding2D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.ZeroPadding2D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
index 310277fe67..64d35d9447 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
@@ -1,7 +1,7 @@
 path: "tensorflow.keras.layers.ZeroPadding3D"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.ZeroPadding3D\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
index 88eb237cec..18be9c9701 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
@@ -1,8 +1,8 @@
 path: "tensorflow.keras.models.Model"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.training.Model\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Network\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.network.Network\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
index 34f10f01ad..b934632922 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
@@ -1,9 +1,9 @@
 path: "tensorflow.keras.models.Sequential"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.models.Sequential\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.sequential.Sequential\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.training.Model\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Network\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.network.Network\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
   member {
-- 
GitLab


From 917136b3bb7d83a1674bb24d3c0b0892ad77e056 Mon Sep 17 00:00:00 2001
From: Guangda Lai <laigd@google.com>
Date: Fri, 23 Feb 2018 18:18:15 -0800
Subject: [PATCH 269/575] Exclude more tests for cuda_on_cpu.

PiperOrigin-RevId: 186851831
---
 tensorflow/contrib/lite/testing/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD
index b5960d6f8d..83b9e21427 100644
--- a/tensorflow/contrib/lite/testing/BUILD
+++ b/tensorflow/contrib/lite/testing/BUILD
@@ -317,7 +317,10 @@ tf_cc_test(
         "//tensorflow/contrib/lite:testdata/multi_add.bin",
         "//tensorflow/contrib/lite:testdata/multi_add.pb",
     ],
-    tags = ["no_oss"],
+    tags = [
+        "no_cuda_on_cpu_tap",
+        "no_oss",
+    ],
     deps = [
         ":tflite_diff_flags",
         ":tflite_diff_util",
-- 
GitLab


From b9af4308064dc560c4501523a5508de553000fb0 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Sat, 27 Jan 2018 19:48:35 +0000
Subject: [PATCH 270/575] Enable multi-dimensional and axis support for
 tf.unique_with_counts

This fix tries to address the issue raised in 16499 to bring
multi-dimensional and axis support for `unique_with_counts`.

When `UniqueV2` kernel was added in 12952, it actually supports
multi-dimensional and axis support for `unique_with_counts` as well,
just not registered.

This fix:
1. Register `UniqueWithCountsV2` kernel to have axis support.
2. Hide both `UniqueWithCounts` and `UniqueWithCountsV2`
3. Add python unique_with_counts wrapper to call `gen_array_ops._unique_with_counts`
4. If APi review passes and the PR merges, `unique_with_counts` will switch to `gen_array_ops._unique_with_counts_v2` (in 3 weeks).

This fix fixes 16499.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/core/kernels/unique_op.cc | 10 ++++++++++
 tensorflow/core/ops/array_ops.cc     | 17 +++++++++++++++++
 2 files changed, 27 insertions(+)

diff --git a/tensorflow/core/kernels/unique_op.cc b/tensorflow/core/kernels/unique_op.cc
index 0ef8724b10..31388e4290 100644
--- a/tensorflow/core/kernels/unique_op.cc
+++ b/tensorflow/core/kernels/unique_op.cc
@@ -223,6 +223,16 @@ class UniqueOp : public OpKernel {
                               .Device(DEVICE_CPU)                \
                               .TypeConstraint<type>("T")         \
                               .TypeConstraint<int64>("out_idx"), \
+                          UniqueOp<type, int64>);                \
+  REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsV2")             \
+                              .Device(DEVICE_CPU)                \
+                              .TypeConstraint<type>("T")         \
+                              .TypeConstraint<int32>("out_idx"), \
+                          UniqueOp<type, int32>)                 \
+  REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsV2")             \
+                              .Device(DEVICE_CPU)                \
+                              .TypeConstraint<type>("T")         \
+                              .TypeConstraint<int64>("out_idx"), \
                           UniqueOp<type, int64>)
 TF_CALL_REAL_NUMBER_TYPES(REGISTER_UNIQUE);
 REGISTER_UNIQUE(string)
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 267ce88440..2fab62ea5c 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -1201,6 +1201,23 @@ REGISTER_OP("UniqueWithCounts")
       return Status::OK();
     });
 
+REGISTER_OP("UniqueWithCountsV2")
+    .Input("x: T")
+    .Input("axis: Taxis")
+    .Output("y: T")
+    .Output("idx: out_idx")
+    .Output("count: out_idx")
+    .Attr("T: type")
+    .Attr("Taxis: {int32,int64} = DT_INT64")
+    .Attr("out_idx: {int32, int64} = DT_INT32")
+    .SetShapeFn([](InferenceContext* c) {
+      auto uniq = c->Vector(InferenceContext::kUnknownDim);
+      c->set_output(0, uniq);
+      c->set_output(1, c->input(0));
+      c->set_output(2, uniq);
+      return Status::OK();
+    });
+
 namespace {
 
 Status ShapeShapeFn(InferenceContext* c) {
-- 
GitLab


From 17cc97ca34de33aadd136ef804a9c4fbeabf73b6 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Sat, 27 Jan 2018 19:56:33 +0000
Subject: [PATCH 271/575] Hide UniqueWithCounts and UniqueWithCountsV2 in
 hidden_ops.txt

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/python/ops/hidden_ops.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
index f6ef6f3f3d..9b8172bf26 100644
--- a/tensorflow/python/ops/hidden_ops.txt
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -32,6 +32,8 @@ TileGrad  # Exported through array_grad instead of array_ops.
 ZerosLike  # TODO(josh11b): Use this instead of the Python version.
 Unique
 UniqueV2
+UniqueWithCounts
+UniqueWithCountsV2
 Unpack
 
 # candidate_sampling_ops
-- 
GitLab


From 812eac93168881c6472fc08b90bdc4a9695b3220 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Sat, 27 Jan 2018 19:57:38 +0000
Subject: [PATCH 272/575] Add python wrapper for unique_with_counts

to call gen_array_ops._unique_with_counts

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 tensorflow/python/ops/array_ops.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 08db8a17b5..14824962ea 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1319,6 +1319,18 @@ def unique(x, out_idx=dtypes.int32, name=None):
 unique.__doc__ = gen_array_ops._unique.__doc__
 
 
+@tf_export("unique_with_counts")
+def unique_with_counts(x, out_idx=dtypes.int32, name=None):
+  # TODO(yongtang): switch to v2 once API deprecation
+  # period (3 weeks) pass.
+  # TODO(yongtang): The documentation should also
+  # be updated when switch  to v2.
+  return gen_array_ops._unique_with_counts(x, out_idx, name)
+
+
+unique_with_counts.__doc__ = gen_array_ops._unique_with_counts.__doc__
+
+
 @tf_export("split")
 def split(value, num_or_size_splits, axis=0, num=None, name="split"):
   """Splits a tensor into sub tensors.
-- 
GitLab


From a347f14c8aa14e81710c0cb33bf1a0bd23f3bcfd Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Sat, 27 Jan 2018 19:58:54 +0000
Subject: [PATCH 273/575] Add test cases for unique_with_counts_v2

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 .../python/kernel_tests/unique_op_test.py     | 33 +++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/tensorflow/python/kernel_tests/unique_op_test.py b/tensorflow/python/kernel_tests/unique_op_test.py
index 6366d2e181..4498fd9fe9 100644
--- a/tensorflow/python/kernel_tests/unique_op_test.py
+++ b/tensorflow/python/kernel_tests/unique_op_test.py
@@ -133,6 +133,39 @@ class UniqueWithCountsTest(test.TestCase):
       v = [1 if x[i] == value.decode('ascii') else 0 for i in range(7000)]
       self.assertEqual(count, sum(v))
 
+  def testInt32Axis(self):
+    for dtype in [np.int32, np.int64]:
+      x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]])
+      with self.test_session() as sess:
+        y0, idx0, count0 = gen_array_ops._unique_with_counts_v2(
+            x, axis=np.array([0], dtype))
+        tf_y0, tf_idx0, tf_count0 = sess.run([y0, idx0, count0])
+        y1, idx1, count1 = gen_array_ops._unique_with_counts_v2(
+            x, axis=np.array([1], dtype))
+        tf_y1, tf_idx1, tf_count1 = sess.run([y1, idx1, count1])
+      self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]]))
+      self.assertAllEqual(tf_idx0, np.array([0, 0, 1]))
+      self.assertAllEqual(tf_count0, np.array([2, 1]))
+      self.assertAllEqual(tf_y1, np.array([[1, 0], [1, 0], [2, 0]]))
+      self.assertAllEqual(tf_idx1, np.array([0, 1, 1]))
+      self.assertAllEqual(tf_count1, np.array([1, 2]))
+
+  def testInt32V2(self):
+    # This test is only temporary, once V2 is used
+    # by default, the axis will be wrapped to allow `axis=None`.
+    x = np.random.randint(2, high=10, size=7000)
+    with self.test_session() as sess:
+      y, idx, count = gen_array_ops._unique_with_counts_v2(
+          x, axis=np.array([], np.int32))
+      tf_y, tf_idx, tf_count = sess.run([y, idx, count])
+
+    self.assertEqual(len(x), len(tf_idx))
+    self.assertEqual(len(tf_y), len(np.unique(x)))
+    for i in range(len(x)):
+      self.assertEqual(x[i], tf_y[tf_idx[i]])
+    for value, count in zip(tf_y, tf_count):
+      self.assertEqual(count, np.sum(x == value))
+
 
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From ddbe17802d508619e749522e998dfb323c363921 Mon Sep 17 00:00:00 2001
From: Yong Tang <yong.tang.github@outlook.com>
Date: Sat, 24 Feb 2018 02:44:13 +0000
Subject: [PATCH 274/575] Update api_def for
 UniqueWithCounts/UniqueWithCountsV2

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
 .../base_api/api_def_UniqueWithCountsV2.pbtxt | 85 +++++++++++++++++++
 .../python_api/api_def_UniqueWithCounts.pbtxt |  4 +
 .../api_def_UniqueWithCountsV2.pbtxt          |  4 +
 3 files changed, 93 insertions(+)
 create mode 100644 tensorflow/core/api_def/base_api/api_def_UniqueWithCountsV2.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_UniqueWithCounts.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_UniqueWithCountsV2.pbtxt

diff --git a/tensorflow/core/api_def/base_api/api_def_UniqueWithCountsV2.pbtxt b/tensorflow/core/api_def/base_api/api_def_UniqueWithCountsV2.pbtxt
new file mode 100644
index 0000000000..e21f56ba5b
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_UniqueWithCountsV2.pbtxt
@@ -0,0 +1,85 @@
+op {
+  graph_op_name: "UniqueWithCountsV2"
+  in_arg {
+    name: "x"
+    description: <<END
+A `Tensor`.
+END
+  }
+  in_arg {
+    name: "axis"
+    description: <<END
+A `Tensor` of type `int32` (default: None). The axis of the Tensor to
+find the unique elements.
+END
+  }
+  out_arg {
+    name: "y"
+    description: <<END
+A `Tensor`. Unique elements along the `axis` of `Tensor` x.
+END
+  }
+  out_arg {
+    name: "idx"
+    description: <<END
+A 1-D Tensor. Has the same type as x that contains the index of each
+value of x in the output y.
+END
+  }
+  out_arg {
+    name: "count"
+    description: <<END
+A 1-D Tensor. The count of each value of x in the output y.
+END
+  }
+  summary: "Finds unique elements along an axis of a tensor."
+  description: <<END
+This operation either returns a tensor `y` containing unique elements
+along the `axis` of a tensor. The returned unique elements is sorted
+in the same order as they occur along `axis` in `x`.
+This operation also returns a tensor `idx` and a tensor `count`
+that are the same size as the number of the elements in `x` along the
+`axis` dimension. The `idx` contains the index in the unique output `y`
+and the `count` contains the count in the unique output `y`.
+In other words, for an `1-D` tensor `x` with `axis = None:
+
+`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
+
+For example:
+
+```
+# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+y, idx, count = unique_with_counts(x)
+y ==> [1, 2, 4, 7, 8]
+idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
+count ==> [2, 1, 3, 1, 2]
+```
+
+For an `2-D` tensor `x` with `axis = 0`:
+
+```
+# tensor 'x' is [[1, 0, 0],
+#                [1, 0, 0],
+#                [2, 0, 0]]
+y, idx, count = unique_with_counts(x, axis=0)
+y ==> [[1, 0, 0],
+       [2, 0, 0]]
+idx ==> [0, 0, 1]
+count ==> [2, 1]
+```
+
+For an `2-D` tensor `x` with `axis = 1`:
+
+```
+# tensor 'x' is [[1, 0, 0],
+#                [1, 0, 0],
+#                [2, 0, 0]]
+y, idx, count = unique_with_counts(x, axis=1)
+y ==> [[1, 0],
+       [1, 0],
+       [2, 0]]
+idx ==> [0, 1, 1]
+count ==> [1, 2]
+```
+END
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_UniqueWithCounts.pbtxt b/tensorflow/core/api_def/python_api/api_def_UniqueWithCounts.pbtxt
new file mode 100644
index 0000000000..71b35eaab5
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_UniqueWithCounts.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "UniqueWithCounts"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_UniqueWithCountsV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_UniqueWithCountsV2.pbtxt
new file mode 100644
index 0000000000..7876e55cf3
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_UniqueWithCountsV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "UniqueWithCountsV2"
+  visibility: HIDDEN
+}
-- 
GitLab


From 0220d128c78f4061595a13d40037aebc865239cb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 24 Feb 2018 06:35:12 -0800
Subject: [PATCH 275/575] Use the new inspect_utils API to to get the
 function's namespace.

PiperOrigin-RevId: 186884307
---
 tensorflow/contrib/py2tf/impl/conversion.py | 22 +++++++--------------
 1 file changed, 7 insertions(+), 15 deletions(-)

diff --git a/tensorflow/contrib/py2tf/impl/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py
index 4bf698f207..044de33568 100644
--- a/tensorflow/contrib/py2tf/impl/conversion.py
+++ b/tensorflow/contrib/py2tf/impl/conversion.py
@@ -19,7 +19,6 @@ from __future__ import division
 from __future__ import print_function
 
 import gast
-import six
 
 from tensorflow.contrib.py2tf import utils
 from tensorflow.contrib.py2tf.converters import asserts
@@ -36,6 +35,7 @@ from tensorflow.contrib.py2tf.converters import side_effect_guards
 from tensorflow.contrib.py2tf.impl import config
 from tensorflow.contrib.py2tf.impl import naming
 from tensorflow.contrib.py2tf.pyct import context
+from tensorflow.contrib.py2tf.pyct import inspect_utils
 from tensorflow.contrib.py2tf.pyct import parser
 from tensorflow.contrib.py2tf.pyct import qual_names
 from tensorflow.contrib.py2tf.pyct.static_analysis import activity
@@ -155,7 +155,7 @@ def class_to_graph(c, conversion_map):
   if not members:
     raise ValueError('Cannot convert %s: it has no member methods.')
 
-  class_globals = None
+  class_namespace = None
   for _, m in members:
     node, _ = function_to_graph(
         m,
@@ -164,10 +164,10 @@ def class_to_graph(c, conversion_map):
         arg_types={'self': (c.__name__, c)},
         owner_type=c)
     # TODO(mdan): Do not assume all members have the same view of globals.
-    if class_globals is None:
-      class_globals = six.get_function_globals(m)
+    if class_namespace is None:
+      class_namespace = inspect_utils.getnamespace(m)
     converted_members[m] = node
-  namer = conversion_map.new_namer(class_globals)
+  namer = conversion_map.new_namer(class_namespace)
   class_name = namer.compiled_class_name(c.__name__, c)
   node = gast.ClassDef(
       class_name,
@@ -202,19 +202,11 @@ def function_to_graph(f, conversion_map, arg_values, arg_types,
   """Specialization of `entity_to_graph` for callable functions."""
   node, source = parser.parse_entity(f)
   node = node.body[0]
-  namespace = six.get_function_globals(f)
-
-  # This is needed for non-global functions.
-  closure = six.get_function_closure(f)
-  if closure:
-    for e in closure:
-      if callable(e.cell_contents):
-        fn = e.cell_contents
-        namespace[fn.__name__] = fn
 
+  namespace = inspect_utils.getnamespace(f)
   _add_self_references(namespace, conversion_map.api_module)
-
   namer = conversion_map.new_namer(namespace)
+
   ctx = context.EntityContext(
       namer=namer,
       source_code=source,
-- 
GitLab


From a9bf219028e4c37e3d826b52699c39619da7bb1b Mon Sep 17 00:00:00 2001
From: Nick Kreeger <nick.kreeger@gmail.com>
Date: Sat, 24 Feb 2018 11:19:21 -0800
Subject: [PATCH 276/575] Ship TF Eager header with libtensorflow tarballs.
 (#17230)

* Ship TF Eager header with libtensorflow tarballs.
---
 tensorflow/tools/lib_package/BUILD | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD
index 614457e899..3fbdb5cacd 100644
--- a/tensorflow/tools/lib_package/BUILD
+++ b/tensorflow/tools/lib_package/BUILD
@@ -27,6 +27,7 @@ pkg_tar(
         ":cheaders",
         ":clib",
         ":clicenses",
+        ":eager_cheaders",
     ],
 )
 
@@ -57,7 +58,6 @@ pkg_tar(
     name = "cheaders",
     files = [
         "//tensorflow/c:headers",
-        "//tensorflow/c/eager:headers",
     ],
     package_dir = "include/tensorflow/c",
     # Mark as "manual" till
@@ -68,6 +68,20 @@ pkg_tar(
     tags = ["manual"],
 )
 
+pkg_tar(
+    name = "eager_cheaders",
+    files = [
+        "//tensorflow/c/eager:headers",
+    ],
+    package_dir = "include/tensorflow/c/eager",
+    # Mark as "manual" till
+    # https://github.com/bazelbuild/bazel/issues/2352
+    # and https://github.com/bazelbuild/bazel/issues/1580
+    # are resolved, otherwise these rules break when built
+    # with Python 3.
+    tags = ["manual"],
+)
+
 pkg_tar(
     name = "clib",
     files = ["//tensorflow:libtensorflow.so"],
-- 
GitLab


From d187c21c85e15db6521147a3b5c8434dafca44d8 Mon Sep 17 00:00:00 2001
From: Ming Li <14131823+minggli@users.noreply.github.com>
Date: Sun, 25 Feb 2018 14:41:12 +0800
Subject: [PATCH 277/575] update documentation to {0, 1} (#17245)

---
 tensorflow/python/ops/losses/losses_impl.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py
index a39417139e..7386976e93 100644
--- a/tensorflow/python/ops/losses/losses_impl.py
+++ b/tensorflow/python/ops/losses/losses_impl.py
@@ -654,7 +654,7 @@ def sigmoid_cross_entropy(
 
   Args:
     multi_class_labels: `[batch_size, num_classes]` target integer labels in
-      `(0, 1)`.
+      `{0, 1}`.
     logits: Float `[batch_size, num_classes]` logits outputs of the network.
     weights: Optional `Tensor` whose rank is either 0, or the same rank as
       `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
-- 
GitLab


From eb0792340efaca19e75adcb73b6f3250dfd36ca0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Sat, 24 Feb 2018 23:51:54 -0800
Subject: [PATCH 278/575] Re-enables moving_average_optimizer_test. Resource
 variable bug fixed by apassos@

PiperOrigin-RevId: 186921623
---
 tensorflow/contrib/opt/BUILD | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 52e88348c1..827279bd47 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -70,9 +70,6 @@ py_test(
     srcs = ["python/training/moving_average_optimizer_test.py"],
     srcs_version = "PY2AND3",
     tags = [
-        "manual",
-        "no_oss",
-        "notap",
         "notsan",  # b/31055119
     ],
     deps = [
-- 
GitLab


From 020408675695bce8133076d2dd6cc7188adde534 Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Sun, 25 Feb 2018 02:55:29 -0800
Subject: [PATCH 279/575] [XLA] Remove bitcast-converts between same shape.

PiperOrigin-RevId: 186929931
---
 tensorflow/compiler/xla/service/algebraic_simplifier.cc | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 4391462c1c..5ddd8ec377 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -122,6 +122,8 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault {
 
   Status HandleBitcast(HloInstruction* bitcast) override;
 
+  Status HandleBitcastConvert(HloInstruction* bitcast) override;
+
   Status HandleBroadcast(HloInstruction* broadcast) override;
 
   Status HandleConcatenate(HloInstruction* concatenate) override;
@@ -411,6 +413,13 @@ Status AlgebraicSimplifierVisitor::HandleBitcast(HloInstruction* bitcast) {
   return Status::OK();
 }
 
+Status AlgebraicSimplifierVisitor::HandleBitcastConvert(
+    HloInstruction* bitcast) {
+  // Eliminate bitcast converts between same shape.
+  ReplaceInstructionIfSameShape(bitcast, bitcast->mutable_operand(0));
+  return Status::OK();
+}
+
 Status AlgebraicSimplifierVisitor::HandleCopy(HloInstruction* copy) {
   // If a copy feeds a copy, make it a single copy.
   if (copy->operand(0)->opcode() == HloOpcode::kCopy) {
-- 
GitLab


From 26ae3287a12c71fccaec9ea74f55b6a51a3d33c6 Mon Sep 17 00:00:00 2001
From: ManHyuk <manhyuk@kw.ac.kr>
Date: Mon, 26 Feb 2018 01:09:28 +0900
Subject: [PATCH 280/575] Fix typo (#17258)

* fix typo
---
 tensorflow/contrib/slim/python/slim/data/parallel_reader.py     | 2 +-
 .../python/kernel_tests/linalg/linear_operator_diag_test.py     | 2 +-
 tensorflow/python/ops/distributions/special_math.py             | 2 +-
 tensorflow/python/ops/linalg/linear_operator_diag.py            | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/data/parallel_reader.py b/tensorflow/contrib/slim/python/slim/data/parallel_reader.py
index ad5e985487..b3343aef47 100644
--- a/tensorflow/contrib/slim/python/slim/data/parallel_reader.py
+++ b/tensorflow/contrib/slim/python/slim/data/parallel_reader.py
@@ -221,7 +221,7 @@ def parallel_read(data_sources,
         the data will be cycled through indefinitely.
     num_readers: a integer, number of Readers to create.
     reader_kwargs: an optional dict, of kwargs for the reader.
-    shuffle: boolean, wether should shuffle the files and the records by using
+    shuffle: boolean, whether should shuffle the files and the records by using
       RandomShuffleQueue as common_queue.
     dtypes:  A list of types.  The length of dtypes must equal the number
         of elements in each record. If it is None it will default to
diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py
index 343d158498..8cb9f9e621 100644
--- a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py
+++ b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py
@@ -129,7 +129,7 @@ class LinearOperatorDiagTest(
     with self.test_session() as sess:
       x = random_ops.random_normal(shape=(2, 2, 3, 4))
 
-      # This LinearOperatorDiag will be brodacast to (2, 2, 3, 3) during solve
+      # This LinearOperatorDiag will be broadcast to (2, 2, 3, 3) during solve
       # and matmul with 'x' as the argument.
       diag = random_ops.random_uniform(shape=(2, 1, 3))
       operator = linalg.LinearOperatorDiag(diag, is_self_adjoint=True)
diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py
index bed4cbb2c1..1d605c5dfc 100644
--- a/tensorflow/python/ops/distributions/special_math.py
+++ b/tensorflow/python/ops/distributions/special_math.py
@@ -213,7 +213,7 @@ def _ndtri(p):
 
   # Compute x for p <= exp(-2): x = z - log(z)/z - (1/z) P(1/z) / Q(1/z),
   # where z = sqrt(-2. * log(p)), and P/Q are chosen between two different
-  # arrays based on wether p < exp(-32).
+  # arrays based on whether p < exp(-32).
   z = math_ops.sqrt(-2. * math_ops.log(sanitized_mcp))
   first_term = z - math_ops.log(z) / z
   second_term_small_p = (_create_polynomial(1. / z, p2)
diff --git a/tensorflow/python/ops/linalg/linear_operator_diag.py b/tensorflow/python/ops/linalg/linear_operator_diag.py
index b3ec3d5b7c..e180e83026 100644
--- a/tensorflow/python/ops/linalg/linear_operator_diag.py
+++ b/tensorflow/python/ops/linalg/linear_operator_diag.py
@@ -67,7 +67,7 @@ class LinearOperatorDiag(linear_operator.LinearOperator):
   operator = LinearOperatorDiag(diag)
 
   # Create a shape [2, 1, 4, 2] vector.  Note that this shape is compatible
-  # since the batch dimensions, [2, 1], are brodcast to
+  # since the batch dimensions, [2, 1], are broadcast to
   # operator.batch_shape = [2, 3].
   y = tf.random_normal(shape=[2, 1, 4, 2])
   x = operator.solve(y)
-- 
GitLab


From 27adc952de9aa38d75fa513d972f2e7012da1d0f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 07:21:08 -0800
Subject: [PATCH 281/575] Annotate attribute nodes with the value or type of
 their parent. This helps with resolving function owners, since using
 reflection to do it is unreliable.

PiperOrigin-RevId: 187017742
---
 tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py    | 2 ++
 .../contrib/py2tf/pyct/static_analysis/live_values_test.py      | 1 +
 2 files changed, 3 insertions(+)

diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py
index 9c0a9a9e74..0388be5d25 100644
--- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py
+++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values.py
@@ -86,6 +86,7 @@ class LiveValueResolver(transformer.Base):
       if not hasattr(parent_object, node.attr):
         raise AttributeError('%s has no attribute %s' % (parent_object,
                                                          node.attr))
+      anno.setanno(node, 'parent_type', type(parent_object))
       anno.setanno(node, 'live_val', getattr(parent_object, node.attr))
       anno.setanno(node, 'fqn', anno.getanno(node.value, 'fqn') + (node.attr,))
     # TODO(mdan): Investigate the role built-in annotations can play here.
@@ -96,6 +97,7 @@ class LiveValueResolver(transformer.Base):
         # This would not hold for dynamic members like function attributes.
         # For the dynamic case, we simply leave the node without an annotation,
         # and let downstream consumers figure out what to do.
+        anno.setanno(node, 'parent_type', parent_type)
         anno.setanno(node, 'live_val', getattr(parent_type, node.attr))
         anno.setanno(node, 'fqn',
                      anno.getanno(node.value, 'type_fqn') + (node.attr,))
diff --git a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py
index 1e81bc70a8..c133a455b3 100644
--- a/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py
+++ b/tensorflow/contrib/py2tf/pyct/static_analysis/live_values_test.py
@@ -103,6 +103,7 @@ class LiveValuesResolverTest(test.TestCase):
         arg_types={'self': (TestClass.__name__, TestClass)})
     func_node = node.body[0].body[0].value.func
     self.assertEquals(TestClass.member, anno.getanno(func_node, 'live_val'))
+    self.assertEquals(TestClass, anno.getanno(func_node, 'parent_type'))
     self.assertEquals(('TestClass', 'member'), anno.getanno(func_node, 'fqn'))
 
 
-- 
GitLab


From 546d30232a07c790de55ea75795f24614312c12a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 08:04:09 -0800
Subject: [PATCH 282/575] Drop the getcallargs extension as its logic had to be
 moved to a higher level into api.py.

PiperOrigin-RevId: 187022717
---
 .../contrib/py2tf/pyct/inspect_utils.py       | 27 --------------
 .../contrib/py2tf/pyct/inspect_utils_test.py  | 36 -------------------
 2 files changed, 63 deletions(-)

diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils.py b/tensorflow/contrib/py2tf/pyct/inspect_utils.py
index c1af95e2ab..d19c6ed75e 100644
--- a/tensorflow/contrib/py2tf/pyct/inspect_utils.py
+++ b/tensorflow/contrib/py2tf/pyct/inspect_utils.py
@@ -50,33 +50,6 @@ def getnamespace(f):
   return namespace
 
 
-def getcallargs(c, *args, **kwargs):
-  """Extension of getcallargs to non-function callables."""
-  if tf_inspect.isfunction(c) or tf_inspect.ismethod(c):
-    # The traditional getcallargs
-    return tf_inspect.getcallargs(c, *args, **kwargs)
-
-  if tf_inspect.isclass(c):
-    # Constructors: use a sentinel to remove the self argument.
-    self_sentinel = object()
-    arg_map = tf_inspect.getcallargs(
-        c.__init__, self_sentinel, *args, **kwargs)
-    # Find and remove the self arg. We cannot assume it's called 'self'.
-    self_arg_name = None
-    for name, value in arg_map.items():
-      if value is self_sentinel:
-        self_arg_name = name
-        break
-    del arg_map[self_arg_name]
-    return arg_map
-
-  if hasattr(c, '__call__'):
-    # Callable objects: map self to the object itself
-    return tf_inspect.getcallargs(c.__call__, *args, **kwargs)
-
-  raise NotImplementedError('unknown callable "%s"' % type(c))
-
-
 def getmethodclass(m):
   """Resolves a function's owner, e.g. a method's class.
 
diff --git a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
index d96c3df547..5528ac851f 100644
--- a/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
+++ b/tensorflow/contrib/py2tf/pyct/inspect_utils_test.py
@@ -127,42 +127,6 @@ class InspectUtilsTest(test.TestCase):
     self.assertEqual(ns['closed_over_primitive'], closed_over_primitive)
     self.assertTrue('local_var' not in ns)
 
-  def test_getcallargs_constructor(self):
-
-    class TestSuperclass(object):
-
-      def __init__(self, x):
-        pass
-
-    class TestCallable(TestSuperclass):
-      pass
-
-    self.assertDictEqual({
-        'x': 1
-    }, inspect_utils.getcallargs(TestCallable, 1))
-
-  def test_getcallargs_object(self):
-
-    class TestCallable(object):
-
-      def __call__(self, x):
-        pass
-
-    obj = TestCallable()
-    self.assertDictEqual({
-        'self': obj,
-        'x': 1
-    }, inspect_utils.getcallargs(obj, 1))
-
-  def test_getcallargs_function(self):
-
-    def test_fn(x):
-      return x + 1
-
-    self.assertDictEqual({
-        'x': 1
-    }, inspect_utils.getcallargs(test_fn, 1))
-
   def test_getmethodclass(self):
 
     self.assertEqual(
-- 
GitLab


From d2ecfc5ab0a22be088e4385c2d601c2ba8ad8816 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 08:58:48 -0800
Subject: [PATCH 283/575] Add __str__ method to _RefVariableProcessor.

PiperOrigin-RevId: 187029027
---
 tensorflow/python/training/optimizer.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py
index 678d6322aa..454cc3add5 100644
--- a/tensorflow/python/training/optimizer.py
+++ b/tensorflow/python/training/optimizer.py
@@ -98,6 +98,9 @@ class _RefVariableProcessor(_OptimizableVariable):
   def __init__(self, v):
     self._v = v
 
+  def __str__(self):
+    return "<_RefVariableProcessor(%s)>" % self._v
+
   def target(self):
     return self._v._ref()  # pylint: disable=protected-access
 
-- 
GitLab


From c76dd17b2086b760ac38e1e12ec3d4df6268d0b3 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Mon, 26 Feb 2018 09:24:38 -0800
Subject: [PATCH 284/575] [XLA:GPU] Fix HLO profiling when multiple streams are
 involved.

We were enqueueing the timer on the main stream, but not blocking the
substreams, so the results were nonsensical.

PiperOrigin-RevId: 187032412
---
 .../xla/service/gpu/gpu_executable.cc         | 21 ++++++++++++-------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
index 623d6714de..04b37d913e 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc
@@ -46,12 +46,14 @@ namespace {
 class HloExecutionProfiler {
  public:
   // If profiling is enabled, start an execution timer running.
-  explicit HloExecutionProfiler(bool do_profile, HloExecutionProfile* profile,
-                                se::Stream* stream,
-                                const HloComputation* computation)
+  explicit HloExecutionProfiler(
+      bool do_profile, HloExecutionProfile* profile, se::Stream* stream,
+      const std::vector<Pool<se::Stream>::SmartPtr>& sub_streams,
+      const HloComputation* computation)
       : do_profile_(do_profile),
         profile_(profile),
         stream_(stream),
+        sub_streams_(sub_streams),
         computation_(computation) {
     if (do_profile_) {
       clock_rate_ghz_ =
@@ -70,6 +72,7 @@ class HloExecutionProfiler {
     CHECK(!finished_execution_) << "Call FinishExecution only once!";
     finished_execution_ = true;
     if (do_profile_) {
+      stream_->ThenWaitFor(&sub_streams_);
       stream_->ThenStopTimer(execution_timer_.get());
       stream_->BlockHostUntilDone().IgnoreError();
       profile_->set_total_cycles_executed(
@@ -88,6 +91,7 @@ class HloExecutionProfiler {
   // that the hlo_instruction took to execute in the profile.
   void FinishOperation(const HloInstruction* hlo_instruction) {
     if (do_profile_) {
+      stream_->ThenWaitFor(&sub_streams_);
       stream_->ThenStopTimer(per_op_timer_.get());
       stream_->BlockHostUntilDone().IgnoreError();
       profile_->SetCyclesTakenBy(
@@ -100,6 +104,7 @@ class HloExecutionProfiler {
   double clock_rate_ghz_;
   HloExecutionProfile* profile_;
   se::Stream* stream_;
+  const std::vector<Pool<se::Stream>::SmartPtr>& sub_streams_;
   const HloComputation* computation_;
   std::unique_ptr<se::Timer> execution_timer_;
   std::unique_ptr<se::Timer> per_op_timer_;
@@ -147,13 +152,9 @@ Status GpuExecutable::ExecuteThunks(
     LOG(WARNING) << "PROFILING: profiling is enabled";
   }
 
-  HloExecutionProfiler profiler(do_profile, hlo_execution_profile, main_stream,
-                                hlo_module_->entry_computation());
-
-  uint64 start_micros = tensorflow::Env::Default()->NowMicros();
-
   // Stream 0 indicates `main_stream` and substreams start from stream 1.
   std::vector<Pool<se::Stream>::SmartPtr> sub_streams;
+  sub_streams.reserve(thunk_schedule_->StreamCount() - 1);
   while (sub_streams.size() + 1 < thunk_schedule_->StreamCount()) {
     sub_streams.emplace_back();
     TF_ASSIGN_OR_RETURN(
@@ -161,6 +162,10 @@ Status GpuExecutable::ExecuteThunks(
         run_options->BorrowStream(main_stream->parent()->device_ordinal()));
   }
 
+  HloExecutionProfiler profiler(do_profile, hlo_execution_profile, main_stream,
+                                sub_streams, hlo_module_->entry_computation());
+  uint64 start_micros = tensorflow::Env::Default()->NowMicros();
+
   // The next event enqueued on stream N must not run until the thunk at
   // last_blocking_thunk_for_stream[N] completes.
   std::map<int32, const Thunk*> last_blocking_thunk_for_stream;
-- 
GitLab


From 9e823230c42b9e2ba08726ef711ebaff7e1de7af Mon Sep 17 00:00:00 2001
From: Rui Zhao <rzhao@google.com>
Date: Mon, 26 Feb 2018 09:32:47 -0800
Subject: [PATCH 285/575] Fix print format error.

PiperOrigin-RevId: 187033623
---
 .../python/grappler/hierarchical_controller.py       | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/grappler/hierarchical_controller.py b/tensorflow/python/grappler/hierarchical_controller.py
index 655e43e78f..b06fb3c6d0 100644
--- a/tensorflow/python/grappler/hierarchical_controller.py
+++ b/tensorflow/python/grappler/hierarchical_controller.py
@@ -612,10 +612,10 @@ class HierarchicalController(Controller):
     num_inter_group_connections = num_connections - num_intra_group_connections
     if verbose:
       print("grouping evaluation metric")
-      print("num_connections={} num_intra_group_connections={} "
-            "num_inter_group_connections={}").format(
-                num_connections, num_intra_group_connections,
-                num_inter_group_connections)
+      print(("num_connections={} num_intra_group_connections={} "
+             "num_inter_group_connections={}").format(
+                 num_connections, num_intra_group_connections,
+                 num_inter_group_connections))
     self.dag_matrix = dag_matrix
 
     # output_shape
@@ -972,8 +972,8 @@ class HierarchicalController(Controller):
             controller_ops["reward"]["ph"][child_id]: reward,
         })
     if verbose:
-      print("run_time={:<.5f} reward={:<.5f} "
-            "best_reward={:<.5f}").format(run_time, reward, best_reward)
+      print(("run_time={:<.5f} reward={:<.5f} "
+             "best_reward={:<.5f}").format(run_time, reward, best_reward))
 
     # Reward is a double, best_reward a float: allow for some slack in the
     # comparison.
-- 
GitLab


From f4e70be18b104fbb2efeefeb83bea190aec12727 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 10:07:09 -0800
Subject: [PATCH 286/575] Fix pip install examples to match text: Use pip and
 point to Py2 packages

PiperOrigin-RevId: 187038889
---
 tensorflow/docs_src/install/install_mac.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index 5be38ae1ef..623ca6bb79 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -118,8 +118,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python 2.7, the command to install
      TensorFlow in the active Virtualenv is as follows:
 
-     <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl</b></pre>
+     <pre> $ <b>pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -241,8 +241,8 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
-     <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py3-none-any.whl</b> </pre>
+     <pre> $ <b>sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.6.0rc1-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
-- 
GitLab


From 3b08cd35bc108f48b4f63d73af7a53eb8a1169f9 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Mon, 26 Feb 2018 10:17:15 -0800
Subject: [PATCH 287/575] Generalize the gather_indices dimension that stores
 indices

This is now exposed as a index_vector_dim dimension number.

Also fixed an off-by-one error in ValidateGatherDimensionNumbers in the
expression computing output_shape_rank.

PiperOrigin-RevId: 187040748
---
 .../compiler/xla/service/hlo_instruction.cc   |   9 +-
 .../compiler/xla/service/hlo_instruction.h    |   3 +-
 .../xla/service/hlo_instruction_test.cc       |  43 +++-
 .../compiler/xla/service/shape_inference.cc   |  42 ++--
 .../xla/service/shape_inference_test.cc       | 191 ++++++++++++++----
 tensorflow/compiler/xla/xla_data.proto        |   4 +
 .../performance/xla/operation_semantics.md    |  61 ++++--
 7 files changed, 274 insertions(+), 79 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index b7dd055d7c..a534d8ff06 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1172,7 +1172,8 @@ bool HloInstruction::HasSideEffect() const {
 /* static */ GatherDimensionNumbers HloInstruction::MakeGatherDimNumbers(
     tensorflow::gtl::ArraySlice<int64> output_window_dims,
     tensorflow::gtl::ArraySlice<int64> elided_window_dims,
-    tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims) {
+    tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims,
+    int64 index_vector_dim) {
   GatherDimensionNumbers gather_dim_numbers;
   for (int64 output_window_dim : output_window_dims) {
     gather_dim_numbers.add_output_window_dims(output_window_dim);
@@ -1184,6 +1185,7 @@ bool HloInstruction::HasSideEffect() const {
     gather_dim_numbers.add_gather_dims_to_operand_dims(gather_dim_to_input_dim);
   }
 
+  gather_dim_numbers.set_index_vector_dim(index_vector_dim);
   return gather_dim_numbers;
 }
 
@@ -3369,9 +3371,12 @@ string HloInstruction::GatherDimensionNumbersToString() const {
   string gather_dims_to_operand_dims = StrCat(
       "gather_dims_to_operand_dims={",
       Join(gather_dimension_numbers_->gather_dims_to_operand_dims(), ","), "}");
+  string index_vector_dim = StrCat(
+      "index_vector_dim=", gather_dimension_numbers_->index_vector_dim());
 
   return Join<std::initializer_list<string>>(
-      {output_window_dims, elided_window_dims, gather_dims_to_operand_dims},
+      {output_window_dims, elided_window_dims, gather_dims_to_operand_dims,
+       index_vector_dim},
       ", ");
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index e4d22e5703..e4c86214c2 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -502,7 +502,8 @@ class HloInstruction {
   static GatherDimensionNumbers MakeGatherDimNumbers(
       tensorflow::gtl::ArraySlice<int64> output_window_dims,
       tensorflow::gtl::ArraySlice<int64> elided_window_dims,
-      tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims);
+      tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims,
+      int64 index_vector_dim);
 
   // Returns the opcode for this instruction.
   HloOpcode opcode() const { return opcode_; }
diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
index 32d3ed272b..f2980d309d 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
@@ -1271,7 +1271,7 @@ TEST_F(HloInstructionTest, Stringification) {
             "true_computation=%TransposeDot, false_computation=%TransposeDot");
 }
 
-TEST_F(HloInstructionTest, StringifyGather) {
+TEST_F(HloInstructionTest, StringifyGather_0) {
   Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46});
   Shape gather_indices_tensor_shape =
       ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5});
@@ -1291,7 +1291,8 @@ TEST_F(HloInstructionTest, StringifyGather) {
           HloInstruction::MakeGatherDimNumbers(
               /*output_window_dims=*/{4, 5, 6, 7, 8},
               /*elided_window_dims=*/{},
-              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/4),
           /*window_bounds=*/{30, 29, 28, 27, 26}));
 
   HloModule module(TestName());
@@ -1303,7 +1304,43 @@ TEST_F(HloInstructionTest, StringifyGather) {
             "s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), "
             "output_window_dims={4,5,6,7,8}, elided_window_dims={}, "
             "gather_dims_to_operand_dims={0,1,2,3,4}, "
-            "window_bounds={30,29,28,27,26}");
+            "index_vector_dim=4, window_bounds={30,29,28,27,26}");
+}
+
+TEST_F(HloInstructionTest, StringifyGather_1) {
+  Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46});
+  Shape gather_indices_tensor_shape =
+      ShapeUtil::MakeShape(S64, {10, 9, 5, 7, 6});
+  Shape gather_result_shape =
+      ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26});
+
+  HloComputation::Builder builder("Gather");
+  HloInstruction* input = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_tensor_shape, "input_tensor"));
+  HloInstruction* gather_indices =
+      builder.AddInstruction(HloInstruction::CreateParameter(
+          1, gather_indices_tensor_shape, "gather_indices"));
+
+  HloInstruction* gather_instruction =
+      builder.AddInstruction(HloInstruction::CreateGather(
+          gather_result_shape, input, gather_indices,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{4, 5, 6, 7, 8},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/2),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  HloModule module(TestName());
+  module.AddEntryComputation(builder.Build());
+
+  EXPECT_EQ(gather_instruction->ToString(),
+            "%gather = f32[10,9,7,6,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} "
+            "gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, "
+            "s64[10,9,5,7,6]{4,3,2,1,0} %gather_indices), "
+            "output_window_dims={4,5,6,7,8}, elided_window_dims={}, "
+            "gather_dims_to_operand_dims={0,1,2,3,4}, "
+            "index_vector_dim=2, window_bounds={30,29,28,27,26}");
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index c9692757b2..607a672025 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -2467,27 +2467,27 @@ static Status ValidateGatherDimensionNumbers(
 
   const int64 output_window_dim_count = dim_numbers.output_window_dims_size();
   const int64 output_shape_rank =
-      output_window_dim_count + gather_indices_shape.size();
+      output_window_dim_count + gather_indices_shape.size() - 1;
 
   for (int i = 0; i < dim_numbers.output_window_dims_size(); ++i) {
     int64 window_index = dim_numbers.output_window_dims(i);
     if (window_index < 0 || window_index >= output_shape_rank) {
       return InvalidArgument(
           "Window index %d in gather op is out of bounds; got %lld, but should "
-          "have been in"
-          "[0,%lld)",
+          "have been in [0,%lld)",
           i, window_index, output_shape_rank);
     }
   }
 
   if (dim_numbers.gather_dims_to_operand_dims_size() !=
-      gather_indices_shape.back()) {
+      gather_indices_shape[dim_numbers.index_vector_dim()]) {
     return InvalidArgument(
-        "There must be exactly as many elements in gather_dims_to_operand_dims "
-        "as there are elements in the last dimension of %%gather_indices; got: "
-        "%d, expected %lld",
+        "Gather op has %d elements in gather_dims_to_operand_dims and the "
+        "bound of dimension index_vector_dim=%lld of gather_indices is "
+        "%lld. These two numbers must be equal.",
         dim_numbers.gather_dims_to_operand_dims_size(),
-        gather_indices_shape.back());
+        dim_numbers.index_vector_dim(),
+        gather_indices_shape[dim_numbers.index_vector_dim()]);
   }
 
   for (int i = 0; i < dim_numbers.gather_dims_to_operand_dims_size(); i++) {
@@ -2550,24 +2550,33 @@ static Status ValidateGatherDimensionNumbers(
   TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(
       gather_indices_shape, "gather indices operand of gather op"));
 
-  if (gather_indices_shape.dimensions_size() < 1) {
+  if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) {
     return InvalidArgument(
-        "Gather indices parameter must at least of rank 1; got %s",
+        "Gather indices parameter must be an integral tensor; got %s",
         ShapeUtil::HumanString(gather_indices_shape).c_str());
   }
 
-  if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) {
+  // We implicitly reshape gather indices of shape P[A,B,C] to P[A,B,C,1] if
+  // index_vector_dim is rank(P).  The bounds of this expanded shape is
+  // stored in expanded_gather_indices_shape.
+
+  if (gather_indices_shape.dimensions_size() <
+          gather_dim_numbers.index_vector_dim() ||
+      gather_dim_numbers.index_vector_dim() < 0) {
     return InvalidArgument(
-        "Gather indices parameter must be an integral tensor; got %s",
-        ShapeUtil::HumanString(gather_indices_shape).c_str());
+        "Gather index leaf dimension must be within [0, rank(gather_indices) + "
+        "1). rank(gather_indices) is %d and gather index leaf dimension is "
+        "%lld.",
+        gather_indices_shape.dimensions_size(),
+        gather_dim_numbers.index_vector_dim());
   }
 
   std::vector<int64> expanded_gather_indices_shape;
-  // We implicitly reshape gather indices of shape P[N] to P[N,1].
   expanded_gather_indices_shape.reserve(gather_indices_shape.dimensions_size());
   c_copy(gather_indices_shape.dimensions(),
          std::back_inserter(expanded_gather_indices_shape));
-  if (expanded_gather_indices_shape.size() == 1) {
+  if (expanded_gather_indices_shape.size() ==
+      gather_dim_numbers.index_vector_dim()) {
     expanded_gather_indices_shape.push_back(1);
   }
 
@@ -2632,6 +2641,9 @@ static Status ValidateGatherDimensionNumbers(
       }
       current_bound = window_bounds[window_dims_seen++];
     } else {
+      if (gather_dims_seen == gather_dim_numbers.index_vector_dim()) {
+        gather_dims_seen++;
+      }
       current_bound = expanded_gather_indices_shape[gather_dims_seen++];
     }
 
diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc
index 7eb120843f..029d2b3b86 100644
--- a/tensorflow/compiler/xla/service/shape_inference_test.cc
+++ b/tensorflow/compiler/xla/service/shape_inference_test.cc
@@ -1530,11 +1530,17 @@ TEST_F(ShapeInferenceTest, BadSlice) {
 
 class GatherShapeInferenceTest : public ShapeInferenceTest {
  protected:
+  const Shape s64_scalar_ = ShapeUtil::MakeShape(S64, {});
+  const Shape s64_vector_5_ = ShapeUtil::MakeShape(S64, {5});
   const Shape s64_vector_32_ = ShapeUtil::MakeShape(S64, {32});
   const Shape s64_4d_tensor_10_9_8_7_1_ =
       ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 1});
   const Shape s64_4d_tensor_10_9_8_7_5_ =
       ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5});
+  const Shape s64_4d_tensor_5_10_9_7_6_ =
+      ShapeUtil::MakeShape(S64, {5, 10, 9, 7, 6});
+  const Shape s64_4d_tensor_10_9_5_7_6_ =
+      ShapeUtil::MakeShape(S64, {10, 9, 5, 7, 6});
   const Shape f32_5d_tensor_50_49_48_47_46_ =
       ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46});
   const Shape tuple_shape_ = ShapeUtil::MakeTupleShape(
@@ -1548,7 +1554,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGather) {
                                        HloInstruction::MakeGatherDimNumbers(
                                            /*output_window_dims=*/{0},
                                            /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
+                                           /*gather_dims_to_operand_dims=*/{1},
+                                           /*index_vector_dim=*/1),
                                        /*window_bounds=*/{64, 1}));
   EXPECT_TRUE(
       ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {64, 32})))
@@ -1562,7 +1569,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGatherV2) {
                                        HloInstruction::MakeGatherDimNumbers(
                                            /*output_window_dims=*/{1},
                                            /*elided_window_dims=*/{0},
-                                           /*gather_dims_to_operand_dims=*/{0}),
+                                           /*gather_dims_to_operand_dims=*/{0},
+                                           /*index_vector_dim=*/1),
                                        /*window_bounds=*/{1, 48}));
   EXPECT_TRUE(
       ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {32, 48})))
@@ -1576,7 +1584,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGatherNd) {
                                        HloInstruction::MakeGatherDimNumbers(
                                            /*output_window_dims=*/{4},
                                            /*elided_window_dims=*/{0},
-                                           /*gather_dims_to_operand_dims=*/{0}),
+                                           /*gather_dims_to_operand_dims=*/{0},
+                                           /*index_vector_dim=*/4),
                                        /*window_bounds=*/{1, 48}));
   EXPECT_TRUE(ShapeUtil::Equal(gather_shape,
                                ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 48})))
@@ -1591,7 +1600,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) {
           HloInstruction::MakeGatherDimNumbers(
               /*output_window_dims=*/{4, 5, 6, 7, 8},
               /*elided_window_dims=*/{},
-              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/4),
           /*window_bounds=*/{30, 29, 28, 27, 26}));
   EXPECT_TRUE(ShapeUtil::Equal(
       gather_shape,
@@ -1599,12 +1609,85 @@ TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) {
       << ShapeUtil::HumanString(gather_shape);
 }
 
+TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_A) {
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(
+          f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{4, 5, 6, 7, 8},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/2),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  EXPECT_TRUE(ShapeUtil::Equal(
+      gather_shape,
+      ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_B) {
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(
+          f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_5_10_9_7_6_,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{4, 5, 6, 7, 8},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/0),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  EXPECT_TRUE(ShapeUtil::Equal(
+      gather_shape,
+      ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, NoOutputGatherDims) {
+  // This is equivalent to a dynamic slice.
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(
+          f32_5d_tensor_50_49_48_47_46_, s64_vector_5_,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{0, 1, 2, 3, 4},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/0),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  EXPECT_TRUE(ShapeUtil::Equal(gather_shape,
+                               ShapeUtil::MakeShape(F32, {30, 29, 28, 27, 26})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, ScalarGatherIndices) {
+  // The gather indices "tensor" is a scalar S here that's used to slice out
+  // [S,0,0,0,0]..[S,30,29,28,27] into a [30,29,28,27] shaped result.
+  TF_ASSERT_OK_AND_ASSIGN(Shape gather_shape,
+                          ShapeInference::InferGatherShape(
+                              f32_5d_tensor_50_49_48_47_46_, s64_scalar_,
+                              HloInstruction::MakeGatherDimNumbers(
+                                  /*output_window_dims=*/{0, 1, 2, 3},
+                                  /*elided_window_dims=*/{0},
+                                  /*gather_dims_to_operand_dims=*/{0},
+                                  /*index_vector_dim=*/0),
+                              /*window_bounds=*/{1, 30, 29, 28, 27}));
+
+  EXPECT_TRUE(ShapeUtil::Equal(gather_shape,
+                               ShapeUtil::MakeShape(F32, {30, 29, 28, 27})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
 TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) {
   StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
       tuple_shape_, s64_vector_32_,
       HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
                                            /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
+                                           /*gather_dims_to_operand_dims=*/{1},
+                                           /*index_vector_dim=*/1),
       /*window_bounds=*/{64, 1});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1617,7 +1700,8 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) {
       s64_vector_32_, tuple_shape_,
       HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
                                            /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
+                                           /*gather_dims_to_operand_dims=*/{1},
+                                           /*index_vector_dim=*/0),
       /*window_bounds=*/{64, 1});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1625,25 +1709,13 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) {
       << statusor.status();
 }
 
-TEST_F(GatherShapeInferenceTest, ScalarGatherIndicesInput) {
-  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
-      s64_vector_32_, s32_,
-      HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
-                                           /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
-      /*window_bounds=*/{64, 1});
-  ASSERT_FALSE(statusor.ok());
-  EXPECT_THAT(statusor.status().error_message(),
-              HasSubstr("Gather indices parameter must at least of rank 1"))
-      << statusor.status();
-}
-
 TEST_F(GatherShapeInferenceTest, FloatingPointGatherIndicesInput) {
   StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
       s64_vector_32_, vector_32_,
       HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
                                            /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
+                                           /*gather_dims_to_operand_dims=*/{1},
+                                           /*index_vector_dim=*/0),
       /*window_bounds=*/{64, 1});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1658,7 +1730,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 8, 7},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1674,7 +1747,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 7},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1690,7 +1764,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 99, 100, 101},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1698,6 +1773,22 @@ TEST_F(GatherShapeInferenceTest,
       << statusor.status();
 }
 
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_WindowIndexBarelyOutOfBounds) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 9},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Window index 4 in gather op is out of bounds"))
+      << statusor.status();
+}
+
 TEST_F(GatherShapeInferenceTest,
        InvalidGatherDimNumbers_MismatchingElidedWindowDims) {
   StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
@@ -1705,7 +1796,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{4},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1722,7 +1814,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{0, 1, 2, 3, 19},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1738,7 +1831,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{0, 1, 2, 3, 3},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1755,15 +1849,15 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
       statusor.status().error_message(),
-      HasSubstr(
-          "There must be exactly as many elements in "
-          "gather_dims_to_operand_dims "
-          "as there are elements in the last dimension of %gather_indices"))
+      HasSubstr("Gather op has 4 elements in gather_dims_to_operand_dims and "
+                "the bound of dimension index_vector_dim=4 of "
+                "gather_indices is 5. These two numbers must be equal."))
       << statusor.status();
 }
 
@@ -1774,7 +1868,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1791,7 +1886,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1808,7 +1904,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{2, 1},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{1, 1, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1822,7 +1919,8 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowBoundsTooLarge) {
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7},
           /*elided_window_dims=*/{2},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 1, 300, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1838,7 +1936,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1855,7 +1954,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7},
           /*elided_window_dims=*/{1},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 26, 20});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1864,5 +1964,22 @@ TEST_F(GatherShapeInferenceTest,
       << statusor.status();
 }
 
+TEST_F(GatherShapeInferenceTest, OutOfBoundsGatherIndicesLeafDim) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/32),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Gather index leaf dimension must be within [0, "
+                        "rank(gather_indices) + 1)"))
+      << statusor.status();
+}
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 28620c3b86..1f16e6d251 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -418,6 +418,10 @@ message GatherDimensionNumbers {
   // transforms the gather index looked up from the gather_indices tensor into
   // the starting index in the input space.
   repeated int64 gather_dims_to_operand_dims = 3;
+
+  // The dimension in the gather_indices input that contains the starting
+  // indices.
+  int64 index_vector_dim = 4;
 }
 
 // Operation requests that are all collected as a tagged union with a oneof
diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index 1f7a3a1e2c..eaf6aeba3d 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -1050,6 +1050,9 @@ For a more intuitive description, see the "Informal Description" section below.
 :                  :                         : indices of the slices we're     :
 :                  :                         : we're stitching together into   :
 :                  :                         : the output tensor.              :
+|`index_vector_dim`  | `int64`               | The dimension in                |
+:                  :                         : `gather_indices` that contains  :
+:                  :                         : the starting indices.           :
 |`output_window_dims` | `ArraySlice<int64>`  | The set of dimensions in the    |
 :                  :                         : output shape that are _window   :
 :                  :                         : dimensions_ (defined below).    :
@@ -1066,22 +1069,20 @@ For a more intuitive description, see the "Informal Description" section below.
 :                  :            : `output_window_dims`) and the window         :
 :                  :            : dimensions that are elided (via              :
 :                  :            : `elided_window_dims`).                       :
-|`gather_dims_to_operand_dims` | `ArraySlice<int64>` | A dimension map (the  |
+|`gather_dims_to_operand_dims` | `ArraySlice<int64>` | A dimension map (the    |
 :                  :            : array is interpreted as mapping `i` to       :
 :                  :            : `gather_dims_to_operand_dims[i]`)  from      :
 :                  :            : the gather indices in `gather_indices` to    :
 :                  :            : the operand index space.  It has to be       :
 :                  :            : one-to-one and total.                        :
 
-If `gather_indices` is a vector with `N` elements then we implicitly reshape it
-to a tensor of shape `[N,1]` before proceeding.
-
 For every index `Out` in the output tensor, we compute two things (more
 precisely described later):
 
-  - An index into the first `gather_indices.rank` - `1` dimensions of
-    `gather_indices`, which gives us a starting index of a slice, _operand
-    slice_, in the operand tensor.
+  - An index into `gather_indices.rank` - `1` dimensions of `gather_indices`,
+    which gives us a starting index of a slice, _operand slice_, in the operand
+    tensor.  These `gather_indices.rank` - `1` dimensions are all the dimensions
+    in `gather_indices` except `index_vector_dim`.
 
   - A _window index_ that has the same rank as the operand.  This index is
     composed of the values in `Out` at dimensions `output_window_dims`, embedded
@@ -1093,29 +1094,42 @@ should be present in the output at index `Out`.
 The output is a tensor of rank `output_window_dims.size` + `gather_indices.rank`
 - `1`.  Additionally, as a shorthand, we define `output_gather_dims` of type
 `ArraySlice<int64>` as the set of dimensions in the output shape but not in
-`output_window_dims`, in ascending order.  E.g. if the output tensor has rank 5,
-`output_window_dims` is {`2`, `4`} then `output_gather_dims` is {`0`, `1`, `3`}
+`output_window_dims`, in ascending order.  E.g. if the output tensor has rank
+`5`, `output_window_dims` is {`2`, `4`} then `output_gather_dims` is {`0`, `1`,
+`3`}
+
+If `index_vector_dim` is equal to `gather_indices.rank` we implicitly
+consider `gather_indices` to have a trailing `1` dimension (i.e. if
+`gather_indices` was of shape `[6,7]` and `index_vector_dim` is `2` then
+we implicitly consider the shape of `gather_indices` to be `[6,7,1]`).
 
 The bounds for the output tensor along dimension `i` is computed as follows:
 
   1. If `i` is present in `output_gather_dims` (i.e. is equal to
-    `output_gather_dims[k]` for some `k`) then we pick the corresponding
-    dimension bounds out of `gather_indices.shape` (i.e. pick
-    `gather_indices.shape.dims[k]`).
+     `output_gather_dims[k]` for some `k`) then we pick the corresponding
+     dimension bounds out of `gather_indices.shape`, skipping
+     `index_vector_dim` (i.e. pick `gather_indices.shape.dims`[`k`] if `k`
+     < `index_vector_dim` and `gather_indices.shape.dims`[`k`+`1`]
+     otherwise).
   2. If `i` is present in `output_window_dims` (i.e. equal to
-     `output_window_dims[k]` for some `k`) then we pick the corresponding bound
-     out of `window_bounds` after accounting for `elided_window_dims` (i.e. we
-     pick `adjusted_window_bounds[k]` where `adjusted_window_bounds` is
-     `window_bounds` with the bounds at indices `elided_window_dims` removed).
+     `output_window_dims`[`k`] for some `k`) then we pick the corresponding
+     bound out of `window_bounds` after accounting for `elided_window_dims`
+     (i.e. we pick `adjusted_window_bounds`[`k`] where `adjusted_window_bounds`
+     is `window_bounds` with the bounds at indices `elided_window_dims`
+     removed).
 
 The operand index `In` corresponding to an output index `Out` is computed as
 follows:
 
   1. Let `G` = { `Out`[`k`] for `k` in `output_gather_dims` }.  Use `G` to slice
-     out vector `S` such that `S`[`i`] = `gather_indices`[`G`, `i`].
-  2. Create an index, `S`<sub>`in`</sub>, into `operand` using `S` by scattering
-     `S` using the `gather_dims_to_operand_dims` map (`S`<sub>`in`</sub> is the
-     starting indices for _operand slice_ mentioned above.).  More precisely:
+     out vector `S` such that `S`[`i`] = `gather_indices`[Combine(`G`, `i`)]
+     where Combine(A, b) inserts b at position `index_vector_dim` into A.
+     Note that this is well defined even if `G` is empty -- if `G` is empty then
+     `S` = `gather_indices`.
+  2. Create an index, `S`<sub>`in`</sub>, into `operand` using `S` by
+     scattering `S` using the `gather_dims_to_operand_dims` map
+     (`S`<sub>`in`</sub> is the starting indices for _operand slice_ mentioned
+     above).  More precisely:
        1. `S`<sub>`in`</sub>[`gather_dims_to_operand_dims`[`k`]] = `S`[`k`] if `k` <
           `gather_dims_to_operand_dims.size`.
        2. `S`<sub>`in`</sub>[`_`] = `0` otherwise.
@@ -1136,7 +1150,12 @@ follows:
 `operand.rank` is `6` and `elided_window_dims` is {`0`, `2`} then
 `window_dims_to_operand_dims` is {`0`→`1`, `1`→`3`, `2`→`4`, `3`→`5`}.
 
-### Informal Description
+### Informal Description and Examples
+
+`index_vector_dim` is set to `gather_indices.rank` - `1` in all of the
+examples that follow.  More interesting values for `index_vector_dim`
+does not change the operation fundamentally, but makes the visual representation
+more cumbersome.
 
 To get an intuition on how all of the above fits together, let's look at an
 example that gathers 5 slices of shape `[8,6]` from a `[16,11]` tensor.  The
-- 
GitLab


From c6807e0c7c998f0e38e6930fca4a8cf667f791c6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 10:24:08 -0800
Subject: [PATCH 288/575] Arithemtic optimization: Rewite Sub(0, y) => Neg(y)

PiperOrigin-RevId: 187041872
---
 .../grappler/optimizers/constant_folding.cc    | 18 +++++++++++++++++-
 .../grappler/optimizers/constant_folding.h     |  1 +
 .../optimizers/constant_folding_test.cc        |  7 +++----
 3 files changed, 21 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 182e03f04e..10ca7dcce0 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -1434,6 +1434,17 @@ void ConstantFolding::ReplaceDivisionOfOnesByReciprocal(NodeDef* node,
   graph_modified_ = true;
 }
 
+void ConstantFolding::ReplaceSubtractionFromZeroByNegation(NodeDef* node,
+                                                           GraphDef* graph) {
+  node->set_op("Neg");
+  node->mutable_input()->SwapElements(0, 1);
+  const string ctrl_dep =
+      AddControlDependency(node->input(1), graph, node_map_.get());
+  node_map_->UpdateInput(node->name(), node->input(1), ctrl_dep);
+  node->set_input(1, ctrl_dep);
+  graph_modified_ = true;
+}
+
 Status ConstantFolding::ReplaceOperationWithConstant(
     double value, const TensorShapeProto& shape, NodeDef* node,
     GraphDef* graph) {
@@ -1636,12 +1647,17 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
       const bool y_matches_output_shape = ShapesEqual(output_shape, y_shape);
       if (y_matches_output_shape &&
           ((is_mul && x_is_one) || (is_add && x_is_zero))) {
-        // TODO(rmlarsen): Handle subtraction 0 - y.
         // 1 * y = y or 0 + y = y.
         ReplaceOperationWithSnapshot(1, node, output);
         continue;
       }
 
+      if (y_matches_output_shape && (is_sub && x_is_zero)) {
+        // Replace 0 - y with Neg(y).
+        ReplaceSubtractionFromZeroByNegation(node, output);
+        continue;
+      }
+
       // Replace 1 / y with Reciprocal op.
       if (y_matches_output_shape && is_any_div && x_is_one) {
         DataType type = node->attr().at("T").type();
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index 232b2f9fa0..2fd59c7f9c 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -82,6 +82,7 @@ class ConstantFolding : public GraphOptimizer {
                                     GraphDef* graph);
   void ReplaceOperationWithSnapshot(int input_to_forward, NodeDef* node,
                                     GraphDef* graph);
+  void ReplaceSubtractionFromZeroByNegation(NodeDef* node, GraphDef* graph);
   Status ReplaceOperationWithConstant(double value,
                                       const TensorShapeProto& shape,
                                       NodeDef* node, GraphDef* graph);
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 219f3bd5ec..c6540192d7 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -286,10 +286,9 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
         EXPECT_EQ("x", node.input(0));
         EXPECT_EQ("^zeros", node.input(1));
       } else if (name == "sub2") {
-        // We don't handle this case yet.
-        EXPECT_EQ("Sub", node.op());
-        EXPECT_EQ("zeros", node.input(0));
-        EXPECT_EQ("y", node.input(1));
+        EXPECT_EQ("Neg", node.op());
+        EXPECT_EQ("y", node.input(0));
+        EXPECT_EQ("^zeros", node.input(1));
       }
       const std::set<string> square_zero_const{"mul1", "mul2",    "mul5",
                                                "mul6", "matmul1", "matmul2"};
-- 
GitLab


From 3ce1adbdf7b1f9a4a53d5438985d12b6526dbd14 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 26 Feb 2018 10:24:56 -0800
Subject: [PATCH 289/575] Move accumulate_n_v2 to core.

PiperOrigin-RevId: 187042001
---
 tensorflow/contrib/framework/BUILD            |  38 ------
 .../framework/python/ops/accumulate_n_v2.py   | 111 ------------------
 tensorflow/python/kernel_tests/BUILD          |  34 ++++++
 .../kernel_tests/accumulate_n_eager_test.py}  |  27 ++---
 .../kernel_tests/accumulate_n_test.py}        |  34 +++---
 tensorflow/python/ops/math_ops.py             |  81 ++++++-------
 6 files changed, 99 insertions(+), 226 deletions(-)
 delete mode 100644 tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
 rename tensorflow/{contrib/framework/python/ops/accumulate_n_v2_eager_test.py => python/kernel_tests/accumulate_n_eager_test.py} (72%)
 rename tensorflow/{contrib/framework/python/ops/accumulate_n_v2_test.py => python/kernel_tests/accumulate_n_test.py} (79%)

diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD
index dbdb5cfaac..1accb319d2 100644
--- a/tensorflow/contrib/framework/BUILD
+++ b/tensorflow/contrib/framework/BUILD
@@ -28,7 +28,6 @@ tf_custom_op_py_library(
         "python/framework/graph_util.py",
         "python/framework/tensor_util.py",
         "python/ops/__init__.py",
-        "python/ops/accumulate_n_v2.py",
         "python/ops/arg_scope.py",
         "python/ops/audio_ops.py",
         "python/ops/checkpoint_ops.py",
@@ -161,23 +160,6 @@ py_test(
     ],
 )
 
-py_test(
-    name = "accumulate_n_v2_test",
-    size = "small",
-    srcs = ["python/ops/accumulate_n_v2_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":framework_py",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:gradients",
-        "//tensorflow/python:platform_test",
-        "//tensorflow/python:variables",
-        "//third_party/py/numpy",
-    ],
-)
-
 cuda_py_test(
     name = "critical_section_test",
     size = "medium",
@@ -196,26 +178,6 @@ cuda_py_test(
     ],
 )
 
-py_test(
-    name = "accumulate_n_v2_eager_test",
-    size = "small",
-    srcs = ["python/ops/accumulate_n_v2_eager_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":framework_py",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:gradients",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:resource_variable_ops",
-        "//tensorflow/python/eager:backprop",
-        "//tensorflow/python/eager:context",
-        "//tensorflow/python/eager:tape",
-        "//third_party/py/numpy",
-    ],
-)
-
 py_test(
     name = "ops_test",
     size = "small",
diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py b/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
deleted file mode 100644
index 476528b0dd..0000000000
--- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Ops that will eventually be folded into tensorflow/python/ops/math_ops.py
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from tensorflow.python.eager import context
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import gen_math_ops
-from tensorflow.python.ops import math_ops
-
-
-
-def accumulate_n_v2(inputs, shape=None, tensor_dtype=None, name=None):
-  """Returns the element-wise sum of a list of tensors.
-
-  Optionally, pass `shape` and `tensor_dtype` for shape and type checking,
-  otherwise, these are inferred.
-
-  `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not
-  wait for all of its inputs to be ready before beginning to sum. This can
-  save memory if inputs are ready at different times, since minimum temporary
-  storage is proportional to the output size rather than the inputs size.
-
-  Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable.
-
-  For example:
-
-  ```python
-  a = tf.constant([[1, 2], [3, 4]])
-  b = tf.constant([[5, 0], [0, 6]])
-  tf.accumulate_n_v2([a, b, a])  # [[7, 4], [6, 14]]
-
-  # Explicitly pass shape and type
-  tf.accumulate_n_v2([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
-                                                                   # [[7,  4],
-                                                                   #  [6, 14]]
-  ```
-
-  Args:
-    inputs: A list of `Tensor` objects, each with same shape and type.
-    shape: Shape of elements of `inputs`.
-    tensor_dtype: The type of `inputs`.
-    name: A name for the operation (optional).
-
-  Returns:
-    A `Tensor` of same shape and type as the elements of `inputs`.
-
-  Raises:
-    ValueError: If `inputs` don't all have same shape and dtype or the shape
-    cannot be inferred.
-  """
-  _INPUTS_ERR_MSG = ValueError("inputs must be a list of at least one Tensor"
-                               "with the same dtype and shape")
-  if not inputs or not isinstance(inputs, (list, tuple)):
-    raise _INPUTS_ERR_MSG
-  inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs)
-  if not all(isinstance(x, ops.Tensor) for x in inputs):
-    raise _INPUTS_ERR_MSG
-  if not all(x.dtype == inputs[0].dtype for x in inputs):
-    raise _INPUTS_ERR_MSG
-  if shape is not None:
-    shape = tensor_shape.as_shape(shape)
-  else:
-    shape = tensor_shape.unknown_shape()
-  for input_tensor in inputs:
-    if isinstance(input_tensor, ops.Tensor):
-      shape = shape.merge_with(input_tensor.get_shape())
-
-  # tensor_dtype is for safety only; operator's output type computed in C++
-  if tensor_dtype is not None and tensor_dtype != inputs[0].dtype:
-    raise TypeError("tensor_dtype is {}, but input is of type {}"
-                    .format(tensor_dtype, inputs[0].dtype))
-
-  if len(inputs) == 1 and name is None:
-    return inputs[0]
-  elif len(inputs) == 1 and name is not None:
-    return array_ops.identity(inputs[0], name=name)
-  elif context.in_eager_mode():
-    # TemporaryVariable not currently supported in eager mode; fall back
-    # onto AddN for now.
-    # TODO(frreiss) remove this once the lifetime of eager variables gets
-    # addressed
-    return math_ops.add_n(inputs, name=name)
-  else:
-    return gen_math_ops._accumulate_nv2(inputs, name=name, shape=shape)
-
-# The following code should eventually be merged into
-# tensorflow/python/ops/math_grad.py
-@ops.RegisterGradient("AccumulateNV2")
-def _AddNGrad(op, grad):
-  """Same as gradient for AddN. Copies the gradient to all inputs."""
-  # Not broadcasting.
-  return [grad] * len(op.inputs)
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index d4ceb2e489..c9aa4a252d 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2892,6 +2892,40 @@ tf_py_test(
     ],
 )
 
+tf_py_test(
+    name = "accumulate_n_test",
+    size = "small",
+    srcs = ["accumulate_n_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:variables",
+    ],
+)
+
+tf_py_test(
+    name = "accumulate_n_eager_test",
+    size = "small",
+    srcs = ["accumulate_n_eager_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:resource_variable_ops",
+        "//tensorflow/python/eager:backprop",
+        "//tensorflow/python/eager:context",
+        "//tensorflow/python/eager:tape",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py b/tensorflow/python/kernel_tests/accumulate_n_eager_test.py
similarity index 72%
rename from tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py
rename to tensorflow/python/kernel_tests/accumulate_n_eager_test.py
index 35974b9e21..dc11b7dece 100644
--- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py
+++ b/tensorflow/python/kernel_tests/accumulate_n_eager_test.py
@@ -12,48 +12,41 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for new version of accumulate_n op that will eventually go into
-`ops.math_ops`.
-
-These test cases spefically exercise the `eager` APIs. They need to be in a
-separate file from the remaining tests because eager mode is currently something
-you can turn on but can't turn off for the lifetime of the current process."""
+"""Tests for new version of accumulate_n op."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.framework.python.ops import accumulate_n_v2 as av2
-
 from tensorflow.python.eager import backprop
 
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import test
 
 
-
 class AccumulateNV2EagerTest(test_util.TensorFlowTestCase):
-  """Tests of the new, differentiable version of accumulate_n"""
+  """Tests of the new, differentiable version of accumulate_n."""
 
   def testMinimalEagerMode(self):
     forty = constant_op.constant(40)
     two = constant_op.constant(2)
-    answer = av2.accumulate_n_v2([forty, two])
+    answer = math_ops.accumulate_n([forty, two])
     self.assertEqual(42, answer.numpy())
 
-
   def testFloat(self):
     np.random.seed(12345)
     x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
     tf_x = ops.convert_n_to_tensor(x)
     with self.test_session(use_gpu=True):
-      self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).numpy())
-      self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).numpy())
+      self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).numpy())
+      self.assertAllClose(x[0] * 5,
+                          math_ops.accumulate_n([tf_x[0]] * 5).numpy())
 
   def testGrad(self):
     np.random.seed(42)
@@ -65,16 +58,14 @@ class AccumulateNV2EagerTest(test_util.TensorFlowTestCase):
     ]
 
     def fn(first, second, third):
-      return av2.accumulate_n_v2([first, second, third])
+      return math_ops.accumulate_n([first, second, third])
 
     grad_fn = backprop.gradients_function(fn)
     grad = grad_fn(input_vars[0], input_vars[1], input_vars[2])
-    self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1
+    self.assertAllEqual(np.repeat(1.0, num_inputs),  # d/dx (x + y + ...) = 1
                         [elem.numpy() for elem in grad])
 
 
-
 if __name__ == "__main__":
   ops.enable_eager_execution()
   test.main()
-
diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py b/tensorflow/python/kernel_tests/accumulate_n_test.py
similarity index 79%
rename from tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py
rename to tensorflow/python/kernel_tests/accumulate_n_test.py
index 45962098e9..0a6d4aea37 100644
--- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py
+++ b/tensorflow/python/kernel_tests/accumulate_n_test.py
@@ -12,42 +12,42 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for new version of accumulate_n op that will eventually go into
-`ops.math_ops`."""
+"""Tests for new version of accumulate_n op."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.framework.python.ops import accumulate_n_v2 as av2
-
 from tensorflow.python.framework import dtypes as dtypes_lib
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import gradients
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import googletest
 
 
 class AccumulateNV2Test(test_util.TensorFlowTestCase):
-  """Tests of the new, differentiable version of accumulate_n"""
+  """Tests of the new, differentiable version of accumulate_n."""
 
   def testFloat(self):
     np.random.seed(12345)
     x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
     tf_x = ops.convert_n_to_tensor(x)
     with self.test_session(use_gpu=True):
-      self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).eval())
-      self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).eval())
+      self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).eval())
+      self.assertAllClose(x[0] * 5,
+                          math_ops.accumulate_n([tf_x[0]] * 5).eval())
 
   def testInt(self):
     np.random.seed(54321)
     x = [np.random.randint(-128, 128, (5, 4, 3, 2, 1)) for _ in range(6)]
     tf_x = ops.convert_n_to_tensor(x)
     with self.test_session(use_gpu=True):
-      self.assertAllEqual(sum(x), av2.accumulate_n_v2(tf_x).eval())
-      self.assertAllEqual(x[0] * 6, av2.accumulate_n_v2([tf_x[0]] * 6).eval())
+      self.assertAllEqual(sum(x), math_ops.accumulate_n(tf_x).eval())
+      self.assertAllEqual(x[0] * 6,
+                          math_ops.accumulate_n([tf_x[0]] * 6).eval())
 
   def testGrad(self):
     np.random.seed(42)
@@ -55,9 +55,9 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase):
       with self.test_session(use_gpu=True) as sess:
         input_vars = [
             variables.Variable(10.0 * np.random.random())
-            for i in range(0, num_inputs)
+            for _ in range(0, num_inputs)
         ]
-        accum_n = av2.accumulate_n_v2(input_vars)
+        accum_n = math_ops.accumulate_n(input_vars)
         sess.run(variables.global_variables_initializer())
         accum_n_grad = gradients.gradients(accum_n, input_vars)
         self.assertAllEqual(
@@ -77,7 +77,7 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase):
           ops.convert_to_tensor(x, dtype=dtypes_lib.float32)
           for x in random_arrays
       ]
-      tf_val = av2.accumulate_n_v2(random_tensors)
+      tf_val = math_ops.accumulate_n(random_tensors)
       np_val = random_arrays[0]
       for random_array in random_arrays[1:]:
         np_val += random_array
@@ -86,7 +86,7 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase):
   def testZeroArgs(self):
     with self.test_session():
       with self.assertRaises(ValueError):
-        tf_val = av2.accumulate_n_v2([])
+        tf_val = math_ops.accumulate_n([])
         tf_val.eval()
 
   def testWrongShape(self):
@@ -94,28 +94,28 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase):
       with self.assertRaises(ValueError):
         a = variables.Variable(0.2)
         b = variables.Variable(0.1)
-        tf_val = av2.accumulate_n_v2([a, b], shape=[2, 2])  # Should be shape=[]
+        math_ops.accumulate_n([a, b], shape=[2, 2])  # Should be shape=[]
 
   def testIncompatibleShapes(self):
     with self.test_session():
       with self.assertRaises(ValueError):
         a = variables.Variable(np.array([0.1, 0.2]))
         b = variables.Variable(np.array([[0.3], [0.4]]))
-        tf_val = av2.accumulate_n_v2([a, b])
+        math_ops.accumulate_n([a, b])
 
   def testWrongType(self):
     with self.test_session():
       with self.assertRaises(TypeError):
         a = variables.Variable(0.2, dtype=np.float32)
         b = variables.Variable(0.1, dtype=np.float32)
-        tf_val = av2.accumulate_n_v2([a, b], tensor_dtype=np.int32)
+        math_ops.accumulate_n([a, b], tensor_dtype=np.int32)
 
   def testWrongTypeOneInput(self):
     # Scenario that used to trigger a bug, even when testWrongType() worked
     with self.test_session():
       with self.assertRaises(TypeError):
         a = variables.Variable(0.2, dtype=np.float32)
-        tf_val = av2.accumulate_n_v2([a], tensor_dtype=np.int32)
+        math_ops.accumulate_n([a], tensor_dtype=np.int32)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index a09540028f..c3899c7e12 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -158,14 +158,11 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import gen_control_flow_ops
 from tensorflow.python.ops import gen_data_flow_ops
 from tensorflow.python.ops import gen_math_ops
 from tensorflow.python.ops import gen_nn_ops
 from tensorflow.python.ops import gen_sparse_ops
 from tensorflow.python.ops import gen_spectral_ops
-from tensorflow.python.ops import gen_state_ops
-from tensorflow.python.ops import state_ops
 # go/tf-wildcard-import
 # pylint: disable=wildcard-import
 from tensorflow.python.ops.gen_math_ops import *
@@ -2181,14 +2178,12 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
   Optionally, pass `shape` and `tensor_dtype` for shape and type checking,
   otherwise, these are inferred.
 
-  NOTE: This operation is not differentiable and cannot be used if inputs depend
-  on trainable variables. Please use `tf.add_n` for such cases.
+  `tf.accumulate_n` performs the same operation as `tf.add_n`, but does not
+  wait for all of its inputs to be ready before beginning to sum. This can
+  save memory if inputs are ready at different times, since minimum temporary
+  storage is proportional to the output size rather than the inputs size.
 
-  Aside from differentiability, `tf.accumulate_n` performs the same operation as
-  `tf.add_n`, but does not wait for all of its inputs to be ready before
-  beginning to sum. This can save memory if inputs are ready at different times,
-  since minimum temporary storage is proportional to the output size rather than
-  the inputs size.
+  `accumulate_n` is differentiable (but wasn't previous to TensorFlow 1.7).
 
   For example:
 
@@ -2198,8 +2193,9 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
   tf.accumulate_n([a, b, a])  # [[7, 4], [6, 14]]
 
   # Explicitly pass shape and type
-  tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)  # [[7,  4],
-                                                                   #  [6, 14]]
+  tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
+                                                                 # [[7,  4],
+                                                                 #  [6, 14]]
   ```
 
   Args:
@@ -2215,20 +2211,17 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
     ValueError: If `inputs` don't all have same shape and dtype or the shape
     cannot be inferred.
   """
-  if context.in_eager_mode():
-    # TODO(apassos) remove this once the lifetime of eager variables gets
-    # addressed.
-    raise ValueError("accumulate_n not supported in eager mode")
+  def _input_error():
+    return ValueError(
+        "inputs must be a list of at least one Tensor with the "
+        "same dtype and shape")
   if not inputs or not isinstance(inputs, (list, tuple)):
-    raise ValueError("inputs must be a list of at least one Tensor with the "
-                     "same dtype and shape")
+    raise _input_error()
   inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs)
   if not all(isinstance(x, ops.Tensor) for x in inputs):
-    raise ValueError("inputs must be a list of at least one Tensor with the "
-                     "same dtype and shape")
+    raise _input_error()
   if not all(x.dtype == inputs[0].dtype for x in inputs):
-    raise ValueError("inputs must be a list of at least one Tensor with the "
-                     "same dtype and shape")
+    raise _input_error()
   if shape is not None:
     shape = tensor_shape.as_shape(shape)
   else:
@@ -2236,27 +2229,31 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
   for input_tensor in inputs:
     if isinstance(input_tensor, ops.Tensor):
       shape = shape.merge_with(input_tensor.get_shape())
-  if tensor_dtype is None:
-    tensor_dtype = inputs[0].dtype
-  if tensor_dtype != inputs[0].dtype:
-    raise TypeError("tensor_dtype is {}, but input is of type {}".format(
-        tensor_dtype, inputs[0].dtype))
-  if len(inputs) == 1:
+
+  # tensor_dtype is for safety only; operator's output type computed in C++
+  if tensor_dtype is not None and tensor_dtype != inputs[0].dtype:
+    raise TypeError("tensor_dtype is {}, but input is of type {}"
+                    .format(tensor_dtype, inputs[0].dtype))
+
+  if len(inputs) == 1 and name is None:
     return inputs[0]
-  with ops.name_scope(name, "AccumulateN", inputs) as name:
-    var = gen_state_ops._temporary_variable(
-        shape=tensor_shape.vector(0), dtype=tensor_dtype)
-    with ops.colocate_with(var):
-      zeros = array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0])
-      zeros.set_shape(shape)
-      ref = state_ops.assign(var, zeros, validate_shape=False)
-      update_ops = [
-          state_ops.assign_add(ref, input_tensor, use_locking=True)
-          for input_tensor in inputs
-      ]
-      with ops.control_dependencies(update_ops):
-        return gen_state_ops._destroy_temporary_variable(
-            ref, var_name=var.op.name, name=name)
+  elif len(inputs) == 1 and name is not None:
+    return array_ops.identity(inputs[0], name=name)
+  elif context.in_eager_mode():
+    # TemporaryVariable not currently supported in eager mode; fall back
+    # onto AddN for now.
+    # TODO(frreiss) remove this once the lifetime of eager variables gets
+    # addressed
+    return add_n(inputs, name=name)
+  else:
+    return gen_math_ops._accumulate_nv2(inputs, name=name, shape=shape)  # pylint: disable=protected-access
+
+
+@ops.RegisterGradient("AccumulateNV2")
+def _accumulate_n_grad(op, grad):
+  """Same as gradient for AddN. Copies the gradient to all inputs."""
+  # Not broadcasting.
+  return [grad] * len(op.inputs)
 
 
 @tf_export("nn.sigmoid", "sigmoid")
-- 
GitLab


From 0b94d6270866789d210d1914e60937b6f231a669 Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Mon, 26 Feb 2018 10:41:44 -0800
Subject: [PATCH 290/575] Deleting references to outdated `translate/seq2seq`
 tutorial.

PiperOrigin-RevId: 187044697
---
 tensorflow/tools/ci_build/builds/test_tutorials.sh | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/tensorflow/tools/ci_build/builds/test_tutorials.sh b/tensorflow/tools/ci_build/builds/test_tutorials.sh
index 67e5af5564..db335f14ca 100755
--- a/tensorflow/tools/ci_build/builds/test_tutorials.sh
+++ b/tensorflow/tools/ci_build/builds/test_tutorials.sh
@@ -277,17 +277,6 @@ test_ptb_word_lm() {
   fi
 }
 
-
-# -----------------------------------------------------------
-# translate_test
-test_translate_test() {
-  LOG_FILE=$1
-
-  run_in_directory "${TEST_DIR}" "${LOG_FILE}" \
-    "${TF_MODELS_DIR}/tutorials/rnn/translate/translate.py" --self_test=True
-}
-
-
 # Run the tutorial tests
 test_runner "tutorial test-on-install" \
     "${TUT_TESTS}" "${TF_BUILD_TUT_TEST_BLACKLIST}" "${LOGS_DIR}"
-- 
GitLab


From ca328de4d8805a7495485e787811484d843c43a2 Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu <yuanzx@google.com>
Date: Mon, 26 Feb 2018 10:42:59 -0800
Subject: [PATCH 291/575] [XLA] Add kConvert to
 EffectiveOperandPrecisionIsOutputPrecision list.

PiperOrigin-RevId: 187044921
---
 tensorflow/compiler/xla/service/bfloat16_support.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/service/bfloat16_support.cc b/tensorflow/compiler/xla/service/bfloat16_support.cc
index 3fd9e24601..07b4b14b5e 100644
--- a/tensorflow/compiler/xla/service/bfloat16_support.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_support.cc
@@ -79,6 +79,7 @@ bool BFloat16Support::EffectiveOperandPrecisionIsOutputPrecision(
     case HloOpcode::kBroadcast:
     case HloOpcode::kClamp:
     case HloOpcode::kConcatenate:
+    case HloOpcode::kConvert:
     case HloOpcode::kCopy:
     case HloOpcode::kGetTupleElement:
     case HloOpcode::kMaximum:
-- 
GitLab


From 7735b2db761fba6e76c170066b2e5c3b7f10688b Mon Sep 17 00:00:00 2001
From: Michael Kuperstein <mkuper@google.com>
Date: Mon, 26 Feb 2018 10:52:05 -0800
Subject: [PATCH 292/575] [XLA] Do not recompute flattened sets inside layout
 assignment.

Cache the flattened sets instead of recomputing them. This matters for large graphs, since we may request the flattened set thousands of times on the same instruction, and it may be fairly expensive to construct for large tuples.

PiperOrigin-RevId: 187046642
---
 .../compiler/xla/service/layout_assignment.cc | 31 ++++++++++++++-----
 .../compiler/xla/service/layout_assignment.h  | 10 ++++++
 2 files changed, 34 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index 0668f66051..4929300f7d 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -192,17 +192,34 @@ LayoutConstraints::LayoutConstraints(
   }
 }
 
+PointsToSet::BufferSet* LayoutConstraints::GetBufferSet(
+    const HloInstruction* instruction) const {
+  auto it = buffer_sets_cache_.find(instruction);
+  if (it != buffer_sets_cache_.end()) {
+    return it->second.get();
+  }
+  auto& buffer_set =
+      buffer_sets_cache_
+          .emplace(instruction, MakeUnique<PointsToSet::BufferSet>())
+          .first->second;
+  const auto& points_to_set = points_to_analysis_.GetPointsToSet(instruction);
+  points_to_set.ForEachElement(
+      [&buffer_set](const ShapeIndex& /*index*/,
+                    const PointsToSet::BufferList& buffers) {
+        buffer_set->insert(buffers.begin(), buffers.end());
+      });
+  return buffer_set.get();
+}
+
 bool LayoutConstraints::OperandBufferForwarded(
     const HloInstruction* instruction, int64 operand_no) const {
   // The operand is potentially forwarded if the intersection of points-to sets
   // of the operand and the instruction is non-empty.
-  auto output_buffers =
-      points_to_analysis_.GetPointsToSet(instruction).CreateFlattenedSet();
-  auto operand_buffers =
-      points_to_analysis_.GetPointsToSet(instruction->operand(operand_no))
-          .CreateFlattenedSet();
-  for (const LogicalBuffer* output_buffer : output_buffers) {
-    if (operand_buffers.count(output_buffer) > 0) {
+  PointsToSet::BufferSet* output_buffers = GetBufferSet(instruction);
+  PointsToSet::BufferSet* operand_buffers =
+      GetBufferSet(instruction->operand(operand_no));
+  for (const LogicalBuffer* output_buffer : *output_buffers) {
+    if (operand_buffers->count(output_buffer) > 0) {
       return true;
     }
   }
diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h
index 2901858448..7126cb50cf 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.h
+++ b/tensorflow/compiler/xla/service/layout_assignment.h
@@ -38,6 +38,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/gtl/flatmap.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace xla {
@@ -199,6 +200,11 @@ class LayoutConstraints {
   string ToString() const;
 
  private:
+  // Find a bufferset in the bufferset cache. This is useful since we can
+  // currently create the flattened buffer set for the same instruction many
+  // times, which is often slow.
+  PointsToSet::BufferSet* GetBufferSet(const HloInstruction* instruction) const;
+
   // The set of BufferLayoutConstraints applied to the computation.
   std::unordered_map<const LogicalBuffer*, BufferLayoutConstraint>
       buffer_constraints_;
@@ -221,6 +227,10 @@ class LayoutConstraints {
   // Array-shaped buffers which have not yet been constrained.
   std::set<LogicalBuffer::Id> unconstrained_buffer_ids_;
 
+  mutable tensorflow::gtl::FlatMap<const HloInstruction*,
+                                   std::unique_ptr<PointsToSet::BufferSet>>
+      buffer_sets_cache_;
+
   HloComputation* computation_;
 };
 
-- 
GitLab


From 5a657b47f724b96730f764d3fb21c89e342e9c35 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Mon, 26 Feb 2018 10:54:31 -0800
Subject: [PATCH 293/575] Integrate ClusterResolvers with TPUEstimator.

PiperOrigin-RevId: 187047094
---
 tensorflow/contrib/cluster_resolver/BUILD     |   1 +
 .../python/training/cluster_resolver.py       |  23 +-
 .../python/training/cluster_resolver_test.py  |   2 +
 .../python/training/gce_cluster_resolver.py   |   3 +
 .../python/training/tpu_cluster_resolver.py   | 150 +++++++++---
 .../training/tpu_cluster_resolver_test.py     | 226 +++++++++++++-----
 .../contrib/tpu/python/tpu/tpu_config.py      |  31 +++
 7 files changed, 345 insertions(+), 91 deletions(-)

diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD
index 6b03df2b8e..1a124eca36 100644
--- a/tensorflow/contrib/cluster_resolver/BUILD
+++ b/tensorflow/contrib/cluster_resolver/BUILD
@@ -110,5 +110,6 @@ tf_py_test(
         "//tensorflow/python:platform_test",
         "//tensorflow/python:training",
     ],
+    grpc_enabled = True,
     main = "python/training/tpu_cluster_resolver_test.py",
 )
diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
index b04822fa9d..1c480b2513 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
@@ -53,11 +53,16 @@ class ClusterResolver(object):
     raise NotImplementedError(
         'cluster_spec is not implemented for {}.'.format(self))
 
+  @abc.abstractmethod
+  def master(self):
+    """..."""
+    raise NotImplementedError('master is not implemented for {}.'.format(self))
+
 
 class SimpleClusterResolver(ClusterResolver):
   """Simple implementation of ClusterResolver that accepts a ClusterSpec."""
 
-  def __init__(self, cluster_spec):
+  def __init__(self, cluster_spec, master=''):
     """Creates a SimpleClusterResolver from a ClusterSpec."""
     super(SimpleClusterResolver, self).__init__()
 
@@ -65,10 +70,18 @@ class SimpleClusterResolver(ClusterResolver):
       raise TypeError('cluster_spec must be a ClusterSpec.')
     self._cluster_spec = cluster_spec
 
+    if not isinstance(master, str):
+      raise TypeError('master must be a string.')
+    self._master = master
+
   def cluster_spec(self):
     """Returns the ClusterSpec passed into the constructor."""
     return self._cluster_spec
 
+  def master(self):
+    """Returns the master address to use when creating a session."""
+    return self._master
+
 
 class UnionClusterResolver(ClusterResolver):
   """Performs a union on underlying ClusterResolvers.
@@ -87,9 +100,13 @@ class UnionClusterResolver(ClusterResolver):
 
     Raises:
       TypeError: If any argument is not a subclass of `ClusterResolvers`.
+      ValueError: If there are no arguments passed.
     """
     super(UnionClusterResolver, self).__init__()
 
+    if not args:
+      raise ValueError('At least one ClusterResolver is required.')
+
     for cluster_resolver in args:
       if not isinstance(cluster_resolver, ClusterResolver):
         raise TypeError('All arguments must be a sub-class of '
@@ -169,3 +186,7 @@ class UnionClusterResolver(ClusterResolver):
           merged_cluster[job_name].update(task_dict)
 
     return ClusterSpec(merged_cluster)
+
+  def master(self):
+    """master returns the master address from the first cluster resolver."""
+    return self._cluster_resolvers[0].master()
diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py
index dbfb77723c..d9c97d53eb 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py
@@ -234,5 +234,7 @@ class UnionClusterResolverTest(test.TestCase):
     self._verifyClusterSpecEquality(cluster_spec, expected_proto)
 
 
+# TODO(saeta): Include tests for master resolution
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
index d6f2eced93..3f58241289 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
@@ -134,3 +134,6 @@ class GceClusterResolver(ClusterResolver):
 
     worker_list.sort()
     return ClusterSpec({self._job_name: worker_list})
+
+  def master(self):
+    return ''
diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
index a6a6e642e4..aeccf4c06b 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
@@ -23,7 +23,8 @@ from six.moves.urllib.request import Request
 from six.moves.urllib.request import urlopen
 
 from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import ClusterResolver
-from tensorflow.python.training.server_lib import ClusterSpec
+from tensorflow.python.training import server_lib
+from tensorflow.python.util import compat
 
 _GOOGLE_API_CLIENT_INSTALLED = True
 try:
@@ -46,13 +47,23 @@ class TPUClusterResolver(ClusterResolver):
     req = Request('http://metadata/computeMetadata/v1/%s' % path,
                   headers={'Metadata-Flavor': 'Google'})
     resp = urlopen(req)
-    return resp.read()
+    return compat.as_bytes(resp.read())
+
+  def _shouldResolve(self):
+    if (self._tpu == compat.as_bytes('') or
+        self._tpu == compat.as_bytes('local') or
+        self._tpu.startswith(compat.as_bytes('/bns')) or
+        self._tpu.startswith(compat.as_bytes('grpc://'))):
+      return False
+    return True
 
   def __init__(self,
-               tpu_names,
+               tpu,
                zone=None,
                project=None,
-               job_name='tpu_worker',
+               job_name='worker',
+               coordinator_name='coordinator',
+               coordinator_address=None,
                credentials='default',
                service=None):
     """Creates a new TPUClusterResolver object.
@@ -61,7 +72,11 @@ class TPUClusterResolver(ClusterResolver):
     for the IP addresses and ports of each Cloud TPU listed.
 
     Args:
-      tpu_names: A list of names of the target Cloud TPUs.
+      tpu: Either a string, or a list of strings corresponding to the TPUs to
+        use. If the single string is the empty string, the string 'local', or a
+        string that begins with 'grpc://' or '/bns', then it is assumed to not
+        correspond with a Cloud TPU and will instead be passed as the session
+        master and no ClusterSpec propagation will be done.
       zone: Zone where the TPUs are located. If omitted or empty, we will assume
         that the zone of the TPU is the same as the zone of the GCE VM, which we
         will try to discover from the GCE metadata service.
@@ -69,6 +84,12 @@ class TPUClusterResolver(ClusterResolver):
         empty, we will try to discover the project name of the GCE VM from the
         GCE metadata service.
       job_name: Name of the TensorFlow job the TPUs belong to.
+      coordinator_name: The name to use for the coordinator. Set to None if the
+        coordinator should not be included in the computed ClusterSpec.
+      coordinator_address: The address of the coordinator (typically an ip:port
+        pair). If set to None, a TF server will be started. If coordinator_name
+        is None, a TF server will not be started even if coordinator_address is
+        None.
       credentials: GCE Credentials. If None, then we use default credentials
         from the oauth2client
       service: The GCE API object returned by the googleapiclient.discovery
@@ -77,26 +98,36 @@ class TPUClusterResolver(ClusterResolver):
 
     Raises:
       ImportError: If the googleapiclient is not installed.
+      ValueError: If no TPUs are specified.
     """
+    if isinstance(tpu, list):
+      if not tpu:
+        raise ValueError('At least one TPU must be specified.')
+      if len(tpu) != 1:
+        raise NotImplementedError(
+            'Using multiple TPUs in a single session is not yet implemented')
+      tpu = tpu[0]
+    self._tpu = compat.as_bytes(tpu)  # self._tpu is always bytes
+    self._job_name = job_name
+    self._credentials = credentials
 
-    if not project:
-      project = self._requestComputeMetadata('/project/project-id')
+    should_resolve = self._shouldResolve()
 
-    if not zone:
-      zone_path = self._requestComputeMetadata('/instance/zone')
+    if not project and should_resolve:
+      project = self._requestComputeMetadata('project/project-id')
+
+    if not zone and should_resolve:
+      zone_path = self._requestComputeMetadata('instance/zone')
       zone = zone_path.split('/')[-1]
 
     self._project = project
     self._zone = zone
-    self._tpu_names = tpu_names
-    self._job_name = job_name
-    self._credentials = credentials
 
-    if credentials == 'default':
+    if credentials == 'default' and should_resolve:
       if _GOOGLE_API_CLIENT_INSTALLED:
         self._credentials = GoogleCredentials.get_application_default()
 
-    if service is None:
+    if service is None and should_resolve:
       if not _GOOGLE_API_CLIENT_INSTALLED:
         raise ImportError('googleapiclient must be installed before using the '
                           'TPU cluster resolver')
@@ -107,25 +138,41 @@ class TPUClusterResolver(ClusterResolver):
     else:
       self._service = service
 
-  def get_master(self):
-    """Get the ClusterSpec grpc master path.
+    self._coordinator_name = coordinator_name
+    if coordinator_name and not coordinator_address and should_resolve:
+      self._start_local_server()
+    else:
+      self._coordinator_address = coordinator_address
+
+  def master(self):
+    """Get the Master string to be used for the session.
+
+    In the normal case, this returns the grpc path (grpc://1.2.3.4:8470) of
+    first instance in the ClusterSpec returned by the cluster_spec function.
 
-    This returns the grpc path (grpc://1.2.3.4:8470) of first instance in the
-    ClusterSpec returned by the cluster_spec function. This is suitable for use
-    for the `master` argument in tf.Session() when you are using one TPU.
+    If a non-TPU name is used when constructing a TPUClusterResolver, that will
+    be returned instead (e.g. If the tpus argument's value when constructing
+    this TPUClusterResolver was 'grpc://10.240.1.2:8470',
+    'grpc://10.240.1.2:8470' will be returned).
 
     Returns:
-      string, the grpc path of the first instance in the ClusterSpec.
+      string, the connection string to use when creating a session.
 
     Raises:
       ValueError: If none of the TPUs specified exists.
     """
+    if not self._shouldResolve():
+      return self._tpu
+
     job_tasks = self.cluster_spec().job_tasks(self._job_name)
     if not job_tasks:
       raise ValueError('No TPUs exists with the specified names exist.')
 
     return 'grpc://' + job_tasks[0]
 
+  def get_master(self):
+    return self.master()
+
   def cluster_spec(self):
     """Returns a ClusterSpec object based on the latest TPU information.
 
@@ -134,17 +181,54 @@ class TPUClusterResolver(ClusterResolver):
 
     Returns:
       A ClusterSpec containing host information returned from Cloud TPUs.
-    """
-    worker_list = []
-
-    for tpu_name in self._tpu_names:
-      full_name = 'projects/%s/locations/%s/nodes/%s' % (
-          self._project, self._zone, tpu_name)
-      request = self._service.projects().locations().nodes().get(name=full_name)
-      response = request.execute()
 
-      if 'health' in response and response['health'] == 'HEALTHY':
-        instance_url = '%s:%s' % (response['ipAddress'], response['port'])
-        worker_list.append(instance_url)
-
-    return ClusterSpec({self._job_name: worker_list})
+    Raises:
+      RuntimeError: If the provided TPU is not healthy.
+    """
+    if not self._shouldResolve():
+      return server_lib.ClusterSpec({})
+
+    full_name = 'projects/%s/locations/%s/nodes/%s' % (
+        self._project, self._zone, compat.as_text(self._tpu))
+    request = self._service.projects().locations().nodes().get(name=full_name)
+    response = request.execute()
+
+    if 'health' in response and response['health'] != 'HEALTHY':
+      raise RuntimeError('TPU "%s" is unhealthy: "%s"' % (self._tpu,
+                                                          response['health']))
+
+    if 'networkEndpoints' in response:
+      worker_list = [
+          '%s:%s' % (endpoint['ipAddress'], endpoint['port'])
+          for endpoint in response['networkEndpoints']
+      ]
+    else:
+      # Fall back to the deprecated response format
+      instance_url = '%s:%s' % (response['ipAddress'], response['port'])
+      worker_list = [instance_url]
+
+    cluster_spec = {self._job_name: worker_list}
+
+    if self._coordinator_address:
+      cluster_spec[self._coordinator_name] = [self._coordinator_address]
+
+    return server_lib.ClusterSpec(cluster_spec)
+
+  def _start_local_server(self):
+    address = self._requestComputeMetadata('instance/network-interfaces/0/ip')
+    self._server = server_lib.Server(
+        {
+            'local': ['0.0.0.0:0']
+        }, protocol='grpc', config=None, start=True)
+    # self._server.target is of the form: grpc://ipaddress:port
+    target = compat.as_bytes(self._server.target)
+    splits = target.split(compat.as_bytes(':'))
+    assert len(splits) == 3, self._server.target
+    assert splits[0] == compat.as_bytes('grpc'), self._server.target
+    self._coordinator_port = compat.as_text(splits[2])
+    self._coordinator_address = '%s:%s' % (
+        address, compat.as_text(self._coordinator_port))
+
+  def __deepcopy__(self, memo):
+    # TODO(b/73668574): Remove this once RunConfig avoids performing deepcopy.
+    return self
diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py
index 4fd34629cf..6b4a155152 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py
@@ -21,7 +21,7 @@ from __future__ import print_function
 from tensorflow.contrib.cluster_resolver.python.training.tpu_cluster_resolver import TPUClusterResolver
 from tensorflow.python.platform import test
 from tensorflow.python.training import server_lib
-
+from tensorflow.python.util import compat
 
 mock = test.mock
 
@@ -50,10 +50,12 @@ class MockNodeClass(object):
 
 def mock_request_compute_metadata(cls, *args, **kwargs):
   del cls, kwargs  # Unused.
-  if args[0] == '/project/project-id':
+  if args[0] == 'project/project-id':
     return 'test-project'
-  elif args[0] == '/instance/zone':
+  elif args[0] == 'instance/zone':
     return 'projects/test-project/locations/us-central1-c'
+  elif args[0] == 'instance/network-interfaces/0/ip':
+    return '10.128.1.2'
   return ''
 
 
@@ -113,17 +115,26 @@ class TPUClusterResolverTest(test.TestCase):
     tpu_cluster_resolver = TPUClusterResolver(
         project=None,
         zone=None,
-        tpu_names=['test-tpu-1'],
+        tpu=['test-tpu-1'],
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
 
     actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
     expected_proto = """
-    job { name: 'tpu_worker' tasks { key: 0 value: '10.1.2.3:8470' } }
-    """
-    self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
+    job {
+      name: 'coordinator'
+      tasks { key: 0 value: '10.128.1.2:%s' }
+    }
+    job {
+      name: 'worker'
+      tasks { key: 0 value: '10.1.2.3:8470' }
+    }
+    """ % tpu_cluster_resolver._coordinator_port
+    self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto))
 
-  def testSimpleSuccessfulRetrieval(self):
+  @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata',
+                     mock_request_compute_metadata)
+  def testRetrieveProjectAndZoneFromMetadataNoCoordinator(self):
     tpu_map = {
         'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
             'ipAddress': '10.1.2.3',
@@ -133,116 +144,217 @@ class TPUClusterResolverTest(test.TestCase):
     }
 
     tpu_cluster_resolver = TPUClusterResolver(
-        project='test-project',
-        zone='us-central1-c',
-        tpu_names=['test-tpu-1'],
+        project=None,
+        zone=None,
+        tpu=['test-tpu-1'],
+        coordinator_name=None,
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
 
     actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
     expected_proto = """
-    job { name: 'tpu_worker' tasks { key: 0 value: '10.1.2.3:8470' } }
+    job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } }
     """
     self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
 
-  def testMultipleSuccessfulRetrieval(self):
+  def testSimpleSuccessfulRetrieval(self):
     tpu_map = {
         'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
             'ipAddress': '10.1.2.3',
             'port': '8470',
             'health': 'HEALTHY'
-        },
-        'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': {
-            'ipAddress': '10.4.5.6',
-            'port': '8470',
-            'health': 'HEALTHY'
         }
     }
 
     tpu_cluster_resolver = TPUClusterResolver(
         project='test-project',
         zone='us-central1-c',
-        tpu_names=['test-tpu-2', 'test-tpu-1'],
+        tpu=['test-tpu-1'],
+        coordinator_address='10.128.1.5:10203',
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
 
     actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
     expected_proto = """
-    job { name: 'tpu_worker' tasks { key: 0 value: '10.4.5.6:8470' }
-                             tasks { key: 1 value: '10.1.2.3:8470' } }
+    job { name: 'coordinator' tasks { key: 0 value: '10.128.1.5:10203' } }
+    job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } }
     """
     self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
 
-  def testHealthyTpuNodeRetrieval(self):
+  def testNewNetworkEndpointFormat(self):
     tpu_map = {
         'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
-            'ipAddress': '10.1.2.3',
-            'port': '8470',
-            'health': 'HEALTHY'
-        },
-        'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': {
-            'ipAddress': '10.4.5.6',
-            'port': '8470',
-        },
-        'projects/test-project/locations/us-central1-c/nodes/test-tpu-3': {
-            'ipAddress': '10.7.8.9',
-            'port': '8470',
-            'health': 'UNHEALTHY'
+            'health': 'HEALTHY',
+            'networkEndpoints': [{
+                'ipAddress': '10.2.3.4',
+                'port': 8470,
+            }]
         }
     }
 
     tpu_cluster_resolver = TPUClusterResolver(
         project='test-project',
         zone='us-central1-c',
-        tpu_names=['test-tpu-2', 'test-tpu-1', 'test-tpu-3'],
+        tpu='test-tpu-1',
+        coordinator_address='10.128.1.5:10203',
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
 
     actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
     expected_proto = """
-    job {
-      name: 'tpu_worker'
-      tasks {
-        key: 0
-        value: '10.1.2.3:8470'
-      }
-    }
+    job { name: 'coordinator' tasks { key: 0 value: '10.128.1.5:10203' } }
+    job { name: 'worker' tasks { key: 0 value: '10.2.3.4:8470' } }
     """
     self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
+    self.assertEqual('grpc://10.2.3.4:8470', tpu_cluster_resolver.master())
 
-  def testGetMasterMultipleEntries(self):
+  @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata',
+                     mock_request_compute_metadata)
+  def testPodResolution(self):
     tpu_map = {
         'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
-            'ipAddress': '10.1.2.3',
-            'port': '8470',
-            'health': 'HEALTHY'
-        },
-        'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': {
-            'ipAddress': '10.4.5.6',
-            'port': '8470',
-            'health': 'HEALTHY'
+            'health':
+                'HEALTHY',
+            'networkEndpoints': [
+                {
+                    'ipAddress': '10.2.3.4',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.5',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.6',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.7',
+                    'port': 8470,
+                },
+            ]
+        }
+    }
+
+    tpu_cluster_resolver = TPUClusterResolver(
+        tpu='test-tpu-1',
+        credentials=None,
+        service=self.mock_service_client(tpu_map=tpu_map))
+
+    actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
+    expected_proto = """
+    job {
+      name: 'coordinator',
+      tasks { key: 0 value: '10.128.1.2:%s'}
+    }
+    job {
+      name: 'worker'
+      tasks { key: 0 value: '10.2.3.4:8470' }
+      tasks { key: 1 value: '10.2.3.5:8470' }
+      tasks { key: 2 value: '10.2.3.6:8470' }
+      tasks { key: 3 value: '10.2.3.7:8470' }
+    }
+    """ % tpu_cluster_resolver._coordinator_port
+    self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto))
+
+  def testPodResolutionNoCoordinator(self):
+    tpu_map = {
+        'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
+            'health':
+                'HEALTHY',
+            'networkEndpoints': [
+                {
+                    'ipAddress': '10.2.3.4',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.5',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.6',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.7',
+                    'port': 8470,
+                },
+            ]
         }
     }
 
     tpu_cluster_resolver = TPUClusterResolver(
         project='test-project',
         zone='us-central1-c',
-        tpu_names=['test-tpu-2', 'test-tpu-1'],
+        tpu='test-tpu-1',
+        coordinator_name=None,
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
-    self.assertEqual('grpc://10.4.5.6:8470', tpu_cluster_resolver.get_master())
+
+    actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
+    expected_proto = """
+    job {
+      name: 'worker'
+      tasks { key: 0 value: '10.2.3.4:8470' }
+      tasks { key: 1 value: '10.2.3.5:8470' }
+      tasks { key: 2 value: '10.2.3.6:8470' }
+      tasks { key: 3 value: '10.2.3.7:8470' }
+    }
+    """
+    self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
 
   def testGetMasterNoEntries(self):
     tpu_map = {}
 
+    with self.assertRaises(ValueError):
+      TPUClusterResolver(
+          project='test-project',
+          zone='us-central1-c',
+          tpu=[],
+          coordinator_name=None,
+          credentials=None,
+          service=self.mock_service_client(tpu_map=tpu_map))
+
+  # TODO(saeta): Convert to parameterized test when included in OSS TF.
+  def verifyShouldResolve(self, tpu, should_resolve):
     tpu_cluster_resolver = TPUClusterResolver(
         project='test-project',
         zone='us-central1-c',
-        tpu_names=[],
+        tpu=tpu,
+        coordinator_name=None,
         credentials=None,
-        service=self.mock_service_client(tpu_map=tpu_map))
-    with self.assertRaises(ValueError):
-      tpu_cluster_resolver.get_master()
+        service=self.mock_service_client(tpu_map={}))
+    self.assertEqual(should_resolve, tpu_cluster_resolver._shouldResolve(),
+                     "TPU: '%s'" % tpu)
+
+  def testShouldResolveNoName(self):
+    self.verifyShouldResolve('', False)
+
+  def testShouldResolveLocal(self):
+    self.verifyShouldResolve('local', False)
+
+  def testShouldResolveGrpc(self):
+    self.verifyShouldResolve('grpc://10.1.2.3:8470', False)
+
+  def testShouldResolveBns(self):
+    self.verifyShouldResolve('/bns/foo/bar', False)
+
+  def testShouldResolveName(self):
+    self.verifyShouldResolve('mytpu', True)
+
+  def testShouldResolveList(self):
+    self.verifyShouldResolve(['myothertpu'], True)
+
+  def testShouldResolveGrpcPrefix(self):
+    self.verifyShouldResolve('grpctpu', True)
+
+  def testNoCallComputeMetadata(self):
+    tpu_cluster_resolver = TPUClusterResolver(tpu='/bns/foo/bar')
+    self.assertEqual(compat.as_bytes('/bns/foo/bar'),
+                     tpu_cluster_resolver.master())
+    self.assertEqual(
+        server_lib.ClusterSpec({}), tpu_cluster_resolver.cluster_spec())
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config.py b/tensorflow/contrib/tpu/python/tpu/tpu_config.py
index 6440702182..7ceb4069cf 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_config.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_config.py
@@ -26,6 +26,7 @@ import os
 import numpy as np
 
 from tensorflow.contrib.tpu.python.tpu import util as util_lib
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.estimator import run_config as run_config_lib
 from tensorflow.python.platform import tf_logging as logging
 
@@ -140,6 +141,7 @@ class RunConfig(run_config_lib.RunConfig):
                tpu_config=None,
                evaluation_master=None,
                master=None,
+               cluster=None,
                **kwargs):
     """Constructs a RunConfig.
 
@@ -148,15 +150,26 @@ class RunConfig(run_config_lib.RunConfig):
       evaluation_master: a string. The address of the master to use for eval.
         Defaults to master if not set.
       master: a string. The address of the master to use for training.
+      cluster: a ClusterResolver
       **kwargs: keyword config parameters.
+
+    Raises:
+      ValueError: if cluster is not None and the provided session_config has a
+        cluster_def already.
     """
     super(RunConfig, self).__init__(**kwargs)
     self._tpu_config = tpu_config or TPUConfig()
+    self._cluster = cluster
 
     # If user sets master and/or evaluation_master explicilty, including empty
     # string '', take it. Otherwise, take the values set by parent class.
     if master is not None:
+      if cluster is not None:
+        raise ValueError('Both master and cluster are set.')
       self._master = master
+    else:
+      if cluster:
+        self._master = cluster.master()
 
     if evaluation_master is not None:
       self._evaluation_master = evaluation_master
@@ -170,6 +183,20 @@ class RunConfig(run_config_lib.RunConfig):
       # evaluation_master to master, unless user overwrites it.
       self._evaluation_master = self._master
 
+    # Set the ClusterSpec to use
+    if cluster:
+      self._cluster_spec = cluster.cluster_spec()
+
+      # Merge the cluster_def into the ConfigProto.
+      if self._session_config is None:  # pylint: disable=access-member-before-definition
+        self._session_config = config_pb2.ConfigProto(allow_soft_placement=True)
+      if self._session_config.HasField('cluster_def'):
+        raise ValueError(
+            'You cannot provide a ClusterResolver and '
+            'session_config.cluster_def.')
+      self._session_config.cluster_def.CopyFrom(
+          self._cluster_spec.as_cluster_def())
+
   @property
   def evaluation_master(self):
     return self._evaluation_master
@@ -182,6 +209,10 @@ class RunConfig(run_config_lib.RunConfig):
   def tpu_config(self):
     return self._tpu_config
 
+  @property
+  def cluster(self):
+    return self._cluster
+
   def replace(self, **kwargs):
     if 'tpu_config' not in kwargs:
       return super(RunConfig, self).replace(**kwargs)
-- 
GitLab


From 24c619b6c4dd38fc4ef0f51b92e5f16809cc4ec8 Mon Sep 17 00:00:00 2001
From: Guangda Lai <laigd@google.com>
Date: Mon, 26 Feb 2018 10:59:54 -0800
Subject: [PATCH 294/575] Automated g4 rollback of changelist 185324160

PiperOrigin-RevId: 187048135
---
 tensorflow/contrib/cmake/tf_core_cpu.cmake    |   7 ++
 tensorflow/contrib/makefile/Makefile          |   1 +
 .../core/common_runtime/gpu/gpu_id_manager.cc |  50 +++++++--
 .../core/common_runtime/gpu/gpu_id_manager.h  |  14 ++-
 tensorflow/core/grappler/clusters/BUILD       |  26 ++++-
 .../core/grappler/clusters/single_machine.cc  |  17 ++-
 tensorflow/core/grappler/clusters/utils.cc    |  71 ++++++++-----
 tensorflow/core/grappler/clusters/utils.h     |   3 +-
 .../core/grappler/clusters/utils_test.cc      | 100 ++++++++++++++++++
 tensorflow/core/grappler/costs/BUILD          |   1 +
 tensorflow/core/grappler/costs/utils.cc       |  18 +++-
 11 files changed, 262 insertions(+), 46 deletions(-)
 create mode 100644 tensorflow/core/grappler/clusters/utils_test.cc

diff --git a/tensorflow/contrib/cmake/tf_core_cpu.cmake b/tensorflow/contrib/cmake/tf_core_cpu.cmake
index 96ac60d095..a54cbff33b 100644
--- a/tensorflow/contrib/cmake/tf_core_cpu.cmake
+++ b/tensorflow/contrib/cmake/tf_core_cpu.cmake
@@ -63,6 +63,12 @@ file(GLOB_RECURSE tf_core_cpu_exclude_srcs
     "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
     "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc"
 )
+file(GLOB_RECURSE tf_core_cpu_whitelisted_srcs
+    "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id.h"
+    "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id.cc"
+    "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc"
+)
+list(REMOVE_ITEM tf_core_cpu_exclude_srcs ${tf_core_cpu_whitelisted_srcs})
 list(REMOVE_ITEM tf_core_cpu_srcs ${tf_core_cpu_exclude_srcs})
 
 if (tensorflow_ENABLE_GPU)
@@ -79,6 +85,7 @@ if (tensorflow_ENABLE_GPU)
      "${tensorflow_source_dir}/tensorflow/core/*test*.cc"
   )
   list(REMOVE_ITEM tf_core_gpu_srcs ${tf_core_gpu_exclude_srcs})
+  list(REMOVE_ITEM tf_core_gpu_srcs ${tf_core_cpu_whitelisted_srcs})
   list(APPEND tf_core_cpu_srcs ${tf_core_gpu_srcs})
 endif()
 
diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index 81327407d4..05e8d9064b 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -677,6 +677,7 @@ endif  # TEGRA
 TF_CC_SRCS := $(filter-out $(CORE_CC_EXCLUDE_SRCS), $(CORE_CC_ALL_SRCS))
 # Add in any extra files that don't fit the patterns easily
 TF_CC_SRCS += tensorflow/contrib/makefile/downloads/fft2d/fftsg.c
+TF_CC_SRCS += tensorflow/core/common_runtime/gpu/gpu_id_manager.cc
 # Also include the op and kernel definitions.
 TF_CC_SRCS += $(shell cat $(MAKEFILE_DIR)/tf_op_files.txt)
 PBT_CC_SRCS := $(shell cat $(MAKEFILE_DIR)/tf_pb_text_files.txt)
diff --git a/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc b/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc
index 207afdca75..7dfff3269c 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc
@@ -18,7 +18,10 @@ limitations under the License.
 #include <unordered_map>
 
 #include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/mutex.h"
 
 namespace tensorflow {
@@ -27,8 +30,8 @@ namespace {
 class TfToCudaGpuIdMap {
  public:
   static TfToCudaGpuIdMap* singleton() {
-    static auto* manager = new TfToCudaGpuIdMap;
-    return manager;
+    static auto* id_map = new TfToCudaGpuIdMap;
+    return id_map;
   }
 
   void InsertOrDie(TfGpuId tf_gpu_id, CudaGpuId cuda_gpu_id)
@@ -47,18 +50,41 @@ class TfToCudaGpuIdMap {
     }
   }
 
-  int32 FindOrDie(TfGpuId tf_gpu_id) const LOCKS_EXCLUDED(mu_) {
+  CudaGpuId FindOrDie(TfGpuId tf_gpu_id) const LOCKS_EXCLUDED(mu_) {
     mutex_lock lock(mu_);
+    return FindOrDieLocked(tf_gpu_id);
+  }
+
+  bool Find(TfGpuId tf_gpu_id, CudaGpuId* cuda_gpu_id) const
+      LOCKS_EXCLUDED(mu_) {
+    mutex_lock lock(mu_);
+    if (id_map_.count(tf_gpu_id.value()) == 0) return false;
+    *cuda_gpu_id = FindOrDieLocked(tf_gpu_id);
+    return true;
+  }
+
+ private:
+  TfToCudaGpuIdMap() = default;
+
+  CudaGpuId FindOrDieLocked(TfGpuId tf_gpu_id) const
+      EXCLUSIVE_LOCKS_REQUIRED(mu_) {
     auto result = id_map_.find(tf_gpu_id.value());
     CHECK(result != id_map_.end())
         << "Could not find the mapping for TfGpuId: " << tf_gpu_id;
-    return result->second;
+    return CudaGpuId(result->second);
+  }
+
+  void TestOnlyReset() LOCKS_EXCLUDED(mu_) {
+    mutex_lock lock(mu_);
+    id_map_.clear();
   }
 
- private:
   using IdMapType = std::unordered_map<int32, int32>;
   mutable mutex mu_;
   IdMapType id_map_ GUARDED_BY(mu_);
+
+  friend class ::tensorflow::GpuIdManager;
+  TF_DISALLOW_COPY_AND_ASSIGN(TfToCudaGpuIdMap);
 };
 }  // namespace
 
@@ -67,8 +93,20 @@ void GpuIdManager::InsertTfCudaGpuIdPair(TfGpuId tf_gpu_id,
   TfToCudaGpuIdMap::singleton()->InsertOrDie(tf_gpu_id, cuda_gpu_id);
 }
 
+Status GpuIdManager::TfToCudaGpuId(TfGpuId tf_gpu_id, CudaGpuId* cuda_gpu_id) {
+  if (TfToCudaGpuIdMap::singleton()->Find(tf_gpu_id, cuda_gpu_id)) {
+    return Status::OK();
+  }
+  return errors::NotFound("TF GPU device with id ", tf_gpu_id.value(),
+                          " was not registered");
+}
+
 CudaGpuId GpuIdManager::TfToCudaGpuId(TfGpuId tf_gpu_id) {
-  return CudaGpuId(TfToCudaGpuIdMap::singleton()->FindOrDie(tf_gpu_id));
+  return TfToCudaGpuIdMap::singleton()->FindOrDie(tf_gpu_id);
+}
+
+void GpuIdManager::TestOnlyReset() {
+  TfToCudaGpuIdMap::singleton()->TestOnlyReset();
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/gpu/gpu_id_manager.h b/tensorflow/core/common_runtime/gpu/gpu_id_manager.h
index 33925d8c36..2b54cc184c 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_id_manager.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_id_manager.h
@@ -17,15 +17,25 @@ limitations under the License.
 #define TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_ID_MANAGER_H_
 
 #include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
 
-// Class that manages the translation between Tensorflow GPU ids and CUDA GPU
-// ids.
+// Class that maintains a map from TfGpuId to CudaGpuId, and manages the
+// translation between them.
 class GpuIdManager {
  public:
+  // Adds a mapping from tf_gpu_id to cuda_gpu_id.
   static void InsertTfCudaGpuIdPair(TfGpuId tf_gpu_id, CudaGpuId cuda_gpu_id);
+
+  // Gets the cuda_gpu_id associated with tf_gpu_id. Returns OK if found.
+  static Status TfToCudaGpuId(TfGpuId tf_gpu_id, CudaGpuId* cuda_gpu_id);
+  // Similar to the above version, but returns the result, and checks fail if
+  // no result is found.
   static CudaGpuId TfToCudaGpuId(TfGpuId tf_gpu_id);
+
+  // Clears the map. Used in unit tests only.
+  static void TestOnlyReset();
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index b8f8e13c9a..b653f902e8 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -1,7 +1,12 @@
 licenses(["notice"])  # Apache 2.0
 
+load("//tensorflow:tensorflow.bzl", "if_cuda")
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
 load("//tensorflow:tensorflow.bzl", "tf_cuda_library")
+load(
+    "//tensorflow/core:platform/default/build_config_root.bzl",
+    "tf_cuda_tests_tags",
+)
 
 filegroup(
     name = "all_files",
@@ -26,13 +31,12 @@ config_setting(
 tf_cuda_library(
     name = "utils",
     srcs = ["utils.cc"],
-    hdrs = [
-        "utils.h",
-    ],
+    hdrs = ["utils.h"],
     visibility = ["//visibility:public"],
     deps = [
         "//third_party/eigen3",
         "//tensorflow/core:framework",
+        "//tensorflow/core:gpu_id",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
     ] + select({
@@ -41,6 +45,21 @@ tf_cuda_library(
     }),
 )
 
+tf_cc_test(
+    name = "utils_test",
+    srcs = ["utils_test.cc"],
+    linkstatic = if_cuda(1, 0),
+    tags = tf_cuda_tests_tags(),
+    deps = [
+        ":utils",
+        "//tensorflow/core:gpu_id",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
 cc_library(
     name = "cluster",
     srcs = ["cluster.cc"],
@@ -104,6 +123,7 @@ cc_library(
         "//tensorflow/core:core_cpu_lib",
         "//tensorflow/core:direct_session",
         "//tensorflow/core:framework",
+        "//tensorflow/core:gpu_id",
         "//tensorflow/core:lib",
         "//tensorflow/core/grappler:utils",
         "//tensorflow/core/kernels:ops_util",
diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index cc7f418d49..8e236c9ee8 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -21,6 +21,8 @@ limitations under the License.
 #include "tensorflow/cc/training/queue_runner.h"
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
 #include "tensorflow/core/grappler/clusters/utils.h"
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/kernels/ops_util.h"
@@ -80,13 +82,24 @@ Status SingleMachine::Provision() {
 
   std::vector<DeviceAttributes> devices;
   TF_RETURN_IF_ERROR(session_->ListDevices(&devices));
-  int gpu_id = 0;
   for (const auto& dev : devices) {
     DeviceProperties attr;
     if (dev.device_type() == "CPU") {
       attr = GetLocalCPUInfo();
     } else if (dev.device_type() == "GPU") {
-      attr = GetLocalGPUInfo(gpu_id++);
+      DeviceNameUtils::ParsedName parsed;
+      if (!DeviceNameUtils::ParseFullName(dev.name(), &parsed)) {
+        return errors::InvalidArgument(
+            strings::StrCat("Not able to parse GPU device name: ", dev.name()));
+      }
+      TfGpuId tf_gpu_id(parsed.id);
+      CudaGpuId cuda_gpu_id;
+      Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id);
+      if (!s.ok()) {
+        return errors::Unavailable("Unknown TF GPU device with id ",
+                                   tf_gpu_id.value(), ": ", s.ToString());
+      }
+      attr = GetLocalGPUInfo(cuda_gpu_id);
     } else if (dev.device_type().find("XLA") == string::npos) {
       // Filter out the fake XLA devices to avoid double counting the actual
       // hardware resources that are available.
diff --git a/tensorflow/core/grappler/clusters/utils.cc b/tensorflow/core/grappler/clusters/utils.cc
index 607e10e1ab..b54b34959a 100644
--- a/tensorflow/core/grappler/clusters/utils.cc
+++ b/tensorflow/core/grappler/clusters/utils.cc
@@ -27,6 +27,9 @@ limitations under the License.
 #include "include/libxsmm.h"
 #endif
 
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
+#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/cpu_info.h"
@@ -66,36 +69,40 @@ DeviceProperties GetLocalCPUInfo() {
   return device;
 }
 
-DeviceProperties GetLocalGPUInfo(int gpu_id) {
+DeviceProperties GetLocalGPUInfo(CudaGpuId cuda_gpu_id) {
   DeviceProperties device;
   device.set_type("GPU");
 
 #if GOOGLE_CUDA
   cudaDeviceProp properties;
-  cudaError_t error = cudaGetDeviceProperties(&properties, gpu_id);
-  if (error == cudaSuccess) {
-    device.set_vendor("NVidia");
-    device.set_model(properties.name);
-    device.set_frequency(properties.clockRate * 1e-3);
-    device.set_num_cores(properties.multiProcessorCount);
-    device.set_num_registers(properties.regsPerMultiprocessor);
-    // For compute capability less than 5, l1 cache size is configurable to
-    // either 16 KB or 48 KB. We use the initial configuration 16 KB here. For
-    // compute capability larger or equal to 5, l1 cache (unified with texture
-    // cache) size is 24 KB. This number may need to be updated for future
-    // compute capabilities.
-    device.set_l1_cache_size((properties.major < 5) ? 16 * 1024 : 24 * 1024);
-    device.set_l2_cache_size(properties.l2CacheSize);
-    device.set_l3_cache_size(0);
-    device.set_shared_memory_size_per_multiprocessor(
-        properties.sharedMemPerMultiprocessor);
-    device.set_memory_size(properties.totalGlobalMem);
-    // 8 is the number of bits per byte. 2 is accounted for
-    // double data rate (DDR).
-    device.set_bandwidth(properties.memoryBusWidth / 8 *
-                         properties.memoryClockRate * 2);
+  cudaError_t error = cudaGetDeviceProperties(&properties, cuda_gpu_id.value());
+  if (error != cudaSuccess) {
+    device.set_type("UNKNOWN");
+    LOG(ERROR) << "Failed to get device properties, error code: " << error;
+    return device;
   }
 
+  device.set_vendor("NVIDIA");
+  device.set_model(properties.name);
+  device.set_frequency(properties.clockRate * 1e-3);
+  device.set_num_cores(properties.multiProcessorCount);
+  device.set_num_registers(properties.regsPerMultiprocessor);
+  // For compute capability less than 5, l1 cache size is configurable to
+  // either 16 KB or 48 KB. We use the initial configuration 16 KB here. For
+  // compute capability larger or equal to 5, l1 cache (unified with texture
+  // cache) size is 24 KB. This number may need to be updated for future
+  // compute capabilities.
+  device.set_l1_cache_size((properties.major < 5) ? 16 * 1024 : 24 * 1024);
+  device.set_l2_cache_size(properties.l2CacheSize);
+  device.set_l3_cache_size(0);
+  device.set_shared_memory_size_per_multiprocessor(
+      properties.sharedMemPerMultiprocessor);
+  device.set_memory_size(properties.totalGlobalMem);
+  // 8 is the number of bits per byte. 2 is accounted for
+  // double data rate (DDR).
+  device.set_bandwidth(properties.memoryBusWidth / 8 *
+                       properties.memoryClockRate * 2);
+
   (*device.mutable_environment())["architecture"] =
       strings::StrCat(properties.major, ".", properties.minor);
   (*device.mutable_environment())["cuda"] = strings::StrCat(CUDA_VERSION);
@@ -106,18 +113,26 @@ DeviceProperties GetLocalGPUInfo(int gpu_id) {
 }
 
 DeviceProperties GetDeviceInfo(const DeviceNameUtils::ParsedName& device) {
+  DeviceProperties unknown;
+  unknown.set_type("UNKNOWN");
+
   if (device.type == "CPU") {
     return GetLocalCPUInfo();
   } else if (device.type == "GPU") {
     if (device.has_id) {
-      return GetLocalGPUInfo(device.id);
+      TfGpuId tf_gpu_id(device.id);
+      CudaGpuId cuda_gpu_id;
+      Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id);
+      if (!s.ok()) {
+        LOG(ERROR) << s;
+        return unknown;
+      }
+      return GetLocalGPUInfo(cuda_gpu_id);
     } else {
-      return GetLocalGPUInfo(0);
+      return GetLocalGPUInfo(CudaGpuId(0));
     }
   }
-  DeviceProperties result;
-  result.set_type("UNKNOWN");
-  return result;
+  return unknown;
 }
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/clusters/utils.h b/tensorflow/core/grappler/clusters/utils.h
index 191942040a..df8e7dca44 100644
--- a/tensorflow/core/grappler/clusters/utils.h
+++ b/tensorflow/core/grappler/clusters/utils.h
@@ -16,6 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_GRAPPLER_CLUSTERS_UTILS_H_
 #define TENSORFLOW_GRAPPLER_CLUSTERS_UTILS_H_
 
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
 #include "tensorflow/core/protobuf/device_properties.pb.h"
 #include "tensorflow/core/util/device_name_utils.h"
 
@@ -27,7 +28,7 @@ DeviceProperties GetLocalCPUInfo();
 
 // Returns the DeviceProperties for the specified GPU attached to the server on
 // which grappler is running.
-DeviceProperties GetLocalGPUInfo(int gpu_id);
+DeviceProperties GetLocalGPUInfo(CudaGpuId cuda_gpu_id);
 
 // Returns the DeviceProperties of the specified device
 DeviceProperties GetDeviceInfo(const DeviceNameUtils::ParsedName& device);
diff --git a/tensorflow/core/grappler/clusters/utils_test.cc b/tensorflow/core/grappler/clusters/utils_test.cc
new file mode 100644
index 0000000000..74218adbac
--- /dev/null
+++ b/tensorflow/core/grappler/clusters/utils_test.cc
@@ -0,0 +1,100 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/clusters/utils.h"
+
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/protobuf/device_properties.pb.h"
+
+namespace tensorflow {
+namespace grappler {
+namespace {
+
+TEST(UtilsTest, GetLocalGPUInfo) {
+  GpuIdManager::TestOnlyReset();
+#if GOOGLE_CUDA
+  LOG(INFO) << "CUDA is enabled.";
+  DeviceProperties properties;
+
+  // Invalid CUDA GPU ID.
+  properties = GetLocalGPUInfo(CudaGpuId(100));
+  EXPECT_EQ("UNKNOWN", properties.type());
+
+  // Succeed when a valid CUDA GPU id was inserted.
+  properties = GetLocalGPUInfo(CudaGpuId(0));
+  EXPECT_EQ("GPU", properties.type());
+  EXPECT_EQ("NVIDIA", properties.vendor());
+#else
+  LOG(INFO) << "CUDA is not enabled.";
+  DeviceProperties properties;
+
+  properties = GetLocalGPUInfo(CudaGpuId(0));
+  EXPECT_EQ("GPU", properties.type());
+
+  properties = GetLocalGPUInfo(CudaGpuId(100));
+  EXPECT_EQ("GPU", properties.type());
+#endif
+}
+
+TEST(UtilsTest, GetDeviceInfo) {
+  GpuIdManager::TestOnlyReset();
+  DeviceNameUtils::ParsedName device;
+  DeviceProperties properties;
+
+  // Invalid type.
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("UNKNOWN", properties.type());
+
+  // Cpu info.
+  device.type = "CPU";
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("CPU", properties.type());
+
+  // No TF GPU id provided.
+  device.type = "GPU";
+  device.has_id = false;
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("GPU", properties.type());
+#if GOOGLE_CUDA
+  EXPECT_EQ("NVIDIA", properties.vendor());
+#endif
+
+  // TF to CUDA GPU id mapping entry doesn't exist.
+  device.has_id = true;
+  device.id = 0;
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("UNKNOWN", properties.type());
+
+#if GOOGLE_CUDA
+  // Invalid CUDA GPU id.
+  GpuIdManager::InsertTfCudaGpuIdPair(TfGpuId(0), CudaGpuId(100));
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("UNKNOWN", properties.type());
+
+  // Valid CUDA GPU id.
+  GpuIdManager::InsertTfCudaGpuIdPair(TfGpuId(1), CudaGpuId(0));
+  device.id = 1;
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("GPU", properties.type());
+  EXPECT_EQ("NVIDIA", properties.vendor());
+#endif
+}
+
+}  // namespace
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/costs/BUILD b/tensorflow/core/grappler/costs/BUILD
index 0fe01e9c9e..5336df1f51 100644
--- a/tensorflow/core/grappler/costs/BUILD
+++ b/tensorflow/core/grappler/costs/BUILD
@@ -142,6 +142,7 @@ tf_cuda_library(
         "//third_party/eigen3",
         "//tensorflow/core:framework",
         "//tensorflow/core:graph",
+        "//tensorflow/core:gpu_id",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_proto_parsing",
         "//tensorflow/core:protos_all_cc",
diff --git a/tensorflow/core/grappler/costs/utils.cc b/tensorflow/core/grappler/costs/utils.cc
index 602f69f12e..076945d5c6 100644
--- a/tensorflow/core/grappler/costs/utils.cc
+++ b/tensorflow/core/grappler/costs/utils.cc
@@ -26,6 +26,8 @@ limitations under the License.
 #include "cuda/include/cudnn.h"
 #endif
 
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
 #include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/op.h"
@@ -200,17 +202,25 @@ std::vector<OpInfo::TensorProperties> FindInputFeatures(
 }
 
 DeviceProperties GetDeviceInfo(const string& device_str) {
+  DeviceProperties unknown;
+  unknown.set_type("UNKNOWN");
+
   DeviceNameUtils::ParsedName parsed;
   if (DeviceNameUtils::ParseFullName(device_str, &parsed)) {
     if (parsed.type == "GPU") {
-      return GetLocalGPUInfo(parsed.id);
+      TfGpuId tf_gpu_id(parsed.id);
+      CudaGpuId cuda_gpu_id;
+      Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id);
+      if (!s.ok()) {
+        LOG(ERROR) << s;
+        return unknown;
+      }
+      return GetLocalGPUInfo(cuda_gpu_id);
     } else if (parsed.type == "CPU") {
       return GetLocalCPUInfo();
     }
   }
-  DeviceProperties device;
-  device.set_type("UNKNOWN");
-  return device;
+  return unknown;
 }
 
 DeviceProperties GetDeviceInfo(const CostGraphDef::Node& node) {
-- 
GitLab


From 49b666dbbd58958a7499fa3961c1c8c75757ad7c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 11:08:54 -0800
Subject: [PATCH 295/575] Bring in `isbuiltin`.

PiperOrigin-RevId: 187049824
---
 tensorflow/python/util/tf_inspect.py      |  5 +++++
 tensorflow/python/util/tf_inspect_test.py | 13 +++++++++++++
 2 files changed, 18 insertions(+)

diff --git a/tensorflow/python/util/tf_inspect.py b/tensorflow/python/util/tf_inspect.py
index c2fe6fc449..a7cead5555 100644
--- a/tensorflow/python/util/tf_inspect.py
+++ b/tensorflow/python/util/tf_inspect.py
@@ -149,6 +149,11 @@ def getsource(object):  # pylint: disable=redefined-builtin
   return _inspect.getsource(tf_decorator.unwrap(object)[1])
 
 
+def isbuiltin(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.isbuiltin."""
+  return _inspect.isbuiltin(tf_decorator.unwrap(object)[1])
+
+
 def isclass(object):  # pylint: disable=redefined-builtin
   """TFDecorator-aware replacement for inspect.isclass."""
   return _inspect.isclass(tf_decorator.unwrap(object)[1])
diff --git a/tensorflow/python/util/tf_inspect_test.py b/tensorflow/python/util/tf_inspect_test.py
index 8903e1156b..129408449e 100644
--- a/tensorflow/python/util/tf_inspect_test.py
+++ b/tensorflow/python/util/tf_inspect_test.py
@@ -144,6 +144,19 @@ def test_decorated_function_with_defaults(a, b=2, c='Hello'):
     self.assertEqual(
         expected, tf_inspect.getsource(test_decorated_function_with_defaults))
 
+  def testIsBuiltin(self):
+    self.assertEqual(
+        tf_inspect.isbuiltin(TestDecoratedClass),
+        inspect.isbuiltin(TestDecoratedClass))
+    self.assertEqual(
+        tf_inspect.isbuiltin(test_decorated_function),
+        inspect.isbuiltin(test_decorated_function))
+    self.assertEqual(
+        tf_inspect.isbuiltin(test_undecorated_function),
+        inspect.isbuiltin(test_undecorated_function))
+    self.assertEqual(tf_inspect.isbuiltin(range), inspect.isbuiltin(range))
+    self.assertEqual(tf_inspect.isbuiltin(max), inspect.isbuiltin(max))
+
   def testIsClass(self):
     self.assertTrue(tf_inspect.isclass(TestDecoratedClass))
     self.assertFalse(tf_inspect.isclass(test_decorated_function))
-- 
GitLab


From 59e59b7b1065715e0e59ee134e769f625ec28edd Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Mon, 26 Feb 2018 11:10:20 -0800
Subject: [PATCH 296/575] eager/examples/resnet50: Fix breakage.

PiperOrigin-RevId: 187050075
---
 .../contrib/eager/python/examples/resnet50/resnet50_test.py   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
index c106ab0a06..65dcc53aab 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
@@ -194,11 +194,11 @@ class ResNet50Benchmarks(tf.test.Benchmark):
     with tf.device(device):
       images, _ = random_batch(batch_size)
       for _ in xrange(num_burn):
-        model(images).cpu()
+        model(images, training=False).cpu()
       gc.collect()
       start = time.time()
       for _ in xrange(num_iters):
-        model(images).cpu()
+        model(images, training=False).cpu()
       self._report(label, start, num_iters, device, batch_size, data_format)
 
   def benchmark_eager_apply(self):
-- 
GitLab


From 98f38b608073e761d75227373b2b2c7d26c483e5 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Mon, 26 Feb 2018 11:12:04 -0800
Subject: [PATCH 297/575] Add support for parsing the "gather" HLO

PiperOrigin-RevId: 187050345
---
 .../compiler/xla/tools/parser/hlo_parser.cc   | 37 +++++++++++++++++--
 .../xla/tools/parser/hlo_parser_test.cc       | 24 ++++++++++++
 2 files changed, 58 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index cd2b843ad3..e60a5a4919 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -1049,9 +1049,40 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder,
           HloInstruction::CreateDot(shape, operands[0], operands[1], dnum));
       break;
     }
-    case HloOpcode::kGather:
-      // TODO(b/72710576): HLO parsing is not implemented for Gather.
-      return TokenError("HLO parsing is not implemented for Gather");
+    case HloOpcode::kGather: {
+      optional<std::vector<int64>> output_window_dims;
+      attrs["output_window_dims"] = {
+          /*required=*/true, AttrTy::kBracedInt64List, &output_window_dims};
+      optional<std::vector<int64>> elided_window_dims;
+      attrs["elided_window_dims"] = {
+          /*required=*/true, AttrTy::kBracedInt64List, &elided_window_dims};
+      optional<std::vector<int64>> gather_dims_to_operand_dims;
+      attrs["gather_dims_to_operand_dims"] = {/*required=*/true,
+                                              AttrTy::kBracedInt64List,
+                                              &gather_dims_to_operand_dims};
+      optional<int64> index_vector_dim;
+      attrs["index_vector_dim"] = {/*required=*/true, AttrTy::kInt64,
+                                   &index_vector_dim};
+      optional<std::vector<int64>> window_bounds;
+      attrs["window_bounds"] = {/*required=*/true, AttrTy::kBracedInt64List,
+                                &window_bounds};
+
+      if (!ParseOperands(&operands, /*expected_size=*/2) ||
+          !ParseAttributes(attrs)) {
+        return false;
+      }
+
+      GatherDimensionNumbers dim_numbers = HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/*output_window_dims,
+          /*elided_window_dims=*/*elided_window_dims,
+          /*gather_dims_to_operand_dims=*/*gather_dims_to_operand_dims,
+          /*index_vector_dim=*/*index_vector_dim);
+
+      instruction = builder->AddInstruction(HloInstruction::CreateGather(
+          shape, /*operand=*/operands[0], /*gather_indices=*/operands[1],
+          dim_numbers, *window_bounds));
+      break;
+    }
     case HloOpcode::kTrace:
       return TokenError(StrCat("parsing not yet implemented for op: ",
                                HloOpcodeString(opcode)));
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
index b8c6b59204..863081d654 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
@@ -716,6 +716,18 @@ ENTRY %sparse_f32_r1 () -> f32[9] {
   ROOT %foo = f32[9]sparse{10} constant(f32[9]{1: 2, 3: 4, 5: 6})
 }
 
+)"
+},
+{
+"gather",
+R"(HloModule StringifyGather
+
+ENTRY %Gather (input_tensor: f32[50,49,48,47,46], gather_indices: s64[10,9,8,7,5]) -> f32[10,9,8,7,30,29,28,27,26] {
+  %input_tensor = f32[50,49,48,47,46]{4,3,2,1,0} parameter(0)
+  %gather_indices = s64[10,9,8,7,5]{4,3,2,1,0} parameter(1)
+  ROOT %gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), output_window_dims={4,5,6,7,8}, elided_window_dims={}, gather_dims_to_operand_dims={0,1,2,3,4}, index_vector_dim=4, window_bounds={30,29,28,27,26}
+}
+
 )"
 },
   });
@@ -860,6 +872,18 @@ ENTRY dot {
   ROOT dot = f32[2,3]{1,0} dot(a, b), lhs_batch_dims={0}, lhs_contracting_dims={1}, rhs_contracting_dims={0}
 }
 
+)"
+},
+{
+"gather",
+R"(HloModule gather
+
+ENTRY Gather {
+  input_tensor = f32[50,49,48,47,46]{4,3,2,1,0} parameter(0)
+  gather_indices = s64[10,9,8,7,5]{4,3,2,1,0} parameter(1)
+  ROOT gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} gather(input_tensor, gather_indices), output_window_dims={4,5,6,7,8}, elided_window_dims={}, gather_dims_to_operand_dims={0,1,2,3,4}, index_vector_dim=4, window_bounds={30,29,28,27,26}
+}
+
 )"
 },
   });
-- 
GitLab


From b7b4fe66ee8adf936b1c2508a298c1e26a858af1 Mon Sep 17 00:00:00 2001
From: Mingsheng Hong <hongm@google.com>
Date: Mon, 26 Feb 2018 11:13:09 -0800
Subject: [PATCH 298/575] Added const to Node* in various parts of the code
 base.

PiperOrigin-RevId: 187050526
---
 tensorflow/compiler/tf2xla/const_analysis.cc   |  4 ++--
 tensorflow/compiler/tf2xla/graph_compiler.cc   |  2 +-
 .../core/common_runtime/shape_refiner.cc       |  4 ++--
 .../core/distributed_runtime/scheduler.cc      | 18 +++++++++---------
 .../core/distributed_runtime/scheduler.h       |  6 +++---
 tensorflow/core/graph/costmodel.cc             |  2 +-
 tensorflow/core/graph/graph.cc                 |  2 +-
 tensorflow/core/graph/graph.h                  |  2 +-
 tensorflow/core/graph/graph_constructor.cc     |  2 +-
 tensorflow/core/graph/graph_partition.cc       |  6 +++---
 tensorflow/core/graph/node_builder.cc          |  6 +++---
 tensorflow/core/graph/node_builder.h           |  6 +++---
 tensorflow/core/graph/optimizer_cse.cc         | 16 ++++++++--------
 13 files changed, 38 insertions(+), 38 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/const_analysis.cc b/tensorflow/compiler/tf2xla/const_analysis.cc
index 82923722c5..6f46532419 100644
--- a/tensorflow/compiler/tf2xla/const_analysis.cc
+++ b/tensorflow/compiler/tf2xla/const_analysis.cc
@@ -37,7 +37,7 @@ Status BackwardsConstAnalysis(const Graph& g,
   };
 
   Status status;
-  std::unordered_set<Node*> must_be_const;
+  std::unordered_set<const Node*> must_be_const;
   auto visit = [&status, &metadata_ops, &must_be_const,
                 compile_time_const_args](Node* node) {
     if (!status.ok()) return;
@@ -55,7 +55,7 @@ Status BackwardsConstAnalysis(const Graph& g,
         compile_time_const_args->at(index) = true;
         return;
       }
-      for (Node* pred : node->in_nodes()) {
+      for (const Node* pred : node->in_nodes()) {
         must_be_const.insert(pred);
       }
       return;
diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc
index 058a1f2621..b20c1ffc7d 100644
--- a/tensorflow/compiler/tf2xla/graph_compiler.cc
+++ b/tensorflow/compiler/tf2xla/graph_compiler.cc
@@ -130,7 +130,7 @@ Status GraphCompiler::Compile() {
     // Set up inputs from outputs of previous nodes.
     for (auto* e : n->in_edges()) {
       if (e->IsControlEdge()) continue;
-      Node* src = e->src();
+      const Node* src = e->src();
       TF_RET_CHECK(src->id() < output_registry.size());
       const NodeOutputs& src_outputs = output_registry[src->id()];
 
diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index 45cdab98e0..2acaa31d32 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -211,14 +211,14 @@ Status ShapeRefiner::AddNode(const Node* node) {
   // For each 'input' of this node, fetch the corresponding shape
   // from 'input's InferenceContext, and store into a vector
   // indexed by 'node's input.
-  std::vector<Node*> input_nodes(node->num_inputs());
+  std::vector<const Node*> input_nodes(node->num_inputs());
   std::vector<ShapeHandle> input_shapes(node->num_inputs());
   std::vector<std::unique_ptr<std::vector<ShapeAndType>>>
       input_handle_shapes_and_types(node->num_inputs());
   for (const Edge* e : node->in_edges()) {
     if (e->IsControlEdge()) continue;
 
-    Node* input = e->src();
+    const Node* input = e->src();
     auto it = node_to_context_.find(input);
     if (it == node_to_context_.end()) {
       return errors::FailedPrecondition(
diff --git a/tensorflow/core/distributed_runtime/scheduler.cc b/tensorflow/core/distributed_runtime/scheduler.cc
index 9dae5b3b92..8403636197 100644
--- a/tensorflow/core/distributed_runtime/scheduler.cc
+++ b/tensorflow/core/distributed_runtime/scheduler.cc
@@ -80,7 +80,7 @@ Microseconds SlackAnalysis::ComputeAsap(std::vector<Microseconds>* asap_times) {
   std::vector<int> pending_count(graph_->num_node_ids());
   InitializePending(graph_, &pending_count);
 
-  std::deque<Node*> queue;
+  std::deque<const Node*> queue;
   Node* srcNode = graph_->source_node();
   queue.push_back(srcNode);
   (*asap_times)[srcNode->id()] = 0;
@@ -92,7 +92,7 @@ Microseconds SlackAnalysis::ComputeAsap(std::vector<Microseconds>* asap_times) {
     for (const Edge* out_edge : curr->out_edges()) {
       // The time needed for 'out' to get its input from 'curr'.
       Microseconds copy_time(0);
-      Node* out = out_edge->dst();
+      const Node* out = out_edge->dst();
       if (!out_edge->IsControlEdge() &&
           curr->assigned_device_name() != out->assigned_device_name()) {
         // Add an arbitrary 10microsecs for each copy.
@@ -137,7 +137,7 @@ Microseconds SlackAnalysis::ComputeAlap(std::vector<Microseconds>* alap_times) {
     }
   }
 
-  std::deque<Node*> queue;
+  std::deque<const Node*> queue;
   Node* sinkNode = graph_->sink_node();
   queue.push_back(sinkNode);
   (*alap_times)[sinkNode->id()] = 0;
@@ -148,7 +148,7 @@ Microseconds SlackAnalysis::ComputeAlap(std::vector<Microseconds>* alap_times) {
     for (const Edge* in_edge : curr->in_edges()) {
       // The time needed for 'curr' to get its input from 'src'.
       Microseconds copy_time(0);
-      Node* src = in_edge->src();
+      const Node* src = in_edge->src();
       if (!in_edge->IsControlEdge() &&
           src->assigned_device_name() != curr->assigned_device_name()) {
         // TODO(yuanbyu): Use the real cost model
@@ -236,7 +236,7 @@ Microseconds GreedyScheduler::ComputeSchedule(
 
       for (const Edge* out_edge : event.node->out_edges()) {
         Microseconds copy_time(0);
-        Node* out = out_edge->dst();
+        const Node* out = out_edge->dst();
         if (!out_edge->IsControlEdge() &&
             event.node->assigned_device_name() != out->assigned_device_name()) {
           // TODO(yuanbyu): Use below with the real cost model.
@@ -277,11 +277,11 @@ Microseconds GreedyScheduler::ComputeSchedule(
   return max_completion;
 }
 
-Node* GreedyScheduler::GetNodeWithHighestPriority(
-    const std::vector<Node*>& nodes) {
-  Node* curr_node = nullptr;
+const Node* GreedyScheduler::GetNodeWithHighestPriority(
+    const std::vector<const Node*>& nodes) {
+  const Node* curr_node = nullptr;
   int64 curr_priority = kint64max;
-  for (Node* n : nodes) {
+  for (const Node* n : nodes) {
     if ((*priority_)[n->id()] < curr_priority) {
       curr_node = n;
       curr_priority = (*priority_)[n->id()];
diff --git a/tensorflow/core/distributed_runtime/scheduler.h b/tensorflow/core/distributed_runtime/scheduler.h
index ef87b9834d..bf9d0d1bec 100644
--- a/tensorflow/core/distributed_runtime/scheduler.h
+++ b/tensorflow/core/distributed_runtime/scheduler.h
@@ -57,11 +57,11 @@ class GreedyScheduler {
   struct Sim {
     int degree_parallelism;
     int num_running;
-    std::vector<Node*> ready_nodes;
+    std::vector<const Node*> ready_nodes;
   };
 
   struct Event {
-    Node* node;
+    const Node* node;
     Microseconds time;
     bool is_completion;
 
@@ -79,7 +79,7 @@ class GreedyScheduler {
 
  private:
   // Returns the ready node with the highest priority for a sim.
-  Node* GetNodeWithHighestPriority(const std::vector<Node*>& nodes);
+  const Node* GetNodeWithHighestPriority(const std::vector<const Node*>& nodes);
 
   const DeviceSet* devices_;
   const CostModel* cost_model_;
diff --git a/tensorflow/core/graph/costmodel.cc b/tensorflow/core/graph/costmodel.cc
index 4f3a6ec38c..1df45d9b89 100644
--- a/tensorflow/core/graph/costmodel.cc
+++ b/tensorflow/core/graph/costmodel.cc
@@ -427,7 +427,7 @@ static void AssignSizes(const Graph& g, CostModel* cost_model) {
     if (e->IsControlEdge()) {
       continue;
     }
-    Node* src = e->src();
+    const Node* src = e->src();
 
     // TODO(josh11b): Get an estimate from the Op
     Bytes size(1);
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index 9b56216f1f..a7af5e2312 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -339,7 +339,7 @@ Node* Graph::AddNode(const NodeDef& node_def, Status* status) {
   return node;
 }
 
-Node* Graph::CopyNode(Node* node) {
+Node* Graph::CopyNode(const Node* node) {
   DCHECK(!node->IsSource());
   DCHECK(!node->IsSink());
   Node* copy = AllocateNode(node->props_, node);
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index 9d96cd4654..cbd58b051a 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -422,7 +422,7 @@ class Graph {
   // Copies *node, which may belong to another graph, to a new node,
   // which is returned.  Does not copy any edges.  *this owns the
   // returned instance.
-  Node* CopyNode(Node* node);
+  Node* CopyNode(const Node* node);
 
   // Removes a node from this graph, including all edges from or to it.
   // *node should not be accessed after calling this function.
diff --git a/tensorflow/core/graph/graph_constructor.cc b/tensorflow/core/graph/graph_constructor.cc
index 0629ff32d0..627309078a 100644
--- a/tensorflow/core/graph/graph_constructor.cc
+++ b/tensorflow/core/graph/graph_constructor.cc
@@ -1271,7 +1271,7 @@ void CopyGraph(const Graph& src, Graph* dest) {
   dest->set_versions(src.versions());
 
   // Copy the nodes
-  std::unordered_map<Node*, Node*>
+  std::unordered_map<const Node*, Node*>
       node_map;  // "Node in src" -> "Node in *dest"
   node_map[src.source_node()] = dest->source_node();
   node_map[src.sink_node()] = dest->sink_node();
diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc
index add80eda23..17a174101b 100644
--- a/tensorflow/core/graph/graph_partition.cc
+++ b/tensorflow/core/graph/graph_partition.cc
@@ -123,8 +123,8 @@ bool NeedSameDeviceSendRecv(const Edge* edge, const GraphInfo& info) {
     return false;
   }
 
-  Node* src = edge->src();
-  Node* dst = edge->dst();
+  const Node* src = edge->src();
+  const Node* dst = edge->dst();
   if (src->assigned_device_name() == dst->assigned_device_name()) {
     int src_port = edge->src_output();
     int dst_port = edge->dst_input();
@@ -141,7 +141,7 @@ bool NeedSameDeviceSendRecv(const Edge* edge, const GraphInfo& info) {
 
 // Return true iff (dst, dst_input) is specified on host memory.
 bool IsDstInputOnHost(const Edge* edge, const GraphInfo& info) {
-  Node* dst = edge->dst();
+  const Node* dst = edge->dst();
   int dst_port = edge->dst_input();
   if (info.device_types[dst->id()] != DEVICE_CPU) {
     if (edge->IsControlEdge()) return false;
diff --git a/tensorflow/core/graph/node_builder.cc b/tensorflow/core/graph/node_builder.cc
index 138952dcb3..114962c0e4 100644
--- a/tensorflow/core/graph/node_builder.cc
+++ b/tensorflow/core/graph/node_builder.cc
@@ -88,7 +88,7 @@ NodeBuilder& NodeBuilder::ControlInput(Node* src_node) {
 NodeBuilder& NodeBuilder::ControlInputs(gtl::ArraySlice<Node*> src_nodes) {
   control_inputs_.insert(control_inputs_.end(), src_nodes.begin(),
                          src_nodes.end());
-  for (Node* src_node : src_nodes) {
+  for (const Node* src_node : src_nodes) {
     def_builder_.ControlInput(src_node->name());
   }
   return *this;
@@ -127,7 +127,7 @@ Status NodeBuilder::Finalize(Graph* graph, Node** created_node) const {
   return Status::OK();
 }
 
-void NodeBuilder::AddIndexError(Node* node, int i) {
+void NodeBuilder::AddIndexError(const Node* node, int i) {
   if (node == nullptr) {
     errors_.emplace_back(
         strings::StrCat("Attempt to add nullptr Node to node with type ",
@@ -140,7 +140,7 @@ void NodeBuilder::AddIndexError(Node* node, int i) {
   }
 }
 
-bool NodeBuilder::GetOutputType(Node* node, int i, DataType* dt) {
+bool NodeBuilder::GetOutputType(const Node* node, int i, DataType* dt) {
   bool error;
   *dt = SafeGetOutput(node, i, &error);
   if (error) AddIndexError(node, i);
diff --git a/tensorflow/core/graph/node_builder.h b/tensorflow/core/graph/node_builder.h
index 86647a49c1..f6b7b5674b 100644
--- a/tensorflow/core/graph/node_builder.h
+++ b/tensorflow/core/graph/node_builder.h
@@ -120,7 +120,7 @@ class NodeBuilder {
   const OpDef& op_def() const { return def_builder_.op_def(); }
 
  private:
-  static DataType SafeGetOutput(Node* node, int i, bool* error) {
+  static DataType SafeGetOutput(const Node* node, int i, bool* error) {
     if (node != nullptr && i >= 0 && i < node->num_outputs()) {
       *error = false;
       return node->output_type(i);
@@ -131,11 +131,11 @@ class NodeBuilder {
   }
 
   // If SafeGetOutput indicates a range error, add it to errors_.
-  void AddIndexError(Node* node, int i);
+  void AddIndexError(const Node* node, int i);
 
   // Set *dt and returns true if i is in range. Combines
   // SafeGetOutput() and AddIndexError().
-  bool GetOutputType(Node* node, int i, DataType* dt);
+  bool GetOutputType(const Node* node, int i, DataType* dt);
 
   NodeDefBuilder def_builder_;
   std::vector<NodeOut> inputs_;
diff --git a/tensorflow/core/graph/optimizer_cse.cc b/tensorflow/core/graph/optimizer_cse.cc
index 6b452a1d5d..4073255db3 100644
--- a/tensorflow/core/graph/optimizer_cse.cc
+++ b/tensorflow/core/graph/optimizer_cse.cc
@@ -65,8 +65,8 @@ class OptimizerCSE {
 };
 
 static void FillInputs(const Node* n,
-                       gtl::InlinedVector<Node*, 4>* control_edges,
-                       gtl::InlinedVector<std::pair<Node*, int>, 4>* in) {
+                       gtl::InlinedVector<const Node*, 4>* control_edges,
+                       gtl::InlinedVector<std::pair<const Node*, int>, 4>* in) {
   DCHECK_EQ(in->size(), n->num_inputs());
   control_edges->clear();
   for (const Edge* e : n->in_edges()) {
@@ -96,8 +96,8 @@ size_t OptimizerCSE::NodeHash(const Node* n) {
 
   const int N_in = n->num_inputs();
   strings::StrAppend(&str_to_hash, N_in);
-  gtl::InlinedVector<Node*, 4> control_edges;
-  gtl::InlinedVector<std::pair<Node*, int>, 4> in(N_in);
+  gtl::InlinedVector<const Node*, 4> control_edges;
+  gtl::InlinedVector<std::pair<const Node*, int>, 4> in(N_in);
   FillInputs(n, &control_edges, &in);
   for (const auto& edge : in) {
     strings::StrAppend(&str_to_hash, edge.first->id(), edge.second);
@@ -147,10 +147,10 @@ bool OptimizerCSE::Equivalent(const Node* a, const Node* b,
   // Compare input sources
   if (a->num_inputs() != b->num_inputs()) return false;
   const int N_in = a->num_inputs();
-  gtl::InlinedVector<Node*, 4> a_control_edges;
-  gtl::InlinedVector<Node*, 4> b_control_edges;
-  gtl::InlinedVector<std::pair<Node*, int>, 4> a_in(N_in);
-  gtl::InlinedVector<std::pair<Node*, int>, 4> b_in(N_in);
+  gtl::InlinedVector<const Node*, 4> a_control_edges;
+  gtl::InlinedVector<const Node*, 4> b_control_edges;
+  gtl::InlinedVector<std::pair<const Node*, int>, 4> a_in(N_in);
+  gtl::InlinedVector<std::pair<const Node*, int>, 4> b_in(N_in);
   FillInputs(a, &a_control_edges, &a_in);
   FillInputs(b, &b_control_edges, &b_in);
   if (a_in != b_in) return false;
-- 
GitLab


From e5b73fc9a8df0d87cb964ed49e946d2477c73e19 Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Mon, 26 Feb 2018 11:22:43 -0800
Subject: [PATCH 299/575] TFLite: Ensures pointers to tensors won't be
 invalidated unless 16+ tensors are added. PiperOrigin-RevId: 187052100

---
 tensorflow/contrib/lite/interpreter.cc      | 13 +++----
 tensorflow/contrib/lite/interpreter.h       | 20 +++++++++++
 tensorflow/contrib/lite/interpreter_test.cc | 40 +++++++++++++++++++++
 3 files changed, 64 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc
index 370e495527..0f5e17f0de 100644
--- a/tensorflow/contrib/lite/interpreter.cc
+++ b/tensorflow/contrib/lite/interpreter.cc
@@ -27,13 +27,6 @@ limitations under the License.
 #include "tensorflow/contrib/lite/nnapi_delegate.h"
 #include "tensorflow/contrib/lite/schema/schema_generated.h"
 
-namespace {
-
-// std::vector preallocation tuning.
-constexpr const int kSlotsToReserve = 128;
-
-}  // namespace
-
 namespace tflite {
 
 // A trivial implementation of GraphInfo around the Interpreter.
@@ -85,8 +78,8 @@ Interpreter::Interpreter(ErrorReporter* error_reporter)
   context_.GetExecutionPlan = nullptr;
 
   // Reserve some space for the tensors to avoid excessive resizing.
-  tensors_.reserve(kSlotsToReserve);
-  nodes_and_registration_.reserve(kSlotsToReserve);
+  tensors_.reserve(kTensorsReservedCapacity);
+  nodes_and_registration_.reserve(kTensorsReservedCapacity);
   next_execution_plan_index_to_prepare_ = 0;
   UseNNAPI(false);
 }
@@ -353,6 +346,7 @@ TfLiteStatus Interpreter::PrepareOpsStartingAt(
     TfLiteNode& node = nodes_and_registration_[node_index].first;
     const TfLiteRegistration& registration =
         nodes_and_registration_[node_index].second;
+    EnsureTensorsVectorCapacity();
     if (OpPrepare(registration, &node) == kTfLiteError) {
       return kTfLiteError;
     }
@@ -430,6 +424,7 @@ TfLiteStatus Interpreter::Invoke() {
     TfLiteNode& node = nodes_and_registration_[node_index].first;
     const TfLiteRegistration& registration =
         nodes_and_registration_[node_index].second;
+    EnsureTensorsVectorCapacity();
     if (OpInvoke(registration, &node) == kTfLiteError) {
       status = kTfLiteError;
     }
diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h
index a9df2627e0..04c19644a0 100644
--- a/tensorflow/contrib/lite/interpreter.h
+++ b/tensorflow/contrib/lite/interpreter.h
@@ -265,6 +265,14 @@ class Interpreter {
   void set_model(const Model* model) { model_ = const_cast<Model*>(model); }
   Model* model() const { return model_; }
 
+  // The default capacity of `tensors_` vector.
+  static constexpr int kTensorsReservedCapacity = 128;
+  // The capacity headroom of `tensors_` vector before calling ops'
+  // `prepare` and `invoke` function. In these functions, it's guaranteed
+  // allocating up to `kTensorsCapacityHeadroom` more tensors won't invalidate
+  // pointers to existing tensors.
+  static constexpr int kTensorsCapacityHeadroom = 16;
+
  private:
   // Give 'op_reg' a chance to initialize itself using the contents of
   // 'buffer'.
@@ -377,6 +385,18 @@ class Interpreter {
   static TfLiteStatus GetExecutionPlan(struct TfLiteContext* context,
                                        TfLiteIntArray** execution_plan);
 
+  // Ensures that `tensors_` has at least `kTensorsCapacityHeadroom` extra
+  // capacity. Calling this function may invalidate existing pointers to
+  // tensors. After calling this function, adding `kTensorsCapacityHeadroom`
+  // more tensors won't invalidate the pointer to existing tensors.
+  void EnsureTensorsVectorCapacity() {
+    const int required_capacity = tensors_size() + kTensorsCapacityHeadroom;
+    if (required_capacity > tensors_.capacity()) {
+      tensors_.reserve(required_capacity);
+      context_.tensors = tensors_.data();
+    }
+  }
+
   // A pure C data structure used to communicate with the pure C plugin
   // interface. To avoid copying tensor metadata, this is also the definitive
   // structure to store tensors.
diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc
index 28c96e5dde..2e6727b323 100644
--- a/tensorflow/contrib/lite/interpreter_test.cc
+++ b/tensorflow/contrib/lite/interpreter_test.cc
@@ -561,6 +561,46 @@ TEST(BasicInterpreter, TestCustomErrorReporter) {
   ASSERT_EQ(reporter.calls, 1);
 }
 
+TEST(InterpreterTensorsCapacityTest, TestWithinHeadroom) {
+  Interpreter interpreter;
+  ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity),
+            kTfLiteOk);
+  TfLiteRegistration registration = {nullptr, nullptr, nullptr, nullptr};
+  registration.prepare = [](TfLiteContext* context, TfLiteNode* node) {
+    TfLiteTensor* first_tensor = context->tensors;
+
+    int new_tensor_index;
+    context->AddTensors(context, Interpreter::kTensorsCapacityHeadroom,
+                        &new_tensor_index);
+    EXPECT_EQ(first_tensor, context->tensors);
+    return kTfLiteOk;
+  };
+  ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr,
+                                              &registration),
+            kTfLiteOk);
+  ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk);
+}
+
+TEST(InterpreterTensorsCapacityTest, TestExceedHeadroom) {
+  Interpreter interpreter;
+  ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity),
+            kTfLiteOk);
+  TfLiteRegistration registration = {nullptr, nullptr, nullptr, nullptr};
+  registration.prepare = [](TfLiteContext* context, TfLiteNode* node) {
+    TfLiteTensor* first_tensor = context->tensors;
+
+    int new_tensor_index;
+    context->AddTensors(context, Interpreter::kTensorsCapacityHeadroom + 1,
+                        &new_tensor_index);
+    EXPECT_NE(first_tensor, context->tensors);
+    return kTfLiteOk;
+  };
+  ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr,
+                                              &registration),
+            kTfLiteOk);
+  ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk);
+}
+
 // Test fixture that allows playing with execution plans. It creates a two
 // node graph that can be executed in either [0,1] order or [1,0] order.
 // The CopyOp records when it is invoked in the class member run_order_
-- 
GitLab


From 0f8ee19ef830fc7d28ae611194bcd66f4383b038 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Mon, 26 Feb 2018 11:43:14 -0800
Subject: [PATCH 300/575] Actually expose smart_cond and smart_constant_value
 in tf.contrib.framework

Also moves these methods into their own file in python/framework. This avoids further bloating control_flow_ops.py and makes the BUILD deps easier for a future change I'm working on.

PiperOrigin-RevId: 187055501
---
 tensorflow/contrib/framework/BUILD            |  1 +
 tensorflow/contrib/framework/__init__.py      |  7 +-
 tensorflow/python/BUILD                       | 26 ++++++
 tensorflow/python/framework/smart_cond.py     | 79 +++++++++++++++++++
 .../python/framework/smart_cond_test.py       | 66 ++++++++++++++++
 tensorflow/python/layers/utils.py             |  5 +-
 tensorflow/python/ops/control_flow_ops.py     | 56 -------------
 .../python/ops/control_flow_ops_test.py       | 36 ---------
 8 files changed, 180 insertions(+), 96 deletions(-)
 create mode 100644 tensorflow/python/framework/smart_cond.py
 create mode 100644 tensorflow/python/framework/smart_cond_test.py

diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD
index 1accb319d2..50868c6d6c 100644
--- a/tensorflow/contrib/framework/BUILD
+++ b/tensorflow/contrib/framework/BUILD
@@ -63,6 +63,7 @@ tf_custom_op_py_library(
         "//tensorflow/python:platform",
         "//tensorflow/python:pywrap_tensorflow",
         "//tensorflow/python:script_ops",
+        "//tensorflow/python:smart_cond",
         "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:state_ops_gen",
diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py
index deeb5bec79..8063250091 100644
--- a/tensorflow/contrib/framework/__init__.py
+++ b/tensorflow/contrib/framework/__init__.py
@@ -87,6 +87,9 @@ See the @{$python/contrib.framework} guide.
 
 @@get_placeholders
 
+@@smart_cond
+@@smart_constant_value
+
 @@CriticalSection
 
 @@BoundedTensorSpec
@@ -104,10 +107,10 @@ from tensorflow.contrib.framework.python.ops import *
 
 from tensorflow.python.framework.ops import prepend_name_scope
 from tensorflow.python.framework.ops import strip_name_scope
+from tensorflow.python.framework.smart_cond import smart_cond
+from tensorflow.python.framework.smart_cond import smart_constant_value
 from tensorflow.python.framework.tensor_spec import BoundedTensorSpec
 from tensorflow.python.framework.tensor_spec import TensorSpec
-from tensorflow.python.ops.control_flow_ops import smart_cond
-from tensorflow.python.ops.control_flow_ops import smart_constant_value
 from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = ['nest']
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 4c8c73548c..b0cb48c80c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -765,6 +765,31 @@ py_library(
     ],
 )
 
+py_library(
+    name = "smart_cond",
+    srcs = ["framework/smart_cond.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":control_flow_ops",
+        ":tensor_util",
+    ],
+)
+
+py_test(
+    name = "smart_cond_test",
+    size = "small",
+    srcs = ["framework/smart_cond_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":client_testlib",
+        ":constant_op",
+        ":framework_ops",
+        ":math_ops",
+        ":session",
+        ":smart_cond",
+    ],
+)
+
 py_library(
     name = "sparse_tensor",
     srcs = ["framework/sparse_tensor.py"],
@@ -4091,6 +4116,7 @@ py_library(
         ":control_flow_ops",
         ":framework_for_generated_wrappers",
         ":platform",
+        ":smart_cond",
         ":tensor_util",
         ":util",
         ":variable_scope",
diff --git a/tensorflow/python/framework/smart_cond.py b/tensorflow/python/framework/smart_cond.py
new file mode 100644
index 0000000000..f97bb01f54
--- /dev/null
+++ b/tensorflow/python/framework/smart_cond.py
@@ -0,0 +1,79 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""smart_cond and related utilties."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import control_flow_ops
+
+
+def smart_cond(pred, true_fn=None, false_fn=None, name=None):
+  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
+
+  If `pred` is a bool or has a constant value, we return either `true_fn()`
+  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
+
+  Arguments:
+    pred: A scalar determining whether to return the result of `true_fn` or
+      `false_fn`.
+    true_fn: The callable to be performed if pred is true.
+    false_fn: The callable to be performed if pred is false.
+    name: Optional name prefix when using `tf.cond`.
+
+  Returns:
+    Tensors returned by the call to either `true_fn` or `false_fn`.
+
+  Raises:
+    TypeError: If `true_fn` or `false_fn` is not callable.
+  """
+  if not callable(true_fn):
+    raise TypeError("`true_fn` must be callable.")
+  if not callable(false_fn):
+    raise TypeError("`false_fn` must be callable.")
+
+  pred_value = smart_constant_value(pred)
+  if pred_value is not None:
+    if pred_value:
+      return true_fn()
+    else:
+      return false_fn()
+  else:
+    return control_flow_ops.cond(pred, true_fn=true_fn, false_fn=false_fn,
+                                 name=name)
+
+
+def smart_constant_value(pred):
+  """Return the bool value for `pred`, or None if `pred` had a dynamic value.
+
+  Arguments:
+    pred: A scalar, either a Python bool or tensor.
+
+  Returns:
+    True or False if `pred` has a constant boolean value, None otherwise.
+
+  Raises:
+    TypeError: If `pred` is not a Tensor or bool.
+  """
+  if isinstance(pred, bool):
+    pred_value = pred
+  elif isinstance(pred, ops.Tensor):
+    pred_value = tensor_util.constant_value(pred)
+  else:
+    raise TypeError("`pred` must be a Tensor or a Python bool.")
+  return pred_value
diff --git a/tensorflow/python/framework/smart_cond_test.py b/tensorflow/python/framework/smart_cond_test.py
new file mode 100644
index 0000000000..b682506da0
--- /dev/null
+++ b/tensorflow/python/framework/smart_cond_test.py
@@ -0,0 +1,66 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.client import session
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import smart_cond
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import googletest
+
+
+@test_util.with_c_api
+class SmartCondTest(test_util.TensorFlowTestCase):
+
+  def testSmartCondTrue(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(2)
+        y = constant_op.constant(5)
+        z = smart_cond.smart_cond(True, lambda: math_ops.multiply(x, 16),
+                                  lambda: math_ops.multiply(y, 5))
+        self.assertEqual(z.eval(), 32)
+
+  def testSmartCondFalse(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(4)
+        y = constant_op.constant(3)
+        z = smart_cond.smart_cond(False, lambda: math_ops.multiply(x, 16),
+                                  lambda: math_ops.multiply(y, 3))
+        self.assertEqual(z.eval(), 9)
+
+  def testSmartCondMissingArg1(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(1)
+        with self.assertRaises(TypeError):
+          smart_cond.smart_cond(True, false_fn=lambda: x)
+
+  def testSmartCondMissingArg2(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(1)
+        with self.assertRaises(TypeError):
+          smart_cond.smart_cond(True, lambda: x)
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py
index 484c6fc466..3b156c36a2 100644
--- a/tensorflow/python/layers/utils.py
+++ b/tensorflow/python/layers/utils.py
@@ -24,6 +24,7 @@ from tensorflow.python.eager import context
 from tensorflow.python.ops import variables
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import smart_cond as smart_module
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.util import nest
 
@@ -201,7 +202,7 @@ def smart_cond(pred, true_fn=None, false_fn=None, name=None):
   if isinstance(pred, variables.Variable):
     return control_flow_ops.cond(
         pred, true_fn=true_fn, false_fn=false_fn, name=name)
-  return control_flow_ops.smart_cond(
+  return smart_module.smart_cond(
       pred, true_fn=true_fn, false_fn=false_fn, name=name)
 
 
@@ -228,7 +229,7 @@ def constant_value(pred):
 
   if isinstance(pred, variables.Variable):
     return None
-  return control_flow_ops.smart_constant_value(pred)
+  return smart_module.smart_constant_value(pred)
 
 
 def object_list_uid(object_list):
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index c78a5aa8c2..8d5ab72670 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -23,7 +23,6 @@ See the @{$python/control_flow_ops} guide.
 @@no_op
 @@count_up_to
 @@cond
-@@smart_cond
 @@case
 @@while_loop
 @@logical_and
@@ -2130,61 +2129,6 @@ def cond(pred,
 # pylint: enable=redefined-outer-name
 
 
-def smart_cond(pred, true_fn=None, false_fn=None, name=None):
-  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
-
-  If `pred` is a bool or has a constant value, we return either `true_fn()`
-  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
-
-  Arguments:
-    pred: A scalar determining whether to return the result of `true_fn` or
-      `false_fn`.
-    true_fn: The callable to be performed if pred is true.
-    false_fn: The callable to be performed if pred is false.
-    name: Optional name prefix when using `tf.cond`.
-
-  Returns:
-    Tensors returned by the call to either `true_fn` or `false_fn`.
-
-  Raises:
-    TypeError: If `true_fn` or `false_fn` is not callable.
-  """
-  if not callable(true_fn):
-    raise TypeError("`true_fn` must be callable.")
-  if not callable(false_fn):
-    raise TypeError("`false_fn` must be callable.")
-
-  pred_value = smart_constant_value(pred)
-  if pred_value is not None:
-    if pred_value:
-      return true_fn()
-    else:
-      return false_fn()
-  else:
-    return cond(pred, true_fn=true_fn, false_fn=false_fn, name=name)
-
-
-def smart_constant_value(pred):
-  """Return the bool value for `pred`, or None if `pred` had a dynamic value.
-
-  Arguments:
-    pred: A scalar, either a Python bool or tensor.
-
-  Returns:
-    True or False if `pred` has a constant boolean value, None otherwise.
-
-  Raises:
-    TypeError: If `pred` is not a Tensor or bool.
-  """
-  if isinstance(pred, bool):
-    pred_value = pred
-  elif isinstance(pred, ops.Tensor):
-    pred_value = tensor_util.constant_value(pred)
-  else:
-    raise TypeError("`pred` must be a Tensor or a Python bool.")
-  return pred_value
-
-
 def _resource_safe_shape(t):
   """Returns the shape of t or the variable it points to."""
   if t.dtype == dtypes.resource:
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index adc8c51e11..f22f3059d1 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -349,42 +349,6 @@ class SwitchTestCase(test_util.TensorFlowTestCase):
       self.assertEquals(grad_x_false.eval(), 0.)
 
 
-@test_util.with_c_api
-class SmartCondTest(test_util.TensorFlowTestCase):
-
-  def testSmartCondTrue(self):
-    with ops.Graph().as_default():
-      with session.Session():
-        x = constant_op.constant(2)
-        y = constant_op.constant(5)
-        z = control_flow_ops.smart_cond(True, lambda: math_ops.multiply(x, 16),
-                                        lambda: math_ops.multiply(y, 5))
-        self.assertEqual(z.eval(), 32)
-
-  def testSmartCondFalse(self):
-    with ops.Graph().as_default():
-      with session.Session():
-        x = constant_op.constant(4)
-        y = constant_op.constant(3)
-        z = control_flow_ops.smart_cond(False, lambda: math_ops.multiply(x, 16),
-                                        lambda: math_ops.multiply(y, 3))
-        self.assertEqual(z.eval(), 9)
-
-  def testSmartCondMissingArg1(self):
-    with ops.Graph().as_default():
-      with session.Session():
-        x = constant_op.constant(1)
-        with self.assertRaises(TypeError):
-          control_flow_ops.smart_cond(True, false_fn=lambda: x)
-
-  def testSmartCondMissingArg2(self):
-    with ops.Graph().as_default():
-      with session.Session():
-        x = constant_op.constant(1)
-        with self.assertRaises(TypeError):
-          control_flow_ops.smart_cond(True, lambda: x)
-
-
 @test_util.with_c_api
 class CondTest(test_util.TensorFlowTestCase):
 
-- 
GitLab


From 72eef4b7cf49956a3c675c6dc9d0488176a224cb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 11:50:49 -0800
Subject: [PATCH 301/575] Add the internal module name prefix to the white
 list.

PiperOrigin-RevId: 187056701
---
 tensorflow/contrib/py2tf/impl/config.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/py2tf/impl/config.py b/tensorflow/contrib/py2tf/impl/config.py
index c90e85c96b..bdbc6663dd 100644
--- a/tensorflow/contrib/py2tf/impl/config.py
+++ b/tensorflow/contrib/py2tf/impl/config.py
@@ -31,12 +31,16 @@ PYTHON_LITERALS = {
 DEFAULT_UNCOMPILED_MODULES = set((
     ('tensorflow',),
     (utils.__name__,),
+
+    # All of tensorflow's subpackages. Unlike the root tf module, they don't
+    # have well-known names. Not refering to the module directly to avoid
+    # circular imports.
+    (utils.__name__[:-len('.contrib.py2tf.utils')],),
 ))
 
 NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',))
 
 # TODO(mdan): Also allow controlling the generated names (for testability).
-# TODO(mdan): Make sure copybara renames the reference below.
 COMPILED_IMPORT_STATEMENTS = (
     'from __future__ import print_function',
     'import tensorflow as tf',
-- 
GitLab


From fd1a54b00b265a09d7026c05c074af6b8839e593 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Mon, 26 Feb 2018 11:52:26 -0800
Subject: [PATCH 302/575] Internal change.

PiperOrigin-RevId: 187056963
---
 tensorflow/tools/api/tests/api_compatibility_test.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/api/tests/api_compatibility_test.py b/tensorflow/tools/api/tests/api_compatibility_test.py
index c1e09cc531..2a784973e1 100644
--- a/tensorflow/tools/api/tests/api_compatibility_test.py
+++ b/tensorflow/tools/api/tests/api_compatibility_test.py
@@ -165,7 +165,7 @@ class ApiCompatibilityTest(test.TestCase):
       logging.error('%d differences found between API and golden.', diff_count)
       messages = verbose_diffs if verbose else diffs
       for i in range(diff_count):
-        logging.error('Issue %d\t: %s', i + 1, messages[i])
+        print('Issue %d\t: %s' % (i + 1, messages[i]), file=sys.stderr)
 
       if update_goldens:
         # Write files if requested.
-- 
GitLab


From 16dbf4b8b08a587329900c71da5cb1bcab075b19 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 26 Feb 2018 11:57:30 -0800
Subject: [PATCH 303/575] Use optimized ops to handle GPU memory swapping: this
 avoids the need for 2 pairs of extra _send/_recv nodes which speeds things up
 a bit. This also ensures that performance doesn't depend on the recv
 scheduling built in TF, which isn't always optimal.

PiperOrigin-RevId: 187057831
---
 tensorflow/core/grappler/optimizers/BUILD     | 36 +++++++-
 .../optimizers/gpu_swapping_kernels.cc        | 88 +++++++++++++++++++
 .../grappler/optimizers/gpu_swapping_ops.cc   | 58 ++++++++++++
 .../grappler/optimizers/memory_optimizer.cc   |  9 +-
 .../optimizers/memory_optimizer_test.cc       | 65 +++++++++++---
 tensorflow/core/grappler/utils/BUILD          |  1 +
 .../core/grappler/utils/grappler_test.cc      | 17 ++++
 .../core/grappler/utils/grappler_test.h       |  3 +
 8 files changed, 258 insertions(+), 19 deletions(-)
 create mode 100644 tensorflow/core/grappler/optimizers/gpu_swapping_kernels.cc
 create mode 100644 tensorflow/core/grappler/optimizers/gpu_swapping_ops.cc

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 50ba48ea7a..908e58bcc7 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -1,6 +1,8 @@
 licenses(["notice"])  # Apache 2.0
 
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
+load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu")
+load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
 
 filegroup(
     name = "all_files",
@@ -282,18 +284,48 @@ tf_cc_test(
     ],
 )
 
+tf_kernel_library(
+    name = "gpu_swapping_kernels",
+    srcs = [
+        "gpu_swapping_kernels.cc",
+    ],
+    deps = [
+        "//tensorflow/core:core_cpu_base",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_library(
+    name = "gpu_swapping_ops",
+    srcs = [
+        "gpu_swapping_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/core:core_cpu_base",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+    ],
+    alwayslink = 1,
+)
+
 cc_library(
     name = "memory_optimizer",
-    srcs = ["memory_optimizer.cc"],
+    srcs = [
+        "memory_optimizer.cc",
+    ],
     hdrs = [
         "memory_optimizer.h",
     ],
     visibility = ["//visibility:public"],
     deps = [
+        ":gpu_swapping_kernels",
+        ":gpu_swapping_ops",
         ":graph_optimizer",
         ":graph_rewriter",
         ":static_schedule",
         "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:graph_view",
         "//tensorflow/core/grappler:grappler_item",
@@ -307,7 +339,7 @@ cc_library(
     ],
 )
 
-tf_cc_test(
+tf_cc_test_gpu(
     name = "memory_optimizer_test",
     srcs = ["memory_optimizer_test.cc"],
     deps = [
diff --git a/tensorflow/core/grappler/optimizers/gpu_swapping_kernels.cc b/tensorflow/core/grappler/optimizers/gpu_swapping_kernels.cc
new file mode 100644
index 0000000000..1820af6844
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/gpu_swapping_kernels.cc
@@ -0,0 +1,88 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Op kernels used to swap data in and out of GPU memory.
+
+#include "tensorflow/core/common_runtime/device.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+namespace {
+
+class CopyFromGpuToHostKernel : public AsyncOpKernel {
+ public:
+  explicit CopyFromGpuToHostKernel(OpKernelConstruction* context)
+      : AsyncOpKernel(context) {}
+  void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override {
+    const Tensor& input = ctx->input(0);
+    OP_REQUIRES_ASYNC(
+        ctx, !ctx->input_alloc_attr(0).on_host(),
+        errors::Internal("The input tensor to the _CopyFromGpuToHost kernel "
+                         "must reside on the device."),
+        done);
+
+    AllocatorAttributes alloc_attrs;
+    alloc_attrs.set_gpu_compatible(true);
+    alloc_attrs.set_on_host(true);
+    Tensor* output;
+    OP_REQUIRES_OK_ASYNC(
+        ctx, ctx->allocate_output(0, input.shape(), &output, alloc_attrs),
+        done);
+
+    ctx->op_device_context()->CopyDeviceTensorToCPU(
+        &input, "CopyFromGpuToHost", static_cast<Device*>(ctx->device()),
+        output, [ctx, done](const Status& s) {
+          ctx->SetStatus(s);
+          done();
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("_CopyFromGpuToHost").Device(DEVICE_GPU).HostMemory("output"),
+    CopyFromGpuToHostKernel);
+
+class CopyFromHostToGpuKernel : public AsyncOpKernel {
+ public:
+  explicit CopyFromHostToGpuKernel(OpKernelConstruction* context)
+      : AsyncOpKernel(context) {}
+  void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override {
+    const Tensor& input = ctx->input(0);
+    OP_REQUIRES_ASYNC(
+        ctx, ctx->input_alloc_attr(0).on_host(),
+        errors::Internal("The input tensor to the _CopyFromHostToGpu kernel "
+                         "must reside on the host."),
+        done);
+
+    Tensor* output;
+    OP_REQUIRES_OK_ASYNC(ctx, ctx->allocate_output(0, input.shape(), &output),
+                         done);
+
+    ctx->op_device_context()->CopyCPUTensorToDevice(
+        &input, static_cast<Device*>(ctx->device()), output,
+        [ctx, done](const Status& s) {
+          ctx->SetStatus(s);
+          done();
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("_CopyFromHostToGpu").Device(DEVICE_GPU).HostMemory("input"),
+    CopyFromHostToGpuKernel);
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/gpu_swapping_ops.cc b/tensorflow/core/grappler/optimizers/gpu_swapping_ops.cc
new file mode 100644
index 0000000000..46828346da
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/gpu_swapping_ops.cc
@@ -0,0 +1,58 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Definition for the ops used to swap data in and out of GPU memory.
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+namespace {
+
+// The _CopyFromGpuToHost op copies its input tensor to the host. The input must
+// reside on GPU. The op itself must be placed on GPU.
+REGISTER_OP("_CopyFromGpuToHost")
+    .Input("input: T")
+    .Output("output: T")
+    .Attr("T: type")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      c->set_output(0, c->input(0));
+      auto* handle_data = c->input_handle_shapes_and_types(0);
+      if (handle_data != nullptr) {
+        c->set_output_handle_shapes_and_types(0, *handle_data);
+      }
+      return Status::OK();
+    })
+    .Doc("Copies the input tensor from gpu to the host.");
+
+// The _CopyFromHostToGpu op copies its input tensor from the host to the GPU.
+// The input must reside on CPU. The op itself must be placed on GPU.
+REGISTER_OP("_CopyFromHostToGpu")
+    .Input("input: T")
+    .Output("output: T")
+    .Attr("T: type")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      c->set_output(0, c->input(0));
+      auto* handle_data = c->input_handle_shapes_and_types(0);
+      if (handle_data != nullptr) {
+        c->set_output_handle_shapes_and_types(0, *handle_data);
+      }
+      return Status::OK();
+    })
+    .Doc("Copies the input tensor from the host to the GPU.");
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index dec4f04a1c..694139fa50 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -720,18 +720,19 @@ Status BuildSwapPair(NodeDef* node, int input_to_swap,
   // Force the tensor to be copied to cpu.
   NodeDef* swap_out_node = graph->add_node();
   swap_out_node->set_name(swap_out_name);
-  swap_out_node->set_op("Identity");
-  swap_out_node->set_device("/device:CPU:0");
+  swap_out_node->set_op("_CopyFromGpuToHost");
 
   // Force the tensor to be restored to the device.
   NodeDef* swap_in_node = graph->add_node();
   swap_in_node->set_name(swap_in_name);
-  swap_in_node->set_op("Identity");
+  swap_in_node->set_op("_CopyFromHostToGpu");
   *swap_in_node->add_input() = swap_out_node->name();
 
-  // Colocate the swap_in_ node with the node itself.
+  // Colocate the swap_out_ and swap_in_ nodes with the node itself.
+  swap_out_node->set_device(node->device());
   swap_in_node->set_device(node->device());
   string coloc_group = strings::StrCat("loc@", tensor_to_swap);
+  (*swap_out_node->mutable_attr())["_class"].mutable_list()->add_s(coloc_group);
   (*swap_in_node->mutable_attr())["_class"].mutable_list()->add_s(coloc_group);
   (*node->mutable_attr())["_class"].mutable_list()->add_s(coloc_group);
 
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
index 5d7913e0c0..9595936e9e 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
@@ -221,16 +221,20 @@ TEST_F(MemoryOptimizerTest, SimpleSwapping) {
   // Build a simple graph with an op that's marked for swapping.
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Variable(s.WithOpName("a"), {10, 10}, DT_FLOAT);
-  Output b = ops::AddN(s.WithOpName("b"), {a});
-  Output c = ops::AddN(s.WithOpName("c"), {b});
-  Output d = ops::AddN(s.WithOpName("d"), {c});
-  Output e = ops::AddN(s.WithOpName("e"), {b, d});
+  Output a =
+      ops::Variable(s.WithOpName("a").WithDevice("/gpu:0"), {10, 10}, DT_FLOAT);
+  Output b = ops::AddN(s.WithOpName("b").WithDevice("/gpu:0"), {a});
+  Output c = ops::AddN(s.WithOpName("c").WithDevice("/gpu:0"), {b});
+  Output d = ops::AddN(s.WithOpName("d").WithDevice("/gpu:0"), {c});
+  Output e = ops::AddN(s.WithOpName("e").WithDevice("/gpu:0"), {b, d});
+
+  Output constant = ops::Const(s.WithOpName("constant"), 0.0f, {10, 10});
+  Output init = ops::Assign(s.WithOpName("init"), a, constant);
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
-  EXPECT_EQ(5, item.graph.node_size());
+  EXPECT_EQ(7, item.graph.node_size());
   EXPECT_EQ(NodeName(e.name()), item.graph.node(4).name());
   AttrValue& val =
       (*item.graph.mutable_node(4)->mutable_attr())["_swap_to_host"];
@@ -243,32 +247,43 @@ TEST_F(MemoryOptimizerTest, SimpleSwapping) {
   Status status = optimizer.Optimize(cluster.get(), item, &output);
   TF_EXPECT_OK(status);
 
-  EXPECT_EQ(7, output.node_size());
-  const NodeDef& new_e = output.node(4);
+  EXPECT_EQ(9, output.node_size());
+  const NodeDef& new_e = output.node(6);
   EXPECT_EQ(NodeName(e.name()), new_e.name());
 
   EXPECT_EQ(2, new_e.input_size());
   EXPECT_EQ(NodeName(d.name()), new_e.input(1));
   EXPECT_EQ("swap_in_e_0", new_e.input(0));
 
-  const NodeDef& swap_out = output.node(5);
+  const NodeDef& swap_out = output.node(7);
   EXPECT_EQ("swap_out_e_0", swap_out.name());
+  EXPECT_EQ("_CopyFromGpuToHost", swap_out.op());
 
-  const NodeDef& swap_in = output.node(6);
+  const NodeDef& swap_in = output.node(8);
   EXPECT_EQ("swap_in_e_0", swap_in.name());
+  EXPECT_EQ("_CopyFromHostToGpu", swap_in.op());
 
   EXPECT_EQ(NodeName(b.name()), swap_out.input(0));
   EXPECT_EQ(NodeName(swap_out.name()), swap_in.input(0));
   EXPECT_EQ("^c", swap_in.input(1));
 
-  const NodeDef& new_c = output.node(2);
+  const NodeDef& new_c = output.node(4);
   EXPECT_EQ(NodeName(c.name()), new_c.name());
   EXPECT_EQ("^swap_out_e_0", new_c.input(1));
 
   // Run the optimizer a second time to ensure it's idempotent.
-  item.graph.Swap(&output);
-  status = optimizer.Optimize(cluster.get(), item, &output);
+  GrapplerItem item_copy(item, std::move(output));
+  status = optimizer.Optimize(cluster.get(), item_copy, &output);
   TF_EXPECT_OK(status);
+
+#if GOOGLE_CUDA
+  item.fetch = {"e"};
+  item.init_ops = {init.name()};
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
+#endif
 }
 
 TEST_F(MemoryOptimizerTest, SwappingHeuristics) {
@@ -287,9 +302,13 @@ TEST_F(MemoryOptimizerTest, SwappingHeuristics) {
   Output h = ops::Exp(s.WithOpName("h").WithDevice("/gpu:0"), c);
   Output i = ops::Log(s.WithOpName("i").WithDevice("/gpu:0"), d);
 
+  Output constant = ops::Const(s.WithOpName("constant"), 0.0f, {128, 128, 8});
+  Output init = ops::Assign(s.WithOpName("init"), v, constant);
+
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
   item.fetch = {"e", "f", "g", "h", "i"};
+  item.init_ops = {init.name()};
 
   std::unique_ptr<VirtualCluster> cluster(CreateVirtualCluster());
 
@@ -308,6 +327,15 @@ TEST_F(MemoryOptimizerTest, SwappingHeuristics) {
       EXPECT_EQ("axis", node.input(4));
     }
   }
+
+#if GOOGLE_CUDA
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  for (int i = 0; i < item.fetch.size(); ++i) {
+    test::ExpectTensorEqual<float>(tensors_expected[i], tensors[i]);
+  }
+#endif
 }
 
 TEST_F(MemoryOptimizerTest, UnswappableInputs) {
@@ -325,9 +353,13 @@ TEST_F(MemoryOptimizerTest, UnswappableInputs) {
   Output e =
       ops::Concat(s.WithOpName("e").WithDevice("/gpu:0"), {b, c, d}, axis);
 
+  Output constant = ops::Const(s.WithOpName("constant"), 0.0f, {128, 128, 8});
+  Output init = ops::Assign(s.WithOpName("init"), v, constant);
+
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
   item.fetch = {"e"};
+  item.init_ops = {init.name()};
 
   std::unique_ptr<VirtualCluster> cluster(CreateVirtualCluster());
 
@@ -344,6 +376,13 @@ TEST_F(MemoryOptimizerTest, UnswappableInputs) {
       EXPECT_EQ("^swap_out_d_2", node.input(4));
     }
   }
+
+#if GOOGLE_CUDA
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
+#endif
 }
 
 TEST_F(MemoryOptimizerTest, AccumulationRewrites) {
diff --git a/tensorflow/core/grappler/utils/BUILD b/tensorflow/core/grappler/utils/BUILD
index 0a9dbe22cf..5d32609434 100644
--- a/tensorflow/core/grappler/utils/BUILD
+++ b/tensorflow/core/grappler/utils/BUILD
@@ -142,6 +142,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
+        "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:utils",
     ],
 )
diff --git a/tensorflow/core/grappler/utils/grappler_test.cc b/tensorflow/core/grappler/utils/grappler_test.cc
index fed46c05fb..fef8e97b6e 100644
--- a/tensorflow/core/grappler/utils/grappler_test.cc
+++ b/tensorflow/core/grappler/utils/grappler_test.cc
@@ -35,6 +35,23 @@ std::vector<Tensor> GrapplerTest::EvaluateNodes(
   return output_tensors;
 }
 
+std::vector<Tensor> GrapplerTest::EvaluateFetchNodes(const GrapplerItem& item) {
+  SessionOptions options;
+  std::unique_ptr<tensorflow::Session> session(NewSession(options));
+  TF_CHECK_OK(session->Create(item.graph));
+  RunOptions run_options;
+  if (!item.init_ops.empty()) {
+    std::vector<Tensor> dummy;
+    TF_CHECK_OK(
+        session->Run(run_options, {}, {}, item.init_ops, &dummy, nullptr));
+  }
+  std::vector<Tensor> output_tensors;
+  TF_CHECK_OK(
+      session->Run(run_options, {}, item.fetch, {}, &output_tensors, nullptr));
+  TF_CHECK_OK(session->Close());
+  return output_tensors;
+}
+
 void GrapplerTest::AddNode(const string& name, const string& op,
                            const std::vector<string>& inputs, GraphDef* graph) {
   auto* node = graph->add_node();
diff --git a/tensorflow/core/grappler/utils/grappler_test.h b/tensorflow/core/grappler/utils/grappler_test.h
index 042b616aa4..fd6809b6e2 100644
--- a/tensorflow/core/grappler/utils/grappler_test.h
+++ b/tensorflow/core/grappler/utils/grappler_test.h
@@ -20,6 +20,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
@@ -30,6 +31,8 @@ class GrapplerTest : public ::testing::Test {
   std::vector<Tensor> EvaluateNodes(const GraphDef& graph,
                                     const std::vector<string>& node_names);
 
+  std::vector<Tensor> EvaluateFetchNodes(const GrapplerItem& item);
+
   void AddNode(const string& name, const string& op,
                const std::vector<string>& inputs, GraphDef* graph);
 
-- 
GitLab


From 63d4c46a613c4d0e44d966c040bdfbbd0b16d13d Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 26 Feb 2018 12:10:01 -0800
Subject: [PATCH 304/575] Fix bug calling gradients_function inside
 custom_gradient

PiperOrigin-RevId: 187059871
---
 tensorflow/python/eager/backprop_test.py   | 13 +++++++++++++
 tensorflow/python/eager/custom_gradient.py |  9 ++++-----
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index 734558dee2..48fd170764 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -115,6 +115,19 @@ class BackpropTest(test.TestCase):
     with self.assertRaises(RuntimeError):
       backprop.gradients_function(f)(constant_op.constant(1.0))
 
+  def testGradientsFunctionInCustomGradient(self):
+
+    @custom_gradient.custom_gradient
+    def f(x):
+      (y,) = backprop.gradients_function(lambda x: x * x)(x)
+
+      def grad(dy):
+        return [2 * dy]
+
+      return y, grad
+
+    self.assertAllEqual(f(1.0), 2.0)
+
   def testImplicitGradOverEmbeddingLookup(self):
     batch_size = 8
     embedding_size = 512
diff --git a/tensorflow/python/eager/custom_gradient.py b/tensorflow/python/eager/custom_gradient.py
index 05460ff996..fb932a9372 100644
--- a/tensorflow/python/eager/custom_gradient.py
+++ b/tensorflow/python/eager/custom_gradient.py
@@ -71,11 +71,10 @@ def custom_gradient(f):
 
     input_tensors = [tf_ops.convert_to_tensor(x) for x in args]
 
-    with tape.stop_recording():
-      result, grad_fn = f(*args, **kwargs)
-      flat_result = nest.flatten(result)
-      # TODO(apassos) consider removing the identity below.
-      flat_result = [gen_array_ops.identity(x) for x in flat_result]
+    result, grad_fn = f(*args, **kwargs)
+    flat_result = nest.flatten(result)
+    # TODO(apassos) consider removing the identity below.
+    flat_result = [gen_array_ops.identity(x) for x in flat_result]
 
     def actual_grad_fn(*outputs):
       return nest.flatten(grad_fn(*outputs))
-- 
GitLab


From 1120deaf0bf5a51db5351c12b548994b35ba71c8 Mon Sep 17 00:00:00 2001
From: Jeremy Lau <lauj@google.com>
Date: Mon, 26 Feb 2018 12:23:36 -0800
Subject: [PATCH 305/575] Internal change.

PiperOrigin-RevId: 187061863
---
 tensorflow/contrib/bayesflow/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 08b29fb6bc..270c309ec3 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -210,7 +210,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "hmc_test",
-    size = "medium",
+    size = "large",
     srcs = ["python/kernel_tests/hmc_test.py"],
     additional_deps = [
         ":bayesflow_py",
-- 
GitLab


From da492741630f62bfd4f8475fa532ef216f0d2bfd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 12:33:17 -0800
Subject: [PATCH 306/575] Maintain a cache of output dtypes of ops in
 TFE_Context.

PiperOrigin-RevId: 187062992
---
 tensorflow/c/eager/c_api.cc   | 20 ++++++++++++++++++++
 tensorflow/c/eager/runtime.cc | 15 ++++++++++++---
 tensorflow/c/eager/runtime.h  |  6 ++++++
 3 files changed, 38 insertions(+), 3 deletions(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index c27a7129fa..bebb63c746 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
+#include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/rendezvous.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
@@ -823,6 +824,25 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
       delete kernel;
       return;
     }
+    // Update output_dtypes inside `kernel`.
+    const tensorflow::OpDef* op_def = nullptr;
+    const tensorflow::FunctionDef* function_def =
+        ctx->func_lib_def.Find(ndef.op());
+    if (function_def != nullptr) {
+      op_def = &(function_def->signature());
+    }
+    if (op_def == nullptr) {
+      status->status = OpDefForOp(ndef.op().c_str(), &op_def);
+      if (!status->status.ok()) {
+        return;
+      }
+    }
+    tensorflow::DataTypeVector input_dtypes;
+    status->status = InOutTypesForNode(ndef, *op_def, &input_dtypes,
+                                       kernel->output_dtypes());
+    if (!status->status.ok()) {
+      return;
+    }
     tensorflow::mutex_lock ml(ctx->cache_mu);
     tensorflow::gtl::InsertOrUpdate(&(ctx->kernel_cache), cache_key, kernel);
   }
diff --git a/tensorflow/c/eager/runtime.cc b/tensorflow/c/eager/runtime.cc
index f77a937f1f..4bf24fec2c 100644
--- a/tensorflow/c/eager/runtime.cc
+++ b/tensorflow/c/eager/runtime.cc
@@ -41,17 +41,26 @@ const uint32 kIsList = 1U << 31;
 
 }  // namespace
 
+Status OpDefForOp(const char* op_name, const OpDef** op_def) {
+  const OpRegistrationData* op_reg_data = nullptr;
+  Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data);
+  if (s.ok()) {
+    *op_def = &op_reg_data->op_def;
+  }
+  return s;
+}
+
 Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out) {
   mutex_lock l(g_op_name_to_attr_type_map_lock);
   *out = gtl::FindPtrOrNull(*OpNameToAttrTypeMap(), op_name);
   if (*out != nullptr) return Status::OK();
-  const OpRegistrationData* op_reg_data = nullptr;
-  Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data);
+  const OpDef* op_def = nullptr;
+  Status s = OpDefForOp(op_name, &op_def);
   if (!s.ok()) return s;
   std::unique_ptr<AttrTypeMap> m(new AttrTypeMap);
   // TODO(agarwal): Avoid having to create this "registry" at runtime,
   // perhaps can be done at op registration time?
-  for (const auto& attr : op_reg_data->op_def.attr()) {
+  for (const auto& attr : op_def->attr()) {
     string type = attr.type();
     const bool is_list = (type.length() > 6 && type.compare(0, 4, "list") == 0);
     if (is_list) {
diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h
index 4d20b5244a..7fede4dae9 100644
--- a/tensorflow/c/eager/runtime.h
+++ b/tensorflow/c/eager/runtime.h
@@ -39,6 +39,9 @@ namespace tensorflow {
 // represent the TF_AttrType type of the values in the list.
 typedef std::unordered_map<string, uint32> AttrTypeMap;
 
+// Look up OpDef for `op_name`.
+Status OpDefForOp(const char* op_name, const OpDef** op_def);
+
 // Returns the AttrTypeMap for the TensorFlow operation named op_name.
 Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out);
 
@@ -180,12 +183,15 @@ class KernelAndDevice {
 
   const OpKernel* kernel() const { return kernel_.get(); }
 
+  DataTypeVector* output_dtypes() { return &output_dtypes_; }
+
  private:
   std::unique_ptr<OpKernel> kernel_;
   Device* device_;
   FunctionLibraryRuntime* flib_;
   checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_;
   Rendezvous* rendez_;
+  DataTypeVector output_dtypes_;
 };
 
 }  // namespace tensorflow
-- 
GitLab


From c7ea6ace71ed503a316cc5eb3dd087c5e7709725 Mon Sep 17 00:00:00 2001
From: Richard Wei <rxwei@google.com>
Date: Mon, 26 Feb 2018 13:06:59 -0800
Subject: [PATCH 307/575] Include c_api_experimental in libtensorflow.so's
 dependencies.

PiperOrigin-RevId: 187068103
---
 tensorflow/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 2e71783b0d..a4e7602bea 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -779,6 +779,7 @@ tf_cc_shared_object(
     }),
     deps = [
         "//tensorflow/c:c_api",
+        "//tensorflow/c:c_api_experimental",
         "//tensorflow/c:exported_symbols.lds",
         "//tensorflow/c:version_script.lds",
         "//tensorflow/c/eager:c_api",
-- 
GitLab


From acb1ef68f5aea3b6f7f1e14db588b74134719b5e Mon Sep 17 00:00:00 2001
From: Daniel Trebbien <dtrebbien@gmail.com>
Date: Mon, 26 Feb 2018 13:42:07 -0800
Subject: [PATCH 308/575] Add missing `override' (#17098)

This fixes a warning produced by clang:
./tensorflow/core/common_runtime/gpu/gpu_device.h:70:10: warning: 'FillContextMap' overrides a member function but is not marked 'override' [-Winconsistent-missing-override]
  Status FillContextMap(const Graph* graph,
         ^
./tensorflow/core/common_runtime/device.h:124:18: note: overridden virtual function is here
  virtual Status FillContextMap(const Graph* graph,
---
 tensorflow/core/common_runtime/gpu/gpu_device.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.h b/tensorflow/core/common_runtime/gpu/gpu_device.h
index c88daa8ff8..d817c7dd1f 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.h
@@ -68,7 +68,7 @@ class BaseGPUDevice : public LocalDevice {
       const TensorReferenceVector& tensor_refs) override;
 
   Status FillContextMap(const Graph* graph,
-                        DeviceContextMap* device_context_map);
+                        DeviceContextMap* device_context_map) override;
 
   void Compute(OpKernel* op_kernel, OpKernelContext* context) override;
 
-- 
GitLab


From ba2cc572f99b09ddd6a60e0557059cb1da51b356 Mon Sep 17 00:00:00 2001
From: Akshay Agrawal <akshayka@google.com>
Date: Mon, 26 Feb 2018 13:54:02 -0800
Subject: [PATCH 309/575] Update eager uniform replay buffer microbenchmarks to
 compare against graph functions when possible.

PiperOrigin-RevId: 187075418
---
 .../contrib/framework/python/ops/critical_section_ops.py | 6 ++++--
 tensorflow/python/framework/ops.py                       | 9 ++++++++-
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/framework/python/ops/critical_section_ops.py b/tensorflow/contrib/framework/python/ops/critical_section_ops.py
index 3c5c55ed65..ab603cc18e 100644
--- a/tensorflow/contrib/framework/python/ops/critical_section_ops.py
+++ b/tensorflow/contrib/framework/python/ops/critical_section_ops.py
@@ -143,7 +143,7 @@ class CriticalSection(object):
   def _init_from_args(self, name, shared_name):  # pylint: disable=invalid-name
     """Initialize the CriticalSection from constructor arguments."""
     with ops.name_scope(name, "CriticalSection", []) as name:
-      with ops.control_dependencies(None):
+      with ops.init_scope():
         # pylint: disable=protected-access
         container = ops.get_default_graph()._container
         # pylint: enable=protected-access
@@ -226,7 +226,9 @@ class CriticalSection(object):
         # mode.  This is generally ok; since eager mode (as of
         # writing) executes sequentially anyway.
         for sg in ops.get_collection(CRITICAL_SECTION_EXECUTIONS):
-          if sg.handle.name == self._handle.name:
+          sg_handle_name = ops.convert_to_tensor(sg.handle).name
+          self_handle_name = ops.convert_to_tensor(self._handle).name
+          if sg_handle_name == self_handle_name:
             # Other executions in the same critical section are allowed.
             continue
           if not (exclusive_resource_access or sg.exclusive_resource_access):
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 5a14ea4176..b0d2704c07 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -4805,7 +4805,14 @@ def container(container_name):
 @tf_export("colocate_with")
 def colocate_with(op, ignore_existing=False):
   if context.in_graph_mode():
-    return get_default_graph().colocate_with(op, ignore_existing)
+    default_graph = get_default_graph()
+    if isinstance(op, EagerTensor):
+      if default_graph.building_function:
+        op = internal_convert_to_tensor(op)
+      else:
+        raise ValueError("Encountered an Eager-defined Tensor during graph "
+                         "construction, but a function was not being built.")
+    return default_graph.colocate_with(op, ignore_existing)
   else:
     if op is not None:
       return device(op.device)
-- 
GitLab


From 7765066e6a686c7d6b1bed44248fafaa859db4eb Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Mon, 26 Feb 2018 14:00:07 -0800
Subject: [PATCH 310/575] TFTS: Switch to using core feature columns

This fixes some shape issues that came up when using the tf.contrib.layers
parsing functions.

Adds a string -> embedding column API example to the LSTM example.

PiperOrigin-RevId: 187076400
---
 .../examples/data/multivariate_periods.csv    | 200 +++++++++---------
 .../timeseries/examples/known_anomaly.py      |   8 +-
 .../contrib/timeseries/examples/lstm.py       |  26 ++-
 .../python/timeseries/estimators.py           |  53 +++--
 .../timeseries/python/timeseries/model.py     |  38 ++--
 .../state_space_models/state_space_model.py   |  10 +-
 6 files changed, 177 insertions(+), 158 deletions(-)

diff --git a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
index b49a0662c2..9b15b4f0b2 100644
--- a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
+++ b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
@@ -1,100 +1,100 @@
-0,0.926906299771,1.99107237682,2.56546245685,3.07914768197,4.04839057867,1.,0.
-1,0.108010001864,1.41645361423,2.1686839775,2.94963962176,4.1263503303,1.,0.
-2,-0.800567600028,1.0172132907,1.96434754116,2.99885333086,4.04300485864,1.,0.
-3,0.0607042871898,0.719540073421,1.9765012584,2.89265588817,4.0951014426,1.,0.
-4,0.933712200629,0.28052120776,1.41018552514,2.69232603996,4.06481164223,1.,0.
-5,-0.171730652974,0.260054421028,1.48770816369,2.62199129293,4.44572807842,1.,0.
-6,-1.00180162933,0.333045158863,1.50006392277,2.88888309683,4.24755865606,1.,0.
-7,0.0580061875336,0.688929398826,1.56543458772,2.99840358953,4.52726873347,1.,0.
-8,0.764139447412,1.24704875327,1.77649279698,3.13578593851,4.63238922951,1.,0.
-9,-0.230331874785,1.47903998963,2.03547545751,3.20624030377,4.77980005228,1.,0.
-10,-1.03846045211,2.01133000781,2.31977503972,3.67951536251,5.09716775897,1.,0.
-11,0.188643592253,2.23285349038,2.68338482249,3.49817168611,5.24928239634,1.,0.
-12,0.91207302309,2.24244446841,2.71362604985,3.96332587625,5.37802271594,1.,0.
-13,-0.296588665881,2.02594634141,3.07733910479,3.99698324956,5.56365901394,1.,0.
-14,-0.959961476551,1.45078629833,3.18996420137,4.3763059609,5.65356015609,1.,0.
-15,0.46313530679,1.01141441548,3.4980215948,4.20224896882,5.88842247449,1.,0.
-16,0.929354125798,0.626635305936,3.70508262244,4.51791573544,5.73945973251,1.,0.
-17,-0.519110731957,0.269249223148,3.39866823332,4.46802003061,5.82768174382,1.,0.
-18,-0.924330981367,0.349602834684,3.21762413294,4.72803587499,5.94918925767,1.,0.
-19,0.253239387885,0.345158023497,3.11071425333,4.79311566935,5.9489259713,1.,0.
-20,0.637408390225,0.698996675371,3.25232492145,4.73814732384,5.9612010251,1.,0.
-21,-0.407396859412,1.17456342803,2.49526823723,4.59323415742,5.82501686811,1.,0.
-22,-0.967485452118,1.66655933642,2.47284606244,4.58316034754,5.88721406681,1.,0.
-23,0.474480867904,1.95018556323,2.0228950072,4.48651142819,5.8255943735,1.,0.
-24,1.04309652155,2.23519892356,1.91924131572,4.19094661783,5.87457348436,1.,0.
-25,-0.517861513772,2.12501967336,1.70266619979,4.05280882887,5.72160912899,1.,0.
-26,-0.945301585146,1.65464653549,1.81567174251,3.92309850635,5.58270493814,1.,0.
-27,0.501153868974,1.40600764889,1.53991387719,3.72853247942,5.60169001727,1.,0.
-28,0.972859524418,1.00344321868,1.5175642828,3.64092376655,5.10567722582,1.,0.
-29,-0.70553406135,0.465306263885,1.7038540803,3.33236870312,5.09182481555,1.,0.
-30,-0.946093634916,0.294539309453,1.88052827037,2.93011492669,4.97354922696,1.,0.
-31,0.47922123231,0.308465865031,2.03445883031,2.90772899045,4.86241793548,1.,0.
-32,0.754030014252,0.549752241167,2.46115815089,2.95063349534,4.71834614627,1.,0.
-33,-0.64875949826,0.894615488148,2.5922463381,2.81269864022,4.43480095104,1.,0.
-34,-0.757829951086,1.39123914261,2.69258079904,2.61834837315,4.36580046156,1.,0.
-35,0.565653301088,1.72360022693,2.97794913834,2.80403840334,4.27327248459,1.,0.
-36,0.867440092372,2.21100730052,3.38648090792,2.84057515729,4.12210169576,1.,0.
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diff --git a/tensorflow/contrib/timeseries/examples/known_anomaly.py b/tensorflow/contrib/timeseries/examples/known_anomaly.py
index 7659dd308a..c08c0b0acb 100644
--- a/tensorflow/contrib/timeseries/examples/known_anomaly.py
+++ b/tensorflow/contrib/timeseries/examples/known_anomaly.py
@@ -46,12 +46,12 @@ def train_and_evaluate_exogenous(csv_file_name=_DATA_FILE, train_steps=300):
 
   # Indicate the format of our exogenous feature, in this case a string
   # representing a boolean value.
-  string_feature = tf.contrib.layers.sparse_column_with_keys(
-      column_name="is_changepoint", keys=["no", "yes"])
+  string_feature = tf.feature_column.categorical_column_with_vocabulary_list(
+      key="is_changepoint", vocabulary_list=["no", "yes"])
   # Specify the way this feature is presented to the model, here using a one-hot
   # encoding.
-  one_hot_feature = tf.contrib.layers.one_hot_column(
-      sparse_id_column=string_feature)
+  one_hot_feature = tf.feature_column.indicator_column(
+      categorical_column=string_feature)
 
   estimator = tf.contrib.timeseries.StructuralEnsembleRegressor(
       periodicities=12,
diff --git a/tensorflow/contrib/timeseries/examples/lstm.py b/tensorflow/contrib/timeseries/examples/lstm.py
index f37cafcc50..2eee878196 100644
--- a/tensorflow/contrib/timeseries/examples/lstm.py
+++ b/tensorflow/contrib/timeseries/examples/lstm.py
@@ -59,10 +59,10 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel):
       num_units: The number of units in the model's LSTMCell.
       num_features: The dimensionality of the time series (features per
         timestep).
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects representing features which are inputs to the model but are
-          not predicted by it. These must then be present for training,
-          evaluation, and prediction.
+      exogenous_feature_columns: A list of `tf.feature_column`s representing
+          features which are inputs to the model but are not predicted by
+          it. These must then be present for training, evaluation, and
+          prediction.
       dtype: The floating point data type to use.
     """
     super(_LSTMModel, self).__init__(
@@ -189,12 +189,16 @@ def train_and_predict(
     export_directory=None):
   """Train and predict using a custom time series model."""
   # Construct an Estimator from our LSTM model.
+  categorical_column = tf.feature_column.categorical_column_with_hash_bucket(
+      key="categorical_exogenous_feature", hash_bucket_size=16)
   exogenous_feature_columns = [
       # Exogenous features are not part of the loss, but can inform
       # predictions. In this example the features have no extra information, but
       # are included as an API example.
-      tf.contrib.layers.real_valued_column(
-          "2d_exogenous_feature", dimension=2)]
+      tf.feature_column.numeric_column(
+          "2d_exogenous_feature", shape=(2,)),
+      tf.feature_column.embedding_column(
+          categorical_column=categorical_column, dimension=10)]
   estimator = ts_estimators.TimeSeriesRegressor(
       model=_LSTMModel(num_features=5, num_units=128,
                        exogenous_feature_columns=exogenous_feature_columns),
@@ -205,7 +209,11 @@ def train_and_predict(
       csv_file_name,
       column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,)
                     + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5
-                    + ("2d_exogenous_feature",) * 2))
+                    + ("2d_exogenous_feature",) * 2
+                    + ("categorical_exogenous_feature",)),
+      # Data types other than for `times` need to be specified if they aren't
+      # float32. In this case one of our exogenous features has string dtype.
+      column_dtypes=((tf.int64,) + (tf.float32,) * 7 + (tf.string,)))
   train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
       reader, batch_size=4, window_size=32)
   estimator.train(input_fn=train_input_fn, steps=training_steps)
@@ -215,7 +223,9 @@ def train_and_predict(
   predict_exogenous_features = {
       "2d_exogenous_feature": numpy.concatenate(
           [numpy.ones([1, 100, 1]), numpy.zeros([1, 100, 1])],
-          axis=-1)}
+          axis=-1),
+      "categorical_exogenous_feature": numpy.array(
+          ["strkey"] * 100)[None, :, None]}
   (predictions,) = tuple(estimator.predict(
       input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
           evaluation, steps=100,
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators.py b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
index f8355f366f..8d13343e82 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/estimators.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
@@ -18,8 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.layers.python.layers import feature_column
-
 from tensorflow.contrib.timeseries.python.timeseries import ar_model
 from tensorflow.contrib.timeseries.python.timeseries import feature_keys
 from tensorflow.contrib.timeseries.python.timeseries import head as ts_head_lib
@@ -31,10 +29,12 @@ from tensorflow.contrib.timeseries.python.timeseries.state_space_models.filterin
 
 from tensorflow.python.estimator import estimator_lib
 from tensorflow.python.estimator.export import export_lib
+from tensorflow.python.feature_column import feature_column
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import parsing_ops
 from tensorflow.python.training import training as train
 
 
@@ -117,22 +117,29 @@ class TimeSeriesRegressor(estimator_lib.Estimator):
                   dtype=self._model.dtype),
               shape=(default_batch_size, default_series_length,
                      self._model.num_features)))
-      with ops.Graph().as_default():
-        # Default placeholders have only an unknown batch dimension. Make them
-        # in a separate graph, then splice in the series length to the shapes
-        # and re-create them in the outer graph.
-        exogenous_feature_shapes = {
-            key: (value.get_shape(), value.dtype) for key, value
-            in feature_column.make_place_holder_tensors_for_base_features(
-                self._model.exogenous_feature_columns).items()}
-      for feature_key, (batch_only_feature_shape, value_dtype) in (
-          exogenous_feature_shapes.items()):
-        batch_only_feature_shape = batch_only_feature_shape.with_rank_at_least(
-            1).as_list()
-        feature_shape = ([default_batch_size, default_series_length]
-                         + batch_only_feature_shape[1:])
-        placeholders[feature_key] = array_ops.placeholder(
-            dtype=value_dtype, name=feature_key, shape=feature_shape)
+      if self._model.exogenous_feature_columns:
+        with ops.Graph().as_default():
+          # Default placeholders have only an unknown batch dimension. Make them
+          # in a separate graph, then splice in the series length to the shapes
+          # and re-create them in the outer graph.
+          parsed_features = (
+              feature_column.make_parse_example_spec(
+                  self._model.exogenous_feature_columns))
+          placeholder_features = parsing_ops.parse_example(
+              serialized=array_ops.placeholder(
+                  shape=[None], dtype=dtypes.string),
+              features=parsed_features)
+          exogenous_feature_shapes = {
+              key: (value.get_shape(), value.dtype) for key, value
+              in placeholder_features.items()}
+        for feature_key, (batch_only_feature_shape, value_dtype) in (
+            exogenous_feature_shapes.items()):
+          batch_only_feature_shape = (
+              batch_only_feature_shape.with_rank_at_least(1).as_list())
+          feature_shape = ([default_batch_size, default_series_length]
+                           + batch_only_feature_shape[1:])
+          placeholders[feature_key] = array_ops.placeholder(
+              dtype=value_dtype, name=feature_key, shape=feature_shape)
       # Models may not know the shape of their state without creating some
       # variables/ops. Avoid polluting the default graph by making a new one. We
       # use only static metadata from the returned Tensors.
@@ -333,11 +340,11 @@ class StructuralEnsembleRegressor(StateSpaceRegressor):
           determine the model size. Learning autoregressive coefficients
           typically requires more steps and a smaller step size than other
           components.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+          `tf.feature_column.embedding_column`) corresponding to exogenous
+          features which provide extra information to the model but are not part
+          of the series to be predicted. Passed to
+          `tf.feature_column.input_layer`.
       exogenous_update_condition: A function taking two Tensor arguments,
           `times` (shape [batch size]) and `features` (a dictionary mapping
           exogenous feature keys to Tensors with shapes [batch size, ...]), and
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py
index bac7d1ebf5..7644764a74 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/model.py
@@ -21,18 +21,17 @@ from __future__ import print_function
 import abc
 import collections
 
-from tensorflow.contrib import layers
-from tensorflow.contrib.layers import feature_column
-
 from tensorflow.contrib.timeseries.python.timeseries import math_utils
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
 
+from tensorflow.python.feature_column import feature_column
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope
 
@@ -66,11 +65,11 @@ class TimeSeriesModel(object):
 
     Args:
       num_features: Number of features for the time series
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+           `tf.feature_column.embedding_column`) corresponding to exogenous
+           features which provide extra information to the model but are not
+           part of the series to be predicted. Passed to
+           `tf.feature_column.input_layer`.
       dtype: The floating point datatype to use.
     """
     if exogenous_feature_columns:
@@ -86,7 +85,7 @@ class TimeSeriesModel(object):
 
   @property
   def exogenous_feature_columns(self):
-    """`FeatureColumn` objects for features which are not predicted."""
+    """`tf.feature_colum`s for features which are not predicted."""
     return self._exogenous_feature_columns
 
   # TODO(allenl): Move more of the generic machinery for generating and
@@ -265,11 +264,14 @@ class TimeSeriesModel(object):
     if not self._exogenous_feature_columns:
       return (0,)
     with ops.Graph().as_default():
-      placeholder_features = (
-          feature_column.make_place_holder_tensors_for_base_features(
+      parsed_features = (
+          feature_column.make_parse_example_spec(
               self._exogenous_feature_columns))
-      embedded = layers.input_from_feature_columns(
-          columns_to_tensors=placeholder_features,
+      placeholder_features = parsing_ops.parse_example(
+          serialized=array_ops.placeholder(shape=[None], dtype=dtypes.string),
+          features=parsed_features)
+      embedded = feature_column.input_layer(
+          features=placeholder_features,
           feature_columns=self._exogenous_feature_columns)
       return embedded.get_shape().as_list()[1:]
 
@@ -308,13 +310,13 @@ class TimeSeriesModel(object):
         # Avoid shape warnings when embedding "scalar" exogenous features (those
         # with only batch and window dimensions); input_from_feature_columns
         # expects input ranks to match the embedded rank.
-        if tensor.get_shape().ndims == 1:
+        if tensor.get_shape().ndims == 1 and tensor.dtype != dtypes.string:
           exogenous_features_single_batch_dimension[name] = tensor[:, None]
         else:
           exogenous_features_single_batch_dimension[name] = tensor
       embedded_exogenous_features_single_batch_dimension = (
-          layers.input_from_feature_columns(
-              columns_to_tensors=exogenous_features_single_batch_dimension,
+          feature_column.input_layer(
+              features=exogenous_features_single_batch_dimension,
               feature_columns=self._exogenous_feature_columns,
               trainable=True))
       exogenous_regressors = array_ops.reshape(
@@ -381,8 +383,8 @@ class SequentialTimeSeriesModel(TimeSeriesModel):
           may use _scale_back_data or _scale_back_variance to return predictions
           to the input scale.
       dtype: The floating point datatype to use.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects. See `TimeSeriesModel`.
+      exogenous_feature_columns: A list of `tf.feature_column`s objects. See
+          `TimeSeriesModel`.
       exogenous_update_condition: A function taking two Tensor arguments `times`
           (shape [batch size]) and `features` (a dictionary mapping exogenous
           feature keys to Tensors with shapes [batch size, ...]) and returning a
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
index 6257002647..951c6546d5 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
@@ -112,11 +112,11 @@ class StateSpaceModelConfiguration(
       exogenous_noise_decreases: If True, exogenous regressors can "set" model
           state, decreasing uncertainty. If both this parameter and
           exogenous_noise_increases are False, exogenous regressors are ignored.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+          `tf.feature_column.embedding_column`) corresponding to exogenous
+          features which provide extra information to the model but are not part
+          of the series to be predicted. Passed to
+          `tf.feature_column.input_layer`.
       exogenous_update_condition: A function taking two Tensor arguments `times`
           (shape [batch size]) and `features` (a dictionary mapping exogenous
           feature keys to Tensors with shapes [batch size, ...]) and returning a
-- 
GitLab


From a05488be720fc803ac56738c8bc0222fb8a36d7f Mon Sep 17 00:00:00 2001
From: Shivani Agrawal <shivaniagrawal@google.com>
Date: Mon, 26 Feb 2018 14:11:08 -0800
Subject: [PATCH 311/575] Adding documentation for dataset/iterator
 checkpointing.

PiperOrigin-RevId: 187078347
---
 .../docs_src/programmers_guide/datasets.md    | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/programmers_guide/datasets.md
index d19200e80c..d38fbddfa1 100644
--- a/tensorflow/docs_src/programmers_guide/datasets.md
+++ b/tensorflow/docs_src/programmers_guide/datasets.md
@@ -327,6 +327,35 @@ same op/node (created by `Iterator.get_next()`). Therefore,  evaluating *any* of
 these tensors will advance the iterator for all components. A typical consumer
 of an iterator will include all components in a single expression.
 
+### Saving iterator state
+
+The @{tf.contrib.data.make_saveable_from_iterator} function creates a
+`SaveableObject` from an iterator, which can be used to save and
+restore the current state of the iterator (and, effectively, the whole input
+pipeline). A saveable object thus created can be added to @{tf.train.Saver}
+variables list or the `tf.GraphKeys.SAVEABLE_OBJECTS` collection for saving and
+restoring in the same manner as a @{tf.Variable}. Refer to
+@{$saved_model$Saving and Restoring} for details on how to save and restore
+variables.
+
+```python
+# Create saveable object from iterator.
+saveable = tf.contrib.data.make_saveable_from_iterator(iterator)
+
+# Save the iterator state by adding it to the saveable objects collection.
+tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, saveable)
+saver = tf.train.Saver()
+
+with tf.Session() as sess:
+
+  if should_checkpoint:
+    saver.save(path_to_checkpoint)
+
+# Restore the iterator state.
+with tf.Session() as sess:
+  saver.restore(sess, path_to_checkpoint)
+```
+
 ## Reading input data
 
 ### Consuming NumPy arrays
-- 
GitLab


From d98e7fc5720c1597b6f2034ba2ad62438ac5ef39 Mon Sep 17 00:00:00 2001
From: Michael Kuperstein <mkuper@google.com>
Date: Mon, 26 Feb 2018 14:19:56 -0800
Subject: [PATCH 312/575] [XLA] GTE of a certain element of the tuple does not
 need not keep other elements alive.

This achieves two things:
1. Heap simulation runtime is no longer quadratic in the number of tuple elements (as we don't add each GetTupleElement to the liveset of each buffer defined by the tuple).
2. A reduction in the heap memory footprint.

PiperOrigin-RevId: 187079787
---
 .../compiler/xla/service/heap_simulator.cc    | 135 ++++++++++--------
 .../xla/service/heap_simulator_test.cc        |  50 +++++++
 2 files changed, 127 insertions(+), 58 deletions(-)

diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc
index a2d13c013c..3dd4c4a079 100644
--- a/tensorflow/compiler/xla/service/heap_simulator.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator.cc
@@ -27,38 +27,6 @@ namespace xla {
 using tensorflow::gtl::FlatMap;
 using tensorflow::gtl::FlatSet;
 
-namespace {
-
-// Returns the set of buffers that may be sources of all operands of the given
-// instruction.  The returned buffers are guaranteed to have no duplicates, and
-// to be sorted in a deterministic order.
-std::vector<const LogicalBuffer*> UniqueOperandSourceBuffers(
-    const HloInstruction* instruction,
-    const TuplePointsToAnalysis& points_to_analysis) {
-  std::vector<const LogicalBuffer*> buffers;
-  for (const HloInstruction* operand : instruction->operands()) {
-    points_to_analysis.GetPointsToSet(operand).ForEachElement(
-        [&](const ShapeIndex& /*index*/,
-            const PointsToSet::BufferList& points_to) {
-          buffers.insert(buffers.end(), points_to.begin(), points_to.end());
-        });
-  }
-
-  // Sort and then remove duplicates from buffers.
-  std::sort(buffers.begin(), buffers.end(),
-            [](const LogicalBuffer* a, const LogicalBuffer* b) {
-              return a->id() < b->id();
-            });
-  buffers.erase(std::unique(buffers.begin(), buffers.end(),
-                            [](const LogicalBuffer* a, const LogicalBuffer* b) {
-                              return a->id() == b->id();
-                            }),
-                buffers.end());
-  return buffers;
-}
-
-}  // namespace
-
 /*static*/
 StatusOr<HeapSimulator::Result> HeapSimulator::Run(
     std::unique_ptr<HeapAlgorithm> algorithm, const HloModule& module,
@@ -93,6 +61,7 @@ Status HeapSimulator::RunComputation(
     const HloComputation& computation,
     const std::vector<const HloInstruction*>& instruction_sequence,
     const TuplePointsToAnalysis& points_to_analysis) {
+  VLOG(3) << "Computation:\n" << computation.ToString();
   // The goal here is to minimize memory usage, assuming the given sequential
   // ordering of instructions.  The strategy is to walk through the instruction
   // sequence, calling Alloc and Free on the underlying heap algorithm.  The
@@ -101,7 +70,51 @@ Status HeapSimulator::RunComputation(
   // 'live_buffers' tracks the liveness of each buffer that we assign, by
   // associating it with a set of HloInstructions that need to be visited.  When
   // the set becomes empty, the buffer is no longer used, and can be freed.
+  // 'used_buffers' is the reverse map - it tracks which buffers were used by an
+  // instruction, so that we can remove the instructions from a buffer's live
+  // set after they are visited.
   FlatMap<const LogicalBuffer*, FlatSet<const HloInstruction*>> live_buffers;
+  FlatMap<const HloInstruction*, FlatSet<const LogicalBuffer*>> used_buffers;
+  auto add_user_to_buffer = [this, &live_buffers, &used_buffers](
+                                const HloInstruction* user,
+                                const LogicalBuffer* buffer) {
+    if (!IgnoreBuffer(buffer)) {
+      VLOG(4) << "  Adding user " << user->name() << " to buffer "
+              << buffer->ToString();
+      live_buffers[buffer].insert(user);
+      used_buffers[user].insert(buffer);
+    }
+  };
+
+  // Initialize live_buffers for each buffer that we're going to assign.  The
+  // set of instructions that need to be visited contains all users of all
+  // aliases, that is, all users of all instructions that have the buffer
+  // contained in their points-to set.
+  for (const HloInstruction* instruction : instruction_sequence) {
+    const PointsToSet& points_to =
+        points_to_analysis.GetPointsToSet(instruction);
+    const PointsToSet::BufferSet& buffer_set = points_to.CreateFlattenedSet();
+    for (const HloInstruction* user : instruction->users()) {
+      if (user->opcode() != HloOpcode::kGetTupleElement) {
+        for (const LogicalBuffer* buffer : buffer_set) {
+          add_user_to_buffer(user, buffer);
+        }
+      } else {
+        // A GetTupleElement doesn't need to keep all of its operand's buffers
+        // alive. It only needs the buffers that relate to the element its
+        // extracting, and the tuple it's extracting from, but not the buffers
+        // for the other elements.
+        for (const LogicalBuffer* buffer : points_to.element({})) {
+          add_user_to_buffer(user, buffer);
+        }
+        const PointsToSet& gte_points_to =
+            points_to_analysis.GetPointsToSet(user);
+        for (const LogicalBuffer* buffer : gte_points_to.CreateFlattenedSet()) {
+          add_user_to_buffer(user, buffer);
+        }
+      }
+    }
+  }
 
   const HloInstruction* root = computation.root_instruction();
   auto output_source_buffers =
@@ -114,34 +127,17 @@ Status HeapSimulator::RunComputation(
         buffers_defined_by_instruction =
             points_to_analysis.GetBuffersDefinedByInstruction(instruction);
 
-    // Initialize live_buffers for each buffer that we're going to assign.  The
-    // set of instructions that need to be visited contains all users of all
-    // aliases.  The alias itself is not necessary; if it has users, the users
-    // are necessarily scheduled after the alias.  And if it has no users, it is
-    // either a dead value or an output, both of which are handled below.
-    //
-    // We ignore control dependencies here. The reasoning is that the control
-    // dependencies have already been accounted for in the ordering of the given
-    // 'instruction_sequence', and should not otherwise artificially extend the
-    // lifetime of buffers that aren't already connected by a data dependency.
+    VLOG(3) << "Instruction: " << instruction->ToString();
+    for (const LogicalBuffer* buffer : buffers_defined_by_instruction) {
+      VLOG(4) << "  Defines: " << buffer->ToString()
+              << (IgnoreBuffer(buffer) ? " (Ignored)" : "");
+    }
+
     dead_buffers_to_free.clear();
     for (const LogicalBuffer* buffer : buffers_defined_by_instruction) {
       if (IgnoreBuffer(buffer)) {
         continue;
       }
-      FlatSet<const HloInstruction*>* live_set = nullptr;
-      for (const BufferAlias& alias :
-           points_to_analysis.GetBufferAliases(*buffer)) {
-        const std::vector<HloInstruction*>& users =
-            alias.instruction()->users();
-        if (!users.empty()) {
-          if (live_set == nullptr) {
-            live_set = &live_buffers[buffer];
-          }
-          live_set->insert(users.begin(), users.end());
-        }
-      }
-
       // Add a nullptr sentry to ensure entry parameters and output source
       // buffers are not freed until the very end.
       const bool entry_parameter =
@@ -165,11 +161,12 @@ Status HeapSimulator::RunComputation(
     // have no instructions left to visit are moved from live_buffers to
     // operand_buffers_to_free.
     operand_buffers_to_free.clear();
-    for (const LogicalBuffer* operand_buffer :
-         UniqueOperandSourceBuffers(instruction, points_to_analysis)) {
+    for (const LogicalBuffer* operand_buffer : used_buffers[instruction]) {
       if (IgnoreBuffer(operand_buffer)) {
         continue;
       }
+      VLOG(4) << "  Removing user " << instruction->name() << " from buffer "
+              << operand_buffer->ToString();
       auto it = live_buffers.find(operand_buffer);
       FlatSet<const HloInstruction*>* live_set = &it->second;
       live_set->erase(instruction);
@@ -178,6 +175,11 @@ Status HeapSimulator::RunComputation(
         operand_buffers_to_free.push_back(operand_buffer);
       }
     }
+    // Sort to get a deterministic iteration order.
+    std::sort(operand_buffers_to_free.begin(), operand_buffers_to_free.end(),
+              [](const LogicalBuffer* x, const LogicalBuffer* y) {
+                return x->id() < y->id();
+              });
 
     // Allocate buffers defined by this instruction.  This is the latest point
     // that we can allocate; right before the buffer is first used.  This must
@@ -203,6 +205,8 @@ Status HeapSimulator::RunComputation(
               CanShareOperandBufferWithUser(
                   operand_buffer->instruction(), operand_buffer->index(),
                   buffer->instruction(), buffer->index(), points_to_analysis)) {
+            VLOG(3) << "  Sharing: " << buffer->ToString() << " with "
+                    << operand_buffer->ToString();
             ShareBuffer(buffer, operand_buffer, instruction);
             shared = true;
             break;
@@ -211,6 +215,7 @@ Status HeapSimulator::RunComputation(
       }
 
       if (!shared) {
+        VLOG(3) << "  Allocating: " << buffer->ToString();
         Alloc(buffer, instruction);
       }
     }
@@ -244,20 +249,34 @@ Status HeapSimulator::RunComputation(
     // Free buffers that are no longer live.  This is the earliest point that we
     // can de-allocate; right after the last use of the buffer.
     for (const LogicalBuffer* buffer : dead_buffers_to_free) {
+      VLOG(3) << "  Freeing dead: " << buffer->ToString();
       Free(buffer, instruction);
     }
     for (const LogicalBuffer* buffer : operand_buffers_to_free) {
+      VLOG(3) << "  Freeing operand: " << buffer->ToString();
       Free(buffer, instruction);
     }
   }
 
   // Any remaining live buffers must be entry parameters or output source
-  // buffers, which had a nullptr sentry added.  Free them now.
+  // buffers, which had a nullptr sentry added.  Free them now, in a
+  // deterministic order.
+  std::vector<const LogicalBuffer*> to_free;
+  to_free.reserve(live_buffers.size());
   for (const auto& buffer_pending : live_buffers) {
     const LogicalBuffer* buffer = buffer_pending.first;
     const FlatSet<const HloInstruction*>& pending = buffer_pending.second;
     CHECK_EQ(pending.size(), 1) << *buffer;
     CHECK(*pending.begin() == nullptr) << *buffer;
+    to_free.push_back(buffer);
+  }
+
+  std::sort(to_free.begin(), to_free.end(),
+            [](const LogicalBuffer* x, const LogicalBuffer* y) {
+              return x->id() < y->id();
+            });
+  for (const LogicalBuffer* buffer : to_free) {
+    VLOG(3) << "Freeing pending: " << buffer->ToString();
     Free(buffer, root);
   }
 
diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc
index 387b649a73..688a271712 100644
--- a/tensorflow/compiler/xla/service/heap_simulator_test.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc
@@ -410,6 +410,56 @@ TEST_F(HeapSimulatorTest, MultiplyDotDotTuple) {
   });
 }
 
+TEST_F(HeapSimulatorTest, IndependentTupleElements) {
+  auto builder = HloComputation::Builder(TestName());
+  auto paramA = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, f32scalar_, "paramA"));
+  auto paramB = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, f32scalar_, "paramB"));
+  auto mul = builder.AddInstruction(HloInstruction::CreateBinary(
+      f32scalar_, HloOpcode::kMultiply, paramA, paramB));
+  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
+      f32scalar_, HloOpcode::kAdd, paramA, paramB));
+  auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({mul, add}));
+  auto element0 = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(f32scalar_, tuple, 0));
+  auto broadcast = builder.AddInstruction(
+      HloInstruction::CreateBroadcast(f32vec4_, element0, {0}));
+  auto sub = builder.AddInstruction(HloInstruction::CreateBinary(
+      f32scalar_, HloOpcode::kSubtract, paramA, paramB));
+  auto element1 = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(f32scalar_, tuple, 1));
+  auto output = builder.AddInstruction(
+      HloInstruction::CreateTuple({broadcast, sub, element1}));
+
+  HeapSimulatorTracker tracker(TestName(), builder.Build(),
+                               {paramA, paramB, mul, add, tuple, element0,
+                                broadcast, sub, element1, output});
+  tracker.ExpectCallSequence({
+      {kAlloc, tracker.BufferAt(paramA, {})},
+      {kAlloc, tracker.BufferAt(paramB, {})},
+      {kAlloc, tracker.BufferAt(mul, {})},
+      {kAlloc, tracker.BufferAt(add, {})},
+      {kAlloc, tracker.BufferAt(tuple, {})},
+      {kAlloc, tracker.BufferAt(broadcast, {})},
+      // The mul can be freed right after the broadcast happens, even though
+      // The other GetTupleElement is still alive.
+      {kFree, tracker.BufferAt(mul, {})},
+      {kAlloc, tracker.BufferAt(sub, {})},
+      // The temporary tuple is now dead.
+      {kFree, tracker.BufferAt(tuple, {})},
+      {kAlloc, tracker.BufferAt(output, {})},
+      // All params and outputs are freed at the end.
+      {kFree, tracker.BufferAt(paramA, {})},
+      {kFree, tracker.BufferAt(paramB, {})},
+      {kFree, tracker.BufferAt(add, {})},
+      {kFree, tracker.BufferAt(broadcast, {})},
+      {kFree, tracker.BufferAt(sub, {})},
+      {kFree, tracker.BufferAt(output, {})},
+      {kFinish, nullptr},
+  });
+}
+
 TEST_F(HeapSimulatorTest, WholeModule) {
   HeapSimulatorTracker tracker(TestName());
 
-- 
GitLab


From 5b7f78c767b30076850f9b9f88b8730767a0437c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 14:25:30 -0800
Subject: [PATCH 313/575] 1st version of sequential feature columns.

PiperOrigin-RevId: 187080635
---
 tensorflow/contrib/feature_column/BUILD       |  31 +-
 .../sequential_feature_column.py              | 308 +++++++++++-
 .../sequential_feature_column_test.py         | 471 ++++++++++++++++++
 3 files changed, 808 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py

diff --git a/tensorflow/contrib/feature_column/BUILD b/tensorflow/contrib/feature_column/BUILD
index 6fc053759c..a53e36c2d5 100644
--- a/tensorflow/contrib/feature_column/BUILD
+++ b/tensorflow/contrib/feature_column/BUILD
@@ -33,5 +33,34 @@ py_library(
     name = "sequential_feature_column",
     srcs = ["python/feature_column/sequential_feature_column.py"],
     srcs_version = "PY2AND3",
-    deps = [],
+    deps = [
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:check_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python/feature_column",
+    ],
+)
+
+py_test(
+    name = "sequential_feature_column_test",
+    srcs = ["python/feature_column/sequential_feature_column_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":sequential_feature_column",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:training",
+        "//tensorflow/python/feature_column",
+        "//third_party/py/numpy",
+    ],
 )
diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py
index 690a44ff43..4ed7268e7a 100644
--- a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py
@@ -12,8 +12,314 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Experimental methods for tf.feature_column sequential input."""
+"""Experimental methods for tf.feature_column sequence input."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+
+
+import abc
+import collections
+
+
+from tensorflow.python.feature_column import feature_column as fc
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import parsing_ops
+from tensorflow.python.ops import sparse_ops
+from tensorflow.python.ops import variable_scope
+
+# TODO(b/73160931): Fix pydoc.
+# pylint: disable=g-doc-args,missing-docstring,protected-access
+# TODO(b/73827486): Support SequenceExample.
+
+
+def sequence_input_layer(
+    features,
+    feature_columns,
+    weight_collections=None,
+    trainable=True,
+    scope=None):
+  """"Builds input layer for sequence input.
+
+  All `feature_columns` must be sequence dense columns with the same
+  `sequence_length`. The output of this method can be fed into sequence
+  networks, such as RNN.
+
+  The output of this method is a 3D `Tensor` of shape `[batch_size, T, D]`.
+  `T` is the maximum sequence length for this batch, which could differ from
+  batch to batch.
+
+  If multiple `feature_columns` are given with `Di` `num_elements` each, their
+  outputs are concatenated. So, the final `Tensor` has shape
+  `[batch_size, T, D0 + D1 + ... + Dn]`.
+
+  Example:
+
+  ```python
+  rating = sequence_numeric_column('rating')
+  watches = sequence_categorical_column_with_identity(
+      'watches', num_buckets=1000)
+  watches_embedding = embedding_column(watches, dimension=10)
+  columns = [rating, watches]
+
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  input_layer, sequence_length = sequence_input_layer(features, columns)
+
+  rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
+  outputs, state = tf.nn.dynamic_rnn(
+      rnn_cell, inputs=input_layer, sequence_length=sequence_length)
+  ```
+
+  Returns:
+    An `(input_layer, sequence_length)` tuple where:
+    - input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
+        `T` is the maximum sequence length for this batch, which could differ
+        from batch to batch. `D` is the sum of `num_elements` for all
+        `feature_columns`.
+    - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence
+        length for each example.
+  Raises:
+    ValueError: If any of the `feature_columns` is the wrong type.
+  """
+  feature_columns = fc._clean_feature_columns(feature_columns)
+  for c in feature_columns:
+    if not isinstance(c, _SequenceDenseColumn):
+      raise ValueError(
+          'All feature_columns must be of type _SequenceDenseColumn. '
+          'Given (type {}): {}'.format(type(c), c))
+
+  with variable_scope.variable_scope(
+      scope, default_name='sequence_input_layer', values=features.values()):
+    builder = fc._LazyBuilder(features)
+    output_tensors = []
+    sequence_lengths = []
+    ordered_columns = []
+    for column in sorted(feature_columns, key=lambda x: x.name):
+      ordered_columns.append(column)
+      with variable_scope.variable_scope(
+          None, default_name=column._var_scope_name):
+        dense_tensor, sequence_length = column._get_sequence_dense_tensor(
+            builder,
+            weight_collections=weight_collections,
+            trainable=trainable)
+        # Flattens the final dimension to produce a 3D Tensor.
+        num_elements = column._variable_shape.num_elements()
+        shape = array_ops.shape(dense_tensor)
+        output_tensors.append(
+            array_ops.reshape(
+                dense_tensor,
+                shape=array_ops.concat([shape[:2], [num_elements]], axis=0)))
+        sequence_lengths.append(sequence_length)
+    fc._verify_static_batch_size_equality(output_tensors, ordered_columns)
+    # TODO(b/73160931): Verify sequence_length equality.
+    return array_ops.concat(output_tensors, -1), sequence_lengths[0]
+
+
+# TODO(b/73160931): Add remaining categorical columns.
+def sequence_categorical_column_with_identity(
+    key, num_buckets, default_value=None):
+  return _SequenceCategoricalColumn(
+      fc.categorical_column_with_identity(
+          key=key,
+          num_buckets=num_buckets,
+          default_value=default_value))
+
+
+# TODO(b/73160931): Merge with embedding_column
+def _sequence_embedding_column(
+    categorical_column, dimension, initializer=None, ckpt_to_load_from=None,
+    tensor_name_in_ckpt=None, max_norm=None, trainable=True):
+  if not isinstance(categorical_column, _SequenceCategoricalColumn):
+    raise ValueError(
+        'categorical_column must be of type _SequenceCategoricalColumn. '
+        'Given (type {}): {}'.format(
+            type(categorical_column), categorical_column))
+  return _SequenceEmbeddingColumn(
+      fc.embedding_column(
+          categorical_column,
+          dimension=dimension,
+          initializer=initializer,
+          ckpt_to_load_from=ckpt_to_load_from,
+          tensor_name_in_ckpt=tensor_name_in_ckpt,
+          max_norm=max_norm,
+          trainable=trainable))
+
+
+def sequence_numeric_column(
+    key,
+    shape=(1,),
+    default_value=0.,
+    dtype=dtypes.float32):
+  # TODO(b/73160931): Add validations.
+  return _SequenceNumericColumn(
+      key,
+      shape=shape,
+      default_value=default_value,
+      dtype=dtype)
+
+
+class _SequenceDenseColumn(fc._FeatureColumn):
+  """Represents dense sequence data."""
+
+  __metaclass__ = abc.ABCMeta
+
+  TensorSequenceLengthPair = collections.namedtuple(  # pylint: disable=invalid-name
+      'TensorSequenceLengthPair', ['dense_tensor', 'sequence_length'])
+
+  @abc.abstractproperty
+  def _variable_shape(self):
+    """`TensorShape` without batch and sequence dimensions."""
+    pass
+
+  @abc.abstractmethod
+  def _get_sequence_dense_tensor(
+      self, inputs, weight_collections=None, trainable=None):
+    """Returns a `TensorSequenceLengthPair`."""
+    pass
+
+
+def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1):
+  with ops.name_scope(None, 'sequence_length') as name_scope:
+    row_ids = sp_tensor.indices[:, 0]
+    column_ids = sp_tensor.indices[:, 1]
+    column_ids += array_ops.ones_like(column_ids)
+    seq_length = (
+        math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements)
+    # If the last n rows do not have ids, seq_length will have shape
+    # [batch_size - n]. Pad the remaining values with zeros.
+    n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1]
+    padding = array_ops.zeros(n_pad, dtype=seq_length.dtype)
+    return array_ops.concat([seq_length, padding], axis=0, name=name_scope)
+
+
+class _SequenceCategoricalColumn(
+    fc._CategoricalColumn,
+    collections.namedtuple(
+        '_SequenceCategoricalColumn', ['categorical_column'])):
+
+  @property
+  def name(self):
+    return self.categorical_column.name
+
+  @property
+  def _parse_example_spec(self):
+    return self.categorical_column._parse_example_spec
+
+  def _transform_feature(self, inputs):
+    return self.categorical_column._transform_feature(inputs)
+
+  @property
+  def _num_buckets(self):
+    return self.categorical_column._num_buckets
+
+  def _get_sparse_tensors(self, inputs, weight_collections=None,
+                          trainable=None):
+    sparse_tensors = self.categorical_column._get_sparse_tensors(inputs)
+    id_tensor = sparse_tensors.id_tensor
+    weight_tensor = sparse_tensors.weight_tensor
+    # Expands final dimension, so that embeddings are not combined during
+    # embedding lookup.
+    check_id_rank = check_ops.assert_equal(
+        array_ops.rank(id_tensor), 2,
+        data=[
+            'Column {} expected ID tensor of rank 2. '.format(self.name),
+            'id_tensor shape: ', array_ops.shape(id_tensor)])
+    with ops.control_dependencies([check_id_rank]):
+      id_tensor = sparse_ops.sparse_reshape(
+          id_tensor,
+          shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0))
+    if weight_tensor is not None:
+      check_weight_rank = check_ops.assert_equal(
+          array_ops.rank(weight_tensor), 2,
+          data=[
+              'Column {} expected weight tensor of rank 2.'.format(self.name),
+              'weight_tensor shape:', array_ops.shape(weight_tensor)])
+      with ops.control_dependencies([check_weight_rank]):
+        weight_tensor = sparse_ops.sparse_reshape(
+            weight_tensor,
+            shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0))
+    return fc._CategoricalColumn.IdWeightPair(id_tensor, weight_tensor)
+
+  def _sequence_length(self, inputs):
+    sparse_tensors = self.categorical_column._get_sparse_tensors(inputs)
+    return _sequence_length_from_sparse_tensor(sparse_tensors.id_tensor)
+
+
+class _SequenceEmbeddingColumn(
+    _SequenceDenseColumn,
+    collections.namedtuple('_SequenceEmbeddingColumn', ['embedding_column'])):
+
+  @property
+  def name(self):
+    return self.embedding_column.name
+
+  @property
+  def _parse_example_spec(self):
+    return self.embedding_column._parse_example_spec
+
+  def _transform_feature(self, inputs):
+    return self.embedding_column._transform_feature(inputs)
+
+  @property
+  def _variable_shape(self):
+    return self.embedding_column._variable_shape
+
+  def _get_sequence_dense_tensor(
+      self, inputs, weight_collections=None, trainable=None):
+    dense_tensor = self.embedding_column._get_dense_tensor(
+        inputs=inputs,
+        weight_collections=weight_collections,
+        trainable=trainable)
+    sequence_length = self.embedding_column.categorical_column._sequence_length(
+        inputs)
+    return _SequenceDenseColumn.TensorSequenceLengthPair(
+        dense_tensor=dense_tensor, sequence_length=sequence_length)
+
+
+class _SequenceNumericColumn(
+    _SequenceDenseColumn,
+    collections.namedtuple(
+        '_SequenceNumericColumn',
+        ['key', 'shape', 'default_value', 'dtype'])):
+
+  @property
+  def name(self):
+    return self.key
+
+  @property
+  def _parse_example_spec(self):
+    return {self.key: parsing_ops.VarLenFeature(self.dtype)}
+
+  def _transform_feature(self, inputs):
+    return inputs.get(self.key)
+
+  @property
+  def _variable_shape(self):
+    return tensor_shape.TensorShape(self.shape)
+
+  def _get_sequence_dense_tensor(
+      self, inputs, weight_collections=None, trainable=None):
+    # Do nothing with weight_collections and trainable since no variables are
+    # created in this function.
+    del weight_collections
+    del trainable
+    sp_tensor = inputs.get(self)
+    dense_tensor = sparse_ops.sparse_tensor_to_dense(
+        sp_tensor, default_value=self.default_value)
+    # Reshape into [batch_size, T, variable_shape].
+    dense_shape = array_ops.concat(
+        [array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape],
+        axis=0)
+    dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
+    sequence_length = _sequence_length_from_sparse_tensor(
+        sp_tensor, num_elements=self._variable_shape.num_elements())
+    return _SequenceDenseColumn.TensorSequenceLengthPair(
+        dense_tensor=dense_tensor, sequence_length=sequence_length)
+
+# pylint: enable=g-doc-args,missing-docstring,protected-access
diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py
new file mode 100644
index 0000000000..59674869a2
--- /dev/null
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py
@@ -0,0 +1,471 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for sequential_feature_column."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.feature_column.python.feature_column import sequential_feature_column as sfc
+from tensorflow.python.feature_column.feature_column import _LazyBuilder
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.platform import test
+from tensorflow.python.training import monitored_session
+
+
+class SequenceInputLayerTest(test.TestCase):
+
+  def test_embedding_column(self):
+    vocabulary_size = 3
+    sparse_input_a = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(2, 0, 1),
+        dense_shape=(2, 2))
+    sparse_input_b = sparse_tensor.SparseTensorValue(
+        # example 0, ids [1]
+        # example 1, ids [2, 0]
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, 2, 0),
+        dense_shape=(2, 2))
+
+    embedding_dimension_a = 2
+    embedding_values_a = (
+        (1., 2.),  # id 0
+        (3., 4.),  # id 1
+        (5., 6.)  # id 2
+    )
+    embedding_dimension_b = 3
+    embedding_values_b = (
+        (11., 12., 13.),  # id 0
+        (14., 15., 16.),  # id 1
+        (17., 18., 19.)  # id 2
+    )
+    def _get_initializer(embedding_dimension, embedding_values):
+      def _initializer(shape, dtype, partition_info):
+        self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+        self.assertEqual(dtypes.float32, dtype)
+        self.assertIsNone(partition_info)
+        return embedding_values
+      return _initializer
+
+    expected_input_layer = [
+        # example 0, ids_a [2], ids_b [1]
+        [[5., 6., 14., 15., 16.], [0., 0., 0., 0., 0.]],
+        # example 1, ids_a [0, 1], ids_b [2, 0]
+        [[1., 2., 17., 18., 19.], [3., 4., 11., 12., 13.]],
+    ]
+    expected_sequence_length = [1, 2]
+
+    categorical_column_a = sfc.sequence_categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column_a = sfc._sequence_embedding_column(
+        categorical_column_a, dimension=embedding_dimension_a,
+        initializer=_get_initializer(embedding_dimension_a, embedding_values_a))
+    categorical_column_b = sfc.sequence_categorical_column_with_identity(
+        key='bbb', num_buckets=vocabulary_size)
+    embedding_column_b = sfc._sequence_embedding_column(
+        categorical_column_b, dimension=embedding_dimension_b,
+        initializer=_get_initializer(embedding_dimension_b, embedding_values_b))
+
+    input_layer, sequence_length = sfc.sequence_input_layer(
+        features={
+            'aaa': sparse_input_a,
+            'bbb': sparse_input_b,
+        },
+        # Test that columns are reordered alphabetically.
+        feature_columns=[embedding_column_b, embedding_column_a])
+
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('sequence_input_layer/aaa_embedding/embedding_weights:0',
+         'sequence_input_layer/bbb_embedding/embedding_weights:0'),
+        tuple([v.name for v in global_vars]))
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(embedding_values_a, global_vars[0].eval(session=sess))
+      self.assertAllEqual(embedding_values_b, global_vars[1].eval(session=sess))
+      self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess))
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_numeric_column(self):
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0.], [1]]
+        # example 1, [[10.]]
+        indices=((0, 0), (0, 1), (1, 0)),
+        values=(0., 1., 10.),
+        dense_shape=(2, 2))
+    expected_input_layer = [
+        [[0.], [1.]],
+        [[10.], [0.]],
+    ]
+    expected_sequence_length = [2, 1]
+    numeric_column = sfc.sequence_numeric_column('aaa')
+
+    input_layer, sequence_length = sfc.sequence_input_layer(
+        features={'aaa': sparse_input},
+        feature_columns=[numeric_column])
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess))
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_numeric_column_multi_dim(self):
+    """Tests sequence_input_layer for multi-dimensional numeric_column."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[[0., 1.],  [2., 3.]], [[4., 5.],  [6., 7.]]]
+        # example 1, [[[10., 11.],  [12., 13.]]]
+        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7),
+                 (1, 0), (1, 1), (1, 2), (1, 3)),
+        values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.),
+        dense_shape=(2, 8))
+    # The output of numeric_column._get_dense_tensor should be flattened.
+    expected_input_layer = [
+        [[0., 1., 2., 3.], [4., 5., 6., 7.]],
+        [[10., 11., 12., 13.], [0., 0., 0., 0.]],
+    ]
+    expected_sequence_length = [2, 1]
+    numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2))
+
+    input_layer, sequence_length = sfc.sequence_input_layer(
+        features={'aaa': sparse_input},
+        feature_columns=[numeric_column])
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess))
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+
+def _assert_sparse_tensor_value(test_case, expected, actual):
+  test_case.assertEqual(np.int64, np.array(actual.indices).dtype)
+  test_case.assertAllEqual(expected.indices, actual.indices)
+
+  test_case.assertEqual(
+      np.array(expected.values).dtype, np.array(actual.values).dtype)
+  test_case.assertAllEqual(expected.values, actual.values)
+
+  test_case.assertEqual(np.int64, np.array(actual.dense_shape).dtype)
+  test_case.assertAllEqual(expected.dense_shape, actual.dense_shape)
+
+
+class SequenceCategoricalColumnWithIdentityTest(test.TestCase):
+
+  def test_get_sparse_tensors(self):
+    column = sfc.sequence_categorical_column_with_identity(
+        'aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, 2, 0),
+        dense_shape=(2, 2))
+    expected_sparse_ids = sparse_tensor.SparseTensorValue(
+        indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)),
+        values=np.array((1, 2, 0), dtype=np.int64),
+        dense_shape=(2, 2, 1))
+
+    id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with monitored_session.MonitoredSession() as sess:
+      _assert_sparse_tensor_value(
+          self,
+          expected_sparse_ids,
+          id_weight_pair.id_tensor.eval(session=sess))
+
+  def test_get_sparse_tensors_inputs3d(self):
+    """Tests _get_sparse_tensors when the input is already 3D Tensor."""
+    column = sfc.sequence_categorical_column_with_identity(
+        'aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)),
+        values=(1, 2, 0),
+        dense_shape=(2, 2, 1))
+
+    with self.assertRaisesRegexp(
+        errors.InvalidArgumentError,
+        r'Column aaa expected ID tensor of rank 2\.\s*'
+        r'id_tensor shape:\s*\[2 2 1\]'):
+      id_weight_pair = column._get_sparse_tensors(
+          _LazyBuilder({'aaa': inputs}))
+      with monitored_session.MonitoredSession() as sess:
+        id_weight_pair.id_tensor.eval(session=sess)
+
+  def test_sequence_length(self):
+    column = sfc.sequence_categorical_column_with_identity(
+        'aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, 2, 0),
+        dense_shape=(2, 2))
+    expected_sequence_length = [1, 2]
+
+    sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_sequence_length_with_zeros(self):
+    column = sfc.sequence_categorical_column_with_identity(
+        'aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((1, 0), (3, 0), (3, 1)),
+        values=(1, 2, 0),
+        dense_shape=(5, 2))
+    expected_sequence_length = [0, 1, 0, 2, 0]
+
+    sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+
+class SequenceEmbeddingColumnTest(test.TestCase):
+
+  def test_get_sequence_dense_tensor(self):
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 1), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(4, 2))
+
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    expected_lookups = [
+        # example 0, ids [2]
+        [[7., 11.], [0., 0.]],
+        # example 1, ids [0, 1]
+        [[1., 2.], [3., 5.]],
+        # example 2, ids []
+        [[0., 0.], [0., 0.]],
+        # example 3, ids [1]
+        [[3., 5.], [0., 0.]],
+    ]
+
+    categorical_column = sfc.sequence_categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = sfc._sequence_embedding_column(
+        categorical_column, dimension=embedding_dimension,
+        initializer=_initializer)
+
+    embedding_lookup, _ = embedding_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('embedding_weights:0',), tuple([v.name for v in global_vars]))
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess))
+      self.assertAllEqual(expected_lookups, embedding_lookup.eval(session=sess))
+
+  def test_sequence_length(self):
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(2, 0, 1),
+        dense_shape=(2, 2))
+    expected_sequence_length = [1, 2]
+
+    categorical_column = sfc.sequence_categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = sfc._sequence_embedding_column(
+        categorical_column, dimension=2)
+
+    _, sequence_length = embedding_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_sequence_length_with_empty_rows(self):
+    """Tests _sequence_length when some examples do not have ids."""
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids []
+        # example 1, ids [2]
+        # example 2, ids [0, 1]
+        # example 3, ids []
+        # example 4, ids [1]
+        # example 5, ids []
+        indices=((1, 0), (2, 0), (2, 1), (4, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(6, 2))
+    expected_sequence_length = [0, 1, 2, 0, 1, 0]
+
+    categorical_column = sfc.sequence_categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = sfc._sequence_embedding_column(
+        categorical_column, dimension=2)
+
+    _, sequence_length = embedding_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+
+class SequenceNumericColumnTest(test.TestCase):
+
+  def test_get_sequence_dense_tensor(self):
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0.], [1]]
+        # example 1, [[10.]]
+        indices=((0, 0), (0, 1), (1, 0)),
+        values=(0., 1., 10.),
+        dense_shape=(2, 2))
+    expected_dense_tensor = [
+        [[0.], [1.]],
+        [[10.], [0.]],
+    ]
+    numeric_column = sfc.sequence_numeric_column('aaa')
+
+    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_dense_tensor, dense_tensor.eval(session=sess))
+
+  def test_get_sequence_dense_tensor_with_shape(self):
+    """Tests get_sequence_dense_tensor with shape !=(1,)."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0., 1., 2.], [3., 4., 5.]]
+        # example 1, [[10., 11., 12.]]
+        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5),
+                 (1, 0), (1, 1), (1, 2)),
+        values=(0., 1., 2., 3., 4., 5., 10., 11., 12.),
+        dense_shape=(2, 6))
+    expected_dense_tensor = [
+        [[0., 1., 2.], [3., 4., 5.]],
+        [[10., 11., 12.], [0., 0., 0.]],
+    ]
+    numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,))
+
+    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_dense_tensor, dense_tensor.eval(session=sess))
+
+  def test_get_dense_tensor_multi_dim(self):
+    """Tests get_sequence_dense_tensor for multi-dim numeric_column."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[[0., 1.],  [2., 3.]], [[4., 5.],  [6., 7.]]]
+        # example 1, [[[10., 11.],  [12., 13.]]]
+        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7),
+                 (1, 0), (1, 1), (1, 2), (1, 3)),
+        values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.),
+        dense_shape=(2, 8))
+    expected_dense_tensor = [
+        [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]],
+        [[[10., 11.], [12., 13.]], [[0., 0.], [0., 0.]]],
+    ]
+    numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2))
+
+    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_dense_tensor, dense_tensor.eval(session=sess))
+
+  def test_sequence_length(self):
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0., 1., 2.], [3., 4., 5.]]
+        # example 1, [[10., 11., 12.]]
+        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5),
+                 (1, 0), (1, 1), (1, 2)),
+        values=(0., 1., 2., 3., 4., 5., 10., 11., 12.),
+        dense_shape=(2, 6))
+    expected_sequence_length = [2, 1]
+    numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,))
+
+    _, sequence_length = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_sequence_length_with_shape(self):
+    """Tests _sequence_length with shape !=(1,)."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0.], [1]]
+        # example 1, [[10.]]
+        indices=((0, 0), (0, 1), (1, 0)),
+        values=(0., 1., 10.),
+        dense_shape=(2, 2))
+    expected_sequence_length = [2, 1]
+    numeric_column = sfc.sequence_numeric_column('aaa')
+
+    _, sequence_length = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_sequence_length_with_empty_rows(self):
+    """Tests _sequence_length when some examples do not have ids."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values []
+        # example 1, values [[0.], [1.]]
+        # example 2, [[2.]]
+        # example 3, values []
+        # example 4, [[3.]]
+        # example 5, values []
+        indices=((1, 0), (1, 1), (2, 0), (4, 0)),
+        values=(0., 1., 2., 3.),
+        dense_shape=(6, 2))
+    expected_sequence_length = [0, 2, 1, 0, 1, 0]
+    numeric_column = sfc.sequence_numeric_column('aaa')
+
+    _, sequence_length = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From ecace69b5e28f508f76264e66778935e84c37715 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 14:25:37 -0800
Subject: [PATCH 314/575] Add a function that allows to dynamically verify
 whether a function is white listed for graph mode.

PiperOrigin-RevId: 187080654
---
 tensorflow/contrib/py2tf/impl/conversion.py    | 18 ++++++++++++++++++
 .../contrib/py2tf/impl/conversion_test.py      | 11 +++++++++++
 2 files changed, 29 insertions(+)

diff --git a/tensorflow/contrib/py2tf/impl/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py
index 044de33568..d95469ea53 100644
--- a/tensorflow/contrib/py2tf/impl/conversion.py
+++ b/tensorflow/contrib/py2tf/impl/conversion.py
@@ -97,6 +97,24 @@ class ConversionMap(object):
     self.dependency_cache[original_entity] = converted_ast
 
 
+def is_whitelisted_for_graph(o):
+  """Check whether an entity is whitelisted for use in graph mode.
+
+  Examples of whitelisted entities include all members of the tensorflow
+  package.
+
+  Args:
+    o: A Python entity.
+  Returns:
+    Boolean
+  """
+  m = tf_inspect.getmodule(o)
+  for prefix, in config.DEFAULT_UNCOMPILED_MODULES:
+    if m.__name__.startswith(prefix):
+      return True
+  return False
+
+
 def entity_to_graph(o, conversion_map, arg_values, arg_types):
   """Compile a Python entity into equivalent TensorFlow.
 
diff --git a/tensorflow/contrib/py2tf/impl/conversion_test.py b/tensorflow/contrib/py2tf/impl/conversion_test.py
index 7816f95857..9ff256aace 100644
--- a/tensorflow/contrib/py2tf/impl/conversion_test.py
+++ b/tensorflow/contrib/py2tf/impl/conversion_test.py
@@ -20,12 +20,23 @@ from __future__ import print_function
 
 import gast
 
+from tensorflow.contrib.py2tf import utils
 from tensorflow.contrib.py2tf.impl import conversion
+from tensorflow.python.framework import constant_op
 from tensorflow.python.platform import test
 
 
 class ConversionTest(test.TestCase):
 
+  def test_is_whitelisted_for_graph(self):
+
+    def test_fn():
+      return constant_op.constant(1)
+
+    self.assertFalse(conversion.is_whitelisted_for_graph(test_fn))
+    self.assertTrue(conversion.is_whitelisted_for_graph(utils))
+    self.assertTrue(conversion.is_whitelisted_for_graph(constant_op.constant))
+
   def test_entity_to_graph_unsupported_types(self):
     with self.assertRaises(ValueError):
       conversion_map = conversion.ConversionMap(True, (), (), None)
-- 
GitLab


From 26a765f95acc7cbc762b8e1fef94921cab8f181d Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Mon, 26 Feb 2018 14:31:29 -0800
Subject: [PATCH 315/575] [TF:XLA] Bump open source llvm revision to r326083

PiperOrigin-RevId: 187081592
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index d6ac7be8b5..5b09c5e67d 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -475,11 +475,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/8f7bcdf3c65b9a47e35653d525135beb18f3ac25.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/8f7bcdf3c65b9a47e35653d525135beb18f3ac25.tar.gz",
       ],
-      sha256 = "f5721d9cc18a9109c9e9f847f48e69b710b961cee83e6691227e310cb3b5da58",
-      strip_prefix = "llvm-fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14",
+      sha256 = "63d4da54dc7bc9a79e2ad266d230f4f759520cccb344a2dd49c2c6383ab75285",
+      strip_prefix = "llvm-8f7bcdf3c65b9a47e35653d525135beb18f3ac25",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
   )
 
-- 
GitLab


From a80896d3b3a2358f324dc4cd429409ea9acc8a09 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Mon, 26 Feb 2018 14:32:08 -0800
Subject: [PATCH 316/575] Track DebugOptions in AotCompilationOptions

In particular, I need this for supporting HLO profiling in the AOT backend.

PiperOrigin-RevId: 187081674
---
 tensorflow/compiler/xla/service/compile_only_service.cc | 3 +--
 tensorflow/compiler/xla/service/compiler.cc             | 3 +++
 tensorflow/compiler/xla/service/compiler.h              | 6 +++++-
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc
index dab73596e1..6664496ab6 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.cc
+++ b/tensorflow/compiler/xla/service/compile_only_service.cc
@@ -72,8 +72,7 @@ CompileOnlyService::CompileAheadOfTime(
     VersionedComputationHandle versioned_handle =
         user_computation->GetVersionedHandle();
 
-    // TODO(b/63773457): Track DebugOptions in AotCompilationOptions.
-    DebugOptions debug_options = legacy_flags::GetDebugOptionsFromFlags();
+    const DebugOptions& debug_options = options.debug_options();
 
     // Dump computation proto state if flag is set.
     const string& directory_path = debug_options.xla_dump_computations_to();
diff --git a/tensorflow/compiler/xla/service/compiler.cc b/tensorflow/compiler/xla/service/compiler.cc
index e2e9d2a0c0..0392d4af48 100644
--- a/tensorflow/compiler/xla/service/compiler.cc
+++ b/tensorflow/compiler/xla/service/compiler.cc
@@ -86,4 +86,7 @@ Compiler::GetPlatformCompilers() {
   return compilers->at(platform->id()).get();
 }
 
+AotCompilationOptions::AotCompilationOptions()
+    : debug_options_(legacy_flags::GetDebugOptionsFromFlags()) {}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h
index 74fd24edf8..33e19efc72 100644
--- a/tensorflow/compiler/xla/service/compiler.h
+++ b/tensorflow/compiler/xla/service/compiler.h
@@ -79,11 +79,15 @@ class AotCompilationOptions {
     device_allocator_ = device_allocator;
   }
 
+  const DebugOptions& debug_options() const { return debug_options_; }
+  DebugOptions* mutable_debug_options() { return &debug_options_; }
+
  protected:
-  AotCompilationOptions() = default;
+  AotCompilationOptions();
 
  private:
   DeviceMemoryAllocator* device_allocator_ = nullptr;
+  DebugOptions debug_options_;
 };
 
 // Abstract compiler interface that is subclassed for compilation on a
-- 
GitLab


From 153e10a037c5e348834108ff46d9dccdf0cfb9a9 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Mon, 26 Feb 2018 14:38:31 -0800
Subject: [PATCH 317/575] Enable de/serialization of nested control flow.

This is a follow-up to the previous commit
(https://github.com/tensorflow/tensorflow/commit/23851760b7b099214bdd4f1b88156d7ac2bdd2a2).
It adds the new proto schemas, enables the behavior for reading and
writing the new protos, and adds a test for de/serializing nested
while loops.

There's still a bug preventing deserializing conds, which will be addressed
in another change.

PiperOrigin-RevId: 187082713
---
 tensorflow/core/protobuf/control_flow.proto | 17 ++++++-
 tensorflow/python/ops/control_flow_ops.py   | 54 ++++++--------------
 tensorflow/python/training/saver_test.py    | 56 +++++++++++++++++++++
 3 files changed, 88 insertions(+), 39 deletions(-)

diff --git a/tensorflow/core/protobuf/control_flow.proto b/tensorflow/core/protobuf/control_flow.proto
index 2c9476a08a..3c05b4f0e2 100644
--- a/tensorflow/core/protobuf/control_flow.proto
+++ b/tensorflow/core/protobuf/control_flow.proto
@@ -17,6 +17,15 @@ message ValuesDef {
   map<string, string> external_values = 2;
 }
 
+// Container for any kind of control flow context. Any other control flow
+// contexts that are added below should also be added here.
+message ControlFlowContextDef {
+  oneof ctxt {
+    CondContextDef cond_ctxt = 1;
+    WhileContextDef while_ctxt = 2;
+  }
+}
+
 // Protocol buffer representing a CondContext object.
 message CondContextDef {
   // Name of the context.
@@ -33,6 +42,9 @@ message CondContextDef {
 
   // Values and external values in control flow context.
   ValuesDef values_def = 5;
+
+  // Contexts contained inside this context (e.g. nested conds).
+  repeated ControlFlowContextDef nested_contexts = 6;
 }
 
 // Protocol buffer representing a WhileContext object.
@@ -70,5 +82,8 @@ message WhileContextDef {
   // Optional name of the maximum_iterations tensor.
   string maximum_iterations_name = 11;
 
-  // Next available id: 12.
+  // Contexts contained inside this context (e.g. nested whiles).
+  repeated ControlFlowContextDef nested_contexts = 12;
+
+  // Next available id: 13.
 }
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 8d5ab72670..85944efbe8 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1767,13 +1767,9 @@ class CondContext(ControlFlowContext):
       context_def.branch = self._branch
       context_def.values_def.MergeFrom(super(CondContext, self)._to_values_def(
           export_scope))
-      # TODO(b/72868227): enable this once the corresponding control_flow.proto
-      # changes have been checked in (they aren't checked in and this is
-      # disabled for now to ensure forwards compatibility).
-      if False:  # pylint: disable=using-constant-test
-        for nested in self._nested_contexts:
-          nested_def = context_def.nested_contexts.add()
-          nested.to_control_flow_context_def(nested_def)
+      for nested in self._nested_contexts:
+        nested_def = context_def.nested_contexts.add()
+        nested.to_control_flow_context_def(nested_def)
 
       return context_def
     else:
@@ -1785,14 +1781,10 @@ class CondContext(ControlFlowContext):
     ret = CondContext(context_def=context_def,
                       import_scope=import_scope)
 
-    # TODO(b/72868227): remove "if hasattr(...)" once the corresponding
-    # control_flow.proto changes have been checked in (they aren't checked in
-    # and this is here for now to ensure forwards compatibility).
-    if hasattr(context_def, "nested_contexts"):
-      ret.Enter()
-      for nested_def in context_def.nested_contexts:
-        from_control_flow_context_def(nested_def)
-      ret.Exit()
+    ret.Enter()
+    for nested_def in context_def.nested_contexts:
+      from_control_flow_context_def(nested_def)
+    ret.Exit()
     return ret
 
   def to_control_flow_context_def(self, context_def, export_scope=None):
@@ -2110,10 +2102,7 @@ def cond(pred,
     # Only add non-nested conds to the collection. Any nested control flow will
     # be encapsulated in the root context.
     assert context_t.outer_context == context_f.outer_context
-    # TODO(b/72868227): remove "if True..." once the corresponding
-    # control_flow.proto changes have been checked in (they aren't checked in
-    # and this is disabled for now to ensure forwards compatibility).
-    if True or context_t.outer_context is None:
+    if context_t.outer_context is None:
       ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_t)
       ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_f)
 
@@ -2336,13 +2325,9 @@ class WhileContext(ControlFlowContext):
       context_def.values_def.MergeFrom(
           super(WhileContext, self)._to_values_def(
               export_scope=export_scope))
-      # TODO(b/72868227): remove "if True..." once the corresponding
-      # control_flow.proto changes have been checked in (they aren't checked in
-      # and this is disabled for now to ensure forwards compatibility).
-      if False:  # pylint: disable=using-constant-test
-        for nested in self._nested_contexts:
-          nested_def = context_def.nested_contexts.add()
-          nested.to_control_flow_context_def(nested_def)
+      for nested in self._nested_contexts:
+        nested_def = context_def.nested_contexts.add()
+        nested.to_control_flow_context_def(nested_def)
 
       return context_def
     else:
@@ -2364,14 +2349,10 @@ class WhileContext(ControlFlowContext):
     """
     ret = WhileContext(context_def=context_def,
                        import_scope=import_scope)
-    # TODO(b/72868227): remove "if hasattr(...)" once the corresponding
-    # control_flow.proto changes have been checked in (they aren't checked in
-    # and this is disabled for now to ensure forwards compatibility).
-    if hasattr(context_def, "nested_contexts"):
-      ret.Enter()
-      for nested_def in context_def.nested_contexts:
-        from_control_flow_context_def(nested_def, import_scope=import_scope)
-      ret.Exit()
+    ret.Enter()
+    for nested_def in context_def.nested_contexts:
+      from_control_flow_context_def(nested_def, import_scope=import_scope)
+    ret.Exit()
     return ret
 
   def GetWhileContext(self):
@@ -3216,10 +3197,7 @@ def while_loop(cond,
         swap_memory=swap_memory)
     # Only add non-nested loops to the collection. Any nested control flow will
     # be encapsulated in the root context.
-    # TODO(b/72868227): enable condition once the corresponding
-    # control_flow.proto changes have been checked in (they aren't checked in
-    # and this is disabled for now to ensure forwards compatibility).
-    if True or loop_context.outer_context is None:
+    if loop_context.outer_context is None:
       ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context)
     result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
     if maximum_iterations is not None:
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index f00f98db00..b366ed30f3 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -53,6 +53,7 @@ from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import partitioned_variables
@@ -2040,6 +2041,61 @@ class MetaGraphTest(test.TestCase):
     self._testGraphExtensionRestore(test_dir)
     self._testRestoreFromTrainGraphWithControlContext(test_dir)
 
+  def testNestedWhileLoops(self):
+    test_dir = self._get_test_dir("nested_whiles")
+    filename = os.path.join(test_dir, "metafile")
+    saver_ckpt = os.path.join(test_dir, "saver.ckpt")
+
+    # Create two simple nested while loops.
+    with ops_lib.Graph().as_default():
+      def body(i, x):
+        _, r = control_flow_ops.while_loop(lambda j, y: j < 3,
+                                           lambda j, y: (j + 1, y + x),
+                                           [0, 0])
+        return i + 1, x + r
+
+      var = variables.Variable(0)
+      var_name = var.name
+
+      _, output = control_flow_ops.while_loop(lambda i, x: i < 5, body,
+                                              [0, var])
+      output_name = output.name
+
+      init_op = variables.global_variables_initializer()
+
+      # Generate a MetaGraphDef containing the nested loops.
+      with session.Session() as sess:
+        sess.run(init_op)
+        sess.run(output)
+        saver = saver_module.Saver()
+        saver.save(sess, saver_ckpt)
+        saver.export_meta_graph(filename)
+
+      # Build and run the gradients of the nested while loop. We use this below
+      # to verify that the gradients are correct with an imported MetaGraphDef.
+      grad = gradients_impl.gradients([output], [var])
+      with session.Session() as sess:
+        sess.run(init_op)
+        expected_grad_value = sess.run(grad)
+
+    # Restore the MetaGraphDef into a new Graph.
+    with ops_lib.Graph().as_default():
+      with session.Session() as sess:
+        saver = saver_module.import_meta_graph(filename)
+        saver.restore(sess, saver_ckpt)
+
+      # Make sure we can still build gradients and get the same result.
+      var = ops_lib.get_default_graph().get_tensor_by_name(var_name)
+      output = ops_lib.get_default_graph().get_tensor_by_name(output_name)
+      grad = gradients_impl.gradients([output], [var])
+
+      init_op = variables.global_variables_initializer()
+
+      with session.Session() as sess:
+        sess.run(init_op)
+        actual_grad_value = sess.run(grad)
+        self.assertEqual(expected_grad_value, actual_grad_value)
+
   def testStrippedOpListDef(self):
     with self.test_session():
       # Creates a graph.
-- 
GitLab


From 95d36c770b24a343008d32eda85e8f91278f6df0 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Mon, 26 Feb 2018 15:37:27 -0800
Subject: [PATCH 318/575] [XLA::Interpreter] Add support for kCall to
 HloEvaluator. Also enable xla/tests/call_test to run on interpreter.

PiperOrigin-RevId: 187092587
---
 .../compiler/xla/service/hlo_evaluator.cc     | 20 +++++++++++++++++++
 .../compiler/xla/service/hlo_evaluator.h      |  2 ++
 tensorflow/compiler/xla/tests/BUILD           |  3 +++
 3 files changed, 25 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 15ae53128a..fd06b19144 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -2445,6 +2445,26 @@ Status HloEvaluator::HandleCopy(HloInstruction* copy) {
   return Status::OK();
 }
 
+Status HloEvaluator::HandleCall(HloInstruction* call) {
+  auto* computation = call->to_apply();
+  auto operands = call->operands();
+
+  std::vector<const Literal*> arg_literals;
+  arg_literals.reserve(operands.size());
+  for (auto operand : operands) {
+    const Literal& arg_literal = GetEvaluatedLiteralFor(operand);
+    arg_literals.push_back(&arg_literal);
+  }
+
+  HloEvaluator embedded_evaluator;
+  std::unique_ptr<Literal> result =
+      embedded_evaluator.Evaluate<const Literal*>(*computation, arg_literals)
+          .ConsumeValueOrDie();
+
+  evaluated_[call] = std::move(result);
+  return Status::OK();
+}
+
 Status HloEvaluator::Preprocess(HloInstruction* hlo) {
   VLOG(2) << "About to visit HLO: " << hlo->ToString();
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h
index 3b2b697e49..c65d9915e3 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.h
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.h
@@ -153,6 +153,8 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
 
   Status HandleCopy(HloInstruction* copy) override;
 
+  Status HandleCall(HloInstruction* call) override;
+
  private:
   // Returns the already-evaluated literal result for the instruction.
   // A Constant instruction is considered evaluated and its literal will be
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 97abf217d7..33fde9737d 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1143,6 +1143,9 @@ xla_test(
 xla_test(
     name = "call_test",
     srcs = ["call_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
-- 
GitLab


From aa2f0b68fb7052ea46547bf15fb8a46f6447f182 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 26 Feb 2018 15:37:40 -0800
Subject: [PATCH 319/575] Uses a thread pool for graph functions in eager mode
 with inter_op_parallelism_threads.

PiperOrigin-RevId: 187092622
---
 tensorflow/c/eager/BUILD            |  1 +
 tensorflow/c/eager/c_api.cc         |  4 ++--
 tensorflow/c/eager/c_api_internal.h | 14 +++++++++++++-
 tensorflow/c/eager/runtime.cc       | 14 ++++++++++----
 tensorflow/c/eager/runtime.h        |  3 +++
 tensorflow/c/eager/runtime_test.cc  | 12 ++++++------
 6 files changed, 35 insertions(+), 13 deletions(-)

diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD
index e55cb672e9..16a2a15072 100644
--- a/tensorflow/c/eager/BUILD
+++ b/tensorflow/c/eager/BUILD
@@ -21,6 +21,7 @@ tf_cuda_library(
     visibility = ["//visibility:public"],
     deps = select({
         "//tensorflow:android": [
+            "//tensorflow/core:lib",
             "//tensorflow/core:android_tensorflow_lib_lite",
         ],
         "//conditions:default": [
diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index bebb63c746..b233dd5b93 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -818,8 +818,8 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
     // See WARNING comment below - would be nice to rework to avoid this
     // subtlety.
     tensorflow::tf_shared_lock l(ctx->functions_mu);
-    status->status =
-        tensorflow::KernelAndDevice::Init(ndef, ctx->func_lib(device), kernel);
+    status->status = tensorflow::KernelAndDevice::Init(
+        ndef, ctx->func_lib(device), &ctx->runner, kernel);
     if (!status->status.ok()) {
       delete kernel;
       return;
diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h
index 3356054cd0..29944df4c2 100644
--- a/tensorflow/c/eager/c_api_internal.h
+++ b/tensorflow/c/eager/c_api_internal.h
@@ -31,6 +31,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
 #include "tensorflow/core/framework/rendezvous.h"
+#include "tensorflow/core/lib/core/threadpool.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -45,7 +46,15 @@ struct TFE_ContextOptions {
 
 struct TFE_Context {
   explicit TFE_Context(const TFE_ContextOptions& opts, TF_Session* s)
-      : policy(opts.policy),
+      : thread_pool(new tensorflow::thread::ThreadPool(
+            opts.session_options.options.env, "EagerCompute",
+            opts.session_options.options.config
+                        .inter_op_parallelism_threads() != 0
+                ? opts.session_options.options.config
+                      .inter_op_parallelism_threads()
+                : tensorflow::port::NumSchedulableCPUs())),
+        runner([this](std::function<void()> f) { thread_pool->Schedule(f); }),
+        policy(opts.policy),
         session(s),
         rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)),
         pflr(new tensorflow::ProcessFunctionLibraryRuntime(
@@ -54,6 +63,9 @@ struct TFE_Context {
         log_device_placement(
             opts.session_options.options.config.log_device_placement()) {}
 
+  const std::unique_ptr<tensorflow::thread::ThreadPool> thread_pool;
+  std::function<void(std::function<void()>)> runner;
+
   const TFE_ContextDevicePlacementPolicy policy;
 
   // Note: we cannot use C++11 thread_local here as there is no concept of a
diff --git a/tensorflow/c/eager/runtime.cc b/tensorflow/c/eager/runtime.cc
index 4bf24fec2c..b9618420f0 100644
--- a/tensorflow/c/eager/runtime.cc
+++ b/tensorflow/c/eager/runtime.cc
@@ -255,17 +255,22 @@ Status KernelAndDevice::InitOp(Device* device, const NodeDef& ndef,
   out->device_ = device;
   out->kernel_.reset(k);
   out->flib_ = nullptr;
+  out->runner_ = nullptr;
+  out->default_runner_ = [](std::function<void()> f) { f(); };
   return s;
 }
 
 // static
 Status KernelAndDevice::Init(const NodeDef& ndef, FunctionLibraryRuntime* flib,
+                             std::function<void(std::function<void()>)>* runner,
                              KernelAndDevice* out) {
   OpKernel* k = nullptr;
   Status s = flib->CreateKernel(ndef, &k);
   out->device_ = flib->device();
   out->kernel_.reset(k);
   out->flib_ = flib;
+  out->runner_ = runner;
+  out->default_runner_ = [](std::function<void()> f) { f(); };
   return s;
 }
 
@@ -296,10 +301,11 @@ Status KernelAndDevice::Run(std::vector<Tensor>* input_tensors,
   if (stats != nullptr) {
     params.track_allocations = true;
   }
-  // TODO(apassos): use a thread pool.
-  std::function<void(std::function<void()>)> runner =
-      [](std::function<void()> f) { f(); };
-  params.runner = &runner;
+  if (runner_ == nullptr) {
+    params.runner = &default_runner_;
+  } else {
+    params.runner = runner_;
+  }
 
   OpKernelContext context(&params);
   device_->Compute(kernel_.get(), &context);
diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h
index 7fede4dae9..fa5f839977 100644
--- a/tensorflow/c/eager/runtime.h
+++ b/tensorflow/c/eager/runtime.h
@@ -169,6 +169,7 @@ class KernelAndDevice {
   // the FunctionLibraryRuntime is pushed on to the caller (see locking in
   // c_api.cc).
   static Status Init(const NodeDef& ndef, FunctionLibraryRuntime* flib,
+                     std::function<void(std::function<void()>)>* runner,
                      KernelAndDevice* out);
   // TODO(ashankar): Remove this
   static Status InitOp(Device* device, const NodeDef& ndef,
@@ -188,6 +189,8 @@ class KernelAndDevice {
  private:
   std::unique_ptr<OpKernel> kernel_;
   Device* device_;
+  std::function<void(std::function<void()>)>* runner_;
+  std::function<void(std::function<void()>)> default_runner_;
   FunctionLibraryRuntime* flib_;
   checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_;
   Rendezvous* rendez_;
diff --git a/tensorflow/c/eager/runtime_test.cc b/tensorflow/c/eager/runtime_test.cc
index 643153058c..ab0b535e1a 100644
--- a/tensorflow/c/eager/runtime_test.cc
+++ b/tensorflow/c/eager/runtime_test.cc
@@ -92,8 +92,8 @@ TEST(KernelAndDevice, Run) {
                    .BuildNodeDef());
   TestEnv env;
   KernelAndDevice kernel(nullptr);
-  Status s =
-      KernelAndDevice::Init(ndef, env.function_library_runtime(), &kernel);
+  Status s = KernelAndDevice::Init(ndef, env.function_library_runtime(),
+                                   nullptr, &kernel);
   ASSERT_TRUE(s.ok()) << s;
   std::vector<Tensor> outputs;
   s = kernel.Run(&inputs, &outputs, nullptr);
@@ -158,8 +158,8 @@ void BM_KernelAndDeviceInit(int iters) {
   KernelAndDevice k(nullptr);
   tensorflow::testing::StartTiming();
   for (int i = 0; i < iters; ++i) {
-    TF_CHECK_OK(
-        KernelAndDevice::Init(ndef, env.function_library_runtime(), &k));
+    TF_CHECK_OK(KernelAndDevice::Init(ndef, env.function_library_runtime(),
+                                      nullptr, &k));
   }
 }
 BENCHMARK(BM_KernelAndDeviceInit);
@@ -179,8 +179,8 @@ void BM_KernelAndDeviceRun(int iters) {
                    .BuildNodeDef());
   TestEnv env;
   KernelAndDevice kernel(nullptr);
-  TF_CHECK_OK(
-      KernelAndDevice::Init(ndef, env.function_library_runtime(), &kernel));
+  TF_CHECK_OK(KernelAndDevice::Init(ndef, env.function_library_runtime(),
+                                    nullptr, &kernel));
   tensorflow::testing::StartTiming();
   for (int i = 0; i < iters; ++i) {
     TF_CHECK_OK(kernel.Run(&inputs, &outputs, nullptr));
-- 
GitLab


From 175730d3791618a496a5c66d7d6fef9c7768cf34 Mon Sep 17 00:00:00 2001
From: Bixia Zheng <bixia@google.com>
Date: Mon, 26 Feb 2018 15:42:52 -0800
Subject: [PATCH 320/575] [XLA] Fix #17090 a problem in
 IrArray::Index::SourceIndexOfTranspose.

Agebraic simplification transforms bitcast-equivalent transpose/reshape
instructions to bitcast instructions before IR emission. As such, we should
skip the checking on whether a transpose/reshape instruction is
bitcast-equivalent or not during IR emission. Remove the call from
IrArray::Index::SourceIndexOfTranspose to ShapeUtil::TransposeIsBitcast. Also
remove the call from IrArray::Index::SourceIndexOfReshape to
ShapeUtil::ReshapeIsBitcast.

Remove the calls to ShapeUtil::TransposeIsBitcast and
ShapeUtil::ReshapeIsBitcast from NotWorthHoistingIndividually
because layout assignment hasn't been done there yet. Instead, returns true
when the input is a transpose or reshape instruction, to prevent it from
being hoisted out of loops.

Add a check to ShapeUtil::TransposeIsBitcast and ShapeUtil::ReshapeIsBitcast
to make sure that both input shape and output shape have layouts.

Add two test cases.

PiperOrigin-RevId: 187093399
---
 .../xla/service/layout_assignment_test.cc     | 79 +++++++++++++++++++
 .../compiler/xla/service/llvm_ir/ir_array.cc  |  8 +-
 .../while_loop_invariant_code_motion.cc       | 12 +--
 tensorflow/compiler/xla/shape_util.cc         | 14 +---
 tensorflow/compiler/xla/shape_util.h          |  4 +
 5 files changed, 95 insertions(+), 22 deletions(-)

diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index 88e5caaf47..62feb7c1e9 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -590,6 +590,85 @@ TEST_F(LayoutAssignmentTest, TransposeToBitcastToUser) {
                                             transpose->shape(), {2, 3, 0, 1}));
 }
 
+// TransposeIsBitcast shouldn't be called without layout information.
+TEST_F(LayoutAssignmentTest, TransposeIsBitcastFail) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2});
+  Shape input_shape_with_layout(input_shape);
+  *input_shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout({2, 1, 0});
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_shape_with_layout, "param"));
+  auto hlo = builder.AddInstruction(
+      HloInstruction::CreateTranspose(input_shape, param, {0, 2, 1}));
+  // Clear the default layout assigned to the instruction.
+  LayoutUtil::ClearLayout(hlo->mutable_shape());
+  EXPECT_DEATH(ShapeUtil::TransposeIsBitcast(hlo->operand(0)->shape(),
+                                             hlo->shape(), hlo->dimensions()),
+               "LayoutUtil::HasLayout");
+}
+
+// ReshapeIsBitcast shouldn't be called without layout information.
+TEST_F(LayoutAssignmentTest, ReshapeIsBitcastFail) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2});
+  Shape input_shape_with_layout(input_shape);
+  *input_shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout({2, 1, 0});
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_shape_with_layout, "param"));
+  auto hlo =
+      builder.AddInstruction(HloInstruction::CreateReshape(input_shape, param));
+  // Clear the default layout assigned to the instruction.
+  LayoutUtil::ClearLayout(hlo->mutable_shape());
+  EXPECT_DEATH(
+      ShapeUtil::ReshapeIsBitcast(hlo->operand(0)->shape(), hlo->shape()),
+      "LayoutUtil::HasLayout");
+}
+
+// Check that the computation below doesn't crash the compiler.
+//
+// Within a fusion computation, only the parameters and result get assigned a
+// layout.  When we run the algebraic simplifier on this computation post layout
+// assignment, it should not call TransposeIsBitcast on the `transpose` node
+// inside the fusion computation as TransposeIsBitcast checks both input_shape
+// and output_shape have layouts.
+TEST_F(LayoutAssignmentTest, TransposeWithinFusionDoesNotCrash) {
+  const char* module_str = R"(
+    HloModule test_module
+
+    fused_computation {
+      param_1 = f32[2,2,2]{2,1,0} parameter(1)
+      transpose = f32[2,2,2]{2,1,0} transpose(param_1), dimensions={0,2,1}
+      reduce_1 = f32[] parameter(0)
+      broadcast_1 = f32[2,2,2]{2,1,0} broadcast(reduce_1), dimensions={}
+      ROOT divide_1 = f32[2,2,2]{2,1,0} divide(transpose, broadcast_1)
+    }
+
+    ENTRY entry_computation {
+      fusion.1 = f32[2,2,2]{2,1,0} parameter(1)
+      reduce.1 = f32[] parameter(0)
+      fusion.2 = f32[2,2,2]{2,1,0} fusion(reduce.1, fusion.1), kind=kLoop, calls=fused_computation
+     ROOT tuple.1 = (f32[2,2,2]{2,1,0}) tuple(fusion.2)
+    }
+  )";
+
+  auto module = tools::Parse(module_str).ValueOrDie();
+
+  module =
+      backend()
+          .compiler()
+          ->RunHloPasses(std::move(module), backend().default_stream_executor(),
+                         /*device_allocator=*/nullptr)
+          .ConsumeValueOrDie();
+
+  EXPECT_EQ(
+      ::tensorflow::Status::OK(),
+      backend()
+          .compiler()
+          ->RunBackend(std::move(module), backend().default_stream_executor(),
+                       /*device_allocator=*/nullptr)
+          .status());
+}
+
 // A GTE inside of a fusion node inherits the layout of its operand (which
 // should, if we keep following operands, eventually be a parameter).
 TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) {
diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
index 6384c7f46f..f3642cf0a1 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
@@ -160,7 +160,8 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape(
     }
   }
 
-  if (linear() != nullptr &&
+  if (linear() != nullptr && LayoutUtil::HasLayout(input_shape) &&
+      LayoutUtil::HasLayout(output_shape) &&
       ShapeUtil::ReshapeIsBitcast(input_shape, output_shape)) {
     return Index(source_multidim_index, linear(), input_shape);
   }
@@ -195,10 +196,13 @@ IrArray::Index IrArray::Index::SourceIndexOfTranspose(
     llvm::IRBuilder<>* builder) const {
   std::vector<llvm::Value*> operand_multidim_index =
       Permute(dimension_mapping, multidim());
-  if (linear() != nullptr &&
+
+  if (linear() != nullptr && LayoutUtil::HasLayout(operand_shape) &&
+      LayoutUtil::HasLayout(shape) &&
       ShapeUtil::TransposeIsBitcast(operand_shape, shape, dimension_mapping)) {
     return Index(operand_multidim_index, linear(), operand_shape);
   }
+
   return Index(operand_multidim_index);
 }
 
diff --git a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
index a5f9b01f01..3ef0cdff67 100644
--- a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
+++ b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
@@ -106,20 +106,12 @@ static bool NotWorthHoistingIndividually(const HloInstruction& instruction) {
     case HloOpcode::kBitcast:
     case HloOpcode::kBroadcast:
     case HloOpcode::kConstant:
+    case HloOpcode::kReshape:
     case HloOpcode::kReverse:
     case HloOpcode::kSlice:
+    case HloOpcode::kTranspose:
     case HloOpcode::kTuple:
       return true;
-
-    case HloOpcode::kTranspose:
-      return ShapeUtil::TransposeIsBitcast(
-          /*input_shape=*/instruction.operand(0)->shape(),
-          /*output_shape=*/instruction.shape(), instruction.dimensions());
-
-    case HloOpcode::kReshape:
-      return ShapeUtil::ReshapeIsBitcast(
-          /*input_shape=*/instruction.operand(0)->shape(),
-          /*output_shape=*/instruction.shape());
   }
 }
 
diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc
index 604e0173e7..3152789016 100644
--- a/tensorflow/compiler/xla/shape_util.cc
+++ b/tensorflow/compiler/xla/shape_util.cc
@@ -1073,11 +1073,8 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape,
 /* static */ bool ShapeUtil::TransposeIsBitcast(
     const Shape& input_shape, const Shape& output_shape,
     tensorflow::gtl::ArraySlice<int64> dimension_mapping) {
-  // Can't insert bitcasts without layout information.
-  if (!LayoutUtil::HasLayout(input_shape) &&
-      !LayoutUtil::HasLayout(output_shape)) {
-    return false;
-  }
+  CHECK(LayoutUtil::HasLayout(input_shape) &&
+        LayoutUtil::HasLayout(output_shape));
 
   // Padding is not handled.
   if (LayoutUtil::IsPadded(input_shape) && LayoutUtil::IsPadded(output_shape)) {
@@ -1106,11 +1103,8 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape,
 
 /* static */ bool ShapeUtil::ReshapeIsBitcast(const Shape& input_shape,
                                               const Shape& output_shape) {
-  // Can't convert reshapes into bitcasts without layout information.
-  if (!LayoutUtil::HasLayout(input_shape) ||
-      !LayoutUtil::HasLayout(output_shape)) {
-    return false;
-  }
+  CHECK(LayoutUtil::HasLayout(input_shape) &&
+        LayoutUtil::HasLayout(output_shape));
 
   // Padding is not handled.
   if (LayoutUtil::IsPadded(input_shape) || LayoutUtil::IsPadded(output_shape)) {
diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index 19b1aa93bd..8ee263fe5e 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -522,12 +522,16 @@ class ShapeUtil {
   // Returns whether a transpose from input_shape to output_shape with dimension
   // mapping "dimension_mapping" produces a result which is bit-wise identical
   // to its input and thus may be replaced with a bitcast.
+  //
+  // Precondition: Both input_shape and output_shape have explicit layouts.
   static bool TransposeIsBitcast(
       const Shape& input_shape, const Shape& output_shape,
       tensorflow::gtl::ArraySlice<int64> dimension_mapping);
 
   // Returns whether a reshape from "input_shape" to "output_shape" is a
   // bitcast.
+  //
+  // Precondition: Both input_shape and output_shape have explicit layouts.
   static bool ReshapeIsBitcast(const Shape& input_shape,
                                const Shape& output_shape);
 
-- 
GitLab


From 7c512d5461eeff635acf1c7d0f301f5bb880b6b3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 16:01:04 -0800
Subject: [PATCH 321/575] [XLA] Add more supported dtypes to the local Python
 client.

PiperOrigin-RevId: 187096144
---
 tensorflow/compiler/xla/python/xla_client.py | 38 ++++++++++++--------
 1 file changed, 24 insertions(+), 14 deletions(-)

diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py
index 3b8ec851d5..90cda42f32 100644
--- a/tensorflow/compiler/xla/python/xla_client.py
+++ b/tensorflow/compiler/xla/python/xla_client.py
@@ -30,9 +30,9 @@ from tensorflow.compiler.xla import xla_data_pb2
 from tensorflow.compiler.xla.python import pywrap_xla as c_api
 
 
-# Most functions are snake_case for consistency with other modules,
-# whereas method names of ComputationBuilder and LocalComputation are
-# CamelCase for consistency with XLA.
+# Most functions are snake_case for consistency with other modules, whereas
+# method names of ComputationBuilder and LocalComputation are CamelCase for
+# consistency with XLA.
 # pylint: disable=invalid-name
 
 
@@ -123,24 +123,34 @@ _BINARY_OPS = [
     'Pow',
 ]
 
+
 XLA_ELEMENT_TYPE_TO_DTYPE = {
-    xla_data_pb2.F32: np.dtype(np.float32),
-    xla_data_pb2.F64: np.dtype(np.float64),
-    xla_data_pb2.S32: np.dtype(np.int32),
-    xla_data_pb2.S64: np.dtype(np.int64),
-    xla_data_pb2.U32: np.dtype(np.uint32),
-    xla_data_pb2.U64: np.dtype(np.uint64),
-    xla_data_pb2.PRED: np.dtype(np.bool),
+    xla_data_pb2.PRED: np.dtype('bool'),
+    xla_data_pb2.S8: np.dtype('int8'),
+    xla_data_pb2.S16: np.dtype('int16'),
+    xla_data_pb2.S32: np.dtype('int32'),
+    xla_data_pb2.S64: np.dtype('int64'),
+    xla_data_pb2.U8: np.dtype('uint8'),
+    xla_data_pb2.U16: np.dtype('uint16'),
+    xla_data_pb2.U32: np.dtype('uint32'),
+    xla_data_pb2.U64: np.dtype('uint64'),
+    xla_data_pb2.F16: np.dtype('float16'),
+    xla_data_pb2.F32: np.dtype('float32'),
+    xla_data_pb2.F64: np.dtype('float64'),
+    xla_data_pb2.C64: np.dtype('complex64'),
     xla_data_pb2.TUPLE: np.dtype(np.object),
 }
 
 # Note the conversion on the key. Numpy has a known issue wherein dtype hashing
 # doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus,
 # when keying by dtype in this dict, we use the string form of dtypes.
-DTYPE_TO_XLA_ELEMENT_TYPE = {
-    str(v): k
-    for k, v in XLA_ELEMENT_TYPE_TO_DTYPE.items()
-}
+DTYPE_TO_XLA_ELEMENT_TYPE = {str(dt): et
+                             for et, dt in XLA_ELEMENT_TYPE_TO_DTYPE.items()}
+
+
+def dtype_to_etype(dtype):
+  """Convenience function for reading DTYPE_TO_XLA_ELEMENT_TYPE."""
+  return DTYPE_TO_XLA_ELEMENT_TYPE[str(np.dtype(dtype))]
 
 
 class LocalBuffer(object):
-- 
GitLab


From 511cf67f2327e9186124a92c9469dc60fd64a6a2 Mon Sep 17 00:00:00 2001
From: Martin Wicke <wicke@google.com>
Date: Mon, 26 Feb 2018 16:23:46 -0800
Subject: [PATCH 322/575] Deprecate tf.contrib.learn.

RELNOTES: Deprecated tf.contrib.learn. Please check contrib/learn/README.md for instructions on how to convert existing code.
PiperOrigin-RevId: 187099439
---
 .../python/framework/experimental_test.py     |   1 -
 tensorflow/contrib/learn/README.md            | 143 ++++++++++++++++++
 tensorflow/contrib/learn/__init__.py          |   7 +-
 tensorflow/contrib/learn/python/__init__.py   |   7 +-
 .../contrib/learn/python/learn/__init__.py    |   7 +-
 .../python/learn/basic_session_run_hooks.py   |  43 +++++-
 .../learn/python/learn/datasets/__init__.py   |  12 +-
 .../learn/python/learn/datasets/base.py       |  26 +++-
 .../learn/python/learn/datasets/mnist.py      |  23 ++-
 .../learn/datasets/produce_small_datasets.py  |   7 +-
 .../learn/python/learn/datasets/synthetic.py  |  10 +-
 .../python/learn/datasets/text_datasets.py    |  10 +-
 .../learn/python/learn/estimators/__init__.py |   7 +-
 .../learn/python/learn/estimators/_sklearn.py |   4 +-
 .../learn/estimators/composable_model.py      |  17 ++-
 .../python/learn/estimators/constants.py      |   8 +-
 .../learn/python/learn/estimators/debug.py    |  14 +-
 .../learn/python/learn/estimators/dnn.py      |  19 ++-
 .../learn/estimators/dnn_linear_combined.py   |  19 ++-
 .../learn/estimators/dynamic_rnn_estimator.py |  13 +-
 .../python/learn/estimators/estimator.py      |  27 +++-
 .../learn/estimators/estimator_test_utils.py  |   7 +-
 .../learn/python/learn/estimators/head.py     |  20 ++-
 .../learn/python/learn/estimators/kmeans.py   |   9 +-
 .../learn/python/learn/estimators/linear.py   |  19 ++-
 .../learn/estimators/logistic_regressor.py    |  10 +-
 .../python/learn/estimators/metric_key.py     |  10 +-
 .../learn/python/learn/estimators/model_fn.py |  22 ++-
 .../python/learn/estimators/prediction_key.py |   8 +-
 .../python/learn/estimators/rnn_common.py     |   7 +-
 .../python/learn/estimators/run_config.py     |  19 ++-
 .../estimators/state_saving_rnn_estimator.py  |  13 +-
 .../learn/python/learn/estimators/svm.py      |  11 +-
 .../learn/estimators/tensor_signature.py      |  11 +-
 .../python/learn/estimators/test_data.py      |   7 +-
 .../contrib/learn/python/learn/evaluable.py   |  11 +-
 .../contrib/learn/python/learn/experiment.py  |  24 +--
 .../learn/python/learn/export_strategy.py     |  14 +-
 .../learn/python/learn/graph_actions.py       |   8 +-
 .../learn/python/learn/learn_io/__init__.py   |   7 +-
 .../learn/python/learn/learn_io/dask_io.py    |  11 +-
 .../python/learn/learn_io/data_feeder.py      |  29 +++-
 .../python/learn/learn_io/generator_io.py     |   9 +-
 .../learn/python/learn/learn_io/graph_io.py   |  16 +-
 .../learn/python/learn/learn_io/numpy_io.py   |   9 +-
 .../learn/python/learn/learn_io/pandas_io.py  |  12 +-
 .../learn/python/learn/learn_runner.py        |  10 +-
 .../learn/python/learn/learn_runner_lib.py    |   6 +-
 .../contrib/learn/python/learn/metric_spec.py |  13 +-
 .../contrib/learn/python/learn/models.py      |  14 +-
 .../learn/python/learn/monitored_session.py   |   6 +-
 .../contrib/learn/python/learn/monitors.py    |  68 ++++++++-
 .../learn/python/learn/ops/__init__.py        |   7 +-
 .../learn/python/learn/ops/embeddings_ops.py  |   6 +-
 .../learn/python/learn/ops/losses_ops.py      |   7 +-
 .../learn/python/learn/ops/seq2seq_ops.py     |  12 +-
 .../python/learn/preprocessing/__init__.py    |   7 +-
 .../python/learn/preprocessing/categorical.py |  15 +-
 .../preprocessing/categorical_vocabulary.py   |  13 +-
 .../learn/python/learn/preprocessing/text.py  |  26 +++-
 .../learn/python/learn/session_run_hook.py    |   6 +-
 .../python/learn/summary_writer_cache.py      |   5 +-
 .../contrib/learn/python/learn/trainable.py   |   9 +-
 .../learn/python/learn/utils/__init__.py      |   7 +-
 .../learn/python/learn/utils/export.py        |   9 +-
 .../contrib/learn/python/learn/utils/gc.py    |  13 +-
 .../python/learn/utils/input_fn_utils.py      |  16 +-
 .../python/learn/utils/inspect_checkpoint.py  |   2 +-
 .../learn/utils/saved_model_export_utils.py   |  30 +++-
 tensorflow/python/util/decorator_utils.py     |   2 +-
 70 files changed, 945 insertions(+), 111 deletions(-)
 create mode 100644 tensorflow/contrib/learn/README.md

diff --git a/tensorflow/contrib/framework/python/framework/experimental_test.py b/tensorflow/contrib/framework/python/framework/experimental_test.py
index 8e54e09e04..cfdc7df7d8 100644
--- a/tensorflow/contrib/framework/python/framework/experimental_test.py
+++ b/tensorflow/contrib/framework/python/framework/experimental_test.py
@@ -49,7 +49,6 @@ class ExperimentalTest(test.TestCase):
                      "\nTHIS FUNCTION IS EXPERIMENTAL. It may change or "
                      "be removed at any time, and without warning."
                      "\n"
-                     "\n"
                      "\nArgs:"
                      "\n  arg0: Arg 0."
                      "\n  arg1: Arg 1."
diff --git a/tensorflow/contrib/learn/README.md b/tensorflow/contrib/learn/README.md
new file mode 100644
index 0000000000..d516bffc5e
--- /dev/null
+++ b/tensorflow/contrib/learn/README.md
@@ -0,0 +1,143 @@
+EVERYTHING IN THIS DIRECTORY IS DEPRECATED.
+
+Using functions or classes will result in warnings.
+
+Instructions for converting to current alternatives are included in the
+warnings. A high-level overview is below.
+
+## Canned Estimators
+
+Many canned estimators (subclasses of `Estimator`) have equivalents in core:
+`DNNClassifier`, `DNNRegressor`, `DNNEstimator`, `LinearClassifier`,
+`LinearRegressor`, `DNNLinearCombinedClassifier` and
+`DNNLinearCombinedRegressor`. They are exposed under `tf.estimator`.
+`DNNEstimator`, `LinearEstimator` and `DNNLinearCombinedEstimator`
+are exposed under `tf.contrib.estimator`.
+
+To migrate to the new api, users need to take the following steps:
+
+* Replace `tf.contrib.learn` with `tf.estimator`.
+* If you subclass any of the estimators, stop doing that. You should be able to
+  write a factory method that returns a canned estimator instead. If this is not
+  possible (if you override methods from the canned estimator), consider writing
+  a custom estimator instead. See `tf.estimator.Estimator`.
+* Set `loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE` to preserve loss
+  reduction as the average over batch.
+* Some optimizer-related arguments are no longer passed in the estimator
+  constructor. Instead, we provide methods that perform the same job by wrapping
+  an optimizer. Specifically:
+  *  `gradient_clip_norm`: Use `tf.contrib.estimator.clip_gradients_by_norm`
+  *  `embedding_lr_multipliers`: Not supported.
+  Other arguments:
+  * `input_layer_min_slice_size`: Replaced by `input_layer_partitioner`
+  * `enable_centered_bias`: Not supported. Dropping this argument is unlikely to
+    harm your model.
+  * `feature_engineering_fn`: Not supported. You can call your
+    `feature_engineering_fn` inside your input_fn:
+    ```python
+    def new_input_fn():
+      features, labels = old_input_fn()
+      return feature_engineering_fn(features, labels)
+    ```
+* Use `tf.reshape` to reshape labels in your `input_fn`. `tf.estimator`
+  classifiers and regressors expect labels as a 2D Tensor of shape
+  `[batch_size, 1]`, or `[batch_size, n_labels]`. In contrast,
+  `tf.contrib.learn` classifiers and regressors supported labels with shape
+  `[batch_size]`.
+* If you pass custom metrics from the `evaluate()` method call, use
+  `tf.contrib.estimator.add_metrics`.
+* Replace your `serving_input_fn` with a `serving_input_receiver_fn`.
+  Note this should be entirely distinct from your training `input_fn`, so if you
+  previously had one `input_fn` with different "modes", you should now factor
+  that apart.  Where the former returned either a simple `(features, labels)`
+  tuple or `InputFnOps`, you should now return a `ServingInputReceiver`.
+  If you were generating your `serving_input_fn` using the
+  `build_parsing_serving_input_fn` helper, you can simply drop in the
+  replacement `build_parsing_serving_input_receiver_fn`.
+
+Some remaining estimators/classes:
+
+* `DynamicRnnEstimator`:  Consider a custom `model_fn`.
+* `KMeansClustering`: Use `tf.contrib.factorization.KMeansClustering`.
+* `LogisticRegressor`: Not supported. Instead, use `binary_classification_head`
+  with a custom `model_fn`, or with `DNNEstimator`.
+* `StateSavingRnnEstimator`: Consider a custom `model_fn`.
+* SVM: Consider a custom `model_fn`.
+* `LinearComposableModel` and `DNNComposableModel`: Not supported. 
+  Consider `tf.contrib.estimator.DNNEstimator`, or write a custom model_fn.
+* `MetricSpec`: Deprecated. For adding custom metrics to canned Estimators, use
+  `tf.contrib.estimator.add_metrics`.
+
+## Estimator
+`tf.contrib.learn.Estimator` is migrated to `tf.estimator.Estimator`.
+
+To migrate, users need to take the following steps:
+
+* Replace `tf.contrib.learn.Estimator` with `tf.estimator.Estimator`.
+* If you pass a `config` argument to `Estimator`, this must be
+  `tf.estimator.RunConfig`. You may need to edit your code accordingly.
+* Edit your `model_fn` to return `tf.estimator.EstimatorSpec`. Refer to
+  `EstimatorSpec` for documentation of specific fields.
+* If your `model_fn` uses the `mode` argument, use `tf.estimator.ModeKeys`.
+
+Some related classes:
+* `Evaluable`, `Trainable`: Not supported, merged into `tf.estimator.Estimator`.
+* ExportStrategy: Replaced by `tf.estimator.Exporter`.
+
+## Head/MultiHead
+These classes are now supported under `tf.contrib.estimator`, e.g.
+`tf.contrib.estimator.multi_class_head` and `tf.contrib.estimator.multi_head`.
+
+Some differences:
+
+* `multi_class_head`: If you use `tf.contrib.learn.multi_class_head` with
+  `n_classes=2`, switch to `tf.contrib.estimator.binary_classification_head`.
+* `loss_only_head`: Not supported.
+* `poisson_regression_head`: Not supported (yet).
+* `binary_svm_head`: Not supported (yet).
+* `no_op_train_fn`: Replace it with `tf.no_op`.
+
+Some arguments are renamed, please refer to documentation. In addition:
+
+* `loss_fn`: Supported for `multi_label_head`. If you need it for other heads,
+  please open an issue.
+* `metric_class_ids`: Not supported (yet).
+* `enable_centered_bias`: Not supported. Dropping this argument is unlikely to
+  harm your model.
+* `label_name`: Not needed in `tf.estimator`. If you don’t use `multi_head`,
+  drop this argument. If you use `multi_head`, refer to
+  `tf.contrib.estimator.multi_head` documentation.
+
+## Experiment Class - Distributed Training Tooling
+
+Switch to `tf.estimator.train_and_evaluate`. Some differences:
+
+* Most of the constructor arguments, like `train_input_fn`, `eval_input_fn`,
+  should be wrapped into `tf.estimator.TrainSpec` and `tf.estimator.EvalSpec`.
+* Remove the `experiment_fn`. Instead, create the `Estimator`,
+  `train_spec` and `eval_spec`, then call `tf.estimator.train_and_evaluate`
+  directly.
+* Inside `tf.estimator.EvalSpec`, the `exporter` field is the replacement
+  for `export_strategy`. To be precise, `tf.estimator.LatestExporter` is the
+  replacement for `tf.contrib.learn.make_export_strategy`. If you want to export
+  only at the end of training  use `tf.estimator.FinalExporter`.
+* If the `TF_CONFIG` environment variable is constructed manually, please read
+  the `train_and_evaluate` documentation for the new requirementds (in
+  particular, the chief node and evaluator node).
+
+## Others Classes and Functions
+
+* `tf.contrib.learn.datasets` is deprecated. We are adding ready to use datasets
+  to tensorflow/models. Many smaller datasets are available from other sources,
+  such as scikits.learn. Some Python processing may have to be written, but this
+  is straightforward to implement using the standard modules.
+* `tf.contrib.learn.preprocessing`: Deprecated. The python-only preprocessing
+  functions are not a good fit for TensorFlow. Please use `tf.data`, and
+  consider tensorflow/transform for more complex use cases.
+* `tf.contrib.learn.models`: Not supported, use canned estimators instead.
+* `tf.contrib.learn.monitors`: Implement `SessionRunHook` instead. Hook
+  implementations are in `tf.train`.
+* `tf.contrib.learn.learn_io`: Use the methods in `tf.estimator.inputs`, such as
+  `tf.estimator.inputs.numpy_input_fn`. Some utility functions have no
+  equivalent, we encourage the use of `tf.data`.
+
diff --git a/tensorflow/contrib/learn/__init__.py b/tensorflow/contrib/learn/__init__.py
index 3698af027e..79bd73faaf 100644
--- a/tensorflow/contrib/learn/__init__.py
+++ b/tensorflow/contrib/learn/__init__.py
@@ -13,8 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-# TODO(ptucker,ipolosukhin): Improve descriptions.
-"""High level API for learning.
+"""High level API for learning (DEPRECATED).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 See the @{$python/contrib.learn} guide.
 
diff --git a/tensorflow/contrib/learn/python/__init__.py b/tensorflow/contrib/learn/python/__init__.py
index bbebd5ab97..df23aeb2c4 100644
--- a/tensorflow/contrib/learn/python/__init__.py
+++ b/tensorflow/contrib/learn/python/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""High level API for learning with TensorFlow."""
+"""High level API for learning with TensorFlow (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/__init__.py b/tensorflow/contrib/learn/python/learn/__init__.py
index cdc67c77d5..76e0e8ac8f 100644
--- a/tensorflow/contrib/learn/python/learn/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""High level API for learning with TensorFlow."""
+"""High level API for learning with TensorFlow (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py b/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py
index 2284ec46e9..fed1c44d19 100644
--- a/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py
+++ b/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py
@@ -12,20 +12,47 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Some common SessionRunHook classes."""
+"""Some common SessionRunHook classes (deprected).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.training import basic_session_run_hooks
+from tensorflow.python.util.deprecation import deprecated_alias
 
 # pylint: disable=invalid-name
-LoggingTensorHook = basic_session_run_hooks.LoggingTensorHook
-StopAtStepHook = basic_session_run_hooks.StopAtStepHook
-CheckpointSaverHook = basic_session_run_hooks.CheckpointSaverHook
-StepCounterHook = basic_session_run_hooks.StepCounterHook
-NanLossDuringTrainingError = basic_session_run_hooks.NanLossDuringTrainingError
-NanTensorHook = basic_session_run_hooks.NanTensorHook
-SummarySaverHook = basic_session_run_hooks.SummarySaverHook
+LoggingTensorHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.LoggingTensorHook',
+    'tf.train.LoggingTensorHook',
+    basic_session_run_hooks.LoggingTensorHook)
+StopAtStepHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.StopAtStepHook',
+    'tf.train.StopAtStepHook',
+    basic_session_run_hooks.StopAtStepHook)
+CheckpointSaverHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.CheckpointSaverHook',
+    'tf.train.CheckpointSaverHook',
+    basic_session_run_hooks.CheckpointSaverHook)
+StepCounterHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.StepCounterHook',
+    'tf.train.StepCounterHook',
+    basic_session_run_hooks.StepCounterHook)
+NanLossDuringTrainingError = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.NanLossDuringTrainingError',
+    'tf.train.NanLossDuringTrainingError',
+    basic_session_run_hooks.NanLossDuringTrainingError)
+NanTensorHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.NanTensorHook',
+    'tf.train.NanTensorHook',
+    basic_session_run_hooks.NanTensorHook)
+SummarySaverHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.SummarySaverHook',
+    'tf.train.SummarySaverHook',
+    basic_session_run_hooks.SummarySaverHook)
 # pylint: enable=invalid-name
diff --git a/tensorflow/contrib/learn/python/learn/datasets/__init__.py b/tensorflow/contrib/learn/python/learn/datasets/__init__.py
index 7240b0de14..3c34712ac8 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/__init__.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Dataset utilities and synthetic/reference datasets."""
+"""Dataset utilities and synthetic/reference datasets (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -27,6 +32,7 @@ from tensorflow.contrib.learn.python.learn.datasets import base
 from tensorflow.contrib.learn.python.learn.datasets import mnist
 from tensorflow.contrib.learn.python.learn.datasets import synthetic
 from tensorflow.contrib.learn.python.learn.datasets import text_datasets
+from tensorflow.python.util.deprecation import deprecated
 
 # Export load_iris and load_boston.
 load_iris = base.load_iris
@@ -51,6 +57,7 @@ SYNTHETIC = {
 }
 
 
+@deprecated(None, 'Please use tf.data.')
 def load_dataset(name, size='small', test_with_fake_data=False):
   """Loads dataset by name.
 
@@ -73,8 +80,9 @@ def load_dataset(name, size='small', test_with_fake_data=False):
     return DATASETS[name]()
 
 
+@deprecated(None, 'Please use tf.data.')
 def make_dataset(name, n_samples=100, noise=None, seed=42, *args, **kwargs):
-  """Creates binary synthetic datasets
+  """Creates binary synthetic datasets.
 
   Args:
     name: str, name of the dataset to generate
diff --git a/tensorflow/contrib/learn/python/learn/datasets/base.py b/tensorflow/contrib/learn/python/learn/datasets/base.py
index ca720ae5ed..3b5c9b97c0 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/base.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/base.py
@@ -12,7 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Base utilities for loading datasets."""
+
+"""Base utilities for loading datasets (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -29,11 +35,14 @@ import numpy as np
 from six.moves import urllib
 
 from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
+
 
 Dataset = collections.namedtuple('Dataset', ['data', 'target'])
 Datasets = collections.namedtuple('Datasets', ['train', 'validation', 'test'])
 
 
+@deprecated(None, 'Use tf.data instead.')
 def load_csv_with_header(filename,
                          target_dtype,
                          features_dtype,
@@ -53,6 +62,7 @@ def load_csv_with_header(filename,
   return Dataset(data=data, target=target)
 
 
+@deprecated(None, 'Use tf.data instead.')
 def load_csv_without_header(filename,
                             target_dtype,
                             features_dtype,
@@ -70,6 +80,7 @@ def load_csv_without_header(filename,
   return Dataset(data=data, target=target)
 
 
+@deprecated(None, 'Use tf.data instead.')
 def shrink_csv(filename, ratio):
   """Create a smaller dataset of only 1/ratio of original data."""
   filename_small = filename.replace('.', '_small.')
@@ -84,6 +95,7 @@ def shrink_csv(filename, ratio):
         i += 1
 
 
+@deprecated(None, 'Use scikits.learn.datasets.')
 def load_iris(data_path=None):
   """Load Iris dataset.
 
@@ -100,6 +112,7 @@ def load_iris(data_path=None):
       data_path, target_dtype=np.int, features_dtype=np.float)
 
 
+@deprecated(None, 'Use scikits.learn.datasets.')
 def load_boston(data_path=None):
   """Load Boston housing dataset.
 
@@ -116,7 +129,12 @@ def load_boston(data_path=None):
       data_path, target_dtype=np.float, features_dtype=np.float)
 
 
-def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None):
+@deprecated(None, 'Use the retry module or similar alternatives.')
+def retry(initial_delay,
+          max_delay,
+          factor=2.0,
+          jitter=0.25,
+          is_retriable=None):
   """Simple decorator for wrapping retriable functions.
 
   Args:
@@ -152,7 +170,7 @@ def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None):
       for delay in delays():
         try:
           return fn(*args, **kwargs)
-        except Exception as e:  # pylint: disable=broad-except)
+        except Exception as e:  # pylint: disable=broad-except
           if is_retriable is None:
             continue
 
@@ -176,11 +194,13 @@ def _is_retriable(e):
   return isinstance(e, IOError) and e.errno in _RETRIABLE_ERRNOS
 
 
+@deprecated(None, 'Please use urllib or similar directly.')
 @retry(initial_delay=1.0, max_delay=16.0, is_retriable=_is_retriable)
 def urlretrieve_with_retry(url, filename=None):
   return urllib.request.urlretrieve(url, filename)
 
 
+@deprecated(None, 'Please write your own downloading logic.')
 def maybe_download(filename, work_directory, source_url):
   """Download the data from source url, unless it's already here.
 
diff --git a/tensorflow/contrib/learn/python/learn/datasets/mnist.py b/tensorflow/contrib/learn/python/learn/datasets/mnist.py
index 37f9175015..abbb44c2f5 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/mnist.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/mnist.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Functions for downloading and reading MNIST data."""
+"""Functions for downloading and reading MNIST data (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -27,6 +32,7 @@ from tensorflow.contrib.learn.python.learn.datasets import base
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import random_seed
 from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
 
 # CVDF mirror of http://yann.lecun.com/exdb/mnist/
 DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
@@ -37,6 +43,7 @@ def _read32(bytestream):
   return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
 
 
+@deprecated(None, 'Please use tf.data to implement this functionality.')
 def extract_images(f):
   """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
@@ -65,6 +72,7 @@ def extract_images(f):
     return data
 
 
+@deprecated(None, 'Please use tf.one_hot on tensors.')
 def dense_to_one_hot(labels_dense, num_classes):
   """Convert class labels from scalars to one-hot vectors."""
   num_labels = labels_dense.shape[0]
@@ -74,6 +82,7 @@ def dense_to_one_hot(labels_dense, num_classes):
   return labels_one_hot
 
 
+@deprecated(None, 'Please use tf.data to implement this functionality.')
 def extract_labels(f, one_hot=False, num_classes=10):
   """Extract the labels into a 1D uint8 numpy array [index].
 
@@ -103,7 +112,15 @@ def extract_labels(f, one_hot=False, num_classes=10):
 
 
 class DataSet(object):
+  """Container class for a dataset (deprecated).
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please use alternatives such as official/mnist/dataset.py'
+              ' from tensorflow/models.')
   def __init__(self,
                images,
                labels,
@@ -210,6 +227,8 @@ class DataSet(object):
       return self._images[start:end], self._labels[start:end]
 
 
+@deprecated(None, 'Please use alternatives such as official/mnist/dataset.py'
+            ' from tensorflow/models.')
 def read_data_sets(train_dir,
                    fake_data=False,
                    one_hot=False,
@@ -275,5 +294,7 @@ def read_data_sets(train_dir,
   return base.Datasets(train=train, validation=validation, test=test)
 
 
+@deprecated(None, 'Please use alternatives such as official/mnist/dataset.py'
+            ' from tensorflow/models.')
 def load_mnist(train_dir='MNIST-data'):
   return read_data_sets(train_dir)
diff --git a/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py b/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py
index 6e0ba38941..a4848fa64a 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Produce DBpedia datasets of a smaller size."""
+"""Produce DBpedia datasets of a smaller size (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py
index 9a843168c2..6a0e3350b3 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Synthetic dataset generators."""
+"""Synthetic dataset generators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -21,8 +26,10 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib.learn.python.learn.datasets.base import Dataset
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Consider using synthetic datasets from scikits.learn.')
 def circles(n_samples=100,
             noise=None,
             seed=None,
@@ -93,6 +100,7 @@ def circles(n_samples=100,
   return Dataset(data=X[indices], target=y[indices])
 
 
+@deprecated(None, 'Consider using synthetic datasets from scikits.learn.')
 def spirals(n_samples=100,
             noise=None,
             seed=None,
diff --git a/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py b/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py
index 2596a2ecaf..ce94663017 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Text datasets."""
+"""Text datasets (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -26,10 +31,12 @@ import numpy as np
 
 from tensorflow.contrib.learn.python.learn.datasets import base
 from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
 
 DBPEDIA_URL = 'https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz'
 
 
+@deprecated(None, 'See contrib/learn/README.md')
 def maybe_download_dbpedia(data_dir):
   """Download if DBpedia data is not present."""
   train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv')
@@ -41,6 +48,7 @@ def maybe_download_dbpedia(data_dir):
     tfile.extractall(data_dir)
 
 
+@deprecated(None, 'See contrib/learn/README.md')
 def load_dbpedia(size='small', test_with_fake_data=False):
   """Get DBpedia datasets from CSV files."""
   if not test_with_fake_data:
diff --git a/tensorflow/contrib/learn/python/learn/estimators/__init__.py b/tensorflow/contrib/learn/python/learn/estimators/__init__.py
index 4981750c94..3e64595f31 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""An estimator is a rule for calculating an estimate of a given quantity.
+"""An estimator is a rule for calculating an estimate of a given quantity (deprecated).
+
+These classes are deprecated and replaced with `tf.estimator`.
+
+See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 # Estimators
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
index 15277415a1..1f0e4663d0 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-"""sklearn cross-support."""
+"""sklearn cross-support (deprecated)."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -132,6 +132,8 @@ class _TransformerMixin():
 class NotFittedError(ValueError, AttributeError):
   """Exception class to raise if estimator is used before fitting.
 
+  USE OF THIS EXCEPTION IS DEPRECATED.
+
   This class inherits from both ValueError and AttributeError to help with
   exception handling and backward compatibility.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/composable_model.py b/tensorflow/contrib/learn/python/learn/estimators/composable_model.py
index a02c726c74..1fa58271e2 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/composable_model.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/composable_model.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""TensorFlow composable models used as building blocks for estimators."""
+"""TensorFlow composable models used as building blocks for estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -34,6 +39,7 @@ from tensorflow.python.ops import nn
 from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.summary import summary
+from tensorflow.python.util.deprecation import deprecated
 
 
 class _ComposableModel(object):
@@ -46,6 +52,7 @@ class _ComposableModel(object):
   _ComposableModel and its subclasses are not part of the public tf.learn API.
   """
 
+  @deprecated(None, "Please use model_fns in tf.estimator.")
   def __init__(self,
                num_label_columns,
                optimizer,
@@ -141,6 +148,10 @@ class _ComposableModel(object):
 class LinearComposableModel(_ComposableModel):
   """A _ComposableModel that implements linear regression.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Instances of this class can be used to build estimators through the use
   of composition.
   """
@@ -252,6 +263,10 @@ class LinearComposableModel(_ComposableModel):
 class DNNComposableModel(_ComposableModel):
   """A _ComposableModel that implements a DNN.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Instances of this class can be used to build estimators through the use
   of composition.
   """
diff --git a/tensorflow/contrib/learn/python/learn/estimators/constants.py b/tensorflow/contrib/learn/python/learn/estimators/constants.py
index fc69e81024..d2548946bc 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/constants.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/constants.py
@@ -13,9 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Constants regarding Estimators.
+"""Constants regarding Estimators (deprecated).
 
-This file is obsoleted in the move of Estimator to core.
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -25,6 +27,8 @@ from __future__ import print_function
 class ProblemType(object):
   """Enum-like values for the type of problem that the model solves.
 
+  THIS CLASS IS DEPRECATED.
+
   These values are used when exporting the model to produce the appropriate
   signature function for serving.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug.py b/tensorflow/contrib/learn/python/learn/estimators/debug.py
index 9d5f6c2bf9..24b067b7e3 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Debug estimators.
+"""Debug estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Debug estimators are bias-only estimators that can be used for debugging
 and as simple baselines.
@@ -118,6 +122,10 @@ def debug_model_fn(features, labels, mode, params, config=None):
 class DebugClassifier(estimator.Estimator):
   """A classifier for TensorFlow Debug models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
@@ -237,6 +245,10 @@ class DebugClassifier(estimator.Estimator):
 class DebugRegressor(estimator.Estimator):
   """A regressor for TensorFlow Debug models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn.py b/tensorflow/contrib/learn/python/learn/estimators/dnn.py
index c17b41c0f7..eabebb7e88 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dnn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dnn.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Deep Neural Network estimators."""
+"""Deep Neural Network estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -212,6 +217,10 @@ def _dnn_model_fn(features, labels, mode, params, config=None):
 class DNNClassifier(estimator.Estimator):
   """A classifier for TensorFlow DNN models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
@@ -521,6 +530,10 @@ class DNNClassifier(estimator.Estimator):
 class DNNRegressor(estimator.Estimator):
   """A regressor for TensorFlow DNN models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
@@ -796,6 +809,10 @@ class DNNRegressor(estimator.Estimator):
 class DNNEstimator(estimator.Estimator):
   """A Estimator for TensorFlow DNN models with user specified _Head.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
index 7266122350..3d85533d92 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow estimators for Linear and DNN joined training models."""
+"""TensorFlow estimators for Linear and DNN joined training models (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -372,6 +377,10 @@ def _dnn_linear_combined_model_fn(features, labels, mode, params, config=None):
 class DNNLinearCombinedEstimator(estimator.Estimator):
   """An estimator for TensorFlow Linear and DNN joined training models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note: New users must set `fix_global_step_increment_bug=True` when creating an
   estimator.
 
@@ -490,6 +499,10 @@ class DNNLinearCombinedEstimator(estimator.Estimator):
 class DNNLinearCombinedClassifier(estimator.Estimator):
   """A classifier for TensorFlow Linear and DNN joined training models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note: New users must set `fix_global_step_increment_bug=True` when creating an
   estimator.
 
@@ -832,6 +845,10 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
 class DNNLinearCombinedRegressor(estimator.Estimator):
   """A regressor for TensorFlow Linear and DNN joined training models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note: New users must set `fix_global_step_increment_bug=True` when creating an
   estimator.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py
index 69440e823e..a703dc66e9 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Estimator for Dynamic RNNs."""
+"""Estimator for Dynamic RNNs (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -540,6 +545,12 @@ def _get_dynamic_rnn_model_fn(
 
 
 class DynamicRnnEstimator(estimator.Estimator):
+  """Dynamically unrolled RNN (deprecated).
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self,
                problem_type,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index 4b63e08ab3..5262e04e16 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Base Estimator class."""
+"""Base Estimator class (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -138,6 +143,7 @@ def _get_input_fn(x, y, input_fn, feed_fn, batch_size, shuffle=False, epochs=1):
   return df.input_builder, df.get_feed_dict_fn()
 
 
+@deprecated(None, 'Please specify feature columns explicitly.')
 def infer_real_valued_columns_from_input_fn(input_fn):
   """Creates `FeatureColumn` objects for inputs defined by `input_fn`.
 
@@ -158,6 +164,7 @@ def infer_real_valued_columns_from_input_fn(input_fn):
     return layers.infer_real_valued_columns(features)
 
 
+@deprecated(None, 'Please specify feature columns explicitly.')
 def infer_real_valued_columns_from_input(x):
   """Creates `FeatureColumn` objects for inputs defined by input `x`.
 
@@ -389,6 +396,10 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
                     trainable.Trainable):
   """Abstract BaseEstimator class to train and evaluate TensorFlow models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Users should not instantiate or subclass this class. Instead, use an
   `Estimator`.
   """
@@ -399,6 +410,8 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
   # TODO(wicke): Remove this once launcher takes over config functionality
   _Config = run_config.RunConfig  # pylint: disable=invalid-name
 
+  @deprecated(None, 'Please replace uses of any Estimator from tf.contrib.learn'
+              ' with an Estimator from tf.estimator.*')
   def __init__(self, model_dir=None, config=None):
     """Initializes a BaseEstimator instance.
 
@@ -1074,6 +1087,10 @@ def _identity_feature_engineering_fn(features, labels):
 
 class Estimator(BaseEstimator):
   """Estimator class is the basic TensorFlow model trainer/evaluator.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
   """
 
   def __init__(self,
@@ -1458,8 +1475,14 @@ class Estimator(BaseEstimator):
 # For time of deprecation x,y from Estimator allow direct access.
 # pylint: disable=protected-access
 class SKCompat(sklearn.BaseEstimator):
-  """Scikit learn wrapper for TensorFlow Learn Estimator."""
+  """Scikit learn wrapper for TensorFlow Learn Estimator.
+  
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please switch to the Estimator interface.')
   def __init__(self, estimator):
     self._estimator = estimator
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py b/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py
index fd47710e30..e4c31396ba 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utils for Estimator."""
+"""Utils for Estimator (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index 9b124b2c19..2b4b6eff39 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -12,8 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Abstractions for the head(s) of a model.
+"""Abstractions for the head(s) of a model (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 """
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -47,11 +52,16 @@ from tensorflow.python.summary import summary
 from tensorflow.python.training import training
 from tensorflow.python.util import tf_decorator
 from tensorflow.python.util import tf_inspect
+from tensorflow.python.util.deprecation import deprecated
 
 
 class Head(object):
   """Interface for the head/top of a model.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Given logits (or output of a hidden layer), a Head knows how to compute
   predictions, loss, default metric and export signature. It is meant to,
 
@@ -177,6 +187,7 @@ class Head(object):
     raise NotImplementedError("Calling an abstract method.")
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def regression_head(label_name=None,
                     weight_column_name=None,
                     label_dimension=1,
@@ -216,6 +227,7 @@ def regression_head(label_name=None,
       link_fn=(link_fn if link_fn is not None else array_ops.identity))
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def poisson_regression_head(label_name=None,
                             weight_column_name=None,
                             label_dimension=1,
@@ -254,6 +266,7 @@ def poisson_regression_head(label_name=None,
 # TODO(zakaria): Consider adding a _RegressionHead for logistic_regression
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def multi_class_head(n_classes,
                      label_name=None,
                      weight_column_name=None,
@@ -335,6 +348,7 @@ def multi_class_head(n_classes,
       label_keys=label_keys)
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def binary_svm_head(
     label_name=None,
     weight_column_name=None,
@@ -370,6 +384,7 @@ def binary_svm_head(
       thresholds=thresholds)
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def multi_label_head(n_classes,
                      label_name=None,
                      weight_column_name=None,
@@ -430,6 +445,7 @@ def multi_label_head(n_classes,
       loss_fn=_wrap_custom_loss_fn(loss_fn) if loss_fn else None)
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def loss_only_head(loss_fn, head_name=None):
   """Creates a Head that contains only loss terms.
 
@@ -447,6 +463,7 @@ def loss_only_head(loss_fn, head_name=None):
   return _LossOnlyHead(loss_fn, head_name=head_name)
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def multi_head(heads, loss_weights=None):
   """Creates a MultiHead stemming from same logits/hidden layer.
 
@@ -479,6 +496,7 @@ def multi_head(heads, loss_weights=None):
   return _MultiHead(heads, loss_merger=_weighted_loss_merger)
 
 
+@deprecated(None, "Use 'lambda _: tf.no_op()'.")
 def no_op_train_fn(loss):
   del loss
   return control_flow_ops.no_op()
diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
index 8f9d6fc318..66ebcfd1d8 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Implementation of k-means clustering on top of `Estimator` API.
+"""Implementation of k-means clustering on top of `Estimator` API (deprecated).
 
 This module is deprecated. Please use
 @{tf.contrib.factorization.KMeansClustering} instead of
@@ -153,7 +153,12 @@ def _kmeans_clustering_model_fn(features, labels, mode, params, config):
 
 # TODO(agarwal,ands): support sharded input.
 class KMeansClustering(estimator.Estimator):
-  """An Estimator for K-Means clustering."""
+  """An Estimator for K-Means clustering.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
   SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE
   COSINE_DISTANCE = clustering_ops.COSINE_DISTANCE
   RANDOM_INIT = clustering_ops.RANDOM_INIT
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear.py b/tensorflow/contrib/learn/python/learn/estimators/linear.py
index 37aa8b3396..64d7ecc68e 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Linear Estimators."""
+"""Linear Estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -305,6 +310,10 @@ class _SdcaUpdateWeightsHook(session_run_hook.SessionRunHook):
 class LinearClassifier(estimator.Estimator):
   """Linear classifier model.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Train a linear model to classify instances into one of multiple possible
   classes. When number of possible classes is 2, this is binary classification.
 
@@ -625,6 +634,10 @@ class LinearClassifier(estimator.Estimator):
 class LinearRegressor(estimator.Estimator):
   """Linear regressor model.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Train a linear regression model to predict label value given observation of
   feature values.
 
@@ -860,6 +873,10 @@ class LinearRegressor(estimator.Estimator):
 class LinearEstimator(estimator.Estimator):
   """Linear model with user specified head.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Train a generalized linear model to predict label value given observation of
   feature values.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py
index fb339160d5..3cbcc6e98d 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Logistic regression (aka binary classifier) class.
+"""Logistic regression (aka binary classifier) class (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 This defines some useful basic metrics for using logistic regression to classify
 a binary event (0 vs 1).
@@ -75,6 +79,10 @@ def LogisticRegressor(  # pylint: disable=invalid-name
     feature_engineering_fn=None):
   """Builds a logistic regression Estimator for binary classification.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   This method provides a basic Estimator with some additional metrics for custom
   binary classification models, including AUC, precision/recall and accuracy.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/metric_key.py b/tensorflow/contrib/learn/python/learn/estimators/metric_key.py
index 99388f116b..f264248e44 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/metric_key.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/metric_key.py
@@ -12,14 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Enum for metric keys."""
+"""Enum for metric keys (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 
 class MetricKey(object):
-  """Metric key strings."""
+  """Metric key strings (deprecated)."""
+  
   LOSS = "loss"
   AUC = "auc"
   AUC_PR = "auc_precision_recall"
diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
index 44e6c7c52d..dcb161180c 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Classes and methods related to model_fn."""
+"""Classes and methods related to model_fn (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -37,10 +42,13 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.training import session_run_hook
+from tensorflow.python.util.deprecation import deprecated
 
 
 class ModeKeys(object):
-  """Standard names for model modes.
+  """Standard names for model modes (deprecated).
+
+  THIS CLASS IS DEPRECATED.
 
   The following standard keys are defined:
 
@@ -65,8 +73,16 @@ class ModelFnOps(
         'output_alternatives', 'training_chief_hooks', 'training_hooks',
         'scaffold', 'mode'
     ])):
-  """Ops returned from a model_fn."""
+  """Ops returned from a model_fn.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'When switching to tf.estimator.Estimator, use '
+              'tf.estimator.EstimatorSpec. You can use the `estimator_spec`'
+              ' method to create an equivalent one.')
   def __new__(cls,
               mode,
               predictions=None,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py b/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py
index f8d87b8914..6fd2fc9d59 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Enum for model prediction keys.
+"""Enum for model prediction keys (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 This file is obsoleted in the move of Estimator to core.
 """
@@ -22,6 +26,8 @@ from __future__ import print_function
 
 
 class PredictionKey(object):
+  """THIS CLASS IS DEPRECATED."""
+
   CLASSES = "classes"
   PROBABILITIES = "probabilities"
   LOGITS = "logits"
diff --git a/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py b/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py
index 2752bc2d90..215022e5d9 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Common operations for RNN Estimators."""
+"""Common operations for RNN Estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index fd90fd1cc6..1d161093de 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Run Config."""
+"""Run Config (deprecated, use tf.estimator.RunConfig instead).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -29,11 +34,12 @@ from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.estimator import run_config as core_run_config
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import server_lib
+from tensorflow.python.util.deprecation import deprecated
 
 
 # A list of the property names in RunConfig user allows to change. They will
 # not affect the execution framework, so when execution framework checks the
-# `uid` of the RunConfig, it should be ingored.
+# `uid` of the RunConfig, it should be ignored.
 _DEFAULT_UID_WHITE_LIST = [
     'tf_random_seed',
     'save_summary_steps',
@@ -47,6 +53,7 @@ _DEFAULT_UID_WHITE_LIST = [
 
 
 class Environment(object):
+  """DEPRECATED CLASS."""
   # For running general distributed training.
   CLOUD = 'cloud'
   # For running Google-internal distributed training.
@@ -56,6 +63,7 @@ class Environment(object):
 
 
 class TaskType(object):
+  """DEPRECATED CLASS."""
   MASTER = 'master'
   PS = 'ps'
   WORKER = 'worker'
@@ -64,6 +72,8 @@ class TaskType(object):
 class ClusterConfig(object):
   """This class specifies the configurations for a distributed run.
 
+  THIS CLASS IS DEPRECATED. Use tf.estimator.RunConfig instead.
+
   If you're using an `Estimator`, you should probably use the subclass
   RunConfig instead.
   """
@@ -211,10 +221,13 @@ class ClusterConfig(object):
 class RunConfig(ClusterConfig, core_run_config.RunConfig):
   """This class specifies the configurations for an `Estimator` run.
 
-  This class is the implementation of @{tf.estimator.RunConfig} interface.
+  This class is a deprecated implementation of @{tf.estimator.RunConfig}
+  interface.
   """
   _USE_DEFAULT = 0
 
+  @deprecated(None, 'When switching to tf.estimator.Estimator, use'
+              ' tf.estimator.RunConfig instead.')
   def __init__(self,
                master=None,
                num_cores=0,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
index 0cea35e219..de78c72c3a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Estimator for State Saving RNNs."""
+"""Estimator for State Saving RNNs (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -528,6 +533,12 @@ def _get_rnn_model_fn(cell_type,
 
 
 class StateSavingRnnEstimator(estimator.Estimator):
+  """RNN with static unrolling and state saving (deprecated).
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self,
                problem_type,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/svm.py b/tensorflow/contrib/learn/python/learn/estimators/svm.py
index 72920d73c0..3459997bab 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/svm.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/svm.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Support Vector Machine (SVM) Estimator."""
+"""Support Vector Machine (SVM) Estimator (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -36,6 +41,10 @@ def _as_iterable(preds, output):
 class SVM(estimator.Estimator):
   """Support Vector Machine (SVM) model for binary classification.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Currently, only linear SVMs are supported. For the underlying optimization
   problem, the `SDCAOptimizer` is used. For performance and convergence tuning,
   the num_loss_partitions parameter passed to `SDCAOptimizer` (see `__init__()`
diff --git a/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py b/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py
index a120bc6cc3..71b5658dd1 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorSignature class and utilities."""
+"""TensorSignature class and utilities (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -33,6 +38,10 @@ class TensorSignature(collections.namedtuple(
     "TensorSignature", ["dtype", "shape", "is_sparse"])):
   """Signature of the `Tensor` object.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Useful to check compatibility of tensors.
 
   Example:
diff --git a/tensorflow/contrib/learn/python/learn/estimators/test_data.py b/tensorflow/contrib/learn/python/learn/estimators/test_data.py
index ed201bfc58..e4b057b4f5 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/test_data.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/test_data.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Test data utilities."""
+"""Test data utilities (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/evaluable.py b/tensorflow/contrib/learn/python/learn/evaluable.py
index 8f6cd39864..10881ca885 100644
--- a/tensorflow/contrib/learn/python/learn/evaluable.py
+++ b/tensorflow/contrib/learn/python/learn/evaluable.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""`Evaluable` interface."""
+"""`Evaluable` interface (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -23,6 +28,10 @@ import abc
 
 class Evaluable(object):
   """Interface for objects that are evaluatable by, e.g., `Experiment`.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
   """
   __metaclass__ = abc.ABCMeta
 
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 331bc11549..9a7c4cd685 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Experiment class collecting information needed for a single training run."""
+"""Experiment class collecting information for a single training run (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -25,7 +30,6 @@ import os
 import time
 
 from tensorflow.contrib.framework import deprecated
-from tensorflow.contrib.framework import deprecated_args
 from tensorflow.contrib.framework.python.framework import experimental
 from tensorflow.contrib.learn.python.learn import evaluable
 from tensorflow.contrib.learn.python.learn import export_strategy
@@ -118,6 +122,10 @@ class _EvalAndExportListener(basic_session_run_hooks.CheckpointSaverListener):
 class Experiment(object):
   """Experiment is a class containing all information needed to train a model.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   After an experiment is created (by passing an Estimator and inputs for
   training and evaluation), an Experiment instance knows how to invoke training
   and eval loops in a sensible fashion for distributed training.
@@ -125,16 +133,8 @@ class Experiment(object):
 
   # TODO(ispir): remove delay_workers_by_global_step and make global step based
   # waiting as only behavior.
-  @deprecated_args(
-      "2016-10-23",
-      "local_eval_frequency is deprecated as local_run will be renamed to "
-      "train_and_evaluate. Use min_eval_frequency and call train_and_evaluate "
-      "instead. Note, however, that the default for min_eval_frequency is 1, "
-      "meaning models will be evaluated every time a new checkpoint is "
-      "available. In contrast, the default for local_eval_frequency is None, "
-      "resulting in evaluation occurring only after training has completed. "
-      "min_eval_frequency is ignored when calling the deprecated local_run.",
-      "local_eval_frequency")
+  @deprecated(None, "Please switch to tf.estimator.train_and_evaluate. You will"
+              " also have to convert to a tf.estimator.Estimator.")
   def __init__(self,
                estimator,
                train_input_fn,
diff --git a/tensorflow/contrib/learn/python/learn/export_strategy.py b/tensorflow/contrib/learn/python/learn/export_strategy.py
index 55a8b82431..075cab536e 100644
--- a/tensorflow/contrib/learn/python/learn/export_strategy.py
+++ b/tensorflow/contrib/learn/python/learn/export_strategy.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""ExportStrategy class represents different flavors of model export."""
+"""ExportStrategy class represents different flavors of model export (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -21,6 +26,7 @@ from __future__ import print_function
 import collections
 
 from tensorflow.python.util import tf_inspect
+from tensorflow.python.util.deprecation import deprecated
 
 __all__ = ['ExportStrategy']
 
@@ -30,6 +36,10 @@ class ExportStrategy(
                            ['name', 'export_fn', 'strip_default_attrs'])):
   """A class representing a type of model export.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Typically constructed by a utility function specific to the exporter, such as
   `saved_model_export_utils.make_export_strategy()`.
 
@@ -56,6 +66,8 @@ class ExportStrategy(
         forward compatibility of the resulting `SavedModel`.
   """
 
+  @deprecated(None, 'Please switch to tf.estimator.train_and_evaluate, and use '
+              'tf.estimator.Exporter.')
   def __new__(cls, name, export_fn, strip_default_attrs=None):
     return super(ExportStrategy, cls).__new__(
         cls, name, export_fn, strip_default_attrs)
diff --git a/tensorflow/contrib/learn/python/learn/graph_actions.py b/tensorflow/contrib/learn/python/learn/graph_actions.py
index 98365c05f6..a997fab723 100644
--- a/tensorflow/contrib/learn/python/learn/graph_actions.py
+++ b/tensorflow/contrib/learn/python/learn/graph_actions.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""High level operations on graphs."""
+"""High level operations on graphs (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -68,6 +73,7 @@ def clear_summary_writers():
   return summary_io.SummaryWriterCache.clear()
 
 
+@deprecated(None, 'Use `SummaryWriterCache.get` directly.')
 def get_summary_writer(logdir):
   """Returns single SummaryWriter per logdir in current run.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/__init__.py b/tensorflow/contrib/learn/python/learn/learn_io/__init__.py
index 06c3782a47..8b133a4440 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/__init__.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tools to allow different io formats."""
+"""Tools to allow different io formats (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py b/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py
index 7d666391ce..e0a1948d95 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Methods to allow dask.DataFrame."""
+"""Methods to allow dask.DataFrame (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -21,6 +26,8 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.util.deprecation import deprecated
+
 try:
   # pylint: disable=g-import-not-at-top
   import dask.dataframe as dd
@@ -60,6 +67,7 @@ def _construct_dask_df_with_divisions(df):
     return dd.Series(merge(dsk, df.dask), name, df.name, divisions)
 
 
+@deprecated(None, 'Please feed input to tf.data to support dask.')
 def extract_dask_data(data):
   """Extract data from dask.Series or dask.DataFrame for predictors.
 
@@ -81,6 +89,7 @@ def extract_dask_data(data):
     return data
 
 
+@deprecated(None, 'Please feed input to tf.data to support dask.')
 def extract_dask_labels(labels):
   """Extract data from dask.Series or dask.DataFrame for labels.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py
index 96be8b1bc4..c45b1d1864 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Implementations of different data feeders to provide data for TF trainer."""
+"""Implementations of different data feeders to provide data for TF trainer (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 # TODO(ipolosukhin): Replace this module with feed-dict queue runners & queues.
 
@@ -31,6 +36,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util.deprecation import deprecated
 
 # pylint: disable=g-multiple-import,g-bad-import-order
 from .pandas_io import HAS_PANDAS, extract_pandas_data, extract_pandas_matrix, extract_pandas_labels
@@ -101,6 +107,7 @@ def _is_iterable(x):
   return hasattr(x, 'next') or hasattr(x, '__next__')
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 def setup_train_data_feeder(x,
                             y,
                             n_classes,
@@ -188,6 +195,7 @@ def _batch_data(x, batch_size=None):
     yield np.matrix(chunk)
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 def setup_predict_data_feeder(x, batch_size=None):
   """Returns an iterable for feeding into predict step.
 
@@ -219,6 +227,7 @@ def setup_predict_data_feeder(x, batch_size=None):
   return [x]
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 def setup_processor_data_feeder(x):
   """Sets up processor iterable.
 
@@ -233,6 +242,7 @@ def setup_processor_data_feeder(x):
   return x
 
 
+@deprecated(None, 'Please convert numpy dtypes explicitly.')
 def check_array(array, dtype):
   """Checks array on dtype and converts it if different.
 
@@ -275,8 +285,14 @@ def _check_dtype(dtype):
 
 
 class DataFeeder(object):
-  """Data feeder is an example class to sample data for TF trainer."""
+  """Data feeder is an example class to sample data for TF trainer.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data.')
   def __init__(self,
                x,
                y,
@@ -563,6 +579,10 @@ class DataFeeder(object):
 class StreamingDataFeeder(DataFeeder):
   """Data feeder for TF trainer that reads data from iterator.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Streaming data feeder allows to read data as it comes it from disk or
   somewhere else. It's custom to have this iterators rotate infinetly over
   the dataset, to allow control of how much to learn on the trainer side.
@@ -771,11 +791,16 @@ class StreamingDataFeeder(DataFeeder):
 class DaskDataFeeder(object):
   """Data feeder for that reads data from dask.Series and dask.DataFrame.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Numpy arrays can be serialized to disk and it's possible to do random seeks
   into them. DaskDataFeeder will remove requirement to have full dataset in the
   memory and still do random seeks for sampling of batches.
   """
 
+  @deprecated(None, 'Please feed input to tf.data to support dask.')
   def __init__(self,
                x,
                y,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py
index 884faf8335..f8aaa0c9e3 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Methods to allow generator of dict with numpy arrays."""
+"""Methods to allow generator of dict with numpy arrays (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -23,8 +28,10 @@ from types import FunctionType
 from types import GeneratorType
 
 from tensorflow.python.estimator.inputs.queues.feeding_functions import _enqueue_data as enqueue_data
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Please use tf.data.')
 def generator_input_fn(x,
                        target_key=None,
                        batch_size=128,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
index 3a46c23968..9e816f54b6 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Methods to read data in the graph."""
+"""Methods to read data in the graph (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -34,11 +39,13 @@ from tensorflow.python.platform import gfile
 from tensorflow.python.summary import summary
 from tensorflow.python.training import input as input_ops
 from tensorflow.python.training import queue_runner
+from tensorflow.python.util.deprecation import deprecated
 
 # Default name for key in the feature dict.
 KEY_FEATURE_NAME = '__key__'
 
 
+@deprecated(None, 'Use tf.data.')
 def read_batch_examples(file_pattern,
                         batch_size,
                         reader,
@@ -106,6 +113,7 @@ def read_batch_examples(file_pattern,
   return examples
 
 
+@deprecated(None, 'Use tf.data.')
 def read_keyed_batch_examples(file_pattern,
                               batch_size,
                               reader,
@@ -175,6 +183,7 @@ def read_keyed_batch_examples(file_pattern,
       seed=seed)
 
 
+@deprecated(None, 'Use tf.data.')
 def read_keyed_batch_examples_shared_queue(file_pattern,
                                            batch_size,
                                            reader,
@@ -452,6 +461,7 @@ def _read_keyed_batch_examples_helper(file_pattern,
     return queued_examples_with_keys
 
 
+@deprecated(None, 'Use tf.data.')
 def read_keyed_batch_features(file_pattern,
                               batch_size,
                               features,
@@ -540,6 +550,7 @@ def read_keyed_batch_features(file_pattern,
         name=scope)
 
 
+@deprecated(None, 'Use tf.data.')
 def read_keyed_batch_features_shared_queue(file_pattern,
                                            batch_size,
                                            features,
@@ -620,6 +631,7 @@ def read_keyed_batch_features_shared_queue(file_pattern,
         name=scope)
 
 
+@deprecated(None, 'Use tf.data.')
 def queue_parsed_features(parsed_features,
                           keys=None,
                           feature_queue_capacity=100,
@@ -742,6 +754,7 @@ def queue_parsed_features(parsed_features,
     return dequeued_keys, dequeued_parsed_features
 
 
+@deprecated(None, 'Use tf.data.')
 def read_batch_features(file_pattern,
                         batch_size,
                         features,
@@ -821,6 +834,7 @@ def read_batch_features(file_pattern,
   return features
 
 
+@deprecated(None, 'Use tf.data.')
 def read_batch_record_features(file_pattern,
                                batch_size,
                                features,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py b/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py
index 692438807f..29552d24f1 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py
@@ -12,15 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Methods to allow dict of numpy arrays."""
+"""Methods to allow dict of numpy arrays (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.estimator.inputs.numpy_io import numpy_input_fn as core_numpy_input_fn
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Use tf.estimator.inputs.numpy_input_fn.')
 def numpy_input_fn(x,
                    y=None,
                    batch_size=128,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py
index ede7558eaf..b4ef055f5a 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py
@@ -13,13 +13,19 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Methods to allow pandas.DataFrame."""
+"""Methods to allow pandas.DataFrame (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.estimator.inputs.pandas_io import pandas_input_fn as core_pandas_input_fn
+from tensorflow.python.util.deprecation import deprecated
 
 try:
   # pylint: disable=g-import-not-at-top
@@ -47,6 +53,7 @@ PANDAS_DTYPES = {
 }
 
 
+@deprecated(None, 'Please use tf.estimator.inputs.pandas_input_fn')
 def pandas_input_fn(x,
                     y=None,
                     batch_size=128,
@@ -66,6 +73,7 @@ def pandas_input_fn(x,
                               target_column=target_column)
 
 
+@deprecated(None, 'Please access pandas data directly.')
 def extract_pandas_data(data):
   """Extract data from pandas.DataFrame for predictors.
 
@@ -96,6 +104,7 @@ def extract_pandas_data(data):
                      'float, or bool. Found: ' + ', '.join(error_report))
 
 
+@deprecated(None, 'Please access pandas data directly.')
 def extract_pandas_matrix(data):
   """Extracts numpy matrix from pandas DataFrame.
 
@@ -111,6 +120,7 @@ def extract_pandas_matrix(data):
   return data.as_matrix()
 
 
+@deprecated(None, 'Please access pandas data directly.')
 def extract_pandas_labels(labels):
   """Extract data from pandas.DataFrame for labels.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_runner.py b/tensorflow/contrib/learn/python/learn/learn_runner.py
index 2af723a0d6..d719a3e488 100644
--- a/tensorflow/contrib/learn/python/learn/learn_runner.py
+++ b/tensorflow/contrib/learn/python/learn/learn_runner.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Runs an Experiment."""
+"""Runs an Experiment (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,6 +27,7 @@ from tensorflow.contrib.learn.python.learn.estimators import run_config as run_c
 from tensorflow.contrib.learn.python.learn.experiment import Experiment
 from tensorflow.contrib.training.python.training import hparam as hparam_lib
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util.deprecation import deprecated
 
 
 # TODO(xiejw): Refactor the learn_runner to make code reusable.
@@ -99,6 +105,7 @@ def _wrapped_experiment_fn_with_uid_check(experiment_fn, require_hparams=False):
   return wrapped_experiment_fn
 
 
+@deprecated(None, 'Use tf.estimator.train_and_evaluate.')
 def run(experiment_fn, output_dir=None, schedule=None, run_config=None,
         hparams=None):
   """Make and run an experiment.
@@ -218,6 +225,7 @@ def run(experiment_fn, output_dir=None, schedule=None, run_config=None,
   return _execute_schedule(experiment, schedule)
 
 
+@deprecated(None, 'Use tf.estimator.train_and_evaluate.')
 def tune(experiment_fn, tuner):
   """Tune an experiment with hyper-parameters.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_runner_lib.py b/tensorflow/contrib/learn/python/learn/learn_runner_lib.py
index 7d9b1c7716..ba2d067787 100644
--- a/tensorflow/contrib/learn/python/learn/learn_runner_lib.py
+++ b/tensorflow/contrib/learn/python/learn/learn_runner_lib.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utilities to run and tune an Experiment.
+"""Utilities to run and tune an Experiment (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 @@run
 @@tune
diff --git a/tensorflow/contrib/learn/python/learn/metric_spec.py b/tensorflow/contrib/learn/python/learn/metric_spec.py
index 6440bc204b..97220365d5 100644
--- a/tensorflow/contrib/learn/python/learn/metric_spec.py
+++ b/tensorflow/contrib/learn/python/learn/metric_spec.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The metric spec class to flexibly connect models and metrics."""
+"""The metric spec class to flexibly connect models and metrics (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,6 +27,7 @@ import six
 
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import tf_inspect
+from tensorflow.python.util.deprecation import deprecated
 
 
 def _assert_named_args(sentinel):
@@ -223,6 +229,10 @@ def _adapt_metric_fn(
 class MetricSpec(object):
   """MetricSpec connects a model to metric functions.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   The MetricSpec class contains all information necessary to connect the
   output of a `model_fn` to the metrics (usually, streaming metrics) that are
   used in evaluation.
@@ -284,6 +294,7 @@ class MetricSpec(object):
 
   """
 
+  @deprecated(None, 'Use tf.estimator.EstimatorSpec.eval_metric_ops.')
   def __init__(self,
                metric_fn,
                prediction_key=None,
diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py
index 4283240d01..bd4bbf9f8c 100644
--- a/tensorflow/contrib/learn/python/learn/models.py
+++ b/tensorflow/contrib/learn/python/learn/models.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Various high level TF models."""
+"""Various high level TF models (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -28,8 +33,10 @@ from tensorflow.python.ops import array_ops as array_ops_
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.summary import summary
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Consider using a tf.estimator.LinearRegressor')
 def linear_regression_zero_init(x, y):
   """Linear regression subgraph with zero-value initial weights and bias.
 
@@ -43,6 +50,7 @@ def linear_regression_zero_init(x, y):
   return linear_regression(x, y, init_mean=0.0, init_stddev=0.0)
 
 
+@deprecated(None, 'Consider using a class from tf.estimator.LinearClassifier')
 def logistic_regression_zero_init(x, y):
   """Logistic regression subgraph with zero-value initial weights and bias.
 
@@ -56,6 +64,7 @@ def logistic_regression_zero_init(x, y):
   return logistic_regression(x, y, init_mean=0.0, init_stddev=0.0)
 
 
+@deprecated(None, 'Consider using a class from tf.estimator.')
 def linear_regression(x, y, init_mean=None, init_stddev=1.0):
   """Creates linear regression TensorFlow subgraph.
 
@@ -107,6 +116,7 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0):
     return losses_ops.mean_squared_error_regressor(x, y, weights, bias)
 
 
+@deprecated(None, 'Consider using a class from tf.estimator.')
 def logistic_regression(x,
                         y,
                         class_weight=None,
@@ -203,6 +213,7 @@ def _reverse_seq(input_seq, lengths):
   return result
 
 
+@deprecated(None, 'Please consider `tf.nn.bidirectional_dynamic_rnn`.')
 def bidirectional_rnn(cell_fw,
                       cell_bw,
                       inputs,
@@ -283,6 +294,7 @@ def bidirectional_rnn(cell_fw,
 # End of TensorFlow 0.7
 
 
+@deprecated(None, 'Please consider tensorflow/tensor2tensor.')
 def get_rnn_model(rnn_size, cell_type, num_layers, input_op_fn, bidirectional,
                   target_predictor_fn, sequence_length, initial_state,
                   attn_length, attn_size, attn_vec_size):
diff --git a/tensorflow/contrib/learn/python/learn/monitored_session.py b/tensorflow/contrib/learn/python/learn/monitored_session.py
index 22602e9f69..ac0433f177 100644
--- a/tensorflow/contrib/learn/python/learn/monitored_session.py
+++ b/tensorflow/contrib/learn/python/learn/monitored_session.py
@@ -13,7 +13,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""A wrapper of Session API which runs hooks."""
+"""A wrapper of Session API which runs hooks (deprecated).
+
+These are deprecated aliases for classes and functions in `tf.train`. Please use
+those directly.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py
index 9457a73ecf..77f7c73d54 100644
--- a/tensorflow/contrib/learn/python/learn/monitors.py
+++ b/tensorflow/contrib/learn/python/learn/monitors.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Monitors instrument the training process.
+"""Monitors instrument the training process (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 @@get_default_monitors
 @@BaseMonitor
@@ -59,6 +63,10 @@ from tensorflow.python.util import tf_inspect
 class BaseMonitor(object):
   """Base class for Monitors.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Defines basic interfaces of Monitors.
   Monitors can either be run on all workers or, more commonly, restricted
   to run exclusively on the elected chief worker.
@@ -229,6 +237,10 @@ def _extract_output(outputs, request):
 class EveryN(BaseMonitor):
   """Base class for monitors that execute callbacks every N steps.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   This class adds three new callbacks:
     - every_n_step_begin
     - every_n_step_end
@@ -418,6 +430,10 @@ class StopAtStep(BaseMonitor):
 class PrintTensor(EveryN):
   """Prints given tensors every N steps.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   This is an `EveryN` monitor and has consistent semantic for `every_n`
   and `first_n`.
 
@@ -455,9 +471,12 @@ class PrintTensor(EveryN):
 class LoggingTrainable(EveryN):
   """Writes trainable variable values into log every N steps.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Write the tensors in trainable variables `every_n` steps,
   starting with the `first_n`th step.
-
   """
 
   def __init__(self, scope=None, every_n=100, first_n=1):
@@ -493,7 +512,12 @@ class LoggingTrainable(EveryN):
 
 
 class SummarySaver(EveryN):
-  """Saves summaries every N steps."""
+  """Saves summaries every N steps.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self,
                summary_op,
@@ -554,6 +578,10 @@ class SummarySaver(EveryN):
 class ValidationMonitor(EveryN):
   """Runs evaluation of a given estimator, at most every N steps.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note that the evaluation is done based on the saved checkpoint, which will
   usually be older than the current step.
 
@@ -756,6 +784,10 @@ class ValidationMonitor(EveryN):
 class CaptureVariable(EveryN):
   """Captures a variable's values into a collection.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   This monitor is useful for unit testing. You should exercise caution when
   using this monitor in production, since it never discards values.
 
@@ -794,6 +826,7 @@ class CaptureVariable(EveryN):
     self._var_values[step] = _extract_output(outputs, self._var_name)
 
 
+@deprecation.deprecated(None, "Use tf.train.MonitoredTrainingSession.")
 def get_default_monitors(loss_op=None,
                          summary_op=None,
                          save_summary_steps=100,
@@ -828,6 +861,10 @@ def get_default_monitors(loss_op=None,
 class GraphDump(BaseMonitor):
   """Dumps almost all tensors in the graph at every step.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note, this is very expensive, prefer `PrintTensor` in production.
   """
 
@@ -917,7 +954,12 @@ class GraphDump(BaseMonitor):
 
 
 class ExportMonitor(EveryN):
-  """Monitor that exports Estimator every N steps."""
+  """Monitor that exports Estimator every N steps.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   @deprecation.deprecated("2017-03-25",
                           "ExportMonitor is deprecated. Please pass an "
@@ -1040,7 +1082,12 @@ class ExportMonitor(EveryN):
 
 
 class CheckpointSaver(BaseMonitor):
-  """Saves checkpoints every N steps or N seconds."""
+  """Saves checkpoints every N steps or N seconds.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self,
                checkpoint_dir,
@@ -1125,7 +1172,12 @@ class CheckpointSaver(BaseMonitor):
 
 
 class StepCounter(EveryN):
-  """Steps per second monitor."""
+  """Steps per second monitor.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self, every_n_steps=100, output_dir=None, summary_writer=None):
     super(StepCounter, self).__init__(every_n_steps=every_n_steps)
@@ -1165,6 +1217,10 @@ class NanLossDuringTrainingError(RuntimeError):
 class NanLoss(EveryN):
   """NaN Loss monitor.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Monitors loss and stops training if loss is NaN.
   Can either fail with exception or just stop training.
   """
diff --git a/tensorflow/contrib/learn/python/learn/ops/__init__.py b/tensorflow/contrib/learn/python/learn/ops/__init__.py
index 33962e34cc..efb1f47cf5 100644
--- a/tensorflow/contrib/learn/python/learn/ops/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/ops/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Various TensorFlow Ops."""
+"""Various TensorFlow Ops (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py b/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py
index fa3b7323e3..b3b067b8e1 100644
--- a/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py
+++ b/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py
@@ -13,7 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow Ops to work with embeddings.
+"""TensorFlow Ops to work with embeddings (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Note: categorical variables are handled via embeddings in many cases.
 For example, in case of words.
diff --git a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
index b040ab3bb6..92976d1539 100644
--- a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
+++ b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow Ops for loss computation."""
+"""TensorFlow Ops for loss computation (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py
index 45727faab4..aa37cb4a76 100644
--- a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py
+++ b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow Ops for Sequence to Sequence models."""
+"""TensorFlow Ops for Sequence to Sequence models (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -26,8 +31,10 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import variable_scope as vs
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Please use tf.nn/tf.layers directly.')
 def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
   """Returns predictions and loss for sequence of predictions.
 
@@ -57,6 +64,7 @@ def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
     return array_ops.stack(predictions, axis=1), loss
 
 
+@deprecated(None, 'Please use tf.nn/tf.layers directly.')
 def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None):
   """Processes inputs for Sequence to Sequence models.
 
@@ -87,6 +95,7 @@ def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None):
     return in_x, in_y, out_y
 
 
+@deprecated(None, 'Please use tf.nn/tf.layers directly.')
 def rnn_decoder(decoder_inputs, initial_state, cell, scope=None):
   """RNN Decoder that creates training and sampling sub-graphs.
 
@@ -123,6 +132,7 @@ def rnn_decoder(decoder_inputs, initial_state, cell, scope=None):
   return outputs, states, sampling_outputs, sampling_states
 
 
+@deprecated(None, 'Please use tf.nn/tf.layers directly.')
 def rnn_seq2seq(encoder_inputs,
                 decoder_inputs,
                 encoder_cell,
diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py b/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py
index 7bcc177d4e..e8c6e1acf8 100644
--- a/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Preprocessing tools useful for building models."""
+"""Preprocessing tools useful for building models (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py b/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py
index 154739d497..faba3b2025 100644
--- a/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py
+++ b/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Implements preprocessing transformers for categorical variables."""
+"""Implements preprocessing transformers for categorical variables (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,6 +27,8 @@ from __future__ import print_function
 import math
 import numpy as np
 
+from tensorflow.python.util.deprecation import deprecated
+
 # pylint: disable=g-bad-import-order
 from . import categorical_vocabulary
 from ..learn_io.data_feeder import setup_processor_data_feeder
@@ -31,10 +38,16 @@ from ..learn_io.data_feeder import setup_processor_data_feeder
 class CategoricalProcessor(object):
   """Maps documents to sequences of word ids.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   As a common convention, Nan values are handled as unknown tokens.
   Both float('nan') and np.nan are accepted.
   """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data for sequence '
+              'processing.')
   def __init__(self, min_frequency=0, share=False, vocabularies=None):
     """Initializes a CategoricalProcessor instance.
 
diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py b/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py
index 5709955c49..3ac370a6ab 100644
--- a/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py
+++ b/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py
@@ -13,7 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Categorical vocabulary classes to map categories to indexes.
+"""Categorical vocabulary classes to map categories to indexes (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Can be used for categorical variables, sparse variables and words.
 """
@@ -25,14 +29,21 @@ from __future__ import print_function
 import collections
 import six
 
+from tensorflow.python.util.deprecation import deprecated
+
 
 class CategoricalVocabulary(object):
   """Categorical variables vocabulary class.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Accumulates and provides mapping from classes to indexes.
   Can be easily used for words.
   """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data.')
   def __init__(self, unknown_token="<UNK>", support_reverse=True):
     self._unknown_token = unknown_token
     self._mapping = {unknown_token: 0}
diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/text.py b/tensorflow/contrib/learn/python/learn/preprocessing/text.py
index 3af2074c2a..f2b6776be7 100644
--- a/tensorflow/contrib/learn/python/learn/preprocessing/text.py
+++ b/tensorflow/contrib/learn/python/learn/preprocessing/text.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Implements a number of text preprocessing utilities."""
+"""Implements a number of text preprocessing utilities (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -24,6 +29,7 @@ import numpy as np
 import six
 
 from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
 
 from .categorical_vocabulary import CategoricalVocabulary  # pylint: disable=g-bad-import-order
 
@@ -38,6 +44,7 @@ TOKENIZER_RE = re.compile(r"[A-Z]{2,}(?![a-z])|[A-Z][a-z]+(?=[A-Z])|[\'\w\-]+",
                           re.UNICODE)
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 def tokenizer(iterator):
   """Tokenizer generator.
 
@@ -51,9 +58,16 @@ def tokenizer(iterator):
     yield TOKENIZER_RE.findall(value)
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 class ByteProcessor(object):
-  """Maps documents into sequence of ids for bytes."""
+  """Maps documents into sequence of ids for bytes.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data.')
   def __init__(self, max_document_length):
     self.max_document_length = max_document_length
 
@@ -108,8 +122,14 @@ class ByteProcessor(object):
 
 
 class VocabularyProcessor(object):
-  """Maps documents to sequences of word ids."""
+  """Maps documents to sequences of word ids.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data.')
   def __init__(self,
                max_document_length,
                min_frequency=0,
diff --git a/tensorflow/contrib/learn/python/learn/session_run_hook.py b/tensorflow/contrib/learn/python/learn/session_run_hook.py
index a8ba2be972..87edc9b720 100644
--- a/tensorflow/contrib/learn/python/learn/session_run_hook.py
+++ b/tensorflow/contrib/learn/python/learn/session_run_hook.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""This file is deprecated. Use tensorflow.python.training.session_run_hook."""
+"""This file is deprecated. Use `tensorflow.python.training.session_run_hook`.
+
+See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/summary_writer_cache.py b/tensorflow/contrib/learn/python/learn/summary_writer_cache.py
index 919d415c30..d663cf5fb7 100644
--- a/tensorflow/contrib/learn/python/learn/summary_writer_cache.py
+++ b/tensorflow/contrib/learn/python/learn/summary_writer_cache.py
@@ -12,7 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Wrapper for a Session-like object that handles threads and recovery.
+"""Wrapper for a Session-like object that handles threads and recovery (deprecated).
+
+These are deprecated aliases for classes and functions in `tf.train`. Please use
+those directly.
 
 Based on an original design of Illia Polosukhin.
 """
diff --git a/tensorflow/contrib/learn/python/learn/trainable.py b/tensorflow/contrib/learn/python/learn/trainable.py
index 429b6040be..a1a3f20dcd 100644
--- a/tensorflow/contrib/learn/python/learn/trainable.py
+++ b/tensorflow/contrib/learn/python/learn/trainable.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""`Trainable` interface."""
+"""`Trainable` interface (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -23,6 +28,8 @@ import abc
 
 class Trainable(object):
   """Interface for objects that are trainable by, e.g., `Experiment`.
+
+  THIS CLASS IS DEPRECATED.
   """
   __metaclass__ = abc.ABCMeta
 
diff --git a/tensorflow/contrib/learn/python/learn/utils/__init__.py b/tensorflow/contrib/learn/python/learn/utils/__init__.py
index 48978d0ac3..66d8dc6fd4 100644
--- a/tensorflow/contrib/learn/python/learn/utils/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/utils/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow Learn Utils."""
+"""TensorFlow Learn Utils (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/utils/export.py b/tensorflow/contrib/learn/python/learn/utils/export.py
index cb34cb1d26..3eacac7a3d 100644
--- a/tensorflow/contrib/learn/python/learn/utils/export.py
+++ b/tensorflow/contrib/learn/python/learn/utils/export.py
@@ -13,14 +13,18 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Export utilities."""
+"""Export utilities (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.framework import deprecated
-from tensorflow.python.training import training_util
 from tensorflow.contrib.session_bundle import exporter
 from tensorflow.contrib.session_bundle import gc
 from tensorflow.python.client import session as tf_session
@@ -32,6 +36,7 @@ from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import saver as tf_saver
+from tensorflow.python.training import training_util
 
 
 @deprecated('2017-03-25', 'Please use Estimator.export_savedmodel() instead.')
diff --git a/tensorflow/contrib/learn/python/learn/utils/gc.py b/tensorflow/contrib/learn/python/learn/utils/gc.py
index 226915987a..916aecbea8 100644
--- a/tensorflow/contrib/learn/python/learn/utils/gc.py
+++ b/tensorflow/contrib/learn/python/learn/utils/gc.py
@@ -13,7 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-r"""System for specifying garbage collection (GC) of path based data.
+r"""System for specifying garbage collection (GC) of path based data (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 This framework allows for GC of data specified by path names, for example files
 on disk.  gc.Path objects each represent a single item stored at a path and may
@@ -73,10 +77,12 @@ import os
 
 from tensorflow.python.platform import gfile
 from tensorflow.python.util import compat
+from tensorflow.python.util.deprecation import deprecated
 
 Path = collections.namedtuple('Path', 'path export_version')
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def largest_export_versions(n):
   """Creates a filter that keeps the largest n export versions.
 
@@ -97,6 +103,7 @@ def largest_export_versions(n):
   return keep
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def one_of_every_n_export_versions(n):
   """Creates a filter that keeps one of every n export versions.
 
@@ -128,6 +135,7 @@ def one_of_every_n_export_versions(n):
   return keep
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def mod_export_version(n):
   """Creates a filter that keeps every export that is a multiple of n.
 
@@ -146,6 +154,7 @@ def mod_export_version(n):
   return keep
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def union(lf, rf):
   """Creates a filter that keeps the union of two filters.
 
@@ -163,6 +172,7 @@ def union(lf, rf):
   return keep
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def negation(f):
   """Negate a filter.
 
@@ -179,6 +189,7 @@ def negation(f):
   return keep
 
 
+@deprecated(None, 'Please implement your own file name management.')
 def get_paths(base_dir, parser):
   """Gets a list of Paths in a given directory.
 
diff --git a/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py b/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py
index b2521933e5..b92eb9fea8 100644
--- a/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py
+++ b/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utilities for creating input_fns.
+"""Utilities for creating input_fns (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Contents of this file are moved to tensorflow/python/estimator/export.py.
 InputFnOps is renamed to ServingInputReceiver.
@@ -32,13 +36,17 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import parsing_ops
+from tensorflow.python.util.deprecation import deprecated
 
 
 class InputFnOps(collections.namedtuple('InputFnOps',
                                         ['features',
                                          'labels',
                                          'default_inputs'])):
-  """A return type for an input_fn.
+  """A return type for an input_fn (deprecated).
+
+  THIS CLASS IS DEPRECATED. Please use tf.estimator.export.ServingInputReceiver
+  instead.
 
   This return type is currently only supported for serving input_fn.
   Training and eval input_fn should return a `(features, labels)` tuple.
@@ -56,6 +64,8 @@ class InputFnOps(collections.namedtuple('InputFnOps',
   """
 
 
+@deprecated(None, 'Please use '
+            'tf.estimator.export.build_parsing_serving_input_receiver_fn.')
 def build_parsing_serving_input_fn(feature_spec, default_batch_size=None):
   """Build an input_fn appropriate for serving, expecting fed tf.Examples.
 
@@ -84,6 +94,8 @@ def build_parsing_serving_input_fn(feature_spec, default_batch_size=None):
   return input_fn
 
 
+@deprecated(None, 'Please use '
+            'tf.estimator.export.build_raw_serving_input_receiver_fn.')
 def build_default_serving_input_fn(features, default_batch_size=None):
   """Build an input_fn appropriate for serving, expecting feature Tensors.
 
diff --git a/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py b/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py
index 6a63fb545a..6dbaa15f83 100644
--- a/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py
+++ b/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""A simple script for inspect checkpoint files."""
+"""A simple script for inspect checkpoint files (deprecated)."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
index 1593380007..213619a187 100644
--- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
+++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utilities supporting export to SavedModel.
+"""Utilities supporting export to SavedModel (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Some contents of this file are moved to tensorflow/python/estimator/export.py:
 
@@ -52,8 +56,9 @@ from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.saved_model import signature_def_utils
 from tensorflow.python.summary import summary_iterator
 from tensorflow.python.training import saver
-
 from tensorflow.python.util import compat
+from tensorflow.python.util.deprecation import deprecated
+
 
 # A key for use in the input_alternatives dict indicating the default input.
 # This is the input that will be expected when a serving request does not
@@ -77,6 +82,7 @@ FEATURES_INPUT_ALTERNATIVE_KEY = 'features_input_alternative'
 _FALLBACK_DEFAULT_OUTPUT_ALTERNATIVE_KEY = 'default_output_alternative'
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def build_standardized_signature_def(input_tensors, output_tensors,
                                      problem_type):
   """Build a SignatureDef using problem type and input and output Tensors.
@@ -156,6 +162,7 @@ def _is_regression_problem(problem_type, input_tensors, output_tensors):
           len(input_tensors) == 1 and len(output_tensors) == 1)
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_input_alternatives(input_ops):
   """Obtain all input alternatives using the input_fn output and heuristics."""
   input_alternatives = {}
@@ -181,6 +188,7 @@ def get_input_alternatives(input_ops):
   return input_alternatives, features
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_output_alternatives(model_fn_ops, default_output_alternative_key=None):
   """Obtain all output alternatives using the model_fn output and heuristics.
 
@@ -246,6 +254,7 @@ def get_output_alternatives(model_fn_ops, default_output_alternative_key=None):
                        sorted(output_alternatives.keys())))
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def build_all_signature_defs(input_alternatives, output_alternatives,
                              actual_default_output_alternative_key):
   """Build `SignatureDef`s from all pairs of input and output alternatives."""
@@ -279,6 +288,7 @@ def build_all_signature_defs(input_alternatives, output_alternatives,
 MAX_DIRECTORY_CREATION_ATTEMPTS = 10
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_timestamped_export_dir(export_dir_base):
   """Builds a path to a new subdirectory within the base directory.
 
@@ -317,6 +327,7 @@ def get_timestamped_export_dir(export_dir_base):
                      '{} attempts.'.format(MAX_DIRECTORY_CREATION_ATTEMPTS))
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_temp_export_dir(timestamped_export_dir):
   """Builds a directory name based on the argument but starting with 'temp-'.
 
@@ -344,6 +355,7 @@ def _export_version_parser(path):
   return path._replace(export_version=int(filename))
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_most_recent_export(export_dir_base):
   """Locate the most recent SavedModel export in a directory of many exports.
 
@@ -363,6 +375,7 @@ def get_most_recent_export(export_dir_base):
   return next(iter(results or []), None)
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def garbage_collect_exports(export_dir_base, exports_to_keep):
   """Deletes older exports, retaining only a given number of the most recent.
 
@@ -387,6 +400,7 @@ def garbage_collect_exports(export_dir_base, exports_to_keep):
       logging.warn('Can not delete %s recursively: %s', p.path, e)
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def make_export_strategy(serving_input_fn,
                          default_output_alternative_key=None,
                          assets_extra=None,
@@ -469,6 +483,8 @@ def make_export_strategy(serving_input_fn,
   return export_strategy.ExportStrategy('Servo', export_fn, strip_default_attrs)
 
 
+@deprecated(None,
+            'Use tf.estimator.export.build_parsing_serving_input_receiver_fn')
 def make_parsing_export_strategy(feature_columns,
                                  default_output_alternative_key=None,
                                  assets_extra=None,
@@ -555,8 +571,14 @@ def _default_compare_fn(curr_best_eval_result, cand_eval_result):
 
 
 class BestModelSelector(object):
-  """A helper that keeps track of export selection candidates."""
+  """A helper that keeps track of export selection candidates.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
   def __init__(self, event_file_pattern=None, compare_fn=None):
     """Constructor of this class.
 
@@ -622,6 +644,7 @@ class BestModelSelector(object):
     return best_eval_result
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def make_best_model_export_strategy(
     serving_input_fn,
     exports_to_keep=1,
@@ -707,6 +730,7 @@ def make_best_model_export_strategy(
 
 # TODO(b/67013778): Revisit this approach when corresponding changes to
 # TF Core are finalized.
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def extend_export_strategy(base_export_strategy,
                            post_export_fn,
                            post_export_name=None):
diff --git a/tensorflow/python/util/decorator_utils.py b/tensorflow/python/util/decorator_utils.py
index df259c7f7c..7b4363c0e4 100644
--- a/tensorflow/python/util/decorator_utils.py
+++ b/tensorflow/python/util/decorator_utils.py
@@ -82,7 +82,7 @@ def add_notice_to_docstring(
     lines = _normalize_docstring(doc).splitlines()
     lines[0] += ' ' + suffix_str
 
-  notice = [''] + notice + [instructions]
+  notice = [''] + notice + ([instructions] if instructions else [])
 
   if len(lines) > 1:
     # Make sure that we keep our distance from the main body
-- 
GitLab


From 29bc0d92967d8853c872ba7f736462f1ea2fbd81 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Mon, 26 Feb 2018 16:24:54 -0800
Subject: [PATCH 323/575] [XLA] In HloEvaluator, fix an issue for HandleAbs to
 handle complex numbers more correctly:

- abs([complex numbers]) would yield floats. However since the specilization for
HandleAbs is based on the return type (float), we'd CHECK fail due to float !=
complex when accessing the elements of the operand (complex).
- enable unary_op_test for interpreter.

PiperOrigin-RevId: 187099576
---
 .../compiler/xla/service/hlo_evaluator.cc     | 32 +++++++++++++++++--
 tensorflow/compiler/xla/tests/BUILD           |  1 +
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index fd06b19144..cf8b35908f 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -57,6 +57,12 @@ struct is_complex_t : public std::false_type {};
 template <>
 struct is_complex_t<complex64> : public std::true_type {};
 
+template <typename T>
+struct is_complex64_t : public std::false_type {};
+
+template <>
+struct is_complex64_t<complex64> : public std::true_type {};
+
 template <typename OperandT>
 StatusOr<std::unique_ptr<Literal>> Compare(const Shape& shape, HloOpcode opcode,
                                            const Literal& lhs_literal,
@@ -248,17 +254,37 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
   template <
       typename NativeT,
-      typename std::enable_if<std::is_signed<NativeT>::value ||
-                              is_complex_t<NativeT>::value>::type* = nullptr>
+      typename std::enable_if<std::is_signed<NativeT>::value>::type* = nullptr>
   Status HandleAbs(HloInstruction* abs) {
     TF_ASSIGN_OR_RETURN(parent_->evaluated_[abs],
-                        ElementWiseUnaryOp(abs, [](ElementwiseT elem_operand) {
+                        ElementWiseUnaryOp(abs, [](NativeT elem_operand) {
                           return std::abs(elem_operand);
                         }));
     return Status::OK();
   }
 
+  template <
+      typename NativeT,
+      typename std::enable_if<is_complex64_t<NativeT>::value>::type* = nullptr>
+  Status HandleAbs(HloInstruction* abs) {
+    const Literal& operand_literal =
+        parent_->GetEvaluatedLiteralFor(abs->operand(0));
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[abs],
+        (ElementWiseUnaryOpImpl<float, NativeT>(
+            abs, [](NativeT elem_operand) { return std::abs(elem_operand); },
+            operand_literal)));
+
+    return Status::OK();
+  }
+
   Status HandleAbs(HloInstruction* abs) override {
+    // If the operand is of C64 type, the return type of abs will be F32.
+    // However, ElementwiseT would still be the return type, F32, and thus
+    // specifying the ElementwiseT explicitly as C64 is needed below.
+    if (abs->operand(0)->shape().element_type() == C64) {
+      return HandleAbs<complex64>(abs);
+    }
     return HandleAbs<ElementwiseT>(abs);
   }
 
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 33fde9737d..f3ecfc1604 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -494,6 +494,7 @@ xla_test(
 xla_test(
     name = "unary_op_test",
     srcs = ["unary_op_test.cc"],
+    tags = ["enable_for_xla_interpreter"],
     deps = [
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
-- 
GitLab


From e37a7ae2277a2a2f7b50ad5ef361e41c30edeb41 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 26 Feb 2018 17:01:24 -0800
Subject: [PATCH 324/575] Only link the swapping code when compiling TensorFlow
 with CUDA support.

PiperOrigin-RevId: 187104273
---
 tensorflow/core/grappler/optimizers/BUILD | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 908e58bcc7..a52d1c8df2 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -3,6 +3,7 @@ licenses(["notice"])  # Apache 2.0
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
 load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu")
 load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
+load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
 
 filegroup(
     name = "all_files",
@@ -319,8 +320,6 @@ cc_library(
     ],
     visibility = ["//visibility:public"],
     deps = [
-        ":gpu_swapping_kernels",
-        ":gpu_swapping_ops",
         ":graph_optimizer",
         ":graph_rewriter",
         ":static_schedule",
@@ -336,7 +335,10 @@ cc_library(
         "//tensorflow/core/grappler/costs:graph_properties",
         "//tensorflow/core/grappler/utils:topological_sort",
         "//tensorflow/core/grappler/utils:traversal",
-    ],
+    ] + if_cuda([
+        ":gpu_swapping_kernels",
+        ":gpu_swapping_ops",
+    ]),
 )
 
 tf_cc_test_gpu(
-- 
GitLab


From 49d4e9233cebdff001ffcc2e3d703e815ba0a881 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 17:04:09 -0800
Subject: [PATCH 325/575] Consolidate the builtin function overrides into a
 single module, and use a generic `dynamic_builtin` function to dispatch
 between implementations. Use the generic dispatcher in the generated code.

PiperOrigin-RevId: 187104685
---
 .../py2tf/converters/builtin_functions.py     | 13 ++++---
 tensorflow/contrib/py2tf/utils/BUILD          | 12 +-----
 tensorflow/contrib/py2tf/utils/__init__.py    |  4 +-
 .../py2tf/utils/{printing.py => builtins.py}  | 32 +++++++++++++--
 .../{printing_test.py => builtins_test.py}    | 39 +++++++++++++++----
 tensorflow/contrib/py2tf/utils/misc.py        | 13 -------
 tensorflow/contrib/py2tf/utils/misc_test.py   | 27 +------------
 7 files changed, 72 insertions(+), 68 deletions(-)
 rename tensorflow/contrib/py2tf/utils/{printing.py => builtins.py} (62%)
 rename tensorflow/contrib/py2tf/utils/{printing_test.py => builtins_test.py} (56%)

diff --git a/tensorflow/contrib/py2tf/converters/builtin_functions.py b/tensorflow/contrib/py2tf/converters/builtin_functions.py
index e69038aced..b5aa9756da 100644
--- a/tensorflow/contrib/py2tf/converters/builtin_functions.py
+++ b/tensorflow/contrib/py2tf/converters/builtin_functions.py
@@ -36,23 +36,24 @@ class BuiltinFunctionTransformer(transformer.Base):
 
   # pylint:disable=invalid-name
 
-  def _convert_len(self, node):
+  def _convert_builtin(self, node):
     template = """
-      py2tf_utils.dynamic_len(args)
+      py2tf_utils.dynamic_builtin(func, args)
     """
-    return templates.replace(template, args=node.args)[0].value
+    return templates.replace(template, func=node.func, args=node.args)[0].value
 
   def _convert_print(self, node):
     template = """
-      py2tf_utils.call_print(args)
+      py2tf_utils.dynamic_print(args)
     """
     return templates.replace(template, args=node.args)[0].value
 
   def visit_Call(self, node):
     self.generic_visit(node)
     # TODO(mdan): This won't work if the function was hidden.
-    if isinstance(node.func, gast.Name) and node.func.id == 'len':
-      return self._convert_len(node)
+    if isinstance(node.func, gast.Name) and node.func.id in ('len',):
+      return self._convert_builtin(node)
+    # Print needs to be handled separately because it can be read as statement.
     if isinstance(node.func, gast.Name) and node.func.id == 'print':
       return self._convert_print(node)
     return node
diff --git a/tensorflow/contrib/py2tf/utils/BUILD b/tensorflow/contrib/py2tf/utils/BUILD
index c2fdd40707..2086a9ef60 100644
--- a/tensorflow/contrib/py2tf/utils/BUILD
+++ b/tensorflow/contrib/py2tf/utils/BUILD
@@ -20,10 +20,10 @@ py_library(
     name = "utils",
     srcs = [
         "__init__.py",
+        "builtins.py",
         "context_managers.py",
         "misc.py",
         "multiple_dispatch.py",
-        "printing.py",
         "py_func.py",
         "tensor_list.py",
         "type_check.py",
@@ -76,16 +76,6 @@ py_test(
     ],
 )
 
-py_test(
-    name = "printing_test",
-    srcs = ["printing_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":utils",
-        "//tensorflow/python:client_testlib",
-    ],
-)
-
 py_test(
     name = "type_check_test",
     srcs = ["type_check_test.py"],
diff --git a/tensorflow/contrib/py2tf/utils/__init__.py b/tensorflow/contrib/py2tf/utils/__init__.py
index d931322bf3..19bf2272bc 100644
--- a/tensorflow/contrib/py2tf/utils/__init__.py
+++ b/tensorflow/contrib/py2tf/utils/__init__.py
@@ -18,11 +18,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.py2tf.utils.builtins import dynamic_builtin
+from tensorflow.contrib.py2tf.utils.builtins import dynamic_print
 from tensorflow.contrib.py2tf.utils.context_managers import control_dependency_on_returns
 from tensorflow.contrib.py2tf.utils.misc import alias_tensors
-from tensorflow.contrib.py2tf.utils.misc import dynamic_len
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_cond
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_while
-from tensorflow.contrib.py2tf.utils.printing import call_print
 from tensorflow.contrib.py2tf.utils.py_func import wrap_py_func
 from tensorflow.contrib.py2tf.utils.type_check import is_tensor
diff --git a/tensorflow/contrib/py2tf/utils/printing.py b/tensorflow/contrib/py2tf/utils/builtins.py
similarity index 62%
rename from tensorflow/contrib/py2tf/utils/printing.py
rename to tensorflow/contrib/py2tf/utils/builtins.py
index 95a62bd80b..0a50b80b60 100644
--- a/tensorflow/contrib/py2tf/utils/printing.py
+++ b/tensorflow/contrib/py2tf/utils/builtins.py
@@ -12,14 +12,40 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""TensorFlow printing support utilities."""
+"""Builtin conversion utilities."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.py2tf.utils import py_func
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import logging_ops
+from tensorflow.python.util import tf_inspect
+
+
+def dynamic_builtin(f, *args, **kwargs):
+  """Converts a builtin function call inline."""
+  if not tf_inspect.isbuiltin(f):
+    return f(*args, **kwargs)
+
+  if f is len:
+    return dynamic_len(*args, **kwargs)
+
+  raise NotImplementedError('The "%s" builtin is not yet supported.' % f)
+
+
+def dynamic_len(list_or_tensor):
+  """Implementation of len using dynamic dispatch."""
+  if tensor_util.is_tensor(list_or_tensor):
+    shape = list_or_tensor.shape
+    if not shape:
+      raise ValueError(
+          'len requires non-zero rank for tensor "%s"' % list_or_tensor)
+    return array_ops.shape(list_or_tensor)[0]
+
+  return len(list_or_tensor)
 
 
 def is_tf_print_compatible(value):
@@ -30,8 +56,8 @@ def is_tf_print_compatible(value):
   return False
 
 
-def call_print(*values):
-  """Compiled counterpart of the print builtin.
+def dynamic_print(*values):
+  """Implementartion of print using dynamic dispatch.
 
   The function attempts to use tf.Print if all the values are compatible.
   Otherwise, it will fall back to py_func.
diff --git a/tensorflow/contrib/py2tf/utils/printing_test.py b/tensorflow/contrib/py2tf/utils/builtins_test.py
similarity index 56%
rename from tensorflow/contrib/py2tf/utils/printing_test.py
rename to tensorflow/contrib/py2tf/utils/builtins_test.py
index 2070deb304..19a72c63ec 100644
--- a/tensorflow/contrib/py2tf/utils/printing_test.py
+++ b/tensorflow/contrib/py2tf/utils/builtins_test.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for printing module."""
+"""Tests for builtins module."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,28 +22,53 @@ import sys
 
 import six
 
-from tensorflow.contrib.py2tf.utils import printing
+from tensorflow.contrib.py2tf.utils import builtins
+from tensorflow.python.framework import constant_op
 from tensorflow.python.platform import test
 
 
-class ContextManagersTest(test.TestCase):
+class BuiltinsTest(test.TestCase):
 
-  def test_call_print_tf(self):
+  def test_dynamic_len_tf_scalar(self):
+    a = constant_op.constant(1)
+
+    with self.assertRaises(ValueError):
+      with self.test_session() as sess:
+        sess.run(builtins.dynamic_builtin(len, a))
+
+  def test_dynamic_len_tf_array(self):
+    a = constant_op.constant([1, 2, 3])
+
+    with self.test_session() as sess:
+      self.assertEqual(3, sess.run(builtins.dynamic_builtin(len, a)))
+
+  def test_dynamic_len_tf_matrix(self):
+    a = constant_op.constant([[1, 2], [3, 4]])
+
+    with self.test_session() as sess:
+      self.assertEqual(2, sess.run(builtins.dynamic_builtin(len, a)))
+
+  def test_dynamic_len_py_list(self):
+    a = [3] * 5
+
+    self.assertEqual(5, builtins.dynamic_builtin(len, a))
+
+  def test_dynamic_print_tf(self):
     try:
       out_capturer = six.StringIO()
       sys.stdout = out_capturer
       with self.test_session() as sess:
-        sess.run(printing.call_print('test message', 1))
+        sess.run(builtins.dynamic_print('test message', 1))
         self.assertEqual(out_capturer.getvalue(), 'test message 1\n')
     finally:
       sys.stdout = sys.__stdout__
 
-  def test_call_print_py_func(self):
+  def test_dynamic_print_complex(self):
     try:
       out_capturer = six.StringIO()
       sys.stdout = out_capturer
       with self.test_session() as sess:
-        sess.run(printing.call_print('test message', [1, 2]))
+        sess.run(builtins.dynamic_print('test message', [1, 2]))
         self.assertEqual(out_capturer.getvalue(), 'test message [1, 2]\n')
     finally:
       sys.stdout = sys.__stdout__
diff --git a/tensorflow/contrib/py2tf/utils/misc.py b/tensorflow/contrib/py2tf/utils/misc.py
index 7548048388..1b06caf0bd 100644
--- a/tensorflow/contrib/py2tf/utils/misc.py
+++ b/tensorflow/contrib/py2tf/utils/misc.py
@@ -19,22 +19,9 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 
 
-def dynamic_len(list_or_tensor):
-  """Implementation of len using dynamic dispatch."""
-  if tensor_util.is_tensor(list_or_tensor):
-    shape = list_or_tensor.shape
-    if not shape:
-      raise ValueError(
-          'len requires non-zero rank for tensor "%s"' % list_or_tensor)
-    return array_ops.shape(list_or_tensor)[0]
-
-  return len(list_or_tensor)
-
-
 def alias_tensors(*args):
   """Wrap any Tensor arguments with an identity op.
 
diff --git a/tensorflow/contrib/py2tf/utils/misc_test.py b/tensorflow/contrib/py2tf/utils/misc_test.py
index ec88e7cb74..8aedd4cd64 100644
--- a/tensorflow/contrib/py2tf/utils/misc_test.py
+++ b/tensorflow/contrib/py2tf/utils/misc_test.py
@@ -19,37 +19,12 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.py2tf.utils.misc import alias_tensors
-from tensorflow.contrib.py2tf.utils.misc import dynamic_len
 from tensorflow.python.framework.constant_op import constant
 from tensorflow.python.ops.variables import Variable
 from tensorflow.python.platform import test
 
 
-class ContextManagersTest(test.TestCase):
-
-  def test_dynamic_len_tf_scalar(self):
-    a = constant(1)
-
-    with self.assertRaises(ValueError):
-      with self.test_session() as sess:
-        sess.run(dynamic_len(a))
-
-  def test_dynamic_len_tf_array(self):
-    a = constant([1, 2, 3])
-
-    with self.test_session() as sess:
-      self.assertEqual(3, sess.run(dynamic_len(a)))
-
-  def test_dynamic_len_tf_matrix(self):
-    a = constant([[1, 2], [3, 4]])
-
-    with self.test_session() as sess:
-      self.assertEqual(2, sess.run(dynamic_len(a)))
-
-  def test_dynamic_len_py_list(self):
-    a = [3] * 5
-
-    self.assertEqual(5, dynamic_len(a))
+class MiscTest(test.TestCase):
 
   def test_alias_single_tensor(self):
     a = constant(1)
-- 
GitLab


From c7c8f4e82ede4fec5b21f9acd61bcc221d87efdc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 17:27:20 -0800
Subject: [PATCH 326/575] Fix buffer assignment for conditional instruction.

PiperOrigin-RevId: 187107432
---
 .../compiler/xla/service/buffer_assignment.cc | 358 +++++++++---------
 .../compiler/xla/service/copy_insertion.cc    |  72 +++-
 2 files changed, 241 insertions(+), 189 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index b1e693da9d..d44d3d71d9 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -48,6 +48,183 @@ using ::tensorflow::strings::HumanReadableNumBytes;
 using ::tensorflow::strings::Printf;
 using ::tensorflow::strings::StrAppend;
 
+namespace {
+
+template <typename T>
+string ColocatedBufferSetsToString(const T& container, const char* title) {
+  string result;
+  StrAppend(&result, title, "\n");
+  for (const auto& it : container) {
+    StrAppend(&result, "\t", it->ToString(), "\n");
+  }
+  return result;
+}
+
+// Walk the call graph of the HLO module and place each computation into either
+// thread_local_computations or global_computations depending upon whether the
+// computation requires thread-local allocations or global allocations. The
+// elements in thread_local_computations and global_computations are in post
+// order (if computation A has an instruction which calls computation B, then A
+// will appear after B in the vector).
+Status GatherComputationsByAllocationType(
+    const HloModule* module,
+    std::vector<const HloComputation*>* thread_local_computations,
+    std::vector<const HloComputation*>* global_computations) {
+  // Create a worklist of computations paired with whether the allocation must
+  // be thread-local.
+  std::deque<std::pair<const HloComputation*, bool>> worklist;
+  worklist.push_back(std::make_pair(module->entry_computation(),
+                                    /*is_thread_local*/ false));
+
+  // Sets for quickly checking membership. Computations are returned in vectors
+  // for stable iteration.
+  FlatSet<const HloComputation*> thread_local_set;
+  FlatSet<const HloComputation*> global_set;
+
+  while (!worklist.empty()) {
+    auto worklist_front = worklist.front();
+    worklist.pop_front();
+    const HloComputation* computation = worklist_front.first;
+    bool is_thread_local = worklist_front.second;
+    bool in_thread_local_set = thread_local_set.count(computation) > 0;
+    bool in_global_set = global_set.count(computation) > 0;
+
+    // If the computation has already been added to the respective set, then
+    // nothing to do.
+    if ((is_thread_local && in_thread_local_set) ||
+        (!is_thread_local && in_global_set)) {
+      continue;
+    }
+
+    // If the computation has already been added to the other set this is an
+    // error condition because the global call to the computation (eg,
+    // while/call) may return a reference to one of the thread-local buffers to
+    // the calling computation which will become a dangling reference when the
+    // thread-local is deallocated with the call return.
+    if ((is_thread_local && in_global_set) ||
+        (!is_thread_local && in_thread_local_set)) {
+      return InvalidArgument(
+          "computation %s has conflicting allocation requirements (global "
+          "and thread-local)",
+          computation->name().c_str());
+    }
+
+    if (is_thread_local) {
+      thread_local_set.insert(computation);
+    } else {
+      global_set.insert(computation);
+    }
+
+    for (auto* instruction : computation->instructions()) {
+      for (HloComputation* subcomputation :
+           instruction->called_computations()) {
+        switch (instruction->opcode()) {
+          case HloOpcode::kCall:
+          case HloOpcode::kConditional:
+          case HloOpcode::kWhile:
+            // Call and while must be called from a computation with global
+            // allocations as they may return references to buffers inside the
+            // called computation which cannot be thread-local.
+            if (is_thread_local) {
+              return InvalidArgument(
+                  "computation %s cannot contain call/while op because it "
+                  "requires thread-local buffer allocations",
+                  computation->name().c_str());
+            }
+            worklist.push_back(std::make_pair(subcomputation,
+                                              false));  // Not thread local.
+            break;
+          case HloOpcode::kMap:
+          case HloOpcode::kReduce:
+          case HloOpcode::kReduceWindow:
+          case HloOpcode::kSelectAndScatter:
+          case HloOpcode::kFusion:
+            // Map/reduce etc computations are always thread-local.
+            worklist.push_back(std::make_pair(subcomputation,
+                                              true));  // Thread local.
+            break;
+          default:
+            return InternalError(
+                "Unexpected calling opcode: %s",
+                HloOpcodeString(instruction->opcode()).c_str());
+        }
+      }
+    }
+  }
+
+  // Add the computations to the vectors in post order.
+  for (auto* computation : module->MakeComputationPostOrder()) {
+    if (thread_local_set.count(computation) > 0) {
+      thread_local_computations->push_back(computation);
+    } else if (global_set.count(computation) > 0) {
+      global_computations->push_back(computation);
+    }
+    // If the computation is not reachable from the entry computation, then it
+    // will not appear in either thread_local_set or global_set. We don't bother
+    // assigning buffers for these.
+  }
+  return Status::OK();
+}
+
+// Checks that points-to set of 'instruction' is unambiguous and distinct
+// (ensured by CopyInsertion), then adds the buffer from the points-to set at
+// 'index' to 'colocated_set'.
+const LogicalBuffer* AddBufferToColocatedSet(
+    const HloInstruction* instruction, const ShapeIndex& index,
+    const TuplePointsToAnalysis& points_to_analysis,
+    std::vector<const LogicalBuffer*>* colocated_set) {
+  // CopyInsertion ensures root points-to set is unambiguous and distinct.
+  const auto& points_to = points_to_analysis.GetPointsToSet(instruction);
+  DCHECK(!points_to.IsAmbiguous());
+  colocated_set->push_back(points_to.element(index)[0]);
+  return colocated_set->back();
+}
+
+// Given the interference map of a graph (the list of interfering node indices
+// for each node), perform graph coloring such that interfering nodes are
+// assigned to different colors. Returns the assigned color of the nodes, where
+// the colors are represented as integer values [0, color_count).
+std::vector<int64> ColorInterferenceGraph(
+    const std::vector<std::vector<int64>>& interference_map) {
+  const int64 node_count = interference_map.size();
+
+  // Sort the nodes such that we assign nodes with more interference first. This
+  // relies on the common heuristic of assigning the most constrained node
+  // first, but it would be good to investigate other ordering heuristics too.
+  std::vector<int64> nodes(node_count);
+  std::iota(nodes.begin(), nodes.end(), 0);
+  std::sort(nodes.begin(), nodes.end(),
+            [&interference_map](const int64 i, const int64 j) {
+              return interference_map[i].size() > interference_map[j].size();
+            });
+
+  const int64 kColorUnassigned = -1;
+  std::vector<int64> assigned_colors(node_count, kColorUnassigned);
+  for (int64 node : nodes) {
+    // Mark the colors that are already assigned to the neighbors.
+    std::vector<bool> available_colors(node_count, true);
+    for (int64 neighbor : interference_map[node]) {
+      int64 color = assigned_colors[neighbor];
+      if (color != kColorUnassigned) {
+        available_colors[color] = false;
+      }
+    }
+
+    // Find the color that is not yet assigned to the neighbors.
+    int64 color = kColorUnassigned;
+    for (color = 0; color < available_colors.size(); ++color) {
+      if (available_colors[color]) {
+        break;
+      }
+    }
+    CHECK_NE(color, kColorUnassigned);
+    assigned_colors[node] = color;
+  }
+  return assigned_colors;
+}
+
+}  // namespace
+
 size_t BufferAllocation::Slice::Hasher::operator()(Slice s) const {
   uint64 h = std::hash<int64>()(s.index());
   h = tensorflow::Hash64Combine(h, std::hash<int64>()(s.offset()));
@@ -523,116 +700,6 @@ BufferAssignmentProto BufferAssignment::ToProto() const {
   return proto;
 }
 
-namespace {
-
-// Walk the call graph of the HLO module and place each computation into either
-// thread_local_computations or global_computations depending upon whether the
-// computation requires thread-local allocations or global allocations. The
-// elements in thread_local_computations and global_computations are in post
-// order (if computation A has an instruction which calls computation B, then A
-// will appear after B in the vector).
-Status GatherComputationsByAllocationType(
-    const HloModule* module,
-    std::vector<const HloComputation*>* thread_local_computations,
-    std::vector<const HloComputation*>* global_computations) {
-  // Create a worklist of computations paired with whether the allocation must
-  // be thread-local.
-  std::deque<std::pair<const HloComputation*, bool>> worklist;
-  worklist.push_back(std::make_pair(module->entry_computation(),
-                                    /*is_thread_local*/ false));
-
-  // Sets for quickly checking membership. Computations are returned in vectors
-  // for stable iteration.
-  FlatSet<const HloComputation*> thread_local_set;
-  FlatSet<const HloComputation*> global_set;
-
-  while (!worklist.empty()) {
-    auto worklist_front = worklist.front();
-    worklist.pop_front();
-    const HloComputation* computation = worklist_front.first;
-    bool is_thread_local = worklist_front.second;
-    bool in_thread_local_set = thread_local_set.count(computation) > 0;
-    bool in_global_set = global_set.count(computation) > 0;
-
-    // If the computation has already been added to the respective set, then
-    // nothing to do.
-    if ((is_thread_local && in_thread_local_set) ||
-        (!is_thread_local && in_global_set)) {
-      continue;
-    }
-
-    // If the computation has already been added to the other set this is an
-    // error condition because the global call to the computation (eg,
-    // while/call) may return a reference to one of the thread-local buffers to
-    // the calling computation which will become a dangling reference when the
-    // thread-local is deallocated with the call return.
-    if ((is_thread_local && in_global_set) ||
-        (!is_thread_local && in_thread_local_set)) {
-      return InvalidArgument(
-          "computation %s has conflicting allocation requirements (global "
-          "and thread-local)",
-          computation->name().c_str());
-    }
-
-    if (is_thread_local) {
-      thread_local_set.insert(computation);
-    } else {
-      global_set.insert(computation);
-    }
-
-    for (auto* instruction : computation->instructions()) {
-      for (HloComputation* subcomputation :
-           instruction->called_computations()) {
-        switch (instruction->opcode()) {
-          case HloOpcode::kCall:
-          case HloOpcode::kConditional:
-          case HloOpcode::kWhile:
-            // Call and while must be called from a computation with global
-            // allocations as they may return references to buffers inside the
-            // called computation which cannot be thread-local.
-            if (is_thread_local) {
-              return InvalidArgument(
-                  "computation %s cannot contain call/while op because it "
-                  "requires thread-local buffer allocations",
-                  computation->name().c_str());
-            }
-            worklist.push_back(std::make_pair(subcomputation,
-                                              false));  // Not thread local.
-            break;
-          case HloOpcode::kMap:
-          case HloOpcode::kReduce:
-          case HloOpcode::kReduceWindow:
-          case HloOpcode::kSelectAndScatter:
-          case HloOpcode::kFusion:
-            // Map/reduce etc computations are always thread-local.
-            worklist.push_back(std::make_pair(subcomputation,
-                                              true));  // Thread local.
-            break;
-          default:
-            return InternalError(
-                "Unexpected calling opcode: %s",
-                HloOpcodeString(instruction->opcode()).c_str());
-        }
-      }
-    }
-  }
-
-  // Add the computations to the vectors in post order.
-  for (auto* computation : module->MakeComputationPostOrder()) {
-    if (thread_local_set.count(computation) > 0) {
-      thread_local_computations->push_back(computation);
-    } else if (global_set.count(computation) > 0) {
-      global_computations->push_back(computation);
-    }
-    // If the computation is not reachable from the entry computation, then it
-    // will not appear in either thread_local_set or global_set. We don't bother
-    // assigning buffers for these.
-  }
-  return Status::OK();
-}
-
-}  // namespace
-
 /* static */
 StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::Run(
     const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
@@ -1085,7 +1152,8 @@ void BufferAssigner::AddSetToColocatedBufferSets(
   if (colocated_set.empty()) {
     return;
   }
-
+  VLOG(5) << ColocatedBufferSetsToString(colocated_set,
+                                         "Adding colocated buffer set");
   // Find existing sets that overlap with at least one buffer from the
   // colocated_set. The resulting 'overlap_set_indices' will have at most
   // colocated_buffer_sets->size() entries, and will be in increasing order.
@@ -1093,6 +1161,10 @@ void BufferAssigner::AddSetToColocatedBufferSets(
   for (size_t index = 0; index < colocated_buffer_sets->size(); ++index) {
     for (const LogicalBuffer* buffer : colocated_set) {
       if ((*colocated_buffer_sets)[index].count(buffer) > 0) {
+        VLOG(5) << "Found overlap with existing set on buffer "
+                << buffer->ToString() << "\n"
+                << ColocatedBufferSetsToString((*colocated_buffer_sets)[index],
+                                               "Overlapping set");
         overlap_set_indices.push_back(index);
         break;
       }
@@ -1104,6 +1176,7 @@ void BufferAssigner::AddSetToColocatedBufferSets(
     colocated_buffer_sets->emplace_back();
     colocated_buffer_sets->back().insert(colocated_set.begin(),
                                          colocated_set.end());
+    VLOG(5) << "No overlap found, new group created";
     return;
   }
 
@@ -1115,6 +1188,8 @@ void BufferAssigner::AddSetToColocatedBufferSets(
     first->insert(overlap_set.begin(), overlap_set.end());
   }
   first->insert(colocated_set.begin(), colocated_set.end());
+  VLOG(5) << ColocatedBufferSetsToString(
+      *first, "Result of the colocated buffer set merging");
 
   // Remove overlap sets that we just merged. The offset accounts for the fact
   // that as elements are erased, the indices need to be adjusted. Keep in mind
@@ -1125,67 +1200,6 @@ void BufferAssigner::AddSetToColocatedBufferSets(
   }
 }
 
-namespace {
-
-// Checks that points-to set of 'instruction' is unambiguous and distinct
-// (ensured by CopyInsertion), then adds the buffer from the points-to set at
-// 'index' to 'colocated_set'.
-const LogicalBuffer* AddBufferToColocatedSet(
-    const HloInstruction* instruction, const ShapeIndex& index,
-    const TuplePointsToAnalysis& points_to_analysis,
-    std::vector<const LogicalBuffer*>* colocated_set) {
-  // CopyInsertion ensures root points-to set is unambiguous and distinct.
-  const auto& points_to = points_to_analysis.GetPointsToSet(instruction);
-  DCHECK(!points_to.IsAmbiguous());
-  colocated_set->push_back(points_to.element(index)[0]);
-  return colocated_set->back();
-}
-
-// Given the interference map of a graph (the list of interfering node indices
-// for each node), perform graph coloring such that interfering nodes are
-// assigned to different colors. Returns the assigned color of the nodes, where
-// the colors are represented as integer values [0, color_count).
-std::vector<int64> ColorInterferenceGraph(
-    const std::vector<std::vector<int64>>& interference_map) {
-  const int64 node_count = interference_map.size();
-
-  // Sort the nodes such that we assign nodes with more interference first. This
-  // relies on the common heuristic of assigning the most constrained node
-  // first, but it would be good to investigate other ordering heuristics too.
-  std::vector<int64> nodes(node_count);
-  std::iota(nodes.begin(), nodes.end(), 0);
-  std::sort(nodes.begin(), nodes.end(),
-            [&interference_map](const int64 i, const int64 j) {
-              return interference_map[i].size() > interference_map[j].size();
-            });
-
-  const int64 kColorUnassigned = -1;
-  std::vector<int64> assigned_colors(node_count, kColorUnassigned);
-  for (int64 node : nodes) {
-    // Mark the colors that are already assigned to the neighbors.
-    std::vector<bool> available_colors(node_count, true);
-    for (int64 neighbor : interference_map[node]) {
-      int64 color = assigned_colors[neighbor];
-      if (color != kColorUnassigned) {
-        available_colors[color] = false;
-      }
-    }
-
-    // Find the color that is not yet assigned to the neighbors.
-    int64 color = kColorUnassigned;
-    for (color = 0; color < available_colors.size(); ++color) {
-      if (available_colors[color]) {
-        break;
-      }
-    }
-    CHECK_NE(color, kColorUnassigned);
-    assigned_colors[node] = color;
-  }
-  return assigned_colors;
-}
-
-}  // namespace
-
 std::vector<BufferAssigner::ColocatedBufferSet>
 BufferAssigner::MergeColocatedBufferSets(
     const std::vector<ColocatedBufferSet>& colocated_buffer_sets,
diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc
index cc195879a6..df73c28597 100644
--- a/tensorflow/compiler/xla/service/copy_insertion.cc
+++ b/tensorflow/compiler/xla/service/copy_insertion.cc
@@ -58,6 +58,45 @@ bool ValueIsReadOnly(const HloValue& value) {
   return IsConstantValue(value) || IsEntryParameterValue(value);
 }
 
+// Data structure describing the action which should be taken on parts of a
+// computation buffers, with respect to the adding of special case copies.
+struct SpecialCaseCopyPolicy {
+  // Insert a copy if the same buffer is found at multiple indices within the
+  // output tuple.
+  bool copy_root_replicated_buffers = false;
+  // If true, insert a copy if a buffer coming from a constant or a parameter
+  // is found wihtin the output tuple.
+  bool copy_parameters_and_constants = false;
+};
+
+SpecialCaseCopyPolicy GetSpecialCaseCopyPolicy(const CallGraphNode& node,
+                                               HloModule* module,
+                                               HloComputation* computation) {
+  SpecialCaseCopyPolicy policy;
+  if (computation == module->entry_computation()) {
+    policy.copy_parameters_and_constants = true;
+    policy.copy_root_replicated_buffers = true;
+  }
+  for (const CallSite& site : node.caller_callsites()) {
+    // The kWhile instruction does not have an handling here, as the
+    // AddCopiesForWhile() API takes care of adding its own copies.
+    if (site.instruction()->opcode() == HloOpcode::kConditional) {
+      policy.copy_parameters_and_constants = true;
+      policy.copy_root_replicated_buffers = true;
+    }
+  }
+  return policy;
+}
+
+bool ShouldCopyRootValue(const HloValue& value,
+                         const SpecialCaseCopyPolicy& policy) {
+  if (policy.copy_parameters_and_constants) {
+    return IsConstantValue(value) ||
+           value.defining_instruction()->opcode() == HloOpcode::kParameter;
+  }
+  return false;
+}
+
 // Deep copy the given instructions 'from' and 'to' at the ShapeIndexes given in
 // 'indices_to_copy'. Add control edges from the respective kCopy instructions
 // in deep copy of 'from' to the respective kCopy instruction in the deep copy
@@ -957,7 +996,8 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) {
     }
     TF_RET_CHECK(node.context() == CallContext::kSequential);
 
-    const bool is_entry = computation == module->entry_computation();
+    SpecialCaseCopyPolicy policy =
+        GetSpecialCaseCopyPolicy(node, module, computation);
     HloInstruction* root = computation->root_instruction();
 
     // Mark nondistinct/ambiguous indices.
@@ -970,27 +1010,26 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) {
           for (const HloBuffer* buffer : buffers_at_index) {
             buffer_seen_before |= !seen.insert(buffer).second;
           }
-          if (buffers_at_index.size() > 1 || (buffer_seen_before && is_entry)) {
-            VLOG(2) << "Index " << index << " of root of computation "
+          if (buffers_at_index.size() > 1 ||
+              (buffer_seen_before && policy.copy_root_replicated_buffers)) {
+            VLOG(2) << "Index " << index << " of computation "
                     << computation->name() << " (" << root->name()
                     << ") has ambiguous or non-distinct buffer. Copying.";
             add_index_to_copy(root, index);
           }
         });
 
-    // For entry instructions, mark any parameter or constant values.
-    if (is_entry) {
-      for (const auto& pair :
-           alias_analysis->dataflow_analysis().GetInstructionValueSet(root)) {
-        const ShapeIndex& index = pair.first;
-        const HloValueSet& value_set = pair.second;
-        for (const HloValue* value : value_set.values()) {
-          if (ValueIsReadOnly(*value)) {
-            VLOG(2) << "Root of entry computation (" << root->name()
-                    << ") has constant or entry parameter value at index "
-                    << index << ". Copying.";
-            add_index_to_copy(root, index);
-          }
+    for (const auto& pair :
+         alias_analysis->dataflow_analysis().GetInstructionValueSet(root)) {
+      const ShapeIndex& index = pair.first;
+      const HloValueSet& value_set = pair.second;
+      for (const HloValue* value : value_set.values()) {
+        if (ShouldCopyRootValue(*value, policy)) {
+          VLOG(2) << "Root of (" << root->name() << ") of computation("
+                  << computation->name()
+                  << ") has constant or parameter value at index " << index
+                  << ". Copying.";
+          add_index_to_copy(root, index);
         }
       }
     }
@@ -1012,7 +1051,6 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) {
       instruction->parent()->set_root_instruction(deep_copy);
     }
   }
-
   return Status::OK();
 }
 
-- 
GitLab


From dedace82ecf34c7906647361a811c8bf99f13da7 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Mon, 26 Feb 2018 17:55:31 -0800
Subject: [PATCH 327/575] [XLA::Interpreter] Add support for kConditional to
 HloEvaluator. Also enable xla/tests/conditional_tests to run on interpreter.

PiperOrigin-RevId: 187110438
---
 .../compiler/xla/service/hlo_evaluator.cc     | 28 +++++++++++++++++++
 .../compiler/xla/service/hlo_evaluator.h      |  2 ++
 tensorflow/compiler/xla/tests/BUILD           |  1 +
 3 files changed, 31 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index cf8b35908f..afbfdac05e 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -2491,6 +2491,34 @@ Status HloEvaluator::HandleCall(HloInstruction* call) {
   return Status::OK();
 }
 
+Status HloEvaluator::HandleConditional(HloInstruction* conditional) {
+  const auto& pred = GetEvaluatedLiteralFor(conditional->operand(0));
+  const auto& true_computation_arg =
+      GetEvaluatedLiteralFor(conditional->operand(1));
+  const auto& false_computation_arg =
+      GetEvaluatedLiteralFor(conditional->operand(2));
+
+  auto* true_computation = conditional->true_computation();
+  auto* false_computation = conditional->false_computation();
+
+  auto result = Literal::CreateFromShape(conditional->shape());
+  HloEvaluator embedded_evaluator;
+  if (pred.Get<bool>({})) {
+    result = embedded_evaluator
+                 .Evaluate<const Literal*>(*true_computation,
+                                           {&true_computation_arg})
+                 .ConsumeValueOrDie();
+  } else {
+    result = embedded_evaluator
+                 .Evaluate<const Literal*>(*false_computation,
+                                           {&false_computation_arg})
+                 .ConsumeValueOrDie();
+  }
+
+  evaluated_[conditional] = std::move(result);
+  return Status::OK();
+}
+
 Status HloEvaluator::Preprocess(HloInstruction* hlo) {
   VLOG(2) << "About to visit HLO: " << hlo->ToString();
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h
index c65d9915e3..fc82011630 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.h
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.h
@@ -153,6 +153,8 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
 
   Status HandleCopy(HloInstruction* copy) override;
 
+  Status HandleConditional(HloInstruction* conditional) override;
+
   Status HandleCall(HloInstruction* call) override;
 
  private:
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index f3ecfc1604..19b3dfae4e 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -478,6 +478,7 @@ xla_test(
 xla_test(
     name = "conditional_test",
     srcs = ["conditional_test.cc"],
+    tags = ["enable_for_xla_interpreter"],
     deps = [
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
-- 
GitLab


From 4aa3d3ce252a9af2e09cdbd5460262ccb5378a3a Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Mon, 26 Feb 2018 17:56:15 -0800
Subject: [PATCH 328/575] Support configurable stats publishers in the grpc
 server.

PiperOrigin-RevId: 187110497
---
 .../distributed_runtime/rpc/grpc_server_lib.cc    | 15 ++++++++++++---
 .../distributed_runtime/rpc/grpc_server_lib.h     |  6 ++++++
 2 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc
index c4ac92d809..a6f4be3eaf 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc
@@ -106,7 +106,8 @@ GrpcServer::~GrpcServer() {
 Status GrpcServer::Init(
     ServiceInitFunction service_func,
     const RendezvousMgrCreationFunction& rendezvous_mgr_func,
-    const WorkerCreationFunction& worker_func) {
+    const WorkerCreationFunction& worker_func,
+    const StatsPublisherFactory& stats_factory) {
   mutex_lock l(mu_);
   CHECK_EQ(state_, NEW);
   master_env_.env = env_;
@@ -218,7 +219,7 @@ Status GrpcServer::Init(
   master_env_.ops = OpRegistry::Global();
   master_env_.worker_cache = worker_cache;
   master_env_.master_session_factory =
-      [config](
+      [config, stats_factory](
           SessionOptions options, const MasterEnv* env,
           std::unique_ptr<std::vector<std::unique_ptr<Device>>> remote_devs,
           std::unique_ptr<WorkerCacheInterface> worker_cache,
@@ -226,7 +227,7 @@ Status GrpcServer::Init(
         options.config.MergeFrom(config);
         return new MasterSession(options, env, std::move(remote_devs),
                                  std::move(worker_cache), std::move(device_set),
-                                 CreateNoOpStatsPublisher);
+                                 stats_factory);
       };
   master_env_.worker_cache_factory =
       [this](const WorkerCacheFactoryOptions& options,
@@ -241,6 +242,14 @@ Status GrpcServer::Init(
   return Status::OK();
 }
 
+Status GrpcServer::Init(
+    ServiceInitFunction service_func,
+    const RendezvousMgrCreationFunction& rendezvous_mgr_func,
+    const WorkerCreationFunction& worker_func) {
+  return Init(std::move(service_func), rendezvous_mgr_func, worker_func,
+              CreateNoOpStatsPublisher);
+}
+
 Status GrpcServer::Init(
     ServiceInitFunction service_func,
     const RendezvousMgrCreationFunction& rendezvous_mgr_func) {
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h
index 8b12ac1461..7c2f06f618 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include "grpc++/security/credentials.h"
 
 #include "tensorflow/core/common_runtime/process_util.h"
+#include "tensorflow/core/common_runtime/stats_publisher_interface.h"
 #include "tensorflow/core/distributed_runtime/master_env.h"
 #include "tensorflow/core/distributed_runtime/rpc/async_service_interface.h"
 #include "tensorflow/core/distributed_runtime/rpc/grpc_channel.h"
@@ -68,6 +69,11 @@ class GrpcServer : public ServerInterface {
   const string target() const override;
 
  protected:
+  Status Init(ServiceInitFunction service_func,
+              const RendezvousMgrCreationFunction& rendezvous_mgr_func,
+              const WorkerCreationFunction& worker_func,
+              const StatsPublisherFactory& stats_factory);
+
   Status Init(ServiceInitFunction service_func,
               const RendezvousMgrCreationFunction& rendezvous_mgr_func,
               const WorkerCreationFunction& worker_func);
-- 
GitLab


From 19f18e377d8ee2f624406527b21444128da344df Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Mon, 26 Feb 2018 18:04:55 -0800
Subject: [PATCH 329/575] Modify retrain script to output TFLite compatible
 quantized models.

-Also fix flaky input name selection introduced by last PR.
-Also rely on tf.contrib.quantize to do graph transformations.
-Also, update retrain script to use new float mobilenet_v1 and quantized mobilenet_v1 models.

PiperOrigin-RevId: 187111533
---
 .../examples/image_retraining/retrain.py      | 317 +++++++++++-------
 .../examples/image_retraining/retrain_test.py |  44 ++-
 2 files changed, 229 insertions(+), 132 deletions(-)

diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 25e09fecbf..99a71206ac 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -75,13 +75,16 @@ python tensorflow/examples/image_retraining/retrain.py \
     --image_dir ~/flower_photos --architecture mobilenet_1.0_224
 ```
 
-Run quantized version of mobilenet:
+Run mobilenet, instrumented for quantization:
 
 ```bash
 python tensorflow/examples/image_retraining/retrain.py \
-    --image_dir ~/flower_photos/   --architecture mobilenet_1.0_224_quantized
+    --image_dir ~/flower_photos/   --architecture mobilenet_1.0_224_quant
 ```
 
+These instrumented models can be converted to fully quantized mobile models via
+TensorFlow Lite.
+
 There are 32 different Mobilenet models to choose from, with a variety of file
 size and latency options. The first number can be '1.0', '0.75', '0.50', or
 '0.25' to control the size, and the second controls the input image size, either
@@ -121,7 +124,6 @@ import numpy as np
 from six.moves import urllib
 import tensorflow as tf
 
-from tensorflow.contrib.quantize.python import quant_ops
 from tensorflow.python.framework import graph_util
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.platform import gfile
@@ -135,6 +137,9 @@ FLAGS = None
 # need to update these to reflect the values in the network you're using.
 MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1  # ~134M
 
+# The location where variable checkpoints will be stored.
+CHECKPOINT_NAME = '/tmp/_retrain_checkpoint'
+
 
 def create_image_lists(image_dir, testing_percentage, validation_percentage):
   """Builds a list of training images from the file system.
@@ -745,9 +750,9 @@ def variable_summaries(var):
     tf.summary.histogram('histogram', var)
 
 
-def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
-                           bottleneck_tensor_size, quantize_layer):
-  """Adds a new softmax and fully-connected layer for training.
+def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
+                          bottleneck_tensor_size, quantize_layer, is_training):
+  """Adds a new softmax and fully-connected layer for training and eval.
 
   We need to retrain the top layer to identify our new classes, so this function
   adds the right operations to the graph, along with some variables to hold the
@@ -763,7 +768,9 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
     bottleneck_tensor: The output of the main CNN graph.
     bottleneck_tensor_size: How many entries in the bottleneck vector.
     quantize_layer: Boolean, specifying whether the newly added layer should be
-        quantized.
+        instrumented for quantized.
+    is_training: Boolean, specifying whether the newly add layer is for training
+        or eval.
 
   Returns:
     The tensors for the training and cross entropy results, and tensors for the
@@ -778,50 +785,41 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
     ground_truth_input = tf.placeholder(
         tf.int64, [None], name='GroundTruthInput')
 
-  # Organizing the following ops as `final_training_ops` so they're easier
-  # to see in TensorBoard
-  layer_name = 'final_training_ops'
+  # Organizing the following ops so they are easier to see in TensorBoard.
+  layer_name = 'final_retrain_ops'
   with tf.name_scope(layer_name):
     with tf.name_scope('weights'):
       initial_value = tf.truncated_normal(
           [bottleneck_tensor_size, class_count], stddev=0.001)
       layer_weights = tf.Variable(initial_value, name='final_weights')
-      if quantize_layer:
-        quantized_layer_weights = quant_ops.MovingAvgQuantize(
-            layer_weights, is_training=True)
-        variable_summaries(quantized_layer_weights)
-
       variable_summaries(layer_weights)
+
     with tf.name_scope('biases'):
       layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
-      if quantize_layer:
-        quantized_layer_biases = quant_ops.MovingAvgQuantize(
-            layer_biases, is_training=True)
-        variable_summaries(quantized_layer_biases)
-
       variable_summaries(layer_biases)
 
     with tf.name_scope('Wx_plus_b'):
-      if quantize_layer:
-        logits = tf.matmul(bottleneck_input,
-                           quantized_layer_weights) + quantized_layer_biases
-        logits = quant_ops.MovingAvgQuantize(
-            logits,
-            init_min=-32.0,
-            init_max=32.0,
-            is_training=True,
-            num_bits=8,
-            narrow_range=False,
-            ema_decay=0.5)
-        tf.summary.histogram('pre_activations', logits)
-      else:
-        logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
-        tf.summary.histogram('pre_activations', logits)
+      logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
+      tf.summary.histogram('pre_activations', logits)
 
   final_tensor = tf.nn.softmax(logits, name=final_tensor_name)
 
+  # The tf.contrib.quantize functions rewrite the graph in place for
+  # quantization. The imported model graph has already been rewritten, so upon
+  # calling these rewrites, only the newly added final layer will be
+  # transformed.
+  if quantize_layer:
+    if is_training:
+      tf.contrib.quantize.create_training_graph()
+    else:
+      tf.contrib.quantize.create_eval_graph()
+
   tf.summary.histogram('activations', final_tensor)
 
+  # If this is an eval graph, we don't need to add loss ops or an optimizer.
+  if not is_training:
+    return None, None, bottleneck_input, ground_truth_input, final_tensor
+
   with tf.name_scope('cross_entropy'):
     cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
         labels=ground_truth_input, logits=logits)
@@ -857,13 +855,91 @@ def add_evaluation_step(result_tensor, ground_truth_tensor):
   return evaluation_step, prediction
 
 
-def save_graph_to_file(sess, graph, graph_file_name):
+def run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor,
+                   decoded_image_tensor, resized_image_tensor,
+                   bottleneck_tensor):
+  """Runs a final evaluation on an eval graph using the test data set.
+
+  Args:
+    sess: Session for the train graph.
+    model_info: Model info dictionary from create_model_info()
+    class_count: Number of classes
+    image_lists: Dictionary of training images for each label.
+    jpeg_data_tensor: The layer to feed jpeg image data into.
+    decoded_image_tensor: The output of decoding and resizing the image.
+    resized_image_tensor: The input node of the recognition graph.
+    bottleneck_tensor: The bottleneck output layer of the CNN graph.
+  """
+  (sess, bottleneck_input, ground_truth_input, evaluation_step,
+   prediction) = build_eval_session(model_info, class_count)
+
+  test_bottlenecks, test_ground_truth, test_filenames = (
+      get_random_cached_bottlenecks(sess, image_lists, FLAGS.test_batch_size,
+                                    'testing', FLAGS.bottleneck_dir,
+                                    FLAGS.image_dir, jpeg_data_tensor,
+                                    decoded_image_tensor, resized_image_tensor,
+                                    bottleneck_tensor, FLAGS.architecture))
+  test_accuracy, predictions = sess.run(
+      [evaluation_step, prediction],
+      feed_dict={
+          bottleneck_input: test_bottlenecks,
+          ground_truth_input: test_ground_truth
+      })
+  tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
+                  (test_accuracy * 100, len(test_bottlenecks)))
+
+  if FLAGS.print_misclassified_test_images:
+    tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
+    for i, test_filename in enumerate(test_filenames):
+      if predictions[i] != test_ground_truth[i]:
+        tf.logging.info('%70s  %s' % (test_filename,
+                                      list(image_lists.keys())[predictions[i]]))
+
+
+def build_eval_session(model_info, class_count):
+  """Builds an restored eval session without train operations for exporting.
+
+  Args:
+    model_info: Model info dictionary from create_model_info()
+    class_count: Number of classes
+
+  Returns:
+    Eval session containing the restored eval graph.
+    The bottleneck input, ground truth, eval step, and prediction tensors.
+  """
+  # If quantized, we need to create the correct eval graph for exporting.
+  eval_graph, bottleneck_tensor, _ = create_model_graph(model_info)
+
+  eval_sess = tf.Session(graph=eval_graph)
+  with eval_graph.as_default():
+    # Add the new layer for exporting.
+    (_, _, bottleneck_input,
+     ground_truth_input, final_tensor) = add_final_retrain_ops(
+         class_count, FLAGS.final_tensor_name, bottleneck_tensor,
+         model_info['bottleneck_tensor_size'], model_info['quantize_layer'],
+         False)
+
+    # Now we need to restore the values from the training graph to the eval
+    # graph.
+    tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME)
+
+    evaluation_step, prediction = add_evaluation_step(final_tensor,
+                                                      ground_truth_input)
+
+  return (eval_sess, bottleneck_input, ground_truth_input, evaluation_step,
+          prediction)
+
+
+def save_graph_to_file(graph, graph_file_name, model_info, class_count):
+  """Saves an graph to file, creating a valid quantized one if necessary."""
+  sess, _, _, _, _ = build_eval_session(model_info, class_count)
+  graph = sess.graph
+
   output_graph_def = graph_util.convert_variables_to_constants(
       sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
 
   with gfile.FastGFile(graph_file_name, 'wb') as f:
     f.write(output_graph_def.SerializeToString())
-  return
 
 
 def prepare_file_system():
@@ -916,11 +992,10 @@ def create_model_info(architecture):
       return None
     version_string = parts[1]
     if (version_string != '1.0' and version_string != '0.75' and
-        version_string != '0.50' and version_string != '0.25'):
+        version_string != '0.5' and version_string != '0.25'):
       tf.logging.error(
-          """"The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25',
-  but found '%s' for architecture '%s'""",
-          version_string, architecture)
+          """"The Mobilenet version should be '1.0', '0.75', '0.5', or '0.25',
+  but found '%s' for architecture '%s'""", version_string, architecture)
       return None
     size_string = parts[2]
     if (size_string != '224' and size_string != '192' and
@@ -933,35 +1008,26 @@ def create_model_info(architecture):
     if len(parts) == 3:
       is_quantized = False
     else:
-      if parts[3] != 'quantized':
+      if parts[3] != 'quant':
         tf.logging.error(
             "Couldn't understand architecture suffix '%s' for '%s'", parts[3],
             architecture)
         return None
       is_quantized = True
 
+    data_url = 'http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/'
+    model_name = 'mobilenet_v1_' + version_string + '_' + size_string
     if is_quantized:
-      data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
-      data_url += version_string + '_' + size_string + '_quantized_frozen.tgz'
-      bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
-      resized_input_tensor_name = 'Placeholder:0'
-      model_dir_name = ('mobilenet_v1_' + version_string + '_' + size_string +
-                        '_quantized_frozen')
-      model_base_name = 'quantized_frozen_graph.pb'
-
-    else:
-      data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
-      data_url += version_string + '_' + size_string + '_frozen.tgz'
-      bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
-      resized_input_tensor_name = 'input:0'
-      model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string
-      model_base_name = 'frozen_graph.pb'
+      model_name += '_quant'
+    data_url += model_name + '.tgz'
+    bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
+    resized_input_tensor_name = 'input:0'
+    model_file_name = model_name + '_frozen.pb'
 
     bottleneck_tensor_size = 1001
     input_width = int(size_string)
     input_height = int(size_string)
     input_depth = 3
-    model_file_name = os.path.join(model_dir_name, model_base_name)
     input_mean = 127.5
     input_std = 127.5
   else:
@@ -1011,43 +1077,45 @@ def add_jpeg_decoding(input_width, input_height, input_depth, input_mean,
   return jpeg_data, mul_image
 
 
-def export_model(sess, architecture, saved_model_dir):
+def export_model(model_info, class_count, saved_model_dir):
   """Exports model for serving.
 
   Args:
-    sess: Current active TensorFlow Session.
-    architecture: Model architecture.
+    model_info: The modelinfo for the current model.
+    class_count: The number of classes.
     saved_model_dir: Directory in which to save exported model and variables.
   """
-  if architecture == 'inception_v3':
-    input_tensor = 'DecodeJpeg/contents:0'
-  elif architecture.startswith('mobilenet_'):
-    input_tensor = 'input:0'
-  else:
-    raise ValueError('Unknown architecture', architecture)
-  in_image = sess.graph.get_tensor_by_name(input_tensor)
-  inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}
-
-  out_classes = sess.graph.get_tensor_by_name('final_result:0')
-  outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_classes)}
+  # The SavedModel should hold the eval graph.
+  sess, _, _, _, _ = build_eval_session(model_info, class_count)
+  graph = sess.graph
+  with graph.as_default():
+    input_tensor = model_info['resized_input_tensor_name']
+    in_image = sess.graph.get_tensor_by_name(input_tensor)
+    inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}
+
+    out_classes = sess.graph.get_tensor_by_name('final_result:0')
+    outputs = {
+        'prediction': tf.saved_model.utils.build_tensor_info(out_classes)
+    }
 
-  signature = tf.saved_model.signature_def_utils.build_signature_def(
-      inputs=inputs,
-      outputs=outputs,
-      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
+    signature = tf.saved_model.signature_def_utils.build_signature_def(
+        inputs=inputs,
+        outputs=outputs,
+        method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
 
-  legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
+    legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
 
-  # Save out the SavedModel.
-  builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
-  builder.add_meta_graph_and_variables(
-      sess, [tf.saved_model.tag_constants.SERVING],
-      signature_def_map={
-          tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
-              signature
-      },
-      legacy_init_op=legacy_init_op)
-  builder.save()
+    # Save out the SavedModel.
+    builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
+    builder.add_meta_graph_and_variables(
+        sess, [tf.saved_model.tag_constants.SERVING],
+        signature_def_map={
+            tf.saved_model.signature_constants.
+            DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+                signature
+        },
+        legacy_init_op=legacy_init_op)
+    builder.save()
 
 
 def main(_):
@@ -1064,11 +1132,6 @@ def main(_):
     tf.logging.error('Did not recognize architecture flag')
     return -1
 
-  # Set up the pre-trained graph.
-  maybe_download_and_extract(model_info['data_url'])
-  graph, bottleneck_tensor, resized_image_tensor = (
-      create_model_graph(model_info))
-
   # Look at the folder structure, and create lists of all the images.
   image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage,
                                    FLAGS.validation_percentage)
@@ -1087,6 +1150,19 @@ def main(_):
       FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
       FLAGS.random_brightness)
 
+  # Set up the pre-trained graph.
+  maybe_download_and_extract(model_info['data_url'])
+  graph, bottleneck_tensor, resized_image_tensor = (
+      create_model_graph(model_info))
+
+  # Add the new layer that we'll be training.
+  with graph.as_default():
+    (train_step, cross_entropy, bottleneck_input,
+     ground_truth_input, final_tensor) = add_final_retrain_ops(
+         class_count, FLAGS.final_tensor_name, bottleneck_tensor,
+         model_info['bottleneck_tensor_size'], model_info['quantize_layer'],
+         True)
+
   with tf.Session(graph=graph) as sess:
     # Set up the image decoding sub-graph.
     jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding(
@@ -1110,15 +1186,8 @@ def main(_):
                         decoded_image_tensor, resized_image_tensor,
                         bottleneck_tensor, FLAGS.architecture)
 
-    # Add the new layer that we'll be training.
-    (train_step, cross_entropy, bottleneck_input, ground_truth_input,
-     final_tensor) = add_final_training_ops(
-         len(image_lists.keys()), FLAGS.final_tensor_name, bottleneck_tensor,
-         model_info['bottleneck_tensor_size'], model_info['quantize_layer'])
-
     # Create the operations we need to evaluate the accuracy of our new layer.
-    evaluation_step, prediction = add_evaluation_step(
-        final_tensor, ground_truth_input)
+    evaluation_step, _ = add_evaluation_step(final_tensor, ground_truth_input)
 
     # Merge all the summaries and write them out to the summaries_dir
     merged = tf.summary.merge_all()
@@ -1128,6 +1197,10 @@ def main(_):
     validation_writer = tf.summary.FileWriter(
         FLAGS.summaries_dir + '/validation')
 
+    # Create a train saver that is used to restore values into an eval graph
+    # when exporting models.
+    train_saver = tf.train.Saver()
+
     # Set up all our weights to their initial default values.
     init = tf.global_variables_initializer()
     sess.run(init)
@@ -1168,6 +1241,9 @@ def main(_):
                         (datetime.now(), i, train_accuracy * 100))
         tf.logging.info('%s: Step %d: Cross entropy = %f' %
                         (datetime.now(), i, cross_entropy_value))
+        # TODO(suharshs): Make this use an eval graph, to avoid quantization
+        # moving averages being updated by the validation set, though in
+        # practice this makes a negligable difference.
         validation_bottlenecks, validation_ground_truth, _ = (
             get_random_cached_bottlenecks(
                 sess, image_lists, FLAGS.validation_batch_size, 'validation',
@@ -1190,42 +1266,32 @@ def main(_):
 
       if (intermediate_frequency > 0 and (i % intermediate_frequency == 0)
           and i > 0):
+        # If we want to do an intermediate save, save a checkpoint of the train
+        # graph, to restore into the eval graph.
+        train_saver.save(sess, CHECKPOINT_NAME)
         intermediate_file_name = (FLAGS.intermediate_output_graphs_dir +
                                   'intermediate_' + str(i) + '.pb')
         tf.logging.info('Save intermediate result to : ' +
                         intermediate_file_name)
-        save_graph_to_file(sess, graph, intermediate_file_name)
+        save_graph_to_file(graph, intermediate_file_name, model_info,
+                           class_count)
+
+    # After training is complete, force one last save of the train checkpoint.
+    train_saver.save(sess, CHECKPOINT_NAME)
 
     # We've completed all our training, so run a final test evaluation on
     # some new images we haven't used before.
-    test_bottlenecks, test_ground_truth, test_filenames = (
-        get_random_cached_bottlenecks(
-            sess, image_lists, FLAGS.test_batch_size, 'testing',
-            FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
-            decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
-            FLAGS.architecture))
-    test_accuracy, predictions = sess.run(
-        [evaluation_step, prediction],
-        feed_dict={bottleneck_input: test_bottlenecks,
-                   ground_truth_input: test_ground_truth})
-    tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
-                    (test_accuracy * 100, len(test_bottlenecks)))
-
-    if FLAGS.print_misclassified_test_images:
-      tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
-      for i, test_filename in enumerate(test_filenames):
-        if predictions[i] != test_ground_truth[i]:
-          tf.logging.info('%70s  %s' %
-                          (test_filename,
-                           list(image_lists.keys())[predictions[i]]))
+    run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor,
+                   decoded_image_tensor, resized_image_tensor,
+                   bottleneck_tensor)
 
     # Write out the trained graph and labels with the weights stored as
     # constants.
-    save_graph_to_file(sess, graph, FLAGS.output_graph)
+    save_graph_to_file(graph, FLAGS.output_graph, model_info, class_count)
     with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
       f.write('\n'.join(image_lists.keys()) + '\n')
 
-    export_model(sess, FLAGS.architecture, FLAGS.saved_model_dir)
+    export_model(model_info, class_count, FLAGS.saved_model_dir)
 
 
 if __name__ == '__main__':
@@ -1406,8 +1472,9 @@ if __name__ == '__main__':
       form 'mobilenet_<parameter size>_<input_size>[_quantized]'. For example,
       'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224
       pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much
-      less accurate, but smaller and faster network that's 920 KB on disk and
-      takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
+      smaller and less accurate model, taking 128x128 images, and instrumented
+      for eventual quantization via TensorFlow Lite.
+      See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
       for more information on Mobilenet.\
       """)
   parser.add_argument(
diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py
index 8b8dd45fd7..fb7324c58a 100644
--- a/tensorflow/examples/image_retraining/retrain_test.py
+++ b/tensorflow/examples/image_retraining/retrain_test.py
@@ -67,22 +67,52 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
         self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0'))
 
   @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
-  def testAddFinalTrainingOps(self, flags_mock):
+  def testAddFinalRetrainOps(self, flags_mock):
     with tf.Graph().as_default():
       with tf.Session() as sess:
         bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
-        # Test creating final training op with quantization
-        retrain.add_final_training_ops(5, 'final', bottleneck, 1024, False)
+        # Test creating final training op with quantization.
+        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, False,
+                                      False)
         self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
 
   @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
-  def testAddFinalTrainingOpsQuantized(self, flags_mock):
-    with tf.Graph().as_default():
+  def testAddFinalRetrainOpsQuantized(self, flags_mock):
+    # Ensure that the training and eval graph for quantized models are correctly
+    # created.
+    with tf.Graph().as_default() as g:
+      with tf.Session() as sess:
+        bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
+        # Test creating final training op with quantization, set is_training to
+        # true.
+        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, True)
+        self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
+        found_fake_quant = 0
+        for op in g.get_operations():
+          if op.type == 'FakeQuantWithMinMaxVars':
+            found_fake_quant += 1
+            # Ensure that the inputs of each FakeQuant operations has 2 Assign
+            # operations in the training graph (Assign[Min,Max]Last,
+            # Assign[Min,Max]Ema)
+            self.assertEqual(2,
+                             len([i for i in op.inputs if 'Assign' in i.name]))
+        self.assertEqual(found_fake_quant, 2)
+    with tf.Graph().as_default() as g:
       with tf.Session() as sess:
         bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
-        # Test creating final training op with quantization
-        retrain.add_final_training_ops(5, 'final', bottleneck, 1024, True)
+        # Test creating final training op with quantization, set is_training to
+        # false.
+        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, False)
         self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
+        found_fake_quant = 0
+        for op in g.get_operations():
+          if op.type == 'FakeQuantWithMinMaxVars':
+            found_fake_quant += 1
+            for i in op.inputs:
+              # Ensure that no operations are Assign operation since this is the
+              # evaluation graph.
+              self.assertTrue('Assign' not in i.name)
+        self.assertEqual(found_fake_quant, 2)
 
   def testAddEvaluationStep(self):
     with tf.Graph().as_default():
-- 
GitLab


From 60a4b676df017b4ac51ca84a5e5e3a998912cebc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 18:05:59 -0800
Subject: [PATCH 330/575] Remove old implementation of the adaptive shared
 batcher, the in flight batches implemntation delivers similar performance but
 is simpler and requires less tuning.

PiperOrigin-RevId: 187111685
---
 .../adaptive_shared_batch_scheduler.h         | 172 +-----
 .../adaptive_shared_batch_scheduler_test.cc   | 488 +++++-------------
 2 files changed, 140 insertions(+), 520 deletions(-)

diff --git a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
index 25c5f9cf42..661ed239d3 100644
--- a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
+++ b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
@@ -50,43 +50,26 @@ class ASBSQueue;
 // track of a number of queues (one per model or model version) which are
 // continuously enqueuing requests. The scheduler groups the requests into
 // batches which it periodically sends off for processing (see
-// shared_batch_scheduler.h for more details). The AdaptiveSharedBatchScheduler
-// prioritizes batches by age (i.e. the batch's oldest request) irrespective of
-// queue or batch size.
+// shared_batch_scheduler.h for more details). AdaptiveSharedBatchScheduler
+// (ASBS) prioritizes batches by age (i.e. the batch's oldest request)
+// irrespective of queue or batch size.
 //
-// The scheduling decision currently exists in two flavors, controlled by the
-// option use_in_flight_batches_implementation. It is expected that setting this
-// option to true will give universally better results; after a period of
-// testing to confirm, the old implementation will be removed.
-//
-// If use_in_flight_batches_implementation is set to true, the scheduler
-// limits the number of batches which can be processed concurrently.  If a new
-// batch is created, and the number of in flight batches is below the limit,
-// the next (i.e. oldest) batch is immediately scheduled.  Similarly, when a
-// batch finishes processing, the limit is rechecked, and another batch may be
-// scheduled.  To avoid the need to carefully tune the limit for workload,
-// model type, platform, etc, it is dynamically adjusted in order to provide the
-// lowest latency.
-//
-// If use_in_flight_batches_implementation is set to false, the scheduler will
-// process the oldest batch at an adjustable rate, regardless of batch size.
-// The user can provide feedback to help set this rate to achieve some goal
-// (i.e. minimize overall latency, limit cpu usage, etc). The rate (or rather,
-// the corresponding period) is adjusted each time a batch is processed, using
-// an exponentially weighted moving average to smooth noisy feedback:
-// ewma_feedback = ((N - 1) * ewma_feedback + feedback()) / N
-// period *= (1 + K * emwa_feedback)
+// ASBS tries to keep the system busy by maintaining an adjustable number of
+// concurrently processed batches.  If a new batch is created, and the number of
+// in flight batches is below the target, the next (i.e. oldest) batch is
+// immediately scheduled.  Similarly, when a batch finishes processing, the
+// target is rechecked, and another batch may be scheduled.  To avoid the need
+// to carefully tune the target for workload, model type, platform, etc, it is
+// dynamically adjusted in order to provide the lowest average latency.
 //
 // Some potential use cases:
 // Hardware Accelerators (GPUs & TPUs) - If some phase of batch processing
 //   involves serial processing by a device, from a latency perspective it is
 //   desirable to keep the device evenly loaded, avoiding the need to wait for
 //   the device to process prior batches.
-//   feedback = num_pending_on_device() - desired_pending.
 // CPU utilization - If the batch processing is cpu dominated, you can reap
 //   latency gains when underutilized by increasing the processing rate, but
 //   back the rate off when the load increases to avoid overload.
-//   feedback = cpu_rate() - desired_cpu_rate.
 
 template <typename TaskType>
 class AdaptiveSharedBatchScheduler
@@ -101,13 +84,17 @@ class AdaptiveSharedBatchScheduler
   struct Options {
     // The name to use for the pool of batch threads.
     string thread_pool_name = {"batch_threads"};
-    // Number of batch processing threads; equivalently the maximum number of
-    // concurrently running batches.
+    // Number of batch processing threads - the maximum value of
+    // in_flight_batches_limit_.  It is recommended that this value be set by
+    // running the system under load, observing the learned value for
+    // in_flight_batches_limit_, and setting this maximum to ~ 2x the value.
+    // Under low load, in_flight_batches_limit_ has no substantial effect on
+    // latency and therefore undergoes a random walk.  Unreasonably large values
+    // for num_batch_threads allows for large in_flight_batches_limit_, which
+    // will harm latency for some time once load increases again.
     int64 num_batch_threads = port::NumSchedulableCPUs();
     // The environment to use (typically only overridden by test code).
     Env* env = Env::Default();
-    // Which implementation to use (described in class comments above).
-    bool use_in_flight_batches_implementation = false;
     // Initial limit for number of batches being concurrently processed.
     // Non-integer values correspond to probabilistic limits - i.e. a value of
     // 3.2 results in an actual cap of 3 80% of the time, and 4 20% of the time.
@@ -116,28 +103,6 @@ class AdaptiveSharedBatchScheduler
     // numbers will give less noisy latency measurements, but will be less
     // responsive to changes in workload.
     int64 batches_to_average_over = 1000;
-
-    // TODO(kte): remove the rate based implementation and corresponding options
-    // below once testing confirms the superiority of the in flight batches
-    // implementation.
-    // Initial batch scheduling period in microseconds. Will be altered for
-    // non-zero rate_feedback.
-    double initial_scheduling_period_micros = 500;
-    // Minimum batch scheduling period in microseconds. Recommend setting this
-    // value greater than 0, otherwise it may take a while to recover from a
-    // sustained time of negative scheduling_period_feedback (which may occur
-    // under low load).
-    double min_scheduling_period_micros = 100;
-    // Maximum batch scheduling period in microseconds.
-    double max_scheduling_period_micros = 10000;
-    // Feedback function used to modify the scheduling period each time a batch
-    // is scheduled.  Should return values roughly O(1), with positive values
-    // resulting in an increased period.
-    std::function<double()> scheduling_period_feedback{[] { return 0.; }};
-    // To handle potentially noisy scheduling_period_feedback, the period is
-    // adjusted using an exponentially weighted moving average over the previous
-    // feedback_smoothing_batches batches.  Must be greater than 0.
-    int64 feedback_smoothing_batches = 10;
   };
 
   // Ownership is shared between the caller of Create() and any queues created
@@ -171,17 +136,11 @@ class AdaptiveSharedBatchScheduler
 
   explicit AdaptiveSharedBatchScheduler(const Options& options);
 
-  // Batch scheduling function which runs every scheduling_period_ microseconds.
-  // Only used when options_.use_in_flight_batches_implementation == false.
-  void ProcessOneBatch();
-
   // Tracks processing latency and adjusts in_flight_batches_limit to minimize.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   void CallbackWrapper(const internal::ASBSBatch<TaskType>* batch,
                        BatchProcessor callback);
 
   // Schedules batch if in_flight_batches_limit_ is not met.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   void MaybeScheduleNextBatch() EXCLUSIVE_LOCKS_REQUIRED(mu_);
 
   // Notifies scheduler of non-empty batch which is eligible for processing.
@@ -212,41 +171,22 @@ class AdaptiveSharedBatchScheduler
 
   mutex mu_;
 
-  // Responsible for running ProcessOneBatch. PeriodicFunction was used in order
-  // to check for deletion so that the thread can be shut down.
-  // Only used when options_.use_in_flight_batches_implementation == false.
-  std::unique_ptr<PeriodicFunction> scheduling_thread_;
-
   // Responsible for running the batch processing callbacks.
   std::unique_ptr<thread::ThreadPool> batch_thread_pool_;
 
-  // Time interval in microseconds between successive ProcessOneBatch calls.
-  // Only used when options_.use_in_flight_batches_implementation == false.
-  double scheduling_period_;
-
-  // Exponentially weighted moving average of
-  // options_.scheduling_period_feedback() evaluated in each ProcessOneBatch
-  // call.
-  // Only used when options_.use_in_flight_batches_implementation == false.
-  double ewma_feedback_ = 0;
-
   // Limit on number of batches which can be concurrently processed.
   // Non-integer values correspond to probabilistic limits - i.e. a value of 3.2
   // results in an actual cap of 3 80% of the time, and 4 20% of the time.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   double in_flight_batches_limit_ GUARDED_BY(mu_);
 
   // Number of batches currently being processed.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   int64 in_flight_batches_ GUARDED_BY(mu_) = 0;
 
   // RNG engine and distribution.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   std::default_random_engine rand_engine_;
   std::uniform_real_distribution<double> rand_double_;
 
   // Fields controlling the dynamic adjustment of in_flight_batches_limit_.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   // Number of batches since the last in_flight_batches_limit_ adjustment.
   int64 batch_count_ GUARDED_BY(mu_) = 0;
   // Sum of processing latency for batches counted by batch_count_.
@@ -348,32 +288,6 @@ Status AdaptiveSharedBatchScheduler<TaskType>::Create(
     return errors::InvalidArgument("num_batch_threads must be positive; was ",
                                    options.num_batch_threads);
   }
-  if (options.min_scheduling_period_micros < 0) {
-    return errors::InvalidArgument(
-        "min_scheduling_period_micros must be >= 0; was ",
-        options.min_scheduling_period_micros);
-  }
-  if (options.min_scheduling_period_micros >
-      options.initial_scheduling_period_micros) {
-    return errors::InvalidArgument(
-        "initial_scheduling_period_micros (",
-        options.initial_scheduling_period_micros,
-        ") must be >= min_scheduling_period_micros (",
-        options.min_scheduling_period_micros, ")");
-  }
-  if (options.initial_scheduling_period_micros >
-      options.max_scheduling_period_micros) {
-    return errors::InvalidArgument(
-        "initial_scheduling_period_micros (",
-        options.initial_scheduling_period_micros,
-        ") must be <= max_scheduling_period_micros (",
-        options.max_scheduling_period_micros, ")");
-  }
-  if (options.feedback_smoothing_batches < 1) {
-    return errors::InvalidArgument(
-        "feedback_smoothing_batches must be positive; was ",
-        options.feedback_smoothing_batches);
-  }
   if (options.initial_in_flight_batches_limit > options.num_batch_threads) {
     return errors::InvalidArgument(
         "initial_in_flight_batches_limit (",
@@ -401,20 +315,12 @@ template <typename TaskType>
 AdaptiveSharedBatchScheduler<TaskType>::AdaptiveSharedBatchScheduler(
     const Options& options)
     : options_(options),
-      scheduling_period_(options.initial_scheduling_period_micros),
       in_flight_batches_limit_(options.initial_in_flight_batches_limit),
       rand_double_(0.0, 1.0) {
   std::random_device device;
   rand_engine_.seed(device());
-  PeriodicFunction::Options opts;
-  opts.thread_name_prefix = "scheduling_thread";
-  opts.env = GetEnv();
   batch_thread_pool_.reset(new thread::ThreadPool(
       GetEnv(), options.thread_pool_name, options.num_batch_threads));
-  if (!options.use_in_flight_batches_implementation) {
-    scheduling_thread_.reset(
-        new PeriodicFunction([this] { ProcessOneBatch(); }, 0, opts));
-  }
 }
 
 template <typename TaskType>
@@ -443,9 +349,7 @@ void AdaptiveSharedBatchScheduler<TaskType>::AddBatch(
     const internal::ASBSBatch<TaskType>* batch) {
   mutex_lock l(mu_);
   batches_.push(batch);
-  if (options_.use_in_flight_batches_implementation) {
-    MaybeScheduleNextBatch();
-  }
+  MaybeScheduleNextBatch();
 }
 
 template <typename TaskType>
@@ -523,44 +427,6 @@ void AdaptiveSharedBatchScheduler<TaskType>::CallbackWrapper(
   MaybeScheduleNextBatch();
 }
 
-template <typename TaskType>
-void AdaptiveSharedBatchScheduler<TaskType>::ProcessOneBatch() {
-  static const double kFeedbackMultiplier = .001;
-  const internal::ASBSBatch<TaskType>* batch = nullptr;
-  BatchProcessor callback;
-  const int64 start_time_micros = GetEnv()->NowMicros();
-  {
-    mutex_lock l(mu_);
-    if (!batches_.empty()) {
-      batch = batches_.top();
-      batches_.pop();
-      callback = queues_and_callbacks_[batch->queue()];
-    }
-  }
-  if (batch != nullptr) {
-    double feedback = options_.scheduling_period_feedback();
-    const int64 N = options_.feedback_smoothing_batches;
-    ewma_feedback_ = ((N - 1) * ewma_feedback_ + feedback) / N;
-    scheduling_period_ *= (1 + kFeedbackMultiplier * ewma_feedback_);
-    if (scheduling_period_ < options_.min_scheduling_period_micros) {
-      scheduling_period_ = options_.min_scheduling_period_micros;
-    } else if (scheduling_period_ > options_.max_scheduling_period_micros) {
-      scheduling_period_ = options_.max_scheduling_period_micros;
-    }
-    // Queue may destroy itself after ReleaseBatch is called.
-    batch->queue()->ReleaseBatch(batch);
-    batch_thread_pool_->Schedule([callback, batch] {
-      callback(std::unique_ptr<Batch<TaskType>>(
-          const_cast<internal::ASBSBatch<TaskType>*>(batch)));
-    });
-  }
-  const int64 sleep_time =
-      scheduling_period_ - (GetEnv()->NowMicros() - start_time_micros);
-  if (sleep_time > 0) {
-    GetEnv()->SleepForMicroseconds(sleep_time);
-  }
-}
-
 template <typename TaskType>
 bool AdaptiveSharedBatchScheduler<TaskType>::BatchCompare::operator()(
     const internal::ASBSBatch<TaskType>* a,
diff --git a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
index 8ae8ca02ec..109234287e 100644
--- a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
+++ b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
@@ -64,59 +64,6 @@ std::unique_ptr<Thread> CreateFakeClockAdvancerThread(
       }));
 }
 
-TEST(AdaptiveSharedBatchSchedulerTest, Basic) {
-  for (const bool delete_scheduler_early : {false, true}) {
-    for (const bool delete_queue_1_early : {false, true}) {
-      int queue_0_tasks = 0;
-      auto queue_0_callback =
-          [&queue_0_tasks](std::unique_ptr<Batch<FakeTask>> batch) {
-            ASSERT_TRUE(batch->IsClosed());
-            EXPECT_GT(batch->num_tasks(), 0);
-            for (int i = 0; i < batch->num_tasks(); i++) {
-              queue_0_tasks += batch->task(i).size();
-            }
-          };
-      int queue_1_tasks = 0;
-      auto queue_1_callback =
-          [&queue_1_tasks](std::unique_ptr<Batch<FakeTask>> batch) {
-            ASSERT_TRUE(batch->IsClosed());
-            EXPECT_GT(batch->num_tasks(), 0);
-            for (int i = 0; i < batch->num_tasks(); i++) {
-              queue_1_tasks += batch->task(i).size();
-            }
-          };
-      {
-        std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-        TF_ASSERT_OK(
-            AdaptiveSharedBatchScheduler<FakeTask>::Create({}, &scheduler));
-
-        // Create two queues.
-        std::unique_ptr<BatchScheduler<FakeTask>> queue_0;
-        TF_ASSERT_OK(scheduler->AddQueue({}, queue_0_callback, &queue_0));
-        std::unique_ptr<BatchScheduler<FakeTask>> queue_1;
-        TF_ASSERT_OK(scheduler->AddQueue({}, queue_1_callback, &queue_1));
-
-        if (delete_scheduler_early) {
-          // Delete our copy of the scheduler. The queues should keep it alive
-          // under the covers.
-          scheduler = nullptr;
-        }
-        // Submit tasks to the two queues, and (optionally) remove the queues.
-        TF_ASSERT_OK(ScheduleTask(1, queue_0.get()));
-        TF_ASSERT_OK(ScheduleTask(2, queue_1.get()));
-        TF_ASSERT_OK(ScheduleTask(3, queue_0.get()));
-        TF_ASSERT_OK(ScheduleTask(4, queue_1.get()));
-        if (delete_queue_1_early) {
-          queue_1 = nullptr;
-        }
-        TF_ASSERT_OK(ScheduleTask(5, queue_0.get()));
-      }
-      EXPECT_EQ(queue_0_tasks, 9);
-      EXPECT_EQ(queue_1_tasks, 6);
-    }
-  }
-}
-
 TEST(AdaptiveSharedBatchSchedulerTest, BadOptions) {
   using Scheduler = AdaptiveSharedBatchScheduler<FakeTask>;
   std::shared_ptr<Scheduler> scheduler;
@@ -124,24 +71,6 @@ TEST(AdaptiveSharedBatchSchedulerTest, BadOptions) {
   options.num_batch_threads = 0;
   EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
   options = Scheduler::Options();
-  options.min_scheduling_period_micros = 50;
-  options.max_scheduling_period_micros = 100;
-  options.initial_scheduling_period_micros = 1;
-  EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
-  options = Scheduler::Options();
-  options.min_scheduling_period_micros = 50;
-  options.max_scheduling_period_micros = 100;
-  options.initial_scheduling_period_micros = 1000;
-  EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
-  options = Scheduler::Options();
-  options.min_scheduling_period_micros = 100;
-  options.max_scheduling_period_micros = 50;
-  options.initial_scheduling_period_micros = 75;
-  EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
-  options = Scheduler::Options();
-  options.feedback_smoothing_batches = 0;
-  EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
-  options = Scheduler::Options();
   options.initial_in_flight_batches_limit = 0.5;
   EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
   options = Scheduler::Options();
@@ -153,301 +82,8 @@ TEST(AdaptiveSharedBatchSchedulerTest, BadOptions) {
   EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
 }
 
-TEST(AdaptiveSharedBatchSchedulerTest, ObeysQueueOptions) {
-  test_util::FakeClockEnv env(Env::Default());
-  Notification start_teardown, stop_teardown;
-  std::unique_ptr<Thread> teardown_thread =
-      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
-  {
-    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-    options.initial_scheduling_period_micros = 1000;
-    options.env = &env;
-    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-    TF_ASSERT_OK(
-        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
-    std::unique_ptr<BatchScheduler<FakeTask>> queue_0;
-    std::unique_ptr<BatchScheduler<FakeTask>> queue_1;
-    int queue_0_tasks = 0;
-    int queue_1_tasks = 0;
-    auto queue_0_callback = [&queue_0_tasks,
-                             &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        queue_0_tasks += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-    auto queue_1_callback = [&queue_1_tasks,
-                             &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        queue_1_tasks += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-    AdaptiveSharedBatchScheduler<FakeTask>::QueueOptions queue_options;
-    queue_options.max_batch_size = 10;
-    queue_options.max_enqueued_batches = 0;
-    // Queue must have max_enqueued_batchs > 1.
-    EXPECT_FALSE(
-        scheduler->AddQueue(queue_options, queue_0_callback, &queue_0).ok());
-    queue_options.max_enqueued_batches = 2;
-    TF_ASSERT_OK(
-        scheduler->AddQueue(queue_options, queue_0_callback, &queue_0));
-    EXPECT_EQ(10, queue_0->max_task_size());
-    queue_options.max_batch_size = 0;
-    // Queue must have max_batch_size > 0.
-    EXPECT_FALSE(
-        scheduler->AddQueue(queue_options, queue_1_callback, &queue_1).ok());
-    queue_options.max_batch_size = 2;
-    queue_options.max_enqueued_batches = 1;
-    TF_ASSERT_OK(
-        scheduler->AddQueue(queue_options, queue_1_callback, &queue_1));
-
-    // Wait for scheduling_thread to sleep.
-    env.BlockUntilThreadsAsleep(1);
-    // Task larger than max_batch_size shouldn't schedule.
-    EXPECT_FALSE(ScheduleTask(15, queue_0.get()).ok());
-    TF_ASSERT_OK(ScheduleTask(5, queue_0.get()));
-    TF_ASSERT_OK(ScheduleTask(5, queue_0.get()));
-    env.AdvanceByMicroseconds(1);
-
-    // Task larger than max_batch_size shouldn't schedule.
-    EXPECT_FALSE(ScheduleTask(3, queue_1.get()).ok());
-    TF_ASSERT_OK(ScheduleTask(1, queue_1.get()));
-    TF_ASSERT_OK(ScheduleTask(1, queue_1.get()));
-    env.AdvanceByMicroseconds(1);
-    // Exceeds max_enqueued_batches, shouldn't schedule.
-    EXPECT_FALSE(ScheduleTask(1, queue_1.get()).ok());
-
-    TF_ASSERT_OK(ScheduleTask(5, queue_0.get()));
-    // Exceeds max_enqueued_batches, shouldn't schedule.
-    EXPECT_FALSE(ScheduleTask(6, queue_0.get()).ok());
-    TF_ASSERT_OK(ScheduleTask(4, queue_0.get()));
-
-    // Batches should be processed in order from oldest to newest.
-    env.AdvanceByMicroseconds(1000);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(queue_0_tasks, 10);
-    EXPECT_EQ(queue_1_tasks, 0);
-
-    env.AdvanceByMicroseconds(1000);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(queue_0_tasks, 10);
-    EXPECT_EQ(queue_1_tasks, 2);
-
-    env.AdvanceByMicroseconds(1000);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(queue_0_tasks, 19);
-    EXPECT_EQ(queue_1_tasks, 2);
-    start_teardown.Notify();
-  }
-  stop_teardown.Notify();
-}
-
-TEST(AdaptiveSharedBatchSchedulerTest, RateFeedback) {
-  test_util::FakeClockEnv env(Env::Default());
-  Notification start_teardown, stop_teardown;
-  std::unique_ptr<Thread> teardown_thread =
-      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
-  {
-    double feedback = 0;
-    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-    options.initial_scheduling_period_micros = 1000;
-    options.min_scheduling_period_micros = 200;
-    options.max_scheduling_period_micros = 2000;
-    options.env = &env;
-    options.scheduling_period_feedback = [&feedback] { return feedback; };
-    options.feedback_smoothing_batches = 1;
-    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-    TF_ASSERT_OK(
-        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
-    std::unique_ptr<BatchScheduler<FakeTask>> queue;
-    int scheduled_items = 0;
-    auto queue_callback = [&scheduled_items,
-                           &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      scheduled_items = 0;
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        scheduled_items += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-
-    TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
-
-    // Wait for scheduling_thread to sleep.
-    env.BlockUntilThreadsAsleep(1);
-    // Enqueue 6 batches.
-    for (int i = 0; i < 6; i++) {
-      TF_ASSERT_OK(ScheduleTask(900 + i, queue.get()));
-      env.AdvanceByMicroseconds(1);
-    }
-    feedback = -500;
-    env.AdvanceByMicroseconds(994);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 500 usec.
-    EXPECT_EQ(scheduled_items, 900);
-    env.AdvanceByMicroseconds(500);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 250 usec.
-    EXPECT_EQ(scheduled_items, 901);
-    feedback = 0;
-    env.AdvanceByMicroseconds(250);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 250 usec.
-    EXPECT_EQ(scheduled_items, 902);
-    feedback = 10000;  // large feedback should hit max_scheduling_period.
-    env.AdvanceByMicroseconds(250);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 2000 usec.
-    EXPECT_EQ(scheduled_items, 903);
-    feedback = -10000;  // large feedback should hit min_scheduling_period.
-    env.AdvanceByMicroseconds(1999);
-    // No callback scheduled, only scheduling thread sleeping.
-    env.BlockUntilThreadsAsleep(1);
-    EXPECT_EQ(scheduled_items, 903);
-    env.AdvanceByMicroseconds(1);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 200 usec.
-    EXPECT_EQ(scheduled_items, 904);
-    env.AdvanceByMicroseconds(200);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(scheduled_items, 905);
-    start_teardown.Notify();
-  }
-  stop_teardown.Notify();
-}
-
-TEST(AdaptiveSharedBatchSchedulerTest, FeedbackSmoothing) {
-  test_util::FakeClockEnv env(Env::Default());
-  Notification start_teardown, stop_teardown;
-  std::unique_ptr<Thread> teardown_thread =
-      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
-  {
-    double feedback = 0;
-    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-    options.initial_scheduling_period_micros = 1000;
-    options.env = &env;
-    options.scheduling_period_feedback = [&feedback] { return feedback; };
-    options.feedback_smoothing_batches = 3;
-    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-    TF_ASSERT_OK(
-        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
-    std::unique_ptr<BatchScheduler<FakeTask>> queue;
-    int scheduled_items = 0;
-    auto queue_callback = [&scheduled_items,
-                           &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      scheduled_items = 0;
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        scheduled_items += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-
-    TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
-
-    // Wait for scheduling_thread to sleep.
-    env.BlockUntilThreadsAsleep(1);
-    // Enqueue 4 batches.
-    for (int i = 0; i < 4; i++) {
-      TF_ASSERT_OK(ScheduleTask(900 + i, queue.get()));
-      env.AdvanceByMicroseconds(1);
-    }
-    feedback = -300;
-    env.AdvanceByMicroseconds(996);
-    env.BlockUntilThreadsAsleep(2);
-    // ewma_feedback = 100, scheduling_period = 900.
-    EXPECT_EQ(scheduled_items, 900);
-    env.AdvanceByMicroseconds(899);
-    // No callback scheduled, only scheduling thread sleeping.
-    env.BlockUntilThreadsAsleep(1);
-    EXPECT_EQ(scheduled_items, 900);
-    env.AdvanceByMicroseconds(1);
-    env.BlockUntilThreadsAsleep(2);
-    // ewma_feedback = 167, scheduling_period = 750.
-    EXPECT_EQ(scheduled_items, 901);
-    env.AdvanceByMicroseconds(749);
-    // No callback scheduled, only scheduling thread sleeping.
-    env.BlockUntilThreadsAsleep(1);
-    EXPECT_EQ(scheduled_items, 901);
-    feedback = 1000 / 3.;
-    env.AdvanceByMicroseconds(1);
-    env.BlockUntilThreadsAsleep(2);
-    // emwa_feedback = 0, scheduling_period = 750.
-    EXPECT_EQ(scheduled_items, 902);
-    env.AdvanceByMicroseconds(749);
-    // No callback scheduled, only scheduling thread sleeping.
-    env.BlockUntilThreadsAsleep(1);
-    EXPECT_EQ(scheduled_items, 902);
-    env.AdvanceByMicroseconds(1);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(scheduled_items, 903);
-    start_teardown.Notify();
-  }
-  stop_teardown.Notify();
-}
-
-TEST(AdaptiveSharedBatchSchedulerTest, QueueCapacityInfo) {
-  test_util::FakeClockEnv env(Env::Default());
-  Notification start_teardown, stop_teardown;
-  std::unique_ptr<Thread> teardown_thread =
-      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
-  {
-    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-    options.initial_scheduling_period_micros = 1000;
-    options.env = &env;
-    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-    TF_ASSERT_OK(
-        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
-    std::unique_ptr<BatchScheduler<FakeTask>> queue;
-    int scheduled_items = 0;
-    auto queue_callback = [&scheduled_items,
-                           &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      scheduled_items = 0;
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        scheduled_items += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-    AdaptiveSharedBatchScheduler<FakeTask>::QueueOptions queue_options;
-    queue_options.max_batch_size = 10;
-    queue_options.max_enqueued_batches = 10;
-    TF_ASSERT_OK(scheduler->AddQueue(queue_options, queue_callback, &queue));
-
-    // Wait for scheduling_thread to sleep.
-    env.BlockUntilThreadsAsleep(1);
-    // Enqueue 3 tasks.
-    EXPECT_EQ(queue->NumEnqueuedTasks(), 0);
-    EXPECT_EQ(queue->SchedulingCapacity(), 100);
-    TF_ASSERT_OK(ScheduleTask(5, queue.get()));
-    EXPECT_EQ(queue->NumEnqueuedTasks(), 1);
-    EXPECT_EQ(queue->SchedulingCapacity(), 95);
-    env.AdvanceByMicroseconds(1);
-    TF_ASSERT_OK(ScheduleTask(6, queue.get()));
-    EXPECT_EQ(queue->NumEnqueuedTasks(), 2);
-    EXPECT_EQ(queue->SchedulingCapacity(), 84);
-    env.AdvanceByMicroseconds(1);
-    TF_ASSERT_OK(ScheduleTask(1, queue.get()));
-    EXPECT_EQ(queue->NumEnqueuedTasks(), 3);
-    EXPECT_EQ(queue->SchedulingCapacity(), 83);
-
-    env.AdvanceByMicroseconds(998);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(scheduled_items, 5);
-    env.AdvanceByMicroseconds(1000);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(scheduled_items, 7);
-    start_teardown.Notify();
-  }
-  stop_teardown.Notify();
-}
-
-TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesImplementation) {
+TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesLimit) {
   AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-  options.use_in_flight_batches_implementation = true;
   options.initial_in_flight_batches_limit = 2;
   options.batches_to_average_over = 1000;
   mutex mu;
@@ -476,7 +112,7 @@ TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesImplementation) {
   std::unique_ptr<BatchScheduler<FakeTask>> queue;
   TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
 
-  // Enqueue 3 batches.
+  // Enqueue 3 tasks, should result in 3 batches.
   for (int i = 0; i < 3; i++) {
     TF_ASSERT_OK(ScheduleTask(100, queue.get()));
   }
@@ -490,7 +126,6 @@ TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesLimitTuning) {
   {
     AdaptiveSharedBatchScheduler<FakeTask>::Options options;
     options.env = &env;
-    options.use_in_flight_batches_implementation = true;
     options.initial_in_flight_batches_limit = 2;
     options.batches_to_average_over = 1;
     auto queue_callback = [&env](std::unique_ptr<Batch<FakeTask>> batch) {
@@ -544,6 +179,125 @@ TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesLimitTuning) {
   }
   stop_teardown.Notify();
 }
+
+TEST(AdaptiveSharedBatchSchedulerTest, DeleteQueue) {
+  AdaptiveSharedBatchScheduler<FakeTask>::Options options;
+  options.initial_in_flight_batches_limit = 1;
+  options.batches_to_average_over = 1000;
+  mutex mu;
+  int processed_batches = 0;
+  Notification finish_processing;
+  auto queue_callback = [&mu, &processed_batches, &finish_processing](
+                            std::unique_ptr<Batch<FakeTask>> batch) {
+    ASSERT_TRUE(batch->IsClosed());
+    EXPECT_GT(batch->num_tasks(), 0);
+    finish_processing.WaitForNotification();
+    mu.lock();
+    processed_batches++;
+    mu.unlock();
+  };
+
+  std::unique_ptr<Thread> queue_deleter;
+  std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
+  TF_ASSERT_OK(
+      AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
+  std::unique_ptr<BatchScheduler<FakeTask>> queue;
+  TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
+
+  // Enqueue 2 tasks, should result in 2 batches.
+  for (int i = 0; i < 2; i++) {
+    TF_ASSERT_OK(ScheduleTask(100, queue.get()));
+  }
+  // Delete queue, should be kept alive until empty.
+  queue_deleter.reset(Env::Default()->StartThread(
+      {}, "QueueDeleterThread", [&queue, &mu, &processed_batches] {
+        queue.reset();
+        mutex_lock l(mu);
+        EXPECT_EQ(processed_batches, 2);
+      }));
+  // Give queue_deleter thread time to delete queue.
+  Env::Default()->SleepForMicroseconds(1000);
+  finish_processing.Notify();
+}
+
+TEST(AdaptiveSharedBatchSchedulerTest, DeleteScheduler) {
+  AdaptiveSharedBatchScheduler<FakeTask>::Options options;
+  options.initial_in_flight_batches_limit = 1;
+  options.batches_to_average_over = 1000;
+  mutex mu;
+  int processed_batches = 0;
+  Notification finish_processing;
+  auto queue_callback = [&mu, &processed_batches, &finish_processing](
+                            std::unique_ptr<Batch<FakeTask>> batch) {
+    ASSERT_TRUE(batch->IsClosed());
+    EXPECT_GT(batch->num_tasks(), 0);
+    finish_processing.WaitForNotification();
+    mu.lock();
+    processed_batches++;
+    mu.unlock();
+  };
+
+  std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
+  TF_ASSERT_OK(
+      AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
+  std::unique_ptr<BatchScheduler<FakeTask>> queue;
+  TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
+
+  // Enqueue 2 tasks, should result in 2 batches.
+  for (int i = 0; i < 2; i++) {
+    TF_ASSERT_OK(ScheduleTask(100, queue.get()));
+  }
+  // Delete scheduler, should be kept alive until queues are empty.
+  scheduler.reset();
+  finish_processing.Notify();
+  while (true) {
+    mutex_lock l(mu);
+    if (processed_batches == 2) break;
+  }
+}
+
+TEST(AdaptiveSharedBatchSchedulerTest, QueueCapacityInfo) {
+  AdaptiveSharedBatchScheduler<FakeTask>::Options options;
+  options.initial_in_flight_batches_limit = 1;
+  options.batches_to_average_over = 1000;
+  mutex mu;
+  int processed_batches = 0;
+  Notification finish_processing;
+  auto queue_callback = [&mu, &processed_batches, &finish_processing](
+                            std::unique_ptr<Batch<FakeTask>> batch) {
+    ASSERT_TRUE(batch->IsClosed());
+    EXPECT_GT(batch->num_tasks(), 0);
+    mu.lock();
+    int batch_num = ++processed_batches;
+    mu.unlock();
+    if (batch_num == 1) {
+      finish_processing.WaitForNotification();
+    }
+  };
+  std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
+  TF_ASSERT_OK(
+      AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
+  std::unique_ptr<BatchScheduler<FakeTask>> queue;
+  TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
+
+  // Enqueue 2 tasks, should result in 2 batches.
+  for (int i = 0; i < 2; i++) {
+    TF_ASSERT_OK(ScheduleTask(100, queue.get()));
+  }
+  // First batch was immediately processed, no longer counts as enqueued.
+  EXPECT_EQ(queue->NumEnqueuedTasks(), 1);
+  EXPECT_EQ(queue->SchedulingCapacity(), 9 * 1000 + 900);
+  // Enqueue 2 more tasks, should fall in same batch.
+  TF_ASSERT_OK(ScheduleTask(100, queue.get()));
+  TF_ASSERT_OK(ScheduleTask(200, queue.get()));
+  EXPECT_EQ(queue->NumEnqueuedTasks(), 3);
+  EXPECT_EQ(queue->SchedulingCapacity(), 9 * 1000 + 600);
+  // Enqueue 1 more task, should create new batch.
+  TF_ASSERT_OK(ScheduleTask(700, queue.get()));
+  EXPECT_EQ(queue->NumEnqueuedTasks(), 4);
+  EXPECT_EQ(queue->SchedulingCapacity(), 8 * 1000 + 300);
+  finish_processing.Notify();
+}
 }  // namespace anonymous
 }  // namespace serving
 }  // namespace tensorflow
-- 
GitLab


From 8a2d00f57c8bce6be7550dc447036b62567d1d82 Mon Sep 17 00:00:00 2001
From: Martin Wicke <577277+martinwicke@users.noreply.github.com>
Date: Mon, 26 Feb 2018 18:32:36 -0800
Subject: [PATCH 331/575] Fix bad wrong jpeg/nasm mirror (#17277)

---
 tensorflow/workspace.bzl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 85f423f236..278a225f76 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -215,6 +215,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       urls = [
           "https://mirror.bazel.build/www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2",
           "http://pkgs.fedoraproject.org/repo/pkgs/nasm/nasm-2.12.02.tar.bz2/d15843c3fb7db39af80571ee27ec6fad/nasm-2.12.02.tar.bz2",
+          "http://www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2",
       ],
       sha256 = "00b0891c678c065446ca59bcee64719d0096d54d6886e6e472aeee2e170ae324",
       strip_prefix = "nasm-2.12.02",
@@ -226,7 +227,6 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
       urls = [
           "https://mirror.bazel.build/github.com/libjpeg-turbo/libjpeg-turbo/archive/1.5.1.tar.gz",
           "https://github.com/libjpeg-turbo/libjpeg-turbo/archive/1.5.1.tar.gz",
-          "http://www.nasm.us/pub/nasm/releasebuilds/2.12.02/nasm-2.12.02.tar.bz2",
       ],
       sha256 = "c15a9607892113946379ccea3ca8b85018301b200754f209453ab21674268e77",
       strip_prefix = "libjpeg-turbo-1.5.1",
-- 
GitLab


From 4a9d929868c57d742512d65634cceada8c11c6ab Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 19:46:27 -0800
Subject: [PATCH 332/575] Make sure rounding and handling of denormals in
 Grappler is the same as in TensorFlow. Enable constant folding for more
 types, particularly on GPUs.

PiperOrigin-RevId: 187120456
---
 tensorflow/core/grappler/op_types.cc          |  6 +-
 .../grappler/optimizers/constant_folding.cc   | 96 ++++++++++++-------
 tensorflow/core/kernels/constant_op.cc        | 11 +++
 3 files changed, 74 insertions(+), 39 deletions(-)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index e225e99a9e..9b3755ddce 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -354,7 +354,8 @@ bool IsFreeOfSideEffect(const NodeDef& node) {
     return false;
   }
   const OpDef* op_def = nullptr;
-  Status status = OpRegistry::Global()->LookUpOpDef(node.op(), &op_def);
+  const string& op_name = node.op();
+  Status status = OpRegistry::Global()->LookUpOpDef(op_name, &op_def);
   if (!status.ok()) {
     return false;
   }
@@ -368,7 +369,8 @@ bool IsFreeOfSideEffect(const NodeDef& node) {
     }
   }
   // Some nodes do in-place updates on regular tensor inputs.
-  if (GetBoolAttr(node, "in_place") || GetBoolAttr(node, "inplace")) {
+  if (GetBoolAttr(node, "in_place") || GetBoolAttr(node, "inplace") ||
+      StringPiece(op_name).starts_with("Inplace")) {
     return false;
   }
   return true;
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 10ca7dcce0..a5417aaa51 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -35,7 +35,9 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/denormal.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/setround.h"
 #include "tensorflow/core/platform/tensor_coding.h"
 #include "tensorflow/core/public/version.h"
 #include "tensorflow/core/util/bcast.h"
@@ -51,7 +53,14 @@ class EigenThreadPoolWrapper : public Eigen::ThreadPoolInterface {
   explicit EigenThreadPoolWrapper(thread::ThreadPool* pool) : pool_(pool) {}
   ~EigenThreadPoolWrapper() override {}
   void Schedule(std::function<void()> fn) override {
-    pool_->Schedule(std::move(fn));
+    auto wrapped = [=]() {
+      // TensorFlow flushes denormals to zero and rounds to nearest, so we do
+      // the same here.
+      port::ScopedFlushDenormal flush;
+      port::ScopedSetRound round(FE_TONEAREST);
+      fn();
+    };
+    pool_->Schedule(std::move(wrapped));
   }
   int NumThreads() const override { return pool_->NumThreads(); }
   int CurrentThreadId() const override { return pool_->CurrentThreadId(); }
@@ -292,16 +301,16 @@ Status ConstantFolding::MaterializeShapes(const GraphProperties& properties) {
   // graph.
   const int node_count = graph_->node_size();
   for (int i = 0; i < node_count; ++i) {
-    NodeDef& node = *graph_->mutable_node(i);
-    const string op = node.op();
+    NodeDef* node = graph_->mutable_node(i);
+    const string op = node->op();
     if (op != "Shape" && op != "Size" && op != "Rank" && op != "ShapeN") {
       continue;
     }
 
     const std::vector<OpInfo::TensorProperties>& output =
-        properties.GetOutputProperties(node.name());
+        properties.GetOutputProperties(node->name());
     const std::vector<OpInfo::TensorProperties>& input =
-        properties.GetInputProperties(node.name());
+        properties.GetInputProperties(node->name());
     if (input.empty() || output.empty()) {
       continue;
     }
@@ -328,35 +337,35 @@ Status ConstantFolding::MaterializeShapes(const GraphProperties& properties) {
         // could have multiple outputs).
         if (op == "Shape" || op == "Size" || op == "Rank") {
           // Replace the node with the corresponding constant.
-          node.set_op("Const");
-          node.clear_attr();
-          (*node.mutable_attr())["dtype"].set_type(type);
+          node->set_op("Const");
+          node->clear_attr();
+          (*node->mutable_attr())["dtype"].set_type(type);
           value.AsProtoTensorContent(
-              (*node.mutable_attr())["value"].mutable_tensor());
+              (*node->mutable_attr())["value"].mutable_tensor());
 
           // Turn the data input into a control dependency: this is needed to
           // ensure that the constant value will only be run in the
           // cases where the shape/rank/size would have been run in
           // the original graph. Additional inputs are extra control
           string ctrl_dep =
-              AddControlDependency(node.input(0), graph_, node_map_.get());
-          node.set_input(0, ctrl_dep);
-          node_map_->AddOutput(NodeName(ctrl_dep), node.name());
+              AddControlDependency(node->input(0), graph_, node_map_.get());
+          node->set_input(0, ctrl_dep);
+          node_map_->AddOutput(NodeName(ctrl_dep), node->name());
         } else {
-          auto outputs = node_map_->GetOutputs(node.name());
+          auto outputs = node_map_->GetOutputs(node->name());
           for (const auto& output : outputs) {
             for (int k = 0; k < output->input_size(); ++k) {
               int port;
               string node_name = ParseNodeName(output->input(k), &port);
-              if (node_name == node.name() && port == j) {
+              if (node_name == node->name() && port == j) {
                 // Create a const node as ShapeN's output if not already.
                 const string const_name =
-                    OptimizedNodeName(node, strings::StrCat("-matshapes-", j));
+                    OptimizedNodeName(*node, strings::StrCat("-matshapes-", j));
                 if (node_map_->GetNode(const_name) == nullptr) {
                   NodeDef* added_node = graph_->add_node();
                   added_node->set_name(const_name);
                   added_node->set_op("Const");
-                  added_node->set_device(node.device());
+                  added_node->set_device(node->device());
                   node_map_->AddNode(added_node->name(), added_node);
                   (*added_node->mutable_attr())["dtype"].set_type(type);
                   value.AsProtoTensorContent(
@@ -364,7 +373,7 @@ Status ConstantFolding::MaterializeShapes(const GraphProperties& properties) {
                   // We add a control dependency to the original ShapeN node,
                   // so that the node will only be run if all inputs of the
                   // original ShapeN node are run.
-                  string ctrl_dep = AddControlDependency(node.name(), graph_,
+                  string ctrl_dep = AddControlDependency(node->name(), graph_,
                                                          node_map_.get());
                   *added_node->add_input() = ctrl_dep;
                   node_map_->AddOutput(NodeName(ctrl_dep), added_node->name());
@@ -679,7 +688,7 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
       nodes_whitelist_.find(node.name()) == nodes_whitelist_.end()) {
     return false;
   }
-  // Skip control flow nodes, they can't be folded
+  // Skip control flow nodes, they can't be folded.
   if (ModifiesFrameInfo(node)) {
     return false;
   }
@@ -688,12 +697,16 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
 
-  // Skips ops that don't benefit from folding.
-  const string& op = node.op();
+  // Don't fold stateful ops such as TruncatedNormal.
+  if (!IsFreeOfSideEffect(node)) {
+    return false;
+  }
 
-  if (op.find("Placeholder") == 0) {
+  // Skips ops that don't benefit from folding.
+  if (IsPlaceholder(node)) {
     return false;
   }
+  const string& op = node.op();
   if (op.find("Save") != string::npos || op.find("Restore") != string::npos ||
       op.find("Reader") != string::npos) {
     return false;
@@ -705,16 +718,12 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
 
-  // Don't fold stateful ops such as TruncatedNormal.
   const OpDef* op_def = nullptr;
   Status status = OpRegistry::Global()->LookUpOpDef(node.op(), &op_def);
   if (!status.ok()) {
     return false;
   }
-  if (op_def->is_stateful()) {
-    return false;
-  }
-
+  // Don't fold ops without outputs.
   if (op_def->output_arg_size() == 0) {
     return false;
   }
@@ -779,8 +788,11 @@ Status CreateConstantTensorAttrValue(DataType type, double value,
     SET_TENSOR_VAL_CASE(DT_FLOAT, float, float);
     SET_TENSOR_VAL_CASE(DT_DOUBLE, double, double);
     SET_TENSOR_VAL_CASE(DT_INT64, int64, int64);
+    SET_TENSOR_VAL_CASE(DT_UINT64, int64, int64);
     SET_TENSOR_VAL_CASE(DT_INT32, int32, int);
+    SET_TENSOR_VAL_CASE(DT_UINT32, int32, int);
     SET_TENSOR_VAL_CASE(DT_INT16, int32, int);
+    SET_TENSOR_VAL_CASE(DT_UINT16, int32, int);
     SET_TENSOR_VAL_CASE(DT_INT8, int32, int);
     SET_TENSOR_VAL_CASE(DT_UINT8, int32, int);
     SET_TENSOR_VAL_CASE(DT_BOOL, bool, bool);
@@ -843,10 +855,16 @@ Status ConstantFolding::CreateNodeDef(const string& name,
         POPULATE_TENSOR_PROTO(tensor, t, double, double);
       case DT_INT64:
         POPULATE_TENSOR_PROTO(tensor, t, int64, int64);
+      case DT_UINT64:
+        POPULATE_TENSOR_PROTO(tensor, t, uint64, int64);
       case DT_INT32:
         POPULATE_TENSOR_PROTO(tensor, t, int32, int);
+      case DT_UINT32:
+        POPULATE_TENSOR_PROTO(tensor, t, uint32, int);
       case DT_INT16:
         POPULATE_TENSOR_PROTO(tensor, t, int16, int);
+      case DT_UINT16:
+        POPULATE_TENSOR_PROTO(tensor, t, uint16, int);
       case DT_INT8:
         POPULATE_TENSOR_PROTO(tensor, t, int8, int);
       case DT_UINT8:
@@ -1166,9 +1184,8 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
   std::unordered_set<string> processed_nodes;
   std::deque<NodeDef*> queue;
   for (int i = 0; i < graph_->node_size(); i++) {
-    auto node = graph_->mutable_node(i);
-    if (IsFoldable(*node)) {
-      queue.push_back(node);
+    if (IsFoldable(graph_->node(i))) {
+      queue.push_back(graph_->mutable_node(i));
     }
   }
   while (!queue.empty()) {
@@ -1203,8 +1220,8 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
   int last = output->node_size() - 1;
   for (int i = output->node_size() - 1; i >= 0; --i) {
     const NodeDef& node = output->node(i);
-    auto outputs = node_map_->GetOutputs(node.name());
-    if (outputs.empty()) {
+    auto fanout = node_map_->GetOutputs(node.name());
+    if (fanout.empty()) {
       output->mutable_node()->SwapElements(i, last);
       last--;
     }
@@ -1216,8 +1233,8 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
     // If no fetch nodes is provided, we conservatively
     // keep all nodes in the original graph in case users need to fetch
     // their values.
-    auto outputs = node_map_->GetOutputs(node.name());
-    if (!outputs.empty() || !has_fetch_ ||
+    auto fanout = node_map_->GetOutputs(node.name());
+    if (!fanout.empty() || !has_fetch_ ||
         nodes_to_preserve_.find(node.name()) != nodes_to_preserve_.end()) {
       auto added_node = output->add_node();
       *added_node = node;
@@ -1331,14 +1348,14 @@ bool ConstantFolding::IsOnes(const NodeDef& node) const {
     //    IS_ONES_CASE(DT_HALF);
     IS_ONES_CASE(DT_FLOAT);
     IS_ONES_CASE(DT_DOUBLE);
+    IS_ONES_CASE(DT_COMPLEX64);
+    IS_ONES_CASE(DT_COMPLEX128);
     IS_ONES_CASE(DT_UINT8);
     IS_ONES_CASE(DT_INT8);
     IS_ONES_CASE(DT_UINT16);
     IS_ONES_CASE(DT_INT16);
     IS_ONES_CASE(DT_INT32);
     IS_ONES_CASE(DT_INT64);
-    IS_ONES_CASE(DT_COMPLEX64);
-    IS_ONES_CASE(DT_COMPLEX128);
     default:
       VLOG(1) << "Unsupported type " << DataTypeString(dtype);
       return false;
@@ -1362,14 +1379,14 @@ bool ConstantFolding::IsZeros(const NodeDef& node) const {
     //    IS_ZEROS_CASE(DT_HALF);
     IS_ZEROS_CASE(DT_FLOAT);
     IS_ZEROS_CASE(DT_DOUBLE);
+    IS_ZEROS_CASE(DT_COMPLEX64);
+    IS_ZEROS_CASE(DT_COMPLEX128);
     IS_ZEROS_CASE(DT_UINT8);
     IS_ZEROS_CASE(DT_INT8);
     IS_ZEROS_CASE(DT_UINT16);
     IS_ZEROS_CASE(DT_INT16);
     IS_ZEROS_CASE(DT_INT32);
     IS_ZEROS_CASE(DT_INT64);
-    IS_ZEROS_CASE(DT_COMPLEX64);
-    IS_ZEROS_CASE(DT_COMPLEX128);
     default:
       VLOG(1) << "Unsupported type " << DataTypeString(dtype);
       return false;
@@ -1869,6 +1886,11 @@ Status ConstantFolding::RunOptimizationPass(Cluster* cluster,
 
 Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
                                  GraphDef* output) {
+  // TensorFlow flushes denormals to zero and rounds to nearest, so we do
+  // the same here.
+  port::ScopedFlushDenormal flush;
+  port::ScopedSetRound round(FE_TONEAREST);
+
   nodes_to_preserve_ = item.NodesToPreserve();
   for (const auto& feed : item.feed) {
     feed_nodes_.insert(NodeName(feed.first));
diff --git a/tensorflow/core/kernels/constant_op.cc b/tensorflow/core/kernels/constant_op.cc
index fdb03a5aae..312c1a41d3 100644
--- a/tensorflow/core/kernels/constant_op.cc
+++ b/tensorflow/core/kernels/constant_op.cc
@@ -105,7 +105,12 @@ REGISTER_KERNEL(GPU, int8);
 REGISTER_KERNEL(GPU, qint8);
 REGISTER_KERNEL(GPU, uint16);
 REGISTER_KERNEL(GPU, int16);
+REGISTER_KERNEL(GPU, qint16);
+REGISTER_KERNEL(GPU, quint16);
+REGISTER_KERNEL(GPU, uint32);
+REGISTER_KERNEL(GPU, qint32);
 REGISTER_KERNEL(GPU, int64);
+REGISTER_KERNEL(GPU, uint64);
 REGISTER_KERNEL(GPU, complex64);
 REGISTER_KERNEL(GPU, complex128);
 REGISTER_KERNEL(GPU, bool);
@@ -122,9 +127,15 @@ REGISTER_SYCL_KERNEL(SYCL, float);
 REGISTER_SYCL_KERNEL(SYCL, double);
 REGISTER_SYCL_KERNEL(SYCL, uint8);
 REGISTER_SYCL_KERNEL(SYCL, int8);
+REGISTER_SYCL_KERNEL(SYCL, qint8);
 REGISTER_SYCL_KERNEL(SYCL, uint16);
 REGISTER_SYCL_KERNEL(SYCL, int16);
+REGISTER_SYCL_KERNEL(SYCL, qint16);
+REGISTER_SYCL_KERNEL(SYCL, quint16);
+REGISTER_SYCL_KERNEL(SYCL, uint32);
+REGISTER_SYCL_KERNEL(SYCL, qint32);
 REGISTER_SYCL_KERNEL(SYCL, int64);
+REGISTER_SYCL_KERNEL(SYCL, uint64);
 REGISTER_SYCL_KERNEL(SYCL, bool);
 #undef REGISTER_SYCL_KERNEL
 #endif
-- 
GitLab


From 4774889094d3f1787a38cfbeb0670cb4fb6e24ff Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Mon, 26 Feb 2018 19:57:42 -0800
Subject: [PATCH 333/575] Fixes and simplification in the Keras training
 engine. - Explicitly disallow sample/class weighting in eager (it was never
 supported) - Remove tests for it (which were actually ignoring sample/class
 weights) - Make sample weight placeholders placeholder_with_default, and do
 not create all-ones numpy arrays to feed them when no sample weights are
 provided (this might lead to better performance)

PiperOrigin-RevId: 187121215
---
 .../python/keras/_impl/keras/backend.py       |  11 +-
 .../python/keras/_impl/keras/callbacks.py     |  20 +-
 .../keras/_impl/keras/engine/training.py      | 151 +++---
 .../_impl/keras/engine/training_eager.py      |  17 +-
 .../_impl/keras/engine/training_eager_test.py | 436 ------------------
 .../keras/_impl/keras/engine/training_test.py |   8 -
 6 files changed, 110 insertions(+), 533 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py
index a2db05f6cf..2b75666b9e 100644
--- a/tensorflow/python/keras/_impl/keras/backend.py
+++ b/tensorflow/python/keras/_impl/keras/backend.py
@@ -2749,7 +2749,7 @@ class Function(object):
       self.updates_op = control_flow_ops.group(*updates_ops)
     self.name = name
     # additional tensor substitutions
-    self.feed_dict = session_kwargs.pop('feed_dict', {})
+    self.feed_dict = session_kwargs.pop('feed_dict', None)
     # additional operations
     self.fetches = session_kwargs.pop('fetches', [])
     if not isinstance(self.fetches, list):
@@ -2759,8 +2759,15 @@ class Function(object):
   def __call__(self, inputs):
     if not isinstance(inputs, (list, tuple)):
       raise TypeError('`inputs` should be a list or tuple.')
-    feed_dict = self.feed_dict.copy()
+
+    if self.feed_dict:
+      feed_dict = self.feed_dict.copy()
+    else:
+      feed_dict = {}
+
     for tensor, value in zip(self.inputs, inputs):
+      if value is None:
+        continue
       if is_sparse(tensor):
         sparse_coo = value.tocoo()
         indices = np.concatenate((np.expand_dims(sparse_coo.row, 1),
diff --git a/tensorflow/python/keras/_impl/keras/callbacks.py b/tensorflow/python/keras/_impl/keras/callbacks.py
index f6c4661425..deb1e8867d 100644
--- a/tensorflow/python/keras/_impl/keras/callbacks.py
+++ b/tensorflow/python/keras/_impl/keras/callbacks.py
@@ -778,16 +778,24 @@ class TensorBoard(Callback):
         while i < val_size:
           step = min(self.batch_size, val_size - i)
           batch_val = []
-          batch_val.append(val_data[0][i:i + step])
-          batch_val.append(val_data[1][i:i + step])
-          batch_val.append(val_data[2][i:i + step])
+          batch_val.append(val_data[0][i:i + step]
+                           if val_data[0] is not None else None)
+          batch_val.append(val_data[1][i:i + step]
+                           if val_data[1] is not None else None)
+          batch_val.append(val_data[2][i:i + step]
+                           if val_data[2] is not None else None)
           if self.model.uses_learning_phase:
             # do not slice the learning phase
-            batch_val = [x[i:i + step] for x in val_data[:-1]]
+            batch_val = [x[i:i + step] if x is not None else None
+                         for x in val_data[:-1]]
             batch_val.append(val_data[-1])
           else:
-            batch_val = [x[i:i + step] for x in val_data]
-          feed_dict = dict(zip(tensors, batch_val))
+            batch_val = [x[i:i + step] if x is not None else None
+                         for x in val_data]
+          feed_dict = {}
+          for key, val in zip(tensors, batch_val):
+            if val is not None:
+              feed_dict[key] = val
           result = self.sess.run([self.merged], feed_dict=feed_dict)
           summary_str = result[0]
           self.writer.add_summary(summary_str, epoch)
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index 57451ad470..63bea08ac5 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -40,6 +40,7 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches
 from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
 from tensorflow.python.layers.base import _DeferredTensor
+from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import optimizer as tf_optimizer_module
 from tensorflow.python.util.tf_export import tf_export
@@ -225,9 +226,9 @@ def _check_array_lengths(inputs, targets, weights=None):
     # return a set with the variation between
     # different shapes, with None => 0
     if x is None:
-      return {0}
+      return {}
     else:
-      return set([0 if y is None else y.shape[0] for y in x])
+      return set([y.shape[0] for y in x if y is not None])
 
   set_x = set_of_lengths(inputs)
   set_y = set_of_lengths(targets)
@@ -259,7 +260,8 @@ def _check_array_lengths(inputs, targets, weights=None):
 def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
   """Does validation on the compatibility of targets and loss functions.
 
-  This helps prevent users from using loss functions incorrectly.
+  This helps prevent users from using loss functions incorrectly. This check
+  is purely for UX purposes.
 
   Arguments:
       targets: list of Numpy arrays of targets.
@@ -275,7 +277,7 @@ def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
       losses.categorical_crossentropy
   }
   for y, loss, shape in zip(targets, loss_fns, output_shapes):
-    if y is None or loss is None:
+    if y is None or loss is None or tensor_util.is_tensor(y):
       continue
     if loss is losses.categorical_crossentropy:
       if y.shape[-1] == 1:
@@ -507,10 +509,7 @@ def _standardize_weights(y,
                        (existing_classes - existing_class_weight))
     return weights
   else:
-    if sample_weight_mode is None:
-      return np.ones((y.shape[0],), dtype=K.floatx())
-    else:
-      return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx())
+    return None
 
 
 @tf_export('keras.models.Model', 'keras.Model')
@@ -862,12 +861,12 @@ class Model(Network):
           sample_weights.append(None)
         else:
           if sample_weight_mode == 'temporal':
-            sample_weights.append(
-                K.placeholder(ndim=2, name=name + '_sample_weights'))
+            sample_weights.append(array_ops.placeholder_with_default(
+                [[1.]], shape=[None, None], name=name + '_sample_weights'))
             sample_weight_modes.append('temporal')
           else:
-            sample_weights.append(
-                K.placeholder(ndim=1, name=name + '_sample_weights'))
+            sample_weights.append(array_ops.placeholder_with_default(
+                [1.], shape=[None], name=name + '_sample_weights'))
             sample_weight_modes.append(None)
     self.sample_weight_modes = sample_weight_modes
     self._feed_sample_weight_modes = []
@@ -1314,7 +1313,7 @@ class Model(Network):
         for batch_index, (batch_start, batch_end) in enumerate(batches):
           batch_ids = index_array[batch_start:batch_end]
           try:
-            if isinstance(ins[-1], float):
+            if isinstance(ins[-1], int):
               # Do not slice the training phase flag.
               ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
             else:
@@ -1424,7 +1423,7 @@ class Model(Network):
       index_array = np.arange(num_samples)
       for batch_index, (batch_start, batch_end) in enumerate(batches):
         batch_ids = index_array[batch_start:batch_end]
-        if ins and isinstance(ins[-1], float):
+        if ins and isinstance(ins[-1], int):
           # Do not slice the training phase flag.
           ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
         else:
@@ -1518,7 +1517,7 @@ class Model(Network):
       index_array = np.arange(num_samples)
       for batch_index, (batch_start, batch_end) in enumerate(batches):
         batch_ids = index_array[batch_start:batch_end]
-        if isinstance(ins[-1], float):
+        if isinstance(ins[-1], int):
           # Do not slice the training phase flag.
           ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
         else:
@@ -2070,10 +2069,6 @@ class Model(Network):
           val_y,
           sample_weight=val_sample_weight,
           batch_size=batch_size)
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        val_ins = val_x + val_y + val_sample_weights + [0.]
-      else:
-        val_ins = val_x + val_y + val_sample_weights
 
     elif validation_split and 0. < validation_split < 1.:
       do_validation = True
@@ -2085,36 +2080,34 @@ class Model(Network):
       y, val_y = (slice_arrays(y, 0, split_at), slice_arrays(y, split_at))
       sample_weights, val_sample_weights = (slice_arrays(
           sample_weights, 0, split_at), slice_arrays(sample_weights, split_at))
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        val_ins = val_x + val_y + val_sample_weights + [0.]
-      else:
-        val_ins = val_x + val_y + val_sample_weights
-
     elif validation_steps:
+      val_x = []
+      val_y = []
+      val_sample_weights = []
       do_validation = True
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        val_ins = [0.]
-
-    # Prepare input arrays and training function.
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + y + sample_weights + [1.]
-    else:
-      ins = x + y + sample_weights
 
     # Prepare display labels.
     out_labels = self.metrics_names
 
     if context.in_eager_mode():
+      if any([w is not None for w in sample_weights]):
+        raise ValueError('`sample_weight` and `class_weight` is not supported '
+                         'when eager execution is enabled, for now.')
+
       if do_validation:
+        if any([w is not None for w in val_sample_weights]):
+          raise ValueError('`sample_weight` and `class_weight` is not supported'
+                           ' when eager execution is enabled, for now.')
         callback_metrics = copy.copy(out_labels) + [
             'val_' + n for n in out_labels
         ]
+        val_ins = val_x + val_y
       else:
         callback_metrics = copy.copy(out_labels)
 
       return training_eager.fit_loop(
           self,
-          ins,
+          x + y,
           out_labels=out_labels,
           batch_size=batch_size,
           epochs=epochs,
@@ -2127,18 +2120,25 @@ class Model(Network):
           steps_per_epoch=steps_per_epoch,
           validation_steps=validation_steps)
     else:
+      # Prepare input arrays and training function.
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + y + sample_weights + [1]
+      else:
+        ins = x + y + sample_weights
+
       self._make_train_function()
       f = self.train_function
 
       if do_validation:
-        if context.in_graph_mode():
-          self._make_test_function()
-          val_f = self.test_function
-        else:
-          val_f = None
+        self._make_test_function()
+        val_f = self.test_function
         callback_metrics = copy.copy(out_labels) + [
             'val_' + n for n in out_labels
         ]
+        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+          val_ins = val_x + val_y + val_sample_weights + [0]
+        else:
+          val_ins = val_x + val_y + val_sample_weights
       else:
         val_f = None
         callback_metrics = copy.copy(out_labels)
@@ -2229,16 +2229,20 @@ class Model(Network):
         y,
         sample_weight=sample_weight,
         batch_size=batch_size)
-    # Prepare inputs, delegate logic to `_test_loop`.
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + y + sample_weights + [0.]
-    else:
-      ins = x + y + sample_weights
 
     if context.in_eager_mode():
+      if any([w is not None for w in sample_weights]):
+        raise ValueError('`sample_weight` and `class_weight` is not supported '
+                         'when eager execution is enabled, for now.')
       return training_eager.test_loop(
-          self, ins, batch_size=batch_size, verbose=verbose, steps=steps)
+          self, x + y, batch_size=batch_size, verbose=verbose, steps=steps)
     else:
+      # Prepare inputs, delegate logic to `_test_loop`.
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + y + sample_weights + [0]
+      else:
+        ins = x + y + sample_weights
+
       self._make_test_function()
       f = self.test_function
       return self._test_loop(
@@ -2276,16 +2280,16 @@ class Model(Network):
                        'argument.')
     x, _, _ = self._standardize_user_data(x)
 
-    # Prepare inputs, delegate logic to `_predict_loop`.
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + [0.]
-    else:
-      ins = x
-
     if context.in_eager_mode():
       return training_eager.predict_loop(
-          self, ins, batch_size=batch_size, verbose=verbose, steps=steps)
+          self, x, batch_size=batch_size, verbose=verbose, steps=steps)
     else:
+      # Prepare inputs, delegate logic to `_predict_loop`.
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + [0]
+      else:
+        ins = x
+
       self._make_predict_function()
       f = self.predict_function
 
@@ -2327,20 +2331,26 @@ class Model(Network):
         and/or metrics). The attribute `model.metrics_names` will give you
         the display labels for the scalar outputs.
 
+    Raises:
+      ValueError: In case of invalid user-provided arguments.
     """
     x, y, sample_weights = self._standardize_user_data(
         x,
         y,
         sample_weight=sample_weight,
         class_weight=class_weight)
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + y + sample_weights + [1.]
-    else:
-      ins = x + y + sample_weights
 
     if context.in_eager_mode():
-      outputs = training_eager.train_on_batch(self, ins)
+      if any([w is not None for w in sample_weights]):
+        raise ValueError('`sample_weight` and `class_weight` is not supported '
+                         'when eager execution is enabled, for now.')
+      outputs = training_eager.train_on_batch(self, x + y)
     else:
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + y + sample_weights + [1]
+      else:
+        ins = x + y + sample_weights
+
       self._make_train_function()
       outputs = self.train_function(ins)
 
@@ -2377,18 +2387,21 @@ class Model(Network):
         the display labels for the scalar outputs.
 
     Raises:
-        ValueError: in case of invalid arguments.
+        ValueError: In case of invalid user-provided arguments.
     """
     x, y, sample_weights = self._standardize_user_data(
         x, y, sample_weight=sample_weight)
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + y + sample_weights + [0.]
-    else:
-      ins = x + y + sample_weights
 
     if context.in_eager_mode():
-      outputs = training_eager.test_on_batch(self, ins)
+      if any([w is not None for w in sample_weights]):
+        raise ValueError('`sample_weight` and `class_weight` is not supported '
+                         'when eager execution is enabled, for now.')
+      outputs = training_eager.test_on_batch(self, x + y)
     else:
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + y + sample_weights + [0]
+      else:
+        ins = x + y + sample_weights
       self._make_test_function()
       outputs = self.test_function(ins)
 
@@ -2408,14 +2421,9 @@ class Model(Network):
     """
     x, _, _ = self._standardize_user_data(x)
 
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + [0.]
-    else:
-      ins = x
-
     if context.in_eager_mode():
       ins_batch_converted = []
-      for ib in ins:
+      for ib in x:
         ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
 
       eager_model_inputs = []
@@ -2426,6 +2434,11 @@ class Model(Network):
       return outs
 
     if context.in_graph_mode():
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + [0]
+      else:
+        ins = x
+
       self._make_predict_function()
       outputs = self.predict_function(ins)
       if len(outputs) == 1:
@@ -2643,7 +2656,7 @@ class Model(Network):
           val_data = val_x + val_y + val_sample_weights
           if self.uses_learning_phase and not isinstance(
               K.learning_phase(), int):
-            val_data += [0.]
+            val_data += [0]
           for cbk in callbacks:
             cbk.validation_data = val_data
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 282dd0dc0d..cdf189adef 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -139,6 +139,8 @@ def _model_loss(model, inputs, targets, training=False):
                                    model.output_names[i])
       loss_metrics.append(K.mean(output_loss))
 
+      # TODO(fchollet): support masking; in practice `_keras_mask` is never
+      # set in this context currently.
       mask = outs[i]._keras_mask
       # adapted from weighted_loss_fn
       if mask is not None:
@@ -148,17 +150,7 @@ def _model_loss(model, inputs, targets, training=False):
         #  to the number of unmasked samples.
         output_loss /= K.mean(mask)
 
-      # adapted from weighted_loss_fn
-      # apply sample weighting
-      if model.sample_weights:
-        # reduce score_array to same ndim as weight array
-        ndim = K.ndim(output_loss)
-        weight_ndim = K.ndim(model.sample_weights)
-        output_loss = K.mean(output_loss, axis=list(range(weight_ndim, ndim)))
-        output_loss *= model.sample_weights
-        output_loss /= K.mean(K.cast(K.not_equal(model.sample_weights, 0),
-                                     K.floatx()))
-        output_loss = K.mean(output_loss)
+      # TODO(fchollet): support sample weighting
 
       loss_weight = model.loss_weights_list[i]
       if total_loss is None:
@@ -231,7 +223,8 @@ def train_on_batch(model, ins):
   """
   ins_batch_converted = []
   for ib in ins:
-    ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
+    if ib is not None:
+      ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
   eager_model_inputs = []
   eager_model_outputs = []
   for i in range(len(model.inputs)):
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
index 3d94b7537f..550b86a71d 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
@@ -24,9 +24,7 @@ import numpy as np
 from tensorflow.python.framework import ops
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
-from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
 from tensorflow.python.platform import test
-from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 
@@ -311,440 +309,6 @@ class TrainingTest(test.TestCase):
                     optimizer='rms')
 
 
-class LossWeightingTest(test.TestCase):
-
-  def test_class_weights(self):
-    num_classes = 5
-    batch_size = 5
-    epochs = 5
-    weighted_class = 3
-    train_samples = 3000
-    test_samples = 3000
-    input_dim = 5
-
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
-    model.add(keras.layers.Activation('relu'))
-    model.add(keras.layers.Dense(num_classes))
-    model.add(keras.layers.Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer=RMSPropOptimizer(learning_rate=0.001))
-
-    np.random.seed(1337)
-    (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-    int_y_test = y_test.copy()
-    int_y_train = y_train.copy()
-    # convert class vectors to binary class matrices
-    y_train = keras.utils.to_categorical(y_train, num_classes)
-    y_test = keras.utils.to_categorical(y_test, num_classes)
-    test_ids = np.where(int_y_test == np.array(weighted_class))[0]
-
-    class_weight = dict([(i, 1.) for i in range(num_classes)])
-    class_weight[weighted_class] = 2.
-
-    sample_weight = np.ones((y_train.shape[0]))
-    sample_weight[int_y_train == weighted_class] = 2.
-
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 3,
-        verbose=0,
-        class_weight=class_weight,
-        validation_data=(x_train, y_train, sample_weight))
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 2,
-        verbose=0,
-        class_weight=class_weight)
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 2,
-        verbose=0,
-        class_weight=class_weight,
-        validation_split=0.1)
-
-    model.train_on_batch(
-        x_train[:batch_size], y_train[:batch_size], class_weight=class_weight)
-    ref_score = model.evaluate(x_test, y_test, verbose=0)
-    score = model.evaluate(
-        x_test[test_ids, :], y_test[test_ids, :], verbose=0)
-    self.assertLess(score, ref_score)
-
-  def test_sample_weights(self):
-    num_classes = 5
-    batch_size = 5
-    epochs = 5
-    weighted_class = 3
-    train_samples = 3000
-    test_samples = 3000
-    input_dim = 5
-
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
-    model.add(keras.layers.Activation('relu'))
-    model.add(keras.layers.Dense(num_classes))
-    model.add(keras.layers.Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer=RMSPropOptimizer(learning_rate=0.001))
-
-    np.random.seed(43)
-    (x_train, y_train), _ = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-    int_y_train = y_train.copy()
-    y_train = keras.utils.to_categorical(y_train, num_classes)
-
-    class_weight = dict([(i, 1.) for i in range(num_classes)])
-    class_weight[weighted_class] = 2.
-
-    sample_weight = np.ones((y_train.shape[0]))
-    sample_weight[int_y_train == weighted_class] = 2.
-
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 3,
-        verbose=0,
-        sample_weight=sample_weight)
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 3,
-        verbose=0,
-        sample_weight=sample_weight,
-        validation_split=0.1)
-    model.train_on_batch(
-        x_train[:batch_size],
-        y_train[:batch_size],
-        sample_weight=sample_weight[:batch_size])
-    model.test_on_batch(
-        x_train[:batch_size],
-        y_train[:batch_size],
-        sample_weight=sample_weight[:batch_size])
-
-  def test_temporal_sample_weights(self):
-    num_classes = 5
-    weighted_class = 3
-    train_samples = 1000
-    test_samples = 1000
-    input_dim = 5
-    timesteps = 3
-
-    model = keras.models.Sequential()
-    model.add(
-        keras.layers.TimeDistributed(
-            keras.layers.Dense(num_classes),
-            input_shape=(timesteps, input_dim)))
-    model.add(keras.layers.Activation('softmax'))
-
-    np.random.seed(1337)
-    (_, y_train), _ = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-    int_y_train = y_train.copy()
-    # convert class vectors to binary class matrices
-    y_train = keras.utils.to_categorical(y_train, num_classes)
-
-    class_weight = dict([(i, 1.) for i in range(num_classes)])
-    class_weight[weighted_class] = 2.
-
-    sample_weight = np.ones((y_train.shape[0]))
-    sample_weight[int_y_train == weighted_class] = 2.
-    with self.assertRaises(ValueError):
-      model.compile(
-          loss='binary_crossentropy',
-          optimizer=RMSPropOptimizer(learning_rate=0.001),
-          sample_weight_mode='temporal')
-
-  def test_class_weight_invalid_use_case(self):
-    num_classes = 5
-    train_samples = 1000
-    test_samples = 1000
-    input_dim = 5
-    timesteps = 3
-
-    model = keras.models.Sequential()
-    model.add(
-        keras.layers.TimeDistributed(
-            keras.layers.Dense(num_classes),
-            input_shape=(timesteps, input_dim)))
-    model.add(keras.layers.Activation('softmax'))
-    model.compile(
-        loss='binary_crossentropy',
-        optimizer=RMSPropOptimizer(learning_rate=0.001))
-
-    (x_train, y_train), _ = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-    # convert class vectors to binary class matrices
-    y_train = keras.utils.to_categorical(y_train, num_classes)
-    class_weight = dict([(i, 1.) for i in range(num_classes)])
-
-    del class_weight[1]
-    with self.assertRaises(ValueError):
-      model.fit(x_train, y_train,
-                epochs=0, verbose=0, class_weight=class_weight)
-
-    with self.assertRaises(ValueError):
-      model.compile(
-          loss='binary_crossentropy',
-          optimizer=RMSPropOptimizer(learning_rate=0.001),
-          sample_weight_mode=[])
-
-    # Build multi-output model
-    x = keras.Input((3,))
-    y1 = keras.layers.Dense(4, name='1')(x)
-    y2 = keras.layers.Dense(4, name='2')(x)
-    model = keras.models.Model(x, [y1, y2])
-    model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse')
-    x_np = np.random.random((10, 3))
-    y_np = np.random.random((10, 4))
-    w_np = np.random.random((10,))
-    # This will work
-    model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': w_np})
-    # These will not
-    with self.assertRaises(ValueError):
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=[w_np])
-    with self.assertRaises(TypeError):
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=w_np)
-    with self.assertRaises(ValueError):
-      bad_w_np = np.random.random((11,))
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
-    with self.assertRaises(ValueError):
-      bad_w_np = np.random.random((10, 2))
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
-    with self.assertRaises(ValueError):
-      bad_w_np = np.random.random((10, 2, 2))
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
-
-
-class TestDynamicTrainability(test.TestCase):
-
-  def test_trainable_warning(self):
-    x = np.random.random((5, 3))
-    y = np.random.random((5, 2))
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(2, input_dim=3))
-    model.trainable = False
-    model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
-    model.trainable = True
-    with test.mock.patch.object(logging, 'warning') as mock_log:
-      model.train_on_batch(x, y)
-      self.assertRegexpMatches(str(mock_log.call_args),
-                               'trainable weights is empty')
-
-  def test_trainable_argument(self):
-    x = np.random.random((5, 3))
-    y = np.random.random((5, 2))
-
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(2, input_dim=3, trainable=False))
-    model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
-    out = model.predict(x)
-    with test.mock.patch.object(logging, 'warning') as mock_log:
-      model.train_on_batch(x, y)
-      self.assertRegexpMatches(str(mock_log.call_args),
-                               'trainable weights is empty')
-    out_2 = model.predict(x)
-    self.assertAllClose(out, out_2)
-
-    # test with nesting
-    inputs = keras.layers.Input(shape=(3,))
-    output = model(inputs)
-    model = keras.models.Model(inputs, output)
-    model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
-    out = model.predict(x)
-    with test.mock.patch.object(logging, 'warning') as mock_log:
-      model.train_on_batch(x, y)
-      self.assertRegexpMatches(str(mock_log.call_args),
-                               'trainable weights is empty')
-    out_2 = model.predict(x)
-    self.assertAllClose(out, out_2)
-
-  def test_layer_trainability_switch(self):
-    # with constructor argument, in Sequential
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(2, trainable=False, input_dim=1))
-    self.assertListEqual(model.trainable_weights, [])
-
-    # by setting the `trainable` argument, in Sequential
-    model = keras.models.Sequential()
-    layer = keras.layers.Dense(2, input_dim=1)
-    model.add(layer)
-    self.assertListEqual(model.trainable_weights, layer.trainable_weights)
-    layer.trainable = False
-    self.assertListEqual(model.trainable_weights, [])
-
-    # with constructor argument, in Model
-    x = keras.layers.Input(shape=(1,))
-    y = keras.layers.Dense(2, trainable=False)(x)
-    model = keras.models.Model(x, y)
-    self.assertListEqual(model.trainable_weights, [])
-
-    # by setting the `trainable` argument, in Model
-    x = keras.layers.Input(shape=(1,))
-    layer = keras.layers.Dense(2)
-    y = layer(x)
-    model = keras.models.Model(x, y)
-    self.assertListEqual(model.trainable_weights, layer.trainable_weights)
-    layer.trainable = False
-    self.assertListEqual(model.trainable_weights, [])
-
-  def test_model_trainability_switch(self):
-    # a non-trainable model has no trainable weights
-    x = keras.layers.Input(shape=(1,))
-    y = keras.layers.Dense(2)(x)
-    model = keras.models.Model(x, y)
-    model.trainable = False
-    self.assertListEqual(model.trainable_weights, [])
-
-    # same for Sequential
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(2, input_dim=1))
-    model.trainable = False
-    self.assertListEqual(model.trainable_weights, [])
-
-  def test_nested_model_trainability(self):
-
-    # a Sequential inside a Model
-    inner_model = keras.models.Sequential()
-    inner_model.add(keras.layers.Dense(2, input_dim=1))
-
-    x = keras.layers.Input(shape=(1,))
-    y = inner_model(x)
-    outer_model = keras.models.Model(x, y)
-    self.assertListEqual(outer_model.trainable_weights,
-                         inner_model.trainable_weights)
-    inner_model.trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-    inner_model.trainable = True
-    inner_model.layers[-1].trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-
-    # a Sequential inside a Sequential
-    inner_model = keras.models.Sequential()
-    inner_model.add(keras.layers.Dense(2, input_dim=1))
-    outer_model = keras.models.Sequential()
-    outer_model.add(inner_model)
-    self.assertListEqual(outer_model.trainable_weights,
-                         inner_model.trainable_weights)
-    inner_model.trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-    inner_model.trainable = True
-    inner_model.layers[-1].trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-
-    # a Model inside a Model
-    x = keras.layers.Input(shape=(1,))
-    y = keras.layers.Dense(2)(x)
-    inner_model = keras.models.Model(x, y)
-    x = keras.layers.Input(shape=(1,))
-    y = inner_model(x)
-    outer_model = keras.models.Model(x, y)
-    self.assertListEqual(outer_model.trainable_weights,
-                         inner_model.trainable_weights)
-    inner_model.trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-    inner_model.trainable = True
-    inner_model.layers[-1].trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-
-    # a Model inside a Sequential
-    x = keras.layers.Input(shape=(1,))
-    y = keras.layers.Dense(2)(x)
-    inner_model = keras.models.Model(x, y)
-    outer_model = keras.models.Sequential()
-    outer_model.add(inner_model)
-    self.assertListEqual(outer_model.trainable_weights,
-                         inner_model.trainable_weights)
-    inner_model.trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-    inner_model.trainable = True
-    inner_model.layers[-1].trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-
-
-class TestTrainingUtils(test.TestCase):
-
-  def test_check_array_lengths(self):
-    keras.engine.training._check_array_lengths(None, None, None)
-    a_np = np.random.random((4, 3, 3))
-    keras.engine.training._check_array_lengths(a_np, a_np, a_np)
-    keras.engine.training._check_array_lengths(
-        [a_np, a_np], [a_np, a_np], [a_np, a_np])
-    keras.engine.training._check_array_lengths([None], [None], [None])
-
-    b_np = np.random.random((3, 4))
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths(a_np, None, None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths(a_np, a_np, None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], [None], None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], [b_np], None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], None, [b_np])
-
-  def test_slice_arrays(self):
-    input_a = np.random.random((10, 3))
-    slice_arrays(None)
-    slice_arrays(input_a, 0)
-    slice_arrays(input_a, 0, 1)
-    slice_arrays(input_a, stop=2)
-    input_a = [None, [1, 1], None, [1, 1]]
-    slice_arrays(input_a, 0)
-    slice_arrays(input_a, 0, 1)
-    slice_arrays(input_a, stop=2)
-    input_a = [None]
-    slice_arrays(input_a, 0)
-    slice_arrays(input_a, 0, 1)
-    slice_arrays(input_a, stop=2)
-    input_a = None
-    slice_arrays(input_a, 0)
-    slice_arrays(input_a, 0, 1)
-    slice_arrays(input_a, stop=2)
-
-  def test_fit_with_BatchNorm(self):
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(10, input_dim=4))
-    model.add(keras.layers.BatchNormalization())
-    model.add(keras.layers.Activation('tanh'))
-    model.add(keras.layers.Dropout(0.2))
-
-    input_a_np = np.random.random((10, 4))
-    output_b_np = np.random.random((10, 10))
-
-    model.compile(loss='binary_crossentropy', optimizer=RMSPropOptimizer(0.001))
-    model.fit(input_a_np, output_b_np, epochs=1, batch_size=5, verbose=0)
-
-  def test_fit_with_regularization(self):
-    model = keras.models.Sequential()
-    with self.assertRaises(ValueError):
-      model.add(
-          keras.layers.Dense(4, input_dim=3,
-                             kernel_regularizer=keras.regularizers.l2(0.01),
-                             activity_regularizer=keras.regularizers.l1(0.01)))
-
-
 if __name__ == '__main__':
   # Bazel sets these environment variables to very long paths.
   # Tempfile uses them to create long paths, and in turn multiprocessing
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py
index 9651eb9f14..6ca5941e9a 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py
@@ -1045,16 +1045,8 @@ class TestTrainingUtils(test.TestCase):
     keras.engine.training._check_array_lengths([None], [None], [None])
 
     b_np = np.random.random((3, 4))
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths(a_np, None, None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths(a_np, a_np, None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], [None], None)
     with self.assertRaises(ValueError):
       keras.engine.training._check_array_lengths([a_np], [b_np], None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], None, [b_np])
 
   def test_slice_arrays(self):
     input_a = np.random.random((10, 3))
-- 
GitLab


From 6825af46c53e6ad0b1260e5a96a4ef46b7703e46 Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Mon, 26 Feb 2018 19:58:18 -0800
Subject: [PATCH 334/575] Fix bug in deserializing CondContexts.

PiperOrigin-RevId: 187121244
---
 tensorflow/python/ops/control_flow_ops.py | 11 ++++-
 tensorflow/python/training/saver_test.py  | 49 ++++++++++++++++-------
 2 files changed, 43 insertions(+), 17 deletions(-)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 85944efbe8..fb9e2188d7 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1718,8 +1718,15 @@ class CondContext(ControlFlowContext):
     self._pivot = g.as_graph_element(
         ops.prepend_name_scope(context_def.pivot_name, import_scope))
     self._branch = context_def.branch
-    super(CondContext, self).__init__(
-        values_def=context_def.values_def, import_scope=import_scope)
+    super(CondContext, self).__init__(values_def=context_def.values_def,
+                                      import_scope=import_scope)
+    # The predicate and pivot ops appear in self._values, but don't have self
+    # set as their control context. The __init__ call above will set self for
+    # all values, so manually override the predicate and pivot contexts here.
+    # pylint: disable=protected-access
+    self._pred.op._set_control_flow_context(self.outer_context)
+    self._pivot.op._set_control_flow_context(self.outer_context)
+    # pylint: enable=protected-access
 
   @property
   def pred(self):
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index b366ed30f3..b758ceaab0 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -2041,29 +2041,24 @@ class MetaGraphTest(test.TestCase):
     self._testGraphExtensionRestore(test_dir)
     self._testRestoreFromTrainGraphWithControlContext(test_dir)
 
-  def testNestedWhileLoops(self):
-    test_dir = self._get_test_dir("nested_whiles")
+  def _testWhileLoopAndGradientSerDes(self, outer_body_fn):
+    # Build a while loop with `outer_body_fn`, export it, and verify that it can
+    # be imported and the gradient can be built and run correctly.
+
+    test_dir = self._get_test_dir("nested_control_flow")
     filename = os.path.join(test_dir, "metafile")
     saver_ckpt = os.path.join(test_dir, "saver.ckpt")
 
-    # Create two simple nested while loops.
+    # Create while loop using `outer_body_fn`.
     with ops_lib.Graph().as_default():
-      def body(i, x):
-        _, r = control_flow_ops.while_loop(lambda j, y: j < 3,
-                                           lambda j, y: (j + 1, y + x),
-                                           [0, 0])
-        return i + 1, x + r
-
       var = variables.Variable(0)
       var_name = var.name
-
-      _, output = control_flow_ops.while_loop(lambda i, x: i < 5, body,
+      _, output = control_flow_ops.while_loop(lambda i, x: i < 5, outer_body_fn,
                                               [0, var])
       output_name = output.name
-
       init_op = variables.global_variables_initializer()
 
-      # Generate a MetaGraphDef containing the nested loops.
+      # Generate a MetaGraphDef containing the while loop.
       with session.Session() as sess:
         sess.run(init_op)
         sess.run(output)
@@ -2071,8 +2066,8 @@ class MetaGraphTest(test.TestCase):
         saver.save(sess, saver_ckpt)
         saver.export_meta_graph(filename)
 
-      # Build and run the gradients of the nested while loop. We use this below
-      # to verify that the gradients are correct with an imported MetaGraphDef.
+      # Build and run the gradients of the while loop. We use this below to
+      # verify that the gradients are correct with an imported MetaGraphDef.
       grad = gradients_impl.gradients([output], [var])
       with session.Session() as sess:
         sess.run(init_op)
@@ -2096,6 +2091,30 @@ class MetaGraphTest(test.TestCase):
         actual_grad_value = sess.run(grad)
         self.assertEqual(expected_grad_value, actual_grad_value)
 
+  def testNestedWhileLoopsSerDes(self):
+    # Test two simple nested while loops.
+    def body(i, x):
+      _, r = control_flow_ops.while_loop(lambda j, y: j < 3,
+                                         lambda j, y: (j + 1, y + x),
+                                         [0, 0])
+      return i + 1, x + r
+    self._testWhileLoopAndGradientSerDes(body)
+
+  def testNestedControlFlowSerDes(self):
+    # Test while loop in a cond in a while loop.
+    # pylint: disable=g-long-lambda
+    def body(i, x):
+      cond_result = control_flow_ops.cond(
+          i > 0,
+          lambda: control_flow_ops.while_loop(
+              lambda j, y: j < 3,
+              lambda j, y: (j + 1, y + x),
+              [0, 0])[1],
+          lambda: x)
+      return i + 1, cond_result
+    # pylint: enable=g-long-lambda
+    self._testWhileLoopAndGradientSerDes(body)
+
   def testStrippedOpListDef(self):
     with self.test_session():
       # Creates a graph.
-- 
GitLab


From bac2cb076281a90902609cea5ee2b28c5d821657 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Mon, 26 Feb 2018 20:21:07 -0800
Subject: [PATCH 335/575] Add helpers to stream data from the GCE VM to a Cloud
 TPU.

PiperOrigin-RevId: 187122870
---
 tensorflow/contrib/tpu/BUILD                  |  28 +++
 tensorflow/contrib/tpu/python/tpu/datasets.py | 192 ++++++++++++++++++
 .../contrib/tpu/python/tpu/datasets_test.py   | 181 +++++++++++++++++
 3 files changed, 401 insertions(+)
 create mode 100644 tensorflow/contrib/tpu/python/tpu/datasets.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/datasets_test.py

diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD
index c48e84ddfa..095b4821f1 100644
--- a/tensorflow/contrib/tpu/BUILD
+++ b/tensorflow/contrib/tpu/BUILD
@@ -163,6 +163,7 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        ":datasets",
         ":profiler",
         ":tpu_py",
         "//tensorflow/contrib/tpu/proto:topology_proto_py",
@@ -181,6 +182,33 @@ py_library(
     ],
 )
 
+py_library(
+    name = "datasets",
+    srcs = [
+        "python/tpu/datasets.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/data/python/ops:transformation_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:function",
+        "//tensorflow/python:functional_ops",
+        "//tensorflow/python/data/ops:dataset_ops",
+        "//tensorflow/python/data/ops:iterator_ops",
+        "//tensorflow/python/data/ops:readers",
+    ],
+)
+
+tf_py_test(
+    name = "datasets_test",
+    srcs = ["python/tpu/datasets_test.py"],
+    additional_deps = [
+        "//tensorflow/python:client_testlib",
+        ":datasets",
+    ],
+    grpc_enabled = True,
+)
+
 tf_py_test(
     name = "tpu_test",
     size = "small",
diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py
new file mode 100644
index 0000000000..29aea98542
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/datasets.py
@@ -0,0 +1,192 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ======================================
+"""Library of Cloud TPU helper functions for data loading."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.data.python.ops import batching
+from tensorflow.contrib.data.python.ops import interleave_ops
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.ops import iterator_ops
+from tensorflow.python.data.ops import readers
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import function
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import functional_ops
+
+
+def _TextLineDataset(filename):
+  buffer_size = 8 * 1024 * 1024  # 8 MiB per file
+  dataset = readers.TextLineDataset(filename, buffer_size=buffer_size)
+  return dataset
+
+
+def _TFRecordDataset(filename):
+  buffer_size = 8 * 1024 * 1024  # 8 MiB per file
+  dataset = readers.TFRecordDataset(filename, buffer_size=buffer_size)
+  return dataset
+
+
+_FILETYPE_MAP = {
+    'tfrecord': _TFRecordDataset,
+    'textline': _TextLineDataset,
+    'text': _TextLineDataset,
+}
+
+
+def StreamingFilesDataset(files,
+                          filetype=None,
+                          file_reader_job=None,
+                          worker_job=None,
+                          num_epochs=None,
+                          filename_shuffle_buffer_size=None,
+                          num_parallel_reads=None,
+                          batch_transfer_size=None,
+                          sloppy=None):
+  """StreamingFilesDataset constructs a dataset to stream from workers (GCE VM).
+
+  Because Cloud TPUs are allocated over the network, a Cloud TPU cannot read
+  files local to your GCE VM. In order to train using files stored on your local
+  VM (e.g. on local SSD for extreme performance), use the StreamingFilesDataset
+  helper to generate a dataset to feed your Cloud TPU with files from your GCE
+  VM.
+
+  The resulting dataset may return an OutOfRangeError if there are no files
+  found as a result of the fileglob expansion.
+
+  Note: StreamingFilesDataset assumes that the session is using a
+  TPUClusterResolver and has therefore a worker and a coordinator job. File
+  loading will be done on the coordinator job.
+
+  Args:
+    files: A string glob to match files, or a `tf.data.Dataset` generating file
+      names.
+    filetype: A string (one of 'tfrecord', or 'textline') or a single-argument
+      TensorFlow function that when given a filename returns a dataset.
+    file_reader_job: An optional string that corresponds to the job that should
+      perform the file reads.
+    worker_job: An optional string that corresponds to the job that should
+      process the tensors (i.e. your GPU or TPU worker).
+    num_epochs: The number of epochs through the training set that should be
+      generated. By default, it will repeat infinitely.
+    filename_shuffle_buffer_size: An optional integer whose value controls the
+      shuffling of the file names. If you would like to read from the files in
+      the same order, set to 0 or False.
+    num_parallel_reads: An optional integer controlling the number of files to
+      read from concurrently. (Set to 1 for no parallelism.)
+    batch_transfer_size: An optional integer controlling the batching used to
+      amortize the remote function invocation overhead. Set to a very large
+      number to increase throughput. Set to a very small number to reduce memory
+      consumption. Set to False to skip batching.
+    sloppy: (Optional.) If `True`, read input data as fast as possible, without
+      maintaining a deterministic order. Defaults to `False`.
+  Returns:
+    A `tf.data.Dataset` with an infinite stream of elements generated by a
+    parallel interleaving of the set of files matched (or generated) by `files`
+    with a type is the output of the dataset specified by `filetype`.
+
+  Raises:
+    ValueError: if any argument is not of the expected type.
+  """
+  if filetype is None:
+    filetype = 'tfrecord'
+
+  if isinstance(filetype, str):
+    if filetype not in _FILETYPE_MAP:
+      raise ValueError('Unexpected filetype: %s' % filetype)
+    reader_fn = _FILETYPE_MAP[filetype]
+  elif callable(filetype):
+    reader_fn = filetype
+  else:
+    raise ValueError('filetype should be a string or a callable')
+
+  file_reader_job = file_reader_job or 'coordinator'
+
+  worker_job = worker_job or 'worker'
+
+  if filename_shuffle_buffer_size is None:
+    filename_shuffle_buffer_size = 4096
+
+  num_parallel_reads = num_parallel_reads or 8
+
+  if batch_transfer_size is None:
+    batch_transfer_size = 1024
+
+  if sloppy is None:
+    sloppy = False
+
+  with ops.device('/job:%s' % file_reader_job):
+    if isinstance(files, str):
+      source_dataset = dataset_ops.Dataset.list_files(files)
+    elif isinstance(files, dataset_ops.Dataset):
+      source_dataset = files
+    else:
+      raise ValueError('files was not a string or a dataset: %s' % files)
+
+    if filename_shuffle_buffer_size:
+      source_dataset = source_dataset.shuffle(
+          buffer_size=filename_shuffle_buffer_size)
+
+    # NOTE: We perform the `repeat` on the source dataset, because the output
+    # dataset does not currently have enough information to recreate an iterator
+    # over the source dataset when it reaches the end.
+    source_dataset = source_dataset.repeat(num_epochs)
+
+    source_dataset = source_dataset.apply(
+        interleave_ops.parallel_interleave(
+            reader_fn, cycle_length=num_parallel_reads, sloppy=sloppy))
+
+    if batch_transfer_size:
+      # Note: we can safely call batch_and_drop_remainder because we have an
+      # infinite stream of TFRecords.
+      source_dataset = source_dataset.apply(
+          batching.batch_and_drop_remainder(batch_transfer_size))
+
+    source_dataset = source_dataset.prefetch(1)
+
+    source_iterator = source_dataset.make_one_shot_iterator()
+    source_handle = source_iterator.string_handle()
+
+  @function.Defun(dtypes.string)
+  def LoadingFunc(h):
+    remote_iterator = iterator_ops.Iterator.from_string_handle(
+        h, source_dataset.output_types, source_dataset.output_shapes)
+    return remote_iterator.get_next()
+
+  def MapFn(unused_input):
+    return functional_ops.remote_call(
+        args=[source_handle],
+        Tout=[dtypes.string],
+        f=LoadingFunc,
+        target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job)
+
+  with ops.device('/job:%s' % worker_job):
+    # TODO(saeta,mrry): Switch to using _GeneratorDataset.
+
+    # identity = lambda x: x
+    # dummy = constant_op.constant(0)
+    # output_dataset = dataset_ops._GeneratorDataset(dummy, identity, MapFn,
+    #                                                identity)
+
+    output_dataset = dataset_ops.Dataset.range(2).repeat().map(MapFn)
+    output_dataset = output_dataset.prefetch(1)
+
+    if batch_transfer_size:
+      # Undo the batching used during the transfer.
+      output_dataset = output_dataset.apply(batching.unbatch()).prefetch(1)
+
+  return output_dataset
diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py
new file mode 100644
index 0000000000..2c40797792
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py
@@ -0,0 +1,181 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""TPU datasets tests."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from tensorflow.contrib.tpu.python.tpu import datasets
+from tensorflow.core.protobuf import cluster_pb2
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.ops import readers
+from tensorflow.python.lib.io import python_io
+from tensorflow.python.platform import test
+from tensorflow.python.training import server_lib
+from tensorflow.python.util import compat
+
+_NUM_FILES = 10
+_NUM_ENTRIES = 200
+
+
+class DatasetsTest(test.TestCase):
+
+  def setUp(self):
+    super(DatasetsTest, self).setUp()
+    self._coord = server_lib.Server.create_local_server()
+    self._worker = server_lib.Server.create_local_server()
+
+    self._cluster_def = cluster_pb2.ClusterDef()
+    worker_job = self._cluster_def.job.add()
+    worker_job.name = 'worker'
+    worker_job.tasks[0] = self._worker.target[len('grpc://'):]
+    coord_job = self._cluster_def.job.add()
+    coord_job.name = 'coordinator'
+    coord_job.tasks[0] = self._coord.target[len('grpc://'):]
+
+    session_config = config_pb2.ConfigProto(cluster_def=self._cluster_def)
+
+    self._sess = session.Session(self._worker.target, config=session_config)
+
+  def testTextLineDataset(self):
+    all_contents = []
+    for i in range(_NUM_FILES):
+      filename = os.path.join(self.get_temp_dir(), 'text_line.%d.txt' % i)
+      contents = []
+      for j in range(_NUM_ENTRIES):
+        contents.append(compat.as_bytes('%d: %d' % (i, j)))
+      with open(filename, 'wb') as f:
+        f.write(b'\n'.join(contents))
+      all_contents.extend(contents)
+
+    dataset = datasets.StreamingFilesDataset(
+        os.path.join(self.get_temp_dir(), 'text_line.*.txt'), filetype='text')
+
+    iterator = dataset.make_initializable_iterator()
+    self._sess.run(iterator.initializer)
+    get_next = iterator.get_next()
+
+    retrieved_values = []
+    for _ in range(2 * len(all_contents)):
+      retrieved_values.append(compat.as_bytes(self._sess.run(get_next)))
+
+    self.assertEqual(set(all_contents), set(retrieved_values))
+
+  def testTFRecordDataset(self):
+    all_contents = []
+    for i in range(_NUM_FILES):
+      filename = os.path.join(self.get_temp_dir(), 'tf_record.%d' % i)
+      writer = python_io.TFRecordWriter(filename)
+      for j in range(_NUM_ENTRIES):
+        record = compat.as_bytes('Record %d of file %d' % (j, i))
+        writer.write(record)
+        all_contents.append(record)
+      writer.close()
+
+    dataset = datasets.StreamingFilesDataset(
+        os.path.join(self.get_temp_dir(), 'tf_record*'), filetype='tfrecord')
+
+    iterator = dataset.make_initializable_iterator()
+    self._sess.run(iterator.initializer)
+    get_next = iterator.get_next()
+
+    retrieved_values = []
+    for _ in range(2 * len(all_contents)):
+      retrieved_values.append(compat.as_bytes(self._sess.run(get_next)))
+
+    self.assertEqual(set(all_contents), set(retrieved_values))
+
+  def testTFRecordDatasetFromDataset(self):
+    filenames = []
+    all_contents = []
+    for i in range(_NUM_FILES):
+      filename = os.path.join(self.get_temp_dir(), 'tf_record.%d' % i)
+      filenames.append(filename)
+      writer = python_io.TFRecordWriter(filename)
+      for j in range(_NUM_ENTRIES):
+        record = compat.as_bytes('Record %d of file %d' % (j, i))
+        writer.write(record)
+        all_contents.append(record)
+      writer.close()
+
+    filenames = dataset_ops.Dataset.from_tensor_slices(filenames)
+
+    dataset = datasets.StreamingFilesDataset(filenames, filetype='tfrecord')
+
+    iterator = dataset.make_initializable_iterator()
+    self._sess.run(iterator.initializer)
+    get_next = iterator.get_next()
+
+    retrieved_values = []
+    for _ in range(2 * len(all_contents)):
+      retrieved_values.append(compat.as_bytes(self._sess.run(get_next)))
+
+    self.assertEqual(set(all_contents), set(retrieved_values))
+
+  def testArbitraryReaderFunc(self):
+
+    def MakeRecord(i, j):
+      return compat.as_bytes('%04d-%04d' % (i, j))
+
+    record_bytes = len(MakeRecord(10, 200))
+
+    all_contents = []
+    for i in range(_NUM_FILES):
+      filename = os.path.join(self.get_temp_dir(), 'fixed_length.%d' % i)
+      with open(filename, 'wb') as f:
+        for j in range(_NUM_ENTRIES):
+          record = MakeRecord(i, j)
+          f.write(record)
+          all_contents.append(record)
+
+    def FixedLengthFile(filename):
+      return readers.FixedLengthRecordDataset(filename, record_bytes)
+
+    dataset = datasets.StreamingFilesDataset(
+        os.path.join(self.get_temp_dir(), 'fixed_length*'),
+        filetype=FixedLengthFile)
+
+    iterator = dataset.make_initializable_iterator()
+    self._sess.run(iterator.initializer)
+    get_next = iterator.get_next()
+
+    retrieved_values = []
+    for _ in range(2 * len(all_contents)):
+      retrieved_values.append(compat.as_bytes(self._sess.run(get_next)))
+
+    self.assertEqual(set(all_contents), set(retrieved_values))
+
+  def testUnexpectedFiletypeString(self):
+    with self.assertRaises(ValueError):
+      datasets.StreamingFilesDataset(
+          os.path.join(self.get_temp_dir(), '*'), filetype='foo')
+
+  def testUnexpectedFiletypeType(self):
+    with self.assertRaises(ValueError):
+      datasets.StreamingFilesDataset(
+          os.path.join(self.get_temp_dir(), '*'), filetype=3)
+
+  def testUnexpectedFilesType(self):
+    with self.assertRaises(ValueError):
+      datasets.StreamingFilesDataset(123, filetype='tfrecord')
+
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From 0bde713c06895b9ce2de61d6aea1bff5415ddcbc Mon Sep 17 00:00:00 2001
From: Nick Kreeger <nick.kreeger@gmail.com>
Date: Mon, 26 Feb 2018 21:11:36 -0800
Subject: [PATCH 336/575] Upgrade Jenkins/Docker build scripts to Bazel 0.11.0.
 (#17280)

The 0.10.0 bazel has problems with static-linking on linux:
https://github.com/bazelbuild/bazel/issues/4474. This PR bumps to the
latest bazel that produces proper binaries w/o the linking issue.
---
 tensorflow/tools/ci_build/install/install_bazel.sh | 2 +-
 tensorflow/tools/docker/Dockerfile.devel           | 2 +-
 tensorflow/tools/docker/Dockerfile.devel-gpu       | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/tools/ci_build/install/install_bazel.sh b/tensorflow/tools/ci_build/install/install_bazel.sh
index 1df6a84d7c..3e27a94cf2 100755
--- a/tensorflow/tools/ci_build/install/install_bazel.sh
+++ b/tensorflow/tools/ci_build/install/install_bazel.sh
@@ -15,7 +15,7 @@
 # ==============================================================================
 
 # Select bazel version.
-BAZEL_VERSION="0.10.0"
+BAZEL_VERSION="0.11.0"
 
 set +e
 local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}')
diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel
index d16761c367..22c73c3fe1 100644
--- a/tensorflow/tools/docker/Dockerfile.devel
+++ b/tensorflow/tools/docker/Dockerfile.devel
@@ -57,7 +57,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc
 RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
     >>/etc/bazel.bazelrc
 # Install the most recent bazel release.
-ENV BAZEL_VERSION 0.8.0
+ENV BAZEL_VERSION 0.11.0
 WORKDIR /
 RUN mkdir /bazel && \
     cd /bazel && \
diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu
index 4ef37881bc..69ba340f92 100644
--- a/tensorflow/tools/docker/Dockerfile.devel-gpu
+++ b/tensorflow/tools/docker/Dockerfile.devel-gpu
@@ -66,7 +66,7 @@ RUN echo "startup --batch" >>/etc/bazel.bazelrc
 RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \
     >>/etc/bazel.bazelrc
 # Install the most recent bazel release.
-ENV BAZEL_VERSION 0.8.0
+ENV BAZEL_VERSION 0.11.0
 WORKDIR /
 RUN mkdir /bazel && \
     cd /bazel && \
-- 
GitLab


From 50daa198f85f21f3295dd6e1ad2951f38cc6c825 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 21:09:30 -0800
Subject: [PATCH 337/575] Automated g4 rollback of changelist 187092622

PiperOrigin-RevId: 187125995
---
 tensorflow/c/eager/BUILD            |  1 -
 tensorflow/c/eager/c_api.cc         |  4 ++--
 tensorflow/c/eager/c_api_internal.h | 14 +-------------
 tensorflow/c/eager/runtime.cc       | 14 ++++----------
 tensorflow/c/eager/runtime.h        |  3 ---
 tensorflow/c/eager/runtime_test.cc  | 12 ++++++------
 6 files changed, 13 insertions(+), 35 deletions(-)

diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD
index 16a2a15072..e55cb672e9 100644
--- a/tensorflow/c/eager/BUILD
+++ b/tensorflow/c/eager/BUILD
@@ -21,7 +21,6 @@ tf_cuda_library(
     visibility = ["//visibility:public"],
     deps = select({
         "//tensorflow:android": [
-            "//tensorflow/core:lib",
             "//tensorflow/core:android_tensorflow_lib_lite",
         ],
         "//conditions:default": [
diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index b233dd5b93..bebb63c746 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -818,8 +818,8 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
     // See WARNING comment below - would be nice to rework to avoid this
     // subtlety.
     tensorflow::tf_shared_lock l(ctx->functions_mu);
-    status->status = tensorflow::KernelAndDevice::Init(
-        ndef, ctx->func_lib(device), &ctx->runner, kernel);
+    status->status =
+        tensorflow::KernelAndDevice::Init(ndef, ctx->func_lib(device), kernel);
     if (!status->status.ok()) {
       delete kernel;
       return;
diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h
index 29944df4c2..3356054cd0 100644
--- a/tensorflow/c/eager/c_api_internal.h
+++ b/tensorflow/c/eager/c_api_internal.h
@@ -31,7 +31,6 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
 #include "tensorflow/core/framework/rendezvous.h"
-#include "tensorflow/core/lib/core/threadpool.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -46,15 +45,7 @@ struct TFE_ContextOptions {
 
 struct TFE_Context {
   explicit TFE_Context(const TFE_ContextOptions& opts, TF_Session* s)
-      : thread_pool(new tensorflow::thread::ThreadPool(
-            opts.session_options.options.env, "EagerCompute",
-            opts.session_options.options.config
-                        .inter_op_parallelism_threads() != 0
-                ? opts.session_options.options.config
-                      .inter_op_parallelism_threads()
-                : tensorflow::port::NumSchedulableCPUs())),
-        runner([this](std::function<void()> f) { thread_pool->Schedule(f); }),
-        policy(opts.policy),
+      : policy(opts.policy),
         session(s),
         rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)),
         pflr(new tensorflow::ProcessFunctionLibraryRuntime(
@@ -63,9 +54,6 @@ struct TFE_Context {
         log_device_placement(
             opts.session_options.options.config.log_device_placement()) {}
 
-  const std::unique_ptr<tensorflow::thread::ThreadPool> thread_pool;
-  std::function<void(std::function<void()>)> runner;
-
   const TFE_ContextDevicePlacementPolicy policy;
 
   // Note: we cannot use C++11 thread_local here as there is no concept of a
diff --git a/tensorflow/c/eager/runtime.cc b/tensorflow/c/eager/runtime.cc
index b9618420f0..4bf24fec2c 100644
--- a/tensorflow/c/eager/runtime.cc
+++ b/tensorflow/c/eager/runtime.cc
@@ -255,22 +255,17 @@ Status KernelAndDevice::InitOp(Device* device, const NodeDef& ndef,
   out->device_ = device;
   out->kernel_.reset(k);
   out->flib_ = nullptr;
-  out->runner_ = nullptr;
-  out->default_runner_ = [](std::function<void()> f) { f(); };
   return s;
 }
 
 // static
 Status KernelAndDevice::Init(const NodeDef& ndef, FunctionLibraryRuntime* flib,
-                             std::function<void(std::function<void()>)>* runner,
                              KernelAndDevice* out) {
   OpKernel* k = nullptr;
   Status s = flib->CreateKernel(ndef, &k);
   out->device_ = flib->device();
   out->kernel_.reset(k);
   out->flib_ = flib;
-  out->runner_ = runner;
-  out->default_runner_ = [](std::function<void()> f) { f(); };
   return s;
 }
 
@@ -301,11 +296,10 @@ Status KernelAndDevice::Run(std::vector<Tensor>* input_tensors,
   if (stats != nullptr) {
     params.track_allocations = true;
   }
-  if (runner_ == nullptr) {
-    params.runner = &default_runner_;
-  } else {
-    params.runner = runner_;
-  }
+  // TODO(apassos): use a thread pool.
+  std::function<void(std::function<void()>)> runner =
+      [](std::function<void()> f) { f(); };
+  params.runner = &runner;
 
   OpKernelContext context(&params);
   device_->Compute(kernel_.get(), &context);
diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h
index fa5f839977..7fede4dae9 100644
--- a/tensorflow/c/eager/runtime.h
+++ b/tensorflow/c/eager/runtime.h
@@ -169,7 +169,6 @@ class KernelAndDevice {
   // the FunctionLibraryRuntime is pushed on to the caller (see locking in
   // c_api.cc).
   static Status Init(const NodeDef& ndef, FunctionLibraryRuntime* flib,
-                     std::function<void(std::function<void()>)>* runner,
                      KernelAndDevice* out);
   // TODO(ashankar): Remove this
   static Status InitOp(Device* device, const NodeDef& ndef,
@@ -189,8 +188,6 @@ class KernelAndDevice {
  private:
   std::unique_ptr<OpKernel> kernel_;
   Device* device_;
-  std::function<void(std::function<void()>)>* runner_;
-  std::function<void(std::function<void()>)> default_runner_;
   FunctionLibraryRuntime* flib_;
   checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_;
   Rendezvous* rendez_;
diff --git a/tensorflow/c/eager/runtime_test.cc b/tensorflow/c/eager/runtime_test.cc
index ab0b535e1a..643153058c 100644
--- a/tensorflow/c/eager/runtime_test.cc
+++ b/tensorflow/c/eager/runtime_test.cc
@@ -92,8 +92,8 @@ TEST(KernelAndDevice, Run) {
                    .BuildNodeDef());
   TestEnv env;
   KernelAndDevice kernel(nullptr);
-  Status s = KernelAndDevice::Init(ndef, env.function_library_runtime(),
-                                   nullptr, &kernel);
+  Status s =
+      KernelAndDevice::Init(ndef, env.function_library_runtime(), &kernel);
   ASSERT_TRUE(s.ok()) << s;
   std::vector<Tensor> outputs;
   s = kernel.Run(&inputs, &outputs, nullptr);
@@ -158,8 +158,8 @@ void BM_KernelAndDeviceInit(int iters) {
   KernelAndDevice k(nullptr);
   tensorflow::testing::StartTiming();
   for (int i = 0; i < iters; ++i) {
-    TF_CHECK_OK(KernelAndDevice::Init(ndef, env.function_library_runtime(),
-                                      nullptr, &k));
+    TF_CHECK_OK(
+        KernelAndDevice::Init(ndef, env.function_library_runtime(), &k));
   }
 }
 BENCHMARK(BM_KernelAndDeviceInit);
@@ -179,8 +179,8 @@ void BM_KernelAndDeviceRun(int iters) {
                    .BuildNodeDef());
   TestEnv env;
   KernelAndDevice kernel(nullptr);
-  TF_CHECK_OK(KernelAndDevice::Init(ndef, env.function_library_runtime(),
-                                    nullptr, &kernel));
+  TF_CHECK_OK(
+      KernelAndDevice::Init(ndef, env.function_library_runtime(), &kernel));
   tensorflow::testing::StartTiming();
   for (int i = 0; i < iters; ++i) {
     TF_CHECK_OK(kernel.Run(&inputs, &outputs, nullptr));
-- 
GitLab


From b053b1006abdfcf1f790a729a412001ebbaf679f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 21:25:22 -0800
Subject: [PATCH 338/575] Improve error handling in strided_slice_op to fail
 more gracefully and return an error status instead of crashing.

PiperOrigin-RevId: 187126888
---
 tensorflow/core/kernels/strided_slice_op.cc | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc
index 7745effe2a..1e3e92a68a 100644
--- a/tensorflow/core/kernels/strided_slice_op.cc
+++ b/tensorflow/core/kernels/strided_slice_op.cc
@@ -109,17 +109,27 @@ class StridedSliceOp : public OpKernel {
     if (is_identity) {
       VLOG(1) << "Strided slice identity ";
       Tensor tmp;
-      CHECK(tmp.CopyFrom(input, final_shape));
+      OP_REQUIRES(context, tmp.CopyFrom(input, final_shape),
+                  errors::Internal("Copy failed"));
       context->set_output(0, tmp);
       return;
     }
 
     // Optimization #2, slice is memory contiguous (only occurs in dim 0)
     if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), begin[0], end[0])) {
-      CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
+      OP_REQUIRES(context, input.dims() >= 1,
+                  errors::InvalidArgument(
+                      "Input must have rank at least 1, got: ", input.dims()));
+      // Otherwise, is_identity should be true.
       VLOG(1) << "Strided slice dim 0: " << input.shape().DebugString();
+      OP_REQUIRES(
+          context, begin[0] <= end[0],
+          errors::InvalidArgument("begin[0] (", begin[0],
+                                  ") must less or equal to end[0] (", end[0]));
+      Tensor slice = input.Slice(begin[0], end[0]);
       Tensor tmp;
-      CHECK(tmp.CopyFrom(input.Slice(begin[0], end[0]), final_shape));
+      OP_REQUIRES(context, tmp.CopyFrom(slice, final_shape),
+                  errors::Internal("Copy failed"));
       context->set_output(0, tmp);
       return;
     }
@@ -238,7 +248,8 @@ class StridedSliceGradOp : public OpKernel {
 
     if (processing_shape.dims() == 0) {
       auto in = context->input(4);
-      CHECK(result->CopyFrom(in, processing_shape));
+      OP_REQUIRES(context, result->CopyFrom(in, processing_shape),
+                  errors::Internal("Copy failed"));
       return;
     }
 
-- 
GitLab


From 4faee3942d9983e0c96091b32095cc0d9ff494e0 Mon Sep 17 00:00:00 2001
From: Yun Peng <pcloudy@google.com>
Date: Tue, 27 Feb 2018 07:36:01 +0100
Subject: [PATCH 339/575] Fix some breakages in TensorFlow Windows build
 (#17271)

* Fix configure.py

* Add quantization_utils for building quantize_weights, quantize_nodes, round_weights

Caused by https://github.com/tensorflow/tensorflow/pull/16121
---
 configure.py                            |  8 ++++++--
 tensorflow/core/kernels/BUILD           | 16 ++++++++++++----
 tensorflow/tools/graph_transforms/BUILD |  2 +-
 3 files changed, 19 insertions(+), 7 deletions(-)

diff --git a/configure.py b/configure.py
index 7d2e30cd8a..97f46757ee 100644
--- a/configure.py
+++ b/configure.py
@@ -250,7 +250,11 @@ def reset_tf_configure_bazelrc(workspace_path):
       if _TF_BAZELRC_FILENAME in l:
         continue
       f.write('%s\n' % l)
-    f.write('import %s\n' % _TF_BAZELRC)
+    if is_windows():
+      tf_bazelrc_path = _TF_BAZELRC.replace("\\", "/")
+    else:
+      tf_bazelrc_path = _TF_BAZELRC
+    f.write('import %s\n' % tf_bazelrc_path)
 
 
 def cleanup_makefile():
@@ -444,7 +448,7 @@ def check_bazel_version(min_version):
   if which('bazel') is None:
     print('Cannot find bazel. Please install bazel.')
     sys.exit(0)
-  curr_version = run_shell(['bazel', '--batch', 'version'])
+  curr_version = run_shell(['bazel', '--batch', '--bazelrc=/dev/null', 'version'])
 
   for line in curr_version.split('\n'):
     if 'Build label: ' in line:
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 3426cf6e40..78786de16b 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -5128,7 +5128,6 @@ tf_kernel_library(
     srcs = [
         "dequantize_op.cc",
         "meta_support.cc",
-        "quantization_utils.cc",
         "quantize_down_and_shrink_range.cc",
         "quantize_op.cc",
         "quantized_activation_ops.cc",
@@ -5149,7 +5148,6 @@ tf_kernel_library(
     ],
     hdrs = [
         "meta_support.h",
-        "quantization_utils.h",
         "reference_gemm.h",
     ],
     deps = [
@@ -5160,6 +5158,7 @@ tf_kernel_library(
         ":image_resizer_state",
         ":ops_util",
         ":pooling_ops",
+        ":quantization_utils",
         "//tensorflow/core:array_ops_op_lib",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
@@ -5706,6 +5705,16 @@ tf_kernel_library(
     ],
 )
 
+cc_library(
+    name = "quantization_utils",
+    srcs = ["quantization_utils.cc"],
+    hdrs = ["quantization_utils.h"],
+    deps = [
+        "//tensorflow/core:framework",
+        "@gemmlowp",
+    ],
+)
+
 cc_library(
     name = "remote_fused_graph_execute_utils",
     srcs = [
@@ -6081,7 +6090,6 @@ cc_library(
     srcs = [
         "cwise_ops_common.cc",
         "meta_support.cc",
-        "quantization_utils.cc",
     ],
     hdrs = [
         "cwise_ops.h",
@@ -6090,10 +6098,10 @@ cc_library(
         "cwise_ops_gpu_gradients.cu.h",
         "cwise_ops_gradients.h",
         "meta_support.h",
-        "quantization_utils.h",
     ],
     deps = [
         ":bounds_check",
+        ":quantization_utils",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//third_party/eigen3",
diff --git a/tensorflow/tools/graph_transforms/BUILD b/tensorflow/tools/graph_transforms/BUILD
index ad3668fa02..4fe4fc3b13 100644
--- a/tensorflow/tools/graph_transforms/BUILD
+++ b/tensorflow/tools/graph_transforms/BUILD
@@ -134,8 +134,8 @@ cc_library(
         "//tensorflow/core:tensorflow",
         "//tensorflow/contrib/rnn:gru_ops_op_lib",
         "//tensorflow/contrib/rnn:lstm_ops_op_lib",
+        "//tensorflow/core/kernels:quantization_utils",
     ] + if_not_windows([
-        "//tensorflow/core/kernels:quantized_ops",
         "//tensorflow/core/kernels:remote_fused_graph_rewriter_transform",
         "//tensorflow/core/kernels/hexagon:hexagon_rewriter_transform",
     ]),
-- 
GitLab


From e4b294e080dc5f339d1e639e1e9907b53461b754 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 01:02:36 -0800
Subject: [PATCH 340/575] Add documentation to Grappler RewriterConfig to give
 a short description for each of the optimizer on what they do.

PiperOrigin-RevId: 187143156
---
 tensorflow/core/protobuf/rewriter_config.proto | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 504ed5d819..875e4663db 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -30,12 +30,17 @@ message RewriterConfig {
   }
 
   // Optimize tensor layouts (default is ON)
+  // e.g. This will try to use NCHW layout on GPU which is faster.
   Toggle layout_optimizer = 1;
   // Fold constants (default is ON)
+  // Statically infer the value of tensors when possible, and materialize the
+  // result using constants.
   Toggle constant_folding = 3;
   // Arithmetic optimizations (default is ON)
+  // e.g. Simplify arithmetic ops; merge ops with same value (like constants).
   Toggle arithmetic_optimization = 7;
   // Control dependency optimizations (default is ON).
+  // Remove redundant control dependencies, which may enable other optimization.
   Toggle dependency_optimization = 8;
   // Loop optimizations (default is OFF).
   Toggle loop_optimization = 9;
@@ -49,12 +54,20 @@ message RewriterConfig {
     NO_MEM_OPT = 1;
     // Driven by manual op-level annotations.
     MANUAL = 2;
+
     // Driven by heuristics. The behavior of these heuristics is subject to
     // change. Currently includes an experimental recomputation and swapping
     // heuristics. Manual annotations are respected, but additional nodes are
     // selected automatically.
+
+    // Swapping heuristic will move a tensor from the GPU to the CPU and move
+    // it back when needed to reduce peak memory usage.
     SWAPPING_HEURISTICS = 4;
+    // Recomputation heuristics will recompute ops (such as Relu activation)
+    // during backprop instead of storing them, reducing peak memory usage.
     RECOMPUTATION_HEURISTICS = 5;
+    // Scheduling will split big ops such as AddN and try to enforce a schedule
+    // of the new computations that decreases peak memory usage.
     SCHEDULING_HEURISTICS = 6;
     // Use any combination of swapping and recomputation heuristics.
     HEURISTICS = 3;
-- 
GitLab


From 7f25c9d127e8535170d0575c038fd42222887dd4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 06:00:21 -0800
Subject: [PATCH 341/575] Enable dynamic function calls. These are compiled
 just in time by inserting a call to compile.

PiperOrigin-RevId: 187165096
---
 tensorflow/contrib/py2tf/__init__.py          |  4 +-
 tensorflow/contrib/py2tf/converters/BUILD     | 13 +--
 .../contrib/py2tf/converters/call_trees.py    | 76 +++++++-------
 .../py2tf/converters/call_trees_test.py       | 16 +++
 .../py2tf/converters/converter_test_base.py   | 32 ++++--
 tensorflow/contrib/py2tf/impl/api.py          | 99 ++++++++++++++-----
 6 files changed, 163 insertions(+), 77 deletions(-)

diff --git a/tensorflow/contrib/py2tf/__init__.py b/tensorflow/contrib/py2tf/__init__.py
index 379fa7fd5c..6531183cb5 100644
--- a/tensorflow/contrib/py2tf/__init__.py
+++ b/tensorflow/contrib/py2tf/__init__.py
@@ -23,6 +23,7 @@ from __future__ import print_function
 
 from tensorflow.contrib.py2tf import utils
 from tensorflow.contrib.py2tf.impl.api import convert
+from tensorflow.contrib.py2tf.impl.api import converted_call
 from tensorflow.contrib.py2tf.impl.api import graph_ready
 from tensorflow.contrib.py2tf.impl.api import to_code
 from tensorflow.contrib.py2tf.impl.api import to_graph
@@ -30,7 +31,8 @@ from tensorflow.contrib.py2tf.pyct.transformer import PyFlowParseError
 from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = [
-    'to_graph', 'to_code', 'convert', 'graph_ready', 'utils', 'PyFlowParseError'
+    'to_graph', 'to_code', 'convert', 'graph_ready', 'converted_call', 'utils',
+    'PyFlowParseError'
 ]
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD
index 42baaaaba7..78f46bc05f 100644
--- a/tensorflow/contrib/py2tf/converters/BUILD
+++ b/tensorflow/contrib/py2tf/converters/BUILD
@@ -46,6 +46,7 @@ py_library(
     visibility = ["//tensorflow:__subpackages__"],
     deps = [
         ":converters",
+        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/contrib/py2tf/pyct/static_analysis",
         "//tensorflow/contrib/py2tf/utils",
         "@gast_archive//:gast",
@@ -59,7 +60,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -70,7 +70,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -81,7 +80,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -92,7 +90,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
+        "//tensorflow/contrib/py2tf/impl",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -103,7 +101,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -114,7 +111,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -125,7 +121,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -136,7 +131,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -157,7 +151,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -168,7 +161,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -184,7 +176,6 @@ py_test(
     ],
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
diff --git a/tensorflow/contrib/py2tf/converters/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py
index 1050ba654c..f18f9f6086 100644
--- a/tensorflow/contrib/py2tf/converters/call_trees.py
+++ b/tensorflow/contrib/py2tf/converters/call_trees.py
@@ -27,6 +27,7 @@ import types
 import gast
 
 from tensorflow.contrib.py2tf.pyct import anno
+from tensorflow.contrib.py2tf.pyct import inspect_utils
 from tensorflow.contrib.py2tf.pyct import parser
 from tensorflow.contrib.py2tf.pyct import templates
 from tensorflow.contrib.py2tf.pyct import transformer
@@ -72,9 +73,8 @@ class CallTreeTransformer(transformer.Base):
     self.uncompiled_modules = uncompiled_modules
     self.nocompile_decorators = nocompile_decorators
 
-  # pylint:disable=invalid-name
-
   def _resolve_name(self, node):
+    """Used to resolve decorator info."""
     if isinstance(node, gast.Call):
       return self._resolve_name(node.func)
     if isinstance(node, gast.Name):
@@ -99,7 +99,13 @@ class CallTreeTransformer(transformer.Base):
                          (owner_type, node.attr))
     return None
 
+  def _function_is_compilable(self, target_entity):
+    """Determines whether an entity can be compiled at all."""
+    # TODO(mdan): This is just a placeholder. Implement.
+    return not isinstance(target_entity, types.BuiltinFunctionType)
+
   def _should_compile(self, node, fqn):
+    """Determines whether an entity should be compiled in the context."""
     for i in range(1, len(fqn)):
       if fqn[:i] in self.uncompiled_modules:
         return False
@@ -141,33 +147,6 @@ class CallTreeTransformer(transformer.Base):
 
     return True
 
-  def _determine_function_owner(self, m):
-    # TODO(mdan): The parent type should be known at analysis. Use that instead.
-    if hasattr(m, 'im_class'):  # Python 2
-      return m.im_class
-    if hasattr(m, '__qualname__'):  # Python 3
-      # Object attributes: should be bound to "self".
-      if hasattr(m, '__self__'):
-        return type(m.__self__)
-
-      # Class attributes: should have the owner name in their namespace.
-      qn = m.__qualname__.split('.')
-      if len(qn) < 2:
-        return None
-      owner_name, func_name = qn[-2:]
-      if func_name != m.__name__:
-        raise ValueError('Inconsistent names detected '
-                         '(__qualname__[1] = "%s", __name__ = "%s") for %s.' %
-                         (func_name, m.__name__, m))
-      if owner_name == '<locals>':
-        return None
-      if owner_name not in self.context.namespace:
-        raise ValueError(
-            'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' %
-            (owner_name, m, self.context.namespace))
-      return self.context.namespace[owner_name]
-    return None
-
   def _rename_compilable_function(self, node):
     assert anno.hasanno(node.func, 'live_val')
     assert anno.hasanno(node.func, 'fqn')
@@ -182,7 +161,11 @@ class CallTreeTransformer(transformer.Base):
           target_fqn, live_entity=target_entity)
       do_rename = True
     else:
-      owner_type = self._determine_function_owner(target_entity)
+      if anno.hasanno(node.func, 'parent_type'):
+        owner_type = anno.getanno(node.func, 'parent_type')
+      else:
+        # Fallback - not reliable.
+        owner_type = inspect_utils.getmethodclass(target_entity)
       new_name, do_rename = self.context.namer.compiled_function_name(
           target_fqn, live_entity=target_entity, owner_type=owner_type)
 
@@ -202,9 +185,32 @@ class CallTreeTransformer(transformer.Base):
     """
     return templates.replace(template, func=node.func, original_args=node.args)
 
-  def _function_is_compilable(self, target_entity):
-    # TODO(mdan): This is just a placeholder. Implement.
-    return not isinstance(target_entity, types.BuiltinFunctionType)
+  def _converted_call(self, node):
+    """Inlines a dynamic conversion for a dynamic function."""
+    # TODO(mdan): Pass information on the statically compiled functions.
+    # Having access to the statically compiled functions can help avoid
+    # unnecessary compilation.
+    # For example, this would lead to function `a` being compiled twice:
+    #
+    #   def a():
+    #     v = b
+    #     b()
+    #   def b():
+    #     a()
+    #
+    # This is really a problem with recursive calls, which currently can
+    # only be gated by a static condition, and should be rare.
+    # TODO(mdan): It probably makes sense to use dynamic conversion every time.
+    # Before we could convert all the time though, we'd need a reasonable
+    # caching mechanism.
+    template = """
+      py2tf_api.converted_call(func, True, False, {}, original_args)
+    """
+    call_expr = templates.replace(
+        template, func=node.func, original_args=node.args)
+    return call_expr[0].value
+
+  # pylint:disable=invalid-name
 
   def visit_Expr(self, node):
     if isinstance(node.value, gast.Call):
@@ -245,9 +251,9 @@ class CallTreeTransformer(transformer.Base):
         raise NotImplementedError('py_func with return values')
     else:
       if self.context.recursive:
-        raise NotImplementedError('Could not resolve target function.')
+        node = self._converted_call(node)
       else:
-        # TODO(mdan): Double check. Is this reachable code?
+        # Unresolved functions are allowed in non-recursive mode.
         pass
     return node
 
diff --git a/tensorflow/contrib/py2tf/converters/call_trees_test.py b/tensorflow/contrib/py2tf/converters/call_trees_test.py
index 777648dc0b..d482a9ef78 100644
--- a/tensorflow/contrib/py2tf/converters/call_trees_test.py
+++ b/tensorflow/contrib/py2tf/converters/call_trees_test.py
@@ -47,6 +47,21 @@ class CallTreesTest(converter_test_base.TestCase):
       result.renamed_test_fn_1 = renamed_test_fn_1
       self.assertEquals(3, result.test_fn_2(1))
 
+  def test_dynamic_function(self):
+
+    def test_fn_1():
+      raise ValueError('This should be masked by the mock.')
+
+    def test_fn_2(f):
+      return f() + 3
+
+    node = self.parse_and_analyze(test_fn_2, {})
+    node = call_trees.transform(node, self.ctx, (), ())
+
+    with self.compiled(node) as result:
+      # 10 = 7 (from the mock) + 3 (from test_fn_2)
+      self.assertEquals(10, result.test_fn_2(test_fn_1))
+
   def test_simple_methods(self):
 
     class TestClass(object):
@@ -59,6 +74,7 @@ class CallTreesTest(converter_test_base.TestCase):
 
     node = self.parse_and_analyze(
         TestClass.test_fn_2, {'TestClass': TestClass},
+        namer=converter_test_base.FakeNoRenameNamer(),
         arg_types={'self': (TestClass.__name__, TestClass)})
     node = call_trees.transform(node, self.ctx, (), ())
 
diff --git a/tensorflow/contrib/py2tf/converters/converter_test_base.py b/tensorflow/contrib/py2tf/converters/converter_test_base.py
index afa5c2f96f..1f98d8469c 100644
--- a/tensorflow/contrib/py2tf/converters/converter_test_base.py
+++ b/tensorflow/contrib/py2tf/converters/converter_test_base.py
@@ -25,6 +25,7 @@ from tensorflow.contrib.py2tf import utils
 from tensorflow.contrib.py2tf.pyct import compiler
 from tensorflow.contrib.py2tf.pyct import context
 from tensorflow.contrib.py2tf.pyct import parser
+from tensorflow.contrib.py2tf.pyct import pretty_printer
 from tensorflow.contrib.py2tf.pyct import qual_names
 from tensorflow.contrib.py2tf.pyct.static_analysis import activity
 from tensorflow.contrib.py2tf.pyct.static_analysis import live_values
@@ -52,26 +53,43 @@ class FakeNamer(object):
     return ('renamed_%s' % '_'.join(original_fqn)), True
 
 
+class FakeNoRenameNamer(FakeNamer):
+
+  def compiled_function_name(self, original_fqn, **_):
+    return str(original_fqn), False
+
+
 class TestCase(test.TestCase):
   """Base class for unit tests in this module. Contains relevant utilities."""
 
   @contextlib.contextmanager
   def compiled(self, node, *symbols):
-    source = '<compile failed>'
+    source = None
+
+    self.dynamic_calls = []
+    def converted_call(*args):
+      """Mock version of api.converted_call."""
+      self.dynamic_calls.append(args)
+      return 7
+
     try:
       result, source = compiler.ast_to_object(node)
-      result.tf = self.make_fake_tf(*symbols)
+      result.tf = self.make_fake_mod('fake_tf', *symbols)
       result.py2tf_utils = utils
+      result.py2tf_api = self.make_fake_mod('fake_api', converted_call)
       yield result
     except Exception:  # pylint:disable=broad-except
-      print('Offending compiled code:\n%s' % source)
+      if source is None:
+        print('Offending AST:\n%s' % pretty_printer.fmt(node, color=False))
+      else:
+        print('Offending compiled code:\n%s' % source)
       raise
 
-  def make_fake_tf(self, *symbols):
-    fake_tf = imp.new_module('fake_tf')
+  def make_fake_mod(self, name, *symbols):
+    fake_mod = imp.new_module(name)
     for s in symbols:
-      setattr(fake_tf, s.__name__, s)
-    return fake_tf
+      setattr(fake_mod, s.__name__, s)
+    return fake_mod
 
   def attach_namespace(self, module, **ns):
     for k, v in ns.items():
diff --git a/tensorflow/contrib/py2tf/impl/api.py b/tensorflow/contrib/py2tf/impl/api.py
index 29d2e038a7..48100aac32 100644
--- a/tensorflow/contrib/py2tf/impl/api.py
+++ b/tensorflow/contrib/py2tf/impl/api.py
@@ -26,7 +26,9 @@ import six
 from tensorflow.contrib.py2tf.impl import config
 from tensorflow.contrib.py2tf.impl import conversion
 from tensorflow.contrib.py2tf.pyct import compiler
+from tensorflow.contrib.py2tf.pyct import inspect_utils
 from tensorflow.contrib.py2tf.pyct import parser
+from tensorflow.contrib.py2tf.utils import builtins
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import tf_inspect
 
@@ -110,28 +112,7 @@ def convert(recursive=False, verbose=False, arg_types=None):
 
     @wraps(f)
     def wrapper(*args, **kwargs):
-      """Wrapper that calls the compiled version of the wrapped function."""
-      partial_types = ()
-      arg_values = {}
-      arg_names = tf_inspect.getargspec(f)[0]
-      for name, arg in zip(arg_names, args):
-        arg_values[name] = arg
-        arg_class = arg.__class__
-        # If arg_value_hints specifies any name, use that instead.
-        if name not in arg_types:
-          arg_types[name] = (arg_class.__name__, arg_class)
-        if name == 'self' and tf_inspect.isclass(arg_class):
-          # Annotated methods need to specify that their owner type is partial,
-          # otherwise other members they call will not be converted.
-          partial_types = (arg_class,)
-      wrapped = to_graph(
-          f,
-          recursive=recursive,
-          verbose=verbose,
-          arg_values=arg_values,
-          arg_types=arg_types,
-          partial_types=partial_types)
-      return wrapped(*args, **kwargs)
+      return converted_call(f, recursive, verbose, arg_types, *args, **kwargs)
 
     # Sometimes the decorator is just desugared, making it impossible to detect.
     # This attribute makes detection easier.
@@ -141,6 +122,78 @@ def convert(recursive=False, verbose=False, arg_types=None):
   return decorator
 
 
+def converted_call(f, recursive, verbose, arg_types, *args, **kwargs):
+  """Compiles a function call inline."""
+  # TODO(mdan): This needs cleanup.
+  # In particular, we may want to avoid renaming functions altogether.
+
+  if conversion.is_whitelisted_for_graph(f):
+    return f(*args, **kwargs)
+
+  unknown_arg_value = object()  # Sentinel for arguments of unknown value
+
+  if tf_inspect.isbuiltin(f):
+    return builtins.dynamic_builtin(f, *args, **kwargs)
+
+  if tf_inspect.isfunction(f) or tf_inspect.ismethod(f):
+    # Regular functions
+    target_entity = f
+    arg_map_target = f
+    effective_args = args
+    f_class = inspect_utils.getmethodclass(f)
+
+    if f_class is not None:
+      partial_types = (f_class,)
+    else:
+      partial_types = ()
+
+  elif tf_inspect.isclass(f):
+    # Constructors
+    target_entity = f
+    arg_map_target = f.__init__
+    effective_args = (unknown_arg_value,) + args
+    partial_types = ()
+
+  elif hasattr(f, '__call__') and hasattr(f, '__class__'):
+    # Callable objects
+    target_entity = f.__call__
+    arg_map_target = f.__call__
+    effective_args = (f,) + args
+    partial_types = (f.__class__,)
+
+  else:
+    NotImplementedError('unknown callable type "%s"' % type(f))
+
+  arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs)
+  for name, arg in arg_values.items():
+    if arg is unknown_arg_value:
+      continue
+    arg_class = arg.__class__
+    # If arg_value_hints specifies any name, use that instead.
+    if name not in arg_types:
+      arg_types[name] = (arg_class.__name__, arg_class)
+
+  # When called from within a decorator, this is the only indication that
+  # the function is a method - it appears that the decorator is applied
+  # before the method is bound.
+  if not partial_types:
+    if 'self' in arg_values:
+      if tf_inspect.isclass(arg_values['self'].__class__):
+        partial_types = (arg_values['self'].__class__,)
+    elif 'cls' in arg_values:
+      if tf_inspect.isclass(arg_values['cls']):
+        partial_types = (arg_values['cls'],)
+
+  converted_f = to_graph(
+      target_entity,
+      recursive=recursive,
+      verbose=verbose,
+      arg_values=arg_values,
+      arg_types=arg_types,
+      partial_types=partial_types)
+  return converted_f(*effective_args, **kwargs)
+
+
 def to_graph(e,
              recursive=True,
              verbose=False,
@@ -189,7 +242,7 @@ def to_graph(e,
   # The compiled code should see everything the entry function saw.
   # TODO(mdan): This might not work well if the call tree spans modules?
   if tf_inspect.isfunction(e):
-    compiled_node.__dict__.update(six.get_function_globals(e))
+    compiled_node.__dict__.update(inspect_utils.getnamespace(e))
   compiled_fn = getattr(compiled_node, name)
 
   if verbose:
-- 
GitLab


From 0c47d9d9622724aabd41425aad482637b2245499 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 08:29:52 -0800
Subject: [PATCH 342/575] Tensorflow: adds additional debugging info to
 feed_dict failure condition.

If you have a large feed dict, determining the type of each object can be difficult, and this additional debugging info helped me in such a case.

PiperOrigin-RevId: 187179551
---
 tensorflow/python/client/session.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index f3c4fecdc0..5737047c4b 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -1085,7 +1085,10 @@ class BaseSession(SessionInterface):
           if isinstance(subfeed_val, ops.Tensor):
             raise TypeError('The value of a feed cannot be a tf.Tensor object. '
                             'Acceptable feed values include Python scalars, '
-                            'strings, lists, numpy ndarrays, or TensorHandles.')
+                            'strings, lists, numpy ndarrays, or TensorHandles.'
+                            'For reference, the tensor object was ' +
+                            str(feed_val) + ' which was passed to the '
+                            'feed with key ' + str(feed) + '.')
 
           subfeed_dtype = subfeed_t.dtype.as_numpy_dtype
           if isinstance(subfeed_val, int) and _convert_to_numpy_obj(
-- 
GitLab


From 67545cd70ebec13c18159d105b0ce17bbfc7ac44 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Tue, 27 Feb 2018 09:52:00 -0800
Subject: [PATCH 343/575] Uses the new automatic control dependencies code for
 functions.

PiperOrigin-RevId: 187189552
---
 tensorflow/python/eager/function.py       | 73 ++++++++++++++---------
 tensorflow/python/eager/function_test.py  | 14 ++---
 tensorflow/python/eager/graph_callable.py | 12 +++-
 3 files changed, 61 insertions(+), 38 deletions(-)

diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py
index b3317bd323..655eaf3a1e 100644
--- a/tensorflow/python/eager/function.py
+++ b/tensorflow/python/eager/function.py
@@ -36,6 +36,7 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes as dtypes_module
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.util import compat
@@ -162,31 +163,15 @@ class CapturingGraph(ops.Graph):
       op_def=None,
       compute_shapes=True,
       compute_device=True):
-    # TODO(apassos) probably control flow has to be handled delicately here as
-    # in if a resource is accessed inside a control flow context we need the
-    # control dependency to point to something outside the context which is
-    # guaranteed to happen after the access.
-    #
     # TODO(apassos) this should do some form of alias analysis as ops which
     # forward the resources such as Identity and Switch can cause serialization
     # to fail.
-    resource_inputs = set()
-    control_inputs = set()
     for i, inp in enumerate(inputs):
       if inp.graph is not self:
         inputs[i] = capture_value(self.captures, inp, inp.dtype, inp.op.name)
-      inp = inputs[i]
-      if inp.dtype == dtypes_module.resource:
-        if inp.name in self._last_op_using_resource_tensor:
-          control_inputs.add(self._last_op_using_resource_tensor[inp.name])
-        resource_inputs.add(inp.name)
-    with self.control_dependencies(list(control_inputs)):
-      op = super(CapturingGraph, self).create_op(
-          op_type, inputs, dtypes, input_types, name, attrs, op_def,
-          compute_shapes, compute_device)
-    for name in resource_inputs:
-      self._last_op_using_resource_tensor[name] = op
-    return op
+    return super(CapturingGraph, self).create_op(
+        op_type, inputs, dtypes, input_types, name, attrs, op_def,
+        compute_shapes, compute_device)
 
 
 # TODO(apassos): it'd be really nice if we could scope this registration.
@@ -636,13 +621,15 @@ def _defun_internal(name, func, args, kwds):
     for collection in curr_graph.collections:
       tmp_graph.get_collection_ref(collection)[:] = curr_graph.get_collection(
           collection)
-    with tmp_graph.as_default():
+    with tmp_graph.as_default(), AutomaticControlDependencies() as a:
       func_inputs = _get_defun_inputs(args)
 
       def convert(x):
         if x is None:
           return None
-        return ops.convert_to_tensor_or_indexed_slices(x)
+        x = ops.convert_to_tensor_or_indexed_slices(x)
+        x = a.mark_as_return(x)
+        return x
 
       with capture_tensors(captures):
         this_tape = tape.push_new_tape()
@@ -887,7 +874,36 @@ class AutomaticControlDependencies(object):
     self._returned_tensors = set()
 
   def mark_as_return(self, tensor):
+    """Acts like identity but marks the `Tensor` as a return value.
+
+    This will possibly return a copy of the `Tensor`. Usage:
+
+    ```
+      with AutomaticControlDependencies() as a:
+       ...
+       t = a.mark_as_return(t)
+      _ = ...(t...)  # i.e. it's safe to use t here
+    ```
+
+    Args:
+      tensor: the `Tensor` to be marked
+
+    Returns:
+      a copy of the `Tensor`.
+    """
+    if isinstance(tensor, ops.IndexedSlices):
+      values = array_ops.identity(tensor.values)
+      indices = array_ops.identity(tensor.indices)
+      self._returned_tensors.add(indices)
+      self._returned_tensors.add(values)
+      return ops.IndexedSlices(values, indices, dense_shape=tensor.dense_shape)
+    # We want to make the return values depend on the stateful operations, but
+    # we don't want to introduce a cycle, so we make the return value the result
+    # of a new identity operation that the stateful operations definitely don't
+    # depend on.
+    tensor = array_ops.identity(tensor)
     self._returned_tensors.add(tensor)
+    return tensor
 
   def __enter__(self):
     if context.in_eager_mode():
@@ -1008,7 +1024,8 @@ class AutomaticControlDependencies(object):
     for op in new_operations:
       control_inputs = set()
       # Ensure stateful ops run
-      if self._graph._registered_ops[op.type].is_stateful:  # pylint: disable=protected-access
+      if (op.type not in self._graph._registered_ops  # pylint: disable=protected-access
+          or self._graph._registered_ops[op.type].is_stateful):  # pylint: disable=protected-access
         ops_which_must_run.add(op)
       # Ignore switches (they're handled separately)
       if op.type == "Switch" and op.inputs[0].dtype == dtypes_module.resource:
@@ -1044,9 +1061,10 @@ class AutomaticControlDependencies(object):
 
     # Ensure all ops which must run do run
     for r in self._returned_tensors:
-      r.op._add_control_inputs(  # pylint: disable=protected-access
-          [o for o in ops_which_must_run
-           if o._control_flow_context is r.op._control_flow_context])  # pylint: disable=protected-access
+      if ops_which_must_run:
+        r.op._add_control_inputs(  # pylint: disable=protected-access
+            [o for o in ops_which_must_run
+             if o._control_flow_context is r.op._control_flow_context])  # pylint: disable=protected-access
 
 
 def automatic_control_dependencies(f):
@@ -1066,8 +1084,7 @@ def automatic_control_dependencies(f):
   def wrapper(*args, **kwds):
     with AutomaticControlDependencies() as a:
       result = f(*args, **kwds)
-      for t in nest.flatten(result):
-        a.mark_as_return(t)
-      return result
+      result_flat = [a.mark_as_return(t) for t in nest.flatten(result)]
+      return nest.pack_sequence_as(result, result_flat)
 
   return tf_decorator.make_decorator(f, wrapper)
diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py
index 431d9388c0..b9cde16867 100644
--- a/tensorflow/python/eager/function_test.py
+++ b/tensorflow/python/eager/function_test.py
@@ -606,7 +606,7 @@ class AutomaticControlDependenciesTest(test.TestCase):
         v.assign(v + 1)
         v.assign(2 * v)
         val = v.read_value()
-        c.mark_as_return(val)
+        val = c.mark_as_return(val)
       self.assertAllEqual(val.eval(), 4.0)
 
   def testCondMustRun(self):
@@ -626,7 +626,7 @@ class AutomaticControlDependenciesTest(test.TestCase):
 
         control_flow_ops.cond(p, true_fn, false_fn)
         val = v.read_value()
-        c.mark_as_return(val)
+        val = c.mark_as_return(val)
       self.assertAllEqual(val.eval(feed_dict={p: False}), 5.0)
       self.assertAllEqual(val.eval(feed_dict={p: True}), 6.0)
 
@@ -647,7 +647,7 @@ class AutomaticControlDependenciesTest(test.TestCase):
 
         control_flow_ops.cond(p, true_fn, false_fn)
         one = constant_op.constant(1.0)
-        c.mark_as_return(one)
+        one = c.mark_as_return(one)
       one.eval(feed_dict={p: False})
       self.assertAllEqual(v.read_value().eval(), 5.0)
       one.eval(feed_dict={p: True})
@@ -681,7 +681,7 @@ class AutomaticControlDependenciesTest(test.TestCase):
         control_flow_ops.cond(p, true_fn, false_fn)
         with ops.name_scope('final'):
           val = v.read_value()
-        c.mark_as_return(val)
+        val = c.mark_as_return(val)
       self.assertAllEqual(val.eval(feed_dict={p: False, q: False}), 3.0)
       self.assertAllEqual(val.eval(feed_dict={p: False, q: True}), 6.0)
       self.assertAllEqual(val.eval(feed_dict={p: True, q: True}), 7.0)
@@ -703,7 +703,7 @@ class AutomaticControlDependenciesTest(test.TestCase):
 
         control_flow_ops.cond(p, true_fn, false_fn)
         val = v.read_value()
-        c.mark_as_return(val)
+        val = c.mark_as_return(val)
       self.assertAllEqual(val.eval(feed_dict={p: False}), 5.0)
       self.assertAllEqual(val.eval(feed_dict={p: True}), 5.0)
 
@@ -724,7 +724,7 @@ class AutomaticControlDependenciesTest(test.TestCase):
 
         control_flow_ops.cond(p, true_fn, false_fn)
         val = v.read_value()
-        c.mark_as_return(val)
+        val = c.mark_as_return(val)
       self.assertAllEqual(val.eval(feed_dict={p: False}), 6.0)
       self.assertAllEqual(val.eval(feed_dict={p: True}), 12.0)
 
@@ -745,7 +745,7 @@ class AutomaticControlDependenciesTest(test.TestCase):
         control_flow_ops.cond(p, true_fn, false_fn)
         v.assign(v * 2)
         val = v.read_value()
-        c.mark_as_return(val)
+        val = c.mark_as_return(val)
       self.assertAllEqual(val.eval(feed_dict={p: False}), 10.0)
       self.assertAllEqual(val.eval(feed_dict={p: True}), 20.0)
 
diff --git a/tensorflow/python/eager/graph_callable.py b/tensorflow/python/eager/graph_callable.py
index 62106bf0e2..623f3564ad 100644
--- a/tensorflow/python/eager/graph_callable.py
+++ b/tensorflow/python/eager/graph_callable.py
@@ -279,9 +279,12 @@ def _graph_callable_internal(func, shape_and_dtypes):
       # scope's view of which variables exist.
       variable_captures = _VariableCapturingScope()
       with variable_captures.initializing_scope(), function.capture_tensors(
-          captures):
+          captures), function.AutomaticControlDependencies() as a:
         func_outputs = func(*func_inputs)
-      outputs_list = nest.flatten(func_outputs)
+        outputs_list = nest.flatten(func_outputs)
+        for i, x in enumerate(outputs_list):
+          if x is not None:
+            outputs_list[i] = a.mark_as_return(x)
       if len(outputs_list) == 1 and outputs_list[0] is None:
         outputs_list = []
       output_shapes = [x.shape for x in outputs_list]
@@ -294,9 +297,12 @@ def _graph_callable_internal(func, shape_and_dtypes):
       # knows about all variables.
       tmp_graph.clear_resource_control_flow_state()
       with variable_captures.capturing_scope(), function.capture_tensors(
-          captures):
+          captures), function.AutomaticControlDependencies() as a:
         captured_outputs = func(*func_inputs)
       captured_outlist = nest.flatten(captured_outputs)
+      for i, x in enumerate(captured_outlist):
+        if x is not None:
+          captured_outlist[i] = a.mark_as_return(x)
       capturing_operations = tmp_graph.get_operations()[
           len(initializing_operations):]
 
-- 
GitLab


From f62f168fc3d59e3f067423fc39b4f5c3bfe2527a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 10:05:22 -0800
Subject: [PATCH 344/575] Make crosstools ready for introduction of
 c++-link-nodeps-dynamic-library

PiperOrigin-RevId: 187191730
---
 third_party/gpus/crosstool/CROSSTOOL_clang.tpl  | 7 +++++++
 third_party/toolchains/gpus/crosstool/CROSSTOOL | 7 +++++++
 2 files changed, 14 insertions(+)

diff --git a/third_party/gpus/crosstool/CROSSTOOL_clang.tpl b/third_party/gpus/crosstool/CROSSTOOL_clang.tpl
index e4363d6045..2f09473ee2 100644
--- a/third_party/gpus/crosstool/CROSSTOOL_clang.tpl
+++ b/third_party/gpus/crosstool/CROSSTOOL_clang.tpl
@@ -49,6 +49,7 @@ toolchain {
     flag_set {
       action: "c++-link-executable"
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         flag: "-lstdc++"
       }
@@ -75,6 +76,7 @@ toolchain {
     name: "alwayslink"
     flag_set {
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       action: "c++-link-executable"
       flag_group {
         flag: "-Wl,-no-as-needed"
@@ -116,6 +118,7 @@ toolchain {
     }
     flag_set {
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         flag: "-Wl,-z,relro,-z,now"
       }
@@ -161,6 +164,7 @@ toolchain {
     flag_set {
       action: "c++-link-executable"
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         # Stamp the binary with a unique identifier.
         flag: "-Wl,--build-id=md5"
@@ -176,6 +180,7 @@ toolchain {
       action: "c++-compile"
       action: "c++-link-executable"
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         flag:"-no-canonical-prefixes"
       }
@@ -199,6 +204,7 @@ toolchain {
     flag_set {
       action: "c++-link-executable"
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         flag: "-B/usr/bin/"
       }
@@ -246,6 +252,7 @@ toolchain {
     }
     flag_set {
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       action: "c++-link-executable"
       flag_group {
         flag: "-Wl,--gc-sections"
diff --git a/third_party/toolchains/gpus/crosstool/CROSSTOOL b/third_party/toolchains/gpus/crosstool/CROSSTOOL
index a47e0c7cd7..16ee2f82c6 100644
--- a/third_party/toolchains/gpus/crosstool/CROSSTOOL
+++ b/third_party/toolchains/gpus/crosstool/CROSSTOOL
@@ -53,6 +53,7 @@ toolchain {
     flag_set {
       action: "c++-link-executable"
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         flag: "-lstdc++"
       }
@@ -79,6 +80,7 @@ toolchain {
     name: "alwayslink"
     flag_set {
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       action: "c++-link-executable"
       flag_group {
         flag: "-Wl,-no-as-needed"
@@ -120,6 +122,7 @@ toolchain {
     }
     flag_set {
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         flag: "-Wl,-z,relro,-z,now"
       }
@@ -165,6 +168,7 @@ toolchain {
     flag_set {
       action: "c++-link-executable"
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         # Stamp the binary with a unique identifier.
         flag: "-Wl,--build-id=md5"
@@ -180,6 +184,7 @@ toolchain {
       action: "c++-compile"
       action: "c++-link-executable"
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         flag:"-no-canonical-prefixes"
       }
@@ -203,6 +208,7 @@ toolchain {
     flag_set {
       action: "c++-link-executable"
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       flag_group {
         flag: "-B/usr/bin/"
       }
@@ -250,6 +256,7 @@ toolchain {
     }
     flag_set {
       action: "c++-link-dynamic-library"
+      action: "c++-link-nodeps-dynamic-library"
       action: "c++-link-executable"
       flag_group {
         flag: "-Wl,--gc-sections"
-- 
GitLab


From 0e5458fb95b0b146838a3c61de31bb9497c613ce Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 10:05:38 -0800
Subject: [PATCH 345/575] Implement partial constant folding of AddN and
 AccumulateNV2. Change AccumulateNV2 to AddN if all inputs are constant, since
 constant folding doesn't work for the fake node type.

PiperOrigin-RevId: 187191772
---
 .../grappler/optimizers/constant_folding.cc   |  78 ++++++++++++
 .../optimizers/constant_folding_test.cc       | 115 ++++++++++++++++--
 2 files changed, 184 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index a5417aaa51..32c8a9b2f5 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -1493,6 +1493,7 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
   const bool is_aggressive = opt_level_ == RewriterConfig::AGGRESSIVE;
   for (int i = 0; i < output->node_size(); ++i) {
     NodeDef* node = output->mutable_node(i);
+
     // Remove Shuffle or Reverse op over scalar values.
     if (use_shape_info &&
         (IsShuffle(*node) || IsReverse(*node) || IsTranspose(*node))) {
@@ -1839,6 +1840,83 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
       std::swap(*node->mutable_input(parent_const_input),
                 *op_child_node->mutable_input(non_const_leaf_input));
       graph_modified_ = true;
+      continue;
+    }
+
+    // Partial constant folding for associative operators:
+    // Split AddN/AccumulateNV2 to enable partial
+    // folding of ops when more than one but not all inputs are constant.
+    // For AddN and AccumulateNV2, we may furthermore reorder inputs, since
+    // addition is commutative.
+    // TODO(rmlarsen): Concat/Pack/ParallelConcat which are not commutative, so
+    // we have to preserve order and can only push consecutive runs of constant
+    // inputs into sub-nodes.
+    if (IsAggregate(*node) && IsCommutative(*node) &&
+        NumNonControlInputs(*node) > 2) {
+      const int num_control_inputs =
+          node->input_size() - NumNonControlInputs(*node);
+      std::vector<int> const_inputs;
+      std::vector<int> nonconst_inputs;
+      for (int i = 0; i < node->input_size(); ++i) {
+        const string& input = node->input(i);
+        const NodeDef* input_node = node_map_->GetNode(NodeName(input));
+        CHECK(input_node != nullptr) << input;
+        if (!IsControlInput(input) && IsReallyConstant(*input_node)) {
+          const_inputs.push_back(i);
+        } else {
+          // Non-const and control inputs.
+          nonconst_inputs.push_back(i);
+        }
+      }
+      // Promote AccumulateNV2 with all constant inputs to AddN, since it is
+      // a fake node that cannot be constant folded by itself.
+      if (const_inputs.size() == NumNonControlInputs(*node) &&
+          node->op() == "AccumulateNV2") {
+        node->set_op("AddN");
+        node->mutable_attr()->erase("shape");
+        graph_modified_ = true;
+        continue;
+      }
+      const string new_node_name = OptimizedNodeName(
+          *node, strings::StrCat("_partial_split_", const_inputs.size()));
+      if (1 < const_inputs.size() &&
+          const_inputs.size() < NumNonControlInputs(*node) &&
+          !node_map_->NodeExists(new_node_name)) {
+        NodeDef* added_node = output->add_node();
+        *added_node = *node;
+        // Always use AddN for the constant node, since AccumulateNV2 is a fake
+        // node that cannot be constant folded, since it does not have a kernel.
+        added_node->set_op("AddN");
+        added_node->mutable_attr()->erase("shape");
+        added_node->set_name(new_node_name);
+        node_map_->AddNode(added_node->name(), added_node);
+        added_node->clear_input();
+        for (int i : const_inputs) {
+          added_node->add_input(node->input(i));
+          node_map_->UpdateOutput(NodeName(node->input(i)), node->name(),
+                                  added_node->name());
+        }
+
+        // Overwrite the first const input with the added node.
+        node->set_input(const_inputs[0], added_node->name());
+        node_map_->AddOutput(added_node->name(), node->name());
+        nonconst_inputs.push_back(const_inputs[0]);
+        // Compact the remaining inputs to the original node.
+        std::sort(nonconst_inputs.begin(), nonconst_inputs.end());
+        int idx = 0;
+        for (int i : nonconst_inputs) {
+          if (idx != i) {
+            node->set_input(idx, node->input(i));
+          }
+          ++idx;
+        }
+        node->mutable_input()->DeleteSubrange(nonconst_inputs.size(),
+                                              const_inputs.size() - 1);
+        (*node->mutable_attr())["N"].set_i(node->input_size() -
+                                           num_control_inputs);
+        (*added_node->mutable_attr())["N"].set_i(const_inputs.size());
+        graph_modified_ = true;
+      }
     }
   }
 
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index c6540192d7..3149e1d53e 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -187,20 +187,21 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
     Output bias_add2 = ops::BiasAdd(s.WithOpName("bias_add2"), zeros, bias);
     Output sub1 = ops::Sub(s.WithOpName("sub1"), x, zeros);
     Output sub2 = ops::Sub(s.WithOpName("sub2"), zeros, y);
-    Output addn =
-        ops::AddN(s.WithOpName("addn"),
-                  {mul1, mul2, mul3, mul4, mul5, mul6, div1, div2, matmul1,
-                   matmul2, add1, add2, bias_add1, bias_add2, sub1, sub2});
+    Output concat =
+        ops::Concat(s.WithOpName("concat"),
+                    {mul1, mul2, mul3, mul4, mul5, mul6, div1, div2, matmul1,
+                     matmul2, add1, add2, bias_add1, bias_add2, sub1, sub2},
+                    0);
     GrapplerItem item;
     TF_CHECK_OK(s.ToGraphDef(&item.graph));
-    item.fetch = {"addn", "matmul3", "matmul4"};
+    item.fetch = {"concat", "matmul3", "matmul4"};
 
     ConstantFolding optimizer(nullptr /* cpu_device */);
     GraphDef output;
     Status status = optimizer.Optimize(nullptr, item, &output);
     TF_EXPECT_OK(status);
 
-    EXPECT_EQ(27, output.node_size());
+    EXPECT_EQ(28, output.node_size());
     for (int i = 0; i < output.node_size(); ++i) {
       const NodeDef& node = output.node(i);
       const string& name = node.name();
@@ -414,7 +415,6 @@ TEST_F(ConstantFoldingTest, NeutralElement_PartialShape_UnknownOutputShape) {
   GraphDef output;
   Status status = optimizer.Optimize(nullptr, item, &output);
   TF_EXPECT_OK(status);
-  LOG(INFO) << output.DebugString();
 
   EXPECT_EQ(15, output.node_size());
   for (int i = 0; i < output.node_size(); ++i) {
@@ -1547,8 +1547,105 @@ TEST_F(ConstantFoldingTest, SwitchIdenticalInputs) {
   EXPECT_EQ(6, found);
 }
 
+TEST_F(ConstantFoldingTest, PartialFolding_AssociativeAndCommutative) {
+  std::function<Output(const Scope&, InputList)> addn_fun =
+      [](const Scope& scope, InputList inputs) {
+        return ops::AddN(scope, inputs);
+      };
+  std::function<Output(const Scope&, InputList)> accumulate_fun =
+      [](const Scope& scope, InputList inputs) {
+        return ops::AccumulateNV2(scope, inputs, TensorShape({2, 2}));
+      };
+  for (bool use_add_n : {true, false}) {
+    auto fun = use_add_n ? addn_fun : accumulate_fun;
+    const string op_name = use_add_n ? "AddN" : "AccumulateNV2";
+    Scope s = Scope::NewRootScope();
+    Output x = ops::Placeholder(s.WithOpName("x"), DT_FLOAT,
+                                ops::Placeholder::Shape(TensorShape({2, 2})));
+    Output y = ops::Placeholder(s.WithOpName("y"), DT_FLOAT,
+                                ops::Placeholder::Shape(TensorShape({2, 2})));
+    Output z = ops::Placeholder(s.WithOpName("z"), DT_FLOAT,
+                                ops::Placeholder::Shape(TensorShape({2, 2})));
+    Output c1 = ops::Const(s.WithOpName("c1"), 1.0f, {2, 2});
+    Output c2 = ops::Const(s.WithOpName("c2"), 2.0f, {2, 2});
+    Output c3 = ops::Const(s.WithOpName("c3"), 3.0f, {2, 2});
+    Output acc0 = fun(s.WithOpName("acc0"), {c1, c2, c3});
+    Output acc1 = fun(s.WithOpName("acc1"), {x, y, z});
+    Output acc2 = fun(s.WithOpName("acc2"), {c1, x, y});
+    Output acc3 = fun(s.WithOpName("acc3"), {c1, c2, z});
+    Output acc4 = fun(s.WithOpName("acc4"), {c1, y, c2});
+    Output acc5 = fun(s.WithOpName("acc5"), {x, c1, c2});
+    Output acc6 = fun(s.WithOpName("acc6"), {x, c1, y, c2});
+    Output concat = ops::Concat(s.WithOpName("concat"),
+                                {acc0, acc1, acc2, acc3, acc4, acc5, acc6}, 0);
+
+    GrapplerItem item;
+    TF_CHECK_OK(s.ToGraphDef(&item.graph));
+    item.fetch = {"concat"};
+
+    ConstantFolding optimizer(nullptr /* cpu_device */);
+    GraphDef output;
+    Status status = optimizer.Optimize(nullptr, item, &output);
+    TF_EXPECT_OK(status);
+
+    EXPECT_EQ(17, output.node_size());
+    for (const NodeDef& node : output.node()) {
+      if (node.name() == "acc0") {
+        EXPECT_EQ("Const", node.op());
+      }
+      if (node.name() == "acc1") {
+        EXPECT_EQ(op_name, node.op());
+        EXPECT_EQ(3, node.input_size());
+        EXPECT_EQ("x", node.input(0));
+        EXPECT_EQ("y", node.input(1));
+        EXPECT_EQ("z", node.input(2));
+      }
+      if (node.name() == "acc2") {
+        EXPECT_EQ(op_name, node.op());
+        EXPECT_EQ(3, node.input_size());
+        EXPECT_EQ("c1", node.input(0));
+        EXPECT_EQ("x", node.input(1));
+        EXPECT_EQ("y", node.input(2));
+      }
+      if (node.name() == "acc3") {
+        EXPECT_EQ(op_name, node.op());
+        EXPECT_EQ(2, node.input_size());
+        EXPECT_EQ("ConstantFolding/acc3_partial_split_2", node.input(0));
+        EXPECT_EQ("z", node.input(1));
+      }
+      if (node.name() == "acc4") {
+        EXPECT_EQ(op_name, node.op());
+        EXPECT_EQ(2, node.input_size());
+        EXPECT_EQ("ConstantFolding/acc4_partial_split_2", node.input(0));
+        EXPECT_EQ("y", node.input(1));
+      }
+      if (node.name() == "acc5") {
+        EXPECT_EQ(op_name, node.op());
+        EXPECT_EQ(2, node.input_size());
+        EXPECT_EQ("x", node.input(0));
+        EXPECT_EQ("ConstantFolding/acc5_partial_split_2", node.input(1));
+      }
+      if (node.name() == "acc6") {
+        EXPECT_EQ(op_name, node.op());
+        EXPECT_EQ(3, node.input_size());
+        EXPECT_EQ("x", node.input(0));
+        EXPECT_EQ("ConstantFolding/acc6_partial_split_2", node.input(1));
+        EXPECT_EQ("y", node.input(2));
+      }
+      if (StringPiece(node.name()).starts_with("ConstantFolding/")) {
+        EXPECT_EQ("Const", node.op());
+      }
+    }
+
+    std::vector<string> fetch = {"acc0"};
+    auto tensors_expected = EvaluateNodes(item.graph, fetch);
+    auto tensors = EvaluateNodes(output, fetch);
+    EXPECT_EQ(1, tensors_expected.size());
+    EXPECT_EQ(1, tensors.size());
+    test::ExpectTensorNear<float>(tensors_expected[0], tensors[0], 1e-6);
+  }
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-
-//  LocalWords:  NewRootScope
-- 
GitLab


From e929b16dc89f62a41bcaba57b98ddd221bf9bf68 Mon Sep 17 00:00:00 2001
From: Martin Wicke <wicke@google.com>
Date: Tue, 27 Feb 2018 10:25:17 -0800
Subject: [PATCH 346/575] Lint fixes.

PiperOrigin-RevId: 187194778
---
 tensorflow/python/util/tf_inspect.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/util/tf_inspect.py b/tensorflow/python/util/tf_inspect.py
index a7cead5555..4ab8a72a83 100644
--- a/tensorflow/python/util/tf_inspect.py
+++ b/tensorflow/python/util/tf_inspect.py
@@ -46,8 +46,10 @@ def getargspec(object):  # pylint: disable=redefined-builtin
 
 
 def getfullargspec(obj):  # pylint: disable=redefined-builtin
-  """TFDecorator-aware replacement for inspect.getfullargspec and fallback to
-  inspect.getargspec in Python 2.
+  """TFDecorator-aware replacement for `inspect.getfullargspec`/`getargspec`.
+
+  This wrapper uses `inspect.getfullargspec` if available and falls back to
+  `inspect.getargspec` in Python 2.
 
   Args:
     obj: A callable, possibly decorated.
-- 
GitLab


From e20be23387a6c1b72f3e34d03d4206c3211c921a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 10:27:28 -0800
Subject: [PATCH 347/575] Make block-based pruning more general, allowing it to
 operate on higher-dimensional arrays that can be squeezed to 2-dimensional.

PiperOrigin-RevId: 187195105
---
 tensorflow/contrib/model_pruning/README.md    |  2 +-
 .../contrib/model_pruning/python/pruning.py   | 21 ++++++++++++-------
 .../model_pruning/python/pruning_test.py      | 17 +++++++++++++++
 3 files changed, 31 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/model_pruning/README.md b/tensorflow/contrib/model_pruning/README.md
index d286750c25..52b659c69f 100644
--- a/tensorflow/contrib/model_pruning/README.md
+++ b/tensorflow/contrib/model_pruning/README.md
@@ -134,7 +134,7 @@ $ bazel-bin/$examples_dir/cifar10/cifar10_eval --run_once
 
 ### Block Sparsity
 
-For some hardware architectures, it may be beneficial to induce spatially correlated sparsity. To train models in which the weight tensors have block sparse structure, set *block_height* and *block_width* hyperparameters to the desired block configuration (2x2, 4x4, 4x1, 1x8, etc). Currently, block sparsity is supported for weight tensors with rank 2 only. The matrix is partitioned into non-overlapping blocks of size *[block_height, block_dim]* and the either the average or max absolute value in this block is taken as a proxy for the entire block (set by *block_pooling_function* hyperparameter).
+For some hardware architectures, it may be beneficial to induce spatially correlated sparsity. To train models in which the weight tensors have block sparse structure, set *block_height* and *block_width* hyperparameters to the desired block configuration (2x2, 4x4, 4x1, 1x8, etc). Currently, block sparsity is only supported for weight tensors which can be squeezed to rank 2. The matrix is partitioned into non-overlapping blocks of size *[block_height, block_dim]* and the either the average or max absolute value in this block is taken as a proxy for the entire block (set by *block_pooling_function* hyperparameter).
 The convolution layer tensors are always pruned used block dimensions of [1,1].
 
 ## References
diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py
index d16af9da19..86963be4b8 100644
--- a/tensorflow/contrib/model_pruning/python/pruning.py
+++ b/tensorflow/contrib/model_pruning/python/pruning.py
@@ -523,7 +523,8 @@ class Pruning(object):
     """Performs block-granular masking of the weights.
 
     Block pruning occurs only if the block_height or block_width is > 1 and
-    if the weight tensor has ndims = 2. Otherwise, elementwise pruning occurs.
+    if the weight tensor, when squeezed, has ndims = 2. Otherwise, elementwise
+    pruning occurs.
     Args:
       weights: The weight tensor that needs to be masked.
       threshold: The current threshold value. The function will compute a new
@@ -540,7 +541,8 @@ class Pruning(object):
     Raises:
       ValueError: if block pooling function is not AVG or MAX
     """
-    if weights.get_shape().ndims != 2 or self._block_dim == [1, 1]:
+    squeezed_weights = array_ops.squeeze(weights)
+    if squeezed_weights.get_shape().ndims != 2 or self._block_dim == [1, 1]:
       return self._update_mask(weights, threshold)
 
     if self._block_pooling_function not in ['AVG', 'MAX']:
@@ -549,9 +551,11 @@ class Pruning(object):
 
     with ops.name_scope(weights.op.name + '_pruning_ops'):
       abs_weights = math_ops.abs(
-          array_ops.reshape(
-              weights, [1, weights.get_shape()[0],
-                        weights.get_shape()[1], 1]))
+          array_ops.reshape(weights, [
+              1,
+              squeezed_weights.get_shape()[0],
+              squeezed_weights.get_shape()[1], 1
+          ]))
       pool_window = [self._block_dim[0], self._block_dim[1]]
       pooled_weights = nn_ops.pool(
           abs_weights,
@@ -572,9 +576,10 @@ class Pruning(object):
                                         array_ops.ones(self._block_dim))
       sliced_mask = array_ops.slice(
           updated_mask, [0, 0],
-          [weights.get_shape()[0],
-           weights.get_shape()[1]])
-    return smoothed_threshold, sliced_mask
+          [squeezed_weights.get_shape()[0],
+           squeezed_weights.get_shape()[1]])
+    return smoothed_threshold, array_ops.reshape(sliced_mask,
+                                                 array_ops.shape(weights))
 
   def _get_mask_assign_ops(self):
     # Make sure the assignment ops have not already been added to the list
diff --git a/tensorflow/contrib/model_pruning/python/pruning_test.py b/tensorflow/contrib/model_pruning/python/pruning_test.py
index 1767b4bb94..89e6571319 100644
--- a/tensorflow/contrib/model_pruning/python/pruning_test.py
+++ b/tensorflow/contrib/model_pruning/python/pruning_test.py
@@ -140,6 +140,23 @@ class PruningTest(test.TestCase):
          [0.0, -0.3, 0.0, -0.4]])
     expected_mask = [[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]]
 
+    self._blockMasking(param_list + ["block_pooling_function=MAX"], weights_max,
+                       expected_mask)
+    self._blockMasking(param_list + ["block_pooling_function=AVG"], weights_avg,
+                       expected_mask)
+
+  def testBlockMaskingWithHigherDimensions(self):
+    param_list = ["block_height=2", "block_width=2", "threshold_decay=0"]
+
+    # Weights as in testBlockMasking, but with one extra dimension.
+    weights_avg = constant_op.constant(
+        [[[0.1, 0.1, 0.2, 0.2], [0.1, 0.1, 0.2, 0.2], [0.3, 0.3, 0.4, 0.4],
+          [0.3, 0.3, 0.4, 0.4]]])
+    weights_max = constant_op.constant(
+        [[[0.1, 0.0, 0.2, 0.0], [0.0, -0.1, 0.0, -0.2], [0.3, 0.0, 0.4, 0.0],
+          [0.0, -0.3, 0.0, -0.4]]])
+    expected_mask = [[[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]]]
+
     self._blockMasking(param_list + ["block_pooling_function=MAX"], weights_max,
                        expected_mask)
     self._blockMasking(param_list + ["block_pooling_function=AVG"],
-- 
GitLab


From 38bda430f4d302c762bc2a0b74721d82b9c5cca4 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 27 Feb 2018 10:30:41 -0800
Subject: [PATCH 348/575] [TF CriticalSection] Bugfix: deref the Mutex before
 calling done_()

This avoids an error wherein the Mutex destructor is called from the same thread
as its threadpool, thus leading to a pthread 35 error.

If the mutex is dereferenced before done_ is called, then the destruction is
delayed until after done_() is called, and this happens in a different thread
from the threadpool.

PiperOrigin-RevId: 187195628
---
 tensorflow/core/kernels/mutex_ops.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/mutex_ops.cc b/tensorflow/core/kernels/mutex_ops.cc
index b8b1fc7679..b02a584d73 100644
--- a/tensorflow/core/kernels/mutex_ops.cc
+++ b/tensorflow/core/kernels/mutex_ops.cc
@@ -190,7 +190,6 @@ class MutexLockOp : public AsyncOpKernel {
                                          // End of bound arguments.
                                          const Status& s,
                                          Mutex::SharedLockReleaser&& lock) {
-                 core::ScopedUnref unref(mutex);
                  VLOG(2) << "Finished locking mutex " << mutex
                          << " with lock: " << lock.shared_lock.get()
                          << " status: " << s.ToString();
@@ -199,6 +198,7 @@ class MutexLockOp : public AsyncOpKernel {
                  } else {
                    c->SetStatus(s);
                  }
+                 mutex->Unref();
                  done_();
                },
                std::move(done), std::placeholders::_1, std::placeholders::_2));
-- 
GitLab


From 8ccc858d11f913e63cf3e35523bc3121684c2a82 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 10:49:41 -0800
Subject: [PATCH 349/575] Add 8bit Tanh support to tflite

Allow output datatypes for custom ops to be more than the output types used in the graph. When an op has multiple outputs, some of them not used will be optimized away. This results in a failure. The change in propagate_array_data_types.cc fix this.

PiperOrigin-RevId: 187198815
---
 .../contrib/lite/kernels/activations.cc       | 40 ++++++++++++++++++-
 .../contrib/lite/kernels/activations_test.cc  | 29 ++++++++++++++
 .../propagate_array_data_types.cc             |  7 +++-
 3 files changed, 72 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc
index 6acded3091..093761c43c 100644
--- a/tensorflow/contrib/lite/kernels/activations.cc
+++ b/tensorflow/contrib/lite/kernels/activations.cc
@@ -63,6 +63,33 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
                                TfLiteIntArrayCopy(input->dims));
 }
 
+TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) {
+  OpData* data = reinterpret_cast<OpData*>(node->user_data);
+
+  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
+  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
+  TfLiteTensor* input = GetInput(context, node, 0);
+  TfLiteTensor* output = GetOutput(context, node, 0);
+  TF_LITE_ENSURE_EQ(context, input->type, output->type);
+
+  if (input->type == kTfLiteUInt8) {
+    static constexpr int kInputIntegerBits = 4;
+
+    const double input_real_multiplier =
+        input->params.scale *
+        static_cast<double>(1 << (31 - kInputIntegerBits));
+
+    QuantizeMultiplierGreaterThanOne(input_real_multiplier,
+                                     &data->input_multiplier,
+                                     &data->input_left_shift);
+    data->input_range_radius =
+        CalculateInputRadius(kInputIntegerBits, data->input_left_shift);
+  }
+
+  return context->ResizeTensor(context, output,
+                               TfLiteIntArrayCopy(input->dims));
+}
+
 TfLiteStatus SigmoidPrepare(TfLiteContext* context, TfLiteNode* node) {
   OpData* data = reinterpret_cast<OpData*>(node->user_data);
 
@@ -180,6 +207,7 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
 }
 
 TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) {
+  OpData* data = reinterpret_cast<OpData*>(node->user_data);
   TfLiteTensor* input = GetInput(context, node, 0);
   TfLiteTensor* output = GetOutput(context, node, 0);
   switch (input->type) {
@@ -191,6 +219,14 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) {
       for (; in < in_end; in++, out++) *out = std::tanh(*in);
       return kTfLiteOk;
     } break;
+    case kTfLiteUInt8: {
+      optimized_ops::Tanh(GetTensorData<uint8_t>(input), GetTensorDims(input),
+                          input->params.zero_point, data->input_range_radius,
+                          data->input_multiplier, data->input_left_shift,
+                          GetTensorData<uint8_t>(output),
+                          GetTensorDims(output));
+      return kTfLiteOk;
+    } break;
     default:
       context->ReportError(context, "Only float32 supported currently.");
       return kTfLiteError;
@@ -376,8 +412,8 @@ TfLiteRegistration* Register_RELU6() {
 }
 
 TfLiteRegistration* Register_TANH() {
-  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
-                                 activations::GenericPrepare,
+  static TfLiteRegistration r = {activations::Init, activations::Free,
+                                 activations::TanhPrepare,
                                  activations::TanhEval};
   return &r;
 }
diff --git a/tensorflow/contrib/lite/kernels/activations_test.cc b/tensorflow/contrib/lite/kernels/activations_test.cc
index 302e52b96d..b9a96e3f79 100644
--- a/tensorflow/contrib/lite/kernels/activations_test.cc
+++ b/tensorflow/contrib/lite/kernels/activations_test.cc
@@ -52,6 +52,14 @@ class BaseActivationsOpModel : public SingleOpModel {
     BuildInterpreter({GetShape(input_)});
   }
 
+  BaseActivationsOpModel(BuiltinOperator type, const TensorData &input,
+                         const TensorData &output) {
+    input_ = AddInput(input);
+    output_ = AddOutput(output);
+    SetBuiltinOp(type, BuiltinOptions_NONE, 0);
+    BuildInterpreter({GetShape(input_)});
+  }
+
  protected:
   int input_;
   int output_;
@@ -143,6 +151,27 @@ TEST(FloatActivationsOpTest, Tanh) {
                              })));
 }
 
+TEST(QuantizedActivationsOpTest, Tanh) {
+  QuantizedActivationsOpModel m(
+      BuiltinOperator_TANH,
+      /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -8, 8},
+      /*output=*/{TensorType_UINT8, {1, 2, 4, 1}, -1, 1});
+  m.SetInput({
+      0, -6, 2, 4,   //
+      -4, -2, 8, 1,  //
+  });
+  m.Invoke();
+  EXPECT_THAT(m.GetDequantizedOutput(),
+              ElementsAreArray(ArrayFloatNear(
+                  {
+                      0.0, -0.999987, 0.964027, 0.999329,     //
+                      -0.996078, -0.96402, 0.99999, 0.76159,  //
+                  },
+                  4 * (1. / 256))));
+  EXPECT_THAT(m.GetOutput(),
+              ElementsAreArray({128, 0, 251, 255, 0, 5, 255, 226}));
+}
+
 TEST(FloatActivationsOpTest, Sigmoid) {
   FloatActivationsOpModel m(BuiltinOperator_LOGISTIC,
                             /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}});
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
index f0d107232b..bde947f78d 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_array_data_types.cc
@@ -97,10 +97,13 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) {
     SetDataTypeForAllOutputs(model, op, data_type);
   } else if (op->type == OperatorType::kTensorFlowUnsupported) {
     auto* unsupported_op = static_cast<TensorFlowUnsupportedOperator*>(op);
-    if (unsupported_op->output_data_types.size() != op->outputs.size()) {
+    // Some output tensors from the op could be eliminated by optimization.
+    // This can make unsupported_op->output_data_types have more elements than
+    // op->outputs.
+    if (unsupported_op->output_data_types.size() < op->outputs.size()) {
       return false;
     }
-    for (int i = 0; i < unsupported_op->output_data_types.size(); ++i) {
+    for (int i = 0; i < op->outputs.size(); ++i) {
       auto output = op->outputs[i];
       auto data_type = unsupported_op->output_data_types[i];
       model->GetArray(output).data_type = data_type;
-- 
GitLab


From 6a6661bbdce2172d27bf501e26baf09e8a658657 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Tue, 27 Feb 2018 11:01:10 -0800
Subject: [PATCH 350/575] Function optimization: added an optimizer to
 automatically inline functions in order to  enable Grappler to optimize the
 body of functions. Inlining also reduces the overhead of  evaluating
 function.

PiperOrigin-RevId: 187200883
---
 .../core/grappler/grappler_item_builder.cc    | 108 --------
 .../core/grappler/grappler_item_builder.h     |   7 -
 .../grappler/grappler_item_builder_test.cc    | 199 ---------------
 tensorflow/core/grappler/optimizers/BUILD     |  38 +++
 .../grappler/optimizers/function_optimizer.cc | 148 +++++++++++
 .../grappler/optimizers/function_optimizer.h  |  43 ++++
 .../optimizers/function_optimizer_test.cc     |  98 ++++++++
 tensorflow/core/grappler/utils/BUILD          |  32 +++
 tensorflow/core/grappler/utils/functions.cc   | 140 +++++++++++
 tensorflow/core/grappler/utils/functions.h    |  39 +++
 .../core/grappler/utils/functions_test.cc     | 232 ++++++++++++++++++
 .../core/grappler/utils/grappler_test.cc      |   4 +-
 12 files changed, 772 insertions(+), 316 deletions(-)
 create mode 100644 tensorflow/core/grappler/optimizers/function_optimizer.cc
 create mode 100644 tensorflow/core/grappler/optimizers/function_optimizer.h
 create mode 100644 tensorflow/core/grappler/optimizers/function_optimizer_test.cc
 create mode 100644 tensorflow/core/grappler/utils/functions.cc
 create mode 100644 tensorflow/core/grappler/utils/functions.h
 create mode 100644 tensorflow/core/grappler/utils/functions_test.cc

diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 5ac52eefe1..606807b9e9 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -518,113 +518,5 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
   return new_item;
 }
 
-std::unique_ptr<GrapplerItem> GrapplerItemFromFunctionDef(
-    const FunctionDef& func,
-    const std::unordered_map<string, AttrValue>& func_attr,
-    const FunctionDefLibrary& library) {
-  if (func.signature().name().empty()) {
-    LOG(ERROR) << "function name must be specified.";
-    return nullptr;
-  }
-  std::unique_ptr<GrapplerItem> new_item(new GrapplerItem());
-  new_item->id = func.signature().name();
-
-  std::unordered_map<string, string> port_map;
-
-  // Add the function inputs as placeholder
-  for (const auto& inp : func.signature().input_arg()) {
-    NodeDef* ph = new_item->graph.add_node();
-    ph->set_name(inp.name());
-    ph->set_op("Placeholder");
-    if (inp.type() != DT_INVALID) {
-      (*ph->mutable_attr())["T"].set_type(inp.type());
-    } else {
-      auto it = func_attr.find(inp.type_attr());
-      if (it == func_attr.end()) {
-        LOG(ERROR) << "Unknown type attribute " << inp.type_attr()
-                   << " for function input " << inp.name();
-        return nullptr;
-      } else {
-        (*ph->mutable_attr())["T"] = it->second;
-      }
-    }
-    port_map[inp.name()] = inp.name();
-  }
-
-  // Add the function body to the graph.
-  FunctionLibraryDefinition func_def(OpRegistry::Global(), library);
-
-  for (const NodeDef& node : func.node_def()) {
-    NodeDef* new_node = new_item->graph.add_node();
-    *new_node = node;
-    // Replace the placeholder attribute values with the specified value.
-    for (auto& attr : *new_node->mutable_attr()) {
-      const string& ph_name = attr.second.placeholder();
-      auto it = func_attr.find(ph_name);
-      if (it != func_attr.end()) {
-        attr.second = it->second;
-      }
-    }
-
-    // Functions use a custom format to encode connectivity. Map these custom
-    // strings to regular ones.
-    const OpRegistrationData* registration;
-    Status status = func_def.LookUp(node.op(), &registration);
-    if (!status.ok()) {
-      LOG(ERROR) << "Op " << node.op() << " not registered: " << status;
-      return nullptr;
-    }
-
-    tensorflow::NameRangeMap inputs;
-    tensorflow::NameRangeMap outputs;
-    status = tensorflow::NameRangesForNode(node, registration->op_def, &inputs,
-                                           &outputs);
-    if (!status.ok()) {
-      LOG(ERROR) << "Op " << node.op() << " invalid: " << status;
-      return nullptr;
-    }
-    for (const auto& name_range : outputs) {
-      string port_prefix =
-          strings::StrCat(node.name(), ":", name_range.first, ":");
-      int index_start = name_range.second.first;
-      int index_end = name_range.second.second;
-      for (int i = index_start; i < index_end; ++i) {
-        string port_id = strings::StrCat(port_prefix, i - index_start);
-        string port_name = strings::StrCat(node.name(), ":", i);
-        port_map[port_id] = port_name;
-      }
-    }
-  }
-
-  for (auto& node : *new_item->graph.mutable_node()) {
-    // Rewrite the inputs to use the normal naming convention.
-    for (int i = 0; i < node.input_size(); ++i) {
-      const string& input = node.input(i);
-      if (IsControlInput(input)) {
-        // No need to remap control dependencies.
-        continue;
-      } else {
-        auto it = port_map.find(input);
-        if (it == port_map.end()) {
-          LOG(ERROR) << "Unknown input: " << input;
-          return nullptr;
-        }
-        node.set_input(i, it->second);
-      }
-    }
-  }
-
-  // Add the function outputs to the list of fetch nodes.
-  for (const auto& out : func.signature().output_arg()) {
-    new_item->fetch.emplace_back(out.name());
-  }
-  // Add the function inputs to the list of feeds.
-  for (const auto& inp : func.signature().input_arg()) {
-    new_item->feed.emplace_back(inp.name(), Tensor());
-  }
-
-  return new_item;
-}
-
 }  // end namespace grappler
 }  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/grappler_item_builder.h b/tensorflow/core/grappler/grappler_item_builder.h
index e892a3f556..c877d91163 100644
--- a/tensorflow/core/grappler/grappler_item_builder.h
+++ b/tensorflow/core/grappler/grappler_item_builder.h
@@ -58,13 +58,6 @@ struct ItemConfig {
 std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
     const string& id, const MetaGraphDef& meta_graph, const ItemConfig& cfg);
 
-// Factory method for creating a GrapplerItem from a FunctionDef.
-// Returns nullptr if the given function def cannot be converted.
-std::unique_ptr<GrapplerItem> GrapplerItemFromFunctionDef(
-    const FunctionDef& func,
-    const std::unordered_map<string, AttrValue>& func_attr,
-    const FunctionDefLibrary& library);
-
 }  // end namespace grappler
 }  // end namespace tensorflow
 
diff --git a/tensorflow/core/grappler/grappler_item_builder_test.cc b/tensorflow/core/grappler/grappler_item_builder_test.cc
index 68437b6041..ef95992af7 100644
--- a/tensorflow/core/grappler/grappler_item_builder_test.cc
+++ b/tensorflow/core/grappler/grappler_item_builder_test.cc
@@ -280,205 +280,6 @@ TEST_F(GrapplerItemBuilderTest, GraphWithFunctions) {
   ASSERT_TRUE(item != nullptr);
 }
 
-TEST_F(GrapplerItemBuilderTest, FromSimpleFunctionDef) {
-  const Tensor kTwo = test::AsScalar<int64>(2);
-  FunctionDef func = FunctionDefHelper::Define(
-      // Name
-      "XTimesTwo",
-      // Args
-      {"x: T"},
-      // Return values
-      {"y: T"},
-      // Attr def
-      {"T: {float, double, int32, int64}"},
-      // Nodes
-      {
-          {{"two"}, "Const", {}, {{"value", kTwo}, {"dtype", DT_INT64}}},
-          {{"scale"}, "Cast", {"two"}, {{"SrcT", DT_INT64}, {"DstT", "$T"}}},
-          {{"y"}, "Mul", {"x", "scale"}, {{"T", "$T"}}},
-      });
-
-  std::unordered_map<string, AttrValue> func_attr;
-  func_attr["T"].set_type(DT_FLOAT);
-  FunctionDefLibrary library;
-  std::unique_ptr<GrapplerItem> item =
-      GrapplerItemFromFunctionDef(func, func_attr, library);
-  CHECK(item);
-  EXPECT_EQ("XTimesTwo", item->id);
-  EXPECT_EQ(4, item->graph.node_size());
-  EXPECT_EQ(std::vector<string>({"y"}), item->fetch);
-  EXPECT_EQ(1, item->feed.size());
-  EXPECT_EQ("x", item->feed[0].first);
-
-  for (const NodeDef &node : item->graph.node()) {
-    if (node.name() == "x") {
-      EXPECT_EQ("Placeholder", node.op());
-      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
-      EXPECT_EQ(0, node.input_size());
-    } else if (node.name() == "two") {
-      EXPECT_EQ("Const", node.op());
-      EXPECT_EQ(0, node.input_size());
-    } else if (node.name() == "scale") {
-      EXPECT_EQ("Cast", node.op());
-      EXPECT_EQ(DT_FLOAT, node.attr().at("DstT").type());
-      EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("two:0", node.input(0));
-    } else if (node.name() == "y") {
-      EXPECT_EQ("Mul", node.op());
-      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
-      EXPECT_EQ(2, node.input_size());
-      EXPECT_EQ("x", node.input(0));
-      EXPECT_EQ("scale:0", node.input(1));
-    }
-  }
-}
-
-TEST_F(GrapplerItemBuilderTest, FromFunctionDefWithMultiOutputNodes) {
-  // Gradient graph for the Subtract operation
-  std::vector<FunctionDefHelper::Node> nodes = {
-      {{"sx"}, "Shape", {"x"}},
-      {{"sy"}, "Shape", {"y"}},
-      {{"gx"}, "Identity", {"dz"}},
-      {{"gy"}, "Neg", {"dz"}},
-      {{"rx", "ry"}, "BroadcastGradientArgs", {"sx", "sy"}},
-      {{"sum_gx"}, "Sum", {"gx", "rx"}},
-      {{"dx"}, "Reshape", {"sum_gx", "sx"}},
-      {{"sum_gy"}, "Sum", {"gy", "ry"}},
-      {{"dy"}, "Reshape", {"sum_gy", "sy"}},
-  };
-
-  for (auto &n : nodes) {
-    // "BroadcastGradientArgs" doesn't need any attrs.
-    if (n.attr.empty() && n.op != "BroadcastGradientArgs") {
-      n.attr = {{"T", "$T"}};
-    }
-  }
-  FunctionDef func = FunctionDefHelper::Define(
-      // Name
-      "SubGrad",
-      // Arg defs
-      {"x: T", "y: T", "dz: T"},
-      // Ret val defs
-      {"dx: T", "dy: T"},
-      // Attr defs
-      {{"T: {half, float, double}"}},
-      // Nodes
-      nodes);
-
-  std::unordered_map<string, AttrValue> func_attr;
-  func_attr["T"].set_type(DT_FLOAT);
-  FunctionDefLibrary library;
-  std::unique_ptr<GrapplerItem> item =
-      GrapplerItemFromFunctionDef(func, func_attr, library);
-  CHECK(item);
-  EXPECT_EQ("SubGrad", item->id);
-  EXPECT_EQ(12, item->graph.node_size());
-  EXPECT_EQ(std::vector<string>({"dx", "dy"}), item->fetch);
-  EXPECT_EQ(3, item->feed.size());
-  EXPECT_EQ("x", item->feed[0].first);
-  EXPECT_EQ("y", item->feed[1].first);
-  EXPECT_EQ("dz", item->feed[2].first);
-
-  for (const NodeDef &node : item->graph.node()) {
-    if (node.name() == "x" || node.name() == "y" || node.name() == "dz") {
-      EXPECT_EQ("Placeholder", node.op());
-      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
-      EXPECT_EQ(0, node.input_size());
-    } else if (node.name() == "rx") {
-      EXPECT_EQ("BroadcastGradientArgs", node.op());
-      EXPECT_EQ(2, node.input_size());
-      EXPECT_EQ("sx:0", node.input(0));
-      EXPECT_EQ("sy:0", node.input(1));
-    } else if (node.name() == "sum_gx") {
-      EXPECT_EQ("Sum", node.op());
-      EXPECT_EQ(2, node.input_size());
-      EXPECT_EQ("gx:0", node.input(0));
-      EXPECT_EQ("rx:0", node.input(1));
-    } else if (node.name() == "sum_gy") {
-      EXPECT_EQ("Sum", node.op());
-      EXPECT_EQ(2, node.input_size());
-      EXPECT_EQ("gy:0", node.input(0));
-      EXPECT_EQ("rx:1", node.input(1));
-    }
-  }
-}
-
-TEST_F(GrapplerItemBuilderTest, FromFunctionDefWithNestedFuncs) {
-  FunctionDefLibrary library;
-  *library.add_function() = FunctionDefHelper::Define(
-      // Name
-      "Swap",
-      // Args
-      {"i0: T", "i1: T"},
-      // Return values
-      {"o0: T", "o1: T"},
-      // Attr def
-      {"T: {float, double}"},
-      // Nodes
-      {{{"o0"}, "Identity", {"i1"}, {{"T", "$T"}}},
-       {{"o1"}, "Identity", {"i0"}, {{"T", "$T"}}}});
-
-  FunctionDef func = FunctionDefHelper::Create(
-      // Name
-      "ManySwapsFirst",
-      // Args
-      {"x: float", "y: float"},
-      // Return values
-      {"o: float"},
-      // attr def
-      {},
-      // Nodes
-      // o = x*x + y*y.  Furthermore, The 1st swap depends on x2, and
-      // y2 depends on the 2nd swap.  The 2nd swap has data dependency
-      // on the 1st swap.
-      {{{"a0"}, "Swap", {"x", "y"}, {{"T", DT_FLOAT}}, {"x2"}},
-       {{"a1"}, "Swap", {"a0:o0:0", "a0:o1:0"}, {{"T", DT_FLOAT}}},
-       {{"x2"}, "Mul", {"x", "x"}, {{"T", DT_FLOAT}}},
-       {{"y2"}, "Mul", {"y", "y"}, {{"T", DT_FLOAT}}, {"a1"}},
-       {{"o"}, "Add", {"x2:z:0", "y2:z:0"}, {{"T", DT_FLOAT}}}},
-      {{"o", "o:z:0"}});
-
-  std::unordered_map<string, AttrValue> func_attr;
-  func_attr["T"].set_type(DT_FLOAT);
-  std::unique_ptr<GrapplerItem> item =
-      GrapplerItemFromFunctionDef(func, func_attr, library);
-
-  for (const NodeDef &node : item->graph.node()) {
-    if (node.name() == "x" || node.name() == "y") {
-      EXPECT_EQ("Placeholder", node.op());
-      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
-      EXPECT_EQ(0, node.input_size());
-    } else if (node.name() == "a0") {
-      EXPECT_EQ("Swap", node.op());
-      EXPECT_EQ(3, node.input_size());
-      EXPECT_EQ("x", node.input(0));
-      EXPECT_EQ("y", node.input(1));
-      EXPECT_EQ("^x2", node.input(2));
-    } else if (node.name() == "a1") {
-      EXPECT_EQ("Swap", node.op());
-      EXPECT_EQ(2, node.input_size());
-      EXPECT_EQ("a0:0", node.input(0));
-      EXPECT_EQ("a0:1", node.input(1));
-    } else if (node.name() == "x2") {
-      EXPECT_EQ("Mul", node.op());
-      EXPECT_EQ(2, node.input_size());
-      EXPECT_EQ("x", node.input(0));
-      EXPECT_EQ("x", node.input(1));
-    } else if (node.name() == "y2") {
-      EXPECT_EQ("Mul", node.op());
-      EXPECT_EQ(3, node.input_size());
-      EXPECT_EQ("y", node.input(0));
-      EXPECT_EQ("y", node.input(1));
-      EXPECT_EQ("^a1", node.input(2));
-    } else if (node.name() == "o") {
-      EXPECT_EQ("Add", node.op());
-      EXPECT_EQ(2, node.input_size());
-      EXPECT_EQ("x2:0", node.input(0));
-      EXPECT_EQ("y2:0", node.input(1));
-    }
-  }
-}
-
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index a52d1c8df2..bd41854c41 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -132,6 +132,44 @@ tf_cc_test(
     ],
 )
 
+cc_library(
+    name = "function_optimizer",
+    srcs = ["function_optimizer.cc"],
+    hdrs = [
+        "function_optimizer.h",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":graph_optimizer",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:op_types",
+        "//tensorflow/core/grappler/utils:functions",
+    ],
+)
+
+tf_cc_test(
+    name = "function_optimizer_test",
+    srcs = ["function_optimizer_test.cc"],
+    deps = [
+        ":function_optimizer",
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:cc_ops_internal",
+        "//tensorflow/core:all_kernels",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:direct_session",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:utils",
+        "//tensorflow/core/grappler/utils:grappler_test",
+    ],
+)
+
 cc_library(
     name = "graph_rewriter",
     srcs = ["graph_rewriter.cc"],
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
new file mode 100644
index 0000000000..efc4f2f4bd
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -0,0 +1,148 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/optimizers/function_optimizer.h"
+#include <unordered_map>
+#include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/framework/versions.pb.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/grappler/utils/functions.h"
+
+namespace tensorflow {
+namespace grappler {
+
+Status InlineFunction(const NodeDef& node, const FunctionDef& func,
+                      GraphDef* graph) {
+  const std::unordered_map<string, AttrValue> attr(node.attr().begin(),
+                                                   node.attr().end());
+  FunctionDefLibrary library;
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromFunctionDef(func, attr, library);
+
+  std::unordered_map<string, int> input_nodes;
+  for (int i = 0; i < func.signature().input_arg_size(); ++i) {
+    const OpDef::ArgDef& arg = func.signature().input_arg(i);
+    input_nodes[arg.name()] = i;
+  }
+
+  // Add an IdentityN op to hook the function inputs to: this ensures that
+  // they're all evaluated before the evaluation of the function body starts.
+  NodeDef* func_inputs = graph->add_node();
+  func_inputs->set_name(strings::StrCat(node.name(), "/", "inlined_inputs"));
+  func_inputs->set_op("IdentityN");
+  *func_inputs->mutable_input() = node.input();
+  AttrValue::ListValue* type_list =
+      (*func_inputs->mutable_attr())["T"].mutable_list();
+  for (const OpDef::ArgDef& arg : func.signature().input_arg()) {
+    auto it = attr.find(arg.type_attr());
+    if (it == attr.end()) {
+      return errors::InvalidArgument("Invalid input argument ", arg.name(),
+                                     " for function ", node.op(),
+                                     " instantiated by ", node.name());
+    }
+    type_list->add_type(it->second.type());
+  }
+
+  for (NodeDef& func_body_node : *item->graph.mutable_node()) {
+    if (input_nodes.find(func_body_node.name()) != input_nodes.end()) {
+      // Turn input placeholders into identity nodes
+      if (IsPlaceholder(func_body_node)) {
+        func_body_node.set_op("Identity");
+      }
+      CHECK_EQ(0, func_body_node.input_size());
+      int input_id = input_nodes[func_body_node.name()];
+      func_body_node.add_input(
+          strings::StrCat(func_inputs->name(), ":", input_id));
+    } else {
+      // Update the input names.
+      for (string& input : *func_body_node.mutable_input()) {
+        input = strings::StrCat(node.name(), "/", input);
+      }
+    }
+
+    // Add the node name as a prefix to avoid collisions after inlining
+    func_body_node.set_name(
+        strings::StrCat(node.name(), "/", func_body_node.name()));
+
+    // Move the node to the main graph
+    graph->add_node()->Swap(&func_body_node);
+  }
+
+  // Add an IdentityN op to hook the function outputs to: this ensures that the
+  // function body is fully evaluated before its fanout gets scheduled.
+  NodeDef* func_outputs = graph->add_node();
+  func_outputs->set_name(node.name());
+  func_outputs->set_op("IdentityN");
+  type_list = (*func_outputs->mutable_attr())["T"].mutable_list();
+  for (const OpDef::ArgDef& arg : func.signature().output_arg()) {
+    auto it = attr.find(arg.type_attr());
+    if (it == attr.end()) {
+      return errors::InvalidArgument("Invalid output argument ", arg.name(),
+                                     " for function ", node.op(),
+                                     " instantiated by ", node.name());
+    }
+    type_list->add_type(it->second.type());
+    func_outputs->add_input(strings::StrCat(node.name(), "/", arg.name()));
+  }
+
+  return Status::OK();
+}
+
+Status FunctionOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
+                                   GraphDef* optimized_graph) {
+  std::unordered_map<string, const FunctionDef*> functions;
+  for (const FunctionDef& func : item.graph.library().function()) {
+    if (func.attr().count("_noinline") == 0) {
+      functions[func.signature().name()] = &func;
+    }
+  }
+
+  // Nothing to do.
+  if (functions.empty()) {
+    *optimized_graph = item.graph;
+    return Status::OK();
+  }
+
+  // Inline functions when possible.
+  for (const NodeDef& node : item.graph.node()) {
+    auto it = functions.find(node.op());
+    if (it == functions.end()) {
+      *optimized_graph->add_node() = node;
+    } else {
+      TF_RETURN_IF_ERROR(InlineFunction(node, *it->second, optimized_graph));
+    }
+  }
+
+  // TODO(bsteiner): specialize the implementation of functions that can't be
+  // inlined based on the context in which they're instantiated.
+
+  // TODO(bsteiner): trim the library to remove unused function definitions
+  *optimized_graph->mutable_library() = item.graph.library();
+  *optimized_graph->mutable_versions() = item.graph.versions();
+
+  return Status::OK();
+}
+
+void FunctionOptimizer::Feedback(Cluster* cluster, const GrapplerItem& item,
+                                 const GraphDef& optimized_graph,
+                                 double result) {
+  // Nothing to do for FunctionOptimizer.
+}
+
+}  // end namespace grappler
+}  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.h b/tensorflow/core/grappler/optimizers/function_optimizer.h
new file mode 100644
index 0000000000..5c80226e9d
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.h
@@ -0,0 +1,43 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_
+#define TENSORFLOW_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_
+
+#include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
+
+namespace tensorflow {
+namespace grappler {
+
+// Remap TensorFlow subgraphs onto alternative operations or collection of
+// operations to make the overall graph more efficient.
+class FunctionOptimizer : public GraphOptimizer {
+ public:
+  FunctionOptimizer() {}
+  ~FunctionOptimizer() override {}
+
+  string name() const override { return "function_optimizer"; };
+
+  Status Optimize(Cluster* cluster, const GrapplerItem& item,
+                  GraphDef* optimized_graph) override;
+
+  void Feedback(Cluster* cluster, const GrapplerItem& item,
+                const GraphDef& optimized_graph, double result) override;
+};
+
+}  // end namespace grappler
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_GRAPPLER_OPTIMIZERS_FUNCTION_OPTIMIZER_H_
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
new file mode 100644
index 0000000000..b8e05a5296
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
@@ -0,0 +1,98 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/optimizers/function_optimizer.h"
+#include "tensorflow/core/framework/function_testlib.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+#include "tensorflow/core/grappler/utils/grappler_test.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+
+namespace tensorflow {
+namespace grappler {
+namespace {
+
+class FunctionOptimizerTest : public GrapplerTest {};
+
+TEST_F(FunctionOptimizerTest, SimpleFunction) {
+  // Build a graph to compute y = XTimesTwo(x)
+  GrapplerItem item;
+  constexpr char device[] = "/device:CPU:0";
+  item.graph = test::function::GDef(
+      {test::function::NDef("x", "Placeholder", {}, {{"dtype", DT_FLOAT}},
+                            device),
+       test::function::NDef("y", "XTimesTwo", {"x"}, {{"T", DT_FLOAT}}, device),
+       test::function::NDef("z", "Identity", {"y"}, {{"T", DT_FLOAT}}, device)},
+      // FunctionLib
+      {
+          test::function::XTimesTwo(),
+      });
+
+  FunctionOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  int count = 0;
+  for (const NodeDef& node : output.node()) {
+    if (node.name() == "y/inlined_inputs") {
+      count++;
+      EXPECT_EQ("IdentityN", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("x", node.input(0));
+    } else if (node.name() == "y/x") {
+      count++;
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y/inlined_inputs:0", node.input(0));
+    } else if (node.name() == "y/two") {
+      count++;
+      EXPECT_EQ("Const", node.op());
+    } else if (node.name() == "y/scale") {
+      count++;
+      EXPECT_EQ("Cast", node.op());
+    } else if (node.name() == "y/y") {
+      count++;
+      EXPECT_EQ("Mul", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("y/x", node.input(0));
+      EXPECT_EQ("y/scale:0", node.input(1));
+    } else if (node.name() == "y") {
+      count++;
+      EXPECT_EQ("IdentityN", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y/y", node.input(0));
+    } else if (node.name() == "z") {
+      count++;
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y", node.input(0));
+    }
+  }
+  EXPECT_EQ(7, count);
+
+  item.fetch = {"z"};
+  Tensor pi(DT_FLOAT, {});
+  pi.flat<float>()(0) = 3.14f;
+  item.feed.emplace_back("x", pi);
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
+}
+
+}  // namespace
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/utils/BUILD b/tensorflow/core/grappler/utils/BUILD
index 5d32609434..fc05713494 100644
--- a/tensorflow/core/grappler/utils/BUILD
+++ b/tensorflow/core/grappler/utils/BUILD
@@ -146,3 +146,35 @@ cc_library(
         "//tensorflow/core/grappler:utils",
     ],
 )
+
+cc_library(
+    name = "functions",
+    srcs = [
+        "functions.cc",
+    ],
+    hdrs = ["functions.h"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/core:core_cpu_internal",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:framework_internal",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core/grappler:grappler_item",
+        "//tensorflow/core/grappler:utils",
+    ],
+)
+
+tf_cc_test(
+    name = "functions_test",
+    srcs = ["functions_test.cc"],
+    deps = [
+        ":functions",
+        "//tensorflow/cc:cc_ops",
+        "//tensorflow/core:all_kernels",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+    ],
+)
diff --git a/tensorflow/core/grappler/utils/functions.cc b/tensorflow/core/grappler/utils/functions.cc
new file mode 100644
index 0000000000..37b00e0a30
--- /dev/null
+++ b/tensorflow/core/grappler/utils/functions.cc
@@ -0,0 +1,140 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/grappler/utils/functions.h"
+
+#include <unordered_map>
+
+#include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/function.h"
+#include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/framework/graph_def_util.h"
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/types.pb.h"
+#include "tensorflow/core/grappler/utils.h"
+
+namespace tensorflow {
+namespace grappler {
+
+std::unique_ptr<GrapplerItem> GrapplerItemFromFunctionDef(
+    const FunctionDef& func,
+    const std::unordered_map<string, AttrValue>& func_attr,
+    const FunctionDefLibrary& library) {
+  if (func.signature().name().empty()) {
+    LOG(ERROR) << "function name must be specified.";
+    return nullptr;
+  }
+  std::unique_ptr<GrapplerItem> new_item(new GrapplerItem());
+  new_item->id = func.signature().name();
+
+  std::unordered_map<string, string> port_map;
+
+  // Add the function inputs as placeholder
+  for (const auto& inp : func.signature().input_arg()) {
+    NodeDef* ph = new_item->graph.add_node();
+    ph->set_name(inp.name());
+    ph->set_op("Placeholder");
+    if (inp.type() != DT_INVALID) {
+      (*ph->mutable_attr())["T"].set_type(inp.type());
+    } else {
+      auto it = func_attr.find(inp.type_attr());
+      if (it == func_attr.end()) {
+        LOG(ERROR) << "Unknown type attribute " << inp.type_attr()
+                   << " for function input " << inp.name();
+        return nullptr;
+      } else {
+        (*ph->mutable_attr())["T"] = it->second;
+      }
+    }
+    port_map[inp.name()] = inp.name();
+  }
+
+  // Add the function body to the graph.
+  FunctionLibraryDefinition func_def(OpRegistry::Global(), library);
+
+  for (const NodeDef& node : func.node_def()) {
+    NodeDef* new_node = new_item->graph.add_node();
+    *new_node = node;
+    // Replace the placeholder attribute values with the specified value.
+    for (auto& attr : *new_node->mutable_attr()) {
+      const string& ph_name = attr.second.placeholder();
+      auto it = func_attr.find(ph_name);
+      if (it != func_attr.end()) {
+        attr.second = it->second;
+      }
+    }
+
+    // Functions use a custom format to encode connectivity. Map these custom
+    // strings to regular ones.
+    const OpRegistrationData* registration;
+    Status status = func_def.LookUp(node.op(), &registration);
+    if (!status.ok()) {
+      LOG(ERROR) << "Op " << node.op() << " not registered: " << status;
+      return nullptr;
+    }
+
+    tensorflow::NameRangeMap inputs;
+    tensorflow::NameRangeMap outputs;
+    status = tensorflow::NameRangesForNode(node, registration->op_def, &inputs,
+                                           &outputs);
+    if (!status.ok()) {
+      LOG(ERROR) << "Op " << node.op() << " invalid: " << status;
+      return nullptr;
+    }
+    for (const auto& name_range : outputs) {
+      string port_prefix =
+          strings::StrCat(node.name(), ":", name_range.first, ":");
+      int index_start = name_range.second.first;
+      int index_end = name_range.second.second;
+      for (int i = index_start; i < index_end; ++i) {
+        string port_id = strings::StrCat(port_prefix, i - index_start);
+        string port_name = strings::StrCat(node.name(), ":", i);
+        port_map[port_id] = port_name;
+      }
+    }
+  }
+
+  for (auto& node : *new_item->graph.mutable_node()) {
+    // Rewrite the inputs to use the normal naming convention.
+    for (int i = 0; i < node.input_size(); ++i) {
+      const string& input = node.input(i);
+      if (IsControlInput(input)) {
+        // No need to remap control dependencies.
+        continue;
+      } else {
+        auto it = port_map.find(input);
+        if (it == port_map.end()) {
+          LOG(ERROR) << "Unknown input: " << input;
+          return nullptr;
+        }
+        node.set_input(i, it->second);
+      }
+    }
+  }
+
+  // Add the function outputs to the list of fetch nodes.
+  for (const auto& out : func.signature().output_arg()) {
+    new_item->fetch.emplace_back(out.name());
+  }
+  // Add the function inputs to the list of feeds.
+  for (const auto& inp : func.signature().input_arg()) {
+    new_item->feed.emplace_back(inp.name(), Tensor());
+  }
+
+  return new_item;
+}
+
+}  // end namespace grappler
+}  // end namespace tensorflow
diff --git a/tensorflow/core/grappler/utils/functions.h b/tensorflow/core/grappler/utils/functions.h
new file mode 100644
index 0000000000..8f9b7d848a
--- /dev/null
+++ b/tensorflow/core/grappler/utils/functions.h
@@ -0,0 +1,39 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_GRAPPLER_UTILS_FUNCTIONS_H_
+#define TENSORFLOW_GRAPPLER_UTILS_FUNCTIONS_H_
+
+#include <memory>
+#include <string>
+#include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/function.pb.h"
+#include "tensorflow/core/grappler/grappler_item.h"
+
+namespace tensorflow {
+
+namespace grappler {
+
+// Factory method for creating a GrapplerItem from a FunctionDef.
+// Returns nullptr if the given function def cannot be converted.
+std::unique_ptr<GrapplerItem> GrapplerItemFromFunctionDef(
+    const FunctionDef& func,
+    const std::unordered_map<string, AttrValue>& func_attr,
+    const FunctionDefLibrary& library);
+
+}  // end namespace grappler
+}  // end namespace tensorflow
+
+#endif  // TENSORFLOW_GRAPPLER_UTILS_FUNCTIONS_H_
diff --git a/tensorflow/core/grappler/utils/functions_test.cc b/tensorflow/core/grappler/utils/functions_test.cc
new file mode 100644
index 0000000000..25ccb50084
--- /dev/null
+++ b/tensorflow/core/grappler/utils/functions_test.cc
@@ -0,0 +1,232 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/utils/functions.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/framework/function_testlib.h"
+#include "tensorflow/core/framework/node_def.pb.h"
+#include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/protobuf/meta_graph.pb.h"
+
+namespace tensorflow {
+namespace grappler {
+namespace {
+
+class FunctionsTest : public ::testing::Test {};
+
+TEST_F(FunctionsTest, FromSimpleFunctionDef) {
+  const Tensor kTwo = test::AsScalar<int64>(2);
+  FunctionDef func = FunctionDefHelper::Define(
+      // Name
+      "XTimesTwo",
+      // Args
+      {"x: T"},
+      // Return values
+      {"y: T"},
+      // Attr def
+      {"T: {float, double, int32, int64}"},
+      // Nodes
+      {
+          {{"two"}, "Const", {}, {{"value", kTwo}, {"dtype", DT_INT64}}},
+          {{"scale"}, "Cast", {"two"}, {{"SrcT", DT_INT64}, {"DstT", "$T"}}},
+          {{"y"}, "Mul", {"x", "scale"}, {{"T", "$T"}}},
+      });
+
+  std::unordered_map<string, AttrValue> func_attr;
+  func_attr["T"].set_type(DT_FLOAT);
+  FunctionDefLibrary library;
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromFunctionDef(func, func_attr, library);
+  CHECK(item);
+  EXPECT_EQ("XTimesTwo", item->id);
+  EXPECT_EQ(4, item->graph.node_size());
+  EXPECT_EQ(std::vector<string>({"y"}), item->fetch);
+  EXPECT_EQ(1, item->feed.size());
+  EXPECT_EQ("x", item->feed[0].first);
+
+  for (const NodeDef &node : item->graph.node()) {
+    if (node.name() == "x") {
+      EXPECT_EQ("Placeholder", node.op());
+      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
+      EXPECT_EQ(0, node.input_size());
+    } else if (node.name() == "two") {
+      EXPECT_EQ("Const", node.op());
+      EXPECT_EQ(0, node.input_size());
+    } else if (node.name() == "scale") {
+      EXPECT_EQ("Cast", node.op());
+      EXPECT_EQ(DT_FLOAT, node.attr().at("DstT").type());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("two:0", node.input(0));
+    } else if (node.name() == "y") {
+      EXPECT_EQ("Mul", node.op());
+      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("x", node.input(0));
+      EXPECT_EQ("scale:0", node.input(1));
+    }
+  }
+}
+
+TEST_F(FunctionsTest, FromFunctionDefWithMultiOutputNodes) {
+  // Gradient graph for the Subtract operation
+  std::vector<FunctionDefHelper::Node> nodes = {
+      {{"sx"}, "Shape", {"x"}},
+      {{"sy"}, "Shape", {"y"}},
+      {{"gx"}, "Identity", {"dz"}},
+      {{"gy"}, "Neg", {"dz"}},
+      {{"rx", "ry"}, "BroadcastGradientArgs", {"sx", "sy"}},
+      {{"sum_gx"}, "Sum", {"gx", "rx"}},
+      {{"dx"}, "Reshape", {"sum_gx", "sx"}},
+      {{"sum_gy"}, "Sum", {"gy", "ry"}},
+      {{"dy"}, "Reshape", {"sum_gy", "sy"}},
+  };
+
+  for (auto &n : nodes) {
+    // "BroadcastGradientArgs" doesn't need any attrs.
+    if (n.attr.empty() && n.op != "BroadcastGradientArgs") {
+      n.attr = {{"T", "$T"}};
+    }
+  }
+  FunctionDef func = FunctionDefHelper::Define(
+      // Name
+      "SubGrad",
+      // Arg defs
+      {"x: T", "y: T", "dz: T"},
+      // Ret val defs
+      {"dx: T", "dy: T"},
+      // Attr defs
+      {{"T: {half, float, double}"}},
+      // Nodes
+      nodes);
+
+  std::unordered_map<string, AttrValue> func_attr;
+  func_attr["T"].set_type(DT_FLOAT);
+  FunctionDefLibrary library;
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromFunctionDef(func, func_attr, library);
+  CHECK(item);
+  EXPECT_EQ("SubGrad", item->id);
+  EXPECT_EQ(12, item->graph.node_size());
+  EXPECT_EQ(std::vector<string>({"dx", "dy"}), item->fetch);
+  EXPECT_EQ(3, item->feed.size());
+  EXPECT_EQ("x", item->feed[0].first);
+  EXPECT_EQ("y", item->feed[1].first);
+  EXPECT_EQ("dz", item->feed[2].first);
+
+  for (const NodeDef &node : item->graph.node()) {
+    if (node.name() == "x" || node.name() == "y" || node.name() == "dz") {
+      EXPECT_EQ("Placeholder", node.op());
+      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
+      EXPECT_EQ(0, node.input_size());
+    } else if (node.name() == "rx") {
+      EXPECT_EQ("BroadcastGradientArgs", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("sx:0", node.input(0));
+      EXPECT_EQ("sy:0", node.input(1));
+    } else if (node.name() == "sum_gx") {
+      EXPECT_EQ("Sum", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("gx:0", node.input(0));
+      EXPECT_EQ("rx:0", node.input(1));
+    } else if (node.name() == "sum_gy") {
+      EXPECT_EQ("Sum", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("gy:0", node.input(0));
+      EXPECT_EQ("rx:1", node.input(1));
+    }
+  }
+}
+
+TEST_F(FunctionsTest, FromFunctionDefWithNestedFuncs) {
+  FunctionDefLibrary library;
+  *library.add_function() = FunctionDefHelper::Define(
+      // Name
+      "Swap",
+      // Args
+      {"i0: T", "i1: T"},
+      // Return values
+      {"o0: T", "o1: T"},
+      // Attr def
+      {"T: {float, double}"},
+      // Nodes
+      {{{"o0"}, "Identity", {"i1"}, {{"T", "$T"}}},
+       {{"o1"}, "Identity", {"i0"}, {{"T", "$T"}}}});
+
+  FunctionDef func = FunctionDefHelper::Create(
+      // Name
+      "ManySwapsFirst",
+      // Args
+      {"x: float", "y: float"},
+      // Return values
+      {"o: float"},
+      // attr def
+      {},
+      // Nodes
+      // o = x*x + y*y.  Furthermore, The 1st swap depends on x2, and
+      // y2 depends on the 2nd swap.  The 2nd swap has data dependency
+      // on the 1st swap.
+      {{{"a0"}, "Swap", {"x", "y"}, {{"T", DT_FLOAT}}, {"x2"}},
+       {{"a1"}, "Swap", {"a0:o0:0", "a0:o1:0"}, {{"T", DT_FLOAT}}},
+       {{"x2"}, "Mul", {"x", "x"}, {{"T", DT_FLOAT}}},
+       {{"y2"}, "Mul", {"y", "y"}, {{"T", DT_FLOAT}}, {"a1"}},
+       {{"o"}, "Add", {"x2:z:0", "y2:z:0"}, {{"T", DT_FLOAT}}}},
+      {{"o", "o:z:0"}});
+
+  std::unordered_map<string, AttrValue> func_attr;
+  func_attr["T"].set_type(DT_FLOAT);
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromFunctionDef(func, func_attr, library);
+
+  for (const NodeDef &node : item->graph.node()) {
+    if (node.name() == "x" || node.name() == "y") {
+      EXPECT_EQ("Placeholder", node.op());
+      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
+      EXPECT_EQ(0, node.input_size());
+    } else if (node.name() == "a0") {
+      EXPECT_EQ("Swap", node.op());
+      EXPECT_EQ(3, node.input_size());
+      EXPECT_EQ("x", node.input(0));
+      EXPECT_EQ("y", node.input(1));
+      EXPECT_EQ("^x2", node.input(2));
+    } else if (node.name() == "a1") {
+      EXPECT_EQ("Swap", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("a0:0", node.input(0));
+      EXPECT_EQ("a0:1", node.input(1));
+    } else if (node.name() == "x2") {
+      EXPECT_EQ("Mul", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("x", node.input(0));
+      EXPECT_EQ("x", node.input(1));
+    } else if (node.name() == "y2") {
+      EXPECT_EQ("Mul", node.op());
+      EXPECT_EQ(3, node.input_size());
+      EXPECT_EQ("y", node.input(0));
+      EXPECT_EQ("y", node.input(1));
+      EXPECT_EQ("^a1", node.input(2));
+    } else if (node.name() == "o") {
+      EXPECT_EQ("Add", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("x2:0", node.input(0));
+      EXPECT_EQ("y2:0", node.input(1));
+    }
+  }
+}
+
+}  // namespace
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/utils/grappler_test.cc b/tensorflow/core/grappler/utils/grappler_test.cc
index fef8e97b6e..79b2aa2808 100644
--- a/tensorflow/core/grappler/utils/grappler_test.cc
+++ b/tensorflow/core/grappler/utils/grappler_test.cc
@@ -46,8 +46,8 @@ std::vector<Tensor> GrapplerTest::EvaluateFetchNodes(const GrapplerItem& item) {
         session->Run(run_options, {}, {}, item.init_ops, &dummy, nullptr));
   }
   std::vector<Tensor> output_tensors;
-  TF_CHECK_OK(
-      session->Run(run_options, {}, item.fetch, {}, &output_tensors, nullptr));
+  TF_CHECK_OK(session->Run(run_options, item.feed, item.fetch, {},
+                           &output_tensors, nullptr));
   TF_CHECK_OK(session->Close());
   return output_tensors;
 }
-- 
GitLab


From 1f18f757042e678cc935f645e9e5c21208ddc9ac Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 11:40:05 -0800
Subject: [PATCH 351/575] Don't crash on missing inputs in dependency analyzer.
 This is a temporary mitigation until the underlying bug is found.

PiperOrigin-RevId: 187207594
---
 tensorflow/core/grappler/optimizers/dependency_optimizer.cc | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc
index edb0db65e9..b47cba5ff7 100644
--- a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc
@@ -286,7 +286,10 @@ void DependencyOptimizer::OptimizeNode(int node_idx,
     std::vector<NodeDef*> input_nodes;
     for (int i = 0; i < num_inputs; ++i) {
       NodeDef* input_node = node_map_->GetNode(node->input(i));
-      CHECK_NE(input_node, nullptr);
+      if (input_node == nullptr) {
+        LOG(ERROR) << "Invalid input " << node->input(i);
+        return;
+      }
       input_nodes.push_back(input_node);
     }
 
-- 
GitLab


From 207af365eb719fa7af3b56e1723fe3f67b0c4f0f Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Tue, 27 Feb 2018 11:48:25 -0800
Subject: [PATCH 352/575] [TF:XLA] Bump open source llvm revision to r326181

PiperOrigin-RevId: 187208788
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 5b09c5e67d..fa3671b4c9 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -475,11 +475,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/8f7bcdf3c65b9a47e35653d525135beb18f3ac25.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/8f7bcdf3c65b9a47e35653d525135beb18f3ac25.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/832f2bf0d8908aea8160bab128708d521764fe8d.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/832f2bf0d8908aea8160bab128708d521764fe8d.tar.gz",
       ],
-      sha256 = "63d4da54dc7bc9a79e2ad266d230f4f759520cccb344a2dd49c2c6383ab75285",
-      strip_prefix = "llvm-8f7bcdf3c65b9a47e35653d525135beb18f3ac25",
+      sha256 = "e6bb793bbdce37ee5643789a27d174f1cdd8e7323a69d5f331376eb34755ee0d",
+      strip_prefix = "llvm-832f2bf0d8908aea8160bab128708d521764fe8d",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
   )
 
-- 
GitLab


From d429fe193f4c235cde8223804ea888c2eaa5ce68 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Tue, 27 Feb 2018 11:57:09 -0800
Subject: [PATCH 353/575] Improve our handling of bitcasts.

 - Do not fuse bitcasts in the CPU backend.  Fused instructions lose their
   layout and a bitcast is meaningless without a layout.  We were explicitly
   testing for this so I've changed the corresponding tests to use a reshape
   instead.
 - Fail the layout assignment if we see a bitcast.  bitcasts are inherently
   layout sensitive and so a bitcast instruction present in the IR before layout
   assignment is a bug.

PiperOrigin-RevId: 187210151
---
 .../xla/service/cpu/cpu_instruction_fusion.cc |  1 -
 .../cpu/cpu_instruction_fusion_test.cc        | 29 +++++++++----------
 .../compiler/xla/service/layout_assignment.cc |  7 +++++
 .../xla/service/layout_assignment_test.cc     | 21 ++++++++++++++
 4 files changed, 41 insertions(+), 17 deletions(-)

diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc
index 482e04052d..0fc5a746bb 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion.cc
@@ -30,7 +30,6 @@ bool CanBeLoopFused(const HloInstruction& hlo) {
   // These are the only ones we fuse since we rely on effective elemental IR
   // generation.
   return hlo.IsElementwise() ||  //
-         hlo.opcode() == HloOpcode::kBitcast ||
          hlo.opcode() == HloOpcode::kBroadcast ||
          hlo.opcode() == HloOpcode::kConcatenate ||
          hlo.opcode() == HloOpcode::kDynamicSlice ||
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc
index 595c3f55b3..6ed1cd31b1 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_instruction_fusion_test.cc
@@ -77,7 +77,7 @@ TEST_F(InstructionFusionTest, DotOperationFusion_Basic_1) {
   EXPECT_THAT(computation->root_instruction(), op::Fusion());
 }
 
-TEST_F(InstructionFusionTest, DotOperationFusion_Bitcast) {
+TEST_F(InstructionFusionTest, DotOperationNoFusion_Bitcast) {
   HloComputation::Builder builder(TestName());
   HloInstruction* arg0 = builder.AddInstruction(HloInstruction::CreateParameter(
       0, ShapeUtil::MakeShape(F32, {2, 512, 2, 128}), "arg0"));
@@ -94,8 +94,7 @@ TEST_F(InstructionFusionTest, DotOperationFusion_Bitcast) {
   auto module = CreateNewModule();
   auto computation = module->AddEntryComputation(builder.Build());
   EXPECT_EQ(dot, computation->root_instruction());
-  EXPECT_TRUE(CpuInstructionFusion().Run(module.get()).ValueOrDie());
-  EXPECT_THAT(computation->root_instruction(), op::Fusion());
+  EXPECT_FALSE(CpuInstructionFusion().Run(module.get()).ValueOrDie());
 }
 
 TEST_F(InstructionFusionTest, DotOperationFusion_Reshape) {
@@ -244,35 +243,33 @@ class OpcodeFusionTest : public InstructionFusionTest {
   }
 };
 
-TEST_F(OpcodeFusionTest, Exponential_Bitcast_Negate) {
+TEST_F(OpcodeFusionTest, Exponential_Reshape_Negate) {
   HloComputation::Builder builder(TestName());
   Shape param_shape = ShapeUtil::MakeShape(F32, {1, 4});
   Shape result_shape = ShapeUtil::MakeShape(F32, {4});
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, param_shape, "param"));
-  // InstructionFusion::ShouldFuse() precludes fusing a bitcast whose operand
-  // is a parameter, so create an operand between the parameter and bitcast.
   HloInstruction* exp1 = builder.AddInstruction(
       HloInstruction::CreateUnary(param_shape, HloOpcode::kExp, param0));
-  HloInstruction* bitcast2 = builder.AddInstruction(
-      HloInstruction::CreateUnary(result_shape, HloOpcode::kBitcast, exp1));
+  HloInstruction* reshape2 =
+      builder.AddInstruction(HloInstruction::CreateReshape(result_shape, exp1));
   builder.AddInstruction(
-      HloInstruction::CreateUnary(result_shape, HloOpcode::kNegate, bitcast2));
+      HloInstruction::CreateUnary(result_shape, HloOpcode::kNegate, reshape2));
 
   auto module = CreateNewModule();
   module->AddEntryComputation(builder.Build());
 
   RunFusionAndCheckOpcodesWereFused(
-      module.get(), {HloOpcode::kNegate, HloOpcode::kBitcast, HloOpcode::kExp,
+      module.get(), {HloOpcode::kNegate, HloOpcode::kReshape, HloOpcode::kExp,
                      HloOpcode::kParameter});
 }
 
-TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) {
+TEST_F(OpcodeFusionTest, Broadcast_Reshape_DynamicSlice_Tanh) {
   HloComputation::Builder builder(TestName());
   Shape param_shape = ShapeUtil::MakeShape(F32, {8});
   Shape starts_shape = ShapeUtil::MakeShape(F32, {2});
   Shape broadcast_shape = ShapeUtil::MakeShape(F32, {1, 8, 8});
-  Shape bitcast_shape = ShapeUtil::MakeShape(F32, {8, 8});
+  Shape reshape_shape = ShapeUtil::MakeShape(F32, {8, 8});
   Shape dynamic_slice_shape = ShapeUtil::MakeShape(F32, {4, 4});
   HloInstruction* param0 = builder.AddInstruction(
       HloInstruction::CreateParameter(0, param_shape, "param"));
@@ -280,11 +277,11 @@ TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) {
       HloInstruction::CreateParameter(1, starts_shape, "starts"));
   HloInstruction* broadcast2 = builder.AddInstruction(
       HloInstruction::CreateBroadcast(broadcast_shape, param0, {1}));
-  HloInstruction* bitcast3 = builder.AddInstruction(HloInstruction::CreateUnary(
-      bitcast_shape, HloOpcode::kBitcast, broadcast2));
+  HloInstruction* reshape3 = builder.AddInstruction(
+      HloInstruction::CreateReshape(reshape_shape, broadcast2));
   HloInstruction* dynamic_slice4 =
       builder.AddInstruction(HloInstruction::CreateDynamicSlice(
-          dynamic_slice_shape, bitcast3, param1, {4, 4}));
+          dynamic_slice_shape, reshape3, param1, {4, 4}));
   builder.AddInstruction(HloInstruction::CreateUnary(
       dynamic_slice_shape, HloOpcode::kTanh, dynamic_slice4));
 
@@ -293,7 +290,7 @@ TEST_F(OpcodeFusionTest, Broadcast_Bitcast_DynamicSlice_Tanh) {
 
   RunFusionAndCheckOpcodesWereFused(
       module.get(),
-      {HloOpcode::kTanh, HloOpcode::kDynamicSlice, HloOpcode::kBitcast,
+      {HloOpcode::kTanh, HloOpcode::kDynamicSlice, HloOpcode::kReshape,
        HloOpcode::kBroadcast, HloOpcode::kParameter, HloOpcode::kParameter});
 }
 
diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index 4929300f7d..39f9120e55 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -1561,6 +1561,13 @@ StatusOr<bool> LayoutAssignment::Run(HloModule* module) {
     // infeeds.  Clearing the layouts here avoids hiding potential bugs in the
     // layout assignment pass that may accidently use the existing layout.
     for (HloInstruction* instruction : computation->instructions()) {
+      if (instruction->opcode() == HloOpcode::kBitcast) {
+        // bitcasts are inherently layout sensitive and so a bitcast instruction
+        // present in the IR before layout assignment is a bug.
+        return InternalError(
+            "Unexpected bitcast operation seen during layout assignment: %s.",
+            instruction->ToString().c_str());
+      }
       if (instruction->opcode() != HloOpcode::kInfeed) {
         LayoutUtil::ClearLayout(instruction->mutable_shape());
       }
diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index 62feb7c1e9..4b1c9bad41 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -796,5 +796,26 @@ TEST_F(LayoutAssignmentTest, ConditionalAsymmetricLayout) {
   EXPECT_THAT(false_result->opcode(), HloOpcode::kCopy);
 }
 
+TEST_F(LayoutAssignmentTest, InternalErrorOnBitcast) {
+  auto builder = HloComputation::Builder(TestName());
+  auto constant0 = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR2WithLayout<float>(
+          {{1.0, 2.0}, {3.0, 4.0}}, LayoutUtil::MakeLayout({0, 1}))));
+  builder.AddInstruction(HloInstruction::CreateUnary(
+      constant0->shape(), HloOpcode::kBitcast, constant0));
+  auto module = CreateNewModule();
+  module->AddEntryComputation(builder.Build());
+
+  ComputationLayout computation_layout(
+      module->entry_computation()->ComputeProgramShape());
+  LayoutAssignment layout_assignment(&computation_layout);
+  Status error_status = layout_assignment.Run(module.get()).status();
+  EXPECT_FALSE(error_status.ok());
+  EXPECT_THAT(
+      error_status.error_message(),
+      ::testing::HasSubstr(
+          "Unexpected bitcast operation seen during layout assignment"));
+}
+
 }  // namespace
 }  // namespace xla
-- 
GitLab


From e504797de0b1112caea5080c3ab2060156c4e8a1 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Tue, 27 Feb 2018 12:05:41 -0800
Subject: [PATCH 354/575] Use a couple of type aliases for brevity; NFC

PiperOrigin-RevId: 187211560
---
 .../compiler/xla/service/hlo_evaluator.cc     | 133 ++++++++----------
 1 file changed, 62 insertions(+), 71 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index afbfdac05e..8c7459099d 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -51,6 +51,10 @@ namespace xla {
 
 namespace {
 
+using tensorflow::gtl::ArraySlice;
+using tensorflow::gtl::FlatSet;
+using tensorflow::gtl::optional;
+
 template <typename T>
 struct is_complex_t : public std::false_type {};
 
@@ -105,11 +109,10 @@ StatusOr<std::unique_ptr<Literal>> Compare(const Shape& shape, HloOpcode opcode,
   }
 
   auto result = Literal::CreateFromShape(shape);
-  TF_RETURN_IF_ERROR(result->Populate<bool>(
-      [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
-        return compare_op(lhs_literal.Get<OperandT>(multi_index),
-                          rhs_literal.Get<OperandT>(multi_index));
-      }));
+  TF_RETURN_IF_ERROR(result->Populate<bool>([&](ArraySlice<int64> multi_index) {
+    return compare_op(lhs_literal.Get<OperandT>(multi_index),
+                      rhs_literal.Get<OperandT>(multi_index));
+  }));
 
   return std::move(result);
 }
@@ -136,11 +139,10 @@ StatusOr<std::unique_ptr<Literal>> Compare<complex64>(
   }
 
   auto result = Literal::CreateFromShape(shape);
-  TF_RETURN_IF_ERROR(result->Populate<bool>(
-      [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
-        return compare_op(lhs_literal.Get<complex64>(multi_index),
-                          rhs_literal.Get<complex64>(multi_index));
-      }));
+  TF_RETURN_IF_ERROR(result->Populate<bool>([&](ArraySlice<int64> multi_index) {
+    return compare_op(lhs_literal.Get<complex64>(multi_index),
+                      rhs_literal.Get<complex64>(multi_index));
+  }));
 
   return std::move(result);
 }
@@ -165,8 +167,8 @@ StatusOr<std::unique_ptr<Literal>> ElementWiseUnaryOpImpl(
 
   auto result = Literal::CreateFromShape(shape);
 
-  TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-      [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+  TF_RETURN_IF_ERROR(
+      result->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
         return unary_op(operand_literal.Get<NativeT>(multi_index));
       }));
   return std::move(result);
@@ -178,7 +180,7 @@ StatusOr<std::unique_ptr<Literal>> ElementWiseUnaryOpImpl(
 // with the base index.
 void IterateThroughWindow(
     const Shape& window_shape, const Window& window, const Shape& base_shape,
-    const tensorflow::gtl::ArraySlice<int64>& window_count_index,
+    const ArraySlice<int64>& window_count_index,
     const std::function<void(const std::vector<int64>&)>& f) {
   const int64 rank = ShapeUtil::Rank(base_shape);
   DimensionVector window_index(rank);
@@ -332,13 +334,12 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
                    operand_to_broadcast.shape().dimensions(i));
     }
 
-    return output->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
-          for (int64 i = 0; i < broadcast->dimensions().size(); ++i) {
-            broadcast_indices[i] = multi_index[broadcast->dimensions(i)];
-          }
-          return operand_to_broadcast.Get<ReturnT>(broadcast_indices);
-        });
+    return output->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
+      for (int64 i = 0; i < broadcast->dimensions().size(); ++i) {
+        broadcast_indices[i] = multi_index[broadcast->dimensions(i)];
+      }
+      return operand_to_broadcast.Get<ReturnT>(broadcast_indices);
+    });
   }
 
   template <
@@ -902,8 +903,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
     auto result = Literal::CreateFromShape(result_shape);
 
-    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> out_index) {
+    TF_RETURN_IF_ERROR(
+        result->Populate<ReturnT>([&](ArraySlice<int64> out_index) {
           std::vector<int64> from_index(out_index.begin(), out_index.end());
           for (const int64 dim : reverse_dimensions) {
             from_index[dim] = result_shape.dimensions(dim) - 1 - out_index[dim];
@@ -978,7 +979,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     DimensionVector rhs_index(rhs_rank);
     DimensionVector rhs_spatial_index(dnums.kernel_spatial_dimensions_size());
 
-    auto func = [&](tensorflow::gtl::ArraySlice<int64> out_index) {
+    auto func = [&](ArraySlice<int64> out_index) {
       ElementwiseT result_val = static_cast<ElementwiseT>(0);
 
       std::fill(lhs_index.begin(), lhs_index.end(), 0);
@@ -1100,9 +1101,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     }
 
     std::vector<int64> rhs_non_batch_non_contracting_dims;
-    tensorflow::gtl::FlatSet<int64> batch_dims_set(
-        dnums.rhs_batch_dimensions().begin(),
-        dnums.rhs_batch_dimensions().end());
+    FlatSet<int64> batch_dims_set(dnums.rhs_batch_dimensions().begin(),
+                                  dnums.rhs_batch_dimensions().end());
     for (int64 i = 0; i < rhs_rank; i++) {
       if (i != rhs_contracting_dimension && batch_dims_set.count(i) == 0) {
         rhs_non_batch_non_contracting_dims.push_back(i);
@@ -1114,8 +1114,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     DimensionVector lhs_index(lhs_rank);
     DimensionVector rhs_index(rhs_rank);
-    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> result_index) {
+    TF_RETURN_IF_ERROR(
+        result->Populate<ReturnT>([&](ArraySlice<int64> result_index) {
           ElementwiseT result_val = static_cast<ElementwiseT>(0);
 
           // Find the corresponding non-contracting indices for lhs and rhs.
@@ -1209,9 +1209,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
         parent_->GetEvaluatedLiteralFor(pad->operand(1)).Get<ReturnT>({});
     auto result = Literal::CreateFromShape(pad->shape());
     TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&scalar](tensorflow::gtl::ArraySlice<int64> multi_index) {
-          return scalar;
-        }));
+        [&scalar](ArraySlice<int64> multi_index) { return scalar; }));
 
     const Literal& evaluated_operand =
         parent_->GetEvaluatedLiteralFor(pad->operand(0));
@@ -1375,8 +1373,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     auto result = Literal::CreateFromShape(map->shape());
 
     HloEvaluator embedded_evaluator;
-    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+    TF_RETURN_IF_ERROR(
+        result->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
           std::vector<std::unique_ptr<Literal>> arg_literals;
           arg_literals.reserve(operands.size());
 
@@ -1466,7 +1464,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
   Status HandleReduce(HloInstruction* reduce) override {
     auto arg = reduce->operand(0);
     auto init_value = reduce->operand(1);
-    tensorflow::gtl::ArraySlice<int64> dimensions(reduce->dimensions());
+    ArraySlice<int64> dimensions(reduce->dimensions());
     HloComputation* function = reduce->to_apply();
     TF_RET_CHECK(ShapeUtil::Rank(reduce->shape()) ==
                  ShapeUtil::Rank(arg->shape()) - dimensions.size());
@@ -1511,8 +1509,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     HloEvaluator embedded_evaluator;
     // For each resulting dimension, calculate and assign computed value.
-    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+    TF_RETURN_IF_ERROR(
+        result->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
           ReturnT result_val = init_scalar;
 
           std::vector<int64> base(arg_dimensions.size());
@@ -1566,9 +1564,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     // Initialize result array with the init value.
     TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> output_index) {
-          return init_scalar;
-        }));
+        [&](ArraySlice<int64> output_index) { return init_scalar; }));
 
     std::vector<int64> window_dimension_sizes;
     for (const auto& window_dimension : window.dimensions()) {
@@ -1601,8 +1597,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
       // 2. Using the selected index, scatter value from `source` to result. We
       // do this by iterating through the window, and compare each index with
       // the selected index.
-      tensorflow::gtl::optional<ReturnT> selected_val;
-      tensorflow::gtl::optional<std::vector<int64>> selected_index;
+      optional<ReturnT> selected_val;
+      optional<std::vector<int64>> selected_index;
 
       IterateThroughWindow(
           window_shape, window, operand_literal.shape(), source_index,
@@ -1698,8 +1694,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     HloEvaluator embedded_evaluator;
     // For each resulting dimension, calculate and assign computed value.
-    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> output_index) {
+    TF_RETURN_IF_ERROR(
+        result->Populate<ReturnT>([&](ArraySlice<int64> output_index) {
           ReturnT result_val = init_scalar;
 
           std::fill(window_index.begin(), window_index.end(), 0);
@@ -1749,7 +1745,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     const int64 rank = ShapeUtil::Rank(operand->shape());
     const Literal& operand_literal = parent_->GetEvaluatedLiteralFor(operand);
-    auto func = [&](tensorflow::gtl::ArraySlice<int64> out_index) {
+    auto func = [&](ArraySlice<int64> out_index) {
       DimensionVector operand_index(rank);
       for (int64 i = 0; i < rank; ++i) {
         operand_index[i] =
@@ -1930,8 +1926,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     std::vector<int64> operand_indices(start.size());
 
     auto result = Literal::CreateFromShape(result_shape);
-    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+    TF_RETURN_IF_ERROR(
+        result->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
           for (int64 i = 0; i < operand_indices.size(); ++i) {
             CHECK_GE(multi_index[i] + start[i], 0);
             // Mod is only used here to be consistent with the existing
@@ -2014,8 +2010,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     auto result = Literal::CreateFromShape(shape);
 
-    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+    TF_RETURN_IF_ERROR(
+        result->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
           return ConvertBinaryFunction(binary_op)(
               lhs_literal.Get<ReturnT>(multi_index),
               rhs_literal.Get<ReturnT>(multi_index));
@@ -2052,8 +2048,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     auto result = Literal::CreateFromShape(shape);
 
-    TF_RETURN_IF_ERROR(result->Populate<ReturnT>(
-        [&](tensorflow::gtl::ArraySlice<int64> multi_index) {
+    TF_RETURN_IF_ERROR(
+        result->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
           return ternary_op(lhs_literal.Get<LhsType>(multi_index),
                             rhs_literal.Get<RhsType>(multi_index),
                             ehs_literal.Get<EhsType>(multi_index));
@@ -2107,8 +2103,7 @@ HloEvaluator::HloEvaluator() {
 
 template <typename LiteralPtr>
 StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate(
-    const HloModule& module,
-    tensorflow::gtl::ArraySlice<LiteralPtr> arg_literals) {
+    const HloModule& module, ArraySlice<LiteralPtr> arg_literals) {
   XLA_VLOG_LINES(2, "HloEvaluator::Evaluate module:\n" + module.ToString());
 
   evaluated_.clear();
@@ -2125,8 +2120,7 @@ StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate(
 
 template <typename LiteralPtr>
 StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate(
-    const HloComputation& computation,
-    tensorflow::gtl::ArraySlice<LiteralPtr> arg_literals) {
+    const HloComputation& computation, ArraySlice<LiteralPtr> arg_literals) {
   XLA_VLOG_LINES(
       2, "HloEvaluator::Evaluate computation:\n" + computation.ToString());
 
@@ -2142,8 +2136,7 @@ StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate(
 
 template <typename LiteralPtr>
 StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate(
-    HloInstruction* instruction,
-    tensorflow::gtl::ArraySlice<LiteralPtr> arg_literals) {
+    HloInstruction* instruction, ArraySlice<LiteralPtr> arg_literals) {
   TF_RET_CHECK(hlo_query::AllOperandsAreParametersOrConstants(*instruction));
   TF_RETURN_IF_ERROR(ShapeUtil::ValidateShape(instruction->shape()));
 
@@ -2268,8 +2261,7 @@ Status HloEvaluator::HandleTranspose(HloInstruction* transpose) {
 }
 
 Status HloEvaluator::HandleConcatenate(HloInstruction* concatenate) {
-  tensorflow::gtl::ArraySlice<HloInstruction*> operands(
-      concatenate->operands());
+  ArraySlice<HloInstruction*> operands(concatenate->operands());
   // The result concatenate dimension is going to be the sum of all
   // concatenate dimensions of the operands taking part of the operation.
   const Shape& reference_shape = operands[0]->shape();
@@ -2532,28 +2524,27 @@ Status HloEvaluator::Postprocess(HloInstruction* hlo) {
 
 // Explicit instantiation of templatized Evaluate* methods.
 //
-template StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate<
-    const Literal*>(const HloModule& module,
-                    tensorflow::gtl::ArraySlice<const Literal*> arg_literals);
+template StatusOr<std::unique_ptr<Literal>>
+HloEvaluator::Evaluate<const Literal*>(const HloModule& module,
+                                       ArraySlice<const Literal*> arg_literals);
 template StatusOr<std::unique_ptr<Literal>>
 HloEvaluator::Evaluate<std::unique_ptr<Literal>>(
-    const HloModule& module,
-    tensorflow::gtl::ArraySlice<std::unique_ptr<Literal>> arg_literals);
+    const HloModule& module, ArraySlice<std::unique_ptr<Literal>> arg_literals);
 
-template StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate<
-    const Literal*>(const HloComputation& computation,
-                    tensorflow::gtl::ArraySlice<const Literal*> arg_literals);
+template StatusOr<std::unique_ptr<Literal>>
+HloEvaluator::Evaluate<const Literal*>(const HloComputation& computation,
+                                       ArraySlice<const Literal*> arg_literals);
 template StatusOr<std::unique_ptr<Literal>>
 HloEvaluator::Evaluate<std::unique_ptr<Literal>>(
     const HloComputation& computation,
-    tensorflow::gtl::ArraySlice<std::unique_ptr<Literal>> arg_literals);
+    ArraySlice<std::unique_ptr<Literal>> arg_literals);
 
-template StatusOr<std::unique_ptr<Literal>> HloEvaluator::Evaluate<
-    const Literal*>(HloInstruction* instruction,
-                    tensorflow::gtl::ArraySlice<const Literal*> arg_literals);
+template StatusOr<std::unique_ptr<Literal>>
+HloEvaluator::Evaluate<const Literal*>(HloInstruction* instruction,
+                                       ArraySlice<const Literal*> arg_literals);
 template StatusOr<std::unique_ptr<Literal>>
 HloEvaluator::Evaluate<std::unique_ptr<Literal>>(
     HloInstruction* instruction,
-    tensorflow::gtl::ArraySlice<std::unique_ptr<Literal>> arg_literals);
+    ArraySlice<std::unique_ptr<Literal>> arg_literals);
 
 }  // namespace xla
-- 
GitLab


From 691f1e6de0ce628ed11406bd6fd2f599763bb7cc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 12:06:33 -0800
Subject: [PATCH 355/575] Add consistency check: for constant arrays (those
 that have a buffer), there must be a shape, and its flat-size must equal the
 buffer length.

PiperOrigin-RevId: 187211685
---
 .../contrib/lite/toco/import_tensorflow.cc    | 37 +++++++++++++++++++
 tensorflow/contrib/lite/toco/model.h          |  4 ++
 tensorflow/contrib/lite/toco/tflite/import.cc |  3 ++
 tensorflow/contrib/lite/toco/tooling_util.cc  | 10 ++++-
 4 files changed, 53 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc
index 27d2f33a8d..52a0512e23 100644
--- a/tensorflow/contrib/lite/toco/import_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc
@@ -272,6 +272,39 @@ void ImportInt64Array(const TensorProto& input_tensor, Array* output_array) {
   }
 }
 
+void ImportBoolArray(const TensorProto& input_tensor, Array* output_array) {
+  CHECK_EQ(input_tensor.dtype(), DT_BOOL);
+  const auto& input_shape = input_tensor.tensor_shape();
+  CHECK_LE(input_shape.dim_size(), 4);
+  ImportShape(input_shape.dim(), output_array->mutable_shape());
+  int input_flat_size = 1;
+  for (int k = 0; k < input_shape.dim_size(); k++) {
+    input_flat_size *= input_shape.dim(k).size();
+  }
+  auto& output_bool_data =
+      output_array->GetMutableBuffer<ArrayDataType::kBool>().data;
+  output_bool_data.resize(RequiredBufferSizeForShape(output_array->shape()),
+                          false);
+  if (input_tensor.bool_val_size()) {
+    for (int i = 0; i < input_tensor.bool_val_size(); i++) {
+      output_bool_data[i] = input_tensor.bool_val(i);
+    }
+  } else if (input_tensor.tensor_content().size() == input_flat_size) {
+    std::vector<char> buf(input_tensor.tensor_content().size());
+    toco::port::CopyToBuffer(input_tensor.tensor_content(), buf.data());
+    for (int i = 0; i < input_tensor.tensor_content().size(); i++) {
+      output_bool_data[i] = static_cast<bool>(buf[i]);
+    }
+  } else {
+    // Some graphs have bool const nodes without actual value...
+    // assuming that 'false' is implied.
+    // So far only encountered that in an array with 1 entry, let's
+    // require that until we encounter a graph where that's not the case.
+    CHECK_EQ(output_bool_data.size(), 1);
+    output_bool_data[0] = false;
+  }
+}
+
 void ImportStringArray(const TensorProto& input_tensor, Array* output_array) {
   CHECK_EQ(input_tensor.dtype(), DT_STRING);
   const auto& input_shape = input_tensor.tensor_shape();
@@ -347,6 +380,10 @@ void ConvertConstOperator(const NodeDef& node,
       array.data_type = ArrayDataType::kString;
       ImportStringArray(tensor, &array);
       break;
+    case DT_BOOL:
+      array.data_type = ArrayDataType::kBool;
+      ImportBoolArray(tensor, &array);
+      break;
     default:
       array.data_type = ArrayDataType::kNone;
       // do nothing, silently ignore the Const data.
diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h
index 346859ab39..d5df0fb951 100644
--- a/tensorflow/contrib/lite/toco/model.h
+++ b/tensorflow/contrib/lite/toco/model.h
@@ -244,6 +244,8 @@ struct GenericBuffer {
   // in containers and have the containers call the right subclass destructor.
   virtual ~GenericBuffer() {}
 
+  virtual int Length() const = 0;
+
   const ArrayDataType type;
 
  protected:
@@ -256,6 +258,8 @@ template <ArrayDataType A>
 struct Buffer : GenericBuffer {
   Buffer() : GenericBuffer(A) {}
 
+  int Length() const override { return data.size(); }
+
   std::vector<DataType<A>> data;
 };
 
diff --git a/tensorflow/contrib/lite/toco/tflite/import.cc b/tensorflow/contrib/lite/toco/tflite/import.cc
index 5b1ab514b2..d2aeb78114 100644
--- a/tensorflow/contrib/lite/toco/tflite/import.cc
+++ b/tensorflow/contrib/lite/toco/tflite/import.cc
@@ -64,6 +64,9 @@ void ImportTensors(const ::tflite::Model& input_model, Model* model) {
 
     auto shape = input_tensor->shape();
     if (shape) {
+      // If the shape is 0-dimensional, make sure to record it as such,
+      // as oppose to leaving the array without a shape.
+      array.mutable_shape()->mutable_dims()->clear();
       for (int i = 0; i < shape->Length(); ++i) {
         auto d = shape->Get(i);
         array.mutable_shape()->mutable_dims()->push_back(d);
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index 9e72582238..1ab7b34331 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -84,6 +84,8 @@ string ArrayDataTypeName(ArrayDataType data_type) {
       return "Uint64";
     case ArrayDataType::kString:
       return "String";
+    case ArrayDataType::kBool:
+      return "Bool";
     case ArrayDataType::kNone:
       return "None";
     default:
@@ -809,9 +811,15 @@ void CheckEachArray(const Model& model) {
     // It's OK to have a buffer or an alloc, but not both.
     // (Since allocs are for transient arrays without a buffer).
     CHECK(!array->buffer || !array->alloc);
-    // If there is a buffer, its type should be consistent with data_type.
     if (array->buffer) {
+      // If there is a buffer, its type should be consistent with data_type.
       CHECK(array->buffer->type == array->data_type);
+      // The presence of a fixed buffer should imply the presence of a fixed
+      // shape.
+      CHECK(array->has_shape());
+      // The shape flat-size should agree with the buffer length.
+      CHECK_EQ(array->buffer->Length(),
+               RequiredBufferSizeForShape(array->shape()));
     }
 
     // Check name.  Either "name_with_suffix_8", "name_with_port:3", but not
-- 
GitLab


From f97d233e79aa7d88057c8b8b355eda6cb3bfea07 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Tue, 27 Feb 2018 12:08:24 -0800
Subject: [PATCH 356/575] Register the function optimizer in the meta
 optimizer. Made sure it's turned OFF by default until more validation is
 done.

PiperOrigin-RevId: 187211957
---
 tensorflow/core/grappler/optimizers/BUILD                | 1 +
 .../core/grappler/optimizers/function_optimizer.cc       | 5 +++++
 .../core/grappler/optimizers/function_optimizer_test.cc  | 7 +++++++
 tensorflow/core/grappler/optimizers/meta_optimizer.cc    | 9 +++++++++
 tensorflow/core/grappler/utils/BUILD                     | 1 -
 tensorflow/core/protobuf/rewriter_config.proto           | 2 ++
 6 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index bd41854c41..7b801db2c8 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -455,6 +455,7 @@ cc_library(
         ":custom_graph_optimizer",
         ":custom_graph_optimizer_registry",
         ":dependency_optimizer",
+        ":function_optimizer",
         ":graph_optimizer",
         ":layout_optimizer",
         ":loop_optimizer",
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index efc4f2f4bd..3c96ff869b 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -45,6 +45,7 @@ Status InlineFunction(const NodeDef& node, const FunctionDef& func,
   NodeDef* func_inputs = graph->add_node();
   func_inputs->set_name(strings::StrCat(node.name(), "/", "inlined_inputs"));
   func_inputs->set_op("IdentityN");
+  func_inputs->set_device(node.device());
   *func_inputs->mutable_input() = node.input();
   AttrValue::ListValue* type_list =
       (*func_inputs->mutable_attr())["T"].mutable_list();
@@ -79,6 +80,9 @@ Status InlineFunction(const NodeDef& node, const FunctionDef& func,
     func_body_node.set_name(
         strings::StrCat(node.name(), "/", func_body_node.name()));
 
+    // Make sure the node is placed
+    func_body_node.set_device(node.device());
+
     // Move the node to the main graph
     graph->add_node()->Swap(&func_body_node);
   }
@@ -88,6 +92,7 @@ Status InlineFunction(const NodeDef& node, const FunctionDef& func,
   NodeDef* func_outputs = graph->add_node();
   func_outputs->set_name(node.name());
   func_outputs->set_op("IdentityN");
+  func_outputs->set_device(node.device());
   type_list = (*func_outputs->mutable_attr())["T"].mutable_list();
   for (const OpDef::ArgDef& arg : func.signature().output_arg()) {
     auto it = attr.find(arg.type_attr());
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
index b8e05a5296..76a5c08d35 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
@@ -50,33 +50,40 @@ TEST_F(FunctionOptimizerTest, SimpleFunction) {
     if (node.name() == "y/inlined_inputs") {
       count++;
       EXPECT_EQ("IdentityN", node.op());
+      EXPECT_EQ(device, node.device());
       EXPECT_EQ(1, node.input_size());
       EXPECT_EQ("x", node.input(0));
     } else if (node.name() == "y/x") {
       count++;
       EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(device, node.device());
       EXPECT_EQ(1, node.input_size());
       EXPECT_EQ("y/inlined_inputs:0", node.input(0));
     } else if (node.name() == "y/two") {
       count++;
       EXPECT_EQ("Const", node.op());
+      EXPECT_EQ(device, node.device());
     } else if (node.name() == "y/scale") {
       count++;
       EXPECT_EQ("Cast", node.op());
+      EXPECT_EQ(device, node.device());
     } else if (node.name() == "y/y") {
       count++;
       EXPECT_EQ("Mul", node.op());
+      EXPECT_EQ(device, node.device());
       EXPECT_EQ(2, node.input_size());
       EXPECT_EQ("y/x", node.input(0));
       EXPECT_EQ("y/scale:0", node.input(1));
     } else if (node.name() == "y") {
       count++;
       EXPECT_EQ("IdentityN", node.op());
+      EXPECT_EQ(device, node.device());
       EXPECT_EQ(1, node.input_size());
       EXPECT_EQ("y/y", node.input(0));
     } else if (node.name() == "z") {
       count++;
       EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(device, node.device());
       EXPECT_EQ(1, node.input_size());
       EXPECT_EQ("y", node.input(0));
     }
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 7ae77207af..93658a6475 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/optimizers/constant_folding.h"
 #include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h"
 #include "tensorflow/core/grappler/optimizers/dependency_optimizer.h"
+#include "tensorflow/core/grappler/optimizers/function_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/layout_optimizer.h"
 #include "tensorflow/core/grappler/optimizers/loop_optimizer.h"
@@ -56,6 +57,9 @@ std::unique_ptr<GraphOptimizer> MetaOptimizer::NewOptimizer(
   if (optimizer == "pruning") {
     graph_optimizer.reset(new ModelPruner());
   }
+  if (optimizer == "function") {
+    graph_optimizer.reset(new FunctionOptimizer());
+  }
   if (optimizer == "constfold") {
     graph_optimizer.reset(new ConstantFolding(cpu_device_));
   }
@@ -90,6 +94,10 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
     if (!cfg_.disable_model_pruning()) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(new ModelPruner()));
     }
+    if (cfg_.function_optimization() == RewriterConfig::ON) {
+      optimizers.push_back(
+          std::unique_ptr<GraphOptimizer>(new FunctionOptimizer()));
+    }
     if (cfg_.constant_folding() != RewriterConfig::OFF) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
           new ConstantFolding(cfg_.constant_folding(), cpu_device_)));
@@ -223,6 +231,7 @@ void MetaOptimizer::Feedback(Cluster* cluster, const GrapplerItem& item,
 bool MetaOptimizerEnabled(const RewriterConfig& cfg) {
   return !cfg.disable_model_pruning() ||
          cfg.layout_optimizer() != RewriterConfig::OFF ||
+         cfg.function_optimization() == RewriterConfig::ON ||
          cfg.constant_folding() != RewriterConfig::OFF ||
          cfg.dependency_optimization() != RewriterConfig::OFF ||
          cfg.loop_optimization() == RewriterConfig::ON ||
diff --git a/tensorflow/core/grappler/utils/BUILD b/tensorflow/core/grappler/utils/BUILD
index fc05713494..3dbad40cae 100644
--- a/tensorflow/core/grappler/utils/BUILD
+++ b/tensorflow/core/grappler/utils/BUILD
@@ -155,7 +155,6 @@ cc_library(
     hdrs = ["functions.h"],
     visibility = ["//visibility:public"],
     deps = [
-        "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:protos_all_cc",
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 875e4663db..9ebf217811 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -44,6 +44,8 @@ message RewriterConfig {
   Toggle dependency_optimization = 8;
   // Loop optimizations (default is OFF).
   Toggle loop_optimization = 9;
+  // Function optimizations (default is OFF).
+  Toggle function_optimization = 10;
   // If true, don't remove unnecessary ops from the graph
   bool disable_model_pruning = 2;
 
-- 
GitLab


From 24a1c89187e49847fbd3575d626f1e374ce9ed18 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Tue, 27 Feb 2018 12:12:32 -0800
Subject: [PATCH 357/575] Allow eager metrics to save internal variables by
 using global_variables.

PiperOrigin-RevId: 187212528
---
 .../contrib/eager/python/metrics_impl.py      | 20 +++++++++++++------
 .../contrib/eager/python/metrics_test.py      | 13 ++++++++++++
 2 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py
index ea8dbf2b46..5571e77c70 100644
--- a/tensorflow/contrib/eager/python/metrics_impl.py
+++ b/tensorflow/contrib/eager/python/metrics_impl.py
@@ -93,11 +93,12 @@ class Metric(object):
   `aggregate()`, it is for use by TensorFlow infrastructure.
   """
 
-  def __init__(self, name=None):
+  def __init__(self, name=None, use_global_variables=False):
     self._built = False
     self._vars = []
     self._initial_values = {}
     self._updates = []
+    self._use_global_variables = use_global_variables
     name = name or self.__class__.__name__
     # Replace things like spaces in name to create a valid scope name.
     scope_name = _to_replace.sub("_", name)
@@ -245,9 +246,14 @@ class Metric(object):
     """***Only for use by descendants of Metric***."""
     if self._built:
       raise RuntimeError("Can't call add_variable() except in build().")
-    collections = None if context.in_eager_mode() else [
-        ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.METRIC_VARIABLES
-    ]
+    if context.in_eager_mode():
+      collections = None
+    else:
+      if self._use_global_variables:
+        collections = [ops.GraphKeys.GLOBAL_VARIABLES]
+      else:
+        collections = [ops.GraphKeys.LOCAL_VARIABLES]
+      collections += [ops.GraphKeys.METRIC_VARIABLES]
     v = variable_scope.get_variable(
         name,
         shape,
@@ -267,8 +273,10 @@ class Mean(Metric):
   # TODO(josh11b): Maybe have a dtype argument that defaults to tf.float64?
   # Or defaults to type of the input if it is tf.float32, else tf.float64?
 
-  def __init__(self, name=None, dtype=dtypes.float64):
-    super(Mean, self).__init__(name=name)
+  def __init__(self, name=None, dtype=dtypes.float64,
+               use_global_variables=False):
+    super(Mean, self).__init__(name=name,
+                               use_global_variables=use_global_variables)
     self.dtype = dtype
 
   def build(self, *args, **kwargs):
diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py
index a9ecaa3f8b..c9106294dc 100644
--- a/tensorflow/contrib/eager/python/metrics_test.py
+++ b/tensorflow/contrib/eager/python/metrics_test.py
@@ -50,6 +50,19 @@ class MetricsTest(test.TestCase):
       self.assertEqual(
           set(m.variables),
           set(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES)))
+      self.assertEqual(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES), [])
+      self.assertEqual(
+          set(m.variables),
+          set(ops.get_collection(ops.GraphKeys.METRIC_VARIABLES)))
+
+  def testUseGlobalVariablesCollections(self):
+    with context.graph_mode(), ops.Graph().as_default():
+      m = metrics.Mean(use_global_variables=True)
+      m(1000)
+      self.assertEqual(
+          set(m.variables),
+          set(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)))
+      self.assertEqual(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES), [])
       self.assertEqual(
           set(m.variables),
           set(ops.get_collection(ops.GraphKeys.METRIC_VARIABLES)))
-- 
GitLab


From 78376e4077f4e9d293811bdbc453c6d1b93db453 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Tue, 27 Feb 2018 12:34:17 -0800
Subject: [PATCH 358/575] Make Layers Checkpointable

(This change is mostly API goldens by volume)

Layers will inherit from CheckpointableBase since they do variable management
themselves. A __setattr__ override would also likely slow down functional layers
significantly.

I believe the plan for Model is to piggyback on its existing __setattr__
override rather than having Model inherit from CheckpointableBase through Layer
and Checkpointable itself.

PiperOrigin-RevId: 187215512
---
 .../eager/python/checkpointable_utils_test.py | 32 ++++---------------
 tensorflow/python/layers/base.py              | 21 +++++++-----
 tensorflow/python/training/checkpointable.py  | 16 +++++++---
 .../api/golden/tensorflow.keras.-model.pbtxt  |  1 +
 .../golden/tensorflow.keras.-sequential.pbtxt |  1 +
 .../tensorflow.keras.layers.-activation.pbtxt |  1 +
 ...eras.layers.-activity-regularization.pbtxt |  1 +
 .../golden/tensorflow.keras.layers.-add.pbtxt |  1 +
 ...nsorflow.keras.layers.-alpha-dropout.pbtxt |  1 +
 ...low.keras.layers.-average-pooling1-d.pbtxt |  1 +
 ...low.keras.layers.-average-pooling2-d.pbtxt |  1 +
 ...low.keras.layers.-average-pooling3-d.pbtxt |  1 +
 .../tensorflow.keras.layers.-average.pbtxt    |  1 +
 ...tensorflow.keras.layers.-avg-pool1-d.pbtxt |  1 +
 ...tensorflow.keras.layers.-avg-pool2-d.pbtxt |  1 +
 ...tensorflow.keras.layers.-avg-pool3-d.pbtxt |  1 +
 ...ow.keras.layers.-batch-normalization.pbtxt |  1 +
 ...nsorflow.keras.layers.-bidirectional.pbtxt |  1 +
 ...tensorflow.keras.layers.-concatenate.pbtxt |  1 +
 ...orflow.keras.layers.-conv-l-s-t-m2-d.pbtxt |  1 +
 .../tensorflow.keras.layers.-conv1-d.pbtxt    |  1 +
 ...flow.keras.layers.-conv2-d-transpose.pbtxt |  1 +
 .../tensorflow.keras.layers.-conv2-d.pbtxt    |  1 +
 ...flow.keras.layers.-conv3-d-transpose.pbtxt |  1 +
 .../tensorflow.keras.layers.-conv3-d.pbtxt    |  1 +
 ...sorflow.keras.layers.-convolution1-d.pbtxt |  1 +
 ...ras.layers.-convolution2-d-transpose.pbtxt |  1 +
 ...sorflow.keras.layers.-convolution2-d.pbtxt |  1 +
 ...ras.layers.-convolution3-d-transpose.pbtxt |  1 +
 ...sorflow.keras.layers.-convolution3-d.pbtxt |  1 +
 ...tensorflow.keras.layers.-cropping1-d.pbtxt |  1 +
 ...tensorflow.keras.layers.-cropping2-d.pbtxt |  1 +
 ...tensorflow.keras.layers.-cropping3-d.pbtxt |  1 +
 .../tensorflow.keras.layers.-dense.pbtxt      |  1 +
 .../golden/tensorflow.keras.layers.-dot.pbtxt |  1 +
 .../tensorflow.keras.layers.-dropout.pbtxt    |  1 +
 .../tensorflow.keras.layers.-e-l-u.pbtxt      |  1 +
 .../tensorflow.keras.layers.-embedding.pbtxt  |  1 +
 .../tensorflow.keras.layers.-flatten.pbtxt    |  1 +
 .../tensorflow.keras.layers.-g-r-u-cell.pbtxt |  1 +
 .../tensorflow.keras.layers.-g-r-u.pbtxt      |  1 +
 ...rflow.keras.layers.-gaussian-dropout.pbtxt |  1 +
 ...sorflow.keras.layers.-gaussian-noise.pbtxt |  1 +
 ...as.layers.-global-average-pooling1-d.pbtxt |  1 +
 ...as.layers.-global-average-pooling2-d.pbtxt |  1 +
 ...as.layers.-global-average-pooling3-d.pbtxt |  1 +
 ...low.keras.layers.-global-avg-pool1-d.pbtxt |  1 +
 ...low.keras.layers.-global-avg-pool2-d.pbtxt |  1 +
 ...low.keras.layers.-global-avg-pool3-d.pbtxt |  1 +
 ...low.keras.layers.-global-max-pool1-d.pbtxt |  1 +
 ...low.keras.layers.-global-max-pool2-d.pbtxt |  1 +
 ...low.keras.layers.-global-max-pool3-d.pbtxt |  1 +
 ....keras.layers.-global-max-pooling1-d.pbtxt |  1 +
 ....keras.layers.-global-max-pooling2-d.pbtxt |  1 +
 ....keras.layers.-global-max-pooling3-d.pbtxt |  1 +
 ...tensorflow.keras.layers.-input-layer.pbtxt |  1 +
 ...ensorflow.keras.layers.-l-s-t-m-cell.pbtxt |  1 +
 .../tensorflow.keras.layers.-l-s-t-m.pbtxt    |  1 +
 .../tensorflow.keras.layers.-lambda.pbtxt     |  1 +
 .../tensorflow.keras.layers.-layer.pbtxt      |  1 +
 ...ensorflow.keras.layers.-leaky-re-l-u.pbtxt |  1 +
 ...w.keras.layers.-locally-connected1-d.pbtxt |  1 +
 ...w.keras.layers.-locally-connected2-d.pbtxt |  1 +
 .../tensorflow.keras.layers.-masking.pbtxt    |  1 +
 ...tensorflow.keras.layers.-max-pool1-d.pbtxt |  1 +
 ...tensorflow.keras.layers.-max-pool2-d.pbtxt |  1 +
 ...tensorflow.keras.layers.-max-pool3-d.pbtxt |  1 +
 ...sorflow.keras.layers.-max-pooling1-d.pbtxt |  1 +
 ...sorflow.keras.layers.-max-pooling2-d.pbtxt |  1 +
 ...sorflow.keras.layers.-max-pooling3-d.pbtxt |  1 +
 .../tensorflow.keras.layers.-maximum.pbtxt    |  1 +
 .../tensorflow.keras.layers.-multiply.pbtxt   |  1 +
 .../tensorflow.keras.layers.-p-re-l-u.pbtxt   |  1 +
 .../tensorflow.keras.layers.-permute.pbtxt    |  1 +
 .../tensorflow.keras.layers.-r-n-n.pbtxt      |  1 +
 ...nsorflow.keras.layers.-repeat-vector.pbtxt |  1 +
 .../tensorflow.keras.layers.-reshape.pbtxt    |  1 +
 ...flow.keras.layers.-separable-conv1-d.pbtxt |  1 +
 ...flow.keras.layers.-separable-conv2-d.pbtxt |  1 +
 ...ras.layers.-separable-convolution1-d.pbtxt |  1 +
 ...ras.layers.-separable-convolution2-d.pbtxt |  1 +
 ...flow.keras.layers.-simple-r-n-n-cell.pbtxt |  1 +
 ...ensorflow.keras.layers.-simple-r-n-n.pbtxt |  1 +
 .../tensorflow.keras.layers.-softmax.pbtxt    |  1 +
 ...low.keras.layers.-spatial-dropout1-d.pbtxt |  1 +
 ...low.keras.layers.-spatial-dropout2-d.pbtxt |  1 +
 ...low.keras.layers.-spatial-dropout3-d.pbtxt |  1 +
 ...ow.keras.layers.-stacked-r-n-n-cells.pbtxt |  1 +
 ...low.keras.layers.-thresholded-re-l-u.pbtxt |  1 +
 ...rflow.keras.layers.-time-distributed.pbtxt |  1 +
 ...sorflow.keras.layers.-up-sampling1-d.pbtxt |  1 +
 ...sorflow.keras.layers.-up-sampling2-d.pbtxt |  1 +
 ...sorflow.keras.layers.-up-sampling3-d.pbtxt |  1 +
 .../tensorflow.keras.layers.-wrapper.pbtxt    |  1 +
 ...orflow.keras.layers.-zero-padding1-d.pbtxt |  1 +
 ...orflow.keras.layers.-zero-padding2-d.pbtxt |  1 +
 ...orflow.keras.layers.-zero-padding3-d.pbtxt |  1 +
 .../tensorflow.keras.models.-model.pbtxt      |  1 +
 .../tensorflow.keras.models.-sequential.pbtxt |  1 +
 ...ensorflow.layers.-average-pooling1-d.pbtxt |  1 +
 ...ensorflow.layers.-average-pooling2-d.pbtxt |  1 +
 ...ensorflow.layers.-average-pooling3-d.pbtxt |  1 +
 ...nsorflow.layers.-batch-normalization.pbtxt |  1 +
 .../golden/tensorflow.layers.-conv1-d.pbtxt   |  1 +
 ...tensorflow.layers.-conv2-d-transpose.pbtxt |  1 +
 .../golden/tensorflow.layers.-conv2-d.pbtxt   |  1 +
 ...tensorflow.layers.-conv3-d-transpose.pbtxt |  1 +
 .../golden/tensorflow.layers.-conv3-d.pbtxt   |  1 +
 .../api/golden/tensorflow.layers.-dense.pbtxt |  1 +
 .../golden/tensorflow.layers.-dropout.pbtxt   |  1 +
 .../golden/tensorflow.layers.-flatten.pbtxt   |  1 +
 .../api/golden/tensorflow.layers.-layer.pbtxt |  1 +
 .../tensorflow.layers.-max-pooling1-d.pbtxt   |  1 +
 .../tensorflow.layers.-max-pooling2-d.pbtxt   |  1 +
 .../tensorflow.layers.-max-pooling3-d.pbtxt   |  1 +
 ...tensorflow.layers.-separable-conv1-d.pbtxt |  1 +
 ...tensorflow.layers.-separable-conv2-d.pbtxt |  1 +
 ...flow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt |  1 +
 ...orflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt |  1 +
 ...nsorflow.nn.rnn_cell.-device-wrapper.pbtxt |  1 +
 ...sorflow.nn.rnn_cell.-dropout-wrapper.pbtxt |  1 +
 .../tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt  |  1 +
 ...tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt |  1 +
 ...orflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt |  1 +
 .../tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt  |  1 +
 ...orflow.nn.rnn_cell.-residual-wrapper.pbtxt |  1 +
 126 files changed, 154 insertions(+), 38 deletions(-)

diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index 3d6a200276..83187b51b5 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -42,24 +42,6 @@ from tensorflow.python.training import saver as core_saver
 from tensorflow.python.training import training_util
 
 
-class CheckpointableDenseLayer(core.Dense, checkpointable.Checkpointable):
-
-  def __init__(self, *args, **kwargs):
-    checkpointable.Checkpointable.__init__(self)
-    core.Dense.__init__(self, *args, **kwargs)
-
-  def add_variable(self, name, shape, **kwargs):
-    # Calls both Checkpointable._add_variable and Layer.add_variable. Eventually
-    # Layer.add_variable should inherit from Checkpointable and simply call
-    # super and then do post-processing.
-    return checkpointable.Checkpointable._add_variable_with_custom_getter(
-        self,
-        name=name,
-        shape=shape,
-        getter=functools.partial(core.Dense.add_variable, self),
-        **kwargs)
-
-
 # pylint: disable=not-callable
 class CheckpointableNetwork(network_lib.Network, checkpointable.Checkpointable):
 
@@ -122,9 +104,9 @@ class MyNetwork(CheckpointableNetwork):
 
   def __init__(self):
     super(MyNetwork, self).__init__()
-    self._named_dense = CheckpointableDenseLayer(1, use_bias=True)
+    self._named_dense = core.Dense(1, use_bias=True)
     self._via_track_layer = self.track_layer(
-        CheckpointableDenseLayer(1, use_bias=False), name="via_track_layer")
+        core.Dense(1, use_bias=False), name="via_track_layer")
     # We can still track Checkpointables which aren't Layers.
     self._non_layer = NonLayerCheckpointable()
 
@@ -326,10 +308,10 @@ class CheckpointingTests(test.TestCase):
         "global_step:0",
         named_variables["optimizer_step" + suffix].name)
     self.assertEqual(
-        "my_network/checkpointable_dense_layer_1/kernel:0",
+        "my_network/dense_1/kernel:0",
         named_variables["network/via_track_layer/kernel" + suffix].name)
     self.assertEqual(
-        "my_network/checkpointable_dense_layer/kernel:0",
+        "my_network/dense/kernel:0",
         named_variables["network/_named_dense/kernel" + suffix].name)
     self.assertEqual(
         "beta1_power:0",
@@ -348,18 +330,18 @@ class CheckpointingTests(test.TestCase):
                      serialized_graph.nodes[optimizer_node.children[0].node_id]
                      .attributes[0].full_name)
     self.assertEqual(
-        "my_network/checkpointable_dense_layer/kernel",
+        "my_network/dense/kernel",
         serialized_graph.nodes[optimizer_node.slot_variables[0]
                                .original_variable_node_id]
         .attributes[0].full_name)
     # We strip off the :0 suffix, as variable.name-based saving does.
     self.assertEqual(
-        "my_network/checkpointable_dense_layer/kernel/Adam",
+        "my_network/dense/kernel/Adam",
         serialized_graph.nodes[optimizer_node.slot_variables[0]
                                .slot_variable_node_id]
         .attributes[0].full_name)
     self.assertEqual(
-        "my_network/checkpointable_dense_layer/kernel/Adam:0",
+        "my_network/dense/kernel/Adam:0",
         optimizer.get_slot(
             var=named_variables["network/_named_dense/kernel" + suffix],
             name="m").name)
diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index 8314c4aa87..2ec9971b88 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -36,12 +36,13 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import checkpointable
 from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import tf_export
 
 
 @tf_export('layers.Layer')
-class Layer(object):
+class Layer(checkpointable.CheckpointableBase):
   """Base layer class.
 
   This is the class from which all layers inherit, implementing common
@@ -532,13 +533,17 @@ class Layer(object):
     with vs.variable_scope(
         self._scope, reuse=reuse, auxiliary_name_scope=False) as scope:
       with ops.name_scope(self._name_scope_name(scope)):
-        variable = vs.get_variable(name,
-                                   shape=shape,
-                                   initializer=initializer,
-                                   dtype=dtypes.as_dtype(dtype),
-                                   constraint=constraint,
-                                   trainable=trainable and self.trainable,
-                                   partitioner=partitioner)
+        variable = self._add_variable_with_custom_getter(
+            name=name,
+            shape=shape,
+            getter=vs.get_variable,
+            # Manage errors in Layer rather than Checkpointable.
+            overwrite=True,
+            initializer=initializer,
+            dtype=dtypes.as_dtype(dtype),
+            constraint=constraint,
+            trainable=trainable and self.trainable,
+            partitioner=partitioner)
 
         if init_graph is not None:  # pylint: disable=protected-access
           # The variable was created and initialized in a graph.
diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py
index 11caa761ae..c5e7f3cdac 100644
--- a/tensorflow/python/training/checkpointable.py
+++ b/tensorflow/python/training/checkpointable.py
@@ -322,7 +322,8 @@ class CheckpointableBase(object):
 
   def _add_variable_with_custom_getter(
       self, name, shape=None, dtype=dtypes.float32,
-      initializer=None, getter=None, **kwargs_for_getter):
+      initializer=None, getter=None, overwrite=False,
+      **kwargs_for_getter):
     """Restore-on-create for a variable be saved with this `Checkpointable`.
 
     If the user has requested that this object or another `Checkpointable` which
@@ -334,12 +335,11 @@ class CheckpointableBase(object):
       name: A name for the variable. Must be unique within this object.
       shape: The shape of the variable.
       dtype: The data type of the variable.
-
       initializer: The initializer to use. Ignored if there is a deferred
         restoration left over from a call to
         `_restore_from_checkpoint_position`.
-
       getter: The getter to wrap which actually fetches the variable.
+      overwrite: If True, disables unique name and type checks.
       **kwargs_for_getter: Passed to the getter.
 
     Returns:
@@ -349,7 +349,7 @@ class CheckpointableBase(object):
       ValueError: If the variable name is not unique.
     """
     self._maybe_initialize_checkpointable()
-    if name in self._dependency_names:
+    if not overwrite and name in self._dependency_names:
       raise ValueError(
           ("A variable named '%s' already exists in this Checkpointable, but "
            "Checkpointable._add_variable called to create another with "
@@ -385,7 +385,13 @@ class CheckpointableBase(object):
     # assign again. It will add this variable to our dependencies, and if there
     # is a non-trivial restoration queued, it will handle that. This also
     # handles slot variables.
-    return self._track_checkpointable(new_variable, name=name)
+    if not overwrite or isinstance(new_variable, CheckpointableBase):
+      return self._track_checkpointable(new_variable, name=name,
+                                        overwrite=overwrite)
+    else:
+      # TODO(allenl): Some variable types are not yet supported. Remove this
+      # fallback once all get_variable() return types are Checkpointable.
+      return new_variable
 
   def _preload_simple_restoration(self, name, shape):
     """Return a dependency's value for restore-on-create.
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
index 241db8956a..7be2f4f61f 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-model.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.network.Network\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
index 9673a508d6..0f2428d77a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.network.Network\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
index 041acf29ff..db8f626b98 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activation.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Activation\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
index 48143b2cd6..809b3a5430 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-activity-regularization.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.ActivityRegularization\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
index 11f78fed97..68d41bb6cc 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-add.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
index 84eb825632..970b777e51 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-alpha-dropout.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.noise.AlphaDropout\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
index ab377a248f..529c64ab29 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling1-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
index c2edd79f52..7e7c330d74 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling2-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
index f3f37eed99..ada8466d74 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average-pooling3-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
index 31d1d1c049..2a5c1cd530 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-average.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
index 6582e1b18e..9a2cb29815 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool1-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
index 12f66095d2..f5e991ea42 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool2-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
index 3a45fa180e..31732214a6 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-avg-pool3-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
index a0f272c178..422eddf10d 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-batch-normalization.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.normalization.BatchNormalization\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
index 9c7d3154ad..9053a37916 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-bidirectional.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.wrappers.Wrapper\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
index 949b225e54..3d536d2182 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-concatenate.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
index a736c84a10..6a7da1aef8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv-l-s-t-m2-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.Recurrent\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
index 95f9afed28..801a033972 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv1-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
index 38ba15400a..13352e264a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d-transpose.pbtxt
@@ -6,6 +6,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
index bc84e2a97e..f400e4a15c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv2-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
index 0802578c22..b3a9f573b8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d-transpose.pbtxt
@@ -6,6 +6,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
index 8ad4646c74..a9be09c0ab 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-conv3-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
index 110e267b75..be1ef5eb92 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution1-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
index 24cfc83af6..30034f7eaf 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d-transpose.pbtxt
@@ -6,6 +6,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
index c56e89187f..189b38054c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution2-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
index 3674f2746c..a76d85c629 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d-transpose.pbtxt
@@ -6,6 +6,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
index 5a8f9d7702..782195d4ad 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-convolution3-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
index caa748be81..2cb7a39ea5 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping1-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Cropping1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
index 97bd4a265a..8080330699 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping2-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Cropping2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
index 20c43eeed1..678f40bbc2 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-cropping3-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.Cropping3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
index 256f0e4bdf..fac826109b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dense.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Dense\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
index d1e53f900c..285d544af2 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
index b010ff6805..b77976974c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
index fffd3854bb..b07714d3f2 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.ELU\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
index 1155fe03fc..e67d4ddfc4 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.embeddings.Embedding\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
index 5e4bebb15b..b2a668e5a8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Flatten\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
index cb9bb3d821..1fd3febad2 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.GRUCell\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
index 9a36e80649..f5f41d879d 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activation"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
index eb32238e15..f4f1a5d51c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.noise.GaussianDropout\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
index 37fc8e29ae..e502df5e17 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.noise.GaussianNoise\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
index 490816458b..9c8d5bfcd8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
index ab49f67f33..8dd65f1f24 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
index 3d7cb3ba49..5e30571cc7 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
index c99ddab4f3..ba90fa4546 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
index 290d2eaebe..8823857758 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
index cf63069641..500ced852b 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
index 2dadc67c09..cf2717ed46 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
index 1a1a1dcf64..a86ff1a469 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
index 44898e23ad..e01cc7c1b0 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
index 941d867d24..259c1fb37c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
index 9a5a6325f8..0c41bf97f7 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
index 7a0c1932f6..bec8817aa3 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.pooling._GlobalPooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
index f679c1d006..17be862229 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.input_layer.InputLayer\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
index ad1e7f2cad..6d2a8c5619 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.LSTMCell\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
index 6dad4b4897..490b5b618c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activation"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
index fa45d8c902..21a65b838a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Lambda\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
index 023d6c0d69..127b04738e 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt
@@ -2,6 +2,7 @@ path: "tensorflow.keras.layers.Layer"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
index e429fced77..87e49f2ed5 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.LeakyReLU\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
index 462568124f..1aa3aad324 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.local.LocallyConnected1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
index 11bf6a2b42..5e9dc7d477 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.local.LocallyConnected2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
index a932448891..0d101e5b68 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Masking\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
index 6ff2adddac..c85cd49ac8 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
index 2957673d4d..4f59e330c9 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
index 2191c10b73..c0ea0eb050 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
index af750ac1b6..ca37ae5131 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
index 9046061510..3ede237834 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
index a40666807b..d87e25a7ba 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
index 65378cef42..e4df7b48ae 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
index b037559e02..6bf7c77743 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.merge._Merge\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
index b3a7f47fa5..c14be132b7 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.PReLU\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
index b2f22f7da3..72ffbceae0 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Permute\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
index 792eacf90d..d3e780c8b2 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
index 5b79a021ca..a27980a9d1 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.RepeatVector\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
index 99c64505ee..67f991276c 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.core.Reshape\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
index d5873ccf76..fccea5e8af 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt
@@ -6,6 +6,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
index 76b4c10a46..d20663bdb0 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt
@@ -6,6 +6,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
index 40cd87de5f..889fa0a1b5 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt
@@ -6,6 +6,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
index c44c0da148..c850f3fedc 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt
@@ -6,6 +6,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
index bd70c31c38..526d88ccba 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.SimpleRNNCell\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
index de717976cf..7fddae3447 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activation"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
index a93b7b8f6e..5b9b62fc97 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.Softmax\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
index 4dc24b195e..769da30999 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
index a3bb1cc414..fca2e42a15 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
index f9a78106fa..36e8de09a9 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
index 5aa21f4022..a96f16fae9 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.StackedRNNCells\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
index 88e8a46572..e1cbd0e150 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.advanced_activations.ThresholdedReLU\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
index f2a7673998..f0d35728fb 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.wrappers.Wrapper\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
index 4db82ddfa9..74efaea6dd 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.UpSampling1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
index 61e65ad56d..dc5bd5fd53 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.UpSampling2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
index 3d9402db4e..e01ccfb74a 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.UpSampling3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
index 0223799ed4..7e6f90f762 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.wrappers.Wrapper\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
index 2e4429833a..4d0d402dad 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.ZeroPadding1D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
index 26cf7b9e49..b353a529bc 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.ZeroPadding2D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
index 64d35d9447..9fe1256e61 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.convolutional.ZeroPadding3D\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
index 18be9c9701..8ccf15f9ab 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-model.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.network.Network\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
index b934632922..102eb32203 100644
--- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt
@@ -5,6 +5,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.network.Network\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.base_layer.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt
index de81206bc8..1c4f550d7f 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling1-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt
index 72d5496464..d2db095269 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling2-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt
index 595e77ff9f..34d9a9df28 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-average-pooling3-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling.AveragePooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt
index 0c4aa2ff26..21ad0efecf 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-batch-normalization.pbtxt
@@ -2,6 +2,7 @@ path: "tensorflow.layers.BatchNormalization"
 tf_class {
   is_instance: "<class \'tensorflow.python.layers.normalization.BatchNormalization\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt
index 5f576d0189..ed38747c76 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv1-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv1D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt
index 675a7c76e5..ff453c6059 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d-transpose.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt
index eaabbf6aab..5583bd22dc 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv2-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv2D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt
index 838e070d79..63f0c32a7c 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d-transpose.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt
index 4bd8cfc1a4..b77726252c 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-conv3-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional.Conv3D\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt
index 57eccb03ff..92db9f6dcd 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-dense.pbtxt
@@ -2,6 +2,7 @@ path: "tensorflow.layers.Dense"
 tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Dense\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt
index a1ec00eeea..80fa846a24 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-dropout.pbtxt
@@ -2,6 +2,7 @@ path: "tensorflow.layers.Dropout"
 tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Dropout\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
index a06943d51a..f63213b3dd 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-flatten.pbtxt
@@ -2,6 +2,7 @@ path: "tensorflow.layers.Flatten"
 tf_class {
   is_instance: "<class \'tensorflow.python.layers.core.Flatten\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt
index 24fda0c87e..4e45b2d513 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-layer.pbtxt
@@ -1,6 +1,7 @@
 path: "tensorflow.layers.Layer"
 tf_class {
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt
index 4c3d00e0e1..19ec33fce7 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling1-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling1D\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt
index f7e2017b0c..76180c333a 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling2-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling2D\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt
index 84780926a3..ded75c8ff0 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-max-pooling3-d.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.pooling.MaxPooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.pooling._Pooling3D\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt
index 05799ecfc9..3dbfa5453f 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv1-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._SeparableConv\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt
index c2aeb35c46..ab171df1d1 100644
--- a/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.layers.-separable-conv2-d.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.layers.convolutional._SeparableConv\'>"
   is_instance: "<class \'tensorflow.python.layers.convolutional._Conv\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
index 44536787f0..9c71a24d05 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
index 768565d3ca..9e19f96b74 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
index 0d253e5dd2..7540aa6286 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DeviceWrapper\'>"
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
index 97edf245f6..fc1ff38669 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.DropoutWrapper\'>"
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
index 6ecc134d4d..751122cfff 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
index 4b3ca1578b..4b6313f395 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
@@ -4,6 +4,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.LayerRNNCell\'>"
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
index 9a6c73a079..00e8c71140 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.MultiRNNCell\'>"
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
index 27488f8e73..3852f90dd6 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
@@ -2,6 +2,7 @@ path: "tensorflow.nn.rnn_cell.RNNCell"
 tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
index 3310836ed2..8f3f0f7506 100644
--- a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
@@ -3,6 +3,7 @@ tf_class {
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.ResidualWrapper\'>"
   is_instance: "<class \'tensorflow.python.ops.rnn_cell_impl.RNNCell\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
     name: "activity_regularizer"
-- 
GitLab


From 7b71b0cfd9f7b4ceb17295cba5b651a04764c37b Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Tue, 27 Feb 2018 13:20:03 -0800
Subject: [PATCH 359/575] Checkpointable: Move the checkpoint-grouping utility
 out of the unit test file

Renames Saver -> CheckpointableSaver in preparation for exposing the necessary
symbols in tf.contrib.eager.

There's a pending change for Optimizers, and Asim is handling Layers/Model. Once
those are checked in, we should be able to save/restore everything in the eager
examples (or at least the mnist one...). Still plenty more to make
Checkpointable, but it should be usable at that point.

PiperOrigin-RevId: 187221803
---
 .../eager/python/checkpointable_utils.py      |  93 ++++++++++++-
 .../eager/python/checkpointable_utils_test.py | 128 +++++++-----------
 2 files changed, 139 insertions(+), 82 deletions(-)

diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py
index e57093bdbc..ed431e02ea 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils.py
@@ -518,7 +518,7 @@ class _SessionWithFeedDictAdditions(session_lib.SessionInterface):
         fetches=fetches, feed_dict=feed_dict, **kwargs)
 
 
-class Saver(object):
+class CheckpointableSaver(object):
   """Saves and restores a `Checkpointable` object and its dependencies.
 
   See `Checkpointable` for details of dependency management. `Saver` wraps
@@ -770,3 +770,94 @@ class Saver(object):
     load_status = CheckpointLoadStatus(
         checkpoint, feed_dict=file_prefix_feed_dict)
     return load_status
+
+
+class Checkpoint(core_checkpointable.Checkpointable):
+  """A utility class which groups `Checkpointable` objects.
+
+  Accepts arbitrary keyword arguments to its constructor and saves those values
+  with a checkpoint. Maintains a `save_counter` for numbering checkpoints.
+
+  Example usage:
+
+  ```python
+  import tensorflow as tf
+  import tensorflow.contrib.eager as tfe
+  import os
+
+  checkpoint_directory = "/tmp/training_checkpoints"
+  checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+
+  root = tfe.Checkpoint(optimizer=optimizer, model=model)
+  root.restore(tf.train.latest_checkpoint(checkpoint_directory))
+  for _ in range(num_training_steps):
+    optimizer.minimize( ... )
+  root.save(file_prefix=checkpoint_prefix)
+  ```
+
+  For more manual control over saving, use `tfe.CheckpointableSaver` directly.
+
+  Attributes:
+    save_counter: Incremented when `save()` is called. Used to number
+      checkpoints.
+  """
+
+  def __init__(self, **kwargs):
+    """Group objects into a training checkpoint.
+
+    Args:
+      **kwargs: Keyword arguments are set as attributes of this object, and are
+        saved with the checkpoint. Attribute values must derive from
+        `CheckpointableBase`.
+    Raises:
+      ValueError: If objects in `kwargs` are not Checkpointable.
+    """
+    super(Checkpoint, self).__init__()
+    for k, v in sorted(kwargs.items(), key=lambda item: item[0]):
+      if not isinstance(v, core_checkpointable.CheckpointableBase):
+        raise ValueError(
+            ("`Checkpoint` was expecting an object derived from "
+             "`CheckpointableBase`, got %s.") % (v,))
+      setattr(self, k, v)
+    self._save_counter = None  # Created lazily for restore-on-create.
+    self._saver = CheckpointableSaver(weakref.ref(self))
+
+  def _maybe_create_save_counter(self):
+    """Create a save counter if it does not yet exist."""
+    if self._save_counter is None:
+      # Initialized to 0 and incremented before saving.
+      self._save_counter = add_variable(
+          self, name="save_counter", initializer=0, dtype=dtypes.int64)
+
+  @property
+  def save_counter(self):
+    """An integer variable which starts at zero and is incremented on save.
+
+    Used to number checkpoints.
+
+    Returns:
+      The save counter variable.
+    """
+    self._maybe_create_save_counter()
+    return self._save_counter
+
+  def save(self, file_prefix, session=None):
+    """Save a checkpoint. Wraps `tfe.CheckpointableSaver.save`."""
+    assign_op = self.save_counter.assign_add(1)
+    if context.in_graph_mode():
+      if session is None:
+        session = ops.get_default_session()
+      session.run(assign_op)
+    return self._saver.save(
+        file_prefix=file_prefix,
+        checkpoint_number=self.save_counter,
+        session=session)
+
+  def restore(self, save_path):
+    """Restore a checkpoint. Wraps `tfe.CheckpointableSaver.restore`."""
+    status = self._saver.restore(save_path=save_path)
+    # Create the save counter now so it gets initialized with other variables
+    # when graph building. Creating it earlier would lead to double
+    # initialization when executing eagerly.
+    self._maybe_create_save_counter()
+    return status
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index 83187b51b5..68f0d93632 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -18,7 +18,6 @@ from __future__ import print_function
 
 import functools
 import os
-import weakref
 
 import six
 
@@ -114,51 +113,6 @@ class MyNetwork(CheckpointableNetwork):
     return self._via_track_layer(self._named_dense(values))
 
 
-class Checkpoint(checkpointable.Checkpointable):
-  """A utility class which groups `Checkpointable` objects."""
-
-  def __init__(self, **kwargs):
-    super(Checkpoint, self).__init__()
-    for k, v in sorted(kwargs.items(), key=lambda item: item[0]):
-      setattr(self, k, v)
-    self._save_counter = None  # Created lazily for restore-on-create.
-    self._saver = checkpointable_utils.Saver(weakref.ref(self))
-
-  @property
-  def save_counter(self):
-    """An integer variable which starts at zero and is incremented on save.
-
-    Used to number checkpoints.
-
-    Returns:
-      The save counter variable.
-    """
-    if self._save_counter is None:
-      # Initialized to 0 and incremented before saving.
-      self._save_counter = checkpointable_utils.add_variable(
-          self, name="save_counter", initializer=0, dtype=dtypes.int64)
-    return self._save_counter
-
-  def save(self, file_prefix, session=None):
-    assign_op = self.save_counter.assign_add(1)
-    if context.in_graph_mode():
-      if session is None:
-        session = ops.get_default_session()
-      session.run(assign_op)
-    return self._saver.save(
-        file_prefix=file_prefix,
-        checkpoint_number=self.save_counter,
-        session=session)
-
-  def restore(self, save_path):
-    status = self._saver.restore(save_path=save_path)
-    # Create the save counter now so it gets initialized with other variables
-    # when graph building. Creating it earlier would lead to double
-    # initialization when executing eagerly.
-    self.save_counter  # pylint: disable=pointless-statement
-    return status
-
-
 class InterfaceTests(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
@@ -256,7 +210,7 @@ class CheckpointingTests(test.TestCase):
     other_network = MyNetwork()
     optimizer = CheckpointableAdam(0.001)
     optimizer_step = training_util.get_or_create_global_step()
-    root_checkpointable = Checkpoint(
+    root_checkpointable = checkpointable_utils.Checkpoint(
         optimizer=optimizer, network=network, optimizer_step=optimizer_step)
     if context.in_eager_mode():
       optimizer.minimize(
@@ -361,7 +315,8 @@ class CheckpointingTests(test.TestCase):
   def testSaveRestore(self):
     network = MyNetwork()
     optimizer = CheckpointableAdam(0.001)
-    root_checkpointable = Checkpoint(optimizer=optimizer, network=network)
+    root_checkpointable = checkpointable_utils.Checkpoint(
+        optimizer=optimizer, network=network)
     input_value = constant_op.constant([[3.]])
     if context.in_eager_mode():
       optimizer.minimize(
@@ -392,7 +347,7 @@ class CheckpointingTests(test.TestCase):
       return  # Restore-on-create is only supported when executing eagerly
     on_create_network = MyNetwork()
     on_create_optimizer = CheckpointableAdam(0.001)
-    on_create_root = Checkpoint(
+    on_create_root = checkpointable_utils.Checkpoint(
         optimizer=on_create_optimizer, network=on_create_network)
     # Deferred restoration
     status = on_create_root.restore(save_path=save_path)
@@ -424,7 +379,7 @@ class CheckpointingTests(test.TestCase):
     for training_continuation in range(3):
       network = MyNetwork()
       optimizer = CheckpointableAdam(0.001)
-      root = Checkpoint(
+      root = checkpointable_utils.Checkpoint(
           optimizer=optimizer, network=network,
           optimizer_step=training_util.get_or_create_global_step())
       root.restore(core_saver.latest_checkpoint(checkpoint_directory))
@@ -448,7 +403,7 @@ class CheckpointingTests(test.TestCase):
         with ops.Graph().as_default():
           network = MyNetwork()
           optimizer = CheckpointableAdam(0.001)
-          root = Checkpoint(
+          root = checkpointable_utils.Checkpoint(
               optimizer=optimizer, network=network,
               global_step=training_util.get_or_create_global_step())
           input_value = constant_op.constant([[3.]])
@@ -485,7 +440,7 @@ class CheckpointingTests(test.TestCase):
           graph=ops.get_default_graph()):
         network = MyNetwork()
         optimizer = CheckpointableAdam(0.001)
-        root = Checkpoint(
+        root = checkpointable_utils.Checkpoint(
             optimizer=optimizer, network=network,
             global_step=training_util.get_or_create_global_step())
         checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory)
@@ -567,9 +522,11 @@ class CheckpointingTests(test.TestCase):
     self.evaluate(state_ops.assign(original.dep.var, 123.))
     checkpoint_directory = self.get_temp_dir()
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
-    save_path = checkpointable_utils.Saver(original).save(checkpoint_prefix)
+    save_path = checkpointable_utils.CheckpointableSaver(
+        original).save(checkpoint_prefix)
     load_into = LateDependencies()
-    status = checkpointable_utils.Saver(load_into).restore(save_path)
+    status = checkpointable_utils.CheckpointableSaver(
+        load_into).restore(save_path)
     with self.assertRaises(AssertionError):
       status.assert_consumed()
     load_into.add_dep()
@@ -598,11 +555,12 @@ class CheckpointingTests(test.TestCase):
     self.evaluate(state_ops.assign(dep_after_var.dep.var, -14.))
     checkpoint_directory = self.get_temp_dir()
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
-    save_path = checkpointable_utils.Saver(dep_after_var).save(
+    save_path = checkpointable_utils.CheckpointableSaver(dep_after_var).save(
         checkpoint_prefix)
 
     loaded_dep_after_var = DepAfterVar()
-    status = checkpointable_utils.Saver(loaded_dep_after_var).restore(save_path)
+    status = checkpointable_utils.CheckpointableSaver(
+        loaded_dep_after_var).restore(save_path)
     loaded_dep_after_var.add_dep()
     status.assert_consumed()
     status.run_restore_ops()
@@ -622,24 +580,26 @@ class CheckpointingTests(test.TestCase):
       # `root`. Create a one-off grouping so that slot variables for `root.var`
       # get initialized too.
       self.evaluate(checkpointable_utils.gather_initializers(
-          Checkpoint(root=root, optimizer=optimizer)))
+          checkpointable_utils.Checkpoint(root=root, optimizer=optimizer)))
       self.evaluate(train_op)
     else:
       optimizer.minimize(root.var.read_value)
     self.evaluate(state_ops.assign(root.var, 12.))
-    no_slots_path = checkpointable_utils.Saver(root).save(
+    no_slots_path = checkpointable_utils.CheckpointableSaver(root).save(
         os.path.join(checkpoint_directory, "no_slots"))
     root.optimizer = optimizer
     self.evaluate(state_ops.assign(root.var, 13.))
     self.evaluate(state_ops.assign(optimizer.get_slot(name="m", var=root.var),
                                    14.))
-    slots_path = checkpointable_utils.Saver(root).save(
+    slots_path = checkpointable_utils.CheckpointableSaver(root).save(
         os.path.join(checkpoint_directory, "with_slots"))
     new_root = checkpointable.Checkpointable()
     # Load the slot-containing checkpoint (deferred), then immediately overwrite
     # the non-slot variable (also deferred).
-    slot_status = checkpointable_utils.Saver(new_root).restore(slots_path)
-    no_slot_status = checkpointable_utils.Saver(new_root).restore(no_slots_path)
+    slot_status = checkpointable_utils.CheckpointableSaver(
+        new_root).restore(slots_path)
+    no_slot_status = checkpointable_utils.CheckpointableSaver(
+        new_root).restore(no_slots_path)
     with self.assertRaises(AssertionError):
       no_slot_status.assert_consumed()
     new_root.var = checkpointable_utils.add_variable(
@@ -679,15 +639,17 @@ class CheckpointingTests(test.TestCase):
     save_root.dep.var = checkpointable_utils.add_variable(
         save_root.dep, name="var", initializer=0.)
     self.evaluate(state_ops.assign(save_root.dep.var, 12.))
-    saver = checkpointable_utils.Saver(save_root)
+    saver = checkpointable_utils.CheckpointableSaver(save_root)
     first_path = saver.save(os.path.join(checkpoint_directory, "first"))
     self.evaluate(state_ops.assign(save_root.dep.var, 13.))
     second_path = saver.save(os.path.join(checkpoint_directory, "second"))
 
     first_root = checkpointable.Checkpointable()
     second_root = checkpointable.Checkpointable()
-    first_status = checkpointable_utils.Saver(first_root).restore(first_path)
-    second_status = checkpointable_utils.Saver(second_root).restore(second_path)
+    first_status = checkpointable_utils.CheckpointableSaver(
+        first_root).restore(first_path)
+    second_status = checkpointable_utils.CheckpointableSaver(
+        second_root).restore(second_path)
     load_dep = checkpointable.Checkpointable()
     load_dep.var = checkpointable_utils.add_variable(
         load_dep, name="var", shape=[])
@@ -704,8 +666,10 @@ class CheckpointingTests(test.TestCase):
     # determines the final value.
     first_root = checkpointable.Checkpointable()
     second_root = checkpointable.Checkpointable()
-    second_status = checkpointable_utils.Saver(second_root).restore(second_path)
-    first_status = checkpointable_utils.Saver(first_root).restore(first_path)
+    second_status = checkpointable_utils.CheckpointableSaver(
+        second_root).restore(second_path)
+    first_status = checkpointable_utils.CheckpointableSaver(
+        first_root).restore(first_path)
     load_dep = checkpointable.Checkpointable()
     load_dep.var = checkpointable_utils.add_variable(
         load_dep, name="var", shape=[])
@@ -730,10 +694,10 @@ class CheckpointingTests(test.TestCase):
     save_root.dep_two.dep_three = dep_three
     checkpointable_utils.add_variable(dep_three, name="var", initializer=0.)
     self.evaluate(checkpointable_utils.gather_initializers(save_root))
-    save_path = checkpointable_utils.Saver(save_root).save(
+    save_path = checkpointable_utils.CheckpointableSaver(save_root).save(
         os.path.join(checkpoint_directory, "ckpt"))
     load_root = checkpointable.Checkpointable()
-    checkpointable_utils.Saver(load_root).restore(save_path)
+    checkpointable_utils.CheckpointableSaver(load_root).restore(save_path)
     load_root.dep_one = checkpointable.Checkpointable()
     load_root.dep_two = checkpointable.Checkpointable()
     load_root.dep_one.dep_three = checkpointable.Checkpointable()
@@ -753,7 +717,7 @@ class CheckpointingTests(test.TestCase):
     checkpointable_utils.add_variable(
         save_root.dep_two, name="var2", initializer=64., dtype=dtypes.float64)
     self.evaluate(checkpointable_utils.gather_initializers(save_root))
-    save_path = checkpointable_utils.Saver(save_root).save(
+    save_path = checkpointable_utils.CheckpointableSaver(save_root).save(
         os.path.join(checkpoint_directory, "ckpt"))
     load_root = checkpointable.Checkpointable()
     load_root.dep_one = checkpointable.Checkpointable()
@@ -762,7 +726,7 @@ class CheckpointingTests(test.TestCase):
         load_root.dep_one, name="var1", shape=[], dtype=dtypes.float64)
     v2 = checkpointable_utils.add_variable(
         load_root.dep_one, name="var2", shape=[], dtype=dtypes.float64)
-    status = checkpointable_utils.Saver(load_root).restore(
+    status = checkpointable_utils.CheckpointableSaver(load_root).restore(
         save_path).assert_consumed()
     status.run_restore_ops()
     self.assertEqual(32., self.evaluate(v1))
@@ -782,12 +746,13 @@ class CheckpointingTests(test.TestCase):
         second, "v2", initializer=[1., 1., 2., 3.])
     self.evaluate(checkpointable_utils.gather_initializers(first))
     checkpoint_directory = self.get_temp_dir()
-    save_path = checkpointable_utils.Saver(first).save(
+    save_path = checkpointable_utils.CheckpointableSaver(first).save(
         os.path.join(checkpoint_directory, "ckpt"))
 
     # Test deferred loading
     first_load = checkpointable.Checkpointable()
-    status = checkpointable_utils.Saver(first_load).restore(save_path)
+    status = checkpointable_utils.CheckpointableSaver(
+        first_load).restore(save_path)
     second_load = checkpointable.Checkpointable()
     first_load.second = second_load
     second_load.first = first_load
@@ -807,7 +772,7 @@ class CheckpointingTests(test.TestCase):
     self.assertAllEqual([2., 7., 1.], self.evaluate(first_load.v))
     self.evaluate(second_load.v.assign([2., 7., 1., 8.]))
     self.assertAllEqual([2., 7., 1., 8.], self.evaluate(second_load.v))
-    status = checkpointable_utils.Saver(first_load).restore(
+    status = checkpointable_utils.CheckpointableSaver(first_load).restore(
         save_path).assert_consumed()
     status.run_restore_ops()
     self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v))
@@ -826,14 +791,15 @@ class CheckpointingTests(test.TestCase):
           name="blah", initializer=0.)
       self.evaluate(first.var1.assign(4.))
       self.evaluate(first.var2.assign(8.))
-      save_path = checkpointable_utils.Saver(first).save(
+      save_path = checkpointable_utils.CheckpointableSaver(first).save(
           checkpoint_prefix)
     restore_graph = ops.Graph()
     with restore_graph.as_default(), self.test_session(restore_graph):
       second = checkpointable.Checkpointable()
       second.var2 = variable_scope.get_variable(
           name="blah", initializer=0.)
-      status = checkpointable_utils.Saver(second).restore(save_path)
+      status = checkpointable_utils.CheckpointableSaver(
+          second).restore(save_path)
       recreated_var1 = variable_scope.get_variable(
           name="outside_var", initializer=0.)
       status.run_restore_ops()
@@ -856,7 +822,7 @@ class CheckpointingTests(test.TestCase):
         obj.opt = CheckpointableAdam(0.1)
         obj.opt.minimize(obj.var.read_value())
         self.evaluate(checkpointable_utils.gather_initializers(obj))
-        saver = checkpointable_utils.Saver(obj)
+        saver = checkpointable_utils.CheckpointableSaver(obj)
         saver.save(checkpoint_prefix)
         before_ops = graph.get_operations()
         saver.save(checkpoint_prefix)
@@ -874,7 +840,7 @@ class CheckpointingTests(test.TestCase):
         obj.opt = CheckpointableAdam(0.1)
         obj.opt.minimize(obj.var.read_value())
         self.evaluate(checkpointable_utils.gather_initializers(obj))
-        saver = checkpointable_utils.Saver(obj)
+        saver = checkpointable_utils.CheckpointableSaver(obj)
         save_path = saver.save(checkpoint_prefix)
         saver.restore(save_path)
         before_ops = graph.get_operations()
@@ -889,7 +855,7 @@ class CheckpointCompatibilityTests(test.TestCase):
     network = MyNetwork()
     optimizer = CheckpointableAdam(0.001)
     optimizer_step = training_util.get_or_create_global_step()
-    root_checkpointable = Checkpoint(
+    root_checkpointable = checkpointable_utils.Checkpoint(
         optimizer=optimizer, network=network, optimizer_step=optimizer_step)
     train_op = optimizer.minimize(
         functools.partial(network, input_value),
@@ -945,7 +911,7 @@ class CheckpointCompatibilityTests(test.TestCase):
     self._set_sentinels(root)
     with self.assertRaises(AssertionError):
       self._check_sentinels(root)
-    object_saver = checkpointable_utils.Saver(root)
+    object_saver = checkpointable_utils.CheckpointableSaver(root)
     status = object_saver.restore(save_path)
     with self.assertRaises(AssertionError):
       status.assert_consumed()
@@ -966,7 +932,7 @@ class CheckpointCompatibilityTests(test.TestCase):
       with save_graph.as_default(), self.test_session(
           graph=save_graph) as session:
         root = self._initialized_model()
-        object_saver = checkpointable_utils.Saver(root)
+        object_saver = checkpointable_utils.CheckpointableSaver(root)
         save_path = object_saver.save(
             session=session, file_prefix=checkpoint_prefix)
     with context.eager_mode():
@@ -980,7 +946,7 @@ class CheckpointCompatibilityTests(test.TestCase):
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
     with context.eager_mode():
       root = self._initialized_model()
-      object_saver = checkpointable_utils.Saver(root)
+      object_saver = checkpointable_utils.CheckpointableSaver(root)
       save_path = object_saver.save(file_prefix=checkpoint_prefix)
     with context.graph_mode():
       save_graph = ops.Graph()
-- 
GitLab


From 142c1f0b9333a6e69fefad18b951944fa4617cd9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 13:22:58 -0800
Subject: [PATCH 360/575] During late import, update model->flags from the
 input-arrays shape information that was read from the graph (e.g. shape
 attribute in Placeholder nodes).

PiperOrigin-RevId: 187222358
---
 tensorflow/contrib/lite/toco/tooling_util.cc | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index 1ab7b34331..d23b3737fc 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -1199,7 +1199,7 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) {
       << "This model does not define output arrays, so a "
          "--output_arrays flag must be given on the command-line.";
 
-  for (const auto& input_array_proto : model->flags.input_arrays()) {
+  for (auto& input_array_proto : *model->flags.mutable_input_arrays()) {
     auto& input_array = model->GetOrCreateArray(input_array_proto.name());
     if (input_array_proto.has_data_type()) {
       const ArrayDataType specified_type =
@@ -1243,6 +1243,11 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) {
         for (int i = 0; i < input_array_dims.size(); i++) {
           CHECK_EQ(input_array_dims[i], input_array_proto.shape().dims(i));
         }
+      } else {
+        for (int i = 0; i < input_array.shape().dimensions_count(); i++) {
+          input_array_proto.mutable_shape()->add_dims(
+              input_array.shape().dims(i));
+        }
       }
     }
 
-- 
GitLab


From 93f5dd54dab124a9ec3b4c5dcb42d31716fe2f95 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 13:36:10 -0800
Subject: [PATCH 361/575] Optimized non-aligned case of split and split_v on
 the first input dimension.

PiperOrigin-RevId: 187224344
---
 tensorflow/core/kernels/batch_kernels.cc    |  14 +-
 tensorflow/core/kernels/split_lib.h         |  32 ++--
 tensorflow/core/kernels/split_lib_cpu.cc    |  32 ++--
 tensorflow/core/kernels/split_lib_gpu.cu.cc |  16 +-
 tensorflow/core/kernels/split_op.cc         | 154 +++++++++++++-------
 tensorflow/core/kernels/split_v_op.cc       | 149 ++++++++++++-------
 tensorflow/core/kernels/tensor_array_ops.cc |  12 +-
 tensorflow/core/kernels/unpack_op.cc        |  14 +-
 8 files changed, 258 insertions(+), 165 deletions(-)

diff --git a/tensorflow/core/kernels/batch_kernels.cc b/tensorflow/core/kernels/batch_kernels.cc
index 546e51be53..8c99ded0a8 100644
--- a/tensorflow/core/kernels/batch_kernels.cc
+++ b/tensorflow/core/kernels/batch_kernels.cc
@@ -146,7 +146,7 @@ Status SplitCPU(OpKernelContext* context, const Tensor& input,
     suffix_dim_size *= input.shape().dim_size(i);
   }
   auto input_reshaped =
-      input.shaped<T, 3>({1, input.shape().dim_size(0), suffix_dim_size});
+      input.shaped<T, 2>({input.shape().dim_size(0), suffix_dim_size});
 
   int64 position = 0;
   for (const int64 size : sizes) {
@@ -155,13 +155,13 @@ Status SplitCPU(OpKernelContext* context, const Tensor& input,
     Tensor output;
     TF_RETURN_IF_ERROR(
         context->allocate_temp(input.dtype(), output_shape, &output));
-    auto output_shaped = output.shaped<T, 3>({1, size, suffix_dim_size});
+    auto output_shaped = output.shaped<T, 2>({size, suffix_dim_size});
 
-    Eigen::DSizes<Eigen::DenseIndex, 3> slice_indices{0, position, 0};
-    Eigen::DSizes<Eigen::DenseIndex, 3> slice_sizes{1, size, suffix_dim_size};
-    functor::Split<CPUDevice, T>()(context->eigen_device<CPUDevice>(),
-                                   output_shaped, input_reshaped, slice_indices,
-                                   slice_sizes);
+    Eigen::DSizes<Eigen::DenseIndex, 2> slice_indices{position, 0};
+    Eigen::DSizes<Eigen::DenseIndex, 2> slice_sizes{size, suffix_dim_size};
+    functor::Split<CPUDevice, T, 2>()(context->eigen_device<CPUDevice>(),
+                                      output_shaped, input_reshaped,
+                                      slice_indices, slice_sizes);
 
     outputs->emplace_back(output);
 
diff --git a/tensorflow/core/kernels/split_lib.h b/tensorflow/core/kernels/split_lib.h
index a08949e626..bc1fa28f8f 100644
--- a/tensorflow/core/kernels/split_lib.h
+++ b/tensorflow/core/kernels/split_lib.h
@@ -31,31 +31,31 @@ struct SplitCustom {
                   const Eigen::DSizes<Eigen::DenseIndex, 2>& slice_sizes);
 };
 
-template <typename Device, typename T>
+template <typename Device, typename T, int NDims>
 struct Split {
-  void operator()(const Device& d, typename TTypes<T, 3>::Tensor output,
-                  typename TTypes<T, 3>::ConstTensor input,
-                  const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_indices,
-                  const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_sizes);
+  void operator()(const Device& d, typename TTypes<T, NDims>::Tensor output,
+                  typename TTypes<T, NDims>::ConstTensor input,
+                  const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_indices,
+                  const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_sizes);
 };
 
-template <typename T>
-struct Split<Eigen::ThreadPoolDevice, T> {
+template <typename T, int NDims>
+struct Split<Eigen::ThreadPoolDevice, T, NDims> {
   void operator()(const Eigen::ThreadPoolDevice& d,
-                  typename TTypes<T, 3>::Tensor output,
-                  typename TTypes<T, 3>::ConstTensor input,
-                  const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_indices,
-                  const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_sizes);
+                  typename TTypes<T, NDims>::Tensor output,
+                  typename TTypes<T, NDims>::ConstTensor input,
+                  const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_indices,
+                  const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_sizes);
 };
 
 #ifdef TENSORFLOW_USE_SYCL
-template <typename T>
+template <typename T, int NDims>
 struct Split<Eigen::SyclDevice, T> {
   void operator()(const Eigen::SyclDevice& d,
-                  typename TTypes<T, 3>::Tensor output,
-                  typename TTypes<T, 3>::ConstTensor input,
-                  const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_indices,
-                  const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_sizes);
+                  typename TTypes<T, NDims>::Tensor output,
+                  typename TTypes<T, NDims>::ConstTensor input,
+                  const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_indices,
+                  const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_sizes);
 };
 #endif  // TENSORFLOW_USE_SYCL
 
diff --git a/tensorflow/core/kernels/split_lib_cpu.cc b/tensorflow/core/kernels/split_lib_cpu.cc
index 771c633b15..a3060e4e90 100644
--- a/tensorflow/core/kernels/split_lib_cpu.cc
+++ b/tensorflow/core/kernels/split_lib_cpu.cc
@@ -24,12 +24,12 @@ limitations under the License.
 namespace tensorflow {
 namespace functor {
 
-template <typename T>
-void Split<Eigen::ThreadPoolDevice, T>::operator()(
-    const Eigen::ThreadPoolDevice& d, typename TTypes<T, 3>::Tensor output,
-    typename TTypes<T, 3>::ConstTensor input,
-    const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_indices,
-    const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_sizes) {
+template <typename T, int NDims>
+void Split<Eigen::ThreadPoolDevice, T, NDims>::operator()(
+    const Eigen::ThreadPoolDevice& d, typename TTypes<T, NDims>::Tensor output,
+    typename TTypes<T, NDims>::ConstTensor input,
+    const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_indices,
+    const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_sizes) {
   if (output.size() < 131072) {
     output = input.slice(slice_indices, slice_sizes);
   } else {
@@ -37,22 +37,26 @@ void Split<Eigen::ThreadPoolDevice, T>::operator()(
   }
 }
 
-#define DEFINE_CPU_KERNELS(T) template struct Split<Eigen::ThreadPoolDevice, T>;
+#define DEFINE_CPU_KERNELS(T)                           \
+  template struct Split<Eigen::ThreadPoolDevice, T, 2>; \
+  template struct Split<Eigen::ThreadPoolDevice, T, 3>;
 
 TF_CALL_ALL_TYPES(DEFINE_CPU_KERNELS)
 DEFINE_CPU_KERNELS(quint8)
 
 #ifdef TENSORFLOW_USE_SYCL
-template <typename T>
-void Split<Eigen::SyclDevice, T>::operator()(
-    const Eigen::SyclDevice& d, typename TTypes<T, 3>::Tensor output,
-    typename TTypes<T, 3>::ConstTensor input,
-    const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_indices,
-    const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_sizes) {
+template <typename T, int NDims>
+void Split<Eigen::SyclDevice, T, NDims>::operator()(
+    const Eigen::SyclDevice& d, typename TTypes<T, NDims>::Tensor output,
+    typename TTypes<T, NDims>::ConstTensor input,
+    const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_indices,
+    const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_sizes) {
   output.device(d) = input.slice(slice_indices, slice_sizes);
 }
 
-#define DEFINE_SYCL_KERNELS(T) template struct Split<Eigen::SyclDevice, T>;
+#define DEFINE_SYCL_KERNELS(T)                    \
+  template struct Split<Eigen::SyclDevice, T, 2>; \
+  template struct Split<Eigen::SyclDevice, T, 3>;
 
 TF_CALL_GPU_NUMBER_TYPES_NO_HALF(DEFINE_SYCL_KERNELS);
 #endif  // TENSORFLOW_USE_SYCL
diff --git a/tensorflow/core/kernels/split_lib_gpu.cu.cc b/tensorflow/core/kernels/split_lib_gpu.cu.cc
index 9f234fc093..393818730b 100644
--- a/tensorflow/core/kernels/split_lib_gpu.cu.cc
+++ b/tensorflow/core/kernels/split_lib_gpu.cu.cc
@@ -29,12 +29,12 @@ limitations under the License.
 namespace tensorflow {
 namespace functor {
 
-template <typename Device, typename T>
-void Split<Device, T>::operator()(
-    const Device& d, typename TTypes<T, 3>::Tensor output,
-    typename TTypes<T, 3>::ConstTensor input,
-    const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_indices,
-    const Eigen::DSizes<Eigen::DenseIndex, 3>& slice_sizes) {
+template <typename Device, typename T, int NDims>
+void Split<Device, T, NDims>::operator()(
+    const Device& d, typename TTypes<T, NDims>::Tensor output,
+    typename TTypes<T, NDims>::ConstTensor input,
+    const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_indices,
+    const Eigen::DSizes<Eigen::DenseIndex, NDims>& slice_sizes) {
   To32Bit(output).device(d) = To32Bit(input).slice(slice_indices, slice_sizes);
 }
 
@@ -47,7 +47,9 @@ void SplitCustom<Device, T>::operator()(
   To32Bit(output).device(d) = To32Bit(input).slice(slice_indices, slice_sizes);
 }
 
-#define DEFINE_GPU_KERNELS(T) template struct Split<Eigen::GpuDevice, T>;
+#define DEFINE_GPU_KERNELS(T)                    \
+  template struct Split<Eigen::GpuDevice, T, 2>; \
+  template struct Split<Eigen::GpuDevice, T, 3>;
 
 TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_KERNELS);
 TF_CALL_complex64(DEFINE_GPU_KERNELS);
diff --git a/tensorflow/core/kernels/split_op.cc b/tensorflow/core/kernels/split_op.cc
index 85f529326d..1bc92a4f70 100644
--- a/tensorflow/core/kernels/split_op.cc
+++ b/tensorflow/core/kernels/split_op.cc
@@ -121,6 +121,77 @@ class SplitOpBase : public OpKernel {
   }
 };
 
+template <typename T, typename InputReshapedType, int NDims>
+class SplitOpCPUImpl {
+ public:
+  template <typename MakeSizesType, typename ReshapeResultType>
+  void operator()(OpKernelContext* context,
+                  const InputReshapedType& input_reshaped,
+                  const TensorShape& input_shape, int32 split_dim,
+                  Eigen::DenseIndex prefix_dim_size,
+                  Eigen::DenseIndex split_dim_size,
+                  Eigen::DenseIndex suffix_dim_size,
+                  const MakeSizesType& make_sizes,
+                  const ReshapeResultType& reshape_result, int32 num_split,
+                  int64 split_dim_output_size) const {
+    const auto num_threads =
+        context->device()->tensorflow_cpu_worker_threads()->num_threads;
+    // TODO(jewillco): Tune heuristic further.
+    const auto input_element_count = input_shape.num_elements();
+    const bool use_parallelism_between_outputs =
+        (num_split >= 4 &&
+         input_element_count >= std::max(num_threads, num_split) * 4096 &&
+         input_element_count < num_split * 180 * 1024);
+    Eigen::DSizes<Eigen::DenseIndex, NDims> indices;
+    for (int i = 0; i < NDims; ++i) {
+      indices[i] = 0;
+    }
+    auto sizes = make_sizes(split_dim_output_size);
+    TensorShape output_shape(input_shape);
+    output_shape.set_dim(split_dim, split_dim_output_size);
+
+    auto range_output_func = [&indices, context, &output_shape, prefix_dim_size,
+                              split_dim_output_size, suffix_dim_size, &sizes,
+                              use_parallelism_between_outputs, &input_reshaped,
+                              &reshape_result](int64 start, int64 limit) {
+      for (int64 i = start; i < limit; ++i) {
+        Tensor* result = nullptr;
+        OP_REQUIRES_OK(context,
+                       context->allocate_output(i, output_shape, &result));
+        if (prefix_dim_size * split_dim_output_size * suffix_dim_size > 0) {
+          Eigen::DSizes<Eigen::DenseIndex, NDims> slice_indices;
+          Eigen::DSizes<Eigen::DenseIndex, NDims> slice_sizes;
+          for (int j = 0; j < NDims; ++j) {
+            slice_indices[j] =
+                (j == NDims - 2 ? i * split_dim_output_size : indices[j]);
+            slice_sizes[j] = sizes[j];
+          }
+
+          auto result_shaped = reshape_result(result, split_dim_output_size);
+
+          if (use_parallelism_between_outputs) {
+            // Use sequential implementation for single output.
+            result_shaped = input_reshaped.slice(slice_indices, slice_sizes);
+          } else {
+            // This implementation may be parallel internally.
+            functor::Split<CPUDevice, T, NDims>()(
+                context->eigen_device<CPUDevice>(), result_shaped,
+                input_reshaped, slice_indices, slice_sizes);
+          }
+        }
+      }
+    };
+    if (use_parallelism_between_outputs) {
+      // Run in parallel, disabling parallelism in functor.
+      context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
+          num_split, input_element_count / num_split, range_output_func);
+    } else {
+      // Run sequentially, but allow internal parallelism in functor.
+      range_output_func(0, num_split);
+    }
+  }
+};
+
 template <typename T>
 class SplitOpCPU : public SplitOpBase<CPUDevice, T> {
  public:
@@ -154,66 +225,37 @@ class SplitOpCPU : public SplitOpBase<CPUDevice, T> {
 
     std::tie(prefix_dim_size, split_dim_size, suffix_dim_size) =
         Base::template SetDims<Eigen::DenseIndex>(input_shape, split_dim);
-    auto input_reshaped =
-        input.shaped<T, 3>({prefix_dim_size, split_dim_size, suffix_dim_size});
 
     const int64 split_dim_output_size = split_dim_size / num_split;
-    TensorShape output_shape(input_shape);
-    output_shape.set_dim(split_dim, split_dim_output_size);
-
-    Eigen::DSizes<Eigen::DenseIndex, 3> indices{0, 0, 0};
-    const Eigen::DSizes<Eigen::DenseIndex, 3> sizes{
-        prefix_dim_size, split_dim_output_size, suffix_dim_size};
-
-    const auto num_threads =
-        context->device()->tensorflow_cpu_worker_threads()->num_threads;
-    // TODO(jewillco): Tune heuristic further.
-    const auto input_element_count = input_shape.num_elements();
-    const bool use_parallelism_between_outputs =
-        (num_split >= 4 &&
-         input_element_count >= std::max(num_threads, num_split) * 4096 &&
-         input_element_count < num_split * 180 * 1024);
-
-    auto range_output_func = [&indices, context, &output_shape, prefix_dim_size,
-                              split_dim_output_size, suffix_dim_size, &sizes,
-                              use_parallelism_between_outputs,
-                              &input_reshaped](int64 start, int64 limit) {
-      for (int64 i = start; i < limit; ++i) {
-        Tensor* result = nullptr;
-        OP_REQUIRES_OK(context,
-                       context->allocate_output(i, output_shape, &result));
-        if (prefix_dim_size * split_dim_output_size * suffix_dim_size > 0) {
-          Eigen::DSizes<Eigen::DenseIndex, 3> slice_indices;
-          Eigen::DSizes<Eigen::DenseIndex, 3> slice_sizes;
-          for (int j = 0; j < 3; ++j) {
-            slice_indices[j] =
-                (j == 1 ? i * split_dim_output_size : indices[j]);
-            slice_sizes[j] = sizes[j];
-          }
-
-          auto result_shaped = result->shaped<T, 3>(
-              {prefix_dim_size, split_dim_output_size, suffix_dim_size});
 
-          if (use_parallelism_between_outputs) {
-            // Use sequential implementation for single output.
-            result_shaped = input_reshaped.slice(slice_indices, slice_sizes);
-          } else {
-            // This implementation may be parallel internally.
-            functor::Split<CPUDevice, T>()(context->eigen_device<CPUDevice>(),
-                                           result_shaped, input_reshaped,
-                                           slice_indices, slice_sizes);
-          }
-        }
-      }
-    };
-    if (use_parallelism_between_outputs) {
-      // Run in parallel, disabling parallelism in functor.
-      Shard(num_split,
-            context->device()->tensorflow_cpu_worker_threads()->workers,
-            num_split, input_element_count / num_split, range_output_func);
+    if (prefix_dim_size == 1) {
+      auto input_reshaped =
+          input.shaped<T, 2>({split_dim_size, suffix_dim_size});
+      auto make_sizes = [&](int64 split_size) {
+        return Eigen::DSizes<Eigen::DenseIndex, 2>{split_size, suffix_dim_size};
+      };
+      auto reshape_result = [&](Tensor* result, int64 split_size) {
+        return result->shaped<T, 2>({split_size, suffix_dim_size});
+      };
+      SplitOpCPUImpl<T, decltype(input_reshaped), 2>{}(
+          context, input_reshaped, input_shape, split_dim, prefix_dim_size,
+          split_dim_size, suffix_dim_size, make_sizes, reshape_result,
+          num_split, split_dim_output_size);
     } else {
-      // Run sequentially, but allow internal parallelism in functor.
-      range_output_func(0, num_split);
+      auto input_reshaped = input.shaped<T, 3>(
+          {prefix_dim_size, split_dim_size, suffix_dim_size});
+      auto make_sizes = [&](int64 split_size) {
+        return Eigen::DSizes<Eigen::DenseIndex, 3>{prefix_dim_size, split_size,
+                                                   suffix_dim_size};
+      };
+      auto reshape_result = [&](Tensor* result, int64 split_size) {
+        return result->shaped<T, 3>(
+            {prefix_dim_size, split_size, suffix_dim_size});
+      };
+      SplitOpCPUImpl<T, decltype(input_reshaped), 3>{}(
+          context, input_reshaped, input_shape, split_dim, prefix_dim_size,
+          split_dim_size, suffix_dim_size, make_sizes, reshape_result,
+          num_split, split_dim_output_size);
     }
   }
 };
diff --git a/tensorflow/core/kernels/split_v_op.cc b/tensorflow/core/kernels/split_v_op.cc
index 7ff5df47d7..16fa890780 100644
--- a/tensorflow/core/kernels/split_v_op.cc
+++ b/tensorflow/core/kernels/split_v_op.cc
@@ -175,6 +175,76 @@ class SplitVOpBase : public OpKernel {
   }
 };
 
+template <typename T, typename Tlen, typename InputReshapedType, int NDims>
+class SplitVOpCPUImpl {
+ public:
+  template <typename MakeSizesType, typename ReshapeResultType>
+  void operator()(OpKernelContext* context,
+                  const InputReshapedType& input_reshaped,
+                  const std::vector<int64>& split_start_points,
+                  const TensorShape& input_shape, int32 split_dim,
+                  Eigen::DenseIndex prefix_dim_size,
+                  Eigen::DenseIndex split_dim_size,
+                  Eigen::DenseIndex suffix_dim_size,
+                  std::vector<Tlen>& split_sizes_vec,
+                  const MakeSizesType& make_sizes,
+                  const ReshapeResultType& reshape_result) const {
+    Eigen::DSizes<Eigen::DenseIndex, NDims> indices;
+    for (int i = 0; i < NDims; ++i) {
+      indices[i] = 0;
+    }
+    const auto num_threads =
+        context->device()->tensorflow_cpu_worker_threads()->num_threads;
+    // TODO(jewillco): Tune heuristic further.
+    const auto input_element_count = input_shape.num_elements();
+    const int num_split = split_start_points.size();
+    const bool use_parallelism_between_outputs =
+        (num_split >= 4 &&
+         input_element_count >= std::max(num_threads, num_split) * 4096 &&
+         input_element_count < num_split * 180 * 1024);
+
+    auto range_output_func = [&indices, context, &input_shape, prefix_dim_size,
+                              split_dim, &split_sizes_vec, &split_start_points,
+                              suffix_dim_size, use_parallelism_between_outputs,
+                              &input_reshaped, &make_sizes,
+                              &reshape_result](int64 start, int64 limit) {
+      for (int64 i = start; i < limit; ++i) {
+        TensorShape output_shape(input_shape);
+        output_shape.set_dim(split_dim, split_sizes_vec[i]);
+        Tensor* result = nullptr;
+        OP_REQUIRES_OK(context,
+                       context->allocate_output(i, output_shape, &result));
+
+        const auto sizes = make_sizes(split_sizes_vec[i]);
+
+        if (sizes.TotalSize() > 0) {
+          auto result_shaped = reshape_result(result, split_sizes_vec[i]);
+
+          auto current_indices = indices;
+          current_indices[NDims - 2] = split_start_points[i];
+          if (use_parallelism_between_outputs) {
+            // Use sequential implementation for single output.
+            result_shaped = input_reshaped.slice(current_indices, sizes);
+          } else {
+            // This implementation may be parallel internally.
+            functor::Split<CPUDevice, T, NDims>()(
+                context->eigen_device<CPUDevice>(), result_shaped,
+                input_reshaped, current_indices, sizes);
+          }
+        }
+      }
+    };
+    if (use_parallelism_between_outputs) {
+      // Run in parallel, disabling parallelism in functor.
+      context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
+          num_split, input_element_count / num_split, range_output_func);
+    } else {
+      // Run sequentially, but allow internal parallelism in functor.
+      range_output_func(0, num_split);
+    }
+  }
+};
+
 template <typename T, typename Tlen>
 class SplitVOpCPU : public SplitVOpBase<CPUDevice, T, Tlen> {
  public:
@@ -209,10 +279,6 @@ class SplitVOpCPU : public SplitVOpBase<CPUDevice, T, Tlen> {
 
     std::tie(prefix_dim_size, split_dim_size, suffix_dim_size) =
         Base::template SetDims<Eigen::DenseIndex>(input_shape, split_dim);
-    auto input_reshaped =
-        input.shaped<T, 3>({prefix_dim_size, split_dim_size, suffix_dim_size});
-
-    Eigen::DSizes<Eigen::DenseIndex, 3> indices{0, 0, 0};
     std::vector<int64> split_start_points(num_split);
     for (int i = 0; i < num_split; ++i) {
       if (i == 0) {
@@ -223,55 +289,34 @@ class SplitVOpCPU : public SplitVOpBase<CPUDevice, T, Tlen> {
       }
     }
 
-    const auto num_threads =
-        context->device()->tensorflow_cpu_worker_threads()->num_threads;
-    // TODO(jewillco): Tune heuristic further.
-    const auto input_element_count = input_shape.num_elements();
-    const bool use_parallelism_between_outputs =
-        (num_split >= 4 &&
-         input_element_count >= std::max(num_threads, num_split) * 4096 &&
-         input_element_count < num_split * 180 * 1024);
-
-    auto range_output_func = [&indices, context, &input_shape, prefix_dim_size,
-                              split_dim, &split_sizes_vec, &split_start_points,
-                              suffix_dim_size, use_parallelism_between_outputs,
-                              &input_reshaped](int64 start, int64 limit) {
-      for (int64 i = start; i < limit; ++i) {
-        TensorShape output_shape(input_shape);
-        output_shape.set_dim(split_dim, split_sizes_vec[i]);
-        Tensor* result = nullptr;
-        OP_REQUIRES_OK(context,
-                       context->allocate_output(i, output_shape, &result));
-
-        Eigen::DSizes<Eigen::DenseIndex, 3> sizes{
-            prefix_dim_size, split_sizes_vec[i], suffix_dim_size};
-
-        if (sizes.TotalSize() > 0) {
-          auto result_shaped = result->shaped<T, 3>(
-              {prefix_dim_size, split_sizes_vec[i], suffix_dim_size});
-
-          auto current_indices = indices;
-          current_indices[1] = split_start_points[i];
-          if (use_parallelism_between_outputs) {
-            // Use sequential implementation for single output.
-            result_shaped = input_reshaped.slice(current_indices, sizes);
-          } else {
-            // This implementation may be parallel internally.
-            functor::Split<CPUDevice, T>()(context->eigen_device<CPUDevice>(),
-                                           result_shaped, input_reshaped,
-                                           current_indices, sizes);
-          }
-        }
-      }
-    };
-    if (use_parallelism_between_outputs) {
-      // Run in parallel, disabling parallelism in functor.
-      Shard(num_split,
-            context->device()->tensorflow_cpu_worker_threads()->workers,
-            num_split, input_element_count / num_split, range_output_func);
+    if (prefix_dim_size == 1) {
+      auto input_reshaped =
+          input.shaped<T, 2>({split_dim_size, suffix_dim_size});
+      auto make_sizes = [&](Tlen split_size) {
+        return Eigen::DSizes<Eigen::DenseIndex, 2>{split_size, suffix_dim_size};
+      };
+      auto reshape_result = [&](Tensor* result, Tlen split_size) {
+        return result->shaped<T, 2>({split_size, suffix_dim_size});
+      };
+      SplitVOpCPUImpl<T, Tlen, decltype(input_reshaped), 2>{}(
+          context, input_reshaped, split_start_points, input_shape, split_dim,
+          prefix_dim_size, split_dim_size, suffix_dim_size, split_sizes_vec,
+          make_sizes, reshape_result);
     } else {
-      // Run sequentially, but allow internal parallelism in functor.
-      range_output_func(0, num_split);
+      auto input_reshaped = input.shaped<T, 3>(
+          {prefix_dim_size, split_dim_size, suffix_dim_size});
+      auto make_sizes = [&](Tlen split_size) {
+        return Eigen::DSizes<Eigen::DenseIndex, 3>{prefix_dim_size, split_size,
+                                                   suffix_dim_size};
+      };
+      auto reshape_result = [&](Tensor* result, Tlen split_size) {
+        return result->shaped<T, 3>(
+            {prefix_dim_size, split_size, suffix_dim_size});
+      };
+      SplitVOpCPUImpl<T, Tlen, decltype(input_reshaped), 3>{}(
+          context, input_reshaped, split_start_points, input_shape, split_dim,
+          prefix_dim_size, split_dim_size, suffix_dim_size, split_sizes_vec,
+          make_sizes, reshape_result);
     }
   }
 };
diff --git a/tensorflow/core/kernels/tensor_array_ops.cc b/tensorflow/core/kernels/tensor_array_ops.cc
index af93d814ec..7ec26d95e6 100644
--- a/tensorflow/core/kernels/tensor_array_ops.cc
+++ b/tensorflow/core/kernels/tensor_array_ops.cc
@@ -1104,9 +1104,9 @@ class TensorArrayUnpackOrScatterOp : public OpKernel {
       indices[1] = i;
 
       if (element_shape.num_elements() > 0) {
-        functor::Split<Device, T>()(ctx->eigen_device<Device>(),
-                                    tensor_value_i_t, tensor_value_t, indices,
-                                    sizes);
+        functor::Split<Device, T, 3>()(ctx->eigen_device<Device>(),
+                                       tensor_value_i_t, tensor_value_t,
+                                       indices, sizes);
       }
 
       write_values.push_back(persistent_tensor);
@@ -1295,9 +1295,9 @@ class TensorArraySplitOp : public OpKernel {
         auto tensor_value_i_t = tensor_value_i->shaped<T, 3>(
             {1, tensor_lengths_t(i), elements_per_row});
 
-        functor::Split<Device, T>()(ctx->eigen_device<Device>(),
-                                    tensor_value_i_t, tensor_value_t, indices,
-                                    sizes);
+        functor::Split<Device, T, 3>()(ctx->eigen_device<Device>(),
+                                       tensor_value_i_t, tensor_value_t,
+                                       indices, sizes);
       }
 
       write_values.push_back(persistent_tensor);
diff --git a/tensorflow/core/kernels/unpack_op.cc b/tensorflow/core/kernels/unpack_op.cc
index 764b6a252a..4376df34be 100644
--- a/tensorflow/core/kernels/unpack_op.cc
+++ b/tensorflow/core/kernels/unpack_op.cc
@@ -104,7 +104,7 @@ class UnpackOp : public OpKernel {
     // Except for shape, unpack is a special case of split, so we reuse the
     // same computational kernels.
     auto input_reshaped =
-        input.shaped<T, 3>({1, before_dim, axis_dim * after_dim});
+        input.shaped<T, 2>({before_dim, axis_dim * after_dim});
 
     for (int i = 0; i < num; ++i) {
       Tensor* output;
@@ -112,12 +112,12 @@ class UnpackOp : public OpKernel {
                      context->allocate_output(i, output_shape, &output));
 
       if (output_shape.num_elements() > 0) {
-        auto output_shaped = output->shaped<T, 3>({1, before_dim, after_dim});
-        Eigen::DSizes<Eigen::DenseIndex, 3> indices{0, 0, i * after_dim};
-        Eigen::DSizes<Eigen::DenseIndex, 3> sizes{1, before_dim, after_dim};
-        functor::Split<Device, T>()(context->eigen_device<Device>(),
-                                    output_shaped, input_reshaped, indices,
-                                    sizes);
+        auto output_shaped = output->shaped<T, 2>({before_dim, after_dim});
+        Eigen::DSizes<Eigen::DenseIndex, 2> indices{0, i * after_dim};
+        Eigen::DSizes<Eigen::DenseIndex, 2> sizes{before_dim, after_dim};
+        functor::Split<Device, T, 2>()(context->eigen_device<Device>(),
+                                       output_shaped, input_reshaped, indices,
+                                       sizes);
       }
     }
   }
-- 
GitLab


From 180c457563271b072b33c90bf2f2fbbea450c943 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 13:38:24 -0800
Subject: [PATCH 362/575] Allow the Ftrl-proximal optimizer parameter
 'initial_accumulator_value' to take zero values.

PiperOrigin-RevId: 187224701
---
 tensorflow/python/training/ftrl.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/training/ftrl.py b/tensorflow/python/training/ftrl.py
index 9d02e694db..4fa081fab7 100644
--- a/tensorflow/python/training/ftrl.py
+++ b/tensorflow/python/training/ftrl.py
@@ -53,7 +53,7 @@ class FtrlOptimizer(optimizer.Optimizer):
       learning_rate: A float value or a constant float `Tensor`.
       learning_rate_power: A float value, must be less or equal to zero.
       initial_accumulator_value: The starting value for accumulators.
-        Only positive values are allowed.
+        Only zero or positive values are allowed.
       l1_regularization_strength: A float value, must be greater than or
         equal to zero.
       l2_regularization_strength: A float value, must be greater than or
@@ -84,9 +84,10 @@ class FtrlOptimizer(optimizer.Optimizer):
     """
     super(FtrlOptimizer, self).__init__(use_locking, name)
 
-    if initial_accumulator_value <= 0.0:
-      raise ValueError("initial_accumulator_value %f needs to be positive" %
-                       initial_accumulator_value)
+    if initial_accumulator_value < 0.0:
+      raise ValueError(
+          "initial_accumulator_value %f needs to be be positive or zero" %
+          initial_accumulator_value)
     if learning_rate_power > 0.0:
       raise ValueError("learning_rate_power %f needs to be negative or zero" %
                        learning_rate_power)
-- 
GitLab


From 1034bb2e69cae7ddd7f26f818e0d8527c5d4c3e9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 13:49:03 -0800
Subject: [PATCH 363/575] Renames sequential_feature_column to
 sequence_feature_column and adds pydoc.

PiperOrigin-RevId: 187226365
---
 tensorflow/contrib/feature_column/BUILD       |  12 +-
 tensorflow/contrib/feature_column/__init__.py |   2 +-
 ...e_column.py => sequence_feature_column.py} | 121 +++++++++++++++++-
 ...est.py => sequence_feature_column_test.py} |   2 +-
 4 files changed, 123 insertions(+), 14 deletions(-)
 rename tensorflow/contrib/feature_column/python/feature_column/{sequential_feature_column.py => sequence_feature_column.py} (72%)
 rename tensorflow/contrib/feature_column/python/feature_column/{sequential_feature_column_test.py => sequence_feature_column_test.py} (99%)

diff --git a/tensorflow/contrib/feature_column/BUILD b/tensorflow/contrib/feature_column/BUILD
index a53e36c2d5..8ba0823a71 100644
--- a/tensorflow/contrib/feature_column/BUILD
+++ b/tensorflow/contrib/feature_column/BUILD
@@ -25,13 +25,13 @@ py_library(
     srcs = ["__init__.py"],
     srcs_version = "PY2AND3",
     deps = [
-        ":sequential_feature_column",
+        ":sequence_feature_column",
     ],
 )
 
 py_library(
-    name = "sequential_feature_column",
-    srcs = ["python/feature_column/sequential_feature_column.py"],
+    name = "sequence_feature_column",
+    srcs = ["python/feature_column/sequence_feature_column.py"],
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/python:array_ops",
@@ -48,12 +48,12 @@ py_library(
 )
 
 py_test(
-    name = "sequential_feature_column_test",
-    srcs = ["python/feature_column/sequential_feature_column_test.py"],
+    name = "sequence_feature_column_test",
+    srcs = ["python/feature_column/sequence_feature_column_test.py"],
     srcs_version = "PY2AND3",
     tags = ["no_pip"],
     deps = [
-        ":sequential_feature_column",
+        ":sequence_feature_column",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:errors",
diff --git a/tensorflow/contrib/feature_column/__init__.py b/tensorflow/contrib/feature_column/__init__.py
index 6da7b12693..650a80144f 100644
--- a/tensorflow/contrib/feature_column/__init__.py
+++ b/tensorflow/contrib/feature_column/__init__.py
@@ -19,7 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 # pylint: disable=unused-import,line-too-long,wildcard-import
-from tensorflow.contrib.feature_column.python.feature_column.sequential_feature_column import *
+from tensorflow.contrib.feature_column.python.feature_column.sequence_feature_column import *
 
 from tensorflow.python.util.all_util import remove_undocumented
 # pylint: enable=unused-import,line-too-long,wildcard-import
diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
similarity index 72%
rename from tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py
rename to tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
index 4ed7268e7a..e99033bbec 100644
--- a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
@@ -34,8 +34,7 @@ from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops import variable_scope
 
-# TODO(b/73160931): Fix pydoc.
-# pylint: disable=g-doc-args,missing-docstring,protected-access
+# pylint: disable=protected-access
 # TODO(b/73827486): Support SequenceExample.
 
 
@@ -43,8 +42,7 @@ def sequence_input_layer(
     features,
     feature_columns,
     weight_collections=None,
-    trainable=True,
-    scope=None):
+    trainable=True):
   """"Builds input layer for sequence input.
 
   All `feature_columns` must be sequence dense columns with the same
@@ -76,6 +74,17 @@ def sequence_input_layer(
       rnn_cell, inputs=input_layer, sequence_length=sequence_length)
   ```
 
+  Args:
+    features: A dict mapping keys to tensors.
+    feature_columns: An iterable of dense sequence columns. Valid columns are
+      - `embedding_column` that wraps a `sequence_categorical_column_with_*`
+      - `sequence_numeric_column`.
+    weight_collections: A list of collection names to which the Variable will be
+      added. Note that variables will also be added to collections
+      `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`.
+    trainable: If `True` also add the variable to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES`.
+
   Returns:
     An `(input_layer, sequence_length)` tuple where:
     - input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
@@ -84,6 +93,7 @@ def sequence_input_layer(
         `feature_columns`.
     - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence
         length for each example.
+
   Raises:
     ValueError: If any of the `feature_columns` is the wrong type.
   """
@@ -95,7 +105,7 @@ def sequence_input_layer(
           'Given (type {}): {}'.format(type(c), c))
 
   with variable_scope.variable_scope(
-      scope, default_name='sequence_input_layer', values=features.values()):
+      None, default_name='sequence_input_layer', values=features.values()):
     builder = fc._LazyBuilder(features)
     output_tensors = []
     sequence_lengths = []
@@ -124,6 +134,35 @@ def sequence_input_layer(
 # TODO(b/73160931): Add remaining categorical columns.
 def sequence_categorical_column_with_identity(
     key, num_buckets, default_value=None):
+  """Returns a feature column that represents sequences of integers.
+
+  Example:
+
+  ```python
+  watches = sequence_categorical_column_with_identity(
+      'watches', num_buckets=1000)
+  watches_embedding = embedding_column(watches, dimension=10)
+  columns = [watches]
+
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  input_layer, sequence_length = sequence_input_layer(features, columns)
+
+  rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
+  outputs, state = tf.nn.dynamic_rnn(
+      rnn_cell, inputs=input_layer, sequence_length=sequence_length)
+  ```
+
+  Args:
+    key: A unique string identifying the input feature.
+    num_buckets: Range of inputs. Namely, inputs are expected to be in the
+      range `[0, num_buckets)`.
+    default_value: If `None`, this column's graph operations will fail for
+      out-of-range inputs. Otherwise, this value must be in the range
+      `[0, num_buckets)`, and will replace out-of-range inputs.
+
+  Returns:
+    A `_SequenceCategoricalColumn`.
+  """
   return _SequenceCategoricalColumn(
       fc.categorical_column_with_identity(
           key=key,
@@ -135,6 +174,46 @@ def sequence_categorical_column_with_identity(
 def _sequence_embedding_column(
     categorical_column, dimension, initializer=None, ckpt_to_load_from=None,
     tensor_name_in_ckpt=None, max_norm=None, trainable=True):
+  """Returns a feature column that represents sequences of embeddings.
+
+  Use this to convert sequence categorical data into dense representation for
+  input to sequence NN, such as RNN.
+
+  Example:
+
+  ```python
+  watches = sequence_categorical_column_with_identity(
+      'watches', num_buckets=1000)
+  watches_embedding = embedding_column(watches, dimension=10)
+  columns = [watches]
+
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  input_layer, sequence_length = sequence_input_layer(features, columns)
+
+  rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
+  outputs, state = tf.nn.dynamic_rnn(
+      rnn_cell, inputs=input_layer, sequence_length=sequence_length)
+  ```
+
+  Args:
+    categorical_column: A `_SequenceCategoricalColumn` created with a
+      `sequence_cateogrical_column_with_*` function.
+    dimension: Integer dimension of the embedding.
+    initializer: Initializer function used to initialize the embeddings.
+    ckpt_to_load_from: String representing checkpoint name/pattern from which to
+      restore column weights. Required if `tensor_name_in_ckpt` is not `None`.
+    tensor_name_in_ckpt: Name of the `Tensor` in `ckpt_to_load_from` from
+      which to restore the column weights. Required if `ckpt_to_load_from` is
+      not `None`.
+    max_norm: If not `None`, embedding values are l2-normalized to this value.
+    trainable: Whether or not the embedding is trainable. Default is True.
+
+  Returns:
+    A `_SequenceEmbeddingColumn`.
+
+  Raises:
+    ValueError: If `categorical_column` is not the right type.
+  """
   if not isinstance(categorical_column, _SequenceCategoricalColumn):
     raise ValueError(
         'categorical_column must be of type _SequenceCategoricalColumn. '
@@ -156,6 +235,33 @@ def sequence_numeric_column(
     shape=(1,),
     default_value=0.,
     dtype=dtypes.float32):
+  """Returns a feature column that represents sequences of numeric data.
+
+  Example:
+
+  ```python
+  temperature = sequence_numeric_column('temperature')
+  columns = [temperature]
+
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  input_layer, sequence_length = sequence_input_layer(features, columns)
+
+  rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
+  outputs, state = tf.nn.dynamic_rnn(
+      rnn_cell, inputs=input_layer, sequence_length=sequence_length)
+  ```
+
+  Args:
+    key: A unique string identifying the input features.
+    shape: The shape of the input data per sequence id. E.g. if `shape=(2,)`,
+      each example must contain `2 * sequence_length` values.
+    default_value: A single value compatible with `dtype` that is used for
+      padding the sparse data into a dense `Tensor`.
+    dtype: The type of values.
+
+  Returns:
+    A `_SequenceNumericColumn`.
+  """
   # TODO(b/73160931): Add validations.
   return _SequenceNumericColumn(
       key,
@@ -202,6 +308,7 @@ class _SequenceCategoricalColumn(
     fc._CategoricalColumn,
     collections.namedtuple(
         '_SequenceCategoricalColumn', ['categorical_column'])):
+  """Represents sequences of categorical data."""
 
   @property
   def name(self):
@@ -254,6 +361,7 @@ class _SequenceCategoricalColumn(
 class _SequenceEmbeddingColumn(
     _SequenceDenseColumn,
     collections.namedtuple('_SequenceEmbeddingColumn', ['embedding_column'])):
+  """Represents sequences of embeddings."""
 
   @property
   def name(self):
@@ -287,6 +395,7 @@ class _SequenceNumericColumn(
     collections.namedtuple(
         '_SequenceNumericColumn',
         ['key', 'shape', 'default_value', 'dtype'])):
+  """Represents sequences of numeric data."""
 
   @property
   def name(self):
@@ -322,4 +431,4 @@ class _SequenceNumericColumn(
     return _SequenceDenseColumn.TensorSequenceLengthPair(
         dense_tensor=dense_tensor, sequence_length=sequence_length)
 
-# pylint: enable=g-doc-args,missing-docstring,protected-access
+# pylint: enable=protected-access
diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
similarity index 99%
rename from tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py
rename to tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
index 59674869a2..8c37ccf11b 100644
--- a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
@@ -20,7 +20,7 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.feature_column.python.feature_column import sequential_feature_column as sfc
+from tensorflow.contrib.feature_column.python.feature_column import sequence_feature_column as sfc
 from tensorflow.python.feature_column.feature_column import _LazyBuilder
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
-- 
GitLab


From 0a799feaea50d4e48e8daa1f3954427fdccd76f1 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Mon, 26 Feb 2018 10:17:15 -0800
Subject: [PATCH 364/575] Generalize the gather_indices dimension that stores
 indices

This is now exposed as a index_vector_dim dimension number.

Also fixed an off-by-one error in ValidateGatherDimensionNumbers in the
expression computing output_shape_rank.

PiperOrigin-RevId: 187040748
---
 .../compiler/xla/service/hlo_instruction.cc   |   9 +-
 .../compiler/xla/service/hlo_instruction.h    |   3 +-
 .../xla/service/hlo_instruction_test.cc       |  43 +++-
 .../compiler/xla/service/shape_inference.cc   |  42 ++--
 .../xla/service/shape_inference_test.cc       | 191 ++++++++++++++----
 tensorflow/compiler/xla/xla_data.proto        |   4 +
 .../performance/xla/operation_semantics.md    |  61 ++++--
 7 files changed, 274 insertions(+), 79 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index b7dd055d7c..a534d8ff06 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -1172,7 +1172,8 @@ bool HloInstruction::HasSideEffect() const {
 /* static */ GatherDimensionNumbers HloInstruction::MakeGatherDimNumbers(
     tensorflow::gtl::ArraySlice<int64> output_window_dims,
     tensorflow::gtl::ArraySlice<int64> elided_window_dims,
-    tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims) {
+    tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims,
+    int64 index_vector_dim) {
   GatherDimensionNumbers gather_dim_numbers;
   for (int64 output_window_dim : output_window_dims) {
     gather_dim_numbers.add_output_window_dims(output_window_dim);
@@ -1184,6 +1185,7 @@ bool HloInstruction::HasSideEffect() const {
     gather_dim_numbers.add_gather_dims_to_operand_dims(gather_dim_to_input_dim);
   }
 
+  gather_dim_numbers.set_index_vector_dim(index_vector_dim);
   return gather_dim_numbers;
 }
 
@@ -3369,9 +3371,12 @@ string HloInstruction::GatherDimensionNumbersToString() const {
   string gather_dims_to_operand_dims = StrCat(
       "gather_dims_to_operand_dims={",
       Join(gather_dimension_numbers_->gather_dims_to_operand_dims(), ","), "}");
+  string index_vector_dim = StrCat(
+      "index_vector_dim=", gather_dimension_numbers_->index_vector_dim());
 
   return Join<std::initializer_list<string>>(
-      {output_window_dims, elided_window_dims, gather_dims_to_operand_dims},
+      {output_window_dims, elided_window_dims, gather_dims_to_operand_dims,
+       index_vector_dim},
       ", ");
 }
 
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index e4d22e5703..e4c86214c2 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -502,7 +502,8 @@ class HloInstruction {
   static GatherDimensionNumbers MakeGatherDimNumbers(
       tensorflow::gtl::ArraySlice<int64> output_window_dims,
       tensorflow::gtl::ArraySlice<int64> elided_window_dims,
-      tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims);
+      tensorflow::gtl::ArraySlice<int64> gather_dims_to_operand_dims,
+      int64 index_vector_dim);
 
   // Returns the opcode for this instruction.
   HloOpcode opcode() const { return opcode_; }
diff --git a/tensorflow/compiler/xla/service/hlo_instruction_test.cc b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
index 32d3ed272b..f2980d309d 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction_test.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction_test.cc
@@ -1271,7 +1271,7 @@ TEST_F(HloInstructionTest, Stringification) {
             "true_computation=%TransposeDot, false_computation=%TransposeDot");
 }
 
-TEST_F(HloInstructionTest, StringifyGather) {
+TEST_F(HloInstructionTest, StringifyGather_0) {
   Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46});
   Shape gather_indices_tensor_shape =
       ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5});
@@ -1291,7 +1291,8 @@ TEST_F(HloInstructionTest, StringifyGather) {
           HloInstruction::MakeGatherDimNumbers(
               /*output_window_dims=*/{4, 5, 6, 7, 8},
               /*elided_window_dims=*/{},
-              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/4),
           /*window_bounds=*/{30, 29, 28, 27, 26}));
 
   HloModule module(TestName());
@@ -1303,7 +1304,43 @@ TEST_F(HloInstructionTest, StringifyGather) {
             "s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), "
             "output_window_dims={4,5,6,7,8}, elided_window_dims={}, "
             "gather_dims_to_operand_dims={0,1,2,3,4}, "
-            "window_bounds={30,29,28,27,26}");
+            "index_vector_dim=4, window_bounds={30,29,28,27,26}");
+}
+
+TEST_F(HloInstructionTest, StringifyGather_1) {
+  Shape input_tensor_shape = ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46});
+  Shape gather_indices_tensor_shape =
+      ShapeUtil::MakeShape(S64, {10, 9, 5, 7, 6});
+  Shape gather_result_shape =
+      ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26});
+
+  HloComputation::Builder builder("Gather");
+  HloInstruction* input = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_tensor_shape, "input_tensor"));
+  HloInstruction* gather_indices =
+      builder.AddInstruction(HloInstruction::CreateParameter(
+          1, gather_indices_tensor_shape, "gather_indices"));
+
+  HloInstruction* gather_instruction =
+      builder.AddInstruction(HloInstruction::CreateGather(
+          gather_result_shape, input, gather_indices,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{4, 5, 6, 7, 8},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/2),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  HloModule module(TestName());
+  module.AddEntryComputation(builder.Build());
+
+  EXPECT_EQ(gather_instruction->ToString(),
+            "%gather = f32[10,9,7,6,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} "
+            "gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, "
+            "s64[10,9,5,7,6]{4,3,2,1,0} %gather_indices), "
+            "output_window_dims={4,5,6,7,8}, elided_window_dims={}, "
+            "gather_dims_to_operand_dims={0,1,2,3,4}, "
+            "index_vector_dim=2, window_bounds={30,29,28,27,26}");
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index c9692757b2..607a672025 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -2467,27 +2467,27 @@ static Status ValidateGatherDimensionNumbers(
 
   const int64 output_window_dim_count = dim_numbers.output_window_dims_size();
   const int64 output_shape_rank =
-      output_window_dim_count + gather_indices_shape.size();
+      output_window_dim_count + gather_indices_shape.size() - 1;
 
   for (int i = 0; i < dim_numbers.output_window_dims_size(); ++i) {
     int64 window_index = dim_numbers.output_window_dims(i);
     if (window_index < 0 || window_index >= output_shape_rank) {
       return InvalidArgument(
           "Window index %d in gather op is out of bounds; got %lld, but should "
-          "have been in"
-          "[0,%lld)",
+          "have been in [0,%lld)",
           i, window_index, output_shape_rank);
     }
   }
 
   if (dim_numbers.gather_dims_to_operand_dims_size() !=
-      gather_indices_shape.back()) {
+      gather_indices_shape[dim_numbers.index_vector_dim()]) {
     return InvalidArgument(
-        "There must be exactly as many elements in gather_dims_to_operand_dims "
-        "as there are elements in the last dimension of %%gather_indices; got: "
-        "%d, expected %lld",
+        "Gather op has %d elements in gather_dims_to_operand_dims and the "
+        "bound of dimension index_vector_dim=%lld of gather_indices is "
+        "%lld. These two numbers must be equal.",
         dim_numbers.gather_dims_to_operand_dims_size(),
-        gather_indices_shape.back());
+        dim_numbers.index_vector_dim(),
+        gather_indices_shape[dim_numbers.index_vector_dim()]);
   }
 
   for (int i = 0; i < dim_numbers.gather_dims_to_operand_dims_size(); i++) {
@@ -2550,24 +2550,33 @@ static Status ValidateGatherDimensionNumbers(
   TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(
       gather_indices_shape, "gather indices operand of gather op"));
 
-  if (gather_indices_shape.dimensions_size() < 1) {
+  if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) {
     return InvalidArgument(
-        "Gather indices parameter must at least of rank 1; got %s",
+        "Gather indices parameter must be an integral tensor; got %s",
         ShapeUtil::HumanString(gather_indices_shape).c_str());
   }
 
-  if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) {
+  // We implicitly reshape gather indices of shape P[A,B,C] to P[A,B,C,1] if
+  // index_vector_dim is rank(P).  The bounds of this expanded shape is
+  // stored in expanded_gather_indices_shape.
+
+  if (gather_indices_shape.dimensions_size() <
+          gather_dim_numbers.index_vector_dim() ||
+      gather_dim_numbers.index_vector_dim() < 0) {
     return InvalidArgument(
-        "Gather indices parameter must be an integral tensor; got %s",
-        ShapeUtil::HumanString(gather_indices_shape).c_str());
+        "Gather index leaf dimension must be within [0, rank(gather_indices) + "
+        "1). rank(gather_indices) is %d and gather index leaf dimension is "
+        "%lld.",
+        gather_indices_shape.dimensions_size(),
+        gather_dim_numbers.index_vector_dim());
   }
 
   std::vector<int64> expanded_gather_indices_shape;
-  // We implicitly reshape gather indices of shape P[N] to P[N,1].
   expanded_gather_indices_shape.reserve(gather_indices_shape.dimensions_size());
   c_copy(gather_indices_shape.dimensions(),
          std::back_inserter(expanded_gather_indices_shape));
-  if (expanded_gather_indices_shape.size() == 1) {
+  if (expanded_gather_indices_shape.size() ==
+      gather_dim_numbers.index_vector_dim()) {
     expanded_gather_indices_shape.push_back(1);
   }
 
@@ -2632,6 +2641,9 @@ static Status ValidateGatherDimensionNumbers(
       }
       current_bound = window_bounds[window_dims_seen++];
     } else {
+      if (gather_dims_seen == gather_dim_numbers.index_vector_dim()) {
+        gather_dims_seen++;
+      }
       current_bound = expanded_gather_indices_shape[gather_dims_seen++];
     }
 
diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc
index 7eb120843f..029d2b3b86 100644
--- a/tensorflow/compiler/xla/service/shape_inference_test.cc
+++ b/tensorflow/compiler/xla/service/shape_inference_test.cc
@@ -1530,11 +1530,17 @@ TEST_F(ShapeInferenceTest, BadSlice) {
 
 class GatherShapeInferenceTest : public ShapeInferenceTest {
  protected:
+  const Shape s64_scalar_ = ShapeUtil::MakeShape(S64, {});
+  const Shape s64_vector_5_ = ShapeUtil::MakeShape(S64, {5});
   const Shape s64_vector_32_ = ShapeUtil::MakeShape(S64, {32});
   const Shape s64_4d_tensor_10_9_8_7_1_ =
       ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 1});
   const Shape s64_4d_tensor_10_9_8_7_5_ =
       ShapeUtil::MakeShape(S64, {10, 9, 8, 7, 5});
+  const Shape s64_4d_tensor_5_10_9_7_6_ =
+      ShapeUtil::MakeShape(S64, {5, 10, 9, 7, 6});
+  const Shape s64_4d_tensor_10_9_5_7_6_ =
+      ShapeUtil::MakeShape(S64, {10, 9, 5, 7, 6});
   const Shape f32_5d_tensor_50_49_48_47_46_ =
       ShapeUtil::MakeShape(F32, {50, 49, 48, 47, 46});
   const Shape tuple_shape_ = ShapeUtil::MakeTupleShape(
@@ -1548,7 +1554,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGather) {
                                        HloInstruction::MakeGatherDimNumbers(
                                            /*output_window_dims=*/{0},
                                            /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
+                                           /*gather_dims_to_operand_dims=*/{1},
+                                           /*index_vector_dim=*/1),
                                        /*window_bounds=*/{64, 1}));
   EXPECT_TRUE(
       ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {64, 32})))
@@ -1562,7 +1569,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGatherV2) {
                                        HloInstruction::MakeGatherDimNumbers(
                                            /*output_window_dims=*/{1},
                                            /*elided_window_dims=*/{0},
-                                           /*gather_dims_to_operand_dims=*/{0}),
+                                           /*gather_dims_to_operand_dims=*/{0},
+                                           /*index_vector_dim=*/1),
                                        /*window_bounds=*/{1, 48}));
   EXPECT_TRUE(
       ShapeUtil::Equal(gather_shape, ShapeUtil::MakeShape(F32, {32, 48})))
@@ -1576,7 +1584,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowGatherNd) {
                                        HloInstruction::MakeGatherDimNumbers(
                                            /*output_window_dims=*/{4},
                                            /*elided_window_dims=*/{0},
-                                           /*gather_dims_to_operand_dims=*/{0}),
+                                           /*gather_dims_to_operand_dims=*/{0},
+                                           /*index_vector_dim=*/4),
                                        /*window_bounds=*/{1, 48}));
   EXPECT_TRUE(ShapeUtil::Equal(gather_shape,
                                ShapeUtil::MakeShape(F32, {10, 9, 8, 7, 48})))
@@ -1591,7 +1600,8 @@ TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) {
           HloInstruction::MakeGatherDimNumbers(
               /*output_window_dims=*/{4, 5, 6, 7, 8},
               /*elided_window_dims=*/{},
-              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/4),
           /*window_bounds=*/{30, 29, 28, 27, 26}));
   EXPECT_TRUE(ShapeUtil::Equal(
       gather_shape,
@@ -1599,12 +1609,85 @@ TEST_F(GatherShapeInferenceTest, TensorFlowBatchDynamicSlice) {
       << ShapeUtil::HumanString(gather_shape);
 }
 
+TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_A) {
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(
+          f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{4, 5, 6, 7, 8},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/2),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  EXPECT_TRUE(ShapeUtil::Equal(
+      gather_shape,
+      ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, NonDefaultGatherIndicesLeafDim_B) {
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(
+          f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_5_10_9_7_6_,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{4, 5, 6, 7, 8},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/0),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  EXPECT_TRUE(ShapeUtil::Equal(
+      gather_shape,
+      ShapeUtil::MakeShape(F32, {10, 9, 7, 6, 30, 29, 28, 27, 26})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, NoOutputGatherDims) {
+  // This is equivalent to a dynamic slice.
+  TF_ASSERT_OK_AND_ASSIGN(
+      Shape gather_shape,
+      ShapeInference::InferGatherShape(
+          f32_5d_tensor_50_49_48_47_46_, s64_vector_5_,
+          HloInstruction::MakeGatherDimNumbers(
+              /*output_window_dims=*/{0, 1, 2, 3, 4},
+              /*elided_window_dims=*/{},
+              /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+              /*index_vector_dim=*/0),
+          /*window_bounds=*/{30, 29, 28, 27, 26}));
+
+  EXPECT_TRUE(ShapeUtil::Equal(gather_shape,
+                               ShapeUtil::MakeShape(F32, {30, 29, 28, 27, 26})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
+TEST_F(GatherShapeInferenceTest, ScalarGatherIndices) {
+  // The gather indices "tensor" is a scalar S here that's used to slice out
+  // [S,0,0,0,0]..[S,30,29,28,27] into a [30,29,28,27] shaped result.
+  TF_ASSERT_OK_AND_ASSIGN(Shape gather_shape,
+                          ShapeInference::InferGatherShape(
+                              f32_5d_tensor_50_49_48_47_46_, s64_scalar_,
+                              HloInstruction::MakeGatherDimNumbers(
+                                  /*output_window_dims=*/{0, 1, 2, 3},
+                                  /*elided_window_dims=*/{0},
+                                  /*gather_dims_to_operand_dims=*/{0},
+                                  /*index_vector_dim=*/0),
+                              /*window_bounds=*/{1, 30, 29, 28, 27}));
+
+  EXPECT_TRUE(ShapeUtil::Equal(gather_shape,
+                               ShapeUtil::MakeShape(F32, {30, 29, 28, 27})))
+      << ShapeUtil::HumanString(gather_shape);
+}
+
 TEST_F(GatherShapeInferenceTest, TupleShapedTensorInput) {
   StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
       tuple_shape_, s64_vector_32_,
       HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
                                            /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
+                                           /*gather_dims_to_operand_dims=*/{1},
+                                           /*index_vector_dim=*/1),
       /*window_bounds=*/{64, 1});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1617,7 +1700,8 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) {
       s64_vector_32_, tuple_shape_,
       HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
                                            /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
+                                           /*gather_dims_to_operand_dims=*/{1},
+                                           /*index_vector_dim=*/0),
       /*window_bounds=*/{64, 1});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1625,25 +1709,13 @@ TEST_F(GatherShapeInferenceTest, TupleShapedGatherIndicesInput) {
       << statusor.status();
 }
 
-TEST_F(GatherShapeInferenceTest, ScalarGatherIndicesInput) {
-  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
-      s64_vector_32_, s32_,
-      HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
-                                           /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
-      /*window_bounds=*/{64, 1});
-  ASSERT_FALSE(statusor.ok());
-  EXPECT_THAT(statusor.status().error_message(),
-              HasSubstr("Gather indices parameter must at least of rank 1"))
-      << statusor.status();
-}
-
 TEST_F(GatherShapeInferenceTest, FloatingPointGatherIndicesInput) {
   StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
       s64_vector_32_, vector_32_,
       HloInstruction::MakeGatherDimNumbers(/*output_window_dims=*/{0},
                                            /*elided_window_dims=*/{1},
-                                           /*gather_dims_to_operand_dims=*/{1}),
+                                           /*gather_dims_to_operand_dims=*/{1},
+                                           /*index_vector_dim=*/0),
       /*window_bounds=*/{64, 1});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1658,7 +1730,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 8, 7},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1674,7 +1747,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 7},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1690,7 +1764,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 99, 100, 101},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1698,6 +1773,22 @@ TEST_F(GatherShapeInferenceTest,
       << statusor.status();
 }
 
+TEST_F(GatherShapeInferenceTest,
+       InvalidGatherDimNumbers_WindowIndexBarelyOutOfBounds) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_8_7_5_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 9},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Window index 4 in gather op is out of bounds"))
+      << statusor.status();
+}
+
 TEST_F(GatherShapeInferenceTest,
        InvalidGatherDimNumbers_MismatchingElidedWindowDims) {
   StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
@@ -1705,7 +1796,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{4},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1722,7 +1814,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{0, 1, 2, 3, 19},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1738,7 +1831,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{0, 1, 2, 3, 3},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1755,15 +1849,15 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
       statusor.status().error_message(),
-      HasSubstr(
-          "There must be exactly as many elements in "
-          "gather_dims_to_operand_dims "
-          "as there are elements in the last dimension of %gather_indices"))
+      HasSubstr("Gather op has 4 elements in gather_dims_to_operand_dims and "
+                "the bound of dimension index_vector_dim=4 of "
+                "gather_indices is 5. These two numbers must be equal."))
       << statusor.status();
 }
 
@@ -1774,7 +1868,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 7},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1791,7 +1886,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 3},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1808,7 +1904,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{2, 1},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{1, 1, 28, 27, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1822,7 +1919,8 @@ TEST_F(GatherShapeInferenceTest, InvalidGatherDimNumbers_WindowBoundsTooLarge) {
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7},
           /*elided_window_dims=*/{2},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 1, 300, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1838,7 +1936,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7, 8},
           /*elided_window_dims=*/{},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 26});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(
@@ -1855,7 +1954,8 @@ TEST_F(GatherShapeInferenceTest,
       HloInstruction::MakeGatherDimNumbers(
           /*output_window_dims=*/{4, 5, 6, 7},
           /*elided_window_dims=*/{1},
-          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4}),
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/4),
       /*window_bounds=*/{30, 29, 28, 26, 20});
   ASSERT_FALSE(statusor.ok());
   EXPECT_THAT(statusor.status().error_message(),
@@ -1864,5 +1964,22 @@ TEST_F(GatherShapeInferenceTest,
       << statusor.status();
 }
 
+TEST_F(GatherShapeInferenceTest, OutOfBoundsGatherIndicesLeafDim) {
+  StatusOr<Shape> statusor = ShapeInference::InferGatherShape(
+      f32_5d_tensor_50_49_48_47_46_, s64_4d_tensor_10_9_5_7_6_,
+      HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/{4, 5, 6, 7, 8},
+          /*elided_window_dims=*/{},
+          /*gather_dims_to_operand_dims=*/{0, 1, 2, 3, 4},
+          /*index_vector_dim=*/32),
+      /*window_bounds=*/{30, 29, 28, 27, 26});
+
+  ASSERT_FALSE(statusor.ok());
+  EXPECT_THAT(statusor.status().error_message(),
+              HasSubstr("Gather index leaf dimension must be within [0, "
+                        "rank(gather_indices) + 1)"))
+      << statusor.status();
+}
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto
index 28620c3b86..1f16e6d251 100644
--- a/tensorflow/compiler/xla/xla_data.proto
+++ b/tensorflow/compiler/xla/xla_data.proto
@@ -418,6 +418,10 @@ message GatherDimensionNumbers {
   // transforms the gather index looked up from the gather_indices tensor into
   // the starting index in the input space.
   repeated int64 gather_dims_to_operand_dims = 3;
+
+  // The dimension in the gather_indices input that contains the starting
+  // indices.
+  int64 index_vector_dim = 4;
 }
 
 // Operation requests that are all collected as a tagged union with a oneof
diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index b0abf5fdd2..b2190c5243 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -1050,6 +1050,9 @@ For a more intuitive description, see the "Informal Description" section below.
 :                  :                         : indices of the slices we're     :
 :                  :                         : we're stitching together into   :
 :                  :                         : the output tensor.              :
+|`index_vector_dim`  | `int64`               | The dimension in                |
+:                  :                         : `gather_indices` that contains  :
+:                  :                         : the starting indices.           :
 |`output_window_dims` | `ArraySlice<int64>`  | The set of dimensions in the    |
 :                  :                         : output shape that are _window   :
 :                  :                         : dimensions_ (defined below).    :
@@ -1066,22 +1069,20 @@ For a more intuitive description, see the "Informal Description" section below.
 :                  :            : `output_window_dims`) and the window         :
 :                  :            : dimensions that are elided (via              :
 :                  :            : `elided_window_dims`).                       :
-|`gather_dims_to_operand_dims` | `ArraySlice<int64>` | A dimension map (the  |
+|`gather_dims_to_operand_dims` | `ArraySlice<int64>` | A dimension map (the    |
 :                  :            : array is interpreted as mapping `i` to       :
 :                  :            : `gather_dims_to_operand_dims[i]`)  from      :
 :                  :            : the gather indices in `gather_indices` to    :
 :                  :            : the operand index space.  It has to be       :
 :                  :            : one-to-one and total.                        :
 
-If `gather_indices` is a vector with `N` elements then we implicitly reshape it
-to a tensor of shape `[N,1]` before proceeding.
-
 For every index `Out` in the output tensor, we compute two things (more
 precisely described later):
 
-  - An index into the first `gather_indices.rank` - `1` dimensions of
-    `gather_indices`, which gives us a starting index of a slice, _operand
-    slice_, in the operand tensor.
+  - An index into `gather_indices.rank` - `1` dimensions of `gather_indices`,
+    which gives us a starting index of a slice, _operand slice_, in the operand
+    tensor.  These `gather_indices.rank` - `1` dimensions are all the dimensions
+    in `gather_indices` except `index_vector_dim`.
 
   - A _window index_ that has the same rank as the operand.  This index is
     composed of the values in `Out` at dimensions `output_window_dims`, embedded
@@ -1093,29 +1094,42 @@ should be present in the output at index `Out`.
 The output is a tensor of rank `output_window_dims.size` + `gather_indices.rank`
 - `1`.  Additionally, as a shorthand, we define `output_gather_dims` of type
 `ArraySlice<int64>` as the set of dimensions in the output shape but not in
-`output_window_dims`, in ascending order.  E.g. if the output tensor has rank 5,
-`output_window_dims` is {`2`, `4`} then `output_gather_dims` is {`0`, `1`, `3`}
+`output_window_dims`, in ascending order.  E.g. if the output tensor has rank
+`5`, `output_window_dims` is {`2`, `4`} then `output_gather_dims` is {`0`, `1`,
+`3`}
+
+If `index_vector_dim` is equal to `gather_indices.rank` we implicitly
+consider `gather_indices` to have a trailing `1` dimension (i.e. if
+`gather_indices` was of shape `[6,7]` and `index_vector_dim` is `2` then
+we implicitly consider the shape of `gather_indices` to be `[6,7,1]`).
 
 The bounds for the output tensor along dimension `i` is computed as follows:
 
   1. If `i` is present in `output_gather_dims` (i.e. is equal to
-    `output_gather_dims[k]` for some `k`) then we pick the corresponding
-    dimension bounds out of `gather_indices.shape` (i.e. pick
-    `gather_indices.shape.dims[k]`).
+     `output_gather_dims[k]` for some `k`) then we pick the corresponding
+     dimension bounds out of `gather_indices.shape`, skipping
+     `index_vector_dim` (i.e. pick `gather_indices.shape.dims`[`k`] if `k`
+     < `index_vector_dim` and `gather_indices.shape.dims`[`k`+`1`]
+     otherwise).
   2. If `i` is present in `output_window_dims` (i.e. equal to
-     `output_window_dims[k]` for some `k`) then we pick the corresponding bound
-     out of `window_bounds` after accounting for `elided_window_dims` (i.e. we
-     pick `adjusted_window_bounds[k]` where `adjusted_window_bounds` is
-     `window_bounds` with the bounds at indices `elided_window_dims` removed).
+     `output_window_dims`[`k`] for some `k`) then we pick the corresponding
+     bound out of `window_bounds` after accounting for `elided_window_dims`
+     (i.e. we pick `adjusted_window_bounds`[`k`] where `adjusted_window_bounds`
+     is `window_bounds` with the bounds at indices `elided_window_dims`
+     removed).
 
 The operand index `In` corresponding to an output index `Out` is computed as
 follows:
 
   1. Let `G` = { `Out`[`k`] for `k` in `output_gather_dims` }.  Use `G` to slice
-     out vector `S` such that `S`[`i`] = `gather_indices`[`G`, `i`].
-  2. Create an index, `S`<sub>`in`</sub>, into `operand` using `S` by scattering
-     `S` using the `gather_dims_to_operand_dims` map (`S`<sub>`in`</sub> is the
-     starting indices for _operand slice_ mentioned above.).  More precisely:
+     out vector `S` such that `S`[`i`] = `gather_indices`[Combine(`G`, `i`)]
+     where Combine(A, b) inserts b at position `index_vector_dim` into A.
+     Note that this is well defined even if `G` is empty -- if `G` is empty then
+     `S` = `gather_indices`.
+  2. Create an index, `S`<sub>`in`</sub>, into `operand` using `S` by
+     scattering `S` using the `gather_dims_to_operand_dims` map
+     (`S`<sub>`in`</sub> is the starting indices for _operand slice_ mentioned
+     above).  More precisely:
        1. `S`<sub>`in`</sub>[`gather_dims_to_operand_dims`[`k`]] = `S`[`k`] if `k` <
           `gather_dims_to_operand_dims.size`.
        2. `S`<sub>`in`</sub>[`_`] = `0` otherwise.
@@ -1136,7 +1150,12 @@ follows:
 `operand.rank` is `6` and `elided_window_dims` is {`0`, `2`} then
 `window_dims_to_operand_dims` is {`0`→`1`, `1`→`3`, `2`→`4`, `3`→`5`}.
 
-### Informal Description
+### Informal Description and Examples
+
+`index_vector_dim` is set to `gather_indices.rank` - `1` in all of the
+examples that follow.  More interesting values for `index_vector_dim`
+does not change the operation fundamentally, but makes the visual representation
+more cumbersome.
 
 To get an intuition on how all of the above fits together, let's look at an
 example that gathers 5 slices of shape `[8,6]` from a `[16,11]` tensor.  The
-- 
GitLab


From 1fc324c6701bc179ca73908731857e8a582437b5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 10:24:08 -0800
Subject: [PATCH 365/575] Arithemtic optimization: Rewite Sub(0, y) => Neg(y)

PiperOrigin-RevId: 187041872
---
 .../grappler/optimizers/constant_folding.cc    | 18 +++++++++++++++++-
 .../grappler/optimizers/constant_folding.h     |  1 +
 .../optimizers/constant_folding_test.cc        |  7 +++----
 3 files changed, 21 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 182e03f04e..10ca7dcce0 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -1434,6 +1434,17 @@ void ConstantFolding::ReplaceDivisionOfOnesByReciprocal(NodeDef* node,
   graph_modified_ = true;
 }
 
+void ConstantFolding::ReplaceSubtractionFromZeroByNegation(NodeDef* node,
+                                                           GraphDef* graph) {
+  node->set_op("Neg");
+  node->mutable_input()->SwapElements(0, 1);
+  const string ctrl_dep =
+      AddControlDependency(node->input(1), graph, node_map_.get());
+  node_map_->UpdateInput(node->name(), node->input(1), ctrl_dep);
+  node->set_input(1, ctrl_dep);
+  graph_modified_ = true;
+}
+
 Status ConstantFolding::ReplaceOperationWithConstant(
     double value, const TensorShapeProto& shape, NodeDef* node,
     GraphDef* graph) {
@@ -1636,12 +1647,17 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
       const bool y_matches_output_shape = ShapesEqual(output_shape, y_shape);
       if (y_matches_output_shape &&
           ((is_mul && x_is_one) || (is_add && x_is_zero))) {
-        // TODO(rmlarsen): Handle subtraction 0 - y.
         // 1 * y = y or 0 + y = y.
         ReplaceOperationWithSnapshot(1, node, output);
         continue;
       }
 
+      if (y_matches_output_shape && (is_sub && x_is_zero)) {
+        // Replace 0 - y with Neg(y).
+        ReplaceSubtractionFromZeroByNegation(node, output);
+        continue;
+      }
+
       // Replace 1 / y with Reciprocal op.
       if (y_matches_output_shape && is_any_div && x_is_one) {
         DataType type = node->attr().at("T").type();
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.h b/tensorflow/core/grappler/optimizers/constant_folding.h
index 232b2f9fa0..2fd59c7f9c 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.h
+++ b/tensorflow/core/grappler/optimizers/constant_folding.h
@@ -82,6 +82,7 @@ class ConstantFolding : public GraphOptimizer {
                                     GraphDef* graph);
   void ReplaceOperationWithSnapshot(int input_to_forward, NodeDef* node,
                                     GraphDef* graph);
+  void ReplaceSubtractionFromZeroByNegation(NodeDef* node, GraphDef* graph);
   Status ReplaceOperationWithConstant(double value,
                                       const TensorShapeProto& shape,
                                       NodeDef* node, GraphDef* graph);
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 219f3bd5ec..c6540192d7 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -286,10 +286,9 @@ TEST_F(ConstantFoldingTest, NeutralElement) {
         EXPECT_EQ("x", node.input(0));
         EXPECT_EQ("^zeros", node.input(1));
       } else if (name == "sub2") {
-        // We don't handle this case yet.
-        EXPECT_EQ("Sub", node.op());
-        EXPECT_EQ("zeros", node.input(0));
-        EXPECT_EQ("y", node.input(1));
+        EXPECT_EQ("Neg", node.op());
+        EXPECT_EQ("y", node.input(0));
+        EXPECT_EQ("^zeros", node.input(1));
       }
       const std::set<string> square_zero_const{"mul1", "mul2",    "mul5",
                                                "mul6", "matmul1", "matmul2"};
-- 
GitLab


From 620348fb6d045dc1f644925a3828ebb12de944d7 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 26 Feb 2018 10:24:56 -0800
Subject: [PATCH 366/575] Move accumulate_n_v2 to core.

PiperOrigin-RevId: 187042001
---
 tensorflow/contrib/framework/BUILD            |  38 ------
 .../framework/python/ops/accumulate_n_v2.py   | 111 ------------------
 tensorflow/python/kernel_tests/BUILD          |  34 ++++++
 .../kernel_tests/accumulate_n_eager_test.py}  |  27 ++---
 .../kernel_tests/accumulate_n_test.py}        |  34 +++---
 tensorflow/python/ops/math_ops.py             |  81 ++++++-------
 6 files changed, 99 insertions(+), 226 deletions(-)
 delete mode 100644 tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
 rename tensorflow/{contrib/framework/python/ops/accumulate_n_v2_eager_test.py => python/kernel_tests/accumulate_n_eager_test.py} (72%)
 rename tensorflow/{contrib/framework/python/ops/accumulate_n_v2_test.py => python/kernel_tests/accumulate_n_test.py} (79%)

diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD
index dbdb5cfaac..1accb319d2 100644
--- a/tensorflow/contrib/framework/BUILD
+++ b/tensorflow/contrib/framework/BUILD
@@ -28,7 +28,6 @@ tf_custom_op_py_library(
         "python/framework/graph_util.py",
         "python/framework/tensor_util.py",
         "python/ops/__init__.py",
-        "python/ops/accumulate_n_v2.py",
         "python/ops/arg_scope.py",
         "python/ops/audio_ops.py",
         "python/ops/checkpoint_ops.py",
@@ -161,23 +160,6 @@ py_test(
     ],
 )
 
-py_test(
-    name = "accumulate_n_v2_test",
-    size = "small",
-    srcs = ["python/ops/accumulate_n_v2_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":framework_py",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:gradients",
-        "//tensorflow/python:platform_test",
-        "//tensorflow/python:variables",
-        "//third_party/py/numpy",
-    ],
-)
-
 cuda_py_test(
     name = "critical_section_test",
     size = "medium",
@@ -196,26 +178,6 @@ cuda_py_test(
     ],
 )
 
-py_test(
-    name = "accumulate_n_v2_eager_test",
-    size = "small",
-    srcs = ["python/ops/accumulate_n_v2_eager_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":framework_py",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:gradients",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:resource_variable_ops",
-        "//tensorflow/python/eager:backprop",
-        "//tensorflow/python/eager:context",
-        "//tensorflow/python/eager:tape",
-        "//third_party/py/numpy",
-    ],
-)
-
 py_test(
     name = "ops_test",
     size = "small",
diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py b/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
deleted file mode 100644
index 476528b0dd..0000000000
--- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Ops that will eventually be folded into tensorflow/python/ops/math_ops.py
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from tensorflow.python.eager import context
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import gen_math_ops
-from tensorflow.python.ops import math_ops
-
-
-
-def accumulate_n_v2(inputs, shape=None, tensor_dtype=None, name=None):
-  """Returns the element-wise sum of a list of tensors.
-
-  Optionally, pass `shape` and `tensor_dtype` for shape and type checking,
-  otherwise, these are inferred.
-
-  `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not
-  wait for all of its inputs to be ready before beginning to sum. This can
-  save memory if inputs are ready at different times, since minimum temporary
-  storage is proportional to the output size rather than the inputs size.
-
-  Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable.
-
-  For example:
-
-  ```python
-  a = tf.constant([[1, 2], [3, 4]])
-  b = tf.constant([[5, 0], [0, 6]])
-  tf.accumulate_n_v2([a, b, a])  # [[7, 4], [6, 14]]
-
-  # Explicitly pass shape and type
-  tf.accumulate_n_v2([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
-                                                                   # [[7,  4],
-                                                                   #  [6, 14]]
-  ```
-
-  Args:
-    inputs: A list of `Tensor` objects, each with same shape and type.
-    shape: Shape of elements of `inputs`.
-    tensor_dtype: The type of `inputs`.
-    name: A name for the operation (optional).
-
-  Returns:
-    A `Tensor` of same shape and type as the elements of `inputs`.
-
-  Raises:
-    ValueError: If `inputs` don't all have same shape and dtype or the shape
-    cannot be inferred.
-  """
-  _INPUTS_ERR_MSG = ValueError("inputs must be a list of at least one Tensor"
-                               "with the same dtype and shape")
-  if not inputs or not isinstance(inputs, (list, tuple)):
-    raise _INPUTS_ERR_MSG
-  inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs)
-  if not all(isinstance(x, ops.Tensor) for x in inputs):
-    raise _INPUTS_ERR_MSG
-  if not all(x.dtype == inputs[0].dtype for x in inputs):
-    raise _INPUTS_ERR_MSG
-  if shape is not None:
-    shape = tensor_shape.as_shape(shape)
-  else:
-    shape = tensor_shape.unknown_shape()
-  for input_tensor in inputs:
-    if isinstance(input_tensor, ops.Tensor):
-      shape = shape.merge_with(input_tensor.get_shape())
-
-  # tensor_dtype is for safety only; operator's output type computed in C++
-  if tensor_dtype is not None and tensor_dtype != inputs[0].dtype:
-    raise TypeError("tensor_dtype is {}, but input is of type {}"
-                    .format(tensor_dtype, inputs[0].dtype))
-
-  if len(inputs) == 1 and name is None:
-    return inputs[0]
-  elif len(inputs) == 1 and name is not None:
-    return array_ops.identity(inputs[0], name=name)
-  elif context.in_eager_mode():
-    # TemporaryVariable not currently supported in eager mode; fall back
-    # onto AddN for now.
-    # TODO(frreiss) remove this once the lifetime of eager variables gets
-    # addressed
-    return math_ops.add_n(inputs, name=name)
-  else:
-    return gen_math_ops._accumulate_nv2(inputs, name=name, shape=shape)
-
-# The following code should eventually be merged into
-# tensorflow/python/ops/math_grad.py
-@ops.RegisterGradient("AccumulateNV2")
-def _AddNGrad(op, grad):
-  """Same as gradient for AddN. Copies the gradient to all inputs."""
-  # Not broadcasting.
-  return [grad] * len(op.inputs)
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index d4ceb2e489..c9aa4a252d 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -2892,6 +2892,40 @@ tf_py_test(
     ],
 )
 
+tf_py_test(
+    name = "accumulate_n_test",
+    size = "small",
+    srcs = ["accumulate_n_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:variables",
+    ],
+)
+
+tf_py_test(
+    name = "accumulate_n_eager_test",
+    size = "small",
+    srcs = ["accumulate_n_eager_test.py"],
+    additional_deps = [
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:resource_variable_ops",
+        "//tensorflow/python/eager:backprop",
+        "//tensorflow/python/eager:context",
+        "//tensorflow/python/eager:tape",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py b/tensorflow/python/kernel_tests/accumulate_n_eager_test.py
similarity index 72%
rename from tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py
rename to tensorflow/python/kernel_tests/accumulate_n_eager_test.py
index 35974b9e21..dc11b7dece 100644
--- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_eager_test.py
+++ b/tensorflow/python/kernel_tests/accumulate_n_eager_test.py
@@ -12,48 +12,41 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for new version of accumulate_n op that will eventually go into
-`ops.math_ops`.
-
-These test cases spefically exercise the `eager` APIs. They need to be in a
-separate file from the remaining tests because eager mode is currently something
-you can turn on but can't turn off for the lifetime of the current process."""
+"""Tests for new version of accumulate_n op."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.framework.python.ops import accumulate_n_v2 as av2
-
 from tensorflow.python.eager import backprop
 
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import test
 
 
-
 class AccumulateNV2EagerTest(test_util.TensorFlowTestCase):
-  """Tests of the new, differentiable version of accumulate_n"""
+  """Tests of the new, differentiable version of accumulate_n."""
 
   def testMinimalEagerMode(self):
     forty = constant_op.constant(40)
     two = constant_op.constant(2)
-    answer = av2.accumulate_n_v2([forty, two])
+    answer = math_ops.accumulate_n([forty, two])
     self.assertEqual(42, answer.numpy())
 
-
   def testFloat(self):
     np.random.seed(12345)
     x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
     tf_x = ops.convert_n_to_tensor(x)
     with self.test_session(use_gpu=True):
-      self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).numpy())
-      self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).numpy())
+      self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).numpy())
+      self.assertAllClose(x[0] * 5,
+                          math_ops.accumulate_n([tf_x[0]] * 5).numpy())
 
   def testGrad(self):
     np.random.seed(42)
@@ -65,16 +58,14 @@ class AccumulateNV2EagerTest(test_util.TensorFlowTestCase):
     ]
 
     def fn(first, second, third):
-      return av2.accumulate_n_v2([first, second, third])
+      return math_ops.accumulate_n([first, second, third])
 
     grad_fn = backprop.gradients_function(fn)
     grad = grad_fn(input_vars[0], input_vars[1], input_vars[2])
-    self.assertAllEqual(np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1
+    self.assertAllEqual(np.repeat(1.0, num_inputs),  # d/dx (x + y + ...) = 1
                         [elem.numpy() for elem in grad])
 
 
-
 if __name__ == "__main__":
   ops.enable_eager_execution()
   test.main()
-
diff --git a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py b/tensorflow/python/kernel_tests/accumulate_n_test.py
similarity index 79%
rename from tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py
rename to tensorflow/python/kernel_tests/accumulate_n_test.py
index 45962098e9..0a6d4aea37 100644
--- a/tensorflow/contrib/framework/python/ops/accumulate_n_v2_test.py
+++ b/tensorflow/python/kernel_tests/accumulate_n_test.py
@@ -12,42 +12,42 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for new version of accumulate_n op that will eventually go into
-`ops.math_ops`."""
+"""Tests for new version of accumulate_n op."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.framework.python.ops import accumulate_n_v2 as av2
-
 from tensorflow.python.framework import dtypes as dtypes_lib
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import gradients
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import googletest
 
 
 class AccumulateNV2Test(test_util.TensorFlowTestCase):
-  """Tests of the new, differentiable version of accumulate_n"""
+  """Tests of the new, differentiable version of accumulate_n."""
 
   def testFloat(self):
     np.random.seed(12345)
     x = [np.random.random((1, 2, 3, 4, 5)) - 0.5 for _ in range(5)]
     tf_x = ops.convert_n_to_tensor(x)
     with self.test_session(use_gpu=True):
-      self.assertAllClose(sum(x), av2.accumulate_n_v2(tf_x).eval())
-      self.assertAllClose(x[0] * 5, av2.accumulate_n_v2([tf_x[0]] * 5).eval())
+      self.assertAllClose(sum(x), math_ops.accumulate_n(tf_x).eval())
+      self.assertAllClose(x[0] * 5,
+                          math_ops.accumulate_n([tf_x[0]] * 5).eval())
 
   def testInt(self):
     np.random.seed(54321)
     x = [np.random.randint(-128, 128, (5, 4, 3, 2, 1)) for _ in range(6)]
     tf_x = ops.convert_n_to_tensor(x)
     with self.test_session(use_gpu=True):
-      self.assertAllEqual(sum(x), av2.accumulate_n_v2(tf_x).eval())
-      self.assertAllEqual(x[0] * 6, av2.accumulate_n_v2([tf_x[0]] * 6).eval())
+      self.assertAllEqual(sum(x), math_ops.accumulate_n(tf_x).eval())
+      self.assertAllEqual(x[0] * 6,
+                          math_ops.accumulate_n([tf_x[0]] * 6).eval())
 
   def testGrad(self):
     np.random.seed(42)
@@ -55,9 +55,9 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase):
       with self.test_session(use_gpu=True) as sess:
         input_vars = [
             variables.Variable(10.0 * np.random.random())
-            for i in range(0, num_inputs)
+            for _ in range(0, num_inputs)
         ]
-        accum_n = av2.accumulate_n_v2(input_vars)
+        accum_n = math_ops.accumulate_n(input_vars)
         sess.run(variables.global_variables_initializer())
         accum_n_grad = gradients.gradients(accum_n, input_vars)
         self.assertAllEqual(
@@ -77,7 +77,7 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase):
           ops.convert_to_tensor(x, dtype=dtypes_lib.float32)
           for x in random_arrays
       ]
-      tf_val = av2.accumulate_n_v2(random_tensors)
+      tf_val = math_ops.accumulate_n(random_tensors)
       np_val = random_arrays[0]
       for random_array in random_arrays[1:]:
         np_val += random_array
@@ -86,7 +86,7 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase):
   def testZeroArgs(self):
     with self.test_session():
       with self.assertRaises(ValueError):
-        tf_val = av2.accumulate_n_v2([])
+        tf_val = math_ops.accumulate_n([])
         tf_val.eval()
 
   def testWrongShape(self):
@@ -94,28 +94,28 @@ class AccumulateNV2Test(test_util.TensorFlowTestCase):
       with self.assertRaises(ValueError):
         a = variables.Variable(0.2)
         b = variables.Variable(0.1)
-        tf_val = av2.accumulate_n_v2([a, b], shape=[2, 2])  # Should be shape=[]
+        math_ops.accumulate_n([a, b], shape=[2, 2])  # Should be shape=[]
 
   def testIncompatibleShapes(self):
     with self.test_session():
       with self.assertRaises(ValueError):
         a = variables.Variable(np.array([0.1, 0.2]))
         b = variables.Variable(np.array([[0.3], [0.4]]))
-        tf_val = av2.accumulate_n_v2([a, b])
+        math_ops.accumulate_n([a, b])
 
   def testWrongType(self):
     with self.test_session():
       with self.assertRaises(TypeError):
         a = variables.Variable(0.2, dtype=np.float32)
         b = variables.Variable(0.1, dtype=np.float32)
-        tf_val = av2.accumulate_n_v2([a, b], tensor_dtype=np.int32)
+        math_ops.accumulate_n([a, b], tensor_dtype=np.int32)
 
   def testWrongTypeOneInput(self):
     # Scenario that used to trigger a bug, even when testWrongType() worked
     with self.test_session():
       with self.assertRaises(TypeError):
         a = variables.Variable(0.2, dtype=np.float32)
-        tf_val = av2.accumulate_n_v2([a], tensor_dtype=np.int32)
+        math_ops.accumulate_n([a], tensor_dtype=np.int32)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 2ae8b610da..ed11fe5348 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -161,14 +161,11 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import gen_control_flow_ops
 from tensorflow.python.ops import gen_data_flow_ops
 from tensorflow.python.ops import gen_math_ops
 from tensorflow.python.ops import gen_nn_ops
 from tensorflow.python.ops import gen_sparse_ops
 from tensorflow.python.ops import gen_spectral_ops
-from tensorflow.python.ops import gen_state_ops
-from tensorflow.python.ops import state_ops
 # go/tf-wildcard-import
 # pylint: disable=wildcard-import
 from tensorflow.python.ops.gen_math_ops import *
@@ -2218,14 +2215,12 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
   Optionally, pass `shape` and `tensor_dtype` for shape and type checking,
   otherwise, these are inferred.
 
-  NOTE: This operation is not differentiable and cannot be used if inputs depend
-  on trainable variables. Please use `tf.add_n` for such cases.
+  `tf.accumulate_n` performs the same operation as `tf.add_n`, but does not
+  wait for all of its inputs to be ready before beginning to sum. This can
+  save memory if inputs are ready at different times, since minimum temporary
+  storage is proportional to the output size rather than the inputs size.
 
-  Aside from differentiability, `tf.accumulate_n` performs the same operation as
-  `tf.add_n`, but does not wait for all of its inputs to be ready before
-  beginning to sum. This can save memory if inputs are ready at different times,
-  since minimum temporary storage is proportional to the output size rather than
-  the inputs size.
+  `accumulate_n` is differentiable (but wasn't previous to TensorFlow 1.7).
 
   For example:
 
@@ -2235,8 +2230,9 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
   tf.accumulate_n([a, b, a])  # [[7, 4], [6, 14]]
 
   # Explicitly pass shape and type
-  tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)  # [[7,  4],
-                                                                   #  [6, 14]]
+  tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
+                                                                 # [[7,  4],
+                                                                 #  [6, 14]]
   ```
 
   Args:
@@ -2252,20 +2248,17 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
     ValueError: If `inputs` don't all have same shape and dtype or the shape
     cannot be inferred.
   """
-  if context.in_eager_mode():
-    # TODO(apassos) remove this once the lifetime of eager variables gets
-    # addressed.
-    raise ValueError("accumulate_n not supported in eager mode")
+  def _input_error():
+    return ValueError(
+        "inputs must be a list of at least one Tensor with the "
+        "same dtype and shape")
   if not inputs or not isinstance(inputs, (list, tuple)):
-    raise ValueError("inputs must be a list of at least one Tensor with the "
-                     "same dtype and shape")
+    raise _input_error()
   inputs = ops.convert_n_to_tensor_or_indexed_slices(inputs)
   if not all(isinstance(x, ops.Tensor) for x in inputs):
-    raise ValueError("inputs must be a list of at least one Tensor with the "
-                     "same dtype and shape")
+    raise _input_error()
   if not all(x.dtype == inputs[0].dtype for x in inputs):
-    raise ValueError("inputs must be a list of at least one Tensor with the "
-                     "same dtype and shape")
+    raise _input_error()
   if shape is not None:
     shape = tensor_shape.as_shape(shape)
   else:
@@ -2273,27 +2266,31 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
   for input_tensor in inputs:
     if isinstance(input_tensor, ops.Tensor):
       shape = shape.merge_with(input_tensor.get_shape())
-  if tensor_dtype is None:
-    tensor_dtype = inputs[0].dtype
-  if tensor_dtype != inputs[0].dtype:
-    raise TypeError("tensor_dtype is {}, but input is of type {}".format(
-        tensor_dtype, inputs[0].dtype))
-  if len(inputs) == 1:
+
+  # tensor_dtype is for safety only; operator's output type computed in C++
+  if tensor_dtype is not None and tensor_dtype != inputs[0].dtype:
+    raise TypeError("tensor_dtype is {}, but input is of type {}"
+                    .format(tensor_dtype, inputs[0].dtype))
+
+  if len(inputs) == 1 and name is None:
     return inputs[0]
-  with ops.name_scope(name, "AccumulateN", inputs) as name:
-    var = gen_state_ops._temporary_variable(
-        shape=tensor_shape.vector(0), dtype=tensor_dtype)
-    with ops.colocate_with(var):
-      zeros = array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0])
-      zeros.set_shape(shape)
-      ref = state_ops.assign(var, zeros, validate_shape=False)
-      update_ops = [
-          state_ops.assign_add(ref, input_tensor, use_locking=True)
-          for input_tensor in inputs
-      ]
-      with ops.control_dependencies(update_ops):
-        return gen_state_ops._destroy_temporary_variable(
-            ref, var_name=var.op.name, name=name)
+  elif len(inputs) == 1 and name is not None:
+    return array_ops.identity(inputs[0], name=name)
+  elif context.in_eager_mode():
+    # TemporaryVariable not currently supported in eager mode; fall back
+    # onto AddN for now.
+    # TODO(frreiss) remove this once the lifetime of eager variables gets
+    # addressed
+    return add_n(inputs, name=name)
+  else:
+    return gen_math_ops._accumulate_nv2(inputs, name=name, shape=shape)  # pylint: disable=protected-access
+
+
+@ops.RegisterGradient("AccumulateNV2")
+def _accumulate_n_grad(op, grad):
+  """Same as gradient for AddN. Copies the gradient to all inputs."""
+  # Not broadcasting.
+  return [grad] * len(op.inputs)
 
 
 @tf_export("nn.sigmoid", "sigmoid")
-- 
GitLab


From feeb6c095ffa15b555298122840f0542ee986eac Mon Sep 17 00:00:00 2001
From: Mark Daoust <markdaoust@google.com>
Date: Mon, 26 Feb 2018 10:41:44 -0800
Subject: [PATCH 367/575] Deleting references to outdated `translate/seq2seq`
 tutorial.

PiperOrigin-RevId: 187044697
---
 tensorflow/tools/ci_build/builds/test_tutorials.sh | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/tensorflow/tools/ci_build/builds/test_tutorials.sh b/tensorflow/tools/ci_build/builds/test_tutorials.sh
index 67e5af5564..db335f14ca 100755
--- a/tensorflow/tools/ci_build/builds/test_tutorials.sh
+++ b/tensorflow/tools/ci_build/builds/test_tutorials.sh
@@ -277,17 +277,6 @@ test_ptb_word_lm() {
   fi
 }
 
-
-# -----------------------------------------------------------
-# translate_test
-test_translate_test() {
-  LOG_FILE=$1
-
-  run_in_directory "${TEST_DIR}" "${LOG_FILE}" \
-    "${TF_MODELS_DIR}/tutorials/rnn/translate/translate.py" --self_test=True
-}
-
-
 # Run the tutorial tests
 test_runner "tutorial test-on-install" \
     "${TUT_TESTS}" "${TF_BUILD_TUT_TEST_BLACKLIST}" "${LOGS_DIR}"
-- 
GitLab


From f487340e7628802b1b8c3b12747f3b9ce9254af3 Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu <yuanzx@google.com>
Date: Mon, 26 Feb 2018 10:42:59 -0800
Subject: [PATCH 368/575] [XLA] Add kConvert to
 EffectiveOperandPrecisionIsOutputPrecision list.

PiperOrigin-RevId: 187044921
---
 tensorflow/compiler/xla/service/bfloat16_support.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/xla/service/bfloat16_support.cc b/tensorflow/compiler/xla/service/bfloat16_support.cc
index 3fd9e24601..07b4b14b5e 100644
--- a/tensorflow/compiler/xla/service/bfloat16_support.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_support.cc
@@ -79,6 +79,7 @@ bool BFloat16Support::EffectiveOperandPrecisionIsOutputPrecision(
     case HloOpcode::kBroadcast:
     case HloOpcode::kClamp:
     case HloOpcode::kConcatenate:
+    case HloOpcode::kConvert:
     case HloOpcode::kCopy:
     case HloOpcode::kGetTupleElement:
     case HloOpcode::kMaximum:
-- 
GitLab


From c6312773dd5473fb47f73c88c2f5c8f41e20c0fa Mon Sep 17 00:00:00 2001
From: Michael Kuperstein <mkuper@google.com>
Date: Mon, 26 Feb 2018 10:52:05 -0800
Subject: [PATCH 369/575] [XLA] Do not recompute flattened sets inside layout
 assignment.

Cache the flattened sets instead of recomputing them. This matters for large graphs, since we may request the flattened set thousands of times on the same instruction, and it may be fairly expensive to construct for large tuples.

PiperOrigin-RevId: 187046642
---
 .../compiler/xla/service/layout_assignment.cc | 31 ++++++++++++++-----
 .../compiler/xla/service/layout_assignment.h  | 10 ++++++
 2 files changed, 34 insertions(+), 7 deletions(-)

diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc
index 0668f66051..4929300f7d 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment.cc
@@ -192,17 +192,34 @@ LayoutConstraints::LayoutConstraints(
   }
 }
 
+PointsToSet::BufferSet* LayoutConstraints::GetBufferSet(
+    const HloInstruction* instruction) const {
+  auto it = buffer_sets_cache_.find(instruction);
+  if (it != buffer_sets_cache_.end()) {
+    return it->second.get();
+  }
+  auto& buffer_set =
+      buffer_sets_cache_
+          .emplace(instruction, MakeUnique<PointsToSet::BufferSet>())
+          .first->second;
+  const auto& points_to_set = points_to_analysis_.GetPointsToSet(instruction);
+  points_to_set.ForEachElement(
+      [&buffer_set](const ShapeIndex& /*index*/,
+                    const PointsToSet::BufferList& buffers) {
+        buffer_set->insert(buffers.begin(), buffers.end());
+      });
+  return buffer_set.get();
+}
+
 bool LayoutConstraints::OperandBufferForwarded(
     const HloInstruction* instruction, int64 operand_no) const {
   // The operand is potentially forwarded if the intersection of points-to sets
   // of the operand and the instruction is non-empty.
-  auto output_buffers =
-      points_to_analysis_.GetPointsToSet(instruction).CreateFlattenedSet();
-  auto operand_buffers =
-      points_to_analysis_.GetPointsToSet(instruction->operand(operand_no))
-          .CreateFlattenedSet();
-  for (const LogicalBuffer* output_buffer : output_buffers) {
-    if (operand_buffers.count(output_buffer) > 0) {
+  PointsToSet::BufferSet* output_buffers = GetBufferSet(instruction);
+  PointsToSet::BufferSet* operand_buffers =
+      GetBufferSet(instruction->operand(operand_no));
+  for (const LogicalBuffer* output_buffer : *output_buffers) {
+    if (operand_buffers->count(output_buffer) > 0) {
       return true;
     }
   }
diff --git a/tensorflow/compiler/xla/service/layout_assignment.h b/tensorflow/compiler/xla/service/layout_assignment.h
index 2901858448..7126cb50cf 100644
--- a/tensorflow/compiler/xla/service/layout_assignment.h
+++ b/tensorflow/compiler/xla/service/layout_assignment.h
@@ -38,6 +38,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/gtl/flatmap.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace xla {
@@ -199,6 +200,11 @@ class LayoutConstraints {
   string ToString() const;
 
  private:
+  // Find a bufferset in the bufferset cache. This is useful since we can
+  // currently create the flattened buffer set for the same instruction many
+  // times, which is often slow.
+  PointsToSet::BufferSet* GetBufferSet(const HloInstruction* instruction) const;
+
   // The set of BufferLayoutConstraints applied to the computation.
   std::unordered_map<const LogicalBuffer*, BufferLayoutConstraint>
       buffer_constraints_;
@@ -221,6 +227,10 @@ class LayoutConstraints {
   // Array-shaped buffers which have not yet been constrained.
   std::set<LogicalBuffer::Id> unconstrained_buffer_ids_;
 
+  mutable tensorflow::gtl::FlatMap<const HloInstruction*,
+                                   std::unique_ptr<PointsToSet::BufferSet>>
+      buffer_sets_cache_;
+
   HloComputation* computation_;
 };
 
-- 
GitLab


From 616de9709cbd1ec2b06a036db628bed04b143560 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Mon, 26 Feb 2018 10:54:31 -0800
Subject: [PATCH 370/575] Integrate ClusterResolvers with TPUEstimator.

PiperOrigin-RevId: 187047094
---
 tensorflow/contrib/cluster_resolver/BUILD     |   1 +
 .../python/training/cluster_resolver.py       |  23 +-
 .../python/training/cluster_resolver_test.py  |   2 +
 .../python/training/gce_cluster_resolver.py   |   3 +
 .../python/training/tpu_cluster_resolver.py   | 150 +++++++++---
 .../training/tpu_cluster_resolver_test.py     | 226 +++++++++++++-----
 .../contrib/tpu/python/tpu/tpu_config.py      |  31 +++
 7 files changed, 345 insertions(+), 91 deletions(-)

diff --git a/tensorflow/contrib/cluster_resolver/BUILD b/tensorflow/contrib/cluster_resolver/BUILD
index 6b03df2b8e..1a124eca36 100644
--- a/tensorflow/contrib/cluster_resolver/BUILD
+++ b/tensorflow/contrib/cluster_resolver/BUILD
@@ -110,5 +110,6 @@ tf_py_test(
         "//tensorflow/python:platform_test",
         "//tensorflow/python:training",
     ],
+    grpc_enabled = True,
     main = "python/training/tpu_cluster_resolver_test.py",
 )
diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
index b04822fa9d..1c480b2513 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py
@@ -53,11 +53,16 @@ class ClusterResolver(object):
     raise NotImplementedError(
         'cluster_spec is not implemented for {}.'.format(self))
 
+  @abc.abstractmethod
+  def master(self):
+    """..."""
+    raise NotImplementedError('master is not implemented for {}.'.format(self))
+
 
 class SimpleClusterResolver(ClusterResolver):
   """Simple implementation of ClusterResolver that accepts a ClusterSpec."""
 
-  def __init__(self, cluster_spec):
+  def __init__(self, cluster_spec, master=''):
     """Creates a SimpleClusterResolver from a ClusterSpec."""
     super(SimpleClusterResolver, self).__init__()
 
@@ -65,10 +70,18 @@ class SimpleClusterResolver(ClusterResolver):
       raise TypeError('cluster_spec must be a ClusterSpec.')
     self._cluster_spec = cluster_spec
 
+    if not isinstance(master, str):
+      raise TypeError('master must be a string.')
+    self._master = master
+
   def cluster_spec(self):
     """Returns the ClusterSpec passed into the constructor."""
     return self._cluster_spec
 
+  def master(self):
+    """Returns the master address to use when creating a session."""
+    return self._master
+
 
 class UnionClusterResolver(ClusterResolver):
   """Performs a union on underlying ClusterResolvers.
@@ -87,9 +100,13 @@ class UnionClusterResolver(ClusterResolver):
 
     Raises:
       TypeError: If any argument is not a subclass of `ClusterResolvers`.
+      ValueError: If there are no arguments passed.
     """
     super(UnionClusterResolver, self).__init__()
 
+    if not args:
+      raise ValueError('At least one ClusterResolver is required.')
+
     for cluster_resolver in args:
       if not isinstance(cluster_resolver, ClusterResolver):
         raise TypeError('All arguments must be a sub-class of '
@@ -169,3 +186,7 @@ class UnionClusterResolver(ClusterResolver):
           merged_cluster[job_name].update(task_dict)
 
     return ClusterSpec(merged_cluster)
+
+  def master(self):
+    """master returns the master address from the first cluster resolver."""
+    return self._cluster_resolvers[0].master()
diff --git a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py
index dbfb77723c..d9c97d53eb 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/cluster_resolver_test.py
@@ -234,5 +234,7 @@ class UnionClusterResolverTest(test.TestCase):
     self._verifyClusterSpecEquality(cluster_spec, expected_proto)
 
 
+# TODO(saeta): Include tests for master resolution
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
index d6f2eced93..3f58241289 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/gce_cluster_resolver.py
@@ -134,3 +134,6 @@ class GceClusterResolver(ClusterResolver):
 
     worker_list.sort()
     return ClusterSpec({self._job_name: worker_list})
+
+  def master(self):
+    return ''
diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
index a6a6e642e4..aeccf4c06b 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py
@@ -23,7 +23,8 @@ from six.moves.urllib.request import Request
 from six.moves.urllib.request import urlopen
 
 from tensorflow.contrib.cluster_resolver.python.training.cluster_resolver import ClusterResolver
-from tensorflow.python.training.server_lib import ClusterSpec
+from tensorflow.python.training import server_lib
+from tensorflow.python.util import compat
 
 _GOOGLE_API_CLIENT_INSTALLED = True
 try:
@@ -46,13 +47,23 @@ class TPUClusterResolver(ClusterResolver):
     req = Request('http://metadata/computeMetadata/v1/%s' % path,
                   headers={'Metadata-Flavor': 'Google'})
     resp = urlopen(req)
-    return resp.read()
+    return compat.as_bytes(resp.read())
+
+  def _shouldResolve(self):
+    if (self._tpu == compat.as_bytes('') or
+        self._tpu == compat.as_bytes('local') or
+        self._tpu.startswith(compat.as_bytes('/bns')) or
+        self._tpu.startswith(compat.as_bytes('grpc://'))):
+      return False
+    return True
 
   def __init__(self,
-               tpu_names,
+               tpu,
                zone=None,
                project=None,
-               job_name='tpu_worker',
+               job_name='worker',
+               coordinator_name='coordinator',
+               coordinator_address=None,
                credentials='default',
                service=None):
     """Creates a new TPUClusterResolver object.
@@ -61,7 +72,11 @@ class TPUClusterResolver(ClusterResolver):
     for the IP addresses and ports of each Cloud TPU listed.
 
     Args:
-      tpu_names: A list of names of the target Cloud TPUs.
+      tpu: Either a string, or a list of strings corresponding to the TPUs to
+        use. If the single string is the empty string, the string 'local', or a
+        string that begins with 'grpc://' or '/bns', then it is assumed to not
+        correspond with a Cloud TPU and will instead be passed as the session
+        master and no ClusterSpec propagation will be done.
       zone: Zone where the TPUs are located. If omitted or empty, we will assume
         that the zone of the TPU is the same as the zone of the GCE VM, which we
         will try to discover from the GCE metadata service.
@@ -69,6 +84,12 @@ class TPUClusterResolver(ClusterResolver):
         empty, we will try to discover the project name of the GCE VM from the
         GCE metadata service.
       job_name: Name of the TensorFlow job the TPUs belong to.
+      coordinator_name: The name to use for the coordinator. Set to None if the
+        coordinator should not be included in the computed ClusterSpec.
+      coordinator_address: The address of the coordinator (typically an ip:port
+        pair). If set to None, a TF server will be started. If coordinator_name
+        is None, a TF server will not be started even if coordinator_address is
+        None.
       credentials: GCE Credentials. If None, then we use default credentials
         from the oauth2client
       service: The GCE API object returned by the googleapiclient.discovery
@@ -77,26 +98,36 @@ class TPUClusterResolver(ClusterResolver):
 
     Raises:
       ImportError: If the googleapiclient is not installed.
+      ValueError: If no TPUs are specified.
     """
+    if isinstance(tpu, list):
+      if not tpu:
+        raise ValueError('At least one TPU must be specified.')
+      if len(tpu) != 1:
+        raise NotImplementedError(
+            'Using multiple TPUs in a single session is not yet implemented')
+      tpu = tpu[0]
+    self._tpu = compat.as_bytes(tpu)  # self._tpu is always bytes
+    self._job_name = job_name
+    self._credentials = credentials
 
-    if not project:
-      project = self._requestComputeMetadata('/project/project-id')
+    should_resolve = self._shouldResolve()
 
-    if not zone:
-      zone_path = self._requestComputeMetadata('/instance/zone')
+    if not project and should_resolve:
+      project = self._requestComputeMetadata('project/project-id')
+
+    if not zone and should_resolve:
+      zone_path = self._requestComputeMetadata('instance/zone')
       zone = zone_path.split('/')[-1]
 
     self._project = project
     self._zone = zone
-    self._tpu_names = tpu_names
-    self._job_name = job_name
-    self._credentials = credentials
 
-    if credentials == 'default':
+    if credentials == 'default' and should_resolve:
       if _GOOGLE_API_CLIENT_INSTALLED:
         self._credentials = GoogleCredentials.get_application_default()
 
-    if service is None:
+    if service is None and should_resolve:
       if not _GOOGLE_API_CLIENT_INSTALLED:
         raise ImportError('googleapiclient must be installed before using the '
                           'TPU cluster resolver')
@@ -107,25 +138,41 @@ class TPUClusterResolver(ClusterResolver):
     else:
       self._service = service
 
-  def get_master(self):
-    """Get the ClusterSpec grpc master path.
+    self._coordinator_name = coordinator_name
+    if coordinator_name and not coordinator_address and should_resolve:
+      self._start_local_server()
+    else:
+      self._coordinator_address = coordinator_address
+
+  def master(self):
+    """Get the Master string to be used for the session.
+
+    In the normal case, this returns the grpc path (grpc://1.2.3.4:8470) of
+    first instance in the ClusterSpec returned by the cluster_spec function.
 
-    This returns the grpc path (grpc://1.2.3.4:8470) of first instance in the
-    ClusterSpec returned by the cluster_spec function. This is suitable for use
-    for the `master` argument in tf.Session() when you are using one TPU.
+    If a non-TPU name is used when constructing a TPUClusterResolver, that will
+    be returned instead (e.g. If the tpus argument's value when constructing
+    this TPUClusterResolver was 'grpc://10.240.1.2:8470',
+    'grpc://10.240.1.2:8470' will be returned).
 
     Returns:
-      string, the grpc path of the first instance in the ClusterSpec.
+      string, the connection string to use when creating a session.
 
     Raises:
       ValueError: If none of the TPUs specified exists.
     """
+    if not self._shouldResolve():
+      return self._tpu
+
     job_tasks = self.cluster_spec().job_tasks(self._job_name)
     if not job_tasks:
       raise ValueError('No TPUs exists with the specified names exist.')
 
     return 'grpc://' + job_tasks[0]
 
+  def get_master(self):
+    return self.master()
+
   def cluster_spec(self):
     """Returns a ClusterSpec object based on the latest TPU information.
 
@@ -134,17 +181,54 @@ class TPUClusterResolver(ClusterResolver):
 
     Returns:
       A ClusterSpec containing host information returned from Cloud TPUs.
-    """
-    worker_list = []
-
-    for tpu_name in self._tpu_names:
-      full_name = 'projects/%s/locations/%s/nodes/%s' % (
-          self._project, self._zone, tpu_name)
-      request = self._service.projects().locations().nodes().get(name=full_name)
-      response = request.execute()
 
-      if 'health' in response and response['health'] == 'HEALTHY':
-        instance_url = '%s:%s' % (response['ipAddress'], response['port'])
-        worker_list.append(instance_url)
-
-    return ClusterSpec({self._job_name: worker_list})
+    Raises:
+      RuntimeError: If the provided TPU is not healthy.
+    """
+    if not self._shouldResolve():
+      return server_lib.ClusterSpec({})
+
+    full_name = 'projects/%s/locations/%s/nodes/%s' % (
+        self._project, self._zone, compat.as_text(self._tpu))
+    request = self._service.projects().locations().nodes().get(name=full_name)
+    response = request.execute()
+
+    if 'health' in response and response['health'] != 'HEALTHY':
+      raise RuntimeError('TPU "%s" is unhealthy: "%s"' % (self._tpu,
+                                                          response['health']))
+
+    if 'networkEndpoints' in response:
+      worker_list = [
+          '%s:%s' % (endpoint['ipAddress'], endpoint['port'])
+          for endpoint in response['networkEndpoints']
+      ]
+    else:
+      # Fall back to the deprecated response format
+      instance_url = '%s:%s' % (response['ipAddress'], response['port'])
+      worker_list = [instance_url]
+
+    cluster_spec = {self._job_name: worker_list}
+
+    if self._coordinator_address:
+      cluster_spec[self._coordinator_name] = [self._coordinator_address]
+
+    return server_lib.ClusterSpec(cluster_spec)
+
+  def _start_local_server(self):
+    address = self._requestComputeMetadata('instance/network-interfaces/0/ip')
+    self._server = server_lib.Server(
+        {
+            'local': ['0.0.0.0:0']
+        }, protocol='grpc', config=None, start=True)
+    # self._server.target is of the form: grpc://ipaddress:port
+    target = compat.as_bytes(self._server.target)
+    splits = target.split(compat.as_bytes(':'))
+    assert len(splits) == 3, self._server.target
+    assert splits[0] == compat.as_bytes('grpc'), self._server.target
+    self._coordinator_port = compat.as_text(splits[2])
+    self._coordinator_address = '%s:%s' % (
+        address, compat.as_text(self._coordinator_port))
+
+  def __deepcopy__(self, memo):
+    # TODO(b/73668574): Remove this once RunConfig avoids performing deepcopy.
+    return self
diff --git a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py
index 4fd34629cf..6b4a155152 100644
--- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py
+++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver_test.py
@@ -21,7 +21,7 @@ from __future__ import print_function
 from tensorflow.contrib.cluster_resolver.python.training.tpu_cluster_resolver import TPUClusterResolver
 from tensorflow.python.platform import test
 from tensorflow.python.training import server_lib
-
+from tensorflow.python.util import compat
 
 mock = test.mock
 
@@ -50,10 +50,12 @@ class MockNodeClass(object):
 
 def mock_request_compute_metadata(cls, *args, **kwargs):
   del cls, kwargs  # Unused.
-  if args[0] == '/project/project-id':
+  if args[0] == 'project/project-id':
     return 'test-project'
-  elif args[0] == '/instance/zone':
+  elif args[0] == 'instance/zone':
     return 'projects/test-project/locations/us-central1-c'
+  elif args[0] == 'instance/network-interfaces/0/ip':
+    return '10.128.1.2'
   return ''
 
 
@@ -113,17 +115,26 @@ class TPUClusterResolverTest(test.TestCase):
     tpu_cluster_resolver = TPUClusterResolver(
         project=None,
         zone=None,
-        tpu_names=['test-tpu-1'],
+        tpu=['test-tpu-1'],
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
 
     actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
     expected_proto = """
-    job { name: 'tpu_worker' tasks { key: 0 value: '10.1.2.3:8470' } }
-    """
-    self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
+    job {
+      name: 'coordinator'
+      tasks { key: 0 value: '10.128.1.2:%s' }
+    }
+    job {
+      name: 'worker'
+      tasks { key: 0 value: '10.1.2.3:8470' }
+    }
+    """ % tpu_cluster_resolver._coordinator_port
+    self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto))
 
-  def testSimpleSuccessfulRetrieval(self):
+  @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata',
+                     mock_request_compute_metadata)
+  def testRetrieveProjectAndZoneFromMetadataNoCoordinator(self):
     tpu_map = {
         'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
             'ipAddress': '10.1.2.3',
@@ -133,116 +144,217 @@ class TPUClusterResolverTest(test.TestCase):
     }
 
     tpu_cluster_resolver = TPUClusterResolver(
-        project='test-project',
-        zone='us-central1-c',
-        tpu_names=['test-tpu-1'],
+        project=None,
+        zone=None,
+        tpu=['test-tpu-1'],
+        coordinator_name=None,
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
 
     actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
     expected_proto = """
-    job { name: 'tpu_worker' tasks { key: 0 value: '10.1.2.3:8470' } }
+    job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } }
     """
     self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
 
-  def testMultipleSuccessfulRetrieval(self):
+  def testSimpleSuccessfulRetrieval(self):
     tpu_map = {
         'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
             'ipAddress': '10.1.2.3',
             'port': '8470',
             'health': 'HEALTHY'
-        },
-        'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': {
-            'ipAddress': '10.4.5.6',
-            'port': '8470',
-            'health': 'HEALTHY'
         }
     }
 
     tpu_cluster_resolver = TPUClusterResolver(
         project='test-project',
         zone='us-central1-c',
-        tpu_names=['test-tpu-2', 'test-tpu-1'],
+        tpu=['test-tpu-1'],
+        coordinator_address='10.128.1.5:10203',
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
 
     actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
     expected_proto = """
-    job { name: 'tpu_worker' tasks { key: 0 value: '10.4.5.6:8470' }
-                             tasks { key: 1 value: '10.1.2.3:8470' } }
+    job { name: 'coordinator' tasks { key: 0 value: '10.128.1.5:10203' } }
+    job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } }
     """
     self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
 
-  def testHealthyTpuNodeRetrieval(self):
+  def testNewNetworkEndpointFormat(self):
     tpu_map = {
         'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
-            'ipAddress': '10.1.2.3',
-            'port': '8470',
-            'health': 'HEALTHY'
-        },
-        'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': {
-            'ipAddress': '10.4.5.6',
-            'port': '8470',
-        },
-        'projects/test-project/locations/us-central1-c/nodes/test-tpu-3': {
-            'ipAddress': '10.7.8.9',
-            'port': '8470',
-            'health': 'UNHEALTHY'
+            'health': 'HEALTHY',
+            'networkEndpoints': [{
+                'ipAddress': '10.2.3.4',
+                'port': 8470,
+            }]
         }
     }
 
     tpu_cluster_resolver = TPUClusterResolver(
         project='test-project',
         zone='us-central1-c',
-        tpu_names=['test-tpu-2', 'test-tpu-1', 'test-tpu-3'],
+        tpu='test-tpu-1',
+        coordinator_address='10.128.1.5:10203',
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
 
     actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
     expected_proto = """
-    job {
-      name: 'tpu_worker'
-      tasks {
-        key: 0
-        value: '10.1.2.3:8470'
-      }
-    }
+    job { name: 'coordinator' tasks { key: 0 value: '10.128.1.5:10203' } }
+    job { name: 'worker' tasks { key: 0 value: '10.2.3.4:8470' } }
     """
     self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
+    self.assertEqual('grpc://10.2.3.4:8470', tpu_cluster_resolver.master())
 
-  def testGetMasterMultipleEntries(self):
+  @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata',
+                     mock_request_compute_metadata)
+  def testPodResolution(self):
     tpu_map = {
         'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
-            'ipAddress': '10.1.2.3',
-            'port': '8470',
-            'health': 'HEALTHY'
-        },
-        'projects/test-project/locations/us-central1-c/nodes/test-tpu-2': {
-            'ipAddress': '10.4.5.6',
-            'port': '8470',
-            'health': 'HEALTHY'
+            'health':
+                'HEALTHY',
+            'networkEndpoints': [
+                {
+                    'ipAddress': '10.2.3.4',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.5',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.6',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.7',
+                    'port': 8470,
+                },
+            ]
+        }
+    }
+
+    tpu_cluster_resolver = TPUClusterResolver(
+        tpu='test-tpu-1',
+        credentials=None,
+        service=self.mock_service_client(tpu_map=tpu_map))
+
+    actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
+    expected_proto = """
+    job {
+      name: 'coordinator',
+      tasks { key: 0 value: '10.128.1.2:%s'}
+    }
+    job {
+      name: 'worker'
+      tasks { key: 0 value: '10.2.3.4:8470' }
+      tasks { key: 1 value: '10.2.3.5:8470' }
+      tasks { key: 2 value: '10.2.3.6:8470' }
+      tasks { key: 3 value: '10.2.3.7:8470' }
+    }
+    """ % tpu_cluster_resolver._coordinator_port
+    self._verifyClusterSpecEquality(actual_cluster_spec, str(expected_proto))
+
+  def testPodResolutionNoCoordinator(self):
+    tpu_map = {
+        'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': {
+            'health':
+                'HEALTHY',
+            'networkEndpoints': [
+                {
+                    'ipAddress': '10.2.3.4',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.5',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.6',
+                    'port': 8470,
+                },
+                {
+                    'ipAddress': '10.2.3.7',
+                    'port': 8470,
+                },
+            ]
         }
     }
 
     tpu_cluster_resolver = TPUClusterResolver(
         project='test-project',
         zone='us-central1-c',
-        tpu_names=['test-tpu-2', 'test-tpu-1'],
+        tpu='test-tpu-1',
+        coordinator_name=None,
         credentials=None,
         service=self.mock_service_client(tpu_map=tpu_map))
-    self.assertEqual('grpc://10.4.5.6:8470', tpu_cluster_resolver.get_master())
+
+    actual_cluster_spec = tpu_cluster_resolver.cluster_spec()
+    expected_proto = """
+    job {
+      name: 'worker'
+      tasks { key: 0 value: '10.2.3.4:8470' }
+      tasks { key: 1 value: '10.2.3.5:8470' }
+      tasks { key: 2 value: '10.2.3.6:8470' }
+      tasks { key: 3 value: '10.2.3.7:8470' }
+    }
+    """
+    self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto)
 
   def testGetMasterNoEntries(self):
     tpu_map = {}
 
+    with self.assertRaises(ValueError):
+      TPUClusterResolver(
+          project='test-project',
+          zone='us-central1-c',
+          tpu=[],
+          coordinator_name=None,
+          credentials=None,
+          service=self.mock_service_client(tpu_map=tpu_map))
+
+  # TODO(saeta): Convert to parameterized test when included in OSS TF.
+  def verifyShouldResolve(self, tpu, should_resolve):
     tpu_cluster_resolver = TPUClusterResolver(
         project='test-project',
         zone='us-central1-c',
-        tpu_names=[],
+        tpu=tpu,
+        coordinator_name=None,
         credentials=None,
-        service=self.mock_service_client(tpu_map=tpu_map))
-    with self.assertRaises(ValueError):
-      tpu_cluster_resolver.get_master()
+        service=self.mock_service_client(tpu_map={}))
+    self.assertEqual(should_resolve, tpu_cluster_resolver._shouldResolve(),
+                     "TPU: '%s'" % tpu)
+
+  def testShouldResolveNoName(self):
+    self.verifyShouldResolve('', False)
+
+  def testShouldResolveLocal(self):
+    self.verifyShouldResolve('local', False)
+
+  def testShouldResolveGrpc(self):
+    self.verifyShouldResolve('grpc://10.1.2.3:8470', False)
+
+  def testShouldResolveBns(self):
+    self.verifyShouldResolve('/bns/foo/bar', False)
+
+  def testShouldResolveName(self):
+    self.verifyShouldResolve('mytpu', True)
+
+  def testShouldResolveList(self):
+    self.verifyShouldResolve(['myothertpu'], True)
+
+  def testShouldResolveGrpcPrefix(self):
+    self.verifyShouldResolve('grpctpu', True)
+
+  def testNoCallComputeMetadata(self):
+    tpu_cluster_resolver = TPUClusterResolver(tpu='/bns/foo/bar')
+    self.assertEqual(compat.as_bytes('/bns/foo/bar'),
+                     tpu_cluster_resolver.master())
+    self.assertEqual(
+        server_lib.ClusterSpec({}), tpu_cluster_resolver.cluster_spec())
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_config.py b/tensorflow/contrib/tpu/python/tpu/tpu_config.py
index 6440702182..7ceb4069cf 100644
--- a/tensorflow/contrib/tpu/python/tpu/tpu_config.py
+++ b/tensorflow/contrib/tpu/python/tpu/tpu_config.py
@@ -26,6 +26,7 @@ import os
 import numpy as np
 
 from tensorflow.contrib.tpu.python.tpu import util as util_lib
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.estimator import run_config as run_config_lib
 from tensorflow.python.platform import tf_logging as logging
 
@@ -140,6 +141,7 @@ class RunConfig(run_config_lib.RunConfig):
                tpu_config=None,
                evaluation_master=None,
                master=None,
+               cluster=None,
                **kwargs):
     """Constructs a RunConfig.
 
@@ -148,15 +150,26 @@ class RunConfig(run_config_lib.RunConfig):
       evaluation_master: a string. The address of the master to use for eval.
         Defaults to master if not set.
       master: a string. The address of the master to use for training.
+      cluster: a ClusterResolver
       **kwargs: keyword config parameters.
+
+    Raises:
+      ValueError: if cluster is not None and the provided session_config has a
+        cluster_def already.
     """
     super(RunConfig, self).__init__(**kwargs)
     self._tpu_config = tpu_config or TPUConfig()
+    self._cluster = cluster
 
     # If user sets master and/or evaluation_master explicilty, including empty
     # string '', take it. Otherwise, take the values set by parent class.
     if master is not None:
+      if cluster is not None:
+        raise ValueError('Both master and cluster are set.')
       self._master = master
+    else:
+      if cluster:
+        self._master = cluster.master()
 
     if evaluation_master is not None:
       self._evaluation_master = evaluation_master
@@ -170,6 +183,20 @@ class RunConfig(run_config_lib.RunConfig):
       # evaluation_master to master, unless user overwrites it.
       self._evaluation_master = self._master
 
+    # Set the ClusterSpec to use
+    if cluster:
+      self._cluster_spec = cluster.cluster_spec()
+
+      # Merge the cluster_def into the ConfigProto.
+      if self._session_config is None:  # pylint: disable=access-member-before-definition
+        self._session_config = config_pb2.ConfigProto(allow_soft_placement=True)
+      if self._session_config.HasField('cluster_def'):
+        raise ValueError(
+            'You cannot provide a ClusterResolver and '
+            'session_config.cluster_def.')
+      self._session_config.cluster_def.CopyFrom(
+          self._cluster_spec.as_cluster_def())
+
   @property
   def evaluation_master(self):
     return self._evaluation_master
@@ -182,6 +209,10 @@ class RunConfig(run_config_lib.RunConfig):
   def tpu_config(self):
     return self._tpu_config
 
+  @property
+  def cluster(self):
+    return self._cluster
+
   def replace(self, **kwargs):
     if 'tpu_config' not in kwargs:
       return super(RunConfig, self).replace(**kwargs)
-- 
GitLab


From 3af99b657f23e52d9c291d488fa3bb2a68e90022 Mon Sep 17 00:00:00 2001
From: Guangda Lai <laigd@google.com>
Date: Mon, 26 Feb 2018 10:59:54 -0800
Subject: [PATCH 371/575] Automated g4 rollback of changelist 185324160

PiperOrigin-RevId: 187048135
---
 tensorflow/contrib/cmake/tf_core_cpu.cmake    |   7 ++
 tensorflow/contrib/makefile/Makefile          |   1 +
 .../core/common_runtime/gpu/gpu_id_manager.cc |  50 +++++++--
 .../core/common_runtime/gpu/gpu_id_manager.h  |  14 ++-
 tensorflow/core/grappler/clusters/BUILD       |  26 ++++-
 .../core/grappler/clusters/single_machine.cc  |  17 ++-
 tensorflow/core/grappler/clusters/utils.cc    |  71 ++++++++-----
 tensorflow/core/grappler/clusters/utils.h     |   3 +-
 .../core/grappler/clusters/utils_test.cc      | 100 ++++++++++++++++++
 tensorflow/core/grappler/costs/BUILD          |   1 +
 tensorflow/core/grappler/costs/utils.cc       |  18 +++-
 11 files changed, 262 insertions(+), 46 deletions(-)
 create mode 100644 tensorflow/core/grappler/clusters/utils_test.cc

diff --git a/tensorflow/contrib/cmake/tf_core_cpu.cmake b/tensorflow/contrib/cmake/tf_core_cpu.cmake
index 96ac60d095..a54cbff33b 100644
--- a/tensorflow/contrib/cmake/tf_core_cpu.cmake
+++ b/tensorflow/contrib/cmake/tf_core_cpu.cmake
@@ -63,6 +63,12 @@ file(GLOB_RECURSE tf_core_cpu_exclude_srcs
     "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
     "${tensorflow_source_dir}/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc"
 )
+file(GLOB_RECURSE tf_core_cpu_whitelisted_srcs
+    "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id.h"
+    "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id.cc"
+    "${tensorflow_source_dir}/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc"
+)
+list(REMOVE_ITEM tf_core_cpu_exclude_srcs ${tf_core_cpu_whitelisted_srcs})
 list(REMOVE_ITEM tf_core_cpu_srcs ${tf_core_cpu_exclude_srcs})
 
 if (tensorflow_ENABLE_GPU)
@@ -79,6 +85,7 @@ if (tensorflow_ENABLE_GPU)
      "${tensorflow_source_dir}/tensorflow/core/*test*.cc"
   )
   list(REMOVE_ITEM tf_core_gpu_srcs ${tf_core_gpu_exclude_srcs})
+  list(REMOVE_ITEM tf_core_gpu_srcs ${tf_core_cpu_whitelisted_srcs})
   list(APPEND tf_core_cpu_srcs ${tf_core_gpu_srcs})
 endif()
 
diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index 81327407d4..05e8d9064b 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -677,6 +677,7 @@ endif  # TEGRA
 TF_CC_SRCS := $(filter-out $(CORE_CC_EXCLUDE_SRCS), $(CORE_CC_ALL_SRCS))
 # Add in any extra files that don't fit the patterns easily
 TF_CC_SRCS += tensorflow/contrib/makefile/downloads/fft2d/fftsg.c
+TF_CC_SRCS += tensorflow/core/common_runtime/gpu/gpu_id_manager.cc
 # Also include the op and kernel definitions.
 TF_CC_SRCS += $(shell cat $(MAKEFILE_DIR)/tf_op_files.txt)
 PBT_CC_SRCS := $(shell cat $(MAKEFILE_DIR)/tf_pb_text_files.txt)
diff --git a/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc b/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc
index 207afdca75..7dfff3269c 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_id_manager.cc
@@ -18,7 +18,10 @@ limitations under the License.
 #include <unordered_map>
 
 #include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/mutex.h"
 
 namespace tensorflow {
@@ -27,8 +30,8 @@ namespace {
 class TfToCudaGpuIdMap {
  public:
   static TfToCudaGpuIdMap* singleton() {
-    static auto* manager = new TfToCudaGpuIdMap;
-    return manager;
+    static auto* id_map = new TfToCudaGpuIdMap;
+    return id_map;
   }
 
   void InsertOrDie(TfGpuId tf_gpu_id, CudaGpuId cuda_gpu_id)
@@ -47,18 +50,41 @@ class TfToCudaGpuIdMap {
     }
   }
 
-  int32 FindOrDie(TfGpuId tf_gpu_id) const LOCKS_EXCLUDED(mu_) {
+  CudaGpuId FindOrDie(TfGpuId tf_gpu_id) const LOCKS_EXCLUDED(mu_) {
     mutex_lock lock(mu_);
+    return FindOrDieLocked(tf_gpu_id);
+  }
+
+  bool Find(TfGpuId tf_gpu_id, CudaGpuId* cuda_gpu_id) const
+      LOCKS_EXCLUDED(mu_) {
+    mutex_lock lock(mu_);
+    if (id_map_.count(tf_gpu_id.value()) == 0) return false;
+    *cuda_gpu_id = FindOrDieLocked(tf_gpu_id);
+    return true;
+  }
+
+ private:
+  TfToCudaGpuIdMap() = default;
+
+  CudaGpuId FindOrDieLocked(TfGpuId tf_gpu_id) const
+      EXCLUSIVE_LOCKS_REQUIRED(mu_) {
     auto result = id_map_.find(tf_gpu_id.value());
     CHECK(result != id_map_.end())
         << "Could not find the mapping for TfGpuId: " << tf_gpu_id;
-    return result->second;
+    return CudaGpuId(result->second);
+  }
+
+  void TestOnlyReset() LOCKS_EXCLUDED(mu_) {
+    mutex_lock lock(mu_);
+    id_map_.clear();
   }
 
- private:
   using IdMapType = std::unordered_map<int32, int32>;
   mutable mutex mu_;
   IdMapType id_map_ GUARDED_BY(mu_);
+
+  friend class ::tensorflow::GpuIdManager;
+  TF_DISALLOW_COPY_AND_ASSIGN(TfToCudaGpuIdMap);
 };
 }  // namespace
 
@@ -67,8 +93,20 @@ void GpuIdManager::InsertTfCudaGpuIdPair(TfGpuId tf_gpu_id,
   TfToCudaGpuIdMap::singleton()->InsertOrDie(tf_gpu_id, cuda_gpu_id);
 }
 
+Status GpuIdManager::TfToCudaGpuId(TfGpuId tf_gpu_id, CudaGpuId* cuda_gpu_id) {
+  if (TfToCudaGpuIdMap::singleton()->Find(tf_gpu_id, cuda_gpu_id)) {
+    return Status::OK();
+  }
+  return errors::NotFound("TF GPU device with id ", tf_gpu_id.value(),
+                          " was not registered");
+}
+
 CudaGpuId GpuIdManager::TfToCudaGpuId(TfGpuId tf_gpu_id) {
-  return CudaGpuId(TfToCudaGpuIdMap::singleton()->FindOrDie(tf_gpu_id));
+  return TfToCudaGpuIdMap::singleton()->FindOrDie(tf_gpu_id);
+}
+
+void GpuIdManager::TestOnlyReset() {
+  TfToCudaGpuIdMap::singleton()->TestOnlyReset();
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/gpu/gpu_id_manager.h b/tensorflow/core/common_runtime/gpu/gpu_id_manager.h
index 33925d8c36..2b54cc184c 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_id_manager.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_id_manager.h
@@ -17,15 +17,25 @@ limitations under the License.
 #define TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_ID_MANAGER_H_
 
 #include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
 
-// Class that manages the translation between Tensorflow GPU ids and CUDA GPU
-// ids.
+// Class that maintains a map from TfGpuId to CudaGpuId, and manages the
+// translation between them.
 class GpuIdManager {
  public:
+  // Adds a mapping from tf_gpu_id to cuda_gpu_id.
   static void InsertTfCudaGpuIdPair(TfGpuId tf_gpu_id, CudaGpuId cuda_gpu_id);
+
+  // Gets the cuda_gpu_id associated with tf_gpu_id. Returns OK if found.
+  static Status TfToCudaGpuId(TfGpuId tf_gpu_id, CudaGpuId* cuda_gpu_id);
+  // Similar to the above version, but returns the result, and checks fail if
+  // no result is found.
   static CudaGpuId TfToCudaGpuId(TfGpuId tf_gpu_id);
+
+  // Clears the map. Used in unit tests only.
+  static void TestOnlyReset();
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/clusters/BUILD b/tensorflow/core/grappler/clusters/BUILD
index b8f8e13c9a..b653f902e8 100644
--- a/tensorflow/core/grappler/clusters/BUILD
+++ b/tensorflow/core/grappler/clusters/BUILD
@@ -1,7 +1,12 @@
 licenses(["notice"])  # Apache 2.0
 
+load("//tensorflow:tensorflow.bzl", "if_cuda")
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
 load("//tensorflow:tensorflow.bzl", "tf_cuda_library")
+load(
+    "//tensorflow/core:platform/default/build_config_root.bzl",
+    "tf_cuda_tests_tags",
+)
 
 filegroup(
     name = "all_files",
@@ -26,13 +31,12 @@ config_setting(
 tf_cuda_library(
     name = "utils",
     srcs = ["utils.cc"],
-    hdrs = [
-        "utils.h",
-    ],
+    hdrs = ["utils.h"],
     visibility = ["//visibility:public"],
     deps = [
         "//third_party/eigen3",
         "//tensorflow/core:framework",
+        "//tensorflow/core:gpu_id",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
     ] + select({
@@ -41,6 +45,21 @@ tf_cuda_library(
     }),
 )
 
+tf_cc_test(
+    name = "utils_test",
+    srcs = ["utils_test.cc"],
+    linkstatic = if_cuda(1, 0),
+    tags = tf_cuda_tests_tags(),
+    deps = [
+        ":utils",
+        "//tensorflow/core:gpu_id",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
+
 cc_library(
     name = "cluster",
     srcs = ["cluster.cc"],
@@ -104,6 +123,7 @@ cc_library(
         "//tensorflow/core:core_cpu_lib",
         "//tensorflow/core:direct_session",
         "//tensorflow/core:framework",
+        "//tensorflow/core:gpu_id",
         "//tensorflow/core:lib",
         "//tensorflow/core/grappler:utils",
         "//tensorflow/core/kernels:ops_util",
diff --git a/tensorflow/core/grappler/clusters/single_machine.cc b/tensorflow/core/grappler/clusters/single_machine.cc
index cc7f418d49..8e236c9ee8 100644
--- a/tensorflow/core/grappler/clusters/single_machine.cc
+++ b/tensorflow/core/grappler/clusters/single_machine.cc
@@ -21,6 +21,8 @@ limitations under the License.
 #include "tensorflow/cc/training/queue_runner.h"
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
 #include "tensorflow/core/grappler/clusters/utils.h"
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/kernels/ops_util.h"
@@ -80,13 +82,24 @@ Status SingleMachine::Provision() {
 
   std::vector<DeviceAttributes> devices;
   TF_RETURN_IF_ERROR(session_->ListDevices(&devices));
-  int gpu_id = 0;
   for (const auto& dev : devices) {
     DeviceProperties attr;
     if (dev.device_type() == "CPU") {
       attr = GetLocalCPUInfo();
     } else if (dev.device_type() == "GPU") {
-      attr = GetLocalGPUInfo(gpu_id++);
+      DeviceNameUtils::ParsedName parsed;
+      if (!DeviceNameUtils::ParseFullName(dev.name(), &parsed)) {
+        return errors::InvalidArgument(
+            strings::StrCat("Not able to parse GPU device name: ", dev.name()));
+      }
+      TfGpuId tf_gpu_id(parsed.id);
+      CudaGpuId cuda_gpu_id;
+      Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id);
+      if (!s.ok()) {
+        return errors::Unavailable("Unknown TF GPU device with id ",
+                                   tf_gpu_id.value(), ": ", s.ToString());
+      }
+      attr = GetLocalGPUInfo(cuda_gpu_id);
     } else if (dev.device_type().find("XLA") == string::npos) {
       // Filter out the fake XLA devices to avoid double counting the actual
       // hardware resources that are available.
diff --git a/tensorflow/core/grappler/clusters/utils.cc b/tensorflow/core/grappler/clusters/utils.cc
index 607e10e1ab..b54b34959a 100644
--- a/tensorflow/core/grappler/clusters/utils.cc
+++ b/tensorflow/core/grappler/clusters/utils.cc
@@ -27,6 +27,9 @@ limitations under the License.
 #include "include/libxsmm.h"
 #endif
 
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
+#include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/cpu_info.h"
@@ -66,36 +69,40 @@ DeviceProperties GetLocalCPUInfo() {
   return device;
 }
 
-DeviceProperties GetLocalGPUInfo(int gpu_id) {
+DeviceProperties GetLocalGPUInfo(CudaGpuId cuda_gpu_id) {
   DeviceProperties device;
   device.set_type("GPU");
 
 #if GOOGLE_CUDA
   cudaDeviceProp properties;
-  cudaError_t error = cudaGetDeviceProperties(&properties, gpu_id);
-  if (error == cudaSuccess) {
-    device.set_vendor("NVidia");
-    device.set_model(properties.name);
-    device.set_frequency(properties.clockRate * 1e-3);
-    device.set_num_cores(properties.multiProcessorCount);
-    device.set_num_registers(properties.regsPerMultiprocessor);
-    // For compute capability less than 5, l1 cache size is configurable to
-    // either 16 KB or 48 KB. We use the initial configuration 16 KB here. For
-    // compute capability larger or equal to 5, l1 cache (unified with texture
-    // cache) size is 24 KB. This number may need to be updated for future
-    // compute capabilities.
-    device.set_l1_cache_size((properties.major < 5) ? 16 * 1024 : 24 * 1024);
-    device.set_l2_cache_size(properties.l2CacheSize);
-    device.set_l3_cache_size(0);
-    device.set_shared_memory_size_per_multiprocessor(
-        properties.sharedMemPerMultiprocessor);
-    device.set_memory_size(properties.totalGlobalMem);
-    // 8 is the number of bits per byte. 2 is accounted for
-    // double data rate (DDR).
-    device.set_bandwidth(properties.memoryBusWidth / 8 *
-                         properties.memoryClockRate * 2);
+  cudaError_t error = cudaGetDeviceProperties(&properties, cuda_gpu_id.value());
+  if (error != cudaSuccess) {
+    device.set_type("UNKNOWN");
+    LOG(ERROR) << "Failed to get device properties, error code: " << error;
+    return device;
   }
 
+  device.set_vendor("NVIDIA");
+  device.set_model(properties.name);
+  device.set_frequency(properties.clockRate * 1e-3);
+  device.set_num_cores(properties.multiProcessorCount);
+  device.set_num_registers(properties.regsPerMultiprocessor);
+  // For compute capability less than 5, l1 cache size is configurable to
+  // either 16 KB or 48 KB. We use the initial configuration 16 KB here. For
+  // compute capability larger or equal to 5, l1 cache (unified with texture
+  // cache) size is 24 KB. This number may need to be updated for future
+  // compute capabilities.
+  device.set_l1_cache_size((properties.major < 5) ? 16 * 1024 : 24 * 1024);
+  device.set_l2_cache_size(properties.l2CacheSize);
+  device.set_l3_cache_size(0);
+  device.set_shared_memory_size_per_multiprocessor(
+      properties.sharedMemPerMultiprocessor);
+  device.set_memory_size(properties.totalGlobalMem);
+  // 8 is the number of bits per byte. 2 is accounted for
+  // double data rate (DDR).
+  device.set_bandwidth(properties.memoryBusWidth / 8 *
+                       properties.memoryClockRate * 2);
+
   (*device.mutable_environment())["architecture"] =
       strings::StrCat(properties.major, ".", properties.minor);
   (*device.mutable_environment())["cuda"] = strings::StrCat(CUDA_VERSION);
@@ -106,18 +113,26 @@ DeviceProperties GetLocalGPUInfo(int gpu_id) {
 }
 
 DeviceProperties GetDeviceInfo(const DeviceNameUtils::ParsedName& device) {
+  DeviceProperties unknown;
+  unknown.set_type("UNKNOWN");
+
   if (device.type == "CPU") {
     return GetLocalCPUInfo();
   } else if (device.type == "GPU") {
     if (device.has_id) {
-      return GetLocalGPUInfo(device.id);
+      TfGpuId tf_gpu_id(device.id);
+      CudaGpuId cuda_gpu_id;
+      Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id);
+      if (!s.ok()) {
+        LOG(ERROR) << s;
+        return unknown;
+      }
+      return GetLocalGPUInfo(cuda_gpu_id);
     } else {
-      return GetLocalGPUInfo(0);
+      return GetLocalGPUInfo(CudaGpuId(0));
     }
   }
-  DeviceProperties result;
-  result.set_type("UNKNOWN");
-  return result;
+  return unknown;
 }
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/clusters/utils.h b/tensorflow/core/grappler/clusters/utils.h
index 191942040a..df8e7dca44 100644
--- a/tensorflow/core/grappler/clusters/utils.h
+++ b/tensorflow/core/grappler/clusters/utils.h
@@ -16,6 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_GRAPPLER_CLUSTERS_UTILS_H_
 #define TENSORFLOW_GRAPPLER_CLUSTERS_UTILS_H_
 
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
 #include "tensorflow/core/protobuf/device_properties.pb.h"
 #include "tensorflow/core/util/device_name_utils.h"
 
@@ -27,7 +28,7 @@ DeviceProperties GetLocalCPUInfo();
 
 // Returns the DeviceProperties for the specified GPU attached to the server on
 // which grappler is running.
-DeviceProperties GetLocalGPUInfo(int gpu_id);
+DeviceProperties GetLocalGPUInfo(CudaGpuId cuda_gpu_id);
 
 // Returns the DeviceProperties of the specified device
 DeviceProperties GetDeviceInfo(const DeviceNameUtils::ParsedName& device);
diff --git a/tensorflow/core/grappler/clusters/utils_test.cc b/tensorflow/core/grappler/clusters/utils_test.cc
new file mode 100644
index 0000000000..74218adbac
--- /dev/null
+++ b/tensorflow/core/grappler/clusters/utils_test.cc
@@ -0,0 +1,100 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/grappler/clusters/utils.h"
+
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/protobuf/device_properties.pb.h"
+
+namespace tensorflow {
+namespace grappler {
+namespace {
+
+TEST(UtilsTest, GetLocalGPUInfo) {
+  GpuIdManager::TestOnlyReset();
+#if GOOGLE_CUDA
+  LOG(INFO) << "CUDA is enabled.";
+  DeviceProperties properties;
+
+  // Invalid CUDA GPU ID.
+  properties = GetLocalGPUInfo(CudaGpuId(100));
+  EXPECT_EQ("UNKNOWN", properties.type());
+
+  // Succeed when a valid CUDA GPU id was inserted.
+  properties = GetLocalGPUInfo(CudaGpuId(0));
+  EXPECT_EQ("GPU", properties.type());
+  EXPECT_EQ("NVIDIA", properties.vendor());
+#else
+  LOG(INFO) << "CUDA is not enabled.";
+  DeviceProperties properties;
+
+  properties = GetLocalGPUInfo(CudaGpuId(0));
+  EXPECT_EQ("GPU", properties.type());
+
+  properties = GetLocalGPUInfo(CudaGpuId(100));
+  EXPECT_EQ("GPU", properties.type());
+#endif
+}
+
+TEST(UtilsTest, GetDeviceInfo) {
+  GpuIdManager::TestOnlyReset();
+  DeviceNameUtils::ParsedName device;
+  DeviceProperties properties;
+
+  // Invalid type.
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("UNKNOWN", properties.type());
+
+  // Cpu info.
+  device.type = "CPU";
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("CPU", properties.type());
+
+  // No TF GPU id provided.
+  device.type = "GPU";
+  device.has_id = false;
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("GPU", properties.type());
+#if GOOGLE_CUDA
+  EXPECT_EQ("NVIDIA", properties.vendor());
+#endif
+
+  // TF to CUDA GPU id mapping entry doesn't exist.
+  device.has_id = true;
+  device.id = 0;
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("UNKNOWN", properties.type());
+
+#if GOOGLE_CUDA
+  // Invalid CUDA GPU id.
+  GpuIdManager::InsertTfCudaGpuIdPair(TfGpuId(0), CudaGpuId(100));
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("UNKNOWN", properties.type());
+
+  // Valid CUDA GPU id.
+  GpuIdManager::InsertTfCudaGpuIdPair(TfGpuId(1), CudaGpuId(0));
+  device.id = 1;
+  properties = GetDeviceInfo(device);
+  EXPECT_EQ("GPU", properties.type());
+  EXPECT_EQ("NVIDIA", properties.vendor());
+#endif
+}
+
+}  // namespace
+}  // namespace grappler
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/costs/BUILD b/tensorflow/core/grappler/costs/BUILD
index 0fe01e9c9e..5336df1f51 100644
--- a/tensorflow/core/grappler/costs/BUILD
+++ b/tensorflow/core/grappler/costs/BUILD
@@ -142,6 +142,7 @@ tf_cuda_library(
         "//third_party/eigen3",
         "//tensorflow/core:framework",
         "//tensorflow/core:graph",
+        "//tensorflow/core:gpu_id",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_proto_parsing",
         "//tensorflow/core:protos_all_cc",
diff --git a/tensorflow/core/grappler/costs/utils.cc b/tensorflow/core/grappler/costs/utils.cc
index 602f69f12e..076945d5c6 100644
--- a/tensorflow/core/grappler/costs/utils.cc
+++ b/tensorflow/core/grappler/costs/utils.cc
@@ -26,6 +26,8 @@ limitations under the License.
 #include "cuda/include/cudnn.h"
 #endif
 
+#include "tensorflow/core/common_runtime/gpu/gpu_id.h"
+#include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
 #include "tensorflow/core/framework/allocation_description.pb.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/op.h"
@@ -200,17 +202,25 @@ std::vector<OpInfo::TensorProperties> FindInputFeatures(
 }
 
 DeviceProperties GetDeviceInfo(const string& device_str) {
+  DeviceProperties unknown;
+  unknown.set_type("UNKNOWN");
+
   DeviceNameUtils::ParsedName parsed;
   if (DeviceNameUtils::ParseFullName(device_str, &parsed)) {
     if (parsed.type == "GPU") {
-      return GetLocalGPUInfo(parsed.id);
+      TfGpuId tf_gpu_id(parsed.id);
+      CudaGpuId cuda_gpu_id;
+      Status s = GpuIdManager::TfToCudaGpuId(tf_gpu_id, &cuda_gpu_id);
+      if (!s.ok()) {
+        LOG(ERROR) << s;
+        return unknown;
+      }
+      return GetLocalGPUInfo(cuda_gpu_id);
     } else if (parsed.type == "CPU") {
       return GetLocalCPUInfo();
     }
   }
-  DeviceProperties device;
-  device.set_type("UNKNOWN");
-  return device;
+  return unknown;
 }
 
 DeviceProperties GetDeviceInfo(const CostGraphDef::Node& node) {
-- 
GitLab


From 387e0e51a3a8b6c7752bb198bf1fdfa1ebf12b60 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 11:08:54 -0800
Subject: [PATCH 372/575] Bring in `isbuiltin`.

PiperOrigin-RevId: 187049824
---
 tensorflow/python/util/tf_inspect.py      |  5 +++++
 tensorflow/python/util/tf_inspect_test.py | 13 +++++++++++++
 2 files changed, 18 insertions(+)

diff --git a/tensorflow/python/util/tf_inspect.py b/tensorflow/python/util/tf_inspect.py
index c2fe6fc449..a7cead5555 100644
--- a/tensorflow/python/util/tf_inspect.py
+++ b/tensorflow/python/util/tf_inspect.py
@@ -149,6 +149,11 @@ def getsource(object):  # pylint: disable=redefined-builtin
   return _inspect.getsource(tf_decorator.unwrap(object)[1])
 
 
+def isbuiltin(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.isbuiltin."""
+  return _inspect.isbuiltin(tf_decorator.unwrap(object)[1])
+
+
 def isclass(object):  # pylint: disable=redefined-builtin
   """TFDecorator-aware replacement for inspect.isclass."""
   return _inspect.isclass(tf_decorator.unwrap(object)[1])
diff --git a/tensorflow/python/util/tf_inspect_test.py b/tensorflow/python/util/tf_inspect_test.py
index 8903e1156b..129408449e 100644
--- a/tensorflow/python/util/tf_inspect_test.py
+++ b/tensorflow/python/util/tf_inspect_test.py
@@ -144,6 +144,19 @@ def test_decorated_function_with_defaults(a, b=2, c='Hello'):
     self.assertEqual(
         expected, tf_inspect.getsource(test_decorated_function_with_defaults))
 
+  def testIsBuiltin(self):
+    self.assertEqual(
+        tf_inspect.isbuiltin(TestDecoratedClass),
+        inspect.isbuiltin(TestDecoratedClass))
+    self.assertEqual(
+        tf_inspect.isbuiltin(test_decorated_function),
+        inspect.isbuiltin(test_decorated_function))
+    self.assertEqual(
+        tf_inspect.isbuiltin(test_undecorated_function),
+        inspect.isbuiltin(test_undecorated_function))
+    self.assertEqual(tf_inspect.isbuiltin(range), inspect.isbuiltin(range))
+    self.assertEqual(tf_inspect.isbuiltin(max), inspect.isbuiltin(max))
+
   def testIsClass(self):
     self.assertTrue(tf_inspect.isclass(TestDecoratedClass))
     self.assertFalse(tf_inspect.isclass(test_decorated_function))
-- 
GitLab


From 2513479d7b39235f9504ede2bf6f61cb78aae923 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Mon, 26 Feb 2018 11:10:20 -0800
Subject: [PATCH 373/575] eager/examples/resnet50: Fix breakage.

PiperOrigin-RevId: 187050075
---
 .../contrib/eager/python/examples/resnet50/resnet50_test.py   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
index c106ab0a06..65dcc53aab 100644
--- a/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
+++ b/tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
@@ -194,11 +194,11 @@ class ResNet50Benchmarks(tf.test.Benchmark):
     with tf.device(device):
       images, _ = random_batch(batch_size)
       for _ in xrange(num_burn):
-        model(images).cpu()
+        model(images, training=False).cpu()
       gc.collect()
       start = time.time()
       for _ in xrange(num_iters):
-        model(images).cpu()
+        model(images, training=False).cpu()
       self._report(label, start, num_iters, device, batch_size, data_format)
 
   def benchmark_eager_apply(self):
-- 
GitLab


From 5a9343b2ac7011593fb2ad2e7c82119181e608ec Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Mon, 26 Feb 2018 11:12:04 -0800
Subject: [PATCH 374/575] Add support for parsing the "gather" HLO

PiperOrigin-RevId: 187050345
---
 .../compiler/xla/tools/parser/hlo_parser.cc   | 37 +++++++++++++++++--
 .../xla/tools/parser/hlo_parser_test.cc       | 24 ++++++++++++
 2 files changed, 58 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
index cd2b843ad3..e60a5a4919 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser.cc
@@ -1049,9 +1049,40 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder,
           HloInstruction::CreateDot(shape, operands[0], operands[1], dnum));
       break;
     }
-    case HloOpcode::kGather:
-      // TODO(b/72710576): HLO parsing is not implemented for Gather.
-      return TokenError("HLO parsing is not implemented for Gather");
+    case HloOpcode::kGather: {
+      optional<std::vector<int64>> output_window_dims;
+      attrs["output_window_dims"] = {
+          /*required=*/true, AttrTy::kBracedInt64List, &output_window_dims};
+      optional<std::vector<int64>> elided_window_dims;
+      attrs["elided_window_dims"] = {
+          /*required=*/true, AttrTy::kBracedInt64List, &elided_window_dims};
+      optional<std::vector<int64>> gather_dims_to_operand_dims;
+      attrs["gather_dims_to_operand_dims"] = {/*required=*/true,
+                                              AttrTy::kBracedInt64List,
+                                              &gather_dims_to_operand_dims};
+      optional<int64> index_vector_dim;
+      attrs["index_vector_dim"] = {/*required=*/true, AttrTy::kInt64,
+                                   &index_vector_dim};
+      optional<std::vector<int64>> window_bounds;
+      attrs["window_bounds"] = {/*required=*/true, AttrTy::kBracedInt64List,
+                                &window_bounds};
+
+      if (!ParseOperands(&operands, /*expected_size=*/2) ||
+          !ParseAttributes(attrs)) {
+        return false;
+      }
+
+      GatherDimensionNumbers dim_numbers = HloInstruction::MakeGatherDimNumbers(
+          /*output_window_dims=*/*output_window_dims,
+          /*elided_window_dims=*/*elided_window_dims,
+          /*gather_dims_to_operand_dims=*/*gather_dims_to_operand_dims,
+          /*index_vector_dim=*/*index_vector_dim);
+
+      instruction = builder->AddInstruction(HloInstruction::CreateGather(
+          shape, /*operand=*/operands[0], /*gather_indices=*/operands[1],
+          dim_numbers, *window_bounds));
+      break;
+    }
     case HloOpcode::kTrace:
       return TokenError(StrCat("parsing not yet implemented for op: ",
                                HloOpcodeString(opcode)));
diff --git a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
index b8c6b59204..863081d654 100644
--- a/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
+++ b/tensorflow/compiler/xla/tools/parser/hlo_parser_test.cc
@@ -716,6 +716,18 @@ ENTRY %sparse_f32_r1 () -> f32[9] {
   ROOT %foo = f32[9]sparse{10} constant(f32[9]{1: 2, 3: 4, 5: 6})
 }
 
+)"
+},
+{
+"gather",
+R"(HloModule StringifyGather
+
+ENTRY %Gather (input_tensor: f32[50,49,48,47,46], gather_indices: s64[10,9,8,7,5]) -> f32[10,9,8,7,30,29,28,27,26] {
+  %input_tensor = f32[50,49,48,47,46]{4,3,2,1,0} parameter(0)
+  %gather_indices = s64[10,9,8,7,5]{4,3,2,1,0} parameter(1)
+  ROOT %gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} gather(f32[50,49,48,47,46]{4,3,2,1,0} %input_tensor, s64[10,9,8,7,5]{4,3,2,1,0} %gather_indices), output_window_dims={4,5,6,7,8}, elided_window_dims={}, gather_dims_to_operand_dims={0,1,2,3,4}, index_vector_dim=4, window_bounds={30,29,28,27,26}
+}
+
 )"
 },
   });
@@ -860,6 +872,18 @@ ENTRY dot {
   ROOT dot = f32[2,3]{1,0} dot(a, b), lhs_batch_dims={0}, lhs_contracting_dims={1}, rhs_contracting_dims={0}
 }
 
+)"
+},
+{
+"gather",
+R"(HloModule gather
+
+ENTRY Gather {
+  input_tensor = f32[50,49,48,47,46]{4,3,2,1,0} parameter(0)
+  gather_indices = s64[10,9,8,7,5]{4,3,2,1,0} parameter(1)
+  ROOT gather = f32[10,9,8,7,30,29,28,27,26]{8,7,6,5,4,3,2,1,0} gather(input_tensor, gather_indices), output_window_dims={4,5,6,7,8}, elided_window_dims={}, gather_dims_to_operand_dims={0,1,2,3,4}, index_vector_dim=4, window_bounds={30,29,28,27,26}
+}
+
 )"
 },
   });
-- 
GitLab


From 4fac98fbc731f742e0121fde561fcf6ed1203423 Mon Sep 17 00:00:00 2001
From: Mingsheng Hong <hongm@google.com>
Date: Mon, 26 Feb 2018 11:13:09 -0800
Subject: [PATCH 375/575] Added const to Node* in various parts of the code
 base.

PiperOrigin-RevId: 187050526
---
 tensorflow/compiler/tf2xla/const_analysis.cc   |  4 ++--
 tensorflow/compiler/tf2xla/graph_compiler.cc   |  2 +-
 .../core/common_runtime/shape_refiner.cc       |  4 ++--
 .../core/distributed_runtime/scheduler.cc      | 18 +++++++++---------
 .../core/distributed_runtime/scheduler.h       |  6 +++---
 tensorflow/core/graph/costmodel.cc             |  2 +-
 tensorflow/core/graph/graph.cc                 |  2 +-
 tensorflow/core/graph/graph.h                  |  2 +-
 tensorflow/core/graph/graph_constructor.cc     |  2 +-
 tensorflow/core/graph/graph_partition.cc       |  6 +++---
 tensorflow/core/graph/node_builder.cc          |  6 +++---
 tensorflow/core/graph/node_builder.h           |  6 +++---
 tensorflow/core/graph/optimizer_cse.cc         | 16 ++++++++--------
 13 files changed, 38 insertions(+), 38 deletions(-)

diff --git a/tensorflow/compiler/tf2xla/const_analysis.cc b/tensorflow/compiler/tf2xla/const_analysis.cc
index 82923722c5..6f46532419 100644
--- a/tensorflow/compiler/tf2xla/const_analysis.cc
+++ b/tensorflow/compiler/tf2xla/const_analysis.cc
@@ -37,7 +37,7 @@ Status BackwardsConstAnalysis(const Graph& g,
   };
 
   Status status;
-  std::unordered_set<Node*> must_be_const;
+  std::unordered_set<const Node*> must_be_const;
   auto visit = [&status, &metadata_ops, &must_be_const,
                 compile_time_const_args](Node* node) {
     if (!status.ok()) return;
@@ -55,7 +55,7 @@ Status BackwardsConstAnalysis(const Graph& g,
         compile_time_const_args->at(index) = true;
         return;
       }
-      for (Node* pred : node->in_nodes()) {
+      for (const Node* pred : node->in_nodes()) {
         must_be_const.insert(pred);
       }
       return;
diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc
index 058a1f2621..b20c1ffc7d 100644
--- a/tensorflow/compiler/tf2xla/graph_compiler.cc
+++ b/tensorflow/compiler/tf2xla/graph_compiler.cc
@@ -130,7 +130,7 @@ Status GraphCompiler::Compile() {
     // Set up inputs from outputs of previous nodes.
     for (auto* e : n->in_edges()) {
       if (e->IsControlEdge()) continue;
-      Node* src = e->src();
+      const Node* src = e->src();
       TF_RET_CHECK(src->id() < output_registry.size());
       const NodeOutputs& src_outputs = output_registry[src->id()];
 
diff --git a/tensorflow/core/common_runtime/shape_refiner.cc b/tensorflow/core/common_runtime/shape_refiner.cc
index 45cdab98e0..2acaa31d32 100644
--- a/tensorflow/core/common_runtime/shape_refiner.cc
+++ b/tensorflow/core/common_runtime/shape_refiner.cc
@@ -211,14 +211,14 @@ Status ShapeRefiner::AddNode(const Node* node) {
   // For each 'input' of this node, fetch the corresponding shape
   // from 'input's InferenceContext, and store into a vector
   // indexed by 'node's input.
-  std::vector<Node*> input_nodes(node->num_inputs());
+  std::vector<const Node*> input_nodes(node->num_inputs());
   std::vector<ShapeHandle> input_shapes(node->num_inputs());
   std::vector<std::unique_ptr<std::vector<ShapeAndType>>>
       input_handle_shapes_and_types(node->num_inputs());
   for (const Edge* e : node->in_edges()) {
     if (e->IsControlEdge()) continue;
 
-    Node* input = e->src();
+    const Node* input = e->src();
     auto it = node_to_context_.find(input);
     if (it == node_to_context_.end()) {
       return errors::FailedPrecondition(
diff --git a/tensorflow/core/distributed_runtime/scheduler.cc b/tensorflow/core/distributed_runtime/scheduler.cc
index 9dae5b3b92..8403636197 100644
--- a/tensorflow/core/distributed_runtime/scheduler.cc
+++ b/tensorflow/core/distributed_runtime/scheduler.cc
@@ -80,7 +80,7 @@ Microseconds SlackAnalysis::ComputeAsap(std::vector<Microseconds>* asap_times) {
   std::vector<int> pending_count(graph_->num_node_ids());
   InitializePending(graph_, &pending_count);
 
-  std::deque<Node*> queue;
+  std::deque<const Node*> queue;
   Node* srcNode = graph_->source_node();
   queue.push_back(srcNode);
   (*asap_times)[srcNode->id()] = 0;
@@ -92,7 +92,7 @@ Microseconds SlackAnalysis::ComputeAsap(std::vector<Microseconds>* asap_times) {
     for (const Edge* out_edge : curr->out_edges()) {
       // The time needed for 'out' to get its input from 'curr'.
       Microseconds copy_time(0);
-      Node* out = out_edge->dst();
+      const Node* out = out_edge->dst();
       if (!out_edge->IsControlEdge() &&
           curr->assigned_device_name() != out->assigned_device_name()) {
         // Add an arbitrary 10microsecs for each copy.
@@ -137,7 +137,7 @@ Microseconds SlackAnalysis::ComputeAlap(std::vector<Microseconds>* alap_times) {
     }
   }
 
-  std::deque<Node*> queue;
+  std::deque<const Node*> queue;
   Node* sinkNode = graph_->sink_node();
   queue.push_back(sinkNode);
   (*alap_times)[sinkNode->id()] = 0;
@@ -148,7 +148,7 @@ Microseconds SlackAnalysis::ComputeAlap(std::vector<Microseconds>* alap_times) {
     for (const Edge* in_edge : curr->in_edges()) {
       // The time needed for 'curr' to get its input from 'src'.
       Microseconds copy_time(0);
-      Node* src = in_edge->src();
+      const Node* src = in_edge->src();
       if (!in_edge->IsControlEdge() &&
           src->assigned_device_name() != curr->assigned_device_name()) {
         // TODO(yuanbyu): Use the real cost model
@@ -236,7 +236,7 @@ Microseconds GreedyScheduler::ComputeSchedule(
 
       for (const Edge* out_edge : event.node->out_edges()) {
         Microseconds copy_time(0);
-        Node* out = out_edge->dst();
+        const Node* out = out_edge->dst();
         if (!out_edge->IsControlEdge() &&
             event.node->assigned_device_name() != out->assigned_device_name()) {
           // TODO(yuanbyu): Use below with the real cost model.
@@ -277,11 +277,11 @@ Microseconds GreedyScheduler::ComputeSchedule(
   return max_completion;
 }
 
-Node* GreedyScheduler::GetNodeWithHighestPriority(
-    const std::vector<Node*>& nodes) {
-  Node* curr_node = nullptr;
+const Node* GreedyScheduler::GetNodeWithHighestPriority(
+    const std::vector<const Node*>& nodes) {
+  const Node* curr_node = nullptr;
   int64 curr_priority = kint64max;
-  for (Node* n : nodes) {
+  for (const Node* n : nodes) {
     if ((*priority_)[n->id()] < curr_priority) {
       curr_node = n;
       curr_priority = (*priority_)[n->id()];
diff --git a/tensorflow/core/distributed_runtime/scheduler.h b/tensorflow/core/distributed_runtime/scheduler.h
index ef87b9834d..bf9d0d1bec 100644
--- a/tensorflow/core/distributed_runtime/scheduler.h
+++ b/tensorflow/core/distributed_runtime/scheduler.h
@@ -57,11 +57,11 @@ class GreedyScheduler {
   struct Sim {
     int degree_parallelism;
     int num_running;
-    std::vector<Node*> ready_nodes;
+    std::vector<const Node*> ready_nodes;
   };
 
   struct Event {
-    Node* node;
+    const Node* node;
     Microseconds time;
     bool is_completion;
 
@@ -79,7 +79,7 @@ class GreedyScheduler {
 
  private:
   // Returns the ready node with the highest priority for a sim.
-  Node* GetNodeWithHighestPriority(const std::vector<Node*>& nodes);
+  const Node* GetNodeWithHighestPriority(const std::vector<const Node*>& nodes);
 
   const DeviceSet* devices_;
   const CostModel* cost_model_;
diff --git a/tensorflow/core/graph/costmodel.cc b/tensorflow/core/graph/costmodel.cc
index 4f3a6ec38c..1df45d9b89 100644
--- a/tensorflow/core/graph/costmodel.cc
+++ b/tensorflow/core/graph/costmodel.cc
@@ -427,7 +427,7 @@ static void AssignSizes(const Graph& g, CostModel* cost_model) {
     if (e->IsControlEdge()) {
       continue;
     }
-    Node* src = e->src();
+    const Node* src = e->src();
 
     // TODO(josh11b): Get an estimate from the Op
     Bytes size(1);
diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index 9b56216f1f..a7af5e2312 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -339,7 +339,7 @@ Node* Graph::AddNode(const NodeDef& node_def, Status* status) {
   return node;
 }
 
-Node* Graph::CopyNode(Node* node) {
+Node* Graph::CopyNode(const Node* node) {
   DCHECK(!node->IsSource());
   DCHECK(!node->IsSink());
   Node* copy = AllocateNode(node->props_, node);
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index 9d96cd4654..cbd58b051a 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -422,7 +422,7 @@ class Graph {
   // Copies *node, which may belong to another graph, to a new node,
   // which is returned.  Does not copy any edges.  *this owns the
   // returned instance.
-  Node* CopyNode(Node* node);
+  Node* CopyNode(const Node* node);
 
   // Removes a node from this graph, including all edges from or to it.
   // *node should not be accessed after calling this function.
diff --git a/tensorflow/core/graph/graph_constructor.cc b/tensorflow/core/graph/graph_constructor.cc
index 0629ff32d0..627309078a 100644
--- a/tensorflow/core/graph/graph_constructor.cc
+++ b/tensorflow/core/graph/graph_constructor.cc
@@ -1271,7 +1271,7 @@ void CopyGraph(const Graph& src, Graph* dest) {
   dest->set_versions(src.versions());
 
   // Copy the nodes
-  std::unordered_map<Node*, Node*>
+  std::unordered_map<const Node*, Node*>
       node_map;  // "Node in src" -> "Node in *dest"
   node_map[src.source_node()] = dest->source_node();
   node_map[src.sink_node()] = dest->sink_node();
diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc
index add80eda23..17a174101b 100644
--- a/tensorflow/core/graph/graph_partition.cc
+++ b/tensorflow/core/graph/graph_partition.cc
@@ -123,8 +123,8 @@ bool NeedSameDeviceSendRecv(const Edge* edge, const GraphInfo& info) {
     return false;
   }
 
-  Node* src = edge->src();
-  Node* dst = edge->dst();
+  const Node* src = edge->src();
+  const Node* dst = edge->dst();
   if (src->assigned_device_name() == dst->assigned_device_name()) {
     int src_port = edge->src_output();
     int dst_port = edge->dst_input();
@@ -141,7 +141,7 @@ bool NeedSameDeviceSendRecv(const Edge* edge, const GraphInfo& info) {
 
 // Return true iff (dst, dst_input) is specified on host memory.
 bool IsDstInputOnHost(const Edge* edge, const GraphInfo& info) {
-  Node* dst = edge->dst();
+  const Node* dst = edge->dst();
   int dst_port = edge->dst_input();
   if (info.device_types[dst->id()] != DEVICE_CPU) {
     if (edge->IsControlEdge()) return false;
diff --git a/tensorflow/core/graph/node_builder.cc b/tensorflow/core/graph/node_builder.cc
index 138952dcb3..114962c0e4 100644
--- a/tensorflow/core/graph/node_builder.cc
+++ b/tensorflow/core/graph/node_builder.cc
@@ -88,7 +88,7 @@ NodeBuilder& NodeBuilder::ControlInput(Node* src_node) {
 NodeBuilder& NodeBuilder::ControlInputs(gtl::ArraySlice<Node*> src_nodes) {
   control_inputs_.insert(control_inputs_.end(), src_nodes.begin(),
                          src_nodes.end());
-  for (Node* src_node : src_nodes) {
+  for (const Node* src_node : src_nodes) {
     def_builder_.ControlInput(src_node->name());
   }
   return *this;
@@ -127,7 +127,7 @@ Status NodeBuilder::Finalize(Graph* graph, Node** created_node) const {
   return Status::OK();
 }
 
-void NodeBuilder::AddIndexError(Node* node, int i) {
+void NodeBuilder::AddIndexError(const Node* node, int i) {
   if (node == nullptr) {
     errors_.emplace_back(
         strings::StrCat("Attempt to add nullptr Node to node with type ",
@@ -140,7 +140,7 @@ void NodeBuilder::AddIndexError(Node* node, int i) {
   }
 }
 
-bool NodeBuilder::GetOutputType(Node* node, int i, DataType* dt) {
+bool NodeBuilder::GetOutputType(const Node* node, int i, DataType* dt) {
   bool error;
   *dt = SafeGetOutput(node, i, &error);
   if (error) AddIndexError(node, i);
diff --git a/tensorflow/core/graph/node_builder.h b/tensorflow/core/graph/node_builder.h
index 86647a49c1..f6b7b5674b 100644
--- a/tensorflow/core/graph/node_builder.h
+++ b/tensorflow/core/graph/node_builder.h
@@ -120,7 +120,7 @@ class NodeBuilder {
   const OpDef& op_def() const { return def_builder_.op_def(); }
 
  private:
-  static DataType SafeGetOutput(Node* node, int i, bool* error) {
+  static DataType SafeGetOutput(const Node* node, int i, bool* error) {
     if (node != nullptr && i >= 0 && i < node->num_outputs()) {
       *error = false;
       return node->output_type(i);
@@ -131,11 +131,11 @@ class NodeBuilder {
   }
 
   // If SafeGetOutput indicates a range error, add it to errors_.
-  void AddIndexError(Node* node, int i);
+  void AddIndexError(const Node* node, int i);
 
   // Set *dt and returns true if i is in range. Combines
   // SafeGetOutput() and AddIndexError().
-  bool GetOutputType(Node* node, int i, DataType* dt);
+  bool GetOutputType(const Node* node, int i, DataType* dt);
 
   NodeDefBuilder def_builder_;
   std::vector<NodeOut> inputs_;
diff --git a/tensorflow/core/graph/optimizer_cse.cc b/tensorflow/core/graph/optimizer_cse.cc
index 6b452a1d5d..4073255db3 100644
--- a/tensorflow/core/graph/optimizer_cse.cc
+++ b/tensorflow/core/graph/optimizer_cse.cc
@@ -65,8 +65,8 @@ class OptimizerCSE {
 };
 
 static void FillInputs(const Node* n,
-                       gtl::InlinedVector<Node*, 4>* control_edges,
-                       gtl::InlinedVector<std::pair<Node*, int>, 4>* in) {
+                       gtl::InlinedVector<const Node*, 4>* control_edges,
+                       gtl::InlinedVector<std::pair<const Node*, int>, 4>* in) {
   DCHECK_EQ(in->size(), n->num_inputs());
   control_edges->clear();
   for (const Edge* e : n->in_edges()) {
@@ -96,8 +96,8 @@ size_t OptimizerCSE::NodeHash(const Node* n) {
 
   const int N_in = n->num_inputs();
   strings::StrAppend(&str_to_hash, N_in);
-  gtl::InlinedVector<Node*, 4> control_edges;
-  gtl::InlinedVector<std::pair<Node*, int>, 4> in(N_in);
+  gtl::InlinedVector<const Node*, 4> control_edges;
+  gtl::InlinedVector<std::pair<const Node*, int>, 4> in(N_in);
   FillInputs(n, &control_edges, &in);
   for (const auto& edge : in) {
     strings::StrAppend(&str_to_hash, edge.first->id(), edge.second);
@@ -147,10 +147,10 @@ bool OptimizerCSE::Equivalent(const Node* a, const Node* b,
   // Compare input sources
   if (a->num_inputs() != b->num_inputs()) return false;
   const int N_in = a->num_inputs();
-  gtl::InlinedVector<Node*, 4> a_control_edges;
-  gtl::InlinedVector<Node*, 4> b_control_edges;
-  gtl::InlinedVector<std::pair<Node*, int>, 4> a_in(N_in);
-  gtl::InlinedVector<std::pair<Node*, int>, 4> b_in(N_in);
+  gtl::InlinedVector<const Node*, 4> a_control_edges;
+  gtl::InlinedVector<const Node*, 4> b_control_edges;
+  gtl::InlinedVector<std::pair<const Node*, int>, 4> a_in(N_in);
+  gtl::InlinedVector<std::pair<const Node*, int>, 4> b_in(N_in);
   FillInputs(a, &a_control_edges, &a_in);
   FillInputs(b, &b_control_edges, &b_in);
   if (a_in != b_in) return false;
-- 
GitLab


From 2d5db0213258da2e97276af7e6e9d85e9a1e2100 Mon Sep 17 00:00:00 2001
From: Yu-Cheng Ling <ycling@google.com>
Date: Mon, 26 Feb 2018 11:22:43 -0800
Subject: [PATCH 376/575] TFLite: Ensures pointers to tensors won't be
 invalidated unless 16+ tensors are added. PiperOrigin-RevId: 187052100

---
 tensorflow/contrib/lite/interpreter.cc      | 13 +++----
 tensorflow/contrib/lite/interpreter.h       | 20 +++++++++++
 tensorflow/contrib/lite/interpreter_test.cc | 40 +++++++++++++++++++++
 3 files changed, 64 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc
index 370e495527..0f5e17f0de 100644
--- a/tensorflow/contrib/lite/interpreter.cc
+++ b/tensorflow/contrib/lite/interpreter.cc
@@ -27,13 +27,6 @@ limitations under the License.
 #include "tensorflow/contrib/lite/nnapi_delegate.h"
 #include "tensorflow/contrib/lite/schema/schema_generated.h"
 
-namespace {
-
-// std::vector preallocation tuning.
-constexpr const int kSlotsToReserve = 128;
-
-}  // namespace
-
 namespace tflite {
 
 // A trivial implementation of GraphInfo around the Interpreter.
@@ -85,8 +78,8 @@ Interpreter::Interpreter(ErrorReporter* error_reporter)
   context_.GetExecutionPlan = nullptr;
 
   // Reserve some space for the tensors to avoid excessive resizing.
-  tensors_.reserve(kSlotsToReserve);
-  nodes_and_registration_.reserve(kSlotsToReserve);
+  tensors_.reserve(kTensorsReservedCapacity);
+  nodes_and_registration_.reserve(kTensorsReservedCapacity);
   next_execution_plan_index_to_prepare_ = 0;
   UseNNAPI(false);
 }
@@ -353,6 +346,7 @@ TfLiteStatus Interpreter::PrepareOpsStartingAt(
     TfLiteNode& node = nodes_and_registration_[node_index].first;
     const TfLiteRegistration& registration =
         nodes_and_registration_[node_index].second;
+    EnsureTensorsVectorCapacity();
     if (OpPrepare(registration, &node) == kTfLiteError) {
       return kTfLiteError;
     }
@@ -430,6 +424,7 @@ TfLiteStatus Interpreter::Invoke() {
     TfLiteNode& node = nodes_and_registration_[node_index].first;
     const TfLiteRegistration& registration =
         nodes_and_registration_[node_index].second;
+    EnsureTensorsVectorCapacity();
     if (OpInvoke(registration, &node) == kTfLiteError) {
       status = kTfLiteError;
     }
diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h
index a9df2627e0..04c19644a0 100644
--- a/tensorflow/contrib/lite/interpreter.h
+++ b/tensorflow/contrib/lite/interpreter.h
@@ -265,6 +265,14 @@ class Interpreter {
   void set_model(const Model* model) { model_ = const_cast<Model*>(model); }
   Model* model() const { return model_; }
 
+  // The default capacity of `tensors_` vector.
+  static constexpr int kTensorsReservedCapacity = 128;
+  // The capacity headroom of `tensors_` vector before calling ops'
+  // `prepare` and `invoke` function. In these functions, it's guaranteed
+  // allocating up to `kTensorsCapacityHeadroom` more tensors won't invalidate
+  // pointers to existing tensors.
+  static constexpr int kTensorsCapacityHeadroom = 16;
+
  private:
   // Give 'op_reg' a chance to initialize itself using the contents of
   // 'buffer'.
@@ -377,6 +385,18 @@ class Interpreter {
   static TfLiteStatus GetExecutionPlan(struct TfLiteContext* context,
                                        TfLiteIntArray** execution_plan);
 
+  // Ensures that `tensors_` has at least `kTensorsCapacityHeadroom` extra
+  // capacity. Calling this function may invalidate existing pointers to
+  // tensors. After calling this function, adding `kTensorsCapacityHeadroom`
+  // more tensors won't invalidate the pointer to existing tensors.
+  void EnsureTensorsVectorCapacity() {
+    const int required_capacity = tensors_size() + kTensorsCapacityHeadroom;
+    if (required_capacity > tensors_.capacity()) {
+      tensors_.reserve(required_capacity);
+      context_.tensors = tensors_.data();
+    }
+  }
+
   // A pure C data structure used to communicate with the pure C plugin
   // interface. To avoid copying tensor metadata, this is also the definitive
   // structure to store tensors.
diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc
index 28c96e5dde..2e6727b323 100644
--- a/tensorflow/contrib/lite/interpreter_test.cc
+++ b/tensorflow/contrib/lite/interpreter_test.cc
@@ -561,6 +561,46 @@ TEST(BasicInterpreter, TestCustomErrorReporter) {
   ASSERT_EQ(reporter.calls, 1);
 }
 
+TEST(InterpreterTensorsCapacityTest, TestWithinHeadroom) {
+  Interpreter interpreter;
+  ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity),
+            kTfLiteOk);
+  TfLiteRegistration registration = {nullptr, nullptr, nullptr, nullptr};
+  registration.prepare = [](TfLiteContext* context, TfLiteNode* node) {
+    TfLiteTensor* first_tensor = context->tensors;
+
+    int new_tensor_index;
+    context->AddTensors(context, Interpreter::kTensorsCapacityHeadroom,
+                        &new_tensor_index);
+    EXPECT_EQ(first_tensor, context->tensors);
+    return kTfLiteOk;
+  };
+  ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr,
+                                              &registration),
+            kTfLiteOk);
+  ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk);
+}
+
+TEST(InterpreterTensorsCapacityTest, TestExceedHeadroom) {
+  Interpreter interpreter;
+  ASSERT_EQ(interpreter.AddTensors(Interpreter::kTensorsReservedCapacity),
+            kTfLiteOk);
+  TfLiteRegistration registration = {nullptr, nullptr, nullptr, nullptr};
+  registration.prepare = [](TfLiteContext* context, TfLiteNode* node) {
+    TfLiteTensor* first_tensor = context->tensors;
+
+    int new_tensor_index;
+    context->AddTensors(context, Interpreter::kTensorsCapacityHeadroom + 1,
+                        &new_tensor_index);
+    EXPECT_NE(first_tensor, context->tensors);
+    return kTfLiteOk;
+  };
+  ASSERT_EQ(interpreter.AddNodeWithParameters({0}, {1}, nullptr, 0, nullptr,
+                                              &registration),
+            kTfLiteOk);
+  ASSERT_EQ(interpreter.AllocateTensors(), kTfLiteOk);
+}
+
 // Test fixture that allows playing with execution plans. It creates a two
 // node graph that can be executed in either [0,1] order or [1,0] order.
 // The CopyOp records when it is invoked in the class member run_order_
-- 
GitLab


From 215af206b0cba3ac3d64fe01ec372c924662f97f Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Mon, 26 Feb 2018 11:43:14 -0800
Subject: [PATCH 377/575] Actually expose smart_cond and smart_constant_value
 in tf.contrib.framework

Also moves these methods into their own file in python/framework. This avoids further bloating control_flow_ops.py and makes the BUILD deps easier for a future change I'm working on.

PiperOrigin-RevId: 187055501
---
 tensorflow/contrib/framework/BUILD            |  1 +
 tensorflow/contrib/framework/__init__.py      |  7 +-
 tensorflow/python/BUILD                       | 26 ++++++
 tensorflow/python/framework/smart_cond.py     | 79 +++++++++++++++++++
 .../python/framework/smart_cond_test.py       | 66 ++++++++++++++++
 tensorflow/python/layers/utils.py             |  5 +-
 tensorflow/python/ops/control_flow_ops.py     | 56 -------------
 .../python/ops/control_flow_ops_test.py       | 36 ---------
 8 files changed, 180 insertions(+), 96 deletions(-)
 create mode 100644 tensorflow/python/framework/smart_cond.py
 create mode 100644 tensorflow/python/framework/smart_cond_test.py

diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD
index 1accb319d2..50868c6d6c 100644
--- a/tensorflow/contrib/framework/BUILD
+++ b/tensorflow/contrib/framework/BUILD
@@ -63,6 +63,7 @@ tf_custom_op_py_library(
         "//tensorflow/python:platform",
         "//tensorflow/python:pywrap_tensorflow",
         "//tensorflow/python:script_ops",
+        "//tensorflow/python:smart_cond",
         "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:state_ops",
         "//tensorflow/python:state_ops_gen",
diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py
index deeb5bec79..8063250091 100644
--- a/tensorflow/contrib/framework/__init__.py
+++ b/tensorflow/contrib/framework/__init__.py
@@ -87,6 +87,9 @@ See the @{$python/contrib.framework} guide.
 
 @@get_placeholders
 
+@@smart_cond
+@@smart_constant_value
+
 @@CriticalSection
 
 @@BoundedTensorSpec
@@ -104,10 +107,10 @@ from tensorflow.contrib.framework.python.ops import *
 
 from tensorflow.python.framework.ops import prepend_name_scope
 from tensorflow.python.framework.ops import strip_name_scope
+from tensorflow.python.framework.smart_cond import smart_cond
+from tensorflow.python.framework.smart_cond import smart_constant_value
 from tensorflow.python.framework.tensor_spec import BoundedTensorSpec
 from tensorflow.python.framework.tensor_spec import TensorSpec
-from tensorflow.python.ops.control_flow_ops import smart_cond
-from tensorflow.python.ops.control_flow_ops import smart_constant_value
 from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = ['nest']
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 4c8c73548c..b0cb48c80c 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -765,6 +765,31 @@ py_library(
     ],
 )
 
+py_library(
+    name = "smart_cond",
+    srcs = ["framework/smart_cond.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":control_flow_ops",
+        ":tensor_util",
+    ],
+)
+
+py_test(
+    name = "smart_cond_test",
+    size = "small",
+    srcs = ["framework/smart_cond_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":client_testlib",
+        ":constant_op",
+        ":framework_ops",
+        ":math_ops",
+        ":session",
+        ":smart_cond",
+    ],
+)
+
 py_library(
     name = "sparse_tensor",
     srcs = ["framework/sparse_tensor.py"],
@@ -4091,6 +4116,7 @@ py_library(
         ":control_flow_ops",
         ":framework_for_generated_wrappers",
         ":platform",
+        ":smart_cond",
         ":tensor_util",
         ":util",
         ":variable_scope",
diff --git a/tensorflow/python/framework/smart_cond.py b/tensorflow/python/framework/smart_cond.py
new file mode 100644
index 0000000000..f97bb01f54
--- /dev/null
+++ b/tensorflow/python/framework/smart_cond.py
@@ -0,0 +1,79 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""smart_cond and related utilties."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import control_flow_ops
+
+
+def smart_cond(pred, true_fn=None, false_fn=None, name=None):
+  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
+
+  If `pred` is a bool or has a constant value, we return either `true_fn()`
+  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
+
+  Arguments:
+    pred: A scalar determining whether to return the result of `true_fn` or
+      `false_fn`.
+    true_fn: The callable to be performed if pred is true.
+    false_fn: The callable to be performed if pred is false.
+    name: Optional name prefix when using `tf.cond`.
+
+  Returns:
+    Tensors returned by the call to either `true_fn` or `false_fn`.
+
+  Raises:
+    TypeError: If `true_fn` or `false_fn` is not callable.
+  """
+  if not callable(true_fn):
+    raise TypeError("`true_fn` must be callable.")
+  if not callable(false_fn):
+    raise TypeError("`false_fn` must be callable.")
+
+  pred_value = smart_constant_value(pred)
+  if pred_value is not None:
+    if pred_value:
+      return true_fn()
+    else:
+      return false_fn()
+  else:
+    return control_flow_ops.cond(pred, true_fn=true_fn, false_fn=false_fn,
+                                 name=name)
+
+
+def smart_constant_value(pred):
+  """Return the bool value for `pred`, or None if `pred` had a dynamic value.
+
+  Arguments:
+    pred: A scalar, either a Python bool or tensor.
+
+  Returns:
+    True or False if `pred` has a constant boolean value, None otherwise.
+
+  Raises:
+    TypeError: If `pred` is not a Tensor or bool.
+  """
+  if isinstance(pred, bool):
+    pred_value = pred
+  elif isinstance(pred, ops.Tensor):
+    pred_value = tensor_util.constant_value(pred)
+  else:
+    raise TypeError("`pred` must be a Tensor or a Python bool.")
+  return pred_value
diff --git a/tensorflow/python/framework/smart_cond_test.py b/tensorflow/python/framework/smart_cond_test.py
new file mode 100644
index 0000000000..b682506da0
--- /dev/null
+++ b/tensorflow/python/framework/smart_cond_test.py
@@ -0,0 +1,66 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.client import session
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import smart_cond
+from tensorflow.python.framework import test_util
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import googletest
+
+
+@test_util.with_c_api
+class SmartCondTest(test_util.TensorFlowTestCase):
+
+  def testSmartCondTrue(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(2)
+        y = constant_op.constant(5)
+        z = smart_cond.smart_cond(True, lambda: math_ops.multiply(x, 16),
+                                  lambda: math_ops.multiply(y, 5))
+        self.assertEqual(z.eval(), 32)
+
+  def testSmartCondFalse(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(4)
+        y = constant_op.constant(3)
+        z = smart_cond.smart_cond(False, lambda: math_ops.multiply(x, 16),
+                                  lambda: math_ops.multiply(y, 3))
+        self.assertEqual(z.eval(), 9)
+
+  def testSmartCondMissingArg1(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(1)
+        with self.assertRaises(TypeError):
+          smart_cond.smart_cond(True, false_fn=lambda: x)
+
+  def testSmartCondMissingArg2(self):
+    with ops.Graph().as_default():
+      with session.Session():
+        x = constant_op.constant(1)
+        with self.assertRaises(TypeError):
+          smart_cond.smart_cond(True, lambda: x)
+
+
+if __name__ == "__main__":
+  googletest.main()
diff --git a/tensorflow/python/layers/utils.py b/tensorflow/python/layers/utils.py
index 484c6fc466..3b156c36a2 100644
--- a/tensorflow/python/layers/utils.py
+++ b/tensorflow/python/layers/utils.py
@@ -24,6 +24,7 @@ from tensorflow.python.eager import context
 from tensorflow.python.ops import variables
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import smart_cond as smart_module
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.util import nest
 
@@ -201,7 +202,7 @@ def smart_cond(pred, true_fn=None, false_fn=None, name=None):
   if isinstance(pred, variables.Variable):
     return control_flow_ops.cond(
         pred, true_fn=true_fn, false_fn=false_fn, name=name)
-  return control_flow_ops.smart_cond(
+  return smart_module.smart_cond(
       pred, true_fn=true_fn, false_fn=false_fn, name=name)
 
 
@@ -228,7 +229,7 @@ def constant_value(pred):
 
   if isinstance(pred, variables.Variable):
     return None
-  return control_flow_ops.smart_constant_value(pred)
+  return smart_module.smart_constant_value(pred)
 
 
 def object_list_uid(object_list):
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 8218e60b53..152578c0c6 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -23,7 +23,6 @@ See the @{$python/control_flow_ops} guide.
 @@no_op
 @@count_up_to
 @@cond
-@@smart_cond
 @@case
 @@while_loop
 @@logical_and
@@ -2128,61 +2127,6 @@ def cond(pred,
 # pylint: enable=redefined-outer-name
 
 
-def smart_cond(pred, true_fn=None, false_fn=None, name=None):
-  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
-
-  If `pred` is a bool or has a constant value, we return either `true_fn()`
-  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
-
-  Arguments:
-    pred: A scalar determining whether to return the result of `true_fn` or
-      `false_fn`.
-    true_fn: The callable to be performed if pred is true.
-    false_fn: The callable to be performed if pred is false.
-    name: Optional name prefix when using `tf.cond`.
-
-  Returns:
-    Tensors returned by the call to either `true_fn` or `false_fn`.
-
-  Raises:
-    TypeError: If `true_fn` or `false_fn` is not callable.
-  """
-  if not callable(true_fn):
-    raise TypeError("`true_fn` must be callable.")
-  if not callable(false_fn):
-    raise TypeError("`false_fn` must be callable.")
-
-  pred_value = smart_constant_value(pred)
-  if pred_value is not None:
-    if pred_value:
-      return true_fn()
-    else:
-      return false_fn()
-  else:
-    return cond(pred, true_fn=true_fn, false_fn=false_fn, name=name)
-
-
-def smart_constant_value(pred):
-  """Return the bool value for `pred`, or None if `pred` had a dynamic value.
-
-  Arguments:
-    pred: A scalar, either a Python bool or tensor.
-
-  Returns:
-    True or False if `pred` has a constant boolean value, None otherwise.
-
-  Raises:
-    TypeError: If `pred` is not a Tensor or bool.
-  """
-  if isinstance(pred, bool):
-    pred_value = pred
-  elif isinstance(pred, ops.Tensor):
-    pred_value = tensor_util.constant_value(pred)
-  else:
-    raise TypeError("`pred` must be a Tensor or a Python bool.")
-  return pred_value
-
-
 def _resource_safe_shape(t):
   """Returns the shape of t or the variable it points to."""
   if t.dtype == dtypes.resource:
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index adc8c51e11..f22f3059d1 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -349,42 +349,6 @@ class SwitchTestCase(test_util.TensorFlowTestCase):
       self.assertEquals(grad_x_false.eval(), 0.)
 
 
-@test_util.with_c_api
-class SmartCondTest(test_util.TensorFlowTestCase):
-
-  def testSmartCondTrue(self):
-    with ops.Graph().as_default():
-      with session.Session():
-        x = constant_op.constant(2)
-        y = constant_op.constant(5)
-        z = control_flow_ops.smart_cond(True, lambda: math_ops.multiply(x, 16),
-                                        lambda: math_ops.multiply(y, 5))
-        self.assertEqual(z.eval(), 32)
-
-  def testSmartCondFalse(self):
-    with ops.Graph().as_default():
-      with session.Session():
-        x = constant_op.constant(4)
-        y = constant_op.constant(3)
-        z = control_flow_ops.smart_cond(False, lambda: math_ops.multiply(x, 16),
-                                        lambda: math_ops.multiply(y, 3))
-        self.assertEqual(z.eval(), 9)
-
-  def testSmartCondMissingArg1(self):
-    with ops.Graph().as_default():
-      with session.Session():
-        x = constant_op.constant(1)
-        with self.assertRaises(TypeError):
-          control_flow_ops.smart_cond(True, false_fn=lambda: x)
-
-  def testSmartCondMissingArg2(self):
-    with ops.Graph().as_default():
-      with session.Session():
-        x = constant_op.constant(1)
-        with self.assertRaises(TypeError):
-          control_flow_ops.smart_cond(True, lambda: x)
-
-
 @test_util.with_c_api
 class CondTest(test_util.TensorFlowTestCase):
 
-- 
GitLab


From 8525e1dbdcab467e545f09ecf60f0be11b48cd28 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 11:50:49 -0800
Subject: [PATCH 378/575] Add the internal module name prefix to the white
 list.

PiperOrigin-RevId: 187056701
---
 tensorflow/contrib/py2tf/impl/config.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/py2tf/impl/config.py b/tensorflow/contrib/py2tf/impl/config.py
index c90e85c96b..bdbc6663dd 100644
--- a/tensorflow/contrib/py2tf/impl/config.py
+++ b/tensorflow/contrib/py2tf/impl/config.py
@@ -31,12 +31,16 @@ PYTHON_LITERALS = {
 DEFAULT_UNCOMPILED_MODULES = set((
     ('tensorflow',),
     (utils.__name__,),
+
+    # All of tensorflow's subpackages. Unlike the root tf module, they don't
+    # have well-known names. Not refering to the module directly to avoid
+    # circular imports.
+    (utils.__name__[:-len('.contrib.py2tf.utils')],),
 ))
 
 NO_SIDE_EFFECT_CONSTRUCTORS = set(('tensorflow',))
 
 # TODO(mdan): Also allow controlling the generated names (for testability).
-# TODO(mdan): Make sure copybara renames the reference below.
 COMPILED_IMPORT_STATEMENTS = (
     'from __future__ import print_function',
     'import tensorflow as tf',
-- 
GitLab


From 5caeb37e5d4314b702cf660db35b93a3bfc29819 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Mon, 26 Feb 2018 11:52:26 -0800
Subject: [PATCH 379/575] Internal change.

PiperOrigin-RevId: 187056963
---
 tensorflow/tools/api/tests/api_compatibility_test.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/tools/api/tests/api_compatibility_test.py b/tensorflow/tools/api/tests/api_compatibility_test.py
index c1e09cc531..2a784973e1 100644
--- a/tensorflow/tools/api/tests/api_compatibility_test.py
+++ b/tensorflow/tools/api/tests/api_compatibility_test.py
@@ -165,7 +165,7 @@ class ApiCompatibilityTest(test.TestCase):
       logging.error('%d differences found between API and golden.', diff_count)
       messages = verbose_diffs if verbose else diffs
       for i in range(diff_count):
-        logging.error('Issue %d\t: %s', i + 1, messages[i])
+        print('Issue %d\t: %s' % (i + 1, messages[i]), file=sys.stderr)
 
       if update_goldens:
         # Write files if requested.
-- 
GitLab


From 0898ee302cb20d9fce50dae4f484816a2dc2d0e2 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 26 Feb 2018 11:57:30 -0800
Subject: [PATCH 380/575] Use optimized ops to handle GPU memory swapping: this
 avoids the need for 2 pairs of extra _send/_recv nodes which speeds things up
 a bit. This also ensures that performance doesn't depend on the recv
 scheduling built in TF, which isn't always optimal.

PiperOrigin-RevId: 187057831
---
 tensorflow/core/grappler/optimizers/BUILD     | 36 +++++++-
 .../optimizers/gpu_swapping_kernels.cc        | 88 +++++++++++++++++++
 .../grappler/optimizers/gpu_swapping_ops.cc   | 58 ++++++++++++
 .../grappler/optimizers/memory_optimizer.cc   |  9 +-
 .../optimizers/memory_optimizer_test.cc       | 65 +++++++++++---
 tensorflow/core/grappler/utils/BUILD          |  1 +
 .../core/grappler/utils/grappler_test.cc      | 17 ++++
 .../core/grappler/utils/grappler_test.h       |  3 +
 8 files changed, 258 insertions(+), 19 deletions(-)
 create mode 100644 tensorflow/core/grappler/optimizers/gpu_swapping_kernels.cc
 create mode 100644 tensorflow/core/grappler/optimizers/gpu_swapping_ops.cc

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 50ba48ea7a..908e58bcc7 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -1,6 +1,8 @@
 licenses(["notice"])  # Apache 2.0
 
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
+load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu")
+load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
 
 filegroup(
     name = "all_files",
@@ -282,18 +284,48 @@ tf_cc_test(
     ],
 )
 
+tf_kernel_library(
+    name = "gpu_swapping_kernels",
+    srcs = [
+        "gpu_swapping_kernels.cc",
+    ],
+    deps = [
+        "//tensorflow/core:core_cpu_base",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+    ],
+)
+
+cc_library(
+    name = "gpu_swapping_ops",
+    srcs = [
+        "gpu_swapping_ops.cc",
+    ],
+    deps = [
+        "//tensorflow/core:core_cpu_base",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+    ],
+    alwayslink = 1,
+)
+
 cc_library(
     name = "memory_optimizer",
-    srcs = ["memory_optimizer.cc"],
+    srcs = [
+        "memory_optimizer.cc",
+    ],
     hdrs = [
         "memory_optimizer.h",
     ],
     visibility = ["//visibility:public"],
     deps = [
+        ":gpu_swapping_kernels",
+        ":gpu_swapping_ops",
         ":graph_optimizer",
         ":graph_rewriter",
         ":static_schedule",
         "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:graph_view",
         "//tensorflow/core/grappler:grappler_item",
@@ -307,7 +339,7 @@ cc_library(
     ],
 )
 
-tf_cc_test(
+tf_cc_test_gpu(
     name = "memory_optimizer_test",
     srcs = ["memory_optimizer_test.cc"],
     deps = [
diff --git a/tensorflow/core/grappler/optimizers/gpu_swapping_kernels.cc b/tensorflow/core/grappler/optimizers/gpu_swapping_kernels.cc
new file mode 100644
index 0000000000..1820af6844
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/gpu_swapping_kernels.cc
@@ -0,0 +1,88 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Op kernels used to swap data in and out of GPU memory.
+
+#include "tensorflow/core/common_runtime/device.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+namespace {
+
+class CopyFromGpuToHostKernel : public AsyncOpKernel {
+ public:
+  explicit CopyFromGpuToHostKernel(OpKernelConstruction* context)
+      : AsyncOpKernel(context) {}
+  void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override {
+    const Tensor& input = ctx->input(0);
+    OP_REQUIRES_ASYNC(
+        ctx, !ctx->input_alloc_attr(0).on_host(),
+        errors::Internal("The input tensor to the _CopyFromGpuToHost kernel "
+                         "must reside on the device."),
+        done);
+
+    AllocatorAttributes alloc_attrs;
+    alloc_attrs.set_gpu_compatible(true);
+    alloc_attrs.set_on_host(true);
+    Tensor* output;
+    OP_REQUIRES_OK_ASYNC(
+        ctx, ctx->allocate_output(0, input.shape(), &output, alloc_attrs),
+        done);
+
+    ctx->op_device_context()->CopyDeviceTensorToCPU(
+        &input, "CopyFromGpuToHost", static_cast<Device*>(ctx->device()),
+        output, [ctx, done](const Status& s) {
+          ctx->SetStatus(s);
+          done();
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("_CopyFromGpuToHost").Device(DEVICE_GPU).HostMemory("output"),
+    CopyFromGpuToHostKernel);
+
+class CopyFromHostToGpuKernel : public AsyncOpKernel {
+ public:
+  explicit CopyFromHostToGpuKernel(OpKernelConstruction* context)
+      : AsyncOpKernel(context) {}
+  void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override {
+    const Tensor& input = ctx->input(0);
+    OP_REQUIRES_ASYNC(
+        ctx, ctx->input_alloc_attr(0).on_host(),
+        errors::Internal("The input tensor to the _CopyFromHostToGpu kernel "
+                         "must reside on the host."),
+        done);
+
+    Tensor* output;
+    OP_REQUIRES_OK_ASYNC(ctx, ctx->allocate_output(0, input.shape(), &output),
+                         done);
+
+    ctx->op_device_context()->CopyCPUTensorToDevice(
+        &input, static_cast<Device*>(ctx->device()), output,
+        [ctx, done](const Status& s) {
+          ctx->SetStatus(s);
+          done();
+        });
+  }
+};
+
+REGISTER_KERNEL_BUILDER(
+    Name("_CopyFromHostToGpu").Device(DEVICE_GPU).HostMemory("input"),
+    CopyFromHostToGpuKernel);
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/gpu_swapping_ops.cc b/tensorflow/core/grappler/optimizers/gpu_swapping_ops.cc
new file mode 100644
index 0000000000..46828346da
--- /dev/null
+++ b/tensorflow/core/grappler/optimizers/gpu_swapping_ops.cc
@@ -0,0 +1,58 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// Definition for the ops used to swap data in and out of GPU memory.
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+namespace {
+
+// The _CopyFromGpuToHost op copies its input tensor to the host. The input must
+// reside on GPU. The op itself must be placed on GPU.
+REGISTER_OP("_CopyFromGpuToHost")
+    .Input("input: T")
+    .Output("output: T")
+    .Attr("T: type")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      c->set_output(0, c->input(0));
+      auto* handle_data = c->input_handle_shapes_and_types(0);
+      if (handle_data != nullptr) {
+        c->set_output_handle_shapes_and_types(0, *handle_data);
+      }
+      return Status::OK();
+    })
+    .Doc("Copies the input tensor from gpu to the host.");
+
+// The _CopyFromHostToGpu op copies its input tensor from the host to the GPU.
+// The input must reside on CPU. The op itself must be placed on GPU.
+REGISTER_OP("_CopyFromHostToGpu")
+    .Input("input: T")
+    .Output("output: T")
+    .Attr("T: type")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      c->set_output(0, c->input(0));
+      auto* handle_data = c->input_handle_shapes_and_types(0);
+      if (handle_data != nullptr) {
+        c->set_output_handle_shapes_and_types(0, *handle_data);
+      }
+      return Status::OK();
+    })
+    .Doc("Copies the input tensor from the host to the GPU.");
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index dec4f04a1c..694139fa50 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -720,18 +720,19 @@ Status BuildSwapPair(NodeDef* node, int input_to_swap,
   // Force the tensor to be copied to cpu.
   NodeDef* swap_out_node = graph->add_node();
   swap_out_node->set_name(swap_out_name);
-  swap_out_node->set_op("Identity");
-  swap_out_node->set_device("/device:CPU:0");
+  swap_out_node->set_op("_CopyFromGpuToHost");
 
   // Force the tensor to be restored to the device.
   NodeDef* swap_in_node = graph->add_node();
   swap_in_node->set_name(swap_in_name);
-  swap_in_node->set_op("Identity");
+  swap_in_node->set_op("_CopyFromHostToGpu");
   *swap_in_node->add_input() = swap_out_node->name();
 
-  // Colocate the swap_in_ node with the node itself.
+  // Colocate the swap_out_ and swap_in_ nodes with the node itself.
+  swap_out_node->set_device(node->device());
   swap_in_node->set_device(node->device());
   string coloc_group = strings::StrCat("loc@", tensor_to_swap);
+  (*swap_out_node->mutable_attr())["_class"].mutable_list()->add_s(coloc_group);
   (*swap_in_node->mutable_attr())["_class"].mutable_list()->add_s(coloc_group);
   (*node->mutable_attr())["_class"].mutable_list()->add_s(coloc_group);
 
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
index 5d7913e0c0..9595936e9e 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer_test.cc
@@ -221,16 +221,20 @@ TEST_F(MemoryOptimizerTest, SimpleSwapping) {
   // Build a simple graph with an op that's marked for swapping.
   tensorflow::Scope s = tensorflow::Scope::NewRootScope();
 
-  Output a = ops::Variable(s.WithOpName("a"), {10, 10}, DT_FLOAT);
-  Output b = ops::AddN(s.WithOpName("b"), {a});
-  Output c = ops::AddN(s.WithOpName("c"), {b});
-  Output d = ops::AddN(s.WithOpName("d"), {c});
-  Output e = ops::AddN(s.WithOpName("e"), {b, d});
+  Output a =
+      ops::Variable(s.WithOpName("a").WithDevice("/gpu:0"), {10, 10}, DT_FLOAT);
+  Output b = ops::AddN(s.WithOpName("b").WithDevice("/gpu:0"), {a});
+  Output c = ops::AddN(s.WithOpName("c").WithDevice("/gpu:0"), {b});
+  Output d = ops::AddN(s.WithOpName("d").WithDevice("/gpu:0"), {c});
+  Output e = ops::AddN(s.WithOpName("e").WithDevice("/gpu:0"), {b, d});
+
+  Output constant = ops::Const(s.WithOpName("constant"), 0.0f, {10, 10});
+  Output init = ops::Assign(s.WithOpName("init"), a, constant);
 
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
 
-  EXPECT_EQ(5, item.graph.node_size());
+  EXPECT_EQ(7, item.graph.node_size());
   EXPECT_EQ(NodeName(e.name()), item.graph.node(4).name());
   AttrValue& val =
       (*item.graph.mutable_node(4)->mutable_attr())["_swap_to_host"];
@@ -243,32 +247,43 @@ TEST_F(MemoryOptimizerTest, SimpleSwapping) {
   Status status = optimizer.Optimize(cluster.get(), item, &output);
   TF_EXPECT_OK(status);
 
-  EXPECT_EQ(7, output.node_size());
-  const NodeDef& new_e = output.node(4);
+  EXPECT_EQ(9, output.node_size());
+  const NodeDef& new_e = output.node(6);
   EXPECT_EQ(NodeName(e.name()), new_e.name());
 
   EXPECT_EQ(2, new_e.input_size());
   EXPECT_EQ(NodeName(d.name()), new_e.input(1));
   EXPECT_EQ("swap_in_e_0", new_e.input(0));
 
-  const NodeDef& swap_out = output.node(5);
+  const NodeDef& swap_out = output.node(7);
   EXPECT_EQ("swap_out_e_0", swap_out.name());
+  EXPECT_EQ("_CopyFromGpuToHost", swap_out.op());
 
-  const NodeDef& swap_in = output.node(6);
+  const NodeDef& swap_in = output.node(8);
   EXPECT_EQ("swap_in_e_0", swap_in.name());
+  EXPECT_EQ("_CopyFromHostToGpu", swap_in.op());
 
   EXPECT_EQ(NodeName(b.name()), swap_out.input(0));
   EXPECT_EQ(NodeName(swap_out.name()), swap_in.input(0));
   EXPECT_EQ("^c", swap_in.input(1));
 
-  const NodeDef& new_c = output.node(2);
+  const NodeDef& new_c = output.node(4);
   EXPECT_EQ(NodeName(c.name()), new_c.name());
   EXPECT_EQ("^swap_out_e_0", new_c.input(1));
 
   // Run the optimizer a second time to ensure it's idempotent.
-  item.graph.Swap(&output);
-  status = optimizer.Optimize(cluster.get(), item, &output);
+  GrapplerItem item_copy(item, std::move(output));
+  status = optimizer.Optimize(cluster.get(), item_copy, &output);
   TF_EXPECT_OK(status);
+
+#if GOOGLE_CUDA
+  item.fetch = {"e"};
+  item.init_ops = {init.name()};
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
+#endif
 }
 
 TEST_F(MemoryOptimizerTest, SwappingHeuristics) {
@@ -287,9 +302,13 @@ TEST_F(MemoryOptimizerTest, SwappingHeuristics) {
   Output h = ops::Exp(s.WithOpName("h").WithDevice("/gpu:0"), c);
   Output i = ops::Log(s.WithOpName("i").WithDevice("/gpu:0"), d);
 
+  Output constant = ops::Const(s.WithOpName("constant"), 0.0f, {128, 128, 8});
+  Output init = ops::Assign(s.WithOpName("init"), v, constant);
+
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
   item.fetch = {"e", "f", "g", "h", "i"};
+  item.init_ops = {init.name()};
 
   std::unique_ptr<VirtualCluster> cluster(CreateVirtualCluster());
 
@@ -308,6 +327,15 @@ TEST_F(MemoryOptimizerTest, SwappingHeuristics) {
       EXPECT_EQ("axis", node.input(4));
     }
   }
+
+#if GOOGLE_CUDA
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  for (int i = 0; i < item.fetch.size(); ++i) {
+    test::ExpectTensorEqual<float>(tensors_expected[i], tensors[i]);
+  }
+#endif
 }
 
 TEST_F(MemoryOptimizerTest, UnswappableInputs) {
@@ -325,9 +353,13 @@ TEST_F(MemoryOptimizerTest, UnswappableInputs) {
   Output e =
       ops::Concat(s.WithOpName("e").WithDevice("/gpu:0"), {b, c, d}, axis);
 
+  Output constant = ops::Const(s.WithOpName("constant"), 0.0f, {128, 128, 8});
+  Output init = ops::Assign(s.WithOpName("init"), v, constant);
+
   GrapplerItem item;
   TF_CHECK_OK(s.ToGraphDef(&item.graph));
   item.fetch = {"e"};
+  item.init_ops = {init.name()};
 
   std::unique_ptr<VirtualCluster> cluster(CreateVirtualCluster());
 
@@ -344,6 +376,13 @@ TEST_F(MemoryOptimizerTest, UnswappableInputs) {
       EXPECT_EQ("^swap_out_d_2", node.input(4));
     }
   }
+
+#if GOOGLE_CUDA
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
+#endif
 }
 
 TEST_F(MemoryOptimizerTest, AccumulationRewrites) {
diff --git a/tensorflow/core/grappler/utils/BUILD b/tensorflow/core/grappler/utils/BUILD
index 0a9dbe22cf..5d32609434 100644
--- a/tensorflow/core/grappler/utils/BUILD
+++ b/tensorflow/core/grappler/utils/BUILD
@@ -142,6 +142,7 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
+        "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:utils",
     ],
 )
diff --git a/tensorflow/core/grappler/utils/grappler_test.cc b/tensorflow/core/grappler/utils/grappler_test.cc
index fed46c05fb..fef8e97b6e 100644
--- a/tensorflow/core/grappler/utils/grappler_test.cc
+++ b/tensorflow/core/grappler/utils/grappler_test.cc
@@ -35,6 +35,23 @@ std::vector<Tensor> GrapplerTest::EvaluateNodes(
   return output_tensors;
 }
 
+std::vector<Tensor> GrapplerTest::EvaluateFetchNodes(const GrapplerItem& item) {
+  SessionOptions options;
+  std::unique_ptr<tensorflow::Session> session(NewSession(options));
+  TF_CHECK_OK(session->Create(item.graph));
+  RunOptions run_options;
+  if (!item.init_ops.empty()) {
+    std::vector<Tensor> dummy;
+    TF_CHECK_OK(
+        session->Run(run_options, {}, {}, item.init_ops, &dummy, nullptr));
+  }
+  std::vector<Tensor> output_tensors;
+  TF_CHECK_OK(
+      session->Run(run_options, {}, item.fetch, {}, &output_tensors, nullptr));
+  TF_CHECK_OK(session->Close());
+  return output_tensors;
+}
+
 void GrapplerTest::AddNode(const string& name, const string& op,
                            const std::vector<string>& inputs, GraphDef* graph) {
   auto* node = graph->add_node();
diff --git a/tensorflow/core/grappler/utils/grappler_test.h b/tensorflow/core/grappler/utils/grappler_test.h
index 042b616aa4..fd6809b6e2 100644
--- a/tensorflow/core/grappler/utils/grappler_test.h
+++ b/tensorflow/core/grappler/utils/grappler_test.h
@@ -20,6 +20,7 @@ limitations under the License.
 
 #include "tensorflow/core/framework/graph.pb.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
@@ -30,6 +31,8 @@ class GrapplerTest : public ::testing::Test {
   std::vector<Tensor> EvaluateNodes(const GraphDef& graph,
                                     const std::vector<string>& node_names);
 
+  std::vector<Tensor> EvaluateFetchNodes(const GrapplerItem& item);
+
   void AddNode(const string& name, const string& op,
                const std::vector<string>& inputs, GraphDef* graph);
 
-- 
GitLab


From 33a447a3df13559d746b86e2446ee9174099cd3b Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 26 Feb 2018 12:10:01 -0800
Subject: [PATCH 381/575] Fix bug calling gradients_function inside
 custom_gradient

PiperOrigin-RevId: 187059871
---
 tensorflow/python/eager/backprop_test.py   | 13 +++++++++++++
 tensorflow/python/eager/custom_gradient.py |  9 ++++-----
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index 734558dee2..48fd170764 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -115,6 +115,19 @@ class BackpropTest(test.TestCase):
     with self.assertRaises(RuntimeError):
       backprop.gradients_function(f)(constant_op.constant(1.0))
 
+  def testGradientsFunctionInCustomGradient(self):
+
+    @custom_gradient.custom_gradient
+    def f(x):
+      (y,) = backprop.gradients_function(lambda x: x * x)(x)
+
+      def grad(dy):
+        return [2 * dy]
+
+      return y, grad
+
+    self.assertAllEqual(f(1.0), 2.0)
+
   def testImplicitGradOverEmbeddingLookup(self):
     batch_size = 8
     embedding_size = 512
diff --git a/tensorflow/python/eager/custom_gradient.py b/tensorflow/python/eager/custom_gradient.py
index 05460ff996..fb932a9372 100644
--- a/tensorflow/python/eager/custom_gradient.py
+++ b/tensorflow/python/eager/custom_gradient.py
@@ -71,11 +71,10 @@ def custom_gradient(f):
 
     input_tensors = [tf_ops.convert_to_tensor(x) for x in args]
 
-    with tape.stop_recording():
-      result, grad_fn = f(*args, **kwargs)
-      flat_result = nest.flatten(result)
-      # TODO(apassos) consider removing the identity below.
-      flat_result = [gen_array_ops.identity(x) for x in flat_result]
+    result, grad_fn = f(*args, **kwargs)
+    flat_result = nest.flatten(result)
+    # TODO(apassos) consider removing the identity below.
+    flat_result = [gen_array_ops.identity(x) for x in flat_result]
 
     def actual_grad_fn(*outputs):
       return nest.flatten(grad_fn(*outputs))
-- 
GitLab


From cfb6e1628cf752f6cb1d844b8bba3a2cfc98b1e3 Mon Sep 17 00:00:00 2001
From: Jeremy Lau <lauj@google.com>
Date: Mon, 26 Feb 2018 12:23:36 -0800
Subject: [PATCH 382/575] Internal change.

PiperOrigin-RevId: 187061863
---
 tensorflow/contrib/bayesflow/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 08b29fb6bc..270c309ec3 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -210,7 +210,7 @@ cuda_py_test(
 
 cuda_py_test(
     name = "hmc_test",
-    size = "medium",
+    size = "large",
     srcs = ["python/kernel_tests/hmc_test.py"],
     additional_deps = [
         ":bayesflow_py",
-- 
GitLab


From 509e51bc809032bd3d9443bd4afc152fb5eaaf93 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 12:33:17 -0800
Subject: [PATCH 383/575] Maintain a cache of output dtypes of ops in
 TFE_Context.

PiperOrigin-RevId: 187062992
---
 tensorflow/c/eager/c_api.cc   | 20 ++++++++++++++++++++
 tensorflow/c/eager/runtime.cc | 15 ++++++++++++---
 tensorflow/c/eager/runtime.h  |  6 ++++++
 3 files changed, 38 insertions(+), 3 deletions(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index c27a7129fa..bebb63c746 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/device_mgr.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
+#include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/rendezvous.h"
 #include "tensorflow/core/framework/tensor_shape.pb.h"
 #include "tensorflow/core/framework/types.h"
@@ -823,6 +824,25 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
       delete kernel;
       return;
     }
+    // Update output_dtypes inside `kernel`.
+    const tensorflow::OpDef* op_def = nullptr;
+    const tensorflow::FunctionDef* function_def =
+        ctx->func_lib_def.Find(ndef.op());
+    if (function_def != nullptr) {
+      op_def = &(function_def->signature());
+    }
+    if (op_def == nullptr) {
+      status->status = OpDefForOp(ndef.op().c_str(), &op_def);
+      if (!status->status.ok()) {
+        return;
+      }
+    }
+    tensorflow::DataTypeVector input_dtypes;
+    status->status = InOutTypesForNode(ndef, *op_def, &input_dtypes,
+                                       kernel->output_dtypes());
+    if (!status->status.ok()) {
+      return;
+    }
     tensorflow::mutex_lock ml(ctx->cache_mu);
     tensorflow::gtl::InsertOrUpdate(&(ctx->kernel_cache), cache_key, kernel);
   }
diff --git a/tensorflow/c/eager/runtime.cc b/tensorflow/c/eager/runtime.cc
index f77a937f1f..4bf24fec2c 100644
--- a/tensorflow/c/eager/runtime.cc
+++ b/tensorflow/c/eager/runtime.cc
@@ -41,17 +41,26 @@ const uint32 kIsList = 1U << 31;
 
 }  // namespace
 
+Status OpDefForOp(const char* op_name, const OpDef** op_def) {
+  const OpRegistrationData* op_reg_data = nullptr;
+  Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data);
+  if (s.ok()) {
+    *op_def = &op_reg_data->op_def;
+  }
+  return s;
+}
+
 Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out) {
   mutex_lock l(g_op_name_to_attr_type_map_lock);
   *out = gtl::FindPtrOrNull(*OpNameToAttrTypeMap(), op_name);
   if (*out != nullptr) return Status::OK();
-  const OpRegistrationData* op_reg_data = nullptr;
-  Status s = OpRegistry::Global()->LookUp(op_name, &op_reg_data);
+  const OpDef* op_def = nullptr;
+  Status s = OpDefForOp(op_name, &op_def);
   if (!s.ok()) return s;
   std::unique_ptr<AttrTypeMap> m(new AttrTypeMap);
   // TODO(agarwal): Avoid having to create this "registry" at runtime,
   // perhaps can be done at op registration time?
-  for (const auto& attr : op_reg_data->op_def.attr()) {
+  for (const auto& attr : op_def->attr()) {
     string type = attr.type();
     const bool is_list = (type.length() > 6 && type.compare(0, 4, "list") == 0);
     if (is_list) {
diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h
index 4d20b5244a..7fede4dae9 100644
--- a/tensorflow/c/eager/runtime.h
+++ b/tensorflow/c/eager/runtime.h
@@ -39,6 +39,9 @@ namespace tensorflow {
 // represent the TF_AttrType type of the values in the list.
 typedef std::unordered_map<string, uint32> AttrTypeMap;
 
+// Look up OpDef for `op_name`.
+Status OpDefForOp(const char* op_name, const OpDef** op_def);
+
 // Returns the AttrTypeMap for the TensorFlow operation named op_name.
 Status AttrTypeMapForOp(const char* op_name, const AttrTypeMap** out);
 
@@ -180,12 +183,15 @@ class KernelAndDevice {
 
   const OpKernel* kernel() const { return kernel_.get(); }
 
+  DataTypeVector* output_dtypes() { return &output_dtypes_; }
+
  private:
   std::unique_ptr<OpKernel> kernel_;
   Device* device_;
   FunctionLibraryRuntime* flib_;
   checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_;
   Rendezvous* rendez_;
+  DataTypeVector output_dtypes_;
 };
 
 }  // namespace tensorflow
-- 
GitLab


From 19c601b53a8444a26fc6694a2766897df37fc336 Mon Sep 17 00:00:00 2001
From: Richard Wei <rxwei@google.com>
Date: Mon, 26 Feb 2018 13:06:59 -0800
Subject: [PATCH 384/575] Include c_api_experimental in libtensorflow.so's
 dependencies.

PiperOrigin-RevId: 187068103
---
 tensorflow/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index dc995d231d..3828ee0ddb 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -787,6 +787,7 @@ tf_cc_shared_object(
     }),
     deps = [
         "//tensorflow/c:c_api",
+        "//tensorflow/c:c_api_experimental",
         "//tensorflow/c:exported_symbols.lds",
         "//tensorflow/c:version_script.lds",
         "//tensorflow/c/eager:c_api",
-- 
GitLab


From 6c99456856973d7cfee31aeeabef8d79014a097f Mon Sep 17 00:00:00 2001
From: Akshay Agrawal <akshayka@google.com>
Date: Mon, 26 Feb 2018 13:54:02 -0800
Subject: [PATCH 385/575] Update eager uniform replay buffer microbenchmarks to
 compare against graph functions when possible.

PiperOrigin-RevId: 187075418
---
 .../contrib/framework/python/ops/critical_section_ops.py | 6 ++++--
 tensorflow/python/framework/ops.py                       | 9 ++++++++-
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/framework/python/ops/critical_section_ops.py b/tensorflow/contrib/framework/python/ops/critical_section_ops.py
index 3c5c55ed65..ab603cc18e 100644
--- a/tensorflow/contrib/framework/python/ops/critical_section_ops.py
+++ b/tensorflow/contrib/framework/python/ops/critical_section_ops.py
@@ -143,7 +143,7 @@ class CriticalSection(object):
   def _init_from_args(self, name, shared_name):  # pylint: disable=invalid-name
     """Initialize the CriticalSection from constructor arguments."""
     with ops.name_scope(name, "CriticalSection", []) as name:
-      with ops.control_dependencies(None):
+      with ops.init_scope():
         # pylint: disable=protected-access
         container = ops.get_default_graph()._container
         # pylint: enable=protected-access
@@ -226,7 +226,9 @@ class CriticalSection(object):
         # mode.  This is generally ok; since eager mode (as of
         # writing) executes sequentially anyway.
         for sg in ops.get_collection(CRITICAL_SECTION_EXECUTIONS):
-          if sg.handle.name == self._handle.name:
+          sg_handle_name = ops.convert_to_tensor(sg.handle).name
+          self_handle_name = ops.convert_to_tensor(self._handle).name
+          if sg_handle_name == self_handle_name:
             # Other executions in the same critical section are allowed.
             continue
           if not (exclusive_resource_access or sg.exclusive_resource_access):
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 5a14ea4176..b0d2704c07 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -4805,7 +4805,14 @@ def container(container_name):
 @tf_export("colocate_with")
 def colocate_with(op, ignore_existing=False):
   if context.in_graph_mode():
-    return get_default_graph().colocate_with(op, ignore_existing)
+    default_graph = get_default_graph()
+    if isinstance(op, EagerTensor):
+      if default_graph.building_function:
+        op = internal_convert_to_tensor(op)
+      else:
+        raise ValueError("Encountered an Eager-defined Tensor during graph "
+                         "construction, but a function was not being built.")
+    return default_graph.colocate_with(op, ignore_existing)
   else:
     if op is not None:
       return device(op.device)
-- 
GitLab


From 01b96c59f410b44a6279627529a643b1e4da4aa5 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Mon, 26 Feb 2018 14:00:07 -0800
Subject: [PATCH 386/575] TFTS: Switch to using core feature columns

This fixes some shape issues that came up when using the tf.contrib.layers
parsing functions.

Adds a string -> embedding column API example to the LSTM example.

PiperOrigin-RevId: 187076400
---
 .../examples/data/multivariate_periods.csv    | 200 +++++++++---------
 .../timeseries/examples/known_anomaly.py      |   8 +-
 .../contrib/timeseries/examples/lstm.py       |  26 ++-
 .../python/timeseries/estimators.py           |  53 +++--
 .../timeseries/python/timeseries/model.py     |  38 ++--
 .../state_space_models/state_space_model.py   |  10 +-
 6 files changed, 177 insertions(+), 158 deletions(-)

diff --git a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
index b49a0662c2..9b15b4f0b2 100644
--- a/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
+++ b/tensorflow/contrib/timeseries/examples/data/multivariate_periods.csv
@@ -1,100 +1,100 @@
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+44,0.650866553108,0.220341648392,2.41694642022,4.2609098519,4.27271645905,1.,0.,strkey
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+70,-0.13095280331,0.337113974483,1.52703079853,3.16687131599,4.86649398514,1.,0.,strkey
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+80,0.124370187893,0.616474412808,3.4284236674,4.76942168327,3.9749536483,1.,0.,strkey
+81,-0.929423379352,0.290977090976,3.34131726136,4.78590392707,4.10190661656,1.,0.,strkey
+82,0.23766302648,0.155302052254,3.49779513794,4.64605656795,4.15571321107,1.,0.,strkey
+83,1.03531486192,0.359702776204,3.4880725919,4.48167586667,4.21134561991,1.,0.,strkey
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+86,0.380034261243,1.70543355622,2.73605932518,4.16703432307,4.63700400788,1.,0.,strkey
+87,1.03734873488,1.97544410562,2.55586572141,3.84976673263,4.55282864289,1.,0.,strkey
+88,-0.177344253372,2.22614526325,2.09565864891,3.77378097953,4.82577400298,1.,0.,strkey
+89,-0.976821526892,2.18385079177,1.78522284118,3.67768223554,5.06302440873,1.,0.,strkey
+90,0.264820472091,1.86981946157,1.50048403865,3.43619796921,5.05651761669,1.,0.,strkey
+91,1.05642344868,1.47568646076,1.51347671977,3.20898518885,5.50149047462,1.,0.,strkey
+92,-0.311607433358,1.04226467636,1.52089650905,3.02291865417,5.4889046232,1.,0.,strkey
+93,-0.724285777937,0.553052311957,1.48573560173,2.7365973598,5.72549174225,1.,0.,strkey
+94,0.519859192905,0.226520626591,1.61543723167,2.84102086852,5.69330622288,1.,0.,strkey
+95,1.0323195039,0.260873217055,1.81913034804,2.83951143848,5.90325028086,1.,0.,strkey
+96,-0.53285682538,0.387695521405,1.70935609313,2.57977050631,5.79579213161,1.,0.,strkey
+97,-0.975127997215,0.920948771589,2.51292643636,2.71004616612,5.87016469227,1.,0.,strkey
+98,0.540246804099,1.36445470181,2.61949412896,2.98482553485,6.02447664937,1.,0.,strkey
+99,0.987764008058,1.85581989607,2.84685706149,2.94760204892,6.0212151724,1.,0.,strkey
diff --git a/tensorflow/contrib/timeseries/examples/known_anomaly.py b/tensorflow/contrib/timeseries/examples/known_anomaly.py
index 7659dd308a..c08c0b0acb 100644
--- a/tensorflow/contrib/timeseries/examples/known_anomaly.py
+++ b/tensorflow/contrib/timeseries/examples/known_anomaly.py
@@ -46,12 +46,12 @@ def train_and_evaluate_exogenous(csv_file_name=_DATA_FILE, train_steps=300):
 
   # Indicate the format of our exogenous feature, in this case a string
   # representing a boolean value.
-  string_feature = tf.contrib.layers.sparse_column_with_keys(
-      column_name="is_changepoint", keys=["no", "yes"])
+  string_feature = tf.feature_column.categorical_column_with_vocabulary_list(
+      key="is_changepoint", vocabulary_list=["no", "yes"])
   # Specify the way this feature is presented to the model, here using a one-hot
   # encoding.
-  one_hot_feature = tf.contrib.layers.one_hot_column(
-      sparse_id_column=string_feature)
+  one_hot_feature = tf.feature_column.indicator_column(
+      categorical_column=string_feature)
 
   estimator = tf.contrib.timeseries.StructuralEnsembleRegressor(
       periodicities=12,
diff --git a/tensorflow/contrib/timeseries/examples/lstm.py b/tensorflow/contrib/timeseries/examples/lstm.py
index f37cafcc50..2eee878196 100644
--- a/tensorflow/contrib/timeseries/examples/lstm.py
+++ b/tensorflow/contrib/timeseries/examples/lstm.py
@@ -59,10 +59,10 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel):
       num_units: The number of units in the model's LSTMCell.
       num_features: The dimensionality of the time series (features per
         timestep).
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects representing features which are inputs to the model but are
-          not predicted by it. These must then be present for training,
-          evaluation, and prediction.
+      exogenous_feature_columns: A list of `tf.feature_column`s representing
+          features which are inputs to the model but are not predicted by
+          it. These must then be present for training, evaluation, and
+          prediction.
       dtype: The floating point data type to use.
     """
     super(_LSTMModel, self).__init__(
@@ -189,12 +189,16 @@ def train_and_predict(
     export_directory=None):
   """Train and predict using a custom time series model."""
   # Construct an Estimator from our LSTM model.
+  categorical_column = tf.feature_column.categorical_column_with_hash_bucket(
+      key="categorical_exogenous_feature", hash_bucket_size=16)
   exogenous_feature_columns = [
       # Exogenous features are not part of the loss, but can inform
       # predictions. In this example the features have no extra information, but
       # are included as an API example.
-      tf.contrib.layers.real_valued_column(
-          "2d_exogenous_feature", dimension=2)]
+      tf.feature_column.numeric_column(
+          "2d_exogenous_feature", shape=(2,)),
+      tf.feature_column.embedding_column(
+          categorical_column=categorical_column, dimension=10)]
   estimator = ts_estimators.TimeSeriesRegressor(
       model=_LSTMModel(num_features=5, num_units=128,
                        exogenous_feature_columns=exogenous_feature_columns),
@@ -205,7 +209,11 @@ def train_and_predict(
       csv_file_name,
       column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,)
                     + (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5
-                    + ("2d_exogenous_feature",) * 2))
+                    + ("2d_exogenous_feature",) * 2
+                    + ("categorical_exogenous_feature",)),
+      # Data types other than for `times` need to be specified if they aren't
+      # float32. In this case one of our exogenous features has string dtype.
+      column_dtypes=((tf.int64,) + (tf.float32,) * 7 + (tf.string,)))
   train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
       reader, batch_size=4, window_size=32)
   estimator.train(input_fn=train_input_fn, steps=training_steps)
@@ -215,7 +223,9 @@ def train_and_predict(
   predict_exogenous_features = {
       "2d_exogenous_feature": numpy.concatenate(
           [numpy.ones([1, 100, 1]), numpy.zeros([1, 100, 1])],
-          axis=-1)}
+          axis=-1),
+      "categorical_exogenous_feature": numpy.array(
+          ["strkey"] * 100)[None, :, None]}
   (predictions,) = tuple(estimator.predict(
       input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
           evaluation, steps=100,
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators.py b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
index f8355f366f..8d13343e82 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/estimators.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/estimators.py
@@ -18,8 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.layers.python.layers import feature_column
-
 from tensorflow.contrib.timeseries.python.timeseries import ar_model
 from tensorflow.contrib.timeseries.python.timeseries import feature_keys
 from tensorflow.contrib.timeseries.python.timeseries import head as ts_head_lib
@@ -31,10 +29,12 @@ from tensorflow.contrib.timeseries.python.timeseries.state_space_models.filterin
 
 from tensorflow.python.estimator import estimator_lib
 from tensorflow.python.estimator.export import export_lib
+from tensorflow.python.feature_column import feature_column
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import parsing_ops
 from tensorflow.python.training import training as train
 
 
@@ -117,22 +117,29 @@ class TimeSeriesRegressor(estimator_lib.Estimator):
                   dtype=self._model.dtype),
               shape=(default_batch_size, default_series_length,
                      self._model.num_features)))
-      with ops.Graph().as_default():
-        # Default placeholders have only an unknown batch dimension. Make them
-        # in a separate graph, then splice in the series length to the shapes
-        # and re-create them in the outer graph.
-        exogenous_feature_shapes = {
-            key: (value.get_shape(), value.dtype) for key, value
-            in feature_column.make_place_holder_tensors_for_base_features(
-                self._model.exogenous_feature_columns).items()}
-      for feature_key, (batch_only_feature_shape, value_dtype) in (
-          exogenous_feature_shapes.items()):
-        batch_only_feature_shape = batch_only_feature_shape.with_rank_at_least(
-            1).as_list()
-        feature_shape = ([default_batch_size, default_series_length]
-                         + batch_only_feature_shape[1:])
-        placeholders[feature_key] = array_ops.placeholder(
-            dtype=value_dtype, name=feature_key, shape=feature_shape)
+      if self._model.exogenous_feature_columns:
+        with ops.Graph().as_default():
+          # Default placeholders have only an unknown batch dimension. Make them
+          # in a separate graph, then splice in the series length to the shapes
+          # and re-create them in the outer graph.
+          parsed_features = (
+              feature_column.make_parse_example_spec(
+                  self._model.exogenous_feature_columns))
+          placeholder_features = parsing_ops.parse_example(
+              serialized=array_ops.placeholder(
+                  shape=[None], dtype=dtypes.string),
+              features=parsed_features)
+          exogenous_feature_shapes = {
+              key: (value.get_shape(), value.dtype) for key, value
+              in placeholder_features.items()}
+        for feature_key, (batch_only_feature_shape, value_dtype) in (
+            exogenous_feature_shapes.items()):
+          batch_only_feature_shape = (
+              batch_only_feature_shape.with_rank_at_least(1).as_list())
+          feature_shape = ([default_batch_size, default_series_length]
+                           + batch_only_feature_shape[1:])
+          placeholders[feature_key] = array_ops.placeholder(
+              dtype=value_dtype, name=feature_key, shape=feature_shape)
       # Models may not know the shape of their state without creating some
       # variables/ops. Avoid polluting the default graph by making a new one. We
       # use only static metadata from the returned Tensors.
@@ -333,11 +340,11 @@ class StructuralEnsembleRegressor(StateSpaceRegressor):
           determine the model size. Learning autoregressive coefficients
           typically requires more steps and a smaller step size than other
           components.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+          `tf.feature_column.embedding_column`) corresponding to exogenous
+          features which provide extra information to the model but are not part
+          of the series to be predicted. Passed to
+          `tf.feature_column.input_layer`.
       exogenous_update_condition: A function taking two Tensor arguments,
           `times` (shape [batch size]) and `features` (a dictionary mapping
           exogenous feature keys to Tensors with shapes [batch size, ...]), and
diff --git a/tensorflow/contrib/timeseries/python/timeseries/model.py b/tensorflow/contrib/timeseries/python/timeseries/model.py
index bac7d1ebf5..7644764a74 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/model.py
@@ -21,18 +21,17 @@ from __future__ import print_function
 import abc
 import collections
 
-from tensorflow.contrib import layers
-from tensorflow.contrib.layers import feature_column
-
 from tensorflow.contrib.timeseries.python.timeseries import math_utils
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
 
+from tensorflow.python.feature_column import feature_column
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope
 
@@ -66,11 +65,11 @@ class TimeSeriesModel(object):
 
     Args:
       num_features: Number of features for the time series
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+           `tf.feature_column.embedding_column`) corresponding to exogenous
+           features which provide extra information to the model but are not
+           part of the series to be predicted. Passed to
+           `tf.feature_column.input_layer`.
       dtype: The floating point datatype to use.
     """
     if exogenous_feature_columns:
@@ -86,7 +85,7 @@ class TimeSeriesModel(object):
 
   @property
   def exogenous_feature_columns(self):
-    """`FeatureColumn` objects for features which are not predicted."""
+    """`tf.feature_colum`s for features which are not predicted."""
     return self._exogenous_feature_columns
 
   # TODO(allenl): Move more of the generic machinery for generating and
@@ -265,11 +264,14 @@ class TimeSeriesModel(object):
     if not self._exogenous_feature_columns:
       return (0,)
     with ops.Graph().as_default():
-      placeholder_features = (
-          feature_column.make_place_holder_tensors_for_base_features(
+      parsed_features = (
+          feature_column.make_parse_example_spec(
               self._exogenous_feature_columns))
-      embedded = layers.input_from_feature_columns(
-          columns_to_tensors=placeholder_features,
+      placeholder_features = parsing_ops.parse_example(
+          serialized=array_ops.placeholder(shape=[None], dtype=dtypes.string),
+          features=parsed_features)
+      embedded = feature_column.input_layer(
+          features=placeholder_features,
           feature_columns=self._exogenous_feature_columns)
       return embedded.get_shape().as_list()[1:]
 
@@ -308,13 +310,13 @@ class TimeSeriesModel(object):
         # Avoid shape warnings when embedding "scalar" exogenous features (those
         # with only batch and window dimensions); input_from_feature_columns
         # expects input ranks to match the embedded rank.
-        if tensor.get_shape().ndims == 1:
+        if tensor.get_shape().ndims == 1 and tensor.dtype != dtypes.string:
           exogenous_features_single_batch_dimension[name] = tensor[:, None]
         else:
           exogenous_features_single_batch_dimension[name] = tensor
       embedded_exogenous_features_single_batch_dimension = (
-          layers.input_from_feature_columns(
-              columns_to_tensors=exogenous_features_single_batch_dimension,
+          feature_column.input_layer(
+              features=exogenous_features_single_batch_dimension,
               feature_columns=self._exogenous_feature_columns,
               trainable=True))
       exogenous_regressors = array_ops.reshape(
@@ -381,8 +383,8 @@ class SequentialTimeSeriesModel(TimeSeriesModel):
           may use _scale_back_data or _scale_back_variance to return predictions
           to the input scale.
       dtype: The floating point datatype to use.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects. See `TimeSeriesModel`.
+      exogenous_feature_columns: A list of `tf.feature_column`s objects. See
+          `TimeSeriesModel`.
       exogenous_update_condition: A function taking two Tensor arguments `times`
           (shape [batch size]) and `features` (a dictionary mapping exogenous
           feature keys to Tensors with shapes [batch size, ...]) and returning a
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
index 6257002647..951c6546d5 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py
@@ -112,11 +112,11 @@ class StateSpaceModelConfiguration(
       exogenous_noise_decreases: If True, exogenous regressors can "set" model
           state, decreasing uncertainty. If both this parameter and
           exogenous_noise_increases are False, exogenous regressors are ignored.
-      exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
-          objects (for example tf.contrib.layers.embedding_column) corresponding
-          to exogenous features which provide extra information to the model but
-          are not part of the series to be predicted. Passed to
-          tf.contrib.layers.input_from_feature_columns.
+      exogenous_feature_columns: A list of `tf.feature_column`s (for example
+          `tf.feature_column.embedding_column`) corresponding to exogenous
+          features which provide extra information to the model but are not part
+          of the series to be predicted. Passed to
+          `tf.feature_column.input_layer`.
       exogenous_update_condition: A function taking two Tensor arguments `times`
           (shape [batch size]) and `features` (a dictionary mapping exogenous
           feature keys to Tensors with shapes [batch size, ...]) and returning a
-- 
GitLab


From 7b944492cbe1ac81ea728ecb84ce4ea272627990 Mon Sep 17 00:00:00 2001
From: Shivani Agrawal <shivaniagrawal@google.com>
Date: Mon, 26 Feb 2018 14:11:08 -0800
Subject: [PATCH 387/575] Adding documentation for dataset/iterator
 checkpointing.

PiperOrigin-RevId: 187078347
---
 .../docs_src/programmers_guide/datasets.md    | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/programmers_guide/datasets.md
index d19200e80c..d38fbddfa1 100644
--- a/tensorflow/docs_src/programmers_guide/datasets.md
+++ b/tensorflow/docs_src/programmers_guide/datasets.md
@@ -327,6 +327,35 @@ same op/node (created by `Iterator.get_next()`). Therefore,  evaluating *any* of
 these tensors will advance the iterator for all components. A typical consumer
 of an iterator will include all components in a single expression.
 
+### Saving iterator state
+
+The @{tf.contrib.data.make_saveable_from_iterator} function creates a
+`SaveableObject` from an iterator, which can be used to save and
+restore the current state of the iterator (and, effectively, the whole input
+pipeline). A saveable object thus created can be added to @{tf.train.Saver}
+variables list or the `tf.GraphKeys.SAVEABLE_OBJECTS` collection for saving and
+restoring in the same manner as a @{tf.Variable}. Refer to
+@{$saved_model$Saving and Restoring} for details on how to save and restore
+variables.
+
+```python
+# Create saveable object from iterator.
+saveable = tf.contrib.data.make_saveable_from_iterator(iterator)
+
+# Save the iterator state by adding it to the saveable objects collection.
+tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, saveable)
+saver = tf.train.Saver()
+
+with tf.Session() as sess:
+
+  if should_checkpoint:
+    saver.save(path_to_checkpoint)
+
+# Restore the iterator state.
+with tf.Session() as sess:
+  saver.restore(sess, path_to_checkpoint)
+```
+
 ## Reading input data
 
 ### Consuming NumPy arrays
-- 
GitLab


From 10aaee0c5d83649959d8b1a6c75ee3127c205259 Mon Sep 17 00:00:00 2001
From: Michael Kuperstein <mkuper@google.com>
Date: Mon, 26 Feb 2018 14:19:56 -0800
Subject: [PATCH 388/575] [XLA] GTE of a certain element of the tuple does not
 need not keep other elements alive.

This achieves two things:
1. Heap simulation runtime is no longer quadratic in the number of tuple elements (as we don't add each GetTupleElement to the liveset of each buffer defined by the tuple).
2. A reduction in the heap memory footprint.

PiperOrigin-RevId: 187079787
---
 .../compiler/xla/service/heap_simulator.cc    | 135 ++++++++++--------
 .../xla/service/heap_simulator_test.cc        |  50 +++++++
 2 files changed, 127 insertions(+), 58 deletions(-)

diff --git a/tensorflow/compiler/xla/service/heap_simulator.cc b/tensorflow/compiler/xla/service/heap_simulator.cc
index a2d13c013c..3dd4c4a079 100644
--- a/tensorflow/compiler/xla/service/heap_simulator.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator.cc
@@ -27,38 +27,6 @@ namespace xla {
 using tensorflow::gtl::FlatMap;
 using tensorflow::gtl::FlatSet;
 
-namespace {
-
-// Returns the set of buffers that may be sources of all operands of the given
-// instruction.  The returned buffers are guaranteed to have no duplicates, and
-// to be sorted in a deterministic order.
-std::vector<const LogicalBuffer*> UniqueOperandSourceBuffers(
-    const HloInstruction* instruction,
-    const TuplePointsToAnalysis& points_to_analysis) {
-  std::vector<const LogicalBuffer*> buffers;
-  for (const HloInstruction* operand : instruction->operands()) {
-    points_to_analysis.GetPointsToSet(operand).ForEachElement(
-        [&](const ShapeIndex& /*index*/,
-            const PointsToSet::BufferList& points_to) {
-          buffers.insert(buffers.end(), points_to.begin(), points_to.end());
-        });
-  }
-
-  // Sort and then remove duplicates from buffers.
-  std::sort(buffers.begin(), buffers.end(),
-            [](const LogicalBuffer* a, const LogicalBuffer* b) {
-              return a->id() < b->id();
-            });
-  buffers.erase(std::unique(buffers.begin(), buffers.end(),
-                            [](const LogicalBuffer* a, const LogicalBuffer* b) {
-                              return a->id() == b->id();
-                            }),
-                buffers.end());
-  return buffers;
-}
-
-}  // namespace
-
 /*static*/
 StatusOr<HeapSimulator::Result> HeapSimulator::Run(
     std::unique_ptr<HeapAlgorithm> algorithm, const HloModule& module,
@@ -93,6 +61,7 @@ Status HeapSimulator::RunComputation(
     const HloComputation& computation,
     const std::vector<const HloInstruction*>& instruction_sequence,
     const TuplePointsToAnalysis& points_to_analysis) {
+  VLOG(3) << "Computation:\n" << computation.ToString();
   // The goal here is to minimize memory usage, assuming the given sequential
   // ordering of instructions.  The strategy is to walk through the instruction
   // sequence, calling Alloc and Free on the underlying heap algorithm.  The
@@ -101,7 +70,51 @@ Status HeapSimulator::RunComputation(
   // 'live_buffers' tracks the liveness of each buffer that we assign, by
   // associating it with a set of HloInstructions that need to be visited.  When
   // the set becomes empty, the buffer is no longer used, and can be freed.
+  // 'used_buffers' is the reverse map - it tracks which buffers were used by an
+  // instruction, so that we can remove the instructions from a buffer's live
+  // set after they are visited.
   FlatMap<const LogicalBuffer*, FlatSet<const HloInstruction*>> live_buffers;
+  FlatMap<const HloInstruction*, FlatSet<const LogicalBuffer*>> used_buffers;
+  auto add_user_to_buffer = [this, &live_buffers, &used_buffers](
+                                const HloInstruction* user,
+                                const LogicalBuffer* buffer) {
+    if (!IgnoreBuffer(buffer)) {
+      VLOG(4) << "  Adding user " << user->name() << " to buffer "
+              << buffer->ToString();
+      live_buffers[buffer].insert(user);
+      used_buffers[user].insert(buffer);
+    }
+  };
+
+  // Initialize live_buffers for each buffer that we're going to assign.  The
+  // set of instructions that need to be visited contains all users of all
+  // aliases, that is, all users of all instructions that have the buffer
+  // contained in their points-to set.
+  for (const HloInstruction* instruction : instruction_sequence) {
+    const PointsToSet& points_to =
+        points_to_analysis.GetPointsToSet(instruction);
+    const PointsToSet::BufferSet& buffer_set = points_to.CreateFlattenedSet();
+    for (const HloInstruction* user : instruction->users()) {
+      if (user->opcode() != HloOpcode::kGetTupleElement) {
+        for (const LogicalBuffer* buffer : buffer_set) {
+          add_user_to_buffer(user, buffer);
+        }
+      } else {
+        // A GetTupleElement doesn't need to keep all of its operand's buffers
+        // alive. It only needs the buffers that relate to the element its
+        // extracting, and the tuple it's extracting from, but not the buffers
+        // for the other elements.
+        for (const LogicalBuffer* buffer : points_to.element({})) {
+          add_user_to_buffer(user, buffer);
+        }
+        const PointsToSet& gte_points_to =
+            points_to_analysis.GetPointsToSet(user);
+        for (const LogicalBuffer* buffer : gte_points_to.CreateFlattenedSet()) {
+          add_user_to_buffer(user, buffer);
+        }
+      }
+    }
+  }
 
   const HloInstruction* root = computation.root_instruction();
   auto output_source_buffers =
@@ -114,34 +127,17 @@ Status HeapSimulator::RunComputation(
         buffers_defined_by_instruction =
             points_to_analysis.GetBuffersDefinedByInstruction(instruction);
 
-    // Initialize live_buffers for each buffer that we're going to assign.  The
-    // set of instructions that need to be visited contains all users of all
-    // aliases.  The alias itself is not necessary; if it has users, the users
-    // are necessarily scheduled after the alias.  And if it has no users, it is
-    // either a dead value or an output, both of which are handled below.
-    //
-    // We ignore control dependencies here. The reasoning is that the control
-    // dependencies have already been accounted for in the ordering of the given
-    // 'instruction_sequence', and should not otherwise artificially extend the
-    // lifetime of buffers that aren't already connected by a data dependency.
+    VLOG(3) << "Instruction: " << instruction->ToString();
+    for (const LogicalBuffer* buffer : buffers_defined_by_instruction) {
+      VLOG(4) << "  Defines: " << buffer->ToString()
+              << (IgnoreBuffer(buffer) ? " (Ignored)" : "");
+    }
+
     dead_buffers_to_free.clear();
     for (const LogicalBuffer* buffer : buffers_defined_by_instruction) {
       if (IgnoreBuffer(buffer)) {
         continue;
       }
-      FlatSet<const HloInstruction*>* live_set = nullptr;
-      for (const BufferAlias& alias :
-           points_to_analysis.GetBufferAliases(*buffer)) {
-        const std::vector<HloInstruction*>& users =
-            alias.instruction()->users();
-        if (!users.empty()) {
-          if (live_set == nullptr) {
-            live_set = &live_buffers[buffer];
-          }
-          live_set->insert(users.begin(), users.end());
-        }
-      }
-
       // Add a nullptr sentry to ensure entry parameters and output source
       // buffers are not freed until the very end.
       const bool entry_parameter =
@@ -165,11 +161,12 @@ Status HeapSimulator::RunComputation(
     // have no instructions left to visit are moved from live_buffers to
     // operand_buffers_to_free.
     operand_buffers_to_free.clear();
-    for (const LogicalBuffer* operand_buffer :
-         UniqueOperandSourceBuffers(instruction, points_to_analysis)) {
+    for (const LogicalBuffer* operand_buffer : used_buffers[instruction]) {
       if (IgnoreBuffer(operand_buffer)) {
         continue;
       }
+      VLOG(4) << "  Removing user " << instruction->name() << " from buffer "
+              << operand_buffer->ToString();
       auto it = live_buffers.find(operand_buffer);
       FlatSet<const HloInstruction*>* live_set = &it->second;
       live_set->erase(instruction);
@@ -178,6 +175,11 @@ Status HeapSimulator::RunComputation(
         operand_buffers_to_free.push_back(operand_buffer);
       }
     }
+    // Sort to get a deterministic iteration order.
+    std::sort(operand_buffers_to_free.begin(), operand_buffers_to_free.end(),
+              [](const LogicalBuffer* x, const LogicalBuffer* y) {
+                return x->id() < y->id();
+              });
 
     // Allocate buffers defined by this instruction.  This is the latest point
     // that we can allocate; right before the buffer is first used.  This must
@@ -203,6 +205,8 @@ Status HeapSimulator::RunComputation(
               CanShareOperandBufferWithUser(
                   operand_buffer->instruction(), operand_buffer->index(),
                   buffer->instruction(), buffer->index(), points_to_analysis)) {
+            VLOG(3) << "  Sharing: " << buffer->ToString() << " with "
+                    << operand_buffer->ToString();
             ShareBuffer(buffer, operand_buffer, instruction);
             shared = true;
             break;
@@ -211,6 +215,7 @@ Status HeapSimulator::RunComputation(
       }
 
       if (!shared) {
+        VLOG(3) << "  Allocating: " << buffer->ToString();
         Alloc(buffer, instruction);
       }
     }
@@ -244,20 +249,34 @@ Status HeapSimulator::RunComputation(
     // Free buffers that are no longer live.  This is the earliest point that we
     // can de-allocate; right after the last use of the buffer.
     for (const LogicalBuffer* buffer : dead_buffers_to_free) {
+      VLOG(3) << "  Freeing dead: " << buffer->ToString();
       Free(buffer, instruction);
     }
     for (const LogicalBuffer* buffer : operand_buffers_to_free) {
+      VLOG(3) << "  Freeing operand: " << buffer->ToString();
       Free(buffer, instruction);
     }
   }
 
   // Any remaining live buffers must be entry parameters or output source
-  // buffers, which had a nullptr sentry added.  Free them now.
+  // buffers, which had a nullptr sentry added.  Free them now, in a
+  // deterministic order.
+  std::vector<const LogicalBuffer*> to_free;
+  to_free.reserve(live_buffers.size());
   for (const auto& buffer_pending : live_buffers) {
     const LogicalBuffer* buffer = buffer_pending.first;
     const FlatSet<const HloInstruction*>& pending = buffer_pending.second;
     CHECK_EQ(pending.size(), 1) << *buffer;
     CHECK(*pending.begin() == nullptr) << *buffer;
+    to_free.push_back(buffer);
+  }
+
+  std::sort(to_free.begin(), to_free.end(),
+            [](const LogicalBuffer* x, const LogicalBuffer* y) {
+              return x->id() < y->id();
+            });
+  for (const LogicalBuffer* buffer : to_free) {
+    VLOG(3) << "Freeing pending: " << buffer->ToString();
     Free(buffer, root);
   }
 
diff --git a/tensorflow/compiler/xla/service/heap_simulator_test.cc b/tensorflow/compiler/xla/service/heap_simulator_test.cc
index 387b649a73..688a271712 100644
--- a/tensorflow/compiler/xla/service/heap_simulator_test.cc
+++ b/tensorflow/compiler/xla/service/heap_simulator_test.cc
@@ -410,6 +410,56 @@ TEST_F(HeapSimulatorTest, MultiplyDotDotTuple) {
   });
 }
 
+TEST_F(HeapSimulatorTest, IndependentTupleElements) {
+  auto builder = HloComputation::Builder(TestName());
+  auto paramA = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, f32scalar_, "paramA"));
+  auto paramB = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, f32scalar_, "paramB"));
+  auto mul = builder.AddInstruction(HloInstruction::CreateBinary(
+      f32scalar_, HloOpcode::kMultiply, paramA, paramB));
+  auto add = builder.AddInstruction(HloInstruction::CreateBinary(
+      f32scalar_, HloOpcode::kAdd, paramA, paramB));
+  auto tuple = builder.AddInstruction(HloInstruction::CreateTuple({mul, add}));
+  auto element0 = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(f32scalar_, tuple, 0));
+  auto broadcast = builder.AddInstruction(
+      HloInstruction::CreateBroadcast(f32vec4_, element0, {0}));
+  auto sub = builder.AddInstruction(HloInstruction::CreateBinary(
+      f32scalar_, HloOpcode::kSubtract, paramA, paramB));
+  auto element1 = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(f32scalar_, tuple, 1));
+  auto output = builder.AddInstruction(
+      HloInstruction::CreateTuple({broadcast, sub, element1}));
+
+  HeapSimulatorTracker tracker(TestName(), builder.Build(),
+                               {paramA, paramB, mul, add, tuple, element0,
+                                broadcast, sub, element1, output});
+  tracker.ExpectCallSequence({
+      {kAlloc, tracker.BufferAt(paramA, {})},
+      {kAlloc, tracker.BufferAt(paramB, {})},
+      {kAlloc, tracker.BufferAt(mul, {})},
+      {kAlloc, tracker.BufferAt(add, {})},
+      {kAlloc, tracker.BufferAt(tuple, {})},
+      {kAlloc, tracker.BufferAt(broadcast, {})},
+      // The mul can be freed right after the broadcast happens, even though
+      // The other GetTupleElement is still alive.
+      {kFree, tracker.BufferAt(mul, {})},
+      {kAlloc, tracker.BufferAt(sub, {})},
+      // The temporary tuple is now dead.
+      {kFree, tracker.BufferAt(tuple, {})},
+      {kAlloc, tracker.BufferAt(output, {})},
+      // All params and outputs are freed at the end.
+      {kFree, tracker.BufferAt(paramA, {})},
+      {kFree, tracker.BufferAt(paramB, {})},
+      {kFree, tracker.BufferAt(add, {})},
+      {kFree, tracker.BufferAt(broadcast, {})},
+      {kFree, tracker.BufferAt(sub, {})},
+      {kFree, tracker.BufferAt(output, {})},
+      {kFinish, nullptr},
+  });
+}
+
 TEST_F(HeapSimulatorTest, WholeModule) {
   HeapSimulatorTracker tracker(TestName());
 
-- 
GitLab


From c3ad72500cd714a39af5ab530ab14f477cc717c6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 14:25:30 -0800
Subject: [PATCH 389/575] 1st version of sequential feature columns.

PiperOrigin-RevId: 187080635
---
 tensorflow/contrib/feature_column/BUILD       |  31 +-
 .../sequential_feature_column.py              | 308 +++++++++++-
 .../sequential_feature_column_test.py         | 471 ++++++++++++++++++
 3 files changed, 808 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py

diff --git a/tensorflow/contrib/feature_column/BUILD b/tensorflow/contrib/feature_column/BUILD
index 6fc053759c..a53e36c2d5 100644
--- a/tensorflow/contrib/feature_column/BUILD
+++ b/tensorflow/contrib/feature_column/BUILD
@@ -33,5 +33,34 @@ py_library(
     name = "sequential_feature_column",
     srcs = ["python/feature_column/sequential_feature_column.py"],
     srcs_version = "PY2AND3",
-    deps = [],
+    deps = [
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:check_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python/feature_column",
+    ],
+)
+
+py_test(
+    name = "sequential_feature_column_test",
+    srcs = ["python/feature_column/sequential_feature_column_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["no_pip"],
+    deps = [
+        ":sequential_feature_column",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:sparse_tensor",
+        "//tensorflow/python:training",
+        "//tensorflow/python/feature_column",
+        "//third_party/py/numpy",
+    ],
 )
diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py
index 690a44ff43..4ed7268e7a 100644
--- a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column.py
@@ -12,8 +12,314 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Experimental methods for tf.feature_column sequential input."""
+"""Experimental methods for tf.feature_column sequence input."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+
+
+import abc
+import collections
+
+
+from tensorflow.python.feature_column import feature_column as fc
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import parsing_ops
+from tensorflow.python.ops import sparse_ops
+from tensorflow.python.ops import variable_scope
+
+# TODO(b/73160931): Fix pydoc.
+# pylint: disable=g-doc-args,missing-docstring,protected-access
+# TODO(b/73827486): Support SequenceExample.
+
+
+def sequence_input_layer(
+    features,
+    feature_columns,
+    weight_collections=None,
+    trainable=True,
+    scope=None):
+  """"Builds input layer for sequence input.
+
+  All `feature_columns` must be sequence dense columns with the same
+  `sequence_length`. The output of this method can be fed into sequence
+  networks, such as RNN.
+
+  The output of this method is a 3D `Tensor` of shape `[batch_size, T, D]`.
+  `T` is the maximum sequence length for this batch, which could differ from
+  batch to batch.
+
+  If multiple `feature_columns` are given with `Di` `num_elements` each, their
+  outputs are concatenated. So, the final `Tensor` has shape
+  `[batch_size, T, D0 + D1 + ... + Dn]`.
+
+  Example:
+
+  ```python
+  rating = sequence_numeric_column('rating')
+  watches = sequence_categorical_column_with_identity(
+      'watches', num_buckets=1000)
+  watches_embedding = embedding_column(watches, dimension=10)
+  columns = [rating, watches]
+
+  features = tf.parse_example(..., features=make_parse_example_spec(columns))
+  input_layer, sequence_length = sequence_input_layer(features, columns)
+
+  rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
+  outputs, state = tf.nn.dynamic_rnn(
+      rnn_cell, inputs=input_layer, sequence_length=sequence_length)
+  ```
+
+  Returns:
+    An `(input_layer, sequence_length)` tuple where:
+    - input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
+        `T` is the maximum sequence length for this batch, which could differ
+        from batch to batch. `D` is the sum of `num_elements` for all
+        `feature_columns`.
+    - sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence
+        length for each example.
+  Raises:
+    ValueError: If any of the `feature_columns` is the wrong type.
+  """
+  feature_columns = fc._clean_feature_columns(feature_columns)
+  for c in feature_columns:
+    if not isinstance(c, _SequenceDenseColumn):
+      raise ValueError(
+          'All feature_columns must be of type _SequenceDenseColumn. '
+          'Given (type {}): {}'.format(type(c), c))
+
+  with variable_scope.variable_scope(
+      scope, default_name='sequence_input_layer', values=features.values()):
+    builder = fc._LazyBuilder(features)
+    output_tensors = []
+    sequence_lengths = []
+    ordered_columns = []
+    for column in sorted(feature_columns, key=lambda x: x.name):
+      ordered_columns.append(column)
+      with variable_scope.variable_scope(
+          None, default_name=column._var_scope_name):
+        dense_tensor, sequence_length = column._get_sequence_dense_tensor(
+            builder,
+            weight_collections=weight_collections,
+            trainable=trainable)
+        # Flattens the final dimension to produce a 3D Tensor.
+        num_elements = column._variable_shape.num_elements()
+        shape = array_ops.shape(dense_tensor)
+        output_tensors.append(
+            array_ops.reshape(
+                dense_tensor,
+                shape=array_ops.concat([shape[:2], [num_elements]], axis=0)))
+        sequence_lengths.append(sequence_length)
+    fc._verify_static_batch_size_equality(output_tensors, ordered_columns)
+    # TODO(b/73160931): Verify sequence_length equality.
+    return array_ops.concat(output_tensors, -1), sequence_lengths[0]
+
+
+# TODO(b/73160931): Add remaining categorical columns.
+def sequence_categorical_column_with_identity(
+    key, num_buckets, default_value=None):
+  return _SequenceCategoricalColumn(
+      fc.categorical_column_with_identity(
+          key=key,
+          num_buckets=num_buckets,
+          default_value=default_value))
+
+
+# TODO(b/73160931): Merge with embedding_column
+def _sequence_embedding_column(
+    categorical_column, dimension, initializer=None, ckpt_to_load_from=None,
+    tensor_name_in_ckpt=None, max_norm=None, trainable=True):
+  if not isinstance(categorical_column, _SequenceCategoricalColumn):
+    raise ValueError(
+        'categorical_column must be of type _SequenceCategoricalColumn. '
+        'Given (type {}): {}'.format(
+            type(categorical_column), categorical_column))
+  return _SequenceEmbeddingColumn(
+      fc.embedding_column(
+          categorical_column,
+          dimension=dimension,
+          initializer=initializer,
+          ckpt_to_load_from=ckpt_to_load_from,
+          tensor_name_in_ckpt=tensor_name_in_ckpt,
+          max_norm=max_norm,
+          trainable=trainable))
+
+
+def sequence_numeric_column(
+    key,
+    shape=(1,),
+    default_value=0.,
+    dtype=dtypes.float32):
+  # TODO(b/73160931): Add validations.
+  return _SequenceNumericColumn(
+      key,
+      shape=shape,
+      default_value=default_value,
+      dtype=dtype)
+
+
+class _SequenceDenseColumn(fc._FeatureColumn):
+  """Represents dense sequence data."""
+
+  __metaclass__ = abc.ABCMeta
+
+  TensorSequenceLengthPair = collections.namedtuple(  # pylint: disable=invalid-name
+      'TensorSequenceLengthPair', ['dense_tensor', 'sequence_length'])
+
+  @abc.abstractproperty
+  def _variable_shape(self):
+    """`TensorShape` without batch and sequence dimensions."""
+    pass
+
+  @abc.abstractmethod
+  def _get_sequence_dense_tensor(
+      self, inputs, weight_collections=None, trainable=None):
+    """Returns a `TensorSequenceLengthPair`."""
+    pass
+
+
+def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1):
+  with ops.name_scope(None, 'sequence_length') as name_scope:
+    row_ids = sp_tensor.indices[:, 0]
+    column_ids = sp_tensor.indices[:, 1]
+    column_ids += array_ops.ones_like(column_ids)
+    seq_length = (
+        math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements)
+    # If the last n rows do not have ids, seq_length will have shape
+    # [batch_size - n]. Pad the remaining values with zeros.
+    n_pad = array_ops.shape(sp_tensor)[:1] - array_ops.shape(seq_length)[:1]
+    padding = array_ops.zeros(n_pad, dtype=seq_length.dtype)
+    return array_ops.concat([seq_length, padding], axis=0, name=name_scope)
+
+
+class _SequenceCategoricalColumn(
+    fc._CategoricalColumn,
+    collections.namedtuple(
+        '_SequenceCategoricalColumn', ['categorical_column'])):
+
+  @property
+  def name(self):
+    return self.categorical_column.name
+
+  @property
+  def _parse_example_spec(self):
+    return self.categorical_column._parse_example_spec
+
+  def _transform_feature(self, inputs):
+    return self.categorical_column._transform_feature(inputs)
+
+  @property
+  def _num_buckets(self):
+    return self.categorical_column._num_buckets
+
+  def _get_sparse_tensors(self, inputs, weight_collections=None,
+                          trainable=None):
+    sparse_tensors = self.categorical_column._get_sparse_tensors(inputs)
+    id_tensor = sparse_tensors.id_tensor
+    weight_tensor = sparse_tensors.weight_tensor
+    # Expands final dimension, so that embeddings are not combined during
+    # embedding lookup.
+    check_id_rank = check_ops.assert_equal(
+        array_ops.rank(id_tensor), 2,
+        data=[
+            'Column {} expected ID tensor of rank 2. '.format(self.name),
+            'id_tensor shape: ', array_ops.shape(id_tensor)])
+    with ops.control_dependencies([check_id_rank]):
+      id_tensor = sparse_ops.sparse_reshape(
+          id_tensor,
+          shape=array_ops.concat([id_tensor.dense_shape, [1]], axis=0))
+    if weight_tensor is not None:
+      check_weight_rank = check_ops.assert_equal(
+          array_ops.rank(weight_tensor), 2,
+          data=[
+              'Column {} expected weight tensor of rank 2.'.format(self.name),
+              'weight_tensor shape:', array_ops.shape(weight_tensor)])
+      with ops.control_dependencies([check_weight_rank]):
+        weight_tensor = sparse_ops.sparse_reshape(
+            weight_tensor,
+            shape=array_ops.concat([weight_tensor.dense_shape, [1]], axis=0))
+    return fc._CategoricalColumn.IdWeightPair(id_tensor, weight_tensor)
+
+  def _sequence_length(self, inputs):
+    sparse_tensors = self.categorical_column._get_sparse_tensors(inputs)
+    return _sequence_length_from_sparse_tensor(sparse_tensors.id_tensor)
+
+
+class _SequenceEmbeddingColumn(
+    _SequenceDenseColumn,
+    collections.namedtuple('_SequenceEmbeddingColumn', ['embedding_column'])):
+
+  @property
+  def name(self):
+    return self.embedding_column.name
+
+  @property
+  def _parse_example_spec(self):
+    return self.embedding_column._parse_example_spec
+
+  def _transform_feature(self, inputs):
+    return self.embedding_column._transform_feature(inputs)
+
+  @property
+  def _variable_shape(self):
+    return self.embedding_column._variable_shape
+
+  def _get_sequence_dense_tensor(
+      self, inputs, weight_collections=None, trainable=None):
+    dense_tensor = self.embedding_column._get_dense_tensor(
+        inputs=inputs,
+        weight_collections=weight_collections,
+        trainable=trainable)
+    sequence_length = self.embedding_column.categorical_column._sequence_length(
+        inputs)
+    return _SequenceDenseColumn.TensorSequenceLengthPair(
+        dense_tensor=dense_tensor, sequence_length=sequence_length)
+
+
+class _SequenceNumericColumn(
+    _SequenceDenseColumn,
+    collections.namedtuple(
+        '_SequenceNumericColumn',
+        ['key', 'shape', 'default_value', 'dtype'])):
+
+  @property
+  def name(self):
+    return self.key
+
+  @property
+  def _parse_example_spec(self):
+    return {self.key: parsing_ops.VarLenFeature(self.dtype)}
+
+  def _transform_feature(self, inputs):
+    return inputs.get(self.key)
+
+  @property
+  def _variable_shape(self):
+    return tensor_shape.TensorShape(self.shape)
+
+  def _get_sequence_dense_tensor(
+      self, inputs, weight_collections=None, trainable=None):
+    # Do nothing with weight_collections and trainable since no variables are
+    # created in this function.
+    del weight_collections
+    del trainable
+    sp_tensor = inputs.get(self)
+    dense_tensor = sparse_ops.sparse_tensor_to_dense(
+        sp_tensor, default_value=self.default_value)
+    # Reshape into [batch_size, T, variable_shape].
+    dense_shape = array_ops.concat(
+        [array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape],
+        axis=0)
+    dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
+    sequence_length = _sequence_length_from_sparse_tensor(
+        sp_tensor, num_elements=self._variable_shape.num_elements())
+    return _SequenceDenseColumn.TensorSequenceLengthPair(
+        dense_tensor=dense_tensor, sequence_length=sequence_length)
+
+# pylint: enable=g-doc-args,missing-docstring,protected-access
diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py
new file mode 100644
index 0000000000..59674869a2
--- /dev/null
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequential_feature_column_test.py
@@ -0,0 +1,471 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for sequential_feature_column."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.feature_column.python.feature_column import sequential_feature_column as sfc
+from tensorflow.python.feature_column.feature_column import _LazyBuilder
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.platform import test
+from tensorflow.python.training import monitored_session
+
+
+class SequenceInputLayerTest(test.TestCase):
+
+  def test_embedding_column(self):
+    vocabulary_size = 3
+    sparse_input_a = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(2, 0, 1),
+        dense_shape=(2, 2))
+    sparse_input_b = sparse_tensor.SparseTensorValue(
+        # example 0, ids [1]
+        # example 1, ids [2, 0]
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, 2, 0),
+        dense_shape=(2, 2))
+
+    embedding_dimension_a = 2
+    embedding_values_a = (
+        (1., 2.),  # id 0
+        (3., 4.),  # id 1
+        (5., 6.)  # id 2
+    )
+    embedding_dimension_b = 3
+    embedding_values_b = (
+        (11., 12., 13.),  # id 0
+        (14., 15., 16.),  # id 1
+        (17., 18., 19.)  # id 2
+    )
+    def _get_initializer(embedding_dimension, embedding_values):
+      def _initializer(shape, dtype, partition_info):
+        self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+        self.assertEqual(dtypes.float32, dtype)
+        self.assertIsNone(partition_info)
+        return embedding_values
+      return _initializer
+
+    expected_input_layer = [
+        # example 0, ids_a [2], ids_b [1]
+        [[5., 6., 14., 15., 16.], [0., 0., 0., 0., 0.]],
+        # example 1, ids_a [0, 1], ids_b [2, 0]
+        [[1., 2., 17., 18., 19.], [3., 4., 11., 12., 13.]],
+    ]
+    expected_sequence_length = [1, 2]
+
+    categorical_column_a = sfc.sequence_categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column_a = sfc._sequence_embedding_column(
+        categorical_column_a, dimension=embedding_dimension_a,
+        initializer=_get_initializer(embedding_dimension_a, embedding_values_a))
+    categorical_column_b = sfc.sequence_categorical_column_with_identity(
+        key='bbb', num_buckets=vocabulary_size)
+    embedding_column_b = sfc._sequence_embedding_column(
+        categorical_column_b, dimension=embedding_dimension_b,
+        initializer=_get_initializer(embedding_dimension_b, embedding_values_b))
+
+    input_layer, sequence_length = sfc.sequence_input_layer(
+        features={
+            'aaa': sparse_input_a,
+            'bbb': sparse_input_b,
+        },
+        # Test that columns are reordered alphabetically.
+        feature_columns=[embedding_column_b, embedding_column_a])
+
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('sequence_input_layer/aaa_embedding/embedding_weights:0',
+         'sequence_input_layer/bbb_embedding/embedding_weights:0'),
+        tuple([v.name for v in global_vars]))
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(embedding_values_a, global_vars[0].eval(session=sess))
+      self.assertAllEqual(embedding_values_b, global_vars[1].eval(session=sess))
+      self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess))
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_numeric_column(self):
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0.], [1]]
+        # example 1, [[10.]]
+        indices=((0, 0), (0, 1), (1, 0)),
+        values=(0., 1., 10.),
+        dense_shape=(2, 2))
+    expected_input_layer = [
+        [[0.], [1.]],
+        [[10.], [0.]],
+    ]
+    expected_sequence_length = [2, 1]
+    numeric_column = sfc.sequence_numeric_column('aaa')
+
+    input_layer, sequence_length = sfc.sequence_input_layer(
+        features={'aaa': sparse_input},
+        feature_columns=[numeric_column])
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess))
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_numeric_column_multi_dim(self):
+    """Tests sequence_input_layer for multi-dimensional numeric_column."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[[0., 1.],  [2., 3.]], [[4., 5.],  [6., 7.]]]
+        # example 1, [[[10., 11.],  [12., 13.]]]
+        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7),
+                 (1, 0), (1, 1), (1, 2), (1, 3)),
+        values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.),
+        dense_shape=(2, 8))
+    # The output of numeric_column._get_dense_tensor should be flattened.
+    expected_input_layer = [
+        [[0., 1., 2., 3.], [4., 5., 6., 7.]],
+        [[10., 11., 12., 13.], [0., 0., 0., 0.]],
+    ]
+    expected_sequence_length = [2, 1]
+    numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2))
+
+    input_layer, sequence_length = sfc.sequence_input_layer(
+        features={'aaa': sparse_input},
+        feature_columns=[numeric_column])
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(expected_input_layer, input_layer.eval(session=sess))
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+
+def _assert_sparse_tensor_value(test_case, expected, actual):
+  test_case.assertEqual(np.int64, np.array(actual.indices).dtype)
+  test_case.assertAllEqual(expected.indices, actual.indices)
+
+  test_case.assertEqual(
+      np.array(expected.values).dtype, np.array(actual.values).dtype)
+  test_case.assertAllEqual(expected.values, actual.values)
+
+  test_case.assertEqual(np.int64, np.array(actual.dense_shape).dtype)
+  test_case.assertAllEqual(expected.dense_shape, actual.dense_shape)
+
+
+class SequenceCategoricalColumnWithIdentityTest(test.TestCase):
+
+  def test_get_sparse_tensors(self):
+    column = sfc.sequence_categorical_column_with_identity(
+        'aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, 2, 0),
+        dense_shape=(2, 2))
+    expected_sparse_ids = sparse_tensor.SparseTensorValue(
+        indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)),
+        values=np.array((1, 2, 0), dtype=np.int64),
+        dense_shape=(2, 2, 1))
+
+    id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+
+    self.assertIsNone(id_weight_pair.weight_tensor)
+    with monitored_session.MonitoredSession() as sess:
+      _assert_sparse_tensor_value(
+          self,
+          expected_sparse_ids,
+          id_weight_pair.id_tensor.eval(session=sess))
+
+  def test_get_sparse_tensors_inputs3d(self):
+    """Tests _get_sparse_tensors when the input is already 3D Tensor."""
+    column = sfc.sequence_categorical_column_with_identity(
+        'aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0, 0), (1, 0, 0), (1, 1, 0)),
+        values=(1, 2, 0),
+        dense_shape=(2, 2, 1))
+
+    with self.assertRaisesRegexp(
+        errors.InvalidArgumentError,
+        r'Column aaa expected ID tensor of rank 2\.\s*'
+        r'id_tensor shape:\s*\[2 2 1\]'):
+      id_weight_pair = column._get_sparse_tensors(
+          _LazyBuilder({'aaa': inputs}))
+      with monitored_session.MonitoredSession() as sess:
+        id_weight_pair.id_tensor.eval(session=sess)
+
+  def test_sequence_length(self):
+    column = sfc.sequence_categorical_column_with_identity(
+        'aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(1, 2, 0),
+        dense_shape=(2, 2))
+    expected_sequence_length = [1, 2]
+
+    sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_sequence_length_with_zeros(self):
+    column = sfc.sequence_categorical_column_with_identity(
+        'aaa', num_buckets=3)
+    inputs = sparse_tensor.SparseTensorValue(
+        indices=((1, 0), (3, 0), (3, 1)),
+        values=(1, 2, 0),
+        dense_shape=(5, 2))
+    expected_sequence_length = [0, 1, 0, 2, 0]
+
+    sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+
+class SequenceEmbeddingColumnTest(test.TestCase):
+
+  def test_get_sequence_dense_tensor(self):
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        # example 2, ids []
+        # example 3, ids [1]
+        indices=((0, 0), (1, 0), (1, 1), (3, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(4, 2))
+
+    embedding_dimension = 2
+    embedding_values = (
+        (1., 2.),  # id 0
+        (3., 5.),  # id 1
+        (7., 11.)  # id 2
+    )
+    def _initializer(shape, dtype, partition_info):
+      self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
+      self.assertEqual(dtypes.float32, dtype)
+      self.assertIsNone(partition_info)
+      return embedding_values
+
+    expected_lookups = [
+        # example 0, ids [2]
+        [[7., 11.], [0., 0.]],
+        # example 1, ids [0, 1]
+        [[1., 2.], [3., 5.]],
+        # example 2, ids []
+        [[0., 0.], [0., 0.]],
+        # example 3, ids [1]
+        [[3., 5.], [0., 0.]],
+    ]
+
+    categorical_column = sfc.sequence_categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = sfc._sequence_embedding_column(
+        categorical_column, dimension=embedding_dimension,
+        initializer=_initializer)
+
+    embedding_lookup, _ = embedding_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
+    self.assertItemsEqual(
+        ('embedding_weights:0',), tuple([v.name for v in global_vars]))
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(embedding_values, global_vars[0].eval(session=sess))
+      self.assertAllEqual(expected_lookups, embedding_lookup.eval(session=sess))
+
+  def test_sequence_length(self):
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids [2]
+        # example 1, ids [0, 1]
+        indices=((0, 0), (1, 0), (1, 1)),
+        values=(2, 0, 1),
+        dense_shape=(2, 2))
+    expected_sequence_length = [1, 2]
+
+    categorical_column = sfc.sequence_categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = sfc._sequence_embedding_column(
+        categorical_column, dimension=2)
+
+    _, sequence_length = embedding_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_sequence_length_with_empty_rows(self):
+    """Tests _sequence_length when some examples do not have ids."""
+    vocabulary_size = 3
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, ids []
+        # example 1, ids [2]
+        # example 2, ids [0, 1]
+        # example 3, ids []
+        # example 4, ids [1]
+        # example 5, ids []
+        indices=((1, 0), (2, 0), (2, 1), (4, 0)),
+        values=(2, 0, 1, 1),
+        dense_shape=(6, 2))
+    expected_sequence_length = [0, 1, 2, 0, 1, 0]
+
+    categorical_column = sfc.sequence_categorical_column_with_identity(
+        key='aaa', num_buckets=vocabulary_size)
+    embedding_column = sfc._sequence_embedding_column(
+        categorical_column, dimension=2)
+
+    _, sequence_length = embedding_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+
+class SequenceNumericColumnTest(test.TestCase):
+
+  def test_get_sequence_dense_tensor(self):
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0.], [1]]
+        # example 1, [[10.]]
+        indices=((0, 0), (0, 1), (1, 0)),
+        values=(0., 1., 10.),
+        dense_shape=(2, 2))
+    expected_dense_tensor = [
+        [[0.], [1.]],
+        [[10.], [0.]],
+    ]
+    numeric_column = sfc.sequence_numeric_column('aaa')
+
+    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_dense_tensor, dense_tensor.eval(session=sess))
+
+  def test_get_sequence_dense_tensor_with_shape(self):
+    """Tests get_sequence_dense_tensor with shape !=(1,)."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0., 1., 2.], [3., 4., 5.]]
+        # example 1, [[10., 11., 12.]]
+        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5),
+                 (1, 0), (1, 1), (1, 2)),
+        values=(0., 1., 2., 3., 4., 5., 10., 11., 12.),
+        dense_shape=(2, 6))
+    expected_dense_tensor = [
+        [[0., 1., 2.], [3., 4., 5.]],
+        [[10., 11., 12.], [0., 0., 0.]],
+    ]
+    numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,))
+
+    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_dense_tensor, dense_tensor.eval(session=sess))
+
+  def test_get_dense_tensor_multi_dim(self):
+    """Tests get_sequence_dense_tensor for multi-dim numeric_column."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[[0., 1.],  [2., 3.]], [[4., 5.],  [6., 7.]]]
+        # example 1, [[[10., 11.],  [12., 13.]]]
+        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7),
+                 (1, 0), (1, 1), (1, 2), (1, 3)),
+        values=(0., 1., 2., 3., 4., 5., 6., 7., 10., 11., 12., 13.),
+        dense_shape=(2, 8))
+    expected_dense_tensor = [
+        [[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]],
+        [[[10., 11.], [12., 13.]], [[0., 0.], [0., 0.]]],
+    ]
+    numeric_column = sfc.sequence_numeric_column('aaa', shape=(2, 2))
+
+    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_dense_tensor, dense_tensor.eval(session=sess))
+
+  def test_sequence_length(self):
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0., 1., 2.], [3., 4., 5.]]
+        # example 1, [[10., 11., 12.]]
+        indices=((0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5),
+                 (1, 0), (1, 1), (1, 2)),
+        values=(0., 1., 2., 3., 4., 5., 10., 11., 12.),
+        dense_shape=(2, 6))
+    expected_sequence_length = [2, 1]
+    numeric_column = sfc.sequence_numeric_column('aaa', shape=(3,))
+
+    _, sequence_length = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_sequence_length_with_shape(self):
+    """Tests _sequence_length with shape !=(1,)."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0.], [1]]
+        # example 1, [[10.]]
+        indices=((0, 0), (0, 1), (1, 0)),
+        values=(0., 1., 10.),
+        dense_shape=(2, 2))
+    expected_sequence_length = [2, 1]
+    numeric_column = sfc.sequence_numeric_column('aaa')
+
+    _, sequence_length = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+  def test_sequence_length_with_empty_rows(self):
+    """Tests _sequence_length when some examples do not have ids."""
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values []
+        # example 1, values [[0.], [1.]]
+        # example 2, [[2.]]
+        # example 3, values []
+        # example 4, [[3.]]
+        # example 5, values []
+        indices=((1, 0), (1, 1), (2, 0), (4, 0)),
+        values=(0., 1., 2., 3.),
+        dense_shape=(6, 2))
+    expected_sequence_length = [0, 2, 1, 0, 1, 0]
+    numeric_column = sfc.sequence_numeric_column('aaa')
+
+    _, sequence_length = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_sequence_length, sequence_length.eval(session=sess))
+
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From 26cb7de9c03a9d73703decec8c917651369ee9ee Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 14:25:37 -0800
Subject: [PATCH 390/575] Add a function that allows to dynamically verify
 whether a function is white listed for graph mode.

PiperOrigin-RevId: 187080654
---
 tensorflow/contrib/py2tf/impl/conversion.py    | 18 ++++++++++++++++++
 .../contrib/py2tf/impl/conversion_test.py      | 11 +++++++++++
 2 files changed, 29 insertions(+)

diff --git a/tensorflow/contrib/py2tf/impl/conversion.py b/tensorflow/contrib/py2tf/impl/conversion.py
index 044de33568..d95469ea53 100644
--- a/tensorflow/contrib/py2tf/impl/conversion.py
+++ b/tensorflow/contrib/py2tf/impl/conversion.py
@@ -97,6 +97,24 @@ class ConversionMap(object):
     self.dependency_cache[original_entity] = converted_ast
 
 
+def is_whitelisted_for_graph(o):
+  """Check whether an entity is whitelisted for use in graph mode.
+
+  Examples of whitelisted entities include all members of the tensorflow
+  package.
+
+  Args:
+    o: A Python entity.
+  Returns:
+    Boolean
+  """
+  m = tf_inspect.getmodule(o)
+  for prefix, in config.DEFAULT_UNCOMPILED_MODULES:
+    if m.__name__.startswith(prefix):
+      return True
+  return False
+
+
 def entity_to_graph(o, conversion_map, arg_values, arg_types):
   """Compile a Python entity into equivalent TensorFlow.
 
diff --git a/tensorflow/contrib/py2tf/impl/conversion_test.py b/tensorflow/contrib/py2tf/impl/conversion_test.py
index 7816f95857..9ff256aace 100644
--- a/tensorflow/contrib/py2tf/impl/conversion_test.py
+++ b/tensorflow/contrib/py2tf/impl/conversion_test.py
@@ -20,12 +20,23 @@ from __future__ import print_function
 
 import gast
 
+from tensorflow.contrib.py2tf import utils
 from tensorflow.contrib.py2tf.impl import conversion
+from tensorflow.python.framework import constant_op
 from tensorflow.python.platform import test
 
 
 class ConversionTest(test.TestCase):
 
+  def test_is_whitelisted_for_graph(self):
+
+    def test_fn():
+      return constant_op.constant(1)
+
+    self.assertFalse(conversion.is_whitelisted_for_graph(test_fn))
+    self.assertTrue(conversion.is_whitelisted_for_graph(utils))
+    self.assertTrue(conversion.is_whitelisted_for_graph(constant_op.constant))
+
   def test_entity_to_graph_unsupported_types(self):
     with self.assertRaises(ValueError):
       conversion_map = conversion.ConversionMap(True, (), (), None)
-- 
GitLab


From f4a396bcecd8b27caba0c10a50e1f6b56dbcf6a9 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Mon, 26 Feb 2018 14:31:29 -0800
Subject: [PATCH 391/575] [TF:XLA] Bump open source llvm revision to r326083

PiperOrigin-RevId: 187081592
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 278a225f76..9009f08163 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -476,11 +476,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/8f7bcdf3c65b9a47e35653d525135beb18f3ac25.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/8f7bcdf3c65b9a47e35653d525135beb18f3ac25.tar.gz",
       ],
-      sha256 = "f5721d9cc18a9109c9e9f847f48e69b710b961cee83e6691227e310cb3b5da58",
-      strip_prefix = "llvm-fc8ba497cd1a1af4ecae19a5b64bdbd71e065e14",
+      sha256 = "63d4da54dc7bc9a79e2ad266d230f4f759520cccb344a2dd49c2c6383ab75285",
+      strip_prefix = "llvm-8f7bcdf3c65b9a47e35653d525135beb18f3ac25",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
   )
 
-- 
GitLab


From c1e22e9fc1b8db5390c466a2ffb5da8b1abf15b4 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Mon, 26 Feb 2018 14:32:08 -0800
Subject: [PATCH 392/575] Track DebugOptions in AotCompilationOptions

In particular, I need this for supporting HLO profiling in the AOT backend.

PiperOrigin-RevId: 187081674
---
 tensorflow/compiler/xla/service/compile_only_service.cc | 3 +--
 tensorflow/compiler/xla/service/compiler.cc             | 3 +++
 tensorflow/compiler/xla/service/compiler.h              | 6 +++++-
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/compile_only_service.cc b/tensorflow/compiler/xla/service/compile_only_service.cc
index dab73596e1..6664496ab6 100644
--- a/tensorflow/compiler/xla/service/compile_only_service.cc
+++ b/tensorflow/compiler/xla/service/compile_only_service.cc
@@ -72,8 +72,7 @@ CompileOnlyService::CompileAheadOfTime(
     VersionedComputationHandle versioned_handle =
         user_computation->GetVersionedHandle();
 
-    // TODO(b/63773457): Track DebugOptions in AotCompilationOptions.
-    DebugOptions debug_options = legacy_flags::GetDebugOptionsFromFlags();
+    const DebugOptions& debug_options = options.debug_options();
 
     // Dump computation proto state if flag is set.
     const string& directory_path = debug_options.xla_dump_computations_to();
diff --git a/tensorflow/compiler/xla/service/compiler.cc b/tensorflow/compiler/xla/service/compiler.cc
index e2e9d2a0c0..0392d4af48 100644
--- a/tensorflow/compiler/xla/service/compiler.cc
+++ b/tensorflow/compiler/xla/service/compiler.cc
@@ -86,4 +86,7 @@ Compiler::GetPlatformCompilers() {
   return compilers->at(platform->id()).get();
 }
 
+AotCompilationOptions::AotCompilationOptions()
+    : debug_options_(legacy_flags::GetDebugOptionsFromFlags()) {}
+
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h
index 74fd24edf8..33e19efc72 100644
--- a/tensorflow/compiler/xla/service/compiler.h
+++ b/tensorflow/compiler/xla/service/compiler.h
@@ -79,11 +79,15 @@ class AotCompilationOptions {
     device_allocator_ = device_allocator;
   }
 
+  const DebugOptions& debug_options() const { return debug_options_; }
+  DebugOptions* mutable_debug_options() { return &debug_options_; }
+
  protected:
-  AotCompilationOptions() = default;
+  AotCompilationOptions();
 
  private:
   DeviceMemoryAllocator* device_allocator_ = nullptr;
+  DebugOptions debug_options_;
 };
 
 // Abstract compiler interface that is subclassed for compilation on a
-- 
GitLab


From 3653257c729f651c787b6fa04788084191478c3e Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Mon, 26 Feb 2018 14:38:31 -0800
Subject: [PATCH 393/575] Enable de/serialization of nested control flow.

This is a follow-up to the previous commit
(https://github.com/tensorflow/tensorflow/commit/23851760b7b099214bdd4f1b88156d7ac2bdd2a2).
It adds the new proto schemas, enables the behavior for reading and
writing the new protos, and adds a test for de/serializing nested
while loops.

There's still a bug preventing deserializing conds, which will be addressed
in another change.

PiperOrigin-RevId: 187082713
---
 tensorflow/core/protobuf/control_flow.proto | 17 ++++++-
 tensorflow/python/ops/control_flow_ops.py   | 54 ++++++--------------
 tensorflow/python/training/saver_test.py    | 56 +++++++++++++++++++++
 3 files changed, 88 insertions(+), 39 deletions(-)

diff --git a/tensorflow/core/protobuf/control_flow.proto b/tensorflow/core/protobuf/control_flow.proto
index 2c9476a08a..3c05b4f0e2 100644
--- a/tensorflow/core/protobuf/control_flow.proto
+++ b/tensorflow/core/protobuf/control_flow.proto
@@ -17,6 +17,15 @@ message ValuesDef {
   map<string, string> external_values = 2;
 }
 
+// Container for any kind of control flow context. Any other control flow
+// contexts that are added below should also be added here.
+message ControlFlowContextDef {
+  oneof ctxt {
+    CondContextDef cond_ctxt = 1;
+    WhileContextDef while_ctxt = 2;
+  }
+}
+
 // Protocol buffer representing a CondContext object.
 message CondContextDef {
   // Name of the context.
@@ -33,6 +42,9 @@ message CondContextDef {
 
   // Values and external values in control flow context.
   ValuesDef values_def = 5;
+
+  // Contexts contained inside this context (e.g. nested conds).
+  repeated ControlFlowContextDef nested_contexts = 6;
 }
 
 // Protocol buffer representing a WhileContext object.
@@ -70,5 +82,8 @@ message WhileContextDef {
   // Optional name of the maximum_iterations tensor.
   string maximum_iterations_name = 11;
 
-  // Next available id: 12.
+  // Contexts contained inside this context (e.g. nested whiles).
+  repeated ControlFlowContextDef nested_contexts = 12;
+
+  // Next available id: 13.
 }
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 152578c0c6..b16901effd 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1765,13 +1765,9 @@ class CondContext(ControlFlowContext):
       context_def.branch = self._branch
       context_def.values_def.MergeFrom(super(CondContext, self)._to_values_def(
           export_scope))
-      # TODO(b/72868227): enable this once the corresponding control_flow.proto
-      # changes have been checked in (they aren't checked in and this is
-      # disabled for now to ensure forwards compatibility).
-      if False:  # pylint: disable=using-constant-test
-        for nested in self._nested_contexts:
-          nested_def = context_def.nested_contexts.add()
-          nested.to_control_flow_context_def(nested_def)
+      for nested in self._nested_contexts:
+        nested_def = context_def.nested_contexts.add()
+        nested.to_control_flow_context_def(nested_def)
 
       return context_def
     else:
@@ -1783,14 +1779,10 @@ class CondContext(ControlFlowContext):
     ret = CondContext(context_def=context_def,
                       import_scope=import_scope)
 
-    # TODO(b/72868227): remove "if hasattr(...)" once the corresponding
-    # control_flow.proto changes have been checked in (they aren't checked in
-    # and this is here for now to ensure forwards compatibility).
-    if hasattr(context_def, "nested_contexts"):
-      ret.Enter()
-      for nested_def in context_def.nested_contexts:
-        from_control_flow_context_def(nested_def)
-      ret.Exit()
+    ret.Enter()
+    for nested_def in context_def.nested_contexts:
+      from_control_flow_context_def(nested_def)
+    ret.Exit()
     return ret
 
   def to_control_flow_context_def(self, context_def, export_scope=None):
@@ -2108,10 +2100,7 @@ def cond(pred,
     # Only add non-nested conds to the collection. Any nested control flow will
     # be encapsulated in the root context.
     assert context_t.outer_context == context_f.outer_context
-    # TODO(b/72868227): remove "if True..." once the corresponding
-    # control_flow.proto changes have been checked in (they aren't checked in
-    # and this is disabled for now to ensure forwards compatibility).
-    if True or context_t.outer_context is None:
+    if context_t.outer_context is None:
       ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_t)
       ops.add_to_collection(ops.GraphKeys.COND_CONTEXT, context_f)
 
@@ -2334,13 +2323,9 @@ class WhileContext(ControlFlowContext):
       context_def.values_def.MergeFrom(
           super(WhileContext, self)._to_values_def(
               export_scope=export_scope))
-      # TODO(b/72868227): remove "if True..." once the corresponding
-      # control_flow.proto changes have been checked in (they aren't checked in
-      # and this is disabled for now to ensure forwards compatibility).
-      if False:  # pylint: disable=using-constant-test
-        for nested in self._nested_contexts:
-          nested_def = context_def.nested_contexts.add()
-          nested.to_control_flow_context_def(nested_def)
+      for nested in self._nested_contexts:
+        nested_def = context_def.nested_contexts.add()
+        nested.to_control_flow_context_def(nested_def)
 
       return context_def
     else:
@@ -2362,14 +2347,10 @@ class WhileContext(ControlFlowContext):
     """
     ret = WhileContext(context_def=context_def,
                        import_scope=import_scope)
-    # TODO(b/72868227): remove "if hasattr(...)" once the corresponding
-    # control_flow.proto changes have been checked in (they aren't checked in
-    # and this is disabled for now to ensure forwards compatibility).
-    if hasattr(context_def, "nested_contexts"):
-      ret.Enter()
-      for nested_def in context_def.nested_contexts:
-        from_control_flow_context_def(nested_def, import_scope=import_scope)
-      ret.Exit()
+    ret.Enter()
+    for nested_def in context_def.nested_contexts:
+      from_control_flow_context_def(nested_def, import_scope=import_scope)
+    ret.Exit()
     return ret
 
   def GetWhileContext(self):
@@ -3214,10 +3195,7 @@ def while_loop(cond,
         swap_memory=swap_memory)
     # Only add non-nested loops to the collection. Any nested control flow will
     # be encapsulated in the root context.
-    # TODO(b/72868227): enable condition once the corresponding
-    # control_flow.proto changes have been checked in (they aren't checked in
-    # and this is disabled for now to ensure forwards compatibility).
-    if True or loop_context.outer_context is None:
+    if loop_context.outer_context is None:
       ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context)
     result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
     if maximum_iterations is not None:
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index f00f98db00..b366ed30f3 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -53,6 +53,7 @@ from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import partitioned_variables
@@ -2040,6 +2041,61 @@ class MetaGraphTest(test.TestCase):
     self._testGraphExtensionRestore(test_dir)
     self._testRestoreFromTrainGraphWithControlContext(test_dir)
 
+  def testNestedWhileLoops(self):
+    test_dir = self._get_test_dir("nested_whiles")
+    filename = os.path.join(test_dir, "metafile")
+    saver_ckpt = os.path.join(test_dir, "saver.ckpt")
+
+    # Create two simple nested while loops.
+    with ops_lib.Graph().as_default():
+      def body(i, x):
+        _, r = control_flow_ops.while_loop(lambda j, y: j < 3,
+                                           lambda j, y: (j + 1, y + x),
+                                           [0, 0])
+        return i + 1, x + r
+
+      var = variables.Variable(0)
+      var_name = var.name
+
+      _, output = control_flow_ops.while_loop(lambda i, x: i < 5, body,
+                                              [0, var])
+      output_name = output.name
+
+      init_op = variables.global_variables_initializer()
+
+      # Generate a MetaGraphDef containing the nested loops.
+      with session.Session() as sess:
+        sess.run(init_op)
+        sess.run(output)
+        saver = saver_module.Saver()
+        saver.save(sess, saver_ckpt)
+        saver.export_meta_graph(filename)
+
+      # Build and run the gradients of the nested while loop. We use this below
+      # to verify that the gradients are correct with an imported MetaGraphDef.
+      grad = gradients_impl.gradients([output], [var])
+      with session.Session() as sess:
+        sess.run(init_op)
+        expected_grad_value = sess.run(grad)
+
+    # Restore the MetaGraphDef into a new Graph.
+    with ops_lib.Graph().as_default():
+      with session.Session() as sess:
+        saver = saver_module.import_meta_graph(filename)
+        saver.restore(sess, saver_ckpt)
+
+      # Make sure we can still build gradients and get the same result.
+      var = ops_lib.get_default_graph().get_tensor_by_name(var_name)
+      output = ops_lib.get_default_graph().get_tensor_by_name(output_name)
+      grad = gradients_impl.gradients([output], [var])
+
+      init_op = variables.global_variables_initializer()
+
+      with session.Session() as sess:
+        sess.run(init_op)
+        actual_grad_value = sess.run(grad)
+        self.assertEqual(expected_grad_value, actual_grad_value)
+
   def testStrippedOpListDef(self):
     with self.test_session():
       # Creates a graph.
-- 
GitLab


From 854a07650f33be545441a08f5db84a0f05a8b88e Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Mon, 26 Feb 2018 15:37:27 -0800
Subject: [PATCH 394/575] [XLA::Interpreter] Add support for kCall to
 HloEvaluator. Also enable xla/tests/call_test to run on interpreter.

PiperOrigin-RevId: 187092587
---
 .../compiler/xla/service/hlo_evaluator.cc     | 20 +++++++++++++++++++
 .../compiler/xla/service/hlo_evaluator.h      |  2 ++
 tensorflow/compiler/xla/tests/BUILD           |  3 +++
 3 files changed, 25 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 15ae53128a..fd06b19144 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -2445,6 +2445,26 @@ Status HloEvaluator::HandleCopy(HloInstruction* copy) {
   return Status::OK();
 }
 
+Status HloEvaluator::HandleCall(HloInstruction* call) {
+  auto* computation = call->to_apply();
+  auto operands = call->operands();
+
+  std::vector<const Literal*> arg_literals;
+  arg_literals.reserve(operands.size());
+  for (auto operand : operands) {
+    const Literal& arg_literal = GetEvaluatedLiteralFor(operand);
+    arg_literals.push_back(&arg_literal);
+  }
+
+  HloEvaluator embedded_evaluator;
+  std::unique_ptr<Literal> result =
+      embedded_evaluator.Evaluate<const Literal*>(*computation, arg_literals)
+          .ConsumeValueOrDie();
+
+  evaluated_[call] = std::move(result);
+  return Status::OK();
+}
+
 Status HloEvaluator::Preprocess(HloInstruction* hlo) {
   VLOG(2) << "About to visit HLO: " << hlo->ToString();
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h
index 3b2b697e49..c65d9915e3 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.h
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.h
@@ -153,6 +153,8 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
 
   Status HandleCopy(HloInstruction* copy) override;
 
+  Status HandleCall(HloInstruction* call) override;
+
  private:
   // Returns the already-evaluated literal result for the instruction.
   // A Constant instruction is considered evaluated and its literal will be
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 97abf217d7..33fde9737d 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1143,6 +1143,9 @@ xla_test(
 xla_test(
     name = "call_test",
     srcs = ["call_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
-- 
GitLab


From acf78b20f71dd8c3a928b1f12ea4de6f5028fc48 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Mon, 26 Feb 2018 15:37:40 -0800
Subject: [PATCH 395/575] Uses a thread pool for graph functions in eager mode
 with inter_op_parallelism_threads.

PiperOrigin-RevId: 187092622
---
 tensorflow/c/eager/BUILD            |  1 +
 tensorflow/c/eager/c_api.cc         |  4 ++--
 tensorflow/c/eager/c_api_internal.h | 14 +++++++++++++-
 tensorflow/c/eager/runtime.cc       | 14 ++++++++++----
 tensorflow/c/eager/runtime.h        |  3 +++
 tensorflow/c/eager/runtime_test.cc  | 12 ++++++------
 6 files changed, 35 insertions(+), 13 deletions(-)

diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD
index e55cb672e9..16a2a15072 100644
--- a/tensorflow/c/eager/BUILD
+++ b/tensorflow/c/eager/BUILD
@@ -21,6 +21,7 @@ tf_cuda_library(
     visibility = ["//visibility:public"],
     deps = select({
         "//tensorflow:android": [
+            "//tensorflow/core:lib",
             "//tensorflow/core:android_tensorflow_lib_lite",
         ],
         "//conditions:default": [
diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index bebb63c746..b233dd5b93 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -818,8 +818,8 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
     // See WARNING comment below - would be nice to rework to avoid this
     // subtlety.
     tensorflow::tf_shared_lock l(ctx->functions_mu);
-    status->status =
-        tensorflow::KernelAndDevice::Init(ndef, ctx->func_lib(device), kernel);
+    status->status = tensorflow::KernelAndDevice::Init(
+        ndef, ctx->func_lib(device), &ctx->runner, kernel);
     if (!status->status.ok()) {
       delete kernel;
       return;
diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h
index 3356054cd0..29944df4c2 100644
--- a/tensorflow/c/eager/c_api_internal.h
+++ b/tensorflow/c/eager/c_api_internal.h
@@ -31,6 +31,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
 #include "tensorflow/core/framework/rendezvous.h"
+#include "tensorflow/core/lib/core/threadpool.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -45,7 +46,15 @@ struct TFE_ContextOptions {
 
 struct TFE_Context {
   explicit TFE_Context(const TFE_ContextOptions& opts, TF_Session* s)
-      : policy(opts.policy),
+      : thread_pool(new tensorflow::thread::ThreadPool(
+            opts.session_options.options.env, "EagerCompute",
+            opts.session_options.options.config
+                        .inter_op_parallelism_threads() != 0
+                ? opts.session_options.options.config
+                      .inter_op_parallelism_threads()
+                : tensorflow::port::NumSchedulableCPUs())),
+        runner([this](std::function<void()> f) { thread_pool->Schedule(f); }),
+        policy(opts.policy),
         session(s),
         rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)),
         pflr(new tensorflow::ProcessFunctionLibraryRuntime(
@@ -54,6 +63,9 @@ struct TFE_Context {
         log_device_placement(
             opts.session_options.options.config.log_device_placement()) {}
 
+  const std::unique_ptr<tensorflow::thread::ThreadPool> thread_pool;
+  std::function<void(std::function<void()>)> runner;
+
   const TFE_ContextDevicePlacementPolicy policy;
 
   // Note: we cannot use C++11 thread_local here as there is no concept of a
diff --git a/tensorflow/c/eager/runtime.cc b/tensorflow/c/eager/runtime.cc
index 4bf24fec2c..b9618420f0 100644
--- a/tensorflow/c/eager/runtime.cc
+++ b/tensorflow/c/eager/runtime.cc
@@ -255,17 +255,22 @@ Status KernelAndDevice::InitOp(Device* device, const NodeDef& ndef,
   out->device_ = device;
   out->kernel_.reset(k);
   out->flib_ = nullptr;
+  out->runner_ = nullptr;
+  out->default_runner_ = [](std::function<void()> f) { f(); };
   return s;
 }
 
 // static
 Status KernelAndDevice::Init(const NodeDef& ndef, FunctionLibraryRuntime* flib,
+                             std::function<void(std::function<void()>)>* runner,
                              KernelAndDevice* out) {
   OpKernel* k = nullptr;
   Status s = flib->CreateKernel(ndef, &k);
   out->device_ = flib->device();
   out->kernel_.reset(k);
   out->flib_ = flib;
+  out->runner_ = runner;
+  out->default_runner_ = [](std::function<void()> f) { f(); };
   return s;
 }
 
@@ -296,10 +301,11 @@ Status KernelAndDevice::Run(std::vector<Tensor>* input_tensors,
   if (stats != nullptr) {
     params.track_allocations = true;
   }
-  // TODO(apassos): use a thread pool.
-  std::function<void(std::function<void()>)> runner =
-      [](std::function<void()> f) { f(); };
-  params.runner = &runner;
+  if (runner_ == nullptr) {
+    params.runner = &default_runner_;
+  } else {
+    params.runner = runner_;
+  }
 
   OpKernelContext context(&params);
   device_->Compute(kernel_.get(), &context);
diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h
index 7fede4dae9..fa5f839977 100644
--- a/tensorflow/c/eager/runtime.h
+++ b/tensorflow/c/eager/runtime.h
@@ -169,6 +169,7 @@ class KernelAndDevice {
   // the FunctionLibraryRuntime is pushed on to the caller (see locking in
   // c_api.cc).
   static Status Init(const NodeDef& ndef, FunctionLibraryRuntime* flib,
+                     std::function<void(std::function<void()>)>* runner,
                      KernelAndDevice* out);
   // TODO(ashankar): Remove this
   static Status InitOp(Device* device, const NodeDef& ndef,
@@ -188,6 +189,8 @@ class KernelAndDevice {
  private:
   std::unique_ptr<OpKernel> kernel_;
   Device* device_;
+  std::function<void(std::function<void()>)>* runner_;
+  std::function<void(std::function<void()>)> default_runner_;
   FunctionLibraryRuntime* flib_;
   checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_;
   Rendezvous* rendez_;
diff --git a/tensorflow/c/eager/runtime_test.cc b/tensorflow/c/eager/runtime_test.cc
index 643153058c..ab0b535e1a 100644
--- a/tensorflow/c/eager/runtime_test.cc
+++ b/tensorflow/c/eager/runtime_test.cc
@@ -92,8 +92,8 @@ TEST(KernelAndDevice, Run) {
                    .BuildNodeDef());
   TestEnv env;
   KernelAndDevice kernel(nullptr);
-  Status s =
-      KernelAndDevice::Init(ndef, env.function_library_runtime(), &kernel);
+  Status s = KernelAndDevice::Init(ndef, env.function_library_runtime(),
+                                   nullptr, &kernel);
   ASSERT_TRUE(s.ok()) << s;
   std::vector<Tensor> outputs;
   s = kernel.Run(&inputs, &outputs, nullptr);
@@ -158,8 +158,8 @@ void BM_KernelAndDeviceInit(int iters) {
   KernelAndDevice k(nullptr);
   tensorflow::testing::StartTiming();
   for (int i = 0; i < iters; ++i) {
-    TF_CHECK_OK(
-        KernelAndDevice::Init(ndef, env.function_library_runtime(), &k));
+    TF_CHECK_OK(KernelAndDevice::Init(ndef, env.function_library_runtime(),
+                                      nullptr, &k));
   }
 }
 BENCHMARK(BM_KernelAndDeviceInit);
@@ -179,8 +179,8 @@ void BM_KernelAndDeviceRun(int iters) {
                    .BuildNodeDef());
   TestEnv env;
   KernelAndDevice kernel(nullptr);
-  TF_CHECK_OK(
-      KernelAndDevice::Init(ndef, env.function_library_runtime(), &kernel));
+  TF_CHECK_OK(KernelAndDevice::Init(ndef, env.function_library_runtime(),
+                                    nullptr, &kernel));
   tensorflow::testing::StartTiming();
   for (int i = 0; i < iters; ++i) {
     TF_CHECK_OK(kernel.Run(&inputs, &outputs, nullptr));
-- 
GitLab


From 260f5b8fe144cd369fde755739806449a2901252 Mon Sep 17 00:00:00 2001
From: Bixia Zheng <bixia@google.com>
Date: Mon, 26 Feb 2018 15:42:52 -0800
Subject: [PATCH 396/575] [XLA] Fix #17090 a problem in
 IrArray::Index::SourceIndexOfTranspose.

Agebraic simplification transforms bitcast-equivalent transpose/reshape
instructions to bitcast instructions before IR emission. As such, we should
skip the checking on whether a transpose/reshape instruction is
bitcast-equivalent or not during IR emission. Remove the call from
IrArray::Index::SourceIndexOfTranspose to ShapeUtil::TransposeIsBitcast. Also
remove the call from IrArray::Index::SourceIndexOfReshape to
ShapeUtil::ReshapeIsBitcast.

Remove the calls to ShapeUtil::TransposeIsBitcast and
ShapeUtil::ReshapeIsBitcast from NotWorthHoistingIndividually
because layout assignment hasn't been done there yet. Instead, returns true
when the input is a transpose or reshape instruction, to prevent it from
being hoisted out of loops.

Add a check to ShapeUtil::TransposeIsBitcast and ShapeUtil::ReshapeIsBitcast
to make sure that both input shape and output shape have layouts.

Add two test cases.

PiperOrigin-RevId: 187093399
---
 .../xla/service/layout_assignment_test.cc     | 79 +++++++++++++++++++
 .../compiler/xla/service/llvm_ir/ir_array.cc  |  8 +-
 .../while_loop_invariant_code_motion.cc       | 12 +--
 tensorflow/compiler/xla/shape_util.cc         | 14 +---
 tensorflow/compiler/xla/shape_util.h          |  4 +
 5 files changed, 95 insertions(+), 22 deletions(-)

diff --git a/tensorflow/compiler/xla/service/layout_assignment_test.cc b/tensorflow/compiler/xla/service/layout_assignment_test.cc
index 88e5caaf47..62feb7c1e9 100644
--- a/tensorflow/compiler/xla/service/layout_assignment_test.cc
+++ b/tensorflow/compiler/xla/service/layout_assignment_test.cc
@@ -590,6 +590,85 @@ TEST_F(LayoutAssignmentTest, TransposeToBitcastToUser) {
                                             transpose->shape(), {2, 3, 0, 1}));
 }
 
+// TransposeIsBitcast shouldn't be called without layout information.
+TEST_F(LayoutAssignmentTest, TransposeIsBitcastFail) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2});
+  Shape input_shape_with_layout(input_shape);
+  *input_shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout({2, 1, 0});
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_shape_with_layout, "param"));
+  auto hlo = builder.AddInstruction(
+      HloInstruction::CreateTranspose(input_shape, param, {0, 2, 1}));
+  // Clear the default layout assigned to the instruction.
+  LayoutUtil::ClearLayout(hlo->mutable_shape());
+  EXPECT_DEATH(ShapeUtil::TransposeIsBitcast(hlo->operand(0)->shape(),
+                                             hlo->shape(), hlo->dimensions()),
+               "LayoutUtil::HasLayout");
+}
+
+// ReshapeIsBitcast shouldn't be called without layout information.
+TEST_F(LayoutAssignmentTest, ReshapeIsBitcastFail) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape input_shape = ShapeUtil::MakeShape(F32, {2, 2, 2});
+  Shape input_shape_with_layout(input_shape);
+  *input_shape_with_layout.mutable_layout() = LayoutUtil::MakeLayout({2, 1, 0});
+  auto param = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, input_shape_with_layout, "param"));
+  auto hlo =
+      builder.AddInstruction(HloInstruction::CreateReshape(input_shape, param));
+  // Clear the default layout assigned to the instruction.
+  LayoutUtil::ClearLayout(hlo->mutable_shape());
+  EXPECT_DEATH(
+      ShapeUtil::ReshapeIsBitcast(hlo->operand(0)->shape(), hlo->shape()),
+      "LayoutUtil::HasLayout");
+}
+
+// Check that the computation below doesn't crash the compiler.
+//
+// Within a fusion computation, only the parameters and result get assigned a
+// layout.  When we run the algebraic simplifier on this computation post layout
+// assignment, it should not call TransposeIsBitcast on the `transpose` node
+// inside the fusion computation as TransposeIsBitcast checks both input_shape
+// and output_shape have layouts.
+TEST_F(LayoutAssignmentTest, TransposeWithinFusionDoesNotCrash) {
+  const char* module_str = R"(
+    HloModule test_module
+
+    fused_computation {
+      param_1 = f32[2,2,2]{2,1,0} parameter(1)
+      transpose = f32[2,2,2]{2,1,0} transpose(param_1), dimensions={0,2,1}
+      reduce_1 = f32[] parameter(0)
+      broadcast_1 = f32[2,2,2]{2,1,0} broadcast(reduce_1), dimensions={}
+      ROOT divide_1 = f32[2,2,2]{2,1,0} divide(transpose, broadcast_1)
+    }
+
+    ENTRY entry_computation {
+      fusion.1 = f32[2,2,2]{2,1,0} parameter(1)
+      reduce.1 = f32[] parameter(0)
+      fusion.2 = f32[2,2,2]{2,1,0} fusion(reduce.1, fusion.1), kind=kLoop, calls=fused_computation
+     ROOT tuple.1 = (f32[2,2,2]{2,1,0}) tuple(fusion.2)
+    }
+  )";
+
+  auto module = tools::Parse(module_str).ValueOrDie();
+
+  module =
+      backend()
+          .compiler()
+          ->RunHloPasses(std::move(module), backend().default_stream_executor(),
+                         /*device_allocator=*/nullptr)
+          .ConsumeValueOrDie();
+
+  EXPECT_EQ(
+      ::tensorflow::Status::OK(),
+      backend()
+          .compiler()
+          ->RunBackend(std::move(module), backend().default_stream_executor(),
+                       /*device_allocator=*/nullptr)
+          .status());
+}
+
 // A GTE inside of a fusion node inherits the layout of its operand (which
 // should, if we keep following operands, eventually be a parameter).
 TEST_F(LayoutAssignmentTest, GTEInheritsLayoutFromOperand) {
diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
index 6384c7f46f..f3642cf0a1 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc
@@ -160,7 +160,8 @@ IrArray::Index IrArray::Index::SourceIndexOfReshape(
     }
   }
 
-  if (linear() != nullptr &&
+  if (linear() != nullptr && LayoutUtil::HasLayout(input_shape) &&
+      LayoutUtil::HasLayout(output_shape) &&
       ShapeUtil::ReshapeIsBitcast(input_shape, output_shape)) {
     return Index(source_multidim_index, linear(), input_shape);
   }
@@ -195,10 +196,13 @@ IrArray::Index IrArray::Index::SourceIndexOfTranspose(
     llvm::IRBuilder<>* builder) const {
   std::vector<llvm::Value*> operand_multidim_index =
       Permute(dimension_mapping, multidim());
-  if (linear() != nullptr &&
+
+  if (linear() != nullptr && LayoutUtil::HasLayout(operand_shape) &&
+      LayoutUtil::HasLayout(shape) &&
       ShapeUtil::TransposeIsBitcast(operand_shape, shape, dimension_mapping)) {
     return Index(operand_multidim_index, linear(), operand_shape);
   }
+
   return Index(operand_multidim_index);
 }
 
diff --git a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
index a5f9b01f01..3ef0cdff67 100644
--- a/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
+++ b/tensorflow/compiler/xla/service/while_loop_invariant_code_motion.cc
@@ -106,20 +106,12 @@ static bool NotWorthHoistingIndividually(const HloInstruction& instruction) {
     case HloOpcode::kBitcast:
     case HloOpcode::kBroadcast:
     case HloOpcode::kConstant:
+    case HloOpcode::kReshape:
     case HloOpcode::kReverse:
     case HloOpcode::kSlice:
+    case HloOpcode::kTranspose:
     case HloOpcode::kTuple:
       return true;
-
-    case HloOpcode::kTranspose:
-      return ShapeUtil::TransposeIsBitcast(
-          /*input_shape=*/instruction.operand(0)->shape(),
-          /*output_shape=*/instruction.shape(), instruction.dimensions());
-
-    case HloOpcode::kReshape:
-      return ShapeUtil::ReshapeIsBitcast(
-          /*input_shape=*/instruction.operand(0)->shape(),
-          /*output_shape=*/instruction.shape());
   }
 }
 
diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc
index 604e0173e7..3152789016 100644
--- a/tensorflow/compiler/xla/shape_util.cc
+++ b/tensorflow/compiler/xla/shape_util.cc
@@ -1073,11 +1073,8 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape,
 /* static */ bool ShapeUtil::TransposeIsBitcast(
     const Shape& input_shape, const Shape& output_shape,
     tensorflow::gtl::ArraySlice<int64> dimension_mapping) {
-  // Can't insert bitcasts without layout information.
-  if (!LayoutUtil::HasLayout(input_shape) &&
-      !LayoutUtil::HasLayout(output_shape)) {
-    return false;
-  }
+  CHECK(LayoutUtil::HasLayout(input_shape) &&
+        LayoutUtil::HasLayout(output_shape));
 
   // Padding is not handled.
   if (LayoutUtil::IsPadded(input_shape) && LayoutUtil::IsPadded(output_shape)) {
@@ -1106,11 +1103,8 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape,
 
 /* static */ bool ShapeUtil::ReshapeIsBitcast(const Shape& input_shape,
                                               const Shape& output_shape) {
-  // Can't convert reshapes into bitcasts without layout information.
-  if (!LayoutUtil::HasLayout(input_shape) ||
-      !LayoutUtil::HasLayout(output_shape)) {
-    return false;
-  }
+  CHECK(LayoutUtil::HasLayout(input_shape) &&
+        LayoutUtil::HasLayout(output_shape));
 
   // Padding is not handled.
   if (LayoutUtil::IsPadded(input_shape) || LayoutUtil::IsPadded(output_shape)) {
diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index 19b1aa93bd..8ee263fe5e 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -522,12 +522,16 @@ class ShapeUtil {
   // Returns whether a transpose from input_shape to output_shape with dimension
   // mapping "dimension_mapping" produces a result which is bit-wise identical
   // to its input and thus may be replaced with a bitcast.
+  //
+  // Precondition: Both input_shape and output_shape have explicit layouts.
   static bool TransposeIsBitcast(
       const Shape& input_shape, const Shape& output_shape,
       tensorflow::gtl::ArraySlice<int64> dimension_mapping);
 
   // Returns whether a reshape from "input_shape" to "output_shape" is a
   // bitcast.
+  //
+  // Precondition: Both input_shape and output_shape have explicit layouts.
   static bool ReshapeIsBitcast(const Shape& input_shape,
                                const Shape& output_shape);
 
-- 
GitLab


From 6db1b213458ea7f0acd4476f70d930e15af8f35f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 16:01:04 -0800
Subject: [PATCH 397/575] [XLA] Add more supported dtypes to the local Python
 client.

PiperOrigin-RevId: 187096144
---
 tensorflow/compiler/xla/python/xla_client.py | 38 ++++++++++++--------
 1 file changed, 24 insertions(+), 14 deletions(-)

diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py
index 3b8ec851d5..90cda42f32 100644
--- a/tensorflow/compiler/xla/python/xla_client.py
+++ b/tensorflow/compiler/xla/python/xla_client.py
@@ -30,9 +30,9 @@ from tensorflow.compiler.xla import xla_data_pb2
 from tensorflow.compiler.xla.python import pywrap_xla as c_api
 
 
-# Most functions are snake_case for consistency with other modules,
-# whereas method names of ComputationBuilder and LocalComputation are
-# CamelCase for consistency with XLA.
+# Most functions are snake_case for consistency with other modules, whereas
+# method names of ComputationBuilder and LocalComputation are CamelCase for
+# consistency with XLA.
 # pylint: disable=invalid-name
 
 
@@ -123,24 +123,34 @@ _BINARY_OPS = [
     'Pow',
 ]
 
+
 XLA_ELEMENT_TYPE_TO_DTYPE = {
-    xla_data_pb2.F32: np.dtype(np.float32),
-    xla_data_pb2.F64: np.dtype(np.float64),
-    xla_data_pb2.S32: np.dtype(np.int32),
-    xla_data_pb2.S64: np.dtype(np.int64),
-    xla_data_pb2.U32: np.dtype(np.uint32),
-    xla_data_pb2.U64: np.dtype(np.uint64),
-    xla_data_pb2.PRED: np.dtype(np.bool),
+    xla_data_pb2.PRED: np.dtype('bool'),
+    xla_data_pb2.S8: np.dtype('int8'),
+    xla_data_pb2.S16: np.dtype('int16'),
+    xla_data_pb2.S32: np.dtype('int32'),
+    xla_data_pb2.S64: np.dtype('int64'),
+    xla_data_pb2.U8: np.dtype('uint8'),
+    xla_data_pb2.U16: np.dtype('uint16'),
+    xla_data_pb2.U32: np.dtype('uint32'),
+    xla_data_pb2.U64: np.dtype('uint64'),
+    xla_data_pb2.F16: np.dtype('float16'),
+    xla_data_pb2.F32: np.dtype('float32'),
+    xla_data_pb2.F64: np.dtype('float64'),
+    xla_data_pb2.C64: np.dtype('complex64'),
     xla_data_pb2.TUPLE: np.dtype(np.object),
 }
 
 # Note the conversion on the key. Numpy has a known issue wherein dtype hashing
 # doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus,
 # when keying by dtype in this dict, we use the string form of dtypes.
-DTYPE_TO_XLA_ELEMENT_TYPE = {
-    str(v): k
-    for k, v in XLA_ELEMENT_TYPE_TO_DTYPE.items()
-}
+DTYPE_TO_XLA_ELEMENT_TYPE = {str(dt): et
+                             for et, dt in XLA_ELEMENT_TYPE_TO_DTYPE.items()}
+
+
+def dtype_to_etype(dtype):
+  """Convenience function for reading DTYPE_TO_XLA_ELEMENT_TYPE."""
+  return DTYPE_TO_XLA_ELEMENT_TYPE[str(np.dtype(dtype))]
 
 
 class LocalBuffer(object):
-- 
GitLab


From c7caa2d87daa37b66811ac99f997ad02acd4ecc8 Mon Sep 17 00:00:00 2001
From: Martin Wicke <wicke@google.com>
Date: Mon, 26 Feb 2018 16:23:46 -0800
Subject: [PATCH 398/575] Deprecate tf.contrib.learn.

RELNOTES: Deprecated tf.contrib.learn. Please check contrib/learn/README.md for instructions on how to convert existing code.
PiperOrigin-RevId: 187099439
---
 .../python/framework/experimental_test.py     |   1 -
 tensorflow/contrib/learn/README.md            | 143 ++++++++++++++++++
 tensorflow/contrib/learn/__init__.py          |   7 +-
 tensorflow/contrib/learn/python/__init__.py   |   7 +-
 .../contrib/learn/python/learn/__init__.py    |   7 +-
 .../python/learn/basic_session_run_hooks.py   |  43 +++++-
 .../learn/python/learn/datasets/__init__.py   |  12 +-
 .../learn/python/learn/datasets/base.py       |  26 +++-
 .../learn/python/learn/datasets/mnist.py      |  23 ++-
 .../learn/datasets/produce_small_datasets.py  |   7 +-
 .../learn/python/learn/datasets/synthetic.py  |  10 +-
 .../python/learn/datasets/text_datasets.py    |  10 +-
 .../learn/python/learn/estimators/__init__.py |   7 +-
 .../learn/python/learn/estimators/_sklearn.py |   4 +-
 .../learn/estimators/composable_model.py      |  17 ++-
 .../python/learn/estimators/constants.py      |   8 +-
 .../learn/python/learn/estimators/debug.py    |  14 +-
 .../learn/python/learn/estimators/dnn.py      |  19 ++-
 .../learn/estimators/dnn_linear_combined.py   |  19 ++-
 .../learn/estimators/dynamic_rnn_estimator.py |  13 +-
 .../python/learn/estimators/estimator.py      |  27 +++-
 .../learn/estimators/estimator_test_utils.py  |   7 +-
 .../learn/python/learn/estimators/head.py     |  20 ++-
 .../learn/python/learn/estimators/kmeans.py   |   9 +-
 .../learn/python/learn/estimators/linear.py   |  19 ++-
 .../learn/estimators/logistic_regressor.py    |  10 +-
 .../python/learn/estimators/metric_key.py     |  10 +-
 .../learn/python/learn/estimators/model_fn.py |  22 ++-
 .../python/learn/estimators/prediction_key.py |   8 +-
 .../python/learn/estimators/rnn_common.py     |   7 +-
 .../python/learn/estimators/run_config.py     |  19 ++-
 .../estimators/state_saving_rnn_estimator.py  |  13 +-
 .../learn/python/learn/estimators/svm.py      |  11 +-
 .../learn/estimators/tensor_signature.py      |  11 +-
 .../python/learn/estimators/test_data.py      |   7 +-
 .../contrib/learn/python/learn/evaluable.py   |  11 +-
 .../contrib/learn/python/learn/experiment.py  |  24 +--
 .../learn/python/learn/export_strategy.py     |  14 +-
 .../learn/python/learn/graph_actions.py       |   8 +-
 .../learn/python/learn/learn_io/__init__.py   |   7 +-
 .../learn/python/learn/learn_io/dask_io.py    |  11 +-
 .../python/learn/learn_io/data_feeder.py      |  29 +++-
 .../python/learn/learn_io/generator_io.py     |   9 +-
 .../learn/python/learn/learn_io/graph_io.py   |  16 +-
 .../learn/python/learn/learn_io/numpy_io.py   |   9 +-
 .../learn/python/learn/learn_io/pandas_io.py  |  12 +-
 .../learn/python/learn/learn_runner.py        |  10 +-
 .../learn/python/learn/learn_runner_lib.py    |   6 +-
 .../contrib/learn/python/learn/metric_spec.py |  13 +-
 .../contrib/learn/python/learn/models.py      |  14 +-
 .../learn/python/learn/monitored_session.py   |   6 +-
 .../contrib/learn/python/learn/monitors.py    |  68 ++++++++-
 .../learn/python/learn/ops/__init__.py        |   7 +-
 .../learn/python/learn/ops/embeddings_ops.py  |   6 +-
 .../learn/python/learn/ops/losses_ops.py      |   7 +-
 .../learn/python/learn/ops/seq2seq_ops.py     |  12 +-
 .../python/learn/preprocessing/__init__.py    |   7 +-
 .../python/learn/preprocessing/categorical.py |  15 +-
 .../preprocessing/categorical_vocabulary.py   |  13 +-
 .../learn/python/learn/preprocessing/text.py  |  26 +++-
 .../learn/python/learn/session_run_hook.py    |   6 +-
 .../python/learn/summary_writer_cache.py      |   5 +-
 .../contrib/learn/python/learn/trainable.py   |   9 +-
 .../learn/python/learn/utils/__init__.py      |   7 +-
 .../learn/python/learn/utils/export.py        |   9 +-
 .../contrib/learn/python/learn/utils/gc.py    |  13 +-
 .../python/learn/utils/input_fn_utils.py      |  16 +-
 .../python/learn/utils/inspect_checkpoint.py  |   2 +-
 .../learn/utils/saved_model_export_utils.py   |  30 +++-
 tensorflow/python/util/decorator_utils.py     |   2 +-
 70 files changed, 945 insertions(+), 111 deletions(-)
 create mode 100644 tensorflow/contrib/learn/README.md

diff --git a/tensorflow/contrib/framework/python/framework/experimental_test.py b/tensorflow/contrib/framework/python/framework/experimental_test.py
index 8e54e09e04..cfdc7df7d8 100644
--- a/tensorflow/contrib/framework/python/framework/experimental_test.py
+++ b/tensorflow/contrib/framework/python/framework/experimental_test.py
@@ -49,7 +49,6 @@ class ExperimentalTest(test.TestCase):
                      "\nTHIS FUNCTION IS EXPERIMENTAL. It may change or "
                      "be removed at any time, and without warning."
                      "\n"
-                     "\n"
                      "\nArgs:"
                      "\n  arg0: Arg 0."
                      "\n  arg1: Arg 1."
diff --git a/tensorflow/contrib/learn/README.md b/tensorflow/contrib/learn/README.md
new file mode 100644
index 0000000000..d516bffc5e
--- /dev/null
+++ b/tensorflow/contrib/learn/README.md
@@ -0,0 +1,143 @@
+EVERYTHING IN THIS DIRECTORY IS DEPRECATED.
+
+Using functions or classes will result in warnings.
+
+Instructions for converting to current alternatives are included in the
+warnings. A high-level overview is below.
+
+## Canned Estimators
+
+Many canned estimators (subclasses of `Estimator`) have equivalents in core:
+`DNNClassifier`, `DNNRegressor`, `DNNEstimator`, `LinearClassifier`,
+`LinearRegressor`, `DNNLinearCombinedClassifier` and
+`DNNLinearCombinedRegressor`. They are exposed under `tf.estimator`.
+`DNNEstimator`, `LinearEstimator` and `DNNLinearCombinedEstimator`
+are exposed under `tf.contrib.estimator`.
+
+To migrate to the new api, users need to take the following steps:
+
+* Replace `tf.contrib.learn` with `tf.estimator`.
+* If you subclass any of the estimators, stop doing that. You should be able to
+  write a factory method that returns a canned estimator instead. If this is not
+  possible (if you override methods from the canned estimator), consider writing
+  a custom estimator instead. See `tf.estimator.Estimator`.
+* Set `loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE` to preserve loss
+  reduction as the average over batch.
+* Some optimizer-related arguments are no longer passed in the estimator
+  constructor. Instead, we provide methods that perform the same job by wrapping
+  an optimizer. Specifically:
+  *  `gradient_clip_norm`: Use `tf.contrib.estimator.clip_gradients_by_norm`
+  *  `embedding_lr_multipliers`: Not supported.
+  Other arguments:
+  * `input_layer_min_slice_size`: Replaced by `input_layer_partitioner`
+  * `enable_centered_bias`: Not supported. Dropping this argument is unlikely to
+    harm your model.
+  * `feature_engineering_fn`: Not supported. You can call your
+    `feature_engineering_fn` inside your input_fn:
+    ```python
+    def new_input_fn():
+      features, labels = old_input_fn()
+      return feature_engineering_fn(features, labels)
+    ```
+* Use `tf.reshape` to reshape labels in your `input_fn`. `tf.estimator`
+  classifiers and regressors expect labels as a 2D Tensor of shape
+  `[batch_size, 1]`, or `[batch_size, n_labels]`. In contrast,
+  `tf.contrib.learn` classifiers and regressors supported labels with shape
+  `[batch_size]`.
+* If you pass custom metrics from the `evaluate()` method call, use
+  `tf.contrib.estimator.add_metrics`.
+* Replace your `serving_input_fn` with a `serving_input_receiver_fn`.
+  Note this should be entirely distinct from your training `input_fn`, so if you
+  previously had one `input_fn` with different "modes", you should now factor
+  that apart.  Where the former returned either a simple `(features, labels)`
+  tuple or `InputFnOps`, you should now return a `ServingInputReceiver`.
+  If you were generating your `serving_input_fn` using the
+  `build_parsing_serving_input_fn` helper, you can simply drop in the
+  replacement `build_parsing_serving_input_receiver_fn`.
+
+Some remaining estimators/classes:
+
+* `DynamicRnnEstimator`:  Consider a custom `model_fn`.
+* `KMeansClustering`: Use `tf.contrib.factorization.KMeansClustering`.
+* `LogisticRegressor`: Not supported. Instead, use `binary_classification_head`
+  with a custom `model_fn`, or with `DNNEstimator`.
+* `StateSavingRnnEstimator`: Consider a custom `model_fn`.
+* SVM: Consider a custom `model_fn`.
+* `LinearComposableModel` and `DNNComposableModel`: Not supported. 
+  Consider `tf.contrib.estimator.DNNEstimator`, or write a custom model_fn.
+* `MetricSpec`: Deprecated. For adding custom metrics to canned Estimators, use
+  `tf.contrib.estimator.add_metrics`.
+
+## Estimator
+`tf.contrib.learn.Estimator` is migrated to `tf.estimator.Estimator`.
+
+To migrate, users need to take the following steps:
+
+* Replace `tf.contrib.learn.Estimator` with `tf.estimator.Estimator`.
+* If you pass a `config` argument to `Estimator`, this must be
+  `tf.estimator.RunConfig`. You may need to edit your code accordingly.
+* Edit your `model_fn` to return `tf.estimator.EstimatorSpec`. Refer to
+  `EstimatorSpec` for documentation of specific fields.
+* If your `model_fn` uses the `mode` argument, use `tf.estimator.ModeKeys`.
+
+Some related classes:
+* `Evaluable`, `Trainable`: Not supported, merged into `tf.estimator.Estimator`.
+* ExportStrategy: Replaced by `tf.estimator.Exporter`.
+
+## Head/MultiHead
+These classes are now supported under `tf.contrib.estimator`, e.g.
+`tf.contrib.estimator.multi_class_head` and `tf.contrib.estimator.multi_head`.
+
+Some differences:
+
+* `multi_class_head`: If you use `tf.contrib.learn.multi_class_head` with
+  `n_classes=2`, switch to `tf.contrib.estimator.binary_classification_head`.
+* `loss_only_head`: Not supported.
+* `poisson_regression_head`: Not supported (yet).
+* `binary_svm_head`: Not supported (yet).
+* `no_op_train_fn`: Replace it with `tf.no_op`.
+
+Some arguments are renamed, please refer to documentation. In addition:
+
+* `loss_fn`: Supported for `multi_label_head`. If you need it for other heads,
+  please open an issue.
+* `metric_class_ids`: Not supported (yet).
+* `enable_centered_bias`: Not supported. Dropping this argument is unlikely to
+  harm your model.
+* `label_name`: Not needed in `tf.estimator`. If you don’t use `multi_head`,
+  drop this argument. If you use `multi_head`, refer to
+  `tf.contrib.estimator.multi_head` documentation.
+
+## Experiment Class - Distributed Training Tooling
+
+Switch to `tf.estimator.train_and_evaluate`. Some differences:
+
+* Most of the constructor arguments, like `train_input_fn`, `eval_input_fn`,
+  should be wrapped into `tf.estimator.TrainSpec` and `tf.estimator.EvalSpec`.
+* Remove the `experiment_fn`. Instead, create the `Estimator`,
+  `train_spec` and `eval_spec`, then call `tf.estimator.train_and_evaluate`
+  directly.
+* Inside `tf.estimator.EvalSpec`, the `exporter` field is the replacement
+  for `export_strategy`. To be precise, `tf.estimator.LatestExporter` is the
+  replacement for `tf.contrib.learn.make_export_strategy`. If you want to export
+  only at the end of training  use `tf.estimator.FinalExporter`.
+* If the `TF_CONFIG` environment variable is constructed manually, please read
+  the `train_and_evaluate` documentation for the new requirementds (in
+  particular, the chief node and evaluator node).
+
+## Others Classes and Functions
+
+* `tf.contrib.learn.datasets` is deprecated. We are adding ready to use datasets
+  to tensorflow/models. Many smaller datasets are available from other sources,
+  such as scikits.learn. Some Python processing may have to be written, but this
+  is straightforward to implement using the standard modules.
+* `tf.contrib.learn.preprocessing`: Deprecated. The python-only preprocessing
+  functions are not a good fit for TensorFlow. Please use `tf.data`, and
+  consider tensorflow/transform for more complex use cases.
+* `tf.contrib.learn.models`: Not supported, use canned estimators instead.
+* `tf.contrib.learn.monitors`: Implement `SessionRunHook` instead. Hook
+  implementations are in `tf.train`.
+* `tf.contrib.learn.learn_io`: Use the methods in `tf.estimator.inputs`, such as
+  `tf.estimator.inputs.numpy_input_fn`. Some utility functions have no
+  equivalent, we encourage the use of `tf.data`.
+
diff --git a/tensorflow/contrib/learn/__init__.py b/tensorflow/contrib/learn/__init__.py
index 3698af027e..79bd73faaf 100644
--- a/tensorflow/contrib/learn/__init__.py
+++ b/tensorflow/contrib/learn/__init__.py
@@ -13,8 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-# TODO(ptucker,ipolosukhin): Improve descriptions.
-"""High level API for learning.
+"""High level API for learning (DEPRECATED).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 See the @{$python/contrib.learn} guide.
 
diff --git a/tensorflow/contrib/learn/python/__init__.py b/tensorflow/contrib/learn/python/__init__.py
index bbebd5ab97..df23aeb2c4 100644
--- a/tensorflow/contrib/learn/python/__init__.py
+++ b/tensorflow/contrib/learn/python/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""High level API for learning with TensorFlow."""
+"""High level API for learning with TensorFlow (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/__init__.py b/tensorflow/contrib/learn/python/learn/__init__.py
index cdc67c77d5..76e0e8ac8f 100644
--- a/tensorflow/contrib/learn/python/learn/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""High level API for learning with TensorFlow."""
+"""High level API for learning with TensorFlow (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py b/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py
index 2284ec46e9..fed1c44d19 100644
--- a/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py
+++ b/tensorflow/contrib/learn/python/learn/basic_session_run_hooks.py
@@ -12,20 +12,47 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Some common SessionRunHook classes."""
+"""Some common SessionRunHook classes (deprected).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.training import basic_session_run_hooks
+from tensorflow.python.util.deprecation import deprecated_alias
 
 # pylint: disable=invalid-name
-LoggingTensorHook = basic_session_run_hooks.LoggingTensorHook
-StopAtStepHook = basic_session_run_hooks.StopAtStepHook
-CheckpointSaverHook = basic_session_run_hooks.CheckpointSaverHook
-StepCounterHook = basic_session_run_hooks.StepCounterHook
-NanLossDuringTrainingError = basic_session_run_hooks.NanLossDuringTrainingError
-NanTensorHook = basic_session_run_hooks.NanTensorHook
-SummarySaverHook = basic_session_run_hooks.SummarySaverHook
+LoggingTensorHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.LoggingTensorHook',
+    'tf.train.LoggingTensorHook',
+    basic_session_run_hooks.LoggingTensorHook)
+StopAtStepHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.StopAtStepHook',
+    'tf.train.StopAtStepHook',
+    basic_session_run_hooks.StopAtStepHook)
+CheckpointSaverHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.CheckpointSaverHook',
+    'tf.train.CheckpointSaverHook',
+    basic_session_run_hooks.CheckpointSaverHook)
+StepCounterHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.StepCounterHook',
+    'tf.train.StepCounterHook',
+    basic_session_run_hooks.StepCounterHook)
+NanLossDuringTrainingError = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.NanLossDuringTrainingError',
+    'tf.train.NanLossDuringTrainingError',
+    basic_session_run_hooks.NanLossDuringTrainingError)
+NanTensorHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.NanTensorHook',
+    'tf.train.NanTensorHook',
+    basic_session_run_hooks.NanTensorHook)
+SummarySaverHook = deprecated_alias(
+    'tf.contrib.learn.basic_session_run_hooks.SummarySaverHook',
+    'tf.train.SummarySaverHook',
+    basic_session_run_hooks.SummarySaverHook)
 # pylint: enable=invalid-name
diff --git a/tensorflow/contrib/learn/python/learn/datasets/__init__.py b/tensorflow/contrib/learn/python/learn/datasets/__init__.py
index 7240b0de14..3c34712ac8 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/__init__.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Dataset utilities and synthetic/reference datasets."""
+"""Dataset utilities and synthetic/reference datasets (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -27,6 +32,7 @@ from tensorflow.contrib.learn.python.learn.datasets import base
 from tensorflow.contrib.learn.python.learn.datasets import mnist
 from tensorflow.contrib.learn.python.learn.datasets import synthetic
 from tensorflow.contrib.learn.python.learn.datasets import text_datasets
+from tensorflow.python.util.deprecation import deprecated
 
 # Export load_iris and load_boston.
 load_iris = base.load_iris
@@ -51,6 +57,7 @@ SYNTHETIC = {
 }
 
 
+@deprecated(None, 'Please use tf.data.')
 def load_dataset(name, size='small', test_with_fake_data=False):
   """Loads dataset by name.
 
@@ -73,8 +80,9 @@ def load_dataset(name, size='small', test_with_fake_data=False):
     return DATASETS[name]()
 
 
+@deprecated(None, 'Please use tf.data.')
 def make_dataset(name, n_samples=100, noise=None, seed=42, *args, **kwargs):
-  """Creates binary synthetic datasets
+  """Creates binary synthetic datasets.
 
   Args:
     name: str, name of the dataset to generate
diff --git a/tensorflow/contrib/learn/python/learn/datasets/base.py b/tensorflow/contrib/learn/python/learn/datasets/base.py
index ca720ae5ed..3b5c9b97c0 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/base.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/base.py
@@ -12,7 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Base utilities for loading datasets."""
+
+"""Base utilities for loading datasets (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -29,11 +35,14 @@ import numpy as np
 from six.moves import urllib
 
 from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
+
 
 Dataset = collections.namedtuple('Dataset', ['data', 'target'])
 Datasets = collections.namedtuple('Datasets', ['train', 'validation', 'test'])
 
 
+@deprecated(None, 'Use tf.data instead.')
 def load_csv_with_header(filename,
                          target_dtype,
                          features_dtype,
@@ -53,6 +62,7 @@ def load_csv_with_header(filename,
   return Dataset(data=data, target=target)
 
 
+@deprecated(None, 'Use tf.data instead.')
 def load_csv_without_header(filename,
                             target_dtype,
                             features_dtype,
@@ -70,6 +80,7 @@ def load_csv_without_header(filename,
   return Dataset(data=data, target=target)
 
 
+@deprecated(None, 'Use tf.data instead.')
 def shrink_csv(filename, ratio):
   """Create a smaller dataset of only 1/ratio of original data."""
   filename_small = filename.replace('.', '_small.')
@@ -84,6 +95,7 @@ def shrink_csv(filename, ratio):
         i += 1
 
 
+@deprecated(None, 'Use scikits.learn.datasets.')
 def load_iris(data_path=None):
   """Load Iris dataset.
 
@@ -100,6 +112,7 @@ def load_iris(data_path=None):
       data_path, target_dtype=np.int, features_dtype=np.float)
 
 
+@deprecated(None, 'Use scikits.learn.datasets.')
 def load_boston(data_path=None):
   """Load Boston housing dataset.
 
@@ -116,7 +129,12 @@ def load_boston(data_path=None):
       data_path, target_dtype=np.float, features_dtype=np.float)
 
 
-def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None):
+@deprecated(None, 'Use the retry module or similar alternatives.')
+def retry(initial_delay,
+          max_delay,
+          factor=2.0,
+          jitter=0.25,
+          is_retriable=None):
   """Simple decorator for wrapping retriable functions.
 
   Args:
@@ -152,7 +170,7 @@ def retry(initial_delay, max_delay, factor=2.0, jitter=0.25, is_retriable=None):
       for delay in delays():
         try:
           return fn(*args, **kwargs)
-        except Exception as e:  # pylint: disable=broad-except)
+        except Exception as e:  # pylint: disable=broad-except
           if is_retriable is None:
             continue
 
@@ -176,11 +194,13 @@ def _is_retriable(e):
   return isinstance(e, IOError) and e.errno in _RETRIABLE_ERRNOS
 
 
+@deprecated(None, 'Please use urllib or similar directly.')
 @retry(initial_delay=1.0, max_delay=16.0, is_retriable=_is_retriable)
 def urlretrieve_with_retry(url, filename=None):
   return urllib.request.urlretrieve(url, filename)
 
 
+@deprecated(None, 'Please write your own downloading logic.')
 def maybe_download(filename, work_directory, source_url):
   """Download the data from source url, unless it's already here.
 
diff --git a/tensorflow/contrib/learn/python/learn/datasets/mnist.py b/tensorflow/contrib/learn/python/learn/datasets/mnist.py
index 37f9175015..abbb44c2f5 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/mnist.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/mnist.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Functions for downloading and reading MNIST data."""
+"""Functions for downloading and reading MNIST data (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -27,6 +32,7 @@ from tensorflow.contrib.learn.python.learn.datasets import base
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import random_seed
 from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
 
 # CVDF mirror of http://yann.lecun.com/exdb/mnist/
 DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
@@ -37,6 +43,7 @@ def _read32(bytestream):
   return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
 
 
+@deprecated(None, 'Please use tf.data to implement this functionality.')
 def extract_images(f):
   """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
@@ -65,6 +72,7 @@ def extract_images(f):
     return data
 
 
+@deprecated(None, 'Please use tf.one_hot on tensors.')
 def dense_to_one_hot(labels_dense, num_classes):
   """Convert class labels from scalars to one-hot vectors."""
   num_labels = labels_dense.shape[0]
@@ -74,6 +82,7 @@ def dense_to_one_hot(labels_dense, num_classes):
   return labels_one_hot
 
 
+@deprecated(None, 'Please use tf.data to implement this functionality.')
 def extract_labels(f, one_hot=False, num_classes=10):
   """Extract the labels into a 1D uint8 numpy array [index].
 
@@ -103,7 +112,15 @@ def extract_labels(f, one_hot=False, num_classes=10):
 
 
 class DataSet(object):
+  """Container class for a dataset (deprecated).
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please use alternatives such as official/mnist/dataset.py'
+              ' from tensorflow/models.')
   def __init__(self,
                images,
                labels,
@@ -210,6 +227,8 @@ class DataSet(object):
       return self._images[start:end], self._labels[start:end]
 
 
+@deprecated(None, 'Please use alternatives such as official/mnist/dataset.py'
+            ' from tensorflow/models.')
 def read_data_sets(train_dir,
                    fake_data=False,
                    one_hot=False,
@@ -275,5 +294,7 @@ def read_data_sets(train_dir,
   return base.Datasets(train=train, validation=validation, test=test)
 
 
+@deprecated(None, 'Please use alternatives such as official/mnist/dataset.py'
+            ' from tensorflow/models.')
 def load_mnist(train_dir='MNIST-data'):
   return read_data_sets(train_dir)
diff --git a/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py b/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py
index 6e0ba38941..a4848fa64a 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/produce_small_datasets.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Produce DBpedia datasets of a smaller size."""
+"""Produce DBpedia datasets of a smaller size (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py
index 9a843168c2..6a0e3350b3 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/synthetic.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/synthetic.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Synthetic dataset generators."""
+"""Synthetic dataset generators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -21,8 +26,10 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib.learn.python.learn.datasets.base import Dataset
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Consider using synthetic datasets from scikits.learn.')
 def circles(n_samples=100,
             noise=None,
             seed=None,
@@ -93,6 +100,7 @@ def circles(n_samples=100,
   return Dataset(data=X[indices], target=y[indices])
 
 
+@deprecated(None, 'Consider using synthetic datasets from scikits.learn.')
 def spirals(n_samples=100,
             noise=None,
             seed=None,
diff --git a/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py b/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py
index 2596a2ecaf..ce94663017 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Text datasets."""
+"""Text datasets (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -26,10 +31,12 @@ import numpy as np
 
 from tensorflow.contrib.learn.python.learn.datasets import base
 from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
 
 DBPEDIA_URL = 'https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz'
 
 
+@deprecated(None, 'See contrib/learn/README.md')
 def maybe_download_dbpedia(data_dir):
   """Download if DBpedia data is not present."""
   train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv')
@@ -41,6 +48,7 @@ def maybe_download_dbpedia(data_dir):
     tfile.extractall(data_dir)
 
 
+@deprecated(None, 'See contrib/learn/README.md')
 def load_dbpedia(size='small', test_with_fake_data=False):
   """Get DBpedia datasets from CSV files."""
   if not test_with_fake_data:
diff --git a/tensorflow/contrib/learn/python/learn/estimators/__init__.py b/tensorflow/contrib/learn/python/learn/estimators/__init__.py
index 4981750c94..3e64595f31 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""An estimator is a rule for calculating an estimate of a given quantity.
+"""An estimator is a rule for calculating an estimate of a given quantity (deprecated).
+
+These classes are deprecated and replaced with `tf.estimator`.
+
+See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 # Estimators
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
index 15277415a1..1f0e4663d0 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-"""sklearn cross-support."""
+"""sklearn cross-support (deprecated)."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -132,6 +132,8 @@ class _TransformerMixin():
 class NotFittedError(ValueError, AttributeError):
   """Exception class to raise if estimator is used before fitting.
 
+  USE OF THIS EXCEPTION IS DEPRECATED.
+
   This class inherits from both ValueError and AttributeError to help with
   exception handling and backward compatibility.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/composable_model.py b/tensorflow/contrib/learn/python/learn/estimators/composable_model.py
index a02c726c74..1fa58271e2 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/composable_model.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/composable_model.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""TensorFlow composable models used as building blocks for estimators."""
+"""TensorFlow composable models used as building blocks for estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -34,6 +39,7 @@ from tensorflow.python.ops import nn
 from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.summary import summary
+from tensorflow.python.util.deprecation import deprecated
 
 
 class _ComposableModel(object):
@@ -46,6 +52,7 @@ class _ComposableModel(object):
   _ComposableModel and its subclasses are not part of the public tf.learn API.
   """
 
+  @deprecated(None, "Please use model_fns in tf.estimator.")
   def __init__(self,
                num_label_columns,
                optimizer,
@@ -141,6 +148,10 @@ class _ComposableModel(object):
 class LinearComposableModel(_ComposableModel):
   """A _ComposableModel that implements linear regression.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Instances of this class can be used to build estimators through the use
   of composition.
   """
@@ -252,6 +263,10 @@ class LinearComposableModel(_ComposableModel):
 class DNNComposableModel(_ComposableModel):
   """A _ComposableModel that implements a DNN.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Instances of this class can be used to build estimators through the use
   of composition.
   """
diff --git a/tensorflow/contrib/learn/python/learn/estimators/constants.py b/tensorflow/contrib/learn/python/learn/estimators/constants.py
index fc69e81024..d2548946bc 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/constants.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/constants.py
@@ -13,9 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Constants regarding Estimators.
+"""Constants regarding Estimators (deprecated).
 
-This file is obsoleted in the move of Estimator to core.
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -25,6 +27,8 @@ from __future__ import print_function
 class ProblemType(object):
   """Enum-like values for the type of problem that the model solves.
 
+  THIS CLASS IS DEPRECATED.
+
   These values are used when exporting the model to produce the appropriate
   signature function for serving.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/debug.py b/tensorflow/contrib/learn/python/learn/estimators/debug.py
index 9d5f6c2bf9..24b067b7e3 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/debug.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/debug.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Debug estimators.
+"""Debug estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Debug estimators are bias-only estimators that can be used for debugging
 and as simple baselines.
@@ -118,6 +122,10 @@ def debug_model_fn(features, labels, mode, params, config=None):
 class DebugClassifier(estimator.Estimator):
   """A classifier for TensorFlow Debug models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
@@ -237,6 +245,10 @@ class DebugClassifier(estimator.Estimator):
 class DebugRegressor(estimator.Estimator):
   """A regressor for TensorFlow Debug models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn.py b/tensorflow/contrib/learn/python/learn/estimators/dnn.py
index c17b41c0f7..eabebb7e88 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dnn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dnn.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Deep Neural Network estimators."""
+"""Deep Neural Network estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -212,6 +217,10 @@ def _dnn_model_fn(features, labels, mode, params, config=None):
 class DNNClassifier(estimator.Estimator):
   """A classifier for TensorFlow DNN models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
@@ -521,6 +530,10 @@ class DNNClassifier(estimator.Estimator):
 class DNNRegressor(estimator.Estimator):
   """A regressor for TensorFlow DNN models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
@@ -796,6 +809,10 @@ class DNNRegressor(estimator.Estimator):
 class DNNEstimator(estimator.Estimator):
   """A Estimator for TensorFlow DNN models with user specified _Head.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Example:
 
   ```python
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
index 7266122350..3d85533d92 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow estimators for Linear and DNN joined training models."""
+"""TensorFlow estimators for Linear and DNN joined training models (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -372,6 +377,10 @@ def _dnn_linear_combined_model_fn(features, labels, mode, params, config=None):
 class DNNLinearCombinedEstimator(estimator.Estimator):
   """An estimator for TensorFlow Linear and DNN joined training models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note: New users must set `fix_global_step_increment_bug=True` when creating an
   estimator.
 
@@ -490,6 +499,10 @@ class DNNLinearCombinedEstimator(estimator.Estimator):
 class DNNLinearCombinedClassifier(estimator.Estimator):
   """A classifier for TensorFlow Linear and DNN joined training models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note: New users must set `fix_global_step_increment_bug=True` when creating an
   estimator.
 
@@ -832,6 +845,10 @@ class DNNLinearCombinedClassifier(estimator.Estimator):
 class DNNLinearCombinedRegressor(estimator.Estimator):
   """A regressor for TensorFlow Linear and DNN joined training models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note: New users must set `fix_global_step_increment_bug=True` when creating an
   estimator.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py
index 69440e823e..a703dc66e9 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Estimator for Dynamic RNNs."""
+"""Estimator for Dynamic RNNs (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -540,6 +545,12 @@ def _get_dynamic_rnn_model_fn(
 
 
 class DynamicRnnEstimator(estimator.Estimator):
+  """Dynamically unrolled RNN (deprecated).
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self,
                problem_type,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index 4b63e08ab3..5262e04e16 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Base Estimator class."""
+"""Base Estimator class (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -138,6 +143,7 @@ def _get_input_fn(x, y, input_fn, feed_fn, batch_size, shuffle=False, epochs=1):
   return df.input_builder, df.get_feed_dict_fn()
 
 
+@deprecated(None, 'Please specify feature columns explicitly.')
 def infer_real_valued_columns_from_input_fn(input_fn):
   """Creates `FeatureColumn` objects for inputs defined by `input_fn`.
 
@@ -158,6 +164,7 @@ def infer_real_valued_columns_from_input_fn(input_fn):
     return layers.infer_real_valued_columns(features)
 
 
+@deprecated(None, 'Please specify feature columns explicitly.')
 def infer_real_valued_columns_from_input(x):
   """Creates `FeatureColumn` objects for inputs defined by input `x`.
 
@@ -389,6 +396,10 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
                     trainable.Trainable):
   """Abstract BaseEstimator class to train and evaluate TensorFlow models.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Users should not instantiate or subclass this class. Instead, use an
   `Estimator`.
   """
@@ -399,6 +410,8 @@ class BaseEstimator(sklearn.BaseEstimator, evaluable.Evaluable,
   # TODO(wicke): Remove this once launcher takes over config functionality
   _Config = run_config.RunConfig  # pylint: disable=invalid-name
 
+  @deprecated(None, 'Please replace uses of any Estimator from tf.contrib.learn'
+              ' with an Estimator from tf.estimator.*')
   def __init__(self, model_dir=None, config=None):
     """Initializes a BaseEstimator instance.
 
@@ -1074,6 +1087,10 @@ def _identity_feature_engineering_fn(features, labels):
 
 class Estimator(BaseEstimator):
   """Estimator class is the basic TensorFlow model trainer/evaluator.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
   """
 
   def __init__(self,
@@ -1458,8 +1475,14 @@ class Estimator(BaseEstimator):
 # For time of deprecation x,y from Estimator allow direct access.
 # pylint: disable=protected-access
 class SKCompat(sklearn.BaseEstimator):
-  """Scikit learn wrapper for TensorFlow Learn Estimator."""
+  """Scikit learn wrapper for TensorFlow Learn Estimator.
+  
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please switch to the Estimator interface.')
   def __init__(self, estimator):
     self._estimator = estimator
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py b/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py
index fd47710e30..e4c31396ba 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator_test_utils.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utils for Estimator."""
+"""Utils for Estimator (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py
index 9b124b2c19..2b4b6eff39 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/head.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/head.py
@@ -12,8 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Abstractions for the head(s) of a model.
+"""Abstractions for the head(s) of a model (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 """
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -47,11 +52,16 @@ from tensorflow.python.summary import summary
 from tensorflow.python.training import training
 from tensorflow.python.util import tf_decorator
 from tensorflow.python.util import tf_inspect
+from tensorflow.python.util.deprecation import deprecated
 
 
 class Head(object):
   """Interface for the head/top of a model.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Given logits (or output of a hidden layer), a Head knows how to compute
   predictions, loss, default metric and export signature. It is meant to,
 
@@ -177,6 +187,7 @@ class Head(object):
     raise NotImplementedError("Calling an abstract method.")
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def regression_head(label_name=None,
                     weight_column_name=None,
                     label_dimension=1,
@@ -216,6 +227,7 @@ def regression_head(label_name=None,
       link_fn=(link_fn if link_fn is not None else array_ops.identity))
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def poisson_regression_head(label_name=None,
                             weight_column_name=None,
                             label_dimension=1,
@@ -254,6 +266,7 @@ def poisson_regression_head(label_name=None,
 # TODO(zakaria): Consider adding a _RegressionHead for logistic_regression
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def multi_class_head(n_classes,
                      label_name=None,
                      weight_column_name=None,
@@ -335,6 +348,7 @@ def multi_class_head(n_classes,
       label_keys=label_keys)
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def binary_svm_head(
     label_name=None,
     weight_column_name=None,
@@ -370,6 +384,7 @@ def binary_svm_head(
       thresholds=thresholds)
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def multi_label_head(n_classes,
                      label_name=None,
                      weight_column_name=None,
@@ -430,6 +445,7 @@ def multi_label_head(n_classes,
       loss_fn=_wrap_custom_loss_fn(loss_fn) if loss_fn else None)
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def loss_only_head(loss_fn, head_name=None):
   """Creates a Head that contains only loss terms.
 
@@ -447,6 +463,7 @@ def loss_only_head(loss_fn, head_name=None):
   return _LossOnlyHead(loss_fn, head_name=head_name)
 
 
+@deprecated(None, "Please switch to tf.contrib.estimator.*_head.")
 def multi_head(heads, loss_weights=None):
   """Creates a MultiHead stemming from same logits/hidden layer.
 
@@ -479,6 +496,7 @@ def multi_head(heads, loss_weights=None):
   return _MultiHead(heads, loss_merger=_weighted_loss_merger)
 
 
+@deprecated(None, "Use 'lambda _: tf.no_op()'.")
 def no_op_train_fn(loss):
   del loss
   return control_flow_ops.no_op()
diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
index 8f9d6fc318..66ebcfd1d8 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Implementation of k-means clustering on top of `Estimator` API.
+"""Implementation of k-means clustering on top of `Estimator` API (deprecated).
 
 This module is deprecated. Please use
 @{tf.contrib.factorization.KMeansClustering} instead of
@@ -153,7 +153,12 @@ def _kmeans_clustering_model_fn(features, labels, mode, params, config):
 
 # TODO(agarwal,ands): support sharded input.
 class KMeansClustering(estimator.Estimator):
-  """An Estimator for K-Means clustering."""
+  """An Estimator for K-Means clustering.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
   SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE
   COSINE_DISTANCE = clustering_ops.COSINE_DISTANCE
   RANDOM_INIT = clustering_ops.RANDOM_INIT
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear.py b/tensorflow/contrib/learn/python/learn/estimators/linear.py
index 37aa8b3396..64d7ecc68e 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Linear Estimators."""
+"""Linear Estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -305,6 +310,10 @@ class _SdcaUpdateWeightsHook(session_run_hook.SessionRunHook):
 class LinearClassifier(estimator.Estimator):
   """Linear classifier model.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Train a linear model to classify instances into one of multiple possible
   classes. When number of possible classes is 2, this is binary classification.
 
@@ -625,6 +634,10 @@ class LinearClassifier(estimator.Estimator):
 class LinearRegressor(estimator.Estimator):
   """Linear regressor model.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Train a linear regression model to predict label value given observation of
   feature values.
 
@@ -860,6 +873,10 @@ class LinearRegressor(estimator.Estimator):
 class LinearEstimator(estimator.Estimator):
   """Linear model with user specified head.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Train a generalized linear model to predict label value given observation of
   feature values.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py
index fb339160d5..3cbcc6e98d 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/logistic_regressor.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Logistic regression (aka binary classifier) class.
+"""Logistic regression (aka binary classifier) class (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 This defines some useful basic metrics for using logistic regression to classify
 a binary event (0 vs 1).
@@ -75,6 +79,10 @@ def LogisticRegressor(  # pylint: disable=invalid-name
     feature_engineering_fn=None):
   """Builds a logistic regression Estimator for binary classification.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   This method provides a basic Estimator with some additional metrics for custom
   binary classification models, including AUC, precision/recall and accuracy.
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/metric_key.py b/tensorflow/contrib/learn/python/learn/estimators/metric_key.py
index 99388f116b..f264248e44 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/metric_key.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/metric_key.py
@@ -12,14 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Enum for metric keys."""
+"""Enum for metric keys (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 
 class MetricKey(object):
-  """Metric key strings."""
+  """Metric key strings (deprecated)."""
+  
   LOSS = "loss"
   AUC = "auc"
   AUC_PR = "auc_precision_recall"
diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
index 44e6c7c52d..dcb161180c 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Classes and methods related to model_fn."""
+"""Classes and methods related to model_fn (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -37,10 +42,13 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.training import session_run_hook
+from tensorflow.python.util.deprecation import deprecated
 
 
 class ModeKeys(object):
-  """Standard names for model modes.
+  """Standard names for model modes (deprecated).
+
+  THIS CLASS IS DEPRECATED.
 
   The following standard keys are defined:
 
@@ -65,8 +73,16 @@ class ModelFnOps(
         'output_alternatives', 'training_chief_hooks', 'training_hooks',
         'scaffold', 'mode'
     ])):
-  """Ops returned from a model_fn."""
+  """Ops returned from a model_fn.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'When switching to tf.estimator.Estimator, use '
+              'tf.estimator.EstimatorSpec. You can use the `estimator_spec`'
+              ' method to create an equivalent one.')
   def __new__(cls,
               mode,
               predictions=None,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py b/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py
index f8d87b8914..6fd2fc9d59 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/prediction_key.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Enum for model prediction keys.
+"""Enum for model prediction keys (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 This file is obsoleted in the move of Estimator to core.
 """
@@ -22,6 +26,8 @@ from __future__ import print_function
 
 
 class PredictionKey(object):
+  """THIS CLASS IS DEPRECATED."""
+
   CLASSES = "classes"
   PROBABILITIES = "probabilities"
   LOGITS = "logits"
diff --git a/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py b/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py
index 2752bc2d90..215022e5d9 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/rnn_common.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Common operations for RNN Estimators."""
+"""Common operations for RNN Estimators (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index fd90fd1cc6..1d161093de 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Run Config."""
+"""Run Config (deprecated, use tf.estimator.RunConfig instead).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -29,11 +34,12 @@ from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.estimator import run_config as core_run_config
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import server_lib
+from tensorflow.python.util.deprecation import deprecated
 
 
 # A list of the property names in RunConfig user allows to change. They will
 # not affect the execution framework, so when execution framework checks the
-# `uid` of the RunConfig, it should be ingored.
+# `uid` of the RunConfig, it should be ignored.
 _DEFAULT_UID_WHITE_LIST = [
     'tf_random_seed',
     'save_summary_steps',
@@ -47,6 +53,7 @@ _DEFAULT_UID_WHITE_LIST = [
 
 
 class Environment(object):
+  """DEPRECATED CLASS."""
   # For running general distributed training.
   CLOUD = 'cloud'
   # For running Google-internal distributed training.
@@ -56,6 +63,7 @@ class Environment(object):
 
 
 class TaskType(object):
+  """DEPRECATED CLASS."""
   MASTER = 'master'
   PS = 'ps'
   WORKER = 'worker'
@@ -64,6 +72,8 @@ class TaskType(object):
 class ClusterConfig(object):
   """This class specifies the configurations for a distributed run.
 
+  THIS CLASS IS DEPRECATED. Use tf.estimator.RunConfig instead.
+
   If you're using an `Estimator`, you should probably use the subclass
   RunConfig instead.
   """
@@ -211,10 +221,13 @@ class ClusterConfig(object):
 class RunConfig(ClusterConfig, core_run_config.RunConfig):
   """This class specifies the configurations for an `Estimator` run.
 
-  This class is the implementation of @{tf.estimator.RunConfig} interface.
+  This class is a deprecated implementation of @{tf.estimator.RunConfig}
+  interface.
   """
   _USE_DEFAULT = 0
 
+  @deprecated(None, 'When switching to tf.estimator.Estimator, use'
+              ' tf.estimator.RunConfig instead.')
   def __init__(self,
                master=None,
                num_cores=0,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
index 0cea35e219..de78c72c3a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/state_saving_rnn_estimator.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Estimator for State Saving RNNs."""
+"""Estimator for State Saving RNNs (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -528,6 +533,12 @@ def _get_rnn_model_fn(cell_type,
 
 
 class StateSavingRnnEstimator(estimator.Estimator):
+  """RNN with static unrolling and state saving (deprecated).
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self,
                problem_type,
diff --git a/tensorflow/contrib/learn/python/learn/estimators/svm.py b/tensorflow/contrib/learn/python/learn/estimators/svm.py
index 72920d73c0..3459997bab 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/svm.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/svm.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Support Vector Machine (SVM) Estimator."""
+"""Support Vector Machine (SVM) Estimator (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -36,6 +41,10 @@ def _as_iterable(preds, output):
 class SVM(estimator.Estimator):
   """Support Vector Machine (SVM) model for binary classification.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Currently, only linear SVMs are supported. For the underlying optimization
   problem, the `SDCAOptimizer` is used. For performance and convergence tuning,
   the num_loss_partitions parameter passed to `SDCAOptimizer` (see `__init__()`
diff --git a/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py b/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py
index a120bc6cc3..71b5658dd1 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/tensor_signature.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorSignature class and utilities."""
+"""TensorSignature class and utilities (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -33,6 +38,10 @@ class TensorSignature(collections.namedtuple(
     "TensorSignature", ["dtype", "shape", "is_sparse"])):
   """Signature of the `Tensor` object.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Useful to check compatibility of tensors.
 
   Example:
diff --git a/tensorflow/contrib/learn/python/learn/estimators/test_data.py b/tensorflow/contrib/learn/python/learn/estimators/test_data.py
index ed201bfc58..e4b057b4f5 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/test_data.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/test_data.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Test data utilities."""
+"""Test data utilities (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/evaluable.py b/tensorflow/contrib/learn/python/learn/evaluable.py
index 8f6cd39864..10881ca885 100644
--- a/tensorflow/contrib/learn/python/learn/evaluable.py
+++ b/tensorflow/contrib/learn/python/learn/evaluable.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""`Evaluable` interface."""
+"""`Evaluable` interface (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -23,6 +28,10 @@ import abc
 
 class Evaluable(object):
   """Interface for objects that are evaluatable by, e.g., `Experiment`.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
   """
   __metaclass__ = abc.ABCMeta
 
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 331bc11549..9a7c4cd685 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Experiment class collecting information needed for a single training run."""
+"""Experiment class collecting information for a single training run (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -25,7 +30,6 @@ import os
 import time
 
 from tensorflow.contrib.framework import deprecated
-from tensorflow.contrib.framework import deprecated_args
 from tensorflow.contrib.framework.python.framework import experimental
 from tensorflow.contrib.learn.python.learn import evaluable
 from tensorflow.contrib.learn.python.learn import export_strategy
@@ -118,6 +122,10 @@ class _EvalAndExportListener(basic_session_run_hooks.CheckpointSaverListener):
 class Experiment(object):
   """Experiment is a class containing all information needed to train a model.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   After an experiment is created (by passing an Estimator and inputs for
   training and evaluation), an Experiment instance knows how to invoke training
   and eval loops in a sensible fashion for distributed training.
@@ -125,16 +133,8 @@ class Experiment(object):
 
   # TODO(ispir): remove delay_workers_by_global_step and make global step based
   # waiting as only behavior.
-  @deprecated_args(
-      "2016-10-23",
-      "local_eval_frequency is deprecated as local_run will be renamed to "
-      "train_and_evaluate. Use min_eval_frequency and call train_and_evaluate "
-      "instead. Note, however, that the default for min_eval_frequency is 1, "
-      "meaning models will be evaluated every time a new checkpoint is "
-      "available. In contrast, the default for local_eval_frequency is None, "
-      "resulting in evaluation occurring only after training has completed. "
-      "min_eval_frequency is ignored when calling the deprecated local_run.",
-      "local_eval_frequency")
+  @deprecated(None, "Please switch to tf.estimator.train_and_evaluate. You will"
+              " also have to convert to a tf.estimator.Estimator.")
   def __init__(self,
                estimator,
                train_input_fn,
diff --git a/tensorflow/contrib/learn/python/learn/export_strategy.py b/tensorflow/contrib/learn/python/learn/export_strategy.py
index 55a8b82431..075cab536e 100644
--- a/tensorflow/contrib/learn/python/learn/export_strategy.py
+++ b/tensorflow/contrib/learn/python/learn/export_strategy.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""ExportStrategy class represents different flavors of model export."""
+"""ExportStrategy class represents different flavors of model export (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -21,6 +26,7 @@ from __future__ import print_function
 import collections
 
 from tensorflow.python.util import tf_inspect
+from tensorflow.python.util.deprecation import deprecated
 
 __all__ = ['ExportStrategy']
 
@@ -30,6 +36,10 @@ class ExportStrategy(
                            ['name', 'export_fn', 'strip_default_attrs'])):
   """A class representing a type of model export.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Typically constructed by a utility function specific to the exporter, such as
   `saved_model_export_utils.make_export_strategy()`.
 
@@ -56,6 +66,8 @@ class ExportStrategy(
         forward compatibility of the resulting `SavedModel`.
   """
 
+  @deprecated(None, 'Please switch to tf.estimator.train_and_evaluate, and use '
+              'tf.estimator.Exporter.')
   def __new__(cls, name, export_fn, strip_default_attrs=None):
     return super(ExportStrategy, cls).__new__(
         cls, name, export_fn, strip_default_attrs)
diff --git a/tensorflow/contrib/learn/python/learn/graph_actions.py b/tensorflow/contrib/learn/python/learn/graph_actions.py
index 98365c05f6..a997fab723 100644
--- a/tensorflow/contrib/learn/python/learn/graph_actions.py
+++ b/tensorflow/contrib/learn/python/learn/graph_actions.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""High level operations on graphs."""
+"""High level operations on graphs (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -68,6 +73,7 @@ def clear_summary_writers():
   return summary_io.SummaryWriterCache.clear()
 
 
+@deprecated(None, 'Use `SummaryWriterCache.get` directly.')
 def get_summary_writer(logdir):
   """Returns single SummaryWriter per logdir in current run.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/__init__.py b/tensorflow/contrib/learn/python/learn/learn_io/__init__.py
index 06c3782a47..8b133a4440 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/__init__.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tools to allow different io formats."""
+"""Tools to allow different io formats (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py b/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py
index 7d666391ce..e0a1948d95 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Methods to allow dask.DataFrame."""
+"""Methods to allow dask.DataFrame (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -21,6 +26,8 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.python.util.deprecation import deprecated
+
 try:
   # pylint: disable=g-import-not-at-top
   import dask.dataframe as dd
@@ -60,6 +67,7 @@ def _construct_dask_df_with_divisions(df):
     return dd.Series(merge(dsk, df.dask), name, df.name, divisions)
 
 
+@deprecated(None, 'Please feed input to tf.data to support dask.')
 def extract_dask_data(data):
   """Extract data from dask.Series or dask.DataFrame for predictors.
 
@@ -81,6 +89,7 @@ def extract_dask_data(data):
     return data
 
 
+@deprecated(None, 'Please feed input to tf.data to support dask.')
 def extract_dask_labels(labels):
   """Extract data from dask.Series or dask.DataFrame for labels.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py
index 96be8b1bc4..c45b1d1864 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Implementations of different data feeders to provide data for TF trainer."""
+"""Implementations of different data feeders to provide data for TF trainer (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 # TODO(ipolosukhin): Replace this module with feed-dict queue runners & queues.
 
@@ -31,6 +36,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util.deprecation import deprecated
 
 # pylint: disable=g-multiple-import,g-bad-import-order
 from .pandas_io import HAS_PANDAS, extract_pandas_data, extract_pandas_matrix, extract_pandas_labels
@@ -101,6 +107,7 @@ def _is_iterable(x):
   return hasattr(x, 'next') or hasattr(x, '__next__')
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 def setup_train_data_feeder(x,
                             y,
                             n_classes,
@@ -188,6 +195,7 @@ def _batch_data(x, batch_size=None):
     yield np.matrix(chunk)
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 def setup_predict_data_feeder(x, batch_size=None):
   """Returns an iterable for feeding into predict step.
 
@@ -219,6 +227,7 @@ def setup_predict_data_feeder(x, batch_size=None):
   return [x]
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 def setup_processor_data_feeder(x):
   """Sets up processor iterable.
 
@@ -233,6 +242,7 @@ def setup_processor_data_feeder(x):
   return x
 
 
+@deprecated(None, 'Please convert numpy dtypes explicitly.')
 def check_array(array, dtype):
   """Checks array on dtype and converts it if different.
 
@@ -275,8 +285,14 @@ def _check_dtype(dtype):
 
 
 class DataFeeder(object):
-  """Data feeder is an example class to sample data for TF trainer."""
+  """Data feeder is an example class to sample data for TF trainer.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data.')
   def __init__(self,
                x,
                y,
@@ -563,6 +579,10 @@ class DataFeeder(object):
 class StreamingDataFeeder(DataFeeder):
   """Data feeder for TF trainer that reads data from iterator.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Streaming data feeder allows to read data as it comes it from disk or
   somewhere else. It's custom to have this iterators rotate infinetly over
   the dataset, to allow control of how much to learn on the trainer side.
@@ -771,11 +791,16 @@ class StreamingDataFeeder(DataFeeder):
 class DaskDataFeeder(object):
   """Data feeder for that reads data from dask.Series and dask.DataFrame.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Numpy arrays can be serialized to disk and it's possible to do random seeks
   into them. DaskDataFeeder will remove requirement to have full dataset in the
   memory and still do random seeks for sampling of batches.
   """
 
+  @deprecated(None, 'Please feed input to tf.data to support dask.')
   def __init__(self,
                x,
                y,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py
index 884faf8335..f8aaa0c9e3 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Methods to allow generator of dict with numpy arrays."""
+"""Methods to allow generator of dict with numpy arrays (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -23,8 +28,10 @@ from types import FunctionType
 from types import GeneratorType
 
 from tensorflow.python.estimator.inputs.queues.feeding_functions import _enqueue_data as enqueue_data
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Please use tf.data.')
 def generator_input_fn(x,
                        target_key=None,
                        batch_size=128,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
index 3a46c23968..9e816f54b6 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Methods to read data in the graph."""
+"""Methods to read data in the graph (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -34,11 +39,13 @@ from tensorflow.python.platform import gfile
 from tensorflow.python.summary import summary
 from tensorflow.python.training import input as input_ops
 from tensorflow.python.training import queue_runner
+from tensorflow.python.util.deprecation import deprecated
 
 # Default name for key in the feature dict.
 KEY_FEATURE_NAME = '__key__'
 
 
+@deprecated(None, 'Use tf.data.')
 def read_batch_examples(file_pattern,
                         batch_size,
                         reader,
@@ -106,6 +113,7 @@ def read_batch_examples(file_pattern,
   return examples
 
 
+@deprecated(None, 'Use tf.data.')
 def read_keyed_batch_examples(file_pattern,
                               batch_size,
                               reader,
@@ -175,6 +183,7 @@ def read_keyed_batch_examples(file_pattern,
       seed=seed)
 
 
+@deprecated(None, 'Use tf.data.')
 def read_keyed_batch_examples_shared_queue(file_pattern,
                                            batch_size,
                                            reader,
@@ -452,6 +461,7 @@ def _read_keyed_batch_examples_helper(file_pattern,
     return queued_examples_with_keys
 
 
+@deprecated(None, 'Use tf.data.')
 def read_keyed_batch_features(file_pattern,
                               batch_size,
                               features,
@@ -540,6 +550,7 @@ def read_keyed_batch_features(file_pattern,
         name=scope)
 
 
+@deprecated(None, 'Use tf.data.')
 def read_keyed_batch_features_shared_queue(file_pattern,
                                            batch_size,
                                            features,
@@ -620,6 +631,7 @@ def read_keyed_batch_features_shared_queue(file_pattern,
         name=scope)
 
 
+@deprecated(None, 'Use tf.data.')
 def queue_parsed_features(parsed_features,
                           keys=None,
                           feature_queue_capacity=100,
@@ -742,6 +754,7 @@ def queue_parsed_features(parsed_features,
     return dequeued_keys, dequeued_parsed_features
 
 
+@deprecated(None, 'Use tf.data.')
 def read_batch_features(file_pattern,
                         batch_size,
                         features,
@@ -821,6 +834,7 @@ def read_batch_features(file_pattern,
   return features
 
 
+@deprecated(None, 'Use tf.data.')
 def read_batch_record_features(file_pattern,
                                batch_size,
                                features,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py b/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py
index 692438807f..29552d24f1 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/numpy_io.py
@@ -12,15 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Methods to allow dict of numpy arrays."""
+"""Methods to allow dict of numpy arrays (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.estimator.inputs.numpy_io import numpy_input_fn as core_numpy_input_fn
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Use tf.estimator.inputs.numpy_input_fn.')
 def numpy_input_fn(x,
                    y=None,
                    batch_size=128,
diff --git a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py
index ede7558eaf..b4ef055f5a 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py
@@ -13,13 +13,19 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Methods to allow pandas.DataFrame."""
+"""Methods to allow pandas.DataFrame (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.estimator.inputs.pandas_io import pandas_input_fn as core_pandas_input_fn
+from tensorflow.python.util.deprecation import deprecated
 
 try:
   # pylint: disable=g-import-not-at-top
@@ -47,6 +53,7 @@ PANDAS_DTYPES = {
 }
 
 
+@deprecated(None, 'Please use tf.estimator.inputs.pandas_input_fn')
 def pandas_input_fn(x,
                     y=None,
                     batch_size=128,
@@ -66,6 +73,7 @@ def pandas_input_fn(x,
                               target_column=target_column)
 
 
+@deprecated(None, 'Please access pandas data directly.')
 def extract_pandas_data(data):
   """Extract data from pandas.DataFrame for predictors.
 
@@ -96,6 +104,7 @@ def extract_pandas_data(data):
                      'float, or bool. Found: ' + ', '.join(error_report))
 
 
+@deprecated(None, 'Please access pandas data directly.')
 def extract_pandas_matrix(data):
   """Extracts numpy matrix from pandas DataFrame.
 
@@ -111,6 +120,7 @@ def extract_pandas_matrix(data):
   return data.as_matrix()
 
 
+@deprecated(None, 'Please access pandas data directly.')
 def extract_pandas_labels(labels):
   """Extract data from pandas.DataFrame for labels.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_runner.py b/tensorflow/contrib/learn/python/learn/learn_runner.py
index 2af723a0d6..d719a3e488 100644
--- a/tensorflow/contrib/learn/python/learn/learn_runner.py
+++ b/tensorflow/contrib/learn/python/learn/learn_runner.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Runs an Experiment."""
+"""Runs an Experiment (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,6 +27,7 @@ from tensorflow.contrib.learn.python.learn.estimators import run_config as run_c
 from tensorflow.contrib.learn.python.learn.experiment import Experiment
 from tensorflow.contrib.training.python.training import hparam as hparam_lib
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.util.deprecation import deprecated
 
 
 # TODO(xiejw): Refactor the learn_runner to make code reusable.
@@ -99,6 +105,7 @@ def _wrapped_experiment_fn_with_uid_check(experiment_fn, require_hparams=False):
   return wrapped_experiment_fn
 
 
+@deprecated(None, 'Use tf.estimator.train_and_evaluate.')
 def run(experiment_fn, output_dir=None, schedule=None, run_config=None,
         hparams=None):
   """Make and run an experiment.
@@ -218,6 +225,7 @@ def run(experiment_fn, output_dir=None, schedule=None, run_config=None,
   return _execute_schedule(experiment, schedule)
 
 
+@deprecated(None, 'Use tf.estimator.train_and_evaluate.')
 def tune(experiment_fn, tuner):
   """Tune an experiment with hyper-parameters.
 
diff --git a/tensorflow/contrib/learn/python/learn/learn_runner_lib.py b/tensorflow/contrib/learn/python/learn/learn_runner_lib.py
index 7d9b1c7716..ba2d067787 100644
--- a/tensorflow/contrib/learn/python/learn/learn_runner_lib.py
+++ b/tensorflow/contrib/learn/python/learn/learn_runner_lib.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utilities to run and tune an Experiment.
+"""Utilities to run and tune an Experiment (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 @@run
 @@tune
diff --git a/tensorflow/contrib/learn/python/learn/metric_spec.py b/tensorflow/contrib/learn/python/learn/metric_spec.py
index 6440bc204b..97220365d5 100644
--- a/tensorflow/contrib/learn/python/learn/metric_spec.py
+++ b/tensorflow/contrib/learn/python/learn/metric_spec.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The metric spec class to flexibly connect models and metrics."""
+"""The metric spec class to flexibly connect models and metrics (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,6 +27,7 @@ import six
 
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import tf_inspect
+from tensorflow.python.util.deprecation import deprecated
 
 
 def _assert_named_args(sentinel):
@@ -223,6 +229,10 @@ def _adapt_metric_fn(
 class MetricSpec(object):
   """MetricSpec connects a model to metric functions.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   The MetricSpec class contains all information necessary to connect the
   output of a `model_fn` to the metrics (usually, streaming metrics) that are
   used in evaluation.
@@ -284,6 +294,7 @@ class MetricSpec(object):
 
   """
 
+  @deprecated(None, 'Use tf.estimator.EstimatorSpec.eval_metric_ops.')
   def __init__(self,
                metric_fn,
                prediction_key=None,
diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py
index 4283240d01..bd4bbf9f8c 100644
--- a/tensorflow/contrib/learn/python/learn/models.py
+++ b/tensorflow/contrib/learn/python/learn/models.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Various high level TF models."""
+"""Various high level TF models (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -28,8 +33,10 @@ from tensorflow.python.ops import array_ops as array_ops_
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.summary import summary
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Consider using a tf.estimator.LinearRegressor')
 def linear_regression_zero_init(x, y):
   """Linear regression subgraph with zero-value initial weights and bias.
 
@@ -43,6 +50,7 @@ def linear_regression_zero_init(x, y):
   return linear_regression(x, y, init_mean=0.0, init_stddev=0.0)
 
 
+@deprecated(None, 'Consider using a class from tf.estimator.LinearClassifier')
 def logistic_regression_zero_init(x, y):
   """Logistic regression subgraph with zero-value initial weights and bias.
 
@@ -56,6 +64,7 @@ def logistic_regression_zero_init(x, y):
   return logistic_regression(x, y, init_mean=0.0, init_stddev=0.0)
 
 
+@deprecated(None, 'Consider using a class from tf.estimator.')
 def linear_regression(x, y, init_mean=None, init_stddev=1.0):
   """Creates linear regression TensorFlow subgraph.
 
@@ -107,6 +116,7 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0):
     return losses_ops.mean_squared_error_regressor(x, y, weights, bias)
 
 
+@deprecated(None, 'Consider using a class from tf.estimator.')
 def logistic_regression(x,
                         y,
                         class_weight=None,
@@ -203,6 +213,7 @@ def _reverse_seq(input_seq, lengths):
   return result
 
 
+@deprecated(None, 'Please consider `tf.nn.bidirectional_dynamic_rnn`.')
 def bidirectional_rnn(cell_fw,
                       cell_bw,
                       inputs,
@@ -283,6 +294,7 @@ def bidirectional_rnn(cell_fw,
 # End of TensorFlow 0.7
 
 
+@deprecated(None, 'Please consider tensorflow/tensor2tensor.')
 def get_rnn_model(rnn_size, cell_type, num_layers, input_op_fn, bidirectional,
                   target_predictor_fn, sequence_length, initial_state,
                   attn_length, attn_size, attn_vec_size):
diff --git a/tensorflow/contrib/learn/python/learn/monitored_session.py b/tensorflow/contrib/learn/python/learn/monitored_session.py
index 22602e9f69..ac0433f177 100644
--- a/tensorflow/contrib/learn/python/learn/monitored_session.py
+++ b/tensorflow/contrib/learn/python/learn/monitored_session.py
@@ -13,7 +13,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""A wrapper of Session API which runs hooks."""
+"""A wrapper of Session API which runs hooks (deprecated).
+
+These are deprecated aliases for classes and functions in `tf.train`. Please use
+those directly.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py
index 9457a73ecf..77f7c73d54 100644
--- a/tensorflow/contrib/learn/python/learn/monitors.py
+++ b/tensorflow/contrib/learn/python/learn/monitors.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Monitors instrument the training process.
+"""Monitors instrument the training process (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 @@get_default_monitors
 @@BaseMonitor
@@ -59,6 +63,10 @@ from tensorflow.python.util import tf_inspect
 class BaseMonitor(object):
   """Base class for Monitors.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Defines basic interfaces of Monitors.
   Monitors can either be run on all workers or, more commonly, restricted
   to run exclusively on the elected chief worker.
@@ -229,6 +237,10 @@ def _extract_output(outputs, request):
 class EveryN(BaseMonitor):
   """Base class for monitors that execute callbacks every N steps.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   This class adds three new callbacks:
     - every_n_step_begin
     - every_n_step_end
@@ -418,6 +430,10 @@ class StopAtStep(BaseMonitor):
 class PrintTensor(EveryN):
   """Prints given tensors every N steps.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   This is an `EveryN` monitor and has consistent semantic for `every_n`
   and `first_n`.
 
@@ -455,9 +471,12 @@ class PrintTensor(EveryN):
 class LoggingTrainable(EveryN):
   """Writes trainable variable values into log every N steps.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Write the tensors in trainable variables `every_n` steps,
   starting with the `first_n`th step.
-
   """
 
   def __init__(self, scope=None, every_n=100, first_n=1):
@@ -493,7 +512,12 @@ class LoggingTrainable(EveryN):
 
 
 class SummarySaver(EveryN):
-  """Saves summaries every N steps."""
+  """Saves summaries every N steps.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self,
                summary_op,
@@ -554,6 +578,10 @@ class SummarySaver(EveryN):
 class ValidationMonitor(EveryN):
   """Runs evaluation of a given estimator, at most every N steps.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note that the evaluation is done based on the saved checkpoint, which will
   usually be older than the current step.
 
@@ -756,6 +784,10 @@ class ValidationMonitor(EveryN):
 class CaptureVariable(EveryN):
   """Captures a variable's values into a collection.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   This monitor is useful for unit testing. You should exercise caution when
   using this monitor in production, since it never discards values.
 
@@ -794,6 +826,7 @@ class CaptureVariable(EveryN):
     self._var_values[step] = _extract_output(outputs, self._var_name)
 
 
+@deprecation.deprecated(None, "Use tf.train.MonitoredTrainingSession.")
 def get_default_monitors(loss_op=None,
                          summary_op=None,
                          save_summary_steps=100,
@@ -828,6 +861,10 @@ def get_default_monitors(loss_op=None,
 class GraphDump(BaseMonitor):
   """Dumps almost all tensors in the graph at every step.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Note, this is very expensive, prefer `PrintTensor` in production.
   """
 
@@ -917,7 +954,12 @@ class GraphDump(BaseMonitor):
 
 
 class ExportMonitor(EveryN):
-  """Monitor that exports Estimator every N steps."""
+  """Monitor that exports Estimator every N steps.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   @deprecation.deprecated("2017-03-25",
                           "ExportMonitor is deprecated. Please pass an "
@@ -1040,7 +1082,12 @@ class ExportMonitor(EveryN):
 
 
 class CheckpointSaver(BaseMonitor):
-  """Saves checkpoints every N steps or N seconds."""
+  """Saves checkpoints every N steps or N seconds.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self,
                checkpoint_dir,
@@ -1125,7 +1172,12 @@ class CheckpointSaver(BaseMonitor):
 
 
 class StepCounter(EveryN):
-  """Steps per second monitor."""
+  """Steps per second monitor.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
   def __init__(self, every_n_steps=100, output_dir=None, summary_writer=None):
     super(StepCounter, self).__init__(every_n_steps=every_n_steps)
@@ -1165,6 +1217,10 @@ class NanLossDuringTrainingError(RuntimeError):
 class NanLoss(EveryN):
   """NaN Loss monitor.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Monitors loss and stops training if loss is NaN.
   Can either fail with exception or just stop training.
   """
diff --git a/tensorflow/contrib/learn/python/learn/ops/__init__.py b/tensorflow/contrib/learn/python/learn/ops/__init__.py
index 33962e34cc..efb1f47cf5 100644
--- a/tensorflow/contrib/learn/python/learn/ops/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/ops/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Various TensorFlow Ops."""
+"""Various TensorFlow Ops (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py b/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py
index fa3b7323e3..b3b067b8e1 100644
--- a/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py
+++ b/tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py
@@ -13,7 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow Ops to work with embeddings.
+"""TensorFlow Ops to work with embeddings (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Note: categorical variables are handled via embeddings in many cases.
 For example, in case of words.
diff --git a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
index b040ab3bb6..92976d1539 100644
--- a/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
+++ b/tensorflow/contrib/learn/python/learn/ops/losses_ops.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow Ops for loss computation."""
+"""TensorFlow Ops for loss computation (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py
index 45727faab4..aa37cb4a76 100644
--- a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py
+++ b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow Ops for Sequence to Sequence models."""
+"""TensorFlow Ops for Sequence to Sequence models (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -26,8 +31,10 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import variable_scope as vs
+from tensorflow.python.util.deprecation import deprecated
 
 
+@deprecated(None, 'Please use tf.nn/tf.layers directly.')
 def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
   """Returns predictions and loss for sequence of predictions.
 
@@ -57,6 +64,7 @@ def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
     return array_ops.stack(predictions, axis=1), loss
 
 
+@deprecated(None, 'Please use tf.nn/tf.layers directly.')
 def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None):
   """Processes inputs for Sequence to Sequence models.
 
@@ -87,6 +95,7 @@ def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None):
     return in_x, in_y, out_y
 
 
+@deprecated(None, 'Please use tf.nn/tf.layers directly.')
 def rnn_decoder(decoder_inputs, initial_state, cell, scope=None):
   """RNN Decoder that creates training and sampling sub-graphs.
 
@@ -123,6 +132,7 @@ def rnn_decoder(decoder_inputs, initial_state, cell, scope=None):
   return outputs, states, sampling_outputs, sampling_states
 
 
+@deprecated(None, 'Please use tf.nn/tf.layers directly.')
 def rnn_seq2seq(encoder_inputs,
                 decoder_inputs,
                 encoder_cell,
diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py b/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py
index 7bcc177d4e..e8c6e1acf8 100644
--- a/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/preprocessing/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Preprocessing tools useful for building models."""
+"""Preprocessing tools useful for building models (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py b/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py
index 154739d497..faba3b2025 100644
--- a/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py
+++ b/tensorflow/contrib/learn/python/learn/preprocessing/categorical.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Implements preprocessing transformers for categorical variables."""
+"""Implements preprocessing transformers for categorical variables (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,6 +27,8 @@ from __future__ import print_function
 import math
 import numpy as np
 
+from tensorflow.python.util.deprecation import deprecated
+
 # pylint: disable=g-bad-import-order
 from . import categorical_vocabulary
 from ..learn_io.data_feeder import setup_processor_data_feeder
@@ -31,10 +38,16 @@ from ..learn_io.data_feeder import setup_processor_data_feeder
 class CategoricalProcessor(object):
   """Maps documents to sequences of word ids.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   As a common convention, Nan values are handled as unknown tokens.
   Both float('nan') and np.nan are accepted.
   """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data for sequence '
+              'processing.')
   def __init__(self, min_frequency=0, share=False, vocabularies=None):
     """Initializes a CategoricalProcessor instance.
 
diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py b/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py
index 5709955c49..3ac370a6ab 100644
--- a/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py
+++ b/tensorflow/contrib/learn/python/learn/preprocessing/categorical_vocabulary.py
@@ -13,7 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Categorical vocabulary classes to map categories to indexes.
+"""Categorical vocabulary classes to map categories to indexes (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Can be used for categorical variables, sparse variables and words.
 """
@@ -25,14 +29,21 @@ from __future__ import print_function
 import collections
 import six
 
+from tensorflow.python.util.deprecation import deprecated
+
 
 class CategoricalVocabulary(object):
   """Categorical variables vocabulary class.
 
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+
   Accumulates and provides mapping from classes to indexes.
   Can be easily used for words.
   """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data.')
   def __init__(self, unknown_token="<UNK>", support_reverse=True):
     self._unknown_token = unknown_token
     self._mapping = {unknown_token: 0}
diff --git a/tensorflow/contrib/learn/python/learn/preprocessing/text.py b/tensorflow/contrib/learn/python/learn/preprocessing/text.py
index 3af2074c2a..f2b6776be7 100644
--- a/tensorflow/contrib/learn/python/learn/preprocessing/text.py
+++ b/tensorflow/contrib/learn/python/learn/preprocessing/text.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Implements a number of text preprocessing utilities."""
+"""Implements a number of text preprocessing utilities (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -24,6 +29,7 @@ import numpy as np
 import six
 
 from tensorflow.python.platform import gfile
+from tensorflow.python.util.deprecation import deprecated
 
 from .categorical_vocabulary import CategoricalVocabulary  # pylint: disable=g-bad-import-order
 
@@ -38,6 +44,7 @@ TOKENIZER_RE = re.compile(r"[A-Z]{2,}(?![a-z])|[A-Z][a-z]+(?=[A-Z])|[\'\w\-]+",
                           re.UNICODE)
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 def tokenizer(iterator):
   """Tokenizer generator.
 
@@ -51,9 +58,16 @@ def tokenizer(iterator):
     yield TOKENIZER_RE.findall(value)
 
 
+@deprecated(None, 'Please use tensorflow/transform or tf.data.')
 class ByteProcessor(object):
-  """Maps documents into sequence of ids for bytes."""
+  """Maps documents into sequence of ids for bytes.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data.')
   def __init__(self, max_document_length):
     self.max_document_length = max_document_length
 
@@ -108,8 +122,14 @@ class ByteProcessor(object):
 
 
 class VocabularyProcessor(object):
-  """Maps documents to sequences of word ids."""
+  """Maps documents to sequences of word ids.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Please use tensorflow/transform or tf.data.')
   def __init__(self,
                max_document_length,
                min_frequency=0,
diff --git a/tensorflow/contrib/learn/python/learn/session_run_hook.py b/tensorflow/contrib/learn/python/learn/session_run_hook.py
index a8ba2be972..87edc9b720 100644
--- a/tensorflow/contrib/learn/python/learn/session_run_hook.py
+++ b/tensorflow/contrib/learn/python/learn/session_run_hook.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""This file is deprecated. Use tensorflow.python.training.session_run_hook."""
+"""This file is deprecated. Use `tensorflow.python.training.session_run_hook`.
+
+See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/summary_writer_cache.py b/tensorflow/contrib/learn/python/learn/summary_writer_cache.py
index 919d415c30..d663cf5fb7 100644
--- a/tensorflow/contrib/learn/python/learn/summary_writer_cache.py
+++ b/tensorflow/contrib/learn/python/learn/summary_writer_cache.py
@@ -12,7 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Wrapper for a Session-like object that handles threads and recovery.
+"""Wrapper for a Session-like object that handles threads and recovery (deprecated).
+
+These are deprecated aliases for classes and functions in `tf.train`. Please use
+those directly.
 
 Based on an original design of Illia Polosukhin.
 """
diff --git a/tensorflow/contrib/learn/python/learn/trainable.py b/tensorflow/contrib/learn/python/learn/trainable.py
index 429b6040be..a1a3f20dcd 100644
--- a/tensorflow/contrib/learn/python/learn/trainable.py
+++ b/tensorflow/contrib/learn/python/learn/trainable.py
@@ -12,7 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""`Trainable` interface."""
+"""`Trainable` interface (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -23,6 +28,8 @@ import abc
 
 class Trainable(object):
   """Interface for objects that are trainable by, e.g., `Experiment`.
+
+  THIS CLASS IS DEPRECATED.
   """
   __metaclass__ = abc.ABCMeta
 
diff --git a/tensorflow/contrib/learn/python/learn/utils/__init__.py b/tensorflow/contrib/learn/python/learn/utils/__init__.py
index 48978d0ac3..66d8dc6fd4 100644
--- a/tensorflow/contrib/learn/python/learn/utils/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/utils/__init__.py
@@ -13,7 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 
-"""TensorFlow Learn Utils."""
+"""TensorFlow Learn Utils (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/utils/export.py b/tensorflow/contrib/learn/python/learn/utils/export.py
index cb34cb1d26..3eacac7a3d 100644
--- a/tensorflow/contrib/learn/python/learn/utils/export.py
+++ b/tensorflow/contrib/learn/python/learn/utils/export.py
@@ -13,14 +13,18 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Export utilities."""
+"""Export utilities (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
+"""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.framework import deprecated
-from tensorflow.python.training import training_util
 from tensorflow.contrib.session_bundle import exporter
 from tensorflow.contrib.session_bundle import gc
 from tensorflow.python.client import session as tf_session
@@ -32,6 +36,7 @@ from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import saver as tf_saver
+from tensorflow.python.training import training_util
 
 
 @deprecated('2017-03-25', 'Please use Estimator.export_savedmodel() instead.')
diff --git a/tensorflow/contrib/learn/python/learn/utils/gc.py b/tensorflow/contrib/learn/python/learn/utils/gc.py
index 226915987a..916aecbea8 100644
--- a/tensorflow/contrib/learn/python/learn/utils/gc.py
+++ b/tensorflow/contrib/learn/python/learn/utils/gc.py
@@ -13,7 +13,11 @@
 # limitations under the License.
 # ==============================================================================
 
-r"""System for specifying garbage collection (GC) of path based data.
+r"""System for specifying garbage collection (GC) of path based data (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 This framework allows for GC of data specified by path names, for example files
 on disk.  gc.Path objects each represent a single item stored at a path and may
@@ -73,10 +77,12 @@ import os
 
 from tensorflow.python.platform import gfile
 from tensorflow.python.util import compat
+from tensorflow.python.util.deprecation import deprecated
 
 Path = collections.namedtuple('Path', 'path export_version')
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def largest_export_versions(n):
   """Creates a filter that keeps the largest n export versions.
 
@@ -97,6 +103,7 @@ def largest_export_versions(n):
   return keep
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def one_of_every_n_export_versions(n):
   """Creates a filter that keeps one of every n export versions.
 
@@ -128,6 +135,7 @@ def one_of_every_n_export_versions(n):
   return keep
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def mod_export_version(n):
   """Creates a filter that keeps every export that is a multiple of n.
 
@@ -146,6 +154,7 @@ def mod_export_version(n):
   return keep
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def union(lf, rf):
   """Creates a filter that keeps the union of two filters.
 
@@ -163,6 +172,7 @@ def union(lf, rf):
   return keep
 
 
+@deprecated(None, 'Please implement your own file management or use Saver.')
 def negation(f):
   """Negate a filter.
 
@@ -179,6 +189,7 @@ def negation(f):
   return keep
 
 
+@deprecated(None, 'Please implement your own file name management.')
 def get_paths(base_dir, parser):
   """Gets a list of Paths in a given directory.
 
diff --git a/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py b/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py
index b2521933e5..b92eb9fea8 100644
--- a/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py
+++ b/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utilities for creating input_fns.
+"""Utilities for creating input_fns (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Contents of this file are moved to tensorflow/python/estimator/export.py.
 InputFnOps is renamed to ServingInputReceiver.
@@ -32,13 +36,17 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import parsing_ops
+from tensorflow.python.util.deprecation import deprecated
 
 
 class InputFnOps(collections.namedtuple('InputFnOps',
                                         ['features',
                                          'labels',
                                          'default_inputs'])):
-  """A return type for an input_fn.
+  """A return type for an input_fn (deprecated).
+
+  THIS CLASS IS DEPRECATED. Please use tf.estimator.export.ServingInputReceiver
+  instead.
 
   This return type is currently only supported for serving input_fn.
   Training and eval input_fn should return a `(features, labels)` tuple.
@@ -56,6 +64,8 @@ class InputFnOps(collections.namedtuple('InputFnOps',
   """
 
 
+@deprecated(None, 'Please use '
+            'tf.estimator.export.build_parsing_serving_input_receiver_fn.')
 def build_parsing_serving_input_fn(feature_spec, default_batch_size=None):
   """Build an input_fn appropriate for serving, expecting fed tf.Examples.
 
@@ -84,6 +94,8 @@ def build_parsing_serving_input_fn(feature_spec, default_batch_size=None):
   return input_fn
 
 
+@deprecated(None, 'Please use '
+            'tf.estimator.export.build_raw_serving_input_receiver_fn.')
 def build_default_serving_input_fn(features, default_batch_size=None):
   """Build an input_fn appropriate for serving, expecting feature Tensors.
 
diff --git a/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py b/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py
index 6a63fb545a..6dbaa15f83 100644
--- a/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py
+++ b/tensorflow/contrib/learn/python/learn/utils/inspect_checkpoint.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""A simple script for inspect checkpoint files."""
+"""A simple script for inspect checkpoint files (deprecated)."""
 
 from __future__ import absolute_import
 from __future__ import division
diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
index 1593380007..213619a187 100644
--- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
+++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py
@@ -12,7 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utilities supporting export to SavedModel.
+"""Utilities supporting export to SavedModel (deprecated).
+
+This module and all its submodules are deprecated. See
+[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+for migration instructions.
 
 Some contents of this file are moved to tensorflow/python/estimator/export.py:
 
@@ -52,8 +56,9 @@ from tensorflow.python.saved_model import signature_constants
 from tensorflow.python.saved_model import signature_def_utils
 from tensorflow.python.summary import summary_iterator
 from tensorflow.python.training import saver
-
 from tensorflow.python.util import compat
+from tensorflow.python.util.deprecation import deprecated
+
 
 # A key for use in the input_alternatives dict indicating the default input.
 # This is the input that will be expected when a serving request does not
@@ -77,6 +82,7 @@ FEATURES_INPUT_ALTERNATIVE_KEY = 'features_input_alternative'
 _FALLBACK_DEFAULT_OUTPUT_ALTERNATIVE_KEY = 'default_output_alternative'
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def build_standardized_signature_def(input_tensors, output_tensors,
                                      problem_type):
   """Build a SignatureDef using problem type and input and output Tensors.
@@ -156,6 +162,7 @@ def _is_regression_problem(problem_type, input_tensors, output_tensors):
           len(input_tensors) == 1 and len(output_tensors) == 1)
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_input_alternatives(input_ops):
   """Obtain all input alternatives using the input_fn output and heuristics."""
   input_alternatives = {}
@@ -181,6 +188,7 @@ def get_input_alternatives(input_ops):
   return input_alternatives, features
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_output_alternatives(model_fn_ops, default_output_alternative_key=None):
   """Obtain all output alternatives using the model_fn output and heuristics.
 
@@ -246,6 +254,7 @@ def get_output_alternatives(model_fn_ops, default_output_alternative_key=None):
                        sorted(output_alternatives.keys())))
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def build_all_signature_defs(input_alternatives, output_alternatives,
                              actual_default_output_alternative_key):
   """Build `SignatureDef`s from all pairs of input and output alternatives."""
@@ -279,6 +288,7 @@ def build_all_signature_defs(input_alternatives, output_alternatives,
 MAX_DIRECTORY_CREATION_ATTEMPTS = 10
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_timestamped_export_dir(export_dir_base):
   """Builds a path to a new subdirectory within the base directory.
 
@@ -317,6 +327,7 @@ def get_timestamped_export_dir(export_dir_base):
                      '{} attempts.'.format(MAX_DIRECTORY_CREATION_ATTEMPTS))
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_temp_export_dir(timestamped_export_dir):
   """Builds a directory name based on the argument but starting with 'temp-'.
 
@@ -344,6 +355,7 @@ def _export_version_parser(path):
   return path._replace(export_version=int(filename))
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def get_most_recent_export(export_dir_base):
   """Locate the most recent SavedModel export in a directory of many exports.
 
@@ -363,6 +375,7 @@ def get_most_recent_export(export_dir_base):
   return next(iter(results or []), None)
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def garbage_collect_exports(export_dir_base, exports_to_keep):
   """Deletes older exports, retaining only a given number of the most recent.
 
@@ -387,6 +400,7 @@ def garbage_collect_exports(export_dir_base, exports_to_keep):
       logging.warn('Can not delete %s recursively: %s', p.path, e)
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def make_export_strategy(serving_input_fn,
                          default_output_alternative_key=None,
                          assets_extra=None,
@@ -469,6 +483,8 @@ def make_export_strategy(serving_input_fn,
   return export_strategy.ExportStrategy('Servo', export_fn, strip_default_attrs)
 
 
+@deprecated(None,
+            'Use tf.estimator.export.build_parsing_serving_input_receiver_fn')
 def make_parsing_export_strategy(feature_columns,
                                  default_output_alternative_key=None,
                                  assets_extra=None,
@@ -555,8 +571,14 @@ def _default_compare_fn(curr_best_eval_result, cand_eval_result):
 
 
 class BestModelSelector(object):
-  """A helper that keeps track of export selection candidates."""
+  """A helper that keeps track of export selection candidates.
+
+  THIS CLASS IS DEPRECATED. See
+  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
+  for general migration instructions.
+  """
 
+  @deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
   def __init__(self, event_file_pattern=None, compare_fn=None):
     """Constructor of this class.
 
@@ -622,6 +644,7 @@ class BestModelSelector(object):
     return best_eval_result
 
 
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def make_best_model_export_strategy(
     serving_input_fn,
     exports_to_keep=1,
@@ -707,6 +730,7 @@ def make_best_model_export_strategy(
 
 # TODO(b/67013778): Revisit this approach when corresponding changes to
 # TF Core are finalized.
+@deprecated(None, 'Switch to tf.estimator.Exporter and associated utilities.')
 def extend_export_strategy(base_export_strategy,
                            post_export_fn,
                            post_export_name=None):
diff --git a/tensorflow/python/util/decorator_utils.py b/tensorflow/python/util/decorator_utils.py
index df259c7f7c..7b4363c0e4 100644
--- a/tensorflow/python/util/decorator_utils.py
+++ b/tensorflow/python/util/decorator_utils.py
@@ -82,7 +82,7 @@ def add_notice_to_docstring(
     lines = _normalize_docstring(doc).splitlines()
     lines[0] += ' ' + suffix_str
 
-  notice = [''] + notice + [instructions]
+  notice = [''] + notice + ([instructions] if instructions else [])
 
   if len(lines) > 1:
     # Make sure that we keep our distance from the main body
-- 
GitLab


From d1ba271902a91a044e7515e248cd9f384a91067b Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Mon, 26 Feb 2018 16:24:54 -0800
Subject: [PATCH 399/575] [XLA] In HloEvaluator, fix an issue for HandleAbs to
 handle complex numbers more correctly:

- abs([complex numbers]) would yield floats. However since the specilization for
HandleAbs is based on the return type (float), we'd CHECK fail due to float !=
complex when accessing the elements of the operand (complex).
- enable unary_op_test for interpreter.

PiperOrigin-RevId: 187099576
---
 .../compiler/xla/service/hlo_evaluator.cc     | 32 +++++++++++++++++--
 tensorflow/compiler/xla/tests/BUILD           |  1 +
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index fd06b19144..cf8b35908f 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -57,6 +57,12 @@ struct is_complex_t : public std::false_type {};
 template <>
 struct is_complex_t<complex64> : public std::true_type {};
 
+template <typename T>
+struct is_complex64_t : public std::false_type {};
+
+template <>
+struct is_complex64_t<complex64> : public std::true_type {};
+
 template <typename OperandT>
 StatusOr<std::unique_ptr<Literal>> Compare(const Shape& shape, HloOpcode opcode,
                                            const Literal& lhs_literal,
@@ -248,17 +254,37 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
   template <
       typename NativeT,
-      typename std::enable_if<std::is_signed<NativeT>::value ||
-                              is_complex_t<NativeT>::value>::type* = nullptr>
+      typename std::enable_if<std::is_signed<NativeT>::value>::type* = nullptr>
   Status HandleAbs(HloInstruction* abs) {
     TF_ASSIGN_OR_RETURN(parent_->evaluated_[abs],
-                        ElementWiseUnaryOp(abs, [](ElementwiseT elem_operand) {
+                        ElementWiseUnaryOp(abs, [](NativeT elem_operand) {
                           return std::abs(elem_operand);
                         }));
     return Status::OK();
   }
 
+  template <
+      typename NativeT,
+      typename std::enable_if<is_complex64_t<NativeT>::value>::type* = nullptr>
+  Status HandleAbs(HloInstruction* abs) {
+    const Literal& operand_literal =
+        parent_->GetEvaluatedLiteralFor(abs->operand(0));
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[abs],
+        (ElementWiseUnaryOpImpl<float, NativeT>(
+            abs, [](NativeT elem_operand) { return std::abs(elem_operand); },
+            operand_literal)));
+
+    return Status::OK();
+  }
+
   Status HandleAbs(HloInstruction* abs) override {
+    // If the operand is of C64 type, the return type of abs will be F32.
+    // However, ElementwiseT would still be the return type, F32, and thus
+    // specifying the ElementwiseT explicitly as C64 is needed below.
+    if (abs->operand(0)->shape().element_type() == C64) {
+      return HandleAbs<complex64>(abs);
+    }
     return HandleAbs<ElementwiseT>(abs);
   }
 
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 33fde9737d..f3ecfc1604 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -494,6 +494,7 @@ xla_test(
 xla_test(
     name = "unary_op_test",
     srcs = ["unary_op_test.cc"],
+    tags = ["enable_for_xla_interpreter"],
     deps = [
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
-- 
GitLab


From 60ff3890e98f53c1037440d5e535f6f79ad42d7d Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Mon, 26 Feb 2018 17:01:24 -0800
Subject: [PATCH 400/575] Only link the swapping code when compiling TensorFlow
 with CUDA support.

PiperOrigin-RevId: 187104273
---
 tensorflow/core/grappler/optimizers/BUILD | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 908e58bcc7..a52d1c8df2 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -3,6 +3,7 @@ licenses(["notice"])  # Apache 2.0
 load("//tensorflow:tensorflow.bzl", "tf_cc_test")
 load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu")
 load("//tensorflow:tensorflow.bzl", "tf_kernel_library")
+load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
 
 filegroup(
     name = "all_files",
@@ -319,8 +320,6 @@ cc_library(
     ],
     visibility = ["//visibility:public"],
     deps = [
-        ":gpu_swapping_kernels",
-        ":gpu_swapping_ops",
         ":graph_optimizer",
         ":graph_rewriter",
         ":static_schedule",
@@ -336,7 +335,10 @@ cc_library(
         "//tensorflow/core/grappler/costs:graph_properties",
         "//tensorflow/core/grappler/utils:topological_sort",
         "//tensorflow/core/grappler/utils:traversal",
-    ],
+    ] + if_cuda([
+        ":gpu_swapping_kernels",
+        ":gpu_swapping_ops",
+    ]),
 )
 
 tf_cc_test_gpu(
-- 
GitLab


From 7bcc7ee1a9da4ec55395a935123a46b4ecb2364f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 17:04:09 -0800
Subject: [PATCH 401/575] Consolidate the builtin function overrides into a
 single module, and use a generic `dynamic_builtin` function to dispatch
 between implementations. Use the generic dispatcher in the generated code.

PiperOrigin-RevId: 187104685
---
 .../py2tf/converters/builtin_functions.py     | 13 ++++---
 tensorflow/contrib/py2tf/utils/BUILD          | 12 +-----
 tensorflow/contrib/py2tf/utils/__init__.py    |  4 +-
 .../py2tf/utils/{printing.py => builtins.py}  | 32 +++++++++++++--
 .../{printing_test.py => builtins_test.py}    | 39 +++++++++++++++----
 tensorflow/contrib/py2tf/utils/misc.py        | 13 -------
 tensorflow/contrib/py2tf/utils/misc_test.py   | 27 +------------
 7 files changed, 72 insertions(+), 68 deletions(-)
 rename tensorflow/contrib/py2tf/utils/{printing.py => builtins.py} (62%)
 rename tensorflow/contrib/py2tf/utils/{printing_test.py => builtins_test.py} (56%)

diff --git a/tensorflow/contrib/py2tf/converters/builtin_functions.py b/tensorflow/contrib/py2tf/converters/builtin_functions.py
index e69038aced..b5aa9756da 100644
--- a/tensorflow/contrib/py2tf/converters/builtin_functions.py
+++ b/tensorflow/contrib/py2tf/converters/builtin_functions.py
@@ -36,23 +36,24 @@ class BuiltinFunctionTransformer(transformer.Base):
 
   # pylint:disable=invalid-name
 
-  def _convert_len(self, node):
+  def _convert_builtin(self, node):
     template = """
-      py2tf_utils.dynamic_len(args)
+      py2tf_utils.dynamic_builtin(func, args)
     """
-    return templates.replace(template, args=node.args)[0].value
+    return templates.replace(template, func=node.func, args=node.args)[0].value
 
   def _convert_print(self, node):
     template = """
-      py2tf_utils.call_print(args)
+      py2tf_utils.dynamic_print(args)
     """
     return templates.replace(template, args=node.args)[0].value
 
   def visit_Call(self, node):
     self.generic_visit(node)
     # TODO(mdan): This won't work if the function was hidden.
-    if isinstance(node.func, gast.Name) and node.func.id == 'len':
-      return self._convert_len(node)
+    if isinstance(node.func, gast.Name) and node.func.id in ('len',):
+      return self._convert_builtin(node)
+    # Print needs to be handled separately because it can be read as statement.
     if isinstance(node.func, gast.Name) and node.func.id == 'print':
       return self._convert_print(node)
     return node
diff --git a/tensorflow/contrib/py2tf/utils/BUILD b/tensorflow/contrib/py2tf/utils/BUILD
index c2fdd40707..2086a9ef60 100644
--- a/tensorflow/contrib/py2tf/utils/BUILD
+++ b/tensorflow/contrib/py2tf/utils/BUILD
@@ -20,10 +20,10 @@ py_library(
     name = "utils",
     srcs = [
         "__init__.py",
+        "builtins.py",
         "context_managers.py",
         "misc.py",
         "multiple_dispatch.py",
-        "printing.py",
         "py_func.py",
         "tensor_list.py",
         "type_check.py",
@@ -76,16 +76,6 @@ py_test(
     ],
 )
 
-py_test(
-    name = "printing_test",
-    srcs = ["printing_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":utils",
-        "//tensorflow/python:client_testlib",
-    ],
-)
-
 py_test(
     name = "type_check_test",
     srcs = ["type_check_test.py"],
diff --git a/tensorflow/contrib/py2tf/utils/__init__.py b/tensorflow/contrib/py2tf/utils/__init__.py
index d931322bf3..19bf2272bc 100644
--- a/tensorflow/contrib/py2tf/utils/__init__.py
+++ b/tensorflow/contrib/py2tf/utils/__init__.py
@@ -18,11 +18,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.py2tf.utils.builtins import dynamic_builtin
+from tensorflow.contrib.py2tf.utils.builtins import dynamic_print
 from tensorflow.contrib.py2tf.utils.context_managers import control_dependency_on_returns
 from tensorflow.contrib.py2tf.utils.misc import alias_tensors
-from tensorflow.contrib.py2tf.utils.misc import dynamic_len
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_cond
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_while
-from tensorflow.contrib.py2tf.utils.printing import call_print
 from tensorflow.contrib.py2tf.utils.py_func import wrap_py_func
 from tensorflow.contrib.py2tf.utils.type_check import is_tensor
diff --git a/tensorflow/contrib/py2tf/utils/printing.py b/tensorflow/contrib/py2tf/utils/builtins.py
similarity index 62%
rename from tensorflow/contrib/py2tf/utils/printing.py
rename to tensorflow/contrib/py2tf/utils/builtins.py
index 95a62bd80b..0a50b80b60 100644
--- a/tensorflow/contrib/py2tf/utils/printing.py
+++ b/tensorflow/contrib/py2tf/utils/builtins.py
@@ -12,14 +12,40 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""TensorFlow printing support utilities."""
+"""Builtin conversion utilities."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.py2tf.utils import py_func
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import logging_ops
+from tensorflow.python.util import tf_inspect
+
+
+def dynamic_builtin(f, *args, **kwargs):
+  """Converts a builtin function call inline."""
+  if not tf_inspect.isbuiltin(f):
+    return f(*args, **kwargs)
+
+  if f is len:
+    return dynamic_len(*args, **kwargs)
+
+  raise NotImplementedError('The "%s" builtin is not yet supported.' % f)
+
+
+def dynamic_len(list_or_tensor):
+  """Implementation of len using dynamic dispatch."""
+  if tensor_util.is_tensor(list_or_tensor):
+    shape = list_or_tensor.shape
+    if not shape:
+      raise ValueError(
+          'len requires non-zero rank for tensor "%s"' % list_or_tensor)
+    return array_ops.shape(list_or_tensor)[0]
+
+  return len(list_or_tensor)
 
 
 def is_tf_print_compatible(value):
@@ -30,8 +56,8 @@ def is_tf_print_compatible(value):
   return False
 
 
-def call_print(*values):
-  """Compiled counterpart of the print builtin.
+def dynamic_print(*values):
+  """Implementartion of print using dynamic dispatch.
 
   The function attempts to use tf.Print if all the values are compatible.
   Otherwise, it will fall back to py_func.
diff --git a/tensorflow/contrib/py2tf/utils/printing_test.py b/tensorflow/contrib/py2tf/utils/builtins_test.py
similarity index 56%
rename from tensorflow/contrib/py2tf/utils/printing_test.py
rename to tensorflow/contrib/py2tf/utils/builtins_test.py
index 2070deb304..19a72c63ec 100644
--- a/tensorflow/contrib/py2tf/utils/printing_test.py
+++ b/tensorflow/contrib/py2tf/utils/builtins_test.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for printing module."""
+"""Tests for builtins module."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,28 +22,53 @@ import sys
 
 import six
 
-from tensorflow.contrib.py2tf.utils import printing
+from tensorflow.contrib.py2tf.utils import builtins
+from tensorflow.python.framework import constant_op
 from tensorflow.python.platform import test
 
 
-class ContextManagersTest(test.TestCase):
+class BuiltinsTest(test.TestCase):
 
-  def test_call_print_tf(self):
+  def test_dynamic_len_tf_scalar(self):
+    a = constant_op.constant(1)
+
+    with self.assertRaises(ValueError):
+      with self.test_session() as sess:
+        sess.run(builtins.dynamic_builtin(len, a))
+
+  def test_dynamic_len_tf_array(self):
+    a = constant_op.constant([1, 2, 3])
+
+    with self.test_session() as sess:
+      self.assertEqual(3, sess.run(builtins.dynamic_builtin(len, a)))
+
+  def test_dynamic_len_tf_matrix(self):
+    a = constant_op.constant([[1, 2], [3, 4]])
+
+    with self.test_session() as sess:
+      self.assertEqual(2, sess.run(builtins.dynamic_builtin(len, a)))
+
+  def test_dynamic_len_py_list(self):
+    a = [3] * 5
+
+    self.assertEqual(5, builtins.dynamic_builtin(len, a))
+
+  def test_dynamic_print_tf(self):
     try:
       out_capturer = six.StringIO()
       sys.stdout = out_capturer
       with self.test_session() as sess:
-        sess.run(printing.call_print('test message', 1))
+        sess.run(builtins.dynamic_print('test message', 1))
         self.assertEqual(out_capturer.getvalue(), 'test message 1\n')
     finally:
       sys.stdout = sys.__stdout__
 
-  def test_call_print_py_func(self):
+  def test_dynamic_print_complex(self):
     try:
       out_capturer = six.StringIO()
       sys.stdout = out_capturer
       with self.test_session() as sess:
-        sess.run(printing.call_print('test message', [1, 2]))
+        sess.run(builtins.dynamic_print('test message', [1, 2]))
         self.assertEqual(out_capturer.getvalue(), 'test message [1, 2]\n')
     finally:
       sys.stdout = sys.__stdout__
diff --git a/tensorflow/contrib/py2tf/utils/misc.py b/tensorflow/contrib/py2tf/utils/misc.py
index 7548048388..1b06caf0bd 100644
--- a/tensorflow/contrib/py2tf/utils/misc.py
+++ b/tensorflow/contrib/py2tf/utils/misc.py
@@ -19,22 +19,9 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 
 
-def dynamic_len(list_or_tensor):
-  """Implementation of len using dynamic dispatch."""
-  if tensor_util.is_tensor(list_or_tensor):
-    shape = list_or_tensor.shape
-    if not shape:
-      raise ValueError(
-          'len requires non-zero rank for tensor "%s"' % list_or_tensor)
-    return array_ops.shape(list_or_tensor)[0]
-
-  return len(list_or_tensor)
-
-
 def alias_tensors(*args):
   """Wrap any Tensor arguments with an identity op.
 
diff --git a/tensorflow/contrib/py2tf/utils/misc_test.py b/tensorflow/contrib/py2tf/utils/misc_test.py
index ec88e7cb74..8aedd4cd64 100644
--- a/tensorflow/contrib/py2tf/utils/misc_test.py
+++ b/tensorflow/contrib/py2tf/utils/misc_test.py
@@ -19,37 +19,12 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.py2tf.utils.misc import alias_tensors
-from tensorflow.contrib.py2tf.utils.misc import dynamic_len
 from tensorflow.python.framework.constant_op import constant
 from tensorflow.python.ops.variables import Variable
 from tensorflow.python.platform import test
 
 
-class ContextManagersTest(test.TestCase):
-
-  def test_dynamic_len_tf_scalar(self):
-    a = constant(1)
-
-    with self.assertRaises(ValueError):
-      with self.test_session() as sess:
-        sess.run(dynamic_len(a))
-
-  def test_dynamic_len_tf_array(self):
-    a = constant([1, 2, 3])
-
-    with self.test_session() as sess:
-      self.assertEqual(3, sess.run(dynamic_len(a)))
-
-  def test_dynamic_len_tf_matrix(self):
-    a = constant([[1, 2], [3, 4]])
-
-    with self.test_session() as sess:
-      self.assertEqual(2, sess.run(dynamic_len(a)))
-
-  def test_dynamic_len_py_list(self):
-    a = [3] * 5
-
-    self.assertEqual(5, dynamic_len(a))
+class MiscTest(test.TestCase):
 
   def test_alias_single_tensor(self):
     a = constant(1)
-- 
GitLab


From cb0984df5549c077621049416f69b914635208ce Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 17:27:20 -0800
Subject: [PATCH 402/575] Fix buffer assignment for conditional instruction.

PiperOrigin-RevId: 187107432
---
 .../compiler/xla/service/buffer_assignment.cc | 358 +++++++++---------
 .../compiler/xla/service/copy_insertion.cc    |  72 +++-
 2 files changed, 241 insertions(+), 189 deletions(-)

diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc
index b1e693da9d..d44d3d71d9 100644
--- a/tensorflow/compiler/xla/service/buffer_assignment.cc
+++ b/tensorflow/compiler/xla/service/buffer_assignment.cc
@@ -48,6 +48,183 @@ using ::tensorflow::strings::HumanReadableNumBytes;
 using ::tensorflow::strings::Printf;
 using ::tensorflow::strings::StrAppend;
 
+namespace {
+
+template <typename T>
+string ColocatedBufferSetsToString(const T& container, const char* title) {
+  string result;
+  StrAppend(&result, title, "\n");
+  for (const auto& it : container) {
+    StrAppend(&result, "\t", it->ToString(), "\n");
+  }
+  return result;
+}
+
+// Walk the call graph of the HLO module and place each computation into either
+// thread_local_computations or global_computations depending upon whether the
+// computation requires thread-local allocations or global allocations. The
+// elements in thread_local_computations and global_computations are in post
+// order (if computation A has an instruction which calls computation B, then A
+// will appear after B in the vector).
+Status GatherComputationsByAllocationType(
+    const HloModule* module,
+    std::vector<const HloComputation*>* thread_local_computations,
+    std::vector<const HloComputation*>* global_computations) {
+  // Create a worklist of computations paired with whether the allocation must
+  // be thread-local.
+  std::deque<std::pair<const HloComputation*, bool>> worklist;
+  worklist.push_back(std::make_pair(module->entry_computation(),
+                                    /*is_thread_local*/ false));
+
+  // Sets for quickly checking membership. Computations are returned in vectors
+  // for stable iteration.
+  FlatSet<const HloComputation*> thread_local_set;
+  FlatSet<const HloComputation*> global_set;
+
+  while (!worklist.empty()) {
+    auto worklist_front = worklist.front();
+    worklist.pop_front();
+    const HloComputation* computation = worklist_front.first;
+    bool is_thread_local = worklist_front.second;
+    bool in_thread_local_set = thread_local_set.count(computation) > 0;
+    bool in_global_set = global_set.count(computation) > 0;
+
+    // If the computation has already been added to the respective set, then
+    // nothing to do.
+    if ((is_thread_local && in_thread_local_set) ||
+        (!is_thread_local && in_global_set)) {
+      continue;
+    }
+
+    // If the computation has already been added to the other set this is an
+    // error condition because the global call to the computation (eg,
+    // while/call) may return a reference to one of the thread-local buffers to
+    // the calling computation which will become a dangling reference when the
+    // thread-local is deallocated with the call return.
+    if ((is_thread_local && in_global_set) ||
+        (!is_thread_local && in_thread_local_set)) {
+      return InvalidArgument(
+          "computation %s has conflicting allocation requirements (global "
+          "and thread-local)",
+          computation->name().c_str());
+    }
+
+    if (is_thread_local) {
+      thread_local_set.insert(computation);
+    } else {
+      global_set.insert(computation);
+    }
+
+    for (auto* instruction : computation->instructions()) {
+      for (HloComputation* subcomputation :
+           instruction->called_computations()) {
+        switch (instruction->opcode()) {
+          case HloOpcode::kCall:
+          case HloOpcode::kConditional:
+          case HloOpcode::kWhile:
+            // Call and while must be called from a computation with global
+            // allocations as they may return references to buffers inside the
+            // called computation which cannot be thread-local.
+            if (is_thread_local) {
+              return InvalidArgument(
+                  "computation %s cannot contain call/while op because it "
+                  "requires thread-local buffer allocations",
+                  computation->name().c_str());
+            }
+            worklist.push_back(std::make_pair(subcomputation,
+                                              false));  // Not thread local.
+            break;
+          case HloOpcode::kMap:
+          case HloOpcode::kReduce:
+          case HloOpcode::kReduceWindow:
+          case HloOpcode::kSelectAndScatter:
+          case HloOpcode::kFusion:
+            // Map/reduce etc computations are always thread-local.
+            worklist.push_back(std::make_pair(subcomputation,
+                                              true));  // Thread local.
+            break;
+          default:
+            return InternalError(
+                "Unexpected calling opcode: %s",
+                HloOpcodeString(instruction->opcode()).c_str());
+        }
+      }
+    }
+  }
+
+  // Add the computations to the vectors in post order.
+  for (auto* computation : module->MakeComputationPostOrder()) {
+    if (thread_local_set.count(computation) > 0) {
+      thread_local_computations->push_back(computation);
+    } else if (global_set.count(computation) > 0) {
+      global_computations->push_back(computation);
+    }
+    // If the computation is not reachable from the entry computation, then it
+    // will not appear in either thread_local_set or global_set. We don't bother
+    // assigning buffers for these.
+  }
+  return Status::OK();
+}
+
+// Checks that points-to set of 'instruction' is unambiguous and distinct
+// (ensured by CopyInsertion), then adds the buffer from the points-to set at
+// 'index' to 'colocated_set'.
+const LogicalBuffer* AddBufferToColocatedSet(
+    const HloInstruction* instruction, const ShapeIndex& index,
+    const TuplePointsToAnalysis& points_to_analysis,
+    std::vector<const LogicalBuffer*>* colocated_set) {
+  // CopyInsertion ensures root points-to set is unambiguous and distinct.
+  const auto& points_to = points_to_analysis.GetPointsToSet(instruction);
+  DCHECK(!points_to.IsAmbiguous());
+  colocated_set->push_back(points_to.element(index)[0]);
+  return colocated_set->back();
+}
+
+// Given the interference map of a graph (the list of interfering node indices
+// for each node), perform graph coloring such that interfering nodes are
+// assigned to different colors. Returns the assigned color of the nodes, where
+// the colors are represented as integer values [0, color_count).
+std::vector<int64> ColorInterferenceGraph(
+    const std::vector<std::vector<int64>>& interference_map) {
+  const int64 node_count = interference_map.size();
+
+  // Sort the nodes such that we assign nodes with more interference first. This
+  // relies on the common heuristic of assigning the most constrained node
+  // first, but it would be good to investigate other ordering heuristics too.
+  std::vector<int64> nodes(node_count);
+  std::iota(nodes.begin(), nodes.end(), 0);
+  std::sort(nodes.begin(), nodes.end(),
+            [&interference_map](const int64 i, const int64 j) {
+              return interference_map[i].size() > interference_map[j].size();
+            });
+
+  const int64 kColorUnassigned = -1;
+  std::vector<int64> assigned_colors(node_count, kColorUnassigned);
+  for (int64 node : nodes) {
+    // Mark the colors that are already assigned to the neighbors.
+    std::vector<bool> available_colors(node_count, true);
+    for (int64 neighbor : interference_map[node]) {
+      int64 color = assigned_colors[neighbor];
+      if (color != kColorUnassigned) {
+        available_colors[color] = false;
+      }
+    }
+
+    // Find the color that is not yet assigned to the neighbors.
+    int64 color = kColorUnassigned;
+    for (color = 0; color < available_colors.size(); ++color) {
+      if (available_colors[color]) {
+        break;
+      }
+    }
+    CHECK_NE(color, kColorUnassigned);
+    assigned_colors[node] = color;
+  }
+  return assigned_colors;
+}
+
+}  // namespace
+
 size_t BufferAllocation::Slice::Hasher::operator()(Slice s) const {
   uint64 h = std::hash<int64>()(s.index());
   h = tensorflow::Hash64Combine(h, std::hash<int64>()(s.offset()));
@@ -523,116 +700,6 @@ BufferAssignmentProto BufferAssignment::ToProto() const {
   return proto;
 }
 
-namespace {
-
-// Walk the call graph of the HLO module and place each computation into either
-// thread_local_computations or global_computations depending upon whether the
-// computation requires thread-local allocations or global allocations. The
-// elements in thread_local_computations and global_computations are in post
-// order (if computation A has an instruction which calls computation B, then A
-// will appear after B in the vector).
-Status GatherComputationsByAllocationType(
-    const HloModule* module,
-    std::vector<const HloComputation*>* thread_local_computations,
-    std::vector<const HloComputation*>* global_computations) {
-  // Create a worklist of computations paired with whether the allocation must
-  // be thread-local.
-  std::deque<std::pair<const HloComputation*, bool>> worklist;
-  worklist.push_back(std::make_pair(module->entry_computation(),
-                                    /*is_thread_local*/ false));
-
-  // Sets for quickly checking membership. Computations are returned in vectors
-  // for stable iteration.
-  FlatSet<const HloComputation*> thread_local_set;
-  FlatSet<const HloComputation*> global_set;
-
-  while (!worklist.empty()) {
-    auto worklist_front = worklist.front();
-    worklist.pop_front();
-    const HloComputation* computation = worklist_front.first;
-    bool is_thread_local = worklist_front.second;
-    bool in_thread_local_set = thread_local_set.count(computation) > 0;
-    bool in_global_set = global_set.count(computation) > 0;
-
-    // If the computation has already been added to the respective set, then
-    // nothing to do.
-    if ((is_thread_local && in_thread_local_set) ||
-        (!is_thread_local && in_global_set)) {
-      continue;
-    }
-
-    // If the computation has already been added to the other set this is an
-    // error condition because the global call to the computation (eg,
-    // while/call) may return a reference to one of the thread-local buffers to
-    // the calling computation which will become a dangling reference when the
-    // thread-local is deallocated with the call return.
-    if ((is_thread_local && in_global_set) ||
-        (!is_thread_local && in_thread_local_set)) {
-      return InvalidArgument(
-          "computation %s has conflicting allocation requirements (global "
-          "and thread-local)",
-          computation->name().c_str());
-    }
-
-    if (is_thread_local) {
-      thread_local_set.insert(computation);
-    } else {
-      global_set.insert(computation);
-    }
-
-    for (auto* instruction : computation->instructions()) {
-      for (HloComputation* subcomputation :
-           instruction->called_computations()) {
-        switch (instruction->opcode()) {
-          case HloOpcode::kCall:
-          case HloOpcode::kConditional:
-          case HloOpcode::kWhile:
-            // Call and while must be called from a computation with global
-            // allocations as they may return references to buffers inside the
-            // called computation which cannot be thread-local.
-            if (is_thread_local) {
-              return InvalidArgument(
-                  "computation %s cannot contain call/while op because it "
-                  "requires thread-local buffer allocations",
-                  computation->name().c_str());
-            }
-            worklist.push_back(std::make_pair(subcomputation,
-                                              false));  // Not thread local.
-            break;
-          case HloOpcode::kMap:
-          case HloOpcode::kReduce:
-          case HloOpcode::kReduceWindow:
-          case HloOpcode::kSelectAndScatter:
-          case HloOpcode::kFusion:
-            // Map/reduce etc computations are always thread-local.
-            worklist.push_back(std::make_pair(subcomputation,
-                                              true));  // Thread local.
-            break;
-          default:
-            return InternalError(
-                "Unexpected calling opcode: %s",
-                HloOpcodeString(instruction->opcode()).c_str());
-        }
-      }
-    }
-  }
-
-  // Add the computations to the vectors in post order.
-  for (auto* computation : module->MakeComputationPostOrder()) {
-    if (thread_local_set.count(computation) > 0) {
-      thread_local_computations->push_back(computation);
-    } else if (global_set.count(computation) > 0) {
-      global_computations->push_back(computation);
-    }
-    // If the computation is not reachable from the entry computation, then it
-    // will not appear in either thread_local_set or global_set. We don't bother
-    // assigning buffers for these.
-  }
-  return Status::OK();
-}
-
-}  // namespace
-
 /* static */
 StatusOr<std::unique_ptr<BufferAssignment>> BufferAssigner::Run(
     const HloModule* module, std::unique_ptr<HloOrdering> hlo_ordering,
@@ -1085,7 +1152,8 @@ void BufferAssigner::AddSetToColocatedBufferSets(
   if (colocated_set.empty()) {
     return;
   }
-
+  VLOG(5) << ColocatedBufferSetsToString(colocated_set,
+                                         "Adding colocated buffer set");
   // Find existing sets that overlap with at least one buffer from the
   // colocated_set. The resulting 'overlap_set_indices' will have at most
   // colocated_buffer_sets->size() entries, and will be in increasing order.
@@ -1093,6 +1161,10 @@ void BufferAssigner::AddSetToColocatedBufferSets(
   for (size_t index = 0; index < colocated_buffer_sets->size(); ++index) {
     for (const LogicalBuffer* buffer : colocated_set) {
       if ((*colocated_buffer_sets)[index].count(buffer) > 0) {
+        VLOG(5) << "Found overlap with existing set on buffer "
+                << buffer->ToString() << "\n"
+                << ColocatedBufferSetsToString((*colocated_buffer_sets)[index],
+                                               "Overlapping set");
         overlap_set_indices.push_back(index);
         break;
       }
@@ -1104,6 +1176,7 @@ void BufferAssigner::AddSetToColocatedBufferSets(
     colocated_buffer_sets->emplace_back();
     colocated_buffer_sets->back().insert(colocated_set.begin(),
                                          colocated_set.end());
+    VLOG(5) << "No overlap found, new group created";
     return;
   }
 
@@ -1115,6 +1188,8 @@ void BufferAssigner::AddSetToColocatedBufferSets(
     first->insert(overlap_set.begin(), overlap_set.end());
   }
   first->insert(colocated_set.begin(), colocated_set.end());
+  VLOG(5) << ColocatedBufferSetsToString(
+      *first, "Result of the colocated buffer set merging");
 
   // Remove overlap sets that we just merged. The offset accounts for the fact
   // that as elements are erased, the indices need to be adjusted. Keep in mind
@@ -1125,67 +1200,6 @@ void BufferAssigner::AddSetToColocatedBufferSets(
   }
 }
 
-namespace {
-
-// Checks that points-to set of 'instruction' is unambiguous and distinct
-// (ensured by CopyInsertion), then adds the buffer from the points-to set at
-// 'index' to 'colocated_set'.
-const LogicalBuffer* AddBufferToColocatedSet(
-    const HloInstruction* instruction, const ShapeIndex& index,
-    const TuplePointsToAnalysis& points_to_analysis,
-    std::vector<const LogicalBuffer*>* colocated_set) {
-  // CopyInsertion ensures root points-to set is unambiguous and distinct.
-  const auto& points_to = points_to_analysis.GetPointsToSet(instruction);
-  DCHECK(!points_to.IsAmbiguous());
-  colocated_set->push_back(points_to.element(index)[0]);
-  return colocated_set->back();
-}
-
-// Given the interference map of a graph (the list of interfering node indices
-// for each node), perform graph coloring such that interfering nodes are
-// assigned to different colors. Returns the assigned color of the nodes, where
-// the colors are represented as integer values [0, color_count).
-std::vector<int64> ColorInterferenceGraph(
-    const std::vector<std::vector<int64>>& interference_map) {
-  const int64 node_count = interference_map.size();
-
-  // Sort the nodes such that we assign nodes with more interference first. This
-  // relies on the common heuristic of assigning the most constrained node
-  // first, but it would be good to investigate other ordering heuristics too.
-  std::vector<int64> nodes(node_count);
-  std::iota(nodes.begin(), nodes.end(), 0);
-  std::sort(nodes.begin(), nodes.end(),
-            [&interference_map](const int64 i, const int64 j) {
-              return interference_map[i].size() > interference_map[j].size();
-            });
-
-  const int64 kColorUnassigned = -1;
-  std::vector<int64> assigned_colors(node_count, kColorUnassigned);
-  for (int64 node : nodes) {
-    // Mark the colors that are already assigned to the neighbors.
-    std::vector<bool> available_colors(node_count, true);
-    for (int64 neighbor : interference_map[node]) {
-      int64 color = assigned_colors[neighbor];
-      if (color != kColorUnassigned) {
-        available_colors[color] = false;
-      }
-    }
-
-    // Find the color that is not yet assigned to the neighbors.
-    int64 color = kColorUnassigned;
-    for (color = 0; color < available_colors.size(); ++color) {
-      if (available_colors[color]) {
-        break;
-      }
-    }
-    CHECK_NE(color, kColorUnassigned);
-    assigned_colors[node] = color;
-  }
-  return assigned_colors;
-}
-
-}  // namespace
-
 std::vector<BufferAssigner::ColocatedBufferSet>
 BufferAssigner::MergeColocatedBufferSets(
     const std::vector<ColocatedBufferSet>& colocated_buffer_sets,
diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc
index cc195879a6..df73c28597 100644
--- a/tensorflow/compiler/xla/service/copy_insertion.cc
+++ b/tensorflow/compiler/xla/service/copy_insertion.cc
@@ -58,6 +58,45 @@ bool ValueIsReadOnly(const HloValue& value) {
   return IsConstantValue(value) || IsEntryParameterValue(value);
 }
 
+// Data structure describing the action which should be taken on parts of a
+// computation buffers, with respect to the adding of special case copies.
+struct SpecialCaseCopyPolicy {
+  // Insert a copy if the same buffer is found at multiple indices within the
+  // output tuple.
+  bool copy_root_replicated_buffers = false;
+  // If true, insert a copy if a buffer coming from a constant or a parameter
+  // is found wihtin the output tuple.
+  bool copy_parameters_and_constants = false;
+};
+
+SpecialCaseCopyPolicy GetSpecialCaseCopyPolicy(const CallGraphNode& node,
+                                               HloModule* module,
+                                               HloComputation* computation) {
+  SpecialCaseCopyPolicy policy;
+  if (computation == module->entry_computation()) {
+    policy.copy_parameters_and_constants = true;
+    policy.copy_root_replicated_buffers = true;
+  }
+  for (const CallSite& site : node.caller_callsites()) {
+    // The kWhile instruction does not have an handling here, as the
+    // AddCopiesForWhile() API takes care of adding its own copies.
+    if (site.instruction()->opcode() == HloOpcode::kConditional) {
+      policy.copy_parameters_and_constants = true;
+      policy.copy_root_replicated_buffers = true;
+    }
+  }
+  return policy;
+}
+
+bool ShouldCopyRootValue(const HloValue& value,
+                         const SpecialCaseCopyPolicy& policy) {
+  if (policy.copy_parameters_and_constants) {
+    return IsConstantValue(value) ||
+           value.defining_instruction()->opcode() == HloOpcode::kParameter;
+  }
+  return false;
+}
+
 // Deep copy the given instructions 'from' and 'to' at the ShapeIndexes given in
 // 'indices_to_copy'. Add control edges from the respective kCopy instructions
 // in deep copy of 'from' to the respective kCopy instruction in the deep copy
@@ -957,7 +996,8 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) {
     }
     TF_RET_CHECK(node.context() == CallContext::kSequential);
 
-    const bool is_entry = computation == module->entry_computation();
+    SpecialCaseCopyPolicy policy =
+        GetSpecialCaseCopyPolicy(node, module, computation);
     HloInstruction* root = computation->root_instruction();
 
     // Mark nondistinct/ambiguous indices.
@@ -970,27 +1010,26 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) {
           for (const HloBuffer* buffer : buffers_at_index) {
             buffer_seen_before |= !seen.insert(buffer).second;
           }
-          if (buffers_at_index.size() > 1 || (buffer_seen_before && is_entry)) {
-            VLOG(2) << "Index " << index << " of root of computation "
+          if (buffers_at_index.size() > 1 ||
+              (buffer_seen_before && policy.copy_root_replicated_buffers)) {
+            VLOG(2) << "Index " << index << " of computation "
                     << computation->name() << " (" << root->name()
                     << ") has ambiguous or non-distinct buffer. Copying.";
             add_index_to_copy(root, index);
           }
         });
 
-    // For entry instructions, mark any parameter or constant values.
-    if (is_entry) {
-      for (const auto& pair :
-           alias_analysis->dataflow_analysis().GetInstructionValueSet(root)) {
-        const ShapeIndex& index = pair.first;
-        const HloValueSet& value_set = pair.second;
-        for (const HloValue* value : value_set.values()) {
-          if (ValueIsReadOnly(*value)) {
-            VLOG(2) << "Root of entry computation (" << root->name()
-                    << ") has constant or entry parameter value at index "
-                    << index << ". Copying.";
-            add_index_to_copy(root, index);
-          }
+    for (const auto& pair :
+         alias_analysis->dataflow_analysis().GetInstructionValueSet(root)) {
+      const ShapeIndex& index = pair.first;
+      const HloValueSet& value_set = pair.second;
+      for (const HloValue* value : value_set.values()) {
+        if (ShouldCopyRootValue(*value, policy)) {
+          VLOG(2) << "Root of (" << root->name() << ") of computation("
+                  << computation->name()
+                  << ") has constant or parameter value at index " << index
+                  << ". Copying.";
+          add_index_to_copy(root, index);
         }
       }
     }
@@ -1012,7 +1051,6 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) {
       instruction->parent()->set_root_instruction(deep_copy);
     }
   }
-
   return Status::OK();
 }
 
-- 
GitLab


From ef7c481b0aa563ab8a3bf387e97121382cbaa588 Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Mon, 26 Feb 2018 17:55:31 -0800
Subject: [PATCH 403/575] [XLA::Interpreter] Add support for kConditional to
 HloEvaluator. Also enable xla/tests/conditional_tests to run on interpreter.

PiperOrigin-RevId: 187110438
---
 .../compiler/xla/service/hlo_evaluator.cc     | 28 +++++++++++++++++++
 .../compiler/xla/service/hlo_evaluator.h      |  2 ++
 tensorflow/compiler/xla/tests/BUILD           |  1 +
 3 files changed, 31 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index cf8b35908f..afbfdac05e 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -2491,6 +2491,34 @@ Status HloEvaluator::HandleCall(HloInstruction* call) {
   return Status::OK();
 }
 
+Status HloEvaluator::HandleConditional(HloInstruction* conditional) {
+  const auto& pred = GetEvaluatedLiteralFor(conditional->operand(0));
+  const auto& true_computation_arg =
+      GetEvaluatedLiteralFor(conditional->operand(1));
+  const auto& false_computation_arg =
+      GetEvaluatedLiteralFor(conditional->operand(2));
+
+  auto* true_computation = conditional->true_computation();
+  auto* false_computation = conditional->false_computation();
+
+  auto result = Literal::CreateFromShape(conditional->shape());
+  HloEvaluator embedded_evaluator;
+  if (pred.Get<bool>({})) {
+    result = embedded_evaluator
+                 .Evaluate<const Literal*>(*true_computation,
+                                           {&true_computation_arg})
+                 .ConsumeValueOrDie();
+  } else {
+    result = embedded_evaluator
+                 .Evaluate<const Literal*>(*false_computation,
+                                           {&false_computation_arg})
+                 .ConsumeValueOrDie();
+  }
+
+  evaluated_[conditional] = std::move(result);
+  return Status::OK();
+}
+
 Status HloEvaluator::Preprocess(HloInstruction* hlo) {
   VLOG(2) << "About to visit HLO: " << hlo->ToString();
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h
index c65d9915e3..fc82011630 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.h
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.h
@@ -153,6 +153,8 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
 
   Status HandleCopy(HloInstruction* copy) override;
 
+  Status HandleConditional(HloInstruction* conditional) override;
+
   Status HandleCall(HloInstruction* call) override;
 
  private:
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index f3ecfc1604..19b3dfae4e 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -478,6 +478,7 @@ xla_test(
 xla_test(
     name = "conditional_test",
     srcs = ["conditional_test.cc"],
+    tags = ["enable_for_xla_interpreter"],
     deps = [
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:computation_builder",
-- 
GitLab


From d888a77dc31bb45dfd0416fa9202c83206f2d07e Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Mon, 26 Feb 2018 17:56:15 -0800
Subject: [PATCH 404/575] Support configurable stats publishers in the grpc
 server.

PiperOrigin-RevId: 187110497
---
 .../distributed_runtime/rpc/grpc_server_lib.cc    | 15 ++++++++++++---
 .../distributed_runtime/rpc/grpc_server_lib.h     |  6 ++++++
 2 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc
index c4ac92d809..a6f4be3eaf 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc
@@ -106,7 +106,8 @@ GrpcServer::~GrpcServer() {
 Status GrpcServer::Init(
     ServiceInitFunction service_func,
     const RendezvousMgrCreationFunction& rendezvous_mgr_func,
-    const WorkerCreationFunction& worker_func) {
+    const WorkerCreationFunction& worker_func,
+    const StatsPublisherFactory& stats_factory) {
   mutex_lock l(mu_);
   CHECK_EQ(state_, NEW);
   master_env_.env = env_;
@@ -218,7 +219,7 @@ Status GrpcServer::Init(
   master_env_.ops = OpRegistry::Global();
   master_env_.worker_cache = worker_cache;
   master_env_.master_session_factory =
-      [config](
+      [config, stats_factory](
           SessionOptions options, const MasterEnv* env,
           std::unique_ptr<std::vector<std::unique_ptr<Device>>> remote_devs,
           std::unique_ptr<WorkerCacheInterface> worker_cache,
@@ -226,7 +227,7 @@ Status GrpcServer::Init(
         options.config.MergeFrom(config);
         return new MasterSession(options, env, std::move(remote_devs),
                                  std::move(worker_cache), std::move(device_set),
-                                 CreateNoOpStatsPublisher);
+                                 stats_factory);
       };
   master_env_.worker_cache_factory =
       [this](const WorkerCacheFactoryOptions& options,
@@ -241,6 +242,14 @@ Status GrpcServer::Init(
   return Status::OK();
 }
 
+Status GrpcServer::Init(
+    ServiceInitFunction service_func,
+    const RendezvousMgrCreationFunction& rendezvous_mgr_func,
+    const WorkerCreationFunction& worker_func) {
+  return Init(std::move(service_func), rendezvous_mgr_func, worker_func,
+              CreateNoOpStatsPublisher);
+}
+
 Status GrpcServer::Init(
     ServiceInitFunction service_func,
     const RendezvousMgrCreationFunction& rendezvous_mgr_func) {
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h
index 8b12ac1461..7c2f06f618 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include "grpc++/security/credentials.h"
 
 #include "tensorflow/core/common_runtime/process_util.h"
+#include "tensorflow/core/common_runtime/stats_publisher_interface.h"
 #include "tensorflow/core/distributed_runtime/master_env.h"
 #include "tensorflow/core/distributed_runtime/rpc/async_service_interface.h"
 #include "tensorflow/core/distributed_runtime/rpc/grpc_channel.h"
@@ -68,6 +69,11 @@ class GrpcServer : public ServerInterface {
   const string target() const override;
 
  protected:
+  Status Init(ServiceInitFunction service_func,
+              const RendezvousMgrCreationFunction& rendezvous_mgr_func,
+              const WorkerCreationFunction& worker_func,
+              const StatsPublisherFactory& stats_factory);
+
   Status Init(ServiceInitFunction service_func,
               const RendezvousMgrCreationFunction& rendezvous_mgr_func,
               const WorkerCreationFunction& worker_func);
-- 
GitLab


From 7a2ba8edbaa6491ff33ae1412d9ba45e80c2cc3c Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Mon, 26 Feb 2018 18:04:55 -0800
Subject: [PATCH 405/575] Modify retrain script to output TFLite compatible
 quantized models.

-Also fix flaky input name selection introduced by last PR.
-Also rely on tf.contrib.quantize to do graph transformations.
-Also, update retrain script to use new float mobilenet_v1 and quantized mobilenet_v1 models.

PiperOrigin-RevId: 187111533
---
 .../examples/image_retraining/retrain.py      | 317 +++++++++++-------
 .../examples/image_retraining/retrain_test.py |  44 ++-
 2 files changed, 229 insertions(+), 132 deletions(-)

diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 25e09fecbf..99a71206ac 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -75,13 +75,16 @@ python tensorflow/examples/image_retraining/retrain.py \
     --image_dir ~/flower_photos --architecture mobilenet_1.0_224
 ```
 
-Run quantized version of mobilenet:
+Run mobilenet, instrumented for quantization:
 
 ```bash
 python tensorflow/examples/image_retraining/retrain.py \
-    --image_dir ~/flower_photos/   --architecture mobilenet_1.0_224_quantized
+    --image_dir ~/flower_photos/   --architecture mobilenet_1.0_224_quant
 ```
 
+These instrumented models can be converted to fully quantized mobile models via
+TensorFlow Lite.
+
 There are 32 different Mobilenet models to choose from, with a variety of file
 size and latency options. The first number can be '1.0', '0.75', '0.50', or
 '0.25' to control the size, and the second controls the input image size, either
@@ -121,7 +124,6 @@ import numpy as np
 from six.moves import urllib
 import tensorflow as tf
 
-from tensorflow.contrib.quantize.python import quant_ops
 from tensorflow.python.framework import graph_util
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.platform import gfile
@@ -135,6 +137,9 @@ FLAGS = None
 # need to update these to reflect the values in the network you're using.
 MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1  # ~134M
 
+# The location where variable checkpoints will be stored.
+CHECKPOINT_NAME = '/tmp/_retrain_checkpoint'
+
 
 def create_image_lists(image_dir, testing_percentage, validation_percentage):
   """Builds a list of training images from the file system.
@@ -745,9 +750,9 @@ def variable_summaries(var):
     tf.summary.histogram('histogram', var)
 
 
-def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
-                           bottleneck_tensor_size, quantize_layer):
-  """Adds a new softmax and fully-connected layer for training.
+def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
+                          bottleneck_tensor_size, quantize_layer, is_training):
+  """Adds a new softmax and fully-connected layer for training and eval.
 
   We need to retrain the top layer to identify our new classes, so this function
   adds the right operations to the graph, along with some variables to hold the
@@ -763,7 +768,9 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
     bottleneck_tensor: The output of the main CNN graph.
     bottleneck_tensor_size: How many entries in the bottleneck vector.
     quantize_layer: Boolean, specifying whether the newly added layer should be
-        quantized.
+        instrumented for quantized.
+    is_training: Boolean, specifying whether the newly add layer is for training
+        or eval.
 
   Returns:
     The tensors for the training and cross entropy results, and tensors for the
@@ -778,50 +785,41 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
     ground_truth_input = tf.placeholder(
         tf.int64, [None], name='GroundTruthInput')
 
-  # Organizing the following ops as `final_training_ops` so they're easier
-  # to see in TensorBoard
-  layer_name = 'final_training_ops'
+  # Organizing the following ops so they are easier to see in TensorBoard.
+  layer_name = 'final_retrain_ops'
   with tf.name_scope(layer_name):
     with tf.name_scope('weights'):
       initial_value = tf.truncated_normal(
           [bottleneck_tensor_size, class_count], stddev=0.001)
       layer_weights = tf.Variable(initial_value, name='final_weights')
-      if quantize_layer:
-        quantized_layer_weights = quant_ops.MovingAvgQuantize(
-            layer_weights, is_training=True)
-        variable_summaries(quantized_layer_weights)
-
       variable_summaries(layer_weights)
+
     with tf.name_scope('biases'):
       layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
-      if quantize_layer:
-        quantized_layer_biases = quant_ops.MovingAvgQuantize(
-            layer_biases, is_training=True)
-        variable_summaries(quantized_layer_biases)
-
       variable_summaries(layer_biases)
 
     with tf.name_scope('Wx_plus_b'):
-      if quantize_layer:
-        logits = tf.matmul(bottleneck_input,
-                           quantized_layer_weights) + quantized_layer_biases
-        logits = quant_ops.MovingAvgQuantize(
-            logits,
-            init_min=-32.0,
-            init_max=32.0,
-            is_training=True,
-            num_bits=8,
-            narrow_range=False,
-            ema_decay=0.5)
-        tf.summary.histogram('pre_activations', logits)
-      else:
-        logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
-        tf.summary.histogram('pre_activations', logits)
+      logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
+      tf.summary.histogram('pre_activations', logits)
 
   final_tensor = tf.nn.softmax(logits, name=final_tensor_name)
 
+  # The tf.contrib.quantize functions rewrite the graph in place for
+  # quantization. The imported model graph has already been rewritten, so upon
+  # calling these rewrites, only the newly added final layer will be
+  # transformed.
+  if quantize_layer:
+    if is_training:
+      tf.contrib.quantize.create_training_graph()
+    else:
+      tf.contrib.quantize.create_eval_graph()
+
   tf.summary.histogram('activations', final_tensor)
 
+  # If this is an eval graph, we don't need to add loss ops or an optimizer.
+  if not is_training:
+    return None, None, bottleneck_input, ground_truth_input, final_tensor
+
   with tf.name_scope('cross_entropy'):
     cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
         labels=ground_truth_input, logits=logits)
@@ -857,13 +855,91 @@ def add_evaluation_step(result_tensor, ground_truth_tensor):
   return evaluation_step, prediction
 
 
-def save_graph_to_file(sess, graph, graph_file_name):
+def run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor,
+                   decoded_image_tensor, resized_image_tensor,
+                   bottleneck_tensor):
+  """Runs a final evaluation on an eval graph using the test data set.
+
+  Args:
+    sess: Session for the train graph.
+    model_info: Model info dictionary from create_model_info()
+    class_count: Number of classes
+    image_lists: Dictionary of training images for each label.
+    jpeg_data_tensor: The layer to feed jpeg image data into.
+    decoded_image_tensor: The output of decoding and resizing the image.
+    resized_image_tensor: The input node of the recognition graph.
+    bottleneck_tensor: The bottleneck output layer of the CNN graph.
+  """
+  (sess, bottleneck_input, ground_truth_input, evaluation_step,
+   prediction) = build_eval_session(model_info, class_count)
+
+  test_bottlenecks, test_ground_truth, test_filenames = (
+      get_random_cached_bottlenecks(sess, image_lists, FLAGS.test_batch_size,
+                                    'testing', FLAGS.bottleneck_dir,
+                                    FLAGS.image_dir, jpeg_data_tensor,
+                                    decoded_image_tensor, resized_image_tensor,
+                                    bottleneck_tensor, FLAGS.architecture))
+  test_accuracy, predictions = sess.run(
+      [evaluation_step, prediction],
+      feed_dict={
+          bottleneck_input: test_bottlenecks,
+          ground_truth_input: test_ground_truth
+      })
+  tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
+                  (test_accuracy * 100, len(test_bottlenecks)))
+
+  if FLAGS.print_misclassified_test_images:
+    tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
+    for i, test_filename in enumerate(test_filenames):
+      if predictions[i] != test_ground_truth[i]:
+        tf.logging.info('%70s  %s' % (test_filename,
+                                      list(image_lists.keys())[predictions[i]]))
+
+
+def build_eval_session(model_info, class_count):
+  """Builds an restored eval session without train operations for exporting.
+
+  Args:
+    model_info: Model info dictionary from create_model_info()
+    class_count: Number of classes
+
+  Returns:
+    Eval session containing the restored eval graph.
+    The bottleneck input, ground truth, eval step, and prediction tensors.
+  """
+  # If quantized, we need to create the correct eval graph for exporting.
+  eval_graph, bottleneck_tensor, _ = create_model_graph(model_info)
+
+  eval_sess = tf.Session(graph=eval_graph)
+  with eval_graph.as_default():
+    # Add the new layer for exporting.
+    (_, _, bottleneck_input,
+     ground_truth_input, final_tensor) = add_final_retrain_ops(
+         class_count, FLAGS.final_tensor_name, bottleneck_tensor,
+         model_info['bottleneck_tensor_size'], model_info['quantize_layer'],
+         False)
+
+    # Now we need to restore the values from the training graph to the eval
+    # graph.
+    tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME)
+
+    evaluation_step, prediction = add_evaluation_step(final_tensor,
+                                                      ground_truth_input)
+
+  return (eval_sess, bottleneck_input, ground_truth_input, evaluation_step,
+          prediction)
+
+
+def save_graph_to_file(graph, graph_file_name, model_info, class_count):
+  """Saves an graph to file, creating a valid quantized one if necessary."""
+  sess, _, _, _, _ = build_eval_session(model_info, class_count)
+  graph = sess.graph
+
   output_graph_def = graph_util.convert_variables_to_constants(
       sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
 
   with gfile.FastGFile(graph_file_name, 'wb') as f:
     f.write(output_graph_def.SerializeToString())
-  return
 
 
 def prepare_file_system():
@@ -916,11 +992,10 @@ def create_model_info(architecture):
       return None
     version_string = parts[1]
     if (version_string != '1.0' and version_string != '0.75' and
-        version_string != '0.50' and version_string != '0.25'):
+        version_string != '0.5' and version_string != '0.25'):
       tf.logging.error(
-          """"The Mobilenet version should be '1.0', '0.75', '0.50', or '0.25',
-  but found '%s' for architecture '%s'""",
-          version_string, architecture)
+          """"The Mobilenet version should be '1.0', '0.75', '0.5', or '0.25',
+  but found '%s' for architecture '%s'""", version_string, architecture)
       return None
     size_string = parts[2]
     if (size_string != '224' and size_string != '192' and
@@ -933,35 +1008,26 @@ def create_model_info(architecture):
     if len(parts) == 3:
       is_quantized = False
     else:
-      if parts[3] != 'quantized':
+      if parts[3] != 'quant':
         tf.logging.error(
             "Couldn't understand architecture suffix '%s' for '%s'", parts[3],
             architecture)
         return None
       is_quantized = True
 
+    data_url = 'http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/'
+    model_name = 'mobilenet_v1_' + version_string + '_' + size_string
     if is_quantized:
-      data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
-      data_url += version_string + '_' + size_string + '_quantized_frozen.tgz'
-      bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
-      resized_input_tensor_name = 'Placeholder:0'
-      model_dir_name = ('mobilenet_v1_' + version_string + '_' + size_string +
-                        '_quantized_frozen')
-      model_base_name = 'quantized_frozen_graph.pb'
-
-    else:
-      data_url = 'http://download.tensorflow.org/models/mobilenet_v1_'
-      data_url += version_string + '_' + size_string + '_frozen.tgz'
-      bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
-      resized_input_tensor_name = 'input:0'
-      model_dir_name = 'mobilenet_v1_' + version_string + '_' + size_string
-      model_base_name = 'frozen_graph.pb'
+      model_name += '_quant'
+    data_url += model_name + '.tgz'
+    bottleneck_tensor_name = 'MobilenetV1/Predictions/Reshape:0'
+    resized_input_tensor_name = 'input:0'
+    model_file_name = model_name + '_frozen.pb'
 
     bottleneck_tensor_size = 1001
     input_width = int(size_string)
     input_height = int(size_string)
     input_depth = 3
-    model_file_name = os.path.join(model_dir_name, model_base_name)
     input_mean = 127.5
     input_std = 127.5
   else:
@@ -1011,43 +1077,45 @@ def add_jpeg_decoding(input_width, input_height, input_depth, input_mean,
   return jpeg_data, mul_image
 
 
-def export_model(sess, architecture, saved_model_dir):
+def export_model(model_info, class_count, saved_model_dir):
   """Exports model for serving.
 
   Args:
-    sess: Current active TensorFlow Session.
-    architecture: Model architecture.
+    model_info: The modelinfo for the current model.
+    class_count: The number of classes.
     saved_model_dir: Directory in which to save exported model and variables.
   """
-  if architecture == 'inception_v3':
-    input_tensor = 'DecodeJpeg/contents:0'
-  elif architecture.startswith('mobilenet_'):
-    input_tensor = 'input:0'
-  else:
-    raise ValueError('Unknown architecture', architecture)
-  in_image = sess.graph.get_tensor_by_name(input_tensor)
-  inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}
-
-  out_classes = sess.graph.get_tensor_by_name('final_result:0')
-  outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_classes)}
+  # The SavedModel should hold the eval graph.
+  sess, _, _, _, _ = build_eval_session(model_info, class_count)
+  graph = sess.graph
+  with graph.as_default():
+    input_tensor = model_info['resized_input_tensor_name']
+    in_image = sess.graph.get_tensor_by_name(input_tensor)
+    inputs = {'image': tf.saved_model.utils.build_tensor_info(in_image)}
+
+    out_classes = sess.graph.get_tensor_by_name('final_result:0')
+    outputs = {
+        'prediction': tf.saved_model.utils.build_tensor_info(out_classes)
+    }
 
-  signature = tf.saved_model.signature_def_utils.build_signature_def(
-      inputs=inputs,
-      outputs=outputs,
-      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
+    signature = tf.saved_model.signature_def_utils.build_signature_def(
+        inputs=inputs,
+        outputs=outputs,
+        method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
 
-  legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
+    legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
 
-  # Save out the SavedModel.
-  builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
-  builder.add_meta_graph_and_variables(
-      sess, [tf.saved_model.tag_constants.SERVING],
-      signature_def_map={
-          tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
-              signature
-      },
-      legacy_init_op=legacy_init_op)
-  builder.save()
+    # Save out the SavedModel.
+    builder = tf.saved_model.builder.SavedModelBuilder(saved_model_dir)
+    builder.add_meta_graph_and_variables(
+        sess, [tf.saved_model.tag_constants.SERVING],
+        signature_def_map={
+            tf.saved_model.signature_constants.
+            DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+                signature
+        },
+        legacy_init_op=legacy_init_op)
+    builder.save()
 
 
 def main(_):
@@ -1064,11 +1132,6 @@ def main(_):
     tf.logging.error('Did not recognize architecture flag')
     return -1
 
-  # Set up the pre-trained graph.
-  maybe_download_and_extract(model_info['data_url'])
-  graph, bottleneck_tensor, resized_image_tensor = (
-      create_model_graph(model_info))
-
   # Look at the folder structure, and create lists of all the images.
   image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage,
                                    FLAGS.validation_percentage)
@@ -1087,6 +1150,19 @@ def main(_):
       FLAGS.flip_left_right, FLAGS.random_crop, FLAGS.random_scale,
       FLAGS.random_brightness)
 
+  # Set up the pre-trained graph.
+  maybe_download_and_extract(model_info['data_url'])
+  graph, bottleneck_tensor, resized_image_tensor = (
+      create_model_graph(model_info))
+
+  # Add the new layer that we'll be training.
+  with graph.as_default():
+    (train_step, cross_entropy, bottleneck_input,
+     ground_truth_input, final_tensor) = add_final_retrain_ops(
+         class_count, FLAGS.final_tensor_name, bottleneck_tensor,
+         model_info['bottleneck_tensor_size'], model_info['quantize_layer'],
+         True)
+
   with tf.Session(graph=graph) as sess:
     # Set up the image decoding sub-graph.
     jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding(
@@ -1110,15 +1186,8 @@ def main(_):
                         decoded_image_tensor, resized_image_tensor,
                         bottleneck_tensor, FLAGS.architecture)
 
-    # Add the new layer that we'll be training.
-    (train_step, cross_entropy, bottleneck_input, ground_truth_input,
-     final_tensor) = add_final_training_ops(
-         len(image_lists.keys()), FLAGS.final_tensor_name, bottleneck_tensor,
-         model_info['bottleneck_tensor_size'], model_info['quantize_layer'])
-
     # Create the operations we need to evaluate the accuracy of our new layer.
-    evaluation_step, prediction = add_evaluation_step(
-        final_tensor, ground_truth_input)
+    evaluation_step, _ = add_evaluation_step(final_tensor, ground_truth_input)
 
     # Merge all the summaries and write them out to the summaries_dir
     merged = tf.summary.merge_all()
@@ -1128,6 +1197,10 @@ def main(_):
     validation_writer = tf.summary.FileWriter(
         FLAGS.summaries_dir + '/validation')
 
+    # Create a train saver that is used to restore values into an eval graph
+    # when exporting models.
+    train_saver = tf.train.Saver()
+
     # Set up all our weights to their initial default values.
     init = tf.global_variables_initializer()
     sess.run(init)
@@ -1168,6 +1241,9 @@ def main(_):
                         (datetime.now(), i, train_accuracy * 100))
         tf.logging.info('%s: Step %d: Cross entropy = %f' %
                         (datetime.now(), i, cross_entropy_value))
+        # TODO(suharshs): Make this use an eval graph, to avoid quantization
+        # moving averages being updated by the validation set, though in
+        # practice this makes a negligable difference.
         validation_bottlenecks, validation_ground_truth, _ = (
             get_random_cached_bottlenecks(
                 sess, image_lists, FLAGS.validation_batch_size, 'validation',
@@ -1190,42 +1266,32 @@ def main(_):
 
       if (intermediate_frequency > 0 and (i % intermediate_frequency == 0)
           and i > 0):
+        # If we want to do an intermediate save, save a checkpoint of the train
+        # graph, to restore into the eval graph.
+        train_saver.save(sess, CHECKPOINT_NAME)
         intermediate_file_name = (FLAGS.intermediate_output_graphs_dir +
                                   'intermediate_' + str(i) + '.pb')
         tf.logging.info('Save intermediate result to : ' +
                         intermediate_file_name)
-        save_graph_to_file(sess, graph, intermediate_file_name)
+        save_graph_to_file(graph, intermediate_file_name, model_info,
+                           class_count)
+
+    # After training is complete, force one last save of the train checkpoint.
+    train_saver.save(sess, CHECKPOINT_NAME)
 
     # We've completed all our training, so run a final test evaluation on
     # some new images we haven't used before.
-    test_bottlenecks, test_ground_truth, test_filenames = (
-        get_random_cached_bottlenecks(
-            sess, image_lists, FLAGS.test_batch_size, 'testing',
-            FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
-            decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
-            FLAGS.architecture))
-    test_accuracy, predictions = sess.run(
-        [evaluation_step, prediction],
-        feed_dict={bottleneck_input: test_bottlenecks,
-                   ground_truth_input: test_ground_truth})
-    tf.logging.info('Final test accuracy = %.1f%% (N=%d)' %
-                    (test_accuracy * 100, len(test_bottlenecks)))
-
-    if FLAGS.print_misclassified_test_images:
-      tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
-      for i, test_filename in enumerate(test_filenames):
-        if predictions[i] != test_ground_truth[i]:
-          tf.logging.info('%70s  %s' %
-                          (test_filename,
-                           list(image_lists.keys())[predictions[i]]))
+    run_final_eval(sess, model_info, class_count, image_lists, jpeg_data_tensor,
+                   decoded_image_tensor, resized_image_tensor,
+                   bottleneck_tensor)
 
     # Write out the trained graph and labels with the weights stored as
     # constants.
-    save_graph_to_file(sess, graph, FLAGS.output_graph)
+    save_graph_to_file(graph, FLAGS.output_graph, model_info, class_count)
     with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
       f.write('\n'.join(image_lists.keys()) + '\n')
 
-    export_model(sess, FLAGS.architecture, FLAGS.saved_model_dir)
+    export_model(model_info, class_count, FLAGS.saved_model_dir)
 
 
 if __name__ == '__main__':
@@ -1406,8 +1472,9 @@ if __name__ == '__main__':
       form 'mobilenet_<parameter size>_<input_size>[_quantized]'. For example,
       'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224
       pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much
-      less accurate, but smaller and faster network that's 920 KB on disk and
-      takes 128x128 images. See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
+      smaller and less accurate model, taking 128x128 images, and instrumented
+      for eventual quantization via TensorFlow Lite.
+      See https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
       for more information on Mobilenet.\
       """)
   parser.add_argument(
diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py
index 8b8dd45fd7..fb7324c58a 100644
--- a/tensorflow/examples/image_retraining/retrain_test.py
+++ b/tensorflow/examples/image_retraining/retrain_test.py
@@ -67,22 +67,52 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
         self.assertIsNotNone(sess.graph.get_tensor_by_name('DistortResult:0'))
 
   @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
-  def testAddFinalTrainingOps(self, flags_mock):
+  def testAddFinalRetrainOps(self, flags_mock):
     with tf.Graph().as_default():
       with tf.Session() as sess:
         bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
-        # Test creating final training op with quantization
-        retrain.add_final_training_ops(5, 'final', bottleneck, 1024, False)
+        # Test creating final training op with quantization.
+        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, False,
+                                      False)
         self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
 
   @tf.test.mock.patch.object(retrain, 'FLAGS', learning_rate=0.01)
-  def testAddFinalTrainingOpsQuantized(self, flags_mock):
-    with tf.Graph().as_default():
+  def testAddFinalRetrainOpsQuantized(self, flags_mock):
+    # Ensure that the training and eval graph for quantized models are correctly
+    # created.
+    with tf.Graph().as_default() as g:
+      with tf.Session() as sess:
+        bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
+        # Test creating final training op with quantization, set is_training to
+        # true.
+        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, True)
+        self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
+        found_fake_quant = 0
+        for op in g.get_operations():
+          if op.type == 'FakeQuantWithMinMaxVars':
+            found_fake_quant += 1
+            # Ensure that the inputs of each FakeQuant operations has 2 Assign
+            # operations in the training graph (Assign[Min,Max]Last,
+            # Assign[Min,Max]Ema)
+            self.assertEqual(2,
+                             len([i for i in op.inputs if 'Assign' in i.name]))
+        self.assertEqual(found_fake_quant, 2)
+    with tf.Graph().as_default() as g:
       with tf.Session() as sess:
         bottleneck = tf.placeholder(tf.float32, [1, 1024], name='bottleneck')
-        # Test creating final training op with quantization
-        retrain.add_final_training_ops(5, 'final', bottleneck, 1024, True)
+        # Test creating final training op with quantization, set is_training to
+        # false.
+        retrain.add_final_retrain_ops(5, 'final', bottleneck, 1024, True, False)
         self.assertIsNotNone(sess.graph.get_tensor_by_name('final:0'))
+        found_fake_quant = 0
+        for op in g.get_operations():
+          if op.type == 'FakeQuantWithMinMaxVars':
+            found_fake_quant += 1
+            for i in op.inputs:
+              # Ensure that no operations are Assign operation since this is the
+              # evaluation graph.
+              self.assertTrue('Assign' not in i.name)
+        self.assertEqual(found_fake_quant, 2)
 
   def testAddEvaluationStep(self):
     with tf.Graph().as_default():
-- 
GitLab


From 9139a571f852d06541b0c9f2343c701ac4b7d4ff Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 18:05:59 -0800
Subject: [PATCH 406/575] Remove old implementation of the adaptive shared
 batcher, the in flight batches implemntation delivers similar performance but
 is simpler and requires less tuning.

PiperOrigin-RevId: 187111685
---
 .../adaptive_shared_batch_scheduler.h         | 172 +-----
 .../adaptive_shared_batch_scheduler_test.cc   | 488 +++++-------------
 2 files changed, 140 insertions(+), 520 deletions(-)

diff --git a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
index 25c5f9cf42..661ed239d3 100644
--- a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
+++ b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
@@ -50,43 +50,26 @@ class ASBSQueue;
 // track of a number of queues (one per model or model version) which are
 // continuously enqueuing requests. The scheduler groups the requests into
 // batches which it periodically sends off for processing (see
-// shared_batch_scheduler.h for more details). The AdaptiveSharedBatchScheduler
-// prioritizes batches by age (i.e. the batch's oldest request) irrespective of
-// queue or batch size.
+// shared_batch_scheduler.h for more details). AdaptiveSharedBatchScheduler
+// (ASBS) prioritizes batches by age (i.e. the batch's oldest request)
+// irrespective of queue or batch size.
 //
-// The scheduling decision currently exists in two flavors, controlled by the
-// option use_in_flight_batches_implementation. It is expected that setting this
-// option to true will give universally better results; after a period of
-// testing to confirm, the old implementation will be removed.
-//
-// If use_in_flight_batches_implementation is set to true, the scheduler
-// limits the number of batches which can be processed concurrently.  If a new
-// batch is created, and the number of in flight batches is below the limit,
-// the next (i.e. oldest) batch is immediately scheduled.  Similarly, when a
-// batch finishes processing, the limit is rechecked, and another batch may be
-// scheduled.  To avoid the need to carefully tune the limit for workload,
-// model type, platform, etc, it is dynamically adjusted in order to provide the
-// lowest latency.
-//
-// If use_in_flight_batches_implementation is set to false, the scheduler will
-// process the oldest batch at an adjustable rate, regardless of batch size.
-// The user can provide feedback to help set this rate to achieve some goal
-// (i.e. minimize overall latency, limit cpu usage, etc). The rate (or rather,
-// the corresponding period) is adjusted each time a batch is processed, using
-// an exponentially weighted moving average to smooth noisy feedback:
-// ewma_feedback = ((N - 1) * ewma_feedback + feedback()) / N
-// period *= (1 + K * emwa_feedback)
+// ASBS tries to keep the system busy by maintaining an adjustable number of
+// concurrently processed batches.  If a new batch is created, and the number of
+// in flight batches is below the target, the next (i.e. oldest) batch is
+// immediately scheduled.  Similarly, when a batch finishes processing, the
+// target is rechecked, and another batch may be scheduled.  To avoid the need
+// to carefully tune the target for workload, model type, platform, etc, it is
+// dynamically adjusted in order to provide the lowest average latency.
 //
 // Some potential use cases:
 // Hardware Accelerators (GPUs & TPUs) - If some phase of batch processing
 //   involves serial processing by a device, from a latency perspective it is
 //   desirable to keep the device evenly loaded, avoiding the need to wait for
 //   the device to process prior batches.
-//   feedback = num_pending_on_device() - desired_pending.
 // CPU utilization - If the batch processing is cpu dominated, you can reap
 //   latency gains when underutilized by increasing the processing rate, but
 //   back the rate off when the load increases to avoid overload.
-//   feedback = cpu_rate() - desired_cpu_rate.
 
 template <typename TaskType>
 class AdaptiveSharedBatchScheduler
@@ -101,13 +84,17 @@ class AdaptiveSharedBatchScheduler
   struct Options {
     // The name to use for the pool of batch threads.
     string thread_pool_name = {"batch_threads"};
-    // Number of batch processing threads; equivalently the maximum number of
-    // concurrently running batches.
+    // Number of batch processing threads - the maximum value of
+    // in_flight_batches_limit_.  It is recommended that this value be set by
+    // running the system under load, observing the learned value for
+    // in_flight_batches_limit_, and setting this maximum to ~ 2x the value.
+    // Under low load, in_flight_batches_limit_ has no substantial effect on
+    // latency and therefore undergoes a random walk.  Unreasonably large values
+    // for num_batch_threads allows for large in_flight_batches_limit_, which
+    // will harm latency for some time once load increases again.
     int64 num_batch_threads = port::NumSchedulableCPUs();
     // The environment to use (typically only overridden by test code).
     Env* env = Env::Default();
-    // Which implementation to use (described in class comments above).
-    bool use_in_flight_batches_implementation = false;
     // Initial limit for number of batches being concurrently processed.
     // Non-integer values correspond to probabilistic limits - i.e. a value of
     // 3.2 results in an actual cap of 3 80% of the time, and 4 20% of the time.
@@ -116,28 +103,6 @@ class AdaptiveSharedBatchScheduler
     // numbers will give less noisy latency measurements, but will be less
     // responsive to changes in workload.
     int64 batches_to_average_over = 1000;
-
-    // TODO(kte): remove the rate based implementation and corresponding options
-    // below once testing confirms the superiority of the in flight batches
-    // implementation.
-    // Initial batch scheduling period in microseconds. Will be altered for
-    // non-zero rate_feedback.
-    double initial_scheduling_period_micros = 500;
-    // Minimum batch scheduling period in microseconds. Recommend setting this
-    // value greater than 0, otherwise it may take a while to recover from a
-    // sustained time of negative scheduling_period_feedback (which may occur
-    // under low load).
-    double min_scheduling_period_micros = 100;
-    // Maximum batch scheduling period in microseconds.
-    double max_scheduling_period_micros = 10000;
-    // Feedback function used to modify the scheduling period each time a batch
-    // is scheduled.  Should return values roughly O(1), with positive values
-    // resulting in an increased period.
-    std::function<double()> scheduling_period_feedback{[] { return 0.; }};
-    // To handle potentially noisy scheduling_period_feedback, the period is
-    // adjusted using an exponentially weighted moving average over the previous
-    // feedback_smoothing_batches batches.  Must be greater than 0.
-    int64 feedback_smoothing_batches = 10;
   };
 
   // Ownership is shared between the caller of Create() and any queues created
@@ -171,17 +136,11 @@ class AdaptiveSharedBatchScheduler
 
   explicit AdaptiveSharedBatchScheduler(const Options& options);
 
-  // Batch scheduling function which runs every scheduling_period_ microseconds.
-  // Only used when options_.use_in_flight_batches_implementation == false.
-  void ProcessOneBatch();
-
   // Tracks processing latency and adjusts in_flight_batches_limit to minimize.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   void CallbackWrapper(const internal::ASBSBatch<TaskType>* batch,
                        BatchProcessor callback);
 
   // Schedules batch if in_flight_batches_limit_ is not met.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   void MaybeScheduleNextBatch() EXCLUSIVE_LOCKS_REQUIRED(mu_);
 
   // Notifies scheduler of non-empty batch which is eligible for processing.
@@ -212,41 +171,22 @@ class AdaptiveSharedBatchScheduler
 
   mutex mu_;
 
-  // Responsible for running ProcessOneBatch. PeriodicFunction was used in order
-  // to check for deletion so that the thread can be shut down.
-  // Only used when options_.use_in_flight_batches_implementation == false.
-  std::unique_ptr<PeriodicFunction> scheduling_thread_;
-
   // Responsible for running the batch processing callbacks.
   std::unique_ptr<thread::ThreadPool> batch_thread_pool_;
 
-  // Time interval in microseconds between successive ProcessOneBatch calls.
-  // Only used when options_.use_in_flight_batches_implementation == false.
-  double scheduling_period_;
-
-  // Exponentially weighted moving average of
-  // options_.scheduling_period_feedback() evaluated in each ProcessOneBatch
-  // call.
-  // Only used when options_.use_in_flight_batches_implementation == false.
-  double ewma_feedback_ = 0;
-
   // Limit on number of batches which can be concurrently processed.
   // Non-integer values correspond to probabilistic limits - i.e. a value of 3.2
   // results in an actual cap of 3 80% of the time, and 4 20% of the time.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   double in_flight_batches_limit_ GUARDED_BY(mu_);
 
   // Number of batches currently being processed.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   int64 in_flight_batches_ GUARDED_BY(mu_) = 0;
 
   // RNG engine and distribution.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   std::default_random_engine rand_engine_;
   std::uniform_real_distribution<double> rand_double_;
 
   // Fields controlling the dynamic adjustment of in_flight_batches_limit_.
-  // Only used when options_.use_in_flight_batches_implementation == true.
   // Number of batches since the last in_flight_batches_limit_ adjustment.
   int64 batch_count_ GUARDED_BY(mu_) = 0;
   // Sum of processing latency for batches counted by batch_count_.
@@ -348,32 +288,6 @@ Status AdaptiveSharedBatchScheduler<TaskType>::Create(
     return errors::InvalidArgument("num_batch_threads must be positive; was ",
                                    options.num_batch_threads);
   }
-  if (options.min_scheduling_period_micros < 0) {
-    return errors::InvalidArgument(
-        "min_scheduling_period_micros must be >= 0; was ",
-        options.min_scheduling_period_micros);
-  }
-  if (options.min_scheduling_period_micros >
-      options.initial_scheduling_period_micros) {
-    return errors::InvalidArgument(
-        "initial_scheduling_period_micros (",
-        options.initial_scheduling_period_micros,
-        ") must be >= min_scheduling_period_micros (",
-        options.min_scheduling_period_micros, ")");
-  }
-  if (options.initial_scheduling_period_micros >
-      options.max_scheduling_period_micros) {
-    return errors::InvalidArgument(
-        "initial_scheduling_period_micros (",
-        options.initial_scheduling_period_micros,
-        ") must be <= max_scheduling_period_micros (",
-        options.max_scheduling_period_micros, ")");
-  }
-  if (options.feedback_smoothing_batches < 1) {
-    return errors::InvalidArgument(
-        "feedback_smoothing_batches must be positive; was ",
-        options.feedback_smoothing_batches);
-  }
   if (options.initial_in_flight_batches_limit > options.num_batch_threads) {
     return errors::InvalidArgument(
         "initial_in_flight_batches_limit (",
@@ -401,20 +315,12 @@ template <typename TaskType>
 AdaptiveSharedBatchScheduler<TaskType>::AdaptiveSharedBatchScheduler(
     const Options& options)
     : options_(options),
-      scheduling_period_(options.initial_scheduling_period_micros),
       in_flight_batches_limit_(options.initial_in_flight_batches_limit),
       rand_double_(0.0, 1.0) {
   std::random_device device;
   rand_engine_.seed(device());
-  PeriodicFunction::Options opts;
-  opts.thread_name_prefix = "scheduling_thread";
-  opts.env = GetEnv();
   batch_thread_pool_.reset(new thread::ThreadPool(
       GetEnv(), options.thread_pool_name, options.num_batch_threads));
-  if (!options.use_in_flight_batches_implementation) {
-    scheduling_thread_.reset(
-        new PeriodicFunction([this] { ProcessOneBatch(); }, 0, opts));
-  }
 }
 
 template <typename TaskType>
@@ -443,9 +349,7 @@ void AdaptiveSharedBatchScheduler<TaskType>::AddBatch(
     const internal::ASBSBatch<TaskType>* batch) {
   mutex_lock l(mu_);
   batches_.push(batch);
-  if (options_.use_in_flight_batches_implementation) {
-    MaybeScheduleNextBatch();
-  }
+  MaybeScheduleNextBatch();
 }
 
 template <typename TaskType>
@@ -523,44 +427,6 @@ void AdaptiveSharedBatchScheduler<TaskType>::CallbackWrapper(
   MaybeScheduleNextBatch();
 }
 
-template <typename TaskType>
-void AdaptiveSharedBatchScheduler<TaskType>::ProcessOneBatch() {
-  static const double kFeedbackMultiplier = .001;
-  const internal::ASBSBatch<TaskType>* batch = nullptr;
-  BatchProcessor callback;
-  const int64 start_time_micros = GetEnv()->NowMicros();
-  {
-    mutex_lock l(mu_);
-    if (!batches_.empty()) {
-      batch = batches_.top();
-      batches_.pop();
-      callback = queues_and_callbacks_[batch->queue()];
-    }
-  }
-  if (batch != nullptr) {
-    double feedback = options_.scheduling_period_feedback();
-    const int64 N = options_.feedback_smoothing_batches;
-    ewma_feedback_ = ((N - 1) * ewma_feedback_ + feedback) / N;
-    scheduling_period_ *= (1 + kFeedbackMultiplier * ewma_feedback_);
-    if (scheduling_period_ < options_.min_scheduling_period_micros) {
-      scheduling_period_ = options_.min_scheduling_period_micros;
-    } else if (scheduling_period_ > options_.max_scheduling_period_micros) {
-      scheduling_period_ = options_.max_scheduling_period_micros;
-    }
-    // Queue may destroy itself after ReleaseBatch is called.
-    batch->queue()->ReleaseBatch(batch);
-    batch_thread_pool_->Schedule([callback, batch] {
-      callback(std::unique_ptr<Batch<TaskType>>(
-          const_cast<internal::ASBSBatch<TaskType>*>(batch)));
-    });
-  }
-  const int64 sleep_time =
-      scheduling_period_ - (GetEnv()->NowMicros() - start_time_micros);
-  if (sleep_time > 0) {
-    GetEnv()->SleepForMicroseconds(sleep_time);
-  }
-}
-
 template <typename TaskType>
 bool AdaptiveSharedBatchScheduler<TaskType>::BatchCompare::operator()(
     const internal::ASBSBatch<TaskType>* a,
diff --git a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
index 8ae8ca02ec..109234287e 100644
--- a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
+++ b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
@@ -64,59 +64,6 @@ std::unique_ptr<Thread> CreateFakeClockAdvancerThread(
       }));
 }
 
-TEST(AdaptiveSharedBatchSchedulerTest, Basic) {
-  for (const bool delete_scheduler_early : {false, true}) {
-    for (const bool delete_queue_1_early : {false, true}) {
-      int queue_0_tasks = 0;
-      auto queue_0_callback =
-          [&queue_0_tasks](std::unique_ptr<Batch<FakeTask>> batch) {
-            ASSERT_TRUE(batch->IsClosed());
-            EXPECT_GT(batch->num_tasks(), 0);
-            for (int i = 0; i < batch->num_tasks(); i++) {
-              queue_0_tasks += batch->task(i).size();
-            }
-          };
-      int queue_1_tasks = 0;
-      auto queue_1_callback =
-          [&queue_1_tasks](std::unique_ptr<Batch<FakeTask>> batch) {
-            ASSERT_TRUE(batch->IsClosed());
-            EXPECT_GT(batch->num_tasks(), 0);
-            for (int i = 0; i < batch->num_tasks(); i++) {
-              queue_1_tasks += batch->task(i).size();
-            }
-          };
-      {
-        std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-        TF_ASSERT_OK(
-            AdaptiveSharedBatchScheduler<FakeTask>::Create({}, &scheduler));
-
-        // Create two queues.
-        std::unique_ptr<BatchScheduler<FakeTask>> queue_0;
-        TF_ASSERT_OK(scheduler->AddQueue({}, queue_0_callback, &queue_0));
-        std::unique_ptr<BatchScheduler<FakeTask>> queue_1;
-        TF_ASSERT_OK(scheduler->AddQueue({}, queue_1_callback, &queue_1));
-
-        if (delete_scheduler_early) {
-          // Delete our copy of the scheduler. The queues should keep it alive
-          // under the covers.
-          scheduler = nullptr;
-        }
-        // Submit tasks to the two queues, and (optionally) remove the queues.
-        TF_ASSERT_OK(ScheduleTask(1, queue_0.get()));
-        TF_ASSERT_OK(ScheduleTask(2, queue_1.get()));
-        TF_ASSERT_OK(ScheduleTask(3, queue_0.get()));
-        TF_ASSERT_OK(ScheduleTask(4, queue_1.get()));
-        if (delete_queue_1_early) {
-          queue_1 = nullptr;
-        }
-        TF_ASSERT_OK(ScheduleTask(5, queue_0.get()));
-      }
-      EXPECT_EQ(queue_0_tasks, 9);
-      EXPECT_EQ(queue_1_tasks, 6);
-    }
-  }
-}
-
 TEST(AdaptiveSharedBatchSchedulerTest, BadOptions) {
   using Scheduler = AdaptiveSharedBatchScheduler<FakeTask>;
   std::shared_ptr<Scheduler> scheduler;
@@ -124,24 +71,6 @@ TEST(AdaptiveSharedBatchSchedulerTest, BadOptions) {
   options.num_batch_threads = 0;
   EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
   options = Scheduler::Options();
-  options.min_scheduling_period_micros = 50;
-  options.max_scheduling_period_micros = 100;
-  options.initial_scheduling_period_micros = 1;
-  EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
-  options = Scheduler::Options();
-  options.min_scheduling_period_micros = 50;
-  options.max_scheduling_period_micros = 100;
-  options.initial_scheduling_period_micros = 1000;
-  EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
-  options = Scheduler::Options();
-  options.min_scheduling_period_micros = 100;
-  options.max_scheduling_period_micros = 50;
-  options.initial_scheduling_period_micros = 75;
-  EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
-  options = Scheduler::Options();
-  options.feedback_smoothing_batches = 0;
-  EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
-  options = Scheduler::Options();
   options.initial_in_flight_batches_limit = 0.5;
   EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
   options = Scheduler::Options();
@@ -153,301 +82,8 @@ TEST(AdaptiveSharedBatchSchedulerTest, BadOptions) {
   EXPECT_FALSE(Scheduler::Create(options, &scheduler).ok());
 }
 
-TEST(AdaptiveSharedBatchSchedulerTest, ObeysQueueOptions) {
-  test_util::FakeClockEnv env(Env::Default());
-  Notification start_teardown, stop_teardown;
-  std::unique_ptr<Thread> teardown_thread =
-      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
-  {
-    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-    options.initial_scheduling_period_micros = 1000;
-    options.env = &env;
-    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-    TF_ASSERT_OK(
-        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
-    std::unique_ptr<BatchScheduler<FakeTask>> queue_0;
-    std::unique_ptr<BatchScheduler<FakeTask>> queue_1;
-    int queue_0_tasks = 0;
-    int queue_1_tasks = 0;
-    auto queue_0_callback = [&queue_0_tasks,
-                             &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        queue_0_tasks += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-    auto queue_1_callback = [&queue_1_tasks,
-                             &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        queue_1_tasks += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-    AdaptiveSharedBatchScheduler<FakeTask>::QueueOptions queue_options;
-    queue_options.max_batch_size = 10;
-    queue_options.max_enqueued_batches = 0;
-    // Queue must have max_enqueued_batchs > 1.
-    EXPECT_FALSE(
-        scheduler->AddQueue(queue_options, queue_0_callback, &queue_0).ok());
-    queue_options.max_enqueued_batches = 2;
-    TF_ASSERT_OK(
-        scheduler->AddQueue(queue_options, queue_0_callback, &queue_0));
-    EXPECT_EQ(10, queue_0->max_task_size());
-    queue_options.max_batch_size = 0;
-    // Queue must have max_batch_size > 0.
-    EXPECT_FALSE(
-        scheduler->AddQueue(queue_options, queue_1_callback, &queue_1).ok());
-    queue_options.max_batch_size = 2;
-    queue_options.max_enqueued_batches = 1;
-    TF_ASSERT_OK(
-        scheduler->AddQueue(queue_options, queue_1_callback, &queue_1));
-
-    // Wait for scheduling_thread to sleep.
-    env.BlockUntilThreadsAsleep(1);
-    // Task larger than max_batch_size shouldn't schedule.
-    EXPECT_FALSE(ScheduleTask(15, queue_0.get()).ok());
-    TF_ASSERT_OK(ScheduleTask(5, queue_0.get()));
-    TF_ASSERT_OK(ScheduleTask(5, queue_0.get()));
-    env.AdvanceByMicroseconds(1);
-
-    // Task larger than max_batch_size shouldn't schedule.
-    EXPECT_FALSE(ScheduleTask(3, queue_1.get()).ok());
-    TF_ASSERT_OK(ScheduleTask(1, queue_1.get()));
-    TF_ASSERT_OK(ScheduleTask(1, queue_1.get()));
-    env.AdvanceByMicroseconds(1);
-    // Exceeds max_enqueued_batches, shouldn't schedule.
-    EXPECT_FALSE(ScheduleTask(1, queue_1.get()).ok());
-
-    TF_ASSERT_OK(ScheduleTask(5, queue_0.get()));
-    // Exceeds max_enqueued_batches, shouldn't schedule.
-    EXPECT_FALSE(ScheduleTask(6, queue_0.get()).ok());
-    TF_ASSERT_OK(ScheduleTask(4, queue_0.get()));
-
-    // Batches should be processed in order from oldest to newest.
-    env.AdvanceByMicroseconds(1000);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(queue_0_tasks, 10);
-    EXPECT_EQ(queue_1_tasks, 0);
-
-    env.AdvanceByMicroseconds(1000);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(queue_0_tasks, 10);
-    EXPECT_EQ(queue_1_tasks, 2);
-
-    env.AdvanceByMicroseconds(1000);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(queue_0_tasks, 19);
-    EXPECT_EQ(queue_1_tasks, 2);
-    start_teardown.Notify();
-  }
-  stop_teardown.Notify();
-}
-
-TEST(AdaptiveSharedBatchSchedulerTest, RateFeedback) {
-  test_util::FakeClockEnv env(Env::Default());
-  Notification start_teardown, stop_teardown;
-  std::unique_ptr<Thread> teardown_thread =
-      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
-  {
-    double feedback = 0;
-    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-    options.initial_scheduling_period_micros = 1000;
-    options.min_scheduling_period_micros = 200;
-    options.max_scheduling_period_micros = 2000;
-    options.env = &env;
-    options.scheduling_period_feedback = [&feedback] { return feedback; };
-    options.feedback_smoothing_batches = 1;
-    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-    TF_ASSERT_OK(
-        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
-    std::unique_ptr<BatchScheduler<FakeTask>> queue;
-    int scheduled_items = 0;
-    auto queue_callback = [&scheduled_items,
-                           &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      scheduled_items = 0;
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        scheduled_items += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-
-    TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
-
-    // Wait for scheduling_thread to sleep.
-    env.BlockUntilThreadsAsleep(1);
-    // Enqueue 6 batches.
-    for (int i = 0; i < 6; i++) {
-      TF_ASSERT_OK(ScheduleTask(900 + i, queue.get()));
-      env.AdvanceByMicroseconds(1);
-    }
-    feedback = -500;
-    env.AdvanceByMicroseconds(994);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 500 usec.
-    EXPECT_EQ(scheduled_items, 900);
-    env.AdvanceByMicroseconds(500);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 250 usec.
-    EXPECT_EQ(scheduled_items, 901);
-    feedback = 0;
-    env.AdvanceByMicroseconds(250);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 250 usec.
-    EXPECT_EQ(scheduled_items, 902);
-    feedback = 10000;  // large feedback should hit max_scheduling_period.
-    env.AdvanceByMicroseconds(250);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 2000 usec.
-    EXPECT_EQ(scheduled_items, 903);
-    feedback = -10000;  // large feedback should hit min_scheduling_period.
-    env.AdvanceByMicroseconds(1999);
-    // No callback scheduled, only scheduling thread sleeping.
-    env.BlockUntilThreadsAsleep(1);
-    EXPECT_EQ(scheduled_items, 903);
-    env.AdvanceByMicroseconds(1);
-    env.BlockUntilThreadsAsleep(2);  // scheduling period = 200 usec.
-    EXPECT_EQ(scheduled_items, 904);
-    env.AdvanceByMicroseconds(200);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(scheduled_items, 905);
-    start_teardown.Notify();
-  }
-  stop_teardown.Notify();
-}
-
-TEST(AdaptiveSharedBatchSchedulerTest, FeedbackSmoothing) {
-  test_util::FakeClockEnv env(Env::Default());
-  Notification start_teardown, stop_teardown;
-  std::unique_ptr<Thread> teardown_thread =
-      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
-  {
-    double feedback = 0;
-    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-    options.initial_scheduling_period_micros = 1000;
-    options.env = &env;
-    options.scheduling_period_feedback = [&feedback] { return feedback; };
-    options.feedback_smoothing_batches = 3;
-    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-    TF_ASSERT_OK(
-        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
-    std::unique_ptr<BatchScheduler<FakeTask>> queue;
-    int scheduled_items = 0;
-    auto queue_callback = [&scheduled_items,
-                           &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      scheduled_items = 0;
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        scheduled_items += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-
-    TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
-
-    // Wait for scheduling_thread to sleep.
-    env.BlockUntilThreadsAsleep(1);
-    // Enqueue 4 batches.
-    for (int i = 0; i < 4; i++) {
-      TF_ASSERT_OK(ScheduleTask(900 + i, queue.get()));
-      env.AdvanceByMicroseconds(1);
-    }
-    feedback = -300;
-    env.AdvanceByMicroseconds(996);
-    env.BlockUntilThreadsAsleep(2);
-    // ewma_feedback = 100, scheduling_period = 900.
-    EXPECT_EQ(scheduled_items, 900);
-    env.AdvanceByMicroseconds(899);
-    // No callback scheduled, only scheduling thread sleeping.
-    env.BlockUntilThreadsAsleep(1);
-    EXPECT_EQ(scheduled_items, 900);
-    env.AdvanceByMicroseconds(1);
-    env.BlockUntilThreadsAsleep(2);
-    // ewma_feedback = 167, scheduling_period = 750.
-    EXPECT_EQ(scheduled_items, 901);
-    env.AdvanceByMicroseconds(749);
-    // No callback scheduled, only scheduling thread sleeping.
-    env.BlockUntilThreadsAsleep(1);
-    EXPECT_EQ(scheduled_items, 901);
-    feedback = 1000 / 3.;
-    env.AdvanceByMicroseconds(1);
-    env.BlockUntilThreadsAsleep(2);
-    // emwa_feedback = 0, scheduling_period = 750.
-    EXPECT_EQ(scheduled_items, 902);
-    env.AdvanceByMicroseconds(749);
-    // No callback scheduled, only scheduling thread sleeping.
-    env.BlockUntilThreadsAsleep(1);
-    EXPECT_EQ(scheduled_items, 902);
-    env.AdvanceByMicroseconds(1);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(scheduled_items, 903);
-    start_teardown.Notify();
-  }
-  stop_teardown.Notify();
-}
-
-TEST(AdaptiveSharedBatchSchedulerTest, QueueCapacityInfo) {
-  test_util::FakeClockEnv env(Env::Default());
-  Notification start_teardown, stop_teardown;
-  std::unique_ptr<Thread> teardown_thread =
-      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
-  {
-    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-    options.initial_scheduling_period_micros = 1000;
-    options.env = &env;
-    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
-    TF_ASSERT_OK(
-        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
-    std::unique_ptr<BatchScheduler<FakeTask>> queue;
-    int scheduled_items = 0;
-    auto queue_callback = [&scheduled_items,
-                           &env](std::unique_ptr<Batch<FakeTask>> batch) {
-      ASSERT_TRUE(batch->IsClosed());
-      EXPECT_GT(batch->num_tasks(), 0);
-      scheduled_items = 0;
-      for (int i = 0; i < batch->num_tasks(); i++) {
-        scheduled_items += batch->task(i).size();
-      }
-      env.SleepForMicroseconds(1);
-    };
-    AdaptiveSharedBatchScheduler<FakeTask>::QueueOptions queue_options;
-    queue_options.max_batch_size = 10;
-    queue_options.max_enqueued_batches = 10;
-    TF_ASSERT_OK(scheduler->AddQueue(queue_options, queue_callback, &queue));
-
-    // Wait for scheduling_thread to sleep.
-    env.BlockUntilThreadsAsleep(1);
-    // Enqueue 3 tasks.
-    EXPECT_EQ(queue->NumEnqueuedTasks(), 0);
-    EXPECT_EQ(queue->SchedulingCapacity(), 100);
-    TF_ASSERT_OK(ScheduleTask(5, queue.get()));
-    EXPECT_EQ(queue->NumEnqueuedTasks(), 1);
-    EXPECT_EQ(queue->SchedulingCapacity(), 95);
-    env.AdvanceByMicroseconds(1);
-    TF_ASSERT_OK(ScheduleTask(6, queue.get()));
-    EXPECT_EQ(queue->NumEnqueuedTasks(), 2);
-    EXPECT_EQ(queue->SchedulingCapacity(), 84);
-    env.AdvanceByMicroseconds(1);
-    TF_ASSERT_OK(ScheduleTask(1, queue.get()));
-    EXPECT_EQ(queue->NumEnqueuedTasks(), 3);
-    EXPECT_EQ(queue->SchedulingCapacity(), 83);
-
-    env.AdvanceByMicroseconds(998);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(scheduled_items, 5);
-    env.AdvanceByMicroseconds(1000);
-    env.BlockUntilThreadsAsleep(2);
-    EXPECT_EQ(scheduled_items, 7);
-    start_teardown.Notify();
-  }
-  stop_teardown.Notify();
-}
-
-TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesImplementation) {
+TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesLimit) {
   AdaptiveSharedBatchScheduler<FakeTask>::Options options;
-  options.use_in_flight_batches_implementation = true;
   options.initial_in_flight_batches_limit = 2;
   options.batches_to_average_over = 1000;
   mutex mu;
@@ -476,7 +112,7 @@ TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesImplementation) {
   std::unique_ptr<BatchScheduler<FakeTask>> queue;
   TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
 
-  // Enqueue 3 batches.
+  // Enqueue 3 tasks, should result in 3 batches.
   for (int i = 0; i < 3; i++) {
     TF_ASSERT_OK(ScheduleTask(100, queue.get()));
   }
@@ -490,7 +126,6 @@ TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesLimitTuning) {
   {
     AdaptiveSharedBatchScheduler<FakeTask>::Options options;
     options.env = &env;
-    options.use_in_flight_batches_implementation = true;
     options.initial_in_flight_batches_limit = 2;
     options.batches_to_average_over = 1;
     auto queue_callback = [&env](std::unique_ptr<Batch<FakeTask>> batch) {
@@ -544,6 +179,125 @@ TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesLimitTuning) {
   }
   stop_teardown.Notify();
 }
+
+TEST(AdaptiveSharedBatchSchedulerTest, DeleteQueue) {
+  AdaptiveSharedBatchScheduler<FakeTask>::Options options;
+  options.initial_in_flight_batches_limit = 1;
+  options.batches_to_average_over = 1000;
+  mutex mu;
+  int processed_batches = 0;
+  Notification finish_processing;
+  auto queue_callback = [&mu, &processed_batches, &finish_processing](
+                            std::unique_ptr<Batch<FakeTask>> batch) {
+    ASSERT_TRUE(batch->IsClosed());
+    EXPECT_GT(batch->num_tasks(), 0);
+    finish_processing.WaitForNotification();
+    mu.lock();
+    processed_batches++;
+    mu.unlock();
+  };
+
+  std::unique_ptr<Thread> queue_deleter;
+  std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
+  TF_ASSERT_OK(
+      AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
+  std::unique_ptr<BatchScheduler<FakeTask>> queue;
+  TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
+
+  // Enqueue 2 tasks, should result in 2 batches.
+  for (int i = 0; i < 2; i++) {
+    TF_ASSERT_OK(ScheduleTask(100, queue.get()));
+  }
+  // Delete queue, should be kept alive until empty.
+  queue_deleter.reset(Env::Default()->StartThread(
+      {}, "QueueDeleterThread", [&queue, &mu, &processed_batches] {
+        queue.reset();
+        mutex_lock l(mu);
+        EXPECT_EQ(processed_batches, 2);
+      }));
+  // Give queue_deleter thread time to delete queue.
+  Env::Default()->SleepForMicroseconds(1000);
+  finish_processing.Notify();
+}
+
+TEST(AdaptiveSharedBatchSchedulerTest, DeleteScheduler) {
+  AdaptiveSharedBatchScheduler<FakeTask>::Options options;
+  options.initial_in_flight_batches_limit = 1;
+  options.batches_to_average_over = 1000;
+  mutex mu;
+  int processed_batches = 0;
+  Notification finish_processing;
+  auto queue_callback = [&mu, &processed_batches, &finish_processing](
+                            std::unique_ptr<Batch<FakeTask>> batch) {
+    ASSERT_TRUE(batch->IsClosed());
+    EXPECT_GT(batch->num_tasks(), 0);
+    finish_processing.WaitForNotification();
+    mu.lock();
+    processed_batches++;
+    mu.unlock();
+  };
+
+  std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
+  TF_ASSERT_OK(
+      AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
+  std::unique_ptr<BatchScheduler<FakeTask>> queue;
+  TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
+
+  // Enqueue 2 tasks, should result in 2 batches.
+  for (int i = 0; i < 2; i++) {
+    TF_ASSERT_OK(ScheduleTask(100, queue.get()));
+  }
+  // Delete scheduler, should be kept alive until queues are empty.
+  scheduler.reset();
+  finish_processing.Notify();
+  while (true) {
+    mutex_lock l(mu);
+    if (processed_batches == 2) break;
+  }
+}
+
+TEST(AdaptiveSharedBatchSchedulerTest, QueueCapacityInfo) {
+  AdaptiveSharedBatchScheduler<FakeTask>::Options options;
+  options.initial_in_flight_batches_limit = 1;
+  options.batches_to_average_over = 1000;
+  mutex mu;
+  int processed_batches = 0;
+  Notification finish_processing;
+  auto queue_callback = [&mu, &processed_batches, &finish_processing](
+                            std::unique_ptr<Batch<FakeTask>> batch) {
+    ASSERT_TRUE(batch->IsClosed());
+    EXPECT_GT(batch->num_tasks(), 0);
+    mu.lock();
+    int batch_num = ++processed_batches;
+    mu.unlock();
+    if (batch_num == 1) {
+      finish_processing.WaitForNotification();
+    }
+  };
+  std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
+  TF_ASSERT_OK(
+      AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
+  std::unique_ptr<BatchScheduler<FakeTask>> queue;
+  TF_ASSERT_OK(scheduler->AddQueue({}, queue_callback, &queue));
+
+  // Enqueue 2 tasks, should result in 2 batches.
+  for (int i = 0; i < 2; i++) {
+    TF_ASSERT_OK(ScheduleTask(100, queue.get()));
+  }
+  // First batch was immediately processed, no longer counts as enqueued.
+  EXPECT_EQ(queue->NumEnqueuedTasks(), 1);
+  EXPECT_EQ(queue->SchedulingCapacity(), 9 * 1000 + 900);
+  // Enqueue 2 more tasks, should fall in same batch.
+  TF_ASSERT_OK(ScheduleTask(100, queue.get()));
+  TF_ASSERT_OK(ScheduleTask(200, queue.get()));
+  EXPECT_EQ(queue->NumEnqueuedTasks(), 3);
+  EXPECT_EQ(queue->SchedulingCapacity(), 9 * 1000 + 600);
+  // Enqueue 1 more task, should create new batch.
+  TF_ASSERT_OK(ScheduleTask(700, queue.get()));
+  EXPECT_EQ(queue->NumEnqueuedTasks(), 4);
+  EXPECT_EQ(queue->SchedulingCapacity(), 8 * 1000 + 300);
+  finish_processing.Notify();
+}
 }  // namespace anonymous
 }  // namespace serving
 }  // namespace tensorflow
-- 
GitLab


From 9ba9cf259b38af8425f4ee3b8967b811575fd149 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 19:46:27 -0800
Subject: [PATCH 407/575] Make sure rounding and handling of denormals in
 Grappler is the same as in TensorFlow. Enable constant folding for more
 types, particularly on GPUs.

PiperOrigin-RevId: 187120456
---
 tensorflow/core/grappler/op_types.cc          |  6 +-
 .../grappler/optimizers/constant_folding.cc   | 96 ++++++++++++-------
 tensorflow/core/kernels/constant_op.cc        | 11 +++
 3 files changed, 74 insertions(+), 39 deletions(-)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index e225e99a9e..9b3755ddce 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -354,7 +354,8 @@ bool IsFreeOfSideEffect(const NodeDef& node) {
     return false;
   }
   const OpDef* op_def = nullptr;
-  Status status = OpRegistry::Global()->LookUpOpDef(node.op(), &op_def);
+  const string& op_name = node.op();
+  Status status = OpRegistry::Global()->LookUpOpDef(op_name, &op_def);
   if (!status.ok()) {
     return false;
   }
@@ -368,7 +369,8 @@ bool IsFreeOfSideEffect(const NodeDef& node) {
     }
   }
   // Some nodes do in-place updates on regular tensor inputs.
-  if (GetBoolAttr(node, "in_place") || GetBoolAttr(node, "inplace")) {
+  if (GetBoolAttr(node, "in_place") || GetBoolAttr(node, "inplace") ||
+      StringPiece(op_name).starts_with("Inplace")) {
     return false;
   }
   return true;
diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 10ca7dcce0..a5417aaa51 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -35,7 +35,9 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/inlined_vector.h"
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/denormal.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/setround.h"
 #include "tensorflow/core/platform/tensor_coding.h"
 #include "tensorflow/core/public/version.h"
 #include "tensorflow/core/util/bcast.h"
@@ -51,7 +53,14 @@ class EigenThreadPoolWrapper : public Eigen::ThreadPoolInterface {
   explicit EigenThreadPoolWrapper(thread::ThreadPool* pool) : pool_(pool) {}
   ~EigenThreadPoolWrapper() override {}
   void Schedule(std::function<void()> fn) override {
-    pool_->Schedule(std::move(fn));
+    auto wrapped = [=]() {
+      // TensorFlow flushes denormals to zero and rounds to nearest, so we do
+      // the same here.
+      port::ScopedFlushDenormal flush;
+      port::ScopedSetRound round(FE_TONEAREST);
+      fn();
+    };
+    pool_->Schedule(std::move(wrapped));
   }
   int NumThreads() const override { return pool_->NumThreads(); }
   int CurrentThreadId() const override { return pool_->CurrentThreadId(); }
@@ -292,16 +301,16 @@ Status ConstantFolding::MaterializeShapes(const GraphProperties& properties) {
   // graph.
   const int node_count = graph_->node_size();
   for (int i = 0; i < node_count; ++i) {
-    NodeDef& node = *graph_->mutable_node(i);
-    const string op = node.op();
+    NodeDef* node = graph_->mutable_node(i);
+    const string op = node->op();
     if (op != "Shape" && op != "Size" && op != "Rank" && op != "ShapeN") {
       continue;
     }
 
     const std::vector<OpInfo::TensorProperties>& output =
-        properties.GetOutputProperties(node.name());
+        properties.GetOutputProperties(node->name());
     const std::vector<OpInfo::TensorProperties>& input =
-        properties.GetInputProperties(node.name());
+        properties.GetInputProperties(node->name());
     if (input.empty() || output.empty()) {
       continue;
     }
@@ -328,35 +337,35 @@ Status ConstantFolding::MaterializeShapes(const GraphProperties& properties) {
         // could have multiple outputs).
         if (op == "Shape" || op == "Size" || op == "Rank") {
           // Replace the node with the corresponding constant.
-          node.set_op("Const");
-          node.clear_attr();
-          (*node.mutable_attr())["dtype"].set_type(type);
+          node->set_op("Const");
+          node->clear_attr();
+          (*node->mutable_attr())["dtype"].set_type(type);
           value.AsProtoTensorContent(
-              (*node.mutable_attr())["value"].mutable_tensor());
+              (*node->mutable_attr())["value"].mutable_tensor());
 
           // Turn the data input into a control dependency: this is needed to
           // ensure that the constant value will only be run in the
           // cases where the shape/rank/size would have been run in
           // the original graph. Additional inputs are extra control
           string ctrl_dep =
-              AddControlDependency(node.input(0), graph_, node_map_.get());
-          node.set_input(0, ctrl_dep);
-          node_map_->AddOutput(NodeName(ctrl_dep), node.name());
+              AddControlDependency(node->input(0), graph_, node_map_.get());
+          node->set_input(0, ctrl_dep);
+          node_map_->AddOutput(NodeName(ctrl_dep), node->name());
         } else {
-          auto outputs = node_map_->GetOutputs(node.name());
+          auto outputs = node_map_->GetOutputs(node->name());
           for (const auto& output : outputs) {
             for (int k = 0; k < output->input_size(); ++k) {
               int port;
               string node_name = ParseNodeName(output->input(k), &port);
-              if (node_name == node.name() && port == j) {
+              if (node_name == node->name() && port == j) {
                 // Create a const node as ShapeN's output if not already.
                 const string const_name =
-                    OptimizedNodeName(node, strings::StrCat("-matshapes-", j));
+                    OptimizedNodeName(*node, strings::StrCat("-matshapes-", j));
                 if (node_map_->GetNode(const_name) == nullptr) {
                   NodeDef* added_node = graph_->add_node();
                   added_node->set_name(const_name);
                   added_node->set_op("Const");
-                  added_node->set_device(node.device());
+                  added_node->set_device(node->device());
                   node_map_->AddNode(added_node->name(), added_node);
                   (*added_node->mutable_attr())["dtype"].set_type(type);
                   value.AsProtoTensorContent(
@@ -364,7 +373,7 @@ Status ConstantFolding::MaterializeShapes(const GraphProperties& properties) {
                   // We add a control dependency to the original ShapeN node,
                   // so that the node will only be run if all inputs of the
                   // original ShapeN node are run.
-                  string ctrl_dep = AddControlDependency(node.name(), graph_,
+                  string ctrl_dep = AddControlDependency(node->name(), graph_,
                                                          node_map_.get());
                   *added_node->add_input() = ctrl_dep;
                   node_map_->AddOutput(NodeName(ctrl_dep), added_node->name());
@@ -679,7 +688,7 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
       nodes_whitelist_.find(node.name()) == nodes_whitelist_.end()) {
     return false;
   }
-  // Skip control flow nodes, they can't be folded
+  // Skip control flow nodes, they can't be folded.
   if (ModifiesFrameInfo(node)) {
     return false;
   }
@@ -688,12 +697,16 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
 
-  // Skips ops that don't benefit from folding.
-  const string& op = node.op();
+  // Don't fold stateful ops such as TruncatedNormal.
+  if (!IsFreeOfSideEffect(node)) {
+    return false;
+  }
 
-  if (op.find("Placeholder") == 0) {
+  // Skips ops that don't benefit from folding.
+  if (IsPlaceholder(node)) {
     return false;
   }
+  const string& op = node.op();
   if (op.find("Save") != string::npos || op.find("Restore") != string::npos ||
       op.find("Reader") != string::npos) {
     return false;
@@ -705,16 +718,12 @@ bool ConstantFolding::IsFoldable(const NodeDef& node) const {
     return false;
   }
 
-  // Don't fold stateful ops such as TruncatedNormal.
   const OpDef* op_def = nullptr;
   Status status = OpRegistry::Global()->LookUpOpDef(node.op(), &op_def);
   if (!status.ok()) {
     return false;
   }
-  if (op_def->is_stateful()) {
-    return false;
-  }
-
+  // Don't fold ops without outputs.
   if (op_def->output_arg_size() == 0) {
     return false;
   }
@@ -779,8 +788,11 @@ Status CreateConstantTensorAttrValue(DataType type, double value,
     SET_TENSOR_VAL_CASE(DT_FLOAT, float, float);
     SET_TENSOR_VAL_CASE(DT_DOUBLE, double, double);
     SET_TENSOR_VAL_CASE(DT_INT64, int64, int64);
+    SET_TENSOR_VAL_CASE(DT_UINT64, int64, int64);
     SET_TENSOR_VAL_CASE(DT_INT32, int32, int);
+    SET_TENSOR_VAL_CASE(DT_UINT32, int32, int);
     SET_TENSOR_VAL_CASE(DT_INT16, int32, int);
+    SET_TENSOR_VAL_CASE(DT_UINT16, int32, int);
     SET_TENSOR_VAL_CASE(DT_INT8, int32, int);
     SET_TENSOR_VAL_CASE(DT_UINT8, int32, int);
     SET_TENSOR_VAL_CASE(DT_BOOL, bool, bool);
@@ -843,10 +855,16 @@ Status ConstantFolding::CreateNodeDef(const string& name,
         POPULATE_TENSOR_PROTO(tensor, t, double, double);
       case DT_INT64:
         POPULATE_TENSOR_PROTO(tensor, t, int64, int64);
+      case DT_UINT64:
+        POPULATE_TENSOR_PROTO(tensor, t, uint64, int64);
       case DT_INT32:
         POPULATE_TENSOR_PROTO(tensor, t, int32, int);
+      case DT_UINT32:
+        POPULATE_TENSOR_PROTO(tensor, t, uint32, int);
       case DT_INT16:
         POPULATE_TENSOR_PROTO(tensor, t, int16, int);
+      case DT_UINT16:
+        POPULATE_TENSOR_PROTO(tensor, t, uint16, int);
       case DT_INT8:
         POPULATE_TENSOR_PROTO(tensor, t, int8, int);
       case DT_UINT8:
@@ -1166,9 +1184,8 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
   std::unordered_set<string> processed_nodes;
   std::deque<NodeDef*> queue;
   for (int i = 0; i < graph_->node_size(); i++) {
-    auto node = graph_->mutable_node(i);
-    if (IsFoldable(*node)) {
-      queue.push_back(node);
+    if (IsFoldable(graph_->node(i))) {
+      queue.push_back(graph_->mutable_node(i));
     }
   }
   while (!queue.empty()) {
@@ -1203,8 +1220,8 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
   int last = output->node_size() - 1;
   for (int i = output->node_size() - 1; i >= 0; --i) {
     const NodeDef& node = output->node(i);
-    auto outputs = node_map_->GetOutputs(node.name());
-    if (outputs.empty()) {
+    auto fanout = node_map_->GetOutputs(node.name());
+    if (fanout.empty()) {
       output->mutable_node()->SwapElements(i, last);
       last--;
     }
@@ -1216,8 +1233,8 @@ Status ConstantFolding::FoldGraph(GraphDef* output) {
     // If no fetch nodes is provided, we conservatively
     // keep all nodes in the original graph in case users need to fetch
     // their values.
-    auto outputs = node_map_->GetOutputs(node.name());
-    if (!outputs.empty() || !has_fetch_ ||
+    auto fanout = node_map_->GetOutputs(node.name());
+    if (!fanout.empty() || !has_fetch_ ||
         nodes_to_preserve_.find(node.name()) != nodes_to_preserve_.end()) {
       auto added_node = output->add_node();
       *added_node = node;
@@ -1331,14 +1348,14 @@ bool ConstantFolding::IsOnes(const NodeDef& node) const {
     //    IS_ONES_CASE(DT_HALF);
     IS_ONES_CASE(DT_FLOAT);
     IS_ONES_CASE(DT_DOUBLE);
+    IS_ONES_CASE(DT_COMPLEX64);
+    IS_ONES_CASE(DT_COMPLEX128);
     IS_ONES_CASE(DT_UINT8);
     IS_ONES_CASE(DT_INT8);
     IS_ONES_CASE(DT_UINT16);
     IS_ONES_CASE(DT_INT16);
     IS_ONES_CASE(DT_INT32);
     IS_ONES_CASE(DT_INT64);
-    IS_ONES_CASE(DT_COMPLEX64);
-    IS_ONES_CASE(DT_COMPLEX128);
     default:
       VLOG(1) << "Unsupported type " << DataTypeString(dtype);
       return false;
@@ -1362,14 +1379,14 @@ bool ConstantFolding::IsZeros(const NodeDef& node) const {
     //    IS_ZEROS_CASE(DT_HALF);
     IS_ZEROS_CASE(DT_FLOAT);
     IS_ZEROS_CASE(DT_DOUBLE);
+    IS_ZEROS_CASE(DT_COMPLEX64);
+    IS_ZEROS_CASE(DT_COMPLEX128);
     IS_ZEROS_CASE(DT_UINT8);
     IS_ZEROS_CASE(DT_INT8);
     IS_ZEROS_CASE(DT_UINT16);
     IS_ZEROS_CASE(DT_INT16);
     IS_ZEROS_CASE(DT_INT32);
     IS_ZEROS_CASE(DT_INT64);
-    IS_ZEROS_CASE(DT_COMPLEX64);
-    IS_ZEROS_CASE(DT_COMPLEX128);
     default:
       VLOG(1) << "Unsupported type " << DataTypeString(dtype);
       return false;
@@ -1869,6 +1886,11 @@ Status ConstantFolding::RunOptimizationPass(Cluster* cluster,
 
 Status ConstantFolding::Optimize(Cluster* cluster, const GrapplerItem& item,
                                  GraphDef* output) {
+  // TensorFlow flushes denormals to zero and rounds to nearest, so we do
+  // the same here.
+  port::ScopedFlushDenormal flush;
+  port::ScopedSetRound round(FE_TONEAREST);
+
   nodes_to_preserve_ = item.NodesToPreserve();
   for (const auto& feed : item.feed) {
     feed_nodes_.insert(NodeName(feed.first));
diff --git a/tensorflow/core/kernels/constant_op.cc b/tensorflow/core/kernels/constant_op.cc
index fdb03a5aae..312c1a41d3 100644
--- a/tensorflow/core/kernels/constant_op.cc
+++ b/tensorflow/core/kernels/constant_op.cc
@@ -105,7 +105,12 @@ REGISTER_KERNEL(GPU, int8);
 REGISTER_KERNEL(GPU, qint8);
 REGISTER_KERNEL(GPU, uint16);
 REGISTER_KERNEL(GPU, int16);
+REGISTER_KERNEL(GPU, qint16);
+REGISTER_KERNEL(GPU, quint16);
+REGISTER_KERNEL(GPU, uint32);
+REGISTER_KERNEL(GPU, qint32);
 REGISTER_KERNEL(GPU, int64);
+REGISTER_KERNEL(GPU, uint64);
 REGISTER_KERNEL(GPU, complex64);
 REGISTER_KERNEL(GPU, complex128);
 REGISTER_KERNEL(GPU, bool);
@@ -122,9 +127,15 @@ REGISTER_SYCL_KERNEL(SYCL, float);
 REGISTER_SYCL_KERNEL(SYCL, double);
 REGISTER_SYCL_KERNEL(SYCL, uint8);
 REGISTER_SYCL_KERNEL(SYCL, int8);
+REGISTER_SYCL_KERNEL(SYCL, qint8);
 REGISTER_SYCL_KERNEL(SYCL, uint16);
 REGISTER_SYCL_KERNEL(SYCL, int16);
+REGISTER_SYCL_KERNEL(SYCL, qint16);
+REGISTER_SYCL_KERNEL(SYCL, quint16);
+REGISTER_SYCL_KERNEL(SYCL, uint32);
+REGISTER_SYCL_KERNEL(SYCL, qint32);
 REGISTER_SYCL_KERNEL(SYCL, int64);
+REGISTER_SYCL_KERNEL(SYCL, uint64);
 REGISTER_SYCL_KERNEL(SYCL, bool);
 #undef REGISTER_SYCL_KERNEL
 #endif
-- 
GitLab


From ccefd0a1307ac5dd39d0a254c49ce71f8c2b93e2 Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Mon, 26 Feb 2018 19:57:42 -0800
Subject: [PATCH 408/575] Fixes and simplification in the Keras training
 engine. - Explicitly disallow sample/class weighting in eager (it was never
 supported) - Remove tests for it (which were actually ignoring sample/class
 weights) - Make sample weight placeholders placeholder_with_default, and do
 not create all-ones numpy arrays to feed them when no sample weights are
 provided (this might lead to better performance)

PiperOrigin-RevId: 187121215
---
 .../python/keras/_impl/keras/backend.py       |  11 +-
 .../python/keras/_impl/keras/callbacks.py     |  20 +-
 .../keras/_impl/keras/engine/training.py      | 151 +++---
 .../_impl/keras/engine/training_eager.py      |  17 +-
 .../_impl/keras/engine/training_eager_test.py | 436 ------------------
 .../keras/_impl/keras/engine/training_test.py |   8 -
 6 files changed, 110 insertions(+), 533 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/backend.py b/tensorflow/python/keras/_impl/keras/backend.py
index a2db05f6cf..2b75666b9e 100644
--- a/tensorflow/python/keras/_impl/keras/backend.py
+++ b/tensorflow/python/keras/_impl/keras/backend.py
@@ -2749,7 +2749,7 @@ class Function(object):
       self.updates_op = control_flow_ops.group(*updates_ops)
     self.name = name
     # additional tensor substitutions
-    self.feed_dict = session_kwargs.pop('feed_dict', {})
+    self.feed_dict = session_kwargs.pop('feed_dict', None)
     # additional operations
     self.fetches = session_kwargs.pop('fetches', [])
     if not isinstance(self.fetches, list):
@@ -2759,8 +2759,15 @@ class Function(object):
   def __call__(self, inputs):
     if not isinstance(inputs, (list, tuple)):
       raise TypeError('`inputs` should be a list or tuple.')
-    feed_dict = self.feed_dict.copy()
+
+    if self.feed_dict:
+      feed_dict = self.feed_dict.copy()
+    else:
+      feed_dict = {}
+
     for tensor, value in zip(self.inputs, inputs):
+      if value is None:
+        continue
       if is_sparse(tensor):
         sparse_coo = value.tocoo()
         indices = np.concatenate((np.expand_dims(sparse_coo.row, 1),
diff --git a/tensorflow/python/keras/_impl/keras/callbacks.py b/tensorflow/python/keras/_impl/keras/callbacks.py
index f6c4661425..deb1e8867d 100644
--- a/tensorflow/python/keras/_impl/keras/callbacks.py
+++ b/tensorflow/python/keras/_impl/keras/callbacks.py
@@ -778,16 +778,24 @@ class TensorBoard(Callback):
         while i < val_size:
           step = min(self.batch_size, val_size - i)
           batch_val = []
-          batch_val.append(val_data[0][i:i + step])
-          batch_val.append(val_data[1][i:i + step])
-          batch_val.append(val_data[2][i:i + step])
+          batch_val.append(val_data[0][i:i + step]
+                           if val_data[0] is not None else None)
+          batch_val.append(val_data[1][i:i + step]
+                           if val_data[1] is not None else None)
+          batch_val.append(val_data[2][i:i + step]
+                           if val_data[2] is not None else None)
           if self.model.uses_learning_phase:
             # do not slice the learning phase
-            batch_val = [x[i:i + step] for x in val_data[:-1]]
+            batch_val = [x[i:i + step] if x is not None else None
+                         for x in val_data[:-1]]
             batch_val.append(val_data[-1])
           else:
-            batch_val = [x[i:i + step] for x in val_data]
-          feed_dict = dict(zip(tensors, batch_val))
+            batch_val = [x[i:i + step] if x is not None else None
+                         for x in val_data]
+          feed_dict = {}
+          for key, val in zip(tensors, batch_val):
+            if val is not None:
+              feed_dict[key] = val
           result = self.sess.run([self.merged], feed_dict=feed_dict)
           summary_str = result[0]
           self.writer.add_summary(summary_str, epoch)
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index 57451ad470..63bea08ac5 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -40,6 +40,7 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches
 from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
 from tensorflow.python.layers.base import _DeferredTensor
+from tensorflow.python.ops import array_ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import optimizer as tf_optimizer_module
 from tensorflow.python.util.tf_export import tf_export
@@ -225,9 +226,9 @@ def _check_array_lengths(inputs, targets, weights=None):
     # return a set with the variation between
     # different shapes, with None => 0
     if x is None:
-      return {0}
+      return {}
     else:
-      return set([0 if y is None else y.shape[0] for y in x])
+      return set([y.shape[0] for y in x if y is not None])
 
   set_x = set_of_lengths(inputs)
   set_y = set_of_lengths(targets)
@@ -259,7 +260,8 @@ def _check_array_lengths(inputs, targets, weights=None):
 def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
   """Does validation on the compatibility of targets and loss functions.
 
-  This helps prevent users from using loss functions incorrectly.
+  This helps prevent users from using loss functions incorrectly. This check
+  is purely for UX purposes.
 
   Arguments:
       targets: list of Numpy arrays of targets.
@@ -275,7 +277,7 @@ def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
       losses.categorical_crossentropy
   }
   for y, loss, shape in zip(targets, loss_fns, output_shapes):
-    if y is None or loss is None:
+    if y is None or loss is None or tensor_util.is_tensor(y):
       continue
     if loss is losses.categorical_crossentropy:
       if y.shape[-1] == 1:
@@ -507,10 +509,7 @@ def _standardize_weights(y,
                        (existing_classes - existing_class_weight))
     return weights
   else:
-    if sample_weight_mode is None:
-      return np.ones((y.shape[0],), dtype=K.floatx())
-    else:
-      return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx())
+    return None
 
 
 @tf_export('keras.models.Model', 'keras.Model')
@@ -862,12 +861,12 @@ class Model(Network):
           sample_weights.append(None)
         else:
           if sample_weight_mode == 'temporal':
-            sample_weights.append(
-                K.placeholder(ndim=2, name=name + '_sample_weights'))
+            sample_weights.append(array_ops.placeholder_with_default(
+                [[1.]], shape=[None, None], name=name + '_sample_weights'))
             sample_weight_modes.append('temporal')
           else:
-            sample_weights.append(
-                K.placeholder(ndim=1, name=name + '_sample_weights'))
+            sample_weights.append(array_ops.placeholder_with_default(
+                [1.], shape=[None], name=name + '_sample_weights'))
             sample_weight_modes.append(None)
     self.sample_weight_modes = sample_weight_modes
     self._feed_sample_weight_modes = []
@@ -1314,7 +1313,7 @@ class Model(Network):
         for batch_index, (batch_start, batch_end) in enumerate(batches):
           batch_ids = index_array[batch_start:batch_end]
           try:
-            if isinstance(ins[-1], float):
+            if isinstance(ins[-1], int):
               # Do not slice the training phase flag.
               ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
             else:
@@ -1424,7 +1423,7 @@ class Model(Network):
       index_array = np.arange(num_samples)
       for batch_index, (batch_start, batch_end) in enumerate(batches):
         batch_ids = index_array[batch_start:batch_end]
-        if ins and isinstance(ins[-1], float):
+        if ins and isinstance(ins[-1], int):
           # Do not slice the training phase flag.
           ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
         else:
@@ -1518,7 +1517,7 @@ class Model(Network):
       index_array = np.arange(num_samples)
       for batch_index, (batch_start, batch_end) in enumerate(batches):
         batch_ids = index_array[batch_start:batch_end]
-        if isinstance(ins[-1], float):
+        if isinstance(ins[-1], int):
           # Do not slice the training phase flag.
           ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
         else:
@@ -2070,10 +2069,6 @@ class Model(Network):
           val_y,
           sample_weight=val_sample_weight,
           batch_size=batch_size)
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        val_ins = val_x + val_y + val_sample_weights + [0.]
-      else:
-        val_ins = val_x + val_y + val_sample_weights
 
     elif validation_split and 0. < validation_split < 1.:
       do_validation = True
@@ -2085,36 +2080,34 @@ class Model(Network):
       y, val_y = (slice_arrays(y, 0, split_at), slice_arrays(y, split_at))
       sample_weights, val_sample_weights = (slice_arrays(
           sample_weights, 0, split_at), slice_arrays(sample_weights, split_at))
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        val_ins = val_x + val_y + val_sample_weights + [0.]
-      else:
-        val_ins = val_x + val_y + val_sample_weights
-
     elif validation_steps:
+      val_x = []
+      val_y = []
+      val_sample_weights = []
       do_validation = True
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        val_ins = [0.]
-
-    # Prepare input arrays and training function.
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + y + sample_weights + [1.]
-    else:
-      ins = x + y + sample_weights
 
     # Prepare display labels.
     out_labels = self.metrics_names
 
     if context.in_eager_mode():
+      if any([w is not None for w in sample_weights]):
+        raise ValueError('`sample_weight` and `class_weight` is not supported '
+                         'when eager execution is enabled, for now.')
+
       if do_validation:
+        if any([w is not None for w in val_sample_weights]):
+          raise ValueError('`sample_weight` and `class_weight` is not supported'
+                           ' when eager execution is enabled, for now.')
         callback_metrics = copy.copy(out_labels) + [
             'val_' + n for n in out_labels
         ]
+        val_ins = val_x + val_y
       else:
         callback_metrics = copy.copy(out_labels)
 
       return training_eager.fit_loop(
           self,
-          ins,
+          x + y,
           out_labels=out_labels,
           batch_size=batch_size,
           epochs=epochs,
@@ -2127,18 +2120,25 @@ class Model(Network):
           steps_per_epoch=steps_per_epoch,
           validation_steps=validation_steps)
     else:
+      # Prepare input arrays and training function.
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + y + sample_weights + [1]
+      else:
+        ins = x + y + sample_weights
+
       self._make_train_function()
       f = self.train_function
 
       if do_validation:
-        if context.in_graph_mode():
-          self._make_test_function()
-          val_f = self.test_function
-        else:
-          val_f = None
+        self._make_test_function()
+        val_f = self.test_function
         callback_metrics = copy.copy(out_labels) + [
             'val_' + n for n in out_labels
         ]
+        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+          val_ins = val_x + val_y + val_sample_weights + [0]
+        else:
+          val_ins = val_x + val_y + val_sample_weights
       else:
         val_f = None
         callback_metrics = copy.copy(out_labels)
@@ -2229,16 +2229,20 @@ class Model(Network):
         y,
         sample_weight=sample_weight,
         batch_size=batch_size)
-    # Prepare inputs, delegate logic to `_test_loop`.
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + y + sample_weights + [0.]
-    else:
-      ins = x + y + sample_weights
 
     if context.in_eager_mode():
+      if any([w is not None for w in sample_weights]):
+        raise ValueError('`sample_weight` and `class_weight` is not supported '
+                         'when eager execution is enabled, for now.')
       return training_eager.test_loop(
-          self, ins, batch_size=batch_size, verbose=verbose, steps=steps)
+          self, x + y, batch_size=batch_size, verbose=verbose, steps=steps)
     else:
+      # Prepare inputs, delegate logic to `_test_loop`.
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + y + sample_weights + [0]
+      else:
+        ins = x + y + sample_weights
+
       self._make_test_function()
       f = self.test_function
       return self._test_loop(
@@ -2276,16 +2280,16 @@ class Model(Network):
                        'argument.')
     x, _, _ = self._standardize_user_data(x)
 
-    # Prepare inputs, delegate logic to `_predict_loop`.
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + [0.]
-    else:
-      ins = x
-
     if context.in_eager_mode():
       return training_eager.predict_loop(
-          self, ins, batch_size=batch_size, verbose=verbose, steps=steps)
+          self, x, batch_size=batch_size, verbose=verbose, steps=steps)
     else:
+      # Prepare inputs, delegate logic to `_predict_loop`.
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + [0]
+      else:
+        ins = x
+
       self._make_predict_function()
       f = self.predict_function
 
@@ -2327,20 +2331,26 @@ class Model(Network):
         and/or metrics). The attribute `model.metrics_names` will give you
         the display labels for the scalar outputs.
 
+    Raises:
+      ValueError: In case of invalid user-provided arguments.
     """
     x, y, sample_weights = self._standardize_user_data(
         x,
         y,
         sample_weight=sample_weight,
         class_weight=class_weight)
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + y + sample_weights + [1.]
-    else:
-      ins = x + y + sample_weights
 
     if context.in_eager_mode():
-      outputs = training_eager.train_on_batch(self, ins)
+      if any([w is not None for w in sample_weights]):
+        raise ValueError('`sample_weight` and `class_weight` is not supported '
+                         'when eager execution is enabled, for now.')
+      outputs = training_eager.train_on_batch(self, x + y)
     else:
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + y + sample_weights + [1]
+      else:
+        ins = x + y + sample_weights
+
       self._make_train_function()
       outputs = self.train_function(ins)
 
@@ -2377,18 +2387,21 @@ class Model(Network):
         the display labels for the scalar outputs.
 
     Raises:
-        ValueError: in case of invalid arguments.
+        ValueError: In case of invalid user-provided arguments.
     """
     x, y, sample_weights = self._standardize_user_data(
         x, y, sample_weight=sample_weight)
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + y + sample_weights + [0.]
-    else:
-      ins = x + y + sample_weights
 
     if context.in_eager_mode():
-      outputs = training_eager.test_on_batch(self, ins)
+      if any([w is not None for w in sample_weights]):
+        raise ValueError('`sample_weight` and `class_weight` is not supported '
+                         'when eager execution is enabled, for now.')
+      outputs = training_eager.test_on_batch(self, x + y)
     else:
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + y + sample_weights + [0]
+      else:
+        ins = x + y + sample_weights
       self._make_test_function()
       outputs = self.test_function(ins)
 
@@ -2408,14 +2421,9 @@ class Model(Network):
     """
     x, _, _ = self._standardize_user_data(x)
 
-    if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-      ins = x + [0.]
-    else:
-      ins = x
-
     if context.in_eager_mode():
       ins_batch_converted = []
-      for ib in ins:
+      for ib in x:
         ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
 
       eager_model_inputs = []
@@ -2426,6 +2434,11 @@ class Model(Network):
       return outs
 
     if context.in_graph_mode():
+      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
+        ins = x + [0]
+      else:
+        ins = x
+
       self._make_predict_function()
       outputs = self.predict_function(ins)
       if len(outputs) == 1:
@@ -2643,7 +2656,7 @@ class Model(Network):
           val_data = val_x + val_y + val_sample_weights
           if self.uses_learning_phase and not isinstance(
               K.learning_phase(), int):
-            val_data += [0.]
+            val_data += [0]
           for cbk in callbacks:
             cbk.validation_data = val_data
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index 282dd0dc0d..cdf189adef 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -139,6 +139,8 @@ def _model_loss(model, inputs, targets, training=False):
                                    model.output_names[i])
       loss_metrics.append(K.mean(output_loss))
 
+      # TODO(fchollet): support masking; in practice `_keras_mask` is never
+      # set in this context currently.
       mask = outs[i]._keras_mask
       # adapted from weighted_loss_fn
       if mask is not None:
@@ -148,17 +150,7 @@ def _model_loss(model, inputs, targets, training=False):
         #  to the number of unmasked samples.
         output_loss /= K.mean(mask)
 
-      # adapted from weighted_loss_fn
-      # apply sample weighting
-      if model.sample_weights:
-        # reduce score_array to same ndim as weight array
-        ndim = K.ndim(output_loss)
-        weight_ndim = K.ndim(model.sample_weights)
-        output_loss = K.mean(output_loss, axis=list(range(weight_ndim, ndim)))
-        output_loss *= model.sample_weights
-        output_loss /= K.mean(K.cast(K.not_equal(model.sample_weights, 0),
-                                     K.floatx()))
-        output_loss = K.mean(output_loss)
+      # TODO(fchollet): support sample weighting
 
       loss_weight = model.loss_weights_list[i]
       if total_loss is None:
@@ -231,7 +223,8 @@ def train_on_batch(model, ins):
   """
   ins_batch_converted = []
   for ib in ins:
-    ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
+    if ib is not None:
+      ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
   eager_model_inputs = []
   eager_model_outputs = []
   for i in range(len(model.inputs)):
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
index 3d94b7537f..550b86a71d 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
@@ -24,9 +24,7 @@ import numpy as np
 from tensorflow.python.framework import ops
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
-from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
 from tensorflow.python.platform import test
-from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training.rmsprop import RMSPropOptimizer
 
 
@@ -311,440 +309,6 @@ class TrainingTest(test.TestCase):
                     optimizer='rms')
 
 
-class LossWeightingTest(test.TestCase):
-
-  def test_class_weights(self):
-    num_classes = 5
-    batch_size = 5
-    epochs = 5
-    weighted_class = 3
-    train_samples = 3000
-    test_samples = 3000
-    input_dim = 5
-
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
-    model.add(keras.layers.Activation('relu'))
-    model.add(keras.layers.Dense(num_classes))
-    model.add(keras.layers.Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer=RMSPropOptimizer(learning_rate=0.001))
-
-    np.random.seed(1337)
-    (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-    int_y_test = y_test.copy()
-    int_y_train = y_train.copy()
-    # convert class vectors to binary class matrices
-    y_train = keras.utils.to_categorical(y_train, num_classes)
-    y_test = keras.utils.to_categorical(y_test, num_classes)
-    test_ids = np.where(int_y_test == np.array(weighted_class))[0]
-
-    class_weight = dict([(i, 1.) for i in range(num_classes)])
-    class_weight[weighted_class] = 2.
-
-    sample_weight = np.ones((y_train.shape[0]))
-    sample_weight[int_y_train == weighted_class] = 2.
-
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 3,
-        verbose=0,
-        class_weight=class_weight,
-        validation_data=(x_train, y_train, sample_weight))
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 2,
-        verbose=0,
-        class_weight=class_weight)
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 2,
-        verbose=0,
-        class_weight=class_weight,
-        validation_split=0.1)
-
-    model.train_on_batch(
-        x_train[:batch_size], y_train[:batch_size], class_weight=class_weight)
-    ref_score = model.evaluate(x_test, y_test, verbose=0)
-    score = model.evaluate(
-        x_test[test_ids, :], y_test[test_ids, :], verbose=0)
-    self.assertLess(score, ref_score)
-
-  def test_sample_weights(self):
-    num_classes = 5
-    batch_size = 5
-    epochs = 5
-    weighted_class = 3
-    train_samples = 3000
-    test_samples = 3000
-    input_dim = 5
-
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
-    model.add(keras.layers.Activation('relu'))
-    model.add(keras.layers.Dense(num_classes))
-    model.add(keras.layers.Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer=RMSPropOptimizer(learning_rate=0.001))
-
-    np.random.seed(43)
-    (x_train, y_train), _ = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-    int_y_train = y_train.copy()
-    y_train = keras.utils.to_categorical(y_train, num_classes)
-
-    class_weight = dict([(i, 1.) for i in range(num_classes)])
-    class_weight[weighted_class] = 2.
-
-    sample_weight = np.ones((y_train.shape[0]))
-    sample_weight[int_y_train == weighted_class] = 2.
-
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 3,
-        verbose=0,
-        sample_weight=sample_weight)
-    model.fit(
-        x_train,
-        y_train,
-        batch_size=batch_size,
-        epochs=epochs // 3,
-        verbose=0,
-        sample_weight=sample_weight,
-        validation_split=0.1)
-    model.train_on_batch(
-        x_train[:batch_size],
-        y_train[:batch_size],
-        sample_weight=sample_weight[:batch_size])
-    model.test_on_batch(
-        x_train[:batch_size],
-        y_train[:batch_size],
-        sample_weight=sample_weight[:batch_size])
-
-  def test_temporal_sample_weights(self):
-    num_classes = 5
-    weighted_class = 3
-    train_samples = 1000
-    test_samples = 1000
-    input_dim = 5
-    timesteps = 3
-
-    model = keras.models.Sequential()
-    model.add(
-        keras.layers.TimeDistributed(
-            keras.layers.Dense(num_classes),
-            input_shape=(timesteps, input_dim)))
-    model.add(keras.layers.Activation('softmax'))
-
-    np.random.seed(1337)
-    (_, y_train), _ = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-    int_y_train = y_train.copy()
-    # convert class vectors to binary class matrices
-    y_train = keras.utils.to_categorical(y_train, num_classes)
-
-    class_weight = dict([(i, 1.) for i in range(num_classes)])
-    class_weight[weighted_class] = 2.
-
-    sample_weight = np.ones((y_train.shape[0]))
-    sample_weight[int_y_train == weighted_class] = 2.
-    with self.assertRaises(ValueError):
-      model.compile(
-          loss='binary_crossentropy',
-          optimizer=RMSPropOptimizer(learning_rate=0.001),
-          sample_weight_mode='temporal')
-
-  def test_class_weight_invalid_use_case(self):
-    num_classes = 5
-    train_samples = 1000
-    test_samples = 1000
-    input_dim = 5
-    timesteps = 3
-
-    model = keras.models.Sequential()
-    model.add(
-        keras.layers.TimeDistributed(
-            keras.layers.Dense(num_classes),
-            input_shape=(timesteps, input_dim)))
-    model.add(keras.layers.Activation('softmax'))
-    model.compile(
-        loss='binary_crossentropy',
-        optimizer=RMSPropOptimizer(learning_rate=0.001))
-
-    (x_train, y_train), _ = testing_utils.get_test_data(
-        train_samples=train_samples,
-        test_samples=test_samples,
-        input_shape=(input_dim,),
-        num_classes=num_classes)
-    # convert class vectors to binary class matrices
-    y_train = keras.utils.to_categorical(y_train, num_classes)
-    class_weight = dict([(i, 1.) for i in range(num_classes)])
-
-    del class_weight[1]
-    with self.assertRaises(ValueError):
-      model.fit(x_train, y_train,
-                epochs=0, verbose=0, class_weight=class_weight)
-
-    with self.assertRaises(ValueError):
-      model.compile(
-          loss='binary_crossentropy',
-          optimizer=RMSPropOptimizer(learning_rate=0.001),
-          sample_weight_mode=[])
-
-    # Build multi-output model
-    x = keras.Input((3,))
-    y1 = keras.layers.Dense(4, name='1')(x)
-    y2 = keras.layers.Dense(4, name='2')(x)
-    model = keras.models.Model(x, [y1, y2])
-    model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse')
-    x_np = np.random.random((10, 3))
-    y_np = np.random.random((10, 4))
-    w_np = np.random.random((10,))
-    # This will work
-    model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': w_np})
-    # These will not
-    with self.assertRaises(ValueError):
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=[w_np])
-    with self.assertRaises(TypeError):
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=w_np)
-    with self.assertRaises(ValueError):
-      bad_w_np = np.random.random((11,))
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
-    with self.assertRaises(ValueError):
-      bad_w_np = np.random.random((10, 2))
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
-    with self.assertRaises(ValueError):
-      bad_w_np = np.random.random((10, 2, 2))
-      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
-
-
-class TestDynamicTrainability(test.TestCase):
-
-  def test_trainable_warning(self):
-    x = np.random.random((5, 3))
-    y = np.random.random((5, 2))
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(2, input_dim=3))
-    model.trainable = False
-    model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
-    model.trainable = True
-    with test.mock.patch.object(logging, 'warning') as mock_log:
-      model.train_on_batch(x, y)
-      self.assertRegexpMatches(str(mock_log.call_args),
-                               'trainable weights is empty')
-
-  def test_trainable_argument(self):
-    x = np.random.random((5, 3))
-    y = np.random.random((5, 2))
-
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(2, input_dim=3, trainable=False))
-    model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
-    out = model.predict(x)
-    with test.mock.patch.object(logging, 'warning') as mock_log:
-      model.train_on_batch(x, y)
-      self.assertRegexpMatches(str(mock_log.call_args),
-                               'trainable weights is empty')
-    out_2 = model.predict(x)
-    self.assertAllClose(out, out_2)
-
-    # test with nesting
-    inputs = keras.layers.Input(shape=(3,))
-    output = model(inputs)
-    model = keras.models.Model(inputs, output)
-    model.compile(RMSPropOptimizer(learning_rate=0.001), 'mse')
-    out = model.predict(x)
-    with test.mock.patch.object(logging, 'warning') as mock_log:
-      model.train_on_batch(x, y)
-      self.assertRegexpMatches(str(mock_log.call_args),
-                               'trainable weights is empty')
-    out_2 = model.predict(x)
-    self.assertAllClose(out, out_2)
-
-  def test_layer_trainability_switch(self):
-    # with constructor argument, in Sequential
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(2, trainable=False, input_dim=1))
-    self.assertListEqual(model.trainable_weights, [])
-
-    # by setting the `trainable` argument, in Sequential
-    model = keras.models.Sequential()
-    layer = keras.layers.Dense(2, input_dim=1)
-    model.add(layer)
-    self.assertListEqual(model.trainable_weights, layer.trainable_weights)
-    layer.trainable = False
-    self.assertListEqual(model.trainable_weights, [])
-
-    # with constructor argument, in Model
-    x = keras.layers.Input(shape=(1,))
-    y = keras.layers.Dense(2, trainable=False)(x)
-    model = keras.models.Model(x, y)
-    self.assertListEqual(model.trainable_weights, [])
-
-    # by setting the `trainable` argument, in Model
-    x = keras.layers.Input(shape=(1,))
-    layer = keras.layers.Dense(2)
-    y = layer(x)
-    model = keras.models.Model(x, y)
-    self.assertListEqual(model.trainable_weights, layer.trainable_weights)
-    layer.trainable = False
-    self.assertListEqual(model.trainable_weights, [])
-
-  def test_model_trainability_switch(self):
-    # a non-trainable model has no trainable weights
-    x = keras.layers.Input(shape=(1,))
-    y = keras.layers.Dense(2)(x)
-    model = keras.models.Model(x, y)
-    model.trainable = False
-    self.assertListEqual(model.trainable_weights, [])
-
-    # same for Sequential
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(2, input_dim=1))
-    model.trainable = False
-    self.assertListEqual(model.trainable_weights, [])
-
-  def test_nested_model_trainability(self):
-
-    # a Sequential inside a Model
-    inner_model = keras.models.Sequential()
-    inner_model.add(keras.layers.Dense(2, input_dim=1))
-
-    x = keras.layers.Input(shape=(1,))
-    y = inner_model(x)
-    outer_model = keras.models.Model(x, y)
-    self.assertListEqual(outer_model.trainable_weights,
-                         inner_model.trainable_weights)
-    inner_model.trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-    inner_model.trainable = True
-    inner_model.layers[-1].trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-
-    # a Sequential inside a Sequential
-    inner_model = keras.models.Sequential()
-    inner_model.add(keras.layers.Dense(2, input_dim=1))
-    outer_model = keras.models.Sequential()
-    outer_model.add(inner_model)
-    self.assertListEqual(outer_model.trainable_weights,
-                         inner_model.trainable_weights)
-    inner_model.trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-    inner_model.trainable = True
-    inner_model.layers[-1].trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-
-    # a Model inside a Model
-    x = keras.layers.Input(shape=(1,))
-    y = keras.layers.Dense(2)(x)
-    inner_model = keras.models.Model(x, y)
-    x = keras.layers.Input(shape=(1,))
-    y = inner_model(x)
-    outer_model = keras.models.Model(x, y)
-    self.assertListEqual(outer_model.trainable_weights,
-                         inner_model.trainable_weights)
-    inner_model.trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-    inner_model.trainable = True
-    inner_model.layers[-1].trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-
-    # a Model inside a Sequential
-    x = keras.layers.Input(shape=(1,))
-    y = keras.layers.Dense(2)(x)
-    inner_model = keras.models.Model(x, y)
-    outer_model = keras.models.Sequential()
-    outer_model.add(inner_model)
-    self.assertListEqual(outer_model.trainable_weights,
-                         inner_model.trainable_weights)
-    inner_model.trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-    inner_model.trainable = True
-    inner_model.layers[-1].trainable = False
-    self.assertListEqual(outer_model.trainable_weights, [])
-
-
-class TestTrainingUtils(test.TestCase):
-
-  def test_check_array_lengths(self):
-    keras.engine.training._check_array_lengths(None, None, None)
-    a_np = np.random.random((4, 3, 3))
-    keras.engine.training._check_array_lengths(a_np, a_np, a_np)
-    keras.engine.training._check_array_lengths(
-        [a_np, a_np], [a_np, a_np], [a_np, a_np])
-    keras.engine.training._check_array_lengths([None], [None], [None])
-
-    b_np = np.random.random((3, 4))
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths(a_np, None, None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths(a_np, a_np, None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], [None], None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], [b_np], None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], None, [b_np])
-
-  def test_slice_arrays(self):
-    input_a = np.random.random((10, 3))
-    slice_arrays(None)
-    slice_arrays(input_a, 0)
-    slice_arrays(input_a, 0, 1)
-    slice_arrays(input_a, stop=2)
-    input_a = [None, [1, 1], None, [1, 1]]
-    slice_arrays(input_a, 0)
-    slice_arrays(input_a, 0, 1)
-    slice_arrays(input_a, stop=2)
-    input_a = [None]
-    slice_arrays(input_a, 0)
-    slice_arrays(input_a, 0, 1)
-    slice_arrays(input_a, stop=2)
-    input_a = None
-    slice_arrays(input_a, 0)
-    slice_arrays(input_a, 0, 1)
-    slice_arrays(input_a, stop=2)
-
-  def test_fit_with_BatchNorm(self):
-    model = keras.models.Sequential()
-    model.add(keras.layers.Dense(10, input_dim=4))
-    model.add(keras.layers.BatchNormalization())
-    model.add(keras.layers.Activation('tanh'))
-    model.add(keras.layers.Dropout(0.2))
-
-    input_a_np = np.random.random((10, 4))
-    output_b_np = np.random.random((10, 10))
-
-    model.compile(loss='binary_crossentropy', optimizer=RMSPropOptimizer(0.001))
-    model.fit(input_a_np, output_b_np, epochs=1, batch_size=5, verbose=0)
-
-  def test_fit_with_regularization(self):
-    model = keras.models.Sequential()
-    with self.assertRaises(ValueError):
-      model.add(
-          keras.layers.Dense(4, input_dim=3,
-                             kernel_regularizer=keras.regularizers.l2(0.01),
-                             activity_regularizer=keras.regularizers.l1(0.01)))
-
-
 if __name__ == '__main__':
   # Bazel sets these environment variables to very long paths.
   # Tempfile uses them to create long paths, and in turn multiprocessing
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py
index 9651eb9f14..6ca5941e9a 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py
@@ -1045,16 +1045,8 @@ class TestTrainingUtils(test.TestCase):
     keras.engine.training._check_array_lengths([None], [None], [None])
 
     b_np = np.random.random((3, 4))
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths(a_np, None, None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths(a_np, a_np, None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], [None], None)
     with self.assertRaises(ValueError):
       keras.engine.training._check_array_lengths([a_np], [b_np], None)
-    with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], None, [b_np])
 
   def test_slice_arrays(self):
     input_a = np.random.random((10, 3))
-- 
GitLab


From 78d10e5800a058c6d1865c5282aaa4094f7bc36d Mon Sep 17 00:00:00 2001
From: Skye Wanderman-Milne <skyewm@google.com>
Date: Mon, 26 Feb 2018 19:58:18 -0800
Subject: [PATCH 409/575] Fix bug in deserializing CondContexts.

PiperOrigin-RevId: 187121244
---
 tensorflow/python/ops/control_flow_ops.py | 11 ++++-
 tensorflow/python/training/saver_test.py  | 49 ++++++++++++++++-------
 2 files changed, 43 insertions(+), 17 deletions(-)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index b16901effd..0815527c96 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1716,8 +1716,15 @@ class CondContext(ControlFlowContext):
     self._pivot = g.as_graph_element(
         ops.prepend_name_scope(context_def.pivot_name, import_scope))
     self._branch = context_def.branch
-    super(CondContext, self).__init__(
-        values_def=context_def.values_def, import_scope=import_scope)
+    super(CondContext, self).__init__(values_def=context_def.values_def,
+                                      import_scope=import_scope)
+    # The predicate and pivot ops appear in self._values, but don't have self
+    # set as their control context. The __init__ call above will set self for
+    # all values, so manually override the predicate and pivot contexts here.
+    # pylint: disable=protected-access
+    self._pred.op._set_control_flow_context(self.outer_context)
+    self._pivot.op._set_control_flow_context(self.outer_context)
+    # pylint: enable=protected-access
 
   @property
   def pred(self):
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index b366ed30f3..b758ceaab0 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -2041,29 +2041,24 @@ class MetaGraphTest(test.TestCase):
     self._testGraphExtensionRestore(test_dir)
     self._testRestoreFromTrainGraphWithControlContext(test_dir)
 
-  def testNestedWhileLoops(self):
-    test_dir = self._get_test_dir("nested_whiles")
+  def _testWhileLoopAndGradientSerDes(self, outer_body_fn):
+    # Build a while loop with `outer_body_fn`, export it, and verify that it can
+    # be imported and the gradient can be built and run correctly.
+
+    test_dir = self._get_test_dir("nested_control_flow")
     filename = os.path.join(test_dir, "metafile")
     saver_ckpt = os.path.join(test_dir, "saver.ckpt")
 
-    # Create two simple nested while loops.
+    # Create while loop using `outer_body_fn`.
     with ops_lib.Graph().as_default():
-      def body(i, x):
-        _, r = control_flow_ops.while_loop(lambda j, y: j < 3,
-                                           lambda j, y: (j + 1, y + x),
-                                           [0, 0])
-        return i + 1, x + r
-
       var = variables.Variable(0)
       var_name = var.name
-
-      _, output = control_flow_ops.while_loop(lambda i, x: i < 5, body,
+      _, output = control_flow_ops.while_loop(lambda i, x: i < 5, outer_body_fn,
                                               [0, var])
       output_name = output.name
-
       init_op = variables.global_variables_initializer()
 
-      # Generate a MetaGraphDef containing the nested loops.
+      # Generate a MetaGraphDef containing the while loop.
       with session.Session() as sess:
         sess.run(init_op)
         sess.run(output)
@@ -2071,8 +2066,8 @@ class MetaGraphTest(test.TestCase):
         saver.save(sess, saver_ckpt)
         saver.export_meta_graph(filename)
 
-      # Build and run the gradients of the nested while loop. We use this below
-      # to verify that the gradients are correct with an imported MetaGraphDef.
+      # Build and run the gradients of the while loop. We use this below to
+      # verify that the gradients are correct with an imported MetaGraphDef.
       grad = gradients_impl.gradients([output], [var])
       with session.Session() as sess:
         sess.run(init_op)
@@ -2096,6 +2091,30 @@ class MetaGraphTest(test.TestCase):
         actual_grad_value = sess.run(grad)
         self.assertEqual(expected_grad_value, actual_grad_value)
 
+  def testNestedWhileLoopsSerDes(self):
+    # Test two simple nested while loops.
+    def body(i, x):
+      _, r = control_flow_ops.while_loop(lambda j, y: j < 3,
+                                         lambda j, y: (j + 1, y + x),
+                                         [0, 0])
+      return i + 1, x + r
+    self._testWhileLoopAndGradientSerDes(body)
+
+  def testNestedControlFlowSerDes(self):
+    # Test while loop in a cond in a while loop.
+    # pylint: disable=g-long-lambda
+    def body(i, x):
+      cond_result = control_flow_ops.cond(
+          i > 0,
+          lambda: control_flow_ops.while_loop(
+              lambda j, y: j < 3,
+              lambda j, y: (j + 1, y + x),
+              [0, 0])[1],
+          lambda: x)
+      return i + 1, cond_result
+    # pylint: enable=g-long-lambda
+    self._testWhileLoopAndGradientSerDes(body)
+
   def testStrippedOpListDef(self):
     with self.test_session():
       # Creates a graph.
-- 
GitLab


From 7b15f7a55dcd5e908211e86ec42b49136b1ccc25 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Mon, 26 Feb 2018 20:21:07 -0800
Subject: [PATCH 410/575] Add helpers to stream data from the GCE VM to a Cloud
 TPU.

PiperOrigin-RevId: 187122870
---
 tensorflow/contrib/tpu/BUILD                  |  28 +++
 tensorflow/contrib/tpu/python/tpu/datasets.py | 192 ++++++++++++++++++
 .../contrib/tpu/python/tpu/datasets_test.py   | 181 +++++++++++++++++
 3 files changed, 401 insertions(+)
 create mode 100644 tensorflow/contrib/tpu/python/tpu/datasets.py
 create mode 100644 tensorflow/contrib/tpu/python/tpu/datasets_test.py

diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD
index c48e84ddfa..095b4821f1 100644
--- a/tensorflow/contrib/tpu/BUILD
+++ b/tensorflow/contrib/tpu/BUILD
@@ -163,6 +163,7 @@ py_library(
     ],
     srcs_version = "PY2AND3",
     deps = [
+        ":datasets",
         ":profiler",
         ":tpu_py",
         "//tensorflow/contrib/tpu/proto:topology_proto_py",
@@ -181,6 +182,33 @@ py_library(
     ],
 )
 
+py_library(
+    name = "datasets",
+    srcs = [
+        "python/tpu/datasets.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/contrib/data/python/ops:transformation_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:function",
+        "//tensorflow/python:functional_ops",
+        "//tensorflow/python/data/ops:dataset_ops",
+        "//tensorflow/python/data/ops:iterator_ops",
+        "//tensorflow/python/data/ops:readers",
+    ],
+)
+
+tf_py_test(
+    name = "datasets_test",
+    srcs = ["python/tpu/datasets_test.py"],
+    additional_deps = [
+        "//tensorflow/python:client_testlib",
+        ":datasets",
+    ],
+    grpc_enabled = True,
+)
+
 tf_py_test(
     name = "tpu_test",
     size = "small",
diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py
new file mode 100644
index 0000000000..29aea98542
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/datasets.py
@@ -0,0 +1,192 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ======================================
+"""Library of Cloud TPU helper functions for data loading."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.data.python.ops import batching
+from tensorflow.contrib.data.python.ops import interleave_ops
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.ops import iterator_ops
+from tensorflow.python.data.ops import readers
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import function
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import functional_ops
+
+
+def _TextLineDataset(filename):
+  buffer_size = 8 * 1024 * 1024  # 8 MiB per file
+  dataset = readers.TextLineDataset(filename, buffer_size=buffer_size)
+  return dataset
+
+
+def _TFRecordDataset(filename):
+  buffer_size = 8 * 1024 * 1024  # 8 MiB per file
+  dataset = readers.TFRecordDataset(filename, buffer_size=buffer_size)
+  return dataset
+
+
+_FILETYPE_MAP = {
+    'tfrecord': _TFRecordDataset,
+    'textline': _TextLineDataset,
+    'text': _TextLineDataset,
+}
+
+
+def StreamingFilesDataset(files,
+                          filetype=None,
+                          file_reader_job=None,
+                          worker_job=None,
+                          num_epochs=None,
+                          filename_shuffle_buffer_size=None,
+                          num_parallel_reads=None,
+                          batch_transfer_size=None,
+                          sloppy=None):
+  """StreamingFilesDataset constructs a dataset to stream from workers (GCE VM).
+
+  Because Cloud TPUs are allocated over the network, a Cloud TPU cannot read
+  files local to your GCE VM. In order to train using files stored on your local
+  VM (e.g. on local SSD for extreme performance), use the StreamingFilesDataset
+  helper to generate a dataset to feed your Cloud TPU with files from your GCE
+  VM.
+
+  The resulting dataset may return an OutOfRangeError if there are no files
+  found as a result of the fileglob expansion.
+
+  Note: StreamingFilesDataset assumes that the session is using a
+  TPUClusterResolver and has therefore a worker and a coordinator job. File
+  loading will be done on the coordinator job.
+
+  Args:
+    files: A string glob to match files, or a `tf.data.Dataset` generating file
+      names.
+    filetype: A string (one of 'tfrecord', or 'textline') or a single-argument
+      TensorFlow function that when given a filename returns a dataset.
+    file_reader_job: An optional string that corresponds to the job that should
+      perform the file reads.
+    worker_job: An optional string that corresponds to the job that should
+      process the tensors (i.e. your GPU or TPU worker).
+    num_epochs: The number of epochs through the training set that should be
+      generated. By default, it will repeat infinitely.
+    filename_shuffle_buffer_size: An optional integer whose value controls the
+      shuffling of the file names. If you would like to read from the files in
+      the same order, set to 0 or False.
+    num_parallel_reads: An optional integer controlling the number of files to
+      read from concurrently. (Set to 1 for no parallelism.)
+    batch_transfer_size: An optional integer controlling the batching used to
+      amortize the remote function invocation overhead. Set to a very large
+      number to increase throughput. Set to a very small number to reduce memory
+      consumption. Set to False to skip batching.
+    sloppy: (Optional.) If `True`, read input data as fast as possible, without
+      maintaining a deterministic order. Defaults to `False`.
+  Returns:
+    A `tf.data.Dataset` with an infinite stream of elements generated by a
+    parallel interleaving of the set of files matched (or generated) by `files`
+    with a type is the output of the dataset specified by `filetype`.
+
+  Raises:
+    ValueError: if any argument is not of the expected type.
+  """
+  if filetype is None:
+    filetype = 'tfrecord'
+
+  if isinstance(filetype, str):
+    if filetype not in _FILETYPE_MAP:
+      raise ValueError('Unexpected filetype: %s' % filetype)
+    reader_fn = _FILETYPE_MAP[filetype]
+  elif callable(filetype):
+    reader_fn = filetype
+  else:
+    raise ValueError('filetype should be a string or a callable')
+
+  file_reader_job = file_reader_job or 'coordinator'
+
+  worker_job = worker_job or 'worker'
+
+  if filename_shuffle_buffer_size is None:
+    filename_shuffle_buffer_size = 4096
+
+  num_parallel_reads = num_parallel_reads or 8
+
+  if batch_transfer_size is None:
+    batch_transfer_size = 1024
+
+  if sloppy is None:
+    sloppy = False
+
+  with ops.device('/job:%s' % file_reader_job):
+    if isinstance(files, str):
+      source_dataset = dataset_ops.Dataset.list_files(files)
+    elif isinstance(files, dataset_ops.Dataset):
+      source_dataset = files
+    else:
+      raise ValueError('files was not a string or a dataset: %s' % files)
+
+    if filename_shuffle_buffer_size:
+      source_dataset = source_dataset.shuffle(
+          buffer_size=filename_shuffle_buffer_size)
+
+    # NOTE: We perform the `repeat` on the source dataset, because the output
+    # dataset does not currently have enough information to recreate an iterator
+    # over the source dataset when it reaches the end.
+    source_dataset = source_dataset.repeat(num_epochs)
+
+    source_dataset = source_dataset.apply(
+        interleave_ops.parallel_interleave(
+            reader_fn, cycle_length=num_parallel_reads, sloppy=sloppy))
+
+    if batch_transfer_size:
+      # Note: we can safely call batch_and_drop_remainder because we have an
+      # infinite stream of TFRecords.
+      source_dataset = source_dataset.apply(
+          batching.batch_and_drop_remainder(batch_transfer_size))
+
+    source_dataset = source_dataset.prefetch(1)
+
+    source_iterator = source_dataset.make_one_shot_iterator()
+    source_handle = source_iterator.string_handle()
+
+  @function.Defun(dtypes.string)
+  def LoadingFunc(h):
+    remote_iterator = iterator_ops.Iterator.from_string_handle(
+        h, source_dataset.output_types, source_dataset.output_shapes)
+    return remote_iterator.get_next()
+
+  def MapFn(unused_input):
+    return functional_ops.remote_call(
+        args=[source_handle],
+        Tout=[dtypes.string],
+        f=LoadingFunc,
+        target='/job:%s/replica:0/task:0/cpu:0' % file_reader_job)
+
+  with ops.device('/job:%s' % worker_job):
+    # TODO(saeta,mrry): Switch to using _GeneratorDataset.
+
+    # identity = lambda x: x
+    # dummy = constant_op.constant(0)
+    # output_dataset = dataset_ops._GeneratorDataset(dummy, identity, MapFn,
+    #                                                identity)
+
+    output_dataset = dataset_ops.Dataset.range(2).repeat().map(MapFn)
+    output_dataset = output_dataset.prefetch(1)
+
+    if batch_transfer_size:
+      # Undo the batching used during the transfer.
+      output_dataset = output_dataset.apply(batching.unbatch()).prefetch(1)
+
+  return output_dataset
diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py
new file mode 100644
index 0000000000..2c40797792
--- /dev/null
+++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py
@@ -0,0 +1,181 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""TPU datasets tests."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from tensorflow.contrib.tpu.python.tpu import datasets
+from tensorflow.core.protobuf import cluster_pb2
+from tensorflow.core.protobuf import config_pb2
+from tensorflow.python.client import session
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.ops import readers
+from tensorflow.python.lib.io import python_io
+from tensorflow.python.platform import test
+from tensorflow.python.training import server_lib
+from tensorflow.python.util import compat
+
+_NUM_FILES = 10
+_NUM_ENTRIES = 200
+
+
+class DatasetsTest(test.TestCase):
+
+  def setUp(self):
+    super(DatasetsTest, self).setUp()
+    self._coord = server_lib.Server.create_local_server()
+    self._worker = server_lib.Server.create_local_server()
+
+    self._cluster_def = cluster_pb2.ClusterDef()
+    worker_job = self._cluster_def.job.add()
+    worker_job.name = 'worker'
+    worker_job.tasks[0] = self._worker.target[len('grpc://'):]
+    coord_job = self._cluster_def.job.add()
+    coord_job.name = 'coordinator'
+    coord_job.tasks[0] = self._coord.target[len('grpc://'):]
+
+    session_config = config_pb2.ConfigProto(cluster_def=self._cluster_def)
+
+    self._sess = session.Session(self._worker.target, config=session_config)
+
+  def testTextLineDataset(self):
+    all_contents = []
+    for i in range(_NUM_FILES):
+      filename = os.path.join(self.get_temp_dir(), 'text_line.%d.txt' % i)
+      contents = []
+      for j in range(_NUM_ENTRIES):
+        contents.append(compat.as_bytes('%d: %d' % (i, j)))
+      with open(filename, 'wb') as f:
+        f.write(b'\n'.join(contents))
+      all_contents.extend(contents)
+
+    dataset = datasets.StreamingFilesDataset(
+        os.path.join(self.get_temp_dir(), 'text_line.*.txt'), filetype='text')
+
+    iterator = dataset.make_initializable_iterator()
+    self._sess.run(iterator.initializer)
+    get_next = iterator.get_next()
+
+    retrieved_values = []
+    for _ in range(2 * len(all_contents)):
+      retrieved_values.append(compat.as_bytes(self._sess.run(get_next)))
+
+    self.assertEqual(set(all_contents), set(retrieved_values))
+
+  def testTFRecordDataset(self):
+    all_contents = []
+    for i in range(_NUM_FILES):
+      filename = os.path.join(self.get_temp_dir(), 'tf_record.%d' % i)
+      writer = python_io.TFRecordWriter(filename)
+      for j in range(_NUM_ENTRIES):
+        record = compat.as_bytes('Record %d of file %d' % (j, i))
+        writer.write(record)
+        all_contents.append(record)
+      writer.close()
+
+    dataset = datasets.StreamingFilesDataset(
+        os.path.join(self.get_temp_dir(), 'tf_record*'), filetype='tfrecord')
+
+    iterator = dataset.make_initializable_iterator()
+    self._sess.run(iterator.initializer)
+    get_next = iterator.get_next()
+
+    retrieved_values = []
+    for _ in range(2 * len(all_contents)):
+      retrieved_values.append(compat.as_bytes(self._sess.run(get_next)))
+
+    self.assertEqual(set(all_contents), set(retrieved_values))
+
+  def testTFRecordDatasetFromDataset(self):
+    filenames = []
+    all_contents = []
+    for i in range(_NUM_FILES):
+      filename = os.path.join(self.get_temp_dir(), 'tf_record.%d' % i)
+      filenames.append(filename)
+      writer = python_io.TFRecordWriter(filename)
+      for j in range(_NUM_ENTRIES):
+        record = compat.as_bytes('Record %d of file %d' % (j, i))
+        writer.write(record)
+        all_contents.append(record)
+      writer.close()
+
+    filenames = dataset_ops.Dataset.from_tensor_slices(filenames)
+
+    dataset = datasets.StreamingFilesDataset(filenames, filetype='tfrecord')
+
+    iterator = dataset.make_initializable_iterator()
+    self._sess.run(iterator.initializer)
+    get_next = iterator.get_next()
+
+    retrieved_values = []
+    for _ in range(2 * len(all_contents)):
+      retrieved_values.append(compat.as_bytes(self._sess.run(get_next)))
+
+    self.assertEqual(set(all_contents), set(retrieved_values))
+
+  def testArbitraryReaderFunc(self):
+
+    def MakeRecord(i, j):
+      return compat.as_bytes('%04d-%04d' % (i, j))
+
+    record_bytes = len(MakeRecord(10, 200))
+
+    all_contents = []
+    for i in range(_NUM_FILES):
+      filename = os.path.join(self.get_temp_dir(), 'fixed_length.%d' % i)
+      with open(filename, 'wb') as f:
+        for j in range(_NUM_ENTRIES):
+          record = MakeRecord(i, j)
+          f.write(record)
+          all_contents.append(record)
+
+    def FixedLengthFile(filename):
+      return readers.FixedLengthRecordDataset(filename, record_bytes)
+
+    dataset = datasets.StreamingFilesDataset(
+        os.path.join(self.get_temp_dir(), 'fixed_length*'),
+        filetype=FixedLengthFile)
+
+    iterator = dataset.make_initializable_iterator()
+    self._sess.run(iterator.initializer)
+    get_next = iterator.get_next()
+
+    retrieved_values = []
+    for _ in range(2 * len(all_contents)):
+      retrieved_values.append(compat.as_bytes(self._sess.run(get_next)))
+
+    self.assertEqual(set(all_contents), set(retrieved_values))
+
+  def testUnexpectedFiletypeString(self):
+    with self.assertRaises(ValueError):
+      datasets.StreamingFilesDataset(
+          os.path.join(self.get_temp_dir(), '*'), filetype='foo')
+
+  def testUnexpectedFiletypeType(self):
+    with self.assertRaises(ValueError):
+      datasets.StreamingFilesDataset(
+          os.path.join(self.get_temp_dir(), '*'), filetype=3)
+
+  def testUnexpectedFilesType(self):
+    with self.assertRaises(ValueError):
+      datasets.StreamingFilesDataset(123, filetype='tfrecord')
+
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From 557611cefba99a7c94dc7dd0932723c0a9f96087 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 21:09:30 -0800
Subject: [PATCH 411/575] Automated g4 rollback of changelist 187092622

PiperOrigin-RevId: 187125995
---
 tensorflow/c/eager/BUILD            |  1 -
 tensorflow/c/eager/c_api.cc         |  4 ++--
 tensorflow/c/eager/c_api_internal.h | 14 +-------------
 tensorflow/c/eager/runtime.cc       | 14 ++++----------
 tensorflow/c/eager/runtime.h        |  3 ---
 tensorflow/c/eager/runtime_test.cc  | 12 ++++++------
 6 files changed, 13 insertions(+), 35 deletions(-)

diff --git a/tensorflow/c/eager/BUILD b/tensorflow/c/eager/BUILD
index 16a2a15072..e55cb672e9 100644
--- a/tensorflow/c/eager/BUILD
+++ b/tensorflow/c/eager/BUILD
@@ -21,7 +21,6 @@ tf_cuda_library(
     visibility = ["//visibility:public"],
     deps = select({
         "//tensorflow:android": [
-            "//tensorflow/core:lib",
             "//tensorflow/core:android_tensorflow_lib_lite",
         ],
         "//conditions:default": [
diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index b233dd5b93..bebb63c746 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -818,8 +818,8 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
     // See WARNING comment below - would be nice to rework to avoid this
     // subtlety.
     tensorflow::tf_shared_lock l(ctx->functions_mu);
-    status->status = tensorflow::KernelAndDevice::Init(
-        ndef, ctx->func_lib(device), &ctx->runner, kernel);
+    status->status =
+        tensorflow::KernelAndDevice::Init(ndef, ctx->func_lib(device), kernel);
     if (!status->status.ok()) {
       delete kernel;
       return;
diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h
index 29944df4c2..3356054cd0 100644
--- a/tensorflow/c/eager/c_api_internal.h
+++ b/tensorflow/c/eager/c_api_internal.h
@@ -31,7 +31,6 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
 #include "tensorflow/core/framework/rendezvous.h"
-#include "tensorflow/core/lib/core/threadpool.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/gtl/stl_util.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -46,15 +45,7 @@ struct TFE_ContextOptions {
 
 struct TFE_Context {
   explicit TFE_Context(const TFE_ContextOptions& opts, TF_Session* s)
-      : thread_pool(new tensorflow::thread::ThreadPool(
-            opts.session_options.options.env, "EagerCompute",
-            opts.session_options.options.config
-                        .inter_op_parallelism_threads() != 0
-                ? opts.session_options.options.config
-                      .inter_op_parallelism_threads()
-                : tensorflow::port::NumSchedulableCPUs())),
-        runner([this](std::function<void()> f) { thread_pool->Schedule(f); }),
-        policy(opts.policy),
+      : policy(opts.policy),
         session(s),
         rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)),
         pflr(new tensorflow::ProcessFunctionLibraryRuntime(
@@ -63,9 +54,6 @@ struct TFE_Context {
         log_device_placement(
             opts.session_options.options.config.log_device_placement()) {}
 
-  const std::unique_ptr<tensorflow::thread::ThreadPool> thread_pool;
-  std::function<void(std::function<void()>)> runner;
-
   const TFE_ContextDevicePlacementPolicy policy;
 
   // Note: we cannot use C++11 thread_local here as there is no concept of a
diff --git a/tensorflow/c/eager/runtime.cc b/tensorflow/c/eager/runtime.cc
index b9618420f0..4bf24fec2c 100644
--- a/tensorflow/c/eager/runtime.cc
+++ b/tensorflow/c/eager/runtime.cc
@@ -255,22 +255,17 @@ Status KernelAndDevice::InitOp(Device* device, const NodeDef& ndef,
   out->device_ = device;
   out->kernel_.reset(k);
   out->flib_ = nullptr;
-  out->runner_ = nullptr;
-  out->default_runner_ = [](std::function<void()> f) { f(); };
   return s;
 }
 
 // static
 Status KernelAndDevice::Init(const NodeDef& ndef, FunctionLibraryRuntime* flib,
-                             std::function<void(std::function<void()>)>* runner,
                              KernelAndDevice* out) {
   OpKernel* k = nullptr;
   Status s = flib->CreateKernel(ndef, &k);
   out->device_ = flib->device();
   out->kernel_.reset(k);
   out->flib_ = flib;
-  out->runner_ = runner;
-  out->default_runner_ = [](std::function<void()> f) { f(); };
   return s;
 }
 
@@ -301,11 +296,10 @@ Status KernelAndDevice::Run(std::vector<Tensor>* input_tensors,
   if (stats != nullptr) {
     params.track_allocations = true;
   }
-  if (runner_ == nullptr) {
-    params.runner = &default_runner_;
-  } else {
-    params.runner = runner_;
-  }
+  // TODO(apassos): use a thread pool.
+  std::function<void(std::function<void()>)> runner =
+      [](std::function<void()> f) { f(); };
+  params.runner = &runner;
 
   OpKernelContext context(&params);
   device_->Compute(kernel_.get(), &context);
diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h
index fa5f839977..7fede4dae9 100644
--- a/tensorflow/c/eager/runtime.h
+++ b/tensorflow/c/eager/runtime.h
@@ -169,7 +169,6 @@ class KernelAndDevice {
   // the FunctionLibraryRuntime is pushed on to the caller (see locking in
   // c_api.cc).
   static Status Init(const NodeDef& ndef, FunctionLibraryRuntime* flib,
-                     std::function<void(std::function<void()>)>* runner,
                      KernelAndDevice* out);
   // TODO(ashankar): Remove this
   static Status InitOp(Device* device, const NodeDef& ndef,
@@ -189,8 +188,6 @@ class KernelAndDevice {
  private:
   std::unique_ptr<OpKernel> kernel_;
   Device* device_;
-  std::function<void(std::function<void()>)>* runner_;
-  std::function<void(std::function<void()>)> default_runner_;
   FunctionLibraryRuntime* flib_;
   checkpoint::TensorSliceReaderCacheWrapper slice_reader_cache_;
   Rendezvous* rendez_;
diff --git a/tensorflow/c/eager/runtime_test.cc b/tensorflow/c/eager/runtime_test.cc
index ab0b535e1a..643153058c 100644
--- a/tensorflow/c/eager/runtime_test.cc
+++ b/tensorflow/c/eager/runtime_test.cc
@@ -92,8 +92,8 @@ TEST(KernelAndDevice, Run) {
                    .BuildNodeDef());
   TestEnv env;
   KernelAndDevice kernel(nullptr);
-  Status s = KernelAndDevice::Init(ndef, env.function_library_runtime(),
-                                   nullptr, &kernel);
+  Status s =
+      KernelAndDevice::Init(ndef, env.function_library_runtime(), &kernel);
   ASSERT_TRUE(s.ok()) << s;
   std::vector<Tensor> outputs;
   s = kernel.Run(&inputs, &outputs, nullptr);
@@ -158,8 +158,8 @@ void BM_KernelAndDeviceInit(int iters) {
   KernelAndDevice k(nullptr);
   tensorflow::testing::StartTiming();
   for (int i = 0; i < iters; ++i) {
-    TF_CHECK_OK(KernelAndDevice::Init(ndef, env.function_library_runtime(),
-                                      nullptr, &k));
+    TF_CHECK_OK(
+        KernelAndDevice::Init(ndef, env.function_library_runtime(), &k));
   }
 }
 BENCHMARK(BM_KernelAndDeviceInit);
@@ -179,8 +179,8 @@ void BM_KernelAndDeviceRun(int iters) {
                    .BuildNodeDef());
   TestEnv env;
   KernelAndDevice kernel(nullptr);
-  TF_CHECK_OK(KernelAndDevice::Init(ndef, env.function_library_runtime(),
-                                    nullptr, &kernel));
+  TF_CHECK_OK(
+      KernelAndDevice::Init(ndef, env.function_library_runtime(), &kernel));
   tensorflow::testing::StartTiming();
   for (int i = 0; i < iters; ++i) {
     TF_CHECK_OK(kernel.Run(&inputs, &outputs, nullptr));
-- 
GitLab


From 46306ad7bd02c613a59aa6074f830f0de011cfbf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 26 Feb 2018 21:25:22 -0800
Subject: [PATCH 412/575] Improve error handling in strided_slice_op to fail
 more gracefully and return an error status instead of crashing.

PiperOrigin-RevId: 187126888
---
 tensorflow/core/kernels/strided_slice_op.cc | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc
index 7745effe2a..1e3e92a68a 100644
--- a/tensorflow/core/kernels/strided_slice_op.cc
+++ b/tensorflow/core/kernels/strided_slice_op.cc
@@ -109,17 +109,27 @@ class StridedSliceOp : public OpKernel {
     if (is_identity) {
       VLOG(1) << "Strided slice identity ";
       Tensor tmp;
-      CHECK(tmp.CopyFrom(input, final_shape));
+      OP_REQUIRES(context, tmp.CopyFrom(input, final_shape),
+                  errors::Internal("Copy failed"));
       context->set_output(0, tmp);
       return;
     }
 
     // Optimization #2, slice is memory contiguous (only occurs in dim 0)
     if (slice_dim0 && IsDim0SliceAligned<T>(input.shape(), begin[0], end[0])) {
-      CHECK_GE(input.dims(), 1);  // Otherwise, is_identity should be true.
+      OP_REQUIRES(context, input.dims() >= 1,
+                  errors::InvalidArgument(
+                      "Input must have rank at least 1, got: ", input.dims()));
+      // Otherwise, is_identity should be true.
       VLOG(1) << "Strided slice dim 0: " << input.shape().DebugString();
+      OP_REQUIRES(
+          context, begin[0] <= end[0],
+          errors::InvalidArgument("begin[0] (", begin[0],
+                                  ") must less or equal to end[0] (", end[0]));
+      Tensor slice = input.Slice(begin[0], end[0]);
       Tensor tmp;
-      CHECK(tmp.CopyFrom(input.Slice(begin[0], end[0]), final_shape));
+      OP_REQUIRES(context, tmp.CopyFrom(slice, final_shape),
+                  errors::Internal("Copy failed"));
       context->set_output(0, tmp);
       return;
     }
@@ -238,7 +248,8 @@ class StridedSliceGradOp : public OpKernel {
 
     if (processing_shape.dims() == 0) {
       auto in = context->input(4);
-      CHECK(result->CopyFrom(in, processing_shape));
+      OP_REQUIRES(context, result->CopyFrom(in, processing_shape),
+                  errors::Internal("Copy failed"));
       return;
     }
 
-- 
GitLab


From 129bb5400e20b322016c4a8f378da63be8d58e5e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 01:02:36 -0800
Subject: [PATCH 413/575] Add documentation to Grappler RewriterConfig to give
 a short description for each of the optimizer on what they do.

PiperOrigin-RevId: 187143156
---
 tensorflow/core/protobuf/rewriter_config.proto | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 504ed5d819..875e4663db 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -30,12 +30,17 @@ message RewriterConfig {
   }
 
   // Optimize tensor layouts (default is ON)
+  // e.g. This will try to use NCHW layout on GPU which is faster.
   Toggle layout_optimizer = 1;
   // Fold constants (default is ON)
+  // Statically infer the value of tensors when possible, and materialize the
+  // result using constants.
   Toggle constant_folding = 3;
   // Arithmetic optimizations (default is ON)
+  // e.g. Simplify arithmetic ops; merge ops with same value (like constants).
   Toggle arithmetic_optimization = 7;
   // Control dependency optimizations (default is ON).
+  // Remove redundant control dependencies, which may enable other optimization.
   Toggle dependency_optimization = 8;
   // Loop optimizations (default is OFF).
   Toggle loop_optimization = 9;
@@ -49,12 +54,20 @@ message RewriterConfig {
     NO_MEM_OPT = 1;
     // Driven by manual op-level annotations.
     MANUAL = 2;
+
     // Driven by heuristics. The behavior of these heuristics is subject to
     // change. Currently includes an experimental recomputation and swapping
     // heuristics. Manual annotations are respected, but additional nodes are
     // selected automatically.
+
+    // Swapping heuristic will move a tensor from the GPU to the CPU and move
+    // it back when needed to reduce peak memory usage.
     SWAPPING_HEURISTICS = 4;
+    // Recomputation heuristics will recompute ops (such as Relu activation)
+    // during backprop instead of storing them, reducing peak memory usage.
     RECOMPUTATION_HEURISTICS = 5;
+    // Scheduling will split big ops such as AddN and try to enforce a schedule
+    // of the new computations that decreases peak memory usage.
     SCHEDULING_HEURISTICS = 6;
     // Use any combination of swapping and recomputation heuristics.
     HEURISTICS = 3;
-- 
GitLab


From efa9a8ec649c72887cd286a78b3a2bf95e34f924 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 06:00:21 -0800
Subject: [PATCH 414/575] Enable dynamic function calls. These are compiled
 just in time by inserting a call to compile.

PiperOrigin-RevId: 187165096
---
 tensorflow/contrib/py2tf/__init__.py          |  4 +-
 tensorflow/contrib/py2tf/converters/BUILD     | 13 +--
 .../contrib/py2tf/converters/call_trees.py    | 76 +++++++-------
 .../py2tf/converters/call_trees_test.py       | 16 +++
 .../py2tf/converters/converter_test_base.py   | 32 ++++--
 tensorflow/contrib/py2tf/impl/api.py          | 99 ++++++++++++++-----
 6 files changed, 163 insertions(+), 77 deletions(-)

diff --git a/tensorflow/contrib/py2tf/__init__.py b/tensorflow/contrib/py2tf/__init__.py
index 379fa7fd5c..6531183cb5 100644
--- a/tensorflow/contrib/py2tf/__init__.py
+++ b/tensorflow/contrib/py2tf/__init__.py
@@ -23,6 +23,7 @@ from __future__ import print_function
 
 from tensorflow.contrib.py2tf import utils
 from tensorflow.contrib.py2tf.impl.api import convert
+from tensorflow.contrib.py2tf.impl.api import converted_call
 from tensorflow.contrib.py2tf.impl.api import graph_ready
 from tensorflow.contrib.py2tf.impl.api import to_code
 from tensorflow.contrib.py2tf.impl.api import to_graph
@@ -30,7 +31,8 @@ from tensorflow.contrib.py2tf.pyct.transformer import PyFlowParseError
 from tensorflow.python.util.all_util import remove_undocumented
 
 _allowed_symbols = [
-    'to_graph', 'to_code', 'convert', 'graph_ready', 'utils', 'PyFlowParseError'
+    'to_graph', 'to_code', 'convert', 'graph_ready', 'converted_call', 'utils',
+    'PyFlowParseError'
 ]
 
 remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/py2tf/converters/BUILD b/tensorflow/contrib/py2tf/converters/BUILD
index 42baaaaba7..78f46bc05f 100644
--- a/tensorflow/contrib/py2tf/converters/BUILD
+++ b/tensorflow/contrib/py2tf/converters/BUILD
@@ -46,6 +46,7 @@ py_library(
     visibility = ["//tensorflow:__subpackages__"],
     deps = [
         ":converters",
+        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/contrib/py2tf/pyct/static_analysis",
         "//tensorflow/contrib/py2tf/utils",
         "@gast_archive//:gast",
@@ -59,7 +60,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -70,7 +70,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -81,7 +80,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -92,7 +90,7 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
+        "//tensorflow/contrib/py2tf/impl",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -103,7 +101,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -114,7 +111,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -125,7 +121,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -136,7 +131,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -157,7 +151,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -168,7 +161,6 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
@@ -184,7 +176,6 @@ py_test(
     ],
     deps = [
         ":test_lib",
-        "//tensorflow/contrib/py2tf/pyct",
         "//tensorflow/python:client_testlib",
     ],
 )
diff --git a/tensorflow/contrib/py2tf/converters/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py
index 1050ba654c..f18f9f6086 100644
--- a/tensorflow/contrib/py2tf/converters/call_trees.py
+++ b/tensorflow/contrib/py2tf/converters/call_trees.py
@@ -27,6 +27,7 @@ import types
 import gast
 
 from tensorflow.contrib.py2tf.pyct import anno
+from tensorflow.contrib.py2tf.pyct import inspect_utils
 from tensorflow.contrib.py2tf.pyct import parser
 from tensorflow.contrib.py2tf.pyct import templates
 from tensorflow.contrib.py2tf.pyct import transformer
@@ -72,9 +73,8 @@ class CallTreeTransformer(transformer.Base):
     self.uncompiled_modules = uncompiled_modules
     self.nocompile_decorators = nocompile_decorators
 
-  # pylint:disable=invalid-name
-
   def _resolve_name(self, node):
+    """Used to resolve decorator info."""
     if isinstance(node, gast.Call):
       return self._resolve_name(node.func)
     if isinstance(node, gast.Name):
@@ -99,7 +99,13 @@ class CallTreeTransformer(transformer.Base):
                          (owner_type, node.attr))
     return None
 
+  def _function_is_compilable(self, target_entity):
+    """Determines whether an entity can be compiled at all."""
+    # TODO(mdan): This is just a placeholder. Implement.
+    return not isinstance(target_entity, types.BuiltinFunctionType)
+
   def _should_compile(self, node, fqn):
+    """Determines whether an entity should be compiled in the context."""
     for i in range(1, len(fqn)):
       if fqn[:i] in self.uncompiled_modules:
         return False
@@ -141,33 +147,6 @@ class CallTreeTransformer(transformer.Base):
 
     return True
 
-  def _determine_function_owner(self, m):
-    # TODO(mdan): The parent type should be known at analysis. Use that instead.
-    if hasattr(m, 'im_class'):  # Python 2
-      return m.im_class
-    if hasattr(m, '__qualname__'):  # Python 3
-      # Object attributes: should be bound to "self".
-      if hasattr(m, '__self__'):
-        return type(m.__self__)
-
-      # Class attributes: should have the owner name in their namespace.
-      qn = m.__qualname__.split('.')
-      if len(qn) < 2:
-        return None
-      owner_name, func_name = qn[-2:]
-      if func_name != m.__name__:
-        raise ValueError('Inconsistent names detected '
-                         '(__qualname__[1] = "%s", __name__ = "%s") for %s.' %
-                         (func_name, m.__name__, m))
-      if owner_name == '<locals>':
-        return None
-      if owner_name not in self.context.namespace:
-        raise ValueError(
-            'Could not resolve name "%s" while analyzing %s. Namespace:\n%s' %
-            (owner_name, m, self.context.namespace))
-      return self.context.namespace[owner_name]
-    return None
-
   def _rename_compilable_function(self, node):
     assert anno.hasanno(node.func, 'live_val')
     assert anno.hasanno(node.func, 'fqn')
@@ -182,7 +161,11 @@ class CallTreeTransformer(transformer.Base):
           target_fqn, live_entity=target_entity)
       do_rename = True
     else:
-      owner_type = self._determine_function_owner(target_entity)
+      if anno.hasanno(node.func, 'parent_type'):
+        owner_type = anno.getanno(node.func, 'parent_type')
+      else:
+        # Fallback - not reliable.
+        owner_type = inspect_utils.getmethodclass(target_entity)
       new_name, do_rename = self.context.namer.compiled_function_name(
           target_fqn, live_entity=target_entity, owner_type=owner_type)
 
@@ -202,9 +185,32 @@ class CallTreeTransformer(transformer.Base):
     """
     return templates.replace(template, func=node.func, original_args=node.args)
 
-  def _function_is_compilable(self, target_entity):
-    # TODO(mdan): This is just a placeholder. Implement.
-    return not isinstance(target_entity, types.BuiltinFunctionType)
+  def _converted_call(self, node):
+    """Inlines a dynamic conversion for a dynamic function."""
+    # TODO(mdan): Pass information on the statically compiled functions.
+    # Having access to the statically compiled functions can help avoid
+    # unnecessary compilation.
+    # For example, this would lead to function `a` being compiled twice:
+    #
+    #   def a():
+    #     v = b
+    #     b()
+    #   def b():
+    #     a()
+    #
+    # This is really a problem with recursive calls, which currently can
+    # only be gated by a static condition, and should be rare.
+    # TODO(mdan): It probably makes sense to use dynamic conversion every time.
+    # Before we could convert all the time though, we'd need a reasonable
+    # caching mechanism.
+    template = """
+      py2tf_api.converted_call(func, True, False, {}, original_args)
+    """
+    call_expr = templates.replace(
+        template, func=node.func, original_args=node.args)
+    return call_expr[0].value
+
+  # pylint:disable=invalid-name
 
   def visit_Expr(self, node):
     if isinstance(node.value, gast.Call):
@@ -245,9 +251,9 @@ class CallTreeTransformer(transformer.Base):
         raise NotImplementedError('py_func with return values')
     else:
       if self.context.recursive:
-        raise NotImplementedError('Could not resolve target function.')
+        node = self._converted_call(node)
       else:
-        # TODO(mdan): Double check. Is this reachable code?
+        # Unresolved functions are allowed in non-recursive mode.
         pass
     return node
 
diff --git a/tensorflow/contrib/py2tf/converters/call_trees_test.py b/tensorflow/contrib/py2tf/converters/call_trees_test.py
index 777648dc0b..d482a9ef78 100644
--- a/tensorflow/contrib/py2tf/converters/call_trees_test.py
+++ b/tensorflow/contrib/py2tf/converters/call_trees_test.py
@@ -47,6 +47,21 @@ class CallTreesTest(converter_test_base.TestCase):
       result.renamed_test_fn_1 = renamed_test_fn_1
       self.assertEquals(3, result.test_fn_2(1))
 
+  def test_dynamic_function(self):
+
+    def test_fn_1():
+      raise ValueError('This should be masked by the mock.')
+
+    def test_fn_2(f):
+      return f() + 3
+
+    node = self.parse_and_analyze(test_fn_2, {})
+    node = call_trees.transform(node, self.ctx, (), ())
+
+    with self.compiled(node) as result:
+      # 10 = 7 (from the mock) + 3 (from test_fn_2)
+      self.assertEquals(10, result.test_fn_2(test_fn_1))
+
   def test_simple_methods(self):
 
     class TestClass(object):
@@ -59,6 +74,7 @@ class CallTreesTest(converter_test_base.TestCase):
 
     node = self.parse_and_analyze(
         TestClass.test_fn_2, {'TestClass': TestClass},
+        namer=converter_test_base.FakeNoRenameNamer(),
         arg_types={'self': (TestClass.__name__, TestClass)})
     node = call_trees.transform(node, self.ctx, (), ())
 
diff --git a/tensorflow/contrib/py2tf/converters/converter_test_base.py b/tensorflow/contrib/py2tf/converters/converter_test_base.py
index afa5c2f96f..1f98d8469c 100644
--- a/tensorflow/contrib/py2tf/converters/converter_test_base.py
+++ b/tensorflow/contrib/py2tf/converters/converter_test_base.py
@@ -25,6 +25,7 @@ from tensorflow.contrib.py2tf import utils
 from tensorflow.contrib.py2tf.pyct import compiler
 from tensorflow.contrib.py2tf.pyct import context
 from tensorflow.contrib.py2tf.pyct import parser
+from tensorflow.contrib.py2tf.pyct import pretty_printer
 from tensorflow.contrib.py2tf.pyct import qual_names
 from tensorflow.contrib.py2tf.pyct.static_analysis import activity
 from tensorflow.contrib.py2tf.pyct.static_analysis import live_values
@@ -52,26 +53,43 @@ class FakeNamer(object):
     return ('renamed_%s' % '_'.join(original_fqn)), True
 
 
+class FakeNoRenameNamer(FakeNamer):
+
+  def compiled_function_name(self, original_fqn, **_):
+    return str(original_fqn), False
+
+
 class TestCase(test.TestCase):
   """Base class for unit tests in this module. Contains relevant utilities."""
 
   @contextlib.contextmanager
   def compiled(self, node, *symbols):
-    source = '<compile failed>'
+    source = None
+
+    self.dynamic_calls = []
+    def converted_call(*args):
+      """Mock version of api.converted_call."""
+      self.dynamic_calls.append(args)
+      return 7
+
     try:
       result, source = compiler.ast_to_object(node)
-      result.tf = self.make_fake_tf(*symbols)
+      result.tf = self.make_fake_mod('fake_tf', *symbols)
       result.py2tf_utils = utils
+      result.py2tf_api = self.make_fake_mod('fake_api', converted_call)
       yield result
     except Exception:  # pylint:disable=broad-except
-      print('Offending compiled code:\n%s' % source)
+      if source is None:
+        print('Offending AST:\n%s' % pretty_printer.fmt(node, color=False))
+      else:
+        print('Offending compiled code:\n%s' % source)
       raise
 
-  def make_fake_tf(self, *symbols):
-    fake_tf = imp.new_module('fake_tf')
+  def make_fake_mod(self, name, *symbols):
+    fake_mod = imp.new_module(name)
     for s in symbols:
-      setattr(fake_tf, s.__name__, s)
-    return fake_tf
+      setattr(fake_mod, s.__name__, s)
+    return fake_mod
 
   def attach_namespace(self, module, **ns):
     for k, v in ns.items():
diff --git a/tensorflow/contrib/py2tf/impl/api.py b/tensorflow/contrib/py2tf/impl/api.py
index 29d2e038a7..48100aac32 100644
--- a/tensorflow/contrib/py2tf/impl/api.py
+++ b/tensorflow/contrib/py2tf/impl/api.py
@@ -26,7 +26,9 @@ import six
 from tensorflow.contrib.py2tf.impl import config
 from tensorflow.contrib.py2tf.impl import conversion
 from tensorflow.contrib.py2tf.pyct import compiler
+from tensorflow.contrib.py2tf.pyct import inspect_utils
 from tensorflow.contrib.py2tf.pyct import parser
+from tensorflow.contrib.py2tf.utils import builtins
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import tf_inspect
 
@@ -110,28 +112,7 @@ def convert(recursive=False, verbose=False, arg_types=None):
 
     @wraps(f)
     def wrapper(*args, **kwargs):
-      """Wrapper that calls the compiled version of the wrapped function."""
-      partial_types = ()
-      arg_values = {}
-      arg_names = tf_inspect.getargspec(f)[0]
-      for name, arg in zip(arg_names, args):
-        arg_values[name] = arg
-        arg_class = arg.__class__
-        # If arg_value_hints specifies any name, use that instead.
-        if name not in arg_types:
-          arg_types[name] = (arg_class.__name__, arg_class)
-        if name == 'self' and tf_inspect.isclass(arg_class):
-          # Annotated methods need to specify that their owner type is partial,
-          # otherwise other members they call will not be converted.
-          partial_types = (arg_class,)
-      wrapped = to_graph(
-          f,
-          recursive=recursive,
-          verbose=verbose,
-          arg_values=arg_values,
-          arg_types=arg_types,
-          partial_types=partial_types)
-      return wrapped(*args, **kwargs)
+      return converted_call(f, recursive, verbose, arg_types, *args, **kwargs)
 
     # Sometimes the decorator is just desugared, making it impossible to detect.
     # This attribute makes detection easier.
@@ -141,6 +122,78 @@ def convert(recursive=False, verbose=False, arg_types=None):
   return decorator
 
 
+def converted_call(f, recursive, verbose, arg_types, *args, **kwargs):
+  """Compiles a function call inline."""
+  # TODO(mdan): This needs cleanup.
+  # In particular, we may want to avoid renaming functions altogether.
+
+  if conversion.is_whitelisted_for_graph(f):
+    return f(*args, **kwargs)
+
+  unknown_arg_value = object()  # Sentinel for arguments of unknown value
+
+  if tf_inspect.isbuiltin(f):
+    return builtins.dynamic_builtin(f, *args, **kwargs)
+
+  if tf_inspect.isfunction(f) or tf_inspect.ismethod(f):
+    # Regular functions
+    target_entity = f
+    arg_map_target = f
+    effective_args = args
+    f_class = inspect_utils.getmethodclass(f)
+
+    if f_class is not None:
+      partial_types = (f_class,)
+    else:
+      partial_types = ()
+
+  elif tf_inspect.isclass(f):
+    # Constructors
+    target_entity = f
+    arg_map_target = f.__init__
+    effective_args = (unknown_arg_value,) + args
+    partial_types = ()
+
+  elif hasattr(f, '__call__') and hasattr(f, '__class__'):
+    # Callable objects
+    target_entity = f.__call__
+    arg_map_target = f.__call__
+    effective_args = (f,) + args
+    partial_types = (f.__class__,)
+
+  else:
+    NotImplementedError('unknown callable type "%s"' % type(f))
+
+  arg_values = tf_inspect.getcallargs(arg_map_target, *args, **kwargs)
+  for name, arg in arg_values.items():
+    if arg is unknown_arg_value:
+      continue
+    arg_class = arg.__class__
+    # If arg_value_hints specifies any name, use that instead.
+    if name not in arg_types:
+      arg_types[name] = (arg_class.__name__, arg_class)
+
+  # When called from within a decorator, this is the only indication that
+  # the function is a method - it appears that the decorator is applied
+  # before the method is bound.
+  if not partial_types:
+    if 'self' in arg_values:
+      if tf_inspect.isclass(arg_values['self'].__class__):
+        partial_types = (arg_values['self'].__class__,)
+    elif 'cls' in arg_values:
+      if tf_inspect.isclass(arg_values['cls']):
+        partial_types = (arg_values['cls'],)
+
+  converted_f = to_graph(
+      target_entity,
+      recursive=recursive,
+      verbose=verbose,
+      arg_values=arg_values,
+      arg_types=arg_types,
+      partial_types=partial_types)
+  return converted_f(*effective_args, **kwargs)
+
+
 def to_graph(e,
              recursive=True,
              verbose=False,
@@ -189,7 +242,7 @@ def to_graph(e,
   # The compiled code should see everything the entry function saw.
   # TODO(mdan): This might not work well if the call tree spans modules?
   if tf_inspect.isfunction(e):
-    compiled_node.__dict__.update(six.get_function_globals(e))
+    compiled_node.__dict__.update(inspect_utils.getnamespace(e))
   compiled_fn = getattr(compiled_node, name)
 
   if verbose:
-- 
GitLab


From 7f53659bc67bba5567ea3f0b69710329843e0228 Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Tue, 27 Feb 2018 10:19:08 -0800
Subject: [PATCH 415/575] Bump the version of CUB in cmake build.

---
 tensorflow/contrib/cmake/external/cub.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/cmake/external/cub.cmake b/tensorflow/contrib/cmake/external/cub.cmake
index 8368898955..98a8c7e736 100644
--- a/tensorflow/contrib/cmake/external/cub.cmake
+++ b/tensorflow/contrib/cmake/external/cub.cmake
@@ -14,8 +14,8 @@
 # ==============================================================================
 include (ExternalProject)
 
-set(cub_URL https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.7.4.zip)
-set(cub_HASH SHA256=20a1a39fd97e5da7f40f5f2e7fd73fd2ea59f9dc4bb8a6c5f228aa543e727e31)
+set(cub_URL https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.8.0.zip)
+set(cub_HASH SHA256=6bfa06ab52a650ae7ee6963143a0bbc667d6504822cbd9670369b598f18c58c3)
 set(cub_BUILD ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
 set(cub_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
 set(cub_ARCHIVE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/cub_archive)
-- 
GitLab


From 246cad289498357523517b67a3f214960dfa0f92 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Tue, 27 Feb 2018 14:32:57 -0800
Subject: [PATCH 416/575] "soft placement" for eager

PiperOrigin-RevId: 187233434
---
 tensorflow/c/eager/c_api.cc         | 69 ++++++++++++++++++++++++++---
 tensorflow/c/eager/c_api.h          |  6 ++-
 tensorflow/c/eager/c_api_internal.h |  8 +++-
 tensorflow/c/eager/runtime.h        |  2 +
 tensorflow/python/eager/ops_test.py | 20 +++++++++
 5 files changed, 96 insertions(+), 9 deletions(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index bebb63c746..29c709b06d 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -31,6 +31,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/copy_tensor.h"
 #include "tensorflow/core/common_runtime/device_factory.h"
 #include "tensorflow/core/common_runtime/device_mgr.h"
+#include "tensorflow/core/common_runtime/device_set.h"
 #include "tensorflow/core/common_runtime/function.h"
 #include "tensorflow/core/common_runtime/rendezvous_mgr.h"
 #include "tensorflow/core/framework/node_def_util.h"
@@ -68,6 +69,18 @@ std::atomic_int_fast64_t func_id_generator(0);
 #endif  // TENSORFLOW_EAGER_USE_XLA
 }  // namespace
 
+TFE_ContextDevicePlacementPolicy PlacementPolicy(
+    bool soft_placement, TFE_ContextDevicePlacementPolicy original_policy) {
+  if (!soft_placement) {
+    return original_policy;
+  }
+  if (original_policy == TFE_DEVICE_PLACEMENT_EXPLICIT ||
+      original_policy == TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32) {
+    return TFE_DEVICE_PLACEMENT_SILENT;
+  }
+  return original_policy;
+}
+
 extern "C" {
 
 TFE_ContextOptions* TFE_NewContextOptions() { return new TFE_ContextOptions; }
@@ -777,15 +790,38 @@ std::unique_ptr<TFE_Op> BuildXlaLaunch(TFE_Op* op, TF_Status* status) {
   return launch_op;
 }
 #endif  // TENSORFLOW_EAGER_USE_XLA
+
+tensorflow::Device* SelectDevice(const tensorflow::NodeDef& ndef,
+                                 TFE_Context* ctx, TF_Status* status) {
+  tensorflow::DeviceSet ds;
+  for (tensorflow::Device* d : ctx->devices()) {
+    ds.AddDevice(d);
+  }
+  tensorflow::DeviceTypeVector final_devices;
+  status->status = tensorflow::SupportedDeviceTypesForNode(
+      ds.PrioritizedDeviceTypeList(), ndef, &final_devices);
+  if (!status->status.ok()) {
+    return nullptr;
+  }
+  if (final_devices.empty()) {
+    status->status = tensorflow::errors::Internal(
+        "Could not find valid device for node ", ndef.DebugString());
+    return nullptr;
+  }
+  for (tensorflow::Device* d : ctx->devices()) {
+    if (d->device_type() == final_devices[0].type_string()) {
+      return d;
+    }
+  }
+  status->status = tensorflow::errors::Unknown(
+      "Could not find a device for node ", ndef.DebugString());
+  return nullptr;
+}
+
 }  // namespace
 
 void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
                  TF_Status* status) {
-  TFE_Context* ctx = op->ctx;
-  // TODO(ashankar): ASSUMPTION: ctx->devices()[0] is always CPU
-  tensorflow::Device* device =
-      (op->device == nullptr) ? ctx->devices()[0] : op->device;
-
 #ifdef TENSORFLOW_EAGER_USE_XLA
   std::unique_ptr<TFE_Op> xla_launch_op;
   if (op->use_xla && op->name != "_XlaLaunch") {
@@ -797,9 +833,17 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
   }
 #endif  // TENSORFLOW_EAGER_USE_XLA
 
+  TFE_Context* ctx = op->ctx;
+  tensorflow::Device* device = op->device;
+  if (!ctx->soft_placement && device == nullptr) {
+    // TODO(ashankar): ASSUMPTION: ctx->devices()[0] is always CPU
+    device = ctx->devices()[0];
+  }
+
   std::vector<tensorflow::Tensor> outputs(1);
   const tensorflow::MemoryTypeVector* output_memory_types = nullptr;
-  tensorflow::Fprint128 cache_key = op->attrs.CacheKey(device->name());
+  tensorflow::Fprint128 cache_key =
+      op->attrs.CacheKey(device == nullptr ? "unspecified" : device->name());
   tensorflow::KernelAndDevice* kernel;
   {
     tensorflow::tf_shared_lock l(ctx->cache_mu);
@@ -807,6 +851,13 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
   }
   if (kernel == nullptr) {
     const tensorflow::NodeDef& ndef = op->attrs.BuildNodeDef();
+    if (ctx->soft_placement && device == nullptr) {
+      device = SelectDevice(ndef, ctx, status);
+      if (!status->status.ok()) {
+        return;
+      }
+    }
+    CHECK(device != nullptr);
     if (ctx->log_device_placement) {
       LOG(INFO) << "Executing op " << ndef.op() << " in device "
                 << device->name();
@@ -846,6 +897,12 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
     tensorflow::mutex_lock ml(ctx->cache_mu);
     tensorflow::gtl::InsertOrUpdate(&(ctx->kernel_cache), cache_key, kernel);
   }
+  if (device == nullptr) {
+    // TODO(apassos) debug how the assignment below might return a different
+    // device from the one requested above.
+    device = kernel->device();
+  }
+
   std::vector<TFE_TensorHandle*> copied_tensors;
   status->status = ValidateInputTypeAndPlacement(
       ctx, ctx->devices()[0], device, op, kernel->kernel(), &copied_tensors);
diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h
index 90cfb7500e..9610ca1b3b 100644
--- a/tensorflow/c/eager/c_api.h
+++ b/tensorflow/c/eager/c_api.h
@@ -61,7 +61,8 @@ TF_CAPI_EXPORT extern void TFE_ContextOptionsSetConfig(
 // Controls how to act when we try to run an operation on a given device but
 // some input tensors are not on that device.
 typedef enum TFE_ContextDevicePlacementPolicy {
-  // Running operations with input tensors on the wrong device will fail.
+  // Running operations with input tensors on the wrong device will fail. When
+  // soft placement is enabled acts like TFE_DEVICE_PLACEMENT_SILENT.
   TFE_DEVICE_PLACEMENT_EXPLICIT = 0,
   // Copy the tensor to the right device but log a warning.
   TFE_DEVICE_PLACEMENT_WARN = 1,
@@ -69,7 +70,8 @@ typedef enum TFE_ContextDevicePlacementPolicy {
   // operation will be blocked till the copy completes.
   TFE_DEVICE_PLACEMENT_SILENT = 2,
   // Default placement policy which silently copies int32 tensors but not other
-  // dtypes.
+  // dtypes.  When soft placement is enabled acts like
+  // TFE_DEVICE_PLACEMENT_SILENT.
   TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3,
 } TFE_ContextDevicePlacementPolicy;
 
diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h
index 3356054cd0..53c21b64cb 100644
--- a/tensorflow/c/eager/c_api_internal.h
+++ b/tensorflow/c/eager/c_api_internal.h
@@ -43,9 +43,14 @@ struct TFE_ContextOptions {
       TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32};
 };
 
+TFE_ContextDevicePlacementPolicy PlacementPolicy(
+    bool soft_placement, TFE_ContextDevicePlacementPolicy original_policy);
+
 struct TFE_Context {
   explicit TFE_Context(const TFE_ContextOptions& opts, TF_Session* s)
-      : policy(opts.policy),
+      : soft_placement(
+            opts.session_options.options.config.allow_soft_placement()),
+        policy(PlacementPolicy(soft_placement, opts.policy)),
         session(s),
         rendezvous(new tensorflow::IntraProcessRendezvous(s->device_mgr)),
         pflr(new tensorflow::ProcessFunctionLibraryRuntime(
@@ -54,6 +59,7 @@ struct TFE_Context {
         log_device_placement(
             opts.session_options.options.config.log_device_placement()) {}
 
+  const bool soft_placement;
   const TFE_ContextDevicePlacementPolicy policy;
 
   // Note: we cannot use C++11 thread_local here as there is no concept of a
diff --git a/tensorflow/c/eager/runtime.h b/tensorflow/c/eager/runtime.h
index 7fede4dae9..985ed96735 100644
--- a/tensorflow/c/eager/runtime.h
+++ b/tensorflow/c/eager/runtime.h
@@ -183,6 +183,8 @@ class KernelAndDevice {
 
   const OpKernel* kernel() const { return kernel_.get(); }
 
+  Device* device() const { return device_; }
+
   DataTypeVector* output_dtypes() { return &output_dtypes_; }
 
  private:
diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py
index f2e70341d9..553571d267 100644
--- a/tensorflow/python/eager/ops_test.py
+++ b/tensorflow/python/eager/ops_test.py
@@ -19,6 +19,7 @@ from __future__ import print_function
 
 import numpy as np
 
+from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.eager import context
 from tensorflow.python.eager import execute
 from tensorflow.python.eager import test
@@ -277,6 +278,25 @@ class OpsTest(test_util.TensorFlowTestCase):
       context._context = context.Context()
     # pylint: enable=protected-access
 
+  def testSoftPlacement(self):
+    if not context.context().num_gpus():
+      self.skipTest('No GPUs found')
+    # Temporarily replace the context
+    # pylint: disable=protected-access
+    del context._context
+    try:
+      context._context = context.Context(
+          device_policy=context.DEVICE_PLACEMENT_SILENT,
+          config=config_pb2.ConfigProto(allow_soft_placement=True))
+      cpu_tensor = constant_op.constant(1.0)
+      result = cpu_tensor + cpu_tensor
+      self.assertEqual(result.device,
+                       '/job:localhost/replica:0/task:0/device:GPU:0')
+    finally:
+      del context._context
+      context._context = context.Context()
+    # pylint: enable=protected-access
+
   def testRandomUniform(self):
     scalar_shape = constant_op.constant([], dtype=dtypes.int32)
 
-- 
GitLab


From 80b6956b7cf4a092ff0780d133cd2faad4cda704 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 14:37:14 -0800
Subject: [PATCH 417/575] Added a TFLite Java API to get last inference latency
 in nanoseconds.

PiperOrigin-RevId: 187234119
---
 .../lite/NativeInterpreterWrapper.java        | 16 +++++++-
 .../src/main/native/duration_utils_jni.cc     | 38 +++++++++++++++++
 .../native/nativeinterpreterwrapper_jni.cc    | 12 +++++-
 .../native/nativeinterpreterwrapper_jni.h     |  9 +++-
 .../lite/NativeInterpreterWrapperTest.java    | 41 +++++++++++++++++++
 .../java/org/tensorflow/lite/TestHelper.java  | 15 +++++++
 6 files changed, 126 insertions(+), 5 deletions(-)
 create mode 100644 tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc

diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java
index 5ee594dec4..7612be0ddd 100644
--- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java
+++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java
@@ -91,8 +91,9 @@ final class NativeInterpreterWrapper implements AutoCloseable {
                 i, inputs.length));
       }
     }
+    inferenceDurationNanoseconds = -1;
     long[] outputsHandles =
-        run(interpreterHandle, errorHandle, sizes, dataTypes, numsOfBytes, inputs);
+        run(interpreterHandle, errorHandle, sizes, dataTypes, numsOfBytes, inputs, this);
     if (outputsHandles == null || outputsHandles.length == 0) {
       throw new IllegalStateException("Interpreter has no outputs.");
     }
@@ -109,7 +110,8 @@ final class NativeInterpreterWrapper implements AutoCloseable {
       Object[] sizes,
       int[] dtypes,
       int[] numsOfBytes,
-      Object[] values);
+      Object[] values,
+      NativeInterpreterWrapper wrapper);
 
   /** Resizes dimensions of a specific input. */
   void resizeInput(int idx, int[] dims) {
@@ -236,6 +238,14 @@ final class NativeInterpreterWrapper implements AutoCloseable {
     }
   }
 
+  /**
+   * Gets the last inference duration in nanoseconds. It returns null if there is no previous
+   * inference run or the last inference run failed.
+   */
+  Long getLastNativeInferenceDurationNanoseconds() {
+    return (inferenceDurationNanoseconds < 0) ? null : inferenceDurationNanoseconds;
+  }
+
   private static final int ERROR_BUFFER_SIZE = 512;
 
   private long errorHandle;
@@ -246,6 +256,8 @@ final class NativeInterpreterWrapper implements AutoCloseable {
 
   private int inputSize;
 
+  private long inferenceDurationNanoseconds = -1;
+
   private MappedByteBuffer modelByteBuffer;
 
   private Map<String, Integer> inputsIndexes;
diff --git a/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc b/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc
new file mode 100644
index 0000000000..0e08a04370
--- /dev/null
+++ b/tensorflow/contrib/lite/java/src/main/native/duration_utils_jni.cc
@@ -0,0 +1,38 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <jni.h>
+#include <time.h>
+
+namespace tflite {
+
+// Gets the elapsed wall-clock timespec.
+timespec getCurrentTime() {
+  timespec time;
+  clock_gettime(CLOCK_MONOTONIC, &time);
+  return time;
+}
+
+// Computes the time diff from two timespecs. Returns '-1' if 'stop' is earlier
+// than 'start'.
+jlong timespec_diff_nanoseconds(struct timespec* start, struct timespec* stop) {
+  jlong result = stop->tv_sec - start->tv_sec;
+  if (result < 0) return -1;
+  result = 1000000000 * result + (stop->tv_nsec - start->tv_nsec);
+  if (result < 0) return -1;
+  return result;
+}
+
+}  // namespace tflite
diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
index c346f9f92e..e405df0745 100644
--- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
+++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
@@ -353,7 +353,7 @@ JNIEXPORT jlongArray JNICALL
 Java_org_tensorflow_lite_NativeInterpreterWrapper_run(
     JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle,
     jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes,
-    jobjectArray values) {
+    jobjectArray values, jobject wrapper) {
   tflite::Interpreter* interpreter =
       convertLongToInterpreter(env, interpreter_handle);
   if (interpreter == nullptr) return nullptr;
@@ -384,6 +384,7 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_run(
   status = setInputs(env, interpreter, input_size, data_types, nums_of_bytes,
                      values);
   if (status != kTfLiteOk) return nullptr;
+  timespec beforeInference = ::tflite::getCurrentTime();
   // runs inference
   if (interpreter->Invoke() != kTfLiteOk) {
     throwException(env, kIllegalArgumentException,
@@ -391,6 +392,15 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_run(
                    error_reporter->CachedErrorMessage());
     return nullptr;
   }
+  timespec afterInference = ::tflite::getCurrentTime();
+  jclass wrapper_clazz = env->GetObjectClass(wrapper);
+  jfieldID fid =
+      env->GetFieldID(wrapper_clazz, "inferenceDurationNanoseconds", "J");
+  if (fid != 0) {
+    env->SetLongField(
+        wrapper, fid,
+        ::tflite::timespec_diff_nanoseconds(&beforeInference, &afterInference));
+  }
   // returns outputs
   const std::vector<int>& results = interpreter->outputs();
   if (results.empty()) {
diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h
index c52a7e4e43..31c8f1bc88 100644
--- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h
+++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h
@@ -18,6 +18,7 @@ limitations under the License.
 
 #include <jni.h>
 #include <stdio.h>
+#include <time.h>
 #include <vector>
 #include "tensorflow/contrib/lite/context.h"
 #include "tensorflow/contrib/lite/interpreter.h"
@@ -28,6 +29,9 @@ limitations under the License.
 namespace tflite {
 // This is to be provided at link-time by a library.
 extern std::unique_ptr<OpResolver> CreateOpResolver();
+extern timespec getCurrentTime();
+extern jlong timespec_diff_nanoseconds(struct timespec* start,
+                                       struct timespec* stop);
 }  // namespace tflite
 
 #ifdef __cplusplus
@@ -104,13 +108,14 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter(
 /*
  *  Class:     org_tensorflow_lite_NativeInterpreterWrapper
  *  Method:
- *  Signature: (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;)[J
+ *  Signature:
+ * (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;Lorg/tensorflow/lite/NativeInterpreterWrapper;)[J
  */
 JNIEXPORT jlongArray JNICALL
 Java_org_tensorflow_lite_NativeInterpreterWrapper_run(
     JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle,
     jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes,
-    jobjectArray values);
+    jobjectArray values, jobject wrapper);
 
 /*
  *  Class:     org_tensorflow_lite_NativeInterpreterWrapper
diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java
index 90323555d8..8c1f2406f7 100644
--- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java
+++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java
@@ -417,4 +417,45 @@ public final class NativeInterpreterWrapperTest {
     assertThat(shape[1]).isEqualTo(3);
     assertThat(shape[2]).isEqualTo(1);
   }
+
+  @Test
+  public void testGetInferenceLatency() {
+    NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH);
+    float[] oneD = {1.23f, 6.54f, 7.81f};
+    float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD};
+    float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD};
+    float[][][][] fourD = {threeD, threeD};
+    Object[] inputs = {fourD};
+    Tensor[] outputs = wrapper.run(inputs);
+    assertThat(outputs.length).isEqualTo(1);
+    assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isGreaterThan(0L);
+    wrapper.close();
+  }
+
+  @Test
+  public void testGetInferenceLatencyWithNewWrapper() {
+    NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH);
+    assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isNull();
+    wrapper.close();
+  }
+
+  @Test
+  public void testGetLatencyAfterFailedInference() {
+    NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH);
+    float[] oneD = {1.23f, 6.54f, 7.81f};
+    float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD};
+    float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD};
+    float[][][][] fourD = {threeD, threeD};
+    Object[] inputs = {fourD};
+    try {
+      wrapper.run(inputs);
+      fail();
+    } catch (IllegalArgumentException e) {
+      assertThat(e)
+          .hasMessageThat()
+          .contains("0-th input dimension should be [?,8,8,3], but found [?,8,7,3]");
+    }
+    assertThat(wrapper.getLastNativeInferenceDurationNanoseconds()).isNull();
+    wrapper.close();
+  }
 }
diff --git a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java
index 8660cabf70..a5c13053d7 100644
--- a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java
+++ b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java
@@ -32,4 +32,19 @@ public class TestHelper {
       throw new IllegalArgumentException("Interpreter has not initialized; Failed to setUseNNAPI.");
     }
   }
+
+  /**
+   * Gets the last inference duration in nanoseconds. It returns null if there is no previous
+   * inference run or the last inference run failed.
+   *
+   * @param interpreter an instance of {@code Interpreter}. If it is not initialized, an {@code
+   *     IllegalArgumentException} will be thrown.
+   */
+  public static Long getLastNativeInferenceDurationNanoseconds(Interpreter interpreter) {
+    if (interpreter != null && interpreter.wrapper != null) {
+      return interpreter.wrapper.getLastNativeInferenceDurationNanoseconds();
+    } else {
+      throw new IllegalArgumentException("Interpreter has not initialized; Failed to get latency.");
+    }
+  }
 }
-- 
GitLab


From e101ce9c1c8399fecd6679293d8cb2065ce8d47f Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Tue, 27 Feb 2018 14:55:13 -0800
Subject: [PATCH 418/575] Properly handle inlining failures

PiperOrigin-RevId: 187237044
---
 .../core/grappler/optimizers/function_optimizer.cc     | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index 3c96ff869b..ba8a76ad5f 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -27,12 +27,15 @@ namespace tensorflow {
 namespace grappler {
 
 Status InlineFunction(const NodeDef& node, const FunctionDef& func,
-                      GraphDef* graph) {
+                      const FunctionDefLibrary& library, GraphDef* graph) {
   const std::unordered_map<string, AttrValue> attr(node.attr().begin(),
                                                    node.attr().end());
-  FunctionDefLibrary library;
   std::unique_ptr<GrapplerItem> item =
       GrapplerItemFromFunctionDef(func, attr, library);
+  if (!item) {
+    return errors::InvalidArgument("Failed to inline function ", node.op(),
+                                   " instantiated by ", node.name());
+  }
 
   std::unordered_map<string, int> input_nodes;
   for (int i = 0; i < func.signature().input_arg_size(); ++i) {
@@ -129,7 +132,8 @@ Status FunctionOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
     if (it == functions.end()) {
       *optimized_graph->add_node() = node;
     } else {
-      TF_RETURN_IF_ERROR(InlineFunction(node, *it->second, optimized_graph));
+      TF_RETURN_IF_ERROR(InlineFunction(node, *it->second, item.graph.library(),
+                                        optimized_graph));
     }
   }
 
-- 
GitLab


From 2c25f08b6f97155bd5ce95aada5a3cc9b916176f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 15:19:47 -0800
Subject: [PATCH 419/575] Implement support for unpartitioning
 tf.nn.embedding_lookup into a single gather.

PiperOrigin-RevId: 187241089
---
 tensorflow/contrib/lite/toco/BUILD            |   1 +
 .../graph_transformations.h                   |   1 +
 .../propagate_fixed_sizes.cc                  |   6 +
 .../remove_trivial_passthrough.cc             |   4 +-
 .../unpartition_embedding_lookup.cc           | 237 ++++++++++++++++++
 .../contrib/lite/toco/import_tensorflow.cc    |  41 +++
 tensorflow/contrib/lite/toco/model.h          |  26 ++
 tensorflow/contrib/lite/toco/toco_tooling.cc  |   1 +
 tensorflow/contrib/lite/toco/tooling_util.cc  |  11 +
 tensorflow/contrib/lite/toco/tooling_util.h   |   6 +-
 10 files changed, 331 insertions(+), 3 deletions(-)
 create mode 100644 tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc

diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD
index 17407f3db2..845bc0460f 100644
--- a/tensorflow/contrib/lite/toco/BUILD
+++ b/tensorflow/contrib/lite/toco/BUILD
@@ -240,6 +240,7 @@ cc_library(
         "graph_transformations/resolve_tensorflow_tile.cc",
         "graph_transformations/resolve_transpose_attributes.cc",
         "graph_transformations/unfuse_activation_functions.cc",
+        "graph_transformations/unpartition_embedding_lookup.cc",
         "graph_transformations/unroll_batch_matmul.cc",
     ],
     hdrs = [
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
index f2c81ebc81..f0739990ad 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
+++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h
@@ -177,6 +177,7 @@ DECLARE_GRAPH_TRANSFORMATION(ResolveConstantStridedSlice)
 DECLARE_GRAPH_TRANSFORMATION(ResolveConstantFill)
 DECLARE_GRAPH_TRANSFORMATION(ResolveMultiplyByZero)
 DECLARE_GRAPH_TRANSFORMATION(Dequantize)
+DECLARE_GRAPH_TRANSFORMATION(UnpartitionEmbeddingLookup)
 
 class ResolveReshapeAttributes : public GraphTransformation {
  public:
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
index 0e2e5ecf30..fc26f997a6 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc
@@ -1542,6 +1542,12 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) {
     case OperatorType::kTranspose:
       ProcessTransposeOperator(model, static_cast<TransposeOperator*>(op));
       break;
+    case OperatorType::kDynamicPartition:
+    case OperatorType::kDynamicStitch:
+      // DynamicPartition/DynamicStitch are currently only supported for
+      // transforms that remove them, so we avoid propagating shapes through
+      // them and let things settle once they've been removed.
+      break;
     default:
       // Unimplemented, another graph transformation should drop it.
       LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(op->type);
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
index 587f171bbf..aa93ace03a 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.cc
@@ -60,7 +60,9 @@ bool RemoveTrivialPassthroughOp(GraphTransformation* transformation,
   for (int i = 0; i < passthru_op->inputs.size(); i++) {
     if (!model->GetArray(passthru_op->inputs[i]).buffer) {
       count_nonconstant_input_arrays++;
-      main_input_array_index = i;
+      if (count_nonconstant_input_arrays == 1) {
+        main_input_array_index = i;
+      }
     }
   }
 
diff --git a/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc b/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc
new file mode 100644
index 0000000000..419fb9a799
--- /dev/null
+++ b/tensorflow/contrib/lite/toco/graph_transformations/unpartition_embedding_lookup.cc
@@ -0,0 +1,237 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h"
+#include "tensorflow/contrib/lite/toco/model.h"
+#include "tensorflow/contrib/lite/toco/tooling_util.h"
+
+namespace toco {
+
+bool UnpartitionEmbeddingLookup::Run(Model* model, std::size_t op_index) {
+  // Collapses a partitioned tf.nn.embedding_lookup back into a single Gather.
+  // https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup
+  // This transform attempts to identify the len(params) > 1 case and collapse
+  // it to the len(params) = 1 case by concatenating the original params and
+  // reversing the partitioning.
+  //
+  // If len(params) to the tf.nn.embedding_lookup == 1, the whole op becomes
+  // simply a gather:
+  // https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/python/ops/embedding_ops.py#L150
+  //
+  // Notes on this implementation:
+  // - only supports partition_strategy='mod'
+  //
+  // A rough graph of a partitioned embedding_lookup looks like:
+  //   (ids)--+-->FloorDiv--+-->DynamicPartition-->[[Gather]]--\
+  //          \-->FloorMod--/                                  |
+  //                 V                                         |
+  //   Range-->DynamicPartition-------->DynamicStitch<---------/
+  //  (const)                                V
+  //                                     (embeddings)
+
+  // First look for the final DynamicStitch.
+  auto op_it = model->operators.begin() + op_index;
+  if (op_it->get()->type != OperatorType::kDynamicStitch) {
+    return false;
+  }
+  auto* stitch_op = static_cast<DynamicStitchOperator*>(op_it->get());
+
+  // Split up the DynamicStitch inputs into the indices and data.
+  std::vector<string> stitch_indices_inputs;
+  std::vector<string> stitch_data_inputs;
+  for (size_t i = 0; i < stitch_op->num_partitions; ++i) {
+    stitch_indices_inputs.push_back(stitch_op->inputs[i]);
+  }
+  for (size_t i = stitch_op->num_partitions; i < stitch_op->num_partitions * 2;
+       ++i) {
+    stitch_data_inputs.push_back(stitch_op->inputs[i]);
+  }
+
+  // Validate all indices come from the same DynamicPartition.
+  DynamicPartitionOperator* indices_partition_op = nullptr;
+  for (const string& indices_partition_output_name : stitch_indices_inputs) {
+    auto* op = GetOpWithOutput(*model, indices_partition_output_name);
+    CHECK(op) << "Source of " << indices_partition_output_name << " not found";
+    if (op->type != OperatorType::kDynamicPartition) {
+      AddMessageF(
+          "Skipping because indices input %s into "
+          "%s is unexpected",
+          LogName(*op), LogName(*stitch_op));
+      return false;
+    }
+    if (!indices_partition_op) {
+      indices_partition_op = static_cast<DynamicPartitionOperator*>(op);
+    } else {
+      // Ensure this is the same op as previous ones.
+      if (op != indices_partition_op) {
+        AddMessageF(
+            "Skipping because indices input %s into "
+            "%s is from a different source op than others",
+            LogName(*op), LogName(*stitch_op));
+        return false;
+      }
+    }
+  }
+  CHECK(indices_partition_op) << "No indices inputs";
+
+  // The data for the indices must be a constant range of the array shape.
+  if (!IsConstantParameterArray(*model, indices_partition_op->inputs[0])) {
+    AddMessageF("Skipping because indices partition data is non-constant");
+    return false;
+  }
+  auto& indices_data_array = model->GetArray(indices_partition_op->inputs[0]);
+  if (indices_data_array.data_type == ArrayDataType::kNone) {
+    // Yield until data types are propagated.
+    return false;
+  }
+  CHECK(indices_data_array.data_type == ArrayDataType::kInt32)
+      << "Indices partition inputs must be int32";
+  const auto& indices_data_buffer =
+      indices_data_array.GetBuffer<ArrayDataType::kInt32>().data;
+  for (size_t i = 0; i < indices_data_buffer.size(); ++i) {
+    CHECK_EQ(indices_data_buffer[i], i) << "Indices range must be identity";
+  }
+
+  // Find all of the gathers used for the data inputs.
+  std::vector<GatherOperator*> gather_ops;
+  for (const string& gather_output_name : stitch_data_inputs) {
+    auto* op = GetOpWithOutput(*model, gather_output_name);
+    CHECK(op) << "Source of " << gather_output_name << " not found";
+    if (op->type != OperatorType::kGather) {
+      AddMessageF(
+          "Skipping because data input %s into %s "
+          "is unexpected",
+          LogName(*op), LogName(*stitch_op));
+      return false;
+    }
+    gather_ops.push_back(static_cast<GatherOperator*>(op));
+  }
+
+  // Validate all gathers come from the same DynamicPartition.
+  DynamicPartitionOperator* data_partition_op = nullptr;
+  for (auto* gather_op : gather_ops) {
+    auto* op = GetOpWithOutput(*model, gather_op->inputs[1]);
+    CHECK(op) << "Source of " << gather_op->inputs[1] << " not found";
+    if (op->type != OperatorType::kDynamicPartition) {
+      AddMessageF(
+          "Skipping because data input %s into "
+          "%s is unexpected",
+          LogName(*op), LogName(*gather_op));
+      return false;
+    }
+    if (!data_partition_op) {
+      data_partition_op = static_cast<DynamicPartitionOperator*>(op);
+    } else {
+      // Ensure this is the same op as previous ones.
+      if (op != data_partition_op) {
+        AddMessageF(
+            "Skipping because data input %s into "
+            "%s is from a different source op than others",
+            LogName(*op), LogName(*gather_op));
+        return false;
+      }
+    }
+  }
+  CHECK(data_partition_op) << "No data inputs";
+
+  // Validate the partition ops have the same sizes.
+  CHECK_EQ(indices_partition_op->num_partitions,
+           data_partition_op->num_partitions)
+      << "Indices and data partition ops have differing dimensions";
+  int num_partitions = indices_partition_op->num_partitions;
+
+  // Partition strategy of 'mod' gives us a FloorMod and FloorDiv.
+  // The gather partition uses the FloorDiv as the data and FloorMod as the
+  // partitions and the indices use the FloorMod as their partitions.
+  Operator* div_op = GetOpWithOutput(*model, data_partition_op->inputs[0]);
+  Operator* mod_op = GetOpWithOutput(*model, data_partition_op->inputs[1]);
+  CHECK(div_op && div_op->type == OperatorType::kFloorDiv)
+      << "Unsupported partition strategy";
+  CHECK(mod_op && mod_op->type == OperatorType::kFloorMod)
+      << "Unsupported partition strategy";
+  CHECK_EQ(mod_op, GetOpWithOutput(*model, indices_partition_op->inputs[1]))
+      << "Indices and data parition ops require the same partition strategy "
+         "and inputs";
+
+  // Glob together all of the gather data. This is not yet in the correct order.
+  auto* gather_params_concat_op = new ConcatenationOperator;
+  for (const auto& gather_op : gather_ops) {
+    gather_params_concat_op->inputs.push_back(gather_op->inputs[0]);
+  }
+  gather_params_concat_op->outputs.push_back(
+      AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_unpartitioned"));
+  op_it = model->operators.emplace(op_it, gather_params_concat_op) + 1;
+  model->GetOrCreateArray(gather_params_concat_op->outputs[0]);
+
+  // Permute the gather params to undo the partitioning that was originally
+  // done.
+  auto* gather_params_permute_op = new GatherOperator;
+  gather_params_permute_op->inputs.push_back(
+      gather_params_concat_op->outputs[0]);
+  gather_params_permute_op->inputs.push_back(
+      AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_permuted/perm"));
+  gather_params_permute_op->outputs.push_back(
+      AvailableArrayName(*model, gather_ops[0]->inputs[0] + "_permuted"));
+  op_it = model->operators.emplace(op_it, gather_params_permute_op) + 1;
+  model->GetOrCreateArray(gather_params_permute_op->outputs[0]);
+  const auto& partition_array = model->GetArray(gather_ops[0]->inputs[0]);
+  const auto& partition_array_dims = partition_array.shape().dims();
+  auto& perm_array =
+      model->GetOrCreateArray(gather_params_permute_op->inputs[1]);
+  perm_array.data_type = ArrayDataType::kInt32;
+  perm_array.mutable_shape()->ReplaceDims(
+      {num_partitions * partition_array_dims[0]});
+  auto& perm_data = perm_array.GetMutableBuffer<ArrayDataType::kInt32>().data;
+  perm_data.resize(RequiredBufferSizeForShape(perm_array.shape()));
+  // NOTE: this is what relies on the partition_strategy.
+  for (int i = 0; i < num_partitions * partition_array_dims[0]; ++i) {
+    int p = i % num_partitions;
+    perm_data[i] = p * partition_array_dims[0] + i / num_partitions;
+  }
+
+  // Insert the new unpartitioned gather op.
+  auto* merged_gather_op = new GatherOperator;
+  merged_gather_op->inputs = {gather_params_permute_op->outputs[0],
+                              mod_op->inputs[0]};
+  merged_gather_op->outputs = {stitch_op->outputs[0]};
+  model->operators.emplace(op_it, merged_gather_op);
+
+  AddMessageF(
+      "Replacing suspected partitioned tf.nn.embedding_lookup (starting at %s "
+      "+ %s and ending at %s) with a single unpartitioned gather %s",
+      LogName(*div_op), LogName(*mod_op), LogName(*stitch_op),
+      LogName(*merged_gather_op));
+
+  // Ensure the stitch output array is dead, as we don't want whatever was in it
+  // previously now that we've redefined it. It'll be recreated when needed.
+  model->EraseArray(stitch_op->outputs[0]);
+  model->GetOrCreateArray(merged_gather_op->outputs[0]);
+
+  // Erase all the original ops.
+  DeleteOpAndArraysIfUnused(model, div_op);
+  DeleteOpAndArraysIfUnused(model, mod_op);
+  for (auto* gather_op : gather_ops) {
+    DeleteOpAndArraysIfUnused(model, gather_op);
+  }
+  DeleteOpAndArraysIfUnused(model, indices_partition_op);
+  DeleteOpAndArraysIfUnused(model, data_partition_op);
+  DeleteOpAndArraysIfUnused(model, stitch_op);
+  return true;
+}
+
+}  // namespace toco
diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc
index 52a0512e23..41abca864d 100644
--- a/tensorflow/contrib/lite/toco/import_tensorflow.cc
+++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc
@@ -1896,6 +1896,42 @@ void ConvertTopKV2Operator(const NodeDef& node,
   op->outputs.push_back(node.name() + ":1");
   model->operators.emplace_back(op.release());
 }
+
+void ConvertDynamicPartitionOperator(
+    const NodeDef& node, const TensorFlowImportFlags& tf_import_flags,
+    Model* model) {
+  auto op = absl::make_unique<DynamicPartitionOperator>();
+  CHECK(HasAttr(node, "num_partitions"));
+  op->num_partitions = GetIntAttr(node, "num_partitions");
+  CheckInputsCount(node, tf_import_flags, 2);
+  op->inputs.push_back(node.input(0));
+  op->inputs.push_back(node.input(1));
+  CHECK_GT(op->num_partitions, 1);
+  op->outputs.push_back(node.name());  // Implicit :0.
+  for (int i = 1; i < op->num_partitions; ++i) {
+    op->outputs.push_back(node.name() + ":" + std::to_string(i));
+  }
+  model->operators.emplace_back(op.release());
+}
+
+void ConvertDynamicStitchOperator(const NodeDef& node,
+                                  const TensorFlowImportFlags& tf_import_flags,
+                                  Model* model) {
+  // The parallel and non-parallel variants are the same besides whether they
+  // have a parallel loop; there are no behavioral differences.
+  CHECK(node.op() == "DynamicStitch" || node.op() == "ParallelDynamicStitch");
+  auto op = absl::make_unique<DynamicStitchOperator>();
+  CHECK(HasAttr(node, "N"));
+  op->num_partitions = GetIntAttr(node, "N");
+  // Expect all ID partitions + all value partitions.
+  CheckInputsCount(node, tf_import_flags, op->num_partitions * 2);
+  for (int i = 0; i < op->num_partitions * 2; ++i) {
+    op->inputs.push_back(node.input(i));
+  }
+  op->outputs.push_back(node.name());
+  model->operators.emplace_back(op.release());
+}
+
 }  // namespace
 
 std::unique_ptr<Model> ImportTensorFlowGraphDef(
@@ -2081,6 +2117,11 @@ std::unique_ptr<Model> ImportTensorFlowGraphDef(
       ConvertExpOperator(node, tf_import_flags, model);
     } else if (node.op() == "TopK" || node.op() == "TopKV2") {
       ConvertTopKV2Operator(node, tf_import_flags, model);
+    } else if (node.op() == "DynamicPartition") {
+      ConvertDynamicPartitionOperator(node, tf_import_flags, model);
+    } else if (node.op() == "DynamicStitch" ||
+               node.op() == "ParallelDynamicStitch") {
+      ConvertDynamicStitchOperator(node, tf_import_flags, model);
     } else {
       ConvertUnsupportedOperator(node, tf_import_flags, model);
     }
diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h
index d5df0fb951..ed0dedc003 100644
--- a/tensorflow/contrib/lite/toco/model.h
+++ b/tensorflow/contrib/lite/toco/model.h
@@ -115,6 +115,8 @@ enum class OperatorType {
   kTensorFlowTile,
   kTranspose,
   kTopK_V2,
+  kDynamicPartition,
+  kDynamicStitch,
   // An unsupported TF operation. It's only needed to be able to represent TF
   // graph internally and is expected to be dropped by graph transformations.
   kTensorFlowUnsupported,
@@ -1414,6 +1416,30 @@ struct TopKV2Operator : Operator {
   TopKV2Operator() : Operator(OperatorType::kTopK_V2) {}
 };
 
+// DynamicPartition operator:
+//
+// Inputs:
+//  inputs[0]: required: data.
+//  inputs[1]: required: partitions.
+//
+// TensorFlow equivalent: DynamicPartition
+struct DynamicPartitionOperator : Operator {
+  DynamicPartitionOperator() : Operator(OperatorType::kDynamicPartition) {}
+  int num_partitions;
+};
+
+// DynamicStitch operator:
+//
+// Inputs:
+//  inputs[0,N): required: indices.
+//  inputs[N,2N): required: data.
+//
+// TensorFlow equivalent: DynamicStitch/ParallelDynamicStitch
+struct DynamicStitchOperator : Operator {
+  DynamicStitchOperator() : Operator(OperatorType::kDynamicStitch) {}
+  int num_partitions;
+};
+
 // Alloc's are used for transient arrays only. An Alloc specifies which interval
 // of the "transient_data" workspace buffer passed to inference functions, is to
 // be used for the transient array at hand. The 'start' and 'end' values are
diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc
index a09a3c4ef5..42e0a89017 100644
--- a/tensorflow/contrib/lite/toco/toco_tooling.cc
+++ b/tensorflow/contrib/lite/toco/toco_tooling.cc
@@ -102,6 +102,7 @@ void MakeGeneralGraphTransformationsSet(
   transformations->Add(new ResolveConstantShapeOrRank);
   transformations->Add(new MakeInitialDequantizeOperator);
   transformations->Add(new ResolveConstantFakeQuant);
+  transformations->Add(new UnpartitionEmbeddingLookup);
 }
 
 bool SupportsQuantization(FileFormat format) {
diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc
index d23b3737fc..f92e10752d 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.cc
+++ b/tensorflow/contrib/lite/toco/tooling_util.cc
@@ -159,6 +159,15 @@ bool DeleteArrayIfUsedOnce(const string& array_name, Model* model) {
   return false;
 }
 
+void DeleteOpAndArraysIfUnused(Model* model, Operator* op) {
+  for (const string& array_name : op->inputs) {
+    DeleteArrayIfUsedOnce(array_name, model);
+  }
+  auto op_it = FindOp(*model, op);
+  CHECK(op_it != model->operators.end());
+  model->operators.erase(op_it);
+}
+
 std::vector<std::unique_ptr<Operator>>::const_iterator FindOpWithOutput(
     const Model& model, const string& array_name) {
   for (auto it = model.operators.begin(); it != model.operators.end(); ++it) {
@@ -347,6 +356,8 @@ const char* OperatorTypeName(OperatorType type) {
     HANDLE_OPERATORTYPENAME_CASE(TopK_V2)
     HANDLE_OPERATORTYPENAME_CASE(TensorFlowUnsupported)
     HANDLE_OPERATORTYPENAME_CASE(Exp)
+    HANDLE_OPERATORTYPENAME_CASE(DynamicPartition)
+    HANDLE_OPERATORTYPENAME_CASE(DynamicStitch)
     default:
       LOG(FATAL) << "Unhandled op type";
 #undef HANDLE_OPERATORTYPENAME_CASE
diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h
index 11208ed667..01917b29de 100644
--- a/tensorflow/contrib/lite/toco/tooling_util.h
+++ b/tensorflow/contrib/lite/toco/tooling_util.h
@@ -64,6 +64,10 @@ int CountOpsWithInput(const Model& model, const string& array_name);
 bool DeleteArrayIfUnused(const string& array_name, Model* model);
 bool DeleteArrayIfUsedOnce(const string& array_name, Model* model);
 
+// Deletes the op and any of its input and output arrays if they are unused
+// after the op has been deleted.
+void DeleteOpAndArraysIfUnused(Model* model, Operator* op);
+
 std::vector<std::unique_ptr<Operator>>::const_iterator FindOpWithOutput(
     const Model& model, const string& array_name);
 Operator* GetOpWithOutput(const Model& model, const string& array_name);
@@ -71,8 +75,6 @@ Operator* GetOpWithOutput(const Model& model, const string& array_name);
 std::vector<std::unique_ptr<Operator>>::iterator FindOpWithOutput(
     Model& model, const string& array_name);
 
-Operator* GetOpWithOutput(const Model& model, const string& array_name);
-
 std::vector<std::unique_ptr<Operator>>::const_iterator FindOpWithInput(
     const Model& model, const string& array_name);
 
-- 
GitLab


From 53b2181ea5cff054d40c583f05da942a9a56a283 Mon Sep 17 00:00:00 2001
From: Jeremy Lau <lauj@google.com>
Date: Tue, 27 Feb 2018 15:32:16 -0800
Subject: [PATCH 420/575] Make RecentRequestIds more efficient.

PiperOrigin-RevId: 187242940
---
 tensorflow/core/distributed_runtime/BUILD           |  1 +
 .../core/distributed_runtime/recent_request_ids.cc  |  9 ++++++---
 .../core/distributed_runtime/recent_request_ids.h   |  6 ++++--
 .../distributed_runtime/recent_request_ids_test.cc  | 13 +++++++++++++
 4 files changed, 24 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD
index 9e152aa082..434626bd2d 100644
--- a/tensorflow/core/distributed_runtime/BUILD
+++ b/tensorflow/core/distributed_runtime/BUILD
@@ -595,6 +595,7 @@ tf_cc_test(
     srcs = ["recent_request_ids_test.cc"],
     deps = [
         ":recent_request_ids",
+        ":request_id",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
diff --git a/tensorflow/core/distributed_runtime/recent_request_ids.cc b/tensorflow/core/distributed_runtime/recent_request_ids.cc
index c30879406c..4f6866c5d1 100644
--- a/tensorflow/core/distributed_runtime/recent_request_ids.cc
+++ b/tensorflow/core/distributed_runtime/recent_request_ids.cc
@@ -15,6 +15,8 @@ limitations under the License.
 
 #include "tensorflow/core/distributed_runtime/recent_request_ids.h"
 
+#include <utility>
+
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 #include "tensorflow/core/platform/logging.h"
@@ -29,12 +31,14 @@ RecentRequestIds::RecentRequestIds(int num_tracked_request_ids)
 Status RecentRequestIds::TrackUnique(int64 request_id,
                                      const string& method_name,
                                      const protobuf::Message& request) {
-  mutex_lock l(mu_);
   if (request_id == 0) {
     // For backwards compatibility, allow all requests with request_id 0.
     return Status::OK();
   }
-  if (set_.count(request_id) > 0) {
+
+  mutex_lock l(mu_);
+  const bool inserted = set_.insert(request_id).second;
+  if (!inserted) {
     // Note: RecentRequestIds is not strict LRU because we don't update
     // request_id's age in the circular_buffer_ if it's tracked again. Strict
     // LRU is not useful here because returning this error will close the
@@ -49,7 +53,6 @@ Status RecentRequestIds::TrackUnique(int64 request_id,
   // when the buffer is not yet full.
   set_.erase(circular_buffer_[next_index_]);
   circular_buffer_[next_index_] = request_id;
-  set_.insert(request_id);
   next_index_ = (next_index_ + 1) % circular_buffer_.size();
   return Status::OK();
 }
diff --git a/tensorflow/core/distributed_runtime/recent_request_ids.h b/tensorflow/core/distributed_runtime/recent_request_ids.h
index e8e45331dd..11cf937c94 100644
--- a/tensorflow/core/distributed_runtime/recent_request_ids.h
+++ b/tensorflow/core/distributed_runtime/recent_request_ids.h
@@ -16,11 +16,13 @@ limitations under the License.
 #ifndef TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RECENT_REQUEST_IDS_H_
 #define TENSORFLOW_CORE_DISTRIBUTED_RUNTIME_RECENT_REQUEST_IDS_H_
 
+#include <string>
+#include <unordered_set>
 #include <vector>
 
 #include "tensorflow/core/lib/core/status.h"
-#include "tensorflow/core/lib/gtl/flatset.h"
 #include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/thread_annotations.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/protobuf/worker.pb.h"
@@ -64,7 +66,7 @@ class RecentRequestIds {
   // request_id.
   int next_index_ GUARDED_BY(mu_) = 0;
   std::vector<int64> circular_buffer_ GUARDED_BY(mu_);
-  gtl::FlatSet<int64> set_ GUARDED_BY(mu_);
+  std::unordered_set<int64> set_ GUARDED_BY(mu_);
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/recent_request_ids_test.cc b/tensorflow/core/distributed_runtime/recent_request_ids_test.cc
index 9a0facf540..8910a50e9c 100644
--- a/tensorflow/core/distributed_runtime/recent_request_ids_test.cc
+++ b/tensorflow/core/distributed_runtime/recent_request_ids_test.cc
@@ -17,8 +17,10 @@ limitations under the License.
 
 #include <algorithm>
 
+#include "tensorflow/core/distributed_runtime/request_id.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/test_benchmark.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/protobuf/worker.pb.h"
 
@@ -93,4 +95,15 @@ TEST(RecentRequestIds, Ordered3) { TestOrdered(3); }
 TEST(RecentRequestIds, Ordered4) { TestOrdered(4); }
 TEST(RecentRequestIds, Ordered5) { TestOrdered(5); }
 
+void BM_TrackUnique(int iters) {
+  RecentRequestIds recent_request_ids(100000);
+  RecvTensorRequest request;
+  for (int i = 0; i < iters; ++i) {
+    TF_CHECK_OK(recent_request_ids.TrackUnique(GetUniqueRequestId(),
+                                               "BM_TrackUnique", request));
+  }
+}
+
+BENCHMARK(BM_TrackUnique);
+
 }  // namespace tensorflow
-- 
GitLab


From c54a6ce4b53172569caa19991ec36be04121a359 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 15:39:58 -0800
Subject: [PATCH 421/575] tf.contrib.data.bucket_by_sequence_length for
 variable length inputs

PiperOrigin-RevId: 187244061
---
 tensorflow/contrib/data/__init__.py           |   2 +
 .../python/kernel_tests/bucketing_test.py     |  90 ++++++++++++++
 .../contrib/data/python/ops/grouping.py       | 115 ++++++++++++++++++
 3 files changed, 207 insertions(+)

diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py
index fcdccdd26c..1777727de8 100644
--- a/tensorflow/contrib/data/__init__.py
+++ b/tensorflow/contrib/data/__init__.py
@@ -25,6 +25,7 @@ See the @{$datasets$Importing Data} Programmer's Guide for an overview.
 @@Counter
 
 @@batch_and_drop_remainder
+@@bucket_by_sequence_length
 @@dense_to_sparse_batch
 @@enumerate_dataset
 @@group_by_window
@@ -58,6 +59,7 @@ from tensorflow.contrib.data.python.ops.counter import Counter
 from tensorflow.contrib.data.python.ops.enumerate_ops import enumerate_dataset
 from tensorflow.contrib.data.python.ops.error_ops import ignore_errors
 from tensorflow.contrib.data.python.ops.get_single_element import get_single_element
+from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length
 from tensorflow.contrib.data.python.ops.grouping import group_by_window
 from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave
 from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave
diff --git a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py
index f1b494e1a6..94f800e8a5 100644
--- a/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/bucketing_test.py
@@ -17,6 +17,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import random
+
 import numpy as np
 
 from tensorflow.contrib.data.python.kernel_tests import dataset_serialization_test_base
@@ -379,5 +381,93 @@ class BucketTest(test.TestCase):
       self.assertEqual(batches, 15)
 
 
+class BucketBySequenceLength(test.TestCase):
+
+  def testBucket(self):
+
+    boundaries = [10, 20, 30]
+    batch_sizes = [10, 8, 4, 2]
+    lengths = [8, 13, 25, 35]
+
+    def element_gen():
+      # Produce 1 batch for each bucket
+      elements = []
+      for batch_size, length in zip(batch_sizes, lengths):
+        for _ in range(batch_size):
+          elements.append([1] * length)
+      random.shuffle(elements)
+      for el in elements:
+        yield (el,)
+
+    element_len = lambda el: array_ops.shape(el)[0]
+    dataset = dataset_ops.Dataset.from_generator(
+        element_gen, (dtypes.int64,), ([None],)).apply(
+            grouping.bucket_by_sequence_length(
+                element_len, boundaries, batch_sizes))
+    batch, = dataset.make_one_shot_iterator().get_next()
+
+    with self.test_session() as sess:
+      batches = []
+      for _ in range(4):
+        batches.append(sess.run(batch))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(batch)
+    batch_sizes_val = []
+    lengths_val = []
+    for batch in batches:
+      batch_size = batch.shape[0]
+      length = batch.shape[1]
+      batch_sizes_val.append(batch_size)
+      lengths_val.append(length)
+    self.assertEqual(sum(batch_sizes_val), sum(batch_sizes))
+    self.assertEqual(sorted(batch_sizes), sorted(batch_sizes_val))
+    self.assertEqual(sorted(lengths), sorted(lengths_val))
+
+  def testPadToBoundary(self):
+
+    boundaries = [10, 20, 30]
+    batch_sizes = [10, 8, 4, 2]
+    lengths = [8, 13, 25]
+
+    def element_gen():
+      # Produce 1 batch for each bucket
+      elements = []
+      for batch_size, length in zip(batch_sizes[:-1], lengths):
+        for _ in range(batch_size):
+          elements.append([1] * length)
+      random.shuffle(elements)
+      for el in elements:
+        yield (el,)
+      for _ in range(batch_sizes[-1]):
+        el = [1] * (boundaries[-1] + 5)
+        yield (el,)
+
+    element_len = lambda el: array_ops.shape(el)[0]
+    dataset = dataset_ops.Dataset.from_generator(
+        element_gen, (dtypes.int64,), ([None],)).apply(
+            grouping.bucket_by_sequence_length(
+                element_len, boundaries, batch_sizes,
+                pad_to_bucket_boundary=True))
+    batch, = dataset.make_one_shot_iterator().get_next()
+
+    with self.test_session() as sess:
+      batches = []
+      for _ in range(3):
+        batches.append(sess.run(batch))
+      with self.assertRaisesOpError("bucket_boundaries"):
+        sess.run(batch)
+    batch_sizes_val = []
+    lengths_val = []
+    for batch in batches:
+      batch_size = batch.shape[0]
+      length = batch.shape[1]
+      batch_sizes_val.append(batch_size)
+      lengths_val.append(length)
+    batch_sizes = batch_sizes[:-1]
+    self.assertEqual(sum(batch_sizes_val), sum(batch_sizes))
+    self.assertEqual(sorted(batch_sizes), sorted(batch_sizes_val))
+    self.assertEqual(sorted(boundaries), sorted(lengths_val))
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py
index 67b085002a..a19be22254 100644
--- a/tensorflow/contrib/data/python/ops/grouping.py
+++ b/tensorflow/contrib/data/python/ops/grouping.py
@@ -17,13 +17,20 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.util import nest
 from tensorflow.python.data.util import sparse
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import gen_dataset_ops
+from tensorflow.python.ops import math_ops
 
 
 def group_by_window(key_func,
@@ -85,6 +92,114 @@ def group_by_window(key_func,
   return _apply_fn
 
 
+def bucket_by_sequence_length(element_length_func,
+                              bucket_boundaries,
+                              bucket_batch_sizes,
+                              padded_shapes=None,
+                              padding_values=None,
+                              pad_to_bucket_boundary=False):
+  """A transformation that buckets elements in a `Dataset` by length.
+
+  Elements of the `Dataset` are grouped together by length and then are padded
+  and batched.
+
+  This is useful for sequence tasks in which the elements have variable length.
+  Grouping together elements that have similar lengths reduces the total
+  fraction of padding in a batch which increases training step efficiency.
+
+  Args:
+    element_length_func: function from element in `Dataset` to `tf.int64`,
+      determines the length of the element, which will determine the bucket it
+      goes into.
+    bucket_boundaries: `list<int>`, upper length boundaries of the buckets.
+    bucket_batch_sizes: `list<int>`, batch size per bucket. Length should be
+      `len(bucket_boundaries) + 1`.
+    padded_shapes: Nested structure of `tf.TensorShape` to pass to
+      @{tf.data.Dataset.padded_batch}. If not provided, will use
+      `dataset.output_shapes`, which will result in variable length dimensions
+      being padded out to the maximum length in each batch.
+    padding_values: Values to pad with, passed to
+      @{tf.data.Dataset.padded_batch}. Defaults to padding with 0.
+    pad_to_bucket_boundary: bool, if `False`, will pad dimensions with unknown
+      size to maximum length in batch. If `True`, will pad dimensions with
+      unknown size to bucket boundary, and caller must ensure that the source
+      `Dataset` does not contain any elements with length longer than
+      `max(bucket_boundaries)`.
+
+  Returns:
+    A `Dataset` transformation function, which can be passed to
+    @{tf.data.Dataset.apply}.
+
+  Raises:
+    ValueError: if `len(bucket_batch_sizes) != len(bucket_boundaries) + 1`.
+  """
+  with ops.name_scope("bucket_by_seq_length"):
+    if len(bucket_batch_sizes) != (len(bucket_boundaries) + 1):
+      raise ValueError(
+          "len(bucket_batch_sizes) must equal len(bucket_boundaries) + 1")
+
+    batch_sizes = constant_op.constant(bucket_batch_sizes, dtype=dtypes.int64)
+
+    def element_to_bucket_id(element):
+      """Return int64 id of the length bucket for this element."""
+      seq_length = element_length_func(element)
+
+      boundaries = list(bucket_boundaries)
+      buckets_min = [np.iinfo(np.int32).min] + boundaries
+      buckets_max = boundaries + [np.iinfo(np.int32).max]
+      conditions_c = math_ops.logical_and(
+          math_ops.less_equal(buckets_min, seq_length),
+          math_ops.less(seq_length, buckets_max))
+      bucket_id = math_ops.reduce_min(array_ops.where(conditions_c))
+
+      return bucket_id
+
+    def window_size_fn(bucket_id):
+      # The window size is set to the batch size for this bucket
+      window_size = batch_sizes[bucket_id]
+      return window_size
+
+    def make_padded_shapes(shapes, none_filler=None):
+      padded = []
+      for shape in nest.flatten(shapes):
+        shape = tensor_shape.TensorShape(shape)
+        shape = [
+            none_filler if d.value is None else d
+            for d in shape
+        ]
+        padded.append(shape)
+      return nest.pack_sequence_as(shapes, padded)
+
+    def batching_fn(bucket_id, grouped_dataset):
+      """Batch elements in dataset."""
+      batch_size = batch_sizes[bucket_id]
+      none_filler = None
+      if pad_to_bucket_boundary:
+        err_msg = ("When pad_to_bucket_boundary=True, elements must have "
+                   "length <= max(bucket_boundaries).")
+        check = check_ops.assert_less(
+            bucket_id,
+            constant_op.constant(len(bucket_batch_sizes) - 1,
+                                 dtype=dtypes.int64),
+            message=err_msg)
+        with ops.control_dependencies([check]):
+          boundaries = constant_op.constant(bucket_boundaries,
+                                            dtype=dtypes.int64)
+          bucket_boundary = boundaries[bucket_id]
+          none_filler = bucket_boundary
+      shapes = make_padded_shapes(
+          padded_shapes or grouped_dataset.output_shapes,
+          none_filler=none_filler)
+      return grouped_dataset.padded_batch(batch_size, shapes, padding_values)
+
+    def _apply_fn(dataset):
+      return dataset.apply(
+          group_by_window(element_to_bucket_id, batching_fn,
+                          window_size_func=window_size_fn))
+
+    return _apply_fn
+
+
 class _VariantDataset(dataset_ops.Dataset):
   """A Dataset wrapper for a tf.variant-typed function argument."""
 
-- 
GitLab


From 64d98b3803e3d53e53f14fadd70fa0332de987a0 Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Tue, 27 Feb 2018 15:41:18 -0800
Subject: [PATCH 422/575] Bump the version of CUB in cmake build.

PiperOrigin-RevId: 187244251
---
 tensorflow/contrib/cmake/external/cub.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/cmake/external/cub.cmake b/tensorflow/contrib/cmake/external/cub.cmake
index 8368898955..98a8c7e736 100644
--- a/tensorflow/contrib/cmake/external/cub.cmake
+++ b/tensorflow/contrib/cmake/external/cub.cmake
@@ -14,8 +14,8 @@
 # ==============================================================================
 include (ExternalProject)
 
-set(cub_URL https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.7.4.zip)
-set(cub_HASH SHA256=20a1a39fd97e5da7f40f5f2e7fd73fd2ea59f9dc4bb8a6c5f228aa543e727e31)
+set(cub_URL https://mirror.bazel.build/github.com/NVlabs/cub/archive/1.8.0.zip)
+set(cub_HASH SHA256=6bfa06ab52a650ae7ee6963143a0bbc667d6504822cbd9670369b598f18c58c3)
 set(cub_BUILD ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
 set(cub_INCLUDE_DIR ${CMAKE_CURRENT_BINARY_DIR}/cub/src/cub)
 set(cub_ARCHIVE_DIR ${CMAKE_CURRENT_BINARY_DIR}/external/cub_archive)
-- 
GitLab


From 3ba1f72f8829c566372208062fcea04ab5695dc6 Mon Sep 17 00:00:00 2001
From: vihanjain <vihanjain.nsk@gmail.com>
Date: Tue, 27 Feb 2018 16:05:26 -0800
Subject: [PATCH 423/575] Pull request for fixing warm-starting device
 placement (#17312)

* Update checkpoint_utils.py

Fix device allocation bug for warm-starting op

* Update checkpoint_utils_test.py

Fix test
---
 tensorflow/python/training/checkpoint_utils.py      | 6 +++++-
 tensorflow/python/training/checkpoint_utils_test.py | 4 +++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py
index 0af1cdecfa..8384d0ae94 100644
--- a/tensorflow/python/training/checkpoint_utils.py
+++ b/tensorflow/python/training/checkpoint_utils.py
@@ -289,7 +289,11 @@ def _set_checkpoint_initializer(variable,
     name: Name of the operation.
   """
   base_type = variable.dtype.base_dtype
-  with ops.colocate_with(variable):
+  # Do not colocate with variable since RestoreV2 op only runs on CPU and
+  # colocation will force variable (and other ops that colocate with variable)
+  # to be on CPU as well. It is okay to place the variable's initializer op on
+  # CPU since it will only be run once at the start.
+  with ops.device(variable.device), ops.device("/cpu:0"):
     restore_op = io_ops.restore_v2(
         ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
     variable._initializer_op = state_ops.assign(variable, restore_op)  # pylint:disable=protected-access
diff --git a/tensorflow/python/training/checkpoint_utils_test.py b/tensorflow/python/training/checkpoint_utils_test.py
index a461b24cbb..f564871315 100644
--- a/tensorflow/python/training/checkpoint_utils_test.py
+++ b/tensorflow/python/training/checkpoint_utils_test.py
@@ -206,7 +206,9 @@ class CheckpointsTest(test.TestCase):
 
       checkpoint_utils.init_from_checkpoint(checkpoint_dir,
                                             {"useful_scope/": "useful_scope/"})
-      self.assertEqual(my4._initializer_op.op.inputs[1].device, "/job:ps")
+      # initializer runs on the same task but always on CPU.
+      self.assertEqual(my4._initializer_op.op.inputs[1].device,
+                       "/job:ps/device:CPU:0")
 
   def testInitFromRootCheckpoint(self):
     checkpoint_dir = self.get_temp_dir()
-- 
GitLab


From e7e63d8b2386f2b3ddd234da77c15125516c65b6 Mon Sep 17 00:00:00 2001
From: Michael Kuperstein <mkuper@google.com>
Date: Tue, 27 Feb 2018 16:41:38 -0800
Subject: [PATCH 424/575] [XLA] Remove an unused function with a typo in its
 name.

PiperOrigin-RevId: 187252967
---
 tensorflow/compiler/xla/service/hlo_module.h | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_module.h b/tensorflow/compiler/xla/service/hlo_module.h
index 06d92f94fd..ca94118763 100644
--- a/tensorflow/compiler/xla/service/hlo_module.h
+++ b/tensorflow/compiler/xla/service/hlo_module.h
@@ -187,11 +187,6 @@ class HloModule {
   // Returns a randomly generated uint64.
   uint64 RandomNew64() const;
 
-  // Returns the unique name for a computation in this module.
-  string GetUniqueCompuationName(const string& prefix) {
-    return computation_name_uniquer_.GetUniqueName(prefix);
-  }
-
   // Returns the NameUniquer for uniquing instruction names in this module.
   NameUniquer& instruction_name_uniquer() { return instruction_name_uniquer_; }
 
-- 
GitLab


From 944423c12057e4a5215fade57c286237dca2b48c Mon Sep 17 00:00:00 2001
From: Martin Wicke <wicke@google.com>
Date: Tue, 27 Feb 2018 17:02:47 -0800
Subject: [PATCH 425/575] Move security.md into the right place.

PiperOrigin-RevId: 187255784
---
 tensorflow/SECURITY.md => SECURITY.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename tensorflow/SECURITY.md => SECURITY.md (100%)

diff --git a/tensorflow/SECURITY.md b/SECURITY.md
similarity index 100%
rename from tensorflow/SECURITY.md
rename to SECURITY.md
-- 
GitLab


From 681327cd00822f9e7620cf8d95141a75447132f1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 17:13:19 -0800
Subject: [PATCH 426/575] Changed back to Shard for SplitV to get better
 performance.

PiperOrigin-RevId: 187257148
---
 tensorflow/core/kernels/split_v_op.cc | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/split_v_op.cc b/tensorflow/core/kernels/split_v_op.cc
index 16fa890780..51d96a17b3 100644
--- a/tensorflow/core/kernels/split_v_op.cc
+++ b/tensorflow/core/kernels/split_v_op.cc
@@ -236,8 +236,9 @@ class SplitVOpCPUImpl {
     };
     if (use_parallelism_between_outputs) {
       // Run in parallel, disabling parallelism in functor.
-      context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
-          num_split, input_element_count / num_split, range_output_func);
+      Shard(num_split,
+            context->device()->tensorflow_cpu_worker_threads()->workers,
+            num_split, input_element_count / num_split, range_output_func);
     } else {
       // Run sequentially, but allow internal parallelism in functor.
       range_output_func(0, num_split);
-- 
GitLab


From 6585008f3dc3ca0f9163a0588b09379eab46c78a Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 27 Feb 2018 17:32:27 -0800
Subject: [PATCH 427/575] Add unit tests for context propagation in ThreadPool
 and a benchmark for ParallelFor.

PiperOrigin-RevId: 187259233
---
 tensorflow/core/BUILD                       |  1 +
 tensorflow/core/lib/core/threadpool_test.cc | 57 ++++++++++++++++++---
 tensorflow/core/platform/default/context.h  |  2 +
 3 files changed, 53 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 1893967cdd..08832b58da 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -339,6 +339,7 @@ cc_library(
         "lib/strings/strcat.h",
         "lib/strings/stringprintf.h",
         "platform/abi.h",
+        "platform/context.h",
         "platform/cpu_feature_guard.h",
         "platform/cpu_info.h",
         "platform/dynamic_annotations.h",
diff --git a/tensorflow/core/lib/core/threadpool_test.cc b/tensorflow/core/lib/core/threadpool_test.cc
index 627ef5a892..320f3ebb83 100644
--- a/tensorflow/core/lib/core/threadpool_test.cc
+++ b/tensorflow/core/lib/core/threadpool_test.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <atomic>
 
+#include "tensorflow/core/platform/context.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/test.h"
@@ -35,6 +36,7 @@ TEST(ThreadPool, Empty) {
 }
 
 TEST(ThreadPool, DoWork) {
+  Context outer_context(ContextKind::kThread);
   for (int num_threads = 1; num_threads < kNumThreads; num_threads++) {
     fprintf(stderr, "Testing with %d threads\n", num_threads);
     const int kWorkItems = 15;
@@ -45,7 +47,9 @@ TEST(ThreadPool, DoWork) {
     {
       ThreadPool pool(Env::Default(), "test", num_threads);
       for (int i = 0; i < kWorkItems; i++) {
-        pool.Schedule([&work, i]() {
+        pool.Schedule([&outer_context, &work, i]() {
+          Context inner_context(ContextKind::kThread);
+          ASSERT_EQ(outer_context, inner_context);
           ASSERT_FALSE(work[i]);
           work[i] = true;
         });
@@ -58,6 +62,7 @@ TEST(ThreadPool, DoWork) {
 }
 
 TEST(ThreadPool, ParallelFor) {
+  Context outer_context(ContextKind::kThread);
   // Make ParallelFor use as many threads as possible.
   int64 kHugeCost = 1 << 30;
   for (int num_threads = 1; num_threads < kNumThreads; num_threads++) {
@@ -68,12 +73,15 @@ TEST(ThreadPool, ParallelFor) {
     for (int i = 0; i < kWorkItems; i++) {
       work[i] = false;
     }
-    pool.ParallelFor(kWorkItems, kHugeCost, [&work](int64 begin, int64 end) {
-      for (int64 i = begin; i < end; ++i) {
-        ASSERT_FALSE(work[i]);
-        work[i] = true;
-      }
-    });
+    pool.ParallelFor(kWorkItems, kHugeCost,
+                     [&outer_context, &work](int64 begin, int64 end) {
+                       Context inner_context(ContextKind::kThread);
+                       ASSERT_EQ(outer_context, inner_context);
+                       for (int64 i = begin; i < end; ++i) {
+                         ASSERT_FALSE(work[i]);
+                         work[i] = true;
+                       }
+                     });
     for (int i = 0; i < kWorkItems; i++) {
       ASSERT_TRUE(work[i]);
     }
@@ -167,5 +175,40 @@ static void BM_Parallel(int iters) {
 }
 BENCHMARK(BM_Parallel);
 
+static void BM_ParallelFor(int iters, int total, int cost_per_unit) {
+  ThreadPool pool(Env::Default(), "test", kNumThreads);
+  // Decrement count concurrently until 0.
+  std::atomic_int_fast32_t count(iters);
+  mutex done_lock;
+  condition_variable done;
+  bool done_flag = false;
+  for (int i = 0; i < iters; ++i) {
+    pool.ParallelFor(
+        total, cost_per_unit,
+        [&count, &done_lock, &done, &done_flag](int64 begin, int64 end) {
+          for (int64 i = begin; i < end; ++i) {
+            if (count.fetch_sub(1) == 1) {
+              mutex_lock l(done_lock);
+              done_flag = true;
+              done.notify_all();
+            }
+          }
+        });
+  }
+  mutex_lock l(done_lock);
+  if (!done_flag) {
+    done.wait(l);
+  }
+}
+BENCHMARK(BM_ParallelFor)
+    ->ArgPair(1 << 10, 1)
+    ->ArgPair(1 << 20, 1)
+    ->ArgPair(1 << 10, 1 << 10)
+    ->ArgPair(1 << 20, 1 << 10)
+    ->ArgPair(1 << 10, 1 << 20)
+    ->ArgPair(1 << 20, 1 << 20)
+    ->ArgPair(1 << 10, 1 << 30)
+    ->ArgPair(1 << 20, 1 << 30);
+
 }  // namespace thread
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/default/context.h b/tensorflow/core/platform/default/context.h
index d8afeb47a9..682f64c26d 100644
--- a/tensorflow/core/platform/default/context.h
+++ b/tensorflow/core/platform/default/context.h
@@ -22,6 +22,8 @@ class Context {
  public:
   Context() {}
   Context(const ContextKind kind) {}
+
+  bool operator==(const Context& other) const { return true; }
 };
 
 class WithContext {
-- 
GitLab


From 72bbc7f03b6bbd996f5bc4e14c29429612978974 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 18:01:13 -0800
Subject: [PATCH 428/575]  Add fields to TfOpStats to store step-related
 information of some host operations. Also include the starting time of a
 device step in StepInfoResult.

PiperOrigin-RevId: 187262025
---
 .../contrib/tpu/profiler/tf_op_stats.proto    | 52 +++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto
index 2094294baa..e5c798aa2f 100644
--- a/tensorflow/contrib/tpu/profiler/tf_op_stats.proto
+++ b/tensorflow/contrib/tpu/profiler/tf_op_stats.proto
@@ -77,6 +77,8 @@ message StepInfoResult {
   // The infeed duration in picoseconds.
   // Can turn into a map if we want a variable number of ops.
   optional uint64 infeed_duration_ps = 3;
+  // The start time of this step in picoseconds.
+  optional uint64 begin_ps = 4;
 }
 
 // Result proto for a sequence of steps.
@@ -155,6 +157,54 @@ message RunEnvironmentResult {
   repeated HostDependentJobInfoResult host_dependent_job_info = 6;
 }
 
+// The types of host operations that are tracked.
+enum HostOp {
+  // Invalid host op.
+  kINVALIDHostOp = 0;
+  // Each of host op type has two parts:
+  // (1) the stage where the op happens and (2) the op name.
+  // stage = Input Data Producer, op = Get Next Batch.
+  kInputDataProducerGetNextBatch = 1;
+  // stage = Input Data Producer, op = Session Run.
+  kInputDataProducerSessionRun = 2;
+  // stage = Input Data Producer, op = Forward Batch.
+  kInputDataProducerForwardBatch = 3;
+  // stage = Infeed Thread, op = Get Next Batch.
+  kInfeedThreadGetNextBatch = 4;
+  // stage = Infeed Thread, op = Session Run.
+  kInfeedThreadSessionRun = 5;
+  // stage = Infeed Thread, op = Forward Batch.
+  kInfeedThreadForwardBatch = 6;
+  // stage = Outfeed Thread, op = Get Next Batch.
+  kOutfeedThreadGetNextBatch = 7;
+  // stage = Outfeed Thread, op = Session Run.
+  kOutfeedThreadSessionRun = 8;
+  // stage = Outfeed Thread, op = Forward Batch.
+  kOutfeedThreadForwardBatch = 9;
+}
+
+// Result proto for the host ops per TPU step.
+message HostOpsPerTpuStep {
+  // Whether the data in this message is valid.
+  optional bool valid = 1 [default = false];
+  // The current TPU step number.
+  optional uint32 tpu_step_num = 2;
+  // The beginning time of the current TPU step on the device in picoseconds.
+  optional uint64 tpu_step_begin_ps = 3;
+  // The ending time of the current TPU step on the device in picoseconds.
+  optional uint64 tpu_step_end_ps = 4;
+  // For each possible host operation, maps to the difference between the TPU
+  // step number that the host op targets and the current TPU step number.
+  // The key is HostOp, value is the step difference.
+  map<int32, int32> step_diffs = 5;
+}
+
+// Result proto for the host ops for all TPU steps.
+message HostOpsResult {
+  // A sequence of HostOpsPerTpuStep (one for each TPU step)
+  repeated HostOpsPerTpuStep host_op_sequence = 1;
+}
+
 // Result proto for TfStatsHelper.
 message TfOpStats {
   // The result for the TF-metric database.
@@ -171,4 +221,6 @@ message TfOpStats {
   optional double matrix_unit_utilization_percent = 6;
   // The run environment of this profiling session.
   optional RunEnvironmentResult run_environment = 7;
+  // The result for the host operations.
+  optional HostOpsResult host_ops = 8;
 }
-- 
GitLab


From 887c54728f713ec76ea486c94c25dfca791a10c1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 19:09:38 -0800
Subject: [PATCH 429/575] Adopt Eigen::DenseIndex in lieu of int64 for a few
 variables (to appease compiler warnings/errors).

PiperOrigin-RevId: 187268113
---
 tensorflow/core/kernels/split_op.cc   | 8 ++++----
 tensorflow/core/kernels/split_v_op.cc | 4 ++--
 tensorflow/core/kernels/unpack_op.cc  | 6 +++---
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/tensorflow/core/kernels/split_op.cc b/tensorflow/core/kernels/split_op.cc
index 1bc92a4f70..7cc3c532c9 100644
--- a/tensorflow/core/kernels/split_op.cc
+++ b/tensorflow/core/kernels/split_op.cc
@@ -231,10 +231,10 @@ class SplitOpCPU : public SplitOpBase<CPUDevice, T> {
     if (prefix_dim_size == 1) {
       auto input_reshaped =
           input.shaped<T, 2>({split_dim_size, suffix_dim_size});
-      auto make_sizes = [&](int64 split_size) {
+      auto make_sizes = [&](Eigen::DenseIndex split_size) {
         return Eigen::DSizes<Eigen::DenseIndex, 2>{split_size, suffix_dim_size};
       };
-      auto reshape_result = [&](Tensor* result, int64 split_size) {
+      auto reshape_result = [&](Tensor* result, Eigen::DenseIndex split_size) {
         return result->shaped<T, 2>({split_size, suffix_dim_size});
       };
       SplitOpCPUImpl<T, decltype(input_reshaped), 2>{}(
@@ -244,11 +244,11 @@ class SplitOpCPU : public SplitOpBase<CPUDevice, T> {
     } else {
       auto input_reshaped = input.shaped<T, 3>(
           {prefix_dim_size, split_dim_size, suffix_dim_size});
-      auto make_sizes = [&](int64 split_size) {
+      auto make_sizes = [&](Eigen::DenseIndex split_size) {
         return Eigen::DSizes<Eigen::DenseIndex, 3>{prefix_dim_size, split_size,
                                                    suffix_dim_size};
       };
-      auto reshape_result = [&](Tensor* result, int64 split_size) {
+      auto reshape_result = [&](Tensor* result, Eigen::DenseIndex split_size) {
         return result->shaped<T, 3>(
             {prefix_dim_size, split_size, suffix_dim_size});
       };
diff --git a/tensorflow/core/kernels/split_v_op.cc b/tensorflow/core/kernels/split_v_op.cc
index 51d96a17b3..0681ff1198 100644
--- a/tensorflow/core/kernels/split_v_op.cc
+++ b/tensorflow/core/kernels/split_v_op.cc
@@ -293,7 +293,7 @@ class SplitVOpCPU : public SplitVOpBase<CPUDevice, T, Tlen> {
     if (prefix_dim_size == 1) {
       auto input_reshaped =
           input.shaped<T, 2>({split_dim_size, suffix_dim_size});
-      auto make_sizes = [&](Tlen split_size) {
+      auto make_sizes = [&](Eigen::DenseIndex split_size) {
         return Eigen::DSizes<Eigen::DenseIndex, 2>{split_size, suffix_dim_size};
       };
       auto reshape_result = [&](Tensor* result, Tlen split_size) {
@@ -306,7 +306,7 @@ class SplitVOpCPU : public SplitVOpBase<CPUDevice, T, Tlen> {
     } else {
       auto input_reshaped = input.shaped<T, 3>(
           {prefix_dim_size, split_dim_size, suffix_dim_size});
-      auto make_sizes = [&](Tlen split_size) {
+      auto make_sizes = [&](Eigen::DenseIndex split_size) {
         return Eigen::DSizes<Eigen::DenseIndex, 3>{prefix_dim_size, split_size,
                                                    suffix_dim_size};
       };
diff --git a/tensorflow/core/kernels/unpack_op.cc b/tensorflow/core/kernels/unpack_op.cc
index 4376df34be..1e1647db5c 100644
--- a/tensorflow/core/kernels/unpack_op.cc
+++ b/tensorflow/core/kernels/unpack_op.cc
@@ -90,16 +90,16 @@ class UnpackOp : public OpKernel {
     }
 #endif  // TENSORFLOW_USE_SYCL
 
-    int64 before_dim = 1;
+    Eigen::DenseIndex before_dim = 1;
     for (int i = 0; i < axis; ++i) {
       before_dim *= input_shape.dim_size(i);
     }
 
-    int64 after_dim = 1;
+    Eigen::DenseIndex after_dim = 1;
     for (int i = axis + 1; i < input_shape.dims(); ++i) {
       after_dim *= input_shape.dim_size(i);
     }
-    const int64 axis_dim = input_shape.dim_size(axis);
+    const Eigen::DenseIndex axis_dim = input_shape.dim_size(axis);
 
     // Except for shape, unpack is a special case of split, so we reuse the
     // same computational kernels.
-- 
GitLab


From f6bda409206dc642d7a6f02842e76b0be7234491 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Tue, 27 Feb 2018 19:11:43 -0800
Subject: [PATCH 430/575] [tf.data] Unify behavior for `Dataset.shuffle(...,
 seed=0)` and `Dataset.shuffle(..., seed=tf.constant(0, dtype=tf.int64))`.

Previously, the Python integer argument would give a deterministic seeding, and the tf.Tensor argument would give a non-deterministic seeding when the graph seed was not set. This change fixes the behavior so that both versions give the same deterministic seeding.

This change also applies the same fix to `tf.contrib.data.shuffle_and_repeat()` and `RandomDataset`.

Fixes #17284.

PiperOrigin-RevId: 187268252
---
 .../contrib/data/python/ops/random_ops.py     | 14 +---
 .../contrib/data/python/ops/shuffle_ops.py    | 14 +---
 .../kernel_tests/shuffle_dataset_op_test.py   | 27 ++++++
 tensorflow/python/data/ops/BUILD              |  1 +
 tensorflow/python/data/ops/dataset_ops.py     | 13 +--
 tensorflow/python/data/util/BUILD             | 24 ++++++
 tensorflow/python/data/util/random_seed.py    | 58 +++++++++++++
 .../python/data/util/random_seed_test.py      | 83 +++++++++++++++++++
 8 files changed, 199 insertions(+), 35 deletions(-)
 create mode 100644 tensorflow/python/data/util/random_seed.py
 create mode 100644 tensorflow/python/data/util/random_seed_test.py

diff --git a/tensorflow/contrib/data/python/ops/random_ops.py b/tensorflow/contrib/data/python/ops/random_ops.py
index 7d727165fe..28ef5e50f3 100644
--- a/tensorflow/contrib/data/python/ops/random_ops.py
+++ b/tensorflow/contrib/data/python/ops/random_ops.py
@@ -19,11 +19,10 @@ from __future__ import print_function
 
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.util import nest
+from tensorflow.python.data.util import random_seed
 from tensorflow.python.data.util import sparse
-from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import gen_dataset_ops
 
@@ -34,16 +33,7 @@ class RandomDataset(dataset_ops.Dataset):
   def __init__(self, seed=None):
     """A `Dataset` of pseudorandom values."""
     super(RandomDataset, self).__init__()
-    seed, seed2 = random_seed.get_seed(seed)
-    if seed is None:
-      self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
-    else:
-      self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
-    if seed2 is None:
-      self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
-    else:
-      self._seed2 = ops.convert_to_tensor(
-          seed2, dtype=dtypes.int64, name="seed2")
+    self._seed, self._seed2 = random_seed.get_seed(seed)
 
   def _as_variant_tensor(self):
     return gen_dataset_ops.random_dataset(
diff --git a/tensorflow/contrib/data/python/ops/shuffle_ops.py b/tensorflow/contrib/data/python/ops/shuffle_ops.py
index 99bb79bc06..f35795abd3 100644
--- a/tensorflow/contrib/data/python/ops/shuffle_ops.py
+++ b/tensorflow/contrib/data/python/ops/shuffle_ops.py
@@ -19,11 +19,11 @@ from __future__ import print_function
 
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.util import nest
+from tensorflow.python.data.util import random_seed
 from tensorflow.python.data.util import sparse
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import gen_dataset_ops
 
 
@@ -45,17 +45,7 @@ class _ShuffleAndRepeatDataset(dataset_ops.Dataset):
     else:
       self._count = ops.convert_to_tensor(
           count, dtype=dtypes.int64, name="count")
-
-    seed, seed2 = random_seed.get_seed(seed)
-    if seed is None:
-      self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
-    else:
-      self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
-    if seed2 is None:
-      self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
-    else:
-      self._seed2 = ops.convert_to_tensor(
-          seed2, dtype=dtypes.int64, name="seed2")
+    self._seed, self._seed2 = random_seed.get_seed(seed)
 
   def _as_variant_tensor(self):
     # pylint: disable=protected-access
diff --git a/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py b/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py
index c089fb08c1..5fcc48831f 100644
--- a/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py
+++ b/tensorflow/python/data/kernel_tests/shuffle_dataset_op_test.py
@@ -132,6 +132,33 @@ class ShuffleDatasetTest(test.TestCase):
       with self.assertRaises(errors.OutOfRangeError):
         sess.run(get_next)
 
+  def testSeedZero(self):
+    """Test for same behavior when the seed is a Python or Tensor zero."""
+    iterator = (
+        dataset_ops.Dataset.range(10).shuffle(10, seed=0)
+        .make_one_shot_iterator())
+    get_next = iterator.get_next()
+
+    elems = []
+    with self.test_session() as sess:
+      for _ in range(10):
+        elems.append(sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+    seed_placeholder = array_ops.placeholder(dtypes.int64, shape=[])
+    iterator = (
+        dataset_ops.Dataset.range(10).shuffle(10, seed=seed_placeholder)
+        .make_initializable_iterator())
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(iterator.initializer, feed_dict={seed_placeholder: 0})
+      for elem in elems:
+        self.assertEqual(elem, sess.run(get_next))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
   def testDefaultArguments(self):
     components = [0, 1, 2, 3, 4]
     iterator = (dataset_ops.Dataset.from_tensor_slices(components).shuffle(5)
diff --git a/tensorflow/python/data/ops/BUILD b/tensorflow/python/data/ops/BUILD
index f12b358a7d..dc293562ab 100644
--- a/tensorflow/python/data/ops/BUILD
+++ b/tensorflow/python/data/ops/BUILD
@@ -23,6 +23,7 @@ py_library(
         "//tensorflow/python:tensor_util",
         "//tensorflow/python:util",
         "//tensorflow/python/data/util:nest",
+        "//tensorflow/python/data/util:random_seed",
         "//tensorflow/python/data/util:sparse",
         "//third_party/py/numpy",
     ],
diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py
index 3fb1f8d547..5751f35fe1 100644
--- a/tensorflow/python/data/ops/dataset_ops.py
+++ b/tensorflow/python/data/ops/dataset_ops.py
@@ -26,13 +26,13 @@ import six
 
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.data.util import nest
+from tensorflow.python.data.util import random_seed
 from tensorflow.python.data.util import sparse
 from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
@@ -1484,16 +1484,7 @@ class ShuffleDataset(Dataset):
     self._input_dataset = input_dataset
     self._buffer_size = ops.convert_to_tensor(
         buffer_size, dtype=dtypes.int64, name="buffer_size")
-    seed, seed2 = random_seed.get_seed(seed)
-    if seed is None:
-      self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
-    else:
-      self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
-    if seed2 is None:
-      self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
-    else:
-      self._seed2 = ops.convert_to_tensor(
-          seed2, dtype=dtypes.int64, name="seed2")
+    self._seed, self._seed2 = random_seed.get_seed(seed)
     if reshuffle_each_iteration is None:
       self._reshuffle_each_iteration = True
     else:
diff --git a/tensorflow/python/data/util/BUILD b/tensorflow/python/data/util/BUILD
index e32c7b54a4..b1bdbdab37 100644
--- a/tensorflow/python/data/util/BUILD
+++ b/tensorflow/python/data/util/BUILD
@@ -86,6 +86,30 @@ py_test(
     ],
 )
 
+py_library(
+    name = "random_seed",
+    srcs = ["random_seed.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework",
+    ],
+)
+
+py_test(
+    name = "random_seed_test",
+    size = "small",
+    srcs = ["random_seed_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":random_seed",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:util",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/python/data/util/random_seed.py b/tensorflow/python/data/util/random_seed.py
new file mode 100644
index 0000000000..e2c9d8672f
--- /dev/null
+++ b/tensorflow/python/data/util/random_seed.py
@@ -0,0 +1,58 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for generating Tensor-valued random seeds."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import random_seed
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+
+
+def get_seed(seed):
+  """Returns the local seeds an operation should use given an op-specific seed.
+
+  See @{tf.get_seed} for more details. This wrapper adds support for the case
+  where `seed` may be a tensor.
+
+  Args:
+    seed: An integer or a @{tf.int64} scalar tensor.
+
+  Returns:
+    A tuple of two @{tf.int64} scalar tensors that should be used for the local
+    seed of the calling dataset.
+  """
+  seed, seed2 = random_seed.get_seed(seed)
+  if seed is None:
+    seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
+  else:
+    seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
+  if seed2 is None:
+    seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
+  else:
+    with ops.name_scope("seed2") as scope:
+      seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64)
+      seed2 = array_ops.where(
+          math_ops.logical_and(
+              math_ops.equal(seed, 0), math_ops.equal(seed2, 0)),
+          constant_op.constant(2**31 - 1, dtype=dtypes.int64),
+          seed2,
+          name=scope)
+  return seed, seed2
diff --git a/tensorflow/python/data/util/random_seed_test.py b/tensorflow/python/data/util/random_seed_test.py
new file mode 100644
index 0000000000..c3a2dc0537
--- /dev/null
+++ b/tensorflow/python/data/util/random_seed_test.py
@@ -0,0 +1,83 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for utilities working with arbitrarily nested structures."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.data.util import random_seed as data_random_seed
+from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import random_seed
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import test
+
+
+class RandomSeedTest(test.TestCase):
+
+  @test_util.run_in_graph_and_eager_modes()
+  def testRandomSeed(self):
+    zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero')
+    one_t = constant_op.constant(1, dtype=dtypes.int64, name='one')
+    intmax_t = constant_op.constant(
+        2**31 - 1, dtype=dtypes.int64, name='intmax')
+    test_cases = [
+        # Each test case is a tuple with input to get_seed:
+        # (input_graph_seed, input_op_seed)
+        # and output from get_seed:
+        # (output_graph_seed, output_op_seed)
+        ((None, None), (0, 0)),
+        ((None, 1), (random_seed.DEFAULT_GRAPH_SEED, 1)),
+        ((1, 1), (1, 1)),
+        ((0, 0), (0, 2**31 - 1)),  # Avoid nondeterministic (0, 0) output
+        ((2**31 - 1, 0), (0, 2**31 - 1)),  # Don't wrap to (0, 0) either
+        ((0, 2**31 - 1), (0, 2**31 - 1)),  # Wrapping for the other argument
+        # Once more, with tensor-valued arguments
+        ((None, one_t), (random_seed.DEFAULT_GRAPH_SEED, 1)),
+        ((1, one_t), (1, 1)),
+        ((0, zero_t), (0, 2**31 - 1)),  # Avoid nondeterministic (0, 0) output
+        ((2**31 - 1, zero_t), (0, 2**31 - 1)),  # Don't wrap to (0, 0) either
+        ((0, intmax_t), (0, 2**31 - 1)),  # Wrapping for the other argument
+    ]
+    for tc in test_cases:
+      tinput, toutput = tc[0], tc[1]
+      random_seed.set_random_seed(tinput[0])
+      g_seed, op_seed = data_random_seed.get_seed(tinput[1])
+      g_seed = self.evaluate(g_seed)
+      op_seed = self.evaluate(op_seed)
+      msg = 'test_case = {0}, got {1}, want {2}'.format(
+          tinput, (g_seed, op_seed), toutput)
+      self.assertEqual((g_seed, op_seed), toutput, msg=msg)
+      random_seed.set_random_seed(None)
+
+    if context.in_graph_mode():
+      random_seed.set_random_seed(1)
+      tinput = (1, None)
+      toutput = (1, ops.get_default_graph()._last_id)  # pylint: disable=protected-access
+      random_seed.set_random_seed(tinput[0])
+      g_seed, op_seed = data_random_seed.get_seed(tinput[1])
+      g_seed = self.evaluate(g_seed)
+      op_seed = self.evaluate(op_seed)
+      msg = 'test_case = {0}, got {1}, want {2}'.format(1, (g_seed, op_seed),
+                                                        toutput)
+      self.assertEqual((g_seed, op_seed), toutput, msg=msg)
+      random_seed.set_random_seed(None)
+
+
+if __name__ == '__main__':
+  test.main()
-- 
GitLab


From 891bf22087c271b26325c3f81e4ef08b6b8af6c1 Mon Sep 17 00:00:00 2001
From: Akshay Modi <nareshmodi@google.com>
Date: Tue, 27 Feb 2018 19:31:17 -0800
Subject: [PATCH 431/575] Cleanup post moving record gradient to C - Remove
 unnecessary tuple build (when not needed) - Stop passing record gradient from
 python

PiperOrigin-RevId: 187269557
---
 .../python/eager/python_eager_op_gen.cc       |  6 +--
 tensorflow/python/eager/pywrap_tfe.h          |  9 ++--
 tensorflow/python/eager/pywrap_tfe_src.cc     | 46 ++++++++---------
 tensorflow/python/eager/pywrap_tfe_test.py    | 49 +++++++++----------
 4 files changed, 49 insertions(+), 61 deletions(-)

diff --git a/tensorflow/python/eager/python_eager_op_gen.cc b/tensorflow/python/eager/python_eager_op_gen.cc
index e6d03297e0..554e29c7e0 100644
--- a/tensorflow/python/eager/python_eager_op_gen.cc
+++ b/tensorflow/python/eager/python_eager_op_gen.cc
@@ -712,9 +712,9 @@ bool GenEagerPythonOp::AddEagerFallbackCode(
 }
 
 void GenEagerPythonOp::AddEagerFastPathExecute() {
-  string fastpath_execute_params = strings::StrCat(
-      "_ctx._handle, _ctx.device_name, \"", op_def_.name(), "\", ",
-      "_execute.record_gradient, name, _ctx._post_execution_callbacks");
+  string fastpath_execute_params =
+      strings::StrCat("_ctx._handle, _ctx.device_name, \"", op_def_.name(),
+                      "\", ", "name, _ctx._post_execution_callbacks");
   string fallback_params;
 
   for (int i = 0; i < api_def_.in_arg_size(); i++) {
diff --git a/tensorflow/python/eager/pywrap_tfe.h b/tensorflow/python/eager/pywrap_tfe.h
index f9692a8910..b1b4a6b214 100644
--- a/tensorflow/python/eager/pywrap_tfe.h
+++ b/tensorflow/python/eager/pywrap_tfe.h
@@ -160,13 +160,10 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace,
 //  Item 2: device_name: Name of the device on which to execute the operation,
 //          or NULL for automatic selection.
 //  Item 3: op_name: Name of the TensorFlow op to execute.
-//  Item 4: record_gradient_callback: Callback that records the gradient of the
-//          result. The callback takes (op_name, inputs, attrs, result, name)
-//          - all sequences and records the gradient.
-//  Item 5: name: An optional name for the operation.
-//  Item 6: List representing all callbacks to execute after successful
+//  Item 4: name: An optional name for the operation.
+//  Item 5: List representing all callbacks to execute after successful
 //  op execute.
-//  Item 7 onwards: inputs - This is a list of inputs followed by a list of
+//  Item 6 onwards: inputs - This is a list of inputs followed by a list of
 //        attrs. It is not necessary for type attrs to be present.
 //
 // This is named _C since there doesn't seem to be any way to make it visible
diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc
index 30e08c8e65..42d97dfe3f 100644
--- a/tensorflow/python/eager/pywrap_tfe_src.cc
+++ b/tensorflow/python/eager/pywrap_tfe_src.cc
@@ -31,6 +31,7 @@ limitations under the License.
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/python/eager/pywrap_tensor.h"
+#include "tensorflow/python/lib/core/safe_ptr.h"
 
 using tensorflow::string;
 using tensorflow::strings::Printf;
@@ -1364,7 +1365,7 @@ PyObject* TFE_Py_TapeGradient(PyObject* tape, PyObject* vspace,
 }
 
 namespace {
-static const int kFastPathExecuteInputStartIndex = 6;
+static const int kFastPathExecuteInputStartIndex = 5;
 
 PyObject* GetPythonObjectFromString(const char* s) {
 #if PY_MAJOR_VERSION >= 3
@@ -1621,46 +1622,43 @@ bool RunCallbacks(bool run_gradient_callback, bool run_post_exec_callbacks,
                   const std::vector<PyObject*>& flattened_inputs,
                   const std::vector<PyObject*>& flattened_attrs,
                   PyObject* flattened_result, PyObject* op_name, PyObject* name,
-                  PyObject* record_gradient_callback, PyObject* callbacks) {
-  PyObject* inputs = PyTuple_New(flattened_inputs.size());
+                  PyObject* callbacks) {
+  tensorflow::Safe_PyObjectPtr inputs =
+      tensorflow::make_safe(PyTuple_New(flattened_inputs.size()));
   for (int i = 0; i < flattened_inputs.size(); i++) {
     PyObject* input = flattened_inputs[i];
     Py_INCREF(input);
-    PyTuple_SET_ITEM(inputs, i, input);
+    PyTuple_SET_ITEM(inputs.get(), i, input);
   }
 
   int num_non_inferred_attrs = PyTuple_GET_SIZE(args) -
                                op_def->input_arg_size() -
                                kFastPathExecuteInputStartIndex;
   int num_attrs = flattened_attrs.size() + num_non_inferred_attrs;
-  PyObject* attrs = PyTuple_New(num_attrs);
+  tensorflow::Safe_PyObjectPtr attrs =
+      tensorflow::make_safe(PyTuple_New(num_attrs));
 
   for (int i = 0; i < num_non_inferred_attrs; i++) {
     auto* attr = PyTuple_GET_ITEM(
         args, kFastPathExecuteInputStartIndex + op_def->input_arg_size() + i);
     Py_INCREF(attr);
-    PyTuple_SET_ITEM(attrs, i, attr);
+    PyTuple_SET_ITEM(attrs.get(), i, attr);
   }
   for (int i = num_non_inferred_attrs; i < num_attrs; i++) {
     // Not INCREFing anything in flattened_attrs as each of those is a new
     // reference, so allow the attrs tuple to steal the reference.
-    PyTuple_SET_ITEM(attrs, i, flattened_attrs.at(i - num_non_inferred_attrs));
+    PyTuple_SET_ITEM(attrs.get(), i,
+                     flattened_attrs.at(i - num_non_inferred_attrs));
   }
 
-  PyObject* callback_args =
-      Py_BuildValue("OOOOO", op_name, inputs, attrs, flattened_result, name);
-
-  auto cleaner = tensorflow::gtl::MakeCleanup([inputs, attrs, callback_args] {
-    Py_DECREF(inputs);
-    Py_DECREF(attrs);
-    Py_DECREF(callback_args);
-  });
-
   if (run_gradient_callback) {
-    RecordGradient(op_name, inputs, attrs, flattened_result, name);
+    RecordGradient(op_name, inputs.get(), attrs.get(), flattened_result, name);
   }
 
   if (run_post_exec_callbacks) {
+    tensorflow::Safe_PyObjectPtr callback_args = tensorflow::make_safe(
+        Py_BuildValue("OOOOO", op_name, inputs.get(), attrs.get(),
+                      flattened_result, name));
     for (Py_ssize_t i = 0; i < PyList_Size(callbacks); i++) {
       PyObject* callback_fn = PyList_GET_ITEM(callbacks, i);
       if (!PyCallable_Check(callback_fn)) {
@@ -1673,7 +1671,7 @@ bool RunCallbacks(bool run_gradient_callback, bool run_post_exec_callbacks,
         return false;
       }
       PyObject* callback_result =
-          PyObject_CallObject(callback_fn, callback_args);
+          PyObject_CallObject(callback_fn, callback_args.get());
       if (!callback_result) {
         return false;
       }
@@ -1703,9 +1701,8 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
   PyObject* op_name = PyTuple_GET_ITEM(args, 2);
   const tensorflow::OpDef* op_def = GetOpDef(op_name);
   if (op_def == nullptr) return nullptr;
-  PyObject* record_gradient_callback = PyTuple_GET_ITEM(args, 3);
-  PyObject* name = PyTuple_GET_ITEM(args, 4);
-  PyObject* callbacks = PyTuple_GET_ITEM(args, 5);
+  PyObject* name = PyTuple_GET_ITEM(args, 3);
+  PyObject* callbacks = PyTuple_GET_ITEM(args, 4);
 
   if (args_size < kFastPathExecuteInputStartIndex + op_def->input_arg_size()) {
     PyErr_SetString(
@@ -1775,9 +1772,8 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
   // (similar to benchmark_tf_gradient_function_*). Also consider using an
   // InlinedVector for flattened_attrs and flattened_inputs if the benchmarks
   // point out problems with heap allocs.
-  bool run_gradient_callback = !*ThreadTapeIsStopped() &&
-                               !GetTapeSet()->empty() &&
-                               record_gradient_callback != Py_None;
+  bool run_gradient_callback =
+      !*ThreadTapeIsStopped() && !GetTapeSet()->empty();
   bool run_post_exec_callbacks =
       callbacks != Py_None && PyList_Size(callbacks) > 0;
   bool run_callbacks = run_gradient_callback || run_post_exec_callbacks;
@@ -1916,7 +1912,7 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
   if (run_callbacks &&
       !RunCallbacks(run_gradient_callback, run_post_exec_callbacks, op_def,
                     args, *flattened_inputs, *flattened_attrs, flat_result,
-                    op_name, name, record_gradient_callback, callbacks)) {
+                    op_name, name, callbacks)) {
     return nullptr;
   }
 
diff --git a/tensorflow/python/eager/pywrap_tfe_test.py b/tensorflow/python/eager/pywrap_tfe_test.py
index 49323e6640..418ed75178 100644
--- a/tensorflow/python/eager/pywrap_tfe_test.py
+++ b/tensorflow/python/eager/pywrap_tfe_test.py
@@ -21,7 +21,6 @@ from __future__ import print_function
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
-from tensorflow.python.eager import execute
 from tensorflow.python.eager import test
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import test_util
@@ -46,15 +45,13 @@ class Tests(test.TestCase):
     self.assertAllClose(
         math_ops.matmul(a_2_by_2, b_2_by_2),
         pywrap_tensorflow.TFE_Py_FastPathExecute(
-            ctx._handle, ctx.device_name, "MatMul", execute.record_gradient,
-            None, None, a_2_by_2, b_2_by_2, "transpose_a", False, "transpose_b",
-            False))
+            ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2,
+            b_2_by_2, "transpose_a", False, "transpose_b", False))
     self.assertAllClose(
         math_ops.matmul(a_100_by_784, b_100_by_784, transpose_b=True),
         pywrap_tensorflow.TFE_Py_FastPathExecute(
-            ctx._handle, ctx.device_name, "MatMul", execute.record_gradient,
-            None, None, a_100_by_784, b_100_by_784, "transpose_a", False,
-            "transpose_b", True))
+            ctx._handle, ctx.device_name, "MatMul", None, None, a_100_by_784,
+            b_100_by_784, "transpose_a", False, "transpose_b", True))
 
   @test_util.assert_no_new_tensors
   @test_util.assert_no_garbage_created
@@ -64,8 +61,8 @@ class Tests(test.TestCase):
       a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
       tape.watch(a_2_by_2)
       z = pywrap_tensorflow.TFE_Py_FastPathExecute(
-          ctx._handle, ctx.device_name, "MatMul", execute.record_gradient, None,
-          None, a_2_by_2, a_2_by_2, "transpose_a", False, "transpose_b", False)
+          ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2,
+          a_2_by_2, "transpose_a", False, "transpose_b", False)
     dz_dy = tape.gradient(z, [a_2_by_2])[0]
     self.assertAllEqual(dz_dy.numpy(),
                         constant_op.constant(4.0, shape=[2, 2]).numpy())
@@ -80,9 +77,9 @@ class Tests(test.TestCase):
 
     self.assertAllClose(
         math_ops.add_n([a_2_by_2, b_2_by_2]),
-        pywrap_tensorflow.TFE_Py_FastPathExecute(
-            ctx._handle, ctx.device_name, "AddN", execute.record_gradient, None,
-            None, [a_2_by_2, b_2_by_2]))
+        pywrap_tensorflow.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
+                                                 "AddN", None, None,
+                                                 [a_2_by_2, b_2_by_2]))
 
   # Tests homogeneous list op
   @test_util.assert_no_new_tensors
@@ -96,8 +93,8 @@ class Tests(test.TestCase):
       tape.watch(a_2_by_2)
       tape.watch(b_2_by_2)
       z1 = pywrap_tensorflow.TFE_Py_FastPathExecute(
-          ctx._handle, ctx.device_name, "AddN", execute.record_gradient, None,
-          None, [a_2_by_2, b_2_by_2])
+          ctx._handle, ctx.device_name, "AddN", None, None,
+          [a_2_by_2, b_2_by_2])
       z2 = math_ops.add_n([a_2_by_2, b_2_by_2])
     dz1_dy = tape.gradient(z1, [a_2_by_2])[0]
     dz2_dy = tape.gradient(z2, [a_2_by_2])[0]
@@ -113,9 +110,9 @@ class Tests(test.TestCase):
 
     self.assertAllClose(
         array_ops.identity_n([a_2_by_2, b_2_by_2]),
-        pywrap_tensorflow.TFE_Py_FastPathExecute(
-            ctx._handle, ctx.device_name, "IdentityN", execute.record_gradient,
-            None, None, [a_2_by_2, b_2_by_2]))
+        pywrap_tensorflow.TFE_Py_FastPathExecute(ctx._handle, ctx.device_name,
+                                                 "IdentityN", None, None,
+                                                 [a_2_by_2, b_2_by_2]))
 
   # Tests heterogeneous list op
   @test_util.assert_no_new_tensors
@@ -129,8 +126,8 @@ class Tests(test.TestCase):
       tape.watch(a_2_by_2)
       tape.watch(b_2_by_2)
       z1 = pywrap_tensorflow.TFE_Py_FastPathExecute(
-          ctx._handle, ctx.device_name, "IdentityN", execute.record_gradient,
-          None, None, [a_2_by_2, b_2_by_2])
+          ctx._handle, ctx.device_name, "IdentityN", None, None,
+          [a_2_by_2, b_2_by_2])
       z2 = array_ops.identity_n([a_2_by_2, b_2_by_2])
     dz1_dy = tape.gradient(z1[0], [a_2_by_2])[0]
     dz2_dy = tape.gradient(z2[0], [a_2_by_2])[0]
@@ -147,22 +144,20 @@ class Tests(test.TestCase):
 
     # Not enough base params
     with self.assertRaisesRegexp(ValueError,
-                                 "at least 6 items in the input tuple"):
+                                 "at least 5 items in the input tuple"):
       pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name,
                                                "Identity")
 
     # Not enough inputs
     with self.assertRaisesRegexp(ValueError,
-                                 "Expected to be at least 7, was 6"):
-      pywrap_tensorflow.TFE_Py_FastPathExecute(
-          ctx_handle, ctx_handle, "Identity", backprop._record_gradient, None,
-          [])
+                                 "Expected to be at least 6, was 5"):
+      pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx_handle,
+                                               "Identity", None, [])
 
     # Bad type
     with self.assertRaisesRegexp(TypeError, "expected a string for op_name"):
-      pywrap_tensorflow.TFE_Py_FastPathExecute(
-          ctx_handle, ctx.device_name, ctx_handle, backprop._record_gradient,
-          None, [], a_2_by_2)
+      pywrap_tensorflow.TFE_Py_FastPathExecute(ctx_handle, ctx.device_name,
+                                               ctx_handle, None, [], a_2_by_2)
 
 
 if __name__ == "__main__":
-- 
GitLab


From ae4c23db58c6436786bbcdea4a15aa814d642220 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 20:16:16 -0800
Subject: [PATCH 432/575] Improve handling of undefined split_dim_tensor in the
 split_v op.

PiperOrigin-RevId: 187272486
---
 tensorflow/core/kernels/split_v_op.cc           |  7 ++++++-
 tensorflow/python/kernel_tests/split_op_test.py | 14 ++++++++++++++
 tensorflow/python/ops/array_ops.py              |  4 +++-
 3 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/split_v_op.cc b/tensorflow/core/kernels/split_v_op.cc
index 0681ff1198..0ce0b552e6 100644
--- a/tensorflow/core/kernels/split_v_op.cc
+++ b/tensorflow/core/kernels/split_v_op.cc
@@ -55,8 +55,13 @@ class SplitVOpBase : public OpKernel {
     const Tensor& input = context->input(0);
     const TensorShape& input_shape = input.shape();
     const Tensor& split_tensor = context->input(1);
+    const Tensor& split_dim_tensor = context->input(2);
 
-    const int32 split_dim_orig = context->input(2).flat<int32>()(0);
+    OP_REQUIRES(context, split_dim_tensor.NumElements() == 1,
+                errors::InvalidArgument("split_dim_tensor must have "
+                                        "exactly one element."));
+
+    const int32 split_dim_orig = split_dim_tensor.flat<int32>()(0);
     const int32 split_dim =
         split_dim_orig < 0 ? split_dim_orig + input.dims() : split_dim_orig;
 
diff --git a/tensorflow/python/kernel_tests/split_op_test.py b/tensorflow/python/kernel_tests/split_op_test.py
index 6171793b14..8cfee3eb93 100644
--- a/tensorflow/python/kernel_tests/split_op_test.py
+++ b/tensorflow/python/kernel_tests/split_op_test.py
@@ -336,6 +336,20 @@ class SplitOpTest(test.TestCase):
     for s in splits:
       self.assertEqual(None, s.get_shape().ndims)
 
+  def testNonexistentDimTensor(self):
+    x = array_ops.placeholder(dtypes.int32)
+    values = np.zeros([5, 30])
+    splits = array_ops.placeholder(dtypes.int32)
+    with self.assertRaisesRegexp(ValueError, "Cannot infer"):
+      y = array_ops.split(values, splits, axis=x)
+
+    splits = array_ops.placeholder(dtypes.int32, [3])
+    y = array_ops.split(values, splits, axis=x)
+    with self.test_session(use_gpu=True) as sess:
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   "must have exactly one element"):
+        sess.run(y, {x: np.array([], dtype=np.int32), splits: [4, 11, 15]})
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index cdfb955f54..3db3d84475 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1380,7 +1380,9 @@ def split(value, num_or_size_splits, axis=0, num=None, name="split"):
         axis=axis, num_split=num_or_size_splits, value=value, name=name)
 
   if num is None:
-    num = size_splits._shape_tuple()[0]
+    size_splits_shape = size_splits._shape_tuple()
+    if size_splits_shape:
+      num = size_splits_shape[0]
     if num is None:
       raise ValueError("Cannot infer num from shape %s" % num_or_size_splits)
 
-- 
GitLab


From c38a16dbcc5de5fa5579a3e48ec12be316a2cb3f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 27 Feb 2018 21:24:24 -0800
Subject: [PATCH 433/575] Adds poisson_regression_head.

PiperOrigin-RevId: 187277651
---
 tensorflow/contrib/estimator/BUILD            |  2 +
 .../estimator/python/estimator/head.py        | 61 ++++++++++++++++
 .../estimator/python/estimator/head_test.py   | 71 +++++++++++++++++++
 3 files changed, 134 insertions(+)

diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index ddccfce3c0..773c6ab6c7 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -170,6 +170,7 @@ py_library(
         "//tensorflow/python:lookup_ops",
         "//tensorflow/python:math_ops",
         "//tensorflow/python:metrics",
+        "//tensorflow/python:nn",
         "//tensorflow/python:sparse_ops",
         "//tensorflow/python:sparse_tensor",
         "//tensorflow/python:summary",
@@ -192,6 +193,7 @@ py_test(
         ":head",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
+        "//tensorflow/python:check_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:control_flow_ops",
diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py
index a45f6934cc..f95fcc8039 100644
--- a/tensorflow/contrib/estimator/python/estimator/head.py
+++ b/tensorflow/contrib/estimator/python/estimator/head.py
@@ -31,6 +31,7 @@ from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import metrics as metrics_lib
+from tensorflow.python.ops import nn
 from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops.losses import losses
 from tensorflow.python.saved_model import signature_constants
@@ -237,6 +238,66 @@ def regression_head(weight_column=None,
       name=name)
 
 
+def poisson_regression_head(
+    weight_column=None,
+    label_dimension=1,
+    loss_reduction=losses.Reduction.SUM,
+    compute_full_loss=True,
+    name=None):
+  """Creates a `_Head` for poisson regression using `tf.nn.log_poisson_loss`.
+
+  The loss is the weighted sum over all input dimensions. Namely, if the input
+  labels have shape `[batch_size, label_dimension]`, the loss is the weighted
+  sum over both `batch_size` and `label_dimension`.
+
+  The head expects `logits` with shape `[D0, D1, ... DN, label_dimension]`.
+  In many applications, the shape is `[batch_size, label_dimension]`.
+
+  The `labels` shape must match `logits`, namely
+  `[D0, D1, ... DN, label_dimension]`. If `label_dimension=1`, shape
+  `[D0, D1, ... DN]` is also supported.
+
+  If `weight_column` is specified, weights must be of shape
+  `[D0, D1, ... DN]`, `[D0, D1, ... DN, 1]` or
+  `[D0, D1, ... DN, label_dimension]`.
+
+  This is implemented as a generalized linear model, see
+  https://en.wikipedia.org/wiki/Generalized_linear_model.
+
+  Args:
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example.
+    label_dimension: Number of regression labels per example. This is the size
+      of the last dimension of the labels `Tensor` (typically, this has shape
+      `[batch_size, label_dimension]`).
+    loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to
+      reduce training loss over batch. Defaults to `SUM`.
+    compute_full_loss: Whether to include the constant `log(z!)` term in
+      computing the poisson loss. See `tf.nn.log_poisson_loss` for the full
+      documentation.
+    name: name of the head. If provided, summary and metrics keys will be
+      suffixed by `"/" + name`. Also used as `name_scope` when creating ops.
+
+  Returns:
+    An instance of `_Head` for poisson regression.
+
+  Raises:
+    ValueError: If `label_dimension` or `loss_reduction` is invalid.
+  """
+  def _poisson_loss(labels, logits):
+    return nn.log_poisson_loss(
+        targets=labels, log_input=logits, compute_full_loss=compute_full_loss)
+  return head_lib._regression_head_with_mean_squared_error_loss(  # pylint:disable=protected-access
+      weight_column=weight_column,
+      label_dimension=label_dimension,
+      loss_reduction=loss_reduction,
+      loss_fn=_poisson_loss,
+      inverse_link_fn=math_ops.exp,
+      name=name)
+
+
 def multi_label_head(n_classes,
                      weight_column=None,
                      thresholds=None,
diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py
index 1411635228..76d050cb28 100644
--- a/tensorflow/contrib/estimator/python/estimator/head_test.py
+++ b/tensorflow/contrib/estimator/python/estimator/head_test.py
@@ -32,6 +32,7 @@ from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import string_ops
@@ -1106,5 +1107,75 @@ class MultiLabelHead(test.TestCase):
         expected_metrics=expected_metrics)
 
 
+class PoissonRegressionHead(test.TestCase):
+
+  def setUp(self):
+    ops.reset_default_graph()
+
+  def test_train(self):
+    head = head_lib.poisson_regression_head()
+
+    # Create estimator spec.
+    logits = np.array([[0], [-1], [1]], dtype=np.float32)
+    labels = np.array([[1], [2], [3]], dtype=np.int32)
+    # With x = exp(logits), z = labels.
+    # loss = -ln(exp(-x) * (x^z) / z!)
+    #      = x - z * ln(x) + ln(z!)
+    #      = exp(logits) - labels * logits - ln(labels!)
+    # But for ln(z!) and z > 1, the Stirling approximation is used
+    # ln(z!) = z*ln(z) - z + 0.5*ln(2*pi*z)
+    # loss = [exp(0) - 1 * 0 + ln(1!),
+    #         exp(-1) - 2 * (-1) + 2*ln(2) - 2 + 0.5*ln(2*pi*2),
+    #         exp(1) - 3 * 1 + 3*ln(3) - 3 + 0.5*ln(2*pi*3)]
+    #      = [1.0, 3.020, 1.482]
+    # sum_loss = 5.502
+    expected_loss = 5.502
+    atol = 0.001
+    expected_train_result = b'my_train_op'
+    def _train_op_fn(loss):
+      with ops.control_dependencies((check_ops.assert_near(
+          math_ops.to_float(expected_loss), math_ops.to_float(loss),
+          atol=atol, name='assert_loss'),)):
+        return constant_op.constant(expected_train_result)
+
+    spec = head.create_estimator_spec(
+        features={'x': np.array(((42.,),), dtype=np.int32)},
+        mode=model_fn.ModeKeys.TRAIN,
+        logits=logits,
+        labels=labels,
+        train_op_fn=_train_op_fn)
+
+    with self.test_session() as sess:
+      _initialize_variables(self, spec.scaffold)
+      loss, train_result = sess.run([spec.loss, spec.train_op])
+      self.assertAlmostEqual(expected_loss, loss, delta=atol)
+      self.assertEqual(expected_train_result, train_result)
+
+  def test_predict(self):
+    head = head_lib.poisson_regression_head()
+
+    # Create estimator spec.
+    logits = np.array([[0], [-1], [1]], dtype=np.float32)
+    expected_predictions = np.exp(logits)
+    spec = head.create_estimator_spec(
+        features={'x': np.array(((42.,),), dtype=np.int32)},
+        mode=model_fn.ModeKeys.PREDICT,
+        logits=logits)
+
+    # Assert spec contains expected tensors.
+    keys = prediction_keys.PredictionKeys
+    self.assertItemsEqual(
+        (keys.PREDICTIONS, keys.LOGITS), spec.predictions.keys())
+    self.assertEqual(dtypes.float32, spec.predictions[keys.PREDICTIONS].dtype)
+    self.assertEqual(dtypes.float32, spec.predictions[keys.LOGITS].dtype)
+
+    # Assert predictions.
+    with self.test_session():
+      _initialize_variables(self, spec.scaffold)
+      self.assertAllClose(
+          expected_predictions, spec.predictions[keys.PREDICTIONS].eval())
+      self.assertAllClose(logits, spec.predictions[keys.LOGITS].eval())
+
+
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From 503d9b522e28272e032bc45a10e3c0f21398a16e Mon Sep 17 00:00:00 2001
From: Jacques Pienaar <jpienaar@google.com>
Date: Wed, 28 Feb 2018 00:07:55 -0800
Subject: [PATCH 434/575] [XLA:Evaluator] Handle while loop.

* Add while loop support to HloEvaluator;
* Add a max_loop_iteration argument to the interpreter's constructor to limit
  the number of loop iterations that will be evaluated (or no bound if -1).

Maintain current constant propagation behavior by setting limit to 0 for evaluators used for CP.

PiperOrigin-RevId: 187287574
---
 .../xla/service/hlo_constant_folding.cc       |  5 ++-
 .../compiler/xla/service/hlo_evaluator.cc     | 41 ++++++++++++++++---
 .../compiler/xla/service/hlo_evaluator.h      | 10 ++++-
 .../xla/service/while_loop_simplifier.cc      |  2 +-
 tensorflow/compiler/xla/tests/BUILD           |  3 ++
 tensorflow/compiler/xla/tests/while_test.cc   |  4 +-
 6 files changed, 55 insertions(+), 10 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_constant_folding.cc b/tensorflow/compiler/xla/service/hlo_constant_folding.cc
index 53450991b6..35ecd4428d 100644
--- a/tensorflow/compiler/xla/service/hlo_constant_folding.cc
+++ b/tensorflow/compiler/xla/service/hlo_constant_folding.cc
@@ -35,7 +35,10 @@ limitations under the License.
 namespace xla {
 
 StatusOr<bool> HloConstantFolding::Run(HloModule* module) {
-  auto evaluator = MakeUnique<HloEvaluator>();
+  // Limit the constant folding to 0 iterations to skip folding loops. This
+  // retains the behavior from before while loop support in HloEvaluator and may
+  // be revised.
+  auto evaluator = MakeUnique<HloEvaluator>(/*max_loop_iterations=*/0);
 
   XLA_VLOG_LINES(2,
                  "HloConstantFolding::Run(), before:\n" + module->ToString());
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index 8c7459099d..c3a3251b7d 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -1372,7 +1372,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     auto result = Literal::CreateFromShape(map->shape());
 
-    HloEvaluator embedded_evaluator;
+    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
     TF_RETURN_IF_ERROR(
         result->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
           std::vector<std::unique_ptr<Literal>> arg_literals;
@@ -1507,7 +1507,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
       }
     }
 
-    HloEvaluator embedded_evaluator;
+    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
     // For each resulting dimension, calculate and assign computed value.
     TF_RETURN_IF_ERROR(
         result->Populate<ReturnT>([&](ArraySlice<int64> multi_index) {
@@ -1581,7 +1581,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
 
     int64 rank = ShapeUtil::Rank(operand_literal.shape());
 
-    HloEvaluator embedded_evaluator;
+    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
     DimensionVector source_index(rank);
 
     std::fill(source_index.begin(), source_index.end(), 0);
@@ -1692,7 +1692,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     DimensionVector window_index(window.dimensions_size());
     DimensionVector operand_index(ShapeUtil::Rank(operand_literal.shape()));
 
-    HloEvaluator embedded_evaluator;
+    HloEvaluator embedded_evaluator(parent_->max_loop_iterations_);
     // For each resulting dimension, calculate and assign computed value.
     TF_RETURN_IF_ERROR(
         result->Populate<ReturnT>([&](ArraySlice<int64> output_index) {
@@ -2069,7 +2069,8 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
   HloEvaluator* parent_;
 };  // class HloEvaluator::TypedVisitor
 
-HloEvaluator::HloEvaluator() {
+HloEvaluator::HloEvaluator(int64 max_loop_iterations)
+    : max_loop_iterations_(max_loop_iterations) {
   typed_visitors_[PRED] = MakeUnique<TypedVisitor<bool>>(this);
   typed_visitors_[U8] = MakeUnique<TypedVisitor<uint8>>(this);
   typed_visitors_[U16] = MakeUnique<FunctionVisitor>([](HloInstruction*) {
@@ -2511,6 +2512,36 @@ Status HloEvaluator::HandleConditional(HloInstruction* conditional) {
   return Status::OK();
 }
 
+Status HloEvaluator::HandleWhile(HloInstruction* while_hlo) {
+  HloComputation* cond_comp = while_hlo->while_condition();
+  HloComputation* body_comp = while_hlo->while_body();
+  // Initialize the loop carried valued with the input to the While instruction.
+  auto lcv = GetEvaluatedLiteralFor(while_hlo->operand(0)).CloneToUnique();
+  bool keep_going = true;
+  int64 iteration_count = 0;
+  HloEvaluator cond_evaluator(max_loop_iterations_);
+  HloEvaluator loop_body_evaluator(max_loop_iterations_);
+  while (keep_going) {
+    if (max_loop_iterations_ >= 0 && iteration_count++ > max_loop_iterations_) {
+      return InvalidArgument("Loop %s exceeded loop iteration limit (%lld).",
+                             while_hlo->name().c_str(), max_loop_iterations_);
+    }
+    TF_ASSIGN_OR_RETURN(auto cond_val, cond_evaluator.Evaluate<Literal*>(
+                                           *cond_comp, {lcv.get()}));
+    keep_going = cond_val->GetFirstElement<bool>();
+    if (keep_going) {
+      TF_ASSIGN_OR_RETURN(auto body_val, loop_body_evaluator.Evaluate<Literal*>(
+                                             *body_comp, {lcv.get()}));
+      VLOG(3) << "Loop iteration result: " << body_val->ToString();
+      lcv = std::move(body_val);
+      cond_evaluator.ResetVisitStates();
+      loop_body_evaluator.ResetVisitStates();
+    }
+  }
+  evaluated_[while_hlo] = std::move(lcv);
+  return Status::OK();
+}
+
 Status HloEvaluator::Preprocess(HloInstruction* hlo) {
   VLOG(2) << "About to visit HLO: " << hlo->ToString();
   return Status::OK();
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.h b/tensorflow/compiler/xla/service/hlo_evaluator.h
index fc82011630..8a27cf9a3a 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.h
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.h
@@ -36,7 +36,10 @@ namespace xla {
 // This class is not thread-safe.
 class HloEvaluator : public DfsHloVisitorWithDefault {
  public:
-  HloEvaluator();
+  // Only evaluate up to max_loop_iterations per while-loop execution if
+  // specified.
+  explicit HloEvaluator(int64 max_loop_iterations = -1);
+
   // Evaluates an HLO module and an array of pointers to literals.
   // Returns the evaluated result as a literal if successful.
   // Precondition: The indices of arg_literals correspond to the parameter
@@ -157,6 +160,8 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
 
   Status HandleCall(HloInstruction* call) override;
 
+  Status HandleWhile(HloInstruction* while_hlo) override;
+
  private:
   // Returns the already-evaluated literal result for the instruction.
   // A Constant instruction is considered evaluated and its literal will be
@@ -194,6 +199,9 @@ class HloEvaluator : public DfsHloVisitorWithDefault {
   // Must be cleared for each evaluation.
   std::vector<const Literal*> arg_literals_;
 
+  // Max loop iterations to execute with no maximum if negative.
+  int64 max_loop_iterations_;
+
   TF_DISALLOW_COPY_AND_ASSIGN(HloEvaluator);
 };
 
diff --git a/tensorflow/compiler/xla/service/while_loop_simplifier.cc b/tensorflow/compiler/xla/service/while_loop_simplifier.cc
index 981de9b220..c9d77c9376 100644
--- a/tensorflow/compiler/xla/service/while_loop_simplifier.cc
+++ b/tensorflow/compiler/xla/service/while_loop_simplifier.cc
@@ -212,7 +212,7 @@ static optional<int64> GetLoopTripCount(HloInstruction* while_op) {
   // Now that we know the index of the induction variable, we can we can try to
   // compute how many times the loop executes.  Start by computing the induction
   // variable's initial value.
-  HloEvaluator evaluator;
+  HloEvaluator evaluator(/*max_loop_iterations=*/0);
   auto* while_init = while_op->mutable_operand(0);
   auto* indvar_init = while_init->mutable_operand(*indvar_tuple_idx);
   StatusOr<std::unique_ptr<Literal>> indvar_init_result =
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index 19b3dfae4e..dc282f2440 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -334,6 +334,9 @@ xla_test(
 xla_test(
     name = "while_test",
     srcs = ["while_test.cc"],
+    tags = [
+        "enable_for_xla_interpreter",
+    ],
     deps = [
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc
index 52157b837c..33d457c70b 100644
--- a/tensorflow/compiler/xla/tests/while_test.cc
+++ b/tensorflow/compiler/xla/tests/while_test.cc
@@ -910,7 +910,7 @@ XLA_TEST_F(WhileTest, WhileWithDynamicUpdateSlice) {
 // Per backend the values generated can be different as the different backends
 // use different random number generators.
 // TODO(b/32240857): Extend test to verify outputs.
-TEST_F(WhileTest, WhileWithPrngScalarResult) {
+TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithPrngScalarResult)) {
   auto v6s32 = ShapeUtil::MakeShape(S32, {6});
 
   // Create a computation for the condition: repeat for count iterations.
@@ -1166,7 +1166,7 @@ XLA_TEST_F(WhileTest, NestedWhileWithScalarResult) {
 // while (f(result).get<0>()) {
 //   result = result + 1;
 // }
-TEST_F(WhileTest, WhileWithCallInsideCondition) {
+TEST_F(WhileTest, DISABLED_ON_INTERPRETER(WhileWithCallInsideCondition)) {
   auto result_shape = ShapeUtil::MakeShape(S32, {});
 
   // Create a computation for the condition: repeat for 5 iterations.
-- 
GitLab


From 6ac343bdfc942678d64dcbfc4d4fc90c0df6a4a0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 03:39:04 -0800
Subject: [PATCH 435/575] [TF:XLA] Fix SplitV implementation to support
 negative split_dim.

Mirror behavior of Split op when a negative split_dim is used.

PiperOrigin-RevId: 187304771
---
 tensorflow/compiler/tests/binary_ops_test.py  | 14 +++++++++++
 .../compiler/tf2xla/kernels/split_op.cc       | 23 ++++++++-----------
 2 files changed, 24 insertions(+), 13 deletions(-)

diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py
index 30a6d3a74d..0e4efaed86 100644
--- a/tensorflow/compiler/tests/binary_ops_test.py
+++ b/tensorflow/compiler/tests/binary_ops_test.py
@@ -1045,6 +1045,20 @@ class BinaryOpsTest(XLATestCase):
             ],
             equality_test=self.ListsAreClose)
 
+      def splitvOp(x, y):  # pylint: disable=invalid-name
+        return array_ops.split(value=y, num_or_size_splits=[2, 3], axis=x)
+      for axis in [1, -1]:
+        self._testBinary(
+            splitvOp,
+            np.int32(axis),
+            np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]],
+                     dtype=dtype),
+            expected=[
+                np.array([[0, 1], [5, 6]], dtype=dtype),
+                np.array([[2, 3, 4], [7, 8, 9]], dtype=dtype),
+            ],
+            equality_test=self.ListsAreClose)
+
   def testTile(self):
     for dtype in self.numeric_types:
       self._testBinary(
diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc
index 79c435c90a..43c15e7538 100644
--- a/tensorflow/compiler/tf2xla/kernels/split_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc
@@ -111,27 +111,24 @@ class SplitVOp : public XlaOpKernel {
 
   void Compile(XlaOpKernelContext* ctx) override {
     const int32 num_split = num_outputs();
+    const TensorShape input_shape = ctx->InputShape(0);
     const TensorShape index_shape = ctx->InputShape(2);
-    xla::Literal literal_index;
-    OP_REQUIRES_OK(ctx, ctx->ConstantInput(2, &literal_index));
 
-    int32 split_dim;
-    OP_REQUIRES(ctx, index_shape.dims() == 0,
-                errors::InvalidArgument("split_dim input to Split Op must be a "
-                                        "scalar"));
-    split_dim = literal_index.Get<int>({});
+    int64 split_dim_orig;
+    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(2, &split_dim_orig));
+    int64 split_dim = split_dim_orig < 0 ? split_dim_orig + input_shape.dims()
+                                         : split_dim_orig;
+    OP_REQUIRES(ctx, 0 <= split_dim && split_dim < input_shape.dims(),
+                errors::InvalidArgument("-input rank(-", input_shape.dims(),
+                                        ") <= split_dim < input rank (",
+                                        input_shape.dims(), "), but got ",
+                                        split_dim_orig));
 
     xla::ComputationDataHandle input = ctx->Input(0);
-    const TensorShape input_shape = ctx->InputShape(0);
 
     OP_REQUIRES(ctx, input_shape.dims() > 0,
                 errors::InvalidArgument("Can't split a 0 dimensional input"));
 
-    OP_REQUIRES(
-        ctx, 0 <= split_dim && split_dim < input_shape.dims(),
-        errors::InvalidArgument("0 <= split_dim < number of input dimensions (",
-                                input_shape.dims(), "), but got ", split_dim));
-
     OP_REQUIRES(
         ctx, num_split > 0,
         errors::InvalidArgument(
-- 
GitLab


From 19538075bb174ba315a8b2711e60238b5fb92805 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 04:17:53 -0800
Subject: [PATCH 436/575] Clarify tutorials/image_retraining regarding the use
 of Mobilenets: - The feature depth multiplier controls the dimension of
 intermediate   activations ("number of neurons"). The size of weight matrices
   depends on its square. - Quantization with TF-Lite only occurs when its
 TOCO tool is run   on the module. That is out of scope here, so discussion of
 quantization   gets replaced by links to TF-Lite and part 2 of the "Poets"
 codelab.

PiperOrigin-RevId: 187307400
---
 .../docs_src/tutorials/image_retraining.md    | 31 ++++++++++++-------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/tensorflow/docs_src/tutorials/image_retraining.md b/tensorflow/docs_src/tutorials/image_retraining.md
index df15bc0a9c..246a420400 100644
--- a/tensorflow/docs_src/tutorials/image_retraining.md
+++ b/tensorflow/docs_src/tutorials/image_retraining.md
@@ -349,31 +349,32 @@ results, but if you intend to deploy your model on mobile devices or other
 resource-constrained environments you may want to trade off a little accuracy
 for much smaller file sizes or faster speeds. To help with that, the
 [retrain.py script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py)
-supports 32 different variations on the [Mobilenet architecture](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html).
+supports different variations on the [Mobilenet architecture](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html).
 
 These are a little less precise than Inception v3, but can result in far
-smaller file sizes (down to less than a megabyte) and can be many times faster
+smaller file sizes (a few megabytes) and can be many times faster
 to run. To train with one of these models, pass in the `--architecture` flag,
 for example:
 
 ```
 python tensorflow/examples/image_retraining/retrain.py \
-    --image_dir ~/flower_photos --architecture mobilenet_0.25_128_quantized
+    --image_dir ~/flower_photos --architecture mobilenet_0.25_128
 ```
 
-This will create a 941KB model file in `/tmp/output_graph.pb`, with 25% of the
-parameters of the full Mobilenet, taking 128x128 sized input images, and with
-its weights quantized down to eight bits on disk. You can choose '1.0', '0.75',
-'0.50', or '0.25' to control the number of weight parameters, and so the file
-size (and to some extent the speed), '224', '192', '160', or '128' for the input
-image size, with smaller sizes giving faster speeds, and an optional
-'_quantized' at the end to indicate whether the file should contain 8-bit or
-32-bit float weights.
+This will create a 1.9MB model file in `/tmp/output_graph.pb`, with only 25% of
+the number of neurons of the full Mobilenet, and trained to take 128x128 sized
+input images.
+
+You can choose '1.0', '0.75', '0.50', or '0.25' to control the number of
+neurons (activations of hidden layers); the number of weights (and hence to
+some extent the file size and speed) shrinks like the square of that fraction.
+You can choose '224', '192', '160', or '128' for the input image size,
+with smaller sizes giving faster speeds.
 
 The speed and size advantages come at a loss to accuracy of course, but for many
 purposes this isn't critical. They can also be somewhat offset with improved
 training data. For example, training with distortions allows me to get above 80%
-accuracy on the flower data set even with the 0.25/128/quantized graph above.
+accuracy on the flower data set even with the 0.25/128 graph above.
 
 If you're going to be using the Mobilenet models in label_image or your own
 programs, you'll need to feed in an image of the specified size converted to a
@@ -395,3 +396,9 @@ python tensorflow/examples/label_image/label_image.py \
 --input_mean=128 --input_std=128 \
 --image=$HOME/flower_photos/daisy/21652746_cc379e0eea_m.jpg
 ```
+
+For more information on deploying the retrained model to a mobile device, see
+the [codelab version](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0)
+of this tutorial, especially [part 2](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/#0), which describes
+[TensorFlow Lite](/mobile/tflite/) and the additional optimizations it offers
+(including quantization of model weights).
-- 
GitLab


From 6399c574c12fc58054dbd5989efde2e2d665e3d6 Mon Sep 17 00:00:00 2001
From: Dan Ringwalt <ringwalt@google.com>
Date: Wed, 28 Feb 2018 07:22:02 -0800
Subject: [PATCH 437/575] Replace deprecated _control_inputs with remove/add to
 avoid warnings.

PiperOrigin-RevId: 187321605
---
 tensorflow/contrib/graph_editor/reroute.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/graph_editor/reroute.py b/tensorflow/contrib/graph_editor/reroute.py
index 7ffdbb7139..95c02a64d4 100644
--- a/tensorflow/contrib/graph_editor/reroute.py
+++ b/tensorflow/contrib/graph_editor/reroute.py
@@ -471,9 +471,10 @@ def remove_control_inputs(op, cops):
     if cop not in op.control_inputs:
       raise ValueError("{} is not a control_input of {}".format(op.name,
                                                                 cop.name))
+  control_inputs = [cop for cop in op.control_inputs if cop not in cops]
   # pylint: disable=protected-access
-  op._control_inputs = [cop for cop in op._control_inputs if cop not in cops]
-  op._recompute_node_def()
+  op._remove_all_control_inputs()
+  op._add_control_inputs(control_inputs)
   # pylint: enable=protected-access
 
 
@@ -496,9 +497,6 @@ def add_control_inputs(op, cops):
     if cop in op.control_inputs:
       raise ValueError("{} is already a control_input of {}".format(cop.name,
                                                                     op.name))
-  # pylint: disable=protected-access
-  op._control_inputs += cops
-  op._recompute_node_def()
-  # pylint: enable=protected-access
+  op._add_control_inputs(cops)  # pylint: disable=protected-access
 
 remove_undocumented(__name__, _allowed_symbols)
-- 
GitLab


From f48d3644d433a00733cbe44be67ef4e8ab2988e2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 07:40:08 -0800
Subject: [PATCH 438/575] Pass 'import_scope' when calling
 from_control_flow_context_def.

PiperOrigin-RevId: 187323218
---
 tensorflow/python/ops/control_flow_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index fb9e2188d7..215c6940df 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1790,7 +1790,7 @@ class CondContext(ControlFlowContext):
 
     ret.Enter()
     for nested_def in context_def.nested_contexts:
-      from_control_flow_context_def(nested_def)
+      from_control_flow_context_def(nested_def, import_scope=import_scope)
     ret.Exit()
     return ret
 
-- 
GitLab


From e5ab5347d695fe3f7f495864329c05a2ff8b512a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 09:54:19 -0800
Subject: [PATCH 439/575] Move Roadmap to a more prominent place.

PiperOrigin-RevId: 187338696
---
 tensorflow/docs_src/about/index.md                  | 1 -
 tensorflow/docs_src/about/leftnav_files             | 1 -
 tensorflow/docs_src/community/index.md              | 1 +
 tensorflow/docs_src/community/leftnav_files         | 1 +
 tensorflow/docs_src/{about => community}/roadmap.md | 0
 5 files changed, 2 insertions(+), 2 deletions(-)
 rename tensorflow/docs_src/{about => community}/roadmap.md (100%)

diff --git a/tensorflow/docs_src/about/index.md b/tensorflow/docs_src/about/index.md
index 5326b1e110..dc1e9af876 100644
--- a/tensorflow/docs_src/about/index.md
+++ b/tensorflow/docs_src/about/index.md
@@ -3,7 +3,6 @@
 This section provides a few documents about TensorFlow itself,
 including the following:
 
-  * @{$roadmap$Roadmap}, which summarizes upcoming additions to TensorFlow.
   * @{$uses$TensorFlow in Use}, which provides a link to our model zoo and
     lists some popular ways that TensorFlow is being used.
   * @{$bib$TensorFlow White Papers}, which provides abstracts of white papers
diff --git a/tensorflow/docs_src/about/leftnav_files b/tensorflow/docs_src/about/leftnav_files
index 28f039e9b5..63763b9d9c 100644
--- a/tensorflow/docs_src/about/leftnav_files
+++ b/tensorflow/docs_src/about/leftnav_files
@@ -1,5 +1,4 @@
 index.md
-roadmap.md
 uses.md
 bib.md
 attribution.md
diff --git a/tensorflow/docs_src/community/index.md b/tensorflow/docs_src/community/index.md
index 8e67022648..b706d9b204 100644
--- a/tensorflow/docs_src/community/index.md
+++ b/tensorflow/docs_src/community/index.md
@@ -5,6 +5,7 @@ This section contains the following documents:
   * @{$welcome$Welcome to the TensorFlow Community}, which explains how
     you can get involved, where to report issues, and where to join
     like-minded TensorFlow enthusiasts online.
+  * @{$roadmap$Roadmap}, which summarizes upcoming additions to TensorFlow.
   * @{$documentation$Writing TensorFlow Documentation}, which explains
     TensorFlow's documentation conventions.  If you are modifying
     TensorFlow source code or documentation, please read this guide.
diff --git a/tensorflow/docs_src/community/leftnav_files b/tensorflow/docs_src/community/leftnav_files
index c1595d3c95..fab35024ad 100644
--- a/tensorflow/docs_src/community/leftnav_files
+++ b/tensorflow/docs_src/community/leftnav_files
@@ -1,5 +1,6 @@
 index.md
 welcome.md
+roadmap.md
 documentation.md
 style_guide.md
 benchmarks.md
diff --git a/tensorflow/docs_src/about/roadmap.md b/tensorflow/docs_src/community/roadmap.md
similarity index 100%
rename from tensorflow/docs_src/about/roadmap.md
rename to tensorflow/docs_src/community/roadmap.md
-- 
GitLab


From 120fdaa4a2869a9bde183ec42398df527bbcc6e0 Mon Sep 17 00:00:00 2001
From: Akshay Agrawal <akshayka@google.com>
Date: Wed, 28 Feb 2018 09:59:49 -0800
Subject: [PATCH 440/575] BUILD file visibility change. END_PUBLIC

RELNOTES: n/a

BEGIN_PUBLIC
Automated g4 rollback of changelist 187222292

PiperOrigin-RevId: 187339609
---
 tensorflow/compiler/jit/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index af259e0564..c7c9e9bd7a 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -205,6 +205,7 @@ cc_library(
     name = "graph_to_functiondef",
     srcs = ["graph_to_functiondef.cc"],
     hdrs = ["graph_to_functiondef.h"],
+    visibility = [":friends"],
     deps = [
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
-- 
GitLab


From 3c9cd2576cb9b88b641b5e38248ca7e49aa5c50a Mon Sep 17 00:00:00 2001
From: MandarJKulkarni <33712629+MandarJKulkarni@users.noreply.github.com>
Date: Thu, 1 Mar 2018 00:05:09 +0530
Subject: [PATCH 441/575] Fix typos in profiler.h (#16938)

---
 tensorflow/cc/profiler/profiler.h | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/cc/profiler/profiler.h b/tensorflow/cc/profiler/profiler.h
index 6077c45c58..64edbb5766 100644
--- a/tensorflow/cc/profiler/profiler.h
+++ b/tensorflow/cc/profiler/profiler.h
@@ -61,18 +61,18 @@ class Profiler {
   /// Adds tracing information `run_meta` to profiler. A `run_meta` is
   /// generated by a TensorFlow session run call. `step` is the key
   /// to the `run_meta`. When calling ProfileXXX methods, caller can specify
-  /// `step` in `options` to seletively profile the corresponding `run_meta`.
+  /// `step` in `options` to selectively profile the corresponding `run_meta`.
   /// Multiple different `run_meta` can be keyed by the same `step` in order
   /// to group them together.
   void AddStep(int64 step, const RunMetadata& run_meta);
 
   /// Profiles the model by organizing nodes in graph structure.
-  /// Each node is an op and the nodes are contected by the op inputs/outputs.
+  /// Each node is an op and the nodes are connected by the op inputs/outputs.
   GraphNodeProto ProfileGraph(const Options& options);
 
   /// Profiles the model by organizing nodes in name scope structure.
   /// Each node is an op, and nodes are organized by the ops' name
-  /// scope, similar to a filesystem tree.
+  /// scope, similar to a file system tree.
   /// E.g. /foo is the root of operation /foo/matmul_1 and foo/conv_2.
   GraphNodeProto ProfileNameScope(const Options& options);
 
-- 
GitLab


From 12d8142dc1bb914fa3ff0a9029e9b6b71e36b9f5 Mon Sep 17 00:00:00 2001
From: Shivani Agrawal <shivaniagrawal@google.com>
Date: Wed, 28 Feb 2018 10:43:36 -0800
Subject: [PATCH 442/575] [eager] Typo correction, there is no method
 `tf.data.Dataset.make_iterator`.

PiperOrigin-RevId: 187347001
---
 tensorflow/contrib/eager/python/datasets.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py
index d177bfeab2..36b7d6d009 100644
--- a/tensorflow/contrib/eager/python/datasets.py
+++ b/tensorflow/contrib/eager/python/datasets.py
@@ -71,7 +71,7 @@ class Iterator(object):
     if not context.in_eager_mode():
       raise RuntimeError(
           "{} objects can only be used when eager execution is enabled, use "
-          "tf.data.Dataset.make_iterator or "
+          "tf.data.Dataset.make_initializable_iterator or "
           "tf.data.Dataset.make_one_shot_iterator for graph construction".
           format(type(self)))
     with ops.device("/device:CPU:0"):
-- 
GitLab


From 39a43c4f1d73b0210795d2003b127d3ffa284e98 Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Wed, 28 Feb 2018 11:07:10 -0800
Subject: [PATCH 443/575] Introduce a ShapeUtil::ForEachIndexWithStatus, change
 index type to ArraySlice

This is not used yet, but I need it in a later CL.  I don't specifically need
the argument to be an ArraySlice, but it seemed cleaner than taking a const ref
to a vector.

No functional change intended.

PiperOrigin-RevId: 187352376
---
 tensorflow/compiler/xla/literal_util.cc       |  2 +-
 tensorflow/compiler/xla/literal_util.h        |  2 +-
 tensorflow/compiler/xla/literal_util_test.cc  | 30 +++++++--------
 .../compiler/xla/service/hlo_evaluator.cc     |  6 +--
 tensorflow/compiler/xla/shape_util.h          | 38 ++++++++++++++-----
 tensorflow/compiler/xla/shape_util_test.cc    | 32 ++++++++++++++--
 6 files changed, 77 insertions(+), 33 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 823da43b5a..3962a9b316 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -223,7 +223,7 @@ Status Literal::CopySliceFromInternal(
     Literal::StrideConfig stride_config(src_literal.shape(), shape(),
                                         copy_size);
 
-    auto copy_proc = [&](const std::vector<int64>& indexes) {
+    auto copy_proc = [&](tensorflow::gtl::ArraySlice<int64> indexes) {
       // Map from multi-dimensional index, to source index.
       std::transform(indexes.begin(), indexes.end(), src_base.begin(),
                      src_indexes.begin(), std::plus<int64>());
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index d5ae3fd723..1d58f0cbc7 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -1269,7 +1269,7 @@ Status Literal::Populate(const FnType& generator) {
     int64 minor_dimension_size =
         ShapeUtil::GetDimension(this_shape, stride_config.minor_dimension);
 
-    auto init_function = [&](const std::vector<int64>& indexes) {
+    auto init_function = [&](tensorflow::gtl::ArraySlice<int64> indexes) {
       const int64 index =
           IndexUtil::MultidimensionalIndexToLinearIndex(shape(), indexes);
       std::copy(indexes.begin(), indexes.end(), minor_scan_indexes.begin());
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index ee2f4fe874..9ff0771110 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -30,6 +30,7 @@ limitations under the License.
 namespace xla {
 namespace {
 
+using tensorflow::gtl::ArraySlice;
 using ::testing::ElementsAre;
 using ::testing::HasSubstr;
 
@@ -214,11 +215,11 @@ TEST_F(LiteralUtilTest, CreateSparse) {
   std::vector<int64> expected_values = {8, 9, 7, 10};
 
   EXPECT_EQ(literal->sparse_indices()->data(),
-            tensorflow::gtl::ArraySlice<int64>(
-                expected_indices.data(), expected_indices.num_elements()));
-  EXPECT_EQ(tensorflow::gtl::ArraySlice<int64>(literal->data<int64>().data(),
-                                               expected_values.size()),
-            tensorflow::gtl::ArraySlice<int64>(expected_values));
+            ArraySlice<int64>(expected_indices.data(),
+                              expected_indices.num_elements()));
+  EXPECT_EQ(
+      ArraySlice<int64>(literal->data<int64>().data(), expected_values.size()),
+      ArraySlice<int64>(expected_values));
 }
 
 TEST_F(LiteralUtilTest, LiteralR4F32ProjectedStringifies) {
@@ -290,7 +291,7 @@ TEST_F(LiteralUtilTest, EachCellR2F32) {
   // clang-format on
   std::vector<std::tuple<int64, int64, string>> seen;
   literal->EachCellAsString(
-      [&seen](tensorflow::gtl::ArraySlice<int64> indices, const string& value) {
+      [&seen](ArraySlice<int64> indices, const string& value) {
         seen.emplace_back(indices[0], indices[1], value);
       });
 
@@ -622,11 +623,10 @@ TEST_F(LiteralUtilTest, TransposeR4) {
   // clang-format on
   auto reshape = original->Transpose(/*permutation=*/{2, 3, 0, 1});
 
-  reshape->EachCell<float>(
-      [&](tensorflow::gtl::ArraySlice<int64> indices, float value) {
-        EXPECT_EQ(value, original->Get<float>(
-                             {indices[2], indices[3], indices[0], indices[1]}));
-      });
+  reshape->EachCell<float>([&](ArraySlice<int64> indices, float value) {
+    EXPECT_EQ(value, original->Get<float>(
+                         {indices[2], indices[3], indices[0], indices[1]}));
+  });
 }
 
 TEST_F(LiteralUtilTest, TestR4RelayoutEquivalence) {
@@ -863,7 +863,7 @@ TEST_F(LiteralUtilTest, CopySliceFrom) {
     const int64 zero_base[] = {0, 0, 0, 0};
     const int64 step[] = {1, 1, 1, 1};
     uint32 seqnr = 0;
-    auto init_proc = [&](const std::vector<int64>& indexes) {
+    auto init_proc = [&](ArraySlice<int64> indexes) {
       source->Set(indexes, ++seqnr);
       return true;
     };
@@ -879,7 +879,7 @@ TEST_F(LiteralUtilTest, CopySliceFrom) {
     std::vector<int64> source_indexes(TF_ARRAYSIZE(dimensions), 0);
     std::vector<int64> blank_indexes(TF_ARRAYSIZE(dimensions), 0);
     bool matched = true;
-    auto check_proc = [&](const std::vector<int64>& indexes) {
+    auto check_proc = [&](ArraySlice<int64> indexes) {
       std::copy(indexes.begin(), indexes.end(), source_indexes.begin());
       std::transform(source_indexes.begin(), source_indexes.end(), src_base,
                      source_indexes.begin(), std::plus<int64>());
@@ -1067,7 +1067,7 @@ TEST_F(LiteralUtilTest, Populate) {
         primitive_util::NativeToPrimitiveType<uint32>(), data.dimensions,
         data.layout);
     auto literal = Literal::CreateFromShape(shape);
-    auto generator = [&](tensorflow::gtl::ArraySlice<int64> indexes) -> uint32 {
+    auto generator = [&](ArraySlice<int64> indexes) -> uint32 {
       // Offsets from linear index just to avoid R0 literals to be initialized
       // with zero.
       return IndexUtil::MultidimensionalIndexToLinearIndex(literal->shape(),
@@ -1079,7 +1079,7 @@ TEST_F(LiteralUtilTest, Populate) {
     std::vector<int64> zero_base(data.dimensions.size(), 0);
     std::vector<int64> step(data.dimensions.size(), 1);
     bool matched = true;
-    auto check_function = [&](const std::vector<int64>& indexes) {
+    auto check_function = [&](ArraySlice<int64> indexes) {
       auto value = literal->Get<uint32>(indexes);
       matched = matched && (value == generator(indexes));
       return matched;
diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index c3a3251b7d..edb1ad2360 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -1222,7 +1222,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     // corresponding index of the resulting padded literal.
     const PaddingConfig& pad_config = pad->padding_config();
 
-    auto func = [&](const std::vector<int64>& input_index) {
+    auto func = [&](ArraySlice<int64> input_index) {
       for (auto i = 0; i < input_index.size(); ++i) {
         // Interior padding occurs logically before edge padding, so in the case
         // of negative edge padding elements are removed from the
@@ -1518,7 +1518,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
             base[result_to_arg_index[i]] = multi_index[i];
           }
 
-          auto func = [&](const std::vector<int64>& input_index) {
+          auto func = [&](ArraySlice<int64> input_index) {
             auto curr_val = arg_literal.Get<ReturnT>(input_index);
 
             // Evaluate computation with specified literal operands.
@@ -1954,7 +1954,7 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     auto result = operand_literal.CloneToUnique();
     std::vector<int64> result_index(ShapeUtil::Rank(result->shape()), 0);
 
-    auto func = [&](const std::vector<int64>& update_index) {
+    auto func = [&](ArraySlice<int64> update_index) {
       std::transform(update_index.begin(), update_index.end(), start.begin(),
                      result_index.begin(), std::plus<int64>());
 
diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index 8ee263fe5e..923315e001 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -24,6 +24,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/primitive_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
@@ -564,16 +565,16 @@ class ShapeUtil {
   // The visitor_function visitor function should return true if it wants to
   // continue, or false otherwise.
   //
-  // visitor_function must be a callable of type bool(const std::vector<int64>&)
-  // or compatible.
+  // visitor_function must be a callable of type
+  // StatusOr<bool>(ArraySlice<int64>) or compatible.
   template <typename FnType>
-  static void ForEachIndex(const Shape& shape,
-                           tensorflow::gtl::ArraySlice<int64> base,
-                           tensorflow::gtl::ArraySlice<int64> count,
-                           tensorflow::gtl::ArraySlice<int64> incr,
-                           const FnType& visitor_function) {
+  static Status ForEachIndexWithStatus(const Shape& shape,
+                                       tensorflow::gtl::ArraySlice<int64> base,
+                                       tensorflow::gtl::ArraySlice<int64> count,
+                                       tensorflow::gtl::ArraySlice<int64> incr,
+                                       const FnType& visitor_function) {
     if (ShapeUtil::HasZeroElements(shape)) {
-      return;
+      return Status::OK();
     }
     CHECK_EQ(Rank(shape), base.size());
     CHECK_EQ(incr.size(), base.size());
@@ -583,7 +584,11 @@ class ShapeUtil {
     // once with the proper empty indexes.
     int64 n = -1;
     std::vector<int64> indexes(base.begin(), base.end());
-    while (n < rank && visitor_function(indexes)) {
+    while (n < rank) {
+      TF_ASSIGN_OR_RETURN(bool should_continue, visitor_function(indexes));
+      if (!should_continue) {
+        break;
+      }
       // Increments dimensions in minor to major order.
       for (n = 0; n < rank; ++n) {
         int64 dim = LayoutUtil::Minor(shape.layout(), n);
@@ -594,6 +599,21 @@ class ShapeUtil {
         indexes[dim] = base[dim];
       }
     }
+
+    return Status::OK();
+  }
+
+  template <typename FnType>
+  static void ForEachIndex(const Shape& shape,
+                           tensorflow::gtl::ArraySlice<int64> base,
+                           tensorflow::gtl::ArraySlice<int64> count,
+                           tensorflow::gtl::ArraySlice<int64> incr,
+                           const FnType& visitor_function) {
+    ForEachIndexWithStatus(shape, base, count, incr,
+                           [&](tensorflow::gtl::ArraySlice<int64> indices) {
+                             return StatusOr<bool>(visitor_function(indices));
+                           })
+        .IgnoreError();
   }
 
  private:
diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc
index 4db97d45b2..a357415698 100644
--- a/tensorflow/compiler/xla/shape_util_test.cc
+++ b/tensorflow/compiler/xla/shape_util_test.cc
@@ -573,10 +573,11 @@ TEST(ShapeUtilTest, ForEachIndex) {
     Shape shape = ShapeUtil::MakeShape(F32, data.dimensions);
     // Increments at every invocation.
     int invocations = 0;
-    auto increment_func = [&invocations](const std::vector<int64>& indexes) {
-      invocations++;
-      return true;
-    };
+    auto increment_func =
+        [&invocations](tensorflow::gtl::ArraySlice<int64> indexes) {
+          invocations++;
+          return true;
+        };
 
     std::vector<int64> zero_base(data.dimensions.size(), 0);
     std::vector<int64> step(data.dimensions.size(), 1);
@@ -588,6 +589,29 @@ TEST(ShapeUtilTest, ForEachIndex) {
   }
 }
 
+TEST(ShapeUtilTest, ForEachIndexWithStatus) {
+  Shape shape = ShapeUtil::MakeShape(F32, {10, 10});
+  // Increments at every invocation.
+  int invocations = 0;
+  auto increment_func =
+      [&invocations](
+          tensorflow::gtl::ArraySlice<int64> indexes) -> StatusOr<bool> {
+    if (++invocations == 5) {
+      return Unimplemented("Cannot increment beyond 5.");
+    }
+    return true;
+  };
+
+  Status error_status = ShapeUtil::ForEachIndexWithStatus(
+      shape, /*base=*/{0, 0}, /*count=*/{10, 10}, /*incr=*/{0, 1},
+      increment_func);
+
+  EXPECT_FALSE(error_status.ok());
+  EXPECT_THAT(error_status.error_message(),
+              ::testing::HasSubstr("Cannot increment beyond 5."));
+  EXPECT_EQ(invocations, 5);
+}
+
 TEST(ShapeUtilTest, DimensionsUnmodifiedByReshape_1x1x1x1_to_1x1x1) {
   // All output dimensions should be unmodified. One of the input dimensions is
   // modified because the input rank is larger by one.
-- 
GitLab


From 09d9715460bf4d0d0d2229816fe45eb81676a9ca Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Wed, 28 Feb 2018 11:50:17 -0800
Subject: [PATCH 444/575] Disable GRPC io utils test.

PiperOrigin-RevId: 187360410
---
 tensorflow/core/debug/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/core/debug/BUILD b/tensorflow/core/debug/BUILD
index 40cb8353cd..f6fe9edb02 100644
--- a/tensorflow/core/debug/BUILD
+++ b/tensorflow/core/debug/BUILD
@@ -298,6 +298,9 @@ tf_cc_test(
     size = "small",
     srcs = ["debug_grpc_io_utils_test.cc"],
     linkstatic = tf_kernel_tests_linkstatic(),
+    tags = [
+        "no_oss",  # b/73962011
+    ],
     deps = [
         ":debug_graph_utils",
         ":debug_grpc_testlib",
-- 
GitLab


From 31421c3fa3a0585c01198458fa123c3493c21b62 Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu <yuanzx@google.com>
Date: Wed, 28 Feb 2018 12:13:22 -0800
Subject: [PATCH 445/575] [XLA] Fix BF16 normalization to avoid the pass adding
 new unsupported mixed precision.

Resolve unsupported input/output first, then resolve unsupported mixed
precision.

PiperOrigin-RevId: 187363969
---
 .../xla/service/bfloat16_normalization.cc     | 115 ++++++++++--------
 .../service/bfloat16_normalization_test.cc    |  33 ++++-
 2 files changed, 95 insertions(+), 53 deletions(-)

diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization.cc b/tensorflow/compiler/xla/service/bfloat16_normalization.cc
index b032c040e8..6176f5d209 100644
--- a/tensorflow/compiler/xla/service/bfloat16_normalization.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_normalization.cc
@@ -221,41 +221,37 @@ Status BFloat16NormalizationVisitor::HandleCrossReplicaSum(
 }
 
 Status BFloat16NormalizationVisitor::HandleInstruction(HloInstruction* hlo) {
-  std::vector<int64> bf16_operands;
-  std::vector<int64> f32_operands;
-  bool has_f32 = false;
-  bool has_bf16 = false;
+  int f32_count = 0;
+  int bf16_count = 1;
 
   for (int64 i = 0; i < hlo->operand_count(); ++i) {
     if (hlo->operand(i)->shape().element_type() == F32) {
-      f32_operands.push_back(i);
-      has_f32 = true;
+      f32_count += 1;
     } else if (hlo->operand(i)->shape().element_type() == BF16) {
-      bf16_operands.push_back(i);
-      has_bf16 = true;
+      bf16_count += 1;
     }
   }
 
   if (hlo->shape().element_type() == F32) {
-    has_f32 = true;
+    f32_count += 1;
   } else if (hlo->shape().element_type() == BF16) {
-    has_bf16 = true;
+    bf16_count += 1;
   }
 
   std::vector<HloComputation*> bf16_called_comps;
   for (auto* comp : hlo->called_computations()) {
     bool comp_has_bf16 = false;
     if (comp->root_instruction()->shape().element_type() == F32) {
-      has_f32 = true;
+      f32_count += 1;
     } else if (comp->root_instruction()->shape().element_type() == BF16) {
-      has_bf16 = true;
+      bf16_count += 1;
       comp_has_bf16 = true;
     }
     for (auto* param : comp->parameter_instructions()) {
       if (param->shape().element_type() == F32) {
-        has_f32 = true;
+        f32_count += 1;
       } else if (param->shape().element_type() == BF16) {
-        has_bf16 = true;
+        bf16_count += 1;
         comp_has_bf16 = true;
       }
     }
@@ -264,54 +260,69 @@ Status BFloat16NormalizationVisitor::HandleInstruction(HloInstruction* hlo) {
     }
   }
 
-  if (!bfloat16_support_->SupportsMixedPrecisions(*hlo) && has_bf16 &&
-      has_f32) {
-    // Resolve unsupported mixed precision.
-    //
-    // See if we can change everything to BF16.
-    if (hlo->called_computations().empty() &&
-        hlo->shape().element_type() == BF16) {
-      bool can_use_bf16 = true;
-      for (int i : f32_operands) {
-        if (bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo,
-                                                                          i) &&
-            bfloat16_support_->SupportsBF16Operand(*hlo, i)) {
-          continue;
-        }
-        can_use_bf16 = false;
-        break;
-      }
-      if (can_use_bf16) {
-        for (int i : f32_operands) {
-          TF_RETURN_IF_ERROR(
-              InsertConvertBeforeOperand(hlo, i, BF16, computation_));
-        }
-        return Status::OK();
-      }
-    }
-    if (hlo->shape().element_type() == BF16) {
-      TF_RETURN_IF_ERROR(
-          ChangeOutputTypeThenInsertConvertBack(hlo, F32, computation_));
-    }
-    for (int i : bf16_operands) {
-      TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_));
-    }
-    return ConvertCalledComputations(hlo, bf16_called_comps);
-  }
-
-  for (int i : bf16_operands) {
-    if (!bfloat16_support_->SupportsBF16Operand(*hlo, i)) {
+  // Resolve unsupported BF16 operands.
+  for (int i = 0; i < hlo->operand_count(); ++i) {
+    if (hlo->operand(i)->shape().element_type() == BF16 &&
+        !bfloat16_support_->SupportsBF16Operand(*hlo, i)) {
       TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_));
+      bf16_count -= 1;
+      f32_count += 1;
     }
   }
 
+  // Resolve unsupported BF16 output.
   if (hlo->shape().element_type() == BF16 &&
       !bfloat16_support_->SupportsBF16Output(*hlo)) {
     TF_RETURN_IF_ERROR(
         ChangeOutputTypeThenInsertConvertBack(hlo, F32, computation_));
+    bf16_count -= 1;
+    f32_count += 1;
   }
 
-  return Status::OK();
+  // Resolve unsupported mixed precision after resolving unsupported BF16
+  // operands and output, because the numbers of BF16 operands/output and F32
+  // operands/output may have changed.
+  if (bfloat16_support_->SupportsMixedPrecisions(*hlo) || bf16_count == 0 ||
+      f32_count == 0) {
+    return Status::OK();
+  }
+  // See if we can change everything to BF16.
+  if (hlo->called_computations().empty() &&
+      hlo->shape().element_type() == BF16) {
+    bool can_use_bf16 = true;
+    for (int i = 0; i < hlo->operand_count(); ++i) {
+      if (hlo->operand(i)->shape().element_type() == BF16) {
+        continue;
+      }
+      if ((bfloat16_support_->EffectiveOperandPrecisionIsBF16(*hlo, i) ||
+           bfloat16_support_->EffectiveOperandPrecisionIsOutputPrecision(*hlo,
+                                                                         i)) &&
+          bfloat16_support_->SupportsBF16Operand(*hlo, i)) {
+        continue;
+      }
+      can_use_bf16 = false;
+      break;
+    }
+    if (can_use_bf16) {
+      for (int i = 0; i < hlo->operand_count(); ++i) {
+        if (hlo->operand(i)->shape().element_type() == F32) {
+          TF_RETURN_IF_ERROR(
+              InsertConvertBeforeOperand(hlo, i, BF16, computation_));
+        }
+      }
+      return Status::OK();
+    }
+  }
+  if (hlo->shape().element_type() == BF16) {
+    TF_RETURN_IF_ERROR(
+        ChangeOutputTypeThenInsertConvertBack(hlo, F32, computation_));
+  }
+  for (int i = 0; i < hlo->operand_count(); ++i) {
+    if (hlo->operand(i)->shape().element_type() == BF16) {
+      TF_RETURN_IF_ERROR(InsertConvertBeforeOperand(hlo, i, F32, computation_));
+    }
+  }
+  return ConvertCalledComputations(hlo, bf16_called_comps);
 }
 
 Status BFloat16NormalizationVisitor::DefaultAction(HloInstruction* hlo) {
diff --git a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc
index 66c3085842..fc0f6f1948 100644
--- a/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_normalization_test.cc
@@ -41,13 +41,17 @@ class TestBFloat16Support : public BFloat16Support {
         hlo.opcode() == HloOpcode::kGetTupleElement) {
       return true;
     }
+    if (hlo.opcode() == HloOpcode::kDot) {
+      // Test that only the first operand of kDot supports BF16.
+      return operand_index == 0;
+    }
     return false;
   }
 
   bool SupportsBF16Output(const HloInstruction& hlo) const override {
     if (hlo.opcode() == HloOpcode::kAdd || hlo.opcode() == HloOpcode::kReduce ||
         hlo.opcode() == HloOpcode::kSubtract ||
-        hlo.opcode() == HloOpcode::kTuple ||
+        hlo.opcode() == HloOpcode::kDot || hlo.opcode() == HloOpcode::kTuple ||
         hlo.opcode() == HloOpcode::kGetTupleElement) {
       return true;
     }
@@ -245,4 +249,31 @@ TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleCrossReplicaSum) {
   EXPECT_EQ(ShapeUtil::GetSubshape(crs->shape(), {1}).element_type(), F32);
 }
 
+// Tests that the normalization should not cause unsupported mixed precision due
+// to resolving unsupported BF16 operand.
+TEST_F(BFloat16NormalizationTest, DoNotAddUnsupportedMixedPrecision) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape bf16_shape = ShapeUtil::MakeShape(BF16, {4, 4});
+
+  HloInstruction* a = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, bf16_shape, "a"));
+  HloInstruction* b = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, bf16_shape, "b"));
+
+  HloInstruction* dot = builder.AddInstruction(
+      HloInstruction::CreateBinary(bf16_shape, HloOpcode::kDot, a, b));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(Normalize(module.get()));
+
+  EXPECT_EQ(computation->root_instruction()->opcode(), HloOpcode::kConvert);
+  EXPECT_EQ(dot->shape().element_type(), F32);
+  EXPECT_EQ(dot->operand(0)->shape().element_type(), F32);
+  EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConvert);
+  EXPECT_EQ(dot->operand(1)->shape().element_type(), F32);
+  EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConvert);
+}
+
 }  // namespace xla
-- 
GitLab


From 3dbbf740441cdd41b2dc998e09980d72d2e9d440 Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Wed, 28 Feb 2018 12:14:03 -0800
Subject: [PATCH 446/575] In Grappler item builder, support inferring fetch
 nodes from siganture defs.

PiperOrigin-RevId: 187364078
---
 .../core/grappler/grappler_item_builder.cc    | 76 ++++++++++++++++---
 .../grappler/grappler_item_builder_test.cc    | 53 +++++++++++++
 2 files changed, 117 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/grappler/grappler_item_builder.cc b/tensorflow/core/grappler/grappler_item_builder.cc
index 606807b9e9..33ad426bbf 100644
--- a/tensorflow/core/grappler/grappler_item_builder.cc
+++ b/tensorflow/core/grappler/grappler_item_builder.cc
@@ -168,12 +168,6 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
   // Fill in feed nodes from config, if any provided.
   for (const auto& feed_node : cfg.feed_nodes) {
     const string feed_name = NodeName(feed_node);
-    if (feed_name.empty()) {
-      LOG(ERROR) << "Invalid feed node name " << feed_node
-                 << ", skipping this input.";
-      return nullptr;
-    }
-    VLOG(1) << "Will use feed node " << feed_name;
     new_item->feed.emplace_back(feed_name, Tensor());
   }
 
@@ -182,17 +176,75 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
     const CollectionDef& nodes = meta_graph.collection_def().at("train_op");
     if (nodes.has_node_list()) {
       for (const auto& node : nodes.node_list().value()) {
-        const string name = NodeName(node);
-        if (name.empty()) {
-          LOG(ERROR) << "Invalid fetch node name " << node
-                     << ", skipping this input";
+        new_item->fetch.push_back(NodeName(node));
+      }
+    }
+  }
+
+  // Detect feed and fetch nodes from signature defs.
+  for (const auto& name_and_signature : meta_graph.signature_def()) {
+    for (const auto& name_and_input : name_and_signature.second.inputs()) {
+      const TensorInfo& input = name_and_input.second;
+      if (input.has_coo_sparse()) {
+        // Define the shapes following the comment of CooSparse.
+        PartialTensorShape partial_shape_1d({-1});
+        PartialTensorShape partial_shape_2d({-1, -1});
+        TensorShape shape_1d;
+        TensorShape shape_2d;
+        if (!partial_shape_1d.AsTensorShape(&shape_1d) ||
+            !partial_shape_2d.AsTensorShape(&shape_2d)) {
+          LOG(ERROR) << "Internal error when constructing tensor shapes.";
           return nullptr;
         }
-        VLOG(1) << "Will use fetch node " << name;
-        new_item->fetch.push_back(name);
+
+        new_item->feed.emplace_back(
+            NodeName(input.coo_sparse().values_tensor_name()),
+            Tensor(input.dtype(), shape_1d));
+        new_item->feed.emplace_back(
+            NodeName(input.coo_sparse().indices_tensor_name()),
+            Tensor(DT_INT64, shape_2d));
+        new_item->feed.emplace_back(
+            NodeName(input.coo_sparse().dense_shape_tensor_name()),
+            Tensor(DT_INT64, shape_1d));
+      } else {
+        new_item->feed.emplace_back(
+            NodeName(input.name()),
+            Tensor(input.dtype(), input.tensor_shape()));
       }
     }
+    for (const auto& name_and_output : name_and_signature.second.outputs()) {
+      const TensorInfo& output = name_and_output.second;
+      if (output.has_coo_sparse()) {
+        new_item->fetch.push_back(
+            NodeName(output.coo_sparse().values_tensor_name()));
+        new_item->fetch.push_back(
+            NodeName(output.coo_sparse().indices_tensor_name()));
+        new_item->fetch.push_back(
+            NodeName(output.coo_sparse().dense_shape_tensor_name()));
+      } else {
+        new_item->fetch.push_back(NodeName(output.name()));
+      }
+    }
+  }
+
+  for (const auto& feed : new_item->feed) {
+    if (feed.first.empty()) {
+      LOG(ERROR) << "Invalid feed node name skipping this input";
+      return nullptr;
+    } else {
+      VLOG(1) << "Will use feed node " << feed.first;
+    }
+  }
+
+  for (const auto& fetch : new_item->fetch) {
+    if (fetch.empty()) {
+      LOG(ERROR) << "Invalid fetch node name skipping this input";
+      return nullptr;
+    } else {
+      VLOG(1) << "Will use fetch node " << fetch;
+    }
   }
+
   if (new_item->fetch.empty()) {
     LOG(ERROR) << "Failed to detect the fetch node(s), skipping this input";
     return nullptr;
diff --git a/tensorflow/core/grappler/grappler_item_builder_test.cc b/tensorflow/core/grappler/grappler_item_builder_test.cc
index ef95992af7..78cbff6c90 100644
--- a/tensorflow/core/grappler/grappler_item_builder_test.cc
+++ b/tensorflow/core/grappler/grappler_item_builder_test.cc
@@ -280,6 +280,59 @@ TEST_F(GrapplerItemBuilderTest, GraphWithFunctions) {
   ASSERT_TRUE(item != nullptr);
 }
 
+TEST_F(GrapplerItemBuilderTest, FromGraphWithSignatureDef) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  auto x = ops::Const(s.WithOpName("x"), 0);
+  auto y = ops::Const(s.WithOpName("y"), 1);
+  auto z = ops::Add(s.WithOpName("z"), x, y);
+
+  MetaGraphDef meta_graph;
+  TF_CHECK_OK(s.ToGraphDef(meta_graph.mutable_graph_def()));
+
+  TensorInfo input, output;
+  input.set_name("x");
+  input.set_dtype(DT_FLOAT);
+  output.set_name("z");
+  SignatureDef serving_signature;
+  (*serving_signature.mutable_inputs())["input"] = input;
+  (*serving_signature.mutable_outputs())["output"] = output;
+  (*meta_graph.mutable_signature_def())["serving"] = serving_signature;
+
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromMetaGraphDef("0", meta_graph, ItemConfig());
+  ASSERT_TRUE(item != nullptr);
+
+  EXPECT_EQ(item->feed[0].first, "x");
+  EXPECT_EQ(item->fetch[0], "z");
+}
+
+TEST_F(GrapplerItemBuilderTest, FromGraphWithIncompleteSignatureDef) {
+  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
+  auto x = ops::Const(s.WithOpName("x"), 0);
+  auto y = ops::Const(s.WithOpName("y"), 1);
+
+  MetaGraphDef meta_graph;
+  TF_CHECK_OK(s.ToGraphDef(meta_graph.mutable_graph_def()));
+
+  CollectionDef train_op;
+  train_op.mutable_node_list()->add_value("y");
+  (*meta_graph.mutable_collection_def())["train_op"] = train_op;
+
+  TensorInfo input, output;
+  input.set_name("x");
+  input.set_dtype(DT_FLOAT);
+  // Its coo_sparse proto is incomplete.
+  output.mutable_coo_sparse()->set_values_tensor_name("z");
+  SignatureDef serving_signature;
+  (*serving_signature.mutable_inputs())["input"] = input;
+  (*serving_signature.mutable_outputs())["output"] = output;
+  (*meta_graph.mutable_signature_def())["serving"] = serving_signature;
+
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromMetaGraphDef("0", meta_graph, ItemConfig());
+  ASSERT_TRUE(item == nullptr);
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From b07680459a88224fce83daa7b3b70bcc62b9c896 Mon Sep 17 00:00:00 2001
From: Loo Rong Jie <loorongjie@gmail.com>
Date: Thu, 1 Mar 2018 04:38:30 +0800
Subject: [PATCH 447/575] [Windows] Copy NominalCPUFrequency from Abseil
 (#16905)

* [Windows] Copy NominalCPUFrequency from Abseil

* Add #include <shlwapi.h>
---
 tensorflow/core/platform/windows/port.cc | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/platform/windows/port.cc b/tensorflow/core/platform/windows/port.cc
index 582b232054..f3b27ea394 100644
--- a/tensorflow/core/platform/windows/port.cc
+++ b/tensorflow/core/platform/windows/port.cc
@@ -25,6 +25,7 @@ limitations under the License.
 #endif
 
 #include <Windows.h>
+#include <shlwapi.h>
 
 #include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/demangle.h"
@@ -149,11 +150,16 @@ bool Snappy_Uncompress(const char* input, size_t length, char* output) {
 string Demangle(const char* mangled) { return mangled; }
 
 double NominalCPUFrequency() {
-#ifdef TENSORFLOW_USE_ABSL
-  return absl::base_internal::NominalCPUFrequency();
-#else
+  DWORD data;
+  DWORD data_size = sizeof(data);
+  #pragma comment(lib, "shlwapi.lib")  // For SHGetValue().
+  if (SUCCEEDED(
+          SHGetValueA(HKEY_LOCAL_MACHINE,
+                      "HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0",
+                      "~MHz", nullptr, &data, &data_size))) {
+    return data * 1e6;  // Value is MHz.
+  }
   return 1.0;
-#endif
 }
 
 int64 AvailableRam() {
-- 
GitLab


From 8a31fec675f3f1ade28a9a8f38cc8f72d9573256 Mon Sep 17 00:00:00 2001
From: Bixia Zheng <bixia@google.com>
Date: Wed, 28 Feb 2018 12:55:34 -0800
Subject: [PATCH 448/575] [XLA] FP16 Dot support for the CPU and GPU backends.

Extend the stream interface ThenBlasGemmWithAlgorithm to support F16 matrix
multiplication with computation type FP32.

Extend the stream executor interface DoBlasGemmWithAlgorithm to support F16
GEMM with computation type FP32.

Extend the CPU IR emitter to handle F16 Dot instruction, and add F16 matrix
multiplication implementation to the CPU runtime.

Extend the GPU backend to handle FP16 GEMM Thunk.

Replicate the existing matrix multiplication test cases in
matrix_ops_simple_test and dot_operation_test for FP16.

RELNOTES:
PiperOrigin-RevId: 187369731
---
 tensorflow/compiler/xla/array.h               |  45 +-
 tensorflow/compiler/xla/array2d.h             |  17 +-
 tensorflow/compiler/xla/array3d.h             |   9 +-
 tensorflow/compiler/xla/array4d.h             |   9 +-
 tensorflow/compiler/xla/reference_util.cc     |  56 +-
 tensorflow/compiler/xla/reference_util.h      |  16 +-
 tensorflow/compiler/xla/service/cpu/BUILD     |   1 -
 .../compiler/xla/service/cpu/cpu_runtime.cc   |   4 +
 .../compiler/xla/service/cpu/cpu_runtime.h    |   2 +
 .../xla/service/cpu/dot_op_emitter.cc         |   9 +-
 .../compiler/xla/service/cpu/ir_emitter.cc    |   2 +-
 .../xla/service/cpu/runtime_matmul.cc         |  39 +-
 .../compiler/xla/service/cpu/runtime_matmul.h |   6 +
 .../xla/service/cpu/runtime_matvec.cc         | 110 ---
 .../compiler/xla/service/cpu/runtime_matvec.h |  94 ++-
 .../cpu/runtime_single_threaded_matmul.cc     |  36 +-
 .../cpu/runtime_single_threaded_matmul.h      |   6 +
 .../xla/service/cpu/simple_orc_jit.cc         |   2 +
 .../compiler/xla/service/gpu/gemm_thunk.cc    |  25 +-
 .../xla/service/gpu/ir_emission_utils.cc      |   6 +-
 tensorflow/compiler/xla/shape_util.h          |   9 +
 tensorflow/compiler/xla/tests/BUILD           |   1 +
 .../compiler/xla/tests/convolution_test.cc    |  52 +-
 .../compiler/xla/tests/dot_operation_test.cc  | 673 +++++++++---------
 .../xla/tests/matrix_ops_simple_test.cc       | 375 +++++-----
 tensorflow/stream_executor/blas.cc            |   6 +
 tensorflow/stream_executor/blas.h             |   2 +
 tensorflow/stream_executor/cuda/cuda_blas.cc  |  47 +-
 28 files changed, 868 insertions(+), 791 deletions(-)
 delete mode 100644 tensorflow/compiler/xla/service/cpu/runtime_matvec.cc

diff --git a/tensorflow/compiler/xla/array.h b/tensorflow/compiler/xla/array.h
index 46ee4e64c9..24b58bec11 100644
--- a/tensorflow/compiler/xla/array.h
+++ b/tensorflow/compiler/xla/array.h
@@ -121,10 +121,31 @@ class Array {
     CHECK(idx == num_elements());
   }
 
-  // Creates a 2D array of Eigen::half from the given nested initializer list of
-  // float values.
+  // Creates a 1D array of a floating-point type (half, bfloat16, float,
+  // or double) from an initializer list of float values.
   template <typename T2, typename = typename std::enable_if<
-                             std::is_same<T, Eigen::half>::value &&
+                             (std::is_same<T, Eigen::half>::value ||
+                              std::is_same<T, bfloat16>::value ||
+                              std::is_same<T, float>::value ||
+                              std::is_same<T, double>::value) &&
+                             std::is_same<T2, float>::value>::type>
+  Array(std::initializer_list<T2> values)
+      : Array(ToInt64Vector({values.size()})) {
+    int64 idx = 0;
+    for (const auto& it1 : values) {
+      values_[idx] = static_cast<T>(it1);
+      ++idx;
+    }
+    CHECK(idx == num_elements());
+  }
+
+  // Creates a 2D array of a floating-point type (half, bfloat16, float,
+  // or double) from an initializer list of float values.
+  template <typename T2, typename = typename std::enable_if<
+                             (std::is_same<T, Eigen::half>::value ||
+                              std::is_same<T, bfloat16>::value ||
+                              std::is_same<T, float>::value ||
+                              std::is_same<T, double>::value) &&
                              std::is_same<T2, float>::value>::type>
   Array(std::initializer_list<std::initializer_list<T2>> values)
       : Array(ToInt64Vector({values.size(), values.begin()->size()})) {
@@ -155,10 +176,13 @@ class Array {
     CHECK(idx == num_elements());
   }
 
-  // Creates a 3D array of Eigen::half from the given nested initializer list of
-  // float values.
+  // Creates a 3D array of a floating-point type (half, bfloat16, float,
+  // or double) from an initializer list of float values.
   template <typename T2, typename = typename std::enable_if<
-                             std::is_same<T, Eigen::half>::value &&
+                             (std::is_same<T, Eigen::half>::value ||
+                              std::is_same<T, bfloat16>::value ||
+                              std::is_same<T, float>::value ||
+                              std::is_same<T, double>::value) &&
                              std::is_same<T2, float>::value>::type>
   Array(std::initializer_list<std::initializer_list<std::initializer_list<T2>>>
             values)
@@ -196,10 +220,13 @@ class Array {
     CHECK(idx == num_elements());
   }
 
-  // Creates a 4D array of Eigen::half from the given nested initializer list of
-  // float values.
+  // Creates a 4D array of a floating-point type (half, bfloat16, float,
+  // or double) from an initializer list of float values.
   template <typename T2, typename = typename std::enable_if<
-                             std::is_same<T, Eigen::half>::value &&
+                             (std::is_same<T, Eigen::half>::value ||
+                              std::is_same<T, bfloat16>::value ||
+                              std::is_same<T, float>::value ||
+                              std::is_same<T, double>::value) &&
                              std::is_same<T2, float>::value>::type>
   Array(std::initializer_list<
         std::initializer_list<std::initializer_list<std::initializer_list<T2>>>>
diff --git a/tensorflow/compiler/xla/array2d.h b/tensorflow/compiler/xla/array2d.h
index d30e78ecde..a17e81f448 100644
--- a/tensorflow/compiler/xla/array2d.h
+++ b/tensorflow/compiler/xla/array2d.h
@@ -53,10 +53,13 @@ class Array2D : public Array<T> {
   Array2D(std::initializer_list<std::initializer_list<T>> values)
       : Array<T>(values) {}
 
-  // Creates an array of Eigen::half from the given nested initializer list of
-  // float values.
+  // Creates an array of a floating-point type (half, bfloat16, float,
+  // or double) from the given nested initializer list of float values.
   template <typename T2, typename = typename std::enable_if<
-                             std::is_same<T, Eigen::half>::value &&
+                             (std::is_same<T, Eigen::half>::value ||
+                              std::is_same<T, bfloat16>::value ||
+                              std::is_same<T, float>::value ||
+                              std::is_same<T, double>::value) &&
                              std::is_same<T2, float>::value>::type>
   Array2D(std::initializer_list<std::initializer_list<T2>> values)
       : Array<T>(values) {}
@@ -100,14 +103,16 @@ std::unique_ptr<Array2D<NativeT>> MakeLinspaceArray2D(double from, double to,
                                                       int64 n1, int64 n2) {
   auto array = MakeUnique<Array2D<NativeT>>(n1, n2);
   int64 count = n1 * n2;
-  NativeT step = (count > 1) ? (to - from) / (count - 1) : 0.0f;
+  NativeT step =
+      static_cast<NativeT>((count > 1) ? (to - from) / (count - 1) : 0);
   auto set = [&array, n1, n2](int64 index, NativeT value) {
     (*array)(index / n2, index % n2) = value;
   };
   for (int64 i = 0; i < count - 1; ++i) {
-    set(i, static_cast<NativeT>(from + i * step));
+    set(i, (static_cast<NativeT>(from) +
+            static_cast<NativeT>(i) * static_cast<NativeT>(step)));
   }
-  set(count - 1, to);
+  set(count - 1, static_cast<NativeT>(to));
   return array;
 }
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/array3d.h b/tensorflow/compiler/xla/array3d.h
index e5eb235d45..0e9a0722ae 100644
--- a/tensorflow/compiler/xla/array3d.h
+++ b/tensorflow/compiler/xla/array3d.h
@@ -57,10 +57,13 @@ class Array3D : public Array<T> {
               values)
       : Array<T>(values) {}
 
-  // Creates an array of Eigen::half from the given nested initializer list of
-  // float values.
+  // Creates an array of a floating-point type (half, bfloat16, float,
+  // or double) from the given nested initializer list of float values.
   template <typename T2, typename = typename std::enable_if<
-                             std::is_same<T, Eigen::half>::value &&
+                             (std::is_same<T, Eigen::half>::value ||
+                              std::is_same<T, bfloat16>::value ||
+                              std::is_same<T, float>::value ||
+                              std::is_same<T, double>::value) &&
                              std::is_same<T2, float>::value>::type>
   Array3D(
       std::initializer_list<std::initializer_list<std::initializer_list<T2>>>
diff --git a/tensorflow/compiler/xla/array4d.h b/tensorflow/compiler/xla/array4d.h
index cff70e54ba..a75fffc605 100644
--- a/tensorflow/compiler/xla/array4d.h
+++ b/tensorflow/compiler/xla/array4d.h
@@ -82,10 +82,13 @@ class Array4D : public Array<T> {
               values)
       : Array<T>(values) {}
 
-  // Creates an array of Eigen::half from the given nested initializer list of
-  // float values.
+  // Creates an array of a floating-point type (half, bfloat16, float,
+  // or double) from the given nested initializer list of float values.
   template <typename T2, typename = typename std::enable_if<
-                             std::is_same<T, Eigen::half>::value &&
+                             (std::is_same<T, Eigen::half>::value ||
+                              std::is_same<T, bfloat16>::value ||
+                              std::is_same<T, float>::value ||
+                              std::is_same<T, double>::value) &&
                              std::is_same<T2, float>::value>::type>
   Array4D(std::initializer_list<std::initializer_list<
               std::initializer_list<std::initializer_list<T2>>>>
diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc
index a9acdae380..8711b8aa2e 100644
--- a/tensorflow/compiler/xla/reference_util.cc
+++ b/tensorflow/compiler/xla/reference_util.cc
@@ -30,29 +30,23 @@ limitations under the License.
 
 namespace xla {
 
-/* static */ std::unique_ptr<Array2D<float>> ReferenceUtil::TransposeArray2D(
-    const Array2D<float>& operand) {
-  auto result = MakeUnique<Array2D<float>>(operand.width(), operand.height());
-  for (int64 w = 0; w < operand.width(); ++w) {
-    for (int64 h = 0; h < operand.height(); ++h) {
-      (*result)(w, h) = operand(h, w);
-    }
-  }
-
-  return result;
-}
-
-/* static */ std::unique_ptr<Array2D<float>> ReferenceUtil::MatmulArray2D(
-    const Array2D<float>& lhs, const Array2D<float>& rhs) {
+namespace {
+
+template <typename T>
+std::unique_ptr<Array2D<T>> MatmulArray2DImpl(
+    const Array2D<T>& lhs, const Array2D<T>& rhs,
+    const std::function<void(
+        const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m, int64 n,
+        int64 k, int32 transpose_lhs, int32 transpose_rhs)>& impl_fn) {
   CHECK_EQ(lhs.width(), rhs.height());
   int m = lhs.height();
   int n = rhs.width();
   int k = lhs.width();
-  auto result = MakeUnique<Array2D<float>>(m, n);
+  auto result = MakeUnique<Array2D<T>>(m, n);
   // Because Eigen is a header-oriented library, make sure that the Eigen code
   // is the same as the code used by the CPU backend (otherwise the linker will
   // randomly pick *some* definition).
-  __xla_cpu_runtime_EigenSingleThreadedMatMulF32(
+  impl_fn(
       /*run_options_ptr=*/nullptr, result->data(), rhs.data(), lhs.data(), n, m,
       k,
       /*transpose_lhs=*/0,
@@ -60,22 +54,24 @@ namespace xla {
   return result;
 }
 
+}  // namespace
+
+/* static */ std::unique_ptr<Array2D<Eigen::half>> ReferenceUtil::MatmulArray2D(
+    const Array2D<Eigen::half>& lhs, const Array2D<Eigen::half>& rhs) {
+  return MatmulArray2DImpl<Eigen::half>(
+      lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF16);
+}
+
+/* static */ std::unique_ptr<Array2D<float>> ReferenceUtil::MatmulArray2D(
+    const Array2D<float>& lhs, const Array2D<float>& rhs) {
+  return MatmulArray2DImpl<float>(
+      lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF32);
+}
+
 /* static */ std::unique_ptr<Array2D<double>> ReferenceUtil::MatmulArray2D(
     const Array2D<double>& lhs, const Array2D<double>& rhs) {
-  CHECK_EQ(lhs.width(), rhs.height());
-  int m = lhs.height();
-  int n = rhs.width();
-  int k = lhs.width();
-  auto result = MakeUnique<Array2D<double>>(m, n);
-  // Because Eigen is a header-oriented library, make sure that the Eigen code
-  // is the same as the code used by the CPU backend (otherwise the linker will
-  // randomly pick *some* definition).
-  __xla_cpu_runtime_EigenSingleThreadedMatMulF64(
-      /*run_options_ptr=*/nullptr, result->data(), rhs.data(), lhs.data(), n, m,
-      k,
-      /*transpose_lhs=*/0,
-      /*transpose_rhs=*/0);
-  return result;
+  return MatmulArray2DImpl<double>(
+      lhs, rhs, __xla_cpu_runtime_EigenSingleThreadedMatMulF64);
 }
 
 /* static */ std::unique_ptr<Array2D<double>> ReferenceUtil::Array2DF32ToF64(
diff --git a/tensorflow/compiler/xla/reference_util.h b/tensorflow/compiler/xla/reference_util.h
index 3ec96f2f38..57b0218882 100644
--- a/tensorflow/compiler/xla/reference_util.h
+++ b/tensorflow/compiler/xla/reference_util.h
@@ -39,10 +39,22 @@ namespace xla {
 class ReferenceUtil {
  public:
   // Returns the result of a transpose operation on the input matrix.
-  static std::unique_ptr<Array2D<float>> TransposeArray2D(
-      const Array2D<float>& operand);
+  template <typename T>
+  static std::unique_ptr<Array2D<T>> TransposeArray2D(
+      const Array2D<T>& operand) {
+    auto result = MakeUnique<Array2D<T>>(operand.width(), operand.height());
+    for (int64 w = 0; w < operand.width(); ++w) {
+      for (int64 h = 0; h < operand.height(); ++h) {
+        (*result)(w, h) = operand(h, w);
+      }
+    }
+
+    return result;
+  }
 
   // Returns the result of a matrix multiply `lhs x rhs`.
+  static std::unique_ptr<Array2D<Eigen::half>> MatmulArray2D(
+      const Array2D<Eigen::half>& lhs, const Array2D<Eigen::half>& rhs);
   static std::unique_ptr<Array2D<float>> MatmulArray2D(
       const Array2D<float>& lhs, const Array2D<float>& rhs);
   static std::unique_ptr<Array2D<double>> MatmulArray2D(
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 32be0b0c96..4170e31527 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -514,7 +514,6 @@ cc_library(
 
 cc_library(
     name = "runtime_matvec",
-    srcs = ["runtime_matvec.cc"],
     hdrs = ["runtime_matvec.h"],
     copts = runtime_copts(),
     deps = [
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
index 40ace96327..9a3bd68c80 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.cc
@@ -31,6 +31,8 @@ XfeedManager* GetXfeedManager() {
   return manager;
 }
 
+extern const char* const kEigenMatMulF16SymbolName =
+    "__xla_cpu_runtime_EigenMatMulF16";
 extern const char* const kEigenMatMulF32SymbolName =
     "__xla_cpu_runtime_EigenMatMulF32";
 extern const char* const kEigenMatMulF64SymbolName =
@@ -40,6 +42,8 @@ extern const char* const kEigenConvF16SymbolName =
 extern const char* const kEigenConvF32SymbolName =
     "__xla_cpu_runtime_EigenConvF32";
 extern const char* const kEigenFftSymbolName = "__xla_cpu_runtime_EigenFft";
+extern const char* const kEigenSingleThreadedMatMulF16SymbolName =
+    "__xla_cpu_runtime_EigenSingleThreadedMatMulF16";
 extern const char* const kEigenSingleThreadedMatMulF32SymbolName =
     "__xla_cpu_runtime_EigenSingleThreadedMatMulF32";
 extern const char* const kEigenSingleThreadedMatMulF64SymbolName =
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
index 2141dfe1ce..e61d6ea28b 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
+++ b/tensorflow/compiler/xla/service/cpu/cpu_runtime.h
@@ -41,11 +41,13 @@ namespace runtime {
 //    the actual symbol.
 // 2. When using ahead-of-time compilation, the linker can resolve the name
 //    because it is a symbol in the cpu_runtime library.
+extern const char* const kEigenMatMulF16SymbolName;
 extern const char* const kEigenMatMulF32SymbolName;
 extern const char* const kEigenMatMulF64SymbolName;
 extern const char* const kEigenConvF16SymbolName;
 extern const char* const kEigenConvF32SymbolName;
 extern const char* const kEigenFftSymbolName;
+extern const char* const kEigenSingleThreadedMatMulF16SymbolName;
 extern const char* const kEigenSingleThreadedMatMulF32SymbolName;
 extern const char* const kEigenSingleThreadedMatMulF64SymbolName;
 extern const char* const kEigenSingleThreadedConvF16SymbolName;
diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
index cfe7c9c3af..6f06256e08 100644
--- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc
@@ -919,6 +919,12 @@ tensorflow::Status DotOpEmitter::EmitCallToRuntime() {
   llvm::Type* float_type;
   const char* fn_name;
   switch (type) {
+    case F16:
+      fn_name = multi_threaded_eigen
+                    ? runtime::kEigenMatMulF16SymbolName
+                    : runtime::kEigenSingleThreadedMatMulF16SymbolName;
+      float_type = ir_builder_->getHalfTy();
+      break;
     case F32:
       fn_name = multi_threaded_eigen
                     ? runtime::kEigenMatMulF32SymbolName
@@ -1051,7 +1057,8 @@ static bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape,
   // The inputs and the output must
   // 1) be matrices with no padding, and
   // 2) have an allowed element type.
-  return output_shape.element_type() == F32 &&
+  PrimitiveType output_primitive_type = output_shape.element_type();
+  return (output_primitive_type == F32 || output_primitive_type == F16) &&
          IsRank2WithNoPadding(lhs_shape) && IsRank2WithNoPadding(rhs_shape) &&
          IsRank2WithNoPadding(output_shape);
 }
diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
index 4dffaee87f..3b8056d505 100644
--- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc
@@ -2074,7 +2074,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) {
 
     TF_RETURN_IF_ERROR(ElementTypesSameAndSupported(
         /*instruction=*/*root, /*operands=*/{lhs, rhs},
-        /*supported_types=*/{F32}));
+        /*supported_types=*/{F16, F32}));
 
     llvm_ir::IrArray lhs_array(GetIrArrayFor(lhs));
     llvm_ir::IrArray rhs_array(GetIrArrayFor(rhs));
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
index bff57d33ae..39b13183ff 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul.cc
@@ -63,30 +63,41 @@ void MatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m,
   C.device(*run_options->intra_op_thread_pool()) = A.contract(B, dims);
 }
 
+template <typename T>
+void MatMulImpl(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m,
+                int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) {
+  if (m == 1 || n == 1) {
+    // Despite being single threaded, this version of matrix * vector is faster.
+    xla::EigenMatVec<T>(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
+  } else {
+    MatMul<T>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
+              transpose_rhs);
+  }
+}
+
 }  // namespace
 
+void __xla_cpu_runtime_EigenMatMulF16(const void* run_options_ptr,
+                                      Eigen::half* out, Eigen::half* lhs,
+                                      Eigen::half* rhs, int64 m, int64 n,
+                                      int64 k, int32 transpose_lhs,
+                                      int32 transpose_rhs) {
+  MatMulImpl<Eigen::half>(run_options_ptr, out, lhs, rhs, m, n, k,
+                          transpose_lhs, transpose_rhs);
+}
+
 void __xla_cpu_runtime_EigenMatMulF32(const void* run_options_ptr, float* out,
                                       float* lhs, float* rhs, int64 m, int64 n,
                                       int64 k, int32 transpose_lhs,
                                       int32 transpose_rhs) {
-  if (m == 1 || n == 1) {
-    // Despite being single threaded, this version of matrix * vector is faster.
-    xla::EigenMatVecF32(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
-  } else {
-    MatMul<float>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
-                  transpose_rhs);
-  }
+  MatMulImpl<float>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
+                    transpose_rhs);
 }
 
 void __xla_cpu_runtime_EigenMatMulF64(const void* run_options_ptr, double* out,
                                       double* lhs, double* rhs, int64 m,
                                       int64 n, int64 k, int32 transpose_lhs,
                                       int32 transpose_rhs) {
-  if (m == 1 || n == 1) {
-    // Despite being single threaded, this version of matrix * vector is faster.
-    xla::EigenMatVecF64(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
-  } else {
-    MatMul<double>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
-                   transpose_rhs);
-  }
+  MatMulImpl<double>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
+                     transpose_rhs);
 }
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul.h b/tensorflow/compiler/xla/service/cpu/runtime_matmul.h
index fdb644651d..b5156434f6 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_matmul.h
+++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul.h
@@ -25,6 +25,12 @@ extern "C" {
 // order. 'out' is a pointer to a buffer sufficiently large to hold the result
 // of the operation. Following standard nomenclature: lhs is m x k,
 // rhs is k x n, and out is m x n.
+extern void __xla_cpu_runtime_EigenMatMulF16(
+    const void* /* xla::ExecutableRunOptions* */ run_options_ptr,
+    Eigen::half* out, Eigen::half* lhs, Eigen::half* rhs, tensorflow::int64 m,
+    tensorflow::int64 n, tensorflow::int64 k, tensorflow::int32 transpose_lhs,
+    tensorflow::int32 transpose_rhs);
+
 extern void __xla_cpu_runtime_EigenMatMulF32(
     const void* /* xla::ExecutableRunOptions* */ run_options_ptr, float* out,
     float* lhs, float* rhs, tensorflow::int64 m, tensorflow::int64 n,
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc b/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc
deleted file mode 100644
index 435820cdd3..0000000000
--- a/tensorflow/compiler/xla/service/cpu/runtime_matvec.cc
+++ /dev/null
@@ -1,110 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include <algorithm>
-#include <cassert>
-
-#include "third_party/eigen3/Eigen/Core"
-#include "tensorflow/compiler/xla/service/cpu/runtime_matvec.h"
-
-using tensorflow::int32;
-using tensorflow::int64;
-
-namespace {
-
-// Does mat * x or mat^T * x.
-template <typename T>
-void MatVec(T* out_buf, T* mat_buf, T* x_buf, int64 rows, int64 cols,
-            int32 transpose) {
-  // Use an Eigen Matrix instead of a Tensor, as the GEMV from Matrix seems to
-  // be faster (b/30223679).  See also: the matmul op kernel in TensorFlow,
-  // which implements the same optimization.
-  using Matrix = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>;
-  using MatrixMap = Eigen::Map<Matrix>;
-
-  using Vector = Eigen::Matrix<T, Eigen::Dynamic, 1>;
-  using VectorMap = Eigen::Map<Vector>;
-
-  auto x = VectorMap(x_buf, cols);
-  auto out = VectorMap(out_buf, rows);
-
-  int64 mat_rows = rows;
-  int64 mat_cols = cols;
-
-  if (transpose) {
-    std::swap(mat_rows, mat_cols);
-  }
-
-  auto mat = MatrixMap(mat_buf, mat_rows, mat_cols);
-
-  if (transpose) {
-    out = mat.transpose() * x;
-  } else {
-    out = mat * x;
-  }
-}
-
-// Converts matmul-style args to matvec.
-template <typename T>
-void DispatchMatVec(T* out, T* lhs, T* rhs, int64 m, int64 n, int64 k,
-                    int32 transpose_lhs, int32 transpose_rhs) {
-  // If the input is in the form x * A, where x is the vector, then bring A back
-  // over to the left hand side.  We make use of the identity
-  //
-  //   (x * A)^T = A^T * x^T
-  //
-  // We do not need to take the transpose of x or of the result since taking
-  // the transpose of a vector does not change the memory layout.
-  const int64 cols = k;
-
-  T* mat;
-  T* vec;
-  int64 rows;
-  bool transpose_mat;
-
-  bool is_mat_vec = (n == 1);
-
-  if (is_mat_vec) {
-    mat = lhs;
-    vec = rhs;
-    rows = m;
-    transpose_mat = transpose_lhs;
-  } else {
-    mat = rhs;
-    vec = lhs;
-    rows = n;
-    transpose_mat = !transpose_rhs;
-  }
-
-  MatVec<T>(out, mat, vec, rows, cols, transpose_mat);
-}
-
-}  // namespace
-
-namespace xla {
-
-void EigenMatVecF32(float* out, float* lhs, float* rhs, int64 m, int64 n,
-                    int64 k, int32 transpose_lhs, int32 transpose_rhs) {
-  assert((m == 1 || n == 1) && "not a matrix-vector multiply");
-  DispatchMatVec<float>(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
-}
-
-void EigenMatVecF64(double* out, double* lhs, double* rhs, int64 m, int64 n,
-                    int64 k, int32 transpose_lhs, int32 transpose_rhs) {
-  assert((m == 1 || n == 1) && "not a matrix-vector multiply");
-  DispatchMatVec<double>(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
-}
-
-}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matvec.h b/tensorflow/compiler/xla/service/cpu/runtime_matvec.h
index 1bd8dfb377..70eb98c541 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_matvec.h
+++ b/tensorflow/compiler/xla/service/cpu/runtime_matvec.h
@@ -16,10 +16,86 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_MATVEC_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_RUNTIME_MATVEC_H_
 
+#include "third_party/eigen3/Eigen/Core"
+
 #include "tensorflow/core/platform/types.h"
 
 namespace xla {
 
+namespace detail {
+
+using tensorflow::int32;
+using tensorflow::int64;
+
+// Does mat * x or mat^T * x.
+template <typename T>
+void MatVec(T* out_buf, T* mat_buf, T* x_buf, int64 rows, int64 cols,
+            int32 transpose) {
+  // Use an Eigen Matrix instead of a Tensor, as the GEMV from Matrix seems to
+  // be faster (b/30223679).  See also: the matmul op kernel in TensorFlow,
+  // which implements the same optimization.
+  using Matrix = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>;
+  using MatrixMap = Eigen::Map<Matrix>;
+
+  using Vector = Eigen::Matrix<T, Eigen::Dynamic, 1>;
+  using VectorMap = Eigen::Map<Vector>;
+
+  auto x = VectorMap(x_buf, cols);
+  auto out = VectorMap(out_buf, rows);
+
+  int64 mat_rows = rows;
+  int64 mat_cols = cols;
+
+  if (transpose) {
+    std::swap(mat_rows, mat_cols);
+  }
+
+  auto mat = MatrixMap(mat_buf, mat_rows, mat_cols);
+
+  if (transpose) {
+    out = mat.transpose() * x;
+  } else {
+    out = mat * x;
+  }
+}
+
+// Converts matmul-style args to matvec.
+template <typename T>
+void DispatchMatVec(T* out, T* lhs, T* rhs, int64 m, int64 n, int64 k,
+                    int32 transpose_lhs, int32 transpose_rhs) {
+  // If the input is in the form x * A, where x is the vector, then bring A back
+  // over to the left hand side.  We make use of the identity
+  //
+  //   (x * A)^T = A^T * x^T
+  //
+  // We do not need to take the transpose of x or of the result since taking
+  // the transpose of a vector does not change the memory layout.
+  const int64 cols = k;
+
+  T* mat;
+  T* vec;
+  int64 rows;
+  bool transpose_mat;
+
+  bool is_mat_vec = (n == 1);
+
+  if (is_mat_vec) {
+    mat = lhs;
+    vec = rhs;
+    rows = m;
+    transpose_mat = transpose_lhs;
+  } else {
+    mat = rhs;
+    vec = lhs;
+    rows = n;
+    transpose_mat = !transpose_rhs;
+  }
+
+  MatVec<T>(out, mat, vec, rows, cols, transpose_mat);
+}
+
+}  // namespace detail
+
 // Performs a matrix-vector multiplication using Eigen. 'lhs' and 'rhs' are
 // pointers to buffers containing input matrices in column-major order. 'out' is
 // a pointer to a buffer sufficiently large to hold the result of the
@@ -30,15 +106,15 @@ namespace xla {
 //
 // TODO(b/64684907): Compare runtime performance of these functions with dot
 // simplification.
-void EigenMatVecF32(float* out, float* lhs, float* rhs, tensorflow::int64 m,
-                    tensorflow::int64 n, tensorflow::int64 k,
-                    tensorflow::int32 transpose_lhs,
-                    tensorflow::int32 transpose_rhs);
-
-void EigenMatVecF64(double* out, double* lhs, double* rhs, tensorflow::int64 m,
-                    tensorflow::int64 n, tensorflow::int64 k,
-                    tensorflow::int32 transpose_lhs,
-                    tensorflow::int32 transpose_rhs);
+template <typename T>
+void EigenMatVec(T* out, T* lhs, T* rhs, tensorflow::int64 m,
+                 tensorflow::int64 n, tensorflow::int64 k,
+                 tensorflow::int32 transpose_lhs,
+                 tensorflow::int32 transpose_rhs) {
+  assert((m == 1 || n == 1) && "not a matrix-vector multiply");
+  detail::DispatchMatVec<T>(out, lhs, rhs, m, n, k, transpose_lhs,
+                            transpose_rhs);
+}
 
 }  // namespace xla
 
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
index ee8eb08155..17303e2f0d 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
+++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.cc
@@ -57,26 +57,38 @@ void MatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs, int64 m,
   C = A.contract(B, dims);
 }
 
+template <typename T>
+void SingleThreadedMatMul(const void* run_options_ptr, T* out, T* lhs, T* rhs,
+                          int64 m, int64 n, int64 k, int32 transpose_lhs,
+                          int32 transpose_rhs) {
+  if (m == 1 || n == 1) {
+    xla::EigenMatVec<T>(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
+  } else {
+    MatMul<T>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
+              transpose_rhs);
+  }
+}
+
 }  // namespace
 
+void __xla_cpu_runtime_EigenSingleThreadedMatMulF16(
+    const void* run_options_ptr, Eigen::half* out, Eigen::half* lhs,
+    Eigen::half* rhs, int64 m, int64 n, int64 k, int32 transpose_lhs,
+    int32 transpose_rhs) {
+  SingleThreadedMatMul<Eigen::half>(run_options_ptr, out, lhs, rhs, m, n, k,
+                                    transpose_lhs, transpose_rhs);
+}
+
 void __xla_cpu_runtime_EigenSingleThreadedMatMulF32(
     const void* run_options_ptr, float* out, float* lhs, float* rhs, int64 m,
     int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) {
-  if (m == 1 || n == 1) {
-    xla::EigenMatVecF32(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
-  } else {
-    MatMul<float>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
-                  transpose_rhs);
-  }
+  SingleThreadedMatMul<float>(run_options_ptr, out, lhs, rhs, m, n, k,
+                              transpose_lhs, transpose_rhs);
 }
 
 void __xla_cpu_runtime_EigenSingleThreadedMatMulF64(
     const void* run_options_ptr, double* out, double* lhs, double* rhs, int64 m,
     int64 n, int64 k, int32 transpose_lhs, int32 transpose_rhs) {
-  if (m == 1 || n == 1) {
-    xla::EigenMatVecF64(out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
-  } else {
-    MatMul<double>(run_options_ptr, out, lhs, rhs, m, n, k, transpose_lhs,
-                   transpose_rhs);
-  }
+  SingleThreadedMatMul<double>(run_options_ptr, out, lhs, rhs, m, n, k,
+                               transpose_lhs, transpose_rhs);
 }
diff --git a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h
index 029eb95142..9371a62242 100644
--- a/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h
+++ b/tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h
@@ -25,6 +25,12 @@ extern "C" {
 // 'out' is a pointer to a buffer sufficiently large to hold the result of the
 // operation. Following standard nomenclature: lhs is m x k, rhs is k x n, and
 // out is m x n.
+extern void __xla_cpu_runtime_EigenSingleThreadedMatMulF16(
+    const void* /* xla::ExecutableRunOptions* */ run_options_ptr,
+    Eigen::half* out, Eigen::half* lhs, Eigen::half* rhs, tensorflow::int64 m,
+    tensorflow::int64 n, tensorflow::int64 k, tensorflow::int32 transpose_lhs,
+    tensorflow::int32 transpose_rhs);
+
 extern void __xla_cpu_runtime_EigenSingleThreadedMatMulF32(
     const void* /* xla::ExecutableRunOptions* */ run_options_ptr, float* out,
     float* lhs, float* rhs, tensorflow::int64 m, tensorflow::int64 n,
diff --git a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
index e8a375d637..80c24eaccf 100644
--- a/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
+++ b/tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
@@ -181,10 +181,12 @@ bool RegisterKnownJITSymbols() {
   REGISTER_CPU_RUNTIME_SYMBOL(EigenConvF16);
   REGISTER_CPU_RUNTIME_SYMBOL(EigenConvF32);
   REGISTER_CPU_RUNTIME_SYMBOL(EigenFft);
+  REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF16);
   REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF32);
   REGISTER_CPU_RUNTIME_SYMBOL(EigenMatMulF64);
   REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF16);
   REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedConvF32);
+  REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF16);
   REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF32);
   REGISTER_CPU_RUNTIME_SYMBOL(EigenSingleThreadedMatMulF64);
   REGISTER_CPU_RUNTIME_SYMBOL(ParallelForkJoin);
diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
index ba482793e7..ca54b2eed8 100644
--- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
+++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.cc
@@ -108,11 +108,13 @@ bool DoGemmWithAlgorithm(MatrixDescriptor lhs_matrix,
   return stream
       ->ThenBlasGemmWithAlgorithm(
           lhs_transpose, rhs_transpose, output_matrix.num_rows,
-          output_matrix.num_cols, /*size of reduce dim=*/k, /*alpha=*/1.0,
-          lhs_data, /*leading dim of LHS=*/lhs_matrix.num_rows, rhs_data,
-          /*leading dim of RHS=*/rhs_matrix.num_rows, /*beta=*/0.0,
-          &output_data, /*leading dim of output=*/output_matrix.num_rows,
-          computation_type, algorithm, output_profile_result)
+          output_matrix.num_cols, /*size of reduce dim=*/k,
+          /*alpha=*/static_cast<Element>(1.0f), lhs_data,
+          /*leading dim of LHS=*/lhs_matrix.num_rows, rhs_data,
+          /*leading dim of RHS=*/rhs_matrix.num_rows,
+          /*beta=*/static_cast<Element>(0.0f), &output_data,
+          /*leading dim of output=*/output_matrix.num_rows, computation_type,
+          algorithm, output_profile_result)
       .ok();
 }
 
@@ -161,6 +163,8 @@ StatusOr<se::blas::AlgorithmType> DoGemmAutotune(
 // DoGemm/DoGemmWithAlgorithm/DoGemmAutotune.
 auto GetGemmFn(PrimitiveType type) -> decltype(&DoGemm<float>) {
   switch (type) {
+    case F16:
+      return &DoGemm<Eigen::half>;
     case F32:
       return &DoGemm<float>;
     case F64:
@@ -172,6 +176,8 @@ auto GetGemmFn(PrimitiveType type) -> decltype(&DoGemm<float>) {
 auto GetGemmWithAlgorithmFn(PrimitiveType type)
     -> decltype(&DoGemmWithAlgorithm<float>) {
   switch (type) {
+    case F16:
+      return &DoGemmWithAlgorithm<Eigen::half>;
     case F32:
       return &DoGemmWithAlgorithm<float>;
     case F64:
@@ -182,6 +188,8 @@ auto GetGemmWithAlgorithmFn(PrimitiveType type)
 }
 auto GetGemmAutotuneFn(PrimitiveType type) -> decltype(&DoGemmAutotune<float>) {
   switch (type) {
+    case F16:
+      return &DoGemmAutotune<Eigen::half>;
     case F32:
       return &DoGemmAutotune<float>;
     case F64:
@@ -196,6 +204,10 @@ auto GetGemmAutotuneFn(PrimitiveType type) -> decltype(&DoGemmAutotune<float>) {
 // separately from the precision of the inputs and result.
 se::blas::ComputationType GetBlasComputationType(PrimitiveType type) {
   switch (type) {
+    case F16:
+      // Use F32 as computation type for F16 as we currently only implement the
+      // cuDNN pseudo half configuration for half precision.
+      return se::blas::ComputationType::kF32;
     case F32:
       return se::blas::ComputationType::kF32;
     case F64:
@@ -315,6 +327,9 @@ tensorflow::Status GemmThunk::ExecuteOnStream(
           stream,
           /*output_profile_result=*/nullptr);
     }
+
+    // Autotune will fail when CUDA 8 and GPU sm_50 or older are used.
+    // Use the older Gemm API in this case.
     return GetGemmFn(element_type)(lhs_matrix, rhs_matrix, output_matrix,
                                    stream);
   };
diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc
index 2f65edffea..1b89dfa7ae 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc
@@ -49,8 +49,10 @@ bool AreValidGemmShapes(const Shape& lhs_shape, const Shape& rhs_shape,
   // The inputs and the output must
   // 1) be matrices with no padding and a non-zero number of elements,
   // 2) have an allowed element type.
-  bool type_is_allowed = (output_shape.element_type() == F32 ||
-                          output_shape.element_type() == F64);
+  PrimitiveType output_primitive_type = output_shape.element_type();
+  bool type_is_allowed =
+      (output_primitive_type == F16 || output_primitive_type == F32 ||
+       output_primitive_type == F64);
   return type_is_allowed && IsRank2WithNoPadding(lhs_shape) &&
          IsRank2WithNoPadding(rhs_shape) &&
          IsRank2WithNoPadding(output_shape) &&
diff --git a/tensorflow/compiler/xla/shape_util.h b/tensorflow/compiler/xla/shape_util.h
index 923315e001..fb66f69709 100644
--- a/tensorflow/compiler/xla/shape_util.h
+++ b/tensorflow/compiler/xla/shape_util.h
@@ -321,6 +321,15 @@ class ShapeUtil {
   static Shape MakeShape(PrimitiveType element_type,
                          tensorflow::gtl::ArraySlice<int64> dimensions);
 
+  // Creates a Shape with element type corresponding to T and the given
+  // dimensions
+  template <typename T>
+  static Shape MakeShapeWithType(
+      tensorflow::gtl::ArraySlice<int64> dimensions) {
+    return ShapeUtil::MakeShape(primitive_util::NativeToPrimitiveType<T>(),
+                                dimensions);
+  }
+
   // Constructs a new shape with the given minor_to_major order in its Layout.
   // Returns a value shape such that shape.has_layout().
   static Shape MakeShapeWithLayout(
diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD
index dc282f2440..63f4a4430f 100644
--- a/tensorflow/compiler/xla/tests/BUILD
+++ b/tensorflow/compiler/xla/tests/BUILD
@@ -1299,6 +1299,7 @@ xla_test(
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/tests:client_library_test_base",
         "//tensorflow/compiler/xla/tests:literal_test_util",
+        "//tensorflow/compiler/xla/tests:test_utils",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",
         "//tensorflow/core:lib",
         "//tensorflow/core:test",
diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc
index e2b5c91653..99640f5bb5 100644
--- a/tensorflow/compiler/xla/tests/convolution_test.cc
+++ b/tensorflow/compiler/xla/tests/convolution_test.cc
@@ -53,26 +53,12 @@ class ConvolutionTest : public ClientLibraryTestBase {
 #endif
 };
 
-#if (XLA_TEST_BACKEND_GPU || XLA_TEST_BACKEND_CPU)
-using TestTypes = ::testing::Types<float, Eigen::half>;
-#else
+#ifdef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
 using TestTypes = ::testing::Types<float>;
+#else
+using TestTypes = ::testing::Types<float, Eigen::half>;
 #endif
 
-template <typename T>
-Shape MakeShapeWrapper(tensorflow::gtl::ArraySlice<int64> dimensions);
-
-template <>
-Shape MakeShapeWrapper<float>(tensorflow::gtl::ArraySlice<int64> dimensions) {
-  return ShapeUtil::MakeShape(F32, dimensions);
-}
-
-template <>
-Shape MakeShapeWrapper<Eigen::half>(
-    tensorflow::gtl::ArraySlice<int64> dimensions) {
-  return ShapeUtil::MakeShape(F16, dimensions);
-}
-
 template <typename T>
 class ForwardPassConvolution_3x3x256_256_OutputZ_Iota : public ConvolutionTest {
  public:
@@ -121,8 +107,8 @@ class Convolve_1x1x1x2_1x1x1x2_Valid : public ConvolutionTest {
  public:
   void RunTest() {
     ComputationBuilder builder(client_, TestName());
-    Shape input_shape = MakeShapeWrapper<T>({1, 1, 1, 2});
-    Shape filter_shape = MakeShapeWrapper<T>({1, 1, 1, 2});
+    Shape input_shape = ShapeUtil::MakeShapeWithType<T>({1, 1, 1, 2});
+    Shape filter_shape = ShapeUtil::MakeShapeWithType<T>({1, 1, 1, 2});
     auto input = builder.Parameter(0, input_shape, "input");
     auto filter = builder.Parameter(1, filter_shape, "filter");
     auto conv = builder.Conv(input, filter, {1, 1}, Padding::kValid);
@@ -152,8 +138,8 @@ class Convolve_1x1x4x4_1x1x2x2_Valid : public ConvolutionTest {
  public:
   void RunTest() {
     ComputationBuilder builder(client_, TestName());
-    Shape input_shape = MakeShapeWrapper<T>({1, 1, 4, 4});
-    Shape filter_shape = MakeShapeWrapper<T>({1, 1, 2, 2});
+    Shape input_shape = ShapeUtil::MakeShapeWithType<T>({1, 1, 4, 4});
+    Shape filter_shape = ShapeUtil::MakeShapeWithType<T>({1, 1, 2, 2});
     auto input = builder.Parameter(0, input_shape, "input");
     auto filter = builder.Parameter(1, filter_shape, "filter");
     auto conv = builder.Conv(input, filter, {1, 1}, Padding::kValid);
@@ -186,8 +172,8 @@ class Convolve_1x1x4x4_1x1x2x2_Same : public ConvolutionTest {
  public:
   void RunTest() {
     ComputationBuilder builder(client_, TestName());
-    Shape input_shape = MakeShapeWrapper<T>({1, 1, 4, 4});
-    Shape filter_shape = MakeShapeWrapper<T>({1, 1, 2, 2});
+    Shape input_shape = ShapeUtil::MakeShapeWithType<T>({1, 1, 4, 4});
+    Shape filter_shape = ShapeUtil::MakeShapeWithType<T>({1, 1, 2, 2});
     auto input = builder.Parameter(0, input_shape, "input");
     auto filter = builder.Parameter(1, filter_shape, "filter");
     auto conv = builder.Conv(input, filter, {1, 1}, Padding::kSame);
@@ -222,8 +208,8 @@ class Convolve_1x1x4x4_1x1x3x3_Same : public ConvolutionTest {
  public:
   void RunTest() {
     ComputationBuilder builder(client_, TestName());
-    Shape input_shape = MakeShapeWrapper<T>({1, 1, 4, 4});
-    Shape filter_shape = MakeShapeWrapper<T>({1, 1, 3, 3});
+    Shape input_shape = ShapeUtil::MakeShapeWithType<T>({1, 1, 4, 4});
+    Shape filter_shape = ShapeUtil::MakeShapeWithType<T>({1, 1, 3, 3});
     auto input = builder.Parameter(0, input_shape, "input");
     auto filter = builder.Parameter(1, filter_shape, "filter");
     auto conv = builder.Conv(input, filter, {1, 1}, Padding::kSame);
@@ -280,8 +266,8 @@ class Convolve1D_1x2x5_1x2x2_WithRHSDilation : public ConvolutionTest {
   void RunTest() {
     ComputationBuilder builder(client_, TestName());
     {
-      Shape input_shape = MakeShapeWrapper<T>({1, 2, 5});
-      Shape filter_shape = MakeShapeWrapper<T>({1, 2, 2});
+      Shape input_shape = ShapeUtil::MakeShapeWithType<T>({1, 2, 5});
+      Shape filter_shape = ShapeUtil::MakeShapeWithType<T>({1, 2, 2});
       auto input = builder.Parameter(0, input_shape, "input");
       auto filter = builder.Parameter(1, filter_shape, "filter");
       // Convolution dimensions are bf0_oi0->bo0.
@@ -381,8 +367,8 @@ class Convolve1D_1x2x5_1x2x2_WithPadding : public ConvolutionTest {
   void RunTest() {
     ComputationBuilder builder(client_, TestName());
     {
-      Shape input_shape = MakeShapeWrapper<T>({1, 2, 5});
-      Shape filter_shape = MakeShapeWrapper<T>({1, 2, 2});
+      Shape input_shape = ShapeUtil::MakeShapeWithType<T>({1, 2, 5});
+      Shape filter_shape = ShapeUtil::MakeShapeWithType<T>({1, 2, 2});
       auto input = builder.Parameter(0, input_shape, "input");
       auto filter = builder.Parameter(1, filter_shape, "filter");
       // Convolution dimensions are bf0_oi0->bo0.
@@ -486,8 +472,8 @@ class Convolve2D_1x3x3x5_3x3x5x5_Valid : public ConvolutionTest {
     ComputationBuilder builder(client_, TestName());
     std::vector<int64> input_dims = {1, 3, 3, 5};
     std::vector<int64> filter_dims = {3, 3, 5, 3};
-    Shape input_shape = MakeShapeWrapper<T>(input_dims);
-    Shape filter_shape = MakeShapeWrapper<T>(filter_dims);
+    Shape input_shape = ShapeUtil::MakeShapeWithType<T>(input_dims);
+    Shape filter_shape = ShapeUtil::MakeShapeWithType<T>(filter_dims);
     {
       auto input = builder.Parameter(0, input_shape, "input");
       auto filter = builder.Parameter(1, filter_shape, "filter");
@@ -611,8 +597,8 @@ class Convolve1D1WindowTestBase
                                      input_feature};
     std::vector<int64> filter_dims = {window_size, input_feature,
                                       output_feature};
-    Shape input_shape = MakeShapeWrapper<T>(input_dims);
-    Shape filter_shape = MakeShapeWrapper<T>(filter_dims);
+    Shape input_shape = ShapeUtil::MakeShapeWithType<T>(input_dims);
+    Shape filter_shape = ShapeUtil::MakeShapeWithType<T>(filter_dims);
     {
       auto input = builder.Parameter(0, input_shape, "input");
       auto filter = builder.Parameter(1, filter_shape, "filter");
diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc
index 815962094a..09b1dd283e 100644
--- a/tensorflow/compiler/xla/tests/dot_operation_test.cc
+++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc
@@ -34,169 +34,194 @@ limitations under the License.
 namespace xla {
 namespace {
 
-// TODO(b/34468543): use GUnit typed tests when we can do all tests on all
-// backends.
 class DotOperationTest : public ClientLibraryTestBase {
  public:
   ErrorSpec error_spec_{0.0001, 1e-5};
-
- protected:
-  template <typename Element>
-  void TestOneElementVectorDot();
-  template <typename Element>
-  void TestVectorDot();
-  template <typename Element>
-  void TestSquareMatrixDot(bool lhs_row_major = false,
-                           bool rhs_row_major = false);
-  template <typename Element>
-  void TestNonsquareMatrixDot(bool lhs_row_major = false,
-                              bool rhs_row_major = false);
 };
 
-XLA_TEST_F(DotOperationTest, ZeroElementVectorDotF32) {
-  ComputationBuilder builder(client_, TestName());
-  auto lhs = builder.ConstantR1<float>({});
-  auto rhs = builder.ConstantR1<float>({});
+#if defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16) && \
+    defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT64)
+using TypesF16F32 = ::testing::Types<float>;
+using TypesF16F32F64 = ::testing::Types<float>;
+using TypesF16F32F64CF64 = ::testing::Types<float>;
+#elif !defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16) && \
+    !defined(XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT64)
+using TypesF16F32 = ::testing::Types<Eigen::half, float>;
+using TypesF16F32F64 = ::testing::Types<Eigen::half, float, double>;
+using TypesF16F32F64CF64 =
+    ::testing::Types<Eigen::half, float, double, complex64>;
+#else
+#error "Situation not handled yet"
+#endif
+
+template <typename T>
+class DotOperationTest_F16F32F64CF64 : public DotOperationTest {};
+TYPED_TEST_CASE(DotOperationTest_F16F32F64CF64, TypesF16F32F64CF64);
+
+XLA_TYPED_TEST(DotOperationTest_F16F32F64CF64, ZeroElementVectorDot) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+
+  auto lhs = builder.ConstantR1<T>({});
+  auto rhs = builder.ConstantR1<T>({});
   auto result = builder.Dot(lhs, rhs);
 
-  ComputeAndCompareR0<float>(&builder, 0.0, {}, error_spec_);
+  this->template ComputeAndCompareR0<T>(&builder, static_cast<T>(0.0), {},
+                                        this->error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, TrivialMatrixVectorDotF32) {
-  ComputationBuilder builder(client_, TestName());
-  auto lhs = builder.ConstantR2<float>({{3.0, 4.0}});
-  auto rhs = builder.ConstantR1<float>({3.0, 4.0});
-  auto result = builder.Dot(lhs, rhs);
+template <typename T>
+class DotOperationTest_F16F32F64 : public DotOperationTest {};
+TYPED_TEST_CASE(DotOperationTest_F16F32F64, TypesF16F32F64);
 
-  ComputeAndCompareR1<float>(&builder, {25.0}, {}, error_spec_);
-}
-
-template <typename Element>
-void DotOperationTest::TestOneElementVectorDot() {
-  ComputationBuilder builder(client_, TestName());
-  auto lhs = builder.ConstantR1<Element>({2.0});
-  auto rhs = builder.ConstantR1<Element>({3.0});
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, TrivialMatrixVectorDot) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto lhs = builder.ConstantR2FromArray2D<T>({{3.0f, 4.0f}});
+  auto rhs = builder.ConstantFromArray<T>({3.0f, 4.0f});
   auto result = builder.Dot(lhs, rhs);
 
-  ComputeAndCompareR0<Element>(&builder, 6.0, {}, error_spec_);
+  this->template ComputeAndCompareR1<T>(&builder, {static_cast<T>(25.0f)}, {},
+                                        this->error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, OneElementVectorDotF32) {
-  TestOneElementVectorDot<float>();
-}
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, OneElementVectorDot) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto lhs = builder.ConstantR1<T>({static_cast<T>(2.0f)});
+  auto rhs = builder.ConstantR1<T>({static_cast<T>(3.0f)});
+  auto result = builder.Dot(lhs, rhs);
 
-XLA_TEST_F(DotOperationTest, OneElementVectorDotF64) {
-  TestOneElementVectorDot<double>();
+  this->template ComputeAndCompareR0<T>(&builder, static_cast<T>(6.0f), {},
+                                        this->error_spec_);
 }
 
-template <typename Element>
-void DotOperationTest::TestVectorDot() {
-  ComputationBuilder builder(client_, TestName());
-  auto lhs = builder.ConstantR1<Element>({1.0, 2.5, 42.0});
-  auto rhs = builder.ConstantR1<Element>({11.0, -1.0, 0.5});
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, VectorDot) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto lhs = builder.ConstantFromArray<T>({1.0f, 2.5f, 42.0f});
+  auto rhs = builder.ConstantFromArray<T>({11.0f, -1.0f, 0.5f});
   auto result = builder.Dot(lhs, rhs);
 
-  ComputeAndCompareR0<Element>(&builder, 29.5, {}, error_spec_);
+  this->template ComputeAndCompareR0<T>(&builder, static_cast<T>(29.5f), {},
+                                        this->error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, VectorDotF32) { TestVectorDot<float>(); }
-
-XLA_TEST_F(DotOperationTest, VectorDotF64) { TestVectorDot<double>(); }
-
-namespace {
-
 std::vector<int64> MinorToMajorForIsRowMajor(bool row_major) {
   return {row_major ? 1 : 0, row_major ? 0 : 1};
 }
 
-}  // namespace
-
-XLA_TEST_F(DotOperationTest, Dot_0x2_2x0) {
-  ComputationBuilder builder(client_, TestName());
-  auto lhs = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 2));
-  auto rhs = builder.ConstantR2FromArray2D<float>(Array2D<float>(2, 0));
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x0) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto lhs = builder.ConstantR2FromArray2D<T>(Array2D<T>(0, 2));
+  auto rhs = builder.ConstantR2FromArray2D<T>(Array2D<T>(2, 0));
   auto result = builder.Dot(lhs, rhs);
 
-  ComputeAndCompareR2<float>(&builder, Array2D<float>(0, 0), {}, error_spec_);
+  this->template ComputeAndCompareR2<T>(&builder, Array2D<T>(0, 0), {},
+                                        this->error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, Dot_0x2_2x3) {
-  ComputationBuilder builder(client_, TestName());
-  auto lhs = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 2));
-  auto rhs = builder.ConstantR2<float>({{7.0, 8.0, 9.0}, {42.0, 77.0, 101.0}});
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_0x2_2x3) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto lhs = builder.ConstantR2FromArray2D<T>(Array2D<T>(0, 2));
+  auto rhs = builder.ConstantR2FromArray2D<T>(
+      {{7.0f, 8.0f, 9.0f}, {42.0f, 77.0f, 101.0f}});
   auto result = builder.Dot(lhs, rhs);
 
-  ComputeAndCompareR2<float>(&builder, Array2D<float>(0, 3), {}, error_spec_);
+  this->template ComputeAndCompareR2<T>(&builder, Array2D<T>(0, 3), {},
+                                        this->error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, Dot_3x2_2x0) {
-  ComputationBuilder builder(client_, TestName());
-  auto lhs =
-      builder.ConstantR2<float>({{7.0, 8.0}, {9.0, 42.0}, {77.0, 101.0}});
-  auto rhs = builder.ConstantR2FromArray2D<float>(Array2D<float>(2, 0));
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_3x2_2x0) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto lhs = builder.ConstantR2FromArray2D<T>(
+      {{7.0f, 8.0f}, {9.0f, 42.0f}, {77.0f, 101.0f}});
+  auto rhs = builder.ConstantR2FromArray2D<T>(Array2D<T>(2, 0));
   auto result = builder.Dot(lhs, rhs);
 
-  ComputeAndCompareR2<float>(&builder, Array2D<float>(3, 0), {}, error_spec_);
+  this->template ComputeAndCompareR2<T>(&builder, Array2D<T>(3, 0), {},
+                                        this->error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, Dot_2x0_0x2) {
-  ComputationBuilder builder(client_, TestName());
-  auto lhs = builder.ConstantR2FromArray2D<float>(Array2D<float>(2, 0));
-  auto rhs = builder.ConstantR2FromArray2D<float>(Array2D<float>(0, 2));
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, Dot_2x0_0x2) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto lhs = builder.ConstantR2FromArray2D<T>(Array2D<T>(2, 0));
+  auto rhs = builder.ConstantR2FromArray2D<T>(Array2D<T>(0, 2));
   auto result = builder.Dot(lhs, rhs);
 
-  ComputeAndCompareR2<float>(&builder, Array2D<float>(2, 2, 0.0f), {},
-                             error_spec_);
+  this->template ComputeAndCompareR2<T>(
+      &builder, Array2D<T>(2, 2, static_cast<T>(0.0f)), {}, this->error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, FusedDot) {
-  ComputationBuilder builder(client_, TestName());
-  auto param0 = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 4}), "arg0");
-  auto param1 = builder.Parameter(1, ShapeUtil::MakeShape(F32, {4, 1}), "arg1");
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, FusedDot) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto param0 =
+      builder.Parameter(0, ShapeUtil::MakeShapeWithType<T>({2, 4}), "arg0");
+  auto param1 =
+      builder.Parameter(1, ShapeUtil::MakeShapeWithType<T>({4, 1}), "arg1");
   auto exp0 = builder.Exp(param0);
   auto result = builder.Dot(exp0, param1);
 
-  auto lhs_handle = client_
-                        ->TransferToServer(*Literal::CreateR2<float>(
-                            {{1.0, 2.0, 3.0, 4.0}, {-1.0, -2.0, -3.0, -4.0}}))
-                        .ConsumeValueOrDie();
-  auto rhs_handle = client_
-                        ->TransferToServer(*Literal::CreateR2<float>(
-                            {{1.0}, {2.0}, {3.0}, {4.0}}))
-                        .ConsumeValueOrDie();
-
-  ComputeAndCompareR2<float>(
-      &builder, Array2D<float>({{296.14560492846033}, {0.8611737683031964}}),
-      {lhs_handle.get(), rhs_handle.get()}, error_spec_);
-}
-
-template <typename Element>
-void DotOperationTest::TestSquareMatrixDot(bool lhs_row_major,
-                                           bool rhs_row_major) {
   auto lhs_handle =
-      client_
-          ->TransferToServer(*Literal::CreateR2WithLayout<Element>(
-              {{1.0, 2.0}, {3.0, -4.0}},
-              LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(lhs_row_major))))
-          .ConsumeValueOrDie();
-  auto rhs_handle =
-      client_
-          ->TransferToServer(*Literal::CreateR2WithLayout<Element>(
-              {{1.0, 6.0}, {7.0, -4.0}},
-              LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(rhs_row_major))))
+      this->client_
+          ->TransferToServer(*Literal::CreateR2FromArray2D<T>(
+              {{1.0f, 2.0f, 3.0f, 4.0f}, {-1.0f, -2.0f, -3.0f, -4.0f}}))
           .ConsumeValueOrDie();
+  auto rhs_handle = this->client_
+                        ->TransferToServer(*Literal::CreateR2FromArray2D<T>(
+                            {{1.0f}, {2.0f}, {3.0f}, {4.0f}}))
+                        .ConsumeValueOrDie();
 
-  ComputationBuilder builder(client_, TestName());
-  auto prim_type = primitive_util::NativeToPrimitiveType<Element>();
-  auto result = builder.Dot(
-      builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"),
-      builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs"));
+  if (std::is_same<Eigen::half, T>::value) {
+    this->error_spec_ = ErrorSpec{0.0001, 1e-3};
+  }
 
-  Array2D<Element> expected({{15.0, -2.0}, {-25.0, 34.0}});
-  ComputeAndCompareR2<Element>(
-      &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_);
+  this->template ComputeAndCompareR2<T>(
+      &builder, Array2D<T>({{296.14560492846033f}, {0.8611737683031964f}}),
+      {lhs_handle.get(), rhs_handle.get()}, this->error_spec_);
 }
 
+template <typename T>
+class SquareMatrixDot : public DotOperationTest {
+ public:
+  void TestImpl(bool lhs_row_major, bool rhs_row_major) {
+    auto lhs_handle =
+        client_
+            ->TransferToServer(*Literal::CreateFromArrayWithLayout<T>(
+                {{1.0f, 2.0f}, {3.0f, -4.0f}},
+                LayoutUtil::MakeLayout(
+                    MinorToMajorForIsRowMajor(lhs_row_major))))
+            .ConsumeValueOrDie();
+    auto rhs_handle =
+        client_
+            ->TransferToServer(*Literal::CreateFromArrayWithLayout<T>(
+                {{1.0f, 6.0f}, {7.0f, -4.0f}},
+                LayoutUtil::MakeLayout(
+                    MinorToMajorForIsRowMajor(rhs_row_major))))
+            .ConsumeValueOrDie();
+    ComputationBuilder builder(client_, TestName());
+    auto prim_type = primitive_util::NativeToPrimitiveType<T>();
+    auto result = builder.Dot(
+        builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}), "lhs"),
+        builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 2}), "rhs"));
+
+    Array2D<T> expected({{15.0f, -2.0f}, {-25.0f, 34.0f}});
+    ComputeAndCompareR2<T>(&builder, expected,
+                           {lhs_handle.get(), rhs_handle.get()}, error_spec_);
+  }
+};
+
+TYPED_TEST_CASE(SquareMatrixDot, TypesF16F32F64CF64);
+XLA_TYPED_TEST(SquareMatrixDot, TypesFF) { this->TestImpl(false, false); }
+XLA_TYPED_TEST(SquareMatrixDot, TypesFT) { this->TestImpl(false, true); }
+XLA_TYPED_TEST(SquareMatrixDot, TypesTF) { this->TestImpl(true, false); }
+XLA_TYPED_TEST(SquareMatrixDot, TypesTT) { this->TestImpl(true, true); }
+
 struct DotTestParam {
   int m;
   int k;
@@ -302,14 +327,13 @@ void ParametricDotTest::TestImpl() {
   if (param.has_addend) {
     args.push_back(addend_handle.get());
   }
-
-  ComputeAndCompareR2<NativeT>(&builder, *expected, args, ErrorSpec(0.3, 3e-3));
+  ErrorSpec error_spec(0.3, 3e-3);
+  if (std::is_same<Eigen::half, NativeT>::value) {
+    error_spec = ErrorSpec(0.3, 5e-3);
+  }
+  ComputeAndCompareR2<NativeT>(&builder, *expected, args, error_spec);
 }
 
-XLA_TEST_P(ParametricDotTest, TestF32) { TestImpl<float>(); }
-
-XLA_TEST_P(ParametricDotTest, TestF64) { TestImpl<double>(); }
-
 std::vector<DotTestParam> CreateDotTestParameters() {
   std::vector<DotTestParam> params;
 
@@ -331,6 +355,12 @@ std::vector<DotTestParam> CreateDotTestParameters() {
   return params;
 }
 
+#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
+XLA_TEST_P(ParametricDotTest, TestF16) { TestImpl<Eigen::half>(); }
+#endif
+XLA_TEST_P(ParametricDotTest, TestF32) { TestImpl<float>(); }
+XLA_TEST_P(ParametricDotTest, TestF64) { TestImpl<double>(); }
+
 INSTANTIATE_TEST_CASE_P(DotTests, ParametricDotTest,
                         ::testing::ValuesIn(CreateDotTestParameters()),
                         PrintDotTestParam);
@@ -343,14 +373,6 @@ class ParametricDotTestWithoutLayoutAssignment : public ParametricDotTest {
   }
 };
 
-XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF32) {
-  TestImpl<float>();
-}
-
-XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF64) {
-  TestImpl<double>();
-}
-
 std::vector<DotTestParam> CreateNoLayoutAssignmentDotTestParameters() {
   std::vector<DotTestParam> params;
 
@@ -407,110 +429,60 @@ std::vector<DotTestParam> CreateNoLayoutAssignmentDotTestParameters() {
   return params;
 }
 
-INSTANTIATE_TEST_CASE_P(
-    DotTests, ParametricDotTestWithoutLayoutAssignment,
-    ::testing::ValuesIn(CreateNoLayoutAssignmentDotTestParameters()),
-    PrintDotTestParam);
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorFF) {
-  TestSquareMatrixDot<float>(false, false);
-}
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorFT) {
-  TestSquareMatrixDot<float>(false, true);
-}
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorTF) {
-  TestSquareMatrixDot<float>(true, false);
-}
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotF32MinorToMajorTT) {
-  TestSquareMatrixDot<float>(true, true);
+#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
+XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF16) {
+  TestImpl<Eigen::half>();
 }
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorFF) {
-  TestSquareMatrixDot<complex64>(false, false);
-}
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorFT) {
-  TestSquareMatrixDot<complex64>(false, true);
-}
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorTF) {
-  TestSquareMatrixDot<complex64>(true, false);
-}
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotC64MinorToMajorTT) {
-  TestSquareMatrixDot<complex64>(true, true);
-}
-
-XLA_TEST_F(DotOperationTest, SquareMatrixDotF64) {
-  TestSquareMatrixDot<double>();
-}
-
-template <typename Element>
-void DotOperationTest::TestNonsquareMatrixDot(bool lhs_row_major,
-                                              bool rhs_row_major) {
-  auto lhs_handle =
-      client_
-          ->TransferToServer(*Literal::CreateR2WithLayout<Element>(
-              {{1.0, 2.0, 3.0}, {3.0, -4.0, -1.0}},
-              LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(lhs_row_major))))
-          .ConsumeValueOrDie();
-  auto rhs_handle =
-      client_
-          ->TransferToServer(*Literal::CreateR2WithLayout<Element>(
-              {{1.0, 6.0}, {2.0, 3.0}, {7.0, -4.0}},
-              LayoutUtil::MakeLayout(MinorToMajorForIsRowMajor(rhs_row_major))))
-          .ConsumeValueOrDie();
-
-  ComputationBuilder builder(client_, TestName());
-  auto prim_type = primitive_util::NativeToPrimitiveType<Element>();
-  auto result = builder.Dot(
-      builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"),
-      builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs"));
-
-  Array2D<Element> expected({{26.0, 0.0}, {-12.0, 10.0}});
-
-  ComputeAndCompareR2<Element>(
-      &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_);
-}
-
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorFF) {
-  TestNonsquareMatrixDot<float>(false, false);
-}
-
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorFT) {
-  TestNonsquareMatrixDot<float>(false, true);
-}
-
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorTF) {
-  TestNonsquareMatrixDot<float>(true, false);
-}
-
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF32MajorToMinorTT) {
-  TestNonsquareMatrixDot<float>(true, true);
-}
-
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotF64) {
-  TestNonsquareMatrixDot<double>();
+#endif
+XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF32) {
+  TestImpl<float>();
 }
-
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorFF) {
-  TestNonsquareMatrixDot<complex64>(false, false);
+XLA_TEST_P(ParametricDotTestWithoutLayoutAssignment, TestF64) {
+  TestImpl<double>();
 }
 
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorFT) {
-  TestNonsquareMatrixDot<complex64>(false, true);
-}
+INSTANTIATE_TEST_CASE_P(
+    DotTests, ParametricDotTestWithoutLayoutAssignment,
+    ::testing::ValuesIn(CreateNoLayoutAssignmentDotTestParameters()),
+    PrintDotTestParam);
 
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorTF) {
-  TestNonsquareMatrixDot<complex64>(true, false);
-}
+template <typename T>
+class NonsquareMatrixDot : public DotOperationTest {
+ public:
+  void TestImpl(bool lhs_row_major, bool rhs_row_major) {
+    auto lhs_handle =
+        client_
+            ->TransferToServer(*Literal::CreateFromArrayWithLayout<T>(
+                {{1.0f, 2.0f, 3.0f}, {3.0f, -4.0f, -1.0f}},
+                LayoutUtil::MakeLayout(
+                    MinorToMajorForIsRowMajor(lhs_row_major))))
+            .ConsumeValueOrDie();
+    auto rhs_handle =
+        client_
+            ->TransferToServer(*Literal::CreateFromArrayWithLayout<T>(
+                {{1.0f, 6.0f}, {2.0f, 3.0f}, {7.0f, -4.0f}},
+                LayoutUtil::MakeLayout(
+                    MinorToMajorForIsRowMajor(rhs_row_major))))
+            .ConsumeValueOrDie();
+
+    ComputationBuilder builder(client_, TestName());
+    auto prim_type = primitive_util::NativeToPrimitiveType<T>();
+    auto result = builder.Dot(
+        builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 3}), "lhs"),
+        builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {3, 2}), "rhs"));
+
+    Array2D<T> expected({{26.0f, 0.0f}, {-12.0f, 10.0f}});
+
+    ComputeAndCompareR2<T>(&builder, expected,
+                           {lhs_handle.get(), rhs_handle.get()}, error_spec_);
+  }
+};
 
-XLA_TEST_F(DotOperationTest, NonsquareMatrixDotC64MajorToMinorTT) {
-  TestNonsquareMatrixDot<complex64>(true, true);
-}
+TYPED_TEST_CASE(NonsquareMatrixDot, TypesF16F32F64CF64);
+XLA_TYPED_TEST(NonsquareMatrixDot, TestFF) { this->TestImpl(false, false); }
+XLA_TYPED_TEST(NonsquareMatrixDot, TestFT) { this->TestImpl(false, true); }
+XLA_TYPED_TEST(NonsquareMatrixDot, TestTF) { this->TestImpl(true, false); }
+XLA_TYPED_TEST(NonsquareMatrixDot, TestTT) { this->TestImpl(true, true); }
 
 XLA_TEST_F(DotOperationTest, MatrixVectorC64) {
   auto lhs_handle =
@@ -537,25 +509,35 @@ XLA_TEST_F(DotOperationTest, MatrixVectorC64) {
       &builder, expected, {lhs_handle.get(), rhs_handle.get()}, error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, ConcurrentMatMul) {
-  ComputationBuilder builder(client_, TestName());
-  auto matrix1 = builder.ConstantR2<float>({{1.0, 2.0}, {3.0, 4.0}});
-  auto matrix2 = builder.ConstantR2<float>({{5.0, 6.0}, {7.0, 8.0}});
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, ConcurrentMatMult) {
+  using T = TypeParam;
+
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto matrix1 = builder.ConstantR2FromArray2D<T>({{1.0f, 2.0f}, {3.0f, 4.0f}});
+  auto matrix2 = builder.ConstantR2FromArray2D<T>({{5.0f, 6.0f}, {7.0f, 8.0f}});
   auto matrix12 = builder.Dot(matrix1, matrix2);
   auto matrix21 = builder.Dot(matrix2, matrix1);
   builder.Add(matrix12, matrix21);
 
-  Array2D<float> expected({{42.0, 56.0}, {74.0, 96.0}});
-  ComputeAndCompareR2<float>(&builder, expected, {}, error_spec_);
+  Array2D<T> expected({{42.0f, 56.0f}, {74.0f, 96.0f}});
+  this->template ComputeAndCompareR2<T>(&builder, expected, {},
+                                        this->error_spec_);
 }
 
+template <typename T>
+class DotOperationTestForBatchMatMul : public DotOperationTest {};
+TYPED_TEST_CASE(DotOperationTestForBatchMatMul, TypesF16F32F64);
+
 // Regression test for b/32055648. The root of the graph is a kFusion of 4
 // bitcasts. Although bitcasts don't map to thunks, the root should still be
 // sync-dependent on bitcasts' operands.
-XLA_TEST_F(DotOperationTest, BatchMatMul) {
-  ComputationBuilder builder(client_, TestName());
-  auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2, 2, 2}), "x");
-  auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2, 2, 2}), "y");
+XLA_TYPED_TEST(DotOperationTestForBatchMatMul, Types) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto x =
+      builder.Parameter(0, ShapeUtil::MakeShapeWithType<T>({2, 2, 2, 2}), "x");
+  auto y =
+      builder.Parameter(1, ShapeUtil::MakeShapeWithType<T>({2, 2, 2, 2}), "y");
 
   auto x_flat = builder.Reshape(x, {0, 1, 2, 3}, {4, 2, 2});
   auto y_flat = builder.Reshape(y, {0, 1, 2, 3}, {4, 2, 2});
@@ -576,29 +558,42 @@ XLA_TEST_F(DotOperationTest, BatchMatMul) {
   auto out_flat = builder.ConcatInDim(out_slices, 0);
   builder.Reshape(out_flat, {0, 1, 2}, {2, 2, 2, 2});
 
-  auto x_data = client_
-                    ->TransferToServer(*Literal::CreateR4<float>(
-                        {{{{1000, 100}, {10, 1}}, {{2000, 200}, {20, 2}}},
-                         {{{3000, 300}, {30, 3}}, {{4000, 400}, {40, 4}}}}))
-                    .ConsumeValueOrDie();
-  auto y_data = client_
-                    ->TransferToServer(*Literal::CreateR4<float>(
-                        {{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}},
-                         {{{11, 22}, {33, 44}}, {{55, 66}, {77, 88}}}}))
+  auto x_data = this->client_
+                    ->TransferToServer(*Literal::CreateR4FromArray4D<T>(
+                        {{{{1000.0f, 100.0f}, {10.0f, 1.0f}},
+                          {{2000.0f, 200.0f}, {20.0f, 2.0f}}},
+                         {{{3000.0f, 300.0f}, {30.0f, 3.0f}},
+                          {{4000.0f, 400.0f}, {40.0f, 4.0f}}}}))
                     .ConsumeValueOrDie();
+  auto y_data =
+      this->client_
+          ->TransferToServer(*Literal::CreateR4FromArray4D<T>(
+              {{{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}},
+               {{{11.0f, 22.0f}, {33.0f, 44.0f}},
+                {{55.0f, 66.0f}, {77.0f, 88.0f}}}}))
+          .ConsumeValueOrDie();
 
-  ComputeAndCompareR4<float>(
+  if (std::is_same<Eigen::half, T>::value) {
+    this->error_spec_ = ErrorSpec{0.0001, 1e-3};
+  }
+  this->template ComputeAndCompareR4<T>(
       &builder,
       /*expected=*/
-      {{{{1300, 2400}, {13, 24}}, {{11400, 13600}, {114, 136}}},
-       {{{42900, 79200}, {429, 792}}, {{250800, 299200}, {2508, 2992}}}},
-      {x_data.get(), y_data.get()}, error_spec_);
+      {{{{1300.0f, 2400.0f}, {13.0f, 24.0f}},
+        {{11400.0f, 13600.0f}, {114.0f, 136.0f}}},
+       {{{42900.0f, 79200.0f}, {429.0f, 792.0f}},
+        {{250800.0f, 299200.0f}, {2508.0f, 2992.0f}}}},
+      {x_data.get(), y_data.get()}, this->error_spec_);
 }
 
-XLA_TEST_F(DotOperationTest, GeneralMatMul) {
-  ComputationBuilder builder(client_, TestName());
-  auto x = builder.Parameter(0, ShapeUtil::MakeShape(F32, {2, 2, 2}), "x");
-  auto y = builder.Parameter(1, ShapeUtil::MakeShape(F32, {2, 2, 2}), "y");
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, GeneralMatMul) {
+  using T = TypeParam;
+
+  ComputationBuilder builder(this->client_, this->TestName());
+  auto x =
+      builder.Parameter(0, ShapeUtil::MakeShapeWithType<T>({2, 2, 2}), "x");
+  auto y =
+      builder.Parameter(1, ShapeUtil::MakeShapeWithType<T>({2, 2, 2}), "y");
 
   DotDimensionNumbers dnums;
   dnums.add_lhs_contracting_dimensions(2);
@@ -608,31 +603,34 @@ XLA_TEST_F(DotOperationTest, GeneralMatMul) {
 
   auto out = builder.DotGeneral(x, y, dnums);
 
-  auto x_data = client_
-                    ->TransferToServer(*Literal::CreateR3<float>(
-                        {{{1.0, 2.0}, {3.0, 4.0}}, {{5.0, 6.0}, {7.0, 8.0}}}))
-                    .ConsumeValueOrDie();
+  auto x_data =
+      this->client_
+          ->TransferToServer(*Literal::CreateR3FromArray3D<T>(
+              {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}}))
+          .ConsumeValueOrDie();
 
-  auto y_data = client_
-                    ->TransferToServer(*Literal::CreateR3<float>(
-                        {{{1.0, 0.0}, {0.0, 1.0}}, {{1.0, 0.0}, {0.0, 1.0}}}))
-                    .ConsumeValueOrDie();
+  auto y_data =
+      this->client_
+          ->TransferToServer(*Literal::CreateR3FromArray3D<T>(
+              {{{1.0f, 0.0f}, {0.0f, 1.0f}}, {{1.0f, 0.0f}, {0.0f, 1.0f}}}))
+          .ConsumeValueOrDie();
 
-  ComputeAndCompareR3<float>(
+  this->template ComputeAndCompareR3<T>(
       &builder,
       /*expected=*/
-      {{{1.0, 2.0}, {3.0, 4.0}}, {{5.0, 6.0}, {7.0, 8.0}}},
-      {x_data.get(), y_data.get()}, error_spec_);
+      {{{1.0f, 2.0f}, {3.0f, 4.0f}}, {{5.0f, 6.0f}, {7.0f, 8.0f}}},
+      {x_data.get(), y_data.get()}, this->error_spec_);
 }
 
-TEST_F(DotOperationTest, TransposeFolding) {
+XLA_TYPED_TEST(DotOperationTest_F16F32F64, TransposeFolding) {
+  using T = TypeParam;
   for (bool transpose_lhs : {false, true}) {
     for (bool transpose_rhs : {false, true}) {
       for (bool row_major : {false, true}) {
-        std::unique_ptr<Array2D<float>> lhs(
-            new Array2D<float>({{1.0, 2.0, 3.0}, {3.0, -4.0, -1.0}}));
-        std::unique_ptr<Array2D<float>> rhs(
-            new Array2D<float>({{1.0, 6.0}, {2.0, 3.0}, {7.0, -4.0}}));
+        std::unique_ptr<Array2D<T>> lhs(
+            new Array2D<T>({{1.0f, 2.0f, 3.0f}, {3.0f, -4.0f, -1.0f}}));
+        std::unique_ptr<Array2D<T>> rhs(
+            new Array2D<T>({{1.0f, 6.0f}, {2.0f, 3.0f}, {7.0f, -4.0f}}));
 
         if (transpose_lhs) {
           lhs = ReferenceUtil::TransposeArray2D(*lhs);
@@ -641,22 +639,20 @@ TEST_F(DotOperationTest, TransposeFolding) {
           rhs = ReferenceUtil::TransposeArray2D(*rhs);
         }
         auto lhs_handle =
-            client_
-                ->TransferToServer(
-                    *Literal::CreateR2FromArray2DWithLayout<float>(
-                        *lhs, LayoutUtil::MakeLayout(
-                                  MinorToMajorForIsRowMajor(row_major))))
+            this->client_
+                ->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<T>(
+                    *lhs, LayoutUtil::MakeLayout(
+                              MinorToMajorForIsRowMajor(row_major))))
                 .ConsumeValueOrDie();
         auto rhs_handle =
-            client_
-                ->TransferToServer(
-                    *Literal::CreateR2FromArray2DWithLayout<float>(
-                        *rhs, LayoutUtil::MakeLayout(
-                                  MinorToMajorForIsRowMajor(row_major))))
+            this->client_
+                ->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<T>(
+                    *rhs, LayoutUtil::MakeLayout(
+                              MinorToMajorForIsRowMajor(row_major))))
                 .ConsumeValueOrDie();
 
-        ComputationBuilder builder(client_, TestName());
-        auto prim_type = primitive_util::NativeToPrimitiveType<float>();
+        ComputationBuilder builder(this->client_, this->TestName());
+        auto prim_type = primitive_util::NativeToPrimitiveType<T>();
         auto lhs_arg = builder.Parameter(
             0, ShapeUtil::MakeShape(prim_type, {lhs->height(), lhs->width()}),
             "lhs");
@@ -671,24 +667,27 @@ TEST_F(DotOperationTest, TransposeFolding) {
         }
         auto result = builder.Dot(lhs_arg, rhs_arg);
 
-        Array2D<float> expected({{26.0, 0.0}, {-12.0, 10.0}});
+        Array2D<T> expected({{26.0f, 0.0f}, {-12.0f, 10.0f}});
         VLOG(1) << "TestTransposeFolding " << transpose_lhs << " "
                 << transpose_rhs << " " << row_major;
-        ComputeAndCompareR2<float>(&builder, expected,
-                                   {lhs_handle.get(), rhs_handle.get()},
-                                   error_spec_);
+        this->template ComputeAndCompareR2<T>(
+            &builder, expected, {lhs_handle.get(), rhs_handle.get()},
+            this->error_spec_);
       }
     }
   }
 }
 
-TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstLHS) {
-  auto prim_type = primitive_util::NativeToPrimitiveType<float>();
+XLA_TYPED_TEST(DotOperationTest_F16F32F64,
+               DotOfConcatOptimizationWithConstLHS) {
+  using T = TypeParam;
+  auto prim_type = primitive_util::NativeToPrimitiveType<T>();
 
-  std::unique_ptr<Array2D<float>> constant_lhs_array(new Array2D<float>(
-      {{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, {6.0, 5.0, 4.0, 3.0, 2.0, 1.0}}));
+  std::unique_ptr<Array2D<T>> constant_lhs_array(
+      new Array2D<T>({{1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f},
+                      {6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}));
 
-  ComputationBuilder builder(client_, TestName());
+  ComputationBuilder builder(this->client_, this->TestName());
   auto lhs_constant = builder.ConstantR2FromArray2D(*constant_lhs_array);
   auto rhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}),
                                      "rhs_arg_0");
@@ -699,78 +698,80 @@ TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstLHS) {
   auto result = builder.Dot(
       lhs_constant, builder.ConcatInDim({rhs_arg_0, rhs_arg_1, rhs_arg_2}, 0));
 
-  std::unique_ptr<Array2D<float>> arg_0_value_array(
-      new Array2D<float>({{1.0, 2.0}, {3.0, 4.0}}));
-  std::unique_ptr<Array2D<float>> arg_1_value_array(
-      new Array2D<float>({{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}}));
-  std::unique_ptr<Array2D<float>> arg_2_value_array(
-      new Array2D<float>({{1.0, 2.0}}));
+  std::unique_ptr<Array2D<T>> arg_0_value_array(
+      new Array2D<T>({{1.0f, 2.0f}, {3.0f, 4.0f}}));
+  std::unique_ptr<Array2D<T>> arg_1_value_array(
+      new Array2D<T>({{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}}));
+  std::unique_ptr<Array2D<T>> arg_2_value_array(new Array2D<T>({{1.0f, 2.0f}}));
 
   TF_ASSERT_OK_AND_ASSIGN(
       auto arg_0_value,
-      client_->TransferToServer(
-          *Literal::CreateR2FromArray2D<float>(*arg_0_value_array)));
+      this->client_->TransferToServer(
+          *Literal::CreateR2FromArray2D<T>(*arg_0_value_array)));
   TF_ASSERT_OK_AND_ASSIGN(
       auto arg_1_value,
-      client_->TransferToServer(
-          *Literal::CreateR2FromArray2D<float>(*arg_1_value_array)));
+      this->client_->TransferToServer(
+          *Literal::CreateR2FromArray2D<T>(*arg_1_value_array)));
   TF_ASSERT_OK_AND_ASSIGN(
       auto arg_2_value,
-      client_->TransferToServer(
-          *Literal::CreateR2FromArray2D<float>(*arg_2_value_array)));
+      this->client_->TransferToServer(
+          *Literal::CreateR2FromArray2D<T>(*arg_2_value_array)));
 
-  Array2D<float> expected({{53.0, 74.0}, {45.0, 66.0}});
-  ComputeAndCompareR2<float>(
+  Array2D<T> expected({{53.0f, 74.0f}, {45.0f, 66.0f}});
+  this->template ComputeAndCompareR2<T>(
       &builder, expected,
-      {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, error_spec_);
-}
-
-TEST_F(DotOperationTest, DotOfConcatOptimizationWithConstRHS) {
-  auto prim_type = primitive_util::NativeToPrimitiveType<float>();
-
-  std::unique_ptr<Array2D<float>> constant_rhs_array(
-      new Array2D<float>({{1.0, 2.0},
-                          {3.0, 4.0},
-                          {5.0, 6.0},
-                          {6.0, 5.0},
-                          {4.0, 3.0},
-                          {2.0, 1.0}}));
-
-  ComputationBuilder builder(client_, TestName());
+      {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()},
+      this->error_spec_);
+}
+
+XLA_TYPED_TEST(DotOperationTest_F16F32F64,
+               DotOfConcatOptimizationWithConstRHS) {
+  using T = TypeParam;
+  std::unique_ptr<Array2D<T>> constant_rhs_array(
+      new Array2D<T>({{1.0f, 2.0f},
+                      {3.0f, 4.0f},
+                      {5.0f, 6.0f},
+                      {6.0f, 5.0f},
+                      {4.0f, 3.0f},
+                      {2.0f, 1.0f}}));
+
+  ComputationBuilder builder(this->client_, this->TestName());
   auto rhs_constant = builder.ConstantR2FromArray2D(*constant_rhs_array);
-  auto lhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShape(prim_type, {2, 2}),
+  auto lhs_arg_0 = builder.Parameter(0, ShapeUtil::MakeShapeWithType<T>({2, 2}),
                                      "lhs_arg_0");
-  auto lhs_arg_1 = builder.Parameter(1, ShapeUtil::MakeShape(prim_type, {2, 3}),
+  auto lhs_arg_1 = builder.Parameter(1, ShapeUtil::MakeShapeWithType<T>({2, 3}),
                                      "lhs_arg_1");
-  auto lhs_arg_2 = builder.Parameter(2, ShapeUtil::MakeShape(prim_type, {2, 1}),
+  auto lhs_arg_2 = builder.Parameter(2, ShapeUtil::MakeShapeWithType<T>({2, 1}),
                                      "lhs_arg_2");
   auto result = builder.Dot(
       builder.ConcatInDim({lhs_arg_0, lhs_arg_1, lhs_arg_2}, 1), rhs_constant);
 
-  std::unique_ptr<Array2D<float>> arg_0_value_array(
-      new Array2D<float>({{1.0, 2.0}, {3.0, 4.0}}));
-  std::unique_ptr<Array2D<float>> arg_1_value_array(
-      new Array2D<float>({{1.0, 2.0, 3.0}, {4.0, 5.0, 6.0}}));
-  std::unique_ptr<Array2D<float>> arg_2_value_array(
-      new Array2D<float>({{1.0}, {2.0}}));
+  std::unique_ptr<Array2D<T>> arg_0_value_array(
+      new Array2D<T>({{1.0f, 2.0f}, {3.0f, 4.0f}}));
+  std::unique_ptr<Array2D<T>> arg_1_value_array(
+      new Array2D<T>({{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}));
+  std::unique_ptr<Array2D<T>> arg_2_value_array(
+      new Array2D<T>({{1.0f}, {2.0f}}));
 
   TF_ASSERT_OK_AND_ASSIGN(
       auto arg_0_value,
-      client_->TransferToServer(
-          *Literal::CreateR2FromArray2D<float>(*arg_0_value_array)));
+      this->client_->TransferToServer(
+          *Literal::CreateR2FromArray2D<T>(*arg_0_value_array)));
   TF_ASSERT_OK_AND_ASSIGN(
       auto arg_1_value,
-      client_->TransferToServer(
-          *Literal::CreateR2FromArray2D<float>(*arg_1_value_array)));
+      this->client_->TransferToServer(
+          *Literal::CreateR2FromArray2D<T>(*arg_1_value_array)));
   TF_ASSERT_OK_AND_ASSIGN(
       auto arg_2_value,
-      client_->TransferToServer(
-          *Literal::CreateR2FromArray2D<float>(*arg_2_value_array)));
+      this->client_->TransferToServer(
+          *Literal::CreateR2FromArray2D<T>(*arg_2_value_array)));
 
-  Array2D<float> expected({{38.0, 36.0}, {93.0, 91.0}});
-  ComputeAndCompareR2<float>(
+  Array2D<T> expected({{38.0f, 36.0f}, {93.0f, 91.0f}});
+  this->template ComputeAndCompareR2<T>(
       &builder, expected,
-      {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()}, error_spec_);
+      {arg_0_value.get(), arg_1_value.get(), arg_2_value.get()},
+      this->error_spec_);
 }
+
 }  // namespace
 }  // namespace xla
diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
index 6c86dd5b9e..c42f71388b 100644
--- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc
@@ -29,6 +29,8 @@ limitations under the License.
 #include "tensorflow/compiler/xla/test_helpers.h"
 #include "tensorflow/compiler/xla/tests/client_library_test_base.h"
 #include "tensorflow/compiler/xla/tests/literal_test_util.h"
+#include "tensorflow/compiler/xla/tests/test_macros.h"
+#include "tensorflow/compiler/xla/tests/test_utils.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/logging.h"
@@ -38,258 +40,223 @@ limitations under the License.
 namespace xla {
 namespace {
 
-class MatOpsSimpleTest : public ClientLibraryTestBase {
- protected:
-  Computation BuildSum() {
-    // sum(x, y) = x + y
-    ComputationBuilder builder(client_, "sum");
-    auto x_value =
-        builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x_value");
-    auto y_value =
-        builder.Parameter(1, ShapeUtil::MakeShape(F32, {}), "y_value");
-    builder.Add(x_value, y_value);
-    auto computation_status = builder.Build();
-    TF_CHECK_OK(computation_status.status());
-    return computation_status.ConsumeValueOrDie();
-  }
-
-  void TestLinspaceMax(int64 rows, int64 cols) {
-    float from = -128.0, to = 256.0;
-    std::unique_ptr<Array2D<float>> alhs =
-        MakeLinspaceArray2D(from, to, rows, cols);
-    auto arhs = MakeUnique<Array2D<float>>(rows, cols, 1.0);
-
-    ComputationBuilder builder(
-        client_,
-        tensorflow::strings::Printf("max_%lldx%lld_linspace", rows, cols));
-    auto lhs = builder.ConstantR2FromArray2D<float>(*alhs);
-    auto rhs = builder.ConstantR2FromArray2D<float>(*arhs);
-    auto max = builder.Max(lhs, rhs);
-
-    Array2D<float> aexpected(rows, cols);
-    for (int row = 0; row < rows; ++row) {
-      for (int col = 0; col < cols; ++col) {
-        aexpected(row, col) = std::max((*alhs)(row, col), (*arhs)(row, col));
-      }
-    }
-
-    ComputeAndCompareR2<float>(&builder, aexpected, {}, ErrorSpec(1e-6));
-  }
-};
-
-TEST_F(MatOpsSimpleTest, ExpTwoByTwoValues) {
-  ComputationBuilder builder(client_, "exp_2x2");
-  auto data = builder.ConstantR2<float>({
-      {1.0, 0.0},   // row 0
-      {-1.0, 0.5},  // row 1
+#ifdef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
+using TypesF16F32 = ::testing::Types<float>;
+#else
+using TypesF16F32 = ::testing::Types<Eigen::half, float>;
+#endif
+
+class MatOpsSimpleTest : public ClientLibraryTestBase {};
+
+template <typename T>
+class MatOpsSimpleTest_F16F32 : public MatOpsSimpleTest {};
+
+// TODO(bixia): This test for F16 failed on GPU 02-25-2018.
+#ifdef XLA_TEST_BACKEND_GPU
+TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, ::testing::Types<float>);
+#else
+TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, TypesF16F32);
+#endif
+
+XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, ExpTwoByTwoValues) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, "exp_2x2");
+  auto data = builder.ConstantR2FromArray2D<T>({
+      {1.0f, 0.0f},   // row 0
+      {-1.0f, 0.5f},  // row 1
   });
   builder.Exp(data);
 
   std::unique_ptr<Literal> expected =
-      Literal::CreateR2<float>({{2.71828, 1.00000},    // row 0
-                                {0.36788, 1.64872}});  // row 1
+      Literal::CreateR2FromArray2D<T>({{2.71828f, 1.00000f},    // row 0
+                                       {0.36788f, 1.64872f}});  // row 1
 
-  ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5));
+  this->template ComputeAndCompareLiteral(&builder, *expected, {},
+                                          ErrorSpec(1e-5));
 }
 
-TEST_F(MatOpsSimpleTest, MapTwoByTwo) {
+XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) {
+  using T = TypeParam;
   Computation add_half;
   {
     // add_half(x) = x + 0.5
-    ComputationBuilder builder(client_, "add_half");
+    ComputationBuilder builder(this->client_, "add_half");
     auto x_value =
-        builder.Parameter(0, ShapeUtil::MakeShape(F32, {}), "x_value");
-    auto half = builder.ConstantR0<float>(0.5);
+        builder.Parameter(0, ShapeUtil::MakeShapeWithType<T>({}), "x_value");
+    auto half = builder.ConstantR0<T>(static_cast<T>(0.5));
     builder.Add(x_value, half);
     auto computation_status = builder.Build();
     ASSERT_IS_OK(computation_status.status());
     add_half = computation_status.ConsumeValueOrDie();
   }
 
-  ComputationBuilder builder(client_, "map_2x2");
-  auto data = builder.ConstantR2<float>({
-      {1.0, 0.0},   // row 0
-      {-1.0, 0.5},  // row 1
+  ComputationBuilder builder(this->client_, "map_2x2");
+  auto data = builder.ConstantR2FromArray2D<T>({
+      {1.0f, 0.0f},   // row 0
+      {-1.0f, 0.5f},  // row 1
   });
   auto map = builder.Map({data}, add_half, {0, 1});
 
   std::unique_ptr<Literal> expected =
-      Literal::CreateR2<float>({{1.5, 0.5},     // row 0
-                                {-0.5, 1.0}});  // row 1
-  ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5));
+      Literal::CreateR2FromArray2D<T>({{1.5f, 0.5f},     // row 0
+                                       {-0.5f, 1.0f}});  // row 1
+  this->template ComputeAndCompareLiteral(&builder, *expected, {},
+                                          ErrorSpec(1e-5));
 }
 
-TEST_F(MatOpsSimpleTest, MaxTwoByTwoValues) {
-  ComputationBuilder builder(client_, "max_2x2");
-  auto lhs = builder.ConstantR2<float>({
-      {7.0, 2.0},   // row 0
-      {3.0, -4.0},  // row 1
+XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MaxTwoByTwoValues) {
+  using T = TypeParam;
+  ComputationBuilder builder(this->client_, "max_2x2");
+  auto lhs = builder.ConstantR2FromArray2D<T>({
+      {7.0f, 2.0f},   // row 0
+      {3.0f, -4.0f},  // row 1
   });
-  auto rhs = builder.ConstantR2<float>({
-      {5.0, 6.0},   // row 0
-      {1.0, -8.0},  // row 1
+  auto rhs = builder.ConstantR2FromArray2D<T>({
+      {5.0f, 6.0f},   // row 0
+      {1.0f, -8.0f},  // row 1
   });
   auto max = builder.Max(lhs, rhs);
 
   std::unique_ptr<Literal> expected =
-      Literal::CreateR2<float>({{7.0, 6.0},     // row 0
-                                {3.0, -4.0}});  // row 1
-  ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6));
+      Literal::CreateR2FromArray2D<T>({{7.0f, 6.0f},     // row 0
+                                       {3.0f, -4.0f}});  // row 1
+  this->template ComputeAndCompareLiteral(&builder, *expected, {},
+                                          ErrorSpec(1e-6));
 }
 
-TEST_F(MatOpsSimpleTest, Max1x1Linspace) { TestLinspaceMax(1, 1); }
-
-TEST_F(MatOpsSimpleTest, Max2x2Linspace) { TestLinspaceMax(2, 2); }
-
-TEST_F(MatOpsSimpleTest, Max3x3Linspace) { TestLinspaceMax(3, 3); }
-
-TEST_F(MatOpsSimpleTest, Max4x4Linspace) { TestLinspaceMax(4, 4); }
-
-TEST_F(MatOpsSimpleTest, Max6x6Linspace) { TestLinspaceMax(6, 6); }
-
-TEST_F(MatOpsSimpleTest, Max8x8Linspace) { TestLinspaceMax(8, 8); }
-
-TEST_F(MatOpsSimpleTest, Max12x12Linspace) { TestLinspaceMax(12, 12); }
-
-TEST_F(MatOpsSimpleTest, Max16x16Linspace) { TestLinspaceMax(16, 16); }
+struct TestLinspaceMaxParam {
+  int64 rows;
+  int64 cols;
+};
 
-TEST_F(MatOpsSimpleTest, Max32x8Linspace) { TestLinspaceMax(32, 8); }
+class TestLinspaceMaxParametric
+    : public MatOpsSimpleTest,
+      public ::testing::WithParamInterface<TestLinspaceMaxParam> {
+ public:
+  template <typename T>
+  void TestImpl() {
+    TestLinspaceMaxParam param = GetParam();
+    int64 rows = param.rows;
+    int64 cols = param.cols;
+    float from = -128.0, to = 256.0;
+    std::unique_ptr<Array2D<T>> alhs =
+        MakeLinspaceArray2D<T>(from, to, rows, cols);
+    auto arhs = MakeUnique<Array2D<T>>(rows, cols, static_cast<T>(1.0f));
 
-TEST_F(MatOpsSimpleTest, Max64x8Linspace) { TestLinspaceMax(64, 8); }
+    ComputationBuilder builder(
+        client_,
+        tensorflow::strings::Printf("max_%lldx%lld_linspace", rows, cols));
+    auto lhs = builder.ConstantR2FromArray2D<T>(*alhs);
+    auto rhs = builder.ConstantR2FromArray2D<T>(*arhs);
+    auto max = builder.Max(lhs, rhs);
 
-class MatOpsDotAddTest
-    : public ClientLibraryTestBase,
-      public ::testing::WithParamInterface<std::tuple<bool, bool, bool>> {};
-
-TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2) {
-  bool row_major = std::get<0>(GetParam());
-  bool add_lhs = std::get<1>(GetParam());
-  bool transpose = std::get<2>(GetParam());
-  Array2D<float> lhs({{1.0, 2.0}, {3.0, 4.0}});
-  Array2D<float> rhs({{10.0, 11.0}, {12.0, 13.0}});
-
-  auto minor_to_major = [](bool row_major) -> std::vector<int64> {
-    return {row_major ? 1 : 0, row_major ? 0 : 1};
-  };
-
-  auto prim_type = primitive_util::NativeToPrimitiveType<float>();
-  Shape lhs_shape =
-      ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()});
-  Shape rhs_shape =
-      ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()});
-
-  TF_ASSERT_OK_AND_ASSIGN(
-      auto lhs_handle,
-      client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<float>(
-          lhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
-  TF_ASSERT_OK_AND_ASSIGN(
-      auto rhs_handle,
-      client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<float>(
-          rhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
-
-  ComputationBuilder builder(client_, TestName());
-  auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs");
-  auto lhs_mat_arg = lhs_arg;
-  if (transpose) {
-    lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0});
-  }
-  auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs");
-  auto result = builder.Dot(lhs_mat_arg, rhs_arg);
-  Array2D<float> expected;
-  if (add_lhs) {
-    result = builder.Add(result, lhs_arg);
-    if (transpose) {
-      expected = Array2D<float>({{47, 52}, {71, 78}});
-    } else {
-      expected = Array2D<float>({{35, 39}, {81, 89}});
+    Array2D<T> expected(rows, cols);
+    for (int row = 0; row < rows; ++row) {
+      for (int col = 0; col < cols; ++col) {
+        expected(row, col) = std::max<T>((*alhs)(row, col), (*arhs)(row, col));
+      }
     }
-  } else {
-    result = builder.Add(result, rhs_arg);
-    if (transpose) {
-      expected = Array2D<float>({{56, 61}, {80, 87}});
-    } else {
-      expected = Array2D<float>({{44, 48}, {90, 98}});
+    ErrorSpec error_spec(1e-6);
+    if (std::is_same<Eigen::half, T>::value) {
+      error_spec = ErrorSpec(1e-6, 2e-4);
     }
+    ComputeAndCompareR2<T>(&builder, expected, {}, error_spec);
   }
+};
 
-  ComputeAndCompareR2<float>(&builder, expected,
-                             {lhs_handle.get(), rhs_handle.get()},
-                             ErrorSpec(1e-6));
+string PrintTestLinspaceMaxParam(
+    const ::testing::TestParamInfo<TestLinspaceMaxParam>& test_param) {
+  const TestLinspaceMaxParam& param = test_param.param;
+  return tensorflow::strings::StrCat(param.rows, "r", param.cols, "c");
 }
 
-INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest,
-                        ::testing::Combine(::testing::Bool(), ::testing::Bool(),
-                                           ::testing::Bool()));
+#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
+// TODO(bixia): This test failed on GPU 02-25-2018
+#ifdef XLA_TEST_BACKEND_CPU
+XLA_TEST_P(TestLinspaceMaxParametric, TestF16) { TestImpl<Eigen::half>(); }
+#endif
+#endif
+XLA_TEST_P(TestLinspaceMaxParametric, TestF32) { TestImpl<float>(); }
+
+INSTANTIATE_TEST_CASE_P(
+    TestLinspaceMax, TestLinspaceMaxParametric,
+    ::testing::Values(TestLinspaceMaxParam{1, 1}, TestLinspaceMaxParam{2, 2},
+                      TestLinspaceMaxParam{3, 3}, TestLinspaceMaxParam{4, 4},
+                      TestLinspaceMaxParam{6, 6}, TestLinspaceMaxParam{8, 8},
+                      TestLinspaceMaxParam{12, 12},
+                      TestLinspaceMaxParam{16, 16}, TestLinspaceMaxParam{32, 8},
+                      TestLinspaceMaxParam{64, 8}),
+    PrintTestLinspaceMaxParam);
 
-class MatOpsDotAddTest_bf16
+class MatOpsDotAddTest
     : public ClientLibraryTestBase,
-      public ::testing::WithParamInterface<std::tuple<bool, bool, bool>> {};
-
-TEST_P(MatOpsDotAddTest_bf16, Dot_Add_2x2_2x2) {
-  bool row_major = std::get<0>(GetParam());
-  bool add_lhs = std::get<1>(GetParam());
-  bool transpose = std::get<2>(GetParam());
-  Array2D<bfloat16> lhs(
-      {{bfloat16(1.0f), bfloat16(2.0f)}, {bfloat16(3.0), bfloat16(4.0)}});
-  Array2D<bfloat16> rhs(
-      {{bfloat16(10.0f), bfloat16(11.0f)}, {bfloat16(12.0f), bfloat16(13.0f)}});
-
-  auto minor_to_major = [](bool row_major) -> std::vector<int64> {
-    return {row_major ? 1 : 0, row_major ? 0 : 1};
-  };
-
-  auto prim_type = primitive_util::NativeToPrimitiveType<bfloat16>();
-  Shape lhs_shape =
-      ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()});
-  Shape rhs_shape =
-      ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()});
-
-  TF_ASSERT_OK_AND_ASSIGN(
-      auto lhs_handle,
-      client_->TransferToServer(
-          *Literal::CreateR2FromArray2DWithLayout<bfloat16>(
-              lhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
-  TF_ASSERT_OK_AND_ASSIGN(
-      auto rhs_handle,
-      client_->TransferToServer(
-          *Literal::CreateR2FromArray2DWithLayout<bfloat16>(
-              rhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
-
-  ComputationBuilder builder(client_, TestName());
-  auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs");
-  auto lhs_mat_arg = lhs_arg;
-  if (transpose) {
-    lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0});
-  }
-  auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs");
-  auto result = builder.Dot(lhs_mat_arg, rhs_arg);
-  Array2D<bfloat16> expected;
-  if (add_lhs) {
-    result = builder.Add(result, lhs_arg);
+      public ::testing::WithParamInterface<std::tuple<bool, bool, bool>> {
+ public:
+  template <typename T>
+  void TestImpl() {
+    bool row_major = std::get<0>(GetParam());
+    bool add_lhs = std::get<1>(GetParam());
+    bool transpose = std::get<2>(GetParam());
+    Array2D<T> lhs({{1.0f, 2.0f}, {3.0f, 4.0f}});
+    Array2D<T> rhs({{10.0f, 11.0f}, {12.0f, 13.0f}});
+
+    auto minor_to_major = [](bool row_major) -> std::vector<int64> {
+      return {row_major ? 1 : 0, row_major ? 0 : 1};
+    };
+
+    auto prim_type = primitive_util::NativeToPrimitiveType<T>();
+    Shape lhs_shape =
+        ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()});
+    Shape rhs_shape =
+        ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()});
+
+    TF_ASSERT_OK_AND_ASSIGN(
+        auto lhs_handle,
+        client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<T>(
+            lhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
+    TF_ASSERT_OK_AND_ASSIGN(
+        auto rhs_handle,
+        client_->TransferToServer(*Literal::CreateR2FromArray2DWithLayout<T>(
+            rhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
+
+    ComputationBuilder builder(client_, TestName());
+    auto lhs_arg = builder.Parameter(0, lhs_shape, "lhs");
+    auto lhs_mat_arg = lhs_arg;
     if (transpose) {
-      expected = Array2D<bfloat16>(
-          {{bfloat16(47), bfloat16(52)}, {bfloat16(71), bfloat16(78)}});
-    } else {
-      expected = Array2D<bfloat16>(
-          {{bfloat16(35), bfloat16(39)}, {bfloat16(81), bfloat16(89)}});
+      lhs_mat_arg = builder.Transpose(lhs_mat_arg, {1, 0});
     }
-  } else {
-    result = builder.Add(result, rhs_arg);
-    if (transpose) {
-      expected = Array2D<bfloat16>(
-          {{bfloat16(56), bfloat16(61)}, {bfloat16(80), bfloat16(87)}});
+    auto rhs_arg = builder.Parameter(1, rhs_shape, "rhs");
+    auto result = builder.Dot(lhs_mat_arg, rhs_arg);
+    Array2D<T> expected;
+    if (add_lhs) {
+      result = builder.Add(result, lhs_arg);
+      if (transpose) {
+        expected = Array2D<T>({{47.0f, 52.0f}, {71.0f, 78.0f}});
+      } else {
+        expected = Array2D<T>({{35.0f, 39.0f}, {81.0f, 89.0f}});
+      }
     } else {
-      expected = Array2D<bfloat16>(
-          {{bfloat16(44), bfloat16(48)}, {bfloat16(90), bfloat16(98)}});
+      result = builder.Add(result, rhs_arg);
+      if (transpose) {
+        expected = Array2D<T>({{56.0f, 61.0f}, {80.0f, 87.0f}});
+      } else {
+        expected = Array2D<T>({{44.0f, 48.0f}, {90.0f, 98.0f}});
+      }
     }
+
+    ComputeAndCompareR2<T>(&builder, expected,
+                           {lhs_handle.get(), rhs_handle.get()},
+                           ErrorSpec(1e-6));
   }
+};
 
-  ComputeAndCompareR2<bfloat16>(&builder, expected,
-                                {lhs_handle.get(), rhs_handle.get()},
-                                ErrorSpec(1e-6));
-}
+XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2BF16) { TestImpl<bfloat16>(); }
+#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
+XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2F16) { TestImpl<Eigen::half>(); }
+#endif
+XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2F32) { TestImpl<float>(); }
 
-INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest_bf16,
+INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest,
                         ::testing::Combine(::testing::Bool(), ::testing::Bool(),
                                            ::testing::Bool()));
 
diff --git a/tensorflow/stream_executor/blas.cc b/tensorflow/stream_executor/blas.cc
index da09d84921..31724cf6c9 100644
--- a/tensorflow/stream_executor/blas.cc
+++ b/tensorflow/stream_executor/blas.cc
@@ -79,6 +79,8 @@ string ComputationTypeString(ComputationType ty) {
       return "f32";
     case ComputationType::kF64:
       return "f64";
+    case ComputationType::kI32:
+      return "i32";
     case ComputationType::kComplexF32:
       return "complex f32";
     case ComputationType::kComplexF64:
@@ -88,6 +90,10 @@ string ComputationTypeString(ComputationType ty) {
   }
 }
 
+std::ostream& operator<<(std::ostream& os, ComputationType ty) {
+  return os << ComputationTypeString(ty);
+}
+
 }  // namespace blas
 }  // namespace gputools
 }  // namespace perftools
diff --git a/tensorflow/stream_executor/blas.h b/tensorflow/stream_executor/blas.h
index 072f085546..c5f778a5c7 100644
--- a/tensorflow/stream_executor/blas.h
+++ b/tensorflow/stream_executor/blas.h
@@ -104,6 +104,8 @@ enum class ComputationType {
 // Converts a ComputationType to a string.
 string ComputationTypeString(ComputationType ty);
 
+std::ostream &operator<<(std::ostream &os, ComputationType ty);
+
 // Opaque identifier for an "algorithm" used by a blas routine.  This functions
 // as a hint to the blas library.
 typedef int64 AlgorithmType;
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index 44a3a745ad..c563f8f931 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -13,17 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-// Include cuBLAS headers early, and then set EIGEN_HAS_CUDA_FP16
-// if we have new enough CUDA (which we will only know after including
-// cuda.h). This ensures that Eigen's Half.h does not attempt to make its own
-// __half typedef if CUDA has already defined one (and conversely, that we do
-// not include <cuda_fp16.h> after Half.h has made its typedef).
-#include "cuda/include/cuda.h"
 #include "cuda/include/cublas_v2.h"
-
-#if CUDA_VERSION >= 7050
-#define EIGEN_HAS_CUDA_FP16
-#endif
+#include "cuda/include/cuda.h"
 
 #if CUDA_VERSION >= 8000
 #define SE_CUDA_DATA_HALF CUDA_R_16F
@@ -33,6 +24,34 @@ limitations under the License.
 
 #include "tensorflow/stream_executor/cuda/cuda_blas.h"
 
+// Both Eigen Half.h and CUDA cuda_fp16.h provide similar typedef for __half. As
+// such, there are two ways to get the typedef for __half:
+//
+// (1) Includes cuda_fp16.h and defines EIGEN_HAS_CUDA_FP16.
+// (2) Neither includes cuda_fp16.h nor defines EIGEN_HAS_CUDA_FP16.
+//
+// Due to issue b/73793421, when the first approach is used and NVCC is used to
+// compile this file, NVCC will complain duplicated definition for
+// EIGEN_HAS_CUDA_FP16. On the other hand, when the second approach is used and
+// clang is used to compile this file, clang will not understand __half
+// due to missing the definition and macro EIGEN_HAS_CUDA_FP16.
+//
+// Because this file may be compiled with CLANG but will never be compiled with
+// NVCC, we choose the first approach for CUDA < 9.0. For CUDA >= 9.0, we have
+// to use the second approach because the data member in the __half defined
+// by CUDA > 9.0 is `__x` while Eigen expects it to be `x`.
+//
+// TODO(b/73793421): Remove the following code block to switch to the second
+// approach when the issue is fixed.
+#if CUDA_VERSION < 9000
+#include "cuda/include/cuda_fp16.h"
+#if CUDA_VERSION >= 7050
+#define EIGEN_HAS_CUDA_FP16
+#endif
+#endif
+
+#include "third_party/eigen3/Eigen/Core"
+
 #include <assert.h>
 #include <complex>
 
@@ -2256,6 +2275,14 @@ bool CUDABlas::DoBlasGemmWithAlgorithm(
     DeviceMemory<Eigen::half> *c, int ldc,
     blas::ComputationType computation_type, blas::AlgorithmType algorithm,
     blas::ProfileResult *output_profile_result) {
+  if (computation_type == blas::ComputationType::kF32) {
+    return DoBlasGemmWithAlgorithmImpl(
+        stream, transa, transb, m, n, k, static_cast<float>(alpha), a, lda, b,
+        ldb, static_cast<float>(beta), c, ldc, computation_type, algorithm,
+        output_profile_result);
+  }
+
+  CHECK_EQ(computation_type, blas::ComputationType::kF16);
   return DoBlasGemmWithAlgorithmImpl(
       stream, transa, transb, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
       computation_type, algorithm, output_profile_result);
-- 
GitLab


From 757a71e886fb9328b19b0ba15658e49cfa7cc323 Mon Sep 17 00:00:00 2001
From: Akshay Agrawal <akshayka@google.com>
Date: Wed, 28 Feb 2018 13:00:30 -0800
Subject: [PATCH 449/575] Lift ops to the global graph if all graphs are
 building functions

This change ensures that, when all graphs are building functions, `init_scope`
lifts ops into the global graph.

PiperOrigin-RevId: 187370367
---
 tensorflow/python/framework/ops.py      | 60 +++++++++++++++----------
 tensorflow/python/framework/ops_test.py | 31 +++++++++----
 2 files changed, 59 insertions(+), 32 deletions(-)

diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index b0d2704c07..735ba316d0 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -5103,38 +5103,50 @@ def init_scope():
   """
   # pylint: enable=g-doc-return-or-yield,line-too-long
 
-  in_graph_mode = context.in_graph_mode()
-  # Retrieve the active name scope: entering an `init_scope` preserves
-  # the name scope of the current context.
-  if in_graph_mode:
+  if context.in_eager_mode():
+    # Fastpath.
+    with tape.stop_recording():
+      yield
+  else:
+    # Retrieve the active name scope: entering an `init_scope` preserves
+    # the name scope of the current context.
     default_graph = get_default_graph()
     scope = default_graph.get_name_scope()
-  else:
-    scope = context.context().scope_name
-  if scope and scope[-1] != '/':
-    # Names that end with trailing slashes are treated by `name_scope` as
-    # absolute.
-    scope = scope + '/'
-
-  outer_context = None
-  if in_graph_mode and not _default_graph_stack.stack:
-    outer_context = default_graph.as_default
-  else:
-    for stack_entry in reversed(context.context_stack.stack):
-      if not stack_entry.is_building_function:
-        outer_context = stack_entry.enter_context_fn
-        break
+    if scope and scope[-1] != '/':
+      # Names that end with trailing slashes are treated by `name_scope` as
+      # absolute.
+      scope = scope + '/'
+
+    outer_context = None
+    if not _default_graph_stack.stack:
+      # If the default graph stack is empty, then we cannot be building a
+      # function. Install the global graph (which, in this case, is also the
+      # default graph) as the outer context.
+      if default_graph.building_function:
+        raise RuntimeError("The global graph is building a function.")
+      outer_context = default_graph.as_default
+    else:
+      # Find a context that is not building a function.
+      for stack_entry in reversed(context.context_stack.stack):
+        if not stack_entry.is_building_function:
+          outer_context = stack_entry.enter_context_fn
+          break
 
-  if outer_context is None:
-    raise AssertionError("All graphs are building functions, and no "
+      if outer_context is None:
+        # As a last resort, obtain the global default graph; this graph doesn't
+        # necessarily live on the graph stack (and hence it doesn't necessarily
+        # live on the context stack), but it is stored in the graph stack's
+        # encapsulating object.
+        outer_context = _default_graph_stack._GetGlobalDefaultGraph().as_default  # pylint: disable=protected-access
+
+    if outer_context is None:
+      # Sanity check; this shouldn't be triggered.
+      raise RuntimeError("All graphs are building functions, and no "
                          "eager context was previously active.")
 
-  try:
     with outer_context(), name_scope(scope), control_dependencies(
         None), tape.stop_recording():
       yield
-  finally:
-    pass
 
 
 def enable_eager_execution(config=None, device_policy=None):
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index a141fe6340..1f2dfb8d43 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -2156,14 +2156,6 @@ class InitScopeTest(test_util.TensorFlowTestCase):
           self.assertIs(g, ops.get_default_graph())
           self.assertTrue(context.in_graph_mode())
 
-  def testAllGraphsBuildingFunctionsRaisesError(self):
-    g = ops.Graph()
-    g._building_function = True  # pylint: disable=protected-access
-    with g.as_default():
-      with self.assertRaises(AssertionError):
-        with ops.init_scope():
-          pass
-
   def testStaysInEagerWhenOnlyEagerContextActive(self):
     with context.eager_mode():
       with ops.init_scope():
@@ -2241,6 +2233,29 @@ class InitScopeTest(test_util.TensorFlowTestCase):
       self.assertEqual(4, int(compiled_outer(inner=compiled_inner)))
       self.assertEqual(7, int(compiled_outer(inner=compiled_inner)))
 
+  def testFallsBackToGlobalGraphWhenAllGraphsAreBuildingFunctions(self):
+    with context.graph_mode():
+      ops.reset_default_graph()
+      # This doesn't push anything onto the graph stack, but it does
+      # set the stack's global graph.
+      global_graph = ops.get_default_graph()
+      fn_graph = ops.Graph()
+
+      # pylint: disable=protected-access
+      fn_graph._building_function = True
+      self.assertEqual(len(ops._default_graph_stack.stack), 0)
+      with fn_graph.as_default():
+        self.assertEqual(len(ops._default_graph_stack.stack), 1)
+        with ops.init_scope():
+          self.assertGreater(len(ops._default_graph_stack.stack), 1)
+          dummy = constant_op.constant(1.0)
+        self.assertEqual(len(ops._default_graph_stack.stack), 1)
+      # Note that the global graph is _not_ on the graph stack.
+      self.assertEqual(len(ops._default_graph_stack.stack), 0)
+      # Ensure that `dummy` was added to the global graph.
+      self.assertEqual(global_graph, dummy.graph)
+      # pylint: enable=protected-access
+
   def testInstallsDefaultGraphWhenGraphStackIsEmptyInGraphMode(self):
     with context.graph_mode():
       # pylint: disable=protected-access
-- 
GitLab


From 69f674b473470b44c6a1ca1bbb3bcc6a8c53074b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 13:02:07 -0800
Subject: [PATCH 450/575] Factor out the LstmBatchStep for the various LSTM
 Ops.

PiperOrigin-RevId: 187370622
---
 .../kernels/bidirectional_sequence_lstm.cc    | 183 ++----------------
 .../lite/kernels/internal/kernel_utils.cc     | 147 ++++++++++++++
 .../lite/kernels/internal/kernel_utils.h      |  36 ++++
 tensorflow/contrib/lite/kernels/lstm.cc       | 170 +++++-----------
 .../kernels/unidirectional_sequence_lstm.cc   | 179 +++++------------
 5 files changed, 294 insertions(+), 421 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
index 8d70df5e21..a64ac42bc4 100644
--- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
+++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/contrib/lite/builtin_op_data.h"
 #include "tensorflow/contrib/lite/context.h"
 #include "tensorflow/contrib/lite/kernels/activation_functor.h"
+#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h"
 #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h"
 #include "tensorflow/contrib/lite/kernels/kernel_util.h"
 #include "tensorflow/contrib/lite/kernels/op_macros.h"
@@ -443,166 +444,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   return kTfLiteOk;
 }
 
-// Performs an LSTM batch inference step for input specified by input_ptr_batch.
-// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and
-// biases (*_bias_ptr), and buffers (*_scratch), along with additional
-// parameters:
-//  - params: various LSTM params including activation, clipping, etc.,
-//  - use_cifg: use coupled input forget gates,
-//  - use_peephole: whether to use peephole connection or not,
-//  - n_batch: size of batch,
-//  - n_cell: number of cells (or units),
-//  - n_input: the input size,
-//  - n_output: the output size.
-//
-// The pointers to the hidden state and the output are updated as a result.
-//
-// The pointers with the suffix "_batch" point to data aligned in batch_major
-// order, and each step processes batch_size many inputs from input_ptr_batch,
-// and updates batch_size many outputs and hidden states.
-void LstmBatchStep(
-    const float* input_ptr_batch, const float* input_to_input_weights_ptr,
-    const float* input_to_forget_weights_ptr,
-    const float* input_to_cell_weights_ptr,
-    const float* input_to_output_weights_ptr,
-    const float* recurrent_to_input_weights_ptr,
-    const float* recurrent_to_forget_weights_ptr,
-    const float* recurrent_to_cell_weights_ptr,
-    const float* recurrent_to_output_weights_ptr,
-    const float* cell_to_input_weights_ptr,
-    const float* cell_to_forget_weights_ptr,
-    const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr,
-    const float* forget_gate_bias_ptr, const float* cell_bias_ptr,
-    const float* output_gate_bias_ptr, const float* projection_weights_ptr,
-    const float* projection_bias_ptr, const TfLiteLSTMParams* params,
-    bool use_cifg, bool use_peephole, int n_batch, int n_cell, int n_input,
-    int n_output, float* output_state_ptr, float* cell_state_ptr,
-    float* input_gate_scratch, float* forget_gate_scratch, float* cell_scratch,
-    float* output_gate_scratch, float* output_ptr_time) {
-  // Initialize scratch buffers with bias.
-  if (!use_cifg) {
-    tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch,
-                                          input_gate_scratch);
-  }
-  tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch,
-                                        forget_gate_scratch);
-  tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch,
-                                        cell_scratch);
-  tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch,
-                                        output_gate_scratch);
-
-  // For each batch and cell: compute input_weight * input.
-  if (!use_cifg) {
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
-        input_gate_scratch, /*result_stride=*/1);
-  }
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
-      forget_gate_scratch, /*result_stride=*/1);
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
-      cell_scratch, /*result_stride=*/1);
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
-      output_gate_scratch, /*result_stride=*/1);
-
-  // For each batch and cell: compute recurrent_weight * output_state.
-  if (!use_cifg) {
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr,
-        n_batch, input_gate_scratch,
-        /*result_stride=*/1);
-  }
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr,
-      n_batch, forget_gate_scratch,
-      /*result_stride=*/1);
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr,
-      n_batch, cell_scratch, /*result_stride=*/1);
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr,
-      n_batch, output_gate_scratch,
-      /*result_stride=*/1);
-
-  // For each batch and cell: update input gate.
-  if (!use_cifg) {
-    if (use_peephole) {
-      tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-          cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch,
-          input_gate_scratch);
-    }
-    tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
-                                       input_gate_scratch);
-  }
-
-  // For each batch and cell: update forget gate.
-  if (use_peephole) {
-    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-        cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch,
-        forget_gate_scratch);
-  }
-  tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
-                                     forget_gate_scratch);
-
-  // For each batch and cell: update the cell.
-  tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr,
-                                         n_batch * n_cell, cell_state_ptr);
-  tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell,
-                                        params->activation, cell_scratch);
-  if (use_cifg) {
-    tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell,
-                             forget_gate_scratch);
-    tensor_utils::VectorVectorCwiseProductAccumulate(
-        cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr);
-  } else {
-    tensor_utils::VectorVectorCwiseProductAccumulate(
-        cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr);
-  }
-  if (params->cell_clip > 0.0) {
-    tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell,
-                             params->cell_clip, cell_state_ptr);
-  }
-
-  // For each batch and cell: update the output gate.
-  if (use_peephole) {
-    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-        cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch,
-        output_gate_scratch);
-  }
-  tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
-                                     output_gate_scratch);
-  tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell,
-                                        params->activation, cell_scratch);
-  tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch,
-                                         n_batch * n_cell, output_gate_scratch);
-
-  // For each batch: update the projection and output_state.
-  const bool use_projection_weight = (projection_weights_ptr != nullptr);
-  const bool use_projection_bias = (projection_bias_ptr != nullptr);
-  if (use_projection_weight) {
-    if (use_projection_bias) {
-      tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output,
-                                            n_batch, output_ptr_time);
-    } else {
-      tensor_utils::ZeroVector(output_ptr_time, n_batch * n_output);
-    }
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch,
-        output_ptr_time, /*result_stride=*/1);
-    if (params->proj_clip > 0.0) {
-      tensor_utils::ClipVector(output_ptr_time, n_batch * n_output,
-                               params->proj_clip, output_ptr_time);
-    }
-  } else {
-    tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output,
-                             output_ptr_time);
-  }
-  tensor_utils::CopyVector(output_ptr_time, n_batch * n_output,
-                           output_state_ptr);
-}
-
 // The LSTM Op engine.
 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   auto* params = reinterpret_cast<TfLiteLSTMParams*>(node->builtin_data);
@@ -756,7 +597,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
     const float* input_ptr_batch = input->data.f + t * n_batch * n_input;
     float* output_ptr_time = fw_output->data.f + t * n_batch * n_fw_output;
 
-    LstmBatchStep(
+    kernel_utils::LstmStep(
         input_ptr_batch, fw_input_to_input_weights_ptr,
         fw_input_to_forget_weights->data.f, fw_input_to_cell_weights->data.f,
         fw_input_to_output_weights->data.f, fw_recurrent_to_input_weights_ptr,
@@ -766,11 +607,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
         fw_cell_to_forget_weights_ptr, fw_cell_to_output_weights_ptr,
         fw_input_gate_bias_ptr, fw_forget_gate_bias->data.f,
         fw_cell_bias->data.f, fw_output_gate_bias->data.f,
-        fw_projection_weights_ptr, fw_projection_bias_ptr, params, fw_use_cifg,
-        fw_use_peephole, n_batch, n_fw_cell, n_input, n_fw_output,
-        fw_output_state->data.f, fw_cell_state->data.f, fw_input_gate_scratch,
-        fw_forget_gate_scratch, fw_cell_scratch, fw_output_gate_scratch,
-        output_ptr_time);
+        fw_projection_weights_ptr, fw_projection_bias_ptr, params, n_batch,
+        n_fw_cell, n_input, n_fw_output, fw_output_state->data.f,
+        fw_cell_state->data.f, fw_input_gate_scratch, fw_forget_gate_scratch,
+        fw_cell_scratch, fw_output_gate_scratch, output_ptr_time);
   }
 
   // n_cell and n_output will be the same size when there is no projection.
@@ -828,7 +668,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
     const float* input_ptr_batch = input->data.f + t * n_batch * n_input;
     float* output_ptr_time = bw_output->data.f + t * n_batch * n_bw_output;
 
-    LstmBatchStep(
+    kernel_utils::LstmStep(
         input_ptr_batch, bw_input_to_input_weights_ptr,
         bw_input_to_forget_weights->data.f, bw_input_to_cell_weights->data.f,
         bw_input_to_output_weights->data.f, bw_recurrent_to_input_weights_ptr,
@@ -838,11 +678,10 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
         bw_cell_to_forget_weights_ptr, bw_cell_to_output_weights_ptr,
         bw_input_gate_bias_ptr, bw_forget_gate_bias->data.f,
         bw_cell_bias->data.f, bw_output_gate_bias->data.f,
-        bw_projection_weights_ptr, bw_projection_bias_ptr, params, bw_use_cifg,
-        bw_use_peephole, n_batch, n_bw_cell, n_input, n_bw_output,
-        bw_output_state->data.f, bw_cell_state->data.f, bw_input_gate_scratch,
-        bw_forget_gate_scratch, bw_cell_scratch, bw_output_gate_scratch,
-        output_ptr_time);
+        bw_projection_weights_ptr, bw_projection_bias_ptr, params, n_batch,
+        n_bw_cell, n_input, n_bw_output, bw_output_state->data.f,
+        bw_cell_state->data.f, bw_input_gate_scratch, bw_forget_gate_scratch,
+        bw_cell_scratch, bw_output_gate_scratch, output_ptr_time);
   }
 
   // Backward step.
diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc
index 510395126c..f142374269 100644
--- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc
+++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc
@@ -40,5 +40,152 @@ void RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr,
                                         hidden_state_ptr_batch);
 }
 
+void LstmStep(
+    const float* input_ptr_batch, const float* input_to_input_weights_ptr,
+    const float* input_to_forget_weights_ptr,
+    const float* input_to_cell_weights_ptr,
+    const float* input_to_output_weights_ptr,
+    const float* recurrent_to_input_weights_ptr,
+    const float* recurrent_to_forget_weights_ptr,
+    const float* recurrent_to_cell_weights_ptr,
+    const float* recurrent_to_output_weights_ptr,
+    const float* cell_to_input_weights_ptr,
+    const float* cell_to_forget_weights_ptr,
+    const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr,
+    const float* forget_gate_bias_ptr, const float* cell_bias_ptr,
+    const float* output_gate_bias_ptr, const float* projection_weights_ptr,
+    const float* projection_bias_ptr, const TfLiteLSTMParams* params,
+    int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr,
+    float* cell_state_ptr, float* input_gate_scratch,
+    float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch,
+    float* output_ptr_batch) {
+  // Since we have already checked that weights are all there or none, we can
+  // check the existense of only one to the get the condition.
+  const bool use_cifg = (input_to_input_weights_ptr == nullptr);
+  const bool use_peephole = (cell_to_output_weights_ptr != nullptr);
+  // Initialize scratch buffers with bias.
+  if (!use_cifg) {
+    tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch,
+                                          input_gate_scratch);
+  }
+  tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch,
+                                        forget_gate_scratch);
+  tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch,
+                                        cell_scratch);
+  tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch,
+                                        output_gate_scratch);
+
+  // For each batch and cell: compute input_weight * input.
+  if (!use_cifg) {
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+        input_gate_scratch, /*result_stride=*/1);
+  }
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+      forget_gate_scratch, /*result_stride=*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+      cell_scratch, /*result_stride=*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch,
+      output_gate_scratch, /*result_stride=*/1);
+
+  // For each batch and cell: compute recurrent_weight * output_state.
+  if (!use_cifg) {
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr,
+        n_batch, input_gate_scratch,
+        /*result_stride=*/1);
+  }
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr,
+      n_batch, forget_gate_scratch,
+      /*result_stride=*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr,
+      n_batch, cell_scratch, /*result_stride=*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+      recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr,
+      n_batch, output_gate_scratch,
+      /*result_stride=*/1);
+
+  // For each batch and cell: update input gate.
+  if (!use_cifg) {
+    if (use_peephole) {
+      tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+          cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch,
+          input_gate_scratch);
+    }
+    tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
+                                       input_gate_scratch);
+  }
+
+  // For each batch and cell: update forget gate.
+  if (use_peephole) {
+    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+        cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch,
+        forget_gate_scratch);
+  }
+  tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
+                                     forget_gate_scratch);
+
+  // For each batch and cell: update the cell.
+  tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr,
+                                         n_batch * n_cell, cell_state_ptr);
+  tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell,
+                                        params->activation, cell_scratch);
+  if (use_cifg) {
+    tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell,
+                             forget_gate_scratch);
+    tensor_utils::VectorVectorCwiseProductAccumulate(
+        cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr);
+  } else {
+    tensor_utils::VectorVectorCwiseProductAccumulate(
+        cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr);
+  }
+  if (params->cell_clip > 0.0) {
+    tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell,
+                             params->cell_clip, cell_state_ptr);
+  }
+
+  // For each batch and cell: update the output gate.
+  if (use_peephole) {
+    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
+        cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch,
+        output_gate_scratch);
+  }
+  tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
+                                     output_gate_scratch);
+  tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell,
+                                        params->activation, cell_scratch);
+  tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch,
+                                         n_batch * n_cell, output_gate_scratch);
+
+  // For each batch: update the projection and output_state.
+  const bool use_projection_weight = (projection_weights_ptr != nullptr);
+  const bool use_projection_bias = (projection_bias_ptr != nullptr);
+  if (use_projection_weight) {
+    if (use_projection_bias) {
+      tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output,
+                                            n_batch, output_ptr_batch);
+    } else {
+      tensor_utils::ZeroVector(output_ptr_batch, n_batch * n_output);
+    }
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch,
+        output_ptr_batch, /*result_stride=*/1);
+    if (params->proj_clip > 0.0) {
+      tensor_utils::ClipVector(output_ptr_batch, n_batch * n_output,
+                               params->proj_clip, output_ptr_batch);
+    }
+  } else {
+    tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output,
+                             output_ptr_batch);
+  }
+  tensor_utils::CopyVector(output_ptr_batch, n_batch * n_output,
+                           output_state_ptr);
+}
+
 }  // namespace kernel_utils
 }  // namespace tflite
diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h
index 9872d4500b..3ec60ee57a 100644
--- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h
+++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h
@@ -35,6 +35,42 @@ void RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr,
                   TfLiteFusedActivation activation,
                   float* hidden_state_ptr_batch, float* output_ptr_batch);
 
+// Performs an LSTM batch inference step for input specified by input_ptr_batch.
+// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and
+// biases (*_bias_ptr), and buffers (*_scratch), along with additional
+// parameters:
+//  - params: various LSTM params including activation, clipping, etc.,
+//  - n_batch: size of batch,
+//  - n_cell: number of cells (or units),
+//  - n_input: the input size,
+//  - n_output: the output size.
+//
+// The pointers to the cell and output state and the output are updated. Unless
+// projection is specified output and output state contain the same data.
+//
+// The pointers with the suffix "_batch" point to data aligned in batch_major
+// order, and each step processes batch_size many inputs from input_ptr_batch,
+// and updates batch_size many cell and output states.
+void LstmStep(
+    const float* input_ptr_batch, const float* input_to_input_weights_ptr,
+    const float* input_to_forget_weights_ptr,
+    const float* input_to_cell_weights_ptr,
+    const float* input_to_output_weights_ptr,
+    const float* recurrent_to_input_weights_ptr,
+    const float* recurrent_to_forget_weights_ptr,
+    const float* recurrent_to_cell_weights_ptr,
+    const float* recurrent_to_output_weights_ptr,
+    const float* cell_to_input_weights_ptr,
+    const float* cell_to_forget_weights_ptr,
+    const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr,
+    const float* forget_gate_bias_ptr, const float* cell_bias_ptr,
+    const float* output_gate_bias_ptr, const float* projection_weights_ptr,
+    const float* projection_bias_ptr, const TfLiteLSTMParams* params,
+    int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr,
+    float* cell_state_ptr, float* input_gate_scratch,
+    float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch,
+    float* output_ptr_batch);
+
 }  // namespace kernel_utils
 }  // namespace tflite
 #endif  // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_KERNEL_UTILS_H_
diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc
index 6c06264d84..b9255b23a5 100644
--- a/tensorflow/contrib/lite/kernels/lstm.cc
+++ b/tensorflow/contrib/lite/kernels/lstm.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/contrib/lite/builtin_op_data.h"
 #include "tensorflow/contrib/lite/context.h"
 #include "tensorflow/contrib/lite/kernels/activation_functor.h"
+#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h"
 #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h"
 #include "tensorflow/contrib/lite/kernels/kernel_util.h"
 #include "tensorflow/contrib/lite/kernels/op_macros.h"
@@ -377,127 +378,54 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
     output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch;
   }
 
-  // Initialize scratch buffers with bias.
-  if (!use_cifg) {
-    tensor_utils::VectorBatchVectorAssign(input_gate_bias->data.f, n_cell,
-                                          n_batch, input_gate_scratch);
-  }
-  tensor_utils::VectorBatchVectorAssign(forget_gate_bias->data.f, n_cell,
-                                        n_batch, forget_gate_scratch);
-  tensor_utils::VectorBatchVectorAssign(cell_bias->data.f, n_cell, n_batch,
-                                        cell_scratch);
-  tensor_utils::VectorBatchVectorAssign(output_gate_bias->data.f, n_cell,
-                                        n_batch, output_gate_scratch);
-
-  // For each batch and cell: compute input_weight * input.
-  if (!use_cifg) {
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        input_to_input_weights->data.f, n_cell, n_input, input->data.f, n_batch,
-        input_gate_scratch, /*result_stride=*/1);
-  }
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      input_to_forget_weights->data.f, n_cell, n_input, input->data.f, n_batch,
-      forget_gate_scratch, /*result_stride=*/1);
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      input_to_cell_weights->data.f, n_cell, n_input, input->data.f, n_batch,
-      cell_scratch, /*result_stride=*/1);
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      input_to_output_weights->data.f, n_cell, n_input, input->data.f, n_batch,
-      output_gate_scratch, /*result_stride=*/1);
-
-  // For each batch and cell: compute recurrent_weight * output_state.
-  if (!use_cifg) {
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        recurrent_to_input_weights->data.f, n_cell, n_output,
-        output_state->data.f, n_batch, input_gate_scratch, /*result_stride=*/1);
-  }
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      recurrent_to_forget_weights->data.f, n_cell, n_output,
-      output_state->data.f, n_batch, forget_gate_scratch, /*result_stride=*/1);
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      recurrent_to_cell_weights->data.f, n_cell, n_output, output_state->data.f,
-      n_batch, cell_scratch, /*result_stride=*/1);
-  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      recurrent_to_output_weights->data.f, n_cell, n_output,
-      output_state->data.f, n_batch, output_gate_scratch, /*result_stride=*/1);
-
-  // For each batch and cell: update input gate.
-  if (!use_cifg) {
-    if (use_peephole) {
-      tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-          cell_to_input_weights->data.f, n_cell, cell_state->data.f, n_batch,
-          input_gate_scratch);
-    }
-    tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
-                                       input_gate_scratch);
-  }
-
-  // For each batch and cell: update forget gate.
-  if (use_peephole) {
-    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-        cell_to_forget_weights->data.f, n_cell, cell_state->data.f, n_batch,
-        forget_gate_scratch);
-  }
-  tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
-                                     forget_gate_scratch);
-
-  // For each batch and cell: update the cell.
-  tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch,
-                                         cell_state->data.f, n_batch * n_cell,
-                                         cell_state->data.f);
-  tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell,
-                                        params->activation, cell_scratch);
-  if (use_cifg) {
-    tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell,
-                             forget_gate_scratch);
-    tensor_utils::VectorVectorCwiseProductAccumulate(
-        cell_scratch, forget_gate_scratch, n_batch * n_cell,
-        cell_state->data.f);
-  } else {
-    tensor_utils::VectorVectorCwiseProductAccumulate(
-        cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state->data.f);
-  }
-  if (params->cell_clip > 0.0) {
-    tensor_utils::ClipVector(cell_state->data.f, n_batch * n_cell,
-                             params->cell_clip, cell_state->data.f);
-  }
-
-  // For each batch and cell: update the output gate.
-  if (use_peephole) {
-    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-        cell_to_output_weights->data.f, n_cell, cell_state->data.f, n_batch,
-        output_gate_scratch);
-  }
-  tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
-                                     output_gate_scratch);
-  tensor_utils::ApplyActivationToVector(cell_state->data.f, n_batch * n_cell,
-                                        params->activation, cell_scratch);
-  tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch,
-                                         n_batch * n_cell, output_gate_scratch);
-
-  // For each batch: update the projection and output_state.
-  const bool use_projection_weight = (projection_weights != nullptr);
-  const bool use_projection_bias = (projection_bias != nullptr);
-  if (use_projection_weight) {
-    if (use_projection_bias) {
-      tensor_utils::VectorBatchVectorAssign(projection_bias->data.f, n_output,
-                                            n_batch, output->data.f);
-    } else {
-      tensor_utils::ZeroVector(output->data.f, n_batch * n_output);
-    }
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        projection_weights->data.f, n_output, n_cell, output_gate_scratch,
-        n_batch, output->data.f, /*result_stride=*/1);
-    if (params->proj_clip > 0.0) {
-      tensor_utils::ClipVector(output->data.f, n_batch * n_output,
-                               params->proj_clip, output->data.f);
-    }
-  } else {
-    tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output,
-                             output->data.f);
-  }
-  tensor_utils::CopyVector(output->data.f, n_batch * n_output,
-                           output_state->data.f);
+  // Check optional tensors, the respective pointers can be null.
+  const float* input_to_input_weights_ptr =
+      (use_cifg) ? nullptr : input_to_input_weights->data.f;
+  const float* recurrent_to_input_weights_ptr =
+      (use_cifg) ? nullptr : recurrent_to_input_weights->data.f;
+  const float* input_gate_bias_ptr =
+      (use_cifg) ? nullptr : input_gate_bias->data.f;
+  const float* cell_to_input_weights_ptr =
+      (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr;
+  const float* cell_to_forget_weights_ptr =
+      (use_peephole) ? cell_to_forget_weights->data.f : nullptr;
+  const float* cell_to_output_weights_ptr =
+      (use_peephole) ? cell_to_output_weights->data.f : nullptr;
+  const float* projection_weights_ptr =
+      (projection_weights == nullptr) ? nullptr : projection_weights->data.f;
+  const float* projection_bias_ptr =
+      (projection_bias == nullptr) ? nullptr : projection_bias->data.f;
+
+  // Required tensors, pointers are non-null.
+  const float* input_ptr_batch = input->data.f;
+  const float* input_to_forget_weights_ptr = input_to_forget_weights->data.f;
+  const float* input_to_cell_weights_ptr = input_to_cell_weights->data.f;
+  const float* input_to_output_weights_ptr = input_to_output_weights->data.f;
+  const float* recurrent_to_forget_weights_ptr =
+      recurrent_to_forget_weights->data.f;
+  const float* recurrent_to_cell_weights_ptr =
+      recurrent_to_cell_weights->data.f;
+  const float* recurrent_to_output_weights_ptr =
+      recurrent_to_output_weights->data.f;
+  const float* forget_gate_bias_ptr = forget_gate_bias->data.f;
+  const float* cell_bias_ptr = cell_bias->data.f;
+  const float* output_gate_bias_ptr = output_gate_bias->data.f;
+
+  float* output_state_ptr = output_state->data.f;
+  float* cell_state_ptr = cell_state->data.f;
+  float* output_ptr_batch = output->data.f;
+
+  kernel_utils::LstmStep(
+      input_ptr_batch, input_to_input_weights_ptr, input_to_forget_weights_ptr,
+      input_to_cell_weights_ptr, input_to_output_weights_ptr,
+      recurrent_to_input_weights_ptr, recurrent_to_forget_weights_ptr,
+      recurrent_to_cell_weights_ptr, recurrent_to_output_weights_ptr,
+      cell_to_input_weights_ptr, cell_to_forget_weights_ptr,
+      cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias_ptr,
+      cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr,
+      projection_bias_ptr, params, n_batch, n_cell, n_input, n_output,
+      output_state_ptr, cell_state_ptr, input_gate_scratch, forget_gate_scratch,
+      cell_scratch, output_gate_scratch, output_ptr_batch);
 
   return kTfLiteOk;
 }
diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
index 9cdb58714e..508a570e2e 100644
--- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
+++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc
@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/contrib/lite/builtin_op_data.h"
 #include "tensorflow/contrib/lite/context.h"
 #include "tensorflow/contrib/lite/kernels/activation_functor.h"
+#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h"
 #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h"
 #include "tensorflow/contrib/lite/kernels/kernel_util.h"
 #include "tensorflow/contrib/lite/kernels/op_macros.h"
@@ -380,135 +381,57 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
     output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch;
   }
 
+  // Check optional tensors, the respective pointers can be null.
+  const float* input_to_input_weights_ptr =
+      (use_cifg) ? nullptr : input_to_input_weights->data.f;
+  const float* recurrent_to_input_weights_ptr =
+      (use_cifg) ? nullptr : recurrent_to_input_weights->data.f;
+  const float* input_gate_bias_ptr =
+      (use_cifg) ? nullptr : input_gate_bias->data.f;
+  const float* cell_to_input_weights_ptr =
+      (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr;
+  const float* cell_to_forget_weights_ptr =
+      (use_peephole) ? cell_to_forget_weights->data.f : nullptr;
+  const float* cell_to_output_weights_ptr =
+      (use_peephole) ? cell_to_output_weights->data.f : nullptr;
+  const float* projection_weights_ptr =
+      (projection_weights == nullptr) ? nullptr : projection_weights->data.f;
+  const float* projection_bias_ptr =
+      (projection_bias == nullptr) ? nullptr : projection_bias->data.f;
+
+  // Required tensors, pointers are non-null.
+  const float* input_to_forget_weights_ptr = input_to_forget_weights->data.f;
+  const float* input_to_cell_weights_ptr = input_to_cell_weights->data.f;
+  const float* input_to_output_weights_ptr = input_to_output_weights->data.f;
+  const float* recurrent_to_forget_weights_ptr =
+      recurrent_to_forget_weights->data.f;
+  const float* recurrent_to_cell_weights_ptr =
+      recurrent_to_cell_weights->data.f;
+  const float* recurrent_to_output_weights_ptr =
+      recurrent_to_output_weights->data.f;
+  const float* forget_gate_bias_ptr = forget_gate_bias->data.f;
+  const float* cell_bias_ptr = cell_bias->data.f;
+  const float* output_gate_bias_ptr = output_gate_bias->data.f;
+
+  float* output_state_ptr = output_state->data.f;
+  float* cell_state_ptr = cell_state->data.f;
+
   for (int t = 0; t < max_time; t++) {
-    const float* input_ptr_time = input->data.f + t * n_batch * n_input;
-    // Initialize scratch buffers with bias.
-    if (!use_cifg) {
-      tensor_utils::VectorBatchVectorAssign(input_gate_bias->data.f, n_cell,
-                                            n_batch, input_gate_scratch);
-    }
-    tensor_utils::VectorBatchVectorAssign(forget_gate_bias->data.f, n_cell,
-                                          n_batch, forget_gate_scratch);
-    tensor_utils::VectorBatchVectorAssign(cell_bias->data.f, n_cell, n_batch,
-                                          cell_scratch);
-    tensor_utils::VectorBatchVectorAssign(output_gate_bias->data.f, n_cell,
-                                          n_batch, output_gate_scratch);
-
-    // For each batch and cell: compute input_weight * input.
-    if (!use_cifg) {
-      tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-          input_to_input_weights->data.f, n_cell, n_input, input_ptr_time,
-          n_batch, input_gate_scratch, /*result_stride=*/1);
-    }
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        input_to_forget_weights->data.f, n_cell, n_input, input_ptr_time,
-        n_batch, forget_gate_scratch, /*result_stride=*/1);
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        input_to_cell_weights->data.f, n_cell, n_input, input_ptr_time, n_batch,
-        cell_scratch, /*result_stride=*/1);
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        input_to_output_weights->data.f, n_cell, n_input, input_ptr_time,
-        n_batch, output_gate_scratch, /*result_stride=*/1);
-
-    // For each batch and cell: compute recurrent_weight * output_state.
-    if (!use_cifg) {
-      tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-          recurrent_to_input_weights->data.f, n_cell, n_output,
-          output_state->data.f, n_batch, input_gate_scratch,
-          /*result_stride=*/1);
-    }
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        recurrent_to_forget_weights->data.f, n_cell, n_output,
-        output_state->data.f, n_batch, forget_gate_scratch,
-        /*result_stride=*/1);
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        recurrent_to_cell_weights->data.f, n_cell, n_output,
-        output_state->data.f, n_batch, cell_scratch, /*result_stride=*/1);
-    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        recurrent_to_output_weights->data.f, n_cell, n_output,
-        output_state->data.f, n_batch, output_gate_scratch,
-        /*result_stride=*/1);
-
-    // For each batch and cell: update input gate.
-    if (!use_cifg) {
-      if (use_peephole) {
-        tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-            cell_to_input_weights->data.f, n_cell, cell_state->data.f, n_batch,
-            input_gate_scratch);
-      }
-      tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
-                                         input_gate_scratch);
-    }
-
-    // For each batch and cell: update forget gate.
-    if (use_peephole) {
-      tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-          cell_to_forget_weights->data.f, n_cell, cell_state->data.f, n_batch,
-          forget_gate_scratch);
-    }
-    tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
-                                       forget_gate_scratch);
-
-    // For each batch and cell: update the cell.
-    tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch,
-                                           cell_state->data.f, n_batch * n_cell,
-                                           cell_state->data.f);
-    tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell,
-                                          params->activation, cell_scratch);
-    if (use_cifg) {
-      tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell,
-                               forget_gate_scratch);
-      tensor_utils::VectorVectorCwiseProductAccumulate(
-          cell_scratch, forget_gate_scratch, n_batch * n_cell,
-          cell_state->data.f);
-    } else {
-      tensor_utils::VectorVectorCwiseProductAccumulate(
-          cell_scratch, input_gate_scratch, n_batch * n_cell,
-          cell_state->data.f);
-    }
-    if (params->cell_clip > 0.0) {
-      tensor_utils::ClipVector(cell_state->data.f, n_batch * n_cell,
-                               params->cell_clip, cell_state->data.f);
-    }
-
-    // For each batch and cell: update the output gate.
-    if (use_peephole) {
-      tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-          cell_to_output_weights->data.f, n_cell, cell_state->data.f, n_batch,
-          output_gate_scratch);
-    }
-    tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
-                                       output_gate_scratch);
-    tensor_utils::ApplyActivationToVector(cell_state->data.f, n_batch * n_cell,
-                                          params->activation, cell_scratch);
-    tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch,
-                                           n_batch * n_cell,
-                                           output_gate_scratch);
-
-    // For each batch: update the projection and output_state.
-    const bool use_projection_weight = (projection_weights != nullptr);
-    const bool use_projection_bias = (projection_bias != nullptr);
-    float* output_ptr_time = output->data.f + t * n_batch * n_output;
-    if (use_projection_weight) {
-      if (use_projection_bias) {
-        tensor_utils::VectorBatchVectorAssign(projection_bias->data.f, n_output,
-                                              n_batch, output_ptr_time);
-      } else {
-        tensor_utils::ZeroVector(output_ptr_time, n_batch * n_output);
-      }
-      tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-          projection_weights->data.f, n_output, n_cell, output_gate_scratch,
-          n_batch, output_ptr_time, /*result_stride=*/1);
-      if (params->proj_clip > 0.0) {
-        tensor_utils::ClipVector(output_ptr_time, n_batch * n_output,
-                                 params->proj_clip, output_ptr_time);
-      }
-    } else {
-      tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output,
-                               output_ptr_time);
-    }
-    tensor_utils::CopyVector(output_ptr_time, n_batch * n_output,
-                             output_state->data.f);
+    const float* input_ptr_batch = input->data.f + t * n_batch * n_input;
+    float* output_ptr_batch = output->data.f + t * n_batch * n_output;
+
+    kernel_utils::LstmStep(
+        input_ptr_batch, input_to_input_weights_ptr,
+        input_to_forget_weights_ptr, input_to_cell_weights_ptr,
+        input_to_output_weights_ptr, recurrent_to_input_weights_ptr,
+        recurrent_to_forget_weights_ptr, recurrent_to_cell_weights_ptr,
+        recurrent_to_output_weights_ptr, cell_to_input_weights_ptr,
+        cell_to_forget_weights_ptr, cell_to_output_weights_ptr,
+        input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr,
+        output_gate_bias_ptr, projection_weights_ptr, projection_bias_ptr,
+        params, n_batch, n_cell, n_input, n_output, output_state_ptr,
+        cell_state_ptr, input_gate_scratch, forget_gate_scratch, cell_scratch,
+        output_gate_scratch, output_ptr_batch);
   }
   return kTfLiteOk;
 }
-- 
GitLab


From c1777a2633bd5615a1d654e50f82d0cf75fd60f0 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Wed, 28 Feb 2018 13:17:06 -0800
Subject: [PATCH 451/575] [XLA] Fix up some error messages to conform to XLA's
 error message style.

PiperOrigin-RevId: 187372860
---
 tensorflow/compiler/xla/literal_util.cc        | 18 +++++++++++-------
 tensorflow/compiler/xla/literal_util_test.cc   | 10 +++++-----
 .../compiler/xla/service/allocation_tracker.cc |  2 +-
 .../compiler/xla/service/hlo_instruction.cc    |  6 ++++--
 .../xla/tests/deconstruct_tuple_test.cc        |  2 +-
 5 files changed, 22 insertions(+), 16 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index 3962a9b316..c3eb8caa57 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -343,7 +343,7 @@ Status Literal::Piece::CopyFrom(const Literal::Piece& src) {
 #undef COPY_ELEMENTS
       default:
         return Unimplemented(
-            "Unhandled primitive type %s",
+            "Copying a Literal object with element type %s is not implemented.",
             PrimitiveType_Name(subshape().element_type()).c_str());
     }
   }
@@ -491,7 +491,10 @@ Status Literal::CopySliceFrom(const Literal& src_literal,
     default:
       break;
   }
-  return Unimplemented("Unhandled primitive type %d", shape().element_type());
+  return Unimplemented(
+      "Copying a slice from a Literal object with element type %d is not "
+      "implemented.",
+      shape().element_type());
 }
 
 /* static */ Literal Literal::Zero(PrimitiveType primitive_type) {
@@ -1394,8 +1397,8 @@ StatusOr<std::unique_ptr<Literal>> ConvertIfDestTypeMatches(
       return ConvertToC64<primitive_src_type>(src_literal);
     // Other types are not yet supported.
     default:
-      return InvalidArgument(
-          "Unimplemented: Convert from type %s to type %s",
+      return Unimplemented(
+          "Converting from type %s to type %s is not implemented.",
           PrimitiveType_Name(src_literal.shape().element_type()).c_str(),
           PrimitiveType_Name(primitive_dest_type).c_str());
   }
@@ -1424,9 +1427,10 @@ StatusOr<std::unique_ptr<Literal>> Literal::Convert(
 #undef CONVERT_IF_DEST_TYPE_MATCHES
       // Other types are not yet supported.
     default:
-      return InvalidArgument("Unimplemented: Convert from type %s to type %s",
-                             PrimitiveType_Name(shape().element_type()).c_str(),
-                             PrimitiveType_Name(primitive_dest_type).c_str());
+      return Unimplemented(
+          "Converting from type %s to type %s is not implemented.",
+          PrimitiveType_Name(shape().element_type()).c_str(),
+          PrimitiveType_Name(primitive_dest_type).c_str());
   }
 }
 
diff --git a/tensorflow/compiler/xla/literal_util_test.cc b/tensorflow/compiler/xla/literal_util_test.cc
index 9ff0771110..04e45f0049 100644
--- a/tensorflow/compiler/xla/literal_util_test.cc
+++ b/tensorflow/compiler/xla/literal_util_test.cc
@@ -1232,15 +1232,15 @@ TEST_F(LiteralUtilTest, ConvertIfTypesMatch) {
   EXPECT_EQ(*conv, *c64);
 
   EXPECT_EQ(s32->Convert(TUPLE).status().code(),
-            tensorflow::error::INVALID_ARGUMENT);
+            tensorflow::error::UNIMPLEMENTED);
   EXPECT_EQ(s32->Convert(S16).status().code(),
-            tensorflow::error::INVALID_ARGUMENT);
+            tensorflow::error::UNIMPLEMENTED);
   EXPECT_EQ(s32->Convert(U16).status().code(),
-            tensorflow::error::INVALID_ARGUMENT);
+            tensorflow::error::UNIMPLEMENTED);
   EXPECT_EQ(c64->Convert(F32).status().code(),
-            tensorflow::error::INVALID_ARGUMENT);
+            tensorflow::error::UNIMPLEMENTED);
   EXPECT_EQ(c64->Convert(S32).status().code(),
-            tensorflow::error::INVALID_ARGUMENT);
+            tensorflow::error::UNIMPLEMENTED);
 }
 
 TEST_F(LiteralUtilTest, CopyFromProto_Bool) {
diff --git a/tensorflow/compiler/xla/service/allocation_tracker.cc b/tensorflow/compiler/xla/service/allocation_tracker.cc
index 4e80679c11..7a75c02531 100644
--- a/tensorflow/compiler/xla/service/allocation_tracker.cc
+++ b/tensorflow/compiler/xla/service/allocation_tracker.cc
@@ -109,7 +109,7 @@ StatusOr<std::vector<GlobalDataHandle>> AllocationTracker::DeconstructTuple(
   TF_RET_CHECK(ShapeUtil::IsTuple(shaped_buffer->on_device_shape()));
 
   if (ShapeUtil::IsNestedTuple(shaped_buffer->on_device_shape())) {
-    return Unimplemented("deconstructing nested tuples not yet supported");
+    return Unimplemented("Deconstructing nested tuples is not implemented.");
   }
 
   std::vector<GlobalDataHandle> element_handles;
diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc
index a534d8ff06..af9d772b00 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.cc
+++ b/tensorflow/compiler/xla/service/hlo_instruction.cc
@@ -2682,8 +2682,10 @@ Status HloInstruction::Visit(DfsHloVisitorBase<HloInstructionPtr>* visitor) {
     case HloOpcode::kTrace:
       break;
   }
-  return Unimplemented("unhandled HloOpcode for DfsHloVisitor: %s",
-                       HloOpcodeString(opcode_).c_str());
+  return InternalError(
+      "Unhandled HloOpcode for DfsHloVisitor: %s. This should not happen - "
+      "please file a bug for XLA.",
+      HloOpcodeString(opcode_).c_str());
 }
 
 // Explicit instantiations.
diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
index 032c06cd3c..3ab0ea4ad4 100644
--- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
+++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc
@@ -195,7 +195,7 @@ XLA_TEST_F(DeconstructTupleTest, DeconstructNestedTuple) {
   auto result_status = client_->DeconstructTuple(*global_data);
   EXPECT_FALSE(result_status.ok());
   EXPECT_THAT(result_status.status().error_message(),
-              HasSubstr("deconstructing nested tuples not yet supported"));
+              HasSubstr("Deconstructing nested tuples is not implemented"));
 }
 
 }  // namespace
-- 
GitLab


From c661f2c3de75e3ad58bce52b39b8cc2e7ee07c0e Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Wed, 28 Feb 2018 13:19:01 -0800
Subject: [PATCH 452/575] [TF:XLA] Bump open source llvm revision to r326313

PiperOrigin-RevId: 187373178
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index fa3671b4c9..ea8f42ab8d 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -475,11 +475,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/832f2bf0d8908aea8160bab128708d521764fe8d.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/832f2bf0d8908aea8160bab128708d521764fe8d.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/9a6e78e4adc959d2825f7af35b4ed0e09394d840.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/9a6e78e4adc959d2825f7af35b4ed0e09394d840.tar.gz",
       ],
-      sha256 = "e6bb793bbdce37ee5643789a27d174f1cdd8e7323a69d5f331376eb34755ee0d",
-      strip_prefix = "llvm-832f2bf0d8908aea8160bab128708d521764fe8d",
+      sha256 = "7990b4d446de971e0acc481942920452a182d2f87a8164bdc117fd9b9ace591d",
+      strip_prefix = "llvm-9a6e78e4adc959d2825f7af35b4ed0e09394d840",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
   )
 
-- 
GitLab


From 0f3105c39b079d8e7741e48e3b098c47c81a453a Mon Sep 17 00:00:00 2001
From: Kay Zhu <kayzhu@google.com>
Date: Wed, 28 Feb 2018 13:43:42 -0800
Subject: [PATCH 453/575] [XLA] Add a HLO simplifier pass to fold
 Conditional(constant_predicate, true_computation, false_computation) to
 Call(predicated_computation) and finally inlined computation.

PiperOrigin-RevId: 187376657
---
 tensorflow/compiler/xla/service/BUILD         |  35 ++++
 .../xla/service/conditional_simplifier.cc     | 106 ++++++++++++
 .../xla/service/conditional_simplifier.h      |  38 +++++
 .../service/conditional_simplifier_test.cc    | 153 ++++++++++++++++++
 tensorflow/compiler/xla/service/cpu/BUILD     |   1 +
 .../compiler/xla/service/cpu/cpu_compiler.cc  |   2 +
 tensorflow/compiler/xla/service/gpu/BUILD     |   1 +
 .../compiler/xla/service/gpu/gpu_compiler.cc  |   2 +
 8 files changed, 338 insertions(+)
 create mode 100644 tensorflow/compiler/xla/service/conditional_simplifier.cc
 create mode 100644 tensorflow/compiler/xla/service/conditional_simplifier.h
 create mode 100644 tensorflow/compiler/xla/service/conditional_simplifier_test.cc

diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index e6a6e54927..e4ae812532 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -1213,6 +1213,41 @@ tf_cc_test(
     ],
 )
 
+cc_library(
+    name = "conditional_simplifier",
+    srcs = ["conditional_simplifier.cc"],
+    hdrs = ["conditional_simplifier.h"],
+    deps = [
+        ":call_inliner",
+        ":hlo",
+        ":hlo_pass",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/core:lib",
+    ],
+)
+
+tf_cc_test(
+    name = "conditional_simplifier_test",
+    srcs = ["conditional_simplifier_test.cc"],
+    deps = [
+        ":conditional_simplifier",
+        ":hlo",
+        ":hlo_matchers",
+        "//tensorflow/compiler/xla:literal_util",
+        "//tensorflow/compiler/xla:shape_util",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/tests:hlo_verified_test_base",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:test",
+    ],
+)
+
 cc_library(
     name = "while_loop_simplifier",
     srcs = ["while_loop_simplifier.cc"],
diff --git a/tensorflow/compiler/xla/service/conditional_simplifier.cc b/tensorflow/compiler/xla/service/conditional_simplifier.cc
new file mode 100644
index 0000000000..f35de08085
--- /dev/null
+++ b/tensorflow/compiler/xla/service/conditional_simplifier.cc
@@ -0,0 +1,106 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/conditional_simplifier.h"
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/call_inliner.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+
+namespace xla {
+
+// Tries to replace a conditional with a call operation of the corresponding
+// computation. If the given conditional has a constant predicate, tries to
+// replace it with a call to its true/false computation as appropirate and then
+// inline that computation.
+//
+// Returns true if it made a change to the graph.
+static StatusOr<bool> TryRemoveConditional(HloInstruction* conditional) {
+  CHECK_EQ(conditional->opcode(), HloOpcode::kConditional);
+  // Do not remove conditionals that contain side-effecting instructions or
+  // have control predecessors/successors in either true/false computation.
+  if (!conditional->parent()->IsRemovable(conditional) ||
+      conditional->HasSideEffect()) {
+    VLOG(2) << "Not attempting to remove conditional as it is not removable or "
+               "has side effect: "
+            << conditional->ToShortString();
+    return false;
+  }
+
+  if (conditional->operand(0)->opcode() != HloOpcode::kConstant) {
+    VLOG(2) << "Not attempting to remove conditional as its predicate is not a "
+               "compile-time constant: "
+            << conditional->ToShortString();
+    return false;
+  }
+
+  auto computation = conditional->parent();
+  HloInstruction* call_op;
+  if (conditional->operand(0)->literal().Get<bool>({})) {
+    call_op = computation->AddInstruction(HloInstruction::CreateCall(
+        conditional->shape(), {conditional->mutable_operand(1)},
+        conditional->true_computation()));
+  } else {
+    call_op = computation->AddInstruction(HloInstruction::CreateCall(
+        conditional->shape(), {conditional->mutable_operand(2)},
+        conditional->false_computation()));
+  }
+
+  TF_RETURN_IF_ERROR(computation->ReplaceInstruction(conditional, call_op));
+  TF_RETURN_IF_ERROR(CallInliner::Inline(call_op).status());
+
+  return true;
+}
+
+StatusOr<bool> ConditionalSimplifier::Run(HloModule* module) {
+  XLA_VLOG_LINES(
+      3, "ConditionalSimplifier::Run(), before:\n" + module->ToString());
+  bool changed = false;
+
+  // Gather all the conditional ops in our module. We do this ahead of time so
+  // we don't have to worry about mutating the lists of computations or
+  // instructions as we iterate.
+  std::vector<HloInstruction*> conditional_ops;
+  for (auto* comp : module->computations()) {
+    for (auto* instr : comp->instructions()) {
+      if (instr->opcode() == HloOpcode::kConditional) {
+        conditional_ops.push_back(instr);
+      }
+    }
+  }
+
+  for (HloInstruction* conditional_op : conditional_ops) {
+    TF_ASSIGN_OR_RETURN(bool result, TryRemoveConditional(conditional_op));
+    changed |= result;
+  }
+
+  XLA_VLOG_LINES(3,
+                 "ConditionalSimplifier::Run(), after:\n" + module->ToString());
+  return changed;
+}
+
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/conditional_simplifier.h b/tensorflow/compiler/xla/service/conditional_simplifier.h
new file mode 100644
index 0000000000..063261e26d
--- /dev/null
+++ b/tensorflow/compiler/xla/service/conditional_simplifier.h
@@ -0,0 +1,38 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_
+
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
+
+namespace xla {
+
+// HLO pass that removes kConditional with a constant predicate, replacing them
+// with their true or false computation as appropriate.
+class ConditionalSimplifier : public HloPassInterface {
+ public:
+  tensorflow::StringPiece name() const override {
+    return "simplify-conditional";
+  }
+  StatusOr<bool> Run(HloModule* module) override;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CONDITIONAL_SIMPLIFIER_H_
diff --git a/tensorflow/compiler/xla/service/conditional_simplifier_test.cc b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc
new file mode 100644
index 0000000000..868348547d
--- /dev/null
+++ b/tensorflow/compiler/xla/service/conditional_simplifier_test.cc
@@ -0,0 +1,153 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/conditional_simplifier.h"
+
+#include <string>
+#include <utility>
+
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_matchers.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tests/hlo_verified_test_base.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+namespace {
+
+namespace op = xla::testing::opcode_matchers;
+
+class ConditionalSimplifierTest : public HloVerifiedTestBase {
+ public:
+  // Makes a computation that contains a conditional with constant predicate.
+  HloComputation* MakeConditional(HloModule* module);
+};
+
+HloComputation* ConditionalSimplifierTest::MakeConditional(HloModule* module) {
+  HloComputation::Builder builder(TestName());
+
+  // true_computation returns param+1.
+  HloComputation* true_computation;
+  {
+    HloComputation::Builder true_computation_builder(TestName() +
+                                                     ".true_computation");
+    auto param =
+        true_computation_builder.AddInstruction(HloInstruction::CreateParameter(
+            0, ShapeUtil::MakeShape(S32, {}), "param"));
+    auto one = true_computation_builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(1)));
+
+    true_computation_builder.AddInstruction(HloInstruction::CreateBinary(
+        ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, one));
+
+    true_computation =
+        module->AddEmbeddedComputation(true_computation_builder.Build());
+  }
+
+  // false_computation returns param+42.
+  HloComputation* false_computation;
+  {
+    HloComputation::Builder false_computation_builder(TestName() +
+                                                      ".false_computation");
+    auto param = false_computation_builder.AddInstruction(
+        HloInstruction::CreateParameter(0, ShapeUtil::MakeShape(S32, {}),
+                                        "param"));
+    auto forty_two = false_computation_builder.AddInstruction(
+        HloInstruction::CreateConstant(Literal::CreateR0<int32>(42)));
+
+    false_computation_builder.AddInstruction(HloInstruction::CreateBinary(
+        ShapeUtil::MakeShape(S32, {}), HloOpcode::kAdd, param, forty_two));
+    false_computation =
+        module->AddEmbeddedComputation(false_computation_builder.Build());
+  }
+
+  auto false_instrn = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(false)));
+  auto false_param = builder.AddInstruction(HloInstruction::CreateParameter(
+      0, ShapeUtil::MakeShape(S32, {}), "false_param"));
+  auto one = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<int32>(1)));
+
+  builder.AddInstruction(HloInstruction::CreateConditional(
+      ShapeUtil::MakeShape(S32, {}), false_instrn, one, true_computation,
+      false_param, false_computation));
+
+  return module->AddEntryComputation(builder.Build());
+}
+
+TEST_F(ConditionalSimplifierTest, ConditionalGetsInlined) {
+  HloComputation* computation = MakeConditional(&module());
+  ASSERT_TRUE(ConditionalSimplifier().Run(&module()).ValueOrDie());
+  EXPECT_THAT(computation->root_instruction(),
+              op::Add(op::Parameter(), op::Constant()));
+}
+
+TEST_F(ConditionalSimplifierTest, ConditionalWithControlDependency) {
+  HloComputation* computation = MakeConditional(&module());
+
+  auto* true_op = computation->AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR0<bool>(true)));
+  TF_ASSERT_OK(
+      true_op->AddControlDependencyTo(computation->root_instruction()));
+
+  EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie());
+}
+
+TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsSend) {
+  HloComputation* computation = MakeConditional(&module());
+  auto* conditional = computation->root_instruction();
+  ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional);
+
+  auto* true_computation = conditional->true_computation();
+  auto* send = true_computation->AddInstruction(HloInstruction::CreateSend(
+      true_computation->AddInstruction(
+          HloInstruction::CreateConstant(Literal::CreateR0<bool>(true))),
+      /*channel_id=*/0));
+  true_computation->AddInstruction(HloInstruction::CreateSendDone(send));
+  EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie());
+}
+
+TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsRecv) {
+  HloComputation* computation = MakeConditional(&module());
+  auto* conditional = computation->root_instruction();
+  ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional);
+
+  auto* true_computation = conditional->true_computation();
+  auto* recv = true_computation->AddInstruction(HloInstruction::CreateRecv(
+      ShapeUtil::MakeShape(F32, {1}), /*channel_id=*/0));
+  true_computation->AddInstruction(HloInstruction::CreateRecvDone(recv));
+  EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie());
+}
+
+TEST_F(ConditionalSimplifierTest, NotRemovedIfContainsNonRemovableInstruction) {
+  HloComputation* computation = MakeConditional(&module());
+  auto* conditional = computation->root_instruction();
+  ASSERT_EQ(conditional->opcode(), HloOpcode::kConditional);
+  auto* false_computation = conditional->false_computation();
+  false_computation->AddInstruction(
+      HloInstruction::CreateInfeed(ShapeUtil::MakeShape(F32, {1}), "config"));
+  EXPECT_FALSE(ConditionalSimplifier().Run(&module()).ValueOrDie());
+}
+
+}  // namespace
+}  // namespace xla
diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD
index 4170e31527..38a54fcb64 100644
--- a/tensorflow/compiler/xla/service/cpu/BUILD
+++ b/tensorflow/compiler/xla/service/cpu/BUILD
@@ -105,6 +105,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:buffer_liveness",
         "//tensorflow/compiler/xla/service:call_inliner",
+        "//tensorflow/compiler/xla/service:conditional_simplifier",
         "//tensorflow/compiler/xla/service:dot_decomposer",
         "//tensorflow/compiler/xla/service:executable",
         "//tensorflow/compiler/xla/service:flatten_call_graph",
diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
index 387806e24a..0d15be5a23 100644
--- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc
@@ -47,6 +47,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/buffer_liveness.h"
 #include "tensorflow/compiler/xla/service/call_inliner.h"
+#include "tensorflow/compiler/xla/service/conditional_simplifier.h"
 #include "tensorflow/compiler/xla/service/cpu/compiler_functor.h"
 #include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_copy_insertion.h"
@@ -275,6 +276,7 @@ Status CpuCompiler::RunHloPasses(HloModule* module, bool is_aot_compile) {
     pass.AddPass<HloDCE>();
     pass.AddPass<ReshapeMover>();
     pass.AddPass<HloConstantFolding>();
+    pass.AddPass<ConditionalSimplifier>();
   }
   pipeline.AddPass<TransposeFolding>(
       [](const HloInstruction& dot,
diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD
index 9da4fb97fa..334efff1e6 100644
--- a/tensorflow/compiler/xla/service/gpu/BUILD
+++ b/tensorflow/compiler/xla/service/gpu/BUILD
@@ -510,6 +510,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:buffer_assignment",
         "//tensorflow/compiler/xla/service:buffer_liveness",
         "//tensorflow/compiler/xla/service:call_inliner",
+        "//tensorflow/compiler/xla/service:conditional_simplifier",
         "//tensorflow/compiler/xla/service:dot_decomposer",
         "//tensorflow/compiler/xla/service:executable",
         "//tensorflow/compiler/xla/service:flatten_call_graph",
diff --git a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
index 28ebd034ee..9e37acdf31 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_compiler.cc
@@ -33,6 +33,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/buffer_assignment.h"
 #include "tensorflow/compiler/xla/service/buffer_liveness.h"
 #include "tensorflow/compiler/xla/service/call_inliner.h"
+#include "tensorflow/compiler/xla/service/conditional_simplifier.h"
 #include "tensorflow/compiler/xla/service/dot_decomposer.h"
 #include "tensorflow/compiler/xla/service/flatten_call_graph.h"
 #include "tensorflow/compiler/xla/service/gpu/cudnn_batchnorm_rewriter.h"
@@ -176,6 +177,7 @@ tensorflow::Status OptimizeHloModule(HloModule* hlo_module,
       pass.AddPass<HloDCE>();
       pass.AddPass<ReshapeMover>();
       pass.AddPass<HloConstantFolding>();
+      pass.AddPass<ConditionalSimplifier>();
     }
 
     pipeline.AddPass<TransposeFolding>(
-- 
GitLab


From 9d6c5a06638262f6815717c682fab29ba3524282 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Wed, 28 Feb 2018 13:48:38 -0800
Subject: [PATCH 454/575] Bypasses warnings in eager mode for converting
 indexed slices to tensors.

PiperOrigin-RevId: 187377370
---
 tensorflow/python/ops/gradients_impl.py | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index 1418c0b10f..227316a01e 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -86,17 +86,19 @@ def _IndexedSlicesToTensor(value, dtype=None, name=None, as_ref=False):
         % str(value))
   # TODO(mrry): Consider adding static shape information to
   # IndexedSlices, to avoid using numpy here.
-  dense_shape_value = tensor_util.constant_value(value.dense_shape)
-  if dense_shape_value is not None:
-    num_elements = np.prod(dense_shape_value)
-    if num_elements >= _LARGE_SPARSE_NUM_ELEMENTS:
+  if context.in_graph_mode():
+    dense_shape_value = tensor_util.constant_value(value.dense_shape)
+    if dense_shape_value is not None:
+      num_elements = np.prod(dense_shape_value)
+      if num_elements >= _LARGE_SPARSE_NUM_ELEMENTS:
+        warnings.warn(
+            "Converting sparse IndexedSlices to a dense Tensor with %d "
+            "elements. This may consume a large amount of memory." %
+            num_elements)
+    else:
       warnings.warn(
-          "Converting sparse IndexedSlices to a dense Tensor with %d elements. "
-          "This may consume a large amount of memory." % num_elements)
-  else:
-    warnings.warn(
-        "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
-        "This may consume a large amount of memory.")
+          "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
+          "This may consume a large amount of memory.")
   return math_ops.unsorted_segment_sum(
       value.values, value.indices, value.dense_shape[0], name=name)
 
-- 
GitLab


From a72ece230eb46c1afcb96c52dc5ae6ceabdeaf25 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Wed, 28 Feb 2018 13:55:35 -0800
Subject: [PATCH 455/575] Checkpointable: Handle Optimizer non-slot variables

Overrides the Checkpointable dependency-gathering logic to key Optimizer dependencies to the current graph.

Moves my Checkpointable Adam prototype out of contrib.

Right now there is no check that loading all happens in the same graph. This would be easy enough to do (save a Graph ID with the _Checkpoint object), but it's not clear to me that it's useful; doing deferred restoration in whichever graph the variable is created in seems reasonable. (Let me know if you disagree)

PiperOrigin-RevId: 187378372
---
 .../eager/python/checkpointable_utils_test.py | 115 +++++++++++-------
 tensorflow/python/ops/variables.py            |   3 +
 tensorflow/python/training/checkpointable.py  |  96 ++++++++++++---
 tensorflow/python/training/optimizer.py       |  48 +++++++-
 ...tensorflow.train.-adadelta-optimizer.pbtxt |   1 -
 ...sorflow.train.-adagrad-d-a-optimizer.pbtxt |   1 -
 .../tensorflow.train.-adagrad-optimizer.pbtxt |   1 -
 .../tensorflow.train.-adam-optimizer.pbtxt    |   1 -
 .../tensorflow.train.-ftrl-optimizer.pbtxt    |   1 -
 ...ow.train.-gradient-descent-optimizer.pbtxt |   1 -
 ...tensorflow.train.-momentum-optimizer.pbtxt |   1 -
 .../golden/tensorflow.train.-optimizer.pbtxt  |   1 -
 ...ow.train.-proximal-adagrad-optimizer.pbtxt |   1 -
 ...-proximal-gradient-descent-optimizer.pbtxt |   1 -
 ...nsorflow.train.-r-m-s-prop-optimizer.pbtxt |   1 -
 ...rflow.train.-sync-replicas-optimizer.pbtxt |   1 -
 16 files changed, 196 insertions(+), 78 deletions(-)

diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index 68f0d93632..7367f1b71c 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -23,6 +23,7 @@ import six
 
 from tensorflow.contrib.eager.python import checkpointable_utils
 from tensorflow.contrib.eager.python import network as network_lib
+from tensorflow.python.client import session as session_lib
 from tensorflow.python.eager import context
 from tensorflow.python.eager import test
 from tensorflow.python.framework import constant_op
@@ -56,40 +57,6 @@ class CheckpointableNetwork(network_lib.Network, checkpointable.Checkpointable):
     return super(CheckpointableNetwork, self).track_layer(layer)
 
 
-class CheckpointableAdam(adam.AdamOptimizer, checkpointable.Checkpointable):
-
-  # NOTE: Copied from Optimizer with modifications to use add_variable
-  # for non-slot variables. These contortions are necessary to maintain
-  # checkpoint compatibility with variable.name based saving.
-  # TODO(allenl): Make this cleaner.
-  def _create_non_slot_variable(self, initial_value, name, colocate_with):
-    """Add an extra variable, not associated with a slot."""
-    if context.in_graph_mode():
-      graph = colocate_with.graph
-    else:
-      graph = None
-
-    key = (name, graph)
-    v = self._non_slot_dict.get(key, None)
-    if v is None:
-      with ops.colocate_with(colocate_with):
-        def _variable_getter(name, shape, dtype, initializer):
-          del shape, dtype  # not used, but there for compatibility
-          return variable_scope.variable(
-              name=name, initial_value=initializer, trainable=False)
-
-        initial_value = ops.convert_to_tensor(initial_value)
-        v = self._add_variable_with_custom_getter(
-            name=name,
-            shape=initial_value.get_shape(),
-            initializer=initial_value,
-            getter=_variable_getter)
-
-      self._non_slot_dict[key] = v
-
-    return v
-
-
 class NonLayerCheckpointable(checkpointable.Checkpointable):
 
   def __init__(self):
@@ -208,7 +175,7 @@ class CheckpointingTests(test.TestCase):
     # A nuisance Network using the same optimizer. Its slot variables should not
     # go in the checkpoint, since it is never depended on.
     other_network = MyNetwork()
-    optimizer = CheckpointableAdam(0.001)
+    optimizer = adam.AdamOptimizer(0.001)
     optimizer_step = training_util.get_or_create_global_step()
     root_checkpointable = checkpointable_utils.Checkpoint(
         optimizer=optimizer, network=network, optimizer_step=optimizer_step)
@@ -314,7 +281,7 @@ class CheckpointingTests(test.TestCase):
   @test_util.run_in_graph_and_eager_modes()
   def testSaveRestore(self):
     network = MyNetwork()
-    optimizer = CheckpointableAdam(0.001)
+    optimizer = adam.AdamOptimizer(0.001)
     root_checkpointable = checkpointable_utils.Checkpoint(
         optimizer=optimizer, network=network)
     input_value = constant_op.constant([[3.]])
@@ -346,7 +313,7 @@ class CheckpointingTests(test.TestCase):
     if context.in_graph_mode():
       return  # Restore-on-create is only supported when executing eagerly
     on_create_network = MyNetwork()
-    on_create_optimizer = CheckpointableAdam(0.001)
+    on_create_optimizer = adam.AdamOptimizer(0.001)
     on_create_root = checkpointable_utils.Checkpoint(
         optimizer=on_create_optimizer, network=on_create_network)
     # Deferred restoration
@@ -378,7 +345,7 @@ class CheckpointingTests(test.TestCase):
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
     for training_continuation in range(3):
       network = MyNetwork()
-      optimizer = CheckpointableAdam(0.001)
+      optimizer = adam.AdamOptimizer(0.001)
       root = checkpointable_utils.Checkpoint(
           optimizer=optimizer, network=network,
           optimizer_step=training_util.get_or_create_global_step())
@@ -402,7 +369,7 @@ class CheckpointingTests(test.TestCase):
       for training_continuation in range(3):
         with ops.Graph().as_default():
           network = MyNetwork()
-          optimizer = CheckpointableAdam(0.001)
+          optimizer = adam.AdamOptimizer(0.001)
           root = checkpointable_utils.Checkpoint(
               optimizer=optimizer, network=network,
               global_step=training_util.get_or_create_global_step())
@@ -439,7 +406,7 @@ class CheckpointingTests(test.TestCase):
       with ops.Graph().as_default(), self.test_session(
           graph=ops.get_default_graph()):
         network = MyNetwork()
-        optimizer = CheckpointableAdam(0.001)
+        optimizer = adam.AdamOptimizer(0.001)
         root = checkpointable_utils.Checkpoint(
             optimizer=optimizer, network=network,
             global_step=training_util.get_or_create_global_step())
@@ -573,7 +540,7 @@ class CheckpointingTests(test.TestCase):
     root = checkpointable.Checkpointable()
     root.var = checkpointable_utils.add_variable(
         root, name="var", initializer=0.)
-    optimizer = CheckpointableAdam(0.1)
+    optimizer = adam.AdamOptimizer(0.1)
     if context.in_graph_mode():
       train_op = optimizer.minimize(root.var)
       # Note that `optimizer` has not been added as a dependency of
@@ -607,7 +574,7 @@ class CheckpointingTests(test.TestCase):
     no_slot_status.assert_consumed()
     no_slot_status.run_restore_ops()
     self.assertEqual(12., self.evaluate(new_root.var))
-    new_root.optimizer = CheckpointableAdam(0.1)
+    new_root.optimizer = adam.AdamOptimizer(0.1)
     with self.assertRaisesRegexp(AssertionError, "beta1_power"):
       slot_status.assert_consumed()
     self.assertEqual(12., self.evaluate(new_root.var))
@@ -819,7 +786,7 @@ class CheckpointingTests(test.TestCase):
         checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
         obj = checkpointable.Checkpointable()
         obj.var = variable_scope.get_variable(name="v", initializer=0.)
-        obj.opt = CheckpointableAdam(0.1)
+        obj.opt = adam.AdamOptimizer(0.1)
         obj.opt.minimize(obj.var.read_value())
         self.evaluate(checkpointable_utils.gather_initializers(obj))
         saver = checkpointable_utils.CheckpointableSaver(obj)
@@ -837,7 +804,7 @@ class CheckpointingTests(test.TestCase):
         checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
         obj = checkpointable.Checkpointable()
         obj.var = variable_scope.get_variable(name="v", initializer=0.)
-        obj.opt = CheckpointableAdam(0.1)
+        obj.opt = adam.AdamOptimizer(0.1)
         obj.opt.minimize(obj.var.read_value())
         self.evaluate(checkpointable_utils.gather_initializers(obj))
         saver = checkpointable_utils.CheckpointableSaver(obj)
@@ -847,13 +814,71 @@ class CheckpointingTests(test.TestCase):
         saver.restore(save_path)
         self.assertEqual(before_ops, graph.get_operations())
 
+  def testMultipleGraphsNonSlotVariables(self):
+    with context.graph_mode():
+      checkpoint_directory = self.get_temp_dir()
+      checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+      optimizer = adam.AdamOptimizer(0.001)
+      # Construct a model in one graph
+      first_graph = ops.Graph()
+      first_session = session_lib.Session(graph=first_graph)
+      with first_graph.as_default(), first_session.as_default():
+        first_variable = resource_variable_ops.ResourceVariable([1.])
+        first_root_checkpointable = checkpointable_utils.Checkpoint(
+            optimizer=optimizer, variable=first_variable)
+        train_op = optimizer.minimize(first_variable.read_value)
+        self.evaluate(checkpointable_utils.gather_initializers(
+            first_root_checkpointable))
+        self.evaluate(train_op)
+        self.evaluate(first_variable.assign([1.]))
+        self.evaluate(optimizer.get_slot(
+            var=first_variable, name="m").assign([2.]))
+        beta1_power, _ = optimizer._get_beta_accumulators()
+        self.evaluate(beta1_power.assign(3.))
+
+      # Save and load in a second graph
+      second_graph = ops.Graph()
+      with second_graph.as_default(), session_lib.Session(graph=second_graph):
+        second_variable = resource_variable_ops.ResourceVariable([1.])
+        second_root_checkpointable = checkpointable_utils.Checkpoint(
+            optimizer=optimizer, variable=second_variable)
+        train_op = optimizer.minimize(second_variable.read_value)
+        second_root_checkpointable.restore(None).initialize_or_restore()
+        self.evaluate(train_op)
+        self.evaluate(second_variable.assign([4.]))
+        self.evaluate(optimizer.get_slot(
+            var=second_variable, name="m").assign([5.]))
+        beta1_power, _ = optimizer._get_beta_accumulators()
+        self.evaluate(beta1_power.assign(6.))
+        save_path = second_root_checkpointable.save(checkpoint_prefix)
+        self.evaluate(second_variable.assign([7.]))
+        self.evaluate(optimizer.get_slot(
+            var=second_variable, name="m").assign([8.]))
+        beta1_power, _ = optimizer._get_beta_accumulators()
+        self.assertAllEqual(6., self.evaluate(beta1_power))
+        status = second_root_checkpointable.restore(save_path)
+        status.assert_consumed().run_restore_ops()
+        self.assertAllEqual([4.], self.evaluate(second_variable))
+        self.assertAllEqual([5.], self.evaluate(optimizer.get_slot(
+            var=second_variable, name="m")))
+        beta1_power, _ = optimizer._get_beta_accumulators()
+        self.assertAllEqual(6., self.evaluate(beta1_power))
+
+      # Check that the first graph is unmolested
+      with first_graph.as_default(), first_session.as_default():
+        self.assertAllEqual([1.], self.evaluate(first_variable))
+        self.assertAllEqual([2.], self.evaluate(optimizer.get_slot(
+            var=first_variable, name="m")))
+        beta1_power, _ = optimizer._get_beta_accumulators()
+        self.assertAllEqual(3., self.evaluate(beta1_power))
+
 
 class CheckpointCompatibilityTests(test.TestCase):
 
   def _initialized_model(self):
     input_value = constant_op.constant([[3.]])
     network = MyNetwork()
-    optimizer = CheckpointableAdam(0.001)
+    optimizer = adam.AdamOptimizer(0.001)
     optimizer_step = training_util.get_or_create_global_step()
     root_checkpointable = checkpointable_utils.Checkpoint(
         optimizer=optimizer, network=network, optimizer_step=optimizer_step)
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index d382683858..643a3b7edc 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -307,6 +307,9 @@ class Variable(checkpointable.CheckpointableBase):
     if constraint is not None and not callable(constraint):
       raise ValueError("The `constraint` argument must be a callable.")
 
+    # Store the graph key so optimizers know how to only retrieve variables from
+    # this graph.
+    self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
     if isinstance(initial_value, checkpointable.CheckpointInitialValue):
       self._maybe_initialize_checkpointable()
       self._update_uid = initial_value.checkpoint_position.restore_uid
diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py
index c5e7f3cdac..02c3aebda8 100644
--- a/tensorflow/python/training/checkpointable.py
+++ b/tensorflow/python/training/checkpointable.py
@@ -31,8 +31,8 @@ from tensorflow.python.util import nest
 # creation (avoiding double assignment when executing eagerly).
 VARIABLE_VALUE_KEY = "VARIABLE_VALUE"
 
-_CheckpointableReference = collections.namedtuple(
-    "_CheckpointableReference",
+CheckpointableReference = collections.namedtuple(
+    "CheckpointableReference",
     [
         # The local name for this dependency.
         "name",
@@ -301,14 +301,17 @@ class CheckpointableBase(object):
 
     Not __init__, since most objects will forget to call it.
     """
-    if hasattr(self, "_checkpoint_dependencies"):
+    if hasattr(self, "_unconditional_checkpoint_dependencies"):
       # __init__ already called. This check means that we don't need
       # Checkpointable.__init__() in the constructor of every TensorFlow object.
       return
-    # A list of _CheckpointableReference objects.
-    self._checkpoint_dependencies = []
+    # A list of CheckpointableReference objects. Some classes implementing
+    # `Checkpointable`, notably `Optimizer`s, may override the
+    # _checkpoint_dependencies property with conditional dependencies
+    # (e.g. based on the current graph when saving).
+    self._unconditional_checkpoint_dependencies = []
     # Maps names -> Checkpointable objects
-    self._dependency_names = {}
+    self._unconditional_dependency_names = {}
     # Restorations for other Checkpointable objects on which this object may
     # eventually depend.
     self._deferred_dependencies = {}  # local name -> _CheckpointPosition list
@@ -320,6 +323,32 @@ class CheckpointableBase(object):
           "initialization code was run.")
     self._update_uid = -1
 
+  @property
+  def _checkpoint_dependencies(self):
+    """All dependencies of this object.
+
+    May be overridden to include conditional dependencies.
+
+    Returns:
+      A list of `CheckpointableReference` objects indicating named
+      `Checkpointable` dependencies which should be saved along with this
+      object.
+    """
+    return self._unconditional_checkpoint_dependencies
+
+  def _lookup_dependency(self, name):
+    """Look up a dependency by name.
+
+    May be overridden to include conditional dependencies.
+
+    Args:
+      name: The local name of the dependency.
+    Returns:
+      A `Checkpointable` object, or `None` if no dependency by this name was
+      found.
+    """
+    return self._unconditional_dependency_names.get(name, None)
+
   def _add_variable_with_custom_getter(
       self, name, shape=None, dtype=dtypes.float32,
       initializer=None, getter=None, overwrite=False,
@@ -349,7 +378,7 @@ class CheckpointableBase(object):
       ValueError: If the variable name is not unique.
     """
     self._maybe_initialize_checkpointable()
-    if not overwrite and name in self._dependency_names:
+    if not overwrite and self._lookup_dependency(name) is not None:
       raise ValueError(
           ("A variable named '%s' already exists in this Checkpointable, but "
            "Checkpointable._add_variable called to create another with "
@@ -461,9 +490,10 @@ class CheckpointableBase(object):
       raise TypeError(
           ("Checkpointable._track_checkpointable() passed type %s, not a "
            "Checkpointable.") % (type(checkpointable),))
-    new_reference = _CheckpointableReference(name=name, ref=checkpointable)
-    if (name in self._dependency_names
-        and self._dependency_names[name] is not checkpointable):
+    new_reference = CheckpointableReference(name=name, ref=checkpointable)
+    current_object = self._lookup_dependency(name)
+    if (current_object is not None
+        and current_object is not checkpointable):
       if not overwrite:
         raise ValueError(
             ("Called Checkpointable._track_checkpointable() with name='%s', "
@@ -471,19 +501,47 @@ class CheckpointableBase(object):
              "dependency. Names must be unique (or overwrite=True).") % (name,))
       # This is a weird thing to do, but we're not going to stop people from
       # using __setattr__.
-      for index, (old_name, _) in enumerate(self._checkpoint_dependencies):
+      for index, (old_name, _) in enumerate(
+          self._unconditional_checkpoint_dependencies):
         if name == old_name:
-          self._checkpoint_dependencies[index] = new_reference
+          self._unconditional_checkpoint_dependencies[index] = new_reference
     else:
-      self._checkpoint_dependencies.append(new_reference)
+      self._unconditional_checkpoint_dependencies.append(new_reference)
 
-    self._dependency_names[name] = checkpointable
-    deferred_dependency_list = self._deferred_dependencies.pop(name, None)
-    if deferred_dependency_list is not None:
-      for checkpoint_position in deferred_dependency_list:
-        checkpoint_position.restore(checkpointable=checkpointable)
+    self._unconditional_dependency_names[name] = checkpointable
+    self._handle_deferred_dependencies(name=name, checkpointable=checkpointable)
     return checkpointable
 
+  def _handle_deferred_dependencies(self, name, checkpointable):
+    """Pop and load any deferred checkpoint restores into `checkpointable`.
+
+    This method does not add a new dependency on `checkpointable`, but it does
+    check if any outstanding/deferred dependencies have been queued waiting for
+    this dependency to be added (matched based on `name`). If so,
+    `checkpointable` and its dependencies are restored. The restorations are
+    considered fulfilled and so are deleted.
+
+    `_track_checkpointable` is more appropriate for adding a
+    normal/unconditional dependency, and includes handling for deferred
+    restorations. This method allows objects such as `Optimizer` to use the same
+    restoration logic while managing conditional dependencies themselves, by
+    overriding `_checkpoint_dependencies` and `_lookup_dependency` to change the
+    object's dependencies based on the context it is saved/restored in (a single
+    optimizer instance can have state associated with multiple graphs).
+
+    Args:
+      name: The name of the dependency within this object (`self`), used to
+        match `checkpointable` with values saved in a checkpoint.
+      checkpointable: The Checkpointable object to restore (inheriting from
+        `CheckpointableBase`).
+    """
+    deferred_dependencies_list = self._deferred_dependencies.pop(name, ())
+    for checkpoint_position in sorted(
+        deferred_dependencies_list,
+        key=lambda restore: restore.checkpoint.restore_uid,
+        reverse=True):
+      checkpoint_position.restore(checkpointable)
+
   def _restore_from_checkpoint_position(self, checkpoint_position):
     """Restore this object and its dependencies (may be deferred)."""
     # Attempt a breadth-first traversal, since presumably the user has more
@@ -519,7 +577,7 @@ class CheckpointableBase(object):
       child_position = _CheckpointPosition(
           checkpoint=checkpoint,
           proto_id=child.node_id)
-      local_object = self._dependency_names.get(child.local_name, None)
+      local_object = self._lookup_dependency(child.local_name)
       if local_object is None:
         # We don't yet have a dependency registered with this name. Save it
         # in case we do.
diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py
index 454cc3add5..ba7e087c5a 100644
--- a/tensorflow/python/training/optimizer.py
+++ b/tensorflow/python/training/optimizer.py
@@ -216,7 +216,11 @@ def _get_processor(v):
 
 
 @tf_export("train.Optimizer")
-class Optimizer(checkpointable.Checkpointable):
+class Optimizer(
+    # Optimizers inherit from CheckpointableBase rather than Checkpointable
+    # since they do most of their dependency management themselves (slot
+    # variables are special-cased, and non-slot variables are keyed to graphs).
+    checkpointable.CheckpointableBase):
   """Base class for optimizers.
 
   This class defines the API to add Ops to train a model.  You never use this
@@ -645,7 +649,8 @@ class Optimizer(checkpointable.Checkpointable):
 
   def _create_non_slot_variable(self, initial_value, name, colocate_with):
     """Add an extra variable, not associated with a slot."""
-    if context.in_graph_mode():
+    in_graph_mode = context.in_graph_mode()
+    if in_graph_mode:
       graph = colocate_with.graph
     else:
       graph = None
@@ -653,12 +658,51 @@ class Optimizer(checkpointable.Checkpointable):
     key = (name, graph)
     v = self._non_slot_dict.get(key, None)
     if v is None:
+      self._maybe_initialize_checkpointable()
       with ops.colocate_with(colocate_with):
+        if not in_graph_mode:
+          restored_initial_value = self._preload_simple_restoration(
+              name=name, shape=None)
+          if restored_initial_value is not None:
+            initial_value = restored_initial_value
         v = variable_scope.variable(initial_value, name=name, trainable=False)
+        # Restore this variable by name if necessary, but don't add a
+        # Checkpointable dependency. Optimizers return the current graph's
+        # non-slot variables from _checkpoint_dependencies explicitly rather
+        # than unconditionally adding dependencies (since there may be multiple
+        # non-slot variables with the same name in different graphs, trying to
+        # save all of them would result in errors).
+        self._handle_deferred_dependencies(name=name, checkpointable=v)
       self._non_slot_dict[key] = v
 
     return v
 
+  @property
+  def _checkpoint_dependencies(self):
+    """From Checkpointable. Gather graph-specific non-slot variables to save."""
+    current_graph_non_slot_variables = []
+    current_graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
+    for (name, _), variable_object in sorted(self._non_slot_dict.items(),
+                                             # Avoid comparing graphs
+                                             key=lambda item: item[0][0]):
+      if variable_object._graph_key == current_graph_key:  # pylint: disable=protected-access
+        current_graph_non_slot_variables.append(
+            checkpointable.CheckpointableReference(
+                name=name, ref=variable_object))
+    return (super(Optimizer, self)._checkpoint_dependencies
+            + current_graph_non_slot_variables)
+
+  def _lookup_dependency(self, name):
+    """From Checkpointable. Find a non-slot variable in the current graph."""
+    unconditional = super(Optimizer, self)._lookup_dependency(name)
+    if unconditional is not None:
+      return unconditional
+    if context.in_graph_mode():
+      graph = ops.get_default_graph()
+    else:
+      graph = None
+    return self._get_non_slot_variable(name, graph=graph)
+
   def _get_non_slot_variable(self, name, graph=None):
     return self._non_slot_dict.get((name, graph), None)
 
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt
index c02e54adfb..16bfbf20d5 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-adadelta-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.AdadeltaOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.adadelta.AdadeltaOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt
index 2b619908fc..61cde9181c 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-d-a-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.AdagradDAOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.adagrad_da.AdagradDAOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt
index 2005cf4677..0a998c1afe 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-adagrad-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.AdagradOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.adagrad.AdagradOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt
index 0a2bae1d90..cc59541525 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.AdamOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.adam.AdamOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
index 847f9ad759..1add3a9021 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-ftrl-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.FtrlOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.ftrl.FtrlOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt
index 13a58e0608..ef5bbd6ace 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-gradient-descent-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.GradientDescentOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.gradient_descent.GradientDescentOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt
index bfbc2357a3..3d6e87f5eb 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-momentum-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.MomentumOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.momentum.MomentumOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt
index 437efa0a2b..e73861ff7c 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-optimizer.pbtxt
@@ -1,7 +1,6 @@
 path: "tensorflow.train.Optimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt
index 72f224605f..301b35b199 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-proximal-adagrad-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.ProximalAdagradOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.proximal_adagrad.ProximalAdagradOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt
index 316275b1fb..8815befa93 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.ProximalGradientDescentOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.proximal_gradient_descent.ProximalGradientDescentOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt
index af50a19861..e9819683ba 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-r-m-s-prop-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.RMSPropOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.rmsprop.RMSPropOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
diff --git a/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt b/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt
index 6edc516c93..3db96aff87 100644
--- a/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.train.-sync-replicas-optimizer.pbtxt
@@ -2,7 +2,6 @@ path: "tensorflow.train.SyncReplicasOptimizer"
 tf_class {
   is_instance: "<class \'tensorflow.python.training.sync_replicas_optimizer.SyncReplicasOptimizer\'>"
   is_instance: "<class \'tensorflow.python.training.optimizer.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.Checkpointable\'>"
   is_instance: "<class \'tensorflow.python.training.checkpointable.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
   member {
-- 
GitLab


From 8cd02f550634ea7ae5f75531a49986e099ddf957 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 13:58:18 -0800
Subject: [PATCH 456/575] Fix Markdown syntax of bulleted list.

PiperOrigin-RevId: 187378753
---
 tensorflow/python/ops/distributions/uniform.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/ops/distributions/uniform.py b/tensorflow/python/ops/distributions/uniform.py
index 3580af18f2..e0c554442f 100644
--- a/tensorflow/python/ops/distributions/uniform.py
+++ b/tensorflow/python/ops/distributions/uniform.py
@@ -45,11 +45,12 @@ class Uniform(distribution.Distribution):
   Z = b - a
   ```
 
-  where:
-  * `low = a`,
-  * `high = b`,
-  * `Z` is the normalizing constant, and,
-  * `I[predicate]` is the [indicator function](
+  where
+
+  - `low = a`,
+  - `high = b`,
+  - `Z` is the normalizing constant, and
+  - `I[predicate]` is the [indicator function](
     https://en.wikipedia.org/wiki/Indicator_function) for `predicate`.
 
   The parameters `low` and `high` must be shaped in a way that supports
-- 
GitLab


From 9f95084b53303af50d0a13fd9bb40a183af9104a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 14:22:07 -0800
Subject: [PATCH 457/575] Make fuse_op handle loops in the graph

The current implementation of fuse_op does not work when there are loops in the tensorflow graph.

PiperOrigin-RevId: 187382720
---
 .../contrib/framework/python/framework/graph_util.py       | 7 ++++++-
 .../contrib/framework/python/framework/graph_util_test.py  | 3 ++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/framework/python/framework/graph_util.py b/tensorflow/contrib/framework/python/framework/graph_util.py
index 49eec3a3f1..2703224b1b 100644
--- a/tensorflow/contrib/framework/python/framework/graph_util.py
+++ b/tensorflow/contrib/framework/python/framework/graph_util.py
@@ -85,14 +85,19 @@ def fuse_op(graph_def, input_nodes, output_nodes, output_dtypes,
       if n not in reachable_by_input and n not in output_nodes_set:
         # n is between input and output, i.e., part of the fused op
         next_to_visit = [n]
+        visited = set()
         while next_to_visit:
           cur_node = next_to_visit[0]
+          visited.add(cur_node)
           del next_to_visit[0]
           if cur_node in reachable_by_input and cur_node not in input_nodes_set:
             raise TypeError("Node %s uses input %s not in input_nodes." %
                             (n, cur_node))
           if cur_node not in input_nodes_set:
-            next_to_visit += name_to_input_name[cur_node]
+            next_to_visit += [
+                input_node for input_node in name_to_input_name[cur_node]
+                if input_node not in visited
+            ]
     elif n not in reachable_by_input:
       nodes_post_output.append(n)
 
diff --git a/tensorflow/contrib/framework/python/framework/graph_util_test.py b/tensorflow/contrib/framework/python/framework/graph_util_test.py
index b8a6d109e1..812c5fbd8c 100644
--- a/tensorflow/contrib/framework/python/framework/graph_util_test.py
+++ b/tensorflow/contrib/framework/python/framework/graph_util_test.py
@@ -42,7 +42,8 @@ class GraphUtilTest(test.TestCase):
     graph_def = graph_pb2.GraphDef()
     node_a = GetNewNode('A', 'Placeholder', [])
     node_b = GetNewNode('B', 'Op1', ['A'])
-    node_c = GetNewNode('C', 'Op1', ['B'])
+    # A loop in the part that will be fused.
+    node_c = GetNewNode('C', 'Op1', ['B', 'C'])
     node_d = GetNewNode('D', 'Op1', ['C'])
     node_e = GetNewNode('E', 'Op1', ['D'])
     graph_def.node.extend([node_a, node_b, node_c, node_d, node_e])
-- 
GitLab


From b21969b1305b211cd08f8d628b6a5a0e7a9e16f8 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 28 Feb 2018 14:36:09 -0800
Subject: [PATCH 458/575] [tf.data] Add `num_parallel_reads` argument to
 `tf.data.TFRecordDataset`.

This provides a convenient way to use the `tf.contrib.data.parallel_interleave()` idiom for reading multiple TFRecord files in parallel. In addition, the `filenames` argument to the initializer can now be a `tf.data.Dataset` of strings, which makes it easier to use `TFRecordDataset` with `Dataset.list_files()`.

PiperOrigin-RevId: 187384812
---
 tensorflow/contrib/data/python/ops/BUILD      |   1 +
 .../contrib/data/python/ops/interleave_ops.py |  97 +---------
 .../kernel_tests/reader_dataset_ops_test.py   |  36 +++-
 tensorflow/python/data/ops/BUILD              |   1 +
 tensorflow/python/data/ops/readers.py         | 166 +++++++++++++++++-
 .../tensorflow.data.-t-f-record-dataset.pbtxt |   2 +-
 6 files changed, 200 insertions(+), 103 deletions(-)

diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD
index 789cb9c99a..16fe31675f 100644
--- a/tensorflow/contrib/data/python/ops/BUILD
+++ b/tensorflow/contrib/data/python/ops/BUILD
@@ -126,6 +126,7 @@ py_library(
         "//tensorflow/python:tensor_util",
         "//tensorflow/python:util",
         "//tensorflow/python/data/ops:dataset_ops",
+        "//tensorflow/python/data/ops:readers",
         "//tensorflow/python/data/util:convert",
         "//tensorflow/python/data/util:nest",
         "//tensorflow/python/data/util:sparse",
diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py
index 3124ca1d15..91f19da02d 100644
--- a/tensorflow/contrib/data/python/ops/interleave_ops.py
+++ b/tensorflow/contrib/data/python/ops/interleave_ops.py
@@ -17,101 +17,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.data.ops import dataset_ops
-from tensorflow.python.data.util import convert
-from tensorflow.python.data.util import nest
-from tensorflow.python.data.util import sparse
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import function
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import gen_dataset_ops
+from tensorflow.python.data.ops import readers
 from tensorflow.python.util import deprecation
 
 
-class ParallelInterleaveDataset(dataset_ops.Dataset):
-  """A `Dataset` that maps a function over its input and flattens the result."""
-
-  def __init__(self, input_dataset, map_func, cycle_length, block_length,
-               sloppy, buffer_output_elements, prefetch_input_elements):
-    """See `tf.contrib.data.parallel_interleave()` for details."""
-    super(ParallelInterleaveDataset, self).__init__()
-    self._input_dataset = input_dataset
-
-    @function.Defun(*nest.flatten(
-        sparse.as_dense_types(input_dataset.output_types,
-                              input_dataset.output_classes)))
-    def tf_map_func(*args):
-      """A wrapper for Defun that facilitates shape inference."""
-      # Pass in shape information from the input_dataset.
-      dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes,
-                                            input_dataset.output_classes)
-      for arg, shape in zip(args, nest.flatten(dense_shapes)):
-        arg.set_shape(shape)
-
-      nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
-      nested_args = sparse.deserialize_sparse_tensors(
-          nested_args, input_dataset.output_types, input_dataset.output_shapes,
-          input_dataset.output_classes)
-      if dataset_ops._should_unpack_args(nested_args):  # pylint: disable=protected-access
-        dataset = map_func(*nested_args)
-      else:
-        dataset = map_func(nested_args)
-
-      if not isinstance(dataset, dataset_ops.Dataset):
-        raise TypeError("`map_func` must return a `Dataset` object.")
-
-      self._output_classes = dataset.output_classes
-      self._output_types = dataset.output_types
-      self._output_shapes = dataset.output_shapes
-
-      return dataset._as_variant_tensor()  # pylint: disable=protected-access
-
-    self._map_func = tf_map_func
-    self._map_func.add_to_graph(ops.get_default_graph())
-
-    self._cycle_length = ops.convert_to_tensor(
-        cycle_length, dtype=dtypes.int64, name="cycle_length")
-    self._block_length = ops.convert_to_tensor(
-        block_length, dtype=dtypes.int64, name="block_length")
-    self._sloppy = ops.convert_to_tensor(
-        sloppy, dtype=dtypes.bool, name="sloppy")
-    self._buffer_output_elements = convert.optional_param_to_tensor(
-        "buffer_output_elements",
-        buffer_output_elements,
-        argument_default=2 * block_length)
-    self._prefetch_input_elements = convert.optional_param_to_tensor(
-        "prefetch_input_elements",
-        prefetch_input_elements,
-        argument_default=2 * cycle_length)
-
-  def _as_variant_tensor(self):
-    return gen_dataset_ops.parallel_interleave_dataset(
-        self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
-        self._map_func.captured_inputs,
-        self._cycle_length,
-        self._block_length,
-        self._sloppy,
-        self._buffer_output_elements,
-        self._prefetch_input_elements,
-        f=self._map_func,
-        output_types=nest.flatten(
-            sparse.as_dense_types(self.output_types, self.output_classes)),
-        output_shapes=nest.flatten(
-            sparse.as_dense_shapes(self.output_shapes, self.output_classes)))
-
-  @property
-  def output_classes(self):
-    return self._output_classes
-
-  @property
-  def output_shapes(self):
-    return self._output_shapes
-
-  @property
-  def output_types(self):
-    return self._output_types
-
-
 def parallel_interleave(map_func,
                         cycle_length,
                         block_length=1,
@@ -162,7 +71,7 @@ def parallel_interleave(map_func,
     @{tf.data.Dataset.apply}.
   """
   def _apply_fn(dataset):
-    return ParallelInterleaveDataset(
+    return readers.ParallelInterleaveDataset(
         dataset, map_func, cycle_length, block_length, sloppy,
         buffer_output_elements, prefetch_input_elements)
 
@@ -221,7 +130,7 @@ def sloppy_interleave(map_func, cycle_length, block_length=1):
     @{tf.data.Dataset.apply}.
   """
   def _apply_fn(dataset):
-    return ParallelInterleaveDataset(
+    return readers.ParallelInterleaveDataset(
         dataset,
         map_func,
         cycle_length,
diff --git a/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py b/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py
index d7140088c3..1ddedfda4e 100644
--- a/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py
+++ b/tensorflow/python/data/kernel_tests/reader_dataset_ops_test.py
@@ -21,6 +21,7 @@ import gzip
 import os
 import zlib
 
+from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.data.ops import readers
 from tensorflow.python.framework import constant_op
@@ -736,12 +737,43 @@ class TFRecordDatasetTest(test.TestCase):
     one_mebibyte = 2**20
     d = readers.TFRecordDataset(self.test_filenames, buffer_size=one_mebibyte)
     iterator = d.make_one_shot_iterator()
+    next_element = iterator.get_next()
     with self.test_session() as sess:
       for j in range(self._num_files):
         for i in range(self._num_records):
-          self.assertAllEqual(self._record(j, i), sess.run(iterator.get_next()))
+          self.assertAllEqual(self._record(j, i), sess.run(next_element))
       with self.assertRaises(errors.OutOfRangeError):
-        sess.run(iterator.get_next())
+        sess.run(next_element)
+
+  def testReadFromDatasetOfFiles(self):
+    files = dataset_ops.Dataset.from_tensor_slices(self.test_filenames)
+    d = readers.TFRecordDataset(files)
+    iterator = d.make_one_shot_iterator()
+    next_element = iterator.get_next()
+    with self.test_session() as sess:
+      for j in range(self._num_files):
+        for i in range(self._num_records):
+          self.assertAllEqual(self._record(j, i), sess.run(next_element))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+
+  def testReadTenEpochsFromDatasetOfFilesInParallel(self):
+    files = dataset_ops.Dataset.from_tensor_slices(
+        self.test_filenames).repeat(10)
+    d = readers.TFRecordDataset(files, num_parallel_reads=4)
+    iterator = d.make_one_shot_iterator()
+    next_element = iterator.get_next()
+    expected = []
+    actual = []
+    with self.test_session() as sess:
+      for _ in range(10):
+        for j in range(self._num_files):
+          for i in range(self._num_records):
+            expected.append(self._record(j, i))
+            actual.append(sess.run(next_element))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(next_element)
+      self.assertEqual(sorted(expected), sorted(actual))
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/python/data/ops/BUILD b/tensorflow/python/data/ops/BUILD
index dc293562ab..a8f2154db8 100644
--- a/tensorflow/python/data/ops/BUILD
+++ b/tensorflow/python/data/ops/BUILD
@@ -35,6 +35,7 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":dataset_ops",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:dataset_ops_gen",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
diff --git a/tensorflow/python/data/ops/readers.py b/tensorflow/python/data/ops/readers.py
index fa7601741b..6c493d8163 100644
--- a/tensorflow/python/data/ops/readers.py
+++ b/tensorflow/python/data/ops/readers.py
@@ -17,11 +17,15 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.data.ops.dataset_ops import Dataset
+from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.util import convert
+from tensorflow.python.data.util import nest
+from tensorflow.python.data.util import sparse
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_dataset_ops
 from tensorflow.python.util.tf_export import tf_export
 
@@ -31,7 +35,7 @@ _DEFAULT_READER_BUFFER_SIZE_BYTES = 256 * 1024  # 256 KB
 
 
 @tf_export("data.TextLineDataset")
-class TextLineDataset(Dataset):
+class TextLineDataset(dataset_ops.Dataset):
   """A `Dataset` comprising lines from one or more text files."""
 
   def __init__(self, filenames, compression_type=None, buffer_size=None):
@@ -73,8 +77,7 @@ class TextLineDataset(Dataset):
     return dtypes.string
 
 
-@tf_export("data.TFRecordDataset")
-class TFRecordDataset(Dataset):
+class _TFRecordDataset(dataset_ops.Dataset):
   """A `Dataset` comprising records from one or more TFRecord files."""
 
   def __init__(self, filenames, compression_type=None, buffer_size=None):
@@ -87,7 +90,7 @@ class TFRecordDataset(Dataset):
       buffer_size: (Optional.) A `tf.int64` scalar representing the number of
         bytes in the read buffer. 0 means no buffering.
     """
-    super(TFRecordDataset, self).__init__()
+    super(_TFRecordDataset, self).__init__()
     # Force the type to string even if filenames is an empty list.
     self._filenames = ops.convert_to_tensor(
         filenames, dtypes.string, name="filenames")
@@ -118,8 +121,159 @@ class TFRecordDataset(Dataset):
     return dtypes.string
 
 
+class ParallelInterleaveDataset(dataset_ops.Dataset):
+  """A `Dataset` that maps a function over its input and flattens the result."""
+
+  def __init__(self, input_dataset, map_func, cycle_length, block_length,
+               sloppy, buffer_output_elements, prefetch_input_elements):
+    """See `tf.contrib.data.parallel_interleave()` for details."""
+    super(ParallelInterleaveDataset, self).__init__()
+    self._input_dataset = input_dataset
+
+    @function.Defun(*nest.flatten(
+        sparse.as_dense_types(input_dataset.output_types,
+                              input_dataset.output_classes)))
+    def tf_map_func(*args):
+      """A wrapper for Defun that facilitates shape inference."""
+      # Pass in shape information from the input_dataset.
+      dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes,
+                                            input_dataset.output_classes)
+      for arg, shape in zip(args, nest.flatten(dense_shapes)):
+        arg.set_shape(shape)
+
+      nested_args = nest.pack_sequence_as(input_dataset.output_types, args)
+      nested_args = sparse.deserialize_sparse_tensors(
+          nested_args, input_dataset.output_types, input_dataset.output_shapes,
+          input_dataset.output_classes)
+      if dataset_ops._should_unpack_args(nested_args):  # pylint: disable=protected-access
+        dataset = map_func(*nested_args)
+      else:
+        dataset = map_func(nested_args)
+
+      if not isinstance(dataset, dataset_ops.Dataset):
+        raise TypeError("`map_func` must return a `Dataset` object.")
+
+      self._output_classes = dataset.output_classes
+      self._output_types = dataset.output_types
+      self._output_shapes = dataset.output_shapes
+
+      return dataset._as_variant_tensor()  # pylint: disable=protected-access
+
+    self._map_func = tf_map_func
+    self._map_func.add_to_graph(ops.get_default_graph())
+
+    self._cycle_length = ops.convert_to_tensor(
+        cycle_length, dtype=dtypes.int64, name="cycle_length")
+    self._block_length = ops.convert_to_tensor(
+        block_length, dtype=dtypes.int64, name="block_length")
+    self._sloppy = ops.convert_to_tensor(
+        sloppy, dtype=dtypes.bool, name="sloppy")
+    self._buffer_output_elements = convert.optional_param_to_tensor(
+        "buffer_output_elements",
+        buffer_output_elements,
+        argument_default=2 * block_length)
+    self._prefetch_input_elements = convert.optional_param_to_tensor(
+        "prefetch_input_elements",
+        prefetch_input_elements,
+        argument_default=2 * cycle_length)
+
+  def _as_variant_tensor(self):
+    return gen_dataset_ops.parallel_interleave_dataset(
+        self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
+        self._map_func.captured_inputs,
+        self._cycle_length,
+        self._block_length,
+        self._sloppy,
+        self._buffer_output_elements,
+        self._prefetch_input_elements,
+        f=self._map_func,
+        output_types=nest.flatten(
+            sparse.as_dense_types(self.output_types, self.output_classes)),
+        output_shapes=nest.flatten(
+            sparse.as_dense_shapes(self.output_shapes, self.output_classes)))
+
+  @property
+  def output_classes(self):
+    return self._output_classes
+
+  @property
+  def output_shapes(self):
+    return self._output_shapes
+
+  @property
+  def output_types(self):
+    return self._output_types
+
+
+@tf_export("data.TFRecordDataset")
+class TFRecordDataset(dataset_ops.Dataset):
+  """A `Dataset` comprising records from one or more TFRecord files."""
+
+  def __init__(self, filenames, compression_type=None, buffer_size=None,
+               num_parallel_reads=None):
+    """Creates a `TFRecordDataset` to read for one or more TFRecord files.
+
+    NOTE: The `num_parallel_reads` argument can be used to improve performance
+    when reading from a remote filesystem.
+
+    Args:
+      filenames: A `tf.string` tensor or `tf.data.Dataset` containing one or
+        more filenames.
+      compression_type: (Optional.) A `tf.string` scalar evaluating to one of
+        `""` (no compression), `"ZLIB"`, or `"GZIP"`.
+      buffer_size: (Optional.) A `tf.int64` scalar representing the number of
+        bytes in the read buffer. 0 means no buffering.
+      num_parallel_reads: (Optional.) A `tf.int64` scalar representing the
+        number of files to read in parallel. Defaults to reading files
+        sequentially.
+
+    Raises:
+      TypeError: If any argument does not have the expected type.
+      ValueError: If any argument does not have the expected shape.
+    """
+    super(TFRecordDataset, self).__init__()
+    if isinstance(filenames, dataset_ops.Dataset):
+      if filenames.output_types != dtypes.string:
+        raise TypeError(
+            "`filenames` must be a `tf.data.Dataset` of `tf.string` elements.")
+      if not filenames.output_shapes.is_compatible_with(tensor_shape.scalar()):
+        raise ValueError(
+            "`filenames` must be a `tf.data.Dataset` of scalar `tf.string` "
+            "elements.")
+    else:
+      filenames = ops.convert_to_tensor(filenames, dtype=dtypes.string)
+      filenames = array_ops.reshape(filenames, [-1], name="flat_filenames")
+      filenames = dataset_ops.Dataset.from_tensor_slices(filenames)
+
+    def read_one_file(filename):
+      return _TFRecordDataset(filename, compression_type, buffer_size)
+
+    if num_parallel_reads is None:
+      self._impl = filenames.flat_map(read_one_file)
+    else:
+      self._impl = ParallelInterleaveDataset(
+          filenames, read_one_file, cycle_length=num_parallel_reads,
+          block_length=1, sloppy=False, buffer_output_elements=None,
+          prefetch_input_elements=None)
+
+  def _as_variant_tensor(self):
+    return self._impl._as_variant_tensor()  # pylint: disable=protected-access
+
+  @property
+  def output_classes(self):
+    return self._impl.output_classes
+
+  @property
+  def output_shapes(self):
+    return self._impl.output_shapes
+
+  @property
+  def output_types(self):
+    return self._impl.output_types
+
+
 @tf_export("data.FixedLengthRecordDataset")
-class FixedLengthRecordDataset(Dataset):
+class FixedLengthRecordDataset(dataset_ops.Dataset):
   """A `Dataset` of fixed-length records from one or more binary files."""
 
   def __init__(self,
diff --git a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt
index 9770389e5e..709ec127ce 100644
--- a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt
@@ -17,7 +17,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'filenames\', \'compression_type\', \'buffer_size\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
+    argspec: "args=[\'self\', \'filenames\', \'compression_type\', \'buffer_size\', \'num_parallel_reads\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
   }
   member_method {
     name: "apply"
-- 
GitLab


From 281677dffc41665343d434752df6464fe2b52319 Mon Sep 17 00:00:00 2001
From: Giuseppe <giuscri@gmail.com>
Date: Wed, 28 Feb 2018 23:32:19 +0100
Subject: [PATCH 459/575] Fix markdown error in documentation.

Newline in the middle of links
was preventing their rendering.
---
 tensorflow/docs_src/install/install_sources.md | 3 +--
 tensorflow/docs_src/install/install_windows.md | 3 +--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index 8d83e9f119..acf0af0d9d 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -393,8 +393,7 @@ TensorFlow programs:
 
 <pre>Hello, TensorFlow!</pre>
 
-If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with
-TensorFlow}.
+If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with TensorFlow}.
 
 If the system outputs an error message instead of a greeting, see [Common
 installation problems](#common_installation_problems).
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index dedf485f93..f0a30ee394 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -153,8 +153,7 @@ TensorFlow programs:
 
 <pre>Hello, TensorFlow!</pre>
 
-If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with
-TensorFlow}.
+If you are new to TensorFlow, see @{$get_started/premade_estimators$Getting Started with TensorFlow}.
 
 If the system outputs an error message instead of a greeting, see [Common
 installation problems](#common_installation_problems).
-- 
GitLab


From f28e4d6faf94c08464f430f9cd01ef32dde6ad46 Mon Sep 17 00:00:00 2001
From: Richard Wei <rxwei@google.com>
Date: Wed, 28 Feb 2018 14:43:39 -0800
Subject: [PATCH 460/575] Package c_api_experimental.h in binary release
 distributions.

PiperOrigin-RevId: 187385913
---
 tensorflow/c/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/c/BUILD b/tensorflow/c/BUILD
index 5dfb743681..29ed957c9a 100644
--- a/tensorflow/c/BUILD
+++ b/tensorflow/c/BUILD
@@ -17,7 +17,10 @@ load(
 
 filegroup(
     name = "headers",
-    srcs = ["c_api.h"],
+    srcs = [
+        "c_api.h",
+        "c_api_experimental.h",
+    ],
     visibility = ["//tensorflow:__subpackages__"],
 )
 
-- 
GitLab


From 91d49c7d98114da4e4647c62d9f9b69119296b69 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Wed, 28 Feb 2018 14:50:02 -0800
Subject: [PATCH 461/575] Removing underscore prefixes from hidden generated
 Python functions.

PiperOrigin-RevId: 187386941
---
 tensorflow/compiler/tests/binary_ops_test.py  | 50 +++++------
 tensorflow/compiler/tests/concat_ops_test.py  |  2 +-
 tensorflow/compiler/tests/image_ops_test.py   |  2 +-
 tensorflow/compiler/tests/lrn_ops_test.py     |  8 +-
 .../compiler/tests/pooling_ops_3d_test.py     | 18 ++--
 tensorflow/compiler/tests/pooling_ops_test.py | 10 +--
 .../compiler/tests/spacetobatch_op_test.py    |  4 +-
 tensorflow/compiler/tests/stack_ops_test.py   | 46 +++++-----
 .../compiler/tests/tensor_array_ops_test.py   |  2 +-
 tensorflow/contrib/lookup/lookup_ops.py       | 38 +++-----
 tensorflow/python/__init__.py                 |  4 -
 .../python/debug/lib/debug_gradients.py       |  9 +-
 tensorflow/python/eager/benchmarks_test.py    |  3 +-
 tensorflow/python/eager/ops_test.py           |  8 +-
 .../python/eager/python_eager_op_gen.cc       | 25 ++++--
 tensorflow/python/framework/function_test.py  |  4 +-
 .../python/framework/graph_util_test.py       | 14 +--
 tensorflow/python/framework/ops_test.py       |  2 +-
 tensorflow/python/framework/python_op_gen.cc  | 56 ++++++++++--
 .../python/framework/python_op_gen_internal.h |  3 +
 .../python/framework/tensor_util_test.py      |  2 +-
 .../python/grappler/layout_optimizer_test.py  | 10 +--
 .../python/kernel_tests/array_ops_test.py     |  2 +-
 .../kernel_tests/batchtospace_op_test.py      |  2 +-
 .../python/kernel_tests/bcast_ops_test.py     |  4 +-
 .../kernel_tests/checkpoint_ops_test.py       | 34 ++++----
 .../python/kernel_tests/concat_op_test.py     | 22 ++---
 .../kernel_tests/control_flow_ops_py_test.py  | 10 +--
 .../python/kernel_tests/cwise_ops_test.py     | 10 +--
 .../kernel_tests/determinant_op_test.py       |  2 +-
 .../fractional_avg_pool_op_test.py            | 10 +--
 .../fractional_max_pool_op_test.py            | 28 +++---
 .../matrix_exponential_op_test.py             | 12 +--
 .../kernel_tests/matrix_logarithm_op_test.py  | 14 +--
 .../python/kernel_tests/pooling_ops_test.py   | 86 +++++++++----------
 .../kernel_tests/save_restore_ops_test.py     |  7 +-
 tensorflow/python/kernel_tests/scalar_test.py |  4 +-
 .../kernel_tests/spacetobatch_op_test.py      |  4 +-
 .../kernel_tests/sparse_xent_op_test.py       | 13 +--
 .../python/kernel_tests/stack_ops_test.py     | 82 +++++++++---------
 .../kernel_tests/tensor_array_ops_test.py     |  2 +-
 .../python/kernel_tests/unique_op_test.py     |  6 +-
 .../python/kernel_tests/variable_ops_test.py  | 24 +++---
 .../python/kernel_tests/variables_test.py     |  2 +-
 .../python/kernel_tests/xent_op_test.py       | 12 +--
 .../python/ops/accumulate_n_benchmark.py      |  7 +-
 tensorflow/python/ops/array_grad.py           | 16 +---
 tensorflow/python/ops/array_ops.py            | 77 ++++++++---------
 tensorflow/python/ops/batch_norm_benchmark.py |  5 +-
 .../python/ops/candidate_sampling_ops.py      | 12 +--
 tensorflow/python/ops/control_flow_grad.py    |  1 -
 tensorflow/python/ops/control_flow_ops.py     | 46 ++++------
 tensorflow/python/ops/ctc_ops.py              |  6 +-
 tensorflow/python/ops/data_flow_ops.py        | 42 ++++-----
 tensorflow/python/ops/functional_ops.py       |  2 +-
 tensorflow/python/ops/gradients_impl.py       |  2 +-
 tensorflow/python/ops/histogram_ops.py        |  4 +-
 tensorflow/python/ops/image_grad.py           | 12 +--
 tensorflow/python/ops/image_ops_impl.py       | 12 +--
 tensorflow/python/ops/io_ops.py               | 75 ++++++++--------
 tensorflow/python/ops/linalg/linalg_impl.py   |  8 +-
 tensorflow/python/ops/linalg_ops.py           | 15 ++--
 tensorflow/python/ops/logging_ops.py          | 15 ++--
 tensorflow/python/ops/lookup_ops.py           | 20 ++---
 tensorflow/python/ops/math_grad.py            | 59 +++++--------
 tensorflow/python/ops/math_ops.py             | 80 +++++++++--------
 tensorflow/python/ops/nn_batchnorm_test.py    |  3 +-
 tensorflow/python/ops/nn_grad.py              | 64 +++++++-------
 tensorflow/python/ops/nn_impl.py              |  6 +-
 tensorflow/python/ops/nn_ops.py               | 39 ++++-----
 tensorflow/python/ops/parsing_ops.py          | 23 ++---
 tensorflow/python/ops/random_ops.py           | 16 ++--
 tensorflow/python/ops/script_ops.py           |  8 +-
 tensorflow/python/ops/session_ops.py          | 13 ++-
 tensorflow/python/ops/sparse_grad.py          | 11 +--
 tensorflow/python/ops/sparse_ops.py           | 34 ++++----
 tensorflow/python/ops/standard_ops.py         |  1 -
 tensorflow/python/ops/state_ops.py            | 15 ++--
 tensorflow/python/ops/string_ops.py           |  4 +-
 tensorflow/python/ops/summary_ops.py          |  3 +-
 tensorflow/python/ops/tensor_array_ops.py     | 20 ++---
 tensorflow/python/summary/summary.py          |  9 +-
 tensorflow/python/training/checkpoint_ops.py  |  6 +-
 .../training/learning_rate_decay_test.py      | 20 ++---
 .../python/training/moving_averages_test.py   |  2 +-
 tensorflow/python/training/saver.py           |  6 +-
 .../python/training/saver_test_utils.py       | 12 +--
 tensorflow/python/user_ops/user_ops.py        |  2 +-
 88 files changed, 742 insertions(+), 803 deletions(-)

diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py
index 0e4efaed86..6bcfed7b69 100644
--- a/tensorflow/compiler/tests/binary_ops_test.py
+++ b/tensorflow/compiler/tests/binary_ops_test.py
@@ -71,7 +71,7 @@ class BinaryOpsTest(XLATestCase):
           expected=np.array([[[[False, True], [True, False]]]], dtype=dtype))
 
       self._testBinary(
-          gen_math_ops._real_div,
+          gen_math_ops.real_div,
           np.array([3, 3, -1.5, -8, 44], dtype=dtype),
           np.array([2, -2, 7, -4, 0], dtype=dtype),
           expected=np.array(
@@ -108,57 +108,57 @@ class BinaryOpsTest(XLATestCase):
               [0, np.pi / 4, np.pi / 2, np.pi * 3 / 4, np.pi], dtype=dtype))
 
       self._testBinary(
-          gen_math_ops._reciprocal_grad,
+          gen_math_ops.reciprocal_grad,
           np.array([4, -3, -2, 1], dtype=dtype),
           np.array([5, -6, 7, -8], dtype=dtype),
           expected=np.array([-80, 54, -28, 8], dtype=dtype))
 
       self._testBinary(
-          gen_math_ops._sigmoid_grad,
+          gen_math_ops.sigmoid_grad,
           np.array([4, 3, 2, 1], dtype=dtype),
           np.array([5, 6, 7, 8], dtype=dtype),
           expected=np.array([-60, -36, -14, 0], dtype=dtype))
 
       self._testBinary(
-          gen_math_ops._rsqrt_grad,
+          gen_math_ops.rsqrt_grad,
           np.array([4, 3, 2, 1], dtype=dtype),
           np.array([5, 6, 7, 8], dtype=dtype),
           expected=np.array([-160, -81, -28, -4], dtype=dtype))
 
       self._testBinary(
-          gen_math_ops._sqrt_grad,
+          gen_math_ops.sqrt_grad,
           np.array([4, 3, 2, 1], dtype=dtype),
           np.array([5, 6, 7, 8], dtype=dtype),
           expected=np.array([0.625, 1, 1.75, 4], dtype=dtype))
 
       self._testBinary(
-          gen_nn_ops._softplus_grad,
+          gen_nn_ops.softplus_grad,
           np.array([4, 3, 2, 1], dtype=dtype),
           np.array([5, 6, 7, 8], dtype=dtype),
           expected=np.array(
               [3.97322869, 2.99258232, 1.99817801, 0.99966466], dtype=dtype))
 
       self._testBinary(
-          gen_nn_ops._softsign_grad,
+          gen_nn_ops.softsign_grad,
           np.array([4, 3, 2, 1], dtype=dtype),
           np.array([5, 6, 7, 8], dtype=dtype),
           expected=np.array(
               [0.11111111, 0.06122449, 0.03125, 0.01234568], dtype=dtype))
 
       self._testBinary(
-          gen_math_ops._tanh_grad,
+          gen_math_ops.tanh_grad,
           np.array([4, 3, 2, 1], dtype=dtype),
           np.array([5, 6, 7, 8], dtype=dtype),
           expected=np.array([-75, -48, -21, 0], dtype=dtype))
 
       self._testBinary(
-          gen_nn_ops._elu_grad,
+          gen_nn_ops.elu_grad,
           np.array([1, 2, 3, 4, 5, 6], dtype=dtype),
           np.array([-.6, -.4, -.2, 0, .2, .4], dtype=dtype),
           expected=np.array([0.4, 1.2, 2.4, 4, 5, 6], dtype=dtype))
 
       self._testBinary(
-          gen_nn_ops._selu_grad,
+          gen_nn_ops.selu_grad,
           np.array([1, 2, 3, 4, 5, 6], dtype=dtype),
           np.array([-.6, -.4, -.2, .2, .4, .6], dtype=dtype),
           expected=np.array(
@@ -166,20 +166,20 @@ class BinaryOpsTest(XLATestCase):
                4.202803949422, 5.2535049367774, 6.30420592413], dtype=dtype))
 
       self._testBinary(
-          gen_nn_ops._relu_grad,
+          gen_nn_ops.relu_grad,
           np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtype),
           np.array([0, 0, 0, 0, 0, 0.1, 0.3, 0.5, 0.7, 0.9], dtype=dtype),
           expected=np.array([0, 0, 0, 0, 0, 6, 7, 8, 9, 10], dtype=dtype))
 
       self._testBinary(
-          gen_nn_ops._relu6_grad,
+          gen_nn_ops.relu6_grad,
           np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtype),
           np.array(
               [0, 0, 0, 0, 0, 0.1, 0.3, 0.5, 0.7, 0.9, 6.1, 10.0], dtype=dtype),
           expected=np.array([0, 0, 0, 0, 0, 6, 7, 8, 9, 10, 0, 0], dtype=dtype))
 
       self._testBinary(
-          gen_nn_ops._softmax_cross_entropy_with_logits,
+          gen_nn_ops.softmax_cross_entropy_with_logits,
           np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=dtype),
           np.array([[0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1]], dtype=dtype),
           expected=[
@@ -191,7 +191,7 @@ class BinaryOpsTest(XLATestCase):
           equality_test=self.ListsAreClose)
 
       self._testBinary(
-          gen_nn_ops._sparse_softmax_cross_entropy_with_logits,
+          gen_nn_ops.sparse_softmax_cross_entropy_with_logits,
           np.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8],
                     [0.9, 1.0, 1.1, 1.2]], dtype=dtype),
           np.array([2, 1, 7], dtype=np.int32),
@@ -207,7 +207,7 @@ class BinaryOpsTest(XLATestCase):
   def testIntOps(self):
     for dtype in self.int_types:
       self._testBinary(
-          gen_math_ops._truncate_div,
+          gen_math_ops.truncate_div,
           np.array([3, 3, -1, -9, -8], dtype=dtype),
           np.array([2, -2, 7, 2, -4], dtype=dtype),
           expected=np.array([1, -1, 0, -4, 2], dtype=dtype))
@@ -369,7 +369,7 @@ class BinaryOpsTest(XLATestCase):
           expected=np.array([[[[False, True], [True, False]]]], dtype=dtype))
 
       self._testBinary(
-          gen_math_ops._real_div,
+          gen_math_ops.real_div,
           np.array([3, 3j, -1.5j, -8, 2 + 3j, 2 + 4j], dtype=dtype),
           np.array([2, -2, 7j, -4j, 4 - 6j, 1 + 2j], dtype=dtype),
           expected=np.array(
@@ -378,7 +378,7 @@ class BinaryOpsTest(XLATestCase):
 
       # Test inf/nan scenarios.
       self._testBinary(
-          gen_math_ops._real_div,
+          gen_math_ops.real_div,
           np.array([4 + 3j, 4, 3j, -4, -4j, 2 - 3j], dtype=dtype),
           np.array([0, 0, 0, 0, 0, 0], dtype=dtype),
           expected=np.array(
@@ -418,19 +418,19 @@ class BinaryOpsTest(XLATestCase):
       lhs = np.array([4 + 2j, -3 - 1j, 2j, 1], dtype=dtype)
       rhs = np.array([5, -6j, 7 - 3j, -8j], dtype=dtype)
       self._testBinary(
-          gen_math_ops._reciprocal_grad, lhs, rhs, expected=-rhs * lhs * lhs)
+          gen_math_ops.reciprocal_grad, lhs, rhs, expected=-rhs * lhs * lhs)
 
       self._testBinary(
-          gen_math_ops._sigmoid_grad, lhs, rhs, expected=rhs * lhs * (1 - lhs))
+          gen_math_ops.sigmoid_grad, lhs, rhs, expected=rhs * lhs * (1 - lhs))
 
       self._testBinary(
-          gen_math_ops._rsqrt_grad, lhs, rhs, expected=lhs**3 * rhs / -2)
+          gen_math_ops.rsqrt_grad, lhs, rhs, expected=lhs**3 * rhs / -2)
 
       self._testBinary(
-          gen_math_ops._sqrt_grad, lhs, rhs, expected=rhs / (2 * lhs))
+          gen_math_ops.sqrt_grad, lhs, rhs, expected=rhs / (2 * lhs))
 
       self._testBinary(
-          gen_math_ops._tanh_grad, lhs, rhs, expected=rhs * (1 - lhs * lhs))
+          gen_math_ops.tanh_grad, lhs, rhs, expected=rhs * (1 - lhs * lhs))
 
   def testComplexMath(self):
     for dtype in self.complex_types:
@@ -538,7 +538,7 @@ class BinaryOpsTest(XLATestCase):
 
     if dtype not in self.complex_types:  # floordiv unsupported for complex.
       self._testBinary(
-          gen_math_ops._floor_div,
+          gen_math_ops.floor_div,
           np.array([3, 3, -1, -9, -8], dtype=dtype),
           np.array([2, -2, 7, 2, -4], dtype=dtype),
           expected=np.array([1, -2, -1, -5, 2], dtype=dtype))
@@ -554,12 +554,12 @@ class BinaryOpsTest(XLATestCase):
   def _testRemainder(self, dtype):
     """Test cases for remainder operators."""
     self._testBinary(
-        gen_math_ops._floor_mod,
+        gen_math_ops.floor_mod,
         np.array([3, 3, -1, -8], dtype=dtype),
         np.array([2, -2, 7, -4], dtype=dtype),
         expected=np.array([1, -1, 6, 0], dtype=dtype))
     self._testBinary(
-        gen_math_ops._truncate_mod,
+        gen_math_ops.truncate_mod,
         np.array([3, 3, -1, -8], dtype=dtype),
         np.array([2, -2, 7, -4], dtype=dtype),
         expected=np.array([1, 1, -1, 0], dtype=dtype))
diff --git a/tensorflow/compiler/tests/concat_ops_test.py b/tensorflow/compiler/tests/concat_ops_test.py
index 81734082d9..f10973e19f 100644
--- a/tensorflow/compiler/tests/concat_ops_test.py
+++ b/tensorflow/compiler/tests/concat_ops_test.py
@@ -301,7 +301,7 @@ class ConcatOffsetTest(XLATestCase):
         s0 = constant_op.constant([2, 3, 5], dtypes.int32)
         s1 = constant_op.constant([2, 7, 5], dtypes.int32)
         s2 = constant_op.constant([2, 20, 5], dtypes.int32)
-        off = gen_array_ops._concat_offset(cdim, [s0, s1, s2])
+        off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
         ans = sess.run(off)
         self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
 
diff --git a/tensorflow/compiler/tests/image_ops_test.py b/tensorflow/compiler/tests/image_ops_test.py
index 538fa8e8e5..3bc41b7cfd 100644
--- a/tensorflow/compiler/tests/image_ops_test.py
+++ b/tensorflow/compiler/tests/image_ops_test.py
@@ -426,7 +426,7 @@ class ResizeBilinearTest(XLATestCase):
     with self.test_session() as sess, self.test_scope():
       dtype = dtype or np.float32
       grads = array_ops.placeholder(np.float32)
-      resized = gen_image_ops._resize_bilinear_grad(
+      resized = gen_image_ops.resize_bilinear_grad(
           grads,
           np.zeros([1, input_shape[0], input_shape[1], 1], dtype=dtype),
           align_corners=True)
diff --git a/tensorflow/compiler/tests/lrn_ops_test.py b/tensorflow/compiler/tests/lrn_ops_test.py
index 5d8d89224d..69bd8f7230 100644
--- a/tensorflow/compiler/tests/lrn_ops_test.py
+++ b/tensorflow/compiler/tests/lrn_ops_test.py
@@ -115,11 +115,11 @@ class LRNTest(XLATestCase):
       out_image = constant_op.constant(out_image_vals, shape=shape)
       out_grads = constant_op.constant(out_grads_vals, shape=shape)
       with ops.device(CPU_DEVICE):
-        expected = gen_nn_ops._lrn_grad(out_grads, in_image, out_image,
-                                        depth_radius, bias, alpha, beta)
+        expected = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
+                                       depth_radius, bias, alpha, beta)
       with self.test_scope():
-        actual = gen_nn_ops._lrn_grad(out_grads, in_image, out_image,
-                                      depth_radius, bias, alpha, beta)
+        actual = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
+                                     depth_radius, bias, alpha, beta)
       expected_val = expected.eval()
       actual_val = actual.eval()
     self.assertAllClose(actual_val, expected_val, rtol=1e-3)
diff --git a/tensorflow/compiler/tests/pooling_ops_3d_test.py b/tensorflow/compiler/tests/pooling_ops_3d_test.py
index eb48fe555a..4eed903963 100644
--- a/tensorflow/compiler/tests/pooling_ops_3d_test.py
+++ b/tensorflow/compiler/tests/pooling_ops_3d_test.py
@@ -33,7 +33,7 @@ from tensorflow.python.platform import test
 # MaxPoolGrad.
 def _AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding):
   del outputs  # Unused by average-pooling gradients.
-  return gen_nn_ops._avg_pool3d_grad(
+  return gen_nn_ops.avg_pool3d_grad(
       inputs.get_shape().as_list(),
       output_gradients,
       ksize=ksize,
@@ -263,7 +263,7 @@ class Pooling3DTest(XLATestCase):
   def testMaxPoolGradValidPadding1_1_3d(self):
     self._VerifyGradient(
         nn_ops.max_pool3d,
-        gen_nn_ops._max_pool3d_grad,
+        gen_nn_ops.max_pool3d_grad,
         input_sizes=[1, 3, 3, 3, 1],
         ksize=[1, 1, 1],
         strides=[1, 1, 1],
@@ -272,7 +272,7 @@ class Pooling3DTest(XLATestCase):
   def testMaxPoolGradValidPadding2_1_6_3d(self):
     self._VerifyGradient(
         nn_ops.max_pool3d,
-        gen_nn_ops._max_pool3d_grad,
+        gen_nn_ops.max_pool3d_grad,
         input_sizes=[2, 3, 3, 6, 3],
         ksize=[2, 2, 2],
         strides=[1, 1, 1],
@@ -281,7 +281,7 @@ class Pooling3DTest(XLATestCase):
   def testMaxPoolGradValidPadding2_1_7_3d(self):
     self._VerifyGradient(
         nn_ops.max_pool3d,
-        gen_nn_ops._max_pool3d_grad,
+        gen_nn_ops.max_pool3d_grad,
         input_sizes=[2, 3, 5, 7, 3],
         ksize=[2, 2, 2],
         strides=[1, 1, 1],
@@ -290,7 +290,7 @@ class Pooling3DTest(XLATestCase):
   def testMaxPoolGradValidPadding2_2_3d(self):
     self._VerifyGradient(
         nn_ops.max_pool3d,
-        gen_nn_ops._max_pool3d_grad,
+        gen_nn_ops.max_pool3d_grad,
         input_sizes=[2, 2, 2, 2, 3],
         ksize=[2, 2, 2],
         strides=[2, 2, 2],
@@ -299,7 +299,7 @@ class Pooling3DTest(XLATestCase):
   def testMaxPoolGradSamePadding1_1_3d(self):
     self._VerifyGradient(
         nn_ops.max_pool3d,
-        gen_nn_ops._max_pool3d_grad,
+        gen_nn_ops.max_pool3d_grad,
         input_sizes=[2, 3, 2, 4, 1],
         ksize=[1, 1, 1],
         strides=[1, 1, 1],
@@ -308,7 +308,7 @@ class Pooling3DTest(XLATestCase):
   def testMaxPoolGradSamePadding2_1_3d(self):
     self._VerifyGradient(
         nn_ops.max_pool3d,
-        gen_nn_ops._max_pool3d_grad,
+        gen_nn_ops.max_pool3d_grad,
         input_sizes=[2, 3, 2, 4, 1],
         ksize=[2, 2, 2],
         strides=[1, 1, 1],
@@ -317,7 +317,7 @@ class Pooling3DTest(XLATestCase):
   def testMaxPoolGradSamePadding2_2_3d(self):
     self._VerifyGradient(
         nn_ops.max_pool3d,
-        gen_nn_ops._max_pool3d_grad,
+        gen_nn_ops.max_pool3d_grad,
         input_sizes=[2, 5, 2, 4, 3],
         ksize=[2, 2, 2],
         strides=[2, 2, 2],
@@ -326,7 +326,7 @@ class Pooling3DTest(XLATestCase):
   def testMaxPoolGradSamePadding3_1_3d(self):
     self._VerifyGradient(
         nn_ops.max_pool3d,
-        gen_nn_ops._max_pool3d_grad,
+        gen_nn_ops.max_pool3d_grad,
         input_sizes=[1, 3, 3, 7, 1],
         ksize=[3, 3, 3],
         strides=[1, 1, 1],
diff --git a/tensorflow/compiler/tests/pooling_ops_test.py b/tensorflow/compiler/tests/pooling_ops_test.py
index 7c19a99c4e..e0e85295fe 100644
--- a/tensorflow/compiler/tests/pooling_ops_test.py
+++ b/tensorflow/compiler/tests/pooling_ops_test.py
@@ -459,7 +459,7 @@ class PoolGradTest(XLATestCase):
         padding="SAME")
 
   def testMaxPool(self):
-    self._TestPooling(nn_ops.max_pool, gen_nn_ops._max_pool_grad)
+    self._TestPooling(nn_ops.max_pool, gen_nn_ops.max_pool_grad)
 
   def testAvgPool(self):
     # Wrapper around AvgPoolGrad that ignores extra arguments needed by
@@ -467,7 +467,7 @@ class PoolGradTest(XLATestCase):
     def AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding,
                     data_format):
       del outputs  # Unused by average-pooling gradients.
-      return gen_nn_ops._avg_pool_grad(
+      return gen_nn_ops.avg_pool_grad(
           inputs.get_shape().as_list(),
           output_gradients,
           ksize=ksize,
@@ -483,7 +483,7 @@ class PoolGradTest(XLATestCase):
   def testMaxPoolKernelSmallerThanStrideValid(self):
     self._VerifyValues(
         nn_ops.max_pool,
-        gen_nn_ops._max_pool_grad,
+        gen_nn_ops.max_pool_grad,
         input_sizes=[1, 7, 7, 1],
         ksize=[1, 2, 2, 1],
         strides=[1, 3, 3, 1],
@@ -492,7 +492,7 @@ class PoolGradTest(XLATestCase):
   def testMaxPoolKernelSmallerThanStrideSame(self):
     self._VerifyValues(
         nn_ops.max_pool,
-        gen_nn_ops._max_pool_grad,
+        gen_nn_ops.max_pool_grad,
         input_sizes=[1, 3, 3, 1],
         ksize=[1, 1, 1, 1],
         strides=[1, 2, 2, 1],
@@ -500,7 +500,7 @@ class PoolGradTest(XLATestCase):
 
     self._VerifyValues(
         nn_ops.max_pool,
-        gen_nn_ops._max_pool_grad,
+        gen_nn_ops.max_pool_grad,
         input_sizes=[1, 4, 4, 1],
         ksize=[1, 1, 1, 1],
         strides=[1, 2, 2, 1],
diff --git a/tensorflow/compiler/tests/spacetobatch_op_test.py b/tensorflow/compiler/tests/spacetobatch_op_test.py
index c013f4b50a..92518aadc4 100644
--- a/tensorflow/compiler/tests/spacetobatch_op_test.py
+++ b/tensorflow/compiler/tests/spacetobatch_op_test.py
@@ -75,11 +75,11 @@ class SpaceToBatchTest(XLATestCase):
       for dtype in self.float_types:
         # outputs = space_to_batch(inputs)
         placeholder = array_ops.placeholder(dtype)
-        x_tf = gen_array_ops._space_to_batch(
+        x_tf = gen_array_ops.space_to_batch(
             placeholder, paddings, block_size=block_size)
         self.assertAllEqual(sess.run(x_tf, {placeholder: inputs}), outputs)
         # inputs = batch_to_space(outputs)
-        x_tf = gen_array_ops._batch_to_space(
+        x_tf = gen_array_ops.batch_to_space(
             placeholder, paddings, block_size=block_size)
         self.assertAllEqual(sess.run(x_tf, {placeholder: outputs}), inputs)
 
diff --git a/tensorflow/compiler/tests/stack_ops_test.py b/tensorflow/compiler/tests/stack_ops_test.py
index 2b9c227973..94342f9567 100644
--- a/tensorflow/compiler/tests/stack_ops_test.py
+++ b/tensorflow/compiler/tests/stack_ops_test.py
@@ -34,33 +34,33 @@ class StackOpTest(XLATestCase):
     with self.test_session(), self.test_scope():
       size = array_ops.placeholder(dtypes.int32)
       v = array_ops.placeholder(dtypes.float32)
-      h = gen_data_flow_ops._stack_v2(size, dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push_v2(h, v)
+      h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops.stack_push_v2(h, v)
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
+        c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
       self.assertAllClose([[4.0, 5.0]], c1.eval({size: 5, v: [[4.0, 5.0]]}))
 
   def testStackPushPopSwap(self):
     with self.test_session(), self.test_scope():
       a = np.arange(2000)
       x = array_ops.placeholder(dtypes.float32)
-      h = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push_v2(h, x, swap_memory=True)
+      h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops.stack_push_v2(h, x, swap_memory=True)
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
+        c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
       self.assertAllClose(a, c1.eval({x: a}))
 
   def testMultiStack(self):
     with self.test_session(), self.test_scope():
       v = array_ops.placeholder(dtypes.float32)
-      h1 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
-      c1 = gen_data_flow_ops._stack_push_v2(h1, v)
+      h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
+      c1 = gen_data_flow_ops.stack_push_v2(h1, v)
       with ops.control_dependencies([c1]):
-        c1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32)
-      h2 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="bar")
-      c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0)
+        c1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32)
+      h2 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="bar")
+      c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0)
       with ops.control_dependencies([c2]):
-        c2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32)
+        c2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32)
       r = c1 + c2
       self.assertAllClose(9.0, r.eval({v: 4.0}))
 
@@ -69,15 +69,15 @@ class StackOpTest(XLATestCase):
     with self.test_session() as sess, self.test_scope():
       v1 = array_ops.placeholder(dtypes.float32)
       v2 = array_ops.placeholder(dtypes.float32)
-      h1 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
-      h2 = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
+      h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
+      h2 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
 
-      c1 = gen_data_flow_ops._stack_push_v2(h1, v1)
+      c1 = gen_data_flow_ops.stack_push_v2(h1, v1)
       with ops.control_dependencies([c1]):
-        c2 = gen_data_flow_ops._stack_push_v2(h2, v2)
+        c2 = gen_data_flow_ops.stack_push_v2(h2, v2)
       with ops.control_dependencies([c2]):
-        pop1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32)
-        pop2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32)
+        pop1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32)
+        pop2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32)
 
       out1, out2 = sess.run([pop1, pop2], {v1: 4.0, v2: 5.0})
       self.assertAllClose(out1, 4.0)
@@ -86,17 +86,17 @@ class StackOpTest(XLATestCase):
   def testCloseStack(self):
     with self.test_session() as sess, self.test_scope():
       size = array_ops.placeholder(dtypes.int32)
-      h = gen_data_flow_ops._stack_v2(size, dtypes.float32, stack_name="foo")
-      c1 = gen_data_flow_ops._stack_close_v2(h)
+      h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo")
+      c1 = gen_data_flow_ops.stack_close_v2(h)
       sess.run(c1, {size: 5})
 
   def testPushCloseStack(self):
     with self.test_session() as sess, self.test_scope():
       v = array_ops.placeholder(dtypes.float32)
-      h = gen_data_flow_ops._stack_v2(5, dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push_v2(h, v)
+      h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
+      c = gen_data_flow_ops.stack_push_v2(h, v)
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_close_v2(h)
+        c1 = gen_data_flow_ops.stack_close_v2(h)
       sess.run(c1, {v: [[4.0, 5.0]]})
 
 
diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py
index a62925a181..7624d6e4b2 100644
--- a/tensorflow/compiler/tests/tensor_array_ops_test.py
+++ b/tensorflow/compiler/tests/tensor_array_ops_test.py
@@ -338,7 +338,7 @@ class TensorArrayTest(xla_test.XLATestCase):
         w0 = ta.write(0, [[4.0, 5.0]])
 
         # Test reading wrong datatype.
-        r0_bad = gen_data_flow_ops._tensor_array_read_v3(
+        r0_bad = gen_data_flow_ops.tensor_array_read_v3(
             handle=w0.handle, index=0, dtype=dtype2, flow_in=w0.flow)
         with self.assertRaisesOpError("TensorArray dtype is "):
           r0_bad.eval()
diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index a430dac4ec..62f1c810fc 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -341,23 +341,21 @@ class MutableHashTable(LookupInterface):
     # training to work correctly. Use the node name if no shared_name has been
     # explicitly specified.
     use_node_name_sharing = checkpoint and shared_name is None
-    # pylint: disable=protected-access
     if self._default_value.get_shape().ndims == 0:
-      self._table_ref = gen_lookup_ops._mutable_hash_table_v2(
+      self._table_ref = gen_lookup_ops.mutable_hash_table_v2(
           shared_name=shared_name,
           use_node_name_sharing=use_node_name_sharing,
           key_dtype=key_dtype,
           value_dtype=value_dtype,
           name=name)
     else:
-      self._table_ref = gen_lookup_ops._mutable_hash_table_of_tensors_v2(
+      self._table_ref = gen_lookup_ops.mutable_hash_table_of_tensors_v2(
           shared_name=shared_name,
           use_node_name_sharing=use_node_name_sharing,
           key_dtype=key_dtype,
           value_dtype=value_dtype,
           value_shape=self._default_value.get_shape(),
           name=name)
-    # pylint: enable=protected-access
     super(MutableHashTable, self).__init__(key_dtype, value_dtype,
                                            self._table_ref.op.name.split(
                                                "/")[-1])
@@ -378,9 +376,7 @@ class MutableHashTable(LookupInterface):
     with ops.name_scope(name, "%s_Size" % self._name,
                         [self._table_ref]) as name:
       with ops.colocate_with(self._table_ref):
-
-        # pylint: disable=protected-access
-        return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=name)
+        return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=name)
 
   def lookup(self, keys, name=None):
     """Looks up `keys` in a table, outputs the corresponding values.
@@ -406,8 +402,7 @@ class MutableHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_find" % self._name,
                         (self._table_ref, keys, self._default_value)) as name:
       with ops.colocate_with(self._table_ref):
-        # pylint: disable=protected-access
-        values = gen_lookup_ops._lookup_table_find_v2(
+        values = gen_lookup_ops.lookup_table_find_v2(
             self._table_ref, keys, self._default_value, name=name)
 
         values.set_shape(keys.get_shape().concatenate(self._value_shape))
@@ -437,7 +432,7 @@ class MutableHashTable(LookupInterface):
                         [self._table_ref, keys, values]) as name:
       with ops.colocate_with(self._table_ref):
         # pylint: disable=protected-access
-        op = gen_lookup_ops._lookup_table_insert_v2(
+        op = gen_lookup_ops.lookup_table_insert_v2(
             self._table_ref, keys, values, name=name)
     return op
 
@@ -454,8 +449,7 @@ class MutableHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_export_values" % self._name,
                         [self._table_ref]) as name:
       with ops.colocate_with(self._table_ref):
-        # pylint: disable=protected-access
-        exported_keys, exported_values = gen_lookup_ops._lookup_table_export_v2(
+        exported_keys, exported_values = gen_lookup_ops.lookup_table_export_v2(
             self._table_ref, self._key_dtype, self._value_dtype, name=name)
 
     exported_values.set_shape(exported_keys.get_shape().concatenate(
@@ -477,7 +471,7 @@ class MutableHashTable(LookupInterface):
     def restore(self, restored_tensors, unused_restored_shapes):
       # pylint: disable=protected-access
       with ops.colocate_with(self.op._table_ref):
-        return gen_lookup_ops._lookup_table_import_v2(
+        return gen_lookup_ops.lookup_table_import_v2(
             self.op._table_ref, restored_tensors[0], restored_tensors[1])
 
 
@@ -551,8 +545,7 @@ class MutableDenseHashTable(LookupInterface):
     # explicitly specified.
     use_node_name_sharing = checkpoint and shared_name is None
     empty_key = ops.convert_to_tensor(empty_key, dtype=key_dtype)
-    # pylint: disable=protected-access
-    self._table_ref = gen_lookup_ops._mutable_dense_hash_table_v2(
+    self._table_ref = gen_lookup_ops.mutable_dense_hash_table_v2(
         empty_key=empty_key,
         shared_name=shared_name,
         use_node_name_sharing=use_node_name_sharing,
@@ -560,7 +553,6 @@ class MutableDenseHashTable(LookupInterface):
         value_shape=self._value_shape,
         initial_num_buckets=initial_num_buckets,
         name=name)
-    # pylint: enable=protected-access
     super(MutableDenseHashTable, self).__init__(
         key_dtype, value_dtype, self._table_ref.op.name.split("/")[-1])
 
@@ -580,8 +572,7 @@ class MutableDenseHashTable(LookupInterface):
     with ops.name_scope(name, "%s_Size" % self._name,
                         [self._table_ref]) as name:
       with ops.colocate_with(self._table_ref):
-        # pylint: disable=protected-access
-        return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=name)
+        return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=name)
 
   def lookup(self, keys, name=None):
     """Looks up `keys` in a table, outputs the corresponding values.
@@ -607,8 +598,7 @@ class MutableDenseHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_find" % self._name,
                         [self._table_ref, keys]) as name:
       with ops.colocate_with(self._table_ref):
-        # pylint: disable=protected-access
-        values = gen_lookup_ops._lookup_table_find_v2(
+        values = gen_lookup_ops.lookup_table_find_v2(
             self._table_ref, keys, self._default_value, name=name)
 
     if keys.get_shape().ndims is not None and keys.get_shape().ndims > 0:
@@ -640,8 +630,7 @@ class MutableDenseHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_insert" % self._name,
                         [self._table_ref, keys, values]) as name:
       with ops.colocate_with(self._table_ref):
-        # pylint: disable=protected-access
-        op = gen_lookup_ops._lookup_table_insert_v2(
+        op = gen_lookup_ops.lookup_table_insert_v2(
             self._table_ref, keys, values, name=name)
       return op
 
@@ -658,8 +647,7 @@ class MutableDenseHashTable(LookupInterface):
     with ops.name_scope(name, "%s_lookup_table_export_values" % self._name,
                         [self._table_ref]) as name:
       with ops.colocate_with(self._table_ref):
-        # pylint: disable=protected-access
-        exported_keys, exported_values = gen_lookup_ops._lookup_table_export_v2(
+        exported_keys, exported_values = gen_lookup_ops.lookup_table_export_v2(
             self._table_ref, self._key_dtype, self._value_dtype, name=name)
 
     exported_values.set_shape(exported_keys.get_shape().concatenate(
@@ -681,5 +669,5 @@ class MutableDenseHashTable(LookupInterface):
     def restore(self, restored_tensors, unused_restored_shapes):
       # pylint: disable=protected-access
       with ops.colocate_with(self.op._table_ref):
-        return gen_lookup_ops._lookup_table_import_v2(
+        return gen_lookup_ops.lookup_table_import_v2(
             self.op._table_ref, restored_tensors[0], restored_tensors[1])
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index 02ed5517ca..d6715fa522 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -198,13 +198,9 @@ tf_export('TensorInfo')(TensorInfo)
 _allowed_symbols.extend([
     'arg_max',
     'arg_min',
-    'mul',  # use tf.multiply instead.
-    'neg',  # use tf.negative instead.
-    'sub',  # use tf.subtract instead.
     'create_partitioned_variables',
     'deserialize_many_sparse',
     'lin_space',
-    'list_diff',  # Use tf.listdiff instead.
     'listdiff',  # Use tf.listdiff instead.
     'parse_single_sequence_example',
     'serialize_many_sparse',
diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py
index 16f51a4b32..589a13db7f 100644
--- a/tensorflow/python/debug/lib/debug_gradients.py
+++ b/tensorflow/python/debug/lib/debug_gradients.py
@@ -156,11 +156,12 @@ class GradientsDebugger(object):
     # TODO(cais): Implement value_stack.
     grad_debug_op_name = _tensor_to_grad_debug_op_name(input_tensor, self._uuid)
     # pylint: disable=protected-access
-    identity_op = (gen_array_ops._debug_gradient_ref_identity
-                   if input_tensor.dtype._is_ref_dtype
-                   else gen_array_ops._debug_gradient_identity)
-    debug_grad_identity = identity_op(input_tensor, name=grad_debug_op_name)
+    identity_op = (
+        gen_array_ops.debug_gradient_ref_identity
+        if input_tensor.dtype._is_ref_dtype else
+        gen_array_ops.debug_gradient_identity)
     # pylint: enable=protected-access
+    debug_grad_identity = identity_op(input_tensor, name=grad_debug_op_name)
     assert debug_grad_identity.dtype == input_tensor.dtype
     if debug_grad_identity.op.name != grad_debug_op_name:
       raise ValueError(
diff --git a/tensorflow/python/eager/benchmarks_test.py b/tensorflow/python/eager/benchmarks_test.py
index b56cbe80a7..228ff62b20 100644
--- a/tensorflow/python/eager/benchmarks_test.py
+++ b/tensorflow/python/eager/benchmarks_test.py
@@ -243,7 +243,8 @@ class MicroBenchmarks(test.Benchmark):
 
   def _benchmark_gen_math_ops_matmul(self, m, transpose_b, num_iters):
     def func():
-      gen_math_ops._mat_mul(m, m, transpose_b=transpose_b)
+      gen_math_ops.mat_mul(m, m, transpose_b=transpose_b)
+
     self._run(func, num_iters)
 
   def _benchmark_tfe_py_fastpath_execute_matmul(self, m, transpose_b,
diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py
index 553571d267..f70c7544d6 100644
--- a/tensorflow/python/eager/ops_test.py
+++ b/tensorflow/python/eager/ops_test.py
@@ -131,8 +131,12 @@ class OpsTest(test_util.TensorFlowTestCase):
                                    dtype=dtypes.int64)
     values = constant_op.constant([2, 3, 5, 7, 11])
     shape = constant_op.constant([2, 7], dtype=dtypes.int64)
-    result = sparse_ops.gen_sparse_ops._sparse_split(  # pylint: disable=protected-access
-        split_dim, indices, values, shape, num_split=2)
+    result = sparse_ops.gen_sparse_ops.sparse_split(
+        split_dim,
+        indices,
+        values,
+        shape,
+        num_split=2)
     output_indices, output_values, output_shape = result
     self.assertEqual(2, len(output_indices))
     self.assertEqual(2, len(output_values))
diff --git a/tensorflow/python/eager/python_eager_op_gen.cc b/tensorflow/python/eager/python_eager_op_gen.cc
index 554e29c7e0..3de7445a50 100644
--- a/tensorflow/python/eager/python_eager_op_gen.cc
+++ b/tensorflow/python/eager/python_eager_op_gen.cc
@@ -955,10 +955,10 @@ from tensorflow.python.util.tf_export import tf_export
     if (api_def->visibility() == ApiDef::SKIP) {
       continue;
     }
-
     // An op is hidden if either its ApiDef visibility is HIDDEN
     // or it is in the hidden_ops list.
     bool is_hidden = api_def->visibility() == ApiDef::HIDDEN;
+    bool hidden_by_api_def = is_hidden;
     if (!is_hidden) {
       for (const string& hidden : hidden_ops) {
         if (op_def.name() == hidden) {
@@ -971,13 +971,22 @@ from tensorflow.python.util.tf_export import tf_export
     string function_name;
     python_op_gen_internal::GenerateLowerCaseOpName(op_def.name(),
                                                     &function_name);
-    if (is_hidden) function_name = strings::StrCat("_", function_name);
-
-    // When users create custom python wrappers, they may link in the
-    // default op registry by accident, and because they can't
-    // enumerate all 'hidden' symbols, this guard is to prevent
-    // instantiating a python reserved word in their wrapper.
-    if (python_op_gen_internal::IsPythonReserved(function_name)) {
+    bool is_reserved = python_op_gen_internal::IsPythonReserved(function_name);
+
+    // Prefix an op with underscore if the op is listed in hidden_ops or
+    // name is reserved or it is of the exceptions in IsOpWithUnderscorePrefix.
+    // Do not add underscores to ops set to HIDDEN in ApiDef otherwise.
+    // TODO(annarev): don't prefix with underscores even if op is in hidden_ops.
+    if (is_hidden) {
+      if (!hidden_by_api_def || is_reserved ||
+          python_op_gen_internal::IsOpWithUnderscorePrefix(function_name)) {
+        function_name = strings::StrCat("_", function_name);
+      }
+    } else if (is_reserved) {
+      // When users create custom python wrappers, they may link in the
+      // default op registry by accident, and because they can't
+      // enumerate all 'hidden' symbols, this guard is to prevent
+      // instantiating a python reserved word in their wrapper.
       continue;
     }
 
diff --git a/tensorflow/python/framework/function_test.py b/tensorflow/python/framework/function_test.py
index 52052ba77d..65ca801cbe 100644
--- a/tensorflow/python/framework/function_test.py
+++ b/tensorflow/python/framework/function_test.py
@@ -193,7 +193,7 @@ class FunctionTest(test.TestCase):
 
     @function.Defun(dtypes.float32, dtypes.float32)
     def XSquarePlusOneGrad(x, dy):
-      dx = functional_ops._symbolic_gradient(
+      dx = functional_ops.symbolic_gradient(
           input=[x, dy], Tout=[dtypes.float32], f="XSquarePlusOneFn", name="dx")
       return dx
 
@@ -295,7 +295,7 @@ class FunctionTest(test.TestCase):
       # gradient function is (x, y, dz) -> (dx, dy).  dx's shape
       # should be the same as x's; and dy's shape should be the same
       # as y's.
-      dx, dy = functional_ops._symbolic_gradient(
+      dx, dy = functional_ops.symbolic_gradient(
           input=[x, y, dz], Tout=[dtypes.float32] * 2, f="Foo")
       self.assertEqual(x.get_shape(), dx.get_shape())
       self.assertEqual(y.get_shape(), dy.get_shape())
diff --git a/tensorflow/python/framework/graph_util_test.py b/tensorflow/python/framework/graph_util_test.py
index 0421837d49..1cdd738198 100644
--- a/tensorflow/python/framework/graph_util_test.py
+++ b/tensorflow/python/framework/graph_util_test.py
@@ -47,46 +47,46 @@ class DeviceFunctionsTest(test.TestCase):
 
   def testTwoDeviceFunctions(self):
     with ops.Graph().as_default() as g:
-      var_0 = gen_state_ops._variable(
+      var_0 = gen_state_ops.variable(
           shape=[1],
           dtype=dtypes.float32,
           name="var_0",
           container="",
           shared_name="")
       with g.device(test_device_func_pin_variable_to_cpu):
-        var_1 = gen_state_ops._variable(
+        var_1 = gen_state_ops.variable(
             shape=[1],
             dtype=dtypes.float32,
             name="var_1",
             container="",
             shared_name="")
-      var_2 = gen_state_ops._variable(
+      var_2 = gen_state_ops.variable(
           shape=[1],
           dtype=dtypes.float32,
           name="var_2",
           container="",
           shared_name="")
-      var_3 = gen_state_ops._variable(
+      var_3 = gen_state_ops.variable(
           shape=[1],
           dtype=dtypes.float32,
           name="var_3",
           container="",
           shared_name="")
       with g.device(test_device_func_pin_variable_to_cpu):
-        var_4 = gen_state_ops._variable(
+        var_4 = gen_state_ops.variable(
             shape=[1],
             dtype=dtypes.float32,
             name="var_4",
             container="",
             shared_name="")
         with g.device("/device:GPU:0"):
-          var_5 = gen_state_ops._variable(
+          var_5 = gen_state_ops.variable(
               shape=[1],
               dtype=dtypes.float32,
               name="var_5",
               container="",
               shared_name="")
-        var_6 = gen_state_ops._variable(
+        var_6 = gen_state_ops.variable(
             shape=[1],
             dtype=dtypes.float32,
             name="var_6",
diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py
index 1f2dfb8d43..55576f0e88 100644
--- a/tensorflow/python/framework/ops_test.py
+++ b/tensorflow/python/framework/ops_test.py
@@ -2892,7 +2892,7 @@ class OutputTypesTest(test_util.TensorFlowTestCase):
     with g.as_default():
       x = constant_op.constant([1, 1, 2, 4, 4, 4, 7, 8, 8],
                                dtype=dtypes.double)
-      y, _ = gen_array_ops._unique(x)
+      y, _ = gen_array_ops.unique(x)
       self.assertEqual([types_pb2.DT_DOUBLE, types_pb2.DT_INT32],
                        y.op._output_types)  # pylint: disable=protected-access
 
diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc
index c95149d177..4813458f07 100644
--- a/tensorflow/python/framework/python_op_gen.cc
+++ b/tensorflow/python/framework/python_op_gen.cc
@@ -75,6 +75,38 @@ bool IsPythonReserved(const string& s) {
   return kPythonReserved->count(s) > 0;
 }
 
+bool IsOpWithUnderscorePrefix(const string& s) {
+  static const std::set<string>* const kUnderscoreOps = new std::set<string>(
+      {// Lowercase built-in functions and types in Python, from:
+       // [x for x in dir(__builtins__) if x[0].islower()]
+       // These need to be excluded so they don't conflict with actual built-in
+       // functions since we use '*' imports.
+       "abs", "all", "any", "apply", "bin", "bool", "buffer", "bytearray",
+       "bytes", "callable", "chr", "classmethod", "cmp", "coerce", "compile",
+       "complex", "copyright", "credits", "delattr", "dict", "dir", "divmod",
+       "enumerate", "eval", "execfile", "exit", "file", "filter", "float",
+       "format", "frozenset", "getattr", "globals", "hasattr", "hash", "help",
+       "hex", "id", "input", "int", "intern", "isinstance", "issubclass",
+       "iter", "len", "license", "list", "locals", "long", "map", "max",
+       "memoryview", "min", "next", "object", "oct", "open", "ord", "pow",
+       "print", "property", "quit", "range", "raw_input", "reduce", "reload",
+       "repr", "reversed", "round", "set", "setattr", "slice", "sorted",
+       "staticmethod", "str", "sum", "super", "tuple", "type", "unichr",
+       "unicode", "vars", "xrange", "zip",
+       // These have the same name as ops defined in Python and might be used
+       // incorrectly depending on order of '*' imports.
+       // TODO(annarev): reduce usage of '*' imports and remove these from the
+       // list.
+       "fused_batch_norm", "histogram_fixed_width", "stack",
+       "batch_norm_with_global_normalization",
+       // TODO(annarev): replace these ops in the next change.
+       "add_sparse_to_tensors_map", "add_many_sparse_to_tensors_map",
+       "broadcast_gradient_args", "concat", "enter", "histogram_summary",
+       "ref_enter", "ref_identity", "scalar_summary",
+       "take_many_sparse_from_tensors_map"});
+  return kUnderscoreOps->count(s) > 0;
+}
+
 string AvoidPythonReserved(const string& s) {
   if (IsPythonReserved(s)) return strings::StrCat(s, "_");
   return s;
@@ -816,6 +848,7 @@ from tensorflow.python.util.tf_export import tf_export
     // An op is hidden if either its ApiDef visibility is HIDDEN
     // or it is in the hidden_ops list.
     bool is_hidden = api_def->visibility() == ApiDef::HIDDEN;
+    bool hidden_by_api_def = is_hidden;
     if (!is_hidden) {
       for (const string& hidden : hidden_ops) {
         if (op_def.name() == hidden) {
@@ -828,13 +861,22 @@ from tensorflow.python.util.tf_export import tf_export
     string function_name;
     python_op_gen_internal::GenerateLowerCaseOpName(op_def.name(),
                                                     &function_name);
-    if (is_hidden) function_name = strings::StrCat("_", function_name);
-
-    // When users create custom python wrappers, they may link in the
-    // default op registry by accident, and because they can't
-    // enumerate all 'hidden' symbols, this guard is to prevent
-    // instantiating a python reserved word in their wrapper.
-    if (python_op_gen_internal::IsPythonReserved(function_name)) {
+    bool is_reserved = python_op_gen_internal::IsPythonReserved(function_name);
+
+    // Prefix an op with underscore if the op is listed in hidden_ops or
+    // name is reserved or it is of the exceptions in IsOpWithUnderscorePrefix.
+    // Do not add underscores to ops set to HIDDEN in ApiDef otherwise.
+    // TODO(annarev): don't prefix with underscores even if op is in hidden_ops.
+    if (is_hidden) {
+      if (!hidden_by_api_def || is_reserved ||
+          python_op_gen_internal::IsOpWithUnderscorePrefix(function_name)) {
+        function_name = strings::StrCat("_", function_name);
+      }
+    } else if (is_reserved) {
+      // When users create custom python wrappers, they may link in the
+      // default op registry by accident, and because they can't
+      // enumerate all 'hidden' symbols, this guard is to prevent
+      // instantiating a python reserved word in their wrapper.
       continue;
     }
 
diff --git a/tensorflow/python/framework/python_op_gen_internal.h b/tensorflow/python/framework/python_op_gen_internal.h
index 4319e5a782..e0cfb05f4b 100644
--- a/tensorflow/python/framework/python_op_gen_internal.h
+++ b/tensorflow/python/framework/python_op_gen_internal.h
@@ -29,6 +29,9 @@ namespace python_op_gen_internal {
 // Returns true if s is a Python keyword or built-in.
 bool IsPythonReserved(const string& s);
 
+// Whether the op should be prefixed with underscore.
+bool IsOpWithUnderscorePrefix(const string& s);
+
 // Add a _ to the end of s if necessary to avoid a Python keyword or built-in.
 string AvoidPythonReserved(const string& s);
 
diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py
index bea0ee34fd..6b1b3dd40c 100644
--- a/tensorflow/python/framework/tensor_util_test.py
+++ b/tensorflow/python/framework/tensor_util_test.py
@@ -768,7 +768,7 @@ class ConstantValueTest(test.TestCase):
     self.assertAllClose(np_val, tensor_util.constant_value(tf_val))
 
   def testUnknown(self):
-    tf_val = gen_state_ops._variable(
+    tf_val = gen_state_ops.variable(
         shape=[3, 4, 7],
         dtype=dtypes.float32,
         name="tf_val",
diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py
index 0f51501740..5a84b16a23 100644
--- a/tensorflow/python/grappler/layout_optimizer_test.py
+++ b/tensorflow/python/grappler/layout_optimizer_test.py
@@ -321,7 +321,7 @@ class LayoutOptimizerTest(test.TestCase):
       conv = _two_layer_model(x)
       dim = array_ops.placeholder(dtype='int32')
       sizes = constant_op.constant([50, 10, 4], shape=[3])
-      split = gen_array_ops._split_v(
+      split = gen_array_ops.split_v(
           value=conv, size_splits=sizes, axis=dim, num_split=3)
       output = math_ops.reduce_sum(split[0])
 
@@ -896,7 +896,7 @@ class LayoutOptimizerTest(test.TestCase):
       add = math_ops.add(conv, conv)
       mean = math_ops.reduce_mean(conv)
       condition = math_ops.less(conv, mean)
-      select = gen_math_ops._select(condition, conv, add)
+      select = gen_math_ops.select(condition, conv, add)
       output = array_ops.identity(select)
 
       with session.Session(config=_get_config(False)) as sess:
@@ -926,7 +926,7 @@ class LayoutOptimizerTest(test.TestCase):
       conv = _two_layer_model(x)
       add = math_ops.add(conv, conv)
       condition = array_ops.placeholder(dtype='bool')
-      select = gen_math_ops._select(condition, conv, add)
+      select = gen_math_ops.select(condition, conv, add)
       output = array_ops.identity(select)
 
       condition_val = np.zeros((1, 7, 7, 64))
@@ -957,7 +957,7 @@ class LayoutOptimizerTest(test.TestCase):
       conv = _two_layer_model(x)
       add = math_ops.add(conv, conv)
       condition = constant_op.constant(True)
-      select = gen_math_ops._select(condition, conv, add)
+      select = gen_math_ops.select(condition, conv, add)
       output = array_ops.identity(select)
 
       with session.Session(config=_get_config(False)) as sess:
@@ -1023,7 +1023,7 @@ class LayoutOptimizerTest(test.TestCase):
       conv = _two_layer_model(x)
       ksize = constant_op.constant([1, 2, 3, 1], shape=[4])
       strides = array_ops.placeholder(dtype='int32', shape=[4])
-      max_pool = gen_nn_ops._max_pool_v2(conv, ksize, strides, 'VALID')
+      max_pool = gen_nn_ops.max_pool_v2(conv, ksize, strides, 'VALID')
       output = array_ops.identity(max_pool)
 
       strides_val = [1, 3, 2, 1]
diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py
index 365cf72108..d35f62b186 100644
--- a/tensorflow/python/kernel_tests/array_ops_test.py
+++ b/tensorflow/python/kernel_tests/array_ops_test.py
@@ -1223,7 +1223,7 @@ class SnapshotOpTest(test_util.TensorFlowTestCase):
     for dtype in [dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]:
       with self.test_session(use_gpu=True):
         x = constant_op.constant([0, 1, 2, 3], dtype=dtype)
-        y = gen_array_ops._snapshot(x)
+        y = gen_array_ops.snapshot(x)
         self.assertAllEqual(y.eval(), [0, 1, 2, 3])
 
 
diff --git a/tensorflow/python/kernel_tests/batchtospace_op_test.py b/tensorflow/python/kernel_tests/batchtospace_op_test.py
index 0c802476a0..6143cd3baa 100644
--- a/tensorflow/python/kernel_tests/batchtospace_op_test.py
+++ b/tensorflow/python/kernel_tests/batchtospace_op_test.py
@@ -44,7 +44,7 @@ class CppOpImpl(object):
 
   @staticmethod
   def batch_to_space(*args, **kwargs):
-    return gen_array_ops._batch_to_space(*args, **kwargs)
+    return gen_array_ops.batch_to_space(*args, **kwargs)
 
 
 class BatchToSpaceDepthToSpace(test.TestCase, PythonOpImpl):
diff --git a/tensorflow/python/kernel_tests/bcast_ops_test.py b/tensorflow/python/kernel_tests/bcast_ops_test.py
index 9e51234605..cb46fcb007 100644
--- a/tensorflow/python/kernel_tests/bcast_ops_test.py
+++ b/tensorflow/python/kernel_tests/bcast_ops_test.py
@@ -20,8 +20,8 @@ from __future__ import print_function
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
-from tensorflow.python.ops.gen_array_ops import _broadcast_args
 from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args
+from tensorflow.python.ops.gen_array_ops import broadcast_args
 from tensorflow.python.platform import test
 
 
@@ -29,7 +29,7 @@ class BcastOpsTest(test.TestCase):
 
   def _GetBroadcastShape(self, xs, ys):
     with self.test_session() as sess:
-      return sess.run(_broadcast_args(xs, ys))
+      return sess.run(broadcast_args(xs, ys))
 
   def _GetGradientArgs(self, xs, ys):
     with self.test_session() as sess:
diff --git a/tensorflow/python/kernel_tests/checkpoint_ops_test.py b/tensorflow/python/kernel_tests/checkpoint_ops_test.py
index a786d0a47e..7f147ba53a 100644
--- a/tensorflow/python/kernel_tests/checkpoint_ops_test.py
+++ b/tensorflow/python/kernel_tests/checkpoint_ops_test.py
@@ -50,7 +50,7 @@ class GenerateVocabRemappingTest(test.TestCase):
 
   def test_generate_remapping_with_no_vocab_changes(self):
     """Tests where vocab does not change at all."""
-    remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping(
+    remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
         new_vocab_file=self.old_vocab_file,
         old_vocab_file=self.old_vocab_file,
         num_new_vocab=3,
@@ -63,7 +63,7 @@ class GenerateVocabRemappingTest(test.TestCase):
 
   def test_generate_remapping_with_shifted_vocab(self):
     """Tests where vocab is the same, but shifted / ordered differently."""
-    remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping(
+    remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
         new_vocab_file=self.new_vocab_file,
         old_vocab_file=self.old_vocab_file,
         num_new_vocab=3,
@@ -76,7 +76,7 @@ class GenerateVocabRemappingTest(test.TestCase):
 
   def test_generate_remapping_with_offset(self):
     """Tests offset and num_new_vocab logic."""
-    remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping(
+    remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
         new_vocab_file=self.new_vocab_file,
         old_vocab_file=self.old_vocab_file,
         num_new_vocab=1,
@@ -89,7 +89,7 @@ class GenerateVocabRemappingTest(test.TestCase):
 
   def test_generate_remapping_with_old_vocab_size(self):
     """Tests where old_vocab_size is specified."""
-    remapping, num_present = gen_checkpoint_ops._generate_vocab_remapping(
+    remapping, num_present = gen_checkpoint_ops.generate_vocab_remapping(
         new_vocab_file=self.new_vocab_file,
         old_vocab_file=self.old_vocab_file,
         num_new_vocab=3,
@@ -132,7 +132,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
 
     # No column remapping, new weight matrix has second row, then first row.
     row_remapping = [1, 0]
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=row_remapping,
@@ -147,7 +147,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
     # No row remapping, new weight matrix has third col, then first col.
     row_remapping = list(range(self.old_num_rows))
     col_remapping = [2, 0]
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=row_remapping,
@@ -162,7 +162,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
     # Both row and column remappings.
     row_remapping = [1, 0, 4]
     col_remapping = [1, 15]
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=row_remapping,
@@ -177,7 +177,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
   def test_load_and_remap_with_init(self):
     """Tests the op's load and remap where there are missing entries."""
     init_val = 42
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=[2, -1, 0],
@@ -196,7 +196,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
     """Tests when all the rows are missing and need to be initialized."""
     num_rows = 7
     initializing_values = [42] * num_rows * self.old_num_cols
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=[-1] * num_rows,
@@ -214,7 +214,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
     num_rows = 7
     num_cols = 4
     initializing_values = [42] * num_rows * num_cols
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=[-1] * num_rows,
@@ -235,7 +235,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
     invalid_remapping = [1, 0, 0, 0, 1, 2]
 
     # Invalid row remapping.
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=invalid_remapping,
@@ -247,7 +247,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
       remapped_matrix.eval()
 
     # Invalid column remapping.
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=list(range(self.old_num_rows)),
@@ -260,7 +260,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
 
   def test_load_and_remap_incorrect_initializing_values(self):
     """Tests that errors are raised with incorrect number of init values."""
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=[2, -1, 0],
@@ -275,7 +275,7 @@ class LoadAndRemapMatrixTest(test.TestCase):
     with self.test_session(), self.assertRaises(errors.InvalidArgumentError):
       remapped_matrix.eval()
 
-    remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+    remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
         ckpt_path=[self.bundle_file],
         old_tensor_name=self.old_tensor_name,
         row_remapping=[2, -1, 0],
@@ -314,7 +314,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase):
       num_rows, num_cols = np_value.shape
 
       # Tests loading the entire tensor (except reversed).
-      remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+      remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
           ckpt_path=ckpt_path,
           old_tensor_name=old_tensor_name,
           # Simply reverses the rows of the matrix.
@@ -332,7 +332,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase):
       self.assertGreater(num_rows, 2)
       prefix_rows = 2
       suffix_rows = 3
-      remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+      remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
           ckpt_path=ckpt_path,
           old_tensor_name=old_tensor_name,
           # Reverses the rows of the matrix, then prepends and appends
@@ -353,7 +353,7 @@ class LoadAndRemapMatrixWithMaxRowsTest(test.TestCase):
       # Tests when everything is taken from initializing_values.
       new_rows = 7
       initializing_values = [42] * new_rows * num_cols
-      remapped_matrix = gen_checkpoint_ops._load_and_remap_matrix(
+      remapped_matrix = gen_checkpoint_ops.load_and_remap_matrix(
           ckpt_path=ckpt_path,
           old_tensor_name=old_tensor_name,
           # Nothing is loaded from the old tensor.
diff --git a/tensorflow/python/kernel_tests/concat_op_test.py b/tensorflow/python/kernel_tests/concat_op_test.py
index 127bc6bb20..81c6a4aa6e 100644
--- a/tensorflow/python/kernel_tests/concat_op_test.py
+++ b/tensorflow/python/kernel_tests/concat_op_test.py
@@ -526,7 +526,7 @@ class ConcatOpTest(test.TestCase):
     with self.test_session(use_gpu=True):
       t1 = []
       t2 = []
-      output = gen_array_ops._concat_v2([t1, t2], 0).eval()
+      output = gen_array_ops.concat_v2([t1, t2], 0).eval()
       self.assertFalse(output)  # Checks that output is empty
 
   def testConcatInvalidAxis(self):
@@ -534,20 +534,20 @@ class ConcatOpTest(test.TestCase):
       with self.test_session(use_gpu=True):
         t1 = [1]
         t2 = [2]
-        gen_array_ops._concat_v2([t1, t2], 1).eval()
+        gen_array_ops.concat_v2([t1, t2], 1).eval()
 
   def testConcatNegativeAxis(self):
     with self.test_session(use_gpu=True):
       t1 = [[1, 2, 3], [4, 5, 6]]
       t2 = [[7, 8, 9], [10, 11, 12]]
 
-      c = gen_array_ops._concat_v2([t1, t2], -2)
+      c = gen_array_ops.concat_v2([t1, t2], -2)
       self.assertEqual([4, 3], c.get_shape().as_list())
       output = c.eval()
       self.assertAllEqual([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
                           output)
 
-      c = gen_array_ops._concat_v2([t1, t2], -1)
+      c = gen_array_ops.concat_v2([t1, t2], -1)
       self.assertEqual([2, 6], c.get_shape().as_list())
       output = c.eval()
       self.assertAllEqual([[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]], output)
@@ -615,7 +615,7 @@ class ConcatOffsetTest(test.TestCase):
       s0 = constant_op.constant([2, 3, 5], dtypes.int32)
       s1 = constant_op.constant([2, 7, 5], dtypes.int32)
       s2 = constant_op.constant([2, 20, 5], dtypes.int32)
-      off = gen_array_ops._concat_offset(cdim, [s0, s1, s2])
+      off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
       ans = sess.run(off)
       self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
 
@@ -624,7 +624,7 @@ class ConcatOffsetTest(test.TestCase):
       cdim = constant_op.constant(1, dtypes.int32)
       s0 = constant_op.constant([[2, 3, 5]], dtypes.int32)
       s1 = constant_op.constant([[2, 7, 5]], dtypes.int32)
-      off = gen_array_ops._concat_offset(cdim, [s0, s1])
+      off = gen_array_ops.concat_offset(cdim, [s0, s1])
       with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
                                    r"should be a vector"):
         sess.run(off)
@@ -634,7 +634,7 @@ class ConcatOffsetTest(test.TestCase):
       cdim = constant_op.constant(4, dtypes.int32)
       s0 = constant_op.constant([2, 3, 5], dtypes.int32)
       s1 = constant_op.constant([2, 7, 5], dtypes.int32)
-      off = gen_array_ops._concat_offset(cdim, [s0, s1])
+      off = gen_array_ops.concat_offset(cdim, [s0, s1])
       with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
                                    r"Concat dim is out of range: 4 vs. 3"):
         sess.run(off)
@@ -644,7 +644,7 @@ class ConcatOffsetTest(test.TestCase):
       cdim = constant_op.constant(1, dtypes.int32)
       s0 = constant_op.constant([2, 3, 5], dtypes.int32)
       s1 = constant_op.constant([2, 7, 5, 10], dtypes.int32)
-      off = gen_array_ops._concat_offset(cdim, [s0, s1])
+      off = gen_array_ops.concat_offset(cdim, [s0, s1])
       with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
                                    r"should contain 3 elem"):
         sess.run(off)
@@ -654,7 +654,7 @@ class ConcatOffsetTest(test.TestCase):
       cdim = constant_op.constant(1, dtypes.int32)
       s0 = constant_op.constant([2, 3, 5], dtypes.int32)
       s1 = constant_op.constant([2, 7, 10], dtypes.int32)
-      off = gen_array_ops._concat_offset(cdim, [s0, s1])
+      off = gen_array_ops.concat_offset(cdim, [s0, s1])
       with self.assertRaisesRegexp(
           errors_impl.InvalidArgumentError,
           r"All dimensions except 1 must match. Input 1 has shape \[2 7 10\] "
@@ -667,7 +667,7 @@ class ConcatOffsetTest(test.TestCase):
       s0 = constant_op.constant([2, 3, 5], dtypes.int32)
       s1 = constant_op.constant([2, 7, 5], dtypes.int32)
       s2 = constant_op.constant([2, 20, 5], dtypes.int32)
-      off = gen_array_ops._concat_offset(cdim, [s0, s1, s2])
+      off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
       ans = sess.run(off)
       self.assertAllEqual(ans, [[0, 0, 0], [0, 3, 0], [0, 10, 0]])
 
@@ -675,7 +675,7 @@ class ConcatOffsetTest(test.TestCase):
       s0 = constant_op.constant([2, 3, 5], dtypes.int32)
       s1 = constant_op.constant([1, 3, 5], dtypes.int32)
       s2 = constant_op.constant([3, 3, 5], dtypes.int32)
-      off = gen_array_ops._concat_offset(cdim, [s0, s1, s2])
+      off = gen_array_ops.concat_offset(cdim, [s0, s1, s2])
       ans = sess.run(off)
       self.assertAllEqual(ans, [[0, 0, 0], [2, 0, 0], [3, 0, 0]])
 
diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
index 58f38650eb..b429fa5c42 100644
--- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
@@ -552,7 +552,7 @@ class ControlFlowTest(test.TestCase):
 
   def testCondRef(self):
     with self.test_session():
-      x = gen_state_ops._variable(
+      x = gen_state_ops.variable(
           shape=[1],
           dtype=dtypes.float32,
           name="x",
@@ -580,7 +580,7 @@ class ControlFlowTest(test.TestCase):
 
   def testUninitializedRefIdentity(self):
     with self.test_session() as sess:
-      v = gen_state_ops._variable(
+      v = gen_state_ops.variable(
           shape=[1],
           dtype=dtypes.float32,
           name="v",
@@ -1620,7 +1620,7 @@ class ControlFlowTest(test.TestCase):
 
   def testWhileStack_1(self):
     with self.test_session():
-      s = gen_data_flow_ops._stack_v2(-1, dtypes.int32, stack_name="foo")
+      s = gen_data_flow_ops.stack_v2(-1, dtypes.int32, stack_name="foo")
       i = constant_op.constant(0)
 
       def c(i):
@@ -1629,7 +1629,7 @@ class ControlFlowTest(test.TestCase):
       def b(i):
         ni = math_ops.add(i, 1)
         ni = control_flow_ops.with_dependencies(
-            [gen_data_flow_ops._stack_push_v2(s, i)], ni)
+            [gen_data_flow_ops.stack_push_v2(s, i)], ni)
         return ni
 
       r = control_flow_ops.while_loop(c, b, [i], parallel_iterations=1)
@@ -1641,7 +1641,7 @@ class ControlFlowTest(test.TestCase):
 
       def b1(i, x):
         ni = math_ops.subtract(i, 1)
-        nx = x + gen_data_flow_ops._stack_pop_v2(s, dtypes.int32)
+        nx = x + gen_data_flow_ops.stack_pop_v2(s, dtypes.int32)
         return [ni, nx]
 
       _, rx = control_flow_ops.while_loop(
diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py
index 0d9b46c30d..8db0bb6f0d 100644
--- a/tensorflow/python/kernel_tests/cwise_ops_test.py
+++ b/tensorflow/python/kernel_tests/cwise_ops_test.py
@@ -495,11 +495,11 @@ class UnaryOpTest(test.TestCase):
     dtype_tols = [(np.float32, 5e-4), (np.float64, 1e-6), (np.complex64, 5e-4),
                   (np.complex128, 1e-6)]
     op_range = [
-        (gen_math_ops._reciprocal_grad, [-2, 2]),
-        (gen_math_ops._rsqrt_grad, [0.1, 3]),
-        (gen_math_ops._sigmoid_grad, [-2, 2]),
-        (gen_math_ops._sqrt_grad, [0.1, 3]),
-        (gen_math_ops._tanh_grad, [-2, 2]),
+        (gen_math_ops.reciprocal_grad, [-2, 2]),
+        (gen_math_ops.rsqrt_grad, [0.1, 3]),
+        (gen_math_ops.sigmoid_grad, [-2, 2]),
+        (gen_math_ops.sqrt_grad, [0.1, 3]),
+        (gen_math_ops.tanh_grad, [-2, 2]),
     ]
 
     def rand(dtype):
diff --git a/tensorflow/python/kernel_tests/determinant_op_test.py b/tensorflow/python/kernel_tests/determinant_op_test.py
index 222038b22e..a52b2c0dc3 100644
--- a/tensorflow/python/kernel_tests/determinant_op_test.py
+++ b/tensorflow/python/kernel_tests/determinant_op_test.py
@@ -65,7 +65,7 @@ class DeterminantOpTest(test.TestCase):
       self._compareDeterminantBase(matrix_x,
                                    linalg_ops.matrix_determinant(matrix_x))
       self._compareLogDeterminantBase(
-          matrix_x, gen_linalg_ops._log_matrix_determinant(matrix_x))
+          matrix_x, gen_linalg_ops.log_matrix_determinant(matrix_x))
 
   def testBasic(self):
     # 2x2 matrices
diff --git a/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py b/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py
index feec9934e4..faac7d8365 100644
--- a/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py
+++ b/tensorflow/python/kernel_tests/fractional_avg_pool_op_test.py
@@ -347,7 +347,7 @@ class FractionalAvgPoolGradTest(test.TestCase):
 
   Two types of tests for FractionalAvgPoolGrad.
   1) Test fractional_avg_pool_grad() directly.
-    This type of test relies on gen_nn_ops._avg_pool_grad() returns the
+    This type of test relies on gen_nn_ops.avg_pool_grad() returns the
   correct result. For example:
     * input_tensor_shape = (1, 10, 10, 1)
     * window_size = (1, 2, 2, 1)
@@ -404,13 +404,13 @@ class FractionalAvgPoolGradTest(test.TestCase):
                 num_elements *= dim_size
               output_backprop = (self._PRNG.rand(num_elements) *
                                  1000).reshape(output_data.shape)
-              input_backprop_tensor = gen_nn_ops._avg_pool_grad(
+              input_backprop_tensor = gen_nn_ops.avg_pool_grad(
                   input_tensor.get_shape(), output_backprop, window_size,
                   stride_size, padding)
               input_backprop = input_backprop_tensor.eval()
               row_seq = list(range(0, num_rows + 1, row_window_size))
               col_seq = list(range(0, num_cols + 1, col_window_size))
-              fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad(
+              fap_input_backprop_tensor = gen_nn_ops.fractional_avg_pool_grad(
                   input_tensor.get_shape(),
                   output_backprop,
                   row_seq,
@@ -443,7 +443,7 @@ class FractionalAvgPoolGradTest(test.TestCase):
                 num_elements *= dim_size
               output_backprop = (self._PRNG.rand(num_elements) *
                                  1000).reshape(output_data.shape)
-              input_backprop_tensor = gen_nn_ops._avg_pool_grad(
+              input_backprop_tensor = gen_nn_ops.avg_pool_grad(
                   input_tensor.get_shape(), output_backprop, window_size,
                   stride_size, padding)
               input_backprop = input_backprop_tensor.eval()
@@ -451,7 +451,7 @@ class FractionalAvgPoolGradTest(test.TestCase):
               col_seq = list(range(0, num_cols, col_window_size - 1))
               row_seq[-1] += 1
               col_seq[-1] += 1
-              fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad(
+              fap_input_backprop_tensor = gen_nn_ops.fractional_avg_pool_grad(
                   input_tensor.get_shape(),
                   output_backprop,
                   row_seq,
diff --git a/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py b/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py
index 5983ae7759..6477c9ebc4 100644
--- a/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py
+++ b/tensorflow/python/kernel_tests/fractional_max_pool_op_test.py
@@ -318,7 +318,7 @@ class FractionalMaxPoolGradTest(test.TestCase):
 
   Two types of tests for FractionalMaxPoolGrad.
   1) Test fractional_max_pool_grad() directly.
-    This type of test relies on gen_nn_ops._max_pool_grad() returns the correct
+    This type of test relies on gen_nn_ops.max_pool_grad() returns the correct
   result. For example:
     * input_tensor_shape = (1, 10, 10, 1)
     * window_size = (1, 2, 2, 1)
@@ -384,16 +384,13 @@ class FractionalMaxPoolGradTest(test.TestCase):
                                               stride_size, padding)
               output_data = output_tensor.eval()
               output_backprop = self._PRNG.randint(100, size=output_data.shape)
-              input_backprop_tensor = gen_nn_ops._max_pool_grad(input_tensor,
-                                                                output_tensor,
-                                                                output_backprop,
-                                                                window_size,
-                                                                stride_size,
-                                                                padding)
+              input_backprop_tensor = gen_nn_ops.max_pool_grad(
+                  input_tensor, output_tensor, output_backprop, window_size,
+                  stride_size, padding)
               input_backprop = input_backprop_tensor.eval()
               row_seq = list(range(0, num_rows + 1, row_window_size))
               col_seq = list(range(0, num_cols + 1, col_window_size))
-              fmp_input_backprop_tensor = gen_nn_ops._fractional_max_pool_grad(
+              fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad(
                   input_tensor,
                   output_tensor,
                   output_backprop,
@@ -422,18 +419,15 @@ class FractionalMaxPoolGradTest(test.TestCase):
                                               stride_size, padding)
               output_data = output_tensor.eval()
               output_backprop = self._PRNG.randint(100, size=output_data.shape)
-              input_backprop_tensor = gen_nn_ops._max_pool_grad(input_tensor,
-                                                                output_tensor,
-                                                                output_backprop,
-                                                                window_size,
-                                                                stride_size,
-                                                                padding)
+              input_backprop_tensor = gen_nn_ops.max_pool_grad(
+                  input_tensor, output_tensor, output_backprop, window_size,
+                  stride_size, padding)
               input_backprop = input_backprop_tensor.eval()
               row_seq = list(range(0, num_rows, row_window_size - 1))
               col_seq = list(range(0, num_cols, col_window_size - 1))
               row_seq[-1] += 1
               col_seq[-1] += 1
-              fmp_input_backprop_tensor = gen_nn_ops._fractional_max_pool_grad(
+              fmp_input_backprop_tensor = gen_nn_ops.fractional_max_pool_grad(
                   input_tensor,
                   output_tensor,
                   output_backprop,
@@ -591,7 +585,7 @@ class FractionalMaxPoolGradTest(test.TestCase):
       output_tensor = constant_op.constant(
           output_data_not_overlapping, shape=output_size)
       grad = constant_op.constant(output_backprop, shape=output_size)
-      r = gen_nn_ops._fractional_max_pool_grad(
+      r = gen_nn_ops.fractional_max_pool_grad(
           input_tensor,
           output_tensor,
           grad,
@@ -606,7 +600,7 @@ class FractionalMaxPoolGradTest(test.TestCase):
       # Test when overlapping is True
       output_tensor = constant_op.constant(
           output_data_overlapping, shape=output_size)
-      r = gen_nn_ops._fractional_max_pool_grad(
+      r = gen_nn_ops.fractional_max_pool_grad(
           input_tensor, output_tensor, grad, row_seq, col_seq, overlapping=True)
       input_backprop_overlapping = r.eval()
       self.assertShapeEqual(
diff --git a/tensorflow/python/kernel_tests/matrix_exponential_op_test.py b/tensorflow/python/kernel_tests/matrix_exponential_op_test.py
index 6203a412d7..a0c66c77d8 100644
--- a/tensorflow/python/kernel_tests/matrix_exponential_op_test.py
+++ b/tensorflow/python/kernel_tests/matrix_exponential_op_test.py
@@ -48,7 +48,7 @@ class ExponentialOpTest(test.TestCase):
   def _verifyExponential(self, x, np_type):
     inp = x.astype(np_type)
     with self.test_session(use_gpu=True):
-      tf_ans = gen_linalg_ops._matrix_exponential(inp)
+      tf_ans = gen_linalg_ops.matrix_exponential(inp)
       if x.size == 0:
         np_ans = np.empty(x.shape, dtype=np_type)
       else:
@@ -116,13 +116,13 @@ class ExponentialOpTest(test.TestCase):
     # When the exponential of a non-square matrix is attempted we should return
     # an error
     with self.assertRaises(ValueError):
-      gen_linalg_ops._matrix_exponential(np.array([[1., 2., 3.], [3., 4., 5.]]))
+      gen_linalg_ops.matrix_exponential(np.array([[1., 2., 3.], [3., 4., 5.]]))
 
   def testWrongDimensions(self):
     # The input to the exponential should be at least a 2-dimensional tensor.
     tensor3 = constant_op.constant([1., 2.])
     with self.assertRaises(ValueError):
-      gen_linalg_ops._matrix_exponential(tensor3)
+      gen_linalg_ops.matrix_exponential(tensor3)
 
   def testEmpty(self):
     self._verifyExponentialReal(np.empty([0, 2, 2]))
@@ -143,8 +143,8 @@ class ExponentialOpTest(test.TestCase):
     with self.test_session(use_gpu=True) as sess:
       matrix1 = random_ops.random_normal([5, 5], seed=42)
       matrix2 = random_ops.random_normal([5, 5], seed=42)
-      expm1 = gen_linalg_ops._matrix_exponential(matrix1)
-      expm2 = gen_linalg_ops._matrix_exponential(matrix2)
+      expm1 = gen_linalg_ops.matrix_exponential(matrix1)
+      expm2 = gen_linalg_ops.matrix_exponential(matrix2)
       expm = sess.run([expm1, expm2])
       self.assertAllEqual(expm[0], expm[1])
 
@@ -180,7 +180,7 @@ class MatrixExponentialBenchmark(test.Benchmark):
           session.Session() as sess, \
           ops.device("/cpu:0"):
         matrix = self._GenerateMatrix(shape)
-        expm = gen_linalg_ops._matrix_exponential(matrix)
+        expm = gen_linalg_ops.matrix_exponential(matrix)
         variables.global_variables_initializer().run()
         self.run_op_benchmark(
             sess,
diff --git a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py
index 18ed59828c..24edc4f59f 100644
--- a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py
+++ b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py
@@ -39,8 +39,8 @@ class LogarithmOpTest(test.TestCase):
     inp = x.astype(np_type)
     with self.test_session(use_gpu=True):
       # Verify that expm(logm(A)) == A.
-      tf_ans = gen_linalg_ops._matrix_exponential(
-          gen_linalg_ops._matrix_logarithm(inp))
+      tf_ans = gen_linalg_ops.matrix_exponential(
+          gen_linalg_ops.matrix_logarithm(inp))
       out = tf_ans.eval()
       self.assertAllClose(inp, out, rtol=1e-4, atol=1e-3)
 
@@ -85,14 +85,14 @@ class LogarithmOpTest(test.TestCase):
     # When the logarithm of a non-square matrix is attempted we should return
     # an error
     with self.assertRaises(ValueError):
-      gen_linalg_ops._matrix_logarithm(
+      gen_linalg_ops.matrix_logarithm(
           np.array([[1., 2., 3.], [3., 4., 5.]], dtype=np.complex64))
 
   def testWrongDimensions(self):
     # The input to the logarithm should be at least a 2-dimensional tensor.
     tensor3 = constant_op.constant([1., 2.], dtype=dtypes.complex64)
     with self.assertRaises(ValueError):
-      gen_linalg_ops._matrix_logarithm(tensor3)
+      gen_linalg_ops.matrix_logarithm(tensor3)
 
   def testEmpty(self):
     self._verifyLogarithmComplex(np.empty([0, 2, 2], dtype=np.complex64))
@@ -115,8 +115,8 @@ class LogarithmOpTest(test.TestCase):
           random_ops.random_normal([5, 5], seed=42), dtypes.complex64)
       matrix2 = math_ops.cast(
           random_ops.random_normal([5, 5], seed=42), dtypes.complex64)
-      logm1 = gen_linalg_ops._matrix_logarithm(matrix1)
-      logm2 = gen_linalg_ops._matrix_logarithm(matrix2)
+      logm1 = gen_linalg_ops.matrix_logarithm(matrix1)
+      logm2 = gen_linalg_ops.matrix_logarithm(matrix2)
       logm = sess.run([logm1, logm2])
       self.assertAllEqual(logm[0], logm[1])
 
@@ -152,7 +152,7 @@ class MatrixLogarithmBenchmark(test.Benchmark):
           session.Session() as sess, \
           ops.device("/cpu:0"):
         matrix = self._GenerateMatrix(shape)
-        logm = gen_linalg_ops._matrix_logarithm(matrix)
+        logm = gen_linalg_ops.matrix_logarithm(matrix)
         variables.global_variables_initializer().run()
         self.run_op_benchmark(
             sess,
diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py
index 4466beeec9..a0ac355b60 100644
--- a/tensorflow/python/kernel_tests/pooling_ops_test.py
+++ b/tensorflow/python/kernel_tests/pooling_ops_test.py
@@ -405,7 +405,7 @@ class PoolingTest(test.TestCase):
 
     for v2 in [True, False]:
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 3, 3, 3],
           ksize=[1, 2, 2, 1],
           strides=[1, 2, 2, 1],
@@ -427,7 +427,7 @@ class PoolingTest(test.TestCase):
 
     for v2 in [True, False]:
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 2, 3, 3],
           ksize=[1, 2, 2, 1],
           strides=[1, 2, 2, 1],
@@ -456,7 +456,7 @@ class PoolingTest(test.TestCase):
 
     for v2 in [True, False]:
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 2, 2, 1],
           ksize=[1, 1, 2, 1],
           strides=[1, 1, 1, 1],
@@ -485,7 +485,7 @@ class PoolingTest(test.TestCase):
 
     for v2 in [True, False]:
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 4, 4, 1],
           ksize=[1, 2, 2, 1],
           strides=[1, 1, 2, 1],
@@ -494,7 +494,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu,
           v2=v2)
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 4, 4, 1],
           ksize=[1, 2, 2, 1],
           strides=[1, 2, 1, 1],
@@ -519,7 +519,7 @@ class PoolingTest(test.TestCase):
 
     for v2 in [True, False]:
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 4, 4, 4],
           ksize=[1, 2, 2, 1],
           strides=[1, 2, 2, 1],
@@ -554,7 +554,7 @@ class PoolingTest(test.TestCase):
 
     for v2 in [True, False]:
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 8, 8, 8],
           ksize=[1, 3, 3, 1],
           strides=[1, 2, 2, 1],
@@ -565,7 +565,7 @@ class PoolingTest(test.TestCase):
 
   def _testMaxPoolEmptyInput(self, use_gpu):
     self._VerifyValues(
-        gen_nn_ops._max_pool_v2,
+        gen_nn_ops.max_pool_v2,
         input_sizes=[0, 8, 8, 8],
         ksize=[1, 3, 3, 1],
         strides=[1, 2, 2, 1],
@@ -600,7 +600,7 @@ class PoolingTest(test.TestCase):
 
     for v2 in [True, False]:
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 1, 1, 10],
           ksize=[1, 1, 1, 2],
           strides=[1, 1, 1, 2],
@@ -626,7 +626,7 @@ class PoolingTest(test.TestCase):
 
     for v2 in [True, False]:
       self._VerifyValues(
-          gen_nn_ops._max_pool_v2,
+          gen_nn_ops.max_pool_v2,
           input_sizes=[1, 2, 2, 6],
           ksize=[1, 1, 1, 3],
           strides=[1, 1, 1, 3],
@@ -648,7 +648,7 @@ class PoolingTest(test.TestCase):
 
       for v2 in [True, False]:
         self._VerifyValues(
-            gen_nn_ops._max_pool_v2,
+            gen_nn_ops.max_pool_v2,
             input_sizes=[1, 7, 7, 1],
             ksize=[1, 2, 2, 1],
             strides=[1, 3, 3, 1],
@@ -689,7 +689,7 @@ class PoolingTest(test.TestCase):
 
       for v2 in [True, False]:
         self._VerifyValues(
-            gen_nn_ops._max_pool_v2,
+            gen_nn_ops.max_pool_v2,
             input_sizes=[1, 3, 3, 1],
             ksize=[1, 1, 1, 1],
             strides=[1, 2, 2, 1],
@@ -699,7 +699,7 @@ class PoolingTest(test.TestCase):
             v2=v2)
 
         self._VerifyValues(
-            gen_nn_ops._max_pool_v2,
+            gen_nn_ops.max_pool_v2,
             input_sizes=[1, 4, 4, 1],
             ksize=[1, 1, 1, 1],
             strides=[1, 2, 2, 1],
@@ -764,8 +764,8 @@ class PoolingTest(test.TestCase):
         _, argmax_op = nn_ops.max_pool_with_argmax(t, ksize, strides, padding)
         argmax = argmax_op.eval()
         grad_in = constant_op.constant(tensor_output, shape=output_shape)
-        out_op = gen_nn_ops._max_pool_grad_with_argmax(t, grad_in, argmax,
-                                                       ksize, strides, padding)
+        out_op = gen_nn_ops.max_pool_grad_with_argmax(t, grad_in, argmax, ksize,
+                                                      strides, padding)
         gpu_val = out_op.eval()
         self.assertShapeEqual(gpu_val, out_op)
       with self.test_session(use_gpu=False):
@@ -773,8 +773,8 @@ class PoolingTest(test.TestCase):
         out_op = nn_ops.max_pool(t, ksize, strides, padding)
         orig_out = out_op.eval()
         grad_in = constant_op.constant(tensor_output, shape=output_shape)
-        out_op = gen_nn_ops._max_pool_grad(t, orig_out, grad_in, ksize, strides,
-                                           padding)
+        out_op = gen_nn_ops.max_pool_grad(t, orig_out, grad_in, ksize, strides,
+                                          padding)
         cpu_val = out_op.eval()
         self.assertShapeEqual(cpu_val, out_op)
       # The CPU version accumulates its gradient on fp16, so it's less
@@ -793,7 +793,7 @@ class PoolingTest(test.TestCase):
         _, argmax_op = nn_ops.max_pool_with_argmax(t, ksize, strides, padding)
         argmax = argmax_op.eval()
         grad_in = constant_op.constant(tensor_input, shape=input_shape)
-        out_op = gen_nn_ops._max_pool_grad_grad_with_argmax(
+        out_op = gen_nn_ops.max_pool_grad_grad_with_argmax(
             t, grad_in, argmax, ksize, strides, padding)
         gpu_val = out_op.eval()
         self.assertShapeEqual(gpu_val, out_op)
@@ -802,8 +802,8 @@ class PoolingTest(test.TestCase):
         out_op = nn_ops.max_pool(t, ksize, strides, padding)
         orig_out = out_op.eval()
         grad_in = constant_op.constant(tensor_input, shape=input_shape)
-        out_op = gen_nn_ops._max_pool_grad_grad(t, orig_out, grad_in, ksize,
-                                                strides, padding)
+        out_op = gen_nn_ops.max_pool_grad_grad(t, orig_out, grad_in, ksize,
+                                               strides, padding)
         cpu_val = out_op.eval()
         self.assertShapeEqual(cpu_val, out_op)
       # The CPU version accumulates its gradient on fp16, so it's less
@@ -842,7 +842,7 @@ class PoolingTest(test.TestCase):
       t = constant_op.constant(tensor_input, shape=[1, 2, 2, 1])
       argmax = constant_op.constant(
           tensor_argmax, shape=[1, 2, 2, 1], dtype=dtypes.int64)
-      out_op = gen_nn_ops._max_pool_grad_with_argmax(
+      out_op = gen_nn_ops.max_pool_grad_with_argmax(
           orig_in,
           t,
           argmax,
@@ -865,7 +865,7 @@ class PoolingTest(test.TestCase):
       t = constant_op.constant(tensor_input, shape=[1, 3, 3, 1])
       argmax = constant_op.constant(
           tensor_argmax, shape=[1, 2, 2, 1], dtype=dtypes.int64)
-      out_op = gen_nn_ops._max_pool_grad_grad_with_argmax(
+      out_op = gen_nn_ops.max_pool_grad_grad_with_argmax(
           orig_in,
           t,
           argmax,
@@ -1029,7 +1029,7 @@ class PoolingTest(test.TestCase):
     self.assertLess(err, err_tolerance)
 
   def _testMaxPoolGradValidPadding1_1(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[1, 3, 3, 1],
@@ -1043,7 +1043,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradValidPadding2_1_6(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[2, 6, 6, 3],
@@ -1057,7 +1057,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradValidPadding2_1_7(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[2, 7, 7, 3],
@@ -1071,7 +1071,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradValidPadding1_2(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[1, 3, 3, 1],
@@ -1085,7 +1085,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradValidPadding2_2(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[2, 2, 2, 3],
@@ -1099,7 +1099,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradSamePadding1_1(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[2, 2, 4, 3],
@@ -1113,7 +1113,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradSamePadding1_2(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[2, 2, 4, 3],
@@ -1127,7 +1127,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradSamePadding2_1(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[2, 2, 4, 3],
@@ -1141,7 +1141,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradSamePadding2_2(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[2, 2, 4, 3],
@@ -1155,7 +1155,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradSamePadding3_1(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestGradient(
           pool_func,
           input_sizes=[1, 7, 7, 1],
@@ -1199,7 +1199,7 @@ class PoolingTest(test.TestCase):
     Returns:
       A Tensor.
     """
-    pool_func = gen_nn_ops.max_pool_grad_v2 if v2 else gen_nn_ops._max_pool_grad
+    pool_func = gen_nn_ops.max_pool_grad_v2 if v2 else gen_nn_ops.max_pool_grad
     return pool_func(orig_input, orig_output, grad,
                      [1, window_rows, window_cols, 1],
                      [1, row_stride, col_stride, 1], padding)
@@ -1208,7 +1208,7 @@ class PoolingTest(test.TestCase):
                              expected_input_backprop, input_sizes, output_sizes,
                              window_rows, window_cols, row_stride, col_stride,
                              padding, use_gpu, v2):
-    pool_func = gen_nn_ops._max_pool_v2 if v2 else nn_ops.max_pool
+    pool_func = gen_nn_ops.max_pool_v2 if v2 else nn_ops.max_pool
     with self.test_session(use_gpu=use_gpu):
       input_tensor = constant_op.constant(input_data, shape=input_sizes)
       output_tensor = pool_func(input_tensor, [1, window_rows, window_cols, 1],
@@ -1504,7 +1504,7 @@ class PoolingTest(test.TestCase):
     self._testMaxPoolGradDirectWithNans2_2()
 
   def _testMaxPoolGradGradValidPadding1_1(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestSecondGradient(
           pool_func,
           input_sizes=[1, 3, 3, 1],
@@ -1518,7 +1518,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradGradValidPadding2_1_6(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestSecondGradient(
           pool_func,
           input_sizes=[2, 6, 6, 3],
@@ -1532,7 +1532,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradGradValidPadding2_1_7(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestSecondGradient(
           pool_func,
           input_sizes=[2, 7, 7, 3],
@@ -1546,7 +1546,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradGradValidPadding2_2(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestSecondGradient(
           pool_func,
           input_sizes=[2, 2, 2, 3],
@@ -1560,7 +1560,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradGradSamePadding1_1(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestSecondGradient(
           pool_func,
           input_sizes=[2, 2, 4, 3],
@@ -1574,7 +1574,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradGradSamePadding2_1(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestSecondGradient(
           pool_func,
           input_sizes=[2, 2, 4, 3],
@@ -1588,7 +1588,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradGradSamePadding2_2(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestSecondGradient(
           pool_func,
           input_sizes=[2, 2, 4, 3],
@@ -1602,7 +1602,7 @@ class PoolingTest(test.TestCase):
           use_gpu=use_gpu)
 
   def _testMaxPoolGradGradSamePadding3_1(self, data_format, use_gpu):
-    for pool_func in [gen_nn_ops._max_pool_v2, nn_ops.max_pool]:
+    for pool_func in [gen_nn_ops.max_pool_v2, nn_ops.max_pool]:
       self._ConstructAndTestSecondGradient(
           pool_func,
           input_sizes=[1, 7, 7, 1],
@@ -1644,7 +1644,7 @@ class PoolingTest(test.TestCase):
     Returns:
       A Tensor.
     """
-    return gen_nn_ops._max_pool_grad_grad(
+    return gen_nn_ops.max_pool_grad_grad(
         orig_input, orig_output, grad, [1, window_rows, window_cols, 1],
         [1, row_stride, col_stride, 1], padding)
 
diff --git a/tensorflow/python/kernel_tests/save_restore_ops_test.py b/tensorflow/python/kernel_tests/save_restore_ops_test.py
index 1bdfa9ebd8..cb9aa1e34d 100644
--- a/tensorflow/python/kernel_tests/save_restore_ops_test.py
+++ b/tensorflow/python/kernel_tests/save_restore_ops_test.py
@@ -31,11 +31,10 @@ class ShardedFileOpsTest(test.TestCase):
     with session.Session(
         target="", config=config_pb2.ConfigProto(device_count={"CPU": 2})):
       self.assertEqual(
-          gen_io_ops._sharded_filename("foo", 4, 100).eval(),
+          gen_io_ops.sharded_filename("foo", 4, 100).eval(),
           b"foo-00004-of-00100")
       self.assertEqual(
-          gen_io_ops._sharded_filespec("foo", 100).eval(),
-          b"foo-?????-of-00100")
+          gen_io_ops.sharded_filespec("foo", 100).eval(), b"foo-?????-of-00100")
 
 
 class ShapeInferenceTest(test.TestCase):
@@ -53,7 +52,7 @@ class ShapeInferenceTest(test.TestCase):
                         [dtypes.float32, dtypes.float32])
 
   def testRestoreSlice(self):
-    op = gen_io_ops._restore_slice("model", "var", "3 4 0,1:-", dtypes.float32)
+    op = gen_io_ops.restore_slice("model", "var", "3 4 0,1:-", dtypes.float32)
     self.assertEqual([1, 4], op.get_shape())
 
 
diff --git a/tensorflow/python/kernel_tests/scalar_test.py b/tensorflow/python/kernel_tests/scalar_test.py
index e65241981e..0d8fd23294 100644
--- a/tensorflow/python/kernel_tests/scalar_test.py
+++ b/tensorflow/python/kernel_tests/scalar_test.py
@@ -92,11 +92,11 @@ class ScalarTest(test.TestCase):
     self.check(array_ops.reshape, (7, 1), 'sizes input must be 1-D', [7])
 
   def testShardedFilename(self):
-    self.check(gen_io_ops._sharded_filename, ('foo', 4, [100]),
+    self.check(gen_io_ops.sharded_filename, ('foo', 4, [100]),
                'must be a scalar', b'foo-00004-of-00100')
 
   def testShardedFilespec(self):
-    self.check(gen_io_ops._sharded_filespec, ('foo', [100]), 'must be a scalar',
+    self.check(gen_io_ops.sharded_filespec, ('foo', [100]), 'must be a scalar',
                b'foo-?????-of-00100')
 
   def testUnsortedSegmentSum(self):
diff --git a/tensorflow/python/kernel_tests/spacetobatch_op_test.py b/tensorflow/python/kernel_tests/spacetobatch_op_test.py
index b943dfa4e5..2a9232b6ae 100644
--- a/tensorflow/python/kernel_tests/spacetobatch_op_test.py
+++ b/tensorflow/python/kernel_tests/spacetobatch_op_test.py
@@ -86,11 +86,11 @@ class CppOpImpl(object):
 
   @staticmethod
   def space_to_batch(*args, **kwargs):
-    return gen_array_ops._space_to_batch(*args, **kwargs)
+    return gen_array_ops.space_to_batch(*args, **kwargs)
 
   @staticmethod
   def batch_to_space(*args, **kwargs):
-    return gen_array_ops._batch_to_space(*args, **kwargs)
+    return gen_array_ops.batch_to_space(*args, **kwargs)
 
 
 class SpaceToBatchTest(test.TestCase, PythonOpImpl):
diff --git a/tensorflow/python/kernel_tests/sparse_xent_op_test.py b/tensorflow/python/kernel_tests/sparse_xent_op_test.py
index cd5b711a0e..a841fe83a7 100644
--- a/tensorflow/python/kernel_tests/sparse_xent_op_test.py
+++ b/tensorflow/python/kernel_tests/sparse_xent_op_test.py
@@ -64,7 +64,7 @@ class SparseXentTest(test.TestCase):
   def _testXent(self, np_features, np_labels):
     np_loss, np_backprop = self._npXent(np_features, np_labels)
     with self.test_session(use_gpu=True) as sess:
-      loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits(
+      loss, backprop = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
           np_features, np_labels)
       tf_loss, tf_backprop = sess.run([loss, backprop])
     self.assertAllCloseAccordingToType(np_loss, tf_loss)
@@ -73,7 +73,7 @@ class SparseXentTest(test.TestCase):
   def testSingleClass(self):
     for label_dtype in np.int32, np.int64:
       with self.test_session(use_gpu=True) as sess:
-        loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits(
+        loss, backprop = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
             np.array([[1.], [-1.], [0.]]).astype(np.float32),
             np.array([0, 0, 0]).astype(label_dtype))
         tf_loss, tf_backprop = sess.run([loss, backprop])
@@ -87,8 +87,9 @@ class SparseXentTest(test.TestCase):
 
     if test.is_built_with_cuda() and test.is_gpu_available():
       with self.test_session(use_gpu=True) as sess:
-        loss, backprop = (gen_nn_ops._sparse_softmax_cross_entropy_with_logits(
-            features, labels))
+        loss, backprop = (
+            gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
+                features, labels))
         tf_loss, tf_backprop = sess.run([loss, backprop])
         self.assertAllClose(
             [[np.nan] * 4, [0.25, 0.25, 0.25, -0.75],
@@ -100,8 +101,8 @@ class SparseXentTest(test.TestCase):
             [np.nan, 1.3862, 3.4420, np.nan], tf_loss, rtol=1e-3, atol=1e-3)
 
     with self.test_session(use_gpu=False) as sess:
-      loss, backprop = (gen_nn_ops._sparse_softmax_cross_entropy_with_logits(
-          features, labels))
+      loss, backprop = (
+          gen_nn_ops.sparse_softmax_cross_entropy_with_logits(features, labels))
       with self.assertRaisesOpError("Received a label value of"):
         sess.run([loss, backprop])
 
diff --git a/tensorflow/python/kernel_tests/stack_ops_test.py b/tensorflow/python/kernel_tests/stack_ops_test.py
index aa409336f5..afd2eaffab 100644
--- a/tensorflow/python/kernel_tests/stack_ops_test.py
+++ b/tensorflow/python/kernel_tests/stack_ops_test.py
@@ -34,11 +34,11 @@ class StackOpTest(test.TestCase):
 
   def _testStackPushPop(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
-      h = gen_data_flow_ops._stack_v2(
+      h = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push_v2(h, [[4.0, 5.0]])
+      c = gen_data_flow_ops.stack_push_v2(h, [[4.0, 5.0]])
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
+        c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
       self.assertAllClose([[4.0, 5.0]], c1.eval())
 
   def testStackPushPop(self):
@@ -49,11 +49,11 @@ class StackOpTest(test.TestCase):
     with self.test_session(use_gpu=use_gpu):
       a = np.arange(2000)
       x = constant_op.constant(a, dtype=dtypes.float32)
-      h = gen_data_flow_ops._stack_v2(
+      h = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push_v2(h, x, swap_memory=True)
+      c = gen_data_flow_ops.stack_push_v2(h, x, swap_memory=True)
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
+        c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
       self.assertAllClose(a, c1.eval())
 
   def testStackPushPopSwap(self):
@@ -63,7 +63,7 @@ class StackOpTest(test.TestCase):
   def _testStackWhileSwap(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
       n = constant_op.constant(0)
-      h = gen_data_flow_ops._stack_v2(
+      h = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="foo")
 
       def c(x):
@@ -72,7 +72,7 @@ class StackOpTest(test.TestCase):
       def b(x):
         with ops.control_dependencies([x]):
           a = constant_op.constant(np.ones(2000), dtype=dtypes.float32)
-          v = gen_data_flow_ops._stack_push_v2(h, a, swap_memory=True)
+          v = gen_data_flow_ops.stack_push_v2(h, a, swap_memory=True)
         with ops.control_dependencies([v]):
           return math_ops.add(x, 1)
 
@@ -86,7 +86,7 @@ class StackOpTest(test.TestCase):
 
       def b1(x, y):
         nx = math_ops.subtract(x, 1)
-        ny = y + gen_data_flow_ops._stack_pop_v2(h, dtypes.float32)
+        ny = y + gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
         return [nx, ny]
 
       _, ry = control_flow_ops.while_loop(
@@ -99,16 +99,16 @@ class StackOpTest(test.TestCase):
 
   def _testMultiStack(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
-      h1 = gen_data_flow_ops._stack_v2(
+      h1 = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="foo")
-      c1 = gen_data_flow_ops._stack_push_v2(h1, 4.0)
+      c1 = gen_data_flow_ops.stack_push_v2(h1, 4.0)
       with ops.control_dependencies([c1]):
-        c1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32)
-      h2 = gen_data_flow_ops._stack_v2(
+        c1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32)
+      h2 = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="bar")
-      c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0)
+      c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0)
       with ops.control_dependencies([c2]):
-        c2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32)
+        c2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32)
       r = c1 + c2
       self.assertAllClose(9.0, r.eval())
 
@@ -119,17 +119,17 @@ class StackOpTest(test.TestCase):
   def _testSameNameStacks(self, use_gpu):
     """Different stacks with the same name do not interfere."""
     with self.test_session(use_gpu=use_gpu) as sess:
-      h1 = gen_data_flow_ops._stack_v2(
+      h1 = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="foo")
-      h2 = gen_data_flow_ops._stack_v2(
+      h2 = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="foo")
 
-      c1 = gen_data_flow_ops._stack_push_v2(h1, 4.0)
+      c1 = gen_data_flow_ops.stack_push_v2(h1, 4.0)
       with ops.control_dependencies([c1]):
-        c2 = gen_data_flow_ops._stack_push_v2(h2, 5.0)
+        c2 = gen_data_flow_ops.stack_push_v2(h2, 5.0)
       with ops.control_dependencies([c2]):
-        pop1 = gen_data_flow_ops._stack_pop_v2(h1, dtypes.float32)
-        pop2 = gen_data_flow_ops._stack_pop_v2(h2, dtypes.float32)
+        pop1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32)
+        pop2 = gen_data_flow_ops.stack_pop_v2(h2, dtypes.float32)
 
       out1, out2 = sess.run([pop1, pop2])
       self.assertAllClose(out1, 4.0)
@@ -141,9 +141,9 @@ class StackOpTest(test.TestCase):
 
   def _testCloseStack(self, use_gpu):
     with self.test_session(use_gpu=use_gpu) as sess:
-      h = gen_data_flow_ops._stack_v2(
+      h = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="foo")
-      c1 = gen_data_flow_ops._stack_close_v2(h)
+      c1 = gen_data_flow_ops.stack_close_v2(h)
       sess.run(c1)
 
   def testCloseStack(self):
@@ -152,11 +152,11 @@ class StackOpTest(test.TestCase):
 
   def _testPushCloseStack(self, use_gpu):
     with self.test_session(use_gpu=use_gpu) as sess:
-      h = gen_data_flow_ops._stack_v2(
+      h = gen_data_flow_ops.stack_v2(
           -1, elem_type=dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push_v2(h, [[4.0, 5.0]])
+      c = gen_data_flow_ops.stack_push_v2(h, [[4.0, 5.0]])
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_close_v2(h)
+        c1 = gen_data_flow_ops.stack_close_v2(h)
       sess.run(c1)
 
   def testPushCloseStack(self):
@@ -170,9 +170,9 @@ class StackOpRefTest(test.TestCase):
   def _testStackPushPop(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
       h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push(h, [[4.0, 5.0]])
+      c = gen_data_flow_ops.stack_push(h, [[4.0, 5.0]])
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32)
+        c1 = gen_data_flow_ops.stack_pop(h, dtypes.float32)
       self.assertAllClose([[4.0, 5.0]], c1.eval())
 
   def testStackPushPop(self):
@@ -184,9 +184,9 @@ class StackOpRefTest(test.TestCase):
       a = np.arange(2000)
       x = constant_op.constant(a, dtype=dtypes.float32)
       h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push(h, x, swap_memory=True)
+      c = gen_data_flow_ops.stack_push(h, x, swap_memory=True)
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_pop(h, dtypes.float32)
+        c1 = gen_data_flow_ops.stack_pop(h, dtypes.float32)
       self.assertAllClose(a, c1.eval())
 
   def testStackPushPopSwap(self):
@@ -196,13 +196,13 @@ class StackOpRefTest(test.TestCase):
   def _testMultiStack(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
       h1 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c1 = gen_data_flow_ops._stack_push(h1, 4.0)
+      c1 = gen_data_flow_ops.stack_push(h1, 4.0)
       with ops.control_dependencies([c1]):
-        c1 = gen_data_flow_ops._stack_pop(h1, dtypes.float32)
+        c1 = gen_data_flow_ops.stack_pop(h1, dtypes.float32)
       h2 = gen_data_flow_ops._stack(dtypes.float32, stack_name="bar")
-      c2 = gen_data_flow_ops._stack_push(h2, 5.0)
+      c2 = gen_data_flow_ops.stack_push(h2, 5.0)
       with ops.control_dependencies([c2]):
-        c2 = gen_data_flow_ops._stack_pop(h2, dtypes.float32)
+        c2 = gen_data_flow_ops.stack_pop(h2, dtypes.float32)
       r = c1 + c2
       self.assertAllClose(9.0, r.eval())
 
@@ -217,7 +217,7 @@ class StackOpRefTest(test.TestCase):
       def b(x):
         with ops.control_dependencies([x]):
           a = constant_op.constant(np.ones(2000), dtype=dtypes.float32)
-          v = gen_data_flow_ops._stack_push(h, a, swap_memory=True)
+          v = gen_data_flow_ops.stack_push(h, a, swap_memory=True)
         with ops.control_dependencies([v]):
           return math_ops.add(x, 1)
 
@@ -231,7 +231,7 @@ class StackOpRefTest(test.TestCase):
 
       def b1(x, y):
         nx = math_ops.subtract(x, 1)
-        ny = y + gen_data_flow_ops._stack_pop(h, dtypes.float32)
+        ny = y + gen_data_flow_ops.stack_pop(h, dtypes.float32)
         return [nx, ny]
 
       _, ry = control_flow_ops.while_loop(
@@ -249,9 +249,9 @@ class StackOpRefTest(test.TestCase):
   def _testSameNameStacks(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
       h1 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c1 = gen_data_flow_ops._stack_push(h1, 4.0)
+      c1 = gen_data_flow_ops.stack_push(h1, 4.0)
       h2 = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c2 = gen_data_flow_ops._stack_push(h2, 5.0)
+      c2 = gen_data_flow_ops.stack_push(h2, 5.0)
       _ = c1 + c2
       self.assertNotEqual(h1.eval()[1], h2.eval()[1])
 
@@ -262,7 +262,7 @@ class StackOpRefTest(test.TestCase):
   def _testCloseStack(self, use_gpu):
     with self.test_session(use_gpu=use_gpu) as sess:
       h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c1 = gen_data_flow_ops._stack_close(h)
+      c1 = gen_data_flow_ops.stack_close(h)
       sess.run(c1)
 
   def testCloseStack(self):
@@ -272,9 +272,9 @@ class StackOpRefTest(test.TestCase):
   def _testPushCloseStack(self, use_gpu):
     with self.test_session(use_gpu=use_gpu) as sess:
       h = gen_data_flow_ops._stack(dtypes.float32, stack_name="foo")
-      c = gen_data_flow_ops._stack_push(h, [[4.0, 5.0]])
+      c = gen_data_flow_ops.stack_push(h, [[4.0, 5.0]])
       with ops.control_dependencies([c]):
-        c1 = gen_data_flow_ops._stack_close(h)
+        c1 = gen_data_flow_ops.stack_close(h)
       sess.run(c1)
 
   def testPushCloseStack(self):
diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
index aad2443eea..8f09f3d78b 100644
--- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py
+++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
@@ -437,7 +437,7 @@ class TensorArrayTest(test.TestCase):
 
       # Test reading wrong datatype, which is only possible in graph mode
       if context.in_graph_mode():
-        r0_bad = gen_data_flow_ops._tensor_array_read_v3(
+        r0_bad = gen_data_flow_ops.tensor_array_read_v3(
             handle=w0.handle, index=0, dtype=dtypes.float64, flow_in=w0.flow)
         with self.assertRaisesOpError(
             "TensorArray dtype is float but Op requested dtype double."):
diff --git a/tensorflow/python/kernel_tests/unique_op_test.py b/tensorflow/python/kernel_tests/unique_op_test.py
index 6366d2e181..173d95b258 100644
--- a/tensorflow/python/kernel_tests/unique_op_test.py
+++ b/tensorflow/python/kernel_tests/unique_op_test.py
@@ -66,9 +66,9 @@ class UniqueTest(test.TestCase):
     for dtype in [np.int32, np.int64]:
       x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]])
       with self.test_session() as sess:
-        y0, idx0 = gen_array_ops._unique_v2(x, axis=np.array([0], dtype))
+        y0, idx0 = gen_array_ops.unique_v2(x, axis=np.array([0], dtype))
         tf_y0, tf_idx0 = sess.run([y0, idx0])
-        y1, idx1 = gen_array_ops._unique_v2(x, axis=np.array([1], dtype))
+        y1, idx1 = gen_array_ops.unique_v2(x, axis=np.array([1], dtype))
         tf_y1, tf_idx1 = sess.run([y1, idx1])
       self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]]))
       self.assertAllEqual(tf_idx0, np.array([0, 0, 1]))
@@ -80,7 +80,7 @@ class UniqueTest(test.TestCase):
     # by default, the axis will be wrapped to allow `axis=None`.
     x = np.random.randint(2, high=10, size=7000)
     with self.test_session() as sess:
-      y, idx = gen_array_ops._unique_v2(x, axis=np.array([], np.int32))
+      y, idx = gen_array_ops.unique_v2(x, axis=np.array([], np.int32))
       tf_y, tf_idx = sess.run([y, idx])
 
     self.assertEqual(len(x), len(tf_idx))
diff --git a/tensorflow/python/kernel_tests/variable_ops_test.py b/tensorflow/python/kernel_tests/variable_ops_test.py
index 79071029fd..cf369c0718 100644
--- a/tensorflow/python/kernel_tests/variable_ops_test.py
+++ b/tensorflow/python/kernel_tests/variable_ops_test.py
@@ -165,26 +165,26 @@ class VariableOpTest(test.TestCase):
 
   def testTemporaryVariable(self):
     with self.test_session(use_gpu=True):
-      var = gen_state_ops._temporary_variable(
+      var = gen_state_ops.temporary_variable(
           [1, 2], dtypes.float32, var_name="foo")
       var = state_ops.assign(var, [[4.0, 5.0]])
       var = state_ops.assign_add(var, [[6.0, 7.0]])
-      final = gen_state_ops._destroy_temporary_variable(var, var_name="foo")
+      final = gen_state_ops.destroy_temporary_variable(var, var_name="foo")
       self.assertAllClose([[10.0, 12.0]], final.eval())
 
   def testDestroyNonexistentTemporaryVariable(self):
     with self.test_session(use_gpu=True):
-      var = gen_state_ops._temporary_variable([1, 2], dtypes.float32)
-      final = gen_state_ops._destroy_temporary_variable(var, var_name="bad")
+      var = gen_state_ops.temporary_variable([1, 2], dtypes.float32)
+      final = gen_state_ops.destroy_temporary_variable(var, var_name="bad")
       with self.assertRaises(errors.NotFoundError):
         final.eval()
 
   def testDuplicateTemporaryVariable(self):
     with self.test_session(use_gpu=True):
-      var1 = gen_state_ops._temporary_variable(
+      var1 = gen_state_ops.temporary_variable(
           [1, 2], dtypes.float32, var_name="dup")
       var1 = state_ops.assign(var1, [[1.0, 2.0]])
-      var2 = gen_state_ops._temporary_variable(
+      var2 = gen_state_ops.temporary_variable(
           [1, 2], dtypes.float32, var_name="dup")
       var2 = state_ops.assign(var2, [[3.0, 4.0]])
       final = var1 + var2
@@ -193,25 +193,25 @@ class VariableOpTest(test.TestCase):
 
   def testDestroyTemporaryVariableTwice(self):
     with self.test_session(use_gpu=True):
-      var = gen_state_ops._temporary_variable([1, 2], dtypes.float32)
-      val1 = gen_state_ops._destroy_temporary_variable(var, var_name="dup")
-      val2 = gen_state_ops._destroy_temporary_variable(var, var_name="dup")
+      var = gen_state_ops.temporary_variable([1, 2], dtypes.float32)
+      val1 = gen_state_ops.destroy_temporary_variable(var, var_name="dup")
+      val2 = gen_state_ops.destroy_temporary_variable(var, var_name="dup")
       final = val1 + val2
       with self.assertRaises(errors.NotFoundError):
         final.eval()
 
   def testTemporaryVariableNoLeak(self):
     with self.test_session(use_gpu=True):
-      var = gen_state_ops._temporary_variable(
+      var = gen_state_ops.temporary_variable(
           [1, 2], dtypes.float32, var_name="bar")
       final = array_ops.identity(var)
       final.eval()
 
   def testTwoTemporaryVariablesNoLeaks(self):
     with self.test_session(use_gpu=True):
-      var1 = gen_state_ops._temporary_variable(
+      var1 = gen_state_ops.temporary_variable(
           [1, 2], dtypes.float32, var_name="var1")
-      var2 = gen_state_ops._temporary_variable(
+      var2 = gen_state_ops.temporary_variable(
           [1, 2], dtypes.float32, var_name="var2")
       final = var1 + var2
       final.eval()
diff --git a/tensorflow/python/kernel_tests/variables_test.py b/tensorflow/python/kernel_tests/variables_test.py
index b16c8c002c..27599868b7 100644
--- a/tensorflow/python/kernel_tests/variables_test.py
+++ b/tensorflow/python/kernel_tests/variables_test.py
@@ -687,7 +687,7 @@ class VariableContainerTest(test.TestCase):
         v1 = variables.Variable([1])
         with ops.container("l2"):
           v2 = variables.Variable([2])
-          special_v = gen_state_ops._variable(
+          special_v = gen_state_ops.variable(
               shape=[1],
               dtype=dtypes.float32,
               name="VariableInL3",
diff --git a/tensorflow/python/kernel_tests/xent_op_test.py b/tensorflow/python/kernel_tests/xent_op_test.py
index e152f02d8e..e3e120a4eb 100644
--- a/tensorflow/python/kernel_tests/xent_op_test.py
+++ b/tensorflow/python/kernel_tests/xent_op_test.py
@@ -48,7 +48,7 @@ class XentTest(test.TestCase):
   def _testXent(self, np_features, np_labels, use_gpu=False):
     np_loss, np_backprop = self._npXent(np_features, np_labels)
     with self.test_session(use_gpu=use_gpu) as sess:
-      loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits(
+      loss, backprop = gen_nn_ops.softmax_cross_entropy_with_logits(
           np_features, np_labels)
       tf_loss, tf_backprop = sess.run([loss, backprop])
     self.assertAllCloseAccordingToType(np_loss, tf_loss)
@@ -71,7 +71,7 @@ class XentTest(test.TestCase):
   def _testSingleClass(self, use_gpu=False):
     for dtype in np.float16, np.float32:
       with self.test_session(use_gpu=use_gpu) as sess:
-        loss, backprop = gen_nn_ops._softmax_cross_entropy_with_logits(
+        loss, backprop = gen_nn_ops.softmax_cross_entropy_with_logits(
             np.array([[1.], [-1.], [0.]]).astype(dtype),
             np.array([[-1.], [0.], [1.]]).astype(dtype))
         tf_loss, tf_backprop = sess.run([loss, backprop])
@@ -89,7 +89,7 @@ class XentTest(test.TestCase):
       np_labels = np.array([[[0., 0., 0., 1.]], [[0., .5, .5,
                                                   0.]]]).astype(dtype)
       self.assertRaisesRegexp(ValueError, "must be rank 2",
-                              gen_nn_ops._softmax_cross_entropy_with_logits,
+                              gen_nn_ops.softmax_cross_entropy_with_logits,
                               np_features, np_labels)
 
   def testNpXent(self):
@@ -131,14 +131,14 @@ class XentTest(test.TestCase):
   def testShapeMismatch(self):
     with self.test_session():
       with self.assertRaises(ValueError):
-        gen_nn_ops._softmax_cross_entropy_with_logits(
+        gen_nn_ops.softmax_cross_entropy_with_logits(
             [[0., 1.], [2., 3.]], [[0., 1., 0.], [1., 0., 0.]])
 
   def testNotMatrix(self):
     with self.test_session():
       with self.assertRaises(ValueError):
-        gen_nn_ops._softmax_cross_entropy_with_logits([0., 1., 2., 3.],
-                                                      [0., 1., 0., 1.])
+        gen_nn_ops.softmax_cross_entropy_with_logits([0., 1., 2., 3.],
+                                                     [0., 1., 0., 1.])
 
   def testHalf(self):
     self._testAll(
diff --git a/tensorflow/python/ops/accumulate_n_benchmark.py b/tensorflow/python/ops/accumulate_n_benchmark.py
index c58d36f397..a709066cae 100644
--- a/tensorflow/python/ops/accumulate_n_benchmark.py
+++ b/tensorflow/python/ops/accumulate_n_benchmark.py
@@ -39,7 +39,7 @@ from tensorflow.python.platform import test
 class AccumulateNBenchmark(test.Benchmark):
 
   def _AccumulateNTemplate(self, inputs, init, shape, validate_shape):
-    var = gen_state_ops._temporary_variable(
+    var = gen_state_ops.temporary_variable(
         shape=shape, dtype=inputs[0].dtype.base_dtype)
     ref = state_ops.assign(var, init, validate_shape=validate_shape)
     update_ops = [
@@ -47,8 +47,7 @@ class AccumulateNBenchmark(test.Benchmark):
             ref, tensor, use_locking=True).op for tensor in inputs
     ]
     with ops.control_dependencies(update_ops):
-      return gen_state_ops._destroy_temporary_variable(
-          ref, var_name=var.op.name)
+      return gen_state_ops.destroy_temporary_variable(ref, var_name=var.op.name)
 
   def _AccumulateNInitializedWithFirst(self, inputs):
     return self._AccumulateNTemplate(
@@ -60,7 +59,7 @@ class AccumulateNBenchmark(test.Benchmark):
   def _AccumulateNInitializedWithMerge(self, inputs):
     return self._AccumulateNTemplate(
         inputs,
-        init=array_ops.zeros_like(gen_control_flow_ops._merge(inputs)[0]),
+        init=array_ops.zeros_like(gen_control_flow_ops.merge(inputs)[0]),
         shape=tensor_shape.vector(0),
         validate_shape=False)
 
diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py
index 9745d38dc2..925cf8ef32 100644
--- a/tensorflow/python/ops/array_grad.py
+++ b/tensorflow/python/ops/array_grad.py
@@ -139,7 +139,6 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index):
       # on CPUs and a Maxwell TitanX.  A speedup was seen in a large majority of
       # cases when switching implementations at N=16, but it is possible that
       # there will be a small number of performance regressions.
-      # pylint: disable=protected-access
       if len(sizes) > 16:
         # extract the size of each input along the concat dimension
         sizes = array_ops.squeeze(
@@ -148,10 +147,9 @@ def _ConcatGradHelper(op, grad, start_value_index, end_value_index, dim_index):
                 [1, -1]))
         out_grads = array_ops.split(grad, sizes, non_neg_concat_dim)
       else:
-        offset = gen_array_ops._concat_offset(non_neg_concat_dim, sizes)
+        offset = gen_array_ops.concat_offset(non_neg_concat_dim, sizes)
         for (begin, size) in zip(offset, sizes):
           out_grads.append(array_ops.slice(grad, begin, size))
-      # pylint: enable=protected-access
   elif isinstance(grad, ops.IndexedSlices):
     # Using mod here for convenience since concat_dim is already verified
     # in concat implementation to be within the allowed [-rank, rank) range.
@@ -627,9 +625,7 @@ def _ReverseSequenceGrad(op, grad):
 @ops.RegisterGradient("Reverse")
 def _ReverseGrad(op, grad):
   reverse_dims = op.inputs[1]
-  # pylint: disable=protected-access
-  return gen_array_ops._reverse(grad, reverse_dims), None
-  # pylint: enable=protected-access
+  return gen_array_ops.reverse(grad, reverse_dims), None
 
 
 @ops.RegisterGradient("ReverseV2")
@@ -700,17 +696,13 @@ ops.NotDifferentiable("OneHot")
 @ops.RegisterGradient("MirrorPad")
 def _MirrorPadGrad(op, grad):
   mode = op.get_attr("mode")
-  # pylint: disable=protected-access
-  return [gen_array_ops._mirror_pad_grad(grad, op.inputs[1], mode=mode), None]
-  # pylint: enable=protected-access
+  return [gen_array_ops.mirror_pad_grad(grad, op.inputs[1], mode=mode), None]
 
 
 @ops.RegisterGradient("MirrorPadGrad")
 def _MirrorPadGradGrad(op, grad):
   mode = op.get_attr("mode")
-  # pylint: disable=protected-access
-  return [gen_array_ops._mirror_pad(grad, op.inputs[1], mode=mode), None]
-  # pylint: enable=protected-access
+  return [gen_array_ops.mirror_pad(grad, op.inputs[1], mode=mode), None]
 
 
 @ops.RegisterGradient("QuantizeAndDequantize")
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 3db3d84475..cc559695ed 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -198,7 +198,7 @@ def expand_dims(input, axis=None, name=None, dim=None):
     if axis is not None:
       raise ValueError("can't specify both 'dim' and 'axis'")
     axis = dim
-  return gen_array_ops._expand_dims(input, axis, name)
+  return gen_array_ops.expand_dims(input, axis, name)
 
 
 # pylint: enable=redefined-builtin,protected-access
@@ -211,28 +211,25 @@ def expand_dims(input, axis=None, name=None, dim=None):
     "This op will be removed after the deprecation date. "
     "Please switch to tf.setdiff1d().")
 def listdiff(x, y, out_idx=None, name=None):
-  return gen_array_ops._list_diff(x, y, out_idx, name)
+  return gen_array_ops.list_diff(x, y, out_idx, name)
 
 
-listdiff.__doc__ = gen_array_ops._list_diff.__doc__ + "\n" + listdiff.__doc__
+listdiff.__doc__ = gen_array_ops.list_diff.__doc__ + "\n" + listdiff.__doc__
 
 # pylint: enable=protected-access
 
 
-# pylint: disable=undefined-variable,protected-access
+# pylint: disable=undefined-variable
 @tf_export("setdiff1d")
 def setdiff1d(x, y, index_dtype=dtypes.int32, name=None):
-  return gen_array_ops._list_diff(x, y, index_dtype, name)
+  return gen_array_ops.list_diff(x, y, index_dtype, name)
 
 
-setdiff1d.__doc__ = gen_array_ops._list_diff.__doc__
-
-# pylint: enable=protected-access
+setdiff1d.__doc__ = gen_array_ops.list_diff.__doc__
 
 
 @tf_export("broadcast_dynamic_shape")
 def broadcast_dynamic_shape(shape_x, shape_y):
-  # pylint: disable=protected-access
   """Returns the broadcasted dynamic shape between `shape_x` and `shape_y`.
 
   Args:
@@ -242,8 +239,7 @@ def broadcast_dynamic_shape(shape_x, shape_y):
   Returns:
     A rank 1 integer `Tensor` representing the broadcasted shape.
   """
-  return gen_array_ops._broadcast_args(shape_x, shape_y)
-  # pylint: enable=protected-access
+  return gen_array_ops.broadcast_args(shape_x, shape_y)
 
 
 @tf_export("broadcast_static_shape")
@@ -399,7 +395,7 @@ def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
   with ops.name_scope(name, "Size", [input]) as name:
     if isinstance(input, (sparse_tensor.SparseTensor,
                           sparse_tensor.SparseTensorValue)):
-      return gen_math_ops._prod(
+      return gen_math_ops.prod(
           gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
     else:
       input_tensor = ops.convert_to_tensor(input)
@@ -892,7 +888,7 @@ def parallel_stack(values, name="parallel_stack"):
     output_shape = tensor_shape.TensorShape([len(values)])
     output_shape = output_shape.concatenate(value_shape)
     # expand_dims converts concat to stack.
-    return gen_array_ops._parallel_concat(
+    return gen_array_ops.parallel_concat(
         [expand_dims(value, 0) for value in values], shape=output_shape)
 
 
@@ -950,7 +946,7 @@ def stack(values, axis=0, name="stack"):
       raise ValueError("axis = %d not in [%d, %d)" % (axis, -expanded_num_dims,
                                                       expanded_num_dims))
 
-  return gen_array_ops._pack(values, axis=axis, name=name)
+  return gen_array_ops.pack(values, axis=axis, name=name)
 
 
 # pylint: disable=invalid-name
@@ -994,7 +990,7 @@ def _autopacking_helper(list_or_tuple, dtype, name):
           # convertible-to-tensor types, such as numpy arrays.
           elems_as_tensors.append(
               constant_op.constant(elem, dtype=dtype, name=str(i)))
-      return gen_array_ops._pack(elems_as_tensors, name=scope)
+      return gen_array_ops.pack(elems_as_tensors, name=scope)
     else:
       return converted_elems
 
@@ -1089,7 +1085,7 @@ def unstack(value, num=None, axis=0, name="unstack"):
       num = value_shape[axis].value
   if num is None:
     raise ValueError("Cannot infer num from shape %s" % value_shape)
-  return gen_array_ops._unpack(value, num=num, axis=axis, name=name)
+  return gen_array_ops.unpack(value, num=num, axis=axis, name=name)
 
 
 @tf_export("concat")
@@ -1186,7 +1182,7 @@ def concat(values, axis, name="concat"):
           dtype=dtypes.int32).get_shape().assert_is_compatible_with(
               tensor_shape.scalar())
       return identity(values[0], name=scope)
-  return gen_array_ops._concat_v2(values=values, axis=axis, name=name)
+  return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
 
 
 @tf_export("boolean_mask")
@@ -1254,8 +1250,7 @@ def boolean_mask(tensor, mask, name="boolean_mask", axis=None):
     axis = 0 if axis is None else axis
     shape_tensor[axis:axis + ndims_mask].assert_is_compatible_with(shape_mask)
 
-    leading_size = gen_math_ops._prod(
-        shape(tensor)[axis:axis + ndims_mask], [0])
+    leading_size = gen_math_ops.prod(shape(tensor)[axis:axis + ndims_mask], [0])
     tensor = reshape(tensor,
                      concat([
                          shape(tensor)[:axis], [leading_size],
@@ -1319,10 +1314,10 @@ def unique(x, out_idx=dtypes.int32, name=None):
   # period (3 weeks) pass.
   # TODO(yongtang): The documentation should also
   # be updated when switch  to v2.
-  return gen_array_ops._unique(x, out_idx, name)
+  return gen_array_ops.unique(x, out_idx, name)
 
 
-unique.__doc__ = gen_array_ops._unique.__doc__
+unique.__doc__ = gen_array_ops.unique.__doc__
 
 
 @tf_export("split")
@@ -1376,7 +1371,7 @@ def split(value, num_or_size_splits, axis=0, num=None, name="split"):
   """
   size_splits = ops.convert_to_tensor(num_or_size_splits)
   if size_splits._rank() == 0 and size_splits.dtype.is_integer:
-    return gen_array_ops._split(
+    return gen_array_ops.split(
         axis=axis, num_split=num_or_size_splits, value=value, name=name)
 
   if num is None:
@@ -1386,12 +1381,8 @@ def split(value, num_or_size_splits, axis=0, num=None, name="split"):
     if num is None:
       raise ValueError("Cannot infer num from shape %s" % num_or_size_splits)
 
-  return gen_array_ops._split_v(
-      value=value,
-      size_splits=size_splits,
-      axis=axis,
-      num_split=num,
-      name=name)
+  return gen_array_ops.split_v(
+      value=value, size_splits=size_splits, axis=axis, num_split=num, name=name)
 
 
 @tf_export("transpose")
@@ -1461,7 +1452,7 @@ def transpose(a, perm=None, name="transpose", conjugate=False):
   """
   with ops.name_scope(name, "transpose", [a]) as name:
     transpose_fn = (
-        gen_array_ops._conjugate_transpose
+        gen_array_ops.conjugate_transpose
         if (conjugate and a.dtype.is_complex) else gen_array_ops.transpose)
     if perm is None:
       rank = gen_array_ops.rank(a)
@@ -1639,7 +1630,7 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True):
         return zeros(
             shape_internal(tensor, optimize=optimize), dtype=dtype, name=name)
       with ops.device(tensor.device):
-        return gen_array_ops._zeros_like(tensor, name=name)
+        return gen_array_ops.zeros_like(tensor, name=name)
 
     # For now, variant types must be created via zeros_like; as we need to
     # pass the input variant object to the proper zeros callback.
@@ -1654,7 +1645,7 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True):
       return zeros(
           shape_internal(tensor, optimize=optimize), dtype=dtype, name=name)
     else:
-      return gen_array_ops._zeros_like(tensor, name=name)
+      return gen_array_ops.zeros_like(tensor, name=name)
 
 
 @tf_export("ones_like")
@@ -1775,7 +1766,7 @@ def placeholder(dtype, shape=None, name=None):
     raise RuntimeError("tf.placeholder() is not compatible with "
                        "eager execution.")
 
-  return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
+  return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
 
 
 # pylint: disable=redefined-outer-name
@@ -1919,15 +1910,15 @@ def pad(tensor, paddings, mode="CONSTANT", name=None, constant_values=0):  # pyl
     # TODO(rjryan): Once the forward compatibility period (3 weeks) have passed
     # remove the "Pad" fallback here.
     if constant_values != 0:
-      result = gen_array_ops._pad_v2(
+      result = gen_array_ops.pad_v2(
           tensor, paddings, constant_values, name=name)
     else:
-      result = gen_array_ops._pad(tensor, paddings, name=name)
+      result = gen_array_ops.pad(tensor, paddings, name=name)
   elif mode == "REFLECT":
-    result = gen_array_ops._mirror_pad(
+    result = gen_array_ops.mirror_pad(
         tensor, paddings, mode="REFLECT", name=name)
   elif mode == "SYMMETRIC":
-    result = gen_array_ops._mirror_pad(
+    result = gen_array_ops.mirror_pad(
         tensor, paddings, mode="SYMMETRIC", name=name)
   else:
     raise ValueError("Unknown padding mode: %s" % mode)
@@ -2157,7 +2148,7 @@ def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"):
                             sparse_tensor.SparseTensorValue)):
     raise TypeError("Truth must be a SparseTensor.")
 
-  return gen_array_ops._edit_distance(
+  return gen_array_ops.edit_distance(
       hypothesis.indices,
       hypothesis.values,
       hypothesis.dense_shape,
@@ -2294,7 +2285,7 @@ def space_to_batch(input, paddings, block_size, name=None):  # pylint: disable=r
   return result
 
 
-space_to_batch.__doc__ = gen_array_ops._space_to_batch.__doc__
+space_to_batch.__doc__ = gen_array_ops.space_to_batch.__doc__
 
 
 @tf_export("space_to_depth")
@@ -2324,7 +2315,7 @@ def batch_to_space(input, crops, block_size, name=None):  # pylint: disable=rede
   return result
 
 
-batch_to_space.__doc__ = gen_array_ops._batch_to_space.__doc__
+batch_to_space.__doc__ = gen_array_ops.batch_to_space.__doc__
 
 
 @tf_export("one_hot")
@@ -2468,8 +2459,8 @@ def one_hot(indices,
       raise TypeError("dtype {0} of on_value does not match "
                       "dtype {1} of off_value".format(on_dtype, off_dtype))
 
-    return gen_array_ops._one_hot(indices, depth, on_value, off_value, axis,
-                                  name)
+    return gen_array_ops.one_hot(indices, depth, on_value, off_value, axis,
+                                 name)
 
 
 def _all_dimensions(x):
@@ -2597,7 +2588,7 @@ def squeeze(input, axis=None, name=None, squeeze_dims=None):
     axis = squeeze_dims
   if np.isscalar(axis):
     axis = [axis]
-  return gen_array_ops._squeeze(input, axis, name)
+  return gen_array_ops.squeeze(input, axis, name)
 
 
 @tf_export("where")
@@ -2648,7 +2639,7 @@ def where(condition, x=None, y=None, name=None):
           condition, preferred_dtype=dtypes.bool, name="condition")
       return gen_array_ops.where(condition=condition, name=name)
   elif x is not None and y is not None:
-    return gen_math_ops._select(condition=condition, x=x, y=y, name=name)
+    return gen_math_ops.select(condition=condition, x=x, y=y, name=name)
   else:
     raise ValueError("x and y must both be non-None or both be None.")
 
diff --git a/tensorflow/python/ops/batch_norm_benchmark.py b/tensorflow/python/ops/batch_norm_benchmark.py
index c2ee2b3832..4f65e3771c 100644
--- a/tensorflow/python/ops/batch_norm_benchmark.py
+++ b/tensorflow/python/ops/batch_norm_benchmark.py
@@ -41,9 +41,8 @@ def batch_norm_op(tensor, mean, variance, beta, gamma, scale):
   # _batch_norm_with_global_normalization is deprecated in v9
   ops.get_default_graph().graph_def_versions.producer = 8
   # pylint: disable=protected-access
-  return gen_nn_ops._batch_norm_with_global_normalization(tensor, mean,
-                                                          variance, beta, gamma,
-                                                          0.001, scale)
+  return gen_nn_ops.batch_norm_with_global_normalization(
+      tensor, mean, variance, beta, gamma, 0.001, scale)
   # pylint: enable=protected-access
 
 
diff --git a/tensorflow/python/ops/candidate_sampling_ops.py b/tensorflow/python/ops/candidate_sampling_ops.py
index 220ef1754d..9ea1ea9c92 100644
--- a/tensorflow/python/ops/candidate_sampling_ops.py
+++ b/tensorflow/python/ops/candidate_sampling_ops.py
@@ -77,7 +77,7 @@ def uniform_candidate_sampler(true_classes, num_true, num_sampled, unique,
       of each of `sampled_candidates`.
   """
   seed1, seed2 = random_seed.get_seed(seed)
-  return gen_candidate_sampling_ops._uniform_candidate_sampler(
+  return gen_candidate_sampling_ops.uniform_candidate_sampler(
       true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
       seed2=seed2, name=name)
 
@@ -136,7 +136,7 @@ def log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique,
       of each of `sampled_candidates`.
   """
   seed1, seed2 = random_seed.get_seed(seed)
-  return gen_candidate_sampling_ops._log_uniform_candidate_sampler(
+  return gen_candidate_sampling_ops.log_uniform_candidate_sampler(
       true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
       seed2=seed2, name=name)
 
@@ -193,7 +193,7 @@ def learned_unigram_candidate_sampler(true_classes, num_true, num_sampled,
 
   """
   seed1, seed2 = random_seed.get_seed(seed)
-  return gen_candidate_sampling_ops._learned_unigram_candidate_sampler(
+  return gen_candidate_sampling_ops.learned_unigram_candidate_sampler(
       true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
       seed2=seed2, name=name)
 
@@ -283,7 +283,7 @@ def fixed_unigram_candidate_sampler(true_classes,
 
   """
   seed1, seed2 = random_seed.get_seed(seed)
-  return gen_candidate_sampling_ops._fixed_unigram_candidate_sampler(
+  return gen_candidate_sampling_ops.fixed_unigram_candidate_sampler(
       true_classes, num_true, num_sampled, unique, range_max,
       vocab_file=vocab_file, distortion=distortion,
       num_reserved_ids=num_reserved_ids, num_shards=num_shards, shard=shard,
@@ -321,7 +321,7 @@ def all_candidate_sampler(true_classes, num_true, num_sampled, unique,
       of each of `sampled_candidates`. All returned values are 1.0.
   """
   seed1, seed2 = random_seed.get_seed(seed)
-  return gen_candidate_sampling_ops._all_candidate_sampler(
+  return gen_candidate_sampling_ops.all_candidate_sampler(
       true_classes, num_true, num_sampled, unique, seed=seed1, seed2=seed2,
       name=name)
 
@@ -370,6 +370,6 @@ def compute_accidental_hits(true_classes, sampled_candidates, num_true,
 
   """
   seed1, seed2 = random_seed.get_seed(seed)
-  return gen_candidate_sampling_ops._compute_accidental_hits(
+  return gen_candidate_sampling_ops.compute_accidental_hits(
       true_classes, sampled_candidates, num_true, seed=seed1, seed2=seed2,
       name=name)
diff --git a/tensorflow/python/ops/control_flow_grad.py b/tensorflow/python/ops/control_flow_grad.py
index 97b57177b2..21354b5ae8 100644
--- a/tensorflow/python/ops/control_flow_grad.py
+++ b/tensorflow/python/ops/control_flow_grad.py
@@ -28,7 +28,6 @@ from tensorflow.python.ops import math_ops
 # go/tf-wildcard-import
 # pylint: disable=wildcard-import,undefined-variable
 from tensorflow.python.ops.control_flow_ops import *
-from tensorflow.python.ops.gen_control_flow_ops import *
 # pylint: enable=wildcard-import
 
 
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 215c6940df..689f7cdc8f 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -329,7 +329,7 @@ def exit(data, name=None):  # pylint: disable=redefined-builtin
   data = ops.internal_convert_to_tensor_or_indexed_slices(data, as_ref=True)
   if isinstance(data, ops.Tensor):
     if data.dtype._is_ref_dtype:  # pylint: disable=protected-access
-      return gen_control_flow_ops._ref_exit(data, name)
+      return gen_control_flow_ops.ref_exit(data, name)
     else:
       return gen_control_flow_ops._exit(data, name)
   else:
@@ -371,17 +371,17 @@ def switch(data, pred, dtype=None, name=None):
         data, dtype=dtype, name="data", as_ref=True)
     pred = ops.convert_to_tensor(pred, name="pred")
     if isinstance(data, ops.Tensor):
-      return gen_control_flow_ops._switch(data, pred, name=name)
+      return gen_control_flow_ops.switch(data, pred, name=name)
     else:
       if not isinstance(data, (ops.IndexedSlices, sparse_tensor.SparseTensor)):
         raise TypeError("Type %s not supported" % type(data))
       val, ind = data.values, data.indices
-      val_f, val_t = gen_control_flow_ops._switch(val, pred, name=name)
-      ind_f, ind_t = gen_control_flow_ops._switch(ind, pred, name="indices")
+      val_f, val_t = gen_control_flow_ops.switch(val, pred, name=name)
+      ind_f, ind_t = gen_control_flow_ops.switch(ind, pred, name="indices")
       if isinstance(data, ops.IndexedSlices):
         dense_shape = data.dense_shape
         if dense_shape is not None:
-          dense_shape_f, dense_shape_t = gen_control_flow_ops._switch(
+          dense_shape_f, dense_shape_t = gen_control_flow_ops.switch(
               dense_shape, pred, name="dense_shape")
         else:
           dense_shape_f, dense_shape_t = None, None
@@ -389,7 +389,7 @@ def switch(data, pred, dtype=None, name=None):
                 ops.IndexedSlices(val_t, ind_t, dense_shape_t))
       else:
         dense_shape = data.dense_shape
-        dense_shape_f, dense_shape_t = gen_control_flow_ops._switch(
+        dense_shape_f, dense_shape_t = gen_control_flow_ops.switch(
             data.dense_shape, pred, name="dense_shape")
         return (sparse_tensor.SparseTensor(ind_f, val_f, dense_shape_f),
                 sparse_tensor.SparseTensor(ind_t, val_t, dense_shape_t))
@@ -473,15 +473,15 @@ def merge(inputs, name=None):
     ]
     if all([isinstance(v, ops.Tensor) for v in inputs]):
       if all([v.dtype._is_ref_dtype for v in inputs]):  # pylint: disable=protected-access
-        return gen_control_flow_ops._ref_merge(inputs, name)
+        return gen_control_flow_ops.ref_merge(inputs, name)
       else:
-        return gen_control_flow_ops._merge(inputs, name)
+        return gen_control_flow_ops.merge(inputs, name)
     elif all([isinstance(v, sparse_tensor.SparseTensor) for v in inputs]):
       # Only handle the case when all inputs are SparseTensor.
       values, _ = merge([inp.values for inp in inputs], name=name)
-      indices, chosen_index = gen_control_flow_ops._merge(
+      indices, chosen_index = gen_control_flow_ops.merge(
           [inp.indices for inp in inputs], name="indices")
-      dense_shape, _ = gen_control_flow_ops._merge(
+      dense_shape, _ = gen_control_flow_ops.merge(
           [inp.dense_shape for inp in inputs], name="dense_shape")
       return (sparse_tensor.SparseTensor(indices, values, dense_shape),
               chosen_index)
@@ -489,13 +489,13 @@ def merge(inputs, name=None):
       # For now convert all the inputs as IndexedSlices.
       inputs = math_ops._as_indexed_slices_list(inputs, optimize=False)
       values, _ = merge([inp.values for inp in inputs], name=name)
-      indices, chosen_index = gen_control_flow_ops._merge(
+      indices, chosen_index = gen_control_flow_ops.merge(
           [inp.indices for inp in inputs], name="indices")
       if any(inp.dense_shape is not None for inp in inputs):
         if any(inp.dense_shape is None for inp in inputs):
           raise ValueError("Either all merged IndexedSlices must have a "
                            "dense_shape, or none must have a dense_shape.")
-        dense_shape, _ = gen_control_flow_ops._merge(
+        dense_shape, _ = gen_control_flow_ops.merge(
             [inp.dense_shape for inp in inputs], name="dense_shape")
       else:
         dense_shape = None
@@ -1015,10 +1015,8 @@ class GradLoopState(object):
         else:
           max_size = GetMaxSizeFromNestedMaximumIterations(
               value, self.forward_context)
-        # pylint: disable=protected-access
-        acc = gen_data_flow_ops._stack_v2(
+        acc = gen_data_flow_ops.stack_v2(
             max_size=max_size, elem_type=value.dtype.base_dtype, name="f_acc")
-        # pylint: enable=protected-access
       if curr_ctxt:
         curr_ctxt.Exit()
 
@@ -1031,10 +1029,8 @@ class GradLoopState(object):
       if value_ctxt == self.forward_context:
         # value is not nested in the forward context.
         self.forward_context.Enter()
-        # pylint: disable=protected-access
-        push = gen_data_flow_ops._stack_push_v2(
+        push = gen_data_flow_ops.stack_push_v2(
             enter_acc, value, swap_memory=swap_enabled)
-        # pylint: enable=protected-access
         self.forward_context.Exit()
         # Protect stack push and order it before forward_index.
         self.forward_index.op._add_control_input(push.op)
@@ -1046,18 +1042,14 @@ class GradLoopState(object):
           # The special case for creating a zero tensor for a dead
           # branch of a switch. See ControlFlowState.ZerosLike().
           value_ctxt.outer_context.Enter()
-          # pylint: disable=protected-access
-          push = gen_data_flow_ops._stack_push_v2(
+          push = gen_data_flow_ops.stack_push_v2(
               enter_acc, value, swap_memory=swap_enabled)
-          # pylint: enable=protected-access
           value_ctxt.outer_context.Exit()
           push.op._set_control_flow_context(value_ctxt)
         else:
           value_ctxt.Enter()
-          # pylint: disable=protected-access
-          push = gen_data_flow_ops._stack_push_v2(
+          push = gen_data_flow_ops.stack_push_v2(
               enter_acc, value, swap_memory=swap_enabled)
-          # pylint: enable=protected-access
           value_ctxt.Exit()
         # Protect stack push and order it before forward_sync.
         self.forward_sync._add_control_input(push.op)
@@ -1104,10 +1096,8 @@ class GradLoopState(object):
           pred = cond_ctxt.pred
         branch = (1 - cond_ctxt.branch) if dead_branch else cond_ctxt.branch
         history_value = _SwitchRefOrTensor(history_value, pred)[branch]
-      # pylint: disable=protected-access
-      pop = gen_data_flow_ops._stack_pop_v2(history_value,
-                                            value.dtype.base_dtype)
-      # pylint: enable=protected-access
+      pop = gen_data_flow_ops.stack_pop_v2(history_value,
+                                           value.dtype.base_dtype)
       pop.set_shape(value.get_shape())
       self.grad_context.Exit()
     parallel_iterations = self.grad_context.parallel_iterations
diff --git a/tensorflow/python/ops/ctc_ops.py b/tensorflow/python/ops/ctc_ops.py
index 83da6739db..4b57e2de79 100644
--- a/tensorflow/python/ops/ctc_ops.py
+++ b/tensorflow/python/ops/ctc_ops.py
@@ -148,7 +148,7 @@ def ctc_loss(labels, inputs, sequence_length,
   if not time_major:
     inputs = array_ops.transpose(inputs, [1, 0, 2])  # (B,T,N) => (T,B,N)
 
-  loss, _ = gen_ctc_ops._ctc_loss(
+  loss, _ = gen_ctc_ops.ctc_loss(
       inputs,
       labels.indices,
       labels.values,
@@ -224,7 +224,7 @@ def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True):
         sequence found, the negative of the sum of the greatest logit at each
         timeframe.
   """
-  outputs = gen_ctc_ops._ctc_greedy_decoder(
+  outputs = gen_ctc_ops.ctc_greedy_decoder(
       inputs, sequence_length, merge_repeated=merge_repeated)
   (decoded_ix, decoded_val, decoded_shape, log_probabilities) = outputs
   return ([sparse_tensor.SparseTensor(decoded_ix, decoded_val, decoded_shape)],
@@ -272,7 +272,7 @@ def ctc_beam_search_decoder(inputs, sequence_length, beam_width=100,
   """
 
   decoded_ixs, decoded_vals, decoded_shapes, log_probabilities = (
-      gen_ctc_ops._ctc_beam_search_decoder(
+      gen_ctc_ops.ctc_beam_search_decoder(
           inputs, sequence_length, beam_width=beam_width, top_paths=top_paths,
           merge_repeated=merge_repeated))
 
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 03ed537cfc..052caffd49 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -342,10 +342,10 @@ class QueueBase(object):
         val.get_shape().assert_is_compatible_with(shape)
 
       if self._queue_ref.dtype == _dtypes.resource:
-        return gen_data_flow_ops._queue_enqueue_v2(
+        return gen_data_flow_ops.queue_enqueue_v2(
             self._queue_ref, vals, name=scope)
       else:
-        return gen_data_flow_ops._queue_enqueue(
+        return gen_data_flow_ops.queue_enqueue(
             self._queue_ref, vals, name=scope)
 
   def enqueue_many(self, vals, name=None):
@@ -387,7 +387,7 @@ class QueueBase(object):
             val.get_shape().with_rank_at_least(1)[0])
         val.get_shape()[1:].assert_is_compatible_with(shape)
 
-      return gen_data_flow_ops._queue_enqueue_many_v2(
+      return gen_data_flow_ops.queue_enqueue_many_v2(
           self._queue_ref, vals, name=scope)
 
   def _dequeue_return_value(self, tensors):
@@ -436,10 +436,10 @@ class QueueBase(object):
     if name is None:
       name = "%s_Dequeue" % self._name
     if self._queue_ref.dtype == _dtypes.resource:
-      ret = gen_data_flow_ops._queue_dequeue_v2(
+      ret = gen_data_flow_ops.queue_dequeue_v2(
           self._queue_ref, self._dtypes, name=name)
     else:
-      ret = gen_data_flow_ops._queue_dequeue(
+      ret = gen_data_flow_ops.queue_dequeue(
           self._queue_ref, self._dtypes, name=name)
 
     # NOTE(mrry): Not using a shape function because we need access to
@@ -479,7 +479,7 @@ class QueueBase(object):
     if name is None:
       name = "%s_DequeueMany" % self._name
 
-    ret = gen_data_flow_ops._queue_dequeue_many_v2(
+    ret = gen_data_flow_ops.queue_dequeue_many_v2(
         self._queue_ref, n=n, component_types=self._dtypes, name=name)
 
     # NOTE(mrry): Not using a shape function because we need access to
@@ -523,7 +523,7 @@ class QueueBase(object):
     if name is None:
       name = "%s_DequeueUpTo" % self._name
 
-    ret = gen_data_flow_ops._queue_dequeue_up_to_v2(
+    ret = gen_data_flow_ops.queue_dequeue_up_to_v2(
         self._queue_ref, n=n, component_types=self._dtypes, name=name)
 
     # NOTE(mrry): Not using a shape function because we need access to
@@ -560,12 +560,12 @@ class QueueBase(object):
     if name is None:
       name = "%s_Close" % self._name
     if self._queue_ref.dtype == _dtypes.resource:
-      return gen_data_flow_ops._queue_close_v2(
+      return gen_data_flow_ops.queue_close_v2(
           self._queue_ref,
           cancel_pending_enqueues=cancel_pending_enqueues,
           name=name)
     else:
-      return gen_data_flow_ops._queue_close(
+      return gen_data_flow_ops.queue_close(
           self._queue_ref,
           cancel_pending_enqueues=cancel_pending_enqueues,
           name=name)
@@ -601,9 +601,9 @@ class QueueBase(object):
     if name is None:
       name = "%s_Size" % self._name
     if self._queue_ref.dtype == _dtypes.resource:
-      return gen_data_flow_ops._queue_size_v2(self._queue_ref, name=name)
+      return gen_data_flow_ops.queue_size_v2(self._queue_ref, name=name)
     else:
-      return gen_data_flow_ops._queue_size(self._queue_ref, name=name)
+      return gen_data_flow_ops.queue_size(self._queue_ref, name=name)
 
 
 @tf_export("RandomShuffleQueue")
@@ -683,7 +683,7 @@ class RandomShuffleQueue(QueueBase):
       # the id of the last op created.)
       string = (str(seed1) + shared_name).encode("utf-8")
       seed2 = int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF
-    queue_ref = gen_data_flow_ops._random_shuffle_queue_v2(
+    queue_ref = gen_data_flow_ops.random_shuffle_queue_v2(
         component_types=dtypes,
         shapes=shapes,
         capacity=capacity,
@@ -748,7 +748,7 @@ class FIFOQueue(QueueBase):
     dtypes = _as_type_list(dtypes)
     shapes = _as_shape_list(shapes, dtypes)
     names = _as_name_list(names, dtypes)
-    queue_ref = gen_data_flow_ops._fifo_queue_v2(
+    queue_ref = gen_data_flow_ops.fifo_queue_v2(
         component_types=dtypes,
         shapes=shapes,
         capacity=capacity,
@@ -827,7 +827,7 @@ class PaddingFIFOQueue(QueueBase):
                        "but received %d dtypes and %d shapes." % (len(dtypes),
                                                                   len(shapes)))
 
-    queue_ref = gen_data_flow_ops._padding_fifo_queue_v2(
+    queue_ref = gen_data_flow_ops.padding_fifo_queue_v2(
         component_types=dtypes,
         shapes=shapes,
         capacity=capacity,
@@ -895,7 +895,7 @@ class PriorityQueue(QueueBase):
     types = _as_type_list(types)
     shapes = _as_shape_list(shapes, types)
 
-    queue_ref = gen_data_flow_ops._priority_queue_v2(
+    queue_ref = gen_data_flow_ops.priority_queue_v2(
         component_types=types,
         shapes=shapes,
         capacity=capacity,
@@ -985,7 +985,7 @@ class Barrier(object):
     else:
       self._shapes = [tensor_shape.unknown_shape() for _ in self._types]
 
-    self._barrier_ref = gen_data_flow_ops._barrier(
+    self._barrier_ref = gen_data_flow_ops.barrier(
         component_types=self._types,
         shapes=self._shapes,
         shared_name=shared_name,
@@ -1026,7 +1026,7 @@ class Barrier(object):
     """
     if name is None:
       name = "%s_BarrierInsertMany" % self._name
-    return gen_data_flow_ops._barrier_insert_many(
+    return gen_data_flow_ops.barrier_insert_many(
         self._barrier_ref, keys, values, component_index, name=name)
 
   def take_many(self,
@@ -1073,7 +1073,7 @@ class Barrier(object):
     """
     if name is None:
       name = "%s_BarrierTakeMany" % self._name
-    ret = gen_data_flow_ops._barrier_take_many(
+    ret = gen_data_flow_ops.barrier_take_many(
         self._barrier_ref,
         num_elements,
         self._types,
@@ -1122,7 +1122,7 @@ class Barrier(object):
     """
     if name is None:
       name = "%s_BarrierClose" % self._name
-    return gen_data_flow_ops._barrier_close(
+    return gen_data_flow_ops.barrier_close(
         self._barrier_ref,
         cancel_pending_enqueues=cancel_pending_enqueues,
         name=name)
@@ -1139,7 +1139,7 @@ class Barrier(object):
     """
     if name is None:
       name = "%s_BarrierReadySize" % self._name
-    return gen_data_flow_ops._barrier_ready_size(self._barrier_ref, name=name)
+    return gen_data_flow_ops.barrier_ready_size(self._barrier_ref, name=name)
 
   def incomplete_size(self, name=None):
     """Compute the number of incomplete elements in the given barrier.
@@ -1153,7 +1153,7 @@ class Barrier(object):
     """
     if name is None:
       name = "%s_BarrierIncompleteSize" % self._name
-    return gen_data_flow_ops._barrier_incomplete_size(
+    return gen_data_flow_ops.barrier_incomplete_size(
         self._barrier_ref, name=name)
 
 
diff --git a/tensorflow/python/ops/functional_ops.py b/tensorflow/python/ops/functional_ops.py
index ac03d30fcd..09a0e345f2 100644
--- a/tensorflow/python/ops/functional_ops.py
+++ b/tensorflow/python/ops/functional_ops.py
@@ -41,7 +41,7 @@ from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops.gen_functional_ops import *
 # pylint: enable=wildcard-import
 # pylint: disable=unused-import
-from tensorflow.python.ops.gen_functional_ops import _symbolic_gradient
+from tensorflow.python.ops.gen_functional_ops import symbolic_gradient
 # pylint: enable=unused-import
 from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import tf_export
diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py
index 227316a01e..be61014395 100644
--- a/tensorflow/python/ops/gradients_impl.py
+++ b/tensorflow/python/ops/gradients_impl.py
@@ -356,7 +356,7 @@ def _SymGrad(op, out_grads):
   for k in op.node_def.attr:
     f.attr[k].CopyFrom(op.node_def.attr[k])
   # pylint: disable=protected-access
-  in_grads = functional_ops._symbolic_gradient(input=f_in, Tout=f_types, f=f)
+  in_grads = functional_ops.symbolic_gradient(input=f_in, Tout=f_types, f=f)
   # pylint: enable=protected-access
   return in_grads
 
diff --git a/tensorflow/python/ops/histogram_ops.py b/tensorflow/python/ops/histogram_ops.py
index 6a975160b0..4a1ef54fb5 100644
--- a/tensorflow/python/ops/histogram_ops.py
+++ b/tensorflow/python/ops/histogram_ops.py
@@ -141,5 +141,7 @@ def histogram_fixed_width(values,
   """
   with ops.name_scope(name, 'histogram_fixed_width',
                       [values, value_range, nbins]) as name:
-    return gen_math_ops._histogram_fixed_width(  # pylint: disable=protected-access
+    # pylint: disable=protected-access
+    return gen_math_ops._histogram_fixed_width(
         values, value_range, nbins, dtype=dtype, name=name)
+    # pylint: enable=protected-access
diff --git a/tensorflow/python/ops/image_grad.py b/tensorflow/python/ops/image_grad.py
index 093843cd5b..9f43e3f146 100644
--- a/tensorflow/python/ops/image_grad.py
+++ b/tensorflow/python/ops/image_grad.py
@@ -41,12 +41,10 @@ def _ResizeNearestNeighborGrad(op, grad):
   else:
     image_shape = array_ops.shape(image)[1:3]
 
-  # pylint: disable=protected-access
-  grads = gen_image_ops._resize_nearest_neighbor_grad(
+  grads = gen_image_ops.resize_nearest_neighbor_grad(
       grad,
       image_shape,
       align_corners=op.get_attr("align_corners"))
-  # pylint: enable=protected-access
   return [grads, None]
 
 
@@ -61,10 +59,8 @@ def _ResizeBilinearGrad(op, grad):
   Returns:
     The gradients w.r.t. the input.
   """
-  # pylint: disable=protected-access
-  grad0 = gen_image_ops._resize_bilinear_grad(
+  grad0 = gen_image_ops.resize_bilinear_grad(
       grad, op.inputs[0], align_corners=op.get_attr("align_corners"))
-  # pylint: enable=protected-access
   return [grad0, None]
 
 
@@ -82,10 +78,8 @@ def _ResizeBicubicGrad(op, grad):
   allowed_types = [dtypes.float32, dtypes.float64]
   grad0 = None
   if op.inputs[0].dtype in allowed_types:
-    # pylint: disable=protected-access
-    grad0 = gen_image_ops._resize_bicubic_grad(
+    grad0 = gen_image_ops.resize_bicubic_grad(
         grad, op.inputs[0], align_corners=op.get_attr("align_corners"))
-    # pylint: enable=protected-access
   return [grad0, None]
 
 
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index 53bd108c44..ca8806a095 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -1115,10 +1115,8 @@ def adjust_contrast(images, contrast_factor):
     orig_dtype = images.dtype
     flt_images = convert_image_dtype(images, dtypes.float32)
 
-    # pylint: disable=protected-access
-    adjusted = gen_image_ops._adjust_contrastv2(
+    adjusted = gen_image_ops.adjust_contrastv2(
         flt_images, contrast_factor=contrast_factor, name=name)
-    # pylint: enable=protected-access
 
     return convert_image_dtype(adjusted, orig_dtype, saturate=True)
 
@@ -1732,7 +1730,7 @@ def sample_distorted_bounding_box(image_size,
       Provide as input to `tf.image.draw_bounding_boxes`.
   """
   with ops.name_scope(name, 'sample_distorted_bounding_box'):
-    return gen_image_ops._sample_distorted_bounding_box_v2(  # pylint: disable=protected-access
+    return gen_image_ops.sample_distorted_bounding_box_v2(
         image_size,
         bounding_boxes,
         seed=seed,
@@ -1786,10 +1784,8 @@ def non_max_suppression(boxes,
   """
   with ops.name_scope(name, 'non_max_suppression'):
     iou_threshold = ops.convert_to_tensor(iou_threshold, name='iou_threshold')
-    # pylint: disable=protected-access
-    return gen_image_ops._non_max_suppression_v2(boxes, scores, max_output_size,
-                                                 iou_threshold)
-    # pylint: enable=protected-access
+    return gen_image_ops.non_max_suppression_v2(boxes, scores, max_output_size,
+                                                iou_threshold)
 
 
 _rgb_to_yiq_kernel = [[0.299, 0.59590059,
diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py
index 5e70b3186f..7c782c12a5 100644
--- a/tensorflow/python/ops/io_ops.py
+++ b/tensorflow/python/ops/io_ops.py
@@ -111,10 +111,10 @@ def _save(filename, tensor_names, tensors, tensor_slices=None, name="save"):
     An Operation that saves the tensors.
   """
   if tensor_slices is None:
-    return gen_io_ops._save(filename, tensor_names, tensors, name=name)
+    return gen_io_ops.save(filename, tensor_names, tensors, name=name)
   else:
-    return gen_io_ops._save_slices(filename, tensor_names, tensor_slices,
-                                   tensors, name=name)
+    return gen_io_ops.save_slices(filename, tensor_names, tensor_slices,
+                                  tensors, name=name)
 
 
 def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type,
@@ -136,7 +136,7 @@ def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type,
     A tensor of type "tensor_type".
   """
   base_type = dtypes.as_dtype(tensor_type).base_dtype
-  return gen_io_ops._restore_slice(
+  return gen_io_ops.restore_slice(
       file_pattern, tensor_name, shape_and_slice, base_type,
       preferred_shard, name=name)
 
@@ -208,12 +208,12 @@ class ReaderBase(object):
     else:
       queue_ref = queue.queue_ref
     if self._reader_ref.dtype == dtypes.resource:
-      return gen_io_ops._reader_read_v2(self._reader_ref, queue_ref, name=name)
+      return gen_io_ops.reader_read_v2(self._reader_ref, queue_ref, name=name)
     else:
       # For compatibility with pre-resource queues, create a ref(string) tensor
       # which can be looked up as the same queue by a resource manager.
-      old_queue_op = gen_data_flow_ops._fake_queue(queue_ref)
-      return gen_io_ops._reader_read(self._reader_ref, old_queue_op, name=name)
+      old_queue_op = gen_data_flow_ops.fake_queue(queue_ref)
+      return gen_io_ops.reader_read(self._reader_ref, old_queue_op, name=name)
 
   def read_up_to(self, queue, num_records,  # pylint: disable=invalid-name
                  name=None):
@@ -240,18 +240,18 @@ class ReaderBase(object):
     else:
       queue_ref = queue.queue_ref
     if self._reader_ref.dtype == dtypes.resource:
-      return gen_io_ops._reader_read_up_to_v2(self._reader_ref,
-                                              queue_ref,
-                                              num_records,
-                                              name=name)
+      return gen_io_ops.reader_read_up_to_v2(self._reader_ref,
+                                             queue_ref,
+                                             num_records,
+                                             name=name)
     else:
       # For compatibility with pre-resource queues, create a ref(string) tensor
       # which can be looked up as the same queue by a resource manager.
-      old_queue_op = gen_data_flow_ops._fake_queue(queue_ref)
-      return gen_io_ops._reader_read_up_to(self._reader_ref,
-                                           old_queue_op,
-                                           num_records,
-                                           name=name)
+      old_queue_op = gen_data_flow_ops.fake_queue(queue_ref)
+      return gen_io_ops.reader_read_up_to(self._reader_ref,
+                                          old_queue_op,
+                                          num_records,
+                                          name=name)
 
   def num_records_produced(self, name=None):
     """Returns the number of records this reader has produced.
@@ -267,11 +267,11 @@ class ReaderBase(object):
 
     """
     if self._reader_ref.dtype == dtypes.resource:
-      return gen_io_ops._reader_num_records_produced_v2(self._reader_ref,
-                                                        name=name)
+      return gen_io_ops.reader_num_records_produced_v2(self._reader_ref,
+                                                       name=name)
     else:
-      return gen_io_ops._reader_num_records_produced(self._reader_ref,
-                                                     name=name)
+      return gen_io_ops.reader_num_records_produced(self._reader_ref,
+                                                    name=name)
 
   def num_work_units_completed(self, name=None):
     """Returns the number of work units this reader has finished processing.
@@ -283,11 +283,11 @@ class ReaderBase(object):
       An int64 Tensor.
     """
     if self._reader_ref.dtype == dtypes.resource:
-      return gen_io_ops._reader_num_work_units_completed_v2(self._reader_ref,
-                                                            name=name)
+      return gen_io_ops.reader_num_work_units_completed_v2(self._reader_ref,
+                                                           name=name)
     else:
-      return gen_io_ops._reader_num_work_units_completed(self._reader_ref,
-                                                         name=name)
+      return gen_io_ops.reader_num_work_units_completed(self._reader_ref,
+                                                        name=name)
 
   def serialize_state(self, name=None):
     """Produce a string tensor that encodes the state of a reader.
@@ -302,9 +302,9 @@ class ReaderBase(object):
       A string Tensor.
     """
     if self._reader_ref.dtype == dtypes.resource:
-      return gen_io_ops._reader_serialize_state_v2(self._reader_ref, name=name)
+      return gen_io_ops.reader_serialize_state_v2(self._reader_ref, name=name)
     else:
-      return gen_io_ops._reader_serialize_state(self._reader_ref, name=name)
+      return gen_io_ops.reader_serialize_state(self._reader_ref, name=name)
 
   def restore_state(self, state, name=None):
     """Restore a reader to a previously saved state.
@@ -321,11 +321,10 @@ class ReaderBase(object):
       The created Operation.
     """
     if self._reader_ref.dtype == dtypes.resource:
-      return gen_io_ops._reader_restore_state_v2(
+      return gen_io_ops.reader_restore_state_v2(
           self._reader_ref, state, name=name)
     else:
-      return gen_io_ops._reader_restore_state(
-          self._reader_ref, state, name=name)
+      return gen_io_ops.reader_restore_state(self._reader_ref, state, name=name)
 
   @property
   def supports_serialize(self):
@@ -342,9 +341,9 @@ class ReaderBase(object):
       The created Operation.
     """
     if self._reader_ref.dtype == dtypes.resource:
-      return gen_io_ops._reader_reset_v2(self._reader_ref, name=name)
+      return gen_io_ops.reader_reset_v2(self._reader_ref, name=name)
     else:
-      return gen_io_ops._reader_reset(self._reader_ref, name=name)
+      return gen_io_ops.reader_reset(self._reader_ref, name=name)
 
 
 ops.NotDifferentiable("ReaderRead")
@@ -377,7 +376,7 @@ class WholeFileReader(ReaderBase):
     Args:
       name: A name for the operation (optional).
     """
-    rr = gen_io_ops._whole_file_reader_v2(name=name)
+    rr = gen_io_ops.whole_file_reader_v2(name=name)
     super(WholeFileReader, self).__init__(rr, supports_serialize=True)
 
 
@@ -406,8 +405,8 @@ class TextLineReader(ReaderBase):
         to skip from the beginning of every file.
       name: A name for the operation (optional).
     """
-    rr = gen_io_ops._text_line_reader_v2(skip_header_lines=skip_header_lines,
-                                         name=name)
+    rr = gen_io_ops.text_line_reader_v2(skip_header_lines=skip_header_lines,
+                                        name=name)
     super(TextLineReader, self).__init__(rr)
 
 
@@ -444,7 +443,7 @@ class FixedLengthRecordReader(ReaderBase):
       name: A name for the operation (optional).
       encoding: The type of encoding for the file. Defaults to none.
     """
-    rr = gen_io_ops._fixed_length_record_reader_v2(
+    rr = gen_io_ops.fixed_length_record_reader_v2(
         record_bytes=record_bytes,
         header_bytes=header_bytes,
         footer_bytes=footer_bytes,
@@ -480,7 +479,7 @@ class TFRecordReader(ReaderBase):
     compression_type = python_io.TFRecordOptions.get_compression_type_string(
         options)
 
-    rr = gen_io_ops._tf_record_reader_v2(
+    rr = gen_io_ops.tf_record_reader_v2(
         name=name, compression_type=compression_type)
     super(TFRecordReader, self).__init__(rr)
 
@@ -506,7 +505,7 @@ class LMDBReader(ReaderBase):
       name: A name for the operation (optional).
       options: A LMDBRecordOptions object (optional).
     """
-    rr = gen_io_ops._lmdb_reader(name=name)
+    rr = gen_io_ops.lmdb_reader(name=name)
     super(LMDBReader, self).__init__(rr)
 
 
@@ -534,7 +533,7 @@ class IdentityReader(ReaderBase):
     Args:
       name: A name for the operation (optional).
     """
-    rr = gen_io_ops._identity_reader_v2(name=name)
+    rr = gen_io_ops.identity_reader_v2(name=name)
     super(IdentityReader, self).__init__(rr, supports_serialize=True)
 
 
diff --git a/tensorflow/python/ops/linalg/linalg_impl.py b/tensorflow/python/ops/linalg/linalg_impl.py
index d5bd916f80..2be2d5a3d4 100644
--- a/tensorflow/python/ops/linalg/linalg_impl.py
+++ b/tensorflow/python/ops/linalg/linalg_impl.py
@@ -31,18 +31,16 @@ band_part = array_ops.matrix_band_part
 cholesky = linalg_ops.cholesky
 cholesky_solve = linalg_ops.cholesky_solve
 det = linalg_ops.matrix_determinant
-# pylint: disable=protected-access
-slogdet = gen_linalg_ops._log_matrix_determinant
-# pylint: disable=protected-access
+slogdet = gen_linalg_ops.log_matrix_determinant
 diag = array_ops.matrix_diag
 diag_part = array_ops.matrix_diag_part
 eigh = linalg_ops.self_adjoint_eig
 eigvalsh = linalg_ops.self_adjoint_eigvals
 einsum = special_math_ops.einsum
-expm = gen_linalg_ops._matrix_exponential
+expm = gen_linalg_ops.matrix_exponential
 eye = linalg_ops.eye
 inv = linalg_ops.matrix_inverse
-logm = gen_linalg_ops._matrix_logarithm
+logm = gen_linalg_ops.matrix_logarithm
 lstsq = linalg_ops.matrix_solve_ls
 norm = linalg_ops.norm
 qr = linalg_ops.qr
diff --git a/tensorflow/python/ops/linalg_ops.py b/tensorflow/python/ops/linalg_ops.py
index 9803eed6ae..37470e00d7 100644
--- a/tensorflow/python/ops/linalg_ops.py
+++ b/tensorflow/python/ops/linalg_ops.py
@@ -248,7 +248,7 @@ def matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None):
     and l2_regularizer != 0 due to poor accuracy.
   """
 
-  # pylint: disable=protected-access,long-lambda
+  # pylint: disable=long-lambda
   def _use_composite_impl(fast, tensor_shape):
     """Determines whether to use the composite or specialized CPU kernel.
 
@@ -323,9 +323,8 @@ def matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None):
   if _use_composite_impl(fast, tensor_shape):
     return _composite_impl(matrix, rhs, l2_regularizer)
   else:
-    return gen_linalg_ops._matrix_solve_ls(
+    return gen_linalg_ops.matrix_solve_ls(
         matrix, rhs, l2_regularizer, fast=fast, name=name)
-  # pylint: enable=protected-access
 
 
 @tf_export('self_adjoint_eig', 'linalg.eigh')
@@ -346,8 +345,7 @@ def self_adjoint_eig(tensor, name=None):
     v: Eigenvectors. Shape is `[..., N, N]`. The columns of the inner most
       matrices contain eigenvectors of the corresponding matrices in `tensor`
   """
-  # pylint: disable=protected-access
-  e, v = gen_linalg_ops._self_adjoint_eig_v2(tensor, compute_v=True, name=name)
+  e, v = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=True, name=name)
   return e, v
 
 
@@ -369,8 +367,7 @@ def self_adjoint_eigvals(tensor, name=None):
     e: Eigenvalues. Shape is `[..., N]`. The vector `e[..., :]` contains the `N`
       eigenvalues of `tensor[..., :, :]`.
   """
-  # pylint: disable=protected-access
-  e, _ = gen_linalg_ops._self_adjoint_eig_v2(tensor, compute_v=False, name=name)
+  e, _ = gen_linalg_ops.self_adjoint_eig_v2(tensor, compute_v=False, name=name)
   return e
 
 
@@ -435,10 +432,8 @@ def svd(tensor, full_matrices=False, compute_uv=True, name=None):
   ````
   @end_compatibility
   """
-  # pylint: disable=protected-access
-  s, u, v = gen_linalg_ops._svd(
+  s, u, v = gen_linalg_ops.svd(
       tensor, compute_uv=compute_uv, full_matrices=full_matrices, name=name)
-  # pylint: enable=protected-access
   if compute_uv:
     return math_ops.real(s), u, v
   else:
diff --git a/tensorflow/python/ops/logging_ops.py b/tensorflow/python/ops/logging_ops.py
index 3757109c95..a7ea7dc6e1 100644
--- a/tensorflow/python/ops/logging_ops.py
+++ b/tensorflow/python/ops/logging_ops.py
@@ -170,7 +170,7 @@ def image_summary(tag, tensor, max_images=3, collections=None, name=None):
     buffer.
   """
   with ops.name_scope(name, "ImageSummary", [tag, tensor]) as scope:
-    val = gen_logging_ops._image_summary(
+    val = gen_logging_ops.image_summary(
         tag=tag, tensor=tensor, max_images=max_images, name=scope)
     _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
   return val
@@ -226,11 +226,12 @@ def audio_summary(tag,
   with ops.name_scope(name, "AudioSummary", [tag, tensor]) as scope:
     sample_rate = ops.convert_to_tensor(sample_rate, dtype=dtypes.float32,
                                         name="sample_rate")
-    val = gen_logging_ops._audio_summary_v2(tag=tag,
-                                            tensor=tensor,
-                                            max_outputs=max_outputs,
-                                            sample_rate=sample_rate,
-                                            name=scope)
+    val = gen_logging_ops.audio_summary_v2(
+        tag=tag,
+        tensor=tensor,
+        max_outputs=max_outputs,
+        sample_rate=sample_rate,
+        name=scope)
     _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
   return val
 
@@ -263,7 +264,7 @@ def merge_summary(inputs, collections=None, name=None):
     buffer resulting from the merging.
   """
   with ops.name_scope(name, "MergeSummary", inputs):
-    val = gen_logging_ops._merge_summary(inputs=inputs, name=name)
+    val = gen_logging_ops.merge_summary(inputs=inputs, name=name)
     _Collect(val, collections, [])
   return val
 
diff --git a/tensorflow/python/ops/lookup_ops.py b/tensorflow/python/ops/lookup_ops.py
index f539a7bb68..baf7cc19fa 100644
--- a/tensorflow/python/ops/lookup_ops.py
+++ b/tensorflow/python/ops/lookup_ops.py
@@ -196,9 +196,7 @@ class InitializableLookupTableBase(LookupInterface):
     """
     with ops.name_scope(name, "%s_Size" % self._name,
                         [self._table_ref]) as scope:
-      # pylint: disable=protected-access
-      return gen_lookup_ops._lookup_table_size_v2(self._table_ref, name=scope)
-      # pylint: enable=protected-access
+      return gen_lookup_ops.lookup_table_size_v2(self._table_ref, name=scope)
 
   def lookup(self, keys, name=None):
     """Looks up `keys` in a table, outputs the corresponding values.
@@ -227,10 +225,8 @@ class InitializableLookupTableBase(LookupInterface):
     with ops.name_scope(name, "%s_Lookup" % self._name,
                         (self._table_ref, key_tensor,
                          self._default_value)) as scope:
-      # pylint: disable=protected-access
-      values = gen_lookup_ops._lookup_table_find_v2(
+      values = gen_lookup_ops.lookup_table_find_v2(
           self._table_ref, key_tensor, self._default_value, name=scope)
-      # pylint: enable=protected-access
 
     values.set_shape(key_tensor.get_shape())
     if isinstance(keys, sparse_tensor.SparseTensor):
@@ -274,13 +270,11 @@ class HashTable(InitializableLookupTableBase):
     """
     with ops.name_scope(name, "hash_table", (initializer,
                                              default_value)) as scope:
-      # pylint: disable=protected-access
-      table_ref = gen_lookup_ops._hash_table_v2(
+      table_ref = gen_lookup_ops.hash_table_v2(
           shared_name=shared_name,
           key_dtype=initializer.key_dtype,
           value_dtype=initializer.value_dtype,
           name=scope)
-      # pylint: enable=protected-access
 
       super(HashTable, self).__init__(table_ref, default_value, initializer)
 
@@ -352,10 +346,8 @@ class KeyValueTensorInitializer(TableInitializerBase):
     with ops.name_scope(
         self._name, values=(table.table_ref, self._keys,
                             self._values)) as scope:
-      # pylint: disable=protected-access
-      init_op = gen_lookup_ops._initialize_table_v2(
+      init_op = gen_lookup_ops.initialize_table_v2(
           table.table_ref, self._keys, self._values, name=scope)
-      # pylint: enable=protected-access
     ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op)
     return init_op
 
@@ -518,8 +510,7 @@ class TextFileInitializer(TableInitializerBase):
                         (table.table_ref,)) as scope:
       filename = ops.convert_to_tensor(
           self._filename, dtypes.string, name="asset_filepath")
-      # pylint: disable=protected-access
-      init_op = gen_lookup_ops._initialize_table_from_text_file_v2(
+      init_op = gen_lookup_ops.initialize_table_from_text_file_v2(
           table.table_ref,
           filename,
           self._key_index,
@@ -527,7 +518,6 @@ class TextFileInitializer(TableInitializerBase):
           -1 if self._vocab_size is None else self._vocab_size,
           self._delimiter,
           name=scope)
-      # pylint: enable=protected-access
     ops.add_to_collection(ops.GraphKeys.TABLE_INITIALIZERS, init_op)
     # If the filename tensor is anything other than a string constant (e.g., if
     # it is a placeholder) then it does not make sense to track it as an asset.
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index bf28f74153..51e19b4ad3 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -302,16 +302,14 @@ def _NegGrad(_, grad):
 def _InvGrad(op, grad):
   """Returns -grad * (1 / x^2)."""
   y = op.outputs[0]  # y = 1 / x
-  # pylint: disable=protected-access
-  return gen_math_ops._reciprocal_grad(y, grad)
+  return gen_math_ops.reciprocal_grad(y, grad)
 
 
 @ops.RegisterGradient("Reciprocal")
 def _ReciprocalGrad(op, grad):
   """Returns -grad * (1 / x^2)."""
   y = op.outputs[0]  # y = 1 / x
-  # pylint: disable=protected-access
-  return gen_math_ops._reciprocal_grad(y, grad)
+  return gen_math_ops.reciprocal_grad(y, grad)
 
 
 @ops.RegisterGradient("InvGrad")
@@ -321,8 +319,7 @@ def _InvGradGrad(op, grad):
   with ops.control_dependencies([grad]):
     ca = math_ops.conj(op.inputs[0])
     cg = math_ops.conj(grad)
-    # pylint: disable=protected-access
-    return cg * -2.0 * b * ca, gen_math_ops._reciprocal_grad(ca, grad)
+    return cg * -2.0 * b * ca, gen_math_ops.reciprocal_grad(ca, grad)
 
 
 @ops.RegisterGradient("ReciprocalGrad")
@@ -332,8 +329,7 @@ def _ReciprocalGradGrad(op, grad):
   with ops.control_dependencies([grad]):
     ca = math_ops.conj(op.inputs[0])
     cg = math_ops.conj(grad)
-    # pylint: disable=protected-access
-    return cg * -2.0 * b * ca, gen_math_ops._reciprocal_grad(ca, grad)
+    return cg * -2.0 * b * ca, gen_math_ops.reciprocal_grad(ca, grad)
 
 
 @ops.RegisterGradient("Square")
@@ -348,9 +344,7 @@ def _SquareGrad(op, grad):
 @ops.RegisterGradient("Sqrt")
 def _SqrtGrad(op, grad):
   y = op.outputs[0]  # y = x^(1/2)
-  # pylint: disable=protected-access
-  return gen_math_ops._sqrt_grad(y, grad)
-  # pylint: enable=protected-access
+  return gen_math_ops.sqrt_grad(y, grad)
 
 
 @ops.RegisterGradient("SqrtGrad")
@@ -366,9 +360,7 @@ def _SqrtGradGrad(op, grad):
 def _RsqrtGrad(op, grad):
   """Returns -0.5 * grad * conj(y)^3."""
   y = op.outputs[0]  # y = x^(-1/2)
-  # pylint: disable=protected-access
-  return gen_math_ops._rsqrt_grad(y, grad)
-  # pylint: enable=protected-access
+  return gen_math_ops.rsqrt_grad(y, grad)
 
 
 @ops.RegisterGradient("RsqrtGrad")
@@ -380,8 +372,7 @@ def _RsqrtGradGrad(op, grad):
     ca = math_ops.conj(a)
     cg = math_ops.conj(grad)
     grad_a = -1.5 * cg * b * math_ops.square(ca)
-    # pylint: disable=protected-access
-    grad_b = gen_math_ops._rsqrt_grad(ca, grad)
+    grad_b = gen_math_ops.rsqrt_grad(ca, grad)
     return grad_a, grad_b
 
 
@@ -446,8 +437,7 @@ def _TanhGrad(op, grad):
   y = op.outputs[0]  # y = tanh(x)
   with ops.control_dependencies([grad]):
     y = math_ops.conj(y)
-    # pylint: disable=protected-access
-    return gen_math_ops._tanh_grad(y, grad)
+    return gen_math_ops.tanh_grad(y, grad)
 
 
 @ops.RegisterGradient("Asinh")
@@ -485,8 +475,7 @@ def _TanhGradGrad(op, grad):
   with ops.control_dependencies([grad]):
     a = math_ops.conj(op.inputs[0])
     b = math_ops.conj(op.inputs[1])
-    # pylint: disable=protected-access
-    return grad * -2.0 * b * a, gen_math_ops._tanh_grad(a, grad)
+    return grad * -2.0 * b * a, gen_math_ops.tanh_grad(a, grad)
 
 
 @ops.RegisterGradient("Erf")
@@ -634,8 +623,7 @@ def _SigmoidGrad(op, grad):
   y = op.outputs[0]  # y = sigmoid(x)
   with ops.control_dependencies([grad]):
     y = math_ops.conj(y)
-    # pylint: disable=protected-access
-    return gen_math_ops._sigmoid_grad(y, grad)
+    return gen_math_ops.sigmoid_grad(y, grad)
 
 
 @ops.RegisterGradient("SigmoidGrad")
@@ -644,8 +632,7 @@ def _SigmoidGradGrad(op, grad):
     a = math_ops.conj(op.inputs[0])
     b = math_ops.conj(op.inputs[1])
     gb = grad * b
-    # pylint: disable=protected-access
-    return gb - 2.0 * gb * a, gen_math_ops._sigmoid_grad(a, grad)
+    return gb - 2.0 * gb * a, gen_math_ops.sigmoid_grad(a, grad)
 
 
 @ops.RegisterGradient("Sign")
@@ -792,7 +779,7 @@ def _MulGrad(op, grad):
   if (isinstance(grad, ops.Tensor) and
       _ShapesFullySpecifiedAndEqual(x, y, grad) and
       grad.dtype in (dtypes.int32, dtypes.float32)):
-    return gen_math_ops._mul(grad, y), gen_math_ops._mul(grad, x)
+    return gen_math_ops.mul(grad, y), gen_math_ops.mul(grad, x)
   assert x.dtype.base_dtype == y.dtype.base_dtype, (x.dtype, " vs. ", y.dtype)
   sx = array_ops.shape(x)
   sy = array_ops.shape(y)
@@ -800,9 +787,9 @@ def _MulGrad(op, grad):
   x = math_ops.conj(x)
   y = math_ops.conj(y)
   return (array_ops.reshape(
-      math_ops.reduce_sum(gen_math_ops._mul(grad, y), rx), sx),
+      math_ops.reduce_sum(gen_math_ops.mul(grad, y), rx), sx),
           array_ops.reshape(
-              math_ops.reduce_sum(gen_math_ops._mul(x, grad), ry), sy))
+              math_ops.reduce_sum(gen_math_ops.mul(x, grad), ry), sy))
   # pylint: enable=protected-access
 
 
@@ -976,20 +963,18 @@ def _MatMulGrad(op, grad):
   t_b = op.get_attr("transpose_b")
   a = math_ops.conj(op.inputs[0])
   b = math_ops.conj(op.inputs[1])
-  # pylint: disable=protected-access
   if not t_a and not t_b:
-    grad_a = gen_math_ops._mat_mul(grad, b, transpose_b=True)
-    grad_b = gen_math_ops._mat_mul(a, grad, transpose_a=True)
+    grad_a = gen_math_ops.mat_mul(grad, b, transpose_b=True)
+    grad_b = gen_math_ops.mat_mul(a, grad, transpose_a=True)
   elif not t_a and t_b:
-    grad_a = gen_math_ops._mat_mul(grad, b)
-    grad_b = gen_math_ops._mat_mul(grad, a, transpose_a=True)
+    grad_a = gen_math_ops.mat_mul(grad, b)
+    grad_b = gen_math_ops.mat_mul(grad, a, transpose_a=True)
   elif t_a and not t_b:
-    grad_a = gen_math_ops._mat_mul(b, grad, transpose_b=True)
-    grad_b = gen_math_ops._mat_mul(a, grad)
+    grad_a = gen_math_ops.mat_mul(b, grad, transpose_b=True)
+    grad_b = gen_math_ops.mat_mul(a, grad)
   elif t_a and t_b:
-    grad_a = gen_math_ops._mat_mul(b, grad, transpose_a=True, transpose_b=True)
-    grad_b = gen_math_ops._mat_mul(grad, a, transpose_a=True, transpose_b=True)
-  # pylint: enable=protected-access
+    grad_a = gen_math_ops.mat_mul(b, grad, transpose_a=True, transpose_b=True)
+    grad_b = gen_math_ops.mat_mul(grad, a, transpose_a=True, transpose_b=True)
   return grad_a, grad_b
 
 
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index c3899c7e12..14d6862919 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -89,8 +89,6 @@ See the @{$python/math_ops} guide.
 @@matrix_inverse
 @@cholesky
 @@cholesky_solve
-@@matrix_exponential
-@@matrix_logarithm
 @@matrix_solve
 @@matrix_triangular_solve
 @@matrix_solve_ls
@@ -260,7 +258,7 @@ def abs(x, name=None):  # pylint: disable=redefined-builtin
   with ops.name_scope(name, "Abs", [x]) as name:
     if isinstance(x, sparse_tensor.SparseTensor):
       if x.values.dtype.is_complex:
-        x_abs = gen_math_ops._complex_abs(
+        x_abs = gen_math_ops.complex_abs(
             x.values, Tout=x.values.dtype.real_dtype, name=name)
         return sparse_tensor.SparseTensor(
             indices=x.indices, values=x_abs, dense_shape=x.dense_shape)
@@ -270,7 +268,7 @@ def abs(x, name=None):  # pylint: disable=redefined-builtin
     else:
       x = ops.convert_to_tensor(x, name="x")
       if x.dtype.is_complex:
-        return gen_math_ops._complex_abs(x, Tout=x.dtype.real_dtype, name=name)
+        return gen_math_ops.complex_abs(x, Tout=x.dtype.real_dtype, name=name)
       return gen_math_ops._abs(x, name=name)
 
 
@@ -279,7 +277,7 @@ def abs(x, name=None):  # pylint: disable=redefined-builtin
 
 # pylint: disable=redefined-builtin
 def _bucketize(input, boundaries, name=None):
-  return gen_math_ops._bucketize(input=input, boundaries=boundaries, name=name)
+  return gen_math_ops.bucketize(input=input, boundaries=boundaries, name=name)
 
 
 # pylint: enable=redefined-builtin
@@ -322,10 +320,10 @@ def divide(x, y, name=None):
 
 @tf_export("multiply")
 def multiply(x, y, name=None):
-  return gen_math_ops._mul(x, y, name)
+  return gen_math_ops.mul(x, y, name)
 
 
-multiply.__doc__ = gen_math_ops._mul.__doc__.replace("Mul", "`tf.multiply`")
+multiply.__doc__ = gen_math_ops.mul.__doc__.replace("Mul", "`tf.multiply`")
 
 
 # TODO(aselle): put deprecation in after another round of global code changes
@@ -333,19 +331,19 @@ multiply.__doc__ = gen_math_ops._mul.__doc__.replace("Mul", "`tf.multiply`")
     "2016-12-30",
     "`tf.mul(x, y)` is deprecated, please use `tf.multiply(x, y)` or `x * y`")
 def _mul(x, y, name=None):
-  return gen_math_ops._mul(x, y, name)
+  return gen_math_ops.mul(x, y, name)
 
 
 _mul.__doc__ = (
-    gen_math_ops._mul.__doc__ + ("" if _mul.__doc__ is None else _mul.__doc__))
+    gen_math_ops.mul.__doc__ + ("" if _mul.__doc__ is None else _mul.__doc__))
 
 
 @tf_export("subtract")
 def subtract(x, y, name=None):
-  return gen_math_ops._sub(x, y, name)
+  return gen_math_ops.sub(x, y, name)
 
 
-subtract.__doc__ = gen_math_ops._sub.__doc__.replace("`Sub`", "`tf.subtract`")
+subtract.__doc__ = gen_math_ops.sub.__doc__.replace("`Sub`", "`tf.subtract`")
 
 
 # TODO(aselle): put deprecation in after another round of global code changes
@@ -353,11 +351,11 @@ subtract.__doc__ = gen_math_ops._sub.__doc__.replace("`Sub`", "`tf.subtract`")
     "2016-12-30",
     "`tf.sub(x, y)` is deprecated, please use `tf.subtract(x, y)` or `x - y`")
 def _sub(x, y, name=None):
-  return gen_math_ops._sub(x, y, name)
+  return gen_math_ops.sub(x, y, name)
 
 
 _sub.__doc__ = (
-    gen_math_ops._sub.__doc__ + ("" if _sub.__doc__ is None else _sub.__doc__))
+    gen_math_ops.sub.__doc__ + ("" if _sub.__doc__ is None else _sub.__doc__))
 
 
 # pylint: disable=g-docstring-has-escape
@@ -377,11 +375,11 @@ def negative(x, name=None):
   """
   with ops.name_scope(name, "Neg", [x]) as name:
     if isinstance(x, sparse_tensor.SparseTensor):
-      x_neg = gen_math_ops._neg(x.values, name=name)
+      x_neg = gen_math_ops.neg(x.values, name=name)
       return sparse_tensor.SparseTensor(
           indices=x.indices, values=x_neg, dense_shape=x.dense_shape)
     else:
-      return gen_math_ops._neg(x, name=name)
+      return gen_math_ops.neg(x, name=name)
 
 
 # pylint: enable=g-docstring-has-escape
@@ -895,7 +893,7 @@ def to_bfloat16(x, name="ToBFloat16"):
   return cast(x, dtypes.bfloat16, name=name)
 
 
-ops.Tensor._override_operator("__neg__", gen_math_ops._neg)
+ops.Tensor._override_operator("__neg__", gen_math_ops.neg)
 ops.Tensor._override_operator("__abs__", abs)
 # __invert__ corresponds to the ~ operator.  Here we follow the numpy convention
 # ~ marks an elementwise bit-wise inverse.  This is only implemented for boolean
@@ -1024,7 +1022,7 @@ def _truediv_python3(x, y, name=None):
     if dtype is not None:
       x = cast(x, dtype)
       y = cast(y, dtype)
-    return gen_math_ops._real_div(x, y, name=name)
+    return gen_math_ops.real_div(x, y, name=name)
 
 
 def _div_python2(x, y, name=None):
@@ -1047,9 +1045,9 @@ def _div_python2(x, y, name=None):
       raise TypeError("x and y must have the same dtype, got %r != %r" %
                       (x_dtype, y_dtype))
     if x_dtype.is_floating or x_dtype.is_complex:
-      return gen_math_ops._real_div(x, y, name=name)
+      return gen_math_ops.real_div(x, y, name=name)
     else:
-      return gen_math_ops._floor_div(x, y, name=name)
+      return gen_math_ops.floor_div(x, y, name=name)
 
 
 @tf_export("truediv")
@@ -1107,7 +1105,7 @@ def div(x, y, name=None):
 
 
 # TODO(aselle): This should be removed
-mod = gen_math_ops._floor_mod
+mod = gen_math_ops.floor_mod
 
 
 # TODO(aselle): Deprecate this once all internal functionality uses
@@ -1140,22 +1138,22 @@ def floordiv(x, y, name=None):
     TypeError: If the inputs are complex.
   """
   with ops.name_scope(name, "floordiv", [x, y]) as name:
-    return gen_math_ops._floor_div(x, y, name=name)
+    return gen_math_ops.floor_div(x, y, name=name)
 
 
-realdiv = gen_math_ops._real_div
-truncatediv = gen_math_ops._truncate_div
+realdiv = gen_math_ops.real_div
+truncatediv = gen_math_ops.truncate_div
 # TODO(aselle): Rename this to floordiv when we can.
-floor_div = gen_math_ops._floor_div
-truncatemod = gen_math_ops._truncate_mod
-floormod = gen_math_ops._floor_mod
+floor_div = gen_math_ops.floor_div
+truncatemod = gen_math_ops.truncate_mod
+floormod = gen_math_ops.floor_mod
 
 
 def _mul_dispatch(x, y, name=None):
   """Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse"."""
   is_tensor_y = isinstance(y, ops.Tensor)
   if is_tensor_y:
-    return gen_math_ops._mul(x, y, name=name)
+    return gen_math_ops.mul(x, y, name=name)
   else:
     assert isinstance(y, sparse_tensor.SparseTensor)  # Case: Dense * Sparse.
     new_vals = gen_sparse_ops.sparse_dense_cwise_mul(y.indices, y.values,
@@ -1174,12 +1172,12 @@ _OverrideBinaryOperatorHelper(gen_sparse_ops.sparse_dense_cwise_mul, "mul",
                               sparse_tensor.SparseTensor)
 
 _OverrideBinaryOperatorHelper(gen_math_ops.add, "add")
-_OverrideBinaryOperatorHelper(gen_math_ops._sub, "sub")
+_OverrideBinaryOperatorHelper(gen_math_ops.sub, "sub")
 _OverrideBinaryOperatorHelper(_mul_dispatch, "mul")
 _OverrideBinaryOperatorHelper(_div_python2, "div")
 _OverrideBinaryOperatorHelper(_truediv_python3, "truediv")
 _OverrideBinaryOperatorHelper(floordiv, "floordiv")
-_OverrideBinaryOperatorHelper(gen_math_ops._floor_mod, "mod")
+_OverrideBinaryOperatorHelper(gen_math_ops.floor_mod, "mod")
 _OverrideBinaryOperatorHelper(pow, "pow")
 
 
@@ -1501,7 +1499,7 @@ def reduce_mean(input_tensor,
   if keepdims is None:
     keepdims = False
   return _may_reduce_to_scalar(keepdims, axis, reduction_indices,
-                               gen_math_ops._mean(
+                               gen_math_ops.mean(
                                    input_tensor,
                                    _ReductionDims(input_tensor, axis,
                                                   reduction_indices),
@@ -1551,7 +1549,7 @@ def reduce_prod(input_tensor,
   if keepdims is None:
     keepdims = False
   return _may_reduce_to_scalar(keepdims, axis, reduction_indices,
-                               gen_math_ops._prod(
+                               gen_math_ops.prod(
                                    input_tensor,
                                    _ReductionDims(input_tensor, axis,
                                                   reduction_indices),
@@ -2020,7 +2018,7 @@ def matmul(a,
       if transpose_b:
         b = conj(b)
         adjoint_b = True
-      return gen_math_ops._batch_mat_mul(
+      return gen_math_ops.batch_mat_mul(
           a, b, adj_x=adjoint_a, adj_y=adjoint_b, name=name)
 
     # Neither matmul nor sparse_matmul support adjoint, so we conjugate
@@ -2057,13 +2055,13 @@ def matmul(a,
         ret = cast(ret, dtypes.bfloat16)
       return ret
     else:
-      return gen_math_ops._mat_mul(
+      return gen_math_ops.mat_mul(
           a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
 
 
 _OverrideBinaryOperatorHelper(matmul, "matmul")
 
-sparse_matmul = gen_math_ops._sparse_mat_mul
+sparse_matmul = gen_math_ops.sparse_mat_mul
 
 
 @ops.RegisterStatistics("MatMul", "flops")
@@ -2168,7 +2166,7 @@ def add_n(inputs, name=None):
     if name:
       return array_ops.identity(inputs[0], name=name)
     return inputs[0]
-  return gen_math_ops._add_n(inputs, name=name)
+  return gen_math_ops.add_n(inputs, name=name)
 
 
 @tf_export("accumulate_n")
@@ -2246,7 +2244,7 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None):
     # addressed
     return add_n(inputs, name=name)
   else:
-    return gen_math_ops._accumulate_nv2(inputs, name=name, shape=shape)  # pylint: disable=protected-access
+    return gen_math_ops.accumulate_nv2(inputs, name=name, shape=shape)  # pylint: disable=protected-access
 
 
 @ops.RegisterGradient("AccumulateNV2")
@@ -2276,7 +2274,7 @@ def sigmoid(x, name=None):
   """
   with ops.name_scope(name, "Sigmoid", [x]) as name:
     x = ops.convert_to_tensor(x, name="x")
-    return gen_math_ops._sigmoid(x, name=name)
+    return gen_math_ops.sigmoid(x, name=name)
 
 
 @tf_export("log_sigmoid")
@@ -2295,7 +2293,7 @@ def log_sigmoid(x, name=None):
   """
   with ops.name_scope(name, "LogSigmoid", [x]) as name:
     x = ops.convert_to_tensor(x, name="x")
-    return gen_math_ops._neg(gen_nn_ops.softplus(-x), name=name)
+    return gen_math_ops.neg(gen_nn_ops.softplus(-x), name=name)
 
 
 @tf_export("nn.tanh", "tanh")
@@ -2312,11 +2310,11 @@ def tanh(x, name=None):
   """
   with ops.name_scope(name, "Tanh", [x]) as name:
     if isinstance(x, sparse_tensor.SparseTensor):
-      x_tanh = gen_math_ops._tanh(x.values, name=name)
+      x_tanh = gen_math_ops.tanh(x.values, name=name)
       return sparse_tensor.SparseTensor(
           indices=x.indices, values=x_tanh, dense_shape=x.dense_shape)
     else:
-      return gen_math_ops._tanh(x, name=name)
+      return gen_math_ops.tanh(x, name=name)
 
 
 @tf_export("bincount")
@@ -2505,7 +2503,7 @@ def conj(x, name=None):
   with ops.name_scope(name, "Conj", [x]) as name:
     x = ops.convert_to_tensor(x, name="x")
     if x.dtype.is_complex or x.dtype == dtypes.variant:
-      return gen_math_ops._conj(x, name=name)
+      return gen_math_ops.conj(x, name=name)
     elif x.dtype.is_floating or x.dtype.is_integer:
       return x
     else:
diff --git a/tensorflow/python/ops/nn_batchnorm_test.py b/tensorflow/python/ops/nn_batchnorm_test.py
index eebfb17085..3ac2c8eb17 100644
--- a/tensorflow/python/ops/nn_batchnorm_test.py
+++ b/tensorflow/python/ops/nn_batchnorm_test.py
@@ -57,7 +57,6 @@ class BatchNormalizationTest(test.TestCase):
     test_util.set_producer_version(ops.get_default_graph(), 8)
     return gen_nn_ops._batch_norm_with_global_normalization(
         x, m, v, beta, gamma, epsilon, scale_after_normalization)
-    # pylint: enable=protected-access
 
   def _tfBatchNormV1BW(self, x, m, v, beta, gamma, epsilon,
                        scale_after_normalization):
@@ -223,7 +222,7 @@ class BatchNormalizationTest(test.TestCase):
         for scale_after_normalization in [True, False]:
           # _batch_norm_with_global_normalization_grad is deprecated in v9
           test_util.set_producer_version(ops.get_default_graph(), 8)
-          grad = gen_nn_ops._batch_norm_with_global_normalization_grad(
+          grad = gen_nn_ops.batch_norm_with_global_normalization_grad(
               x, m, v, gamma, backprop, epsilon, scale_after_normalization)
           dx, dm, dv, db, dg = grad
           self.assertEqual(grad.dx, dx)
diff --git a/tensorflow/python/ops/nn_grad.py b/tensorflow/python/ops/nn_grad.py
index dc24b821a5..5582daf2da 100644
--- a/tensorflow/python/ops/nn_grad.py
+++ b/tensorflow/python/ops/nn_grad.py
@@ -150,7 +150,7 @@ def _Conv3DBackpropFilterGrad(op, grad):
 
 @ops.RegisterGradient("AvgPool3D")
 def _AvgPool3DGrad(op, grad):
-  return gen_nn_ops._avg_pool3d_grad(
+  return gen_nn_ops.avg_pool3d_grad(
       array_ops.shape(op.inputs[0]),
       grad,
       ksize=op.get_attr("ksize"),
@@ -172,7 +172,7 @@ def _AvgPool3DGradGrad(op, grad):
 
 @ops.RegisterGradient("MaxPool3D")
 def _MaxPool3DGrad(op, grad):
-  return gen_nn_ops._max_pool3d_grad(
+  return gen_nn_ops.max_pool3d_grad(
       op.inputs[0],
       op.outputs[0],
       grad,
@@ -188,7 +188,7 @@ def _MaxPool3DGradGrad(op, grad):
       shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype),
           array_ops.zeros(
               shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype),
-          gen_nn_ops._max_pool3d_grad_grad(
+          gen_nn_ops.max_pool3d_grad_grad(
               op.inputs[0],
               op.inputs[1],
               grad,
@@ -204,7 +204,7 @@ def _MaxPool3DGradGradGrad(op, grad):
       shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype),
           array_ops.zeros(
               shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype),
-          gen_nn_ops._max_pool3d_grad(
+          gen_nn_ops.max_pool3d_grad(
               op.inputs[0],
               op.inputs[1],
               grad,
@@ -352,13 +352,13 @@ def _BiasAddGradV1(unused_bias_op, received_grad):
 
 @ops.RegisterGradient("Relu")
 def _ReluGrad(op, grad):
-  return gen_nn_ops._relu_grad(grad, op.outputs[0])
+  return gen_nn_ops.relu_grad(grad, op.outputs[0])
 
 
 @ops.RegisterGradient("EluGrad")
 def _EluGradGrad(op, grad):
   elu_x = op.inputs[1]
-  return (gen_nn_ops._elu_grad(grad, op.outputs[0]),
+  return (gen_nn_ops.elu_grad(grad, op.outputs[0]),
           array_ops.where(elu_x < 0, grad * op.inputs[0],
                           array_ops.zeros(
                               shape=array_ops.shape(elu_x), dtype=elu_x.dtype)))
@@ -368,63 +368,63 @@ def _EluGradGrad(op, grad):
 def _SeluGradGrad(op, grad):
   x = op.inputs[1]
   scale_alpha = 1.7580993408473768599402175208123
-  return (gen_nn_ops._elu_grad(grad, op.outputs[0]),
+  return (gen_nn_ops.elu_grad(grad, op.outputs[0]),
           array_ops.where(x < 0.,
-                          gen_nn_ops._elu_grad(grad,
-                                               op.outputs[0] + scale_alpha),
+                          gen_nn_ops.elu_grad(grad,
+                                              op.outputs[0] + scale_alpha),
                           array_ops.zeros(
                               shape=array_ops.shape(x), dtype=x.dtype)))
 
 
 @ops.RegisterGradient("Relu6")
 def _Relu6Grad(op, grad):
-  return gen_nn_ops._relu6_grad(grad, op.outputs[0])  # pylint: disable=protected-access
+  return gen_nn_ops.relu6_grad(grad, op.outputs[0])
 
 
 @ops.RegisterGradient("Relu6Grad")
 def _Relu6GradGrad(op, grad):
   x = op.inputs[1]
-  return (gen_nn_ops._relu6_grad(grad, x),
+  return (gen_nn_ops.relu6_grad(grad, x),
           array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype))
 
 
 @ops.RegisterGradient("Elu")
 def _EluGrad(op, grad):
-  return gen_nn_ops._elu_grad(grad, op.outputs[0])
+  return gen_nn_ops.elu_grad(grad, op.outputs[0])
 
 
 @ops.RegisterGradient("Selu")
 def _SeluGrad(op, grad):
-  return gen_nn_ops._selu_grad(grad, op.outputs[0])
+  return gen_nn_ops.selu_grad(grad, op.outputs[0])
 
 
 @ops.RegisterGradient("Softplus")
 def _SoftplusGrad(op, grad):
-  return gen_nn_ops._softplus_grad(grad, op.inputs[0])
+  return gen_nn_ops.softplus_grad(grad, op.inputs[0])
 
 
 @ops.RegisterGradient("SoftplusGrad")
 def _SoftplusGradGrad(op, grad):
   # Let:
   #   y = tf.nn.softplus(x)
-  #   dx = gen_nn_ops._softplus_grad(dy, x) = dy / (1 + exp(-x))
+  #   dx = gen_nn_ops.softplus_grad(dy, x) = dy / (1 + exp(-x))
   # This op computes (ddy, d2x) from op.inputs == [dy, x] and grad == ddx.
   dy, x = op.inputs
   with ops.control_dependencies([grad]):
-    ddy = gen_nn_ops._softplus_grad(grad, x)  # pylint: disable=protected-access
+    ddy = gen_nn_ops.softplus_grad(grad, x)
     d2x = grad * dy / (math_ops.exp(-x) + 2.0 + math_ops.exp(x))
     return (ddy, d2x)
 
 
 @ops.RegisterGradient("Softsign")
 def _SoftsignGrad(op, grad):
-  return gen_nn_ops._softsign_grad(grad, op.inputs[0])
+  return gen_nn_ops.softsign_grad(grad, op.inputs[0])
 
 
 @ops.RegisterGradient("ReluGrad")
 def _ReluGradGrad(op, grad):
   x = op.inputs[1]
-  return (gen_nn_ops._relu_grad(grad, x),
+  return (gen_nn_ops.relu_grad(grad, x),
           array_ops.zeros(shape=array_ops.shape(x), dtype=x.dtype))
 
 
@@ -565,14 +565,14 @@ def _LRNGrad(op, grad):
   alpha = op.get_attr("alpha")
   beta = op.get_attr("beta")
   return [
-      gen_nn_ops._lrn_grad(grad, op.inputs[0], op.outputs[0], depth_radius,
-                           bias, alpha, beta)
+      gen_nn_ops.lrn_grad(grad, op.inputs[0], op.outputs[0], depth_radius, bias,
+                          alpha, beta)
   ]
 
 
 @ops.RegisterGradient("AvgPool")
 def _AvgPoolGrad(op, grad):
-  return gen_nn_ops._avg_pool_grad(
+  return gen_nn_ops.avg_pool_grad(
       array_ops.shape(op.inputs[0]),
       grad,
       op.get_attr("ksize"),
@@ -584,7 +584,7 @@ def _AvgPoolGrad(op, grad):
 @ops.RegisterGradient("AvgPoolGrad")
 def _AvgPoolGradGrad(op, grad):
   return (array_ops.stop_gradient(op.inputs[0]),
-          gen_nn_ops._avg_pool(
+          gen_nn_ops.avg_pool(
               grad,
               op.get_attr("ksize"),
               op.get_attr("strides"),
@@ -594,7 +594,7 @@ def _AvgPoolGradGrad(op, grad):
 
 @ops.RegisterGradient("MaxPool")
 def _MaxPoolGrad(op, grad):
-  return gen_nn_ops._max_pool_grad(
+  return gen_nn_ops.max_pool_grad(
       op.inputs[0],
       op.outputs[0],
       grad,
@@ -620,7 +620,7 @@ def _MaxPoolGradV2(op, grad):
 
 @ops.RegisterGradient("MaxPoolWithArgmax")
 def _MaxPoolGradWithArgmax(op, grad, unused_argmax_grad):
-  return gen_nn_ops._max_pool_grad_with_argmax(
+  return gen_nn_ops.max_pool_grad_with_argmax(
       op.inputs[0],
       grad,
       op.outputs[1],
@@ -635,7 +635,7 @@ def _MaxPoolGradGrad(op, grad):
       shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype),
           array_ops.zeros(
               shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype),
-          gen_nn_ops._max_pool_grad_grad(
+          gen_nn_ops.max_pool_grad_grad(
               op.inputs[0],
               op.inputs[1],
               grad,
@@ -669,7 +669,7 @@ def _MaxPoolGradGradGrad(op, grad):
       shape=array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype),
           array_ops.zeros(
               shape=array_ops.shape(op.inputs[1]), dtype=op.inputs[1].dtype),
-          gen_nn_ops._max_pool_grad(
+          gen_nn_ops.max_pool_grad(
               op.inputs[0],
               op.inputs[1],
               grad,
@@ -696,8 +696,7 @@ def _FractionalMaxPoolGrad(op, grad_0, unused_grad_1, unused_grad_2):
   Returns:
     Input backprop for FractionalMaxPool op.
   """
-  # pylint: disable=protected-access
-  return gen_nn_ops._fractional_max_pool_grad(
+  return gen_nn_ops.fractional_max_pool_grad(
       op.inputs[0], op.outputs[0], grad_0, op.outputs[1], op.outputs[2],
       op.get_attr("overlapping"))
 
@@ -719,10 +718,9 @@ def _FractionalAvgPoolGrad(op, grad_0, unused_grad_1, unused_grad_2):
   Returns:
     Input backprop for FractionalAvgPool op.
   """
-  # pylint: disable=protected-access
-  return gen_nn_ops._fractional_avg_pool_grad(op.inputs[0].get_shape(), grad_0,
-                                              op.outputs[1], op.outputs[2],
-                                              op.get_attr("overlapping"))
+  return gen_nn_ops.fractional_avg_pool_grad(op.inputs[0].get_shape(), grad_0,
+                                             op.outputs[1], op.outputs[2],
+                                             op.get_attr("overlapping"))
 
 
 @ops.RegisterGradient("BatchNormWithGlobalNormalization")
@@ -746,7 +744,7 @@ def _BatchNormWithGlobalNormalizationGrad(op, grad):
         last dimension.
     dg: Backprop for gamma, which is (grad * ((x - m) * rsqrt(v + epsilon)))
   """
-  dx, dm, dv, db, dg = gen_nn_ops._batch_norm_with_global_normalization_grad(
+  dx, dm, dv, db, dg = gen_nn_ops.batch_norm_with_global_normalization_grad(
       op.inputs[0], op.inputs[1], op.inputs[2], op.inputs[4], grad,
       op.get_attr("variance_epsilon"), op.get_attr("scale_after_normalization"))
   return dx, dm, dv, db, dg
diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 5fa5708114..7814a27311 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -888,12 +888,10 @@ def fused_batch_norm(
   # TODO(reedwm): In a few weeks, switch to using the V2 version exclusively. We
   # currently only use the V2 version for float16 inputs, which is not supported
   # by the V1 version.
-  # pylint: disable=protected-access
   if x.dtype == dtypes.float16 or x.dtype == dtypes.bfloat16:
-    fused_batch_norm_func = gen_nn_ops._fused_batch_norm_v2
+    fused_batch_norm_func = gen_nn_ops.fused_batch_norm_v2
   else:
-    fused_batch_norm_func = gen_nn_ops._fused_batch_norm
-  # pylint: enable=protected-access
+    fused_batch_norm_func = gen_nn_ops._fused_batch_norm  # pylint: disable=protected-access
   y, batch_mean, batch_var, _, _ = fused_batch_norm_func(
       x,
       scale,
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index 8fbe698914..a0d500afce 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -1481,7 +1481,6 @@ def conv3d_transpose(
         name=name)
 
 
-# pylint: disable=protected-access
 @tf_export("nn.bias_add")
 def bias_add(value, bias, data_format=None, name=None):
   """Adds `bias` to `value`.
@@ -1506,10 +1505,9 @@ def bias_add(value, bias, data_format=None, name=None):
   with ops.name_scope(name, "BiasAdd", [value, bias]) as name:
     value = ops.convert_to_tensor(value, name="input")
     bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
-    return gen_nn_ops._bias_add(value, bias, data_format=data_format, name=name)
+    return gen_nn_ops.bias_add(value, bias, data_format=data_format, name=name)
 
 
-# pylint: disable=protected-access
 def bias_add_v1(value, bias, name=None):
   """Adds `bias` to `value`.
 
@@ -1534,7 +1532,7 @@ def bias_add_v1(value, bias, name=None):
   with ops.name_scope(name, "BiasAddV1", [value, bias]) as name:
     value = ops.convert_to_tensor(value, name="input")
     bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias")
-    return gen_nn_ops._bias_add_v1(value, bias, name=name)
+    return gen_nn_ops.bias_add_v1(value, bias, name=name)
 
 
 @tf_export("nn.crelu")
@@ -1580,7 +1578,7 @@ def relu6(features, name=None):
   """
   with ops.name_scope(name, "Relu6", [features]) as name:
     features = ops.convert_to_tensor(features, name="features")
-    return gen_nn_ops._relu6(features, name=name)
+    return gen_nn_ops.relu6(features, name=name)
 
 
 @tf_export("nn.leaky_relu")
@@ -1645,7 +1643,7 @@ def _softmax(logits, compute_op, dim=-1, name=None):
   Args:
     logits: A non-empty `Tensor`. Must be one of the following types: `half`,
       `float32`, `float64`.
-    compute_op: Either gen_nn_ops._softmax or gen_nn_ops._log_softmax
+    compute_op: Either gen_nn_ops.softmax or gen_nn_ops.log_softmax
     dim: The dimension softmax would be performed on. The default is -1 which
       indicates the last dimension.
     name: A name for the operation (optional).
@@ -1739,7 +1737,7 @@ def softmax(logits, axis=None, name=None, dim=None):
   axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim)
   if axis is None:
     axis = -1
-  return _softmax(logits, gen_nn_ops._softmax, axis, name)
+  return _softmax(logits, gen_nn_ops.softmax, axis, name)
 
 
 @tf_export("nn.log_softmax")
@@ -1769,7 +1767,7 @@ def log_softmax(logits, axis=None, name=None, dim=None):
   axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim)
   if axis is None:
     axis = -1
-  return _softmax(logits, gen_nn_ops._log_softmax, axis, name)
+  return _softmax(logits, gen_nn_ops.log_softmax, axis, name)
 
 
 def _ensure_xent_args(name, sentinel, labels, logits):
@@ -1871,7 +1869,7 @@ def softmax_cross_entropy_with_logits_v2(
     # Do the actual op computation.
     # The second output tensor contains the gradients.  We use it in
     # _CrossEntropyGrad() in nn_grad but not here.
-    cost, unused_backprop = gen_nn_ops._softmax_cross_entropy_with_logits(
+    cost, unused_backprop = gen_nn_ops.softmax_cross_entropy_with_logits(
         precise_logits, labels, name=name)
 
     # The output cost shape should be the input minus dim.
@@ -2038,7 +2036,7 @@ def sparse_softmax_cross_entropy_with_logits(
                        (labels_static_shape.ndims, logits.get_shape().ndims))
     # Check if no reshapes are required.
     if logits.get_shape().ndims == 2:
-      cost, _ = gen_nn_ops._sparse_softmax_cross_entropy_with_logits(
+      cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
           precise_logits, labels, name=name)
       if logits.dtype == dtypes.float16:
         return math_ops.cast(cost, dtypes.float16)
@@ -2051,7 +2049,7 @@ def sparse_softmax_cross_entropy_with_logits(
     labels = array_ops.reshape(labels, [-1])
     # The second output tensor contains the gradients.  We use it in
     # _CrossEntropyGrad() in nn_grad but not here.
-    cost, _ = gen_nn_ops._sparse_softmax_cross_entropy_with_logits(
+    cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
         precise_logits, labels, name=name)
     cost = array_ops.reshape(cost, labels_shape)
     cost.set_shape(labels_static_shape)
@@ -2086,7 +2084,7 @@ def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
   """
   with ops.name_scope(name, "AvgPool", [value]) as name:
     value = ops.convert_to_tensor(value, name="input")
-    return gen_nn_ops._avg_pool(
+    return gen_nn_ops.avg_pool(
         value,
         ksize=ksize,
         strides=strides,
@@ -2116,12 +2114,13 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
   """
   with ops.name_scope(name, "MaxPool", [value]) as name:
     value = ops.convert_to_tensor(value, name="input")
-    return gen_nn_ops._max_pool(value,
-                                ksize=ksize,
-                                strides=strides,
-                                padding=padding,
-                                data_format=data_format,
-                                name=name)
+    return gen_nn_ops.max_pool(
+        value,
+        ksize=ksize,
+        strides=strides,
+        padding=padding,
+        data_format=data_format,
+        name=name)
 
 
 @ops.RegisterStatistics("Conv2D", "flops")
@@ -2331,7 +2330,7 @@ def top_k(input, k=1, sorted=True, name=None):  # pylint: disable=redefined-buil
     values: The `k` largest elements along each last dimensional slice.
     indices: The indices of `values` within the last dimension of `input`.
   """
-  return gen_nn_ops._top_kv2(input, k=k, sorted=sorted, name=name)
+  return gen_nn_ops.top_kv2(input, k=k, sorted=sorted, name=name)
 
 
 def nth_element(input, n, reverse=False, name=None):  # pylint: disable=redefined-builtin
@@ -2650,4 +2649,4 @@ def in_top_k(predictions, targets, k, name=None):
     A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`.
   """
   with ops.name_scope(name, "in_top_k"):
-    return gen_nn_ops._in_top_kv2(predictions, targets, k, name=name)
+    return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name)
diff --git a/tensorflow/python/ops/parsing_ops.py b/tensorflow/python/ops/parsing_ops.py
index b0315ceee2..075b38d743 100644
--- a/tensorflow/python/ops/parsing_ops.py
+++ b/tensorflow/python/ops/parsing_ops.py
@@ -700,8 +700,7 @@ def _parse_example_raw(serialized,
     # Finally, convert dense_shapes to TensorShapeProto
     dense_shapes = [shape.as_proto() for shape in dense_shapes]
 
-    # pylint: disable=protected-access
-    outputs = gen_parsing_ops._parse_example(
+    outputs = gen_parsing_ops.parse_example(
         serialized=serialized,
         names=names,
         dense_defaults=dense_defaults_vec,
@@ -710,7 +709,6 @@ def _parse_example_raw(serialized,
         dense_keys=dense_keys,
         dense_shapes=dense_shapes,
         name=name)
-    # pylint: enable=protected-access
 
     (sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs
 
@@ -1132,8 +1130,7 @@ def _parse_single_sequence_example_raw(serialized,
     feature_list_dense_shapes = [tensor_shape.as_shape(shape).as_proto()
                                  for shape in feature_list_dense_shapes]
 
-    # pylint: disable=protected-access
-    outputs = gen_parsing_ops._parse_single_sequence_example(
+    outputs = gen_parsing_ops.parse_single_sequence_example(
         serialized=serialized,
         debug_name=debug_name,
         context_dense_defaults=context_dense_defaults_vec,
@@ -1149,7 +1146,6 @@ def _parse_single_sequence_example_raw(serialized,
         feature_list_dense_missing_assumed_empty=(
             feature_list_dense_missing_assumed_empty),
         name=name)
-    # pylint: enable=protected-access
 
     (context_sparse_indices, context_sparse_values,
      context_sparse_shapes, context_dense_values,
@@ -1182,7 +1178,6 @@ def _parse_single_sequence_example_raw(serialized,
 @tf_export("decode_csv")
 def decode_csv(records, record_defaults, field_delim=",",
                use_quote_delim=True, name=None, na_value=""):
-  # pylint: disable=protected-access
   """Convert CSV records to tensors. Each column maps to one tensor.
 
   RFC 4180 format is expected for the CSV records.
@@ -1211,11 +1206,13 @@ def decode_csv(records, record_defaults, field_delim=",",
     Each tensor will have the same shape as records.
   """
   # TODO(martinwicke), remove the wrapper when new Python API generator is done.
-  return gen_parsing_ops._decode_csv(
-      records=records, record_defaults=record_defaults,
-      field_delim=field_delim, use_quote_delim=use_quote_delim,
-      na_value=na_value, name=name)
-  # pylint: enable=protected-access
+  return gen_parsing_ops.decode_csv(
+      records=records,
+      record_defaults=record_defaults,
+      field_delim=field_delim,
+      use_quote_delim=use_quote_delim,
+      na_value=na_value,
+      name=name)
 
 
 # TODO(b/70890287): Combine the implementation of this op and
@@ -1391,7 +1388,6 @@ def _parse_single_example_v2_raw(serialized, sparse_keys, sparse_types,
     # Finally, convert dense_shapes to TensorShapeProto
     dense_shapes = [shape.as_proto() for shape in dense_shapes]
 
-    # pylint: disable=protected-access
     outputs = gen_parsing_ops.parse_single_example(
         serialized=serialized,
         dense_defaults=dense_defaults_vec,
@@ -1401,7 +1397,6 @@ def _parse_single_example_v2_raw(serialized, sparse_keys, sparse_types,
         dense_keys=dense_keys,
         dense_shapes=dense_shapes,
         name=name)
-    # pylint: enable=protected-access
 
     (sparse_indices, sparse_values, sparse_shapes, dense_values) = outputs
 
diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py
index 2c86358d21..db8159579a 100644
--- a/tensorflow/python/ops/random_ops.py
+++ b/tensorflow/python/ops/random_ops.py
@@ -43,7 +43,6 @@ def _ShapeTensor(shape):
   return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
 
 
-# pylint: disable=protected-access
 @tf_export("random_normal")
 def random_normal(shape,
                   mean=0.0,
@@ -74,7 +73,7 @@ def random_normal(shape,
     mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
     stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
     seed1, seed2 = random_seed.get_seed(seed)
-    rnd = gen_random_ops._random_standard_normal(
+    rnd = gen_random_ops.random_standard_normal(
         shape_tensor, dtype, seed=seed1, seed2=seed2)
     mul = rnd * stddev_tensor
     value = math_ops.add(mul, mean_tensor, name=name)
@@ -126,7 +125,7 @@ def parameterized_truncated_normal(shape,
     minvals_tensor = ops.convert_to_tensor(minvals, dtype=dtype, name="minvals")
     maxvals_tensor = ops.convert_to_tensor(maxvals, dtype=dtype, name="maxvals")
     seed1, seed2 = random_seed.get_seed(seed)
-    rnd = gen_random_ops._parameterized_truncated_normal(
+    rnd = gen_random_ops.parameterized_truncated_normal(
         shape_tensor,
         means_tensor,
         stddevs_tensor,
@@ -171,7 +170,7 @@ def truncated_normal(shape,
     mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
     stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
     seed1, seed2 = random_seed.get_seed(seed)
-    rnd = gen_random_ops._truncated_normal(
+    rnd = gen_random_ops.truncated_normal(
         shape_tensor, dtype, seed=seed1, seed2=seed2)
     mul = rnd * stddev_tensor
     value = math_ops.add(mul, mean_tensor, name=name)
@@ -237,11 +236,10 @@ def random_uniform(shape,
     maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
     seed1, seed2 = random_seed.get_seed(seed)
     if dtype.is_integer:
-      return gen_random_ops._random_uniform_int(
+      return gen_random_ops.random_uniform_int(
           shape, minval, maxval, seed=seed1, seed2=seed2, name=name)
     else:
-      rnd = gen_random_ops._random_uniform(
-          shape, dtype, seed=seed1, seed2=seed2)
+      rnd = gen_random_ops.random_uniform(shape, dtype, seed=seed1, seed2=seed2)
       return math_ops.add(rnd * (maxval - minval), minval, name=name)
 
 
@@ -275,7 +273,7 @@ def random_shuffle(value, seed=None, name=None):
     dimension.
   """
   seed1, seed2 = random_seed.get_seed(seed)
-  return gen_random_ops._random_shuffle(
+  return gen_random_ops.random_shuffle(
       value, seed=seed1, seed2=seed2, name=name)
 
 
@@ -420,7 +418,7 @@ def random_gamma(shape,
     seed1, seed2 = random_seed.get_seed(seed)
     return math_ops.maximum(
         np.finfo(dtype.as_numpy_dtype).tiny,
-        gen_random_ops._random_gamma(
+        gen_random_ops.random_gamma(
             shape, alpha_broadcast, seed=seed1, seed2=seed2) / beta)
 
 ops.NotDifferentiable("RandomGamma")
diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py
index 6fe2f61016..01f0b81684 100644
--- a/tensorflow/python/ops/script_ops.py
+++ b/tensorflow/python/ops/script_ops.py
@@ -219,18 +219,16 @@ def _internal_py_func(func, inp, Tout, stateful=None, eager=False, name=None):
   graph._cleanup_py_funcs_used_in_graph.append(cleanup)
   # pylint: enable=protected-access
 
-  # pylint: disable=protected-access
   if eager:
-    result = gen_script_ops._eager_py_func(
+    result = gen_script_ops.eager_py_func(
         input=inp, token=token, Tout=Tout, name=name)
   else:
     if stateful:
-      result = gen_script_ops._py_func(
+      result = gen_script_ops.py_func(
           input=inp, token=token, Tout=Tout, name=name)
     else:
-      result = gen_script_ops._py_func_stateless(
+      result = gen_script_ops.py_func_stateless(
           input=inp, token=token, Tout=Tout, name=name)
-  # pylint: enable=protected-access
   return result if is_list_or_tuple else result[0]
 
 
diff --git a/tensorflow/python/ops/session_ops.py b/tensorflow/python/ops/session_ops.py
index cedd36c1de..ad38845153 100644
--- a/tensorflow/python/ops/session_ops.py
+++ b/tensorflow/python/ops/session_ops.py
@@ -16,7 +16,6 @@
 """Tensor Handle Operations. See the @{$python/session_ops} guide.
 
 @@get_session_handle
-@@get_session_handle_v2
 @@get_session_tensor
 @@delete_session_tensor
 """
@@ -182,7 +181,7 @@ def get_session_handle(data, name=None):
 
   # Colocate this operation with data.
   with ops.colocate_with(data):
-    return gen_data_flow_ops._get_session_handle(data, name=name)  # pylint: disable=protected-access
+    return gen_data_flow_ops.get_session_handle(data, name=name)
 
 
 @tf_export("get_session_tensor")
@@ -222,7 +221,7 @@ def get_session_tensor(handle, dtype, name=None):
   with ops.device(handle_device):
     holder = array_ops.placeholder(dtypes.string)
     _register_handle_feeder(holder.graph, holder, dtype)
-    tensor = gen_data_flow_ops._get_session_tensor(holder, dtype, name=name)
+    tensor = gen_data_flow_ops.get_session_tensor(holder, dtype, name=name)
   return (holder, tensor)
 
 
@@ -246,7 +245,7 @@ def delete_session_tensor(handle, name=None):
   handle_device = TensorHandle._get_device_name(handle)
   with ops.device(handle_device):
     holder = array_ops.placeholder(dtypes.string)
-    deleter = gen_data_flow_ops._delete_session_tensor(holder, name=name)
+    deleter = gen_data_flow_ops.delete_session_tensor(holder, name=name)
   return (holder, deleter)
 
 
@@ -268,7 +267,7 @@ def _get_handle_reader(graph, handle, dtype):
     with graph.as_default(), graph.device(handle_device):
       holder = array_ops.placeholder(dtypes.string)
       _register_handle_feeder(holder.graph, holder, dtype)
-      reader = gen_data_flow_ops._get_session_tensor(holder, dtype)
+      reader = gen_data_flow_ops.get_session_tensor(holder, dtype)
     result = (holder, reader)
     graph._handle_readers[graph_key] = result
   return result
@@ -289,7 +288,7 @@ def _get_handle_mover(graph, feeder, handle):
     # Create mover if we haven't done it.
     holder, reader = _get_handle_reader(graph, handle, dtype)
     with graph.as_default(), graph.device(feeder.op.device):
-      mover = gen_data_flow_ops._get_session_handle(reader)  # pylint: disable=protected-access
+      mover = gen_data_flow_ops.get_session_handle(reader)
     result = (holder, mover)
     graph._handle_movers[graph_key] = result
   return result
@@ -303,7 +302,7 @@ def _get_handle_deleter(graph, deleter_key, handle):
     handle_device = TensorHandle._get_device_name(handle)
     with graph.as_default(), graph.device(handle_device):
       holder = array_ops.placeholder(dtypes.string)
-      deleter = gen_data_flow_ops._delete_session_tensor(holder)
+      deleter = gen_data_flow_ops.delete_session_tensor(holder)
     result = (holder, deleter)
     graph._handle_deleters[deleter_key] = result
   return result
diff --git a/tensorflow/python/ops/sparse_grad.py b/tensorflow/python/ops/sparse_grad.py
index 5295e7d21c..97353d6c74 100644
--- a/tensorflow/python/ops/sparse_grad.py
+++ b/tensorflow/python/ops/sparse_grad.py
@@ -88,10 +88,8 @@ def _SparseAddGrad(op, *grads):
   # the non-zero elements of the sum, and we will peek into `sum_indices` in the
   # gradient op.
 
-  # pylint: disable=protected-access
-  a_val_grad, b_val_grad = gen_sparse_ops._sparse_add_grad(val_grad, a_indices,
-                                                           b_indices,
-                                                           sum_indices)
+  a_val_grad, b_val_grad = gen_sparse_ops.sparse_add_grad(
+      val_grad, a_indices, b_indices, sum_indices)
   a_val_grad.set_shape(op.inputs[1].get_shape())
   b_val_grad.set_shape(op.inputs[4].get_shape())
   # (a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh)
@@ -151,7 +149,7 @@ def _SparseTensorDenseMatMulGrad(op, grad):
                               "complex gradients.")
 
   # gradient w.r.t. dense
-  b_grad = gen_sparse_ops._sparse_tensor_dense_mat_mul(  # pylint: disable=protected-access
+  b_grad = gen_sparse_ops.sparse_tensor_dense_mat_mul(
       a_indices, a_values, a_shape, grad, adjoint_a=not adj_a)
   if adj_b:
     b_grad = array_ops.transpose(b_grad)
@@ -278,8 +276,7 @@ def _SparseFillEmptyRowsGrad(op, unused_grad_output_indices, output_grad_values,
   """Gradients for SparseFillEmptyRows."""
   reverse_index_map = op.outputs[3]
 
-  # pylint: disable=protected-access
-  d_values, d_default_value = gen_sparse_ops._sparse_fill_empty_rows_grad(
+  d_values, d_default_value = gen_sparse_ops.sparse_fill_empty_rows_grad(
       reverse_index_map=reverse_index_map, grad_values=output_grad_values)
 
   # d_indices, d_values, d_dense_shape, d_default_value.
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index 0fbbf5a805..a01bba632f 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -234,7 +234,7 @@ def sparse_concat(axis,
     ]
 
   output_ind, output_val, output_shape = (
-      gen_sparse_ops._sparse_concat(inds, vals, shapes, axis, name=name))
+      gen_sparse_ops.sparse_concat(inds, vals, shapes, axis, name=name))
 
   return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
 
@@ -302,8 +302,8 @@ def sparse_add(a, b, thresh=0):
     thresh = ops.convert_to_tensor(
         thresh, dtype=a.values.dtype.real_dtype.base_dtype, name="thresh")
     output_ind, output_val, output_shape = (
-        gen_sparse_ops._sparse_add(a.indices, a.values, a.dense_shape,
-                                   b.indices, b.values, b.dense_shape, thresh))
+        gen_sparse_ops.sparse_add(a.indices, a.values, a.dense_shape,
+                                  b.indices, b.values, b.dense_shape, thresh))
 
     # Attempt to get output_shape statically.
     a.get_shape().assert_is_compatible_with(b.get_shape())
@@ -317,8 +317,8 @@ def sparse_add(a, b, thresh=0):
     # swap to make `a` the SparseTensor.
     if isinstance(b, sparse_classes):
       a, b = b, a
-    return gen_sparse_ops._sparse_tensor_dense_add(a.indices, a.values,
-                                                   a.dense_shape, b)
+    return gen_sparse_ops.sparse_tensor_dense_add(a.indices, a.values,
+                                                  a.dense_shape, b)
 
 
 def _sparse_cross(inputs, name=None):
@@ -402,7 +402,7 @@ def _sparse_cross_internal(inputs,
                            num_buckets=0,
                            hash_key=None,
                            name=None):
-  """See gen_sparse_ops._sparse_cross."""
+  """See gen_sparse_ops.sparse_cross."""
   if not isinstance(inputs, list):
     raise TypeError("Inputs must be a list")
   if not all(
@@ -432,7 +432,7 @@ def _sparse_cross_internal(inputs,
       dense_inputs[i] = math_ops.to_int64(dense_inputs[i])
       internal_type = dtypes.int64
 
-  indices_out, values_out, shape_out = gen_sparse_ops._sparse_cross(
+  indices_out, values_out, shape_out = gen_sparse_ops.sparse_cross(
       indices=indices,
       values=values,
       shapes=shapes,
@@ -511,7 +511,7 @@ def sparse_reorder(sp_input, name=None):
   sp_input = _convert_to_sparse_tensor(sp_input)
 
   reordered_ind, reordered_val = (
-      gen_sparse_ops._sparse_reorder(
+      gen_sparse_ops.sparse_reorder(
           sp_input.indices, sp_input.values, sp_input.dense_shape, name=name))
 
   if sp_input.get_shape().is_fully_defined():
@@ -575,7 +575,7 @@ def sparse_reshape(sp_input, shape, name=None):
   shape = math_ops.cast(shape, dtype=dtypes.int64)
 
   with ops.name_scope(name, "SparseReshape", [sp_input]) as name:
-    reshaped_ind, reshaped_shape = gen_sparse_ops._sparse_reshape(
+    reshaped_ind, reshaped_shape = gen_sparse_ops.sparse_reshape(
         sp_input.indices, sp_input.dense_shape, shape, name=name)
 
     reshaped_shape_const = tensor_util.constant_value(shape)
@@ -671,7 +671,7 @@ def sparse_split(keyword_required=KeywordRequired(),
   sp_input = _convert_to_sparse_tensor(sp_input)
 
   output_inds, output_vals, output_shapes = (
-      gen_sparse_ops._sparse_split(
+      gen_sparse_ops.sparse_split(
           axis,
           sp_input.indices,
           sp_input.values,
@@ -782,7 +782,7 @@ def sparse_to_dense(sparse_indices,
     Dense `Tensor` of shape `output_shape`.  Has the same type as
     `sparse_values`.
   """
-  return gen_sparse_ops._sparse_to_dense(
+  return gen_sparse_ops.sparse_to_dense(
       sparse_indices,
       output_shape,
       sparse_values,
@@ -1412,7 +1412,7 @@ def sparse_fill_empty_rows(sp_input, default_value, name=None):
     default_value = ops.convert_to_tensor(
         default_value, dtype=sp_input.values.dtype)
     (output_indices, output_values, empty_row_indicator,
-     unused_reverse_index_map) = gen_sparse_ops._sparse_fill_empty_rows(
+     unused_reverse_index_map) = gen_sparse_ops.sparse_fill_empty_rows(
          indices=sp_input.indices,
          values=sp_input.values,
          dense_shape=sp_input.dense_shape,
@@ -1441,7 +1441,7 @@ def serialize_sparse(sp_input, name=None, out_type=dtypes.string):
   """
   sp_input = _convert_to_sparse_tensor(sp_input)
 
-  return gen_sparse_ops._serialize_sparse(
+  return gen_sparse_ops.serialize_sparse(
       sp_input.indices,
       sp_input.values,
       sp_input.dense_shape,
@@ -1476,7 +1476,7 @@ def serialize_many_sparse(sp_input, name=None, out_type=dtypes.string):
   """
   sp_input = _convert_to_sparse_tensor(sp_input)
 
-  return gen_sparse_ops._serialize_many_sparse(
+  return gen_sparse_ops.serialize_many_sparse(
       sp_input.indices,
       sp_input.values,
       sp_input.dense_shape,
@@ -1541,7 +1541,7 @@ def deserialize_sparse(serialized_sparse, dtype, rank=None, name=None):
 
   """
   output_indices, output_values, output_shape = (
-      gen_sparse_ops._deserialize_sparse(serialized_sparse, dtype, name=name))
+      gen_sparse_ops.deserialize_sparse(serialized_sparse, dtype, name=name))
 
   # Feed rank data back in, if available
   output_indices.set_shape([None, rank])
@@ -1610,7 +1610,7 @@ def deserialize_many_sparse(serialized_sparse, dtype, rank=None, name=None):
     All of the serialized `SparseTensor`s must have had the same rank and type.
   """
   output_indices, output_values, output_shape = (
-      gen_sparse_ops._deserialize_many_sparse(
+      gen_sparse_ops.deserialize_many_sparse(
           serialized_sparse, dtype, name=name))
 
   # Feed rank data back in, if available
@@ -1828,7 +1828,7 @@ def sparse_tensor_dense_matmul(sp_a,
   with ops.name_scope(name, "SparseTensorDenseMatMul",
                       [sp_a.indices, sp_a.values, b]) as name:
     b = ops.convert_to_tensor(b, name="b")
-    return gen_sparse_ops._sparse_tensor_dense_mat_mul(
+    return gen_sparse_ops.sparse_tensor_dense_mat_mul(
         a_indices=sp_a.indices,
         a_values=sp_a.values,
         a_shape=sp_a.dense_shape,
diff --git a/tensorflow/python/ops/standard_ops.py b/tensorflow/python/ops/standard_ops.py
index b62e556967..65b788c31a 100644
--- a/tensorflow/python/ops/standard_ops.py
+++ b/tensorflow/python/ops/standard_ops.py
@@ -186,7 +186,6 @@ _allowed_symbols_array_ops = [
     "quantize_and_dequantize",  # to-doc
 
     # TODO(drpng): legacy symbols to be removed.
-    "list_diff",  # Use tf.listdiff instead.
     "batch_matrix_diag",
     "batch_matrix_band_part",
     "batch_matrix_diag_part",
diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py
index 6c0a090d16..fd4419640a 100644
--- a/tensorflow/python/ops/state_ops.py
+++ b/tensorflow/python/ops/state_ops.py
@@ -99,8 +99,8 @@ def variable_op(shape, dtype, name="Variable", set_shape=True, container="",
   """Deprecated. Used variable_op_v2 instead."""
   if not set_shape:
     shape = tensor_shape.unknown_shape()
-  ret = gen_state_ops._variable(shape=shape, dtype=dtype, name=name,
-                                container=container, shared_name=shared_name)
+  ret = gen_state_ops.variable(shape=shape, dtype=dtype, name=name,
+                               container=container, shared_name=shared_name)
   # TODO(mrry): Move this to where it is used, so we can get rid of this op
   #   wrapper?
   if set_shape:
@@ -127,11 +127,12 @@ def variable_op_v2(shape, dtype, name="Variable", container="", shared_name=""):
   Returns:
     A variable tensor.
   """
-  return gen_state_ops._variable_v2(shape=shape,
-                                    dtype=dtype,
-                                    name=name,
-                                    container=container,
-                                    shared_name=shared_name)
+  return gen_state_ops.variable_v2(
+      shape=shape,
+      dtype=dtype,
+      name=name,
+      container=container,
+      shared_name=shared_name)
 
 
 def init_variable(v, init, name="init"):
diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py
index b8c39d91b4..0335d2456a 100644
--- a/tensorflow/python/ops/string_ops.py
+++ b/tensorflow/python/ops/string_ops.py
@@ -93,10 +93,8 @@ def string_split(source, delimiter=" ", skip_empty=True):  # pylint: disable=inv
   delimiter = ops.convert_to_tensor(delimiter, dtype=dtypes.string)
   source = ops.convert_to_tensor(source, dtype=dtypes.string)
 
-  # pylint: disable=protected-access
-  indices, values, shape = gen_string_ops._string_split(
+  indices, values, shape = gen_string_ops.string_split(
       source, delimiter=delimiter, skip_empty=skip_empty)
-  # pylint: enable=protected-access
   indices.set_shape([None, 2])
   values.set_shape([None])
   shape.set_shape([2])
diff --git a/tensorflow/python/ops/summary_ops.py b/tensorflow/python/ops/summary_ops.py
index 7f4f4ce5ab..037bc9845a 100644
--- a/tensorflow/python/ops/summary_ops.py
+++ b/tensorflow/python/ops/summary_ops.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 # ==============================================================================
 """Summary Operations."""
-# pylint: disable=protected-access
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -74,7 +73,7 @@ def tensor_summary(name,
 
   with summary_op_util.summary_scope(
       name, family, values=[tensor]) as (tag, scope):
-    val = gen_logging_ops._tensor_summary_v2(
+    val = gen_logging_ops.tensor_summary_v2(
         tensor=tensor,
         tag=tag,
         name=scope,
diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py
index 3c08870146..6226f426be 100644
--- a/tensorflow/python/ops/tensor_array_ops.py
+++ b/tensorflow/python/ops/tensor_array_ops.py
@@ -148,7 +148,7 @@ class _GraphTensorArray(object):
         # will retroactively set the device value of this op.
         def create():
           """Create the TensorArray op."""
-          return gen_data_flow_ops._tensor_array_v3(
+          return gen_data_flow_ops.tensor_array_v3(
               dtype=dtype,
               size=size,
               element_shape=element_shape,
@@ -237,7 +237,7 @@ class _GraphTensorArray(object):
       flow = self.flow
     with ops.name_scope(name, "TensorArrayGrad", [self._handle]):
       with ops.colocate_with(self._handle):
-        g_handle, unused_flow = gen_data_flow_ops._tensor_array_grad_v3(
+        g_handle, unused_flow = gen_data_flow_ops.tensor_array_grad_v3(
             handle=self._handle, source=source, flow_in=flow, name=name)
         with ops.control_dependencies([g_handle]):
           flow = array_ops.identity(flow, name="gradient_flow")
@@ -252,7 +252,7 @@ class _GraphTensorArray(object):
 
   def read(self, index, name=None):
     """See TensorArray."""
-    value = gen_data_flow_ops._tensor_array_read_v3(
+    value = gen_data_flow_ops.tensor_array_read_v3(
         handle=self._handle,
         index=index,
         flow_in=self._flow,
@@ -270,7 +270,7 @@ class _GraphTensorArray(object):
       if self._infer_shape:
         self._merge_element_shape(value.shape)
       with self._maybe_colocate_with(value):
-        flow_out = gen_data_flow_ops._tensor_array_write_v3(
+        flow_out = gen_data_flow_ops.tensor_array_write_v3(
             handle=self._handle,
             index=index,
             value=value,
@@ -296,7 +296,7 @@ class _GraphTensorArray(object):
       element_shape = self._element_shape[0]
     else:
       element_shape = tensor_shape.TensorShape(None)
-    value = gen_data_flow_ops._tensor_array_gather_v3(
+    value = gen_data_flow_ops.tensor_array_gather_v3(
         handle=self._handle,
         indices=indices,
         flow_in=self._flow,
@@ -314,7 +314,7 @@ class _GraphTensorArray(object):
           tensor_shape.TensorShape(self._element_shape[0].dims[1:]))
     else:
       element_shape_except0 = tensor_shape.TensorShape(None)
-    value, _ = gen_data_flow_ops._tensor_array_concat_v3(
+    value, _ = gen_data_flow_ops.tensor_array_concat_v3(
         handle=self._handle,
         flow_in=self._flow,
         dtype=self._dtype,
@@ -341,7 +341,7 @@ class _GraphTensorArray(object):
       if self._infer_shape and context.in_graph_mode():
         self._merge_element_shape(value.shape[1:])
       with self._maybe_colocate_with(value):
-        flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
+        flow_out = gen_data_flow_ops.tensor_array_scatter_v3(
             handle=self._handle,
             indices=indices,
             value=value,
@@ -370,7 +370,7 @@ class _GraphTensorArray(object):
               self._merge_element_shape(
                   tensor_shape.TensorShape([clengths[0]]).concatenate(
                       value.shape[1:]))
-        flow_out = gen_data_flow_ops._tensor_array_split_v3(
+        flow_out = gen_data_flow_ops.tensor_array_split_v3(
             handle=self._handle,
             value=value,
             lengths=lengths_64,
@@ -386,13 +386,13 @@ class _GraphTensorArray(object):
 
   def size(self, name=None):
     """See TensorArray."""
-    return gen_data_flow_ops._tensor_array_size_v3(
+    return gen_data_flow_ops.tensor_array_size_v3(
         handle=self._handle, flow_in=self.flow, name=name)
 
   @tf_should_use.should_use_result
   def close(self, name=None):
     """See TensorArray."""
-    return gen_data_flow_ops._tensor_array_close_v3(
+    return gen_data_flow_ops.tensor_array_close_v3(
         handle=self._handle, name=name)
 
 # pylint: enable=protected-access
diff --git a/tensorflow/python/summary/summary.py b/tensorflow/python/summary/summary.py
index b80ad79074..7ff633a654 100644
--- a/tensorflow/python/summary/summary.py
+++ b/tensorflow/python/summary/summary.py
@@ -152,8 +152,7 @@ def image(name, tensor, max_outputs=3, collections=None, family=None):
   """
   with _summary_op_util.summary_scope(
       name, family, values=[tensor]) as (tag, scope):
-    # pylint: disable=protected-access
-    val = _gen_logging_ops._image_summary(
+    val = _gen_logging_ops.image_summary(
         tag=tag, tensor=tensor, max_images=max_outputs, name=scope)
     _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
   return val
@@ -237,10 +236,9 @@ def audio(name, tensor, sample_rate, max_outputs=3, collections=None,
   """
   with _summary_op_util.summary_scope(
       name, family=family, values=[tensor]) as (tag, scope):
-    # pylint: disable=protected-access
     sample_rate = _ops.convert_to_tensor(
         sample_rate, dtype=_dtypes.float32, name='sample_rate')
-    val = _gen_logging_ops._audio_summary_v2(
+    val = _gen_logging_ops.audio_summary_v2(
         tag=tag, tensor=tensor, max_outputs=max_outputs,
         sample_rate=sample_rate, name=scope)
     _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
@@ -286,8 +284,7 @@ def merge(inputs, collections=None, name=None):
         'Use tf.contrib.summary instead.')
   name = _summary_op_util.clean_tag(name)
   with _ops.name_scope(name, 'Merge', inputs):
-    # pylint: disable=protected-access
-    val = _gen_logging_ops._merge_summary(inputs=inputs, name=name)
+    val = _gen_logging_ops.merge_summary(inputs=inputs, name=name)
     _summary_op_util.collect(val, collections, [])
   return val
 
diff --git a/tensorflow/python/training/checkpoint_ops.py b/tensorflow/python/training/checkpoint_ops.py
index 7f92d94d2b..a6e9662b73 100644
--- a/tensorflow/python/training/checkpoint_ops.py
+++ b/tensorflow/python/training/checkpoint_ops.py
@@ -149,7 +149,7 @@ def _load_and_remap_matrix(ckpt_path,
   num_rows_present = num_rows_to_load
   if remap_rows:
     row_remapping, num_rows_present = (
-        gen_checkpoint_ops._generate_vocab_remapping(  # pylint: disable=protected-access
+        gen_checkpoint_ops.generate_vocab_remapping(
             new_vocab_file=new_row_vocab_file,
             old_vocab_file=old_row_vocab_file,
             new_vocab_offset=new_row_vocab_offset,
@@ -168,7 +168,7 @@ def _load_and_remap_matrix(ckpt_path,
   num_cols_present = new_col_vocab_size
   if remap_cols:
     col_remapping, num_cols_present = (
-        gen_checkpoint_ops._generate_vocab_remapping(  # pylint: disable=protected-access
+        gen_checkpoint_ops.generate_vocab_remapping(
             new_vocab_file=new_col_vocab_file,
             old_vocab_file=old_col_vocab_file,
             new_vocab_offset=0,  # Offset is unused for cols (no partitioning).
@@ -178,7 +178,7 @@ def _load_and_remap_matrix(ckpt_path,
       num_rows_to_load * new_col_vocab_size -
       num_rows_present * num_cols_present, 1
   ])
-  return_tensor = gen_checkpoint_ops._load_and_remap_matrix(  # pylint: disable=protected-access
+  return_tensor = gen_checkpoint_ops.load_and_remap_matrix(
       ckpt_path=ckpt_path,
       old_tensor_name=old_tensor_name,
       row_remapping=row_remapping,
diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py
index 1ce8c156a0..23b30632f6 100644
--- a/tensorflow/python/training/learning_rate_decay_test.py
+++ b/tensorflow/python/training/learning_rate_decay_test.py
@@ -43,8 +43,8 @@ class LRDecayTest(test_util.TensorFlowTestCase):
 
   def testStaircase(self):
     with self.test_session():
-      step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
-                                     name="step", container="", shared_name="")
+      step = gen_state_ops.variable(shape=[], dtype=dtypes.int32,
+                                    name="step", container="", shared_name="")
       assign_100 = state_ops.assign(step, 100)
       assign_1 = state_ops.assign(step, 1)
       assign_2 = state_ops.assign(step, 2)
@@ -264,8 +264,8 @@ class ExponentialDecayTest(test_util.TensorFlowTestCase):
     initial_lr = 0.1
     k = 10
     decay_rate = 0.96
-    step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
-                                   name="step", container="", shared_name="")
+    step = gen_state_ops.variable(
+        shape=[], dtype=dtypes.int32, name="step", container="", shared_name="")
     assign_step = state_ops.assign(step, 0)
     increment_step = state_ops.assign_add(step, 1)
     decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr, step,
@@ -281,8 +281,8 @@ class ExponentialDecayTest(test_util.TensorFlowTestCase):
     initial_lr = 0.1
     k = 10
     decay_rate = 0.96
-    step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
-                                   name="step", container="", shared_name="")
+    step = gen_state_ops.variable(
+        shape=[], dtype=dtypes.int32, name="step", container="", shared_name="")
     assign_step = state_ops.assign(step, 0)
     increment_step = state_ops.assign_add(step, 1)
     decayed_lr = learning_rate_decay.natural_exp_decay(initial_lr,
@@ -304,8 +304,8 @@ class InverseDecayTest(test_util.TensorFlowTestCase):
     initial_lr = 0.1
     k = 10
     decay_rate = 0.96
-    step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
-                                   name="step", container="", shared_name="")
+    step = gen_state_ops.variable(
+        shape=[], dtype=dtypes.int32, name="step", container="", shared_name="")
     assign_step = state_ops.assign(step, 0)
     increment_step = state_ops.assign_add(step, 1)
     decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr,
@@ -323,8 +323,8 @@ class InverseDecayTest(test_util.TensorFlowTestCase):
     initial_lr = 0.1
     k = 10
     decay_rate = 0.96
-    step = gen_state_ops._variable(shape=[], dtype=dtypes.int32,
-                                   name="step", container="", shared_name="")
+    step = gen_state_ops.variable(
+        shape=[], dtype=dtypes.int32, name="step", container="", shared_name="")
     assign_step = state_ops.assign(step, 0)
     increment_step = state_ops.assign_add(step, 1)
     decayed_lr = learning_rate_decay.inverse_time_decay(initial_lr,
diff --git a/tensorflow/python/training/moving_averages_test.py b/tensorflow/python/training/moving_averages_test.py
index 6efdeb2866..6717811bbb 100644
--- a/tensorflow/python/training/moving_averages_test.py
+++ b/tensorflow/python/training/moving_averages_test.py
@@ -376,7 +376,7 @@ class ExponentialMovingAverageTest(test.TestCase):
     with ops.device("/job:dev_v0"):
       v0 = variables.Variable(10.0, name="v0")
     with ops.device("/job:dev_v1"):
-      v1 = gen_state_ops._variable(
+      v1 = gen_state_ops.variable(
           shape=[1],
           dtype=dtypes.float32,
           name="v1",
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 9afd1e6643..e8ea5abfbd 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -311,8 +311,7 @@ class BaseSaverBuilder(object):
     Returns:
       A string tensor.
     """
-    # pylint: disable=protected-access
-    return gen_io_ops._sharded_filename(filename_tensor, shard, num_shards)
+    return gen_io_ops.sharded_filename(filename_tensor, shard, num_shards)
 
   def _AddSaveOps(self, filename_tensor, saveables):
     """Add ops to save variables that are on the same shard.
@@ -421,8 +420,7 @@ class BaseSaverBuilder(object):
         sharded_saves.append(self._AddSaveOps(sharded_filename, saveables))
     # Return the sharded name for the save path.
     with ops.control_dependencies([x.op for x in sharded_saves]):
-      # pylint: disable=protected-access
-      return gen_io_ops._sharded_filespec(filename_tensor, num_shards_tensor)
+      return gen_io_ops.sharded_filespec(filename_tensor, num_shards_tensor)
 
   def _AddRestoreOps(self,
                      filename_tensor,
diff --git a/tensorflow/python/training/saver_test_utils.py b/tensorflow/python/training/saver_test_utils.py
index 44b06b357e..0a8b7a09af 100644
--- a/tensorflow/python/training/saver_test_utils.py
+++ b/tensorflow/python/training/saver_test_utils.py
@@ -35,7 +35,7 @@ class CheckpointedOp(object):
   # pylint: disable=protected-access
   def __init__(self, name, table_ref=None):
     if table_ref is None:
-      self.table_ref = gen_lookup_ops._mutable_hash_table_v2(
+      self.table_ref = gen_lookup_ops.mutable_hash_table_v2(
           key_dtype=dtypes.string, value_dtype=dtypes.float32, name=name)
     else:
       self.table_ref = table_ref
@@ -57,10 +57,10 @@ class CheckpointedOp(object):
       return CheckpointedOp.CustomSaveable(self, self.name)
 
   def insert(self, keys, values):
-    return gen_lookup_ops._lookup_table_insert_v2(self.table_ref, keys, values)
+    return gen_lookup_ops.lookup_table_insert_v2(self.table_ref, keys, values)
 
   def lookup(self, keys, default):
-    return gen_lookup_ops._lookup_table_find_v2(self.table_ref, keys, default)
+    return gen_lookup_ops.lookup_table_find_v2(self.table_ref, keys, default)
 
   def keys(self):
     return self._export()[0]
@@ -69,8 +69,8 @@ class CheckpointedOp(object):
     return self._export()[1]
 
   def _export(self):
-    return gen_lookup_ops._lookup_table_export_v2(self.table_ref, dtypes.string,
-                                                  dtypes.float32)
+    return gen_lookup_ops.lookup_table_export_v2(self.table_ref, dtypes.string,
+                                                 dtypes.float32)
 
   class CustomSaveable(saver_module.BaseSaverBuilder.SaveableObject):
     """A custom saveable for CheckpointedOp."""
@@ -86,6 +86,6 @@ class CheckpointedOp(object):
       super(CheckpointedOp.CustomSaveable, self).__init__(table, specs, name)
 
     def restore(self, restore_tensors, shapes):
-      return gen_lookup_ops._lookup_table_import_v2(
+      return gen_lookup_ops.lookup_table_import_v2(
           self.op.table_ref, restore_tensors[0], restore_tensors[1])
   # pylint: enable=protected-access
diff --git a/tensorflow/python/user_ops/user_ops.py b/tensorflow/python/user_ops/user_ops.py
index 17dbab706c..6f9b5d92bb 100644
--- a/tensorflow/python/user_ops/user_ops.py
+++ b/tensorflow/python/user_ops/user_ops.py
@@ -27,4 +27,4 @@ from tensorflow.python.ops.gen_user_ops import *  # pylint: disable=wildcard-imp
 
 def my_fact():
   """Example of overriding the generated code for an Op."""
-  return _gen_user_ops._fact()  # pylint: disable=protected-access
+  return _gen_user_ops.fact()
-- 
GitLab


From 8687aa6f7da68e378d5465914109498f23e300a0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 14:55:50 -0800
Subject: [PATCH 462/575] Remove unnecessary stack operations from graphs. This
 change implements the first such optimization that removes stack pushes
 without corresponding pops.

PiperOrigin-RevId: 187387794
---
 tensorflow/core/grappler/op_types.cc          | 13 ++++
 tensorflow/core/grappler/op_types.h           |  4 ++
 .../grappler/optimizers/loop_optimizer.cc     | 62 ++++++++++++++++++-
 .../optimizers/loop_optimizer_test.cc         | 59 ++++++++++++++++++
 .../grappler/optimizers/meta_optimizer.cc     |  2 +-
 tensorflow/core/grappler/utils.cc             | 17 +++++
 tensorflow/core/grappler/utils.h              |  8 +++
 7 files changed, 161 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/grappler/op_types.cc b/tensorflow/core/grappler/op_types.cc
index 9b3755ddce..fb46b584b2 100644
--- a/tensorflow/core/grappler/op_types.cc
+++ b/tensorflow/core/grappler/op_types.cc
@@ -300,6 +300,19 @@ bool IsSquaredDifference(const NodeDef& node) {
 
 bool IsSqueeze(const NodeDef& node) { return node.op() == "Squeeze"; }
 
+bool IsStackOp(const NodeDef& node) {
+  return node.op() == "Stack" || node.op() == "StackV2";
+}
+bool IsStackCloseOp(const NodeDef& node) {
+  return node.op() == "StackClose" || node.op() == "StackCloseV2";
+}
+bool IsStackPushOp(const NodeDef& node) {
+  return node.op() == "StackPush" || node.op() == "StackPushV2";
+}
+bool IsStackPopOp(const NodeDef& node) {
+  return node.op() == "StackPop" || node.op() == "StackPopV2";
+}
+
 bool IsStopGradient(const NodeDef& node) {
   const auto& op = node.op();
   return op == "StopGradient" || op == "PreventGradient";
diff --git a/tensorflow/core/grappler/op_types.h b/tensorflow/core/grappler/op_types.h
index 1fa43a9b66..a7c33ef97b 100644
--- a/tensorflow/core/grappler/op_types.h
+++ b/tensorflow/core/grappler/op_types.h
@@ -118,6 +118,10 @@ bool IsSplitV(const NodeDef& node);
 bool IsSqrtGrad(const NodeDef& node);
 bool IsSquaredDifference(const NodeDef& node);
 bool IsSqueeze(const NodeDef& node);
+bool IsStackOp(const NodeDef& node);
+bool IsStackCloseOp(const NodeDef& node);
+bool IsStackPushOp(const NodeDef& node);
+bool IsStackPopOp(const NodeDef& node);
 bool IsStopGradient(const NodeDef& node);
 bool IsStridedSlice(const NodeDef& node);
 bool IsStridedSliceGrad(const NodeDef& node);
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.cc b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
index 102526e22f..cc226c01db 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
@@ -22,20 +22,76 @@ limitations under the License.
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/strings/strcat.h"
 
 namespace tensorflow {
 namespace grappler {
+namespace {
 
-Status LoopOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
-                               GraphDef* optimized_graph) {
-  *optimized_graph = item.graph;
+Status RemoveStackOps(const GraphDef& graph, GraphDef* optimized_graph) {
+  SimpleGraphView graph_view;
+  TF_RETURN_IF_ERROR(graph_view.Initialize(graph));
+  const std::unordered_set<string> op_types_to_traverse(
+      {"Stack", "StackV2", "Enter", "Switch", "RefSwitch", "Identity"});
+  std::set<int> nodes_to_delete;
+  for (int node_idx = 0; node_idx < graph.node_size(); ++node_idx) {
+    const NodeDef& node = graph.node(node_idx);
+    if (IsStackOp(node)) {
+      std::set<int> nodes_found;
+      graph_view.DepthFirstSearch(op_types_to_traverse, node_idx, &nodes_found);
+      bool found_pop = false;
+      bool found_unexpected = false;
+      for (int found_idx : nodes_found) {
+        const NodeDef& node = graph.node(found_idx);
+        if (IsStackPushOp(node) || IsStackOp(node) || IsStackCloseOp(node)) {
+          continue;
+        } else if (IsStackPopOp(node)) {
+          found_pop = true;
+        } else {
+          // Don't modify the graph if we found an unexpected op. There may be
+          // a pop hiding behind it.
+          found_unexpected = true;
+        }
+      }
+      if (!found_unexpected && !found_pop) {
+        VLOG(1) << "Found stack node with no pop: " << node.DebugString();
+        // Remove all pushes.
+        for (int found_idx : nodes_found) {
+          const NodeDef& node = graph.node(found_idx);
+          if (IsStackPushOp(node)) {
+            nodes_to_delete.insert(found_idx);
+          }
+        }
+      }
+    }
+  }
 
+  *optimized_graph = graph;
+  if (!nodes_to_delete.empty()) {
+    int last = optimized_graph->node_size() - 1;
+    for (auto it = nodes_to_delete.rbegin(); it != nodes_to_delete.rend();
+         ++it) {
+      const int node_to_delete = *it;
+      optimized_graph->mutable_node()->SwapElements(node_to_delete, last);
+      --last;
+    }
+    optimized_graph->mutable_node()->DeleteSubrange(last + 1,
+                                                    nodes_to_delete.size());
+  }
   return Status::OK();
 }
 
+}  // namespace
+
+Status LoopOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
+                               GraphDef* optimized_graph) {
+  Status status = RemoveStackOps(item.graph, optimized_graph);
+  return status;
+}
+
 void LoopOptimizer::Feedback(Cluster* /*cluster*/, const GrapplerItem& /*item*/,
                              const GraphDef& /*optimized_graph*/,
                              double /*result*/) {
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc b/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
index c09434f609..bb2ee6b02b 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
@@ -57,6 +57,65 @@ TEST_F(LoopOptimizerTest, NoOp) {
   VerifyGraphsEqual(item.graph, output, __FUNCTION__);
 }
 
+namespace {
+NodeDef* AddNode(const string& name, const string& op,
+                 const std::vector<string>& inputs, GraphDef* graph) {
+  NodeDef* node = graph->add_node();
+  node->set_name(name);
+  node->set_op(op);
+  for (const string& input : inputs) {
+    node->add_input(input);
+  }
+  return node;
+}
+}  // namespace
+
+TEST_F(LoopOptimizerTest, RemovePush_NoOp) {
+  GrapplerItem item;
+  GraphDef& graph = item.graph;
+  // Stack with corresponding push/pop.
+  AddNode("stack1", "StackV2", {}, &graph);
+  AddNode("push1", "StackPushV2", {"stack1"}, &graph);
+  AddNode("pop1", "StackPopV2", {"stack1"}, &graph);
+  // Stack with corresponding push/pop behind Enter.
+  AddNode("stack2", "StackV2", {}, &graph);
+  AddNode("push_enter", "Enter", {"stack1"}, &graph);
+  AddNode("push2", "StackPushV2", {"push_enter"}, &graph);
+  AddNode("pop_enter", "Enter", {"stack1"}, &graph);
+  AddNode("pop2", "StackPopV2", {"pop_enter"}, &graph);
+  // Stack with unexpected op type in fanout of Stack.
+  AddNode("stack3", "StackV2", {}, &graph);
+  AddNode("push3", "StackPushV2", {"stack3"}, &graph);
+  AddNode("stop", "StopGradient", {"stack3"}, &graph);
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+  VerifyGraphsEqual(item.graph, output, __FUNCTION__);
+}
+
+TEST_F(LoopOptimizerTest, RemovePushWithoutMatchingPop) {
+  GrapplerItem item;
+  GraphDef& graph = item.graph;
+  AddNode("stack1", "StackV2", {}, &graph);
+  AddNode("push1", "StackPushV2", {"stack1"}, &graph);
+  AddNode("stack2", "StackV2", {}, &graph);
+  AddNode("push_enter", "Enter", {"stack2"}, &graph);
+  AddNode("push2", "StackPushV2", {"push_enter"}, &graph);
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+  EXPECT_EQ(3, output.node_size());
+  int found = 0;
+  for (int i = 0; i < output.node_size(); ++i) {
+    if (output.node(i).name() == "stack1") ++found;
+    if (output.node(i).name() == "push_enter") ++found;
+    if (output.node(i).name() == "stack2") ++found;
+  }
+  EXPECT_EQ(3, found);
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 93658a6475..b674ee1553 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -110,7 +110,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
           new DependencyOptimizer(cfg_.dependency_optimization())));
     }
-    if (cfg_.loop_optimization() != RewriterConfig::OFF) {
+    if (cfg_.loop_optimization() == RewriterConfig::ON) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
           new LoopOptimizer(cfg_.loop_optimization())));
     }
diff --git a/tensorflow/core/grappler/utils.cc b/tensorflow/core/grappler/utils.cc
index 81bb5e6c3b..a611a93086 100644
--- a/tensorflow/core/grappler/utils.cc
+++ b/tensorflow/core/grappler/utils.cc
@@ -348,6 +348,7 @@ inline void STLSortAndRemoveDuplicates(T* v) {
 
 Status SimpleGraphView::Initialize(const GraphDef& graph, bool dedup_inputs,
                                    bool dedup_outputs) {
+  graph_ = &graph;
   const int num_nodes = graph.node_size();
   inputs_.clear();
   inputs_.resize(num_nodes);
@@ -394,6 +395,22 @@ Status SimpleGraphView::Initialize(const GraphDef& graph, bool dedup_inputs,
   return Status::OK();
 }
 
+void SimpleGraphView::DepthFirstSearch(
+    const std::unordered_set<string>& op_types_to_traverse, int node_idx,
+    std::set<int>* nodes_found) const {
+  const NodeDef& node = graph_->node(node_idx);
+  if (op_types_to_traverse.find(node.op()) == op_types_to_traverse.end()) {
+    nodes_found->insert(node_idx);
+    return;
+  }
+  if (nodes_found->find(node_idx) != nodes_found->end()) {
+    return;
+  }
+  for (auto output_idx : this->outputs(node_idx)) {
+    DepthFirstSearch(op_types_to_traverse, output_idx, nodes_found);
+  }
+}
+
 string SimpleGraphView::PrintToString() const {
   string str;
   for (int i = 0; i < num_nodes(); ++i) {
diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h
index 255319693a..1b91a57154 100644
--- a/tensorflow/core/grappler/utils.h
+++ b/tensorflow/core/grappler/utils.h
@@ -194,9 +194,17 @@ class SimpleGraphView {
     return outputs_[node_idx];
   }
 
+  // Traverse the graph starting at `node_idx`, collecting indices of nodes
+  // visited in nodes_found. If a node has an op in `op_types_to_traverse`, the
+  // walk continues to its children. It is assumed that *graph_ was not modified
+  // after the call to Initialize().
+  void DepthFirstSearch(const std::unordered_set<string>& op_types_to_traverse,
+                        int node_idx, std::set<int>* nodes_found) const;
+
   string PrintToString() const;
 
  private:
+  const GraphDef* graph_;  // Not owned.
   std::vector<string> index_to_name_;
   std::unordered_map<string, int> name_to_index_;
   std::vector<gtl::InlinedVector<int, 4>> inputs_;
-- 
GitLab


From d3c8659b27c644268156d15ec4b556e60db21491 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Wed, 28 Feb 2018 15:18:29 -0800
Subject: [PATCH 463/575] keras: Avoid unneccesary call to .call() when
 building models with subclassing.

This fixes a regression in the defun microbenchmarks
(ResNet50Benchmarks.eager_train_with_defun_gpu_batch_32_channels_first etc.)
in tensorflow/contrib/eager/python/examples/resnet50/resnet50_test.py
seen after https://github.com/tensorflow/tensorflow/commit/9a84277be2cb8233c5c14270db6fcdff31ab4d93
(which embeds a model in model)

Without this change, converting a model call to a graph function using
something like:
model.call = tfe.defun(model.call)
could result in redundant nodes being added to the graph function
as the model._set_inputs() call would invoke model.call() again.

PiperOrigin-RevId: 187391494
---
 .../keras/_impl/keras/engine/base_layer.py    |  7 +++--
 .../keras/_impl/keras/engine/training.py      | 28 +++++++++++--------
 2 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/engine/base_layer.py b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
index 142325041b..7f215f5645 100644
--- a/tensorflow/python/keras/_impl/keras/engine/base_layer.py
+++ b/tensorflow/python/keras/_impl/keras/engine/base_layer.py
@@ -240,9 +240,10 @@ class Layer(tf_base_layers.Layer):
     if context.in_eager_mode():
       return output
 
-    # Un-built subclassed network: build it
-    if hasattr(self, '_set_inputs') and not self.inputs:
-      self._set_inputs(inputs, training=kwargs.get('training'))
+    if hasattr(self, '_symbolic_set_inputs') and not self.inputs:
+      # Subclassed network: explicitly set metadata normally set by a call to
+      # self._set_inputs().
+      self._symbolic_set_inputs(inputs, output)
 
     # Update learning phase info.
     output_tensors = generic_utils.to_list(output)
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index 63bea08ac5..c121d819ff 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -1835,14 +1835,17 @@ class Model(Network):
         'output_%d' % (i + 1) for i in range(len(dummy_output_values))]
     self.built = True
 
-  def _symbolic_set_inputs(self, inputs, training=None):
-    """Set model's inputs based on the input data received from the user.
+  def _symbolic_set_inputs(self, inputs, outputs=None, training=None):
+    """Set model's inputs and output specs based.
 
     This is to be used for Model subclasses, which do not know at instantiation
     time what their inputs look like.
 
     Args:
       inputs: Argument `x` (input data) passed by the user upon first model use.
+      outputs: None, a data tensor, or a list of data tensors. If None, the
+        outputs will be determined by invoking self.call(), otherwise the
+        provided value will be used.
       training: Boolean or None. Only relevant in symbolic mode. Specifies
         whether to build the model's graph in inference mode (False), training
         mode (True), or using the Keras learning phase (None).
@@ -1892,17 +1895,18 @@ class Model(Network):
           self._feed_input_names.append(name)
           self._feed_input_shapes.append(K.int_shape(v))
 
-    # Obtain symbolic outputs by calling the model.
-    if len(self.inputs) == 1:
-      if self._expects_training_arg:
-        outputs = self.call(self.inputs[0], training=training)
-      else:
-        outputs = self.call(self.inputs[0])
-    else:
-      if self._expects_training_arg:
-        outputs = self.call(self.inputs, training=training)
+    if outputs is None:
+      # Obtain symbolic outputs by calling the model.
+      if len(self.inputs) == 1:
+        if self._expects_training_arg:
+          outputs = self.call(self.inputs[0], training=training)
+        else:
+          outputs = self.call(self.inputs[0])
       else:
-        outputs = self.call(self.inputs)
+        if self._expects_training_arg:
+          outputs = self.call(self.inputs, training=training)
+        else:
+          outputs = self.call(self.inputs)
     if isinstance(outputs, (list, tuple)):
       outputs = list(outputs)
     else:
-- 
GitLab


From 656055e0c9acd944b7a34bfe01c06ad122f87da8 Mon Sep 17 00:00:00 2001
From: Guangda Lai <laigd@google.com>
Date: Wed, 28 Feb 2018 15:36:39 -0800
Subject: [PATCH 464/575] Exclude more tests for cuda_on_cpu project.

PiperOrigin-RevId: 187394209
---
 tensorflow/core/grappler/optimizers/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 7b801db2c8..b8995ef365 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -382,6 +382,7 @@ cc_library(
 tf_cc_test_gpu(
     name = "memory_optimizer_test",
     srcs = ["memory_optimizer_test.cc"],
+    tags = ["no_cuda_on_cpu_tap"],
     deps = [
         ":memory_optimizer",
         "//tensorflow/cc:cc_ops",
-- 
GitLab


From b98a1f31bca1e773ee215f2c32aa0509843c1247 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 15:44:55 -0800
Subject: [PATCH 465/575] Propagate NaNs for floating point min/max operations.

PiperOrigin-RevId: 187395444
---
 .../compiler/xla/service/hlo_evaluator.cc     | 39 ++++++++---
 .../compiler/xla/service/llvm_ir/llvm_util.cc | 12 ++--
 .../xla/tests/array_elementwise_ops_test.cc   | 70 +++----------------
 .../xla/tests/scalar_computations_test.cc     | 12 ++++
 4 files changed, 59 insertions(+), 74 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_evaluator.cc b/tensorflow/compiler/xla/service/hlo_evaluator.cc
index edb1ad2360..42de7ada61 100644
--- a/tensorflow/compiler/xla/service/hlo_evaluator.cc
+++ b/tensorflow/compiler/xla/service/hlo_evaluator.cc
@@ -613,14 +613,25 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return Status::OK();
   }
 
-  template <
-      typename NativeT,
-      typename std::enable_if<!is_complex_t<NativeT>::value>::type* = nullptr>
+  template <typename NativeT,
+            typename std::enable_if<std::is_integral<NativeT>::value>::type* =
+                nullptr>
+  Status HandleMaximum(HloInstruction* maximum) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[maximum],
+        ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) {
+          return std::max(lhs, rhs);
+        }));
+    return Status::OK();
+  }
+
+  template <typename NativeT, typename std::enable_if<std::is_floating_point<
+                                  NativeT>::value>::type* = nullptr>
   Status HandleMaximum(HloInstruction* maximum) {
     TF_ASSIGN_OR_RETURN(
         parent_->evaluated_[maximum],
         ElementWiseBinaryOp(maximum, [](ElementwiseT lhs, ElementwiseT rhs) {
-          return std::fmax(lhs, rhs);
+          return ((lhs >= rhs) || std::isnan(lhs)) ? lhs : rhs;
         }));
     return Status::OK();
   }
@@ -636,18 +647,30 @@ class HloEvaluator::TypedVisitor : public DfsHloVisitorWithDefault {
     return HandleMaximum<ElementwiseT>(maximum);
   }
 
-  template <
-      typename NativeT,
-      typename std::enable_if<!is_complex_t<NativeT>::value>::type* = nullptr>
+  template <typename NativeT,
+            typename std::enable_if<std::is_integral<NativeT>::value>::type* =
+                nullptr>
   Status HandleMinimum(HloInstruction* minimum) {
     TF_ASSIGN_OR_RETURN(parent_->evaluated_[minimum],
                         ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el,
                                                         ElementwiseT rhs_el) {
-                          return std::fmin(lhs_el, rhs_el);
+                          return std::min(lhs_el, rhs_el);
                         }));
     return Status::OK();
   }
 
+  template <typename NativeT, typename std::enable_if<std::is_floating_point<
+                                  NativeT>::value>::type* = nullptr>
+  Status HandleMinimum(HloInstruction* minimum) {
+    TF_ASSIGN_OR_RETURN(
+        parent_->evaluated_[minimum],
+        ElementWiseBinaryOp(minimum, [](ElementwiseT lhs_el,
+                                        ElementwiseT rhs_el) {
+          return ((lhs_el <= rhs_el) || std::isnan(lhs_el)) ? lhs_el : rhs_el;
+        }));
+    return Status::OK();
+  }
+
   template <
       typename NativeT,
       typename std::enable_if<is_complex_t<NativeT>::value>::type* = nullptr>
diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
index 5c1866311d..2a282f3be7 100644
--- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
+++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc
@@ -106,8 +106,10 @@ llvm::Value* EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value,
     auto cmp = ir_builder->CreateFCmpUGE(lhs_value, rhs_value);
     return ir_builder->CreateSelect(cmp, lhs_value, rhs_value);
   } else {
-    return EmitCallToIntrinsic(llvm::Intrinsic::maxnum, {lhs_value, rhs_value},
-                               {lhs_value->getType()}, ir_builder);
+    auto cmp_ge = ir_builder->CreateFCmpOGE(lhs_value, rhs_value);
+    auto lhs_is_nan = ir_builder->CreateFCmpUNE(lhs_value, lhs_value);
+    auto sel_lhs = ir_builder->CreateOr(cmp_ge, lhs_is_nan);
+    return ir_builder->CreateSelect(sel_lhs, lhs_value, rhs_value);
   }
 }
 
@@ -117,8 +119,10 @@ llvm::Value* EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value,
     auto cmp = ir_builder->CreateFCmpULE(lhs_value, rhs_value);
     return ir_builder->CreateSelect(cmp, lhs_value, rhs_value);
   } else {
-    return EmitCallToIntrinsic(llvm::Intrinsic::minnum, {lhs_value, rhs_value},
-                               {lhs_value->getType()}, ir_builder);
+    auto cmp_le = ir_builder->CreateFCmpOLE(lhs_value, rhs_value);
+    auto lhs_is_nan = ir_builder->CreateFCmpUNE(lhs_value, lhs_value);
+    auto sel_lhs = ir_builder->CreateOr(cmp_le, lhs_is_nan);
+    return ir_builder->CreateSelect(sel_lhs, lhs_value, rhs_value);
   }
 }
 
diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
index 8b35259013..6e21dda25d 100644
--- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
+++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc
@@ -1648,33 +1648,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, SquareIn4DZeroElements) {
   ComputeAndCompareR4<float>(&builder, expected, {}, error_spec_);
 }
 
-// GPU backend emits nvvm intrinsic for fmin and fmax, whose semantics is NOT
-// such
-// * fmin(NaN, x) = x
-// * fmax(NaN, x) = x
-// so we only test NAN on CPU.
-//
-// TODO(b/28180546): Make this compile in a way that is consistent
-// among backends.
 XLA_TEST_F(ArrayElementwiseOpTest, MinF32s) {
   ComputationBuilder builder(client_, TestName());
-#if !defined(XLA_TEST_BACKEND_CPU)
-  auto lhs = builder.ConstantR1<float>({1.0f, 1.0f, 2.25f});
-  auto rhs = builder.ConstantR1<float>({2.0f, -5.0f, 1.0f});
-#else
   SetFastMathDisabled(true);
   auto lhs = builder.ConstantR1<float>({1.0f, 1.0f, 2.25f, NAN, 6.0f});
   auto rhs = builder.ConstantR1<float>({2.0f, -5.0f, 1.0f, 10.0f, NAN});
-#endif
   auto minimum = builder.Min(lhs, rhs);
 
-  ComputeAndCompareR1<float>(&builder,
-#if !defined(XLA_TEST_BACKEND_CPU)
-                             {1.0f, -5.0f, 1.0f},
-#else
-                             {1.0f, -5.0f, 1.0f, 10.0f, 6.0f},
-#endif
-                             {}, error_spec_);
+  ComputeAndCompareR1<float>(&builder, {1.0f, -5.0f, 1.0f, NAN, NAN}, {},
+                             error_spec_);
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, MinZeroElementF32s) {
@@ -1685,50 +1667,26 @@ XLA_TEST_F(ArrayElementwiseOpTest, MinZeroElementF32s) {
   ComputeAndCompareR1<float>(&builder, {}, {}, error_spec_);
 }
 
-// TODO(b/28180546): Make this compile in a way that is consistent
-// among backends. See comment on MinF32s test above.
 XLA_TEST_F(ArrayElementwiseOpTest, MinF64s) {
   ComputationBuilder builder(client_, TestName());
-#if !defined(XLA_TEST_BACKEND_CPU)
-  auto lhs = builder.ConstantR1<double>({1.0, 1.0, 2.25});
-  auto rhs = builder.ConstantR1<double>({2.0, -5.0, 1.0});
-#else
   SetFastMathDisabled(true);
   auto lhs = builder.ConstantR1<double>({1.0, 1.0, 2.25, NAN, 6.0});
   auto rhs = builder.ConstantR1<double>({2.0, -5.0, 1.0, 10.0, NAN});
-#endif
   auto minimum = builder.Min(lhs, rhs);
 
-  ComputeAndCompareR1<double>(&builder,
-#if !defined(XLA_TEST_BACKEND_CPU)
-                              {1.0, -5.0, 1.0},
-#else
-                              {1.0, -5.0, 1.0, 10.0, 6.0},
-#endif
-                              {}, error_spec_);
+  ComputeAndCompareR1<double>(&builder, {1.0, -5.0, 1.0, NAN, NAN}, {},
+                              error_spec_);
 }
 
-// TODO(b/28180546): Make this compile in a way that is consistent
-// among backends. See comment on MinF32s test above.
 XLA_TEST_F(ArrayElementwiseOpTest, MaxF32s) {
   ComputationBuilder builder(client_, TestName());
-#if !defined(XLA_TEST_BACKEND_CPU)
-  auto lhs = builder.ConstantR1<float>({1.0f, 1.0f, 2.25f});
-  auto rhs = builder.ConstantR1<float>({2.0f, -5.0f, 1.0f});
-#else
   SetFastMathDisabled(true);
   auto lhs = builder.ConstantR1<float>({1.0f, 1.0f, 2.25f, NAN, 6.0f});
   auto rhs = builder.ConstantR1<float>({2.0f, -5.0f, 1.0f, 10.0f, NAN});
-#endif
   auto maximum = builder.Max(lhs, rhs);
 
-  ComputeAndCompareR1<float>(&builder,
-#if !defined(XLA_TEST_BACKEND_CPU)
-                             {2.0f, 1.0f, 2.25f},
-#else
-                             {2.0f, 1.0f, 2.25f, 10.0f, 6.0f},
-#endif
-                             {}, error_spec_);
+  ComputeAndCompareR1<float>(&builder, {2.0f, 1.0f, 2.25f, NAN, NAN}, {},
+                             error_spec_);
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, MaxZeroElementF32s) {
@@ -1739,27 +1697,15 @@ XLA_TEST_F(ArrayElementwiseOpTest, MaxZeroElementF32s) {
   ComputeAndCompareR1<float>(&builder, {}, {}, error_spec_);
 }
 
-// TODO(b/28180546): Make this compile in a way that is consistent
-// among backends. See comment on MinF32s test above.
 XLA_TEST_F(ArrayElementwiseOpTest, MaxF64s) {
   ComputationBuilder builder(client_, TestName());
-#if !defined(XLA_TEST_BACKEND_CPU)
-  auto lhs = builder.ConstantR1<double>({1.0, 1.0, 2.25});
-  auto rhs = builder.ConstantR1<double>({2.0, -5.0, 1.0});
-#else
   SetFastMathDisabled(true);
   auto lhs = builder.ConstantR1<double>({1.0, 1.0, 2.25, NAN, 6.0});
   auto rhs = builder.ConstantR1<double>({2.0, -5.0, 1.0, 10.0, NAN});
-#endif
   auto maximum = builder.Max(lhs, rhs);
 
-  ComputeAndCompareR1<double>(&builder,
-#if !defined(XLA_TEST_BACKEND_CPU)
-                              {2.0, 1.0, 2.25},
-#else
-                              {2.0, 1.0, 2.25, 10.0, 6.0},
-#endif
-                              {}, error_spec_);
+  ComputeAndCompareR1<double>(&builder, {2.0, 1.0, 2.25, NAN, NAN}, {},
+                              error_spec_);
 }
 
 XLA_TEST_F(ArrayElementwiseOpTest, MaxS32s) {
diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
index d7bda77e87..0c88bef69d 100644
--- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc
+++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc
@@ -860,6 +860,12 @@ XLA_TEST_F(ScalarComputationsTest, MinF32Below) {
   TestMinMax<float>(-100.1f, 3.1f, -100.1f, &ComputationBuilder::Min);
 }
 
+XLA_TEST_F(ScalarComputationsTest, MinPropagatesNan) {
+  SetFastMathDisabled(true);
+  TestMinMax<float>(NAN, 3.1f, NAN, &ComputationBuilder::Min);
+  TestMinMax<float>(-3.1f, NAN, NAN, &ComputationBuilder::Min);
+}
+
 XLA_TEST_F(ScalarComputationsTest, MaxF32Above) {
   TestMinMax<float>(10.1f, 3.1f, 10.1f, &ComputationBuilder::Max);
 }
@@ -868,6 +874,12 @@ XLA_TEST_F(ScalarComputationsTest, MaxF32Below) {
   TestMinMax<float>(-100.1f, 3.1f, 3.1f, &ComputationBuilder::Max);
 }
 
+XLA_TEST_F(ScalarComputationsTest, MaxPropagatesNan) {
+  SetFastMathDisabled(true);
+  TestMinMax<float>(NAN, 3.1f, NAN, &ComputationBuilder::Max);
+  TestMinMax<float>(-3.1f, NAN, NAN, &ComputationBuilder::Max);
+}
+
 XLA_TEST_F(ScalarComputationsTest, ComplicatedArithmeticExpressionF32) {
   // Compute the expression (1 * (3 - 1) * (7 + 0) - 4) / 20.
   ComputationBuilder b(client_, TestName());
-- 
GitLab


From 9a52edb4760f13dda1b27f9126f8117d6c4f9bc9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 15:47:01 -0800
Subject: [PATCH 466/575] Update a few tests to work with Grappler constant
 folding.

PiperOrigin-RevId: 187395886
---
 tensorflow/python/kernel_tests/pooling_ops_test.py   | 8 ++++++--
 tensorflow/python/kernel_tests/reduction_ops_test.py | 4 +++-
 tensorflow/python/kernel_tests/softmax_op_test.py    | 3 +--
 3 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/kernel_tests/pooling_ops_test.py b/tensorflow/python/kernel_tests/pooling_ops_test.py
index a0ac355b60..2f3bea5825 100644
--- a/tensorflow/python/kernel_tests/pooling_ops_test.py
+++ b/tensorflow/python/kernel_tests/pooling_ops_test.py
@@ -31,6 +31,7 @@ from tensorflow.python.ops import gen_nn_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import gradients_impl
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import variables
 import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging
@@ -731,7 +732,8 @@ class PoolingTest(test.TestCase):
                                             [1, 1, 1, 3], "evenly divide")
     if test.is_gpu_available():
       with self.test_session(use_gpu=True):
-        t = constant_op.constant(1.0, shape=[1, 2, 2, 4])
+        t = variables.Variable(np.ones([1, 2, 2, 4]))
+        variables.global_variables_initializer().run()
         with self.assertRaisesOpError("for CPU devices"):
           nn_ops.max_pool(
               t, ksize=[1, 1, 1, 2], strides=[1, 1, 1, 2],
@@ -1210,7 +1212,9 @@ class PoolingTest(test.TestCase):
                              padding, use_gpu, v2):
     pool_func = gen_nn_ops.max_pool_v2 if v2 else nn_ops.max_pool
     with self.test_session(use_gpu=use_gpu):
-      input_tensor = constant_op.constant(input_data, shape=input_sizes)
+      input_tensor = variables.Variable(
+          np.array(input_data, dtype=np.float32).reshape(input_sizes))
+      variables.global_variables_initializer().run()
       output_tensor = pool_func(input_tensor, [1, window_rows, window_cols, 1],
                                 [1, row_stride, col_stride, 1], padding)
       output_backprop_tensor = constant_op.constant(
diff --git a/tensorflow/python/kernel_tests/reduction_ops_test.py b/tensorflow/python/kernel_tests/reduction_ops_test.py
index d306d1b8d6..589ea54973 100644
--- a/tensorflow/python/kernel_tests/reduction_ops_test.py
+++ b/tensorflow/python/kernel_tests/reduction_ops_test.py
@@ -30,6 +30,7 @@ from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
 # The maximum input rank to test.
@@ -212,7 +213,8 @@ class SumReductionTest(BaseReductionTest):
     arr = np.ones([68000], dtype=np.float16)
 
     with self.test_session(graph=ops.Graph(), use_gpu=True) as sess:
-      tf_arr = array_ops.constant(arr)
+      tf_arr = variables.Variable(arr)
+      variables.global_variables_initializer().run()
       tf_mean = math_ops.reduce_mean(tf_arr, 0, False)
       tf_out_mean = sess.run(tf_mean)
     self.assertAllClose(tf_out_mean, 1.)
diff --git a/tensorflow/python/kernel_tests/softmax_op_test.py b/tensorflow/python/kernel_tests/softmax_op_test.py
index 4d89831aae..2b8e99e18e 100644
--- a/tensorflow/python/kernel_tests/softmax_op_test.py
+++ b/tensorflow/python/kernel_tests/softmax_op_test.py
@@ -20,7 +20,6 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import test_util
@@ -166,7 +165,7 @@ class SoftmaxTest(test.TestCase):
 
   def testEmptyInput(self):
     with self.test_session():
-      x = constant_op.constant([[]], shape=[0, 3])
+      x = array_ops.placeholder(dtypes.float32, shape=[0, 3])
       self.assertEqual(0, array_ops.size(x).eval())
       # reshape would raise if logits is empty
       with self.assertRaises(errors_impl.InvalidArgumentError):
-- 
GitLab


From 8be4ab7b2d2ad00ffa84da82e9cbba88c677877d Mon Sep 17 00:00:00 2001
From: Michael Case <mikecase@google.com>
Date: Wed, 28 Feb 2018 15:51:18 -0800
Subject: [PATCH 467/575] Add all_files target to gcs_smoke_test BUILD file.

PiperOrigin-RevId: 187396477
---
 tensorflow/BUILD                                      |  1 +
 .../integration_tests/gcs_smoke_test/BUILD.bazel      | 11 +++++++++++
 2 files changed, 12 insertions(+)

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index a4e7602bea..4b2facd6b3 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -674,6 +674,7 @@ filegroup(
         "//tensorflow/tools/docs:all_files",
         "//tensorflow/tools/git:all_files",
         "//tensorflow/tools/graph_transforms:all_files",
+        "//tensorflow/tools/integration_tests/gcs_smoke_test:all_files",
         "//tensorflow/tools/mlpbtxt:all_files",
         "//tensorflow/tools/proto_text:all_files",
         "//tensorflow/tools/quantization:all_files",
diff --git a/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel b/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel
index 439d86c5d2..0acc139df9 100755
--- a/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel
+++ b/tensorflow/tools/integration_tests/gcs_smoke_test/BUILD.bazel
@@ -54,3 +54,14 @@ integration_test(
     test_docker_image = toolchain_container_images()["tensorflow"],
     test_type = "MultiMachine",
 )
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+)
-- 
GitLab


From a5b336194f4fd1a26bcd5dfd159d6edf4dfdd081 Mon Sep 17 00:00:00 2001
From: Akshay Modi <nareshmodi@google.com>
Date: Wed, 28 Feb 2018 15:59:33 -0800
Subject: [PATCH 468/575] Remove record_gradient param from benchmark function

PiperOrigin-RevId: 187397610
---
 tensorflow/python/eager/benchmarks_test.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/python/eager/benchmarks_test.py b/tensorflow/python/eager/benchmarks_test.py
index 228ff62b20..527a919ab0 100644
--- a/tensorflow/python/eager/benchmarks_test.py
+++ b/tensorflow/python/eager/benchmarks_test.py
@@ -35,7 +35,6 @@ from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import backprop  # pylint: disable=unused-import
 from tensorflow.python.eager import context
 from tensorflow.python.eager import core
-from tensorflow.python.eager import execute
 from tensorflow.python.eager import function
 from tensorflow.python.eager import test
 from tensorflow.python.framework import dtypes
@@ -60,7 +59,7 @@ def c_tfe_py_fastpath_execute(a,
   ), "The prototype doesn't contain C code for graph construction"
   try:
     return pywrap_tensorflow.TFE_Py_FastPathExecute(
-        ctx._handle, ctx.device_name, "MatMul", execute.record_gradient, name,
+        ctx._handle, ctx.device_name, "MatMul", name,
         ctx._post_execution_callbacks, a, b, "transpose_a", transpose_a,
         "transpose_b", transpose_b)
   except core._NotOkStatusException as e:
-- 
GitLab


From e670c81d85f3353ea3b701569f8f5126714a02bf Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Wed, 28 Feb 2018 16:22:42 -0800
Subject: [PATCH 469/575] GCS: HTTP error code 308 retries during upload.
 Previously, it would only permit 308 when getting the status of an upload.

This matches the behavior of the official library:
https://github.com/google/apitools/blob/master/apitools/base/py/transfer.py#L925

And the general description here:
https://cloud.google.com/storage/docs/json_api/v1/how-tos/resumable-upload

PiperOrigin-RevId: 187400843
---
 .../core/platform/cloud/curl_http_request.cc  |  8 +++--
 .../platform/cloud/gcs_file_system_test.cc    | 33 +++++++++++++------
 2 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/platform/cloud/curl_http_request.cc b/tensorflow/core/platform/cloud/curl_http_request.cc
index 88a5d1e96d..4b5f6974c1 100644
--- a/tensorflow/core/platform/cloud/curl_http_request.cc
+++ b/tensorflow/core/platform/cloud/curl_http_request.cc
@@ -493,14 +493,18 @@ Status CurlHttpRequest::Send() {
     case 303:  // See Other
     case 304:  // Not Modified
     case 307:  // Temporary Redirect
-    case 308:  // Resume Incomplete
     case 412:  // Precondition Failed
     case 413:  // Payload Too Large
       result = errors::FailedPrecondition(error_message);
       break;
 
     // UNAVAILABLE indicates a problem that can go away if the request
-    // is just retried without any modification.
+    // is just retried without any modification. 308 return codes are intended
+    // for write requests that can be retried. See the documentation and the
+    // official library:
+    // https://cloud.google.com/storage/docs/json_api/v1/how-tos/resumable-upload
+    // https://github.com/google/apitools/blob/master/apitools/base/py/transfer.py
+    case 308:  // Resume Incomplete
     case 409:  // Conflict
     case 429:  // Too Many Requests
     case 500:  // Internal Server Error
diff --git a/tensorflow/core/platform/cloud/gcs_file_system_test.cc b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
index d452074ce3..cd9fd3adea 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
@@ -393,7 +393,7 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceeds) {
                            "Timeouts: 5 1 10\n"
                            "Header Content-Range: bytes */17\n"
                            "Put: yes\n",
-                           "", errors::FailedPrecondition("308"), nullptr,
+                           "", errors::Unavailable("308"), nullptr,
                            {{"Range", "0-10"}}, 308),
        new FakeHttpRequest("Uri: https://custom/upload/location\n"
                            "Auth Token: fake_token\n"
@@ -406,13 +406,26 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadSucceeds) {
                            "Timeouts: 5 1 10\n"
                            "Header Content-Range: bytes */17\n"
                            "Put: yes\n",
-                           "", errors::FailedPrecondition("308"), nullptr,
+                           "", errors::Unavailable("308"), nullptr,
                            {{"Range", "bytes=0-12"}}, 308),
        new FakeHttpRequest("Uri: https://custom/upload/location\n"
                            "Auth Token: fake_token\n"
                            "Header Content-Range: bytes 13-16/17\n"
                            "Timeouts: 5 1 30\n"
                            "Put body: ent2\n",
+                           "", errors::Unavailable("308"), 308),
+       new FakeHttpRequest("Uri: https://custom/upload/location\n"
+                           "Auth Token: fake_token\n"
+                           "Timeouts: 5 1 10\n"
+                           "Header Content-Range: bytes */17\n"
+                           "Put: yes\n",
+                           "", errors::Unavailable("308"), nullptr,
+                           {{"Range", "bytes=0-14"}}, 308),
+       new FakeHttpRequest("Uri: https://custom/upload/location\n"
+                           "Auth Token: fake_token\n"
+                           "Header Content-Range: bytes 15-16/17\n"
+                           "Timeouts: 5 1 30\n"
+                           "Put body: t2\n",
                            "")});
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
@@ -521,14 +534,14 @@ TEST(GcsFileSystemTest, NewWritableFile_ResumeUploadAllAttemptsFail) {
                            "Put body: content1,content2\n",
                            "", errors::Unavailable("503"), 503)});
   for (int i = 0; i < 10; i++) {
-    requests.emplace_back(new FakeHttpRequest(
-        "Uri: https://custom/upload/location\n"
-        "Auth Token: fake_token\n"
-        "Timeouts: 5 1 10\n"
-        "Header Content-Range: bytes */17\n"
-        "Put: yes\n",
-        "", errors::FailedPrecondition("important HTTP error 308"), nullptr,
-        {{"Range", "0-10"}}, 308));
+    requests.emplace_back(
+        new FakeHttpRequest("Uri: https://custom/upload/location\n"
+                            "Auth Token: fake_token\n"
+                            "Timeouts: 5 1 10\n"
+                            "Header Content-Range: bytes */17\n"
+                            "Put: yes\n",
+                            "", errors::Unavailable("important HTTP error 308"),
+                            nullptr, {{"Range", "0-10"}}, 308));
     requests.emplace_back(new FakeHttpRequest(
         "Uri: https://custom/upload/location\n"
         "Auth Token: fake_token\n"
-- 
GitLab


From 86061c8e8034c5bee955659bdda8366f640f543d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 16:41:17 -0800
Subject: [PATCH 470/575] Adding the documentation for building the iOS demo
 for TensorFlow Lite.

PiperOrigin-RevId: 187403346
---
 tensorflow/docs_src/mobile/leftnav_files      |  1 +
 tensorflow/docs_src/mobile/tflite/demo_ios.md | 68 +++++++++++++++++++
 2 files changed, 69 insertions(+)
 create mode 100644 tensorflow/docs_src/mobile/tflite/demo_ios.md

diff --git a/tensorflow/docs_src/mobile/leftnav_files b/tensorflow/docs_src/mobile/leftnav_files
index ac50f528ba..4cf134cc3c 100644
--- a/tensorflow/docs_src/mobile/leftnav_files
+++ b/tensorflow/docs_src/mobile/leftnav_files
@@ -2,6 +2,7 @@ index.md
 ### TensorFlow Lite
 tflite/index.md
 tflite/demo_android.md
+tflite/demo_ios.md
 >>>
 ### TensorFlow Mobile
 mobile_intro.md
diff --git a/tensorflow/docs_src/mobile/tflite/demo_ios.md b/tensorflow/docs_src/mobile/tflite/demo_ios.md
new file mode 100644
index 0000000000..3ee9b1cbca
--- /dev/null
+++ b/tensorflow/docs_src/mobile/tflite/demo_ios.md
@@ -0,0 +1,68 @@
+# TensorFlow Lite Demo for iOS
+
+The TensorFlow Lite demo is a camera app that continuously classifies whatever
+it sees from your device's back camera, using a quantized MobileNet model. These
+instructions walk you through building and running the demo on an iOS device.
+
+## Prerequisites
+
+* You must have [Xcode](https://developer.apple.com/xcode/) installed and have a
+  valid Apple Developer ID, and have an iOS device set up and linked to your
+  developer account with all of the appropriate certificates. For these
+  instructions, we assume that you have already been able to build and deploy an
+  app to an iOS device with your current developer environment.
+
+* The demo app requires a camera and must be executed on a real iOS device. You
+  can build it and run with the iPhone Simulator but it won't have any camera
+  information to classify.
+
+* You don't need to build the entire TensorFlow library to run the demo, but you
+  will need to clone the TensorFlow repository if you haven't already:
+
+        git clone https://github.com/tensorflow/tensorflow
+
+* You'll also need the Xcode command-line tools:
+
+        xcode-select --install
+
+    If this is a new install, you will need to run the Xcode application once to
+    agree to the license before continuing.
+
+## Building the iOS Demo App
+
+1. Install CocoaPods if you don't have it:
+
+        sudo gem install cocoapods
+
+2. Download the model files used by the demo app (this is done from inside the
+   cloned directory):
+
+        sh tensorflow/contrib/lite/examples/ios/download_models.sh
+
+3. Install the pod to generate the workspace file:
+
+        cd tensorflow/contrib/lite/examples/ios/camera
+        pod install
+
+    If you have installed this pod before and that command doesn't work, try
+
+        pod update
+
+    At the end of this step you should have a file called 
+    `tflite_camera_example.xcworkspace`.
+
+4. Open the project in Xcode by typing this on the command line:
+
+        open tflite_camera_example.xcworkspace
+
+    This launches Xcode if it isn't open already and opens the
+    `tflite_camera_example` project.
+
+5. Build and run the app in Xcode.
+
+    Note that as mentioned earlier, you must already have a device set up and
+    linked to your Apple Developer account in order to deploy the app on a
+    device.
+
+You'll have to grant permissions for the app to use the device's camera. Point
+the camera at various objects and enjoy seeing how the model classifies things!
-- 
GitLab


From 6a2bb85654655d7dc6e5017de6586e76634ebcd1 Mon Sep 17 00:00:00 2001
From: Billy Lamberta <blamb@google.com>
Date: Wed, 28 Feb 2018 17:18:52 -0800
Subject: [PATCH 471/575] Docs: Update Fixed Point Quantization in performance.

PiperOrigin-RevId: 187408106
---
 tensorflow/docs_src/performance/leftnav_files |   4 +-
 .../docs_src/performance/quantization.md      | 461 +++++++++---------
 2 files changed, 245 insertions(+), 220 deletions(-)

diff --git a/tensorflow/docs_src/performance/leftnav_files b/tensorflow/docs_src/performance/leftnav_files
index 316f023f43..d11a7e5d07 100644
--- a/tensorflow/docs_src/performance/leftnav_files
+++ b/tensorflow/docs_src/performance/leftnav_files
@@ -2,6 +2,7 @@ performance_guide.md
 datasets_performance.md
 performance_models.md
 benchmarks.md
+quantization.md
 
 ### XLA
 xla/index.md
@@ -11,6 +12,3 @@ xla/jit.md
 xla/operation_semantics.md
 xla/shapes.md
 xla/tfcompile.md
-
-### Quantization
-quantization.md
diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md
index 544274cab6..63448c2ebe 100644
--- a/tensorflow/docs_src/performance/quantization.md
+++ b/tensorflow/docs_src/performance/quantization.md
@@ -1,226 +1,253 @@
-# How to Quantize Neural Networks with TensorFlow
-
-When modern neural networks were being developed, the biggest challenge was
-getting them to work at all! That meant that accuracy and speed during training
-were the top priorities. Using floating point arithmetic was the easiest way to
-preserve accuracy, and GPUs were well-equipped to accelerate those calculations,
-so it's natural that not much attention was paid to other numerical formats.
-
-These days, we actually have a lot of models being deployed in commercial
-applications. The computation demands of training grow with the number of
-researchers, but the cycles needed for inference expand in proportion to users.
-That means pure inference efficiency has become a burning issue for a lot of
-teams.
-
-That is where quantization comes in. It's an umbrella term that covers a lot of
-different techniques to store numbers and perform calculations on them in more
-compact formats than 32-bit floating point. I am going to focus on eight-bit
-fixed point, for reasons I'll go into more detail on later.
-
-[TOC]
-
-## Why does Quantization Work?
-
-Training neural networks is done by applying many tiny nudges to the weights,
-and these small increments typically need floating point precision to work
-(though there are research efforts to use quantized representations here too).
-
-Taking a pre-trained model and running inference is very different. One of the
-magical qualities of deep networks is that they tend to cope very well with high
-levels of noise in their inputs. If you think about recognizing an object in a
-photo you've just taken, the network has to ignore all the CCD noise, lighting
-changes, and other non-essential differences between it and the training
-examples it's seen before, and focus on the important similarities instead. This
-ability means that they seem to treat low-precision calculations as just another
-source of noise, and still produce accurate results even with numerical formats
-that hold less information.
-
-## Why Quantize?
-
-Neural network models can take up a lot of space on disk, with the original
-AlexNet being over 200 MB in float format for example. Almost all of that size
-is taken up with the weights for the neural connections, since there are often
-many millions of these in a single model. Because they're all slightly different
-floating point numbers, simple compression formats like zip don't compress them
-well. They are arranged in large layers though, and within each layer the
-weights tend to be normally distributed within a certain range, for example -3.0
-to 6.0.
-
-The simplest motivation for quantization is to shrink file sizes by storing the
-min and max for each layer, and then compressing each float value to an
-eight-bit integer representing the closest real number in a linear set of 256
-within the range. For example with the -3.0 to 6.0 range, a 0 byte would
-represent -3.0, a 255 would stand for 6.0, and 128 would represent about 1.5.
-I'll go into the exact calculations later, since there's some subtleties, but
-this means you can get the benefit of a file on disk that's shrunk by 75%, and
-then convert back to float after loading so that your existing floating-point
-code can work without any changes.
-
-Another reason to quantize is to reduce the computational resources you need to
-do the inference calculations, by running them entirely with eight-bit inputs
-and outputs. This is a lot more difficult since it requires changes everywhere
-you do calculations, but offers a lot of potential rewards. Fetching eight-bit
-values only requires 25% of the memory bandwidth of floats, so you'll make much
-better use of caches and avoid bottlenecking on RAM access. You can also
-typically use SIMD operations that do many more operations per clock cycle. In
-some case you'll have a DSP chip available that can accelerate eight-bit
-calculations too, which can offer a lot of advantages.
-
-Moving calculations over to eight bit will help you run your models faster, and
-use less power (which is especially important on mobile devices). It also opens
-the door to a lot of embedded systems that can't run floating point code
-efficiently, so it can enable a lot of applications in the IoT world.
-
-## Why Not Train in Lower Precision Directly?
-
-There have been some experiments training at lower bit depths, but the results
-seem to indicate that you need higher than eight bit to handle the back
-propagation and gradients. That makes implementing the training more
-complicated, and so starting with inference made sense. We also already have a
-lot of float models already that we use and know well, so being able to convert
-them directly is very convenient.
-
-## How Can You Quantize Your Models?
-
-TensorFlow has production-grade support for eight-bit calculations built in. It
-also has a process for converting many models trained in floating-point over to
-equivalent graphs using quantized calculations for inference. For example,
-here's how you can translate the latest GoogLeNet model into a version that uses
-eight-bit computations:
-
-```sh
-curl -L "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz" |
-  tar -C tensorflow/examples/label_image/data -xz
-bazel build tensorflow/tools/graph_transforms:transform_graph
-bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
-  --in_graph=tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb \
-  --out_graph=/tmp/quantized_graph.pb \
-  --inputs=input \
-  --outputs=InceptionV3/Predictions/Reshape_1 \
-  --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3")
-    remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true)
-    fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes
-    strip_unused_nodes sort_by_execution_order'
+# Fixed Point Quantization
+
+Quantization techniques store and calculate numbers in more compact formats.
+[TensorFlow Lite](/mobile/tflite/) adds quantization that uses an 8-bit fixed
+point representation.
+
+Since a challenge for modern neural networks is optimizing for high accuracy, the
+priority has been improving accuracy and speed during training. Using floating
+point arithmetic is an easy way to preserve accuracy and GPUs are designed to
+accelerate these calculations.
+
+However, as more machine learning models are deployed to mobile devices,
+inference efficiency has become a critical issue. Where the computational demand
+for *training* grows with the amount of models trained on different
+architectures, the computational demand for *inference* grows in proportion to
+the amount of users.
+
+## Quantization benefits
+
+
+Using 8-bit calculations help your models run faster and use less power. This is
+especially important for mobile devices and embedded applications that can't run
+floating point code efficiently, for example, Internet of Things (IoT) and
+robotics devices. There are additional opportunities to extend this support to
+more backends and research lower precision networks.
+
+### Smaller file sizes {: .hide-from-toc}
+
+Neural network models require a lot of space on disk. For example, the original
+AlexNet requires over 200 MB for the float format—almost all of that for the
+model's millions of weights. Because the weights are slightly different
+floating point numbers, simple compression formats perform poorly (like zip).
+
+Weights fall in large layers of numerical values. For each layer, weights tend to
+be normally distributed within a range. Quantization can shrink file sizes by
+storing the minimum and maximum weight for each layer, then compress each
+weight's float value to an 8-bit integer representing the closest real number in
+a linear set of 256 within the range.
+
+### Faster inference {: .hide-from-toc}
+
+Since calculations are run entirely on 8-bit inputs and outputs, quantization
+reduces the computational resources needed for inference calculations. This is
+more involved, requiring changes to all floating point calculations, but results
+in a large speed-up for inference time.
+
+### Memory efficiency {: .hide-from-toc}
+
+Since fetching 8-bit values only requires 25% of the memory bandwidth of floats,
+more efficient caches avoid bottlenecks for RAM access. In many cases, the power
+consumption for running a neural network is dominated by memory access. The
+savings from using fixed-point 8-bit weights and activations are significant. 
+
+Typically, SIMD operations are available that run more operations per clock
+cycle. In some cases, a DSP chip is available that accelerates 8-bit calculations
+resulting in a massive speedup.
+
+## Fixed point quantization techniques
+
+The goal is to use the same precision for weights and activations during both
+training and inference. But an important difference is that training consists of
+a forward pass and a backward pass, while inference only uses a forward pass.
+When we train the model with quantization in the loop, we ensure that the forward
+pass matches precision for both training and inference.
+
+To minimize the loss in accuracy for fully fixed point models (weights and
+activations), train the model with quantization in the loop. This simulates
+quantization in the forward pass of a model so weights tend towards values that
+perform better during quantized inference. The backward pass uses quantized
+weights and activations and models quantization as a straight through estimator.
+(See Bengio et al., [2013](https://arxiv.org/abs/1308.3432))
+
+Additionally, the minimum and maximum values for activations are determined
+during training. This allows a model trained with quantization in the loop to be
+converted to a fixed point inference model with little effort, eliminating the
+need for a separate calibration step.
+
+## Quantization training with TensorFlow
+
+TensorFlow can train models with quantization in the loop. Because training
+requires small gradient adjustments, floating point values are still used. To
+keep models as floating point while adding the quantization error in the training
+loop, @{$array_ops#Fake_quantization} nodes simulate the effect of quantization
+in the forward and backward passes.
+
+Since it's difficult to add these fake quantization operations to all the
+required locations in the model, there's a function available that rewrites the
+training graph. To create a fake quantized training graph:
+
+```
+# Build forward pass of model.
+loss = tf.losses.get_total_loss()
+
+# Call the training rewrite which rewrites the graph in-place with
+# FakeQuantization nodes and folds batchnorm for training. It is
+# often needed to fine tune a floating point model for quantization
+# with this training tool. When training from scratch, quant_delay
+# can be used to activate quantization after training to converge
+# with the float graph, effectively fine-tuning the model.
+tf.contrib.quantize.create_training_graph(quant_delay=2000000)
+
+# Call backward pass optimizer as usual.
+optimizer = tf.train.GradientDescentOptimizer(learning_rate)
+optimizer.minimize(loss)
 ```
 
-This will produce a new model that runs the same operations as the original, but
-with eight bit calculations internally, and all weights quantized as well. If
-you look at the file size, you'll see it's about a quarter of the original (23MB
-versus 91MB). You can still run this model using exactly the same inputs and
-outputs though, and you should get equivalent results. Here's an example:
+The rewritten *eval graph* is non-trivially different from the *training graph*
+since the quantization ops affect the batch normalization step. Because of this,
+we've added a separate rewrite for the *eval graph*:
 
-```sh
-bazel build tensorflow/examples/label_image:label_image
-bazel-bin/tensorflow/examples/label_image/label_image \
---graph=/tmp/quantized_graph.pb \
+```
+# Build eval model
+logits = tf.nn.softmax_cross_entropy_with_logits(...)
+
+# Call the eval rewrite which rewrites the graph in-place with
+# FakeQuantization nodes and fold batchnorm for eval.
+tf.contrib.quantize.create_eval_graph()
+
+# Save the checkpoint and eval graph proto to disk for freezing
+# and providing to TFLite.
+with open(eval_graph_file, ‘w’) as f:
+  f.write(str(g.as_graph_def()))
+saver = tf.train.Saver()
+saver.save(sess, checkpoint_name)
+```
+
+Methods to rewrite the training and eval graphs are an active area of research
+and experimentation. Although rewrites and quantized training might not work or
+improve performance for all models, we are working to generalize these
+techniques.
+
+## Generating fully quantized models
+
+The previously demonstrated after-rewrite eval graph only *simulates*
+quantization. To generate real fixed point computations from a trained
+quantization model, convert it to a fixed point kernel. Tensorflow Lite supports
+this conversion from the graph resulting from `create_eval_graph`.
+
+First, create a frozen graph that will be the input for the TensorFlow Lite
+toolchain:
+
+```
+bazel build tensorflow/python/tools:freeze_graph && \
+  bazel-bin/tensorflow/python/tools/freeze_graph \
+  --input_graph=eval_graph_def.pb \
+  --input_checkpoint=checkpoint \
+  --output_graph=frozen_eval_graph.pb --output_node_names=outputs
 ```
 
-You'll see that this runs the newly-quantized graph, and outputs a very similar
-answer to the original.
-
-You can run the same process on your own models saved out as GraphDefs, with the
-input and output names adapted to those your network requires. I recommend that
-you run them through the freeze_graph script first, to convert checkpoints into
-constants stored in the file.
-
-## How Does the Quantization Process Work?
-
-We've implemented quantization by writing equivalent eight-bit versions of
-operations that are commonly used during inference. These include convolution,
-matrix multiplication, activation functions, pooling operations and
-concatenation. The conversion script first replaces all the individual ops it
-knows about with quantized equivalents. These are small sub-graphs that have
-conversion functions before and after to move the data between float and
-eight-bit. Below is an example of what they look like. First here's the original
-Relu operation, with float inputs and outputs:
-
-![Relu Diagram](https://www.tensorflow.org/images/quantization0.png)
-
-Then, this is the equivalent converted subgraph, still with float inputs and
-outputs, but with internal conversions so the calculations are done in eight
-bit.
-
-![Converted Diagram](https://www.tensorflow.org/images/quantization1.png)
-
-The min and max operations actually look at the values in the input float
-tensor, and then feeds them into the Dequantize operation that converts the
-tensor into eight-bits. There are more details on how the quantized representation
-works later on.
-
-Once the individual operations have been converted, the next stage is to remove
-unnecessary conversions to and from float. If there are consecutive sequences of
-operations that all have float equivalents, then there will be a lot of adjacent
-Dequantize/Quantize ops. This stage spots that pattern, recognizes that they
-cancel each other out, and removes them, like this:
-
-![Stripping Diagram](https://www.tensorflow.org/images/quantization2.png)
-
-Applied on a large scale to models where all of the operations have quantized
-equivalents, this gives a graph where all of the tensor calculations are done in
-eight bit, without having to convert to float.
-
-## What Representation is Used for Quantized Tensors?
-
-We approach converting floating-point arrays of numbers into eight-bit
-representations as a compression problem. We know that the weights and
-activation tensors in trained neural network models tend to have values that are
-distributed across comparatively small ranges (for example you might have -15 to
-+15 for weights, -500 to 1000 for activations on an image model, though the
-exact numbers will vary). We also know from experiment that neural nets tend to
-be very robust in the face of noise, and so the noise-like error produced by
-quantizing down to a small set of values will not hurt the precision of the
-overall results very much. We also want to pick a representation that's easy to
-perform calculations on, especially the large matrix multiplications that form
-the bulk of the work that's needed to run a model.
-
-These led us to pick a representation that has two floats to store the overall
-minimum and maximum values that are represented by the lowest and highest
-quantized value. Each entry in the quantized array represents a float value in
-that range, distributed linearly between the minimum and maximum. For example,
-if we have minimum = -10.0, and maximum = 30.0f, and an eight-bit array, here's
-what the quantized values represent:
+Provide this to the TensorFlow Lite Optimizing Converter (TOCO) to get a fully
+quantized TensorFLow Lite model:
 
 ```
-Quantized | Float
---------- | -----
-0         | -10.0
-255       | 30.0
-128       | 10.0
+bazel build tensorflow/contrib/lite/toco:toco && \
+  ./bazel-bin/third_party/tensorflow/contrib/lite/toco/toco \
+  --input_file=frozen_eval_graph.pb \
+  --output_file=tflite_model.tflite \
+  --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE \
+  --inference_type=QUANTIZED_UINT8 \
+  --input_shape="1,224, 224,3" \
+  --input_array=input \
+  --output_array=outputs \
+  --std_value=127.5 --mean_value=127.5
 ```
 
-The advantages of this format are that it can represent arbitrary magnitudes of
-ranges, they don't have to be symmetrical, it can represent signed and unsigned
-values, and the linear spread makes doing multiplications straightforward. There
-are alternatives like [Song Han's code books](http://arxiv.org/pdf/1510.00149.pdf)
-that can use lower bit depths by non-linearly distributing the float values
-across the representation, but these tend to be more expensive to calculate on.
-
-The advantage of having a strong and clear definition of the quantized format is
-that it's always possible to convert back and forth from float for operations
-that aren't quantization-ready, or to inspect the tensors for debugging
-purposes. One implementation detail in TensorFlow that we're hoping to improve
-in the future is that the minimum and maximum float values need to be passed as
-separate tensors to the one holding the quantized values, so graphs can get a
-bit dense!
-
-The nice thing about the minimum and maximum ranges is that they can often be
-pre-calculated. Weight parameters are constants known at load time, so their
-ranges can also be stored as constants. We often know the ranges for inputs (for
-examples images are usually RGB values in the range 0.0 to 255.0), and many
-activation functions have known ranges too. This can avoid having to analyze the
-outputs of an operation to determine the range, which we need to do for math ops
-like convolution or matrix multiplication which produce 32-bit accumulated
-results from 8-bit inputs.
-
-## What's Next?
-
-We've found that we can get extremely good performance on mobile and embedded
-devices by using eight-bit arithmetic rather than floating-point. You can see
-the framework we use to optimize matrix multiplications at
-[gemmlowp](https://github.com/google/gemmlowp). We still need to apply all the
-lessons we've learned to the TensorFlow ops to get maximum performance on
-mobile, but we're actively working on that. Right now, this quantized
-implementation is a reasonably fast and accurate reference implementation that
-we're hoping will enable wider support for our eight-bit models on a wider
-variety of devices. We also hope that this demonstration will encourage the
-community to explore what's possible with low-precision neural networks.
+See the documentation for @{tf.contrib.quantize} and
+[TensorFlow Lite](/mobile/tflite/).
+
+## Quantized accuracy
+
+Fixed point [MobileNet](https://arxiv.org/abs/1704.0486) models are released with
+8-bit weights and activations. Using the rewriters, these models achieve the
+Top-1 accuracies listed in Table 1. For comparison, the floating point accuracies
+are listed for the same models. The code used to generate these models
+[is available](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md)
+along with links to all of the pretrained mobilenet_v1 models.
+
+<figure>
+  <table>
+    <tr>
+      <th>Image Size</th>
+      <th>Depth</th>
+      <th>Top-1 Accuracy:<br>Floating point</th>
+      <th>Top-1 Accuracy:<br>Fixed point: 8 bit weights and activations</th>
+    </tr>
+    <tr><td>128</td><td>0.25</td><td>0.415</td><td>0.399</td></tr>
+    <tr><td>128</td><td>0.5</td><td>0.563</td><td>0.549</td></tr>
+    <tr><td>128</td><td>0.75</td><td>0.621</td><td>0.598</td></tr>
+    <tr><td>128</td><td>1</td><td>0.652</td><td>0.64</td></tr>
+    <tr><td>160</td><td>0.25</td><td>0.455</td><td>0.435</td></tr>
+    <tr><td>160</td><td>0.5</td><td>0.591</td><td>0.577</td></tr>
+    <tr><td>160</td><td>0.75</td><td>0.653</td><td>0.639</td></tr>
+    <tr><td>160</td><td>1</td><td>0.68</td><td>0.673</td></tr>
+    <tr><td>192</td><td>0.25</td><td>0.477</td><td>0.458</td></tr>
+    <tr><td>192</td><td>0.5</td><td>0.617</td><td>0.604</td></tr>
+    <tr><td>192</td><td>0.75</td><td>0.672</td><td>0.662</td></tr>
+    <tr><td>192</td><td>1</td><td>0.7</td><td>0.69</td></tr>
+    <tr><td>224</td><td>0.25</td><td>0.498</td><td>0.482</td></tr>
+    <tr><td>224</td><td>0.5</td><td>0.633</td><td>0.622</td></tr>
+    <tr><td>224</td><td>0.75</td><td>0.684</td><td>0.679</td></tr>
+    <tr><td>224</td><td>1</td><td>0.709</td><td>0.697</td></tr>
+  </table>
+  <figcaption>
+    <b>Table 1</b>: MobileNet Top-1 accuracy on Imagenet Validation dataset.
+  </figcaption>
+</figure>
+
+## Representation for quantized tensors
+
+TensorFlow approaches the conversion of floating-point arrays of numbers into
+8-bit representations as a compression problem. Since the weights and activation
+tensors in trained neural network models tend to have values that are distributed
+across comparatively small ranges (for example, -15 to +15 for weights or -500 to
+1000 for image model activations). And since neural nets tend to be robust
+handling noise, the error introduced by quantizing to a small set of values
+maintains the precision of the overall results within an acceptable threshold. A
+chosen representation must perform fast calculations, especially the large matrix
+multiplications that comprise the bulk of the computations while running a model.
+
+This is represented with two floats that store the overall minimum and maximum
+values corresponding to the lowest and highest quantized value. Each entry in the
+quantized array represents a float value in that range, distributed linearly
+between the minimum and maximum. For example, with a minimum of -10.0 and maximum
+of 30.0f, and an 8-bit array, the quantized values represent the following:
+
+<figure>
+  <table>
+    <tr><th>Quantized</th><th>Float</th></tr>
+    <tr><td>0</td><td>-10.0</td></tr>
+    <tr><td>255</td><td>30.0</td></tr>
+    <tr><td>128</td><td>10.0</td></tr>
+  </table>
+  <figcaption>
+    <b>Table 2</b>: Example quantized value range
+  </figcaption>
+</figure>
+
+The advantages of this representation format are:
+
+* It efficiently represents an arbitrary magnitude of ranges.
+* The values don't have to be symmetrical.
+* The format represents both signed and unsigned values.
+* The linear spread makes multiplications straightforward.
+
+Alternative techniques use lower bit depths by non-linearly distributing the
+float values across the representation, but currently are more expensive in terms
+of computation time. (See Han et al.,
+[2016](https://arxiv.org/abs/1510.00149).)
+
+The advantage of having a clear definition of the quantized format is that it's
+always possible to convert back and forth from fixed-point to floating-point for
+operations that aren't quantization-ready, or to inspect the tensors for
+debugging.
-- 
GitLab


From 6fdb9ad1baf7686a75f9e660178f7ac595e7fc2e Mon Sep 17 00:00:00 2001
From: 4d55397500 <4d55397500@users.noreply.github.com>
Date: Wed, 28 Feb 2018 17:57:35 -0800
Subject: [PATCH 472/575] Fix return value in sampled_softmax_loss

---
 tensorflow/python/ops/nn_impl.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py
index 5fa5708114..254f0051a4 100644
--- a/tensorflow/python/ops/nn_impl.py
+++ b/tensorflow/python/ops/nn_impl.py
@@ -1345,4 +1345,4 @@ def sampled_softmax_loss(weights,
   sampled_losses = nn_ops.softmax_cross_entropy_with_logits(
       labels=labels, logits=logits)
   # sampled_losses is a [batch_size] tensor.
-  return sampled_losses
+  return sampled_losses
\ No newline at end of file
-- 
GitLab


From f5e2a70e0363c1b08a342e395c4e040114b7a424 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Wed, 28 Feb 2018 17:54:56 -0800
Subject: [PATCH 473/575] Set generated ops to hidden if they are not included
 in TensorFlow Python API. Also, update endpoints in ApiDef files for a few
 ops.

PiperOrigin-RevId: 187412039
---
 tensorflow/core/api_def/python_api/api_def_Abort.pbtxt   | 4 ++++
 .../python_api/api_def_AccumulatorApplyGradient.pbtxt    | 4 ++++
 .../python_api/api_def_AccumulatorNumAccumulated.pbtxt   | 4 ++++
 .../python_api/api_def_AccumulatorSetGlobalStep.pbtxt    | 4 ++++
 .../python_api/api_def_AccumulatorTakeGradient.pbtxt     | 4 ++++
 .../core/api_def/python_api/api_def_AdjustContrast.pbtxt | 4 ++++
 .../core/api_def/python_api/api_def_AdjustHue.pbtxt      | 4 ++++
 .../api_def/python_api/api_def_AdjustSaturation.pbtxt    | 4 ++++
 .../core/api_def/python_api/api_def_ApplyAdadelta.pbtxt  | 4 ++++
 .../core/api_def/python_api/api_def_ApplyAdagrad.pbtxt   | 4 ++++
 .../core/api_def/python_api/api_def_ApplyAdagradDA.pbtxt | 4 ++++
 .../core/api_def/python_api/api_def_ApplyAdam.pbtxt      | 4 ++++
 .../core/api_def/python_api/api_def_ApplyAddSign.pbtxt   | 4 ++++
 .../python_api/api_def_ApplyCenteredRMSProp.pbtxt        | 4 ++++
 .../core/api_def/python_api/api_def_ApplyFtrl.pbtxt      | 4 ++++
 .../core/api_def/python_api/api_def_ApplyFtrlV2.pbtxt    | 4 ++++
 .../python_api/api_def_ApplyGradientDescent.pbtxt        | 4 ++++
 .../core/api_def/python_api/api_def_ApplyMomentum.pbtxt  | 4 ++++
 .../core/api_def/python_api/api_def_ApplyPowerSign.pbtxt | 4 ++++
 .../python_api/api_def_ApplyProximalAdagrad.pbtxt        | 4 ++++
 .../api_def_ApplyProximalGradientDescent.pbtxt           | 4 ++++
 .../core/api_def/python_api/api_def_ApplyRMSProp.pbtxt   | 4 ++++
 .../api_def/python_api/api_def_ApproximateEqual.pbtxt    | 4 ++++
 .../api_def/python_api/api_def_AssignAddVariableOp.pbtxt | 4 ++++
 .../api_def/python_api/api_def_AssignSubVariableOp.pbtxt | 4 ++++
 .../api_def/python_api/api_def_AssignVariableOp.pbtxt    | 4 ++++
 .../core/api_def/python_api/api_def_AvgPool3D.pbtxt      | 6 ++++++
 .../core/api_def/python_api/api_def_BatchDataset.pbtxt   | 4 ++++
 .../api_def/python_api/api_def_BatchMatrixBandPart.pbtxt | 4 ++++
 .../api_def/python_api/api_def_BatchMatrixDiag.pbtxt     | 4 ++++
 .../api_def/python_api/api_def_BatchMatrixDiagPart.pbtxt | 4 ++++
 .../api_def/python_api/api_def_BatchMatrixSetDiag.pbtxt  | 4 ++++
 .../core/api_def/python_api/api_def_BiasAddGrad.pbtxt    | 4 ++++
 .../python_api/api_def_BytesProducedStatsDataset.pbtxt   | 4 ++++
 .../core/api_def/python_api/api_def_CacheDataset.pbtxt   | 4 ++++
 .../core/api_def/python_api/api_def_CholeskyGrad.pbtxt   | 4 ++++
 .../api_def/python_api/api_def_CompareAndBitpack.pbtxt   | 4 ++++
 .../api_def/python_api/api_def_ConcatenateDataset.pbtxt  | 4 ++++
 .../python_api/api_def_ConditionalAccumulator.pbtxt      | 4 ++++
 .../api_def/python_api/api_def_ConsumeMutexLock.pbtxt    | 4 ++++
 .../core/api_def/python_api/api_def_ControlTrigger.pbtxt | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Conv2D.pbtxt  | 6 ++++++
 .../python_api/api_def_Conv2DBackpropFilter.pbtxt        | 6 ++++++
 .../api_def/python_api/api_def_Conv2DBackpropInput.pbtxt | 6 ++++++
 tensorflow/core/api_def/python_api/api_def_Conv3D.pbtxt  | 6 ++++++
 .../python_api/api_def_Conv3DBackpropFilter.pbtxt        | 4 ++++
 .../python_api/api_def_Conv3DBackpropFilterV2.pbtxt      | 6 ++++++
 .../api_def/python_api/api_def_Conv3DBackpropInput.pbtxt | 4 ++++
 .../python_api/api_def_Conv3DBackpropInputV2.pbtxt       | 4 ++++
 .../python_api/api_def_CropAndResizeGradBoxes.pbtxt      | 4 ++++
 .../python_api/api_def_CropAndResizeGradImage.pbtxt      | 4 ++++
 .../api_def/python_api/api_def_DataFormatDimMap.pbtxt    | 4 ++++
 .../python_api/api_def_DataFormatVecPermute.pbtxt        | 4 ++++
 .../python_api/api_def_DatasetToSingleElement.pbtxt      | 4 ++++
 .../api_def/python_api/api_def_DecodeCompressed.pbtxt    | 4 ++++
 .../python_api/api_def_DenseToDenseSetOperation.pbtxt    | 4 ++++
 .../python_api/api_def_DenseToSparseBatchDataset.pbtxt   | 4 ++++
 .../python_api/api_def_DenseToSparseSetOperation.pbtxt   | 4 ++++
 .../api_def/python_api/api_def_DeserializeIterator.pbtxt | 4 ++++
 .../api_def/python_api/api_def_DestroyResourceOp.pbtxt   | 4 ++++
 .../core/api_def/python_api/api_def_Dilation2D.pbtxt     | 6 ++++++
 .../python_api/api_def_Dilation2DBackpropFilter.pbtxt    | 4 ++++
 .../python_api/api_def_Dilation2DBackpropInput.pbtxt     | 4 ++++
 .../python_api/api_def_EnqueueInQueueDataset.pbtxt       | 4 ++++
 tensorflow/core/api_def/python_api/api_def_FFT2D.pbtxt   | 9 +++++++++
 tensorflow/core/api_def/python_api/api_def_FFT3D.pbtxt   | 9 +++++++++
 .../core/api_def/python_api/api_def_FilterDataset.pbtxt  | 4 ++++
 .../python_api/api_def_FixedLengthRecordDataset.pbtxt    | 4 ++++
 .../core/api_def/python_api/api_def_FlatMapDataset.pbtxt | 4 ++++
 .../api_def/python_api/api_def_FusedBatchNormGrad.pbtxt  | 4 ++++
 .../python_api/api_def_FusedBatchNormGradV2.pbtxt        | 4 ++++
 .../core/api_def/python_api/api_def_FusedPadConv2D.pbtxt | 4 ++++
 .../python_api/api_def_FusedResizeAndPadConv2D.pbtxt     | 4 ++++
 .../core/api_def/python_api/api_def_GatherV2.pbtxt       | 4 ++++
 .../api_def/python_api/api_def_GeneratorDataset.pbtxt    | 4 ++++
 .../python_api/api_def_GroupByWindowDataset.pbtxt        | 4 ++++
 tensorflow/core/api_def/python_api/api_def_IFFT2D.pbtxt  | 9 +++++++++
 tensorflow/core/api_def/python_api/api_def_IFFT3D.pbtxt  | 9 +++++++++
 tensorflow/core/api_def/python_api/api_def_IRFFT.pbtxt   | 4 ++++
 tensorflow/core/api_def/python_api/api_def_IRFFT2D.pbtxt | 4 ++++
 tensorflow/core/api_def/python_api/api_def_IRFFT3D.pbtxt | 4 ++++
 .../core/api_def/python_api/api_def_ImmutableConst.pbtxt | 4 ++++
 .../api_def/python_api/api_def_InterleaveDataset.pbtxt   | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Inv.pbtxt     | 4 ++++
 .../core/api_def/python_api/api_def_Iterator.pbtxt       | 4 ++++
 .../python_api/api_def_IteratorFromStringHandle.pbtxt    | 4 ++++
 .../api_def/python_api/api_def_IteratorGetNext.pbtxt     | 4 ++++
 .../api_def/python_api/api_def_IteratorGetNextSync.pbtxt | 4 ++++
 .../python_api/api_def_IteratorSetStatsAggregator.pbtxt  | 4 ++++
 .../python_api/api_def_IteratorToStringHandle.pbtxt      | 4 ++++
 .../api_def/python_api/api_def_LatencyStatsDataset.pbtxt | 4 ++++
 .../core/api_def/python_api/api_def_LoopCond.pbtxt       | 4 ++++
 .../core/api_def/python_api/api_def_MakeIterator.pbtxt   | 4 ++++
 .../api_def/python_api/api_def_MapAndBatchDataset.pbtxt  | 4 ++++
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 .../python_api/api_def_SparseTensorSliceDataset.pbtxt    | 4 ++++
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 create mode 100644 tensorflow/core/api_def/python_api/api_def_OrderedMapStage.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_OrderedMapUnstage.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_OrderedMapUnstageNoKey.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_PaddedBatchDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_ParallelDynamicStitch.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_ParallelInterleaveDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_ParallelMapDataset.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_PlaceholderV2.pbtxt
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 create mode 100644 tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantize.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantizeV2.pbtxt
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 create mode 100644 tensorflow/core/api_def/python_api/api_def_QuantizedAdd.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_QuantizedBiasAdd.pbtxt
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 create mode 100644 tensorflow/core/api_def/python_api/api_def_QuantizedResizeBilinear.pbtxt
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 create mode 100644 tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdadelta.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdagrad.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdagradDA.pbtxt
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 create mode 100644 tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyFtrlV2.pbtxt
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 create mode 100644 tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyRMSProp.pbtxt
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 create mode 100644 tensorflow/core/api_def/python_api/api_def_SparseAccumulatorApplyGradient.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_SparseAccumulatorTakeGradient.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_SparseApplyAdadelta.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_SparseApplyAdagrad.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_SparseApplyAdagradDA.pbtxt
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diff --git a/tensorflow/core/api_def/python_api/api_def_Abort.pbtxt b/tensorflow/core/api_def/python_api/api_def_Abort.pbtxt
new file mode 100644
index 0000000000..3f95aaf12c
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Abort.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Abort"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AccumulatorApplyGradient.pbtxt b/tensorflow/core/api_def/python_api/api_def_AccumulatorApplyGradient.pbtxt
new file mode 100644
index 0000000000..1e76d6dadc
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AccumulatorApplyGradient.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AccumulatorApplyGradient"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AccumulatorNumAccumulated.pbtxt b/tensorflow/core/api_def/python_api/api_def_AccumulatorNumAccumulated.pbtxt
new file mode 100644
index 0000000000..fbe971ab2e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AccumulatorNumAccumulated.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AccumulatorNumAccumulated"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AccumulatorSetGlobalStep.pbtxt b/tensorflow/core/api_def/python_api/api_def_AccumulatorSetGlobalStep.pbtxt
new file mode 100644
index 0000000000..0047b25af6
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AccumulatorSetGlobalStep.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AccumulatorSetGlobalStep"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AccumulatorTakeGradient.pbtxt b/tensorflow/core/api_def/python_api/api_def_AccumulatorTakeGradient.pbtxt
new file mode 100644
index 0000000000..860fbe1245
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AccumulatorTakeGradient.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AccumulatorTakeGradient"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AdjustContrast.pbtxt b/tensorflow/core/api_def/python_api/api_def_AdjustContrast.pbtxt
new file mode 100644
index 0000000000..0311ad92b7
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AdjustContrast.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AdjustContrast"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AdjustHue.pbtxt b/tensorflow/core/api_def/python_api/api_def_AdjustHue.pbtxt
new file mode 100644
index 0000000000..b441167711
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AdjustHue.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AdjustHue"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AdjustSaturation.pbtxt b/tensorflow/core/api_def/python_api/api_def_AdjustSaturation.pbtxt
new file mode 100644
index 0000000000..893219e17a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AdjustSaturation.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AdjustSaturation"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyAdadelta.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyAdadelta.pbtxt
new file mode 100644
index 0000000000..d8776b19f1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyAdadelta.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyAdadelta"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyAdagrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyAdagrad.pbtxt
new file mode 100644
index 0000000000..7e659c1bb3
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyAdagrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyAdagrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyAdagradDA.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyAdagradDA.pbtxt
new file mode 100644
index 0000000000..d647c5eb0a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyAdagradDA.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyAdagradDA"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyAdam.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyAdam.pbtxt
new file mode 100644
index 0000000000..66d9095c8f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyAdam.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyAdam"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyAddSign.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyAddSign.pbtxt
new file mode 100644
index 0000000000..b7fe1aa654
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyAddSign.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyAddSign"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyCenteredRMSProp.pbtxt
new file mode 100644
index 0000000000..56003c5e6f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyCenteredRMSProp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyCenteredRMSProp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyFtrl.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyFtrl.pbtxt
new file mode 100644
index 0000000000..680b3ef480
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyFtrl.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyFtrl"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyFtrlV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyFtrlV2.pbtxt
new file mode 100644
index 0000000000..5ab3bb6efd
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyFtrlV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyFtrlV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyGradientDescent.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyGradientDescent.pbtxt
new file mode 100644
index 0000000000..467bf7db55
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyGradientDescent.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyGradientDescent"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyMomentum.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyMomentum.pbtxt
new file mode 100644
index 0000000000..7c3f0fef95
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyMomentum.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyMomentum"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyPowerSign.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyPowerSign.pbtxt
new file mode 100644
index 0000000000..f376b1dc6e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyPowerSign.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyPowerSign"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyProximalAdagrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyProximalAdagrad.pbtxt
new file mode 100644
index 0000000000..0c6e2a4bb1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyProximalAdagrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyProximalAdagrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyProximalGradientDescent.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyProximalGradientDescent.pbtxt
new file mode 100644
index 0000000000..90c1655fe9
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyProximalGradientDescent.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyProximalGradientDescent"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApplyRMSProp.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApplyRMSProp.pbtxt
new file mode 100644
index 0000000000..18cce1915a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApplyRMSProp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApplyRMSProp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ApproximateEqual.pbtxt b/tensorflow/core/api_def/python_api/api_def_ApproximateEqual.pbtxt
new file mode 100644
index 0000000000..707f6716f9
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ApproximateEqual.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ApproximateEqual"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AssignAddVariableOp.pbtxt b/tensorflow/core/api_def/python_api/api_def_AssignAddVariableOp.pbtxt
new file mode 100644
index 0000000000..e30ec092e6
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AssignAddVariableOp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AssignAddVariableOp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AssignSubVariableOp.pbtxt b/tensorflow/core/api_def/python_api/api_def_AssignSubVariableOp.pbtxt
new file mode 100644
index 0000000000..81290a56ec
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AssignSubVariableOp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AssignSubVariableOp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AssignVariableOp.pbtxt b/tensorflow/core/api_def/python_api/api_def_AssignVariableOp.pbtxt
new file mode 100644
index 0000000000..3ffa4a11c4
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AssignVariableOp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "AssignVariableOp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_AvgPool3D.pbtxt b/tensorflow/core/api_def/python_api/api_def_AvgPool3D.pbtxt
new file mode 100644
index 0000000000..cc16523a15
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_AvgPool3D.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "AvgPool3D"
+  endpoint {
+    name: "nn.avg_pool3d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_BatchDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_BatchDataset.pbtxt
new file mode 100644
index 0000000000..4289c1daf9
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_BatchDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "BatchDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_BatchMatrixBandPart.pbtxt b/tensorflow/core/api_def/python_api/api_def_BatchMatrixBandPart.pbtxt
new file mode 100644
index 0000000000..0a699e2050
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_BatchMatrixBandPart.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "BatchMatrixBandPart"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_BatchMatrixDiag.pbtxt b/tensorflow/core/api_def/python_api/api_def_BatchMatrixDiag.pbtxt
new file mode 100644
index 0000000000..40be51eccc
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_BatchMatrixDiag.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "BatchMatrixDiag"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_BatchMatrixDiagPart.pbtxt b/tensorflow/core/api_def/python_api/api_def_BatchMatrixDiagPart.pbtxt
new file mode 100644
index 0000000000..1ef78fa5ec
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_BatchMatrixDiagPart.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "BatchMatrixDiagPart"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_BatchMatrixSetDiag.pbtxt b/tensorflow/core/api_def/python_api/api_def_BatchMatrixSetDiag.pbtxt
new file mode 100644
index 0000000000..644c1270a2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_BatchMatrixSetDiag.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "BatchMatrixSetDiag"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_BiasAddGrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_BiasAddGrad.pbtxt
new file mode 100644
index 0000000000..9226c6791c
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_BiasAddGrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "BiasAddGrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_BytesProducedStatsDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_BytesProducedStatsDataset.pbtxt
new file mode 100644
index 0000000000..fcf541f903
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_BytesProducedStatsDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "BytesProducedStatsDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_CacheDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_CacheDataset.pbtxt
new file mode 100644
index 0000000000..2bbb4ff9e3
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_CacheDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "CacheDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_CholeskyGrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_CholeskyGrad.pbtxt
new file mode 100644
index 0000000000..3538afb2a7
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_CholeskyGrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "CholeskyGrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_CompareAndBitpack.pbtxt b/tensorflow/core/api_def/python_api/api_def_CompareAndBitpack.pbtxt
new file mode 100644
index 0000000000..493a7e4866
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_CompareAndBitpack.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "CompareAndBitpack"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ConcatenateDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_ConcatenateDataset.pbtxt
new file mode 100644
index 0000000000..c005a4da0f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ConcatenateDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ConcatenateDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ConditionalAccumulator.pbtxt b/tensorflow/core/api_def/python_api/api_def_ConditionalAccumulator.pbtxt
new file mode 100644
index 0000000000..a4663e8eb3
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ConditionalAccumulator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ConditionalAccumulator"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ConsumeMutexLock.pbtxt b/tensorflow/core/api_def/python_api/api_def_ConsumeMutexLock.pbtxt
new file mode 100644
index 0000000000..9559947490
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ConsumeMutexLock.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ConsumeMutexLock"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ControlTrigger.pbtxt b/tensorflow/core/api_def/python_api/api_def_ControlTrigger.pbtxt
new file mode 100644
index 0000000000..33941493af
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ControlTrigger.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ControlTrigger"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Conv2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_Conv2D.pbtxt
new file mode 100644
index 0000000000..2ae75d6da2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Conv2D.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "Conv2D"
+  endpoint {
+    name: "nn.conv2d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Conv2DBackpropFilter.pbtxt b/tensorflow/core/api_def/python_api/api_def_Conv2DBackpropFilter.pbtxt
new file mode 100644
index 0000000000..6f21d8c880
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Conv2DBackpropFilter.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "Conv2DBackpropFilter"
+  endpoint {
+    name: "nn.conv2d_backprop_filter"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Conv2DBackpropInput.pbtxt b/tensorflow/core/api_def/python_api/api_def_Conv2DBackpropInput.pbtxt
new file mode 100644
index 0000000000..ea976799cb
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Conv2DBackpropInput.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "Conv2DBackpropInput"
+  endpoint {
+    name: "nn.conv2d_backprop_input"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Conv3D.pbtxt b/tensorflow/core/api_def/python_api/api_def_Conv3D.pbtxt
new file mode 100644
index 0000000000..ba8d178263
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Conv3D.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "Conv3D"
+  endpoint {
+    name: "nn.conv3d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropFilter.pbtxt b/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropFilter.pbtxt
new file mode 100644
index 0000000000..634545f427
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropFilter.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Conv3DBackpropFilter"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropFilterV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropFilterV2.pbtxt
new file mode 100644
index 0000000000..1da8ee3a25
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropFilterV2.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "Conv3DBackpropFilterV2"
+  endpoint {
+    name: "nn.conv3d_backprop_filter_v2"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropInput.pbtxt b/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropInput.pbtxt
new file mode 100644
index 0000000000..e2b0a0d19f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropInput.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Conv3DBackpropInput"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropInputV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropInputV2.pbtxt
new file mode 100644
index 0000000000..4e5c4f74fe
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Conv3DBackpropInputV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Conv3DBackpropInputV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_CropAndResizeGradBoxes.pbtxt b/tensorflow/core/api_def/python_api/api_def_CropAndResizeGradBoxes.pbtxt
new file mode 100644
index 0000000000..ac44494193
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_CropAndResizeGradBoxes.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "CropAndResizeGradBoxes"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_CropAndResizeGradImage.pbtxt b/tensorflow/core/api_def/python_api/api_def_CropAndResizeGradImage.pbtxt
new file mode 100644
index 0000000000..eecd0536f2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_CropAndResizeGradImage.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "CropAndResizeGradImage"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DataFormatDimMap.pbtxt b/tensorflow/core/api_def/python_api/api_def_DataFormatDimMap.pbtxt
new file mode 100644
index 0000000000..82a39cfc59
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DataFormatDimMap.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DataFormatDimMap"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DataFormatVecPermute.pbtxt b/tensorflow/core/api_def/python_api/api_def_DataFormatVecPermute.pbtxt
new file mode 100644
index 0000000000..9ec292df8f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DataFormatVecPermute.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DataFormatVecPermute"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DatasetToSingleElement.pbtxt b/tensorflow/core/api_def/python_api/api_def_DatasetToSingleElement.pbtxt
new file mode 100644
index 0000000000..e3d34cc15b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DatasetToSingleElement.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DatasetToSingleElement"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DecodeCompressed.pbtxt b/tensorflow/core/api_def/python_api/api_def_DecodeCompressed.pbtxt
new file mode 100644
index 0000000000..f0b7539918
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DecodeCompressed.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DecodeCompressed"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DenseToDenseSetOperation.pbtxt b/tensorflow/core/api_def/python_api/api_def_DenseToDenseSetOperation.pbtxt
new file mode 100644
index 0000000000..1c47ec09c5
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DenseToDenseSetOperation.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DenseToDenseSetOperation"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DenseToSparseBatchDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_DenseToSparseBatchDataset.pbtxt
new file mode 100644
index 0000000000..0a8e068afb
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DenseToSparseBatchDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DenseToSparseBatchDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DenseToSparseSetOperation.pbtxt b/tensorflow/core/api_def/python_api/api_def_DenseToSparseSetOperation.pbtxt
new file mode 100644
index 0000000000..a30757df4d
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DenseToSparseSetOperation.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DenseToSparseSetOperation"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DeserializeIterator.pbtxt b/tensorflow/core/api_def/python_api/api_def_DeserializeIterator.pbtxt
new file mode 100644
index 0000000000..170d37be4e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DeserializeIterator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DeserializeIterator"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DestroyResourceOp.pbtxt b/tensorflow/core/api_def/python_api/api_def_DestroyResourceOp.pbtxt
new file mode 100644
index 0000000000..b9dde0080a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DestroyResourceOp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DestroyResourceOp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Dilation2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_Dilation2D.pbtxt
new file mode 100644
index 0000000000..6d73ecf1bb
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Dilation2D.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "Dilation2D"
+  endpoint {
+    name: "nn.dilation2d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Dilation2DBackpropFilter.pbtxt b/tensorflow/core/api_def/python_api/api_def_Dilation2DBackpropFilter.pbtxt
new file mode 100644
index 0000000000..feb9f083db
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Dilation2DBackpropFilter.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Dilation2DBackpropFilter"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Dilation2DBackpropInput.pbtxt b/tensorflow/core/api_def/python_api/api_def_Dilation2DBackpropInput.pbtxt
new file mode 100644
index 0000000000..9a6b09f5cc
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Dilation2DBackpropInput.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Dilation2DBackpropInput"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_EnqueueInQueueDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_EnqueueInQueueDataset.pbtxt
new file mode 100644
index 0000000000..051cf14c0e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_EnqueueInQueueDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "EnqueueInQueueDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FFT2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_FFT2D.pbtxt
new file mode 100644
index 0000000000..9ed1341dfe
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FFT2D.pbtxt
@@ -0,0 +1,9 @@
+op {
+  graph_op_name: "FFT2D"
+  endpoint {
+    name: "spectral.fft2d"
+  }
+  endpoint {
+    name: "fft2d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FFT3D.pbtxt b/tensorflow/core/api_def/python_api/api_def_FFT3D.pbtxt
new file mode 100644
index 0000000000..5a4e1d6adf
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FFT3D.pbtxt
@@ -0,0 +1,9 @@
+op {
+  graph_op_name: "FFT3D"
+  endpoint {
+    name: "spectral.fft3d"
+  }
+  endpoint {
+    name: "fft3d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FilterDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_FilterDataset.pbtxt
new file mode 100644
index 0000000000..6f91b84218
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FilterDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FilterDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FixedLengthRecordDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_FixedLengthRecordDataset.pbtxt
new file mode 100644
index 0000000000..d0703471d3
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FixedLengthRecordDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FixedLengthRecordDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FlatMapDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_FlatMapDataset.pbtxt
new file mode 100644
index 0000000000..9de61ac263
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FlatMapDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FlatMapDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FusedBatchNormGrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_FusedBatchNormGrad.pbtxt
new file mode 100644
index 0000000000..56409f32d8
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FusedBatchNormGrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FusedBatchNormGrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FusedBatchNormGradV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_FusedBatchNormGradV2.pbtxt
new file mode 100644
index 0000000000..f5a4200b76
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FusedBatchNormGradV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FusedBatchNormGradV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FusedPadConv2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_FusedPadConv2D.pbtxt
new file mode 100644
index 0000000000..03b5fdd5a1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FusedPadConv2D.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FusedPadConv2D"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_FusedResizeAndPadConv2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_FusedResizeAndPadConv2D.pbtxt
new file mode 100644
index 0000000000..52165d9b4d
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_FusedResizeAndPadConv2D.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "FusedResizeAndPadConv2D"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_GatherV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_GatherV2.pbtxt
new file mode 100644
index 0000000000..029bc59b51
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_GatherV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "GatherV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_GeneratorDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_GeneratorDataset.pbtxt
new file mode 100644
index 0000000000..9dcfa0f7d2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_GeneratorDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "GeneratorDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_GroupByWindowDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_GroupByWindowDataset.pbtxt
new file mode 100644
index 0000000000..8d40208e61
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_GroupByWindowDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "GroupByWindowDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IFFT2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_IFFT2D.pbtxt
new file mode 100644
index 0000000000..d6b36a314b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IFFT2D.pbtxt
@@ -0,0 +1,9 @@
+op {
+  graph_op_name: "IFFT2D"
+  endpoint {
+    name: "spectral.ifft2d"
+  }
+  endpoint {
+    name: "ifft2d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IFFT3D.pbtxt b/tensorflow/core/api_def/python_api/api_def_IFFT3D.pbtxt
new file mode 100644
index 0000000000..6def5b36da
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IFFT3D.pbtxt
@@ -0,0 +1,9 @@
+op {
+  graph_op_name: "IFFT3D"
+  endpoint {
+    name: "spectral.ifft3d"
+  }
+  endpoint {
+    name: "ifft3d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IRFFT.pbtxt b/tensorflow/core/api_def/python_api/api_def_IRFFT.pbtxt
new file mode 100644
index 0000000000..8fa74a4317
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IRFFT.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IRFFT"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IRFFT2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_IRFFT2D.pbtxt
new file mode 100644
index 0000000000..2021cad639
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IRFFT2D.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IRFFT2D"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IRFFT3D.pbtxt b/tensorflow/core/api_def/python_api/api_def_IRFFT3D.pbtxt
new file mode 100644
index 0000000000..5d1eab6003
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IRFFT3D.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IRFFT3D"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ImmutableConst.pbtxt b/tensorflow/core/api_def/python_api/api_def_ImmutableConst.pbtxt
new file mode 100644
index 0000000000..997013914b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ImmutableConst.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ImmutableConst"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_InterleaveDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_InterleaveDataset.pbtxt
new file mode 100644
index 0000000000..ef1b06b19c
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_InterleaveDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "InterleaveDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Inv.pbtxt b/tensorflow/core/api_def/python_api/api_def_Inv.pbtxt
new file mode 100644
index 0000000000..ed58a276f6
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Inv.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Inv"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Iterator.pbtxt b/tensorflow/core/api_def/python_api/api_def_Iterator.pbtxt
new file mode 100644
index 0000000000..a021db1534
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Iterator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Iterator"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IteratorFromStringHandle.pbtxt b/tensorflow/core/api_def/python_api/api_def_IteratorFromStringHandle.pbtxt
new file mode 100644
index 0000000000..f9efe2d144
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IteratorFromStringHandle.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IteratorFromStringHandle"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IteratorGetNext.pbtxt b/tensorflow/core/api_def/python_api/api_def_IteratorGetNext.pbtxt
new file mode 100644
index 0000000000..f7066484ce
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IteratorGetNext.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IteratorGetNext"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IteratorGetNextSync.pbtxt b/tensorflow/core/api_def/python_api/api_def_IteratorGetNextSync.pbtxt
new file mode 100644
index 0000000000..d94edbc71d
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IteratorGetNextSync.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IteratorGetNextSync"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IteratorSetStatsAggregator.pbtxt b/tensorflow/core/api_def/python_api/api_def_IteratorSetStatsAggregator.pbtxt
new file mode 100644
index 0000000000..db51ae3873
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IteratorSetStatsAggregator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IteratorSetStatsAggregator"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IteratorToStringHandle.pbtxt b/tensorflow/core/api_def/python_api/api_def_IteratorToStringHandle.pbtxt
new file mode 100644
index 0000000000..8a4251f76b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IteratorToStringHandle.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IteratorToStringHandle"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_LatencyStatsDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_LatencyStatsDataset.pbtxt
new file mode 100644
index 0000000000..94bf6106ad
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_LatencyStatsDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "LatencyStatsDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_LoopCond.pbtxt b/tensorflow/core/api_def/python_api/api_def_LoopCond.pbtxt
new file mode 100644
index 0000000000..4cfa295b2a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_LoopCond.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "LoopCond"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MakeIterator.pbtxt b/tensorflow/core/api_def/python_api/api_def_MakeIterator.pbtxt
new file mode 100644
index 0000000000..acc3342c9b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MakeIterator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MakeIterator"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapAndBatchDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapAndBatchDataset.pbtxt
new file mode 100644
index 0000000000..cffd2910fb
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapAndBatchDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapAndBatchDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapClear.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapClear.pbtxt
new file mode 100644
index 0000000000..67c1c3e2dd
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapClear.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapClear"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapDataset.pbtxt
new file mode 100644
index 0000000000..0b1d2f2c73
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapIncompleteSize.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapIncompleteSize.pbtxt
new file mode 100644
index 0000000000..db7921e13b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapIncompleteSize.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapIncompleteSize"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapPeek.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapPeek.pbtxt
new file mode 100644
index 0000000000..85fab17229
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapPeek.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapPeek"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapSize.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapSize.pbtxt
new file mode 100644
index 0000000000..8b6ed1a0cf
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapSize.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapSize"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapStage.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapStage.pbtxt
new file mode 100644
index 0000000000..3ae70d5d57
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapStage.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapStage"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapUnstage.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapUnstage.pbtxt
new file mode 100644
index 0000000000..e5f92e37db
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapUnstage.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapUnstage"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MapUnstageNoKey.pbtxt b/tensorflow/core/api_def/python_api/api_def_MapUnstageNoKey.pbtxt
new file mode 100644
index 0000000000..2c2a25db21
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MapUnstageNoKey.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MapUnstageNoKey"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MaxPool3D.pbtxt b/tensorflow/core/api_def/python_api/api_def_MaxPool3D.pbtxt
new file mode 100644
index 0000000000..e8576c9ff2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MaxPool3D.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "MaxPool3D"
+  endpoint {
+    name: "nn.max_pool3d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MaxPoolGradGradV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_MaxPoolGradGradV2.pbtxt
new file mode 100644
index 0000000000..534cc90e41
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MaxPoolGradGradV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MaxPoolGradGradV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MaxPoolGradV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_MaxPoolGradV2.pbtxt
new file mode 100644
index 0000000000..e79f839686
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MaxPoolGradV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MaxPoolGradV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MergeV2Checkpoints.pbtxt b/tensorflow/core/api_def/python_api/api_def_MergeV2Checkpoints.pbtxt
new file mode 100644
index 0000000000..ca9f74e0c1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MergeV2Checkpoints.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MergeV2Checkpoints"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MutexLock.pbtxt b/tensorflow/core/api_def/python_api/api_def_MutexLock.pbtxt
new file mode 100644
index 0000000000..74e6e10357
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MutexLock.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MutexLock"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_MutexV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_MutexV2.pbtxt
new file mode 100644
index 0000000000..013f42d855
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_MutexV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "MutexV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_NextIteration.pbtxt b/tensorflow/core/api_def/python_api/api_def_NextIteration.pbtxt
new file mode 100644
index 0000000000..28ac301e41
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_NextIteration.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "NextIteration"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_NthElement.pbtxt b/tensorflow/core/api_def/python_api/api_def_NthElement.pbtxt
new file mode 100644
index 0000000000..ec83858510
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_NthElement.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "NthElement"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OneShotIterator.pbtxt b/tensorflow/core/api_def/python_api/api_def_OneShotIterator.pbtxt
new file mode 100644
index 0000000000..ee9d777b4e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OneShotIterator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OneShotIterator"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OrderedMapClear.pbtxt b/tensorflow/core/api_def/python_api/api_def_OrderedMapClear.pbtxt
new file mode 100644
index 0000000000..b8276b964a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OrderedMapClear.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OrderedMapClear"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OrderedMapIncompleteSize.pbtxt b/tensorflow/core/api_def/python_api/api_def_OrderedMapIncompleteSize.pbtxt
new file mode 100644
index 0000000000..1ba6c5b2fc
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OrderedMapIncompleteSize.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OrderedMapIncompleteSize"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OrderedMapPeek.pbtxt b/tensorflow/core/api_def/python_api/api_def_OrderedMapPeek.pbtxt
new file mode 100644
index 0000000000..8f0c7afd46
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OrderedMapPeek.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OrderedMapPeek"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OrderedMapSize.pbtxt b/tensorflow/core/api_def/python_api/api_def_OrderedMapSize.pbtxt
new file mode 100644
index 0000000000..2e155726da
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OrderedMapSize.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OrderedMapSize"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OrderedMapStage.pbtxt b/tensorflow/core/api_def/python_api/api_def_OrderedMapStage.pbtxt
new file mode 100644
index 0000000000..6222c1fc4c
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OrderedMapStage.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OrderedMapStage"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OrderedMapUnstage.pbtxt b/tensorflow/core/api_def/python_api/api_def_OrderedMapUnstage.pbtxt
new file mode 100644
index 0000000000..5cca8d9f93
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OrderedMapUnstage.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OrderedMapUnstage"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OrderedMapUnstageNoKey.pbtxt b/tensorflow/core/api_def/python_api/api_def_OrderedMapUnstageNoKey.pbtxt
new file mode 100644
index 0000000000..d67b95b65b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OrderedMapUnstageNoKey.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OrderedMapUnstageNoKey"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_PaddedBatchDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_PaddedBatchDataset.pbtxt
new file mode 100644
index 0000000000..c6223b3132
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_PaddedBatchDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "PaddedBatchDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ParallelDynamicStitch.pbtxt b/tensorflow/core/api_def/python_api/api_def_ParallelDynamicStitch.pbtxt
new file mode 100644
index 0000000000..a36ad27364
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ParallelDynamicStitch.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ParallelDynamicStitch"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ParallelInterleaveDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_ParallelInterleaveDataset.pbtxt
new file mode 100644
index 0000000000..93cd5719fe
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ParallelInterleaveDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ParallelInterleaveDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ParallelMapDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_ParallelMapDataset.pbtxt
new file mode 100644
index 0000000000..09d200dd24
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ParallelMapDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ParallelMapDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_PlaceholderV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_PlaceholderV2.pbtxt
new file mode 100644
index 0000000000..a30360d2de
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_PlaceholderV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "PlaceholderV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_PopulationCount.pbtxt b/tensorflow/core/api_def/python_api/api_def_PopulationCount.pbtxt
new file mode 100644
index 0000000000..d35550236a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_PopulationCount.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "PopulationCount"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_PrefetchDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_PrefetchDataset.pbtxt
new file mode 100644
index 0000000000..ec4e214eb5
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_PrefetchDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "PrefetchDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt
new file mode 100644
index 0000000000..228c4047d2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_PrependFromQueueAndPaddedBatchDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "PrependFromQueueAndPaddedBatchDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_PreventGradient.pbtxt b/tensorflow/core/api_def/python_api/api_def_PreventGradient.pbtxt
new file mode 100644
index 0000000000..9565f5632b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_PreventGradient.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "PreventGradient"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantize.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantize.pbtxt
new file mode 100644
index 0000000000..d2468f1b24
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantize.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizeAndDequantize"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantizeV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantizeV2.pbtxt
new file mode 100644
index 0000000000..15e181be20
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantizeV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizeAndDequantizeV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantizeV3.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantizeV3.pbtxt
new file mode 100644
index 0000000000..f1edc6f5fa
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizeAndDequantizeV3.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizeAndDequantizeV3"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizeDownAndShrinkRange.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizeDownAndShrinkRange.pbtxt
new file mode 100644
index 0000000000..9a2a86d25d
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizeDownAndShrinkRange.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizeDownAndShrinkRange"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedAdd.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedAdd.pbtxt
new file mode 100644
index 0000000000..b952d6eccb
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedAdd.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedAdd"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt
new file mode 100644
index 0000000000..e009ada553
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedBatchNormWithGlobalNormalization"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedBiasAdd.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedBiasAdd.pbtxt
new file mode 100644
index 0000000000..3432962e59
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedBiasAdd.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedBiasAdd"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedConv2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedConv2D.pbtxt
new file mode 100644
index 0000000000..2409d12abe
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedConv2D.pbtxt
@@ -0,0 +1,6 @@
+op {
+  graph_op_name: "QuantizedConv2D"
+  endpoint {
+    name: "nn.quantized_conv2d"
+  }
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedInstanceNorm.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedInstanceNorm.pbtxt
new file mode 100644
index 0000000000..47a4931a05
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedInstanceNorm.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedInstanceNorm"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedMatMul.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedMatMul.pbtxt
new file mode 100644
index 0000000000..3ca9d2ae07
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedMatMul.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedMatMul"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedMul.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedMul.pbtxt
new file mode 100644
index 0000000000..c026fba194
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedMul.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedMul"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedRelu.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedRelu.pbtxt
new file mode 100644
index 0000000000..e5da4f25f0
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedRelu.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedRelu"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedRelu6.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedRelu6.pbtxt
new file mode 100644
index 0000000000..ef1e648312
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedRelu6.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedRelu6"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedReshape.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedReshape.pbtxt
new file mode 100644
index 0000000000..7e6d9ed718
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedReshape.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedReshape"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizedResizeBilinear.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizedResizeBilinear.pbtxt
new file mode 100644
index 0000000000..a8da4128c2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizedResizeBilinear.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizedResizeBilinear"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QueueIsClosed.pbtxt b/tensorflow/core/api_def/python_api/api_def_QueueIsClosed.pbtxt
new file mode 100644
index 0000000000..f1d2ef63f1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QueueIsClosed.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QueueIsClosed"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QueueIsClosedV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_QueueIsClosedV2.pbtxt
new file mode 100644
index 0000000000..07cf1a7497
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QueueIsClosedV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QueueIsClosedV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RFFT.pbtxt b/tensorflow/core/api_def/python_api/api_def_RFFT.pbtxt
new file mode 100644
index 0000000000..e9719255ae
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RFFT.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RFFT"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RFFT2D.pbtxt b/tensorflow/core/api_def/python_api/api_def_RFFT2D.pbtxt
new file mode 100644
index 0000000000..1336a64408
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RFFT2D.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RFFT2D"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RFFT3D.pbtxt b/tensorflow/core/api_def/python_api/api_def_RFFT3D.pbtxt
new file mode 100644
index 0000000000..978b5814ff
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RFFT3D.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RFFT3D"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RandomDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_RandomDataset.pbtxt
new file mode 100644
index 0000000000..a5f6f8c6f1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RandomDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RandomDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RandomPoissonV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_RandomPoissonV2.pbtxt
new file mode 100644
index 0000000000..8cc217c50e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RandomPoissonV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RandomPoissonV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RangeDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_RangeDataset.pbtxt
new file mode 100644
index 0000000000..4cd8296b22
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RangeDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RangeDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ReadVariableOp.pbtxt b/tensorflow/core/api_def/python_api/api_def_ReadVariableOp.pbtxt
new file mode 100644
index 0000000000..e250b78eff
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ReadVariableOp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ReadVariableOp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RecordInput.pbtxt b/tensorflow/core/api_def/python_api/api_def_RecordInput.pbtxt
new file mode 100644
index 0000000000..29f798050e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RecordInput.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RecordInput"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RefNextIteration.pbtxt b/tensorflow/core/api_def/python_api/api_def_RefNextIteration.pbtxt
new file mode 100644
index 0000000000..f9dfcf5e97
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RefNextIteration.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RefNextIteration"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RefSelect.pbtxt b/tensorflow/core/api_def/python_api/api_def_RefSelect.pbtxt
new file mode 100644
index 0000000000..8f9909aa86
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RefSelect.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RefSelect"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RefSwitch.pbtxt b/tensorflow/core/api_def/python_api/api_def_RefSwitch.pbtxt
new file mode 100644
index 0000000000..68b0f4a694
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RefSwitch.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RefSwitch"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RemoteCall.pbtxt b/tensorflow/core/api_def/python_api/api_def_RemoteCall.pbtxt
new file mode 100644
index 0000000000..fc069d857d
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RemoteCall.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RemoteCall"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RepeatDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_RepeatDataset.pbtxt
new file mode 100644
index 0000000000..be301da838
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RepeatDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RepeatDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RequantizationRange.pbtxt b/tensorflow/core/api_def/python_api/api_def_RequantizationRange.pbtxt
new file mode 100644
index 0000000000..e327595a38
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RequantizationRange.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RequantizationRange"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Requantize.pbtxt b/tensorflow/core/api_def/python_api/api_def_Requantize.pbtxt
new file mode 100644
index 0000000000..f26f0611ba
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Requantize.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Requantize"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdadelta.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdadelta.pbtxt
new file mode 100644
index 0000000000..e0413a67a3
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdadelta.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyAdadelta"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdagrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdagrad.pbtxt
new file mode 100644
index 0000000000..52b8ba0b0e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdagrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyAdagrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdagradDA.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdagradDA.pbtxt
new file mode 100644
index 0000000000..edfc0a733f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdagradDA.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyAdagradDA"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdam.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdam.pbtxt
new file mode 100644
index 0000000000..ca2713b533
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAdam.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyAdam"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyAddSign.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAddSign.pbtxt
new file mode 100644
index 0000000000..50dd643953
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyAddSign.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyAddSign"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyCenteredRMSProp.pbtxt
new file mode 100644
index 0000000000..20592e38c8
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyCenteredRMSProp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyCenteredRMSProp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyFtrl.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyFtrl.pbtxt
new file mode 100644
index 0000000000..72b49e09d6
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyFtrl.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyFtrl"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyFtrlV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyFtrlV2.pbtxt
new file mode 100644
index 0000000000..af1d24c344
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyFtrlV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyFtrlV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyGradientDescent.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyGradientDescent.pbtxt
new file mode 100644
index 0000000000..75d6afd426
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyGradientDescent.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyGradientDescent"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyMomentum.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyMomentum.pbtxt
new file mode 100644
index 0000000000..3e499cf72e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyMomentum.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyMomentum"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyPowerSign.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyPowerSign.pbtxt
new file mode 100644
index 0000000000..b23ad0d061
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyPowerSign.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyPowerSign"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyProximalAdagrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyProximalAdagrad.pbtxt
new file mode 100644
index 0000000000..6ad124c590
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyProximalAdagrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyProximalAdagrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyProximalGradientDescent.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyProximalGradientDescent.pbtxt
new file mode 100644
index 0000000000..d684a5dd67
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyProximalGradientDescent.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyProximalGradientDescent"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceApplyRMSProp.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceApplyRMSProp.pbtxt
new file mode 100644
index 0000000000..c4c20e1382
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceApplyRMSProp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceApplyRMSProp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceCountUpTo.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceCountUpTo.pbtxt
new file mode 100644
index 0000000000..87376b7447
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceCountUpTo.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceCountUpTo"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceGather.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceGather.pbtxt
new file mode 100644
index 0000000000..714ba4a7ca
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceGather.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceGather"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceScatterAdd.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceScatterAdd.pbtxt
new file mode 100644
index 0000000000..4d4601cafd
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceScatterAdd.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceScatterAdd"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceScatterNdUpdate.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceScatterNdUpdate.pbtxt
new file mode 100644
index 0000000000..54c66708ae
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceScatterNdUpdate.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceScatterNdUpdate"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceScatterUpdate.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceScatterUpdate.pbtxt
new file mode 100644
index 0000000000..30f885bee0
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceScatterUpdate.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceScatterUpdate"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdadelta.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdadelta.pbtxt
new file mode 100644
index 0000000000..a7e4dad138
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdadelta.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyAdadelta"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdagrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdagrad.pbtxt
new file mode 100644
index 0000000000..1388da789c
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdagrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyAdagrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdagradDA.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdagradDA.pbtxt
new file mode 100644
index 0000000000..c5beaa4f58
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyAdagradDA.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyAdagradDA"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt
new file mode 100644
index 0000000000..f3de3d93df
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyCenteredRMSProp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyCenteredRMSProp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyFtrl.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyFtrl.pbtxt
new file mode 100644
index 0000000000..f83833d351
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyFtrl.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyFtrl"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyFtrlV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyFtrlV2.pbtxt
new file mode 100644
index 0000000000..71adbb0bcd
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyFtrlV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyFtrlV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyMomentum.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyMomentum.pbtxt
new file mode 100644
index 0000000000..28a19caacc
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyMomentum.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyMomentum"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyProximalAdagrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyProximalAdagrad.pbtxt
new file mode 100644
index 0000000000..e8cda7f4ed
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyProximalAdagrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyProximalAdagrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyProximalGradientDescent.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyProximalGradientDescent.pbtxt
new file mode 100644
index 0000000000..5fa1ade669
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyProximalGradientDescent.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyProximalGradientDescent"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyRMSProp.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyRMSProp.pbtxt
new file mode 100644
index 0000000000..86cc9a41ae
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceSparseApplyRMSProp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceSparseApplyRMSProp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ResourceStridedSliceAssign.pbtxt b/tensorflow/core/api_def/python_api/api_def_ResourceStridedSliceAssign.pbtxt
new file mode 100644
index 0000000000..ef6e19fea0
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ResourceStridedSliceAssign.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ResourceStridedSliceAssign"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_RestoreV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_RestoreV2.pbtxt
new file mode 100644
index 0000000000..34d07239a1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_RestoreV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "RestoreV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Roll.pbtxt b/tensorflow/core/api_def/python_api/api_def_Roll.pbtxt
new file mode 100644
index 0000000000..9cc919f36f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Roll.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Roll"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SaveV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_SaveV2.pbtxt
new file mode 100644
index 0000000000..617897ee44
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SaveV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SaveV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ScanDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_ScanDataset.pbtxt
new file mode 100644
index 0000000000..e71b655c22
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ScanDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ScanDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ScatterNdNonAliasingAdd.pbtxt b/tensorflow/core/api_def/python_api/api_def_ScatterNdNonAliasingAdd.pbtxt
new file mode 100644
index 0000000000..ecf71cd625
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ScatterNdNonAliasingAdd.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ScatterNdNonAliasingAdd"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SerializeIterator.pbtxt b/tensorflow/core/api_def/python_api/api_def_SerializeIterator.pbtxt
new file mode 100644
index 0000000000..07d2f200fe
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SerializeIterator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SerializeIterator"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SetSize.pbtxt b/tensorflow/core/api_def/python_api/api_def_SetSize.pbtxt
new file mode 100644
index 0000000000..ee9c71036b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SetSize.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SetSize"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ShuffleAndRepeatDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_ShuffleAndRepeatDataset.pbtxt
new file mode 100644
index 0000000000..7b0d2994f0
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ShuffleAndRepeatDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ShuffleAndRepeatDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ShuffleDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_ShuffleDataset.pbtxt
new file mode 100644
index 0000000000..8f0be9197a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ShuffleDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ShuffleDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SkipDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_SkipDataset.pbtxt
new file mode 100644
index 0000000000..96a551c5b6
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SkipDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SkipDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseAccumulatorApplyGradient.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseAccumulatorApplyGradient.pbtxt
new file mode 100644
index 0000000000..5e158c9ca0
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseAccumulatorApplyGradient.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseAccumulatorApplyGradient"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseAccumulatorTakeGradient.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseAccumulatorTakeGradient.pbtxt
new file mode 100644
index 0000000000..5326f23def
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseAccumulatorTakeGradient.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseAccumulatorTakeGradient"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyAdadelta.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyAdadelta.pbtxt
new file mode 100644
index 0000000000..d30a8676e0
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyAdadelta.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyAdadelta"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyAdagrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyAdagrad.pbtxt
new file mode 100644
index 0000000000..cb5ddef212
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyAdagrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyAdagrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyAdagradDA.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyAdagradDA.pbtxt
new file mode 100644
index 0000000000..c3b87b0953
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyAdagradDA.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyAdagradDA"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyCenteredRMSProp.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyCenteredRMSProp.pbtxt
new file mode 100644
index 0000000000..db47328738
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyCenteredRMSProp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyCenteredRMSProp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyFtrl.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyFtrl.pbtxt
new file mode 100644
index 0000000000..14e37b8ba2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyFtrl.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyFtrl"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyFtrlV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyFtrlV2.pbtxt
new file mode 100644
index 0000000000..0d307af9b4
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyFtrlV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyFtrlV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyMomentum.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyMomentum.pbtxt
new file mode 100644
index 0000000000..ed34c0485d
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyMomentum.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyMomentum"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyProximalAdagrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyProximalAdagrad.pbtxt
new file mode 100644
index 0000000000..ff2d3b6731
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyProximalAdagrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyProximalAdagrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyProximalGradientDescent.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyProximalGradientDescent.pbtxt
new file mode 100644
index 0000000000..f342a611bb
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyProximalGradientDescent.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyProximalGradientDescent"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseApplyRMSProp.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseApplyRMSProp.pbtxt
new file mode 100644
index 0000000000..7f337d50e5
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseApplyRMSProp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseApplyRMSProp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseConditionalAccumulator.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseConditionalAccumulator.pbtxt
new file mode 100644
index 0000000000..bad4120795
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseConditionalAccumulator.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseConditionalAccumulator"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseAdd.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseAdd.pbtxt
new file mode 100644
index 0000000000..c5e7c9851f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseAdd.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseDenseCwiseAdd"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseDiv.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseDiv.pbtxt
new file mode 100644
index 0000000000..f72031cf68
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseDiv.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseDenseCwiseDiv"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseMul.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseMul.pbtxt
new file mode 100644
index 0000000000..a87004ee5f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseDenseCwiseMul.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseDenseCwiseMul"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSegmentMeanGrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSegmentMeanGrad.pbtxt
new file mode 100644
index 0000000000..771083cd51
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSegmentMeanGrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSegmentMeanGrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSegmentMeanWithNumSegments.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSegmentMeanWithNumSegments.pbtxt
new file mode 100644
index 0000000000..fcb029535c
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSegmentMeanWithNumSegments.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSegmentMeanWithNumSegments"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtNGrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtNGrad.pbtxt
new file mode 100644
index 0000000000..0682a597bb
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtNGrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSegmentSqrtNGrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtNWithNumSegments.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtNWithNumSegments.pbtxt
new file mode 100644
index 0000000000..7311a093df
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtNWithNumSegments.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSegmentSqrtNWithNumSegments"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSegmentSumWithNumSegments.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSumWithNumSegments.pbtxt
new file mode 100644
index 0000000000..81c2b8554e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSumWithNumSegments.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSegmentSumWithNumSegments"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSparseMaximum.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSparseMaximum.pbtxt
new file mode 100644
index 0000000000..0dbadc01ed
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSparseMaximum.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSparseMaximum"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSparseMinimum.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSparseMinimum.pbtxt
new file mode 100644
index 0000000000..0e3ffcbddf
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSparseMinimum.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSparseMinimum"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseTensorSliceDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseTensorSliceDataset.pbtxt
new file mode 100644
index 0000000000..19c0c7f199
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseTensorSliceDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseTensorSliceDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseToSparseSetOperation.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseToSparseSetOperation.pbtxt
new file mode 100644
index 0000000000..735ee18e14
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseToSparseSetOperation.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseToSparseSetOperation"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SqlDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_SqlDataset.pbtxt
new file mode 100644
index 0000000000..2ab4c3e441
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SqlDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SqlDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Stage.pbtxt b/tensorflow/core/api_def/python_api/api_def_Stage.pbtxt
new file mode 100644
index 0000000000..66de5901bc
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Stage.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Stage"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_StageClear.pbtxt b/tensorflow/core/api_def/python_api/api_def_StageClear.pbtxt
new file mode 100644
index 0000000000..f54a1c1c04
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_StageClear.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "StageClear"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_StagePeek.pbtxt b/tensorflow/core/api_def/python_api/api_def_StagePeek.pbtxt
new file mode 100644
index 0000000000..710394d30d
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_StagePeek.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "StagePeek"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_StageSize.pbtxt b/tensorflow/core/api_def/python_api/api_def_StageSize.pbtxt
new file mode 100644
index 0000000000..472032ac42
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_StageSize.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "StageSize"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_StatsAggregatorHandle.pbtxt b/tensorflow/core/api_def/python_api/api_def_StatsAggregatorHandle.pbtxt
new file mode 100644
index 0000000000..f7bed36602
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_StatsAggregatorHandle.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "StatsAggregatorHandle"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_StatsAggregatorSummary.pbtxt b/tensorflow/core/api_def/python_api/api_def_StatsAggregatorSummary.pbtxt
new file mode 100644
index 0000000000..8b1bab2440
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_StatsAggregatorSummary.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "StatsAggregatorSummary"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_StridedSliceAssign.pbtxt b/tensorflow/core/api_def/python_api/api_def_StridedSliceAssign.pbtxt
new file mode 100644
index 0000000000..bcf1df228e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_StridedSliceAssign.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "StridedSliceAssign"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_StridedSliceGrad.pbtxt b/tensorflow/core/api_def/python_api/api_def_StridedSliceGrad.pbtxt
new file mode 100644
index 0000000000..05d7d57511
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_StridedSliceGrad.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "StridedSliceGrad"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_TFRecordDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_TFRecordDataset.pbtxt
new file mode 100644
index 0000000000..3c270ada3c
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_TFRecordDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "TFRecordDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_TakeDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_TakeDataset.pbtxt
new file mode 100644
index 0000000000..711b335dc1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_TakeDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "TakeDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_TensorDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_TensorDataset.pbtxt
new file mode 100644
index 0000000000..5bc3920c56
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_TensorDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "TensorDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_TensorSliceDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_TensorSliceDataset.pbtxt
new file mode 100644
index 0000000000..89ad016483
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_TensorSliceDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "TensorSliceDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_TextLineDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_TextLineDataset.pbtxt
new file mode 100644
index 0000000000..08d785191b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_TextLineDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "TextLineDataset"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Unstage.pbtxt b/tensorflow/core/api_def/python_api/api_def_Unstage.pbtxt
new file mode 100644
index 0000000000..65eb756b87
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Unstage.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Unstage"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_VarHandleOp.pbtxt b/tensorflow/core/api_def/python_api/api_def_VarHandleOp.pbtxt
new file mode 100644
index 0000000000..2c93a6db93
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_VarHandleOp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "VarHandleOp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_VarIsInitializedOp.pbtxt b/tensorflow/core/api_def/python_api/api_def_VarIsInitializedOp.pbtxt
new file mode 100644
index 0000000000..de5d9850ac
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_VarIsInitializedOp.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "VarIsInitializedOp"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_VariableShape.pbtxt b/tensorflow/core/api_def/python_api/api_def_VariableShape.pbtxt
new file mode 100644
index 0000000000..9b317152dd
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_VariableShape.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "VariableShape"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ZipDataset.pbtxt b/tensorflow/core/api_def/python_api/api_def_ZipDataset.pbtxt
new file mode 100644
index 0000000000..dd1459521f
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ZipDataset.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ZipDataset"
+  visibility: HIDDEN
+}
-- 
GitLab


From 8c557a579384e2665fd438a944fd416f544a2a81 Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Wed, 28 Feb 2018 18:36:57 -0800
Subject: [PATCH 474/575] Use NodeExecStats's output_slot field to identify
 output instead of just using proto index.

PiperOrigin-RevId: 187416101
---
 tensorflow/core/common_runtime/step_stats_collector.cc | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/common_runtime/step_stats_collector.cc b/tensorflow/core/common_runtime/step_stats_collector.cc
index cb900db10a..f21536d586 100644
--- a/tensorflow/core/common_runtime/step_stats_collector.cc
+++ b/tensorflow/core/common_runtime/step_stats_collector.cc
@@ -226,13 +226,14 @@ void StepStatsCollector::BuildCostModel(
       if (node) {
         for (int i = 0; i < stats.output_size(); ++i) {
           const auto& output = stats.output(i);
-          cm->RecordMaxMemorySize(node, i,
+          int output_slot = output.slot();
+          cm->RecordMaxMemorySize(node, output_slot,
                                   Bytes(output.tensor_description()
                                             .allocation_description()
                                             .allocated_bytes()),
-                                  stats.output(i).tensor_description().shape(),
-                                  node->output_types()[i]);
-          cm->RecordAllocationId(node, i,
+                                  output.tensor_description().shape(),
+                                  node->output_types()[output_slot]);
+          cm->RecordAllocationId(node, output_slot,
                                  output.tensor_description()
                                      .allocation_description()
                                      .allocation_id());
-- 
GitLab


From af6cdb9e5eae7e5e41824336fa5b3084402d43e9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 18:56:01 -0800
Subject: [PATCH 475/575] Use half_val instead of int_val to get the bfloat16
 tensor value in MakeNdarray.

PiperOrigin-RevId: 187417908
---
 tensorflow/python/framework/tensor_util.py    |  9 ++++-----
 .../python/framework/tensor_util_test.py      | 20 +++++++++++++++++++
 2 files changed, 24 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py
index 27afaa074a..135562e831 100644
--- a/tensorflow/python/framework/tensor_util.py
+++ b/tensorflow/python/framework/tensor_util.py
@@ -559,16 +559,16 @@ def MakeNdarray(tensor):
   if tensor.tensor_content:
     return (np.frombuffer(tensor.tensor_content, dtype=dtype).copy()
             .reshape(shape))
-  elif tensor_dtype == dtypes.float16:
+  elif tensor_dtype == dtypes.float16 or tensor_dtype == dtypes.bfloat16:
     # the half_val field of the TensorProto stores the binary representation
     # of the fp16: we need to reinterpret this as a proper float16
     if len(tensor.half_val) == 1:
       tmp = np.array(tensor.half_val[0], dtype=np.uint16)
-      tmp.dtype = np.float16
+      tmp.dtype = tensor_dtype.as_numpy_dtype
       return np.repeat(tmp, num_elements).reshape(shape)
     else:
       tmp = np.fromiter(tensor.half_val, dtype=np.uint16)
-      tmp.dtype = np.float16
+      tmp.dtype = tensor_dtype.as_numpy_dtype
       return tmp.reshape(shape)
   elif tensor_dtype == dtypes.float32:
     if len(tensor.float_val) == 1:
@@ -586,8 +586,7 @@ def MakeNdarray(tensor):
       return np.fromiter(tensor.double_val, dtype=dtype).reshape(shape)
   elif tensor_dtype in [
       dtypes.int32, dtypes.uint8, dtypes.uint16, dtypes.int16, dtypes.int8,
-      dtypes.qint32, dtypes.quint8, dtypes.qint8, dtypes.qint16, dtypes.quint16,
-      dtypes.bfloat16
+      dtypes.qint32, dtypes.quint8, dtypes.qint8, dtypes.qint16, dtypes.quint16
   ]:
     if len(tensor.int_val) == 1:
       return np.repeat(np.array(tensor.int_val[0], dtype=dtype),
diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py
index 6b1b3dd40c..35fff80c61 100644
--- a/tensorflow/python/framework/tensor_util_test.py
+++ b/tensorflow/python/framework/tensor_util_test.py
@@ -235,6 +235,26 @@ class TensorUtilTest(test.TestCase):
     self.assertEquals(np.float16, a.dtype)
     self.assertAllClose(np.array([10.0, 20.0], dtype=np.float16), a)
 
+  def testBfloat16(self):
+    test_type = dtypes.bfloat16.as_numpy_dtype
+    t = tensor_util.make_tensor_proto(np.array([10.0, 20.0], dtype=test_type))
+    # 10.0: 16672 = 010000010(130) 0100000: (1+0/2+1/4) * 2^(130-127)
+    # 20.0: 16800 = 010000011(131) 0100000: (1+0/2+1/4) * 2^(131-127)
+    self.assertProtoEquals("""
+      dtype: DT_BFLOAT16
+      tensor_shape {
+        dim {
+          size: 2
+        }
+      }
+      half_val: 16672
+      half_val: 16800
+      """, t)
+
+    a = tensor_util.MakeNdarray(t)
+    self.assertEquals(test_type, a.dtype)
+    self.assertAllClose(np.array([10.0, 20.0], dtype=test_type), a)
+
   def testInt(self):
     t = tensor_util.make_tensor_proto(10)
     self.assertProtoEquals("""
-- 
GitLab


From 63646c32c629f750706c9c63f87735bdbcec4963 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 28 Feb 2018 18:59:41 -0800
Subject: [PATCH 476/575] Add bfloat16 random_op for CPU.

PiperOrigin-RevId: 187418131
---
 tensorflow/core/kernels/random_op.cc          |   1 +
 .../core/lib/random/random_distributions.h    | 119 ++++++++++++++++++
 .../lib/random/random_distributions_test.cc   |  24 +++-
 3 files changed, 142 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/random_op.cc b/tensorflow/core/kernels/random_op.cc
index 78ff7948fb..e37232539f 100644
--- a/tensorflow/core/kernels/random_op.cc
+++ b/tensorflow/core/kernels/random_op.cc
@@ -495,6 +495,7 @@ class RandomGammaOp : public OpKernel {
                           RandomUniformIntOp<CPUDevice, IntType>);
 
 TF_CALL_half(REGISTER);
+TF_CALL_bfloat16(REGISTER);
 TF_CALL_float(REGISTER);
 TF_CALL_double(REGISTER);
 TF_CALL_int32(REGISTER_INT);
diff --git a/tensorflow/core/lib/random/random_distributions.h b/tensorflow/core/lib/random/random_distributions.h
index 3fe1f9bc6c..2ebe608fc9 100644
--- a/tensorflow/core/lib/random/random_distributions.h
+++ b/tensorflow/core/lib/random/random_distributions.h
@@ -32,6 +32,8 @@ namespace random {
 
 // Helper function to convert a 16-bit integer to a half between [0..1).
 PHILOX_DEVICE_INLINE Eigen::half Uint16ToHalf(uint16 x);
+// Helper function to convert a 16-bit integer to a bfloat16 between [0..1).
+PHILOX_DEVICE_INLINE bfloat16 Uint16ToGfloat16(uint16 x);
 // Helper function to convert a 32-bit integer to a float between [0..1).
 PHILOX_DEVICE_INLINE float Uint32ToFloat(uint32 x);
 // Helper function to convert two 32-bit integers to a double between [0..1).
@@ -75,6 +77,30 @@ class UniformDistribution<Generator, Eigen::half> {
   }
 };
 
+template <class Generator>
+class UniformDistribution<Generator, bfloat16> {
+ public:
+  // The number of elements that will be returned.
+  static const int kResultElementCount = Generator::kResultElementCount;
+  // Cost of generation of a single element (in cycles).
+  static const int kElementCost = 3;
+  // Indicate that this distribution may take variable number of samples
+  // during the runtime.
+  static const bool kVariableSamplesPerOutput = false;
+  typedef Array<bfloat16, kResultElementCount> ResultType;
+  typedef bfloat16 ResultElementType;
+
+  PHILOX_DEVICE_INLINE
+  ResultType operator()(Generator* gen) {
+    typename Generator::ResultType sample = (*gen)();
+    ResultType result;
+    for (int i = 0; i < kResultElementCount; ++i) {
+      result[i] = Uint16ToGfloat16(sample[i]);
+    }
+    return result;
+  }
+};
+
 template <class Generator>
 class UniformDistribution<Generator, float> {
  public:
@@ -305,6 +331,36 @@ class NormalDistribution<Generator, Eigen::half> {
   }
 };
 
+template <class Generator>
+class NormalDistribution<Generator, bfloat16> {
+ public:
+  // The number of elements that will be returned.
+  static const int kResultElementCount = Generator::kResultElementCount;
+  // Cost of generation of a single element (in cycles).
+  static const int kElementCost = 70;
+  // Indicate that this distribution may take variable number of samples
+  // during the runtime.
+  static const bool kVariableSamplesPerOutput = false;
+  typedef Array<bfloat16, kResultElementCount> ResultType;
+  typedef bfloat16 ResultElementType;
+
+  PHILOX_DEVICE_INLINE
+  ResultType operator()(Generator* gen) {
+    typename Generator::ResultType sample = (*gen)();
+    ResultType result;
+    static_assert(kResultElementCount % 2 == 0,
+                  "kResultElementCount should be an even number");
+    for (int i = 0; i < kResultElementCount; i += 2) {
+      float f[2];
+      // Box-Muller transform requires processing 2 elements at a time.
+      BoxMullerFloat(sample[i], sample[i + 1], &f[0], &f[1]);
+      result[i] = bfloat16(f[0]);
+      result[i + 1] = bfloat16(f[1]);
+    }
+    return result;
+  }
+};
+
 template <class Generator>
 class NormalDistribution<Generator, float> {
  public:
@@ -414,6 +470,48 @@ class TruncatedNormalDistribution<SingleSampleGenerator, Eigen::half> {
   }
 };
 
+template <class SingleSampleGenerator>
+class TruncatedNormalDistribution<SingleSampleGenerator, bfloat16> {
+ public:
+  // The number of elements that will be returned.
+  static const int kResultElementCount =
+      SingleSampleGenerator::kNativeElementCount;
+  // Cost of generation of a single element (in cycles).
+  static const int kElementCost = 90;
+  // Indicate that this distribution may take variable number of samples
+  // during the runtime.
+  static const bool kVariableSamplesPerOutput = true;
+  // The threshold where the normal distribution is truncated.
+  const float kTruncateValue = 2.0f;
+
+  typedef Array<bfloat16, kResultElementCount> ResultType;
+  typedef bfloat16 ResultElementType;
+
+  PHILOX_DEVICE_INLINE
+  ResultType operator()(SingleSampleGenerator* gen) {
+    ResultType results;
+    int index = 0;
+    while (true) {
+      // Repeatedly take samples from the normal distribution, until we have
+      // the desired number of elements that fall within the pre-defined cutoff
+      // threshold.
+      const uint32 x0 = (*gen)();
+      const uint32 x1 = (*gen)();
+      float f[2];
+      BoxMullerFloat(x0, x1, &f[0], &f[1]);
+
+      for (int i = 0; i < 2; ++i) {
+        if (Eigen::numext::abs(f[i]) < kTruncateValue) {
+          results[index++] = bfloat16(f[i]);
+          if (index >= kResultElementCount) {
+            return results;
+          }
+        }
+      }
+    }
+  }
+};
+
 // Partial specialization for float.
 template <class SingleSampleGenerator>
 class TruncatedNormalDistribution<SingleSampleGenerator, float> {
@@ -567,6 +665,27 @@ PHILOX_DEVICE_INLINE Eigen::half Uint16ToHalf(uint16 x) {
   return result - Eigen::half(1.0);
 }
 
+// Helper function to convert an 16-bit integer to a bfloat16 between [0..1).
+// This can create a uniform distribution of values between [0..1).
+PHILOX_DEVICE_INLINE bfloat16 Uint16ToGfloat16(uint16 x) {
+  // bfloat are formatted as follows (MSB first):
+  //    sign(1) exponent(8) mantissa(7)
+  // Conceptually construct the following:
+  //    sign == 0
+  //    exponent == 127  -- an excess 127 representation of a zero exponent
+  //    mantissa == 7 random bits
+  const uint16 man = x & 0x7fu;  // 7 bit mantissa
+  const uint16 exp = static_cast<uint16>(127);
+  const uint16 val = (exp << 7) | man;
+
+  bfloat16 result;
+  memcpy(&result, &val, sizeof(val));
+  // The mantissa has an implicit leading 1, so the above code creates a value
+  // in [1, 2). The minus will not cause a rounding that makes the result 1.
+  // Instead it will just be close to 1.
+  return result - bfloat16(1.0);
+}
+
 // Helper function to convert an 32-bit integer to a float between [0..1).
 PHILOX_DEVICE_INLINE float Uint32ToFloat(uint32 x) {
   // IEEE754 floats are formatted as follows (MSB first):
diff --git a/tensorflow/core/lib/random/random_distributions_test.cc b/tensorflow/core/lib/random/random_distributions_test.cc
index 85d68f456e..8868672a10 100644
--- a/tensorflow/core/lib/random/random_distributions_test.cc
+++ b/tensorflow/core/lib/random/random_distributions_test.cc
@@ -37,6 +37,10 @@ namespace {
 // unit normal distribution, it should almost definitely never exceed 6.
 static constexpr float kZLimit = 6.0;
 
+// As bfloat16 has much less precision, the largest z-value will should be
+// larger than float32.
+static constexpr float kZLimitBfloat16 = 20.0;
+
 // A utility function to fill the given array with samples from the given
 // distribution, using the single adapter of the underlying generator
 template <class Distribution>
@@ -93,7 +97,7 @@ bool CheckSamplesMoments(const std::vector<T>& samples,
       // mode, given the large number of samples.
       moments_data[i] += moment;
       ++moments_sample_count_data[i];
-      moment *= samples_data[index];
+      moment *= static_cast<double>(samples_data[index]);
     }
   }
 
@@ -125,7 +129,7 @@ bool CheckSamplesMoments(const std::vector<T>& samples,
     const double z_test =
         fabs((moments[i] - moments_i_mean) / sqrt(total_variance));
 
-    if (z_test > z_limit) {
+    if (z_test > static_cast<double>(z_limit)) {
       LOG(ERROR) << "failing z_test:"
                  << " moment: " << i << " stride: " << stride
                  << " z_test: " << z_test << " z_limit: " << z_limit
@@ -252,6 +256,22 @@ void RandomParametersMomentsTest(int count, int max_moments,
   }
 }
 
+TEST(PhiloxRandomTest, UniformBfloat16MomentsTest) {
+  const std::vector<int> strides = {0, 1, 4, 17};
+  UniformMomentsTest<bfloat16>(1 << 20, 40, strides, bfloat16(kZLimitBfloat16));
+}
+
+TEST(PhiloxRandomTest, NormalBfloat16MomentsTest) {
+  const std::vector<int> strides = {0, 1, 4, 17};
+  NormalMomentsTest<bfloat16>(8 << 20, 25, strides, bfloat16(kZLimitBfloat16));
+}
+
+TEST(PhiloxRandomTest, RandomParametersBfloat16MomentsTest) {
+  const std::vector<int> strides = {0, 1, 4, 17};
+  RandomParametersMomentsTest<bfloat16>(1 << 20, 40, strides,
+                                        bfloat16(kZLimitBfloat16));
+}
+
 TEST(PhiloxRandomTest, UniformFloatMomentsTest) {
   const std::vector<int> strides = {0, 1, 4, 17};
   UniformMomentsTest<float>(1 << 20, 40, strides, kZLimit);
-- 
GitLab


From 16b4fbd56f1b460cefa41c6c50864c0245ecad91 Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu <yuanzx@google.com>
Date: Wed, 28 Feb 2018 21:07:39 -0800
Subject: [PATCH 477/575] [XLA] Reshape/Transpose should not be bitcast if
 element type changes.

PiperOrigin-RevId: 187427133
---
 tensorflow/compiler/xla/shape_util.cc | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc
index 3152789016..9810e818f6 100644
--- a/tensorflow/compiler/xla/shape_util.cc
+++ b/tensorflow/compiler/xla/shape_util.cc
@@ -1076,6 +1076,10 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape,
   CHECK(LayoutUtil::HasLayout(input_shape) &&
         LayoutUtil::HasLayout(output_shape));
 
+  if (!SameElementType(input_shape, output_shape)) {
+    return false;
+  }
+
   // Padding is not handled.
   if (LayoutUtil::IsPadded(input_shape) && LayoutUtil::IsPadded(output_shape)) {
     return false;
@@ -1106,6 +1110,10 @@ ShapeUtil::DimensionsUnmodifiedByReshape(const Shape& input_shape,
   CHECK(LayoutUtil::HasLayout(input_shape) &&
         LayoutUtil::HasLayout(output_shape));
 
+  if (!SameElementType(input_shape, output_shape)) {
+    return false;
+  }
+
   // Padding is not handled.
   if (LayoutUtil::IsPadded(input_shape) || LayoutUtil::IsPadded(output_shape)) {
     return false;
-- 
GitLab


From 6c6bd9524764c1b15d2dc791f88f5de8cf0b51c1 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 28 Feb 2018 22:58:19 -0800
Subject: [PATCH 478/575] [tf.data] Add optional `shuffle` argument to
 `Dataset.list_files()`.

This option makes it easier to shuffle a set of filenames on each iteration,
and default to true to match the recommended best practices when training on
a large dataset.

PiperOrigin-RevId: 187434282
---
 .../list_files_dataset_op_test.py             | 49 ++++++++++++++++---
 tensorflow/python/data/ops/dataset_ops.py     | 22 +++++++--
 .../api/golden/tensorflow.data.-dataset.pbtxt |  2 +-
 ...ow.data.-fixed-length-record-dataset.pbtxt |  2 +-
 .../tensorflow.data.-t-f-record-dataset.pbtxt |  2 +-
 .../tensorflow.data.-text-line-dataset.pbtxt  |  2 +-
 6 files changed, 66 insertions(+), 13 deletions(-)

diff --git a/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py b/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py
index 4e7691ee81..6442eb9ff5 100644
--- a/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py
+++ b/tensorflow/python/data/kernel_tests/list_files_dataset_op_test.py
@@ -46,8 +46,9 @@ class ListFilesDatasetOpTest(test.TestCase):
     dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*'))
     with self.test_session() as sess:
       itr = dataset.make_one_shot_iterator()
+      next_element = itr.get_next()
       with self.assertRaises(errors.OutOfRangeError):
-        sess.run(itr.get_next())
+        sess.run(next_element)
 
   def testSimpleDirectory(self):
     filenames = ['a', 'b', 'c']
@@ -56,13 +57,14 @@ class ListFilesDatasetOpTest(test.TestCase):
     dataset = dataset_ops.Dataset.list_files(path.join(self.tmp_dir, '*'))
     with self.test_session() as sess:
       itr = dataset.make_one_shot_iterator()
+      next_element = itr.get_next()
 
       full_filenames = []
       produced_filenames = []
       for filename in filenames:
         full_filenames.append(
             compat.as_bytes(path.join(self.tmp_dir, filename)))
-        produced_filenames.append(compat.as_bytes(sess.run(itr.get_next())))
+        produced_filenames.append(compat.as_bytes(sess.run(next_element)))
       self.assertItemsEqual(full_filenames, produced_filenames)
       with self.assertRaises(errors.OutOfRangeError):
         sess.run(itr.get_next())
@@ -73,12 +75,13 @@ class ListFilesDatasetOpTest(test.TestCase):
 
     with self.test_session() as sess:
       itr = dataset.make_initializable_iterator()
+      next_element = itr.get_next()
       sess.run(
           itr.initializer,
           feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')})
 
       with self.assertRaises(errors.OutOfRangeError):
-        sess.run(itr.get_next())
+        sess.run(next_element)
 
   def testSimpleDirectoryInitializer(self):
     filenames = ['a', 'b', 'c']
@@ -89,6 +92,7 @@ class ListFilesDatasetOpTest(test.TestCase):
 
     with self.test_session() as sess:
       itr = dataset.make_initializable_iterator()
+      next_element = itr.get_next()
       sess.run(
           itr.initializer,
           feed_dict={filename_placeholder: path.join(self.tmp_dir, '*')})
@@ -98,7 +102,7 @@ class ListFilesDatasetOpTest(test.TestCase):
       for filename in filenames:
         full_filenames.append(
             compat.as_bytes(path.join(self.tmp_dir, filename)))
-        produced_filenames.append(compat.as_bytes(sess.run(itr.get_next())))
+        produced_filenames.append(compat.as_bytes(sess.run(next_element)))
 
       self.assertItemsEqual(full_filenames, produced_filenames)
 
@@ -114,6 +118,7 @@ class ListFilesDatasetOpTest(test.TestCase):
 
     with self.test_session() as sess:
       itr = dataset.make_initializable_iterator()
+      next_element = itr.get_next()
       sess.run(
           itr.initializer,
           feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py')})
@@ -123,7 +128,7 @@ class ListFilesDatasetOpTest(test.TestCase):
       for filename in filenames[1:-1]:
         full_filenames.append(
             compat.as_bytes(path.join(self.tmp_dir, filename)))
-        produced_filenames.append(compat.as_bytes(sess.run(itr.get_next())))
+        produced_filenames.append(compat.as_bytes(sess.run(next_element)))
       self.assertItemsEqual(full_filenames, produced_filenames)
 
       with self.assertRaises(errors.OutOfRangeError):
@@ -138,6 +143,7 @@ class ListFilesDatasetOpTest(test.TestCase):
 
     with self.test_session() as sess:
       itr = dataset.make_initializable_iterator()
+      next_element = itr.get_next()
       sess.run(
           itr.initializer,
           feed_dict={filename_placeholder: path.join(self.tmp_dir, '*.py*')})
@@ -147,13 +153,44 @@ class ListFilesDatasetOpTest(test.TestCase):
       for filename in filenames[1:]:
         full_filenames.append(
             compat.as_bytes(path.join(self.tmp_dir, filename)))
-        produced_filenames.append(compat.as_bytes(sess.run(itr.get_next())))
+        produced_filenames.append(compat.as_bytes(sess.run(next_element)))
 
       self.assertItemsEqual(full_filenames, produced_filenames)
 
       with self.assertRaises(errors.OutOfRangeError):
         sess.run(itr.get_next())
 
+  def testNoShuffle(self):
+    filenames = ['a', 'b', 'c']
+    self._touchTempFiles(filenames)
+
+    # Repeat the list twice and ensure that the order is the same each time.
+    # NOTE(mrry): This depends on an implementation detail of `list_files()`,
+    # which is that the list of files is captured when the iterator is
+    # initialized. Otherwise, or if e.g. the iterator were initialized more than
+    # once, it's possible that the non-determinism of `tf.matching_files()`
+    # would cause this test to fail. However, it serves as a useful confirmation
+    # that the `shuffle=False` argument is working as intended.
+    # TODO(b/73959787): Provide some ordering guarantees so that this test is
+    # more meaningful.
+    dataset = dataset_ops.Dataset.list_files(
+        path.join(self.tmp_dir, '*'), shuffle=False).repeat(2)
+    with self.test_session() as sess:
+      itr = dataset.make_one_shot_iterator()
+      next_element = itr.get_next()
+
+      full_filenames = []
+      produced_filenames = []
+      for filename in filenames * 2:
+        full_filenames.append(
+            compat.as_bytes(path.join(self.tmp_dir, filename)))
+        produced_filenames.append(compat.as_bytes(sess.run(next_element)))
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(itr.get_next())
+      self.assertItemsEqual(full_filenames, produced_filenames)
+      self.assertEqual(produced_filenames[:len(filenames)],
+                       produced_filenames[len(filenames):])
+
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py
index 5751f35fe1..7c5aa4c767 100644
--- a/tensorflow/python/data/ops/dataset_ops.py
+++ b/tensorflow/python/data/ops/dataset_ops.py
@@ -36,6 +36,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_dataset_ops
 from tensorflow.python.ops import gen_io_ops
 from tensorflow.python.ops import math_ops
@@ -557,7 +558,7 @@ class Dataset(object):
     return PrefetchDataset(self, buffer_size)
 
   @staticmethod
-  def list_files(file_pattern):
+  def list_files(file_pattern, shuffle=None):
     """A dataset of all files matching a pattern.
 
     Example:
@@ -570,16 +571,31 @@ class Dataset(object):
         - /path/to/dir/b.py
         - /path/to/dir/c.py
 
-    NOTE: The order of the file names returned can be non-deterministic.
+    NOTE: The order of the file names returned can be non-deterministic even
+    when `shuffle` is `False`.
 
     Args:
       file_pattern: A string or scalar string `tf.Tensor`, representing
         the filename pattern that will be matched.
+      shuffle: (Optional.) If `True`, the file names will be shuffled randomly.
+        Defaults to `True`.
 
     Returns:
      Dataset: A `Dataset` of strings corresponding to file names.
     """
-    return Dataset.from_tensor_slices(gen_io_ops.matching_files(file_pattern))
+    # TODO(b/73959787): Add a `seed` argument and make the `shuffle=False`
+    # behavior deterministic (e.g. by sorting the filenames).
+    if shuffle is None:
+      shuffle = True
+    matching_files = gen_io_ops.matching_files(file_pattern)
+    dataset = Dataset.from_tensor_slices(matching_files)
+    if shuffle:
+      # NOTE(mrry): The shuffle buffer size must be greater than zero, but the
+      # list of files might be empty.
+      buffer_size = math_ops.maximum(
+          array_ops.shape(matching_files, out_type=dtypes.int64)[0], 1)
+      dataset = dataset.shuffle(buffer_size)
+    return dataset
 
   def repeat(self, count=None):
     """Repeats this dataset `count` times.
diff --git a/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt
index 42de5c0c80..0900adaf76 100644
--- a/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.data.-dataset.pbtxt
@@ -64,7 +64,7 @@ tf_class {
   }
   member_method {
     name: "list_files"
-    argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "make_initializable_iterator"
diff --git a/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt
index e2fc8d6cb1..7b16ac90c9 100644
--- a/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt
@@ -65,7 +65,7 @@ tf_class {
   }
   member_method {
     name: "list_files"
-    argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "make_initializable_iterator"
diff --git a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt
index 709ec127ce..9cf5f2ae20 100644
--- a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt
@@ -65,7 +65,7 @@ tf_class {
   }
   member_method {
     name: "list_files"
-    argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "make_initializable_iterator"
diff --git a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt b/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt
index 7263230c1c..8c3d669143 100644
--- a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt
@@ -65,7 +65,7 @@ tf_class {
   }
   member_method {
     name: "list_files"
-    argspec: "args=[\'file_pattern\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'file_pattern\', \'shuffle\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "make_initializable_iterator"
-- 
GitLab


From 46355f9065967dd39cd340b17d91a91f70d2c0c1 Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Thu, 1 Mar 2018 01:44:33 -0800
Subject: [PATCH 479/575] Ensure folding of batch norms is idempotent.

Added more rigorous testing.
(Also fixed a couple of naming nits in the code as I looked through)

PiperOrigin-RevId: 187446976
---
 .../quantize/python/fold_batch_norms.py       | 40 +++++++++++++------
 .../python/quantize_parameterized_test.py     | 23 ++++++++---
 2 files changed, 46 insertions(+), 17 deletions(-)

diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py
index 75d9eb0e58..1f0648bbb6 100644
--- a/tensorflow/contrib/quantize/python/fold_batch_norms.py
+++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py
@@ -194,7 +194,7 @@ def _FindFusedBatchNorms(graph):
     layer_op = match_result.get_op(layer_pattern)
     layer_tensor = match_result.get_tensor(layer_pattern)
     bn_op = match_result.get_op(batch_norm_pattern)
-    batch_epsilon_tensor = bn_op.get_attr('epsilon')
+    batch_epsilon = bn_op.get_attr('epsilon')
 
     # In the MatMul case, the output of batch norm is reshaped back into a
     # 2D tensor, so the output_tensor is the output of the Reshape op.
@@ -207,6 +207,11 @@ def _FindFusedBatchNorms(graph):
         continue
       output_tensor = output_reshape_op.outputs[0]
 
+    # Ensure that the output tensor has consumers, otherwise this is a dangling
+    # node and not a match.
+    if not output_tensor.consumers():
+      continue
+
     input_tensor = match_result.get_tensor(input_pattern)
     weight_tensor = match_result.get_tensor(weight_pattern)
     gamma_tensor = match_result.get_tensor(gamma_pattern)
@@ -270,7 +275,7 @@ def _FindFusedBatchNorms(graph):
         moving_variance_tensor=moving_variance_tensor,
         bn_decay_mean_tensor=bn_decay_mean_tensor,
         bn_decay_var_tensor=bn_decay_var_tensor,
-        batch_epsilon_tensor=batch_epsilon_tensor)
+        batch_epsilon=batch_epsilon)
 
 
 def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay,
@@ -313,9 +318,8 @@ def _ComputeBatchNormCorrections(context, match, freeze_batch_norm_delay,
   g = ops.get_default_graph()
   with g.name_scope(context + '/batch_norm_correction'):
     recip_sigma_mv = math_ops.rsqrt(
-        match.moving_variance_tensor + match.batch_epsilon_tensor)
-    recip_sigma = math_ops.rsqrt(
-        match.variance_tensor + match.batch_epsilon_tensor)
+        match.moving_variance_tensor + match.batch_epsilon)
+    recip_sigma = math_ops.rsqrt(match.variance_tensor + match.batch_epsilon)
     correction_scale = math_ops.divide(
         recip_sigma_mv, recip_sigma, name='scale_compute')
     correction_scale = array_ops.identity(
@@ -434,6 +438,9 @@ def _FoldUnfusedBatchNorms(graph, is_training, freeze_batch_norm_delay):
   for bn in common.BatchNormGroups(graph):
     has_scaling = _HasScaling(graph, input_to_ops_map, bn)
 
+    if not _IsValidUnfusedBatchNorm(graph, bn):
+      continue
+
     # The mangling code intimately depends on BatchNorm node's internals.
     original_op, folded_op = _CreateFoldedOp(
         graph,
@@ -462,6 +469,15 @@ def _FoldUnfusedBatchNorms(graph, is_training, freeze_batch_norm_delay):
       raise ValueError('Unexpected inputs to op: %s' % add_bypass.name)
 
 
+def _IsValidUnfusedBatchNorm(graph, context):
+  """Checks that the output of the unfused batch norm has consumers."""
+  add_shift = graph.get_operation_by_name(
+      context + '/BatchNorm/batchnorm/add_1')
+  # Ensure that the output tensor of batch norm has consumers, otherwise this
+  # is a dangling node and not a match.
+  return bool(add_shift.outputs[0].consumers())
+
+
 def _GetBatchNormParams(graph, context, has_scaling):
   """Extracts relevant tensors for folding batch norms.
 
@@ -478,7 +494,7 @@ def _GetBatchNormParams(graph, context, has_scaling):
   batch_variance_tensor = None
   moving_mean_tensor = None
   moving_variance_tensor = None
-  batch_epsilon_tensor = None
+  batch_epsilon = None
   bn_decay_mean_tensor = None
   bn_decay_var_tensor = None
 
@@ -509,7 +525,7 @@ def _GetBatchNormParams(graph, context, has_scaling):
     if op.name.endswith(op_suffix_moving_variance):
       moving_variance_tensor = graph.get_tensor_by_name(op.name + ':0')
     if op.name.endswith(op_suffix_epsilon):
-      batch_epsilon_tensor = graph.get_tensor_by_name(op.name + ':0')
+      batch_epsilon = graph.get_tensor_by_name(op.name + ':0')
     if op.name.endswith(op_suffix_bn_decay_mean):
       bn_decay_mean_tensor = graph.get_tensor_by_name(op.name + ':0')
     if op.name.endswith(op_suffix_bn_decay_var):
@@ -535,7 +551,7 @@ def _GetBatchNormParams(graph, context, has_scaling):
       moving_variance_tensor=moving_variance_tensor,
       bn_decay_mean_tensor=bn_decay_mean_tensor,
       bn_decay_var_tensor=bn_decay_var_tensor,
-      batch_epsilon_tensor=batch_epsilon_tensor)
+      batch_epsilon=batch_epsilon)
 
 
 def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay,
@@ -816,7 +832,7 @@ class _BatchNormMatch(object):
   def __init__(self, layer_op, bn_op, output_tensor, input_tensor,
                weight_tensor, gamma_tensor, beta_tensor, mean_tensor,
                variance_tensor, moving_mean_tensor, moving_variance_tensor,
-               bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon_tensor):
+               bn_decay_mean_tensor, bn_decay_var_tensor, batch_epsilon):
     self._layer_op = layer_op
     self._bn_op = bn_op
     self._output_tensor = output_tensor
@@ -830,7 +846,7 @@ class _BatchNormMatch(object):
     self._moving_variance_tensor = moving_variance_tensor
     self._bn_decay_mean_tensor = bn_decay_mean_tensor
     self._bn_decay_var_tensor = bn_decay_var_tensor
-    self._batch_epsilon_tensor = batch_epsilon_tensor
+    self._batch_epsilon = batch_epsilon
 
   @property
   def layer_op(self):
@@ -877,8 +893,8 @@ class _BatchNormMatch(object):
     return self._moving_variance_tensor
 
   @property
-  def batch_epsilon_tensor(self):
-    return self._batch_epsilon_tensor
+  def batch_epsilon(self):
+    return self._batch_epsilon
 
   @property
   def bn_decay_mean_tensor(self):
diff --git a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py
index 639a7454a9..dd73f6c860 100644
--- a/tensorflow/contrib/quantize/python/quantize_parameterized_test.py
+++ b/tensorflow/contrib/quantize/python/quantize_parameterized_test.py
@@ -87,8 +87,8 @@ class QuantizeTest(test_util.TensorFlowTestCase):
       update_barrier = control_flow_ops.no_op(name='update_barrier')
       with ops.control_dependencies([update_barrier]):
         array_ops.identity(node, name='control_dependency')
-
       quantize.Quantize(graph, True, quant_delay=delay)
+
     quantization_node_name = 'FakeQuantWithMinMaxVars'
     weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' +
                                                 quantization_node_name)
@@ -130,6 +130,7 @@ class QuantizeTest(test_util.TensorFlowTestCase):
     output_op_name = ('test/act_quant/delayed_quant/Switch_1'
                       if delay else 'control_dependency')
     self._AssertOutputGoesToOps(act_quant, graph, [output_op_name])
+    self._TestIdempotent(graph)
 
   def testQuantize_Conv2dWithoutBatchNorm(self):
     self._RunWithoutBatchNormTestOverParameters(
@@ -163,7 +164,6 @@ class QuantizeTest(test_util.TensorFlowTestCase):
       update_barrier = control_flow_ops.no_op(name='update_barrier')
       with ops.control_dependencies([update_barrier]):
         array_ops.identity(node, name='control_dependency')
-
       quantize.Quantize(graph, True, quant_delay=delay)
 
     quantization_node_name = 'FakeQuantWithMinMaxVars'
@@ -205,6 +205,7 @@ class QuantizeTest(test_util.TensorFlowTestCase):
     output_op_name = ('test/act_quant/delayed_quant/Switch_1'
                       if delay else 'control_dependency')
     self._AssertOutputGoesToOps(act_quant, graph, [output_op_name])
+    self._TestIdempotent(graph)
 
   def testQuantize_FCWithoutBatchNorm(self):
     self._RunWithoutBatchNormTestOverParameters(
@@ -239,7 +240,6 @@ class QuantizeTest(test_util.TensorFlowTestCase):
       update_barrier = control_flow_ops.no_op(name='update_barrier')
       with ops.control_dependencies([update_barrier]):
         array_ops.identity(node, name='control_dependency')
-
       quantize.Quantize(graph, True, quant_delay=delay)
 
     quantization_node_name = 'FakeQuantWithMinMaxVars'
@@ -282,6 +282,7 @@ class QuantizeTest(test_util.TensorFlowTestCase):
     output_op_name = ('test/act_quant/delayed_quant/Switch_1'
                       if delay else 'control_dependency')
     self._AssertOutputGoesToOps(act_quant, graph, [output_op_name])
+    self._TestIdempotent(graph)
 
   def testQuantize_DepthwiseConv2dWithoutBatchNorm(self):
     self._RunWithoutBatchNormTestOverParameters(
@@ -364,7 +365,6 @@ class QuantizeTest(test_util.TensorFlowTestCase):
         array_ops.identity(node, name='control_dependency')
 
       fold_batch_norms.FoldBatchNorms(graph, is_training=True)
-
       quantize.Quantize(graph, True, quant_delay=delay)
 
     quantization_node_name = 'FakeQuantWithMinMaxVars'
@@ -404,6 +404,7 @@ class QuantizeTest(test_util.TensorFlowTestCase):
     output_op_name = ('test/act_quant/delayed_quant/Switch_1'
                       if delay else 'control_dependency')
     self._AssertOutputGoesToOps(act_quant, graph, [output_op_name])
+    self._TestIdempotent(graph)
 
   def testQuantize_FCWithBatchNorm(self):
     self._RunBatchNormTestOverParameters(self._TestQuantize_FCWithBatchNorm)
@@ -487,6 +488,7 @@ class QuantizeTest(test_util.TensorFlowTestCase):
     output_op_name = ('test/act_quant/delayed_quant/Switch_1'
                       if delay else 'control_dependency')
     self._AssertOutputGoesToOps(act_quant, graph, [output_op_name])
+    self._TestIdempotent(graph)
 
   def testQuantize_DepthwiseConv2dWithBatchNorm(self):
     self._RunBatchNormTestOverParameters(
@@ -535,8 +537,8 @@ class QuantizeTest(test_util.TensorFlowTestCase):
         array_ops.identity(node, name='control_dependency')
 
       fold_batch_norms.FoldBatchNorms(graph, is_training=True)
-
       quantize.Quantize(graph, True, quant_delay=delay)
+
     quantization_node_name = 'FakeQuantWithMinMaxVars'
     weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' +
                                                 quantization_node_name)
@@ -574,6 +576,17 @@ class QuantizeTest(test_util.TensorFlowTestCase):
     output_op_name = ('test/act_quant/delayed_quant/Switch_1'
                       if delay else 'control_dependency')
     self._AssertOutputGoesToOps(act_quant, graph, [output_op_name])
+    self._TestIdempotent(graph)
+
+  def _TestIdempotent(self, graph):
+    # Ensure that calling the rewrite again doesn't change the graph.
+    graph_def_before = str(graph.as_graph_def())
+    with graph.as_default():
+      # Ensuring that calling the rewrite again doesn't add more nodes.
+      fold_batch_norms.FoldBatchNorms(graph, is_training=True)
+      quantize.Quantize(graph, True)
+    graph_def_after = str(graph.as_graph_def())
+    self.assertEqual(graph_def_before, graph_def_after)
 
   def _BatchNormParams(self, fused=False):
     return {'center': True, 'scale': True, 'decay': 1.0 - 0.003, 'fused': fused}
-- 
GitLab


From 2b7a7ee30666d160929c9aa3e941fbc94c17cc52 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 06:03:38 -0800
Subject: [PATCH 480/575] Add RegexReplace Op that internally calls
 RE2::Replace.

PiperOrigin-RevId: 187467840
---
 .../base_api/api_def_RegexReplace.pbtxt       | 25 ++++++
 tensorflow/core/kernels/BUILD                 |  8 ++
 tensorflow/core/kernels/regex_replace_op.cc   | 76 +++++++++++++++++++
 tensorflow/core/ops/string_ops.cc             | 14 ++++
 tensorflow/python/kernel_tests/BUILD          | 12 +++
 .../kernel_tests/regex_replace_op_test.py     | 71 +++++++++++++++++
 tensorflow/python/ops/string_ops.py           |  2 +
 tensorflow/tools/api/golden/tensorflow.pbtxt  |  4 +
 8 files changed, 212 insertions(+)
 create mode 100644 tensorflow/core/api_def/base_api/api_def_RegexReplace.pbtxt
 create mode 100644 tensorflow/core/kernels/regex_replace_op.cc
 create mode 100644 tensorflow/python/kernel_tests/regex_replace_op_test.py

diff --git a/tensorflow/core/api_def/base_api/api_def_RegexReplace.pbtxt b/tensorflow/core/api_def/base_api/api_def_RegexReplace.pbtxt
new file mode 100644
index 0000000000..70ad521926
--- /dev/null
+++ b/tensorflow/core/api_def/base_api/api_def_RegexReplace.pbtxt
@@ -0,0 +1,25 @@
+op {
+  graph_op_name: "RegexReplace"
+  in_arg {
+    name: "input"
+    description: "The text to be processed."
+  }
+  in_arg {
+    name: "pattern"
+    description: "The regular expression to match the input."
+  }
+  in_arg {
+    name: "rewrite"
+    description: "The rewrite to be applied to the matched expresion."
+  }
+  out_arg {
+    name: "output"
+    description: "The text after applying pattern and rewrite."
+  }
+  attr {
+    name: "replace_global"
+    description: "If True, the replacement is global, otherwise the replacement\nis done only on the first match."
+  }
+  summary: "Replaces the match of pattern in input with rewrite."
+  description: "It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax)"
+}
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index 3426cf6e40..feacee5d63 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -4155,6 +4155,7 @@ cc_library(
         ":as_string_op",
         ":base64_ops",
         ":reduce_join_op",
+        ":regex_replace_op",
         ":string_join_op",
         ":string_split_op",
         ":string_to_hash_bucket_op",
@@ -4189,6 +4190,12 @@ tf_kernel_library(
     deps = STRING_DEPS,
 )
 
+tf_kernel_library(
+    name = "regex_replace_op",
+    prefix = "regex_replace_op",
+    deps = STRING_DEPS + ["@com_googlesource_code_re2//:re2"],
+)
+
 tf_kernel_library(
     name = "string_split_op",
     prefix = "string_split_op",
@@ -5063,6 +5070,7 @@ filegroup(
             "scatter_nd_op*",
             "mutex_ops.*",
             "batch_kernels.*",
+            "regex_replace_op.cc",
         ],
     ),
     visibility = ["//visibility:public"],
diff --git a/tensorflow/core/kernels/regex_replace_op.cc b/tensorflow/core/kernels/regex_replace_op.cc
new file mode 100644
index 0000000000..59ec854a79
--- /dev/null
+++ b/tensorflow/core/kernels/regex_replace_op.cc
@@ -0,0 +1,76 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include <string>
+
+#include "re2/re2.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+class RegexReplaceOp : public OpKernel {
+ public:
+  explicit RegexReplaceOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("replace_global", &replace_global_));
+  }
+
+  void Compute(OpKernelContext* ctx) override {
+    const Tensor* input_tensor;
+    OP_REQUIRES_OK(ctx, ctx->input("input", &input_tensor));
+    const auto& input_flat = input_tensor->flat<string>();
+
+    const Tensor* pattern_tensor;
+    OP_REQUIRES_OK(ctx, ctx->input("pattern", &pattern_tensor));
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(pattern_tensor->shape()),
+                errors::InvalidArgument("Pattern must be scalar, but received ",
+                                        pattern_tensor->shape().DebugString()));
+    const string pattern = pattern_tensor->flat<string>()(0);
+    const RE2 match(pattern);
+    OP_REQUIRES(ctx, match.ok(),
+                errors::InvalidArgument("Invalid pattern: ", pattern,
+                                        ", error: ", match.error()));
+
+    const Tensor* rewrite_tensor;
+    OP_REQUIRES_OK(ctx, ctx->input("rewrite", &rewrite_tensor));
+    OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(rewrite_tensor->shape()),
+                errors::InvalidArgument("Rewrite must be scalar, but received ",
+                                        rewrite_tensor->shape().DebugString()));
+    const string rewrite = rewrite_tensor->flat<string>()(0);
+
+    Tensor* output_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output("output", input_tensor->shape(),
+                                             &output_tensor));
+    auto output_flat = output_tensor->flat<string>();
+    for (size_t i = 0; i < input_flat.size(); ++i) {
+      output_flat(i) = input_flat(i);
+      if (replace_global_) {
+        RE2::GlobalReplace(&output_flat(i), match, rewrite);
+      } else {
+        RE2::Replace(&output_flat(i), match, rewrite);
+      }
+    }
+  }
+
+ private:
+  bool replace_global_;
+};
+
+REGISTER_KERNEL_BUILDER(Name("RegexReplace").Device(DEVICE_CPU),
+                        RegexReplaceOp);
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc
index e4c5bcfb54..05f216a83e 100644
--- a/tensorflow/core/ops/string_ops.cc
+++ b/tensorflow/core/ops/string_ops.cc
@@ -23,6 +23,20 @@ using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
 using shape_inference::ShapeHandle;
 
+REGISTER_OP("RegexReplace")
+    .Input("input: string")
+    .Input("pattern: string")
+    .Input("rewrite: string")
+    .Output("output: string")
+    .Attr("replace_global: bool = true")
+    .SetShapeFn([](InferenceContext* c) {
+      ShapeHandle unused;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
+      c->set_output(0, c->input(0));
+      return Status::OK();
+    });
+
 REGISTER_OP("StringToHashBucketFast")
     .Input("input: string")
     .Output("output: int64")
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index c9aa4a252d..0f13e8bba5 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -712,6 +712,18 @@ cuda_py_test(
     ],
 )
 
+tf_py_test(
+    name = "regex_replace_op_test",
+    size = "small",
+    srcs = ["regex_replace_op_test.py"],
+    additional_deps = [
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:string_ops",
+    ],
+)
+
 tf_py_test(
     name = "save_restore_ops_test",
     size = "small",
diff --git a/tensorflow/python/kernel_tests/regex_replace_op_test.py b/tensorflow/python/kernel_tests/regex_replace_op_test.py
new file mode 100644
index 0000000000..6739ac3224
--- /dev/null
+++ b/tensorflow/python/kernel_tests/regex_replace_op_test.py
@@ -0,0 +1,71 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for RegexReplace op from string_ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import string_ops
+from tensorflow.python.platform import test
+
+
+class RegexReplaceOpTest(test.TestCase):
+
+  def testRemovePrefix(self):
+    values = ["a:foo", "a:bar", "a:foo", "b:baz", "b:qux", "ca:b"]
+    with self.test_session():
+      input_vector = constant_op.constant(values, dtypes.string)
+      stripped = string_ops.regex_replace(
+          input_vector, "^(a:|b:)", "", replace_global=False).eval()
+      self.assertAllEqual([b"foo", b"bar", b"foo", b"baz", b"qux", b"ca:b"],
+                          stripped)
+
+  def testRegexReplace(self):
+    values = ["aba\naba", "abcdabcde"]
+    with self.test_session():
+      input_vector = constant_op.constant(values, dtypes.string)
+      stripped = string_ops.regex_replace(input_vector, "a.*a", "(\\0)").eval()
+      self.assertAllEqual([b"(aba)\n(aba)", b"(abcda)bcde"], stripped)
+
+  def testEmptyMatch(self):
+    values = ["abc", "1"]
+    with self.test_session():
+      input_vector = constant_op.constant(values, dtypes.string)
+      stripped = string_ops.regex_replace(input_vector, "", "x").eval()
+      self.assertAllEqual([b"xaxbxcx", b"x1x"], stripped)
+
+  def testInvalidPattern(self):
+    values = ["abc", "1"]
+    with self.test_session():
+      input_vector = constant_op.constant(values, dtypes.string)
+      invalid_pattern = "A["
+      replace = string_ops.regex_replace(input_vector, invalid_pattern, "x")
+      with self.assertRaisesOpError("Invalid pattern"):
+        replace.eval()
+
+  def testGlobal(self):
+    values = ["ababababab", "abcabcabc", ""]
+    with self.test_session():
+      input_vector = constant_op.constant(values, dtypes.string)
+      stripped = string_ops.regex_replace(input_vector, "ab", "abc",
+                                          True).eval()
+      self.assertAllEqual([b"abcabcabcabcabc", b"abccabccabcc", b""], stripped)
+
+
+if __name__ == "__main__":
+  test.main()
diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py
index 0335d2456a..5bd75b9215 100644
--- a/tensorflow/python/ops/string_ops.py
+++ b/tensorflow/python/ops/string_ops.py
@@ -17,6 +17,7 @@
 
 See the @{$python/string_ops} guide.
 
+@@regex_replace
 @@string_to_hash_bucket_fast
 @@string_to_hash_bucket_strong
 @@string_to_hash_bucket
@@ -139,6 +140,7 @@ def reduce_join(inputs, axis=None,
 reduce_join.__doc__ = deprecation.rewrite_argument_docstring(
     gen_string_ops.reduce_join.__doc__, "reduction_indices", "axis")
 
+ops.NotDifferentiable("RegexReplace")
 ops.NotDifferentiable("StringToHashBucket")
 ops.NotDifferentiable("StringToHashBucketFast")
 ops.NotDifferentiable("StringToHashBucketStrong")
diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt
index 2333736583..8c9e7af89b 100644
--- a/tensorflow/tools/api/golden/tensorflow.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.pbtxt
@@ -1600,6 +1600,10 @@ tf_module {
     name: "reduce_sum"
     argspec: "args=[\'input_tensor\', \'axis\', \'keepdims\', \'name\', \'reduction_indices\', \'keep_dims\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], "
   }
+  member_method {
+    name: "regex_replace"
+    argspec: "args=[\'input\', \'pattern\', \'rewrite\', \'replace_global\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], "
+  }
   member_method {
     name: "register_tensor_conversion_function"
     argspec: "args=[\'base_type\', \'conversion_func\', \'priority\'], varargs=None, keywords=None, defaults=[\'100\'], "
-- 
GitLab


From 8a06526e9ac4cd47c14975bd75640966bd11daf9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 06:18:11 -0800
Subject: [PATCH 481/575] Update ops-related pbtxt files.

PiperOrigin-RevId: 187468981
---
 .../core/ops/compat/ops_history.v1.pbtxt      | 26 +++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 26 +++++++++++++++++++
 2 files changed, 52 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index dddde1624a..35c49658b3 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -37666,6 +37666,32 @@ op {
   }
   allows_uninitialized_input: true
 }
+op {
+  name: "RegexReplace"
+  input_arg {
+    name: "input"
+    type: DT_STRING
+  }
+  input_arg {
+    name: "pattern"
+    type: DT_STRING
+  }
+  input_arg {
+    name: "rewrite"
+    type: DT_STRING
+  }
+  output_arg {
+    name: "output"
+    type: DT_STRING
+  }
+  attr {
+    name: "replace_global"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+}
 op {
   name: "Relu"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 55be0519a7..bf7682712c 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -19353,6 +19353,32 @@ op {
   }
   allows_uninitialized_input: true
 }
+op {
+  name: "RegexReplace"
+  input_arg {
+    name: "input"
+    type: DT_STRING
+  }
+  input_arg {
+    name: "pattern"
+    type: DT_STRING
+  }
+  input_arg {
+    name: "rewrite"
+    type: DT_STRING
+  }
+  output_arg {
+    name: "output"
+    type: DT_STRING
+  }
+  attr {
+    name: "replace_global"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+}
 op {
   name: "Relu"
   input_arg {
-- 
GitLab


From 166980803009ec4577806b4437579159f5e9dd5a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 06:25:38 -0800
Subject: [PATCH 482/575] Support 0 size literals in Literal::Slice

PiperOrigin-RevId: 187469563
---
 tensorflow/compiler/xla/literal_util.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index c3eb8caa57..a345e95a8b 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -813,7 +813,7 @@ std::unique_ptr<Literal> Literal::Slice(
     CHECK_GE(start_indices[dnum], 0);
     CHECK_LE(limit_indices[dnum], shape().dimensions(dnum));
     int64 dimension = limit_indices[dnum] - start_indices[dnum];
-    CHECK_GT(dimension, 0);
+    CHECK_GE(dimension, 0);
     result_dimensions.push_back(dimension);
   }
   const auto result_shape =
-- 
GitLab


From bf048d60fbf68fd731df6b2f2ff36a5722b73bb8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 06:45:58 -0800
Subject: [PATCH 483/575] Go: Update generated wrapper functions for TensorFlow
 ops. PiperOrigin-RevId: 187471483

---
 tensorflow/go/op/wrappers.go | 1486 +++++++++++++++++-----------------
 1 file changed, 743 insertions(+), 743 deletions(-)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index d9e684a661..336df7c2f7 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -384,122 +384,6 @@ func FakeQuantWithMinMaxVarsGradient(scope *Scope, gradients tf.Output, inputs t
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2.
-type MutableHashTableOfTensorsV2Attr func(optionalAttr)
-
-// MutableHashTableOfTensorsV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this table is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this table is shared under the given name across
-// multiple sessions.
-// If not specified, defaults to ""
-func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
-// If not specified, defaults to false
-func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr {
-	return func(m optionalAttr) {
-		m["use_node_name_sharing"] = value
-	}
-}
-
-// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value.
-// If not specified, defaults to <>
-func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr {
-	return func(m optionalAttr) {
-		m["value_shape"] = value
-	}
-}
-
-// Creates an empty hash table.
-//
-// This op creates a mutable hash table, specifying the type of its keys and
-// values. Each value must be a vector. Data can be inserted into the table using
-// the insert operations. It does not support the initialization operation.
-//
-// Arguments:
-//	key_dtype: Type of the table keys.
-//	value_dtype: Type of the table values.
-//
-// Returns Handle to a table.
-func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "MutableHashTableOfTensorsV2",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad.
-type ResourceApplyProximalAdagradAttr func(optionalAttr)
-
-// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
-//
-// value: If True, updating of the var and accum tensors will be protected by
-// a lock; otherwise the behavior is undefined, but may exhibit less contention.
-// If not specified, defaults to false
-func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr {
-	return func(m optionalAttr) {
-		m["use_locking"] = value
-	}
-}
-
-// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
-//
-// accum += grad * grad
-// prox_v = var - lr * grad * (1 / sqrt(accum))
-// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
-//
-// Arguments:
-//	var_: Should be from a Variable().
-//	accum: Should be from a Variable().
-//	lr: Scaling factor. Must be a scalar.
-//	l1: L1 regularization. Must be a scalar.
-//	l2: L2 regularization. Must be a scalar.
-//	grad: The gradient.
-//
-// Returns the created operation.
-func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "ResourceApplyProximalAdagrad",
-		Input: []tf.Input{
-			var_, accum, lr, l1, l2, grad,
-		},
-		Attrs: attrs,
-	}
-	return scope.AddOperation(opspec)
-}
-
 // MutableHashTableV2Attr is an optional argument to MutableHashTableV2.
 type MutableHashTableV2Attr func(optionalAttr)
 
@@ -564,142 +448,6 @@ func MutableHashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.Data
 	return op.Output(0)
 }
 
-// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey.
-type MapUnstageNoKeyAttr func(optionalAttr)
-
-// MapUnstageNoKeyCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
-//
-// REQUIRES: value >= 0
-func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr {
-	return func(m optionalAttr) {
-		m["capacity"] = value
-	}
-}
-
-// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
-//
-// REQUIRES: value >= 0
-func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr {
-	return func(m optionalAttr) {
-		m["memory_limit"] = value
-	}
-}
-
-// MapUnstageNoKeyContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// MapUnstageNoKeySharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// Op removes and returns a random (key, value)
-//
-// from the underlying container.   If the underlying container
-// does not contain elements, the op will block until it does.
-func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"dtypes": dtypes}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "MapUnstageNoKey",
-		Input: []tf.Input{
-			indices,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	if scope.Err() != nil {
-		return
-	}
-	var idx int
-	var err error
-	key = op.Output(idx)
-	if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
-		scope.UpdateErr("MapUnstageNoKey", err)
-		return
-	}
-	return key, values
-}
-
-// HashTableV2Attr is an optional argument to HashTableV2.
-type HashTableV2Attr func(optionalAttr)
-
-// HashTableV2Container sets the optional container attribute to value.
-//
-// value: If non-empty, this table is placed in the given container.
-// Otherwise, a default container is used.
-// If not specified, defaults to ""
-func HashTableV2Container(value string) HashTableV2Attr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// HashTableV2SharedName sets the optional shared_name attribute to value.
-//
-// value: If non-empty, this table is shared under the given name across
-// multiple sessions.
-// If not specified, defaults to ""
-func HashTableV2SharedName(value string) HashTableV2Attr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
-//
-// value: If true and shared_name is empty, the table is shared
-// using the node name.
-// If not specified, defaults to false
-func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr {
-	return func(m optionalAttr) {
-		m["use_node_name_sharing"] = value
-	}
-}
-
-// Creates a non-initialized hash table.
-//
-// This op creates a hash table, specifying the type of its keys and values.
-// Before using the table you will have to initialize it.  After initialization the
-// table will be immutable.
-//
-// Arguments:
-//	key_dtype: Type of the table keys.
-//	value_dtype: Type of the table values.
-//
-// Returns Handle to a table.
-func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "HashTableV2",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Replaces the contents of the table with the specified keys and values.
 //
 // The tensor `keys` must be of the same type as the keys of the table.
@@ -5642,113 +5390,8 @@ func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_f
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// SummaryWriterAttr is an optional argument to SummaryWriter.
-type SummaryWriterAttr func(optionalAttr)
-
-// SummaryWriterSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func SummaryWriterSharedName(value string) SummaryWriterAttr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// SummaryWriterContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func SummaryWriterContainer(value string) SummaryWriterAttr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// Returns a handle to be used to access a summary writer.
-//
-// The summary writer is an in-graph resource which can be used by ops to write
-// summaries to event files.
-//
-// Returns the summary writer resource. Scalar handle.
-func SummaryWriter(scope *Scope, optional ...SummaryWriterAttr) (writer tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "SummaryWriter",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes gradients for SparseSegmentMean.
-//
-// Returns tensor "output" with same shape as grad, except for dimension 0 whose
-// value is output_dim0.
-//
-// Arguments:
-//	grad: gradient propagated to the SparseSegmentMean op.
-//	indices: indices passed to the corresponding SparseSegmentMean op.
-//	segment_ids: segment_ids passed to the corresponding SparseSegmentMean op.
-//	output_dim0: dimension 0 of "data" passed to SparseSegmentMean op.
-func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SparseSegmentMeanGrad",
-		Input: []tf.Input{
-			grad, indices, segment_ids, output_dim0,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Applies softmax to a batched N-D `SparseTensor`.
-//
-// The inputs represent an N-D SparseTensor  with logical shape `[..., B, C]`
-// (where `N >= 2`), and with indices sorted in the canonical lexicographic order.
-//
-// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost
-// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly
-// zero elements do not participate*.  Specifically, the algorithm is equivalent
-// to the following:
-//
-//   (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix
-//       with shape `[B, C]`, along the size-C dimension;
-//   (2) Masks out the original implicitly-zero locations;
-//   (3) Renormalizes the remaining elements.
-//
-// Hence, the `SparseTensor` result has exactly the same non-zero indices and
-// shape.
-//
-// Arguments:
-//	sp_indices: 2-D.  `NNZ x R` matrix with the indices of non-empty values in a
-// SparseTensor, in canonical ordering.
-//	sp_values: 1-D.  `NNZ` non-empty values corresponding to `sp_indices`.
-//	sp_shape: 1-D.  Shape of the input SparseTensor.
-//
-// Returns 1-D.  The `NNZ` values for the result `SparseTensor`.
-func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SparseSoftmax",
-		Input: []tf.Input{
-			sp_indices, sp_values, sp_shape,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// RandomPoissonAttr is an optional argument to RandomPoisson.
-type RandomPoissonAttr func(optionalAttr)
+// RandomPoissonAttr is an optional argument to RandomPoisson.
+type RandomPoissonAttr func(optionalAttr)
 
 // RandomPoissonSeed sets the optional seed attribute to value.
 // If not specified, defaults to 0
@@ -7025,67 +6668,32 @@ func DestroyResourceOp(scope *Scope, resource tf.Output, optional ...DestroyReso
 	return scope.AddOperation(opspec)
 }
 
-// CumprodAttr is an optional argument to Cumprod.
-type CumprodAttr func(optionalAttr)
+// SummaryWriterAttr is an optional argument to SummaryWriter.
+type SummaryWriterAttr func(optionalAttr)
 
-// CumprodExclusive sets the optional exclusive attribute to value.
-//
-// value: If `True`, perform exclusive cumprod.
-// If not specified, defaults to false
-func CumprodExclusive(value bool) CumprodAttr {
+// SummaryWriterSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func SummaryWriterSharedName(value string) SummaryWriterAttr {
 	return func(m optionalAttr) {
-		m["exclusive"] = value
+		m["shared_name"] = value
 	}
 }
 
-// CumprodReverse sets the optional reverse attribute to value.
-//
-// value: A `bool` (default: False).
-// If not specified, defaults to false
-func CumprodReverse(value bool) CumprodAttr {
+// SummaryWriterContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func SummaryWriterContainer(value string) SummaryWriterAttr {
 	return func(m optionalAttr) {
-		m["reverse"] = value
+		m["container"] = value
 	}
 }
 
-// Compute the cumulative product of the tensor `x` along `axis`.
-//
-// By default, this op performs an inclusive cumprod, which means that the first
-// element of the input is identical to the first element of the output:
-//
-// ```python
-// tf.cumprod([a, b, c])  # => [a, a * b, a * b * c]
-// ```
-//
-// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
-// performed instead:
-//
-// ```python
-// tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
-// ```
-//
-// By setting the `reverse` kwarg to `True`, the cumprod is performed in the
-// opposite direction:
-//
-// ```python
-// tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
-// ```
-//
-// This is more efficient than using separate `tf.reverse` ops.
-//
-// The `reverse` and `exclusive` kwargs can also be combined:
+// Returns a handle to be used to access a summary writer.
 //
-// ```python
-// tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
-// ```
+// The summary writer is an in-graph resource which can be used by ops to write
+// summaries to event files.
 //
-// Arguments:
-//	x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
-// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
-// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-//	axis: A `Tensor` of type `int32` (default: 0). Must be in the range
-// `[-rank(x), rank(x))`.
-func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
+// Returns the summary writer resource. Scalar handle.
+func SummaryWriter(scope *Scope, optional ...SummaryWriterAttr) (writer tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
@@ -7094,93 +6702,347 @@ func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr)
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "Cumprod",
-		Input: []tf.Input{
-			x, axis,
-		},
+		Type: "SummaryWriter",
+
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// Computes the mean along segments of a tensor.
-//
-// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// Computes a tensor such that
-// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
-// over `j` such that `segment_ids[j] == i` and `N` is the total number of
-// values summed.
-//
-// If the mean is empty for a given segment ID `i`, `output[i] = 0`.
+// Computes gradients for SparseSegmentMean.
 //
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMean.png" alt>
-// </div>
+// Returns tensor "output" with same shape as grad, except for dimension 0 whose
+// value is output_dim0.
 //
 // Arguments:
-//
-//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
-// first dimension.  Values should be sorted and can be repeated.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
+//	grad: gradient propagated to the SparseSegmentMean op.
+//	indices: indices passed to the corresponding SparseSegmentMean op.
+//	segment_ids: segment_ids passed to the corresponding SparseSegmentMean op.
+//	output_dim0: dimension 0 of "data" passed to SparseSegmentMean op.
+func SparseSegmentMeanGrad(scope *Scope, grad tf.Output, indices tf.Output, segment_ids tf.Output, output_dim0 tf.Output) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
 	opspec := tf.OpSpec{
-		Type: "SegmentMean",
+		Type: "SparseSegmentMeanGrad",
 		Input: []tf.Input{
-			data, segment_ids,
+			grad, indices, segment_ids, output_dim0,
 		},
 	}
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
 
-// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp.
-type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr)
-
-// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
-//
-// value: If `True`, updating of the var, mg, ms, and mom tensors is
-// protected by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr {
-	return func(m optionalAttr) {
-		m["use_locking"] = value
-	}
-}
-
-// Update '*var' according to the centered RMSProp algorithm.
+// Applies softmax to a batched N-D `SparseTensor`.
 //
-// The centered RMSProp algorithm uses an estimate of the centered second moment
-// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
-// uses the (uncentered) second moment. This often helps with training, but is
-// slightly more expensive in terms of computation and memory.
+// The inputs represent an N-D SparseTensor  with logical shape `[..., B, C]`
+// (where `N >= 2`), and with indices sorted in the canonical lexicographic order.
 //
-// Note that in dense implementation of this algorithm, mg, ms, and mom will
-// update even if the grad is zero, but in this sparse implementation, mg, ms,
-// and mom will not update in iterations during which the grad is zero.
+// This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost
+// logical submatrix with shape `[B, C]`, but with the catch that *the implicitly
+// zero elements do not participate*.  Specifically, the algorithm is equivalent
+// to the following:
 //
-// mean_square = decay * mean_square + (1-decay) * gradient ** 2
-// mean_grad = decay * mean_grad + (1-decay) * gradient
-// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+//   (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix
+//       with shape `[B, C]`, along the size-C dimension;
+//   (2) Masks out the original implicitly-zero locations;
+//   (3) Renormalizes the remaining elements.
 //
-// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
-// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
-// var <- var - mom
+// Hence, the `SparseTensor` result has exactly the same non-zero indices and
+// shape.
 //
 // Arguments:
-//	var_: Should be from a Variable().
-//	mg: Should be from a Variable().
-//	ms: Should be from a Variable().
-//	mom: Should be from a Variable().
-//	lr: Scaling factor. Must be a scalar.
+//	sp_indices: 2-D.  `NNZ x R` matrix with the indices of non-empty values in a
+// SparseTensor, in canonical ordering.
+//	sp_values: 1-D.  `NNZ` non-empty values corresponding to `sp_indices`.
+//	sp_shape: 1-D.  Shape of the input SparseTensor.
+//
+// Returns 1-D.  The `NNZ` values for the result `SparseTensor`.
+func SparseSoftmax(scope *Scope, sp_indices tf.Output, sp_values tf.Output, sp_shape tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SparseSoftmax",
+		Input: []tf.Input{
+			sp_indices, sp_values, sp_shape,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
+//
+// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
+// becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
+// are placed in `outputs[i]` in lexicographic order of `js`, and the first
+// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
+// In detail,
+//
+// ```python
+//     outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
+//
+//     outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
+// ```
+//
+// `data.shape` must start with `partitions.shape`.
+//
+// For example:
+//
+// ```python
+//     # Scalar partitions.
+//     partitions = 1
+//     num_partitions = 2
+//     data = [10, 20]
+//     outputs[0] = []  # Empty with shape [0, 2]
+//     outputs[1] = [[10, 20]]
+//
+//     # Vector partitions.
+//     partitions = [0, 0, 1, 1, 0]
+//     num_partitions = 2
+//     data = [10, 20, 30, 40, 50]
+//     outputs[0] = [10, 20, 50]
+//     outputs[1] = [30, 40]
+// ```
+//
+// See `dynamic_stitch` for an example on how to merge partitions back.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicPartition.png" alt>
+// </div>
+//
+// Arguments:
+//
+//	partitions: Any shape.  Indices in the range `[0, num_partitions)`.
+//	num_partitions: The number of partitions to output.
+func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"num_partitions": num_partitions}
+	opspec := tf.OpSpec{
+		Type: "DynamicPartition",
+		Input: []tf.Input{
+			data, partitions,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	if scope.Err() != nil {
+		return
+	}
+	var idx int
+	var err error
+	if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
+		scope.UpdateErr("DynamicPartition", err)
+		return
+	}
+	return outputs
+}
+
+// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad.
+type ResourceApplyAdagradAttr func(optionalAttr)
+
+// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var and accum tensors will be protected
+// by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// Update '*var' according to the adagrad scheme.
+//
+// accum += grad * grad
+// var -= lr * grad * (1 / sqrt(accum))
+//
+// Arguments:
+//	var_: Should be from a Variable().
+//	accum: Should be from a Variable().
+//	lr: Scaling factor. Must be a scalar.
+//	grad: The gradient.
+//
+// Returns the created operation.
+func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResourceApplyAdagrad",
+		Input: []tf.Input{
+			var_, accum, lr, grad,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
+// CumprodAttr is an optional argument to Cumprod.
+type CumprodAttr func(optionalAttr)
+
+// CumprodExclusive sets the optional exclusive attribute to value.
+//
+// value: If `True`, perform exclusive cumprod.
+// If not specified, defaults to false
+func CumprodExclusive(value bool) CumprodAttr {
+	return func(m optionalAttr) {
+		m["exclusive"] = value
+	}
+}
+
+// CumprodReverse sets the optional reverse attribute to value.
+//
+// value: A `bool` (default: False).
+// If not specified, defaults to false
+func CumprodReverse(value bool) CumprodAttr {
+	return func(m optionalAttr) {
+		m["reverse"] = value
+	}
+}
+
+// Compute the cumulative product of the tensor `x` along `axis`.
+//
+// By default, this op performs an inclusive cumprod, which means that the first
+// element of the input is identical to the first element of the output:
+//
+// ```python
+// tf.cumprod([a, b, c])  # => [a, a * b, a * b * c]
+// ```
+//
+// By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
+// performed instead:
+//
+// ```python
+// tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
+// ```
+//
+// By setting the `reverse` kwarg to `True`, the cumprod is performed in the
+// opposite direction:
+//
+// ```python
+// tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
+// ```
+//
+// This is more efficient than using separate `tf.reverse` ops.
+//
+// The `reverse` and `exclusive` kwargs can also be combined:
+//
+// ```python
+// tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
+// ```
+//
+// Arguments:
+//	x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
+// `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
+// `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+//	axis: A `Tensor` of type `int32` (default: 0). Must be in the range
+// `[-rank(x), rank(x))`.
+func Cumprod(scope *Scope, x tf.Output, axis tf.Output, optional ...CumprodAttr) (out tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "Cumprod",
+		Input: []tf.Input{
+			x, axis,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes the mean along segments of a tensor.
+//
+// Read @{$math_ops#segmentation$the section on segmentation} for an explanation of
+// segments.
+//
+// Computes a tensor such that
+// \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
+// over `j` such that `segment_ids[j] == i` and `N` is the total number of
+// values summed.
+//
+// If the mean is empty for a given segment ID `i`, `output[i] = 0`.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMean.png" alt>
+// </div>
+//
+// Arguments:
+//
+//	segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
+// first dimension.  Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SegmentMean(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SegmentMean",
+		Input: []tf.Input{
+			data, segment_ids,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// ResourceSparseApplyCenteredRMSPropAttr is an optional argument to ResourceSparseApplyCenteredRMSProp.
+type ResourceSparseApplyCenteredRMSPropAttr func(optionalAttr)
+
+// ResourceSparseApplyCenteredRMSPropUseLocking sets the optional use_locking attribute to value.
+//
+// value: If `True`, updating of the var, mg, ms, and mom tensors is
+// protected by a lock; otherwise the behavior is undefined, but may exhibit less
+// contention.
+// If not specified, defaults to false
+func ResourceSparseApplyCenteredRMSPropUseLocking(value bool) ResourceSparseApplyCenteredRMSPropAttr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// Update '*var' according to the centered RMSProp algorithm.
+//
+// The centered RMSProp algorithm uses an estimate of the centered second moment
+// (i.e., the variance) for normalization, as opposed to regular RMSProp, which
+// uses the (uncentered) second moment. This often helps with training, but is
+// slightly more expensive in terms of computation and memory.
+//
+// Note that in dense implementation of this algorithm, mg, ms, and mom will
+// update even if the grad is zero, but in this sparse implementation, mg, ms,
+// and mom will not update in iterations during which the grad is zero.
+//
+// mean_square = decay * mean_square + (1-decay) * gradient ** 2
+// mean_grad = decay * mean_grad + (1-decay) * gradient
+// Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
+//
+// ms <- rho * ms_{t-1} + (1-rho) * grad * grad
+// mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
+// var <- var - mom
+//
+// Arguments:
+//	var_: Should be from a Variable().
+//	mg: Should be from a Variable().
+//	ms: Should be from a Variable().
+//	mom: Should be from a Variable().
+//	lr: Scaling factor. Must be a scalar.
 //	rho: Decay rate. Must be a scalar.
 //
 //	epsilon: Ridge term. Must be a scalar.
@@ -7909,63 +7771,6 @@ func ResourceScatterNdUpdate(scope *Scope, ref tf.Output, indices tf.Output, upd
 	return scope.AddOperation(opspec)
 }
 
-// StageSizeAttr is an optional argument to StageSize.
-type StageSizeAttr func(optionalAttr)
-
-// StageSizeCapacity sets the optional capacity attribute to value.
-// If not specified, defaults to 0
-//
-// REQUIRES: value >= 0
-func StageSizeCapacity(value int64) StageSizeAttr {
-	return func(m optionalAttr) {
-		m["capacity"] = value
-	}
-}
-
-// StageSizeMemoryLimit sets the optional memory_limit attribute to value.
-// If not specified, defaults to 0
-//
-// REQUIRES: value >= 0
-func StageSizeMemoryLimit(value int64) StageSizeAttr {
-	return func(m optionalAttr) {
-		m["memory_limit"] = value
-	}
-}
-
-// StageSizeContainer sets the optional container attribute to value.
-// If not specified, defaults to ""
-func StageSizeContainer(value string) StageSizeAttr {
-	return func(m optionalAttr) {
-		m["container"] = value
-	}
-}
-
-// StageSizeSharedName sets the optional shared_name attribute to value.
-// If not specified, defaults to ""
-func StageSizeSharedName(value string) StageSizeAttr {
-	return func(m optionalAttr) {
-		m["shared_name"] = value
-	}
-}
-
-// Op returns the number of elements in the underlying container.
-func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"dtypes": dtypes}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "StageSize",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // NonMaxSuppressionAttr is an optional argument to NonMaxSuppression.
 type NonMaxSuppressionAttr func(optionalAttr)
 
@@ -8702,121 +8507,7 @@ func SparseReduceSum(scope *Scope, input_indices tf.Output, input_values tf.Outp
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
-//
-// For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
-// becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
-// are placed in `outputs[i]` in lexicographic order of `js`, and the first
-// dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
-// In detail,
-//
-// ```python
-//     outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
-//
-//     outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
-// ```
-//
-// `data.shape` must start with `partitions.shape`.
-//
-// For example:
-//
-// ```python
-//     # Scalar partitions.
-//     partitions = 1
-//     num_partitions = 2
-//     data = [10, 20]
-//     outputs[0] = []  # Empty with shape [0, 2]
-//     outputs[1] = [[10, 20]]
-//
-//     # Vector partitions.
-//     partitions = [0, 0, 1, 1, 0]
-//     num_partitions = 2
-//     data = [10, 20, 30, 40, 50]
-//     outputs[0] = [10, 20, 50]
-//     outputs[1] = [30, 40]
-// ```
-//
-// See `dynamic_stitch` for an example on how to merge partitions back.
-//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/DynamicPartition.png" alt>
-// </div>
-//
-// Arguments:
-//
-//	partitions: Any shape.  Indices in the range `[0, num_partitions)`.
-//	num_partitions: The number of partitions to output.
-func DynamicPartition(scope *Scope, data tf.Output, partitions tf.Output, num_partitions int64) (outputs []tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"num_partitions": num_partitions}
-	opspec := tf.OpSpec{
-		Type: "DynamicPartition",
-		Input: []tf.Input{
-			data, partitions,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	if scope.Err() != nil {
-		return
-	}
-	var idx int
-	var err error
-	if outputs, idx, err = makeOutputList(op, idx, "outputs"); err != nil {
-		scope.UpdateErr("DynamicPartition", err)
-		return
-	}
-	return outputs
-}
-
-// ResourceApplyAdagradAttr is an optional argument to ResourceApplyAdagrad.
-type ResourceApplyAdagradAttr func(optionalAttr)
-
-// ResourceApplyAdagradUseLocking sets the optional use_locking attribute to value.
-//
-// value: If `True`, updating of the var and accum tensors will be protected
-// by a lock; otherwise the behavior is undefined, but may exhibit less
-// contention.
-// If not specified, defaults to false
-func ResourceApplyAdagradUseLocking(value bool) ResourceApplyAdagradAttr {
-	return func(m optionalAttr) {
-		m["use_locking"] = value
-	}
-}
-
-// Update '*var' according to the adagrad scheme.
-//
-// accum += grad * grad
-// var -= lr * grad * (1 / sqrt(accum))
-//
-// Arguments:
-//	var_: Should be from a Variable().
-//	accum: Should be from a Variable().
-//	lr: Scaling factor. Must be a scalar.
-//	grad: The gradient.
-//
-// Returns the created operation.
-func ResourceApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, grad tf.Output, optional ...ResourceApplyAdagradAttr) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "ResourceApplyAdagrad",
-		Input: []tf.Input{
-			var_, accum, lr, grad,
-		},
-		Attrs: attrs,
-	}
-	return scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
 // Returns element-wise remainder of division. This emulates C semantics in that
@@ -9482,83 +9173,335 @@ func TensorArrayV3(scope *Scope, size tf.Output, dtype tf.DataType, optional ...
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1)
+	return op.Output(0), op.Output(1)
+}
+
+// MaxPool3DAttr is an optional argument to MaxPool3D.
+type MaxPool3DAttr func(optionalAttr)
+
+// MaxPool3DDataFormat sets the optional data_format attribute to value.
+//
+// value: The data format of the input and output data. With the
+// default format "NDHWC", the data is stored in the order of:
+//     [batch, in_depth, in_height, in_width, in_channels].
+// Alternatively, the format could be "NCDHW", the data storage order is:
+//     [batch, in_channels, in_depth, in_height, in_width].
+// If not specified, defaults to "NDHWC"
+func MaxPool3DDataFormat(value string) MaxPool3DAttr {
+	return func(m optionalAttr) {
+		m["data_format"] = value
+	}
+}
+
+// Performs 3D max pooling on the input.
+//
+// Arguments:
+//	input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
+//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
+// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
+//	strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+//	padding: The type of padding algorithm to use.
+//
+// Returns The max pooled output tensor.
+func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "MaxPool3D",
+		Input: []tf.Input{
+			input,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes the gradients of 3-D convolution with respect to the input.
+//
+// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2
+//
+// Arguments:
+//	input: Shape `[batch, depth, rows, cols, in_channels]`.
+//	filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
+// `in_channels` must match between `input` and `filter`.
+//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
+// out_channels]`.
+//	strides: 1-D tensor of length 5. The stride of the sliding window for each
+// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+//	padding: The type of padding algorithm to use.
+func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"strides": strides, "padding": padding}
+	opspec := tf.OpSpec{
+		Type: "Conv3DBackpropInput",
+		Input: []tf.Input{
+			input, filter, out_backprop,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// ResourceApplyProximalAdagradAttr is an optional argument to ResourceApplyProximalAdagrad.
+type ResourceApplyProximalAdagradAttr func(optionalAttr)
+
+// ResourceApplyProximalAdagradUseLocking sets the optional use_locking attribute to value.
+//
+// value: If True, updating of the var and accum tensors will be protected by
+// a lock; otherwise the behavior is undefined, but may exhibit less contention.
+// If not specified, defaults to false
+func ResourceApplyProximalAdagradUseLocking(value bool) ResourceApplyProximalAdagradAttr {
+	return func(m optionalAttr) {
+		m["use_locking"] = value
+	}
+}
+
+// Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
+//
+// accum += grad * grad
+// prox_v = var - lr * grad * (1 / sqrt(accum))
+// var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
+//
+// Arguments:
+//	var_: Should be from a Variable().
+//	accum: Should be from a Variable().
+//	lr: Scaling factor. Must be a scalar.
+//	l1: L1 regularization. Must be a scalar.
+//	l2: L2 regularization. Must be a scalar.
+//	grad: The gradient.
+//
+// Returns the created operation.
+func ResourceApplyProximalAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, lr tf.Output, l1 tf.Output, l2 tf.Output, grad tf.Output, optional ...ResourceApplyProximalAdagradAttr) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "ResourceApplyProximalAdagrad",
+		Input: []tf.Input{
+			var_, accum, lr, l1, l2, grad,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
+// MutableHashTableOfTensorsV2Attr is an optional argument to MutableHashTableOfTensorsV2.
+type MutableHashTableOfTensorsV2Attr func(optionalAttr)
+
+// MutableHashTableOfTensorsV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this table is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func MutableHashTableOfTensorsV2Container(value string) MutableHashTableOfTensorsV2Attr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// MutableHashTableOfTensorsV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this table is shared under the given name across
+// multiple sessions.
+// If not specified, defaults to ""
+func MutableHashTableOfTensorsV2SharedName(value string) MutableHashTableOfTensorsV2Attr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// MutableHashTableOfTensorsV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
+// If not specified, defaults to false
+func MutableHashTableOfTensorsV2UseNodeNameSharing(value bool) MutableHashTableOfTensorsV2Attr {
+	return func(m optionalAttr) {
+		m["use_node_name_sharing"] = value
+	}
+}
+
+// MutableHashTableOfTensorsV2ValueShape sets the optional value_shape attribute to value.
+// If not specified, defaults to <>
+func MutableHashTableOfTensorsV2ValueShape(value tf.Shape) MutableHashTableOfTensorsV2Attr {
+	return func(m optionalAttr) {
+		m["value_shape"] = value
+	}
+}
+
+// Creates an empty hash table.
+//
+// This op creates a mutable hash table, specifying the type of its keys and
+// values. Each value must be a vector. Data can be inserted into the table using
+// the insert operations. It does not support the initialization operation.
+//
+// Arguments:
+//	key_dtype: Type of the table keys.
+//	value_dtype: Type of the table values.
+//
+// Returns Handle to a table.
+func MutableHashTableOfTensorsV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...MutableHashTableOfTensorsV2Attr) (table_handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "MutableHashTableOfTensorsV2",
+
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// HashTableV2Attr is an optional argument to HashTableV2.
+type HashTableV2Attr func(optionalAttr)
+
+// HashTableV2Container sets the optional container attribute to value.
+//
+// value: If non-empty, this table is placed in the given container.
+// Otherwise, a default container is used.
+// If not specified, defaults to ""
+func HashTableV2Container(value string) HashTableV2Attr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// HashTableV2SharedName sets the optional shared_name attribute to value.
+//
+// value: If non-empty, this table is shared under the given name across
+// multiple sessions.
+// If not specified, defaults to ""
+func HashTableV2SharedName(value string) HashTableV2Attr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// HashTableV2UseNodeNameSharing sets the optional use_node_name_sharing attribute to value.
+//
+// value: If true and shared_name is empty, the table is shared
+// using the node name.
+// If not specified, defaults to false
+func HashTableV2UseNodeNameSharing(value bool) HashTableV2Attr {
+	return func(m optionalAttr) {
+		m["use_node_name_sharing"] = value
+	}
+}
+
+// Creates a non-initialized hash table.
+//
+// This op creates a hash table, specifying the type of its keys and values.
+// Before using the table you will have to initialize it.  After initialization the
+// table will be immutable.
+//
+// Arguments:
+//	key_dtype: Type of the table keys.
+//	value_dtype: Type of the table values.
+//
+// Returns Handle to a table.
+func HashTableV2(scope *Scope, key_dtype tf.DataType, value_dtype tf.DataType, optional ...HashTableV2Attr) (table_handle tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"key_dtype": key_dtype, "value_dtype": value_dtype}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "HashTableV2",
+
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
 }
 
-// MaxPool3DAttr is an optional argument to MaxPool3D.
-type MaxPool3DAttr func(optionalAttr)
+// MapUnstageNoKeyAttr is an optional argument to MapUnstageNoKey.
+type MapUnstageNoKeyAttr func(optionalAttr)
 
-// MaxPool3DDataFormat sets the optional data_format attribute to value.
+// MapUnstageNoKeyCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
 //
-// value: The data format of the input and output data. With the
-// default format "NDHWC", the data is stored in the order of:
-//     [batch, in_depth, in_height, in_width, in_channels].
-// Alternatively, the format could be "NCDHW", the data storage order is:
-//     [batch, in_channels, in_depth, in_height, in_width].
-// If not specified, defaults to "NDHWC"
-func MaxPool3DDataFormat(value string) MaxPool3DAttr {
+// REQUIRES: value >= 0
+func MapUnstageNoKeyCapacity(value int64) MapUnstageNoKeyAttr {
 	return func(m optionalAttr) {
-		m["data_format"] = value
+		m["capacity"] = value
 	}
 }
 
-// Performs 3D max pooling on the input.
+// MapUnstageNoKeyMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
 //
-// Arguments:
-//	input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
-//	ksize: 1-D tensor of length 5. The size of the window for each dimension of
-// the input tensor. Must have `ksize[0] = ksize[4] = 1`.
-//	strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-//	padding: The type of padding algorithm to use.
+// REQUIRES: value >= 0
+func MapUnstageNoKeyMemoryLimit(value int64) MapUnstageNoKeyAttr {
+	return func(m optionalAttr) {
+		m["memory_limit"] = value
+	}
+}
+
+// MapUnstageNoKeyContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func MapUnstageNoKeyContainer(value string) MapUnstageNoKeyAttr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// MapUnstageNoKeySharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func MapUnstageNoKeySharedName(value string) MapUnstageNoKeyAttr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// Op removes and returns a random (key, value)
 //
-// Returns The max pooled output tensor.
-func MaxPool3D(scope *Scope, input tf.Output, ksize []int64, strides []int64, padding string, optional ...MaxPool3DAttr) (output tf.Output) {
+// from the underlying container.   If the underlying container
+// does not contain elements, the op will block until it does.
+func MapUnstageNoKey(scope *Scope, indices tf.Output, dtypes []tf.DataType, optional ...MapUnstageNoKeyAttr) (key tf.Output, values []tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"ksize": ksize, "strides": strides, "padding": padding}
+	attrs := map[string]interface{}{"dtypes": dtypes}
 	for _, a := range optional {
 		a(attrs)
 	}
 	opspec := tf.OpSpec{
-		Type: "MaxPool3D",
+		Type: "MapUnstageNoKey",
 		Input: []tf.Input{
-			input,
+			indices,
 		},
 		Attrs: attrs,
 	}
 	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes the gradients of 3-D convolution with respect to the input.
-//
-// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2
-//
-// Arguments:
-//	input: Shape `[batch, depth, rows, cols, in_channels]`.
-//	filter: Shape `[depth, rows, cols, in_channels, out_channels]`.
-// `in_channels` must match between `input` and `filter`.
-//	out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
-// out_channels]`.
-//	strides: 1-D tensor of length 5. The stride of the sliding window for each
-// dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-//	padding: The type of padding algorithm to use.
-func Conv3DBackpropInput(scope *Scope, input tf.Output, filter tf.Output, out_backprop tf.Output, strides []int64, padding string) (output tf.Output) {
 	if scope.Err() != nil {
 		return
 	}
-	attrs := map[string]interface{}{"strides": strides, "padding": padding}
-	opspec := tf.OpSpec{
-		Type: "Conv3DBackpropInput",
-		Input: []tf.Input{
-			input, filter, out_backprop,
-		},
-		Attrs: attrs,
+	var idx int
+	var err error
+	key = op.Output(idx)
+	if values, idx, err = makeOutputList(op, idx, "values"); err != nil {
+		scope.UpdateErr("MapUnstageNoKey", err)
+		return
 	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
+	return key, values
 }
 
 // Inverse 2D fast Fourier transform.
@@ -12257,6 +12200,63 @@ func MutableDenseHashTableV2(scope *Scope, empty_key tf.Output, value_dtype tf.D
 	return op.Output(0)
 }
 
+// StageSizeAttr is an optional argument to StageSize.
+type StageSizeAttr func(optionalAttr)
+
+// StageSizeCapacity sets the optional capacity attribute to value.
+// If not specified, defaults to 0
+//
+// REQUIRES: value >= 0
+func StageSizeCapacity(value int64) StageSizeAttr {
+	return func(m optionalAttr) {
+		m["capacity"] = value
+	}
+}
+
+// StageSizeMemoryLimit sets the optional memory_limit attribute to value.
+// If not specified, defaults to 0
+//
+// REQUIRES: value >= 0
+func StageSizeMemoryLimit(value int64) StageSizeAttr {
+	return func(m optionalAttr) {
+		m["memory_limit"] = value
+	}
+}
+
+// StageSizeContainer sets the optional container attribute to value.
+// If not specified, defaults to ""
+func StageSizeContainer(value string) StageSizeAttr {
+	return func(m optionalAttr) {
+		m["container"] = value
+	}
+}
+
+// StageSizeSharedName sets the optional shared_name attribute to value.
+// If not specified, defaults to ""
+func StageSizeSharedName(value string) StageSizeAttr {
+	return func(m optionalAttr) {
+		m["shared_name"] = value
+	}
+}
+
+// Op returns the number of elements in the underlying container.
+func StageSize(scope *Scope, dtypes []tf.DataType, optional ...StageSizeAttr) (size tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"dtypes": dtypes}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "StageSize",
+
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Produces the max pool of the input tensor for quantized types.
 //
 // Arguments:
@@ -12999,6 +12999,56 @@ func Neg(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars.
+type FakeQuantWithMinMaxVarsAttr func(optionalAttr)
+
+// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value.
+// If not specified, defaults to 8
+func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr {
+	return func(m optionalAttr) {
+		m["num_bits"] = value
+	}
+}
+
+// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value.
+// If not specified, defaults to false
+func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr {
+	return func(m optionalAttr) {
+		m["narrow_range"] = value
+	}
+}
+
+// Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
+//
+// and `max` to 'outputs' tensor of same shape as `inputs`.
+//
+// `[min; max]` define the clamping range for the `inputs` data.
+// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
+// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
+// then de-quantized and output as floats in `[min; max]` interval.
+// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive.
+//
+// This operation has a gradient and thus allows for training `min` and `max`
+// values.
+func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "FakeQuantWithMinMaxVars",
+		Input: []tf.Input{
+			inputs, min, max,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Writes a `Summary` protocol buffer with a histogram.
 //
 // The generated
@@ -28230,53 +28280,3 @@ func QuantizedInstanceNorm(scope *Scope, x tf.Output, x_min tf.Output, x_max tf.
 	op := scope.AddOperation(opspec)
 	return op.Output(0), op.Output(1), op.Output(2)
 }
-
-// FakeQuantWithMinMaxVarsAttr is an optional argument to FakeQuantWithMinMaxVars.
-type FakeQuantWithMinMaxVarsAttr func(optionalAttr)
-
-// FakeQuantWithMinMaxVarsNumBits sets the optional num_bits attribute to value.
-// If not specified, defaults to 8
-func FakeQuantWithMinMaxVarsNumBits(value int64) FakeQuantWithMinMaxVarsAttr {
-	return func(m optionalAttr) {
-		m["num_bits"] = value
-	}
-}
-
-// FakeQuantWithMinMaxVarsNarrowRange sets the optional narrow_range attribute to value.
-// If not specified, defaults to false
-func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr {
-	return func(m optionalAttr) {
-		m["narrow_range"] = value
-	}
-}
-
-// Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
-//
-// and `max` to 'outputs' tensor of same shape as `inputs`.
-//
-// `[min; max]` define the clamping range for the `inputs` data.
-// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
-// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
-// then de-quantized and output as floats in `[min; max]` interval.
-// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive.
-//
-// This operation has a gradient and thus allows for training `min` and `max`
-// values.
-func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "FakeQuantWithMinMaxVars",
-		Input: []tf.Input{
-			inputs, min, max,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-- 
GitLab


From 5110763dc8e71ca5331144e6a837d0f3886bcbd9 Mon Sep 17 00:00:00 2001
From: ImSheridan <xiaoyudong-512@163.com>
Date: Fri, 2 Mar 2018 00:34:36 +0800
Subject: [PATCH 484/575] Fix some minor typos in get started docs to keep
 consistent (#17357)

---
 tensorflow/docs_src/get_started/checkpoints.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/get_started/checkpoints.md b/tensorflow/docs_src/get_started/checkpoints.md
index dfa2110e69..4aa07c7f2a 100644
--- a/tensorflow/docs_src/get_started/checkpoints.md
+++ b/tensorflow/docs_src/get_started/checkpoints.md
@@ -154,7 +154,7 @@ classifier = tf.estimator.DNNClassifier(
 
 The first time you call an Estimator's `train` method, TensorFlow saves a
 checkpoint to the `model_dir`. Each subsequent call to the Estimator's
-`train`, `eval`, or `predict` method causes the following:
+`train`, `evaluate`, or `predict` method causes the following:
 
 1.  The Estimator builds the model's
     [graph](https://developers.google.com/machine-learning/glossary/#graph)
@@ -222,7 +222,7 @@ does not match the shape stored in checkpoint: [20]
 
 To run experiments in which you train and compare slightly different
 versions of a model, save a copy of the code that created each
-`model-dir`, possibly by creating a separate git branch for each version.
+`model_dir`, possibly by creating a separate git branch for each version.
 This separation will keep your checkpoints recoverable.
 
 ## Summary
-- 
GitLab


From 873768ca8e9eebb1e0985b6fd4fe8d56ad2389ff Mon Sep 17 00:00:00 2001
From: Billy Lamberta <blamb@google.com>
Date: Thu, 1 Mar 2018 08:41:55 -0800
Subject: [PATCH 485/575] Fix link text

PiperOrigin-RevId: 187483166
---
 tensorflow/docs_src/performance/quantization.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md
index 63448c2ebe..411889cb1c 100644
--- a/tensorflow/docs_src/performance/quantization.md
+++ b/tensorflow/docs_src/performance/quantization.md
@@ -80,8 +80,8 @@ need for a separate calibration step.
 TensorFlow can train models with quantization in the loop. Because training
 requires small gradient adjustments, floating point values are still used. To
 keep models as floating point while adding the quantization error in the training
-loop, @{$array_ops#Fake_quantization} nodes simulate the effect of quantization
-in the forward and backward passes.
+loop, @{$array_ops#Fake_quantization$fake quantization} nodes simulate the
+effect of quantization in the forward and backward passes.
 
 Since it's difficult to add these fake quantization operations to all the
 required locations in the model, there's a function available that rewrites the
-- 
GitLab


From 88a13b85c9559e1a14e25f36c26fb4f95fd63dde Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Thu, 1 Mar 2018 08:44:45 -0800
Subject: [PATCH 486/575] [XLA] Fix signatures of c_foo functions and add
 c_any_of.

Embarrassingly, we were often copying the container in c_foo.  Oops.
This fixes that, and also adds some perfect forwarding that was missing.
It also adds a c_any_of function.

PiperOrigin-RevId: 187483435
---
 tensorflow/compiler/xla/util.h | 25 ++++++++++++++++---------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/tensorflow/compiler/xla/util.h b/tensorflow/compiler/xla/util.h
index e14c8cefa1..82e5a59da0 100644
--- a/tensorflow/compiler/xla/util.h
+++ b/tensorflow/compiler/xla/util.h
@@ -427,30 +427,37 @@ std::vector<std::pair<int64, int64>> CommonFactors(
 string SanitizeFileName(string file_name);
 
 template <typename Container, typename Predicate>
-bool c_all_of(Container container, Predicate&& predicate) {
+bool c_all_of(const Container& container, Predicate&& predicate) {
   return std::all_of(std::begin(container), std::end(container),
                      std::forward<Predicate>(predicate));
 }
 
+template <typename Container, typename Predicate>
+bool c_any_of(const Container& container, Predicate&& predicate) {
+  return std::any_of(std::begin(container), std::end(container),
+                     std::forward<Predicate>(predicate));
+}
+
 template <typename InputContainer, typename OutputIterator,
           typename UnaryOperation>
-OutputIterator c_transform(InputContainer input_container,
+OutputIterator c_transform(const InputContainer& input_container,
                            OutputIterator output_iterator,
-                           UnaryOperation unary_op) {
+                           UnaryOperation&& unary_op) {
   return std::transform(std::begin(input_container), std::end(input_container),
-                        output_iterator, unary_op);
+                        output_iterator,
+                        std::forward<UnaryOperation>(unary_op));
 }
 
 template <class InputContainer, class OutputIterator, class UnaryPredicate>
-OutputIterator c_copy_if(InputContainer input_container,
+OutputIterator c_copy_if(const InputContainer& input_container,
                          OutputIterator output_iterator,
-                         UnaryPredicate predicate) {
+                         UnaryPredicate&& predicate) {
   return std::copy_if(std::begin(input_container), std::end(input_container),
-                      output_iterator, predicate);
+                      output_iterator, std::forward<UnaryPredicate>(predicate));
 }
 
 template <class InputContainer, class OutputIterator>
-OutputIterator c_copy(InputContainer input_container,
+OutputIterator c_copy(const InputContainer& input_container,
                       OutputIterator output_iterator) {
   return std::copy(std::begin(input_container), std::end(input_container),
                    output_iterator);
@@ -468,7 +475,7 @@ void c_sort(InputContainer& input_container, Comparator&& comparator) {
 }
 
 template <typename Sequence, typename T>
-bool c_binary_search(Sequence& sequence, T&& value) {
+bool c_binary_search(const Sequence& sequence, T&& value) {
   return std::binary_search(std::begin(sequence), std::end(sequence),
                             std::forward<T>(value));
 }
-- 
GitLab


From c4cc731f4f92f76dfd5f09b87c9c4acbabaace46 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 08:55:41 -0800
Subject: [PATCH 487/575] Fix TF doc style.

PiperOrigin-RevId: 187484534
---
 tensorflow/docs_src/community/roadmap.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/docs_src/community/roadmap.md b/tensorflow/docs_src/community/roadmap.md
index 1f934acab6..a3170a10f2 100644
--- a/tensorflow/docs_src/community/roadmap.md
+++ b/tensorflow/docs_src/community/roadmap.md
@@ -75,8 +75,7 @@ across image recognition, speech, object detection, and
 ### Community and Partner Engagement
 #### Special Interest Groups: 
 * Mobilizing the community to work together in focused domains
-* [tf-distribute](https://groups.google.com/a/tensorflow.org/forum/#!forum/tf-distribute)
-: build and packaging of TensorFlow
+* [tf-distribute](https://groups.google.com/a/tensorflow.org/forum/#!forum/tf-distribute): build and packaging of TensorFlow
 * More to be identified and launched
 
 #### Community:
-- 
GitLab


From 03de984caa1f1403d4417357b67e96dfb7edbc3e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 09:10:17 -0800
Subject: [PATCH 488/575] Correct struct array initialization syntax.

PiperOrigin-RevId: 187486332
---
 tensorflow/python/eager/pywrap_tensor.cc | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc
index 3ec2109d32..d3aaede749 100644
--- a/tensorflow/python/eager/pywrap_tensor.cc
+++ b/tensorflow/python/eager/pywrap_tensor.cc
@@ -520,16 +520,11 @@ PyTypeObject* EagerTensorType = nullptr;
 
 #if PY_MAJOR_VERSION >= 3
 static PyType_Slot EagerTensor_Type_slots[] = {
-    Py_tp_dealloc,
-    reinterpret_cast<void*>(EagerTensor_dealloc),
-    Py_tp_methods,
-    reinterpret_cast<void*>(EagerTensor_methods),
-    Py_tp_getset,
-    reinterpret_cast<void*>(EagerTensor_getseters),
-    Py_tp_init,
-    reinterpret_cast<void*>(EagerTensor_init),
-    0,
-    nullptr,
+    {Py_tp_dealloc, reinterpret_cast<void*>(EagerTensor_dealloc)},
+    {Py_tp_methods, reinterpret_cast<void*>(EagerTensor_methods)},
+    {Py_tp_getset, reinterpret_cast<void*>(EagerTensor_getseters)},
+    {Py_tp_init, reinterpret_cast<void*>(EagerTensor_init)},
+    {0, nullptr},
 };
 
 PyType_Spec EagerTensor_Type_spec = {"EagerTensor", sizeof(EagerTensor), 0,
-- 
GitLab


From c65343d282cdf5ccf4f7d3229f6c492fec344f8d Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Thu, 1 Mar 2018 09:27:57 -0800
Subject: [PATCH 489/575] Keep track of eager op device for tensor handles.
 Force-colocates ops using resources with the resources.

PiperOrigin-RevId: 187488175
---
 tensorflow/c/eager/c_api.cc           | 37 +++++++++++++++++++++------
 tensorflow/c/eager/c_api_internal.h   | 10 ++++++--
 tensorflow/python/eager/core_test.py  | 14 ++++++++++
 tensorflow/python/lib/core/py_func.cc |  5 ++--
 4 files changed, 54 insertions(+), 12 deletions(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index 29c709b06d..252ceab54a 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -159,7 +159,7 @@ TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status) {
   tensorflow::Tensor tensor;
   status->status = tensorflow::TF_TensorToTensor(t, &tensor);
   if (!status->status.ok()) return nullptr;
-  return new TFE_TensorHandle(tensor, nullptr);
+  return new TFE_TensorHandle(tensor, nullptr, nullptr);
 }
 
 void TFE_DeleteTensorHandle(TFE_TensorHandle* h) { delete h; }
@@ -222,7 +222,8 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h,
   // has device type XLA_CPU, and the other CPU.
   const bool both_on_cpu = src_cpu && dst_cpu;
   if (is_same_device || both_on_cpu) {
-    return new TFE_TensorHandle(h->t, dst_cpu ? nullptr : dstd);
+    dstd = dst_cpu ? nullptr : dstd;
+    return new TFE_TensorHandle(h->t, dstd, dstd);
   }
   tensorflow::Tensor* src = &(h->t);
   if (!dst_cpu && (src->dtype() != tensorflow::DT_VARIANT &&
@@ -241,7 +242,8 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h,
   }
   tensorflow::Tensor dst(dstd->GetAllocator(attr), src->dtype(), src->shape());
   if (src->shape().num_elements() == 0) {
-    return new TFE_TensorHandle(dst, dst_cpu ? nullptr : dstd);
+    dstd = dst_cpu ? nullptr : dstd;
+    return new TFE_TensorHandle(dst, dstd, dstd);
   }
   tensorflow::DeviceContext* src_device_context = nullptr;
   if (!src_cpu) {
@@ -269,7 +271,8 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h,
                                  });
   n.WaitForNotification();
   return (TF_GetCode(status) == TF_OK)
-             ? new TFE_TensorHandle(dst, dst_cpu ? nullptr : dstd)
+             ? new TFE_TensorHandle(dst, dst_cpu ? nullptr : dstd,
+                                    dst_cpu ? nullptr : dstd)
              : nullptr;
 }
 
@@ -325,6 +328,7 @@ void TFE_OpAddInput(TFE_Op* op, TFE_TensorHandle* h, TF_Status* status) {
   if (!status->status.ok()) return;
   op->inputs.push_back(h->t);
   op->input_devices.push_back(h->d);
+  op->input_op_devices.push_back(h->op_device);
   op->attrs.NumInputs(op->inputs.size());
 }
 
@@ -540,7 +544,8 @@ tensorflow::Status ValidateInputTypeAndPlacement(
       }
       // We are only here if the policy is warn or silent copies, so we should
       // trigger a copy.
-      TFE_TensorHandle original{op->inputs[i], op->input_devices[i]};
+      TFE_TensorHandle original{op->inputs[i], op->input_devices[i],
+                                op->device};
       TF_Status* s = TF_NewStatus();
       TFE_TensorHandle* copied_tensor = TFE_TensorHandleCopyToDevice(
           &original, ctx, expected_device->name().c_str(), s);
@@ -744,6 +749,7 @@ std::unique_ptr<TFE_Op> BuildXlaLaunch(TFE_Op* op, TF_Status* status) {
   // via `op_input_to_func_input`, adjust the actual inputs accordingly.
   launch_op->inputs = op->inputs;
   launch_op->input_devices = op->input_devices;
+  launch_op->input_op_devices = op->input_op_devices;
   if (!op_input_to_func_input.empty()) {
     DCHECK_EQ(op->inputs.size(), op_input_to_func_input.size());
     if (!op->input_devices.empty()) {
@@ -832,9 +838,24 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
     op = xla_launch_op.get();
   }
 #endif  // TENSORFLOW_EAGER_USE_XLA
-
   TFE_Context* ctx = op->ctx;
   tensorflow::Device* device = op->device;
+  // Ensure all resource-touching ops run in the device the resource is,
+  // regardless of anything else that has been specified. This is identical to
+  // the graph mode behavior.
+  for (int i = 0; i < op->inputs.size(); ++i) {
+    if (op->inputs[i].dtype() == tensorflow::DT_RESOURCE &&
+        op->input_op_devices[i] != device) {
+      tensorflow::Device* d = op->input_op_devices[i] == nullptr
+                                  ? ctx->devices()[0]
+                                  : op->input_op_devices[i];
+      VLOG(1) << "Changing device of operation " << op->name << " to "
+              << d->name() << " because input #" << i
+              << " is a resource in this device.";
+      device = d;
+      op->device = d;
+    }
+  }
   if (!ctx->soft_placement && device == nullptr) {
     // TODO(ashankar): ASSUMPTION: ctx->devices()[0] is always CPU
     device = ctx->devices()[0];
@@ -968,7 +989,7 @@ void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
         (*output_memory_types)[i] == tensorflow::HOST_MEMORY) {
       d = nullptr;
     }
-    retvals[i] = new TFE_TensorHandle(outputs[i], d);
+    retvals[i] = new TFE_TensorHandle(outputs[i], d, device);
   }
 }
 
@@ -994,7 +1015,7 @@ void TFE_ContextAddFunction(TFE_Context* ctx, TF_Function* function,
 }  // extern "C"
 
 TFE_TensorHandle* TFE_NewTensorHandle(const tensorflow::Tensor& t) {
-  return new TFE_TensorHandle(t, nullptr);
+  return new TFE_TensorHandle(t, nullptr, nullptr);
 }
 
 const tensorflow::Tensor* TFE_TensorHandleUnderlyingTensorInHostMemory(
diff --git a/tensorflow/c/eager/c_api_internal.h b/tensorflow/c/eager/c_api_internal.h
index 53c21b64cb..145e4c95cf 100644
--- a/tensorflow/c/eager/c_api_internal.h
+++ b/tensorflow/c/eager/c_api_internal.h
@@ -101,8 +101,9 @@ struct TFE_Context {
 };
 
 struct TFE_TensorHandle {
-  TFE_TensorHandle(const tensorflow::Tensor& t, tensorflow::Device* d)
-      : t(t), d(d) {}
+  TFE_TensorHandle(const tensorflow::Tensor& t, tensorflow::Device* d,
+                   tensorflow::Device* op_device)
+      : t(t), d(d), op_device(op_device) {}
 
   tensorflow::Tensor t;
   // TODO(ashankar): d == nullptr iff local CPU
@@ -114,6 +115,10 @@ struct TFE_TensorHandle {
   // TODO(ashankar): Reference count TFE_Context to ensure that 'd' of a
   // TFE_TensorHandle does not outlive the TFE_Context from which it came?
   tensorflow::Device* d;
+
+  // Device in which the op producing this tensor was executed. Equals to d for
+  // constant tensors.
+  tensorflow::Device* op_device;
 };
 
 struct TFE_Op {
@@ -130,6 +135,7 @@ struct TFE_Op {
   const tensorflow::AttrTypeMap* attr_types;
   std::vector<tensorflow::Tensor> inputs;
   std::vector<tensorflow::Device*> input_devices;
+  std::vector<tensorflow::Device*> input_op_devices;
   tensorflow::Device* device;
   bool use_xla = false;
 };
diff --git a/tensorflow/python/eager/core_test.py b/tensorflow/python/eager/core_test.py
index 0e40d8a5c0..e418be5fae 100644
--- a/tensorflow/python/eager/core_test.py
+++ b/tensorflow/python/eager/core_test.py
@@ -34,7 +34,9 @@ from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gen_resource_variable_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import resource_variable_ops
 
 
 def execute(op_name, num_outputs, inputs, attrs=None):
@@ -181,6 +183,18 @@ class TFETest(test_util.TensorFlowTestCase):
         attrs=('T', x.dtype.as_datatype_enum))[0].cpu().numpy()
     self.assertEqual(3, result)
 
+  def testResourceTensorPlacement(self):
+    if not context.context().num_gpus():
+      self.skipTest('No GPUs found')
+
+    with context.device('gpu:0'):
+      v = resource_variable_ops.ResourceVariable(1.0)
+    with context.device('cpu:0'):
+      # Check that even though we specified the cpu device we'll run the read op
+      # in the device where the handle is.
+      self.assertAllEqual(
+          gen_resource_variable_ops.read_variable_op(v.handle, v.dtype), 1.0)
+
   def testCopyBetweenDevices(self):
     if not context.context().num_gpus():
       self.skipTest('No GPUs found')
diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc
index e0422ef80a..343415b264 100644
--- a/tensorflow/python/lib/core/py_func.cc
+++ b/tensorflow/python/lib/core/py_func.cc
@@ -79,10 +79,11 @@ Status MakeArgTuple(const PyCall* call, PyObject** tuple) {
     const Tensor& t = call->ins[i];
     if (call->eager) {
       if (call->gpu) {
-        arg = EagerTensorFromHandle(new TFE_TensorHandle(t, call->device));
+        arg = EagerTensorFromHandle(
+            new TFE_TensorHandle(t, call->device, call->device));
       } else {
         // TFE_TensorHandle assumes that CPU is identified by `nullptr`.
-        arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr));
+        arg = EagerTensorFromHandle(new TFE_TensorHandle(t, nullptr, nullptr));
       }
       if (arg == nullptr) {
         return errors::Internal("Unable to procure EagerTensor from Tensor.");
-- 
GitLab


From 2c4eca575e1fc36c7b2f1d1c312426ff4c4cec16 Mon Sep 17 00:00:00 2001
From: Justin Lebar <jlebar@google.com>
Date: Thu, 1 Mar 2018 09:31:20 -0800
Subject: [PATCH 490/575] [XLA] Don't dump the "contents" of constants with a
 zero-sized dimension in the HLO graph dumper.

Previously we'd dump e.g. "{ {}, {}, ... }" for an f32[100, 0], which is
just noise.

PiperOrigin-RevId: 187488625
---
 tensorflow/compiler/xla/service/hlo_graph_dumper.cc | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 2861fec39e..99c4932a38 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -782,6 +782,14 @@ string HloDotDumper::GetInstructionNodeInlinedOperands(
   auto stringify_constant = [](const HloInstruction* constant) {
     const auto& shape = constant->shape();
 
+    // If the shape has a dimension of size zero, print it as e.g.
+    // "{} (f32[42, 0, 10])".  The alternative, calling Literal::ToString(),
+    // enumerates all of its empty dimensions (e.g.  "{ { {}, {} }, ..."), which
+    // is just noise.
+    if (ShapeUtil::HasZeroElements(shape)) {
+      return Printf("{} (%s)", ShapeUtil::HumanString(constant->shape()));
+    }
+
     // Print the literal value of constants with <= K elements.
     optional<int64> elem_count;
     if (!ShapeUtil::IsOpaque(shape) && !ShapeUtil::IsTuple(shape)) {
-- 
GitLab


From 7129d6a0746d0798e0a3015f645697b0fee12c37 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 09:52:00 -0800
Subject: [PATCH 491/575] Fixed tf.reduce_sum usage on 2-D tensors.

PiperOrigin-RevId: 187491311
---
 .../resolve_constant_unary.cc                 | 30 +++++++++++++++----
 1 file changed, 25 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc
index f227554bc5..d96b3d522d 100644
--- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc
+++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc
@@ -138,12 +138,32 @@ bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) {
     memcpy(output_float_data.data(), (*input_float_data).data(),
            output_buffer_size * sizeof(output_float_data[0]));
   } else if (unary_op->type == OperatorType::kTensorFlowSum) {
-    // At the moment only full reduction across all dimensions is supported.
-    float sum = 0.f;
-    for (int i = 0; i < input_buffer_size; i++) {
-      sum += (*input_float_data)[i];
+    CHECK_EQ(unary_op->inputs.size(), 2) << "Sum needs 2 inputs";
+    if (!IsConstantParameterArray(*model, unary_op->inputs[1])) {
+      AddMessageF("Axis input is non-constant");
+      return false;
     }
-    for (int i = 0; i < output_buffer_size; ++i) {
+    auto& axis_array = model->GetArray(unary_op->inputs[1]);
+    CHECK(axis_array.data_type == ArrayDataType::kInt32);
+    int axis = axis_array.GetBuffer<ArrayDataType::kInt32>().data[0];
+    CHECK_LT(axis, input_shape.dimensions_count()) << "Axis out of bounds";
+
+    // We currently only handle reduction on axis 0.
+    CHECK_EQ(axis, 0) << "Only reduction along axis 0 is supported";
+    // We currently only handle 1-D and 2-D input tensors.
+    CHECK_LE(input_shape.dimensions_count(), 2) << "Rank >2 not yet supported";
+    // We only support keep_dims=true; shape prop will need to change otherwise.
+    auto sum_op = static_cast<const TensorFlowSumOperator*>(unary_op);
+    CHECK(sum_op->keep_dims) << "Only keep_dims=true is supported";
+
+    std::vector<int> indices(input_shape.dimensions_count());
+    for (int i = 0; i < input_shape.dims(1); ++i) {
+      indices[1] = i;
+      float sum = 0.f;
+      for (int j = 0; j < input_shape.dims(0); ++j) {
+        indices[0] = j;
+        sum += (*input_float_data)[Offset(input_shape, indices)];
+      }
       output_float_data[i] = sum;
     }
   } else if (unary_op->type == OperatorType::kTensorFlowMin) {
-- 
GitLab


From 02b5fe290aea0e3cb8680d9e484f2b485bc92042 Mon Sep 17 00:00:00 2001
From: imsheridan <xiaoyudong0512@gmail.com>
Date: Fri, 2 Mar 2018 01:58:06 +0800
Subject: [PATCH 492/575] Fix the error activation function link in
 custom_estimators

---
 tensorflow/docs_src/get_started/custom_estimators.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/docs_src/get_started/custom_estimators.md b/tensorflow/docs_src/get_started/custom_estimators.md
index 42a246678a..ae89b639b4 100644
--- a/tensorflow/docs_src/get_started/custom_estimators.md
+++ b/tensorflow/docs_src/get_started/custom_estimators.md
@@ -213,7 +213,7 @@ is connected to every node in the preceding layer.  Here's the relevant code:
 ```
 
 * The `units` parameter defines the number of output neurons in a given layer.
-* The `activation` parameter defines the [activation function](https://developers.google.com/machine-learning/glossary/#a) —
+* The `activation` parameter defines the [activation function](https://developers.google.com/machine-learning/glossary/#activation_function) —
   [Relu](https://developers.google.com/machine-learning/glossary/#ReLU) in this
   case.
 
-- 
GitLab


From 0265b5e632b35c2a5dff30e72e06aa5229bf0d45 Mon Sep 17 00:00:00 2001
From: Yunxing Dai <yunxing@google.com>
Date: Thu, 1 Mar 2018 10:23:57 -0800
Subject: [PATCH 493/575] [XLA] Update operation semantics doc for BatchNorm
 operations

- Update formulas for BatchNormGrad. The rendered version of the new formulas can be found here:
https://latexbase.com/d/1ad54ff9-f9d5-4479-beef-156ea26a0632
- Update output table to include the symbol name for each output.
- Fix a typo in BatchNormGrad's input table to correct display the symbol `beta`.

PiperOrigin-RevId: 187496086
---
 .../performance/xla/operation_semantics.md    | 31 ++++++++++---------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md
index eaf6aeba3d..8162382846 100644
--- a/tensorflow/docs_src/performance/xla/operation_semantics.md
+++ b/tensorflow/docs_src/performance/xla/operation_semantics.md
@@ -45,27 +45,30 @@ feature dimension in `operand`), the operation calculates the gradients with
 respect to `operand`, `offset` and `scale` across all the other dimensions. The
 `feature_index` must be a valid index for the feature dimension in `operand`.
 
-The three gradients are defined by the following formulas:
+The three gradients are defined by the following formulas (Assuming a
+4-dimensional tensor as `operand` and (l) is the index for feature dimension):
 
-\\( \nabla x = \nabla y * \gamma * \sqrt{\sigma^2+\epsilon} \\)
+\\( coef_l = \frac{1}{mwh}\sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h (\nabla y_{ijkl} * (x_{ijkl} - \mu_l) / (\sigma^2_{l}+\epsilon)) \\)
 
-\\( \nabla \gamma = sum(\nabla y * (x - \mu) * \sqrt{\sigma^2 + \epsilon}) \\)
+\\( \nabla x_{ijkl} = \gamma_{l} * (1/\sqrt{\sigma^2_{l}+\epsilon}) * [\nabla y_{ijkl} - mean(\nabla y) - (x_{ijkl} - \mu_{l}) * coef_l] \\)
 
-\\( \nabla \beta = sum(\nabla y) \\)
+\\( \nabla \beta_l = \sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h \nabla y_{ijkl} \\)
+
+\\( \nabla \gamma_l = \sum_{i=1}^m\sum_{j=1}^w\sum_{k=1}^h \nabla y_{ijkl} * ((x_{ijkl} - \mu_l) / \sqrt{\sigma^2_{l}+\epsilon}) \\)
 
 The inputs `mean` and `variance` represents moments value
 across batch and spatial dimensions.
 
 The output type is a tuple of three handles:
 
-|Outputs       | Type                    | Semantics                           |
-|------------- | ----------------------- | ------------------------------------|
-|`grad_operand`| `ComputationDataHandle` | gradient with respect to input      |
-:              :                         : `operand`                           :
-|`grad_scale`  | `ComputationDataHandle` | gradient with respect to input      |
-:              :                         : `scale`                             :
-|`grad_offset` | `ComputationDataHandle` | gradient with respect to input      |
-:              :                         : `offset`                            :
+|Outputs       | Type                    | Semantics                            |
+|------------- | ----------------------- | ------------------------------------ |
+|`grad_operand`| `ComputationDataHandle` | gradient with respect to input       |
+:              :                         : `operand` (\\( \nabla x\\))          :
+|`grad_scale`  | `ComputationDataHandle` | gradient with respect to input       |
+:              :                         : `scale` (\\( \nabla \gamma\\))       :
+|`grad_offset` | `ComputationDataHandle` | gradient with respect to input       |
+:              :                         : `offset`(\\( \nabla \beta\\))        :
 
 
 ## BatchNormInference
@@ -119,11 +122,11 @@ Normalizes an array across batch and spatial dimensions.
 | Arguments       | Type                    | Semantics                        |
 | --------------- | ----------------------- | -------------------------------- |
 | `operand`       | `ComputationDataHandle` | n dimensional array to be        |
-:                 :                         : normalized                       :
+:                 :                         : normalized (x)                   :
 | `scale`         | `ComputationDataHandle` | 1 dimensional array              |
 :                 :                         : (\\(\gamma\\))                   :
 | `offset`        | `ComputationDataHandle` | 1 dimensional array              |
-:                 :                         : (\\(\beta\\ )                    :
+:                 :                         : (\\(\beta\\))                    :
 | `epsilon`       | `float`                 | Epsilon value (\\(\epsilon\\))   |
 | `feature_index` | `int64`                 | Index to feature dimension       |
 :                 :                         : in `operand`                     :
-- 
GitLab


From 16f7cb272f4810cb09f8238ba6b87f5945cd2b03 Mon Sep 17 00:00:00 2001
From: Guozhong Zhuang <guozhong.zhuang@intel.com>
Date: Thu, 1 Mar 2018 10:29:11 -0800
Subject: [PATCH 494/575] Fix improper comments such as tf --> TensorFlow

---
 tensorflow/core/kernels/mkl_fused_batch_norm_op.cc | 4 ++--
 tensorflow/core/kernels/mkl_relu_op.cc             | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
index b7dee3fb3e..eccdece5e3 100644
--- a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
+++ b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
@@ -1249,8 +1249,8 @@ class MklFusedBatchNormGradOp : public OpKernel {
         tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T));
       } else {
         dnn_shape_diff_src.SetMklTensor(false);
-        // both src and diff_dst are tf layout,
-        // so get tf shape from anyont should be ok
+        // both src and diff_dst are TensorFlow layout,
+        // so it is OK to get TensorFlow shape.
         tf_shape_diff_src = src_tensor.shape();
       }
       AllocateOutputSetMklShape(context, kDiffSrcIndex, &diff_src_tensor,
diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc
index 924b9da7e0..6c873af566 100644
--- a/tensorflow/core/kernels/mkl_relu_op.cc
+++ b/tensorflow/core/kernels/mkl_relu_op.cc
@@ -600,8 +600,8 @@ class MklReluGradOpBase : public OpKernel {
         tf_shape_diff_src.AddDim(diff_src_pd.get_size() / sizeof(T));
       } else {
         dnn_shape_diff_src.SetMklTensor(false);
-        // both src and diff_dst are tf layout,
-        // so get tf shape from anyone should be ok
+        // both src and diff_dst are TensorFlow layout,
+        // so it is ok to get TensorFlow shape.
         tf_shape_diff_src = src_tensor.shape();
       }
       AllocateOutputSetMklShape(context, diff_src_index, &diff_src_tensor,
-- 
GitLab


From ce8783a0d535b4657ecaab8e621ab7de568b80d6 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Thu, 1 Mar 2018 10:37:45 -0800
Subject: [PATCH 495/575] Remove old note that no longer applies.

PiperOrigin-RevId: 187498339
---
 tensorflow/core/BUILD | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 08832b58da..3271825251 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -2224,8 +2224,6 @@ tf_cuda_library(
     alwayslink = 1,
 )
 
-# This library is deprecated and no longer publicly available.
-# Do not add more uses of it.
 cc_library(
     name = "regexp_internal",
     hdrs = [
-- 
GitLab


From 006d228201a1e9e140aa0651a59c51d3396a2d12 Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Thu, 1 Mar 2018 10:38:27 -0800
Subject: [PATCH 496/575] Fixed the typo in RunConfig pydoc.

PiperOrigin-RevId: 187498424
---
 tensorflow/python/estimator/run_config.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/estimator/run_config.py b/tensorflow/python/estimator/run_config.py
index 3e021242c4..62f035bce5 100644
--- a/tensorflow/python/estimator/run_config.py
+++ b/tensorflow/python/estimator/run_config.py
@@ -345,7 +345,7 @@ class RunConfig(object):
       os.environ['TF_CONFIG'] = json.dumps(
           {'cluster': cluster,
            'task': {'type': 'worker', 'index': 1}})
-      config = ClusterConfig()
+      config = RunConfig()
       assert config.master == 'host4:2222'
       assert config.task_id == 1
       assert config.num_ps_replicas == 2
@@ -363,7 +363,7 @@ class RunConfig(object):
       os.environ['TF_CONFIG'] = json.dumps(
           {'cluster': cluster,
            'task': {'type': 'chief', 'index': 0}})
-      config = ClusterConfig()
+      config = RunConfig()
       assert config.master == 'host0:2222'
       assert config.task_id == 0
       assert config.num_ps_replicas == 2
@@ -381,7 +381,7 @@ class RunConfig(object):
       os.environ['TF_CONFIG'] = json.dumps(
           {'cluster': cluster,
            'task': {'type': 'evaluator', 'index': 0}})
-      config = ClusterConfig()
+      config = RunConfig()
       assert config.master == ''
       assert config.evaluator_master == ''
       assert config.task_id == 0
-- 
GitLab


From 12bd86fb45d1b5981896ea7500a465cc017c3ab8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 11:16:18 -0800
Subject: [PATCH 497/575] Internal cleanup.

PiperOrigin-RevId: 187504966
---
 .../java/org/tensorflow/lite/Interpreter.java |   6 +
 .../lite/NativeInterpreterWrapper.java        |  25 +++-
 .../native/nativeinterpreterwrapper_jni.cc    | 107 ++++++++++++++----
 .../native/nativeinterpreterwrapper_jni.h     |  11 +-
 .../lite/NativeInterpreterWrapperTest.java    |  24 ++++
 5 files changed, 140 insertions(+), 33 deletions(-)

diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
index dd883d69d2..9286814b74 100644
--- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
+++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
@@ -80,6 +80,9 @@ public final class Interpreter implements AutoCloseable {
   /**
    * Runs model inference if the model takes only one input, and provides only one output.
    *
+   * <p>Warning: The API runs much faster if {@link ByteBuffer} is used as input data type. Please
+   * consider using {@link ByteBuffer} to feed input data for better performance.
+   *
    * @param input an array or multidimensional array, or a {@link ByteBuffer} of primitive types
    *     including int, float, long, and byte. {@link ByteBuffer} is the preferred way to pass large
    *     input data. When {@link ByteBuffer} is used, its content should remain unchanged until
@@ -96,6 +99,9 @@ public final class Interpreter implements AutoCloseable {
   /**
    * Runs model inference if the model takes multiple inputs, or returns multiple outputs.
    *
+   * <p>Warning: The API runs much faster if {@link ByteBuffer} is used as input data type. Please
+   * consider using {@link ByteBuffer} to feed input data for better performance.
+   *
    * @param inputs an array of input data. The inputs should be in the same order as inputs of the
    *     model. Each input can be an array or multidimensional array, or a {@link ByteBuffer} of
    *     primitive types including int, float, long, and byte. {@link ByteBuffer} is the preferred
diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java
index 7612be0ddd..bca4a3cae6 100644
--- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java
+++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/NativeInterpreterWrapper.java
@@ -35,6 +35,7 @@ final class NativeInterpreterWrapper implements AutoCloseable {
     errorHandle = createErrorReporter(ERROR_BUFFER_SIZE);
     modelHandle = createModel(modelPath, errorHandle);
     interpreterHandle = createInterpreter(modelHandle, errorHandle);
+    isMemoryAllocated = true;
   }
 
   /**
@@ -47,6 +48,7 @@ final class NativeInterpreterWrapper implements AutoCloseable {
     errorHandle = createErrorReporter(ERROR_BUFFER_SIZE);
     modelHandle = createModelWithBuffer(modelByteBuffer, errorHandle);
     interpreterHandle = createInterpreter(modelHandle, errorHandle);
+    isMemoryAllocated = true;
   }
 
   /** Releases resources associated with this {@code NativeInterpreterWrapper}. */
@@ -59,6 +61,7 @@ final class NativeInterpreterWrapper implements AutoCloseable {
     modelByteBuffer = null;
     inputsIndexes = null;
     outputsIndexes = null;
+    isMemoryAllocated = false;
   }
 
   /** Sets inputs, runs model inference and returns outputs. */
@@ -93,10 +96,19 @@ final class NativeInterpreterWrapper implements AutoCloseable {
     }
     inferenceDurationNanoseconds = -1;
     long[] outputsHandles =
-        run(interpreterHandle, errorHandle, sizes, dataTypes, numsOfBytes, inputs, this);
+        run(
+            interpreterHandle,
+            errorHandle,
+            sizes,
+            dataTypes,
+            numsOfBytes,
+            inputs,
+            this,
+            isMemoryAllocated);
     if (outputsHandles == null || outputsHandles.length == 0) {
       throw new IllegalStateException("Interpreter has no outputs.");
     }
+    isMemoryAllocated = true;
     Tensor[] outputs = new Tensor[outputsHandles.length];
     for (int i = 0; i < outputsHandles.length; ++i) {
       outputs[i] = Tensor.fromHandle(outputsHandles[i]);
@@ -111,14 +123,17 @@ final class NativeInterpreterWrapper implements AutoCloseable {
       int[] dtypes,
       int[] numsOfBytes,
       Object[] values,
-      NativeInterpreterWrapper wrapper);
+      NativeInterpreterWrapper wrapper,
+      boolean memoryAllocated);
 
   /** Resizes dimensions of a specific input. */
   void resizeInput(int idx, int[] dims) {
-    resizeInput(interpreterHandle, errorHandle, idx, dims);
+    if (resizeInput(interpreterHandle, errorHandle, idx, dims)) {
+      isMemoryAllocated = false;
+    }
   }
 
-  private static native void resizeInput(
+  private static native boolean resizeInput(
       long interpreterHandle, long errorHandle, int inputIdx, int[] dims);
 
   void setUseNNAPI(boolean useNNAPI) {
@@ -264,6 +279,8 @@ final class NativeInterpreterWrapper implements AutoCloseable {
 
   private Map<String, Integer> outputsIndexes;
 
+  private boolean isMemoryAllocated = false;
+
   private static native String[] getInputNames(long interpreterHandle);
 
   private static native String[] getOutputNames(long interpreterHandle);
diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
index e405df0745..47bf4c9c9d 100644
--- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
+++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
@@ -149,6 +149,45 @@ TfLiteStatus checkInputs(JNIEnv* env, tflite::Interpreter* interpreter,
   return kTfLiteOk;
 }
 
+// Checks whether there is any difference between dimensions of a tensor and a
+// given dimensions. Returns true if there is difference, else false.
+bool areDimsDifferent(JNIEnv* env, TfLiteTensor* tensor, jintArray dims) {
+  int num_dims = static_cast<int>(env->GetArrayLength(dims));
+  jint* ptr = env->GetIntArrayElements(dims, nullptr);
+  if (ptr == nullptr) {
+    throwException(env, kIllegalArgumentException,
+                   "Empty dimensions of input array.");
+    return true;
+  }
+  if (tensor->dims->size != num_dims) {
+    return true;
+  }
+  for (int i = 0; i < num_dims; ++i) {
+    if (ptr[i] != tensor->dims->data[i]) {
+      return true;
+    }
+  }
+  env->ReleaseIntArrayElements(dims, ptr, JNI_ABORT);
+  return false;
+}
+
+bool areInputDimensionsTheSame(JNIEnv* env, tflite::Interpreter* interpreter,
+                               int input_size, jobjectArray sizes) {
+  if (interpreter->inputs().size() != input_size) {
+    return false;
+  }
+  for (int i = 0; i < input_size; ++i) {
+    int input_idx = interpreter->inputs()[i];
+    jintArray dims =
+        static_cast<jintArray>(env->GetObjectArrayElement(sizes, i));
+    TfLiteTensor* target = interpreter->tensor(input_idx);
+    if (areDimsDifferent(env, target, dims)) return false;
+    env->DeleteLocalRef(dims);
+    if (env->ExceptionCheck()) return false;
+  }
+  return true;
+}
+
 TfLiteStatus resizeInputs(JNIEnv* env, tflite::Interpreter* interpreter,
                           int input_size, jobjectArray sizes) {
   for (int i = 0; i < input_size; ++i) {
@@ -344,6 +383,15 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter(
     throwException(env, kIllegalArgumentException,
                    "Cannot create interpreter: %s",
                    error_reporter->CachedErrorMessage());
+    return 0;
+  }
+  // allocates memory
+  status = interpreter->AllocateTensors();
+  if (status != kTfLiteOk) {
+    throwException(env, kNullPointerException,
+                   "Can not allocate memory for the interpreter",
+                   error_reporter->CachedErrorMessage());
+    return 0;
   }
   return reinterpret_cast<jlong>(interpreter.release());
 }
@@ -353,7 +401,7 @@ JNIEXPORT jlongArray JNICALL
 Java_org_tensorflow_lite_NativeInterpreterWrapper_run(
     JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle,
     jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes,
-    jobjectArray values, jobject wrapper) {
+    jobjectArray values, jobject wrapper, jboolean memory_allocated) {
   tflite::Interpreter* interpreter =
       convertLongToInterpreter(env, interpreter_handle);
   if (interpreter == nullptr) return nullptr;
@@ -365,20 +413,23 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_run(
   TfLiteStatus status = checkInputs(env, interpreter, input_size, data_types,
                                     nums_of_bytes, values, sizes);
   if (status != kTfLiteOk) return nullptr;
-  // resizes inputs
-  status = resizeInputs(env, interpreter, input_size, sizes);
-  if (status != kTfLiteOk) {
-    throwException(env, kNullPointerException, "Can not resize the input: %s",
-                   error_reporter->CachedErrorMessage());
-    return nullptr;
-  }
-  // allocates memory
-  status = interpreter->AllocateTensors();
-  if (status != kTfLiteOk) {
-    throwException(env, kNullPointerException,
-                   "Can not allocate memory for the given inputs: %s",
-                   error_reporter->CachedErrorMessage());
-    return nullptr;
+  if (!memory_allocated ||
+      !areInputDimensionsTheSame(env, interpreter, input_size, sizes)) {
+    // resizes inputs
+    status = resizeInputs(env, interpreter, input_size, sizes);
+    if (status != kTfLiteOk) {
+      throwException(env, kNullPointerException, "Can not resize the input: %s",
+                     error_reporter->CachedErrorMessage());
+      return nullptr;
+    }
+    // allocates memory
+    status = interpreter->AllocateTensors();
+    if (status != kTfLiteOk) {
+      throwException(env, kNullPointerException,
+                     "Can not allocate memory for the given inputs: %s",
+                     error_reporter->CachedErrorMessage());
+      return nullptr;
+    }
   }
   // sets inputs
   status = setInputs(env, interpreter, input_size, data_types, nums_of_bytes,
@@ -448,29 +499,37 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims(
   return outputs;
 }
 
-JNIEXPORT void JNICALL
+JNIEXPORT jboolean JNICALL
 Java_org_tensorflow_lite_NativeInterpreterWrapper_resizeInput(
     JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle,
     jint input_idx, jintArray dims) {
   BufferErrorReporter* error_reporter =
       convertLongToErrorReporter(env, error_handle);
-  if (error_reporter == nullptr) return;
+  if (error_reporter == nullptr) return JNI_FALSE;
   tflite::Interpreter* interpreter =
       convertLongToInterpreter(env, interpreter_handle);
-  if (interpreter == nullptr) return;
+  if (interpreter == nullptr) return JNI_FALSE;
   const int idx = static_cast<int>(input_idx);
   if (idx < 0 || idx >= interpreter->inputs().size()) {
     throwException(env, kIllegalArgumentException,
                    "Can not resize %d-th input for a model having %d inputs.",
                    idx, interpreter->inputs().size());
+    return JNI_FALSE;
   }
-  TfLiteStatus status = interpreter->ResizeInputTensor(
-      interpreter->inputs()[idx], convertJIntArrayToVector(env, dims));
-  if (status != kTfLiteOk) {
-    throwException(env, kIllegalArgumentException,
-                   "Failed to resize %d-th input: %s", idx,
-                   error_reporter->CachedErrorMessage());
+  // check whether it is resizing with the same dimensions.
+  TfLiteTensor* target = interpreter->tensor(input_idx);
+  bool is_changed = areDimsDifferent(env, target, dims);
+  if (is_changed) {
+    TfLiteStatus status = interpreter->ResizeInputTensor(
+        interpreter->inputs()[idx], convertJIntArrayToVector(env, dims));
+    if (status != kTfLiteOk) {
+      throwException(env, kIllegalArgumentException,
+                     "Failed to resize %d-th input: %s", idx,
+                     error_reporter->CachedErrorMessage());
+      return JNI_FALSE;
+    }
   }
+  return is_changed ? JNI_TRUE : JNI_FALSE;
 }
 
 JNIEXPORT void JNICALL Java_org_tensorflow_lite_NativeInterpreterWrapper_delete(
diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h
index 31c8f1bc88..f7c2d9bf82 100644
--- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h
+++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.h
@@ -109,13 +109,13 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter(
  *  Class:     org_tensorflow_lite_NativeInterpreterWrapper
  *  Method:
  *  Signature:
- * (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;Lorg/tensorflow/lite/NativeInterpreterWrapper;)[J
+ * (JJ[Ljava/lang/Object;[I[I[Ljava/lang/Object;Ljava/lang/Object;Z)[J
  */
 JNIEXPORT jlongArray JNICALL
 Java_org_tensorflow_lite_NativeInterpreterWrapper_run(
     JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle,
     jobjectArray sizes, jintArray data_types, jintArray nums_of_bytes,
-    jobjectArray values, jobject wrapper);
+    jobjectArray values, jobject wrapper, jboolean memory_allocated);
 
 /*
  *  Class:     org_tensorflow_lite_NativeInterpreterWrapper
@@ -132,11 +132,12 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_getInputDims(
 /*
  *  Class:     org_tensorflow_lite_NativeInterpreterWrapper
  *  Method:
- *  Signature: (JJI[I)
+ *  Signature: (JJI[I)Z
  *
- * It resizes dimensions of a input.
+ * It returns true if resizing input tensor to different dimensions, else return
+ * false.
  */
-JNIEXPORT void JNICALL
+JNIEXPORT jboolean JNICALL
 Java_org_tensorflow_lite_NativeInterpreterWrapper_resizeInput(
     JNIEnv* env, jclass clazz, jlong interpreter_handle, jlong error_handle,
     jint input_idx, jintArray dims);
diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java
index 8c1f2406f7..6371fb59dc 100644
--- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java
+++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/NativeInterpreterWrapperTest.java
@@ -94,6 +94,30 @@ public final class NativeInterpreterWrapperTest {
     wrapper.close();
   }
 
+  @Test
+  public void testRunWithInputsOfSameDims() {
+    NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(FLOAT_MODEL_PATH);
+    float[] oneD = {1.23f, -6.54f, 7.81f};
+    float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD};
+    float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD};
+    float[][][][] fourD = {threeD, threeD};
+    Object[] inputs = {fourD};
+    Tensor[] outputs = wrapper.run(inputs);
+    assertThat(outputs.length).isEqualTo(1);
+    float[][][][] parsedOutputs = new float[2][8][8][3];
+    outputs[0].copyTo(parsedOutputs);
+    float[] outputOneD = parsedOutputs[0][0][0];
+    float[] expected = {3.69f, -19.62f, 23.43f};
+    assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder();
+    outputs = wrapper.run(inputs);
+    assertThat(outputs.length).isEqualTo(1);
+    parsedOutputs = new float[2][8][8][3];
+    outputs[0].copyTo(parsedOutputs);
+    outputOneD = parsedOutputs[0][0][0];
+    assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder();
+    wrapper.close();
+  }
+
   @Test
   public void testRunWithInt() {
     NativeInterpreterWrapper wrapper = new NativeInterpreterWrapper(INT_MODEL_PATH);
-- 
GitLab


From 34eddebe5127a984a058cb7c2b003c2fd49f5c82 Mon Sep 17 00:00:00 2001
From: Nick Desaulniers <ndesaulniers@google.com>
Date: Thu, 1 Mar 2018 11:46:56 -0800
Subject: [PATCH 498/575] [XLA] Optimize away DynamicUpdateSlice with update
 parameter with a dimension of zero.

A zero sized update has no effect.

PiperOrigin-RevId: 187510099
---
 .../xla/service/algebraic_simplifier.cc       |  8 +++++++
 .../xla/service/algebraic_simplifier_test.cc  | 23 +++++++++++++++++++
 2 files changed, 31 insertions(+)

diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
index 5ddd8ec377..ecaa474336 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc
@@ -1625,6 +1625,14 @@ Status AlgebraicSimplifierVisitor::HandleDynamicUpdateSlice(
   if (IsAll(start_indices, 0) && SameShape(dynamic_update_slice, update)) {
     return ReplaceInstruction(dynamic_update_slice, update);
   }
+
+  // If any dimension of update is 0, elide the DynamicUpdateSlice.  This
+  // optimization becomes invalid should we later prefer to warn about out of
+  // bound indices.
+  if (ShapeUtil::HasZeroElements(update->shape())) {
+    return ReplaceInstruction(dynamic_update_slice,
+                              dynamic_update_slice->mutable_operand(0));
+  }
   return Status::OK();
 }
 
diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
index 667ae01993..451294ef5d 100644
--- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
+++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc
@@ -2800,6 +2800,29 @@ DotOfConcatTestSpec kDotOfConcatTestSpecs[] = {
     {/*m=*/1, /*k=*/16, /*n=*/1},   //
 };
 
+// Test that DynamicUpdateSlice update param with any dimension equal to zero
+// gets removed.
+TEST_F(AlgebraicSimplifierTest, DynamicUpdateSliceZeroUpdate) {
+  HloComputation::Builder builder(TestName());
+  const Shape dslice_shape = ShapeUtil::MakeShape(F32, {10});
+  HloInstruction* const operand = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, dslice_shape, "operand"));
+  const Shape update_shape = ShapeUtil::MakeShape(F32, {0});
+  HloInstruction* const update = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, update_shape, "update"));
+  HloInstruction* const start_indices = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateR1<int>({0})));
+  builder.AddInstruction(HloInstruction::CreateDynamicUpdateSlice(
+      dslice_shape, operand, update, start_indices));
+  const HloComputation* const computation =
+      module().AddEntryComputation(builder.Build());
+
+  AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false,
+                                 non_bitcasting_callback());
+  ASSERT_TRUE(simplifier.Run(&module()).ValueOrDie());
+  EXPECT_THAT(computation->root_instruction(), operand);
+}
+
 INSTANTIATE_TEST_CASE_P(DotOfConcatSimplificationTestInstantiation,
                         DotOfConcatSimplificationTest,
                         ::testing::ValuesIn(kDotOfConcatTestSpecs));
-- 
GitLab


From f176a611605bb26b17ef16d096e66d9d9ab2bda9 Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Thu, 1 Mar 2018 11:59:14 -0800
Subject: [PATCH 499/575] Refactor training part of the Keras engine. Also add
 support for sample/class weights with eager execution.

Structure before:
engine/training.py
engine/training_eager.py

After:

engine/training.py
engine/training_arrays.py
engine/training_eager.py
engine/training_generator.py
engine/training_utils.py

All new files are about 500 lines long. training.py is now 1700 lines long (about 1000 lines of logic). It was previously 3000 lines long.

PiperOrigin-RevId: 187511923
---
 tensorflow/python/keras/BUILD                 |    9 +-
 .../keras/_impl/keras/engine/training.py      | 1494 +----------------
 .../_impl/keras/engine/training_arrays.py     |  495 ++++++
 .../_impl/keras/engine/training_eager.py      |  314 ++--
 .../_impl/keras/engine/training_eager_test.py |  223 +++
 .../_impl/keras/engine/training_generator.py  |  439 +++++
 .../keras/_impl/keras/engine/training_test.py |   14 +-
 .../_impl/keras/engine/training_utils.py      |  534 ++++++
 .../keras/_impl/keras/utils/__init__.py       |    2 +-
 .../{training_utils.py => multi_gpu_utils.py} |    0
 ..._utils_test.py => multi_gpu_utils_test.py} |    0
 tensorflow/python/keras/utils/__init__.py     |    2 +-
 12 files changed, 1966 insertions(+), 1560 deletions(-)
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/training_arrays.py
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/training_generator.py
 create mode 100644 tensorflow/python/keras/_impl/keras/engine/training_utils.py
 rename tensorflow/python/keras/_impl/keras/utils/{training_utils.py => multi_gpu_utils.py} (100%)
 rename tensorflow/python/keras/_impl/keras/utils/{training_utils_test.py => multi_gpu_utils_test.py} (100%)

diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD
index a98d08f928..bd1aac5eae 100755
--- a/tensorflow/python/keras/BUILD
+++ b/tensorflow/python/keras/BUILD
@@ -45,7 +45,10 @@ py_library(
         "_impl/keras/engine/saving.py",
         "_impl/keras/engine/sequential.py",
         "_impl/keras/engine/training.py",
+        "_impl/keras/engine/training_arrays.py",
         "_impl/keras/engine/training_eager.py",
+        "_impl/keras/engine/training_generator.py",
+        "_impl/keras/engine/training_utils.py",
         "_impl/keras/estimator.py",
         "_impl/keras/initializers.py",
         "_impl/keras/layers/__init__.py",
@@ -78,8 +81,8 @@ py_library(
         "_impl/keras/utils/generic_utils.py",
         "_impl/keras/utils/io_utils.py",
         "_impl/keras/utils/layer_utils.py",
+        "_impl/keras/utils/multi_gpu_utils.py",
         "_impl/keras/utils/np_utils.py",
-        "_impl/keras/utils/training_utils.py",
         "_impl/keras/utils/vis_utils.py",
         "_impl/keras/wrappers/__init__.py",
         "_impl/keras/wrappers/scikit_learn.py",
@@ -646,9 +649,9 @@ py_test(
 )
 
 py_test(
-    name = "training_utils_test",
+    name = "multi_gpu_utils_test",
     size = "medium",
-    srcs = ["_impl/keras/utils/training_utils_test.py"],
+    srcs = ["_impl/keras/utils/multi_gpu_utils_test.py"],
     srcs_version = "PY2AND3",
     tags = ["multi_gpu"],
     deps = [
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index c121d819ff..2d040e7c0f 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -18,26 +18,21 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import copy
-
 import numpy as np
 
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras._impl.keras import backend as K
-from tensorflow.python.keras._impl.keras import callbacks as cbks
 from tensorflow.python.keras._impl.keras import losses
 from tensorflow.python.keras._impl.keras import metrics as metrics_module
 from tensorflow.python.keras._impl.keras import optimizers
+from tensorflow.python.keras._impl.keras.engine import training_arrays
 from tensorflow.python.keras._impl.keras.engine import training_eager
+from tensorflow.python.keras._impl.keras.engine import training_generator
+from tensorflow.python.keras._impl.keras.engine import training_utils
 from tensorflow.python.keras._impl.keras.engine.base_layer import Layer
 from tensorflow.python.keras._impl.keras.engine.network import Network
-from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer
-from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer
-from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence
-from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches
-from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
 from tensorflow.python.layers.base import _DeferredTensor
 from tensorflow.python.ops import array_ops
@@ -45,472 +40,6 @@ from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import optimizer as tf_optimizer_module
 from tensorflow.python.util.tf_export import tf_export
 
-try:
-  from scipy.sparse import issparse  # pylint: disable=g-import-not-at-top
-except ImportError:
-  issparse = None
-
-
-def _standardize_input_data(data,
-                            names,
-                            shapes=None,
-                            check_batch_axis=True,
-                            exception_prefix=''):
-  """Normalizes inputs and targets provided by users.
-
-  Users may pass data as a list of arrays, dictionary of arrays,
-  or as a single array. We normalize this to an ordered list of
-  arrays (same order as `names`), while checking that the provided
-  arrays have shapes that match the network's expectations.
-
-  Arguments:
-      data: User-provided input data (polymorphic).
-      names: List of expected array names.
-      shapes: Optional list of expected array shapes.
-      check_batch_axis: Boolean; whether to check that
-          the batch axis of the arrays matches the expected
-          value found in `shapes`.
-      exception_prefix: String prefix used for exception formatting.
-
-  Returns:
-      List of standardized input arrays (one array per model input).
-
-  Raises:
-      ValueError: in case of improperly formatted user-provided data.
-  """
-  if not names:
-    if data is not None and hasattr(data, '__len__') and len(data):
-      raise ValueError('Error when checking model ' + exception_prefix + ': '
-                       'expected no data, but got:', data)
-    return []
-  if data is None:
-    return [None for _ in range(len(names))]
-
-  if isinstance(data, dict):
-    try:
-      data = [
-          data[x].values
-          if data[x].__class__.__name__ == 'DataFrame' else data[x]
-          for x in names
-      ]
-    except KeyError as e:
-      raise ValueError('No data provided for "' + e.args[0] + '". Need data '
-                       'for each key in: ' + str(names))
-  elif isinstance(data, list):
-    if isinstance(data[0], list):
-      data = [np.asarray(d) for d in data]
-    elif len(names) == 1 and isinstance(data[0], (float, int)):
-      data = [np.asarray(data)]
-    else:
-      data = [
-          x.values if x.__class__.__name__ == 'DataFrame' else x for x in data
-      ]
-  else:
-    data = data.values if data.__class__.__name__ == 'DataFrame' else data
-    data = [data]
-  data = [
-      np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data
-  ]
-
-  if len(data) != len(names):
-    if data and hasattr(data[0], 'shape'):
-      raise ValueError('Error when checking model ' + exception_prefix +
-                       ': the list of Numpy arrays that you are passing to '
-                       'your model is not the size the model expected. '
-                       'Expected to see ' + str(len(names)) + ' array(s), '
-                       'but instead got the following list of ' +
-                       str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
-    elif len(names) > 1:
-      raise ValueError(
-          'Error when checking model ' + exception_prefix +
-          ': you are passing a list as input to your model, '
-          'but the model expects a list of ' + str(len(names)) +
-          ' Numpy arrays instead. The list you passed was: ' + str(data)[:200])
-    elif len(data) == 1 and not hasattr(data[0], 'shape'):
-      raise TypeError('Error when checking model ' + exception_prefix +
-                      ': data should be a Numpy array, or list/dict of '
-                      'Numpy arrays. Found: ' + str(data)[:200] + '...')
-    elif len(names) == 1:
-      data = [np.asarray(data)]
-
-  # Check shapes compatibility.
-  if shapes:
-    for i in range(len(names)):
-      if shapes[i] is not None:
-        data_shape = data[i].shape
-        shape = shapes[i]
-        if data[i].ndim != len(shape):
-          raise ValueError('Error when checking ' + exception_prefix +
-                           ': expected ' + names[i] + ' to have ' +
-                           str(len(shape)) + ' dimensions, but got array '
-                           'with shape ' + str(data_shape))
-        if not check_batch_axis:
-          data_shape = data_shape[1:]
-          shape = shape[1:]
-        for dim, ref_dim in zip(data_shape, shape):
-          if ref_dim != dim and ref_dim:
-            raise ValueError(
-                'Error when checking ' + exception_prefix + ': expected ' +
-                names[i] + ' to have shape ' + str(shape) +
-                ' but got array with shape ' + str(data_shape))
-  return data
-
-
-def _standardize_sample_or_class_weights(x_weight, output_names, weight_type):
-  """Maps `sample_weight` or `class_weight` to model outputs.
-
-  Arguments:
-      x_weight: User-provided `sample_weight` or `class_weight` argument.
-      output_names: List of output names (strings) in the model.
-      weight_type: A string used purely for exception printing.
-
-  Returns:
-      A list of `sample_weight` or `class_weight` where there are exactly
-          one element per model output.
-
-  Raises:
-      ValueError: In case of invalid user-provided argument.
-  """
-  if x_weight is None or len(x_weight) == 0:  # pylint: disable=g-explicit-length-test
-    return [None for _ in output_names]
-  if len(output_names) == 1:
-    if isinstance(x_weight, list) and len(x_weight) == 1:
-      return x_weight
-    if isinstance(x_weight, dict) and output_names[0] in x_weight:
-      return [x_weight[output_names[0]]]
-    else:
-      return [x_weight]
-  if isinstance(x_weight, list):
-    if len(x_weight) != len(output_names):
-      raise ValueError('Provided `' + weight_type + '` was a list of ' +
-                       str(len(x_weight)) + ' elements, but the model has ' +
-                       str(len(output_names)) + ' outputs. '
-                       'You should provide one `' + weight_type + '`'
-                       'array per model output.')
-    return x_weight
-  if isinstance(x_weight, dict):
-    x_weights = []
-    for name in output_names:
-      x_weights.append(x_weight.get(name))
-    return x_weights
-  else:
-    raise TypeError(
-        'The model has multiple outputs, so `' + weight_type + '` '
-        'should be either a list or a dict. '
-        'Provided `' + weight_type + '` type not understood: ' + str(x_weight))
-
-
-def _standardize_class_weights(class_weight, output_names):
-  return _standardize_sample_or_class_weights(class_weight, output_names,
-                                              'class_weight')
-
-
-def _standardize_sample_weights(sample_weight, output_names):
-  return _standardize_sample_or_class_weights(sample_weight, output_names,
-                                              'sample_weight')
-
-
-def _check_array_lengths(inputs, targets, weights=None):
-  """Does user input validation for numpy arrays.
-
-  Arguments:
-      inputs: list of Numpy arrays of inputs.
-      targets: list of Numpy arrays of targets.
-      weights: list of Numpy arrays of sample weights.
-
-  Raises:
-      ValueError: in case of incorrectly formatted data.
-  """
-
-  def set_of_lengths(x):
-    # return a set with the variation between
-    # different shapes, with None => 0
-    if x is None:
-      return {}
-    else:
-      return set([y.shape[0] for y in x if y is not None])
-
-  set_x = set_of_lengths(inputs)
-  set_y = set_of_lengths(targets)
-  set_w = set_of_lengths(weights)
-  if len(set_x) > 1:
-    raise ValueError('All input arrays (x) should have '
-                     'the same number of samples. Got array shapes: ' +
-                     str([x.shape for x in inputs]))
-  if len(set_y) > 1:
-    raise ValueError('All target arrays (y) should have '
-                     'the same number of samples. Got array shapes: ' +
-                     str([y.shape for y in targets]))
-  if set_x and set_y and list(set_x)[0] != list(set_y)[0]:
-    raise ValueError('Input arrays should have '
-                     'the same number of samples as target arrays. '
-                     'Found ' + str(list(set_x)[0]) + ' input samples '
-                     'and ' + str(list(set_y)[0]) + ' target samples.')
-  if len(set_w) > 1:
-    raise ValueError('All sample_weight arrays should have '
-                     'the same number of samples. Got array shapes: ' +
-                     str([w.shape for w in weights]))
-  if set_y and set_w and list(set_y)[0] != list(set_w)[0]:
-    raise ValueError('Sample_weight arrays should have '
-                     'the same number of samples as target arrays. Got ' +
-                     str(list(set_y)[0]) + ' input samples and ' +
-                     str(list(set_w)[0]) + ' target samples.')
-
-
-def _check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
-  """Does validation on the compatibility of targets and loss functions.
-
-  This helps prevent users from using loss functions incorrectly. This check
-  is purely for UX purposes.
-
-  Arguments:
-      targets: list of Numpy arrays of targets.
-      loss_fns: list of loss functions.
-      output_shapes: list of shapes of model outputs.
-
-  Raises:
-      ValueError: if a loss function or target array
-          is incompatible with an output.
-  """
-  key_losses = {
-      losses.mean_squared_error, losses.binary_crossentropy,
-      losses.categorical_crossentropy
-  }
-  for y, loss, shape in zip(targets, loss_fns, output_shapes):
-    if y is None or loss is None or tensor_util.is_tensor(y):
-      continue
-    if loss is losses.categorical_crossentropy:
-      if y.shape[-1] == 1:
-        raise ValueError('You are passing a target array of shape ' + str(
-            y.shape) + ' while using as loss `categorical_crossentropy`. '
-                         '`categorical_crossentropy` expects '
-                         'targets to be binary matrices (1s and 0s) '
-                         'of shape (samples, classes). '
-                         'If your targets are integer classes, '
-                         'you can convert them to the expected format via:\n'
-                         '```\n'
-                         'from keras.utils import to_categorical\n'
-                         'y_binary = to_categorical(y_int)\n'
-                         '```\n'
-                         '\n'
-                         'Alternatively, you can use the loss function '
-                         '`sparse_categorical_crossentropy` instead, '
-                         'which does expect integer targets.')
-    if loss in key_losses:
-      for target_dim, out_dim in zip(y.shape[1:], shape[1:]):
-        if out_dim is not None and target_dim != out_dim:
-          raise ValueError('A target array with shape ' + str(y.shape) +
-                           ' was passed for an output of shape ' + str(shape) +
-                           ' while using as loss `' + loss.__name__ + '`. '
-                           'This loss expects '
-                           'targets to have the same shape '
-                           'as the output.')
-
-
-def _collect_metrics(metrics, output_names):
-  """Maps metric functions to model outputs.
-
-  Arguments:
-      metrics: a list or dict of metric functions.
-      output_names: a list of the names (strings) of model outputs.
-
-  Returns:
-      A list (one entry per model output) of lists of metric functions.
-      For instance, if the model has 2 outputs, and for the first output
-      we want to compute "binary_accuracy" and "binary_crossentropy",
-      and just "binary_accuracy" for the second output,
-      the list would look like:
-          `[[binary_accuracy, binary_crossentropy], [binary_accuracy]]`
-
-  Raises:
-      TypeError: if an incorrect type is passed for the `metrics` argument.
-  """
-  if not metrics:
-    return [[] for _ in output_names]
-  if isinstance(metrics, list):
-    # we then apply all metrics to all outputs.
-    return [copy.copy(metrics) for _ in output_names]
-  elif isinstance(metrics, dict):
-    nested_metrics = []
-    for name in output_names:
-      output_metrics = metrics.get(name, [])
-      if not isinstance(output_metrics, list):
-        output_metrics = [output_metrics]
-      nested_metrics.append(output_metrics)
-    return nested_metrics
-  else:
-    raise TypeError('Type of `metrics` argument not understood. '
-                    'Expected a list or dictionary, found: ' + str(metrics))
-
-
-def _batch_shuffle(index_array, batch_size):
-  """Shuffles an array in a batch-wise fashion.
-
-  Useful for shuffling HDF5 arrays
-  (where one cannot access arbitrary indices).
-
-  Arguments:
-      index_array: array of indices to be shuffled.
-      batch_size: integer.
-
-  Returns:
-      The `index_array` array, shuffled in a batch-wise fashion.
-  """
-  batch_count = int(len(index_array) / batch_size)
-  # to reshape we need to be cleanly divisible by batch size
-  # we stash extra items and reappend them after shuffling
-  last_batch = index_array[batch_count * batch_size:]
-  index_array = index_array[:batch_count * batch_size]
-  index_array = index_array.reshape((batch_count, batch_size))
-  np.random.shuffle(index_array)
-  index_array = index_array.flatten()
-  return np.append(index_array, last_batch)
-
-
-def _weighted_masked_objective(fn):
-  """Adds support for masking and sample-weighting to an objective function.
-
-  It transforms an objective function `fn(y_true, y_pred)`
-  into a sample-weighted, cost-masked objective function
-  `fn(y_true, y_pred, weights, mask)`.
-
-  Arguments:
-      fn: The objective function to wrap,
-          with signature `fn(y_true, y_pred)`.
-
-  Returns:
-      A function with signature `fn(y_true, y_pred, weights, mask)`.
-  """
-  if fn is None:
-    return None
-
-  def weighted(y_true, y_pred, weights, mask=None):
-    """Wrapper function.
-
-    Arguments:
-        y_true: `y_true` argument of `fn`.
-        y_pred: `y_pred` argument of `fn`.
-        weights: Weights tensor.
-        mask: Mask tensor.
-
-    Returns:
-        Scalar tensor.
-    """
-    # score_array has ndim >= 2
-    score_array = fn(y_true, y_pred)
-    if mask is not None:
-      # Cast the mask to floatX to avoid float64 upcasting in theano
-      mask = K.cast(mask, K.floatx())
-      # mask should have the same shape as score_array
-      score_array *= mask
-      #  the loss per batch should be proportional
-      #  to the number of unmasked samples.
-      score_array /= K.mean(mask)
-
-    # apply sample weighting
-    if weights is not None:
-      # reduce score_array to same ndim as weight array
-      ndim = K.ndim(score_array)
-      weight_ndim = K.ndim(weights)
-      score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
-      score_array *= weights
-      score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
-    return K.mean(score_array)
-
-  return weighted
-
-
-def _standardize_weights(y,
-                         sample_weight=None,
-                         class_weight=None,
-                         sample_weight_mode=None):
-  """Performs sample weight validation and standardization.
-
-  Everything gets normalized to a single sample-wise (or timestep-wise)
-  weight array.
-
-  Arguments:
-      y: Numpy array of model targets to be weighted.
-      sample_weight: User-provided `sample_weight` argument.
-      class_weight: User-provided `class_weight` argument.
-      sample_weight_mode: One of `None` or `"temporal"`.
-          `"temporal"` indicated that we expect 2D weight data
-          that will be applied to the last 2 dimensions of
-          the targets (i.e. we are weighting timesteps, not samples).
-
-  Returns:
-      A numpy array of target weights, one entry per sample to weight.
-
-  Raises:
-      ValueError: In case of invalid user-provided arguments.
-  """
-  if sample_weight_mode is not None:
-    if sample_weight_mode != 'temporal':
-      raise ValueError('"sample_weight_mode '
-                       'should be None or "temporal". '
-                       'Found: ' + str(sample_weight_mode))
-    if len(y.shape) < 3:
-      raise ValueError('Found a sample_weight array for '
-                       'an input with shape ' + str(y.shape) + '. '
-                       'Timestep-wise sample weighting (use of '
-                       'sample_weight_mode="temporal") is restricted to '
-                       'outputs that are at least 3D, i.e. that have '
-                       'a time dimension.')
-    if sample_weight is not None and len(sample_weight.shape) != 2:
-      raise ValueError('Found a sample_weight array with shape ' +
-                       str(sample_weight.shape) + '. '
-                       'In order to use timestep-wise sample weighting, '
-                       'you should pass a 2D sample_weight array.')
-  else:
-    if sample_weight is not None and len(sample_weight.shape) != 1:
-      raise ValueError('Found a sample_weight array with shape ' +
-                       str(sample_weight.shape) + '. '
-                       'In order to use timestep-wise sample weights, '
-                       'you should specify '
-                       'sample_weight_mode="temporal" '
-                       'in compile(). If you just mean to use '
-                       'sample-wise weights, make sure your '
-                       'sample_weight array is 1D.')
-
-  if sample_weight is not None:
-    if len(sample_weight.shape) > len(y.shape):
-      raise ValueError(
-          'Found a sample_weight with shape' + str(sample_weight.shape) + '.'
-          'Expected sample_weight with rank '
-          'less than or equal to ' + str(len(y.shape)))
-
-    if y.shape[:sample_weight.ndim] != sample_weight.shape:
-      raise ValueError(
-          'Found a sample_weight array with shape ' + str(sample_weight.shape) +
-          ' for an input with shape ' + str(y.shape) + '. '
-          'sample_weight cannot be broadcast.')
-    return sample_weight
-  elif isinstance(class_weight, dict):
-    if len(y.shape) > 2:
-      raise ValueError('`class_weight` not supported for '
-                       '3+ dimensional targets.')
-    if y.shape[1] > 1:
-      y_classes = np.argmax(y, axis=1)
-    elif y.shape[1] == 1:
-      y_classes = np.reshape(y, y.shape[0])
-    else:
-      y_classes = y
-
-    weights = np.asarray(
-        [class_weight[cls] for cls in y_classes if cls in class_weight])
-
-    if len(weights) != len(y_classes):
-      # subtract the sets to pick all missing classes
-      existing_classes = set(y_classes)
-      existing_class_weight = set(class_weight.keys())
-      raise ValueError('`class_weight` must contain all classes in the data.'
-                       ' The classes %s exist in the data but not in '
-                       '`class_weight`.' %
-                       (existing_classes - existing_class_weight))
-    return weights
-  else:
-    return None
-
 
 @tf_export('keras.models.Model', 'keras.Model')
 class Model(Network):
@@ -687,7 +216,8 @@ class Model(Network):
       loss_functions = [loss_function for _ in range(len(self.outputs))]
     self.loss_functions = loss_functions
 
-    weighted_losses = [_weighted_masked_objective(fn) for fn in loss_functions]
+    weighted_losses = [training_utils.weighted_masked_objective(fn)
+                       for fn in loss_functions]
     skip_target_indices = []
     skip_target_weighing_indices = []
     self._feed_outputs = []
@@ -744,7 +274,8 @@ class Model(Network):
       for i in range(len(self.outputs)):
         if len(self.outputs) > 1:
           self.metrics_names.append(self.output_names[i] + '_loss')
-      self.nested_metrics = _collect_metrics(metrics, self.output_names)
+      self.nested_metrics = training_utils.collect_metrics(metrics,
+                                                           self.output_names)
       self._feed_sample_weight_modes = []
       for i in range(len(self.outputs)):
         self._feed_sample_weight_modes.append(None)
@@ -914,9 +445,9 @@ class Model(Network):
 
     # List of same size as output_names.
     # contains tuples (metrics for output, names of metrics).
-    nested_metrics = _collect_metrics(metrics, self.output_names)
-    nested_weighted_metrics = _collect_metrics(weighted_metrics,
-                                               self.output_names)
+    nested_metrics = training_utils.collect_metrics(metrics, self.output_names)
+    nested_weighted_metrics = training_utils.collect_metrics(weighted_metrics,
+                                                             self.output_names)
     self.metrics_updates = []
     self.stateful_metric_names = []
     with K.name_scope('metrics'):
@@ -962,11 +493,13 @@ class Model(Network):
                 suffix = 'acc'
               elif metric in ('crossentropy', 'ce'):
                 suffix = 'ce'
-              weighted_metric_fn = _weighted_masked_objective(metric_fn)
+              weighted_metric_fn = training_utils.weighted_masked_objective(
+                  metric_fn)
               metric_name = metric_name_prefix + suffix
             else:
               metric_fn = metrics_module.get(metric)
-              weighted_metric_fn = _weighted_masked_objective(metric_fn)
+              weighted_metric_fn = training_utils.weighted_masked_objective(
+                  metric_fn)
               # Get metric name as string
               if hasattr(metric_fn, 'name'):
                 metric_name = metric_fn.name
@@ -1104,451 +637,6 @@ class Model(Network):
           name='predict_function',
           **kwargs)
 
-  def _check_num_samples(self,
-                         ins,
-                         batch_size=None,
-                         steps=None,
-                         steps_name='steps'):
-    """Determine the number of samples provided for training and evaluation.
-
-    The number of samples is not defined when running with `steps`,
-    in which case the number of samples is set to `None`.
-
-    Arguments:
-        ins: List of tensors to be fed to the Keras function.
-        batch_size: Integer batch size or `None` if not defined.
-        steps: Total number of steps (batches of samples)
-            before declaring `_predict_loop` finished.
-            Ignored with the default value of `None`.
-        steps_name: The public API's parameter name for `steps`.
-
-    Raises:
-        ValueError: when `steps` is `None` and the attribute `ins.shape`
-        does not exist. Also raises ValueError when `steps` is not `None`
-        and `batch_size` is not `None` because they are mutually
-        exclusive.
-
-    Returns:
-        When steps is `None`, returns the number of samples to be
-        processed based on the size of the first dimension of the
-        first input numpy array. When steps is not `None` and
-        `batch_size` is `None`, returns `None`.
-
-    Raises:
-        ValueError: In case of invalid arguments.
-    """
-    if steps is not None:
-      num_samples = None
-      if batch_size is not None:
-        raise ValueError(
-            'If ' + steps_name + ' is set, the `batch_size` must be None.')
-    elif ins and hasattr(ins[0], 'shape'):
-      num_samples = ins[0].shape[0]
-    else:
-      raise ValueError(
-          'Either the input data should have '
-          'a defined shape, or ' + steps_name + ' should be specified.')
-    return num_samples
-
-  def _fit_loop(self,
-                f,
-                ins,
-                out_labels=None,
-                batch_size=None,
-                epochs=100,
-                verbose=1,
-                callbacks=None,
-                val_f=None,
-                val_ins=None,
-                shuffle=True,
-                callback_metrics=None,
-                initial_epoch=0,
-                steps_per_epoch=None,
-                validation_steps=None):
-    """Abstract fit function for `f(ins)`.
-
-    Assume that f returns a list, labeled by out_labels.
-
-    Arguments:
-        f: Keras function returning a list of tensors
-        ins: List of tensors to be fed to `f`
-        out_labels: List of strings, display names of
-            the outputs of `f`
-        batch_size: Integer batch size or None if unknown.
-        epochs: Number of times to iterate over the data
-        verbose: Verbosity mode, 0, 1 or 2
-        callbacks: List of callbacks to be called during training
-        val_f: Keras function to call for validation
-        val_ins: List of tensors to be fed to `val_f`
-        shuffle: Whether to shuffle the data at the beginning of each epoch
-        callback_metrics: List of strings, the display names of the metrics
-            passed to the callbacks. They should be the
-            concatenation of list the display names of the outputs of
-             `f` and the list of display names of the outputs of `f_val`.
-        initial_epoch: Epoch at which to start training
-            (useful for resuming a previous training run)
-        steps_per_epoch: Total number of steps (batches of samples)
-            before declaring one epoch finished and starting the
-            next epoch. Ignored with the default value of `None`.
-        validation_steps: Number of steps to run validation for
-            (only if doing validation from data tensors).
-            Ignored with the default value of `None`.
-
-    Returns:
-        `History` object.
-
-    Raises:
-        ValueError: in case of invalid arguments.
-    """
-    do_validation = False
-    if val_f and val_ins:
-      do_validation = True
-      if verbose and ins and hasattr(ins[0], 'shape') and hasattr(
-          val_ins[0], 'shape'):
-        print('Train on %d samples, validate on %d samples' %
-              (ins[0].shape[0], val_ins[0].shape[0]))
-    if validation_steps:
-      do_validation = True
-      if steps_per_epoch is None:
-        raise ValueError('Can only use `validation_steps` '
-                         'when doing step-wise '
-                         'training, i.e. `steps_per_epoch` '
-                         'must be set.')
-
-    num_train_samples = self._check_num_samples(
-        ins, batch_size, steps_per_epoch, 'steps_per_epoch')
-    if num_train_samples is not None:
-      index_array = np.arange(num_train_samples)
-
-    self.history = cbks.History()
-    all_callbacks = [cbks.BaseLogger(
-        stateful_metrics=self.stateful_metric_names)]
-    if verbose:
-      if steps_per_epoch is not None:
-        count_mode = 'steps'
-      else:
-        count_mode = 'samples'
-      all_callbacks.append(
-          cbks.ProgbarLogger(
-              count_mode, stateful_metrics=self.stateful_metric_names))
-    all_callbacks += (callbacks or []) + [self.history]
-    callbacks = cbks.CallbackList(all_callbacks)
-    out_labels = out_labels or []
-
-    # it's possible to callback a different model than self
-    # (used by Sequential models)
-    if hasattr(self, 'callback_model') and self.callback_model:
-      callback_model = self.callback_model
-    else:
-      callback_model = self
-
-    callbacks.set_model(callback_model)
-
-    callbacks.set_params({
-        'batch_size': batch_size,
-        'epochs': epochs,
-        'steps': steps_per_epoch,
-        'samples': num_train_samples,
-        'verbose': verbose,
-        'do_validation': do_validation,
-        'metrics': callback_metrics or [],
-    })
-    callbacks.on_train_begin()
-    callback_model.stop_training = False
-    for cbk in callbacks:
-      cbk.validation_data = val_ins
-
-    # To prevent a slowdown, we find beforehand the arrays that need conversion.
-    feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights
-    indices_for_conversion_to_dense = []
-    for i in range(len(feed)):
-      if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
-        indices_for_conversion_to_dense.append(i)
-
-    for epoch in range(initial_epoch, epochs):
-      # Reset stateful metrics
-      for m in self.metrics:
-        if isinstance(m, Layer):
-          m.reset_states()
-      # Update callbacks
-      callbacks.on_epoch_begin(epoch)
-      epoch_logs = {}
-      if steps_per_epoch is not None:
-        for step_index in range(steps_per_epoch):
-          batch_logs = {}
-          batch_logs['batch'] = step_index
-          batch_logs['size'] = 1
-          callbacks.on_batch_begin(step_index, batch_logs)
-          outs = f(ins)
-
-          if not isinstance(outs, list):
-            outs = [outs]
-          for l, o in zip(out_labels, outs):
-            batch_logs[l] = o
-
-          callbacks.on_batch_end(step_index, batch_logs)
-          if callback_model.stop_training:
-            break
-
-        if do_validation:
-          val_outs = self._test_loop(
-              val_f,
-              val_ins,
-              batch_size=batch_size,
-              steps=validation_steps,
-              verbose=0)
-          if not isinstance(val_outs, list):
-            val_outs = [val_outs]
-          # Same labels assumed.
-          for l, o in zip(out_labels, val_outs):
-            epoch_logs['val_' + l] = o
-      else:
-        if shuffle == 'batch':
-          index_array = _batch_shuffle(index_array, batch_size)
-        elif shuffle:
-          np.random.shuffle(index_array)
-
-        batches = make_batches(num_train_samples, batch_size)
-
-        for batch_index, (batch_start, batch_end) in enumerate(batches):
-          batch_ids = index_array[batch_start:batch_end]
-          try:
-            if isinstance(ins[-1], int):
-              # Do not slice the training phase flag.
-              ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
-            else:
-              ins_batch = slice_arrays(ins, batch_ids)
-          except TypeError:
-            raise TypeError('TypeError while preparing batch. '
-                            'If using HDF5 input data, '
-                            'pass shuffle="batch".')
-          batch_logs = {}
-          batch_logs['batch'] = batch_index
-          batch_logs['size'] = len(batch_ids)
-          callbacks.on_batch_begin(batch_index, batch_logs)
-          for i in indices_for_conversion_to_dense:
-            ins_batch[i] = ins_batch[i].toarray()
-
-          outs = f(ins_batch)
-          if not isinstance(outs, list):
-            outs = [outs]
-          for l, o in zip(out_labels, outs):
-            batch_logs[l] = o
-
-          callbacks.on_batch_end(batch_index, batch_logs)
-          if callback_model.stop_training:
-            break
-
-          if batch_index == len(batches) - 1:  # Last batch.
-            if do_validation:
-              val_outs = self._test_loop(
-                  val_f, val_ins, batch_size=batch_size, verbose=0)
-              if not isinstance(val_outs, list):
-                val_outs = [val_outs]
-              # Same labels assumed.
-              for l, o in zip(out_labels, val_outs):
-                epoch_logs['val_' + l] = o
-      callbacks.on_epoch_end(epoch, epoch_logs)
-      if callback_model.stop_training:
-        break
-    callbacks.on_train_end()
-    return self.history
-
-  def _predict_loop(self, f, ins, batch_size=32, verbose=0, steps=None):
-    """Abstract method to loop over some data in batches.
-
-    Arguments:
-        f: Keras function returning a list of tensors.
-        ins: list of tensors to be fed to `f`.
-        batch_size: integer batch size.
-        verbose: verbosity mode.
-        steps: Total number of steps (batches of samples)
-            before declaring `_predict_loop` finished.
-            Ignored with the default value of `None`.
-
-    Returns:
-        Array of predictions (if the model has a single output)
-        or list of arrays of predictions
-        (if the model has multiple outputs).
-    """
-    if hasattr(self, 'metrics'):
-      for m in self.metrics:
-        if isinstance(m, Layer):
-          m.reset_states()
-
-    num_samples = self._check_num_samples(ins, batch_size, steps, 'steps')
-    if verbose == 1:
-      if steps is not None:
-        progbar = Progbar(target=steps,
-                          stateful_metrics=self.stateful_metric_names)
-      else:
-        progbar = Progbar(target=num_samples,
-                          stateful_metrics=self.stateful_metric_names)
-
-    indices_for_conversion_to_dense = []
-    for i in range(len(self._feed_inputs)):
-      if (issparse is not None and issparse(ins[i]) and
-          not K.is_sparse(self._feed_inputs[i])):
-        indices_for_conversion_to_dense.append(i)
-
-    if steps is not None:
-      # Step-based predictions.
-      # Since we do not know how many samples
-      # we will see, we cannot pre-allocate
-      # the returned Numpy arrays.
-      # Instead, we store one array per batch seen
-      # and concatenate them upon returning.
-      unconcatenated_outs = []
-      for step in range(steps):
-        batch_outs = f(ins)
-        if not isinstance(batch_outs, list):
-          batch_outs = [batch_outs]
-        if step == 0:
-          for batch_out in batch_outs:
-            unconcatenated_outs.append([])
-        for i, batch_out in enumerate(batch_outs):
-          unconcatenated_outs[i].append(batch_out)
-        if verbose == 1:
-          progbar.update(step + 1)
-      if len(unconcatenated_outs) == 1:
-        return np.concatenate(unconcatenated_outs[0], axis=0)
-      return [
-          np.concatenate(unconcatenated_outs[i], axis=0)
-          for i in range(len(unconcatenated_outs))
-      ]
-    else:
-      # Sample-based predictions.
-      outs = []
-      batches = make_batches(num_samples, batch_size)
-      index_array = np.arange(num_samples)
-      for batch_index, (batch_start, batch_end) in enumerate(batches):
-        batch_ids = index_array[batch_start:batch_end]
-        if ins and isinstance(ins[-1], int):
-          # Do not slice the training phase flag.
-          ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
-        else:
-          ins_batch = slice_arrays(ins, batch_ids)
-        for i in indices_for_conversion_to_dense:
-          ins_batch[i] = ins_batch[i].toarray()
-
-        batch_outs = f(ins_batch)
-        if not isinstance(batch_outs, list):
-          batch_outs = [batch_outs]
-        if batch_index == 0:
-          # Pre-allocate the results arrays.
-          for batch_out in batch_outs:
-            shape = (num_samples,) + batch_out.shape[1:]
-            outs.append(np.zeros(shape, dtype=batch_out.dtype))
-        for i, batch_out in enumerate(batch_outs):
-          outs[i][batch_start:batch_end] = batch_out
-        if verbose == 1:
-          progbar.update(batch_end)
-      if len(outs) == 1:
-        return outs[0]
-      return outs
-
-  def _test_loop(self, f, ins, batch_size=None, verbose=0, steps=None):
-    """Abstract method to loop over some data in batches.
-
-    Arguments:
-        f: Keras function returning a list of tensors.
-        ins: list of tensors to be fed to `f`.
-        batch_size: integer batch size or `None`.
-        verbose: verbosity mode.
-        steps: Total number of steps (batches of samples)
-            before declaring predictions finished.
-            Ignored with the default value of `None`.
-
-    Returns:
-        Scalar loss (if the model has a single output and no metrics)
-        or list of scalars (if the model has multiple outputs
-        and/or metrics). The attribute `model.metrics_names` will give you
-        the display labels for the scalar outputs.
-    """
-    if hasattr(self, 'metrics'):
-      for m in self.metrics:
-        if isinstance(m, Layer):
-          m.reset_states()
-      stateful_metric_indices = [
-          i for i, name in enumerate(self.metrics_names)
-          if str(name) in self.stateful_metric_names
-      ]
-    else:
-      stateful_metric_indices = []
-
-    num_samples = self._check_num_samples(ins, batch_size, steps, 'steps')
-    outs = []
-    if verbose == 1:
-      if steps is not None:
-        progbar = Progbar(target=steps)
-      else:
-        progbar = Progbar(target=num_samples)
-
-    # To prevent a slowdown, we find beforehand the arrays that need conversion.
-    feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights
-    indices_for_conversion_to_dense = []
-    for i in range(len(feed)):
-      if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
-        indices_for_conversion_to_dense.append(i)
-
-    if steps is not None:
-      for step in range(steps):
-        batch_outs = f(ins)
-        if isinstance(batch_outs, list):
-          if step == 0:
-            for _ in enumerate(batch_outs):
-              outs.append(0.)
-          for i, batch_out in enumerate(batch_outs):
-            if i in stateful_metric_indices:
-              outs[i] = batch_out
-            else:
-              outs[i] += batch_out
-        else:
-          if step == 0:
-            outs.append(0.)
-          outs[0] += batch_outs
-        if verbose == 1:
-          progbar.update(step + 1)
-      for i in range(len(outs)):
-        if i not in stateful_metric_indices:
-          outs[i] /= steps
-    else:
-      batches = make_batches(num_samples, batch_size)
-      index_array = np.arange(num_samples)
-      for batch_index, (batch_start, batch_end) in enumerate(batches):
-        batch_ids = index_array[batch_start:batch_end]
-        if isinstance(ins[-1], int):
-          # Do not slice the training phase flag.
-          ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
-        else:
-          ins_batch = slice_arrays(ins, batch_ids)
-        for i in indices_for_conversion_to_dense:
-          ins_batch[i] = ins_batch[i].toarray()
-
-        batch_outs = f(ins_batch)
-
-        if isinstance(batch_outs, list):
-          if batch_index == 0:
-            for batch_out in enumerate(batch_outs):
-              outs.append(0.)
-          for i, batch_out in enumerate(batch_outs):
-            if i in stateful_metric_indices:
-              outs[i] = batch_out
-            else:
-              outs[i] += batch_out * len(batch_ids)
-        else:
-          if batch_index == 0:
-            outs.append(0.)
-          outs[0] += batch_outs * len(batch_ids)
-        if verbose == 1:
-          progbar.update(batch_end)
-      for i in range(len(outs)):
-        if i not in stateful_metric_indices:
-          outs[i] /= num_samples
-    if len(outs) == 1:
-      return outs[0]
-    return outs
-
   def _standardize_user_data(self,
                              x,
                              y=None,
@@ -1688,7 +776,7 @@ class Model(Network):
       feed_input_shapes = self._feed_input_shapes
 
     # Standardize the inputs.
-    x = _standardize_input_data(
+    x = training_utils.standardize_input_data(
         x,
         feed_input_names,
         feed_input_shapes,
@@ -1727,7 +815,7 @@ class Model(Network):
             feed_output_shapes.append(output_shape)
 
       # Standardize the outputs.
-      y = _standardize_input_data(
+      y = training_utils.standardize_input_data(
           y,
           feed_output_names,
           feed_output_shapes,
@@ -1736,21 +824,21 @@ class Model(Network):
 
       # Generate sample-wise weight values given the `sample_weight` and
       # `class_weight` arguments.
-      sample_weights = _standardize_sample_weights(sample_weight,
-                                                   feed_output_names)
-      class_weights = _standardize_class_weights(class_weight,
-                                                 feed_output_names)
+      sample_weights = training_utils.standardize_sample_weights(
+          sample_weight, feed_output_names)
+      class_weights = training_utils.standardize_class_weights(
+          class_weight, feed_output_names)
       sample_weights = [
-          _standardize_weights(ref, sw, cw, mode)
+          training_utils.standardize_weights(ref, sw, cw, mode)
           for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights,
                                          feed_sample_weight_modes)
       ]
       # Check that all arrays have the same length.
-      _check_array_lengths(x, y, sample_weights)
+      training_utils.check_array_lengths(x, y, sample_weights)
       if self._is_graph_network and not context.in_eager_mode():
         # Additional checks to avoid users mistakenly using improper loss fns.
-        _check_loss_and_target_compatibility(y, self._feed_loss_fns,
-                                             feed_output_shapes)
+        training_utils.check_loss_and_target_compatibility(
+            y, self._feed_loss_fns, feed_output_shapes)
     else:
       y = []
       sample_weights = []
@@ -2052,10 +1140,7 @@ class Model(Network):
         class_weight=class_weight,
         batch_size=batch_size)
     # Prepare validation data.
-    do_validation = False
-    val_ins = []
     if validation_data:
-      do_validation = True
       if len(validation_data) == 2:
         val_x, val_y = validation_data  # pylint: disable=unpacking-non-sequence
         val_sample_weight = None
@@ -2075,7 +1160,6 @@ class Model(Network):
           batch_size=batch_size)
 
     elif validation_split and 0. < validation_split < 1.:
-      do_validation = True
       if hasattr(x[0], 'shape'):
         split_at = int(x[0].shape[0] * (1. - validation_split))
       else:
@@ -2088,78 +1172,40 @@ class Model(Network):
       val_x = []
       val_y = []
       val_sample_weights = []
-      do_validation = True
-
-    # Prepare display labels.
-    out_labels = self.metrics_names
+    else:
+      val_x = None
+      val_y = None
+      val_sample_weights = None
 
     if context.in_eager_mode():
-      if any([w is not None for w in sample_weights]):
-        raise ValueError('`sample_weight` and `class_weight` is not supported '
-                         'when eager execution is enabled, for now.')
-
-      if do_validation:
-        if any([w is not None for w in val_sample_weights]):
-          raise ValueError('`sample_weight` and `class_weight` is not supported'
-                           ' when eager execution is enabled, for now.')
-        callback_metrics = copy.copy(out_labels) + [
-            'val_' + n for n in out_labels
-        ]
-        val_ins = val_x + val_y
-      else:
-        callback_metrics = copy.copy(out_labels)
-
       return training_eager.fit_loop(
           self,
-          x + y,
-          out_labels=out_labels,
+          inputs=x,
+          targets=y,
+          sample_weights=sample_weights,
           batch_size=batch_size,
           epochs=epochs,
           verbose=verbose,
           callbacks=callbacks,
-          val_ins=val_ins,
+          val_inputs=val_x,
+          val_targets=val_y,
+          val_sample_weights=val_sample_weights,
           shuffle=shuffle,
-          callback_metrics=callback_metrics,
           initial_epoch=initial_epoch,
           steps_per_epoch=steps_per_epoch,
           validation_steps=validation_steps)
     else:
-      # Prepare input arrays and training function.
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        ins = x + y + sample_weights + [1]
-      else:
-        ins = x + y + sample_weights
-
-      self._make_train_function()
-      f = self.train_function
-
-      if do_validation:
-        self._make_test_function()
-        val_f = self.test_function
-        callback_metrics = copy.copy(out_labels) + [
-            'val_' + n for n in out_labels
-        ]
-        if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-          val_ins = val_x + val_y + val_sample_weights + [0]
-        else:
-          val_ins = val_x + val_y + val_sample_weights
-      else:
-        val_f = None
-        callback_metrics = copy.copy(out_labels)
-
-      # Delegate logic to `_fit_loop`.
-      return self._fit_loop(
-          f,
-          ins,
-          out_labels=out_labels,
+      return training_arrays.fit_loop(
+          self, x, y,
+          sample_weights=sample_weights,
           batch_size=batch_size,
           epochs=epochs,
           verbose=verbose,
           callbacks=callbacks,
-          val_f=val_f,
-          val_ins=val_ins,
+          val_inputs=val_x,
+          val_targets=val_y,
+          val_sample_weights=val_sample_weights,
           shuffle=shuffle,
-          callback_metrics=callback_metrics,
           initial_epoch=initial_epoch,
           steps_per_epoch=steps_per_epoch,
           validation_steps=validation_steps)
@@ -2235,22 +1281,13 @@ class Model(Network):
         batch_size=batch_size)
 
     if context.in_eager_mode():
-      if any([w is not None for w in sample_weights]):
-        raise ValueError('`sample_weight` and `class_weight` is not supported '
-                         'when eager execution is enabled, for now.')
       return training_eager.test_loop(
-          self, x + y, batch_size=batch_size, verbose=verbose, steps=steps)
+          self, inputs=x, targets=y, sample_weights=sample_weights,
+          batch_size=batch_size, verbose=verbose, steps=steps)
     else:
-      # Prepare inputs, delegate logic to `_test_loop`.
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        ins = x + y + sample_weights + [0]
-      else:
-        ins = x + y + sample_weights
-
-      self._make_test_function()
-      f = self.test_function
-      return self._test_loop(
-          f, ins, batch_size=batch_size, verbose=verbose, steps=steps)
+      return training_arrays.test_loop(
+          self, inputs=x, targets=y, sample_weights=sample_weights,
+          batch_size=batch_size, verbose=verbose, steps=steps)
 
   def predict(self, x, batch_size=None, verbose=0, steps=None):
     """Generates output predictions for the input samples.
@@ -2288,17 +1325,8 @@ class Model(Network):
       return training_eager.predict_loop(
           self, x, batch_size=batch_size, verbose=verbose, steps=steps)
     else:
-      # Prepare inputs, delegate logic to `_predict_loop`.
-      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
-        ins = x + [0]
-      else:
-        ins = x
-
-      self._make_predict_function()
-      f = self.predict_function
-
-      return self._predict_loop(
-          f, ins, batch_size=batch_size, verbose=verbose, steps=steps)
+      return training_arrays.predict_loop(
+          self, x, batch_size=batch_size, verbose=verbose, steps=steps)
 
   def train_on_batch(self, x, y, sample_weight=None, class_weight=None):
     """Runs a single gradient update on a single batch of data.
@@ -2345,10 +1373,8 @@ class Model(Network):
         class_weight=class_weight)
 
     if context.in_eager_mode():
-      if any([w is not None for w in sample_weights]):
-        raise ValueError('`sample_weight` and `class_weight` is not supported '
-                         'when eager execution is enabled, for now.')
-      outputs = training_eager.train_on_batch(self, x + y)
+      outputs = training_eager.train_on_batch(
+          self, x, y, sample_weights=sample_weights)
     else:
       if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
         ins = x + y + sample_weights + [1]
@@ -2397,10 +1423,8 @@ class Model(Network):
         x, y, sample_weight=sample_weight)
 
     if context.in_eager_mode():
-      if any([w is not None for w in sample_weights]):
-        raise ValueError('`sample_weight` and `class_weight` is not supported '
-                         'when eager execution is enabled, for now.')
-      outputs = training_eager.test_on_batch(self, x + y)
+      outputs = training_eager.test_on_batch(
+          self, x, y, sample_weights=sample_weights)
     else:
       if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
         ins = x + y + sample_weights + [0]
@@ -2426,16 +1450,8 @@ class Model(Network):
     x, _, _ = self._standardize_user_data(x)
 
     if context.in_eager_mode():
-      ins_batch_converted = []
-      for ib in x:
-        ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
-
-      eager_model_inputs = []
-      for i in range(len(self.inputs)):
-        eager_model_inputs.append(ins_batch_converted[i])
-
-      outs = self(eager_model_inputs)  # pylint: disable=not-callable
-      return outs
+      inputs = [ops.convert_to_tensor(val, dtype=K.floatx()) for val in x]
+      return self(inputs)  # pylint: disable=not-callable
 
     if context.in_graph_mode():
       if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
@@ -2445,6 +1461,7 @@ class Model(Network):
 
       self._make_predict_function()
       outputs = self.predict_function(ins)
+
       if len(outputs) == 1:
         return outputs[0]
       return outputs
@@ -2560,213 +1577,21 @@ class Model(Network):
       raise NotImplementedError(
           '`fit_generator` is not yet enabled for Model subclasses')
 
-    wait_time = 0.01  # in seconds
-    epoch = initial_epoch
-
-    do_validation = bool(validation_data)
-    self._make_train_function()
-    if do_validation:
-      self._make_test_function()
-
-    is_sequence = isinstance(generator, Sequence)
-    if not is_sequence and use_multiprocessing and workers > 1:
-      logging.warning(
-          UserWarning('Using a generator with `use_multiprocessing=True`'
-                      ' and multiple workers may duplicate your data.'
-                      ' Please consider using the`keras.utils.Sequence'
-                      ' class.'))
-    if steps_per_epoch is None:
-      if is_sequence:
-        steps_per_epoch = len(generator)
-      else:
-        raise ValueError('`steps_per_epoch=None` is only valid for a'
-                         ' generator based on the `keras.utils.Sequence`'
-                         ' class. Please specify `steps_per_epoch` or use'
-                         ' the `keras.utils.Sequence` class.')
-
-    # python 2 has 'next', 3 has '__next__'
-    # avoid any explicit version checks
-    val_gen = (
-        hasattr(validation_data, 'next') or
-        hasattr(validation_data, '__next__') or
-        isinstance(validation_data, Sequence))
-    if (val_gen and not isinstance(validation_data, Sequence) and
-        not validation_steps):
-      raise ValueError('`validation_steps=None` is only valid for a'
-                       ' generator based on the `keras.utils.Sequence`'
-                       ' class. Please specify `validation_steps` or use'
-                       ' the `keras.utils.Sequence` class.')
-
-    # Prepare display labels.
-    out_labels = self.metrics_names
-    callback_metrics = out_labels + ['val_%s' % n for n in out_labels]
-
-    # prepare callbacks
-    self.history = cbks.History()
-    callbacks = [cbks.BaseLogger()] + (callbacks or []) + [self.history]
-    if verbose:
-      callbacks += [cbks.ProgbarLogger(count_mode='steps')]
-    callbacks = cbks.CallbackList(callbacks)
-
-    # it's possible to callback a different model than self:
-    if hasattr(self, 'callback_model') and self.callback_model:
-      callback_model = self.callback_model
-    else:
-      callback_model = self
-    callbacks.set_model(callback_model)
-    callbacks.set_params({
-        'epochs': epochs,
-        'steps': steps_per_epoch,
-        'verbose': verbose,
-        'do_validation': do_validation,
-        'metrics': callback_metrics,
-    })
-    callbacks.on_train_begin()
-
-    enqueuer = None
-    val_enqueuer = None
-
-    try:
-      if do_validation:
-        if val_gen:
-          if workers > 0:
-            if isinstance(validation_data, Sequence):
-              val_enqueuer = OrderedEnqueuer(
-                  validation_data, use_multiprocessing=use_multiprocessing)
-              if validation_steps is None:
-                validation_steps = len(validation_data)
-            else:
-              val_enqueuer = GeneratorEnqueuer(
-                  validation_data,
-                  use_multiprocessing=use_multiprocessing,
-                  wait_time=wait_time)
-            val_enqueuer.start(workers=workers, max_queue_size=max_queue_size)
-            validation_generator = val_enqueuer.get()
-          else:
-            validation_generator = validation_data
-        else:
-          if len(validation_data) == 2:
-            val_x, val_y = validation_data  # pylint: disable=unpacking-non-sequence
-            val_sample_weight = None
-          elif len(validation_data) == 3:
-            val_x, val_y, val_sample_weight = validation_data  # pylint: disable=unpacking-non-sequence
-          else:
-            raise ValueError(
-                '`validation_data` should be a tuple '
-                '`(val_x, val_y, val_sample_weight)` '
-                'or `(val_x, val_y)`. Found: ' + str(validation_data))
-          val_x, val_y, val_sample_weights = self._standardize_user_data(
-              val_x, val_y, val_sample_weight)
-          val_data = val_x + val_y + val_sample_weights
-          if self.uses_learning_phase and not isinstance(
-              K.learning_phase(), int):
-            val_data += [0]
-          for cbk in callbacks:
-            cbk.validation_data = val_data
-
-      if workers > 0:
-        if is_sequence:
-          enqueuer = OrderedEnqueuer(
-              generator,
-              use_multiprocessing=use_multiprocessing,
-              shuffle=shuffle)
-        else:
-          enqueuer = GeneratorEnqueuer(
-              generator,
-              use_multiprocessing=use_multiprocessing,
-              wait_time=wait_time)
-        enqueuer.start(workers=workers, max_queue_size=max_queue_size)
-        output_generator = enqueuer.get()
-      else:
-        output_generator = generator
-
-      callback_model.stop_training = False
-      # Construct epoch logs.
-      epoch_logs = {}
-      while epoch < epochs:
-        callbacks.on_epoch_begin(epoch)
-        steps_done = 0
-        batch_index = 0
-        while steps_done < steps_per_epoch:
-          generator_output = next(output_generator)
-
-          if not hasattr(generator_output, '__len__'):
-            raise ValueError('Output of generator should be '
-                             'a tuple `(x, y, sample_weight)` '
-                             'or `(x, y)`. Found: ' + str(generator_output))
-
-          if len(generator_output) == 2:
-            x, y = generator_output
-            sample_weight = None
-          elif len(generator_output) == 3:
-            x, y, sample_weight = generator_output
-          else:
-            raise ValueError('Output of generator should be '
-                             'a tuple `(x, y, sample_weight)` '
-                             'or `(x, y)`. Found: ' + str(generator_output))
-          # build batch logs
-          batch_logs = {}
-          if isinstance(x, list):
-            batch_size = x[0].shape[0]
-          elif isinstance(x, dict):
-            batch_size = list(x.values())[0].shape[0]
-          else:
-            batch_size = x.shape[0]
-          batch_logs['batch'] = batch_index
-          batch_logs['size'] = batch_size
-          callbacks.on_batch_begin(batch_index, batch_logs)
-
-          outs = self.train_on_batch(
-              x, y, sample_weight=sample_weight, class_weight=class_weight)
-
-          if not isinstance(outs, list):
-            outs = [outs]
-          for l, o in zip(out_labels, outs):
-            batch_logs[l] = o
-
-          callbacks.on_batch_end(batch_index, batch_logs)
-
-          batch_index += 1
-          steps_done += 1
-
-          # Epoch finished.
-          if steps_done >= steps_per_epoch and do_validation:
-            if val_gen:
-              val_outs = self.evaluate_generator(
-                  validation_generator, validation_steps, workers=0)
-            else:
-              # No need for try/except because
-              # data has already been validated.
-              val_outs = self.evaluate(
-                  val_x,
-                  val_y,
-                  batch_size=batch_size,
-                  sample_weight=val_sample_weights,
-                  verbose=0)
-            if not isinstance(val_outs, list):
-              val_outs = [val_outs]
-            # Same labels assumed.
-            for l, o in zip(out_labels, val_outs):
-              epoch_logs['val_' + l] = o
-
-          if callback_model.stop_training:
-            break
-
-        callbacks.on_epoch_end(epoch, epoch_logs)
-        epoch += 1
-        if callback_model.stop_training:
-          break
-
-    finally:
-      try:
-        if enqueuer is not None:
-          enqueuer.stop()
-      finally:
-        if val_enqueuer is not None:
-          val_enqueuer.stop()
-
-    callbacks.on_train_end()
-    return self.history
+    return training_generator.fit_generator(
+        self,
+        generator,
+        steps_per_epoch=steps_per_epoch,
+        epochs=epochs,
+        verbose=verbose,
+        callbacks=callbacks,
+        validation_data=validation_data,
+        validation_steps=validation_steps,
+        class_weight=class_weight,
+        max_queue_size=max_queue_size,
+        workers=workers,
+        use_multiprocessing=use_multiprocessing,
+        shuffle=shuffle,
+        initial_epoch=initial_epoch)
 
   def evaluate_generator(self,
                          generator,
@@ -2819,87 +1644,13 @@ class Model(Network):
       raise NotImplementedError(
           '`evaluate_generator` is not yet enabled for Model subclasses')
 
-    self._make_test_function()
-
-    steps_done = 0
-    wait_time = 0.01
-    all_outs = []
-    batch_sizes = []
-    is_sequence = isinstance(generator, Sequence)
-    if not is_sequence and use_multiprocessing and workers > 1:
-      logging.warning(
-          UserWarning('Using a generator with `use_multiprocessing=True`'
-                      ' and multiple workers may duplicate your data.'
-                      ' Please consider using the`keras.utils.Sequence'
-                      ' class.'))
-    if steps is None:
-      if is_sequence:
-        steps = len(generator)
-      else:
-        raise ValueError('`steps=None` is only valid for a generator'
-                         ' based on the `keras.utils.Sequence` class.'
-                         ' Please specify `steps` or use the'
-                         ' `keras.utils.Sequence` class.')
-    enqueuer = None
-
-    try:
-      if workers > 0:
-        if is_sequence:
-          enqueuer = OrderedEnqueuer(
-              generator, use_multiprocessing=use_multiprocessing)
-        else:
-          enqueuer = GeneratorEnqueuer(
-              generator,
-              use_multiprocessing=use_multiprocessing,
-              wait_time=wait_time)
-        enqueuer.start(workers=workers, max_queue_size=max_queue_size)
-        output_generator = enqueuer.get()
-      else:
-        output_generator = generator
-
-      while steps_done < steps:
-        generator_output = next(output_generator)
-        if not hasattr(generator_output, '__len__'):
-          raise ValueError('Output of generator should be a tuple '
-                           '(x, y, sample_weight) '
-                           'or (x, y). Found: ' + str(generator_output))
-        if len(generator_output) == 2:
-          x, y = generator_output
-          sample_weight = None
-        elif len(generator_output) == 3:
-          x, y, sample_weight = generator_output
-        else:
-          raise ValueError('Output of generator should be a tuple '
-                           '(x, y, sample_weight) '
-                           'or (x, y). Found: ' + str(generator_output))
-        outs = self.test_on_batch(x, y, sample_weight=sample_weight)
-
-        if isinstance(x, list):
-          batch_size = x[0].shape[0]
-        elif isinstance(x, dict):
-          batch_size = list(x.values())[0].shape[0]
-        else:
-          batch_size = x.shape[0]
-        if batch_size == 0:
-          raise ValueError('Received an empty batch. '
-                           'Batches should at least contain one item.')
-        all_outs.append(outs)
-
-        steps_done += 1
-        batch_sizes.append(batch_size)
-
-    finally:
-      if enqueuer is not None:
-        enqueuer.stop()
-
-    if not isinstance(outs, list):
-      return np.average(np.asarray(all_outs), weights=batch_sizes)
-    else:
-      averages = []
-      for i in range(len(outs)):
-        averages.append(
-            np.average([out[i] for out in all_outs], weights=batch_sizes))
-      return averages
+    return training_generator.evaluate_generator(
+        self,
+        generator,
+        steps=steps,
+        max_queue_size=max_queue_size,
+        workers=workers,
+        use_multiprocessing=use_multiprocessing)
 
   def predict_generator(self,
                         generator,
@@ -2947,88 +1698,11 @@ class Model(Network):
       raise NotImplementedError(
           '`predict_generator` is not yet enabled for Model subclasses')
 
-    self._make_predict_function()
-
-    steps_done = 0
-    wait_time = 0.01
-    all_outs = []
-    is_sequence = isinstance(generator, Sequence)
-    if not is_sequence and use_multiprocessing and workers > 1:
-      logging.warning(
-          UserWarning('Using a generator with `use_multiprocessing=True`'
-                      ' and multiple workers may duplicate your data.'
-                      ' Please consider using the`keras.utils.Sequence'
-                      ' class.'))
-    if steps is None:
-      if is_sequence:
-        steps = len(generator)
-      else:
-        raise ValueError('`steps=None` is only valid for a generator'
-                         ' based on the `keras.utils.Sequence` class.'
-                         ' Please specify `steps` or use the'
-                         ' `keras.utils.Sequence` class.')
-    enqueuer = None
-
-    try:
-      if workers > 0:
-        if is_sequence:
-          enqueuer = OrderedEnqueuer(
-              generator, use_multiprocessing=use_multiprocessing)
-        else:
-          enqueuer = GeneratorEnqueuer(
-              generator,
-              use_multiprocessing=use_multiprocessing,
-              wait_time=wait_time)
-        enqueuer.start(workers=workers, max_queue_size=max_queue_size)
-        output_generator = enqueuer.get()
-      else:
-        output_generator = generator
-
-      if verbose == 1:
-        progbar = Progbar(target=steps)
-
-      while steps_done < steps:
-        generator_output = next(output_generator)
-        if isinstance(generator_output, tuple):
-          # Compatibility with the generators
-          # used for training.
-          if len(generator_output) == 2:
-            x, _ = generator_output
-          elif len(generator_output) == 3:
-            x, _, _ = generator_output
-          else:
-            raise ValueError('Output of generator should be '
-                             'a tuple `(x, y, sample_weight)` '
-                             'or `(x, y)`. Found: ' + str(generator_output))
-        else:
-          # Assumes a generator that only
-          # yields inputs (not targets and sample weights).
-          x = generator_output
-
-        outs = self.predict_on_batch(x)
-        if not isinstance(outs, list):
-          outs = [outs]
-
-        if not all_outs:
-          for out in outs:
-            all_outs.append([])
-
-        for i, out in enumerate(outs):
-          all_outs[i].append(out)
-        steps_done += 1
-        if verbose == 1:
-          progbar.update(steps_done)
-
-    finally:
-      if enqueuer is not None:
-        enqueuer.stop()
-
-    if len(all_outs) == 1:
-      if steps_done == 1:
-        return all_outs[0][0]
-      else:
-        return np.concatenate(all_outs[0])
-    if steps_done == 1:
-      return [out[0] for out in all_outs]
-    else:
-      return [np.concatenate(out) for out in all_outs]
+    return training_generator.predict_generator(
+        self,
+        generator,
+        steps=steps,
+        max_queue_size=max_queue_size,
+        workers=workers,
+        use_multiprocessing=use_multiprocessing,
+        verbose=verbose)
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_arrays.py b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py
new file mode 100644
index 0000000000..9291ef5fe6
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/training_arrays.py
@@ -0,0 +1,495 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Part of the Keras training engine related to plain array data.
+"""
+# pylint: disable=protected-access
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+import numpy as np
+
+from tensorflow.python.keras._impl.keras import backend as K
+from tensorflow.python.keras._impl.keras import callbacks as cbks
+from tensorflow.python.keras._impl.keras.engine import training_utils
+from tensorflow.python.keras._impl.keras.engine.base_layer import Layer
+from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches
+from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
+from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
+
+try:
+  from scipy.sparse import issparse  # pylint: disable=g-import-not-at-top
+except ImportError:
+  issparse = None
+
+
+def fit_loop(model,
+             inputs,
+             targets,
+             sample_weights=None,
+             batch_size=None,
+             epochs=100,
+             verbose=1,
+             callbacks=None,
+             val_inputs=None,
+             val_targets=None,
+             val_sample_weights=None,
+             shuffle=True,
+             callback_metrics=None,
+             initial_epoch=0,
+             steps_per_epoch=None,
+             validation_steps=None):
+  """Abstract fit function for arrays of data.
+
+  Arguments:
+      model: Keras Model instance.
+      inputs: List of input arrays.
+      targets: List of target arrays.
+      sample_weights: Optional list of sample weight arrays.
+      batch_size: Integer batch size or None if unknown.
+      epochs: Number of times to iterate over the data
+      verbose: Verbosity mode, 0, 1 or 2
+      callbacks: List of callbacks to be called during training
+      val_inputs: List of input arrays.
+      val_targets: List of target arrays.
+      val_sample_weights: Optional list of sample weight arrays.
+      shuffle: Whether to shuffle the data at the beginning of each epoch
+      callback_metrics: List of strings, the display names of the metrics
+          passed to the callbacks. They should be the
+          concatenation of list the display names of the outputs of
+           `f` and the list of display names of the outputs of `f_val`.
+      initial_epoch: Epoch at which to start training
+          (useful for resuming a previous training run)
+      steps_per_epoch: Total number of steps (batches of samples)
+          before declaring one epoch finished and starting the
+          next epoch. Ignored with the default value of `None`.
+      validation_steps: Number of steps to run validation for
+          (only if doing validation from data tensors).
+          Ignored with the default value of `None`.
+
+  Returns:
+      `History` object.
+
+  Raises:
+      ValueError: in case of invalid arguments.
+  """
+  model._make_train_function()
+  f = model.train_function
+
+  sample_weights = sample_weights or []
+  val_sample_weights = val_sample_weights or []
+  if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
+    ins = inputs + targets + sample_weights + [1]
+    if val_inputs:
+      val_ins = val_inputs + val_targets + val_sample_weights + [1]
+  else:
+    ins = inputs + targets + sample_weights
+    if val_inputs:
+      val_ins = val_inputs + val_targets + val_sample_weights
+  if not val_inputs:
+    val_ins = []
+
+  do_validation = False
+  if val_inputs:
+    do_validation = True
+    if verbose and inputs and hasattr(inputs[0], 'shape') and hasattr(
+        val_inputs[0], 'shape'):
+      print('Train on %d samples, validate on %d samples' %
+            (inputs[0].shape[0], val_inputs[0].shape[0]))
+  if validation_steps:
+    do_validation = True
+    if steps_per_epoch is None:
+      raise ValueError('Can only use `validation_steps` '
+                       'when doing step-wise '
+                       'training, i.e. `steps_per_epoch` '
+                       'must be set.')
+
+  out_labels = model.metrics_names
+  if do_validation:
+    callback_metrics = copy.copy(out_labels) + [
+        'val_' + n for n in out_labels
+    ]
+  else:
+    callback_metrics = copy.copy(out_labels)
+
+  num_train_samples = training_utils.check_num_samples(
+      ins, batch_size, steps_per_epoch, 'steps_per_epoch')
+  if num_train_samples is not None:
+    index_array = np.arange(num_train_samples)
+
+  model.history = cbks.History()
+  all_callbacks = [cbks.BaseLogger(
+      stateful_metrics=model.stateful_metric_names)]
+  if verbose:
+    if steps_per_epoch is not None:
+      count_mode = 'steps'
+    else:
+      count_mode = 'samples'
+    all_callbacks.append(
+        cbks.ProgbarLogger(
+            count_mode, stateful_metrics=model.stateful_metric_names))
+  all_callbacks += (callbacks or []) + [model.history]
+  callbacks = cbks.CallbackList(all_callbacks)
+  out_labels = out_labels or []
+
+  # it's possible to callback a different model than self
+  # (used by Sequential models)
+  if hasattr(model, 'callback_model') and model.callback_model:
+    callback_model = model.callback_model
+  else:
+    callback_model = model
+
+  callbacks.set_model(callback_model)
+
+  callbacks.set_params({
+      'batch_size': batch_size,
+      'epochs': epochs,
+      'steps': steps_per_epoch,
+      'samples': num_train_samples,
+      'verbose': verbose,
+      'do_validation': do_validation,
+      'metrics': callback_metrics or [],
+  })
+  callbacks.on_train_begin()
+  callback_model.stop_training = False
+  for cbk in callbacks:
+    cbk.validation_data = val_ins
+
+  # To prevent a slowdown, we find beforehand the arrays that need conversion.
+  feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
+  indices_for_conversion_to_dense = []
+  for i in range(len(feed)):
+    if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
+      indices_for_conversion_to_dense.append(i)
+
+  for epoch in range(initial_epoch, epochs):
+    # Reset stateful metrics
+    for m in model.metrics:
+      if isinstance(m, Layer):
+        m.reset_states()
+    # Update callbacks
+    callbacks.on_epoch_begin(epoch)
+    epoch_logs = {}
+    if steps_per_epoch is not None:
+      for step_index in range(steps_per_epoch):
+        batch_logs = {}
+        batch_logs['batch'] = step_index
+        batch_logs['size'] = 1
+        callbacks.on_batch_begin(step_index, batch_logs)
+        outs = f(ins)
+
+        if not isinstance(outs, list):
+          outs = [outs]
+        for l, o in zip(out_labels, outs):
+          batch_logs[l] = o
+
+        callbacks.on_batch_end(step_index, batch_logs)
+        if callback_model.stop_training:
+          break
+
+      if do_validation:
+        val_outs = test_loop(
+            model,
+            val_inputs,
+            val_targets,
+            sample_weights=val_sample_weights,
+            batch_size=batch_size,
+            steps=validation_steps,
+            verbose=0)
+        if not isinstance(val_outs, list):
+          val_outs = [val_outs]
+        # Same labels assumed.
+        for l, o in zip(out_labels, val_outs):
+          epoch_logs['val_' + l] = o
+    else:
+      if shuffle == 'batch':
+        index_array = training_utils.batch_shuffle(index_array, batch_size)
+      elif shuffle:
+        np.random.shuffle(index_array)
+
+      batches = make_batches(num_train_samples, batch_size)
+
+      for batch_index, (batch_start, batch_end) in enumerate(batches):
+        batch_ids = index_array[batch_start:batch_end]
+        try:
+          if isinstance(ins[-1], int):
+            # Do not slice the training phase flag.
+            ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
+          else:
+            ins_batch = slice_arrays(ins, batch_ids)
+        except TypeError:
+          raise TypeError('TypeError while preparing batch. '
+                          'If using HDF5 input data, '
+                          'pass shuffle="batch".')
+        batch_logs = {}
+        batch_logs['batch'] = batch_index
+        batch_logs['size'] = len(batch_ids)
+        callbacks.on_batch_begin(batch_index, batch_logs)
+        for i in indices_for_conversion_to_dense:
+          ins_batch[i] = ins_batch[i].toarray()
+
+        outs = f(ins_batch)
+        if not isinstance(outs, list):
+          outs = [outs]
+        for l, o in zip(out_labels, outs):
+          batch_logs[l] = o
+
+        callbacks.on_batch_end(batch_index, batch_logs)
+        if callback_model.stop_training:
+          break
+
+        if batch_index == len(batches) - 1:  # Last batch.
+          if do_validation:
+            val_outs = test_loop(
+                model,
+                val_inputs,
+                val_targets,
+                sample_weights=val_sample_weights,
+                batch_size=batch_size,
+                verbose=0)
+            if not isinstance(val_outs, list):
+              val_outs = [val_outs]
+            # Same labels assumed.
+            for l, o in zip(out_labels, val_outs):
+              epoch_logs['val_' + l] = o
+    callbacks.on_epoch_end(epoch, epoch_logs)
+    if callback_model.stop_training:
+      break
+  callbacks.on_train_end()
+  return model.history
+
+
+def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
+  """Abstract method to loop over some data in batches.
+
+  Arguments:
+      model: Keras Model instance.
+      inputs: list of tensors to be fed to `f`.
+      batch_size: integer batch size.
+      verbose: verbosity mode.
+      steps: Total number of steps (batches of samples)
+          before declaring `_predict_loop` finished.
+          Ignored with the default value of `None`.
+
+  Returns:
+      Array of predictions (if the model has a single output)
+      or list of arrays of predictions
+      (if the model has multiple outputs).
+  """
+  model._make_predict_function()
+  f = model.predict_function
+
+  if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
+    ins = inputs + [0]
+  else:
+    ins = inputs
+
+  if hasattr(model, 'metrics'):
+    for m in model.metrics:
+      if isinstance(m, Layer):
+        m.reset_states()
+
+  num_samples = training_utils.check_num_samples(
+      inputs, batch_size, steps, 'steps')
+  if verbose == 1:
+    if steps is not None:
+      progbar = Progbar(target=steps,
+                        stateful_metrics=model.stateful_metric_names)
+    else:
+      progbar = Progbar(target=num_samples,
+                        stateful_metrics=model.stateful_metric_names)
+
+  indices_for_conversion_to_dense = []
+  for i in range(len(model._feed_inputs)):
+    if (issparse is not None and issparse(inputs[i]) and
+        not K.is_sparse(model._feed_inputs[i])):
+      indices_for_conversion_to_dense.append(i)
+
+  if steps is not None:
+    # Step-based predictions.
+    # Since we do not know how many samples
+    # we will see, we cannot pre-allocate
+    # the returned Numpy arrays.
+    # Instead, we store one array per batch seen
+    # and concatenate them upon returning.
+    unconcatenated_outs = []
+    for step in range(steps):
+      batch_outs = f(ins)
+      if not isinstance(batch_outs, list):
+        batch_outs = [batch_outs]
+      if step == 0:
+        for batch_out in batch_outs:
+          unconcatenated_outs.append([])
+      for i, batch_out in enumerate(batch_outs):
+        unconcatenated_outs[i].append(batch_out)
+      if verbose == 1:
+        progbar.update(step + 1)
+    if len(unconcatenated_outs) == 1:
+      return np.concatenate(unconcatenated_outs[0], axis=0)
+    return [
+        np.concatenate(unconcatenated_outs[i], axis=0)
+        for i in range(len(unconcatenated_outs))
+    ]
+  else:
+    # Sample-based predictions.
+    outs = []
+    batches = make_batches(num_samples, batch_size)
+    index_array = np.arange(num_samples)
+    for batch_index, (batch_start, batch_end) in enumerate(batches):
+      batch_ids = index_array[batch_start:batch_end]
+      if ins and isinstance(ins[-1], int):
+        # Do not slice the training phase flag.
+        ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
+      else:
+        ins_batch = slice_arrays(ins, batch_ids)
+      for i in indices_for_conversion_to_dense:
+        ins_batch[i] = ins_batch[i].toarray()
+
+      batch_outs = f(ins_batch)
+      if not isinstance(batch_outs, list):
+        batch_outs = [batch_outs]
+      if batch_index == 0:
+        # Pre-allocate the results arrays.
+        for batch_out in batch_outs:
+          shape = (num_samples,) + batch_out.shape[1:]
+          outs.append(np.zeros(shape, dtype=batch_out.dtype))
+      for i, batch_out in enumerate(batch_outs):
+        outs[i][batch_start:batch_end] = batch_out
+      if verbose == 1:
+        progbar.update(batch_end)
+    if len(outs) == 1:
+      return outs[0]
+    return outs
+
+
+def test_loop(model, inputs, targets,
+              sample_weights=None,
+              batch_size=None,
+              verbose=0,
+              steps=None):
+  """Abstract method to loop over some data in batches.
+
+  Arguments:
+      model: Keras Model instance.
+      inputs: List of input arrays.
+      targets: List of target arrays.
+      sample_weights: Optional list of sample weight arrays.
+      batch_size: integer batch size or `None`.
+      verbose: verbosity mode.
+      steps: Total number of steps (batches of samples)
+          before declaring predictions finished.
+          Ignored with the default value of `None`.
+
+  Returns:
+      Scalar loss (if the model has a single output and no metrics)
+      or list of scalars (if the model has multiple outputs
+      and/or metrics). The attribute `model.metrics_names` will give you
+      the display labels for the scalar outputs.
+  """
+  model._make_test_function()
+  f = model.test_function
+
+  sample_weights = sample_weights or []
+  if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
+    ins = inputs + targets + sample_weights + [0]
+  else:
+    ins = inputs + targets + sample_weights
+
+  if hasattr(model, 'metrics'):
+    for m in model.metrics:
+      if isinstance(m, Layer):
+        m.reset_states()
+    stateful_metric_indices = [
+        i for i, name in enumerate(model.metrics_names)
+        if str(name) in model.stateful_metric_names
+    ]
+  else:
+    stateful_metric_indices = []
+
+  num_samples = training_utils.check_num_samples(
+      ins, batch_size, steps, 'steps')
+  outs = []
+  if verbose == 1:
+    if steps is not None:
+      progbar = Progbar(target=steps)
+    else:
+      progbar = Progbar(target=num_samples)
+
+  # To prevent a slowdown, we find beforehand the arrays that need conversion.
+  feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
+  indices_for_conversion_to_dense = []
+  for i in range(len(feed)):
+    if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
+      indices_for_conversion_to_dense.append(i)
+
+  if steps is not None:
+    for step in range(steps):
+      batch_outs = f(ins)
+      if isinstance(batch_outs, list):
+        if step == 0:
+          for _ in enumerate(batch_outs):
+            outs.append(0.)
+        for i, batch_out in enumerate(batch_outs):
+          if i in stateful_metric_indices:
+            outs[i] = batch_out
+          else:
+            outs[i] += batch_out
+      else:
+        if step == 0:
+          outs.append(0.)
+        outs[0] += batch_outs
+      if verbose == 1:
+        progbar.update(step + 1)
+    for i in range(len(outs)):
+      if i not in stateful_metric_indices:
+        outs[i] /= steps
+  else:
+    batches = make_batches(num_samples, batch_size)
+    index_array = np.arange(num_samples)
+    for batch_index, (batch_start, batch_end) in enumerate(batches):
+      batch_ids = index_array[batch_start:batch_end]
+      if isinstance(ins[-1], int):
+        # Do not slice the training phase flag.
+        ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
+      else:
+        ins_batch = slice_arrays(ins, batch_ids)
+      for i in indices_for_conversion_to_dense:
+        ins_batch[i] = ins_batch[i].toarray()
+
+      batch_outs = f(ins_batch)
+
+      if isinstance(batch_outs, list):
+        if batch_index == 0:
+          for batch_out in enumerate(batch_outs):
+            outs.append(0.)
+        for i, batch_out in enumerate(batch_outs):
+          if i in stateful_metric_indices:
+            outs[i] = batch_out
+          else:
+            outs[i] += batch_out * len(batch_ids)
+      else:
+        if batch_index == 0:
+          outs.append(0.)
+        outs[0] += batch_outs * len(batch_ids)
+      if verbose == 1:
+        progbar.update(batch_end)
+    for i in range(len(outs)):
+      if i not in stateful_metric_indices:
+        outs[i] /= num_samples
+  if len(outs) == 1:
+    return outs[0]
+  return outs
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager.py b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
index cdf189adef..75c96e6916 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager.py
@@ -12,13 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Keras training and evaluation routines.
+"""Keras training and evaluation routines for eager execution.
 """
 # pylint: disable=protected-access
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+
+import copy
+
 import numpy as np
+
 from tensorflow.python.eager.backprop import GradientTape
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_util
@@ -26,6 +30,7 @@ from tensorflow.python.keras._impl.keras import backend as K
 from tensorflow.python.keras._impl.keras import callbacks as cbks
 from tensorflow.python.keras._impl.keras import losses
 from tensorflow.python.keras._impl.keras import metrics as metrics_module
+from tensorflow.python.keras._impl.keras.engine import training_utils
 from tensorflow.python.keras._impl.keras.utils.generic_utils import make_batches
 from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
@@ -99,15 +104,15 @@ def _eager_metrics_fn(model, outputs, targets):
   return metric_names, metric_results
 
 
-def _model_loss(model, inputs, targets, training=False):
+def _model_loss(model, inputs, targets, sample_weights=None, training=False):
   """Calculates the loss for a given model.
 
   Arguments:
-     model: The model on which metrics are being calculated.
-     inputs: The inputs of the given model. This is typically the mini batch of
-              data that is fed to the model.
-     targets: The predictions or targets of the given model.
-     training: Whether the model should be run in inference or training mode.
+      model: The model on which metrics are being calculated.
+      inputs: List of input arrays.
+      targets: List of target arrays.
+      sample_weights: Optional list of sample weight arrays.
+      training: Whether the model should be run in inference or training mode.
 
   Returns:
      Returns the model output, total loss and loss value calculated using the
@@ -134,23 +139,20 @@ def _model_loss(model, inputs, targets, training=False):
   loss_metrics = []
   with K.name_scope('loss'):
     for i, loss_fn in enumerate(model.loss_functions):
-      # compute the loss
-      output_loss = _eager_loss_fn(outs[i], targets[i], loss_fn,
-                                   model.output_names[i])
-      loss_metrics.append(K.mean(output_loss))
+      if sample_weights:
+        weights = sample_weights[i]
+      else:
+        weights = None
 
       # TODO(fchollet): support masking; in practice `_keras_mask` is never
       # set in this context currently.
       mask = outs[i]._keras_mask
-      # adapted from weighted_loss_fn
-      if mask is not None:
-        # mask should have the same shape as output_loss
-        output_loss *= mask
-        #  the loss per batch should be proportional
-        #  to the number of unmasked samples.
-        output_loss /= K.mean(mask)
 
-      # TODO(fchollet): support sample weighting
+      weighted_masked_fn = training_utils.weighted_masked_objective(loss_fn)
+      with K.name_scope(model.output_names[i] + '_loss'):
+        output_loss = weighted_masked_fn(
+            outs[i], targets[i], weights, mask=mask)
+      loss_metrics.append(K.mean(output_loss))
 
       loss_weight = model.loss_weights_list[i]
       if total_loss is None:
@@ -171,16 +173,20 @@ def _model_loss(model, inputs, targets, training=False):
   return outs, total_loss, loss_metrics
 
 
-def _process_single_batch(eager_model_inputs, eager_model_outputs, model,
+def _process_single_batch(model,
+                          inputs,
+                          targets,
+                          sample_weights=None,
                           training=False):
   """Calculate the loss and gradient for one input batch.
 
      The model weights are updated if training is set to True.
 
   Arguments:
-      eager_model_inputs: Input batch data.
-      eager_model_outputs: Output batch data.
       model: Model whose loss has to be calculated.
+      inputs: List of input arrays.
+      targets: List of target arrays.
+      sample_weights: Optional list of sample weight arrays.
       training: The boolean represents if the weights of the model are updated.
               'fit' methods will set this to True while 'evaluate' methods will
               set this to False.
@@ -193,8 +199,8 @@ def _process_single_batch(eager_model_inputs, eager_model_outputs, model,
   """
   K.set_learning_phase(training)
   with GradientTape() as tape:
-    outs, loss, loss_metrics = _model_loss(model, eager_model_inputs,
-                                           eager_model_outputs,
+    outs, loss, loss_metrics = _model_loss(model, inputs, targets,
+                                           sample_weights=sample_weights,
                                            training=training)
     if loss is None:
       raise ValueError('The model cannot be run '
@@ -211,62 +217,61 @@ def _process_single_batch(eager_model_inputs, eager_model_outputs, model,
   return outs, loss, loss_metrics
 
 
-def train_on_batch(model, ins):
+def train_on_batch(model, inputs, targets, sample_weights=None):
   """Calculates the loss and gradient updates for one input batch.
 
   Arguments:
-      model: Given model on which loss and gradients are calculated.
-      ins: Input and output batch numpy arrays.
+      model: Model whose loss has to be calculated.
+      inputs: Input batch data.
+      targets: Target batch data.
+      sample_weights: Sample weight batch data.
 
   Returns:
       total loss and the loss associated with each output.
   """
-  ins_batch_converted = []
-  for ib in ins:
-    if ib is not None:
-      ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
-  eager_model_inputs = []
-  eager_model_outputs = []
-  for i in range(len(model.inputs)):
-    eager_model_inputs.append(ins_batch_converted[i])
-  for i in range(len(model.inputs), len(ins_batch_converted)):
-    eager_model_outputs.append(ins_batch_converted[i])
+  inputs = [
+      ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs]
+  targets = [
+      ops.convert_to_tensor(val, dtype=K.floatx()) for val in targets]
+  sample_weights = [
+      ops.convert_to_tensor(val, dtype=K.floatx())
+      if val is not None else None for val in sample_weights]
   outs, loss, _ = _process_single_batch(
-      eager_model_inputs, eager_model_outputs, model, training=True)
+      model, inputs, targets, sample_weights=sample_weights, training=True)
   if not isinstance(outs, list):
     outs = [outs]
   _, metrics_results = _eager_metrics_fn(
-      model, outs, eager_model_outputs)
+      model, outs, targets)
   if not isinstance(loss, list):
     loss = [loss]
   return loss + metrics_results
 
 
-def test_on_batch(model, ins):
+def test_on_batch(model, inputs, targets, sample_weights=None):
   """Calculates the loss for one input batch.
 
   Arguments:
-      model: Given model on which loss is calculated.
-      ins: Input and output batch numpy arrays.
+      model: Model whose loss has to be calculated.
+      inputs: Input batch data.
+      targets: Target batch data.
+      sample_weights: Sample weight batch data.
 
   Returns:
       total loss, loss and metrics associated with each output.
   """
-  ins_batch_converted = []
-  for ib in ins:
-    ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
-  eager_model_inputs = []
-  eager_model_outputs = []
-  for i in range(len(model.inputs)):
-    eager_model_inputs.append(ins_batch_converted[i])
-  for i in range(len(model.inputs), len(ins_batch_converted)):
-    eager_model_outputs.append(ins_batch_converted[i])
+  inputs = [
+      ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs]
+  targets = [
+      ops.convert_to_tensor(val, dtype=K.floatx()) for val in targets]
+  sample_weights = [
+      ops.convert_to_tensor(val, dtype=K.floatx())
+      if val is not None else None for val in sample_weights]
   outs, loss, loss_metrics = _process_single_batch(
-      eager_model_inputs, eager_model_outputs, model, training=False)
+      model, inputs, targets, sample_weights=sample_weights, training=False)
   if not isinstance(outs, list):
     outs = [outs]
   metric_names, metrics_results = _eager_metrics_fn(
-      model, outs, eager_model_outputs)
+      model, outs, targets)
   model.metrics_names.append(metric_names)
   if not isinstance(loss, list):
     loss = [loss]
@@ -275,32 +280,35 @@ def test_on_batch(model, ins):
 
 def fit_loop(
     model,
-    ins,
-    out_labels=None,
+    inputs,
+    targets,
+    sample_weights=None,
+    val_inputs=None,
+    val_targets=None,
+    val_sample_weights=None,
     batch_size=None,
     epochs=100,
     verbose=1,
     callbacks=None,
-    val_ins=None,
     shuffle=True,
     callback_metrics=None,
     initial_epoch=0,
     steps_per_epoch=None,
     validation_steps=None):
-  """Abstract fit function for `f(ins)`.
-
-  Assume that f returns a list, labeled by out_labels.
+  """Abstract fit function for eager execution.
 
   Arguments:
       model: Instance of the model that is being executed in Eager mode.
-      ins: List of tensors to be fed to `f`
-      out_labels: List of strings, display names of
-          the outputs of `f`
+      inputs: List of input arrays.
+      targets: List of target arrays.
+      sample_weights: Optional list of sample weight arrays.
+      val_inputs: Input data for validation.
+      val_targets: Target data for validation.
+      val_sample_weights: Sample weight data for validation.
       batch_size: Integer batch size or None if unknown.
       epochs: Number of times to iterate over the data
       verbose: Verbosity mode, 0, 1 or 2
       callbacks: List of callbacks to be called during training
-      val_ins: List of tensors to be fed to `val_f`
       shuffle: Whether to shuffle the data at the beginning of each epoch
       callback_metrics: List of strings, the display names of the metrics
           passed to the callbacks. They should be the
@@ -324,20 +332,35 @@ def fit_loop(
   K.set_learning_phase(True)
 
   do_validation = False
-  if val_ins:
+  if val_inputs:
     do_validation = True
-    if (verbose and ins and hasattr(ins[0], 'shape') and
-        hasattr(val_ins[0], 'shape')):
+    if (verbose and inputs and hasattr(inputs[0], 'shape') and
+        hasattr(val_inputs[0], 'shape')):
       print('Train on %d samples, validate on %d samples' %
-            (ins[0].shape[0], val_ins[0].shape[0]))
+            (inputs[0].shape[0], val_inputs[0].shape[0]))
   if validation_steps:
     if steps_per_epoch is None:
       raise ValueError('Can only use `validation_steps` when doing step-wise '
                        'training, i.e. `steps_per_epoch` must be set.')
     do_validation = True
 
-  num_train_samples = model._check_num_samples(
-      ins, batch_size, steps_per_epoch, 'steps_per_epoch')
+  out_labels = model.metrics_names
+  if do_validation:
+    callback_metrics = copy.copy(out_labels) + [
+        'val_' + n for n in out_labels
+    ]
+  else:
+    callback_metrics = copy.copy(out_labels)
+
+  if sample_weights:
+    feed_data = inputs + targets + sample_weights
+  else:
+    feed_data = inputs + targets
+  num_train_samples = training_utils.check_num_samples(
+      feed_data,
+      batch_size=batch_size,
+      steps=steps_per_epoch,
+      steps_name='steps_per_epoch')
 
   if num_train_samples is not None:
     index_array = np.arange(num_train_samples)
@@ -351,7 +374,6 @@ def fit_loop(
       count_mode = 'samples'
     callbacks += [cbks.ProgbarLogger(count_mode)]
   callbacks = cbks.CallbackList(callbacks)
-  out_labels = out_labels or []
 
   # it's possible to callback a different model than self
   # (used by Sequential models)
@@ -374,7 +396,12 @@ def fit_loop(
   callbacks.on_train_begin()
   callback_model.stop_training = False
   for cbk in callbacks:
-    cbk.validation_data = val_ins
+    if not val_inputs:
+      cbk.validation_data = []
+    elif val_sample_weights:
+      cbk.validation_data = val_inputs + val_targets + val_sample_weights
+    else:
+      cbk.validation_data = val_inputs + val_targets
 
   for epoch in range(initial_epoch, epochs):
     callbacks.on_epoch_begin(epoch)
@@ -389,11 +416,12 @@ def fit_loop(
     for batch_index, (batch_start, batch_end) in enumerate(batches):
       batch_ids = index_array[batch_start:batch_end]
       try:
-        if isinstance(ins[-1], float):
-          # Do not slice the training phase flag.
-          ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
+        inputs_batch = slice_arrays(inputs, batch_ids)
+        targets_batch = slice_arrays(targets, batch_ids)
+        if sample_weights:
+          sample_weights_batch = slice_arrays(sample_weights, batch_ids)
         else:
-          ins_batch = slice_arrays(ins, batch_ids)
+          sample_weights_batch = None
       except TypeError:
         raise TypeError('TypeError while preparing batch. '
                         'If using HDF5 input data, '
@@ -404,21 +432,22 @@ def fit_loop(
 
       callbacks.on_batch_begin(batch_index, batch_logs)
 
-      ins_batch_converted = []
-      for ib in ins_batch:
-        ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
-      eager_model_inputs = []
-      eager_model_outputs = []
-      for i in range(len(model.inputs)):
-        eager_model_inputs.append(ins_batch_converted[i])
-
-      for i in range(len(model.inputs), len(ins_batch_converted)):
-        eager_model_outputs.append(ins_batch_converted[i])
-
-      outs, loss, loss_metrics = _process_single_batch(eager_model_inputs,
-                                                       eager_model_outputs,
-                                                       model,
-                                                       training=True)
+      inputs_batch = [
+          ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs_batch]
+      targets_batch = [
+          ops.convert_to_tensor(val, dtype=K.floatx()) for val in targets_batch]
+      if sample_weights:
+        sample_weights_batch = [
+            ops.convert_to_tensor(val, dtype=K.floatx())
+            if val is not None else None
+            for val in sample_weights_batch]
+
+      outs, loss, loss_metrics = _process_single_batch(
+          model,
+          inputs_batch,
+          targets_batch,
+          sample_weights=sample_weights_batch,
+          training=True)
 
       if not isinstance(outs, list):
         outs = [outs]
@@ -426,8 +455,8 @@ def fit_loop(
       for l, o in zip(out_labels, outs):
         batch_logs[l] = o
       # Required for Eager mode
-      metrics_names, metrics_results = _eager_metrics_fn(model, outs,
-                                                         eager_model_outputs)
+      metrics_names, metrics_results = _eager_metrics_fn(
+          model, outs, targets_batch)
       batch_logs['loss'] = tensor_util.constant_value(K.mean(loss))
 
       # TODO(anjalisridhar): Move this to compile to avoid duplicate code.
@@ -461,7 +490,10 @@ def fit_loop(
       if batch_index == len(batches) - 1:  # Last batch.
         if do_validation:
           val_outs = test_loop(
-              model, val_ins, batch_size=batch_size, verbose=0)
+              model, val_inputs, val_targets,
+              sample_weights=val_sample_weights,
+              batch_size=batch_size,
+              verbose=0)
           if not isinstance(val_outs, list):
             val_outs = [val_outs]
           # Same labels assumed.
@@ -474,12 +506,18 @@ def fit_loop(
   return model.history
 
 
-def test_loop(model, ins, batch_size=None, verbose=0, steps=None):
+def test_loop(model, inputs, targets,
+              sample_weights=None,
+              batch_size=None,
+              verbose=0,
+              steps=None):
   """Abstract method to loop over some data in batches.
 
   Arguments:
       model: Model instance that is being evaluated in Eager mode.
-      ins: list of tensors to be fed to `f`.
+      inputs: List of input arrays.
+      targets: List of target arrays.
+      sample_weights: Optional list of sample weight arrays.
       batch_size: integer batch size or `None`.
       verbose: verbosity mode.
       steps: Total number of steps (batches of samples)
@@ -493,7 +531,11 @@ def test_loop(model, ins, batch_size=None, verbose=0, steps=None):
       the display labels for the scalar outputs.
   """
   K.set_learning_phase(False)
-  num_samples = model._check_num_samples(ins, batch_size, steps, 'steps')
+  feed_data = inputs + targets
+  if sample_weights:
+    feed_data += sample_weights
+  num_samples = training_utils.check_num_samples(
+      feed_data, batch_size=batch_size, steps=steps, steps_name='steps')
   outs = []
   if verbose == 1:
     progbar = Progbar(target=num_samples)
@@ -501,29 +543,30 @@ def test_loop(model, ins, batch_size=None, verbose=0, steps=None):
   index_array = np.arange(num_samples)
   for batch_index, (batch_start, batch_end) in enumerate(batches):
     batch_ids = index_array[batch_start:batch_end]
-    if isinstance(ins[-1], float):
-      # Do not slice the training phase flag.
-      ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
+    inputs_batch = slice_arrays(inputs, batch_ids)
+    targets_batch = slice_arrays(targets, batch_ids)
+    if sample_weights:
+      sample_weights_batch = slice_arrays(sample_weights, batch_ids)
     else:
-      ins_batch = slice_arrays(ins, batch_ids)
-
-    ins_batch_converted = []
-    for ib in ins_batch:
-      ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
-
-    eager_model_inputs = []
-    eager_model_outputs = []
-    for i in range(len(model.inputs)):
-      eager_model_inputs.append(ins_batch_converted[i])
-
-    for i in range(len(model.inputs), len(ins_batch_converted)):
-      eager_model_outputs.append(ins_batch_converted[i])
-
-    loss_outs, loss, loss_metrics = _model_loss(model, eager_model_inputs,
-                                                eager_model_outputs,
-                                                training=False)
-    _, metrics_results = _eager_metrics_fn(model, loss_outs,
-                                           eager_model_outputs)
+      sample_weights_batch = None
+
+    inputs_batch = [
+        ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs_batch]
+    targets_batch = [
+        ops.convert_to_tensor(val, dtype=K.floatx()) for val in targets_batch]
+    if sample_weights:
+      sample_weights_batch = [
+          ops.convert_to_tensor(val, dtype=K.floatx())
+          if val is not None else None
+          for val in sample_weights_batch]
+
+    loss_outs, loss, loss_metrics = _model_loss(
+        model,
+        inputs_batch,
+        targets_batch,
+        sample_weights=sample_weights_batch,
+        training=False)
+    _, metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
     batch_outs = []
     for _, v in zip(model.metrics_names,
                     [K.mean(loss)] + loss_metrics + metrics_results):
@@ -549,12 +592,15 @@ def test_loop(model, ins, batch_size=None, verbose=0, steps=None):
   return outs
 
 
-def predict_loop(model, ins, batch_size=32, verbose=0, steps=None):
+def predict_loop(model, inputs,
+                 batch_size=32,
+                 verbose=0,
+                 steps=None):
   """Abstract method to loop over some data in batches.
 
   Arguments:
       model:
-      ins: list of tensors to be fed to `f`.
+      inputs: List of input arrays.
       batch_size: integer batch size.
       verbose: verbosity mode.
       steps: Total number of steps (batches of samples)
@@ -567,7 +613,8 @@ def predict_loop(model, ins, batch_size=32, verbose=0, steps=None):
       (if the model has multiple outputs).
   """
   K.set_learning_phase(False)
-  num_samples = model._check_num_samples(ins, batch_size, steps, 'steps')
+  num_samples = training_utils.check_num_samples(
+      inputs, batch_size, steps, 'steps')
   if verbose == 1:
     if steps is not None:
       progbar = Progbar(target=steps)
@@ -579,30 +626,21 @@ def predict_loop(model, ins, batch_size=32, verbose=0, steps=None):
   index_array = np.arange(num_samples)
   for batch_index, (batch_start, batch_end) in enumerate(batches):
     batch_ids = index_array[batch_start:batch_end]
-    if ins and isinstance(ins[-1], float):
-      # Do not slice the training phase flag.
-      ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
-    else:
-      ins_batch = slice_arrays(ins, batch_ids)
-
-    ins_batch_converted = []
-    for ib in ins_batch:
-      ins_batch_converted.append(ops.convert_to_tensor(ib, dtype=K.floatx()))
+    inputs_batch = slice_arrays(inputs, batch_ids)
 
-    eager_model_inputs = []
-    for i in range(len(model.inputs)):
-      eager_model_inputs.append(ins_batch_converted[i])
+    inputs_batch = [
+        ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs_batch]
 
-    if len(eager_model_inputs) == 1:
+    if len(inputs_batch) == 1:
       if model._expects_training_arg:
-        batch_outs = model.call(eager_model_inputs[0], training=False)
+        batch_outs = model.call(inputs_batch[0], training=False)
       else:
-        batch_outs = model.call(eager_model_inputs[0])
+        batch_outs = model.call(inputs_batch[0])
     else:
       if model._expects_training_arg:
-        batch_outs = model.call(eager_model_inputs, training=False)
+        batch_outs = model.call(inputs_batch, training=False)
       else:
-        batch_outs = model.call(eager_model_inputs)
+        batch_outs = model.call(inputs_batch)
 
     if not isinstance(batch_outs, list):
       batch_outs = [batch_outs]
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
index 550b86a71d..8848b393d5 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_eager_test.py
@@ -309,6 +309,229 @@ class TrainingTest(test.TestCase):
                     optimizer='rms')
 
 
+class LossWeightingTest(test.TestCase):
+
+  def test_class_weights(self):
+    num_classes = 5
+    batch_size = 5
+    weighted_class = 3
+    train_samples = 300
+    test_samples = 300
+    input_dim = 5
+
+    model = keras.models.Sequential()
+    model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
+    model.add(keras.layers.Activation('relu'))
+    model.add(keras.layers.Dense(num_classes))
+    model.add(keras.layers.Activation('softmax'))
+    model.compile(loss='categorical_crossentropy',
+                  optimizer=RMSPropOptimizer(learning_rate=0.001))
+
+    np.random.seed(1337)
+    (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
+        train_samples=train_samples,
+        test_samples=test_samples,
+        input_shape=(input_dim,),
+        num_classes=num_classes)
+    int_y_test = y_test.copy()
+    int_y_train = y_train.copy()
+    # convert class vectors to binary class matrices
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+    y_test = keras.utils.to_categorical(y_test, num_classes)
+    test_ids = np.where(int_y_test == np.array(weighted_class))[0]
+
+    class_weight = dict([(i, 1.) for i in range(num_classes)])
+    class_weight[weighted_class] = 4.
+
+    sample_weight = np.ones((y_train.shape[0]))
+    sample_weight[int_y_train == weighted_class] = 4.
+
+    model.fit(
+        x_train,
+        y_train,
+        batch_size=batch_size,
+        epochs=2,
+        verbose=0,
+        class_weight=class_weight,
+        validation_data=(x_train, y_train, sample_weight))
+    model.fit(
+        x_train,
+        y_train,
+        batch_size=batch_size,
+        epochs=2,
+        verbose=0,
+        class_weight=class_weight)
+    model.fit(
+        x_train,
+        y_train,
+        batch_size=batch_size,
+        epochs=2,
+        verbose=0,
+        class_weight=class_weight,
+        validation_split=0.1)
+
+    model.train_on_batch(
+        x_train[:batch_size], y_train[:batch_size], class_weight=class_weight)
+    ref_score = model.evaluate(x_test, y_test, verbose=0)
+    score = model.evaluate(
+        x_test[test_ids, :], y_test[test_ids, :], verbose=0)
+    self.assertLess(score, ref_score)
+
+  def test_sample_weights(self):
+    num_classes = 5
+    batch_size = 5
+    weighted_class = 3
+    train_samples = 300
+    test_samples = 300
+    input_dim = 5
+
+    model = keras.models.Sequential()
+    model.add(keras.layers.Dense(10, input_shape=(input_dim,)))
+    model.add(keras.layers.Activation('relu'))
+    model.add(keras.layers.Dense(num_classes))
+    model.add(keras.layers.Activation('softmax'))
+    model.compile(loss='categorical_crossentropy',
+                  optimizer=RMSPropOptimizer(learning_rate=0.001))
+
+    np.random.seed(43)
+    (x_train, y_train), _ = testing_utils.get_test_data(
+        train_samples=train_samples,
+        test_samples=test_samples,
+        input_shape=(input_dim,),
+        num_classes=num_classes)
+    int_y_train = y_train.copy()
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+
+    class_weight = dict([(i, 1.) for i in range(num_classes)])
+    class_weight[weighted_class] = 4.
+
+    sample_weight = np.ones((y_train.shape[0]))
+    sample_weight[int_y_train == weighted_class] = 4.
+
+    model.fit(
+        x_train,
+        y_train,
+        batch_size=batch_size,
+        epochs=2,
+        verbose=0,
+        sample_weight=sample_weight)
+    model.fit(
+        x_train,
+        y_train,
+        batch_size=batch_size,
+        epochs=2,
+        verbose=0,
+        sample_weight=sample_weight,
+        validation_split=0.1)
+    model.train_on_batch(
+        x_train[:batch_size],
+        y_train[:batch_size],
+        sample_weight=sample_weight[:batch_size])
+    model.test_on_batch(
+        x_train[:batch_size],
+        y_train[:batch_size],
+        sample_weight=sample_weight[:batch_size])
+
+  def test_temporal_sample_weights(self):
+    num_classes = 5
+    weighted_class = 3
+    train_samples = 1000
+    test_samples = 1000
+    input_dim = 5
+    timesteps = 3
+
+    model = keras.models.Sequential()
+    model.add(
+        keras.layers.TimeDistributed(
+            keras.layers.Dense(num_classes),
+            input_shape=(timesteps, input_dim)))
+    model.add(keras.layers.Activation('softmax'))
+
+    np.random.seed(1337)
+    (_, y_train), _ = testing_utils.get_test_data(
+        train_samples=train_samples,
+        test_samples=test_samples,
+        input_shape=(input_dim,),
+        num_classes=num_classes)
+    int_y_train = y_train.copy()
+    # convert class vectors to binary class matrices
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+
+    class_weight = dict([(i, 1.) for i in range(num_classes)])
+    class_weight[weighted_class] = 2.
+
+    sample_weight = np.ones((y_train.shape[0]))
+    sample_weight[int_y_train == weighted_class] = 2.
+    with self.assertRaises(ValueError):
+      model.compile(
+          loss='binary_crossentropy',
+          optimizer=RMSPropOptimizer(learning_rate=0.001),
+          sample_weight_mode='temporal')
+
+  def test_class_weight_invalid_use_case(self):
+    num_classes = 5
+    train_samples = 1000
+    test_samples = 1000
+    input_dim = 5
+    timesteps = 3
+
+    model = keras.models.Sequential()
+    model.add(
+        keras.layers.TimeDistributed(
+            keras.layers.Dense(num_classes),
+            input_shape=(timesteps, input_dim)))
+    model.add(keras.layers.Activation('softmax'))
+    model.compile(
+        loss='binary_crossentropy',
+        optimizer=RMSPropOptimizer(learning_rate=0.001))
+
+    (x_train, y_train), _ = testing_utils.get_test_data(
+        train_samples=train_samples,
+        test_samples=test_samples,
+        input_shape=(input_dim,),
+        num_classes=num_classes)
+    # convert class vectors to binary class matrices
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+    class_weight = dict([(i, 1.) for i in range(num_classes)])
+
+    del class_weight[1]
+    with self.assertRaises(ValueError):
+      model.fit(x_train, y_train,
+                epochs=0, verbose=0, class_weight=class_weight)
+
+    with self.assertRaises(ValueError):
+      model.compile(
+          loss='binary_crossentropy',
+          optimizer=RMSPropOptimizer(learning_rate=0.001),
+          sample_weight_mode=[])
+
+    # Build multi-output model
+    x = keras.Input((3,))
+    y1 = keras.layers.Dense(4, name='1')(x)
+    y2 = keras.layers.Dense(4, name='2')(x)
+    model = keras.models.Model(x, [y1, y2])
+    model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse')
+    x_np = np.random.random((10, 3))
+    y_np = np.random.random((10, 4))
+    w_np = np.random.random((10,))
+    # This will work
+    model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': w_np})
+    # These will not
+    with self.assertRaises(ValueError):
+      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=[w_np])
+    with self.assertRaises(TypeError):
+      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight=w_np)
+    with self.assertRaises(ValueError):
+      bad_w_np = np.random.random((11,))
+      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
+    with self.assertRaises(ValueError):
+      bad_w_np = np.random.random((10, 2))
+      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
+    with self.assertRaises(ValueError):
+      bad_w_np = np.random.random((10, 2, 2))
+      model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np})
+
+
 if __name__ == '__main__':
   # Bazel sets these environment variables to very long paths.
   # Tempfile uses them to create long paths, and in turn multiprocessing
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_generator.py b/tensorflow/python/keras/_impl/keras/engine/training_generator.py
new file mode 100644
index 0000000000..4af62c85d5
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/training_generator.py
@@ -0,0 +1,439 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Part of the Keras training engine related to Python generators of array data.
+"""
+# pylint: disable=protected-access
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.keras._impl.keras import backend as K
+from tensorflow.python.keras._impl.keras import callbacks as cbks
+from tensorflow.python.keras._impl.keras.utils.data_utils import GeneratorEnqueuer
+from tensorflow.python.keras._impl.keras.utils.data_utils import OrderedEnqueuer
+from tensorflow.python.keras._impl.keras.utils.data_utils import Sequence
+from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
+from tensorflow.python.platform import tf_logging as logging
+
+
+def fit_generator(model,
+                  generator,
+                  steps_per_epoch=None,
+                  epochs=1,
+                  verbose=1,
+                  callbacks=None,
+                  validation_data=None,
+                  validation_steps=None,
+                  class_weight=None,
+                  max_queue_size=10,
+                  workers=1,
+                  use_multiprocessing=False,
+                  shuffle=True,
+                  initial_epoch=0):
+  """See docstring for `Model.fit_generator`."""
+  wait_time = 0.01  # in seconds
+  epoch = initial_epoch
+
+  do_validation = bool(validation_data)
+  model._make_train_function()
+  if do_validation:
+    model._make_test_function()
+
+  is_sequence = isinstance(generator, Sequence)
+  if not is_sequence and use_multiprocessing and workers > 1:
+    logging.warning(
+        UserWarning('Using a generator with `use_multiprocessing=True`'
+                    ' and multiple workers may duplicate your data.'
+                    ' Please consider using the`keras.utils.Sequence'
+                    ' class.'))
+  if steps_per_epoch is None:
+    if is_sequence:
+      steps_per_epoch = len(generator)
+    else:
+      raise ValueError('`steps_per_epoch=None` is only valid for a'
+                       ' generator based on the `keras.utils.Sequence`'
+                       ' class. Please specify `steps_per_epoch` or use'
+                       ' the `keras.utils.Sequence` class.')
+
+  # python 2 has 'next', 3 has '__next__'
+  # avoid any explicit version checks
+  val_gen = (
+      hasattr(validation_data, 'next') or
+      hasattr(validation_data, '__next__') or
+      isinstance(validation_data, Sequence))
+  if (val_gen and not isinstance(validation_data, Sequence) and
+      not validation_steps):
+    raise ValueError('`validation_steps=None` is only valid for a'
+                     ' generator based on the `keras.utils.Sequence`'
+                     ' class. Please specify `validation_steps` or use'
+                     ' the `keras.utils.Sequence` class.')
+
+  # Prepare display labels.
+  out_labels = model.metrics_names
+  callback_metrics = out_labels + ['val_%s' % n for n in out_labels]
+
+  # prepare callbacks
+  model.history = cbks.History()
+  callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
+  if verbose:
+    callbacks += [cbks.ProgbarLogger(count_mode='steps')]
+  callbacks = cbks.CallbackList(callbacks)
+
+  # it's possible to callback a different model than self:
+  if hasattr(model, 'callback_model') and model.callback_model:
+    callback_model = model.callback_model
+  else:
+    callback_model = model
+  callbacks.set_model(callback_model)
+  callbacks.set_params({
+      'epochs': epochs,
+      'steps': steps_per_epoch,
+      'verbose': verbose,
+      'do_validation': do_validation,
+      'metrics': callback_metrics,
+  })
+  callbacks.on_train_begin()
+
+  enqueuer = None
+  val_enqueuer = None
+
+  try:
+    if do_validation:
+      if val_gen:
+        if workers > 0:
+          if isinstance(validation_data, Sequence):
+            val_enqueuer = OrderedEnqueuer(
+                validation_data, use_multiprocessing=use_multiprocessing)
+            if validation_steps is None:
+              validation_steps = len(validation_data)
+          else:
+            val_enqueuer = GeneratorEnqueuer(
+                validation_data,
+                use_multiprocessing=use_multiprocessing,
+                wait_time=wait_time)
+          val_enqueuer.start(workers=workers, max_queue_size=max_queue_size)
+          validation_generator = val_enqueuer.get()
+        else:
+          validation_generator = validation_data
+      else:
+        if len(validation_data) == 2:
+          val_x, val_y = validation_data  # pylint: disable=unpacking-non-sequence
+          val_sample_weight = None
+        elif len(validation_data) == 3:
+          val_x, val_y, val_sample_weight = validation_data  # pylint: disable=unpacking-non-sequence
+        else:
+          raise ValueError(
+              '`validation_data` should be a tuple '
+              '`(val_x, val_y, val_sample_weight)` '
+              'or `(val_x, val_y)`. Found: ' + str(validation_data))
+        val_x, val_y, val_sample_weights = model._standardize_user_data(
+            val_x, val_y, val_sample_weight)
+        val_data = val_x + val_y + val_sample_weights
+        if model.uses_learning_phase and not isinstance(
+            K.learning_phase(), int):
+          val_data += [0]
+        for cbk in callbacks:
+          cbk.validation_data = val_data
+
+    if workers > 0:
+      if is_sequence:
+        enqueuer = OrderedEnqueuer(
+            generator,
+            use_multiprocessing=use_multiprocessing,
+            shuffle=shuffle)
+      else:
+        enqueuer = GeneratorEnqueuer(
+            generator,
+            use_multiprocessing=use_multiprocessing,
+            wait_time=wait_time)
+      enqueuer.start(workers=workers, max_queue_size=max_queue_size)
+      output_generator = enqueuer.get()
+    else:
+      output_generator = generator
+
+    callback_model.stop_training = False
+    # Construct epoch logs.
+    epoch_logs = {}
+    while epoch < epochs:
+      callbacks.on_epoch_begin(epoch)
+      steps_done = 0
+      batch_index = 0
+      while steps_done < steps_per_epoch:
+        generator_output = next(output_generator)
+
+        if not hasattr(generator_output, '__len__'):
+          raise ValueError('Output of generator should be '
+                           'a tuple `(x, y, sample_weight)` '
+                           'or `(x, y)`. Found: ' + str(generator_output))
+
+        if len(generator_output) == 2:
+          x, y = generator_output
+          sample_weight = None
+        elif len(generator_output) == 3:
+          x, y, sample_weight = generator_output
+        else:
+          raise ValueError('Output of generator should be '
+                           'a tuple `(x, y, sample_weight)` '
+                           'or `(x, y)`. Found: ' + str(generator_output))
+        # build batch logs
+        batch_logs = {}
+        if isinstance(x, list):
+          batch_size = x[0].shape[0]
+        elif isinstance(x, dict):
+          batch_size = list(x.values())[0].shape[0]
+        else:
+          batch_size = x.shape[0]
+        batch_logs['batch'] = batch_index
+        batch_logs['size'] = batch_size
+        callbacks.on_batch_begin(batch_index, batch_logs)
+
+        outs = model.train_on_batch(
+            x, y, sample_weight=sample_weight, class_weight=class_weight)
+
+        if not isinstance(outs, list):
+          outs = [outs]
+        for l, o in zip(out_labels, outs):
+          batch_logs[l] = o
+
+        callbacks.on_batch_end(batch_index, batch_logs)
+
+        batch_index += 1
+        steps_done += 1
+
+        # Epoch finished.
+        if steps_done >= steps_per_epoch and do_validation:
+          if val_gen:
+            val_outs = evaluate_generator(
+                model, validation_generator, validation_steps, workers=0)
+          else:
+            # No need for try/except because
+            # data has already been validated.
+            val_outs = model.evaluate(
+                val_x,
+                val_y,
+                batch_size=batch_size,
+                sample_weight=val_sample_weights,
+                verbose=0)
+          if not isinstance(val_outs, list):
+            val_outs = [val_outs]
+          # Same labels assumed.
+          for l, o in zip(out_labels, val_outs):
+            epoch_logs['val_' + l] = o
+
+        if callback_model.stop_training:
+          break
+
+      callbacks.on_epoch_end(epoch, epoch_logs)
+      epoch += 1
+      if callback_model.stop_training:
+        break
+
+  finally:
+    try:
+      if enqueuer is not None:
+        enqueuer.stop()
+    finally:
+      if val_enqueuer is not None:
+        val_enqueuer.stop()
+
+  callbacks.on_train_end()
+  return model.history
+
+
+def evaluate_generator(model,
+                       generator,
+                       steps=None,
+                       max_queue_size=10,
+                       workers=1,
+                       use_multiprocessing=False):
+  """See docstring for `Model.evaluate_generator`."""
+  model._make_test_function()
+
+  steps_done = 0
+  wait_time = 0.01
+  all_outs = []
+  batch_sizes = []
+  is_sequence = isinstance(generator, Sequence)
+  if not is_sequence and use_multiprocessing and workers > 1:
+    logging.warning(
+        UserWarning('Using a generator with `use_multiprocessing=True`'
+                    ' and multiple workers may duplicate your data.'
+                    ' Please consider using the`keras.utils.Sequence'
+                    ' class.'))
+  if steps is None:
+    if is_sequence:
+      steps = len(generator)
+    else:
+      raise ValueError('`steps=None` is only valid for a generator'
+                       ' based on the `keras.utils.Sequence` class.'
+                       ' Please specify `steps` or use the'
+                       ' `keras.utils.Sequence` class.')
+  enqueuer = None
+
+  try:
+    if workers > 0:
+      if is_sequence:
+        enqueuer = OrderedEnqueuer(
+            generator, use_multiprocessing=use_multiprocessing)
+      else:
+        enqueuer = GeneratorEnqueuer(
+            generator,
+            use_multiprocessing=use_multiprocessing,
+            wait_time=wait_time)
+      enqueuer.start(workers=workers, max_queue_size=max_queue_size)
+      output_generator = enqueuer.get()
+    else:
+      output_generator = generator
+
+    while steps_done < steps:
+      generator_output = next(output_generator)
+      if not hasattr(generator_output, '__len__'):
+        raise ValueError('Output of generator should be a tuple '
+                         '(x, y, sample_weight) '
+                         'or (x, y). Found: ' + str(generator_output))
+      if len(generator_output) == 2:
+        x, y = generator_output
+        sample_weight = None
+      elif len(generator_output) == 3:
+        x, y, sample_weight = generator_output
+      else:
+        raise ValueError('Output of generator should be a tuple '
+                         '(x, y, sample_weight) '
+                         'or (x, y). Found: ' + str(generator_output))
+      outs = model.test_on_batch(x, y, sample_weight=sample_weight)
+
+      if isinstance(x, list):
+        batch_size = x[0].shape[0]
+      elif isinstance(x, dict):
+        batch_size = list(x.values())[0].shape[0]
+      else:
+        batch_size = x.shape[0]
+      if batch_size == 0:
+        raise ValueError('Received an empty batch. '
+                         'Batches should at least contain one item.')
+      all_outs.append(outs)
+
+      steps_done += 1
+      batch_sizes.append(batch_size)
+
+  finally:
+    if enqueuer is not None:
+      enqueuer.stop()
+
+  if not isinstance(outs, list):
+    return np.average(np.asarray(all_outs), weights=batch_sizes)
+  else:
+    averages = []
+    for i in range(len(outs)):
+      averages.append(
+          np.average([out[i] for out in all_outs], weights=batch_sizes))
+    return averages
+
+
+def predict_generator(model,
+                      generator,
+                      steps=None,
+                      max_queue_size=10,
+                      workers=1,
+                      use_multiprocessing=False,
+                      verbose=0):
+  """See docstring for `Model.predict_generator`."""
+  model._make_predict_function()
+
+  steps_done = 0
+  wait_time = 0.01
+  all_outs = []
+  is_sequence = isinstance(generator, Sequence)
+  if not is_sequence and use_multiprocessing and workers > 1:
+    logging.warning(
+        UserWarning('Using a generator with `use_multiprocessing=True`'
+                    ' and multiple workers may duplicate your data.'
+                    ' Please consider using the`keras.utils.Sequence'
+                    ' class.'))
+  if steps is None:
+    if is_sequence:
+      steps = len(generator)
+    else:
+      raise ValueError('`steps=None` is only valid for a generator'
+                       ' based on the `keras.utils.Sequence` class.'
+                       ' Please specify `steps` or use the'
+                       ' `keras.utils.Sequence` class.')
+  enqueuer = None
+
+  try:
+    if workers > 0:
+      if is_sequence:
+        enqueuer = OrderedEnqueuer(
+            generator, use_multiprocessing=use_multiprocessing)
+      else:
+        enqueuer = GeneratorEnqueuer(
+            generator,
+            use_multiprocessing=use_multiprocessing,
+            wait_time=wait_time)
+      enqueuer.start(workers=workers, max_queue_size=max_queue_size)
+      output_generator = enqueuer.get()
+    else:
+      output_generator = generator
+
+    if verbose == 1:
+      progbar = Progbar(target=steps)
+
+    while steps_done < steps:
+      generator_output = next(output_generator)
+      if isinstance(generator_output, tuple):
+        # Compatibility with the generators
+        # used for training.
+        if len(generator_output) == 2:
+          x, _ = generator_output
+        elif len(generator_output) == 3:
+          x, _, _ = generator_output
+        else:
+          raise ValueError('Output of generator should be '
+                           'a tuple `(x, y, sample_weight)` '
+                           'or `(x, y)`. Found: ' + str(generator_output))
+      else:
+        # Assumes a generator that only
+        # yields inputs (not targets and sample weights).
+        x = generator_output
+
+      outs = model.predict_on_batch(x)
+      if not isinstance(outs, list):
+        outs = [outs]
+
+      if not all_outs:
+        for out in outs:
+          all_outs.append([])
+
+      for i, out in enumerate(outs):
+        all_outs[i].append(out)
+      steps_done += 1
+      if verbose == 1:
+        progbar.update(steps_done)
+
+  finally:
+    if enqueuer is not None:
+      enqueuer.stop()
+
+  if len(all_outs) == 1:
+    if steps_done == 1:
+      return all_outs[0][0]
+    else:
+      return np.concatenate(all_outs[0])
+  if steps_done == 1:
+    return [out[0] for out in all_outs]
+  else:
+    return [np.concatenate(out) for out in all_outs]
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_test.py b/tensorflow/python/keras/_impl/keras/engine/training_test.py
index 6ca5941e9a..38ba0f0eae 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training_test.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training_test.py
@@ -25,7 +25,7 @@ import numpy as np
 
 from tensorflow.python.keras._impl import keras
 from tensorflow.python.keras._impl.keras import testing_utils
-from tensorflow.python.keras._impl.keras.engine.training import _weighted_masked_objective
+from tensorflow.python.keras._impl.keras.engine.training_utils import weighted_masked_objective
 from tensorflow.python.keras._impl.keras.utils.generic_utils import slice_arrays
 from tensorflow.python.platform import test
 
@@ -705,7 +705,7 @@ class LossMaskingTest(test.TestCase):
 
   def test_loss_masking(self):
     with self.test_session():
-      weighted_loss = _weighted_masked_objective(keras.losses.get('mae'))
+      weighted_loss = weighted_masked_objective(keras.losses.get('mae'))
       shape = (3, 4, 2)
       x = np.arange(24).reshape(shape)
       y = 2 * x
@@ -1037,16 +1037,16 @@ class TestGeneratorMethods(test.TestCase):
 class TestTrainingUtils(test.TestCase):
 
   def test_check_array_lengths(self):
-    keras.engine.training._check_array_lengths(None, None, None)
+    keras.engine.training_utils.check_array_lengths(None, None, None)
     a_np = np.random.random((4, 3, 3))
-    keras.engine.training._check_array_lengths(a_np, a_np, a_np)
-    keras.engine.training._check_array_lengths(
+    keras.engine.training_utils.check_array_lengths(a_np, a_np, a_np)
+    keras.engine.training_utils.check_array_lengths(
         [a_np, a_np], [a_np, a_np], [a_np, a_np])
-    keras.engine.training._check_array_lengths([None], [None], [None])
+    keras.engine.training_utils.check_array_lengths([None], [None], [None])
 
     b_np = np.random.random((3, 4))
     with self.assertRaises(ValueError):
-      keras.engine.training._check_array_lengths([a_np], [b_np], None)
+      keras.engine.training_utils.check_array_lengths([a_np], [b_np], None)
 
   def test_slice_arrays(self):
     input_a = np.random.random((10, 3))
diff --git a/tensorflow/python/keras/_impl/keras/engine/training_utils.py b/tensorflow/python/keras/_impl/keras/engine/training_utils.py
new file mode 100644
index 0000000000..105638ce10
--- /dev/null
+++ b/tensorflow/python/keras/_impl/keras/engine/training_utils.py
@@ -0,0 +1,534 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Training-related utilities.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+import numpy as np
+
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.keras._impl.keras import backend as K
+from tensorflow.python.keras._impl.keras import losses
+
+
+def check_num_samples(ins,
+                      batch_size=None,
+                      steps=None,
+                      steps_name='steps'):
+  """Determine the number of samples provided for training and evaluation.
+
+  The number of samples is not defined when running with `steps`,
+  in which case the number of samples is set to `None`.
+
+  Arguments:
+      ins: List of tensors to be fed to the Keras function.
+      batch_size: Integer batch size or `None` if not defined.
+      steps: Total number of steps (batches of samples)
+          before declaring `_predict_loop` finished.
+          Ignored with the default value of `None`.
+      steps_name: The public API's parameter name for `steps`.
+
+  Raises:
+      ValueError: when `steps` is `None` and the attribute `ins.shape`
+      does not exist. Also raises ValueError when `steps` is not `None`
+      and `batch_size` is not `None` because they are mutually
+      exclusive.
+
+  Returns:
+      When steps is `None`, returns the number of samples to be
+      processed based on the size of the first dimension of the
+      first input numpy array. When steps is not `None` and
+      `batch_size` is `None`, returns `None`.
+
+  Raises:
+      ValueError: In case of invalid arguments.
+  """
+  if steps is not None:
+    num_samples = None
+    if batch_size is not None:
+      raise ValueError(
+          'If ' + steps_name + ' is set, the `batch_size` must be None.')
+  elif ins and hasattr(ins[0], 'shape'):
+    num_samples = ins[0].shape[0]
+  else:
+    raise ValueError(
+        'Either the input data should have '
+        'a defined shape, or ' + steps_name + ' should be specified.')
+  return num_samples
+
+
+def standardize_input_data(data,
+                           names,
+                           shapes=None,
+                           check_batch_axis=True,
+                           exception_prefix=''):
+  """Normalizes inputs and targets provided by users.
+
+  Users may pass data as a list of arrays, dictionary of arrays,
+  or as a single array. We normalize this to an ordered list of
+  arrays (same order as `names`), while checking that the provided
+  arrays have shapes that match the network's expectations.
+
+  Arguments:
+      data: User-provided input data (polymorphic).
+      names: List of expected array names.
+      shapes: Optional list of expected array shapes.
+      check_batch_axis: Boolean; whether to check that
+          the batch axis of the arrays matches the expected
+          value found in `shapes`.
+      exception_prefix: String prefix used for exception formatting.
+
+  Returns:
+      List of standardized input arrays (one array per model input).
+
+  Raises:
+      ValueError: in case of improperly formatted user-provided data.
+  """
+  if not names:
+    if data is not None and hasattr(data, '__len__') and len(data):
+      raise ValueError('Error when checking model ' + exception_prefix + ': '
+                       'expected no data, but got:', data)
+    return []
+  if data is None:
+    return [None for _ in range(len(names))]
+
+  if isinstance(data, dict):
+    try:
+      data = [
+          data[x].values
+          if data[x].__class__.__name__ == 'DataFrame' else data[x]
+          for x in names
+      ]
+    except KeyError as e:
+      raise ValueError('No data provided for "' + e.args[0] + '". Need data '
+                       'for each key in: ' + str(names))
+  elif isinstance(data, list):
+    if isinstance(data[0], list):
+      data = [np.asarray(d) for d in data]
+    elif len(names) == 1 and isinstance(data[0], (float, int)):
+      data = [np.asarray(data)]
+    else:
+      data = [
+          x.values if x.__class__.__name__ == 'DataFrame' else x for x in data
+      ]
+  else:
+    data = data.values if data.__class__.__name__ == 'DataFrame' else data
+    data = [data]
+  data = [
+      np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data
+  ]
+
+  if len(data) != len(names):
+    if data and hasattr(data[0], 'shape'):
+      raise ValueError('Error when checking model ' + exception_prefix +
+                       ': the list of Numpy arrays that you are passing to '
+                       'your model is not the size the model expected. '
+                       'Expected to see ' + str(len(names)) + ' array(s), '
+                       'but instead got the following list of ' +
+                       str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
+    elif len(names) > 1:
+      raise ValueError(
+          'Error when checking model ' + exception_prefix +
+          ': you are passing a list as input to your model, '
+          'but the model expects a list of ' + str(len(names)) +
+          ' Numpy arrays instead. The list you passed was: ' + str(data)[:200])
+    elif len(data) == 1 and not hasattr(data[0], 'shape'):
+      raise TypeError('Error when checking model ' + exception_prefix +
+                      ': data should be a Numpy array, or list/dict of '
+                      'Numpy arrays. Found: ' + str(data)[:200] + '...')
+    elif len(names) == 1:
+      data = [np.asarray(data)]
+
+  # Check shapes compatibility.
+  if shapes:
+    for i in range(len(names)):
+      if shapes[i] is not None:
+        data_shape = data[i].shape
+        shape = shapes[i]
+        if data[i].ndim != len(shape):
+          raise ValueError('Error when checking ' + exception_prefix +
+                           ': expected ' + names[i] + ' to have ' +
+                           str(len(shape)) + ' dimensions, but got array '
+                           'with shape ' + str(data_shape))
+        if not check_batch_axis:
+          data_shape = data_shape[1:]
+          shape = shape[1:]
+        for dim, ref_dim in zip(data_shape, shape):
+          if ref_dim != dim and ref_dim:
+            raise ValueError(
+                'Error when checking ' + exception_prefix + ': expected ' +
+                names[i] + ' to have shape ' + str(shape) +
+                ' but got array with shape ' + str(data_shape))
+  return data
+
+
+def standardize_sample_or_class_weights(x_weight, output_names, weight_type):
+  """Maps `sample_weight` or `class_weight` to model outputs.
+
+  Arguments:
+      x_weight: User-provided `sample_weight` or `class_weight` argument.
+      output_names: List of output names (strings) in the model.
+      weight_type: A string used purely for exception printing.
+
+  Returns:
+      A list of `sample_weight` or `class_weight` where there are exactly
+          one element per model output.
+
+  Raises:
+      ValueError: In case of invalid user-provided argument.
+  """
+  if x_weight is None or len(x_weight) == 0:  # pylint: disable=g-explicit-length-test
+    return [None for _ in output_names]
+  if len(output_names) == 1:
+    if isinstance(x_weight, list) and len(x_weight) == 1:
+      return x_weight
+    if isinstance(x_weight, dict) and output_names[0] in x_weight:
+      return [x_weight[output_names[0]]]
+    else:
+      return [x_weight]
+  if isinstance(x_weight, list):
+    if len(x_weight) != len(output_names):
+      raise ValueError('Provided `' + weight_type + '` was a list of ' +
+                       str(len(x_weight)) + ' elements, but the model has ' +
+                       str(len(output_names)) + ' outputs. '
+                       'You should provide one `' + weight_type + '`'
+                       'array per model output.')
+    return x_weight
+  if isinstance(x_weight, dict):
+    x_weights = []
+    for name in output_names:
+      x_weights.append(x_weight.get(name))
+    return x_weights
+  else:
+    raise TypeError(
+        'The model has multiple outputs, so `' + weight_type + '` '
+        'should be either a list or a dict. '
+        'Provided `' + weight_type + '` type not understood: ' + str(x_weight))
+
+
+def standardize_class_weights(class_weight, output_names):
+  return standardize_sample_or_class_weights(class_weight, output_names,
+                                             'class_weight')
+
+
+def standardize_sample_weights(sample_weight, output_names):
+  return standardize_sample_or_class_weights(sample_weight, output_names,
+                                             'sample_weight')
+
+
+def check_array_lengths(inputs, targets, weights=None):
+  """Does user input validation for numpy arrays.
+
+  Arguments:
+      inputs: list of Numpy arrays of inputs.
+      targets: list of Numpy arrays of targets.
+      weights: list of Numpy arrays of sample weights.
+
+  Raises:
+      ValueError: in case of incorrectly formatted data.
+  """
+
+  def set_of_lengths(x):
+    # return a set with the variation between
+    # different shapes, with None => 0
+    if x is None:
+      return {}
+    else:
+      return set([y.shape[0] for y in x if y is not None])
+
+  set_x = set_of_lengths(inputs)
+  set_y = set_of_lengths(targets)
+  set_w = set_of_lengths(weights)
+  if len(set_x) > 1:
+    raise ValueError('All input arrays (x) should have '
+                     'the same number of samples. Got array shapes: ' +
+                     str([x.shape for x in inputs]))
+  if len(set_y) > 1:
+    raise ValueError('All target arrays (y) should have '
+                     'the same number of samples. Got array shapes: ' +
+                     str([y.shape for y in targets]))
+  if set_x and set_y and list(set_x)[0] != list(set_y)[0]:
+    raise ValueError('Input arrays should have '
+                     'the same number of samples as target arrays. '
+                     'Found ' + str(list(set_x)[0]) + ' input samples '
+                     'and ' + str(list(set_y)[0]) + ' target samples.')
+  if len(set_w) > 1:
+    raise ValueError('All sample_weight arrays should have '
+                     'the same number of samples. Got array shapes: ' +
+                     str([w.shape for w in weights]))
+  if set_y and set_w and list(set_y)[0] != list(set_w)[0]:
+    raise ValueError('Sample_weight arrays should have '
+                     'the same number of samples as target arrays. Got ' +
+                     str(list(set_y)[0]) + ' input samples and ' +
+                     str(list(set_w)[0]) + ' target samples.')
+
+
+def check_loss_and_target_compatibility(targets, loss_fns, output_shapes):
+  """Does validation on the compatibility of targets and loss functions.
+
+  This helps prevent users from using loss functions incorrectly. This check
+  is purely for UX purposes.
+
+  Arguments:
+      targets: list of Numpy arrays of targets.
+      loss_fns: list of loss functions.
+      output_shapes: list of shapes of model outputs.
+
+  Raises:
+      ValueError: if a loss function or target array
+          is incompatible with an output.
+  """
+  key_losses = {
+      losses.mean_squared_error, losses.binary_crossentropy,
+      losses.categorical_crossentropy
+  }
+  for y, loss, shape in zip(targets, loss_fns, output_shapes):
+    if y is None or loss is None or tensor_util.is_tensor(y):
+      continue
+    if loss is losses.categorical_crossentropy:
+      if y.shape[-1] == 1:
+        raise ValueError('You are passing a target array of shape ' + str(
+            y.shape) + ' while using as loss `categorical_crossentropy`. '
+                         '`categorical_crossentropy` expects '
+                         'targets to be binary matrices (1s and 0s) '
+                         'of shape (samples, classes). '
+                         'If your targets are integer classes, '
+                         'you can convert them to the expected format via:\n'
+                         '```\n'
+                         'from keras.utils import to_categorical\n'
+                         'y_binary = to_categorical(y_int)\n'
+                         '```\n'
+                         '\n'
+                         'Alternatively, you can use the loss function '
+                         '`sparse_categorical_crossentropy` instead, '
+                         'which does expect integer targets.')
+    if loss in key_losses:
+      for target_dim, out_dim in zip(y.shape[1:], shape[1:]):
+        if out_dim is not None and target_dim != out_dim:
+          raise ValueError('A target array with shape ' + str(y.shape) +
+                           ' was passed for an output of shape ' + str(shape) +
+                           ' while using as loss `' + loss.__name__ + '`. '
+                           'This loss expects '
+                           'targets to have the same shape '
+                           'as the output.')
+
+
+def collect_metrics(metrics, output_names):
+  """Maps metric functions to model outputs.
+
+  Arguments:
+      metrics: a list or dict of metric functions.
+      output_names: a list of the names (strings) of model outputs.
+
+  Returns:
+      A list (one entry per model output) of lists of metric functions.
+      For instance, if the model has 2 outputs, and for the first output
+      we want to compute "binary_accuracy" and "binary_crossentropy",
+      and just "binary_accuracy" for the second output,
+      the list would look like:
+          `[[binary_accuracy, binary_crossentropy], [binary_accuracy]]`
+
+  Raises:
+      TypeError: if an incorrect type is passed for the `metrics` argument.
+  """
+  if not metrics:
+    return [[] for _ in output_names]
+  if isinstance(metrics, list):
+    # we then apply all metrics to all outputs.
+    return [copy.copy(metrics) for _ in output_names]
+  elif isinstance(metrics, dict):
+    nested_metrics = []
+    for name in output_names:
+      output_metrics = metrics.get(name, [])
+      if not isinstance(output_metrics, list):
+        output_metrics = [output_metrics]
+      nested_metrics.append(output_metrics)
+    return nested_metrics
+  else:
+    raise TypeError('Type of `metrics` argument not understood. '
+                    'Expected a list or dictionary, found: ' + str(metrics))
+
+
+def batch_shuffle(index_array, batch_size):
+  """Shuffles an array in a batch-wise fashion.
+
+  Useful for shuffling HDF5 arrays
+  (where one cannot access arbitrary indices).
+
+  Arguments:
+      index_array: array of indices to be shuffled.
+      batch_size: integer.
+
+  Returns:
+      The `index_array` array, shuffled in a batch-wise fashion.
+  """
+  batch_count = int(len(index_array) / batch_size)
+  # to reshape we need to be cleanly divisible by batch size
+  # we stash extra items and reappend them after shuffling
+  last_batch = index_array[batch_count * batch_size:]
+  index_array = index_array[:batch_count * batch_size]
+  index_array = index_array.reshape((batch_count, batch_size))
+  np.random.shuffle(index_array)
+  index_array = index_array.flatten()
+  return np.append(index_array, last_batch)
+
+
+def weighted_masked_objective(fn):
+  """Adds support for masking and sample-weighting to an objective function.
+
+  It transforms an objective function `fn(y_true, y_pred)`
+  into a sample-weighted, cost-masked objective function
+  `fn(y_true, y_pred, weights, mask)`.
+
+  Arguments:
+      fn: The objective function to wrap,
+          with signature `fn(y_true, y_pred)`.
+
+  Returns:
+      A function with signature `fn(y_true, y_pred, weights, mask)`.
+  """
+  if fn is None:
+    return None
+
+  def weighted(y_true, y_pred, weights, mask=None):
+    """Wrapper function.
+
+    Arguments:
+        y_true: `y_true` argument of `fn`.
+        y_pred: `y_pred` argument of `fn`.
+        weights: Weights tensor.
+        mask: Mask tensor.
+
+    Returns:
+        Scalar tensor.
+    """
+    # score_array has ndim >= 2
+    score_array = fn(y_true, y_pred)
+    if mask is not None:
+      # Cast the mask to floatX to avoid float64 upcasting in theano
+      mask = K.cast(mask, K.floatx())
+      # mask should have the same shape as score_array
+      score_array *= mask
+      #  the loss per batch should be proportional
+      #  to the number of unmasked samples.
+      score_array /= K.mean(mask)
+
+    # apply sample weighting
+    if weights is not None:
+      # reduce score_array to same ndim as weight array
+      ndim = K.ndim(score_array)
+      weight_ndim = K.ndim(weights)
+      score_array = K.mean(score_array, axis=list(range(weight_ndim, ndim)))
+      score_array *= weights
+      score_array /= K.mean(K.cast(K.not_equal(weights, 0), K.floatx()))
+    return K.mean(score_array)
+
+  return weighted
+
+
+def standardize_weights(y,
+                        sample_weight=None,
+                        class_weight=None,
+                        sample_weight_mode=None):
+  """Performs sample weight validation and standardization.
+
+  Everything gets normalized to a single sample-wise (or timestep-wise)
+  weight array.
+
+  Arguments:
+      y: Numpy array of model targets to be weighted.
+      sample_weight: User-provided `sample_weight` argument.
+      class_weight: User-provided `class_weight` argument.
+      sample_weight_mode: One of `None` or `"temporal"`.
+          `"temporal"` indicated that we expect 2D weight data
+          that will be applied to the last 2 dimensions of
+          the targets (i.e. we are weighting timesteps, not samples).
+
+  Returns:
+      A numpy array of target weights, one entry per sample to weight.
+
+  Raises:
+      ValueError: In case of invalid user-provided arguments.
+  """
+  if sample_weight_mode is not None:
+    if sample_weight_mode != 'temporal':
+      raise ValueError('"sample_weight_mode '
+                       'should be None or "temporal". '
+                       'Found: ' + str(sample_weight_mode))
+    if len(y.shape) < 3:
+      raise ValueError('Found a sample_weight array for '
+                       'an input with shape ' + str(y.shape) + '. '
+                       'Timestep-wise sample weighting (use of '
+                       'sample_weight_mode="temporal") is restricted to '
+                       'outputs that are at least 3D, i.e. that have '
+                       'a time dimension.')
+    if sample_weight is not None and len(sample_weight.shape) != 2:
+      raise ValueError('Found a sample_weight array with shape ' +
+                       str(sample_weight.shape) + '. '
+                       'In order to use timestep-wise sample weighting, '
+                       'you should pass a 2D sample_weight array.')
+  else:
+    if sample_weight is not None and len(sample_weight.shape) != 1:
+      raise ValueError('Found a sample_weight array with shape ' +
+                       str(sample_weight.shape) + '. '
+                       'In order to use timestep-wise sample weights, '
+                       'you should specify '
+                       'sample_weight_mode="temporal" '
+                       'in compile(). If you just mean to use '
+                       'sample-wise weights, make sure your '
+                       'sample_weight array is 1D.')
+
+  if sample_weight is not None:
+    if len(sample_weight.shape) > len(y.shape):
+      raise ValueError(
+          'Found a sample_weight with shape' + str(sample_weight.shape) + '.'
+          'Expected sample_weight with rank '
+          'less than or equal to ' + str(len(y.shape)))
+
+    if y.shape[:sample_weight.ndim] != sample_weight.shape:
+      raise ValueError(
+          'Found a sample_weight array with shape ' + str(sample_weight.shape) +
+          ' for an input with shape ' + str(y.shape) + '. '
+          'sample_weight cannot be broadcast.')
+    return sample_weight
+  elif isinstance(class_weight, dict):
+    if len(y.shape) > 2:
+      raise ValueError('`class_weight` not supported for '
+                       '3+ dimensional targets.')
+    if y.shape[1] > 1:
+      y_classes = np.argmax(y, axis=1)
+    elif y.shape[1] == 1:
+      y_classes = np.reshape(y, y.shape[0])
+    else:
+      y_classes = y
+
+    weights = np.asarray(
+        [class_weight[cls] for cls in y_classes if cls in class_weight])
+
+    if len(weights) != len(y_classes):
+      # subtract the sets to pick all missing classes
+      existing_classes = set(y_classes)
+      existing_class_weight = set(class_weight.keys())
+      raise ValueError('`class_weight` must contain all classes in the data.'
+                       ' The classes %s exist in the data but not in '
+                       '`class_weight`.' %
+                       (existing_classes - existing_class_weight))
+    return weights
+  else:
+    return None
diff --git a/tensorflow/python/keras/_impl/keras/utils/__init__.py b/tensorflow/python/keras/_impl/keras/utils/__init__.py
index 370ae0dd0f..0c9f19a0c8 100644
--- a/tensorflow/python/keras/_impl/keras/utils/__init__.py
+++ b/tensorflow/python/keras/_impl/keras/utils/__init__.py
@@ -31,8 +31,8 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_ke
 from tensorflow.python.keras._impl.keras.utils.io_utils import HDF5Matrix
 from tensorflow.python.keras._impl.keras.utils.layer_utils import convert_all_kernels_in_model
 from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary
+from tensorflow.python.keras._impl.keras.utils.multi_gpu_utils import multi_gpu_model
 from tensorflow.python.keras._impl.keras.utils.np_utils import normalize
 from tensorflow.python.keras._impl.keras.utils.np_utils import to_categorical
-from tensorflow.python.keras._impl.keras.utils.training_utils import multi_gpu_model
 from tensorflow.python.keras._impl.keras.utils.vis_utils import plot_model
 
diff --git a/tensorflow/python/keras/_impl/keras/utils/training_utils.py b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils.py
similarity index 100%
rename from tensorflow/python/keras/_impl/keras/utils/training_utils.py
rename to tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils.py
diff --git a/tensorflow/python/keras/_impl/keras/utils/training_utils_test.py b/tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils_test.py
similarity index 100%
rename from tensorflow/python/keras/_impl/keras/utils/training_utils_test.py
rename to tensorflow/python/keras/_impl/keras/utils/multi_gpu_utils_test.py
diff --git a/tensorflow/python/keras/utils/__init__.py b/tensorflow/python/keras/utils/__init__.py
index 91cc860727..2f74cf031d 100644
--- a/tensorflow/python/keras/utils/__init__.py
+++ b/tensorflow/python/keras/utils/__init__.py
@@ -30,9 +30,9 @@ from tensorflow.python.keras._impl.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras._impl.keras.utils.generic_utils import serialize_keras_object
 from tensorflow.python.keras._impl.keras.utils.io_utils import HDF5Matrix
 from tensorflow.python.keras._impl.keras.utils.layer_utils import convert_all_kernels_in_model
+from tensorflow.python.keras._impl.keras.utils.multi_gpu_utils import multi_gpu_model
 from tensorflow.python.keras._impl.keras.utils.np_utils import normalize
 from tensorflow.python.keras._impl.keras.utils.np_utils import to_categorical
-from tensorflow.python.keras._impl.keras.utils.training_utils import multi_gpu_model
 from tensorflow.python.keras._impl.keras.utils.vis_utils import plot_model
 
 del absolute_import
-- 
GitLab


From 4d631ce22f2902ed11b5e56a6241983dfa5d3eed Mon Sep 17 00:00:00 2001
From: Akshay Agrawal <akshayka@google.com>
Date: Thu, 1 Mar 2018 12:04:59 -0800
Subject: [PATCH 500/575] TFE: Cache `TensorShape` object for `EagerTensor`'s,
 for performance.

PiperOrigin-RevId: 187512946
---
 tensorflow/python/eager/pywrap_tensor.cc | 25 ++++++++++++++++++++++++
 tensorflow/python/framework/ops.py       |  6 +++++-
 2 files changed, 30 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc
index d3aaede749..8338bc4343 100644
--- a/tensorflow/python/eager/pywrap_tensor.cc
+++ b/tensorflow/python/eager/pywrap_tensor.cc
@@ -186,6 +186,10 @@ typedef struct EagerTensor {
   // This stores `_keras_mask` object and is set by Tensorflow layers.
   PyObject* keras_mask;
 
+  // This stores `_tensor_shape`, a cached `TensorShape` object, and is set the
+  // first time that `_EagerTensorBase`'s `shape` property is called.
+  PyObject* tensor_shape;
+
   // We store a status object here as an optimization to avoid allocating a new
   // Status objects on different functions that operate on EagerTensor and need
   // to use a TF_Status object. However note that accesses to `status` are not
@@ -201,6 +205,8 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) {
   self->handle_data = Py_None;
   Py_INCREF(Py_None);
   self->keras_mask = Py_None;
+  Py_INCREF(Py_None);
+  self->tensor_shape = Py_None;
   self->status = TF_NewStatus();
   PyObject* value;
   PyObject* context = nullptr;
@@ -333,6 +339,7 @@ void EagerTensor_dealloc(EagerTensor* self) {
   TF_DeleteStatus(self->status);
   Py_DECREF(self->handle_data);
   Py_DECREF(self->keras_mask);
+  Py_DECREF(self->tensor_shape);
   TFE_DeleteTensorHandle(self->handle);
   self->handle = nullptr;
   // We have the global interpreter lock, so use this chance to perform delayed
@@ -420,6 +427,19 @@ static int EagerTensor_setkeras_mask(EagerTensor* self, PyObject* value,
   self->keras_mask = value;
   return 0;
 }
+
+static PyObject* EagerTensor_tensor_shape(EagerTensor* self, void* unused) {
+  Py_INCREF(self->tensor_shape);
+  return self->tensor_shape;
+}
+
+static int EagerTensor_settensor_shape(EagerTensor* self, PyObject* value,
+                                       void* unused) {
+  Py_DECREF(self->tensor_shape);
+  Py_INCREF(value);
+  self->tensor_shape = value;
+  return 0;
+}
 // Function `_copy_to_device`.
 static PyObject* EagerTensor_copy_to_device(EagerTensor* self, PyObject* args,
                                             PyObject* kwds) {
@@ -484,6 +504,9 @@ static PyGetSetDef EagerTensor_getseters[] = {
     {const_cast<char*>("_keras_mask"), (getter)EagerTensor_keras_mask,
      (setter)EagerTensor_setkeras_mask, const_cast<char*>("_keras_mask"),
      nullptr},
+    {const_cast<char*>("_tensor_shape"), (getter)EagerTensor_tensor_shape,
+     (setter)EagerTensor_settensor_shape, const_cast<char*>("_tensor_shape"),
+     nullptr},
     {nullptr} /* Sentinel */
 };
 
@@ -599,6 +622,8 @@ PyObject* EagerTensorFromHandle(TFE_TensorHandle* handle) {
     t->handle_data = Py_None;
     Py_INCREF(Py_None);
     t->keras_mask = Py_None;
+    Py_INCREF(Py_None);
+    t->tensor_shape = Py_None;
     t->handle = handle;
     t->status = TF_NewStatus();
   }
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 735ba316d0..0a85b153de 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -782,7 +782,11 @@ class _EagerTensorBase(Tensor):
 
   @property
   def shape(self):
-    return tensor_shape.TensorShape(self._shape_tuple())
+    if self._tensor_shape is None:  # pylint: disable=access-member-before-definition
+      # `_tensor_shape` is declared and defined in the definition of
+      # `EagerTensor`, in C.
+      self._tensor_shape = tensor_shape.TensorShape(self._shape_tuple())
+    return self._tensor_shape
 
   def get_shape(self):
     """Alias of Tensor.shape."""
-- 
GitLab


From c953be2e880b3f751e014f947c2d054e4a22c3e2 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Thu, 1 Mar 2018 12:23:37 -0800
Subject: [PATCH 501/575] Remove underscore prefix from the following HIDDEN
 ops: add_sparse_to_tensors_map, add_many_sparse_to_tensors_map and
 take_many_sparse_from_tensors_map.

PiperOrigin-RevId: 187515638
---
 tensorflow/python/framework/python_op_gen.cc | 4 +---
 tensorflow/python/ops/sparse_ops.py          | 6 +++---
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc
index 4813458f07..64d214a07f 100644
--- a/tensorflow/python/framework/python_op_gen.cc
+++ b/tensorflow/python/framework/python_op_gen.cc
@@ -100,10 +100,8 @@ bool IsOpWithUnderscorePrefix(const string& s) {
        "fused_batch_norm", "histogram_fixed_width", "stack",
        "batch_norm_with_global_normalization",
        // TODO(annarev): replace these ops in the next change.
-       "add_sparse_to_tensors_map", "add_many_sparse_to_tensors_map",
        "broadcast_gradient_args", "concat", "enter", "histogram_summary",
-       "ref_enter", "ref_identity", "scalar_summary",
-       "take_many_sparse_from_tensors_map"});
+       "ref_enter", "ref_identity", "scalar_summary"});
   return kUnderscoreOps->count(s) > 0;
 }
 
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index a01bba632f..c580052c32 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -2046,7 +2046,7 @@ def _add_sparse_to_tensors_map(sp_input,
   """
   sp_input = _convert_to_sparse_tensor(sp_input)
 
-  return gen_sparse_ops._add_sparse_to_tensors_map(
+  return gen_sparse_ops.add_sparse_to_tensors_map(
       sp_input.indices,
       sp_input.values,
       sp_input.dense_shape,
@@ -2086,7 +2086,7 @@ def _add_many_sparse_to_tensors_map(sp_input,
   """
   sp_input = _convert_to_sparse_tensor(sp_input)
 
-  return gen_sparse_ops._add_many_sparse_to_tensors_map(
+  return gen_sparse_ops.add_many_sparse_to_tensors_map(
       sp_input.indices,
       sp_input.values,
       sp_input.dense_shape,
@@ -2167,7 +2167,7 @@ def _take_many_sparse_from_tensors_map(sparse_map_op,
   with ops.colocate_with(sparse_map_op):
     shared_name = sparse_map_op.get_attr("shared_name") or sparse_map_op.name
     output_indices, output_values, output_shape = (
-        gen_sparse_ops._take_many_sparse_from_tensors_map(
+        gen_sparse_ops.take_many_sparse_from_tensors_map(
             sparse_handles,
             dtype=sparse_map_op.get_attr("T"),
             container=sparse_map_op.get_attr("container"),
-- 
GitLab


From 1df40b152216bde47dd9ac1fa65bec57434920e1 Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 1 Mar 2018 12:56:05 -0800
Subject: [PATCH 502/575] [XLA] Fully qualify xla::MakeUnique uses in
 shape_tree.h. No functional changes.

PiperOrigin-RevId: 187520283
---
 tensorflow/compiler/xla/shape_tree.h | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tensorflow/compiler/xla/shape_tree.h b/tensorflow/compiler/xla/shape_tree.h
index 280f02e886..ffaa40c2d6 100644
--- a/tensorflow/compiler/xla/shape_tree.h
+++ b/tensorflow/compiler/xla/shape_tree.h
@@ -53,7 +53,7 @@ struct ShapeTreeNode {
   ShapeTreeNode(const ShapeTreeNode& other)
       : data(other.data), children(other.children.size()) {
     for (size_t i = 0; i < children.size(); ++i) {
-      children[i] = MakeUnique<ShapeTreeNode>(*other.children[i]);
+      children[i] = ::xla::MakeUnique<ShapeTreeNode>(*other.children[i]);
     }
   }
 
@@ -62,7 +62,7 @@ struct ShapeTreeNode {
       data = other.data;
       children.resize(other.children.size());
       for (size_t i = 0; i < children.size(); ++i) {
-        children[i] = MakeUnique<ShapeTreeNode>(*other.children[i]);
+        children[i] = ::xla::MakeUnique<ShapeTreeNode>(*other.children[i]);
       }
     }
     return *this;
@@ -445,7 +445,7 @@ class ShapeTreeIterator : public std::iterator<std::forward_iterator_tag,
     for (auto& node_and_index : stack_) {
       index.push_back(node_and_index.second);
     }
-    current_ = MakeUnique<value_type>(index, node_->data);
+    current_ = ::xla::MakeUnique<value_type>(index, node_->data);
     return *current_;
   }
 
@@ -492,7 +492,7 @@ void ShapeTree<T>::InitChildren(const Shape& shape, Node* node) {
 template <typename T>
 ShapeTree<T>::ShapeTree(Shape shape)
     : root_(),
-      shape_storage_(MakeUnique<Shape>(std::move(shape))),
+      shape_storage_(::xla::MakeUnique<Shape>(std::move(shape))),
       shape_(shape_storage_.get()) {
   // The shape_ field is just used to hold the structure of the shape.
   // It should not be relied upon to store layout information.
@@ -508,7 +508,7 @@ ShapeTree<T>::ShapeTree(const Shape* shape) : root_(), shape_(shape) {
 template <typename T>
 ShapeTree<T>::ShapeTree(Shape shape, const T& init_value)
     : root_(init_value),
-      shape_storage_(MakeUnique<Shape>(std::move(shape))),
+      shape_storage_(::xla::MakeUnique<Shape>(std::move(shape))),
       shape_(shape_storage_.get()) {
   // The shape_ field is just used to hold the structure of the shape.
   // It should not be relied upon to store layout information.
-- 
GitLab


From deef58ba3913c4ab9ca93876cd30744db00c4a6a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 13:00:40 -0800
Subject: [PATCH 503/575] Cast sequence_length to an integer.

PiperOrigin-RevId: 187520920
---
 .../feature_column/sequence_feature_column.py     |  2 +-
 .../sequence_feature_column_test.py               | 15 +++++++++------
 2 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
index e99033bbec..e446043bdd 100644
--- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
@@ -295,7 +295,7 @@ def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1):
     row_ids = sp_tensor.indices[:, 0]
     column_ids = sp_tensor.indices[:, 1]
     column_ids += array_ops.ones_like(column_ids)
-    seq_length = (
+    seq_length = math_ops.to_int64(
         math_ops.segment_max(column_ids, segment_ids=row_ids) / num_elements)
     # If the last n rows do not have ids, seq_length will have shape
     # [batch_size - n]. Pad the remaining values with zeros.
diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
index 8c37ccf11b..105213680e 100644
--- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
@@ -221,8 +221,9 @@ class SequenceCategoricalColumnWithIdentityTest(test.TestCase):
     sequence_length = column._sequence_length(_LazyBuilder({'aaa': inputs}))
 
     with monitored_session.MonitoredSession() as sess:
-      self.assertAllEqual(
-          expected_sequence_length, sequence_length.eval(session=sess))
+      sequence_length = sess.run(sequence_length)
+      self.assertAllEqual(expected_sequence_length, sequence_length)
+      self.assertEqual(np.int64, sequence_length.dtype)
 
   def test_sequence_length_with_zeros(self):
     column = sfc.sequence_categorical_column_with_identity(
@@ -311,8 +312,9 @@ class SequenceEmbeddingColumnTest(test.TestCase):
         _LazyBuilder({'aaa': sparse_input}))
 
     with monitored_session.MonitoredSession() as sess:
-      self.assertAllEqual(
-          expected_sequence_length, sequence_length.eval(session=sess))
+      sequence_length = sess.run(sequence_length)
+      self.assertAllEqual(expected_sequence_length, sequence_length)
+      self.assertEqual(np.int64, sequence_length.dtype)
 
   def test_sequence_length_with_empty_rows(self):
     """Tests _sequence_length when some examples do not have ids."""
@@ -423,8 +425,9 @@ class SequenceNumericColumnTest(test.TestCase):
         _LazyBuilder({'aaa': sparse_input}))
 
     with monitored_session.MonitoredSession() as sess:
-      self.assertAllEqual(
-          expected_sequence_length, sequence_length.eval(session=sess))
+      sequence_length = sess.run(sequence_length)
+      self.assertAllEqual(expected_sequence_length, sequence_length)
+      self.assertEqual(np.int64, sequence_length.dtype)
 
   def test_sequence_length_with_shape(self):
     """Tests _sequence_length with shape !=(1,)."""
-- 
GitLab


From 16478853c73d9e6dfab26e73e99d931f4c74043c Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Thu, 1 Mar 2018 13:04:44 -0800
Subject: [PATCH 504/575] Fix parameter name mismatches in
 declarations/definitions.

Reported by clang-tidy

PiperOrigin-RevId: 187521627
---
 .../xla/client/compile_only_client.cc         | 13 +++++------
 .../xla/client/computation_builder.cc         | 23 ++++++++++---------
 .../compiler/xla/client/computation_builder.h |  2 +-
 tensorflow/compiler/xla/client/local_client.h |  2 +-
 4 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/tensorflow/compiler/xla/client/compile_only_client.cc b/tensorflow/compiler/xla/client/compile_only_client.cc
index c7e2c4367b..59662c95ac 100644
--- a/tensorflow/compiler/xla/client/compile_only_client.cc
+++ b/tensorflow/compiler/xla/client/compile_only_client.cc
@@ -39,16 +39,15 @@ CompileOnlyClient::CompileAheadOfTime(
   return compiler_service_->CompileAheadOfTime(service_instances, options);
 }
 
-int64 CompileOnlyClient::PointerSizeForTriple(
-    tensorflow::StringPiece target_triple) {
-  llvm::Triple triple(llvm::Triple::normalize(
-      llvm::StringRef(target_triple.data(), target_triple.size())));
-  if (triple.isArch64Bit()) {
+int64 CompileOnlyClient::PointerSizeForTriple(tensorflow::StringPiece triple) {
+  llvm::Triple llvm_triple(
+      llvm::Triple::normalize(llvm::StringRef(triple.data(), triple.size())));
+  if (llvm_triple.isArch64Bit()) {
     return 8;
-  } else if (triple.isArch32Bit()) {
+  } else if (llvm_triple.isArch32Bit()) {
     return 4;
   } else {
-    CHECK(triple.isArch16Bit());
+    CHECK(llvm_triple.isArch16Bit());
     return 2;
   }
 }
diff --git a/tensorflow/compiler/xla/client/computation_builder.cc b/tensorflow/compiler/xla/client/computation_builder.cc
index 2a6e02649d..4afef6e448 100644
--- a/tensorflow/compiler/xla/client/computation_builder.cc
+++ b/tensorflow/compiler/xla/client/computation_builder.cc
@@ -408,7 +408,7 @@ ComputationDataHandle ComputationBuilder::Reshape(
 
 ComputationDataHandle ComputationBuilder::Collapse(
     const ComputationDataHandle& operand,
-    tensorflow::gtl::ArraySlice<int64> dims_to_collapse) {
+    tensorflow::gtl::ArraySlice<int64> dimensions) {
   if (!first_error_.ok()) {
     return ComputationDataHandle();
   }
@@ -416,8 +416,8 @@ ComputationDataHandle ComputationBuilder::Collapse(
   // Don't support out-of-order collapse here.
   // Checks that the collapsed dimensions are in order and consecutive.
   for (tensorflow::gtl::ArraySlice<int64>::size_type i = 1;
-       i < dims_to_collapse.size(); ++i) {
-    if (dims_to_collapse[i] - 1 != dims_to_collapse[i - 1]) {
+       i < dimensions.size(); ++i) {
+    if (dimensions[i] - 1 != dimensions[i - 1]) {
       NoteError(InvalidArgument(
           "Collapsed dimensions are not in order and consecutive."));
       return ComputationDataHandle();
@@ -434,9 +434,9 @@ ComputationDataHandle ComputationBuilder::Collapse(
 
   VLOG(3) << "original shape: " << ShapeUtil::HumanString(*original_shape);
   VLOG(3) << "dims to collapse: "
-          << tensorflow::str_util::Join(dims_to_collapse, ",");
+          << tensorflow::str_util::Join(dimensions, ",");
 
-  if (dims_to_collapse.size() <= 1) {
+  if (dimensions.size() <= 1) {
     // Not collapsing anything, trivially we can return the operand versus
     // enqueueing a trivial reshape.
     return operand;
@@ -444,7 +444,7 @@ ComputationDataHandle ComputationBuilder::Collapse(
 
   std::vector<int64> new_sizes;
   for (int i = 0; i < ShapeUtil::Rank(*original_shape); ++i) {
-    if (i <= dims_to_collapse.front() || i > dims_to_collapse.back()) {
+    if (i <= dimensions.front() || i > dimensions.back()) {
       new_sizes.push_back(original_shape->dimensions(i));
     } else {
       new_sizes.back() *= original_shape->dimensions(i);
@@ -753,13 +753,13 @@ ComputationDataHandle ComputationBuilder::Infeed(const Shape& shape,
 }
 
 void ComputationBuilder::Outfeed(const ComputationDataHandle& operand,
-                                 const Shape& shape,
+                                 const Shape& shape_with_layout,
                                  const string& outfeed_config) {
   OpRequest op_request;
   OutfeedRequest* request = op_request.mutable_outfeed_request();
   request->set_outfeed_config(outfeed_config);
   *request->mutable_operand() = operand;
-  *request->mutable_shape() = shape;
+  *request->mutable_shape() = shape_with_layout;
   RunOpAndNoteError(&op_request);
 }
 
@@ -1382,15 +1382,16 @@ ComputationDataHandle ComputationBuilder::BatchNormInference(
 
 ComputationDataHandle ComputationBuilder::BatchNormGrad(
     const ComputationDataHandle& operand, const ComputationDataHandle& scale,
-    const ComputationDataHandle& mean, const ComputationDataHandle& var,
+    const ComputationDataHandle& batch_mean,
+    const ComputationDataHandle& batch_var,
     const ComputationDataHandle& grad_output, float epsilon,
     int64 feature_index) {
   OpRequest op_request;
   BatchNormGradRequest* request = op_request.mutable_batch_norm_grad_request();
   *request->mutable_operand() = operand;
   *request->mutable_scale() = scale;
-  *request->mutable_mean() = mean;
-  *request->mutable_variance() = var;
+  *request->mutable_mean() = batch_mean;
+  *request->mutable_variance() = batch_var;
   *request->mutable_grad_output() = grad_output;
   request->set_epsilon(epsilon);
   request->set_feature_index(feature_index);
diff --git a/tensorflow/compiler/xla/client/computation_builder.h b/tensorflow/compiler/xla/client/computation_builder.h
index 377b671639..e085fcb3b1 100644
--- a/tensorflow/compiler/xla/client/computation_builder.h
+++ b/tensorflow/compiler/xla/client/computation_builder.h
@@ -872,7 +872,7 @@ class ComputationBuilder {
                   Window* window);
 
   // Internal helper method that does the building for an arbitrary unary op.
-  ComputationDataHandle UnaryOp(UnaryOperation binop,
+  ComputationDataHandle UnaryOp(UnaryOperation unop,
                                 const ComputationDataHandle& operand);
 
   // Internal helper method that does the building for an arbitrary binary op.
diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h
index b52a30f5a0..de0ed13c43 100644
--- a/tensorflow/compiler/xla/client/local_client.h
+++ b/tensorflow/compiler/xla/client/local_client.h
@@ -69,7 +69,7 @@ class LocalExecutable {
   // of the computation.
   tensorflow::Status ValidateExecutionOptions(
       const tensorflow::gtl::ArraySlice<const ShapedBuffer*> arguments,
-      const ExecutableRunOptions& options, const Backend& backend);
+      const ExecutableRunOptions& run_options, const Backend& backend);
 
   // Records the computation in a SessionModule proto with the arguments used to
   // invoke it, and the result. Enabled by flag: --tla_dump_executions_to.
-- 
GitLab


From 8307faacb96808eae1550ed879fa9a85cf76d897 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 13:09:46 -0800
Subject: [PATCH 505/575] Add support for keyword args for dynamically
 converted functions.

PiperOrigin-RevId: 187522324
---
 tensorflow/contrib/py2tf/converters/call_trees.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/py2tf/converters/call_trees.py b/tensorflow/contrib/py2tf/converters/call_trees.py
index f18f9f6086..ca8726f916 100644
--- a/tensorflow/contrib/py2tf/converters/call_trees.py
+++ b/tensorflow/contrib/py2tf/converters/call_trees.py
@@ -185,7 +185,7 @@ class CallTreeTransformer(transformer.Base):
     """
     return templates.replace(template, func=node.func, original_args=node.args)
 
-  def _converted_call(self, node):
+  def _insert_dynamic_conversion(self, node):
     """Inlines a dynamic conversion for a dynamic function."""
     # TODO(mdan): Pass information on the statically compiled functions.
     # Having access to the statically compiled functions can help avoid
@@ -208,7 +208,10 @@ class CallTreeTransformer(transformer.Base):
     """
     call_expr = templates.replace(
         template, func=node.func, original_args=node.args)
-    return call_expr[0].value
+    new_call = call_expr[0].value
+    # TODO(mdan): Improve the template mechanism to better support this.
+    new_call.keywords = node.keywords
+    return new_call
 
   # pylint:disable=invalid-name
 
@@ -251,7 +254,7 @@ class CallTreeTransformer(transformer.Base):
         raise NotImplementedError('py_func with return values')
     else:
       if self.context.recursive:
-        node = self._converted_call(node)
+        node = self._insert_dynamic_conversion(node)
       else:
         # Unresolved functions are allowed in non-recursive mode.
         pass
-- 
GitLab


From 0abc4c9ecae912676f6070ca4b76b35c80351c26 Mon Sep 17 00:00:00 2001
From: Fred Reiss <frreiss@us.ibm.com>
Date: Thu, 1 Mar 2018 13:25:21 -0800
Subject: [PATCH 506/575] Clean up output formatting of saved_model_cli.py
 (#17235)

---
 .../docs_src/programmers_guide/saved_model.md |  60 ++++----
 tensorflow/python/tools/saved_model_cli.py    |  68 +++++----
 .../python/tools/saved_model_cli_test.py      | 141 +++++++++---------
 3 files changed, 142 insertions(+), 127 deletions(-)

diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/programmers_guide/saved_model.md
index f18d50b282..c54c278584 100644
--- a/tensorflow/docs_src/programmers_guide/saved_model.md
+++ b/tensorflow/docs_src/programmers_guide/saved_model.md
@@ -697,15 +697,15 @@ executing the computation graph later. For example:
 $ saved_model_cli show --dir \
 /tmp/saved_model_dir --tag_set serve --signature_def serving_default
 The given SavedModel SignatureDef contains the following input(s):
-inputs['x'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: x:0
+  inputs['x'] tensor_info:
+      dtype: DT_FLOAT
+      shape: (-1, 1)
+      name: x:0
 The given SavedModel SignatureDef contains the following output(s):
-outputs['y'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y:0
+  outputs['y'] tensor_info:
+      dtype: DT_FLOAT
+      shape: (-1, 1)
+      name: y:0
 Method name is: tensorflow/serving/predict
 ```
 
@@ -717,32 +717,32 @@ $ saved_model_cli show --dir /tmp/saved_model_dir --all
 MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
 
 signature_def['classify_x2_to_y3']:
-The given SavedModel SignatureDef contains the following input(s):
-inputs['inputs'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: x2:0
-The given SavedModel SignatureDef contains the following output(s):
-outputs['scores'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y3:0
-Method name is: tensorflow/serving/classify
+  The given SavedModel SignatureDef contains the following input(s):
+    inputs['inputs'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: x2:0
+  The given SavedModel SignatureDef contains the following output(s):
+    outputs['scores'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: y3:0
+  Method name is: tensorflow/serving/classify
 
 ...
 
 signature_def['serving_default']:
-The given SavedModel SignatureDef contains the following input(s):
-inputs['x'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: x:0
-The given SavedModel SignatureDef contains the following output(s):
-outputs['y'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y:0
-Method name is: tensorflow/serving/predict
+  The given SavedModel SignatureDef contains the following input(s):
+    inputs['x'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: x:0
+  The given SavedModel SignatureDef contains the following output(s):
+    outputs['y'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: y:0
+  Method name is: tensorflow/serving/predict
 ```
 
 
diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py
index 33f6debbcb..b0e9e3e5ed 100644
--- a/tensorflow/python/tools/saved_model_cli.py
+++ b/tensorflow/python/tools/saved_model_cli.py
@@ -115,7 +115,7 @@ def _get_outputs_tensor_info_from_meta_graph_def(meta_graph_def,
                                                       signature_def_key).outputs
 
 
-def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key):
+def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key, indent=0):
   """Prints input and output TensorInfos.
 
   Prints the details of input and output TensorInfos for the SignatureDef mapped
@@ -126,6 +126,7 @@ def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key):
     tag_set: Group of tag(s) of the MetaGraphDef, in string format, separated by
         ','. For tag-set contains multiple tags, all tags must be passed in.
     signature_def_key: A SignatureDef key string.
+    indent: How far (in increments of 2 spaces) to indent each line of output.
   """
   meta_graph_def = saved_model_utils.get_meta_graph_def(saved_model_dir,
                                                         tag_set)
@@ -134,29 +135,39 @@ def _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key):
   outputs_tensor_info = _get_outputs_tensor_info_from_meta_graph_def(
       meta_graph_def, signature_def_key)
 
-  print('The given SavedModel SignatureDef contains the following input(s):')
+  indent_str = "  " * indent
+  def in_print(s):
+    print(indent_str + s)
+
+  in_print('The given SavedModel SignatureDef contains the following input(s):')
   for input_key, input_tensor in sorted(inputs_tensor_info.items()):
-    print('inputs[\'%s\'] tensor_info:' % input_key)
-    _print_tensor_info(input_tensor)
+    in_print('  inputs[\'%s\'] tensor_info:' % input_key)
+    _print_tensor_info(input_tensor, indent+1)
 
-  print('The given SavedModel SignatureDef contains the following output(s):')
+  in_print('The given SavedModel SignatureDef contains the following '
+           'output(s):')
   for output_key, output_tensor in sorted(outputs_tensor_info.items()):
-    print('outputs[\'%s\'] tensor_info:' % output_key)
-    _print_tensor_info(output_tensor)
+    in_print('  outputs[\'%s\'] tensor_info:' % output_key)
+    _print_tensor_info(output_tensor, indent+1)
 
-  print('Method name is: %s' %
-        meta_graph_def.signature_def[signature_def_key].method_name)
+  in_print('Method name is: %s' %
+           meta_graph_def.signature_def[signature_def_key].method_name)
 
 
-def _print_tensor_info(tensor_info):
+def _print_tensor_info(tensor_info, indent=0):
   """Prints details of the given tensor_info.
 
   Args:
     tensor_info: TensorInfo object to be printed.
+    indent: How far (in increments of 2 spaces) to indent each line output
   """
-  print('    dtype: ' +
-        {value: key
-         for (key, value) in types_pb2.DataType.items()}[tensor_info.dtype])
+  indent_str = "  " * indent
+  def in_print(s):
+    print(indent_str + s)
+
+  in_print('    dtype: ' +
+           {value: key
+            for (key, value) in types_pb2.DataType.items()}[tensor_info.dtype])
   # Display shape as tuple.
   if tensor_info.tensor_shape.unknown_rank:
     shape = 'unknown_rank'
@@ -164,8 +175,8 @@ def _print_tensor_info(tensor_info):
     dims = [str(dim.size) for dim in tensor_info.tensor_shape.dim]
     shape = ', '.join(dims)
     shape = '(' + shape + ')'
-  print('    shape: ' + shape)
-  print('    name: ' + tensor_info.name)
+  in_print('    shape: ' + shape)
+  in_print('    name: ' + tensor_info.name)
 
 
 def _show_all(saved_model_dir):
@@ -186,7 +197,8 @@ def _show_all(saved_model_dir):
     signature_def_map = get_signature_def_map(saved_model_dir, tag_set)
     for signature_def_key in sorted(signature_def_map.keys()):
       print('\nsignature_def[\'' + signature_def_key + '\']:')
-      _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key)
+      _show_inputs_outputs(saved_model_dir, tag_set, signature_def_key, 
+                           indent=1)
 
 
 def get_meta_graph_def(saved_model_dir, tag_set):
@@ -614,19 +626,19 @@ def create_parser():
   show_msg = (
       'Usage examples:\n'
       'To show all tag-sets in a SavedModel:\n'
-      '$saved_model_cli show --dir /tmp/saved_model\n'
+      '$saved_model_cli show --dir /tmp/saved_model\n\n'
       'To show all available SignatureDef keys in a '
       'MetaGraphDef specified by its tag-set:\n'
-      '$saved_model_cli show --dir /tmp/saved_model --tag_set serve\n'
+      '$saved_model_cli show --dir /tmp/saved_model --tag_set serve\n\n'
       'For a MetaGraphDef with multiple tags in the tag-set, all tags must be '
       'passed in, separated by \';\':\n'
       '$saved_model_cli show --dir /tmp/saved_model --tag_set serve,gpu\n\n'
       'To show all inputs and outputs TensorInfo for a specific'
       ' SignatureDef specified by the SignatureDef key in a'
       ' MetaGraph.\n'
-      '$saved_model_cli show --dir /tmp/saved_model --tag_set serve '
-      '--signature_def serving_default\n\n'
-      'To show all available information in the SavedModel\n:'
+      '$saved_model_cli show --dir /tmp/saved_model --tag_set serve'
+      ' --signature_def serving_default\n\n'
+      'To show all available information in the SavedModel:\n'
       '$saved_model_cli show --dir /tmp/saved_model --all')
   parser_show = subparsers.add_parser(
       'show',
@@ -658,12 +670,14 @@ def create_parser():
   run_msg = ('Usage example:\n'
              'To run input tensors from files through a MetaGraphDef and save'
              ' the output tensors to files:\n'
-             '$saved_model_cli show --dir /tmp/saved_model --tag_set serve '
-             '--signature_def serving_default '
-             '--inputs input1_key=/tmp/124.npz[x],input2_key=/tmp/123.npy '
-             '--input_exprs \'input3_key=np.ones(2)\' --input_examples '
-             '\'input4_key=[{"id":[26],"weights":[0.5, 0.5]}]\' '
-             '--outdir=/out\n\n'
+             '$saved_model_cli show --dir /tmp/saved_model --tag_set serve \\\n'
+             '   --signature_def serving_default \\\n'
+             '   --inputs input1_key=/tmp/124.npz[x],input2_key=/tmp/123.npy '
+             '\\\n'
+             '   --input_exprs \'input3_key=np.ones(2)\' \\\n'
+             '   --input_examples '
+             '\'input4_key=[{"id":[26],"weights":[0.5, 0.5]}]\' \\\n'
+             '   --outdir=/out\n\n'
              'For more information about input file format, please see:\n'
              'https://www.tensorflow.org/programmers_guide/saved_model_cli\n')
   parser_run = subparsers.add_parser(
diff --git a/tensorflow/python/tools/saved_model_cli_test.py b/tensorflow/python/tools/saved_model_cli_test.py
index d6cbc49ba1..f99c844845 100644
--- a/tensorflow/python/tools/saved_model_cli_test.py
+++ b/tensorflow/python/tools/saved_model_cli_test.py
@@ -61,83 +61,84 @@ class SavedModelCLITestCase(test.TestCase):
     exp_out = """MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
 
 signature_def['classify_x2_to_y3']:
-The given SavedModel SignatureDef contains the following input(s):
-inputs['inputs'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: x2:0
-The given SavedModel SignatureDef contains the following output(s):
-outputs['scores'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y3:0
-Method name is: tensorflow/serving/classify
+  The given SavedModel SignatureDef contains the following input(s):
+    inputs['inputs'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: x2:0
+  The given SavedModel SignatureDef contains the following output(s):
+    outputs['scores'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: y3:0
+  Method name is: tensorflow/serving/classify
 
 signature_def['classify_x_to_y']:
-The given SavedModel SignatureDef contains the following input(s):
-inputs['inputs'] tensor_info:
-    dtype: DT_STRING
-    shape: unknown_rank
-    name: tf_example:0
-The given SavedModel SignatureDef contains the following output(s):
-outputs['scores'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y:0
-Method name is: tensorflow/serving/classify
+  The given SavedModel SignatureDef contains the following input(s):
+    inputs['inputs'] tensor_info:
+        dtype: DT_STRING
+        shape: unknown_rank
+        name: tf_example:0
+  The given SavedModel SignatureDef contains the following output(s):
+    outputs['scores'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: y:0
+  Method name is: tensorflow/serving/classify
 
 signature_def['regress_x2_to_y3']:
-The given SavedModel SignatureDef contains the following input(s):
-inputs['inputs'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: x2:0
-The given SavedModel SignatureDef contains the following output(s):
-outputs['outputs'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y3:0
-Method name is: tensorflow/serving/regress
+  The given SavedModel SignatureDef contains the following input(s):
+    inputs['inputs'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: x2:0
+  The given SavedModel SignatureDef contains the following output(s):
+    outputs['outputs'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: y3:0
+  Method name is: tensorflow/serving/regress
 
 signature_def['regress_x_to_y']:
-The given SavedModel SignatureDef contains the following input(s):
-inputs['inputs'] tensor_info:
-    dtype: DT_STRING
-    shape: unknown_rank
-    name: tf_example:0
-The given SavedModel SignatureDef contains the following output(s):
-outputs['outputs'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y:0
-Method name is: tensorflow/serving/regress
+  The given SavedModel SignatureDef contains the following input(s):
+    inputs['inputs'] tensor_info:
+        dtype: DT_STRING
+        shape: unknown_rank
+        name: tf_example:0
+  The given SavedModel SignatureDef contains the following output(s):
+    outputs['outputs'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: y:0
+  Method name is: tensorflow/serving/regress
 
 signature_def['regress_x_to_y2']:
-The given SavedModel SignatureDef contains the following input(s):
-inputs['inputs'] tensor_info:
-    dtype: DT_STRING
-    shape: unknown_rank
-    name: tf_example:0
-The given SavedModel SignatureDef contains the following output(s):
-outputs['outputs'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y2:0
-Method name is: tensorflow/serving/regress
+  The given SavedModel SignatureDef contains the following input(s):
+    inputs['inputs'] tensor_info:
+        dtype: DT_STRING
+        shape: unknown_rank
+        name: tf_example:0
+  The given SavedModel SignatureDef contains the following output(s):
+    outputs['outputs'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: y2:0
+  Method name is: tensorflow/serving/regress
 
 signature_def['serving_default']:
-The given SavedModel SignatureDef contains the following input(s):
-inputs['x'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: x:0
-The given SavedModel SignatureDef contains the following output(s):
-outputs['y'] tensor_info:
-    dtype: DT_FLOAT
-    shape: (-1, 1)
-    name: y:0
-Method name is: tensorflow/serving/predict"""
+  The given SavedModel SignatureDef contains the following input(s):
+    inputs['x'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: x:0
+  The given SavedModel SignatureDef contains the following output(s):
+    outputs['y'] tensor_info:
+        dtype: DT_FLOAT
+        shape: (-1, 1)
+        name: y:0
+  Method name is: tensorflow/serving/predict"""
     # pylint: enable=line-too-long
+    self.maxDiff = None # Produce a useful error msg if the comparison fails
     self.assertMultiLineEqual(output, exp_out)
     self.assertEqual(err.getvalue().strip(), '')
 
@@ -193,11 +194,11 @@ Method name is: tensorflow/serving/predict"""
     output = out.getvalue().strip()
     expected_output = (
         'The given SavedModel SignatureDef contains the following input(s):\n'
-        'inputs[\'x\'] tensor_info:\n'
-        '    dtype: DT_FLOAT\n    shape: (-1, 1)\n    name: x:0\n'
+        '  inputs[\'x\'] tensor_info:\n'
+        '      dtype: DT_FLOAT\n      shape: (-1, 1)\n      name: x:0\n'
         'The given SavedModel SignatureDef contains the following output(s):\n'
-        'outputs[\'y\'] tensor_info:\n'
-        '    dtype: DT_FLOAT\n    shape: (-1, 1)\n    name: y:0\n'
+        '  outputs[\'y\'] tensor_info:\n'
+        '      dtype: DT_FLOAT\n      shape: (-1, 1)\n      name: y:0\n'
         'Method name is: tensorflow/serving/predict')
     self.assertEqual(output, expected_output)
     self.assertEqual(err.getvalue().strip(), '')
-- 
GitLab


From eec6cbd4a60c8525d6601ceebf50511cefa50ec1 Mon Sep 17 00:00:00 2001
From: Guangda Lai <laigd@google.com>
Date: Thu, 1 Mar 2018 13:37:16 -0800
Subject: [PATCH 507/575] Fix TensorRT build.

PiperOrigin-RevId: 187526192
---
 tensorflow/contrib/tensorrt/BUILD | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD
index 65a0e903a7..3b7b68f61b 100644
--- a/tensorflow/contrib/tensorrt/BUILD
+++ b/tensorflow/contrib/tensorrt/BUILD
@@ -107,6 +107,7 @@ tf_cuda_library(
 
 tf_gen_op_wrapper_py(
     name = "trt_engine_op",
+    gen_locally = True,
     deps = [
         ":trt_engine_op_op_lib",
         ":trt_logging",
-- 
GitLab


From 80710d5c53a8b2896a57dbe026d7f742e71fc03b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 13:43:03 -0800
Subject: [PATCH 508/575] Optimize training with feature selection by avoiding
 any computations on the features that are not selected once we have reached
 our target number of features.

PiperOrigin-RevId: 187526964
---
 .../boosted_trees/kernels/model_ops.cc        |  57 +++
 .../boosted_trees/kernels/training_ops.cc     |  28 +-
 .../contrib/boosted_trees/ops/model_ops.cc    |  27 ++
 .../python/kernel_tests/model_ops_test.py     |  16 +
 .../python/kernel_tests/training_ops_test.py  | 190 +--------
 .../boosted_trees/python/ops/model_ops.py     |   1 +
 .../python/training/functions/gbdt_batch.py   |  34 +-
 .../training/functions/gbdt_batch_test.py     | 376 ++++++++++++++++++
 8 files changed, 517 insertions(+), 212 deletions(-)

diff --git a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
index 754b7bc327..3bf33186ec 100644
--- a/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/model_ops.cc
@@ -137,6 +137,61 @@ class TreeEnsembleDeserializeOp : public OpKernel {
   }
 };
 
+class TreeEnsembleUsedHandlersOp : public OpKernel {
+ public:
+  explicit TreeEnsembleUsedHandlersOp(OpKernelConstruction* context)
+      : OpKernel(context) {
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("num_all_handlers", &num_handlers_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    boosted_trees::models::DecisionTreeEnsembleResource* ensemble_resource;
+
+    OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0),
+                                           &ensemble_resource));
+    tf_shared_lock l(*ensemble_resource->get_mutex());
+    core::ScopedUnref unref_me(ensemble_resource);
+
+    // Get the stamp token.
+    const Tensor* stamp_token_t;
+    OP_REQUIRES_OK(context, context->input("stamp_token", &stamp_token_t));
+    int64 stamp_token = stamp_token_t->scalar<int64>()();
+
+    // Only the Chief should run this Op and it is guaranteed to be in
+    // a consistent state so the stamps must always match.
+    CHECK(ensemble_resource->is_stamp_valid(stamp_token));
+
+    Tensor* output_used_handlers_t = nullptr;
+    OP_REQUIRES_OK(
+        context, context->allocate_output("used_handlers_mask", {num_handlers_},
+                                          &output_used_handlers_t));
+    auto output_used_handlers = output_used_handlers_t->vec<bool>();
+
+    Tensor* output_num_used_handlers_t = nullptr;
+    OP_REQUIRES_OK(context,
+                   context->allocate_output("num_used_handlers", {},
+                                            &output_num_used_handlers_t));
+    int handler_idx = 0;
+    std::vector<int64> used_handlers = ensemble_resource->GetUsedHandlers();
+    output_num_used_handlers_t->scalar<int64>()() = used_handlers.size();
+    for (int64 i = 0; i < num_handlers_; ++i) {
+      if (handler_idx >= used_handlers.size() ||
+          used_handlers[handler_idx] > i) {
+        output_used_handlers(i) = false;
+      } else {
+        OP_REQUIRES(context, used_handlers[handler_idx] == i,
+                    errors::InvalidArgument("Handler IDs should be sorted."));
+        ++handler_idx;
+        output_used_handlers(i) = true;
+      }
+    }
+  }
+
+ private:
+  int64 num_handlers_;
+};
+
 REGISTER_RESOURCE_HANDLE_KERNEL(DecisionTreeEnsembleResource);
 
 REGISTER_KERNEL_BUILDER(
@@ -155,5 +210,7 @@ REGISTER_KERNEL_BUILDER(Name("TreeEnsembleSerialize").Device(DEVICE_CPU),
 REGISTER_KERNEL_BUILDER(Name("TreeEnsembleDeserialize").Device(DEVICE_CPU),
                         TreeEnsembleDeserializeOp);
 
+REGISTER_KERNEL_BUILDER(Name("TreeEnsembleUsedHandlers").Device(DEVICE_CPU),
+                        TreeEnsembleUsedHandlersOp);
 }  // namespace boosted_trees
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
index 7f8dea1d3c..1bfeed3066 100644
--- a/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
+++ b/tensorflow/contrib/boosted_trees/kernels/training_ops.cc
@@ -361,27 +361,10 @@ class GrowTreeEnsembleOp : public OpKernel {
     // Increment attempt stats.
     ensemble_resource->IncrementAttempts();
 
-    // In case we want to do feature selection and we have reached the limit,
-    // build a list of handlers used so far to avoid adding new features.
-    std::vector<int64> allowed_handlers;
-    if (learner_config_.constraints().max_number_of_unique_feature_columns() >
-        0) {
-      allowed_handlers = ensemble_resource->GetUsedHandlers();
-      // TODO(soroush): We can disable handlers that are not going to be used to
-      // avoid unnecessary computations.
-      if (allowed_handlers.size() <
-          learner_config_.constraints()
-              .max_number_of_unique_feature_columns()) {
-        // We have not reached the limit yet. Empty the list of allow features
-        // which means we can keep adding new features.
-        allowed_handlers.clear();
-      }
-    }
-
     // Find best splits for each active partition.
     std::map<int32, SplitCandidate> best_splits;
-    FindBestSplitsPerPartition(context, allowed_handlers, partition_ids_list,
-                               gains_list, splits_list, &best_splits);
+    FindBestSplitsPerPartition(context, partition_ids_list, gains_list,
+                               splits_list, &best_splits);
 
     // No-op if no new splits can be considered.
     if (best_splits.empty()) {
@@ -422,19 +405,12 @@ class GrowTreeEnsembleOp : public OpKernel {
   // and finds the best split for each partition.
   void FindBestSplitsPerPartition(
       OpKernelContext* const context,
-      const std::vector<int64>& allowed_handlers,  // Empty means all handlers.
       const OpInputList& partition_ids_list, const OpInputList& gains_list,
       const OpInputList& splits_list,
       std::map<int32, SplitCandidate>* best_splits) {
     // Find best split per partition going through every feature candidate.
     // TODO(salehay): Is this worth parallelizing?
     for (int64 handler_id = 0; handler_id < num_handlers_; ++handler_id) {
-      if (!allowed_handlers.empty()) {
-        if (!std::binary_search(allowed_handlers.begin(),
-                                allowed_handlers.end(), handler_id)) {
-          continue;
-        }
-      }
       const auto& partition_ids = partition_ids_list[handler_id].vec<int32>();
       const auto& gains = gains_list[handler_id].vec<float>();
       const auto& splits = splits_list[handler_id].vec<string>();
diff --git a/tensorflow/contrib/boosted_trees/ops/model_ops.cc b/tensorflow/contrib/boosted_trees/ops/model_ops.cc
index 0786c41664..9d6343c7e8 100644
--- a/tensorflow/contrib/boosted_trees/ops/model_ops.cc
+++ b/tensorflow/contrib/boosted_trees/ops/model_ops.cc
@@ -110,5 +110,32 @@ stamp_token: Token to use as the new value of the resource stamp.
 tree_ensemble_config: Serialized proto of the ensemble.
 )doc");
 
+REGISTER_OP("TreeEnsembleUsedHandlers")
+    .Attr("num_all_handlers: int >= 0")
+    .Input("tree_ensemble_handle: resource")
+    .Input("stamp_token: int64")
+    .Output("num_used_handlers: int64")
+    .Output("used_handlers_mask: bool")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      shape_inference::ShapeHandle unused_input;
+      TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused_input));
+      c->set_output(0, c->Scalar());
+      int num_all_handlers;
+      c->GetAttr("num_all_handlers", &num_all_handlers).IgnoreError();
+      c->set_output(1, {c->Vector(num_all_handlers)});
+
+      return Status::OK();
+    })
+    .Doc(R"doc(
+Returns the mask of used handlers along with the number of non-zero elements in 
+this mask. Used in feature selection.
+
+tree_ensemble_handle: Handle to the tree ensemble.
+stamp_token: Token to use as the new value of the resource stamp.
+num_used_handlers: number of feature column handlers used in the model.
+used_handlers_mask: A boolean vector of showing which handlers are used in the
+                    model.
+)doc");
+
 }  // namespace boosted_trees
 }  // namespace tensorflow
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
index 27c288bbf7..63b9c5fddf 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py
@@ -310,6 +310,22 @@ class ModelOpsTest(test_util.TensorFlowTestCase):
         # The third tree was added after the save.
         self.assertAllClose(result.eval(), [[-1.1], [-1.1]])
 
+  def testUsedHandlers(self):
+    with self.test_session():
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree_ensemble_config.growing_metadata.used_handler_ids.append(1)
+      tree_ensemble_config.growing_metadata.used_handler_ids.append(5)
+      stamp_token = 3
+      tree_ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=stamp_token,
+          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
+          name="create_tree")
+      resources.initialize_resources(resources.shared_resources()).run()
+      result = model_ops.tree_ensemble_used_handlers(
+          tree_ensemble_handle, stamp_token, num_all_handlers=6)
+      self.assertAllEqual([0, 1, 0, 0, 0, 1], result.used_handlers_mask.eval())
+      self.assertEqual(2, result.num_used_handlers.eval())
+
 
 if __name__ == "__main__":
   googletest.main()
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py
index 8ca1aabaca..3e524efbea 100644
--- a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py
+++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py
@@ -1588,7 +1588,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase):
       self.assertEqual(
           2, tree_ensemble_config.tree_metadata[2].num_tree_weight_updates)
 
-  def testGrowExistingEnsembleTreeWithFeatureSelectionCanStillGrow(self):
+  def testGrowExistingEnsembleTreeWithFeatureSelectionUsedHandlers(self):
     """Test growing a tree with feature selection."""
     with self.test_session() as session:
       # Create existing ensemble with one root split and one bias tree.
@@ -1649,7 +1649,6 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase):
           num_trees_attempted: 2
           num_layers_attempted: 2
           used_handler_ids: 2
-          used_handler_ids: 5
         }
       """, tree_ensemble_config)
       tree_ensemble_handle = model_ops.tree_ensemble_variable(
@@ -1668,183 +1667,8 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase):
           min_node_weight=0,
           pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
           growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
-      # There are 2 handler_ids in used_handler_ids already but one of them
-      # is handler 2, so we can still grow trees.
-      learner_config.constraints.max_number_of_unique_feature_columns = 2
-      learner_config = learner_config.SerializeToString()
-      # Prepare handler inputs.
-      handler1_partitions = np.array([0], dtype=np.int32)
-      handler1_gains = np.array([7.62], dtype=np.float32)
-      handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)]
-      handler2_partitions = np.array([0], dtype=np.int32)
-      handler2_gains = np.array([0.63], dtype=np.float32)
-      handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)]
-      handler3_partitions = np.array([0], dtype=np.int32)
-      handler3_gains = np.array([7.62], dtype=np.float32)
-      handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)]
-
-      # Grow tree ensemble.
-      grow_op = training_ops.grow_tree_ensemble(
-          tree_ensemble_handle,
-          stamp_token=0,
-          next_stamp_token=1,
-          learning_rate=1,
-          partition_ids=[
-              handler1_partitions, handler2_partitions, handler3_partitions
-          ],
-          gains=[handler1_gains, handler2_gains, handler3_gains],
-          splits=[handler1_split, handler2_split, handler3_split],
-          learner_config=learner_config,
-          dropout_seed=123,
-          center_bias=True)
-      session.run(grow_op)
-
-      # Expect a new tree to be added with the split from handler 1.
-      _, serialized = session.run(
-          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
-      tree_ensemble_config.ParseFromString(serialized)
-      self.assertEqual(3, len(tree_ensemble_config.trees))
-      self.assertEqual(
-          2, len(tree_ensemble_config.growing_metadata.used_handler_ids))
-
-  def testGrowExistingEnsembleTreeWithFeatureSelectionEmptyEnsemble(self):
-    """Test growing a tree with feature selection with empty ensemble."""
-    with self.test_session() as session:
-      # Create existing ensemble with one root split and one bias tree.
-      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
-      tree_ensemble_handle = model_ops.tree_ensemble_variable(
-          stamp_token=0,
-          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
-          name="tree_ensemble")
-      resources.initialize_resources(resources.shared_resources()).run()
-
-      # Prepare learner config.
-      learner_config = _gen_learner_config(
-          num_classes=2,
-          l1_reg=0,
-          l2_reg=0,
-          tree_complexity=0,
-          max_depth=1,
-          min_node_weight=0,
-          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
-          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
-      learner_config.constraints.max_number_of_unique_feature_columns = 2
-      learner_config = learner_config.SerializeToString()
-      # Prepare handler inputs.
-      handler1_partitions = np.array([0], dtype=np.int32)
-      handler1_gains = np.array([7.62], dtype=np.float32)
-      handler1_split = [_gen_dense_split_info(5, 0.52, -4.375, 7.143)]
-      handler2_partitions = np.array([0], dtype=np.int32)
-      handler2_gains = np.array([0.63], dtype=np.float32)
-      handler2_split = [_gen_dense_split_info(2, 0.23, -0.6, 0.24)]
-      handler3_partitions = np.array([0], dtype=np.int32)
-      handler3_gains = np.array([7.62], dtype=np.float32)
-      handler3_split = [_gen_categorical_split_info(8, 7, -4.375, 7.143)]
-
-      # Grow tree ensemble.
-      grow_op = training_ops.grow_tree_ensemble(
-          tree_ensemble_handle,
-          stamp_token=0,
-          next_stamp_token=1,
-          learning_rate=1,
-          partition_ids=[
-              handler1_partitions, handler2_partitions, handler3_partitions
-          ],
-          gains=[handler1_gains, handler2_gains, handler3_gains],
-          splits=[handler1_split, handler2_split, handler3_split],
-          learner_config=learner_config,
-          dropout_seed=123,
-          center_bias=True)
-      session.run(grow_op)
-
-      _, serialized = session.run(
-          model_ops.tree_ensemble_serialize(tree_ensemble_handle))
-      tree_ensemble_config.ParseFromString(serialized)
-      self.assertEqual(1, len(tree_ensemble_config.trees))
-      self.assertEqual(
-          1, len(tree_ensemble_config.growing_metadata.used_handler_ids))
-
-  def testGrowExistingEnsembleTreeWithFeatureSelectionCantGrow(self):
-    """Test growing a tree with feature selection with empty ensemble."""
-    with self.test_session() as session:
-      # Create existing ensemble with one root split and one bias tree.
-      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
-      text_format.Merge("""
-        trees {
-          nodes {
-            leaf {
-              vector {
-                value: -0.32
-                value: 0.28
-              }
-            }
-          }
-        }
-        trees {
-          nodes {
-            categorical_id_binary_split {
-              feature_column: 3
-              feature_id: 7
-              left_id: 1
-              right_id: 2
-            }
-            node_metadata {
-              gain: 1.3
-            }
-          }
-          nodes {
-            leaf {
-              sparse_vector {
-                index: 0
-                value: 2.3
-              }
-            }
-          }
-          nodes {
-            leaf {
-              sparse_vector {
-                index: 0
-                value: -0.9
-              }
-            }
-          }
-        }
-        tree_weights: 0.7
-        tree_weights: 1
-        tree_metadata {
-          num_tree_weight_updates: 1
-          num_layers_grown: 1
-          is_finalized: true
-        }
-        tree_metadata {
-          num_tree_weight_updates: 5
-          num_layers_grown: 1
-          is_finalized: true
-        }
-        growing_metadata {
-          num_trees_attempted: 2
-          num_layers_attempted: 2
-          used_handler_ids: 4
-          used_handler_ids: 5
-        }
-      """, tree_ensemble_config)
-      tree_ensemble_handle = model_ops.tree_ensemble_variable(
-          stamp_token=0,
-          tree_ensemble_config=tree_ensemble_config.SerializeToString(),
-          name="tree_ensemble")
-      resources.initialize_resources(resources.shared_resources()).run()
 
-      # Prepare learner config.
-      learner_config = _gen_learner_config(
-          num_classes=2,
-          l1_reg=0,
-          l2_reg=0,
-          tree_complexity=0,
-          max_depth=1,
-          min_node_weight=0,
-          pruning_mode=learner_pb2.LearnerConfig.PRE_PRUNE,
-          growing_mode=learner_pb2.LearnerConfig.WHOLE_TREE)
-      learner_config.constraints.max_number_of_unique_feature_columns = 2
+      learner_config.constraints.max_number_of_unique_feature_columns = 3
       learner_config = learner_config.SerializeToString()
       # Prepare handler inputs.
       handler1_partitions = np.array([0], dtype=np.int32)
@@ -1876,12 +1700,10 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase):
       _, serialized = session.run(
           model_ops.tree_ensemble_serialize(tree_ensemble_handle))
       tree_ensemble_config.ParseFromString(serialized)
-      # We can't grow a tree since we have reached the limit of 2 unique
-      # features [4, 5] and the only available splits are from
-      # handlers [0, 1, 2].
-      self.assertEqual(2, len(tree_ensemble_config.trees))
-      self.assertEqual(
-          2, len(tree_ensemble_config.growing_metadata.used_handler_ids))
+      self.assertEqual(3, len(tree_ensemble_config.trees))
+      # 2 was already used. handler 0 is being added in this tree.
+      self.assertAllEqual(
+          [0, 2], tree_ensemble_config.growing_metadata.used_handler_ids)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/contrib/boosted_trees/python/ops/model_ops.py b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py
index 7a5f509047..25b2c9e2fd 100644
--- a/tensorflow/contrib/boosted_trees/python/ops/model_ops.py
+++ b/tensorflow/contrib/boosted_trees/python/ops/model_ops.py
@@ -25,6 +25,7 @@ from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensem
 from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_serialize
 # pylint: disable=unused-import
 from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_stamp_token
+from tensorflow.contrib.boosted_trees.python.ops.gen_model_ops import tree_ensemble_used_handlers
 # pylint: enable=unused-import
 
 from tensorflow.python.framework import ops
diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
index f0b66dcbbe..233e21f1cf 100644
--- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
+++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
@@ -57,6 +57,8 @@ PREDICTIONS = "predictions"
 PARTITION_IDS = "partition_ids"
 NUM_LAYERS_ATTEMPTED = "num_layers"
 NUM_TREES_ATTEMPTED = "num_trees"
+NUM_USED_HANDLERS = "num_used_handlers"
+USED_HANDLERS_MASK = "used_handlers_mask"
 _FEATURE_NAME_TEMPLATE = "%s_%d"
 
 
@@ -70,7 +72,8 @@ def _get_column_by_index(tensor, indices):
   return array_ops.reshape(array_ops.gather(p_flat, i_flat), [shape[0], -1])
 
 
-def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats):
+def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats,
+                           used_handlers):
   """Returns predictions for the given logits and n_classes.
 
   Args:
@@ -79,6 +82,8 @@ def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats):
         that contains predictions when no dropout was applied.
     partition_ids: A rank 1 `Tensor` with shape [batch_size].
     ensemble_stats: A TreeEnsembleStatsOp result tuple.
+    used_handlers: A TreeEnsembleUsedHandlerOp result tuple of an int and a
+        boolean mask..
 
   Returns:
     A dict of predictions.
@@ -89,6 +94,8 @@ def _make_predictions_dict(stamp, logits, partition_ids, ensemble_stats):
   result[PARTITION_IDS] = partition_ids
   result[NUM_LAYERS_ATTEMPTED] = ensemble_stats.attempted_layers
   result[NUM_TREES_ATTEMPTED] = ensemble_stats.attempted_trees
+  result[NUM_USED_HANDLERS] = used_handlers.num_used_handlers
+  result[USED_HANDLERS_MASK] = used_handlers.used_handlers_mask
   return result
 
 
@@ -361,6 +368,13 @@ class GradientBoostedDecisionTreeModel(object):
     """
     ensemble_stats = training_ops.tree_ensemble_stats(ensemble_handle,
                                                       ensemble_stamp)
+    num_handlers = (
+        len(self._dense_floats) + len(self._sparse_float_shapes) +
+        len(self._sparse_int_shapes))
+    # Used during feature selection.
+    used_handlers = model_ops.tree_ensemble_used_handlers(
+        ensemble_handle, ensemble_stamp, num_all_handlers=num_handlers)
+
     # We don't need dropout info - we can always restore it based on the
     # seed.
     apply_dropout, seed = _dropout_params(mode, ensemble_stats)
@@ -395,7 +409,7 @@ class GradientBoostedDecisionTreeModel(object):
           use_locking=True)
 
     return _make_predictions_dict(ensemble_stamp, predictions, partition_ids,
-                                  ensemble_stats)
+                                  ensemble_stats, used_handlers)
 
   def predict(self, mode):
     """Returns predictions given the features and mode.
@@ -716,6 +730,22 @@ class GradientBoostedDecisionTreeModel(object):
     else:
       active_handlers = array_ops.ones([len(handlers), 2], dtype=dtypes.bool)
 
+    if self._learner_config.constraints.max_number_of_unique_feature_columns:
+      target = (
+          self._learner_config.constraints.max_number_of_unique_feature_columns)
+
+      def _feature_selection_active_handlers():
+        # The active list for current and the next iteration.
+        used_handlers = array_ops.reshape(predictions_dict[USED_HANDLERS_MASK],
+                                          [-1, 1])
+        used_handlers = array_ops.concat([used_handlers, used_handlers], axis=1)
+        return math_ops.logical_and(used_handlers, active_handlers)
+
+      active_handlers = (
+          control_flow_ops.cond(predictions_dict[NUM_USED_HANDLERS] >= target,
+                                _feature_selection_active_handlers,
+                                lambda: active_handlers))
+
     # Prepare empty gradients and hessians when handlers are not ready.
     empty_hess_shape = [1] + hessian_shape.as_list()
     empty_grad_shape = [1] + gradient_shape.as_list()
diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py
index dba51d4f52..6411f57a54 100644
--- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py
+++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch_test.py
@@ -47,6 +47,38 @@ def _squared_loss(label, unused_weights, predictions):
   return loss
 
 
+def _append_to_leaf(leaf, c_id, w):
+  """Helper method for building tree leaves.
+
+  Appends weight contributions for the given class index to a leaf node.
+
+  Args:
+    leaf: leaf node to append to.
+    c_id: class Id for the weight update.
+    w: weight contribution value.
+  """
+  leaf.sparse_vector.index.append(c_id)
+  leaf.sparse_vector.value.append(w)
+
+
+def _set_float_split(split, feat_col, thresh, l_id, r_id):
+  """Helper method for building tree float splits.
+
+  Sets split feature column, threshold and children.
+
+  Args:
+    split: split node to update.
+    feat_col: feature column for the split.
+    thresh: threshold to split on forming rule x <= thresh.
+    l_id: left child Id.
+    r_id: right child Id.
+  """
+  split.feature_column = feat_col
+  split.threshold = thresh
+  split.left_id = l_id
+  split.right_id = r_id
+
+
 class GbdtTest(test_util.TensorFlowTestCase):
 
   def setUp(self):
@@ -917,6 +949,350 @@ class GbdtTest(test_util.TensorFlowTestCase):
           output.trees[0].nodes[2].leaf.sparse_vector.value[0],
           atol=1e-4, rtol=1e-4)
 
+  def testTrainFnChiefFeatureSelectionReachedLimitNoGoodSplit(self):
+    """Tests the train function running on chief with feature selection."""
+    with self.test_session() as sess:
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.max_number_of_unique_feature_columns = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32)
+      # Feature 1 is predictive but it won't be used because we have reached the
+      # limit of num_used_handlers >= max_number_of_unique_feature_columns
+      features["dense_float_1"] = array_ops.constant([0, 0, 1, 1],
+                                                     dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          logits_dimension=1,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32)
+      partition_ids = array_ops.zeros([4], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions":
+              predictions,
+          "predictions_no_dropout":
+              predictions,
+          "partition_ids":
+              partition_ids,
+          "ensemble_stamp":
+              ensemble_stamp,
+          "num_trees":
+              12,
+          "num_used_handlers":
+              array_ops.constant(1, dtype=dtypes.int64),
+          "used_handlers_mask":
+              array_ops.constant([True, False], dtype=dtypes.bool),
+      }
+
+      labels = array_ops.constant([0, 0, 1, 1], dtypes.float32)
+      weights = array_ops.ones([4, 1], dtypes.float32)
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              _squared_loss(labels, weights, predictions)),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect no splits to be chosen because the quantile
+      # buckets will not be ready.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 0)
+      self.assertEquals(len(output.tree_weights), 0)
+      self.assertEquals(stamp_token.eval(), 1)
+
+      # Update the stamp to be able to run a second time.
+      sess.run([ensemble_stamp.assign_add(1)])
+
+      # On second run, expect a trivial split to be chosen to basically
+      # predict the average.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 1)
+      self.assertAllClose(output.tree_weights, [0.1])
+      self.assertEquals(stamp_token.eval(), 2)
+      expected_tree = """
+          nodes {
+            dense_float_binary_split {
+              feature_column: 0
+              threshold: 1.0
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 0
+            }
+          }
+          nodes {
+            leaf {
+              vector {
+                value: -0.25
+              }
+            }
+          }
+          nodes {
+            leaf {
+              vector {
+                value: 0.0
+              }
+            }
+          }"""
+      self.assertProtoEquals(expected_tree, output.trees[0])
+
+  def testTrainFnChiefFeatureSelectionWithGoodSplits(self):
+    """Tests the train function running on chief with feature selection."""
+    with self.test_session() as sess:
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config="", name="tree_ensemble")
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.max_number_of_unique_feature_columns = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32)
+      # Feature 1 is predictive and is in our selected features so it will be
+      # used even when we're at the limit.
+      features["dense_float_1"] = array_ops.constant([0, 0, 1, 1],
+                                                     dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          logits_dimension=1,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32)
+      partition_ids = array_ops.zeros([4], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions":
+              predictions,
+          "predictions_no_dropout":
+              predictions,
+          "partition_ids":
+              partition_ids,
+          "ensemble_stamp":
+              ensemble_stamp,
+          "num_trees":
+              12,
+          "num_used_handlers":
+              array_ops.constant(1, dtype=dtypes.int64),
+          "used_handlers_mask":
+              array_ops.constant([False, True], dtype=dtypes.bool),
+      }
+
+      labels = array_ops.constant([0, 0, 1, 1], dtypes.float32)
+      weights = array_ops.ones([4, 1], dtypes.float32)
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              _squared_loss(labels, weights, predictions)),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect no splits to be chosen because the quantile
+      # buckets will not be ready.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 0)
+      self.assertEquals(len(output.tree_weights), 0)
+      self.assertEquals(stamp_token.eval(), 1)
+
+      # Update the stamp to be able to run a second time.
+      sess.run([ensemble_stamp.assign_add(1)])
+
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+
+      self.assertEquals(len(output.trees), 1)
+      self.assertAllClose(output.tree_weights, [0.1])
+      self.assertEquals(stamp_token.eval(), 2)
+      expected_tree = """
+          nodes {
+            dense_float_binary_split {
+              feature_column: 1
+              left_id: 1
+              right_id: 2
+            }
+            node_metadata {
+              gain: 0.5
+            }
+          }
+          nodes {
+            leaf {
+              vector {
+                value: 0.0
+              }
+            }
+          }
+          nodes {
+            leaf {
+              vector {
+                value: -0.5
+              }
+            }
+          }"""
+      self.assertProtoEquals(expected_tree, output.trees[0])
+
+  def testTrainFnChiefFeatureSelectionReachedLimitIncrementAttemptedLayer(self):
+    """Tests the train function running on chief with feature selection."""
+    with self.test_session() as sess:
+      tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig()
+      tree = tree_ensemble_config.trees.add()
+
+      _set_float_split(tree.nodes.add()
+                       .sparse_float_binary_split_default_right.split, 2, 4.0,
+                       1, 2)
+      _append_to_leaf(tree.nodes.add().leaf, 0, 0.5)
+      _append_to_leaf(tree.nodes.add().leaf, 1, 1.2)
+      tree_ensemble_config.tree_weights.append(1.0)
+      metadata = tree_ensemble_config.tree_metadata.add()
+      metadata.is_finalized = False
+      metadata.num_layers_grown = 1
+      tree_ensemble_config = tree_ensemble_config.SerializeToString()
+      ensemble_handle = model_ops.tree_ensemble_variable(
+          stamp_token=0, tree_ensemble_config=tree_ensemble_config,
+          name="tree_ensemble")
+      learner_config = learner_pb2.LearnerConfig()
+      learner_config.learning_rate_tuner.fixed.learning_rate = 0.1
+      learner_config.num_classes = 2
+      learner_config.regularization.l1 = 0
+      learner_config.regularization.l2 = 0
+      learner_config.constraints.max_tree_depth = 1
+      learner_config.constraints.max_number_of_unique_feature_columns = 1
+      learner_config.constraints.min_node_weight = 0
+      features = {}
+      # Both features will be disabled since the feature selection limit is
+      # already reached.
+      features["dense_float_0"] = array_ops.ones([4, 1], dtypes.float32)
+      features["dense_float_1"] = array_ops.constant([0, 0, 1, 1],
+                                                     dtypes.float32)
+
+      gbdt_model = gbdt_batch.GradientBoostedDecisionTreeModel(
+          is_chief=True,
+          num_ps_replicas=0,
+          center_bias=False,
+          ensemble_handle=ensemble_handle,
+          examples_per_layer=1,
+          learner_config=learner_config,
+          logits_dimension=1,
+          features=features)
+
+      predictions = array_ops.constant(
+          [[0.0], [1.0], [0.0], [2.0]], dtype=dtypes.float32)
+      partition_ids = array_ops.zeros([4], dtypes.int32)
+      ensemble_stamp = variables.Variable(
+          initial_value=0,
+          name="ensemble_stamp",
+          trainable=False,
+          dtype=dtypes.int64)
+
+      predictions_dict = {
+          "predictions":
+              predictions,
+          "predictions_no_dropout":
+              predictions,
+          "partition_ids":
+              partition_ids,
+          "ensemble_stamp":
+              ensemble_stamp,
+          "num_trees":
+              12,
+          # We have somehow reached our limit 1. Both of the handlers will be
+          # disabled.
+          "num_used_handlers":
+              array_ops.constant(1, dtype=dtypes.int64),
+          "used_handlers_mask":
+              array_ops.constant([False, False], dtype=dtypes.bool),
+      }
+
+      labels = array_ops.constant([0, 0, 1, 1], dtypes.float32)
+      weights = array_ops.ones([4, 1], dtypes.float32)
+      # Create train op.
+      train_op = gbdt_model.train(
+          loss=math_ops.reduce_mean(
+              _squared_loss(labels, weights, predictions)),
+          predictions_dict=predictions_dict,
+          labels=labels)
+      variables.global_variables_initializer().run()
+      resources.initialize_resources(resources.shared_resources()).run()
+
+      # On first run, expect no splits to be chosen because the quantile
+      # buckets will not be ready.
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      self.assertEquals(len(output.trees), 1)
+      self.assertEquals(output.growing_metadata.num_layers_attempted, 1)
+      self.assertEquals(stamp_token.eval(), 1)
+
+      # Update the stamp to be able to run a second time.
+      sess.run([ensemble_stamp.assign_add(1)])
+
+      train_op.run()
+      stamp_token, serialized = model_ops.tree_ensemble_serialize(
+          ensemble_handle)
+      output = tree_config_pb2.DecisionTreeEnsembleConfig()
+      output.ParseFromString(serialized.eval())
+      # Make sure the trees are not modified, but the num_layers_attempted is
+      # incremented so that eventually the training stops.
+      self.assertEquals(len(output.trees), 1)
+      self.assertEquals(len(output.trees[0].nodes), 3)
+
+      self.assertEquals(output.growing_metadata.num_layers_attempted, 2)
 
 if __name__ == "__main__":
   googletest.main()
-- 
GitLab


From f8f4a6e26cc1108495c0b9a55d9a7d6e7005c2b5 Mon Sep 17 00:00:00 2001
From: Mingsheng Hong <hongm@google.com>
Date: Thu, 1 Mar 2018 14:15:20 -0800
Subject: [PATCH 509/575] Internal change.

PiperOrigin-RevId: 187532378
---
 tensorflow/c/c_test_util.cc                   | 31 +++++++++++++++++--
 tensorflow/c/c_test_util.h                    |  9 ++++++
 .../common_runtime/graph_execution_state.cc   |  4 +++
 3 files changed, 41 insertions(+), 3 deletions(-)

diff --git a/tensorflow/c/c_test_util.cc b/tensorflow/c/c_test_util.cc
index 3db2852ce6..53346a8cdf 100644
--- a/tensorflow/c/c_test_util.cc
+++ b/tensorflow/c/c_test_util.cc
@@ -34,6 +34,10 @@ static void DoubleDeallocator(void* data, size_t, void* arg) {
   delete[] static_cast<double*>(data);
 }
 
+static void FloatDeallocator(void* data, size_t, void* arg) {
+  delete[] static_cast<float*>(data);
+}
+
 TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values) {
   int64_t num_values = 1;
   for (int i = 0; i < num_dims; ++i) {
@@ -78,13 +82,21 @@ TF_Tensor* DoubleTensor(double v) {
                       &DoubleDeallocator, nullptr);
 }
 
+TF_Tensor* FloatTensor(float v) {
+  const int num_bytes = sizeof(float);
+  float* values = new float[1];
+  values[0] = v;
+  return TF_NewTensor(TF_FLOAT, nullptr, 0, values, num_bytes,
+                      &FloatDeallocator, nullptr);
+}
+
 // All the *Helper methods are used as a workaround for the restrictions that
 // one cannot call ASSERT_* methods in non-void-returning functions (when
 // exceptions are disabled during compilation)
 void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name,
-                       TF_Operation** op) {
+                       TF_DataType dtype, TF_Operation** op) {
   TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
-  TF_SetAttrType(desc, "dtype", TF_INT32);
+  TF_SetAttrType(desc, "dtype", dtype);
   *op = TF_FinishOperation(desc, s);
   ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
   ASSERT_NE(*op, nullptr);
@@ -92,7 +104,14 @@ void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name,
 
 TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name) {
   TF_Operation* op;
-  PlaceholderHelper(graph, s, name, &op);
+  PlaceholderHelper(graph, s, name, TF_INT32, &op);
+  return op;
+}
+
+TF_Operation* PlaceholderFloat(TF_Graph* graph, TF_Status* s,
+                               const char* name) {
+  TF_Operation* op;
+  PlaceholderHelper(graph, s, name, TF_FLOAT, &op);
   return op;
 }
 
@@ -126,6 +145,12 @@ TF_Operation* ScalarConst(double v, TF_Graph* graph, TF_Status* s,
   return Const(tensor.get(), graph, s, name);
 }
 
+TF_Operation* ScalarConst(float v, TF_Graph* graph, TF_Status* s,
+                          const char* name) {
+  unique_tensor_ptr tensor(FloatTensor(v), TF_DeleteTensor);
+  return Const(tensor.get(), graph, s, name);
+}
+
 void AddOpHelper(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name, TF_Operation** op,
                  bool check) {
diff --git a/tensorflow/c/c_test_util.h b/tensorflow/c/c_test_util.h
index 2a70177c72..8cf060f73f 100644
--- a/tensorflow/c/c_test_util.h
+++ b/tensorflow/c/c_test_util.h
@@ -44,8 +44,14 @@ TF_Tensor* Int32Tensor(int32_t v);
 
 TF_Tensor* DoubleTensor(double v);
 
+TF_Tensor* FloatTensor(float v);
+
+// TODO(hongm): Change Placeholder() to take in a TF_DataType parameter, and
+// unify with PlaceholderFloat.
 TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
                           const char* name = "feed");
+TF_Operation* PlaceholderFloat(TF_Graph* graph, TF_Status* s,
+                               const char* name = "feed");
 
 TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,
                     const char* name = "const");
@@ -56,6 +62,9 @@ TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
 TF_Operation* ScalarConst(double v, TF_Graph* graph, TF_Status* s,
                           const char* name = "scalar");
 
+TF_Operation* ScalarConst(float v, TF_Graph* graph, TF_Status* s,
+                          const char* name = "scalar");
+
 TF_Operation* Add(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                   TF_Status* s, const char* name = "add");
 
diff --git a/tensorflow/core/common_runtime/graph_execution_state.cc b/tensorflow/core/common_runtime/graph_execution_state.cc
index 33a5d60eb7..785ec3d227 100644
--- a/tensorflow/core/common_runtime/graph_execution_state.cc
+++ b/tensorflow/core/common_runtime/graph_execution_state.cc
@@ -73,6 +73,10 @@ GraphExecutionState::~GraphExecutionState() {
 /* static */ Status GraphExecutionState::MakeForBaseGraph(
     GraphDef* graph_def, const GraphExecutionStateOptions& options,
     std::unique_ptr<GraphExecutionState>* out_state) {
+#ifndef __ANDROID__
+  VLOG(1) << "Graph proto is " << graph_def->DebugString();
+#endif  // __ANDROID__
+
   std::unique_ptr<GraphExecutionState> ret(
       new GraphExecutionState(graph_def, options));
 
-- 
GitLab


From 3973e772ed84db08cb86b1086558223af29fd64a Mon Sep 17 00:00:00 2001
From: Rui Zhao <rzhao@google.com>
Date: Thu, 1 Mar 2018 14:15:23 -0800
Subject: [PATCH 510/575] Sampling group embeddings for each child.

PiperOrigin-RevId: 187532388
---
 .../grappler/hierarchical_controller.py       | 41 ++++++++++++++-----
 1 file changed, 30 insertions(+), 11 deletions(-)

diff --git a/tensorflow/python/grappler/hierarchical_controller.py b/tensorflow/python/grappler/hierarchical_controller.py
index b06fb3c6d0..c0866c1069 100644
--- a/tensorflow/python/grappler/hierarchical_controller.py
+++ b/tensorflow/python/grappler/hierarchical_controller.py
@@ -258,9 +258,11 @@ class HierarchicalController(Controller):
             "attn_w_2", [self.hparams.hidden_size, self.hparams.hidden_size])
         variable_scope.get_variable("attn_v", [self.hparams.hidden_size, 1])
     seq2seq_input_layer = array_ops.placeholder_with_default(
-        array_ops.zeros([1, self.num_groups, self.group_emb_size],
+        array_ops.zeros([self.hparams.num_children,
+                         self.num_groups,
+                         self.group_emb_size],
                         dtypes.float32),
-        shape=(1, self.num_groups, self.group_emb_size))
+        shape=(self.hparams.num_children, self.num_groups, self.group_emb_size))
     self.seq2seq_input_layer = seq2seq_input_layer
 
   def compute_reward(self, run_time):
@@ -585,12 +587,29 @@ class HierarchicalController(Controller):
     """Approximating the blocks of a TF graph from a graph_def.
 
     Args:
-      grouping_actions: grouping predictions
+      grouping_actions: grouping predictions.
       verbose: print stuffs.
 
     Returns:
       groups: list of groups.
     """
+    groups = [
+        self._create_group_embeddings(grouping_actions, i, verbose) for
+        i in range(self.hparams.num_children)
+    ]
+    return np.stack(groups, axis=0)
+
+  def _create_group_embeddings(self, grouping_actions, child_id, verbose=False):
+    """Approximating the blocks of a TF graph from a graph_def for each child.
+
+    Args:
+      grouping_actions: grouping predictions.
+      child_id: child_id for the group.
+      verbose: print stuffs.
+
+    Returns:
+      groups: group embedding for the child_id.
+    """
     if verbose:
       print("Processing input_graph")
 
@@ -599,13 +618,13 @@ class HierarchicalController(Controller):
     dag_matrix = np.zeros([self.num_groups, self.num_groups], dtype=np.float32)
     for op in self.important_ops:
       topo_op_index = self.name_to_topo_order_index[op.name]
-      # TODO(agoldie) child_id
-      group_index = grouping_actions[0][topo_op_index]
+      group_index = grouping_actions[child_id][topo_op_index]
       for output_op in self.get_node_fanout(op):
         if output_op.name not in self.important_op_names:
           continue
-        output_group_index = grouping_actions[0][self.name_to_topo_order_index[
-            output_op.name]]
+        output_group_index = (
+            grouping_actions[child_id][self.name_to_topo_order_index[
+                output_op.name]])
         dag_matrix[group_index, output_group_index] += 1.0
     num_connections = np.sum(dag_matrix)
     num_intra_group_connections = dag_matrix.trace()
@@ -648,7 +667,8 @@ class HierarchicalController(Controller):
         ],
         dtype=np.float32)
     for op_index, op in enumerate(self.important_ops):
-      group_index = grouping_actions[0][self.name_to_topo_order_index[op.name]]
+      group_index = grouping_actions[child_id][
+          self.name_to_topo_order_index[op.name]]
       type_name = str(op.op)
       type_index = self.type_dict[type_name]
       group_embedding[group_index, type_index] += 1
@@ -675,7 +695,7 @@ class HierarchicalController(Controller):
           shape=[num_children, self.num_groups],
           trainable=False)
 
-    x = array_ops.tile(self.seq2seq_input_layer, [num_children, 1, 1])
+    x = self.seq2seq_input_layer
     last_c, last_h, attn_mem = self.encode(x)
     actions, log_probs = {}, {}
     actions["sample"], log_probs["sample"] = (
@@ -988,8 +1008,7 @@ class HierarchicalController(Controller):
   def generate_placement(self, grouping, sess):
     controller_ops = self.ops["controller"]
     feed_seq2seq_input_dict = {}
-    feed_seq2seq_input_dict[self.seq2seq_input_layer] = np.expand_dims(
-        grouping, axis=0)
+    feed_seq2seq_input_dict[self.seq2seq_input_layer] = grouping
     sess.run(
         controller_ops["y_preds"]["sample"], feed_dict=feed_seq2seq_input_dict)
 
-- 
GitLab


From 759da7754a708f1f64e4b4b2e17cd4d8c42e3ed3 Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Thu, 1 Mar 2018 14:26:07 -0800
Subject: [PATCH 511/575] Set more generated ops to 'hidden'. These ops have
 not been hidden before but instead have corresponding definitions in Python
 files. We don't want tf_export decorators for the generated ops since
 corresponding Python ops have tf_export decorators instead.

PiperOrigin-RevId: 187534113
---
 tensorflow/core/api_def/python_api/api_def_Angle.pbtxt        | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Bincount.pbtxt     | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Cast.pbtxt         | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Cumprod.pbtxt      | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Cumsum.pbtxt       | 4 ++++
 tensorflow/core/api_def/python_api/api_def_DepthToSpace.pbtxt | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Gather.pbtxt       | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Imag.pbtxt         | 4 ++++
 .../api_def/python_api/api_def_IsVariableInitialized.pbtxt    | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Multinomial.pbtxt  | 4 ++++
 tensorflow/core/api_def/python_api/api_def_OnesLike.pbtxt     | 4 ++++
 .../core/api_def/python_api/api_def_ParseSingleExample.pbtxt  | 4 ++++
 tensorflow/core/api_def/python_api/api_def_QuantizeV2.pbtxt   | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Real.pbtxt         | 4 ++++
 tensorflow/core/api_def/python_api/api_def_ReduceJoin.pbtxt   | 4 ++++
 .../core/api_def/python_api/api_def_ReverseSequence.pbtxt     | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Shape.pbtxt        | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Size.pbtxt         | 4 ++++
 tensorflow/core/api_def/python_api/api_def_SpaceToDepth.pbtxt | 4 ++++
 .../core/api_def/python_api/api_def_SparseSegmentMean.pbtxt   | 4 ++++
 .../core/api_def/python_api/api_def_SparseSegmentSqrtN.pbtxt  | 4 ++++
 .../core/api_def/python_api/api_def_SparseSegmentSum.pbtxt    | 4 ++++
 tensorflow/core/api_def/python_api/api_def_StridedSlice.pbtxt | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Transpose.pbtxt    | 4 ++++
 tensorflow/core/api_def/python_api/api_def_Where.pbtxt        | 4 ++++
 25 files changed, 100 insertions(+)
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Angle.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Bincount.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Cast.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Cumprod.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Cumsum.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_DepthToSpace.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Gather.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Imag.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_IsVariableInitialized.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Multinomial.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_OnesLike.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_ParseSingleExample.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_QuantizeV2.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Real.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_ReduceJoin.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_ReverseSequence.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Shape.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Size.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_SpaceToDepth.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_SparseSegmentMean.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtN.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_SparseSegmentSum.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_StridedSlice.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Transpose.pbtxt
 create mode 100644 tensorflow/core/api_def/python_api/api_def_Where.pbtxt

diff --git a/tensorflow/core/api_def/python_api/api_def_Angle.pbtxt b/tensorflow/core/api_def/python_api/api_def_Angle.pbtxt
new file mode 100644
index 0000000000..771e861fd1
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Angle.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Angle"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Bincount.pbtxt b/tensorflow/core/api_def/python_api/api_def_Bincount.pbtxt
new file mode 100644
index 0000000000..551b51db26
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Bincount.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Bincount"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Cast.pbtxt b/tensorflow/core/api_def/python_api/api_def_Cast.pbtxt
new file mode 100644
index 0000000000..428aa62c46
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Cast.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Cast"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Cumprod.pbtxt b/tensorflow/core/api_def/python_api/api_def_Cumprod.pbtxt
new file mode 100644
index 0000000000..8f5e2f061b
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Cumprod.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Cumprod"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Cumsum.pbtxt b/tensorflow/core/api_def/python_api/api_def_Cumsum.pbtxt
new file mode 100644
index 0000000000..715f26fcac
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Cumsum.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Cumsum"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_DepthToSpace.pbtxt b/tensorflow/core/api_def/python_api/api_def_DepthToSpace.pbtxt
new file mode 100644
index 0000000000..fd0766b365
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_DepthToSpace.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "DepthToSpace"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Gather.pbtxt b/tensorflow/core/api_def/python_api/api_def_Gather.pbtxt
new file mode 100644
index 0000000000..5f956930e0
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Gather.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Gather"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Imag.pbtxt b/tensorflow/core/api_def/python_api/api_def_Imag.pbtxt
new file mode 100644
index 0000000000..5632fd4365
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Imag.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Imag"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_IsVariableInitialized.pbtxt b/tensorflow/core/api_def/python_api/api_def_IsVariableInitialized.pbtxt
new file mode 100644
index 0000000000..6a7b078909
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_IsVariableInitialized.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "IsVariableInitialized"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Multinomial.pbtxt b/tensorflow/core/api_def/python_api/api_def_Multinomial.pbtxt
new file mode 100644
index 0000000000..9b65433580
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Multinomial.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Multinomial"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_OnesLike.pbtxt b/tensorflow/core/api_def/python_api/api_def_OnesLike.pbtxt
new file mode 100644
index 0000000000..c058e5b1ab
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_OnesLike.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "OnesLike"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ParseSingleExample.pbtxt b/tensorflow/core/api_def/python_api/api_def_ParseSingleExample.pbtxt
new file mode 100644
index 0000000000..4193bdd091
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ParseSingleExample.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ParseSingleExample"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_QuantizeV2.pbtxt b/tensorflow/core/api_def/python_api/api_def_QuantizeV2.pbtxt
new file mode 100644
index 0000000000..40673234ed
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_QuantizeV2.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "QuantizeV2"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Real.pbtxt b/tensorflow/core/api_def/python_api/api_def_Real.pbtxt
new file mode 100644
index 0000000000..52a9089f4a
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Real.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Real"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ReduceJoin.pbtxt b/tensorflow/core/api_def/python_api/api_def_ReduceJoin.pbtxt
new file mode 100644
index 0000000000..0fde5942ab
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ReduceJoin.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ReduceJoin"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_ReverseSequence.pbtxt b/tensorflow/core/api_def/python_api/api_def_ReverseSequence.pbtxt
new file mode 100644
index 0000000000..f3fc2578df
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_ReverseSequence.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "ReverseSequence"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Shape.pbtxt b/tensorflow/core/api_def/python_api/api_def_Shape.pbtxt
new file mode 100644
index 0000000000..bd7b5ad36c
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Shape.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Shape"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Size.pbtxt b/tensorflow/core/api_def/python_api/api_def_Size.pbtxt
new file mode 100644
index 0000000000..7f76173a5d
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Size.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Size"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SpaceToDepth.pbtxt b/tensorflow/core/api_def/python_api/api_def_SpaceToDepth.pbtxt
new file mode 100644
index 0000000000..d56a7384eb
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SpaceToDepth.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SpaceToDepth"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSegmentMean.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSegmentMean.pbtxt
new file mode 100644
index 0000000000..f12c2e2073
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSegmentMean.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSegmentMean"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtN.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtN.pbtxt
new file mode 100644
index 0000000000..7daaa81482
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSqrtN.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSegmentSqrtN"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_SparseSegmentSum.pbtxt b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSum.pbtxt
new file mode 100644
index 0000000000..e7028efce2
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_SparseSegmentSum.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "SparseSegmentSum"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_StridedSlice.pbtxt b/tensorflow/core/api_def/python_api/api_def_StridedSlice.pbtxt
new file mode 100644
index 0000000000..a55fa98877
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_StridedSlice.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "StridedSlice"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Transpose.pbtxt b/tensorflow/core/api_def/python_api/api_def_Transpose.pbtxt
new file mode 100644
index 0000000000..e22b6a040e
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Transpose.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Transpose"
+  visibility: HIDDEN
+}
diff --git a/tensorflow/core/api_def/python_api/api_def_Where.pbtxt b/tensorflow/core/api_def/python_api/api_def_Where.pbtxt
new file mode 100644
index 0000000000..d4dd25a206
--- /dev/null
+++ b/tensorflow/core/api_def/python_api/api_def_Where.pbtxt
@@ -0,0 +1,4 @@
+op {
+  graph_op_name: "Where"
+  visibility: HIDDEN
+}
-- 
GitLab


From a8bcf9c5b2ea7c88c3034d1b4c5d62c209a6b431 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 14:35:44 -0800
Subject: [PATCH 512/575] Expose native inference latency via TFlite
 interpreter.

PiperOrigin-RevId: 187535695
---
 .../main/java/org/tensorflow/lite/Interpreter.java  | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
index 9286814b74..b071cda5df 100644
--- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
+++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
@@ -167,6 +167,19 @@ public final class Interpreter implements AutoCloseable {
     return wrapper.getOutputIndex(opName);
   }
 
+
+  /**
+   * Returns native inference timing.
+   * <p>IllegalArgumentException will be thrown if the model is not initialized by the
+   * {@link Interpreter}.
+   */
+  public Long getLastNativeInferenceDurationNanoseconds() {
+    if (wrapper == null) {
+      throw new IllegalStateException("The interpreter has already been closed.");
+    }
+    return wrapper.getLastNativeInferenceDurationNanoseconds();
+  }
+
   /** Release resources associated with the {@code Interpreter}. */
   @Override
   public void close() {
-- 
GitLab


From 8b10f9c7a0a67282061275302b00c254b609b7f6 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Thu, 1 Mar 2018 14:49:49 -0800
Subject: [PATCH 513/575] EagerTensor.device reflects the op's device and not
 the tensor's memory space.

This matches graph mode's behavior.

PiperOrigin-RevId: 187537818
---
 tensorflow/c/eager/c_api.cc | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc
index 252ceab54a..4b619dc4e1 100644
--- a/tensorflow/c/eager/c_api.cc
+++ b/tensorflow/c/eager/c_api.cc
@@ -180,12 +180,10 @@ int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index,
 }
 
 const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h, TF_Status* status) {
-  // TODO(apassos) this will be potentially incorrect in the distributed case as
-  // our local device will have a name which depends on the ClusterSpec and
-  // hence will require the context to resolve.
   status->status = tensorflow::Status::OK();
-  return (h->d == nullptr) ? "/job:localhost/replica:0/task:0/device:CPU:0"
-                           : h->d->name().c_str();
+  return (h->op_device == nullptr)
+             ? "/job:localhost/replica:0/task:0/device:CPU:0"
+             : h->op_device->name().c_str();
 }
 
 TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, TF_Status* status) {
-- 
GitLab


From 4d1a2894b7faa7d9576e82e291758c0da0616b47 Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Thu, 1 Mar 2018 15:09:23 -0800
Subject: [PATCH 514/575] Added support for optimization of functions with
 fixed input/output types

PiperOrigin-RevId: 187540982
---
 tensorflow/core/grappler/optimizers/BUILD     |  1 +
 .../grappler/optimizers/function_optimizer.cc | 35 +++++---
 .../optimizers/function_optimizer_test.cc     | 87 +++++++++++++++++++
 .../grappler/optimizers/meta_optimizer.cc     |  2 +-
 4 files changed, 111 insertions(+), 14 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index b8995ef365..037438ee75 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -144,6 +144,7 @@ cc_library(
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:op_types",
+        "//tensorflow/core/grappler:utils",
         "//tensorflow/core/grappler/utils:functions",
     ],
 )
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index ba8a76ad5f..a5cf00c155 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/framework/versions.pb.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/grappler/utils/functions.h"
 
 namespace tensorflow {
@@ -53,13 +54,17 @@ Status InlineFunction(const NodeDef& node, const FunctionDef& func,
   AttrValue::ListValue* type_list =
       (*func_inputs->mutable_attr())["T"].mutable_list();
   for (const OpDef::ArgDef& arg : func.signature().input_arg()) {
-    auto it = attr.find(arg.type_attr());
-    if (it == attr.end()) {
-      return errors::InvalidArgument("Invalid input argument ", arg.name(),
-                                     " for function ", node.op(),
-                                     " instantiated by ", node.name());
+    if (arg.type() != DT_INVALID) {
+      type_list->add_type(arg.type());
+    } else {
+      auto it = attr.find(arg.type_attr());
+      if (it == attr.end()) {
+        return errors::InvalidArgument("Invalid input argument ", arg.name(),
+                                       " for function ", node.op(),
+                                       " instantiated by ", node.name());
+      }
+      type_list->add_type(it->second.type());
     }
-    type_list->add_type(it->second.type());
   }
 
   for (NodeDef& func_body_node : *item->graph.mutable_node()) {
@@ -75,7 +80,7 @@ Status InlineFunction(const NodeDef& node, const FunctionDef& func,
     } else {
       // Update the input names.
       for (string& input : *func_body_node.mutable_input()) {
-        input = strings::StrCat(node.name(), "/", input);
+        input = AddPrefixToNodeName(input, node.name());
       }
     }
 
@@ -98,13 +103,17 @@ Status InlineFunction(const NodeDef& node, const FunctionDef& func,
   func_outputs->set_device(node.device());
   type_list = (*func_outputs->mutable_attr())["T"].mutable_list();
   for (const OpDef::ArgDef& arg : func.signature().output_arg()) {
-    auto it = attr.find(arg.type_attr());
-    if (it == attr.end()) {
-      return errors::InvalidArgument("Invalid output argument ", arg.name(),
-                                     " for function ", node.op(),
-                                     " instantiated by ", node.name());
+    if (arg.type() != DT_INVALID) {
+      type_list->add_type(arg.type());
+    } else {
+      auto it = attr.find(arg.type_attr());
+      if (it == attr.end()) {
+        return errors::InvalidArgument("Invalid output argument ", arg.name(),
+                                       " for function ", node.op(),
+                                       " instantiated by ", node.name());
+      }
+      type_list->add_type(it->second.type());
     }
-    type_list->add_type(it->second.type());
     func_outputs->add_input(strings::StrCat(node.name(), "/", arg.name()));
   }
 
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
index 76a5c08d35..fd61c067ed 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
@@ -100,6 +100,93 @@ TEST_F(FunctionOptimizerTest, SimpleFunction) {
   test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
 }
 
+TEST_F(FunctionOptimizerTest, FixedTypeFunction) {
+  // Create and instantiate a version of the XTimesTwo function that only
+  // accepts floats a inputs.
+  const Tensor kTwo = test::AsScalar<float>(2.0f);
+  FunctionDef x_times_two = FunctionDefHelper::Define(
+      // Name
+      "XTimesTwo",
+      // Args
+      {"x: float"},
+      // Return values
+      {"y: float"},
+      // Attr def
+      {},
+      // Nodes
+      {
+          {{"two"}, "Const", {}, {{"value", kTwo}, {"dtype", DT_FLOAT}}},
+          {{"y"}, "Mul", {"x", "two"}, {{"T", DT_FLOAT}}},
+      });
+
+  constexpr char device[] = "/device:CPU:0";
+  GrapplerItem item;
+  item.graph = test::function::GDef(
+      {test::function::NDef("x", "Placeholder", {}, {{"dtype", DT_FLOAT}},
+                            device),
+       test::function::NDef("y", "XTimesTwo", {"x"}, {}, device),
+       test::function::NDef("z", "Identity", {"y"}, {{"T", DT_FLOAT}}, device)},
+      // FunctionLib
+      {
+          x_times_two,
+      });
+
+  FunctionOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  int count = 0;
+  for (const NodeDef& node : output.node()) {
+    if (node.name() == "y/inlined_inputs") {
+      count++;
+      EXPECT_EQ("IdentityN", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("x", node.input(0));
+    } else if (node.name() == "y/x") {
+      count++;
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y/inlined_inputs:0", node.input(0));
+    } else if (node.name() == "y/two") {
+      count++;
+      EXPECT_EQ("Const", node.op());
+      EXPECT_EQ(device, node.device());
+    } else if (node.name() == "y/y") {
+      count++;
+      EXPECT_EQ("Mul", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("y/x", node.input(0));
+      EXPECT_EQ("y/two:0", node.input(1));
+    } else if (node.name() == "y") {
+      count++;
+      EXPECT_EQ("IdentityN", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y/y", node.input(0));
+    } else if (node.name() == "z") {
+      count++;
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y", node.input(0));
+    }
+  }
+  EXPECT_EQ(6, count);
+
+  item.fetch = {"z"};
+  Tensor pi(DT_FLOAT, {});
+  pi.flat<float>()(0) = 3.14f;
+  item.feed.emplace_back("x", pi);
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index b674ee1553..72d7b94dc8 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -136,7 +136,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
     }
   } else {
     const std::set<string> available_optimizers = {
-        "pruning",      "constfold",  "layout",     "memory",
+        "pruning",      "function",   "constfold",  "layout", "memory",
         "autoparallel", "arithmetic", "dependency", "loop"};
     std::vector<string> custom_optimizer_names;
     for (const auto& optimizer_name : cfg_.optimizers()) {
-- 
GitLab


From ac79486324bda04cc2f3b75e9590935dfe1ef826 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Thu, 1 Mar 2018 15:36:19 -0800
Subject: [PATCH 515/575] Checkpointable: Make Model Checkpointable-compatible

Has Models track Checkpointable dependencies with __setattr__.

Switches subclassed Models to creating ResourceVariables by default, which
removes one source of eager/graph differences. tfe.Network was doing this by
default.

This is necessary for eager/graph agnostic code since tapes currently only work
with ResourceVariables. It's not quite trivial to fix that, and
ResourceVariables by default in more places is a Good Thing anyway. (Not that we
shouldn't also fix the tape code.)

PiperOrigin-RevId: 187544850
---
 tensorflow/contrib/eager/python/BUILD         |   1 +
 .../eager/python/checkpointable_utils_test.py | 154 ++++++++----------
 .../keras/_impl/keras/engine/network.py       |   8 +
 .../keras/_impl/keras/engine/training.py      |   9 +
 4 files changed, 86 insertions(+), 86 deletions(-)

diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD
index a26ec8513f..8c4b0827fd 100644
--- a/tensorflow/contrib/eager/python/BUILD
+++ b/tensorflow/contrib/eager/python/BUILD
@@ -262,6 +262,7 @@ py_test(
         "//tensorflow/python:variables",
         "//tensorflow/python/eager:context",
         "//tensorflow/python/eager:test",
+        "//tensorflow/python/keras",
         "@six_archive//:six",
     ],
 )
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index 7367f1b71c..9424de0835 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -22,7 +22,6 @@ import os
 import six
 
 from tensorflow.contrib.eager.python import checkpointable_utils
-from tensorflow.contrib.eager.python import network as network_lib
 from tensorflow.python.client import session as session_lib
 from tensorflow.python.eager import context
 from tensorflow.python.eager import test
@@ -30,7 +29,7 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
-from tensorflow.python.layers import base
+from tensorflow.python.keras._impl.keras.engine import training
 from tensorflow.python.layers import core
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import resource_variable_ops
@@ -42,21 +41,6 @@ from tensorflow.python.training import saver as core_saver
 from tensorflow.python.training import training_util
 
 
-# pylint: disable=not-callable
-class CheckpointableNetwork(network_lib.Network, checkpointable.Checkpointable):
-
-  def __setattr__(self, name, value):
-    if isinstance(value, base.Layer):
-      self.track_layer(value, name=name)
-    # Checkpointable is next in the method resolution order, so this will catch
-    # Checkpointable objects which aren't Layers.
-    super(CheckpointableNetwork, self).__setattr__(name, value)
-
-  def track_layer(self, layer, name):
-    self._track_checkpointable(layer, name=name)
-    return super(CheckpointableNetwork, self).track_layer(layer)
-
-
 class NonLayerCheckpointable(checkpointable.Checkpointable):
 
   def __init__(self):
@@ -65,19 +49,20 @@ class NonLayerCheckpointable(checkpointable.Checkpointable):
         self, name="a_variable", shape=[])
 
 
-class MyNetwork(CheckpointableNetwork):
-  """A concrete Network for testing."""
+# pylint: disable=not-callable
+class MyModel(training.Model):
+  """A concrete Model for testing."""
 
   def __init__(self):
-    super(MyNetwork, self).__init__()
+    super(MyModel, self).__init__()
     self._named_dense = core.Dense(1, use_bias=True)
-    self._via_track_layer = self.track_layer(
-        core.Dense(1, use_bias=False), name="via_track_layer")
+    self._second = core.Dense(1, use_bias=False)
     # We can still track Checkpointables which aren't Layers.
     self._non_layer = NonLayerCheckpointable()
 
   def call(self, values):
-    return self._via_track_layer(self._named_dense(values))
+    ret = self._second(self._named_dense(values))
+    return ret
 
 
 class InterfaceTests(test.TestCase):
@@ -171,26 +156,26 @@ class CheckpointingTests(test.TestCase):
   @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
   def testNamingWithOptimizer(self):
     input_value = constant_op.constant([[3.]])
-    network = MyNetwork()
-    # A nuisance Network using the same optimizer. Its slot variables should not
+    model = MyModel()
+    # A nuisance Model using the same optimizer. Its slot variables should not
     # go in the checkpoint, since it is never depended on.
-    other_network = MyNetwork()
+    other_model = MyModel()
     optimizer = adam.AdamOptimizer(0.001)
     optimizer_step = training_util.get_or_create_global_step()
     root_checkpointable = checkpointable_utils.Checkpoint(
-        optimizer=optimizer, network=network, optimizer_step=optimizer_step)
+        optimizer=optimizer, model=model, optimizer_step=optimizer_step)
     if context.in_eager_mode():
       optimizer.minimize(
-          lambda: network(input_value),
+          lambda: model(input_value),
           global_step=optimizer_step)
       optimizer.minimize(
-          lambda: other_network(input_value),
+          lambda: other_model(input_value),
           global_step=optimizer_step)
     else:
       train_op = optimizer.minimize(
-          network(input_value), global_step=optimizer_step)
+          model(input_value), global_step=optimizer_step)
       optimizer.minimize(
-          other_network(input_value),
+          other_model(input_value),
           global_step=optimizer_step)
       self.evaluate(checkpointable_utils.gather_initializers(
           root_checkpointable))
@@ -200,24 +185,21 @@ class CheckpointingTests(test.TestCase):
     expected_checkpoint_names = (
         # Created in the root node, so no prefix.
         "optimizer_step",
-        # No name provided to track_checkpointable(), so the position is used
-        # instead (one-based).
-        "network/via_track_layer/kernel",
-        # track_checkpointable() with a name provided, so that's used
-        "network/_named_dense/kernel",
-        "network/_named_dense/bias",
-        # non-Layer dependency of the network
-        "network/_non_layer/a_variable",
+        "model/_second/kernel",
+        "model/_named_dense/kernel",
+        "model/_named_dense/bias",
+        # non-Layer dependency of the model
+        "model/_non_layer/a_variable",
         # The optimizer creates two non-slot variables
         "optimizer/beta1_power",
         "optimizer/beta2_power",
         # Slot variables
-        "network/via_track_layer/kernel/.OPTIMIZER_SLOT/optimizer/m",
-        "network/via_track_layer/kernel/.OPTIMIZER_SLOT/optimizer/v",
-        "network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m",
-        "network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/v",
-        "network/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/m",
-        "network/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/v",
+        "model/_second/kernel/.OPTIMIZER_SLOT/optimizer/m",
+        "model/_second/kernel/.OPTIMIZER_SLOT/optimizer/v",
+        "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m",
+        "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/v",
+        "model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/m",
+        "model/_named_dense/bias/.OPTIMIZER_SLOT/optimizer/v",
     )
     suffix = "/.ATTRIBUTES/VARIABLE_VALUE"
     expected_checkpoint_names = [
@@ -229,11 +211,11 @@ class CheckpointingTests(test.TestCase):
         "global_step:0",
         named_variables["optimizer_step" + suffix].name)
     self.assertEqual(
-        "my_network/dense_1/kernel:0",
-        named_variables["network/via_track_layer/kernel" + suffix].name)
+        "my_model/dense_1/kernel:0",
+        named_variables["model/_second/kernel" + suffix].name)
     self.assertEqual(
-        "my_network/dense/kernel:0",
-        named_variables["network/_named_dense/kernel" + suffix].name)
+        "my_model/dense/kernel:0",
+        named_variables["model/_named_dense/kernel" + suffix].name)
     self.assertEqual(
         "beta1_power:0",
         named_variables["optimizer/beta1_power" + suffix].name)
@@ -251,80 +233,80 @@ class CheckpointingTests(test.TestCase):
                      serialized_graph.nodes[optimizer_node.children[0].node_id]
                      .attributes[0].full_name)
     self.assertEqual(
-        "my_network/dense/kernel",
+        "my_model/dense/kernel",
         serialized_graph.nodes[optimizer_node.slot_variables[0]
                                .original_variable_node_id]
         .attributes[0].full_name)
     # We strip off the :0 suffix, as variable.name-based saving does.
     self.assertEqual(
-        "my_network/dense/kernel/Adam",
+        "my_model/dense/kernel/Adam",
         serialized_graph.nodes[optimizer_node.slot_variables[0]
                                .slot_variable_node_id]
         .attributes[0].full_name)
     self.assertEqual(
-        "my_network/dense/kernel/Adam:0",
+        "my_model/dense/kernel/Adam:0",
         optimizer.get_slot(
-            var=named_variables["network/_named_dense/kernel" + suffix],
+            var=named_variables["model/_named_dense/kernel" + suffix],
             name="m").name)
     self.assertEqual(
-        "network/_named_dense/kernel" + suffix,
+        "model/_named_dense/kernel" + suffix,
         serialized_graph.nodes[
             optimizer_node.slot_variables[0]
             .original_variable_node_id].attributes[0].checkpoint_key)
     self.assertEqual("m", optimizer_node.slot_variables[0].slot_name)
     self.assertEqual(
-        "network/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m" + suffix,
+        "model/_named_dense/kernel/.OPTIMIZER_SLOT/optimizer/m" + suffix,
         serialized_graph.nodes[
             optimizer_node.slot_variables[0]
             .slot_variable_node_id].attributes[0].checkpoint_key)
 
   @test_util.run_in_graph_and_eager_modes()
   def testSaveRestore(self):
-    network = MyNetwork()
+    model = MyModel()
     optimizer = adam.AdamOptimizer(0.001)
     root_checkpointable = checkpointable_utils.Checkpoint(
-        optimizer=optimizer, network=network)
+        optimizer=optimizer, model=model)
     input_value = constant_op.constant([[3.]])
     if context.in_eager_mode():
       optimizer.minimize(
-          lambda: network(input_value))
+          lambda: model(input_value))
     else:
-      train_op = optimizer.minimize(network(input_value))
+      train_op = optimizer.minimize(model(input_value))
       # TODO(allenl): Make initialization more pleasant when graph building.
       root_checkpointable.save_counter  # pylint: disable=pointless-statement
       self.evaluate(checkpointable_utils.gather_initializers(
           root_checkpointable))
       self.evaluate(train_op)
     prefix = os.path.join(self.get_temp_dir(), "ckpt")
-    self.evaluate(state_ops.assign(network._named_dense.variables[1], [42.]))
-    m_bias_slot = optimizer.get_slot(network._named_dense.variables[1], "m")
+    self.evaluate(state_ops.assign(model._named_dense.variables[1], [42.]))
+    m_bias_slot = optimizer.get_slot(model._named_dense.variables[1], "m")
     self.evaluate(state_ops.assign(m_bias_slot, [1.5]))
     save_path = root_checkpointable.save(file_prefix=prefix)
-    self.evaluate(state_ops.assign(network._named_dense.variables[1], [43.]))
+    self.evaluate(state_ops.assign(model._named_dense.variables[1], [43.]))
     self.evaluate(state_ops.assign(root_checkpointable.save_counter, 3))
     optimizer_variables = self.evaluate(optimizer.variables())
     self.evaluate(state_ops.assign(m_bias_slot, [-2.]))
     # Immediate restoration
     status = root_checkpointable.restore(save_path=save_path).assert_consumed()
     status.run_restore_ops()
-    self.assertAllEqual([42.], self.evaluate(network._named_dense.variables[1]))
+    self.assertAllEqual([42.], self.evaluate(model._named_dense.variables[1]))
     self.assertAllEqual(1, self.evaluate(root_checkpointable.save_counter))
     self.assertAllEqual([1.5], self.evaluate(m_bias_slot))
     if context.in_graph_mode():
       return  # Restore-on-create is only supported when executing eagerly
-    on_create_network = MyNetwork()
+    on_create_model = MyModel()
     on_create_optimizer = adam.AdamOptimizer(0.001)
     on_create_root = checkpointable_utils.Checkpoint(
-        optimizer=on_create_optimizer, network=on_create_network)
+        optimizer=on_create_optimizer, model=on_create_model)
     # Deferred restoration
     status = on_create_root.restore(save_path=save_path)
-    on_create_network(constant_op.constant([[3.]]))  # create variables
+    on_create_model(constant_op.constant([[3.]]))  # create variables
     self.assertAllEqual(1, self.evaluate(on_create_root.save_counter))
     self.assertAllEqual([42.],
                         self.evaluate(
-                            on_create_network._named_dense.variables[1]))
+                            on_create_model._named_dense.variables[1]))
     on_create_m_bias_slot = on_create_optimizer.get_slot(
-        on_create_network._named_dense.variables[1], "m")
+        on_create_model._named_dense.variables[1], "m")
     # Optimizer slot variables are created when the original variable is
     # restored.
     self.assertAllEqual([1.5], self.evaluate(on_create_m_bias_slot))
@@ -344,17 +326,17 @@ class CheckpointingTests(test.TestCase):
     checkpoint_directory = self.get_temp_dir()
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
     for training_continuation in range(3):
-      network = MyNetwork()
+      model = MyModel()
       optimizer = adam.AdamOptimizer(0.001)
       root = checkpointable_utils.Checkpoint(
-          optimizer=optimizer, network=network,
+          optimizer=optimizer, model=model,
           optimizer_step=training_util.get_or_create_global_step())
       root.restore(core_saver.latest_checkpoint(checkpoint_directory))
       for _ in range(num_training_steps):
         # TODO(allenl): Use a Dataset and serialize/checkpoint it.
         input_value = constant_op.constant([[3.]])
         optimizer.minimize(
-            lambda: network(input_value),  # pylint: disable=cell-var-from-loop
+            lambda: model(input_value),  # pylint: disable=cell-var-from-loop
             global_step=root.optimizer_step)
       root.save(file_prefix=checkpoint_prefix)
       self.assertEqual((training_continuation + 1) * num_training_steps,
@@ -368,14 +350,14 @@ class CheckpointingTests(test.TestCase):
       checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
       for training_continuation in range(3):
         with ops.Graph().as_default():
-          network = MyNetwork()
+          model = MyModel()
           optimizer = adam.AdamOptimizer(0.001)
           root = checkpointable_utils.Checkpoint(
-              optimizer=optimizer, network=network,
+              optimizer=optimizer, model=model,
               global_step=training_util.get_or_create_global_step())
           input_value = constant_op.constant([[3.]])
           train_op = optimizer.minimize(
-              network(input_value),
+              model(input_value),
               global_step=root.global_step)
           checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory)
           with self.test_session(graph=ops.get_default_graph()) as session:
@@ -405,17 +387,17 @@ class CheckpointingTests(test.TestCase):
     for training_continuation in range(3):
       with ops.Graph().as_default(), self.test_session(
           graph=ops.get_default_graph()):
-        network = MyNetwork()
+        model = MyModel()
         optimizer = adam.AdamOptimizer(0.001)
         root = checkpointable_utils.Checkpoint(
-            optimizer=optimizer, network=network,
+            optimizer=optimizer, model=model,
             global_step=training_util.get_or_create_global_step())
         checkpoint_path = core_saver.latest_checkpoint(checkpoint_directory)
         status = root.restore(save_path=checkpoint_path)
         input_value = constant_op.constant([[3.]])
         train_fn = functools.partial(
             optimizer.minimize,
-            functools.partial(network, input_value),
+            functools.partial(model, input_value),
             global_step=root.global_step)
         if context.in_graph_mode():
           train_fn = functools.partial(self.evaluate, train_fn())
@@ -877,41 +859,41 @@ class CheckpointCompatibilityTests(test.TestCase):
 
   def _initialized_model(self):
     input_value = constant_op.constant([[3.]])
-    network = MyNetwork()
+    model = MyModel()
     optimizer = adam.AdamOptimizer(0.001)
     optimizer_step = training_util.get_or_create_global_step()
     root_checkpointable = checkpointable_utils.Checkpoint(
-        optimizer=optimizer, network=network, optimizer_step=optimizer_step)
+        optimizer=optimizer, model=model, optimizer_step=optimizer_step)
     train_op = optimizer.minimize(
-        functools.partial(network, input_value),
+        functools.partial(model, input_value),
         global_step=optimizer_step)
     self.evaluate(checkpointable_utils.gather_initializers(
         root_checkpointable))
     self.evaluate(train_op)
     # A regular variable, a slot variable, and a non-slot Optimizer variable
     # with known values to check when loading.
-    self.evaluate(network._named_dense.bias.assign([1.]))
+    self.evaluate(model._named_dense.bias.assign([1.]))
     self.evaluate(optimizer.get_slot(
-        var=network._named_dense.bias, name="m").assign([2.]))
+        var=model._named_dense.bias, name="m").assign([2.]))
     beta1_power, _ = optimizer._get_beta_accumulators()
     self.evaluate(beta1_power.assign(3.))
     return root_checkpointable
 
   def _set_sentinels(self, root_checkpointable):
-    self.evaluate(root_checkpointable.network._named_dense.bias.assign([101.]))
+    self.evaluate(root_checkpointable.model._named_dense.bias.assign([101.]))
     self.evaluate(
         root_checkpointable.optimizer.get_slot(
-            var=root_checkpointable.network._named_dense.bias, name="m")
+            var=root_checkpointable.model._named_dense.bias, name="m")
         .assign([102.]))
     beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators()
     self.evaluate(beta1_power.assign(103.))
 
   def _check_sentinels(self, root_checkpointable):
     self.assertAllEqual(
-        [1.], self.evaluate(root_checkpointable.network._named_dense.bias))
+        [1.], self.evaluate(root_checkpointable.model._named_dense.bias))
     self.assertAllEqual([2.], self.evaluate(
         root_checkpointable.optimizer.get_slot(
-            var=root_checkpointable.network._named_dense.bias, name="m")))
+            var=root_checkpointable.model._named_dense.bias, name="m")))
     beta1_power, _ = root_checkpointable.optimizer._get_beta_accumulators()
     self.assertAllEqual(3., self.evaluate(beta1_power))
 
diff --git a/tensorflow/python/keras/_impl/keras/engine/network.py b/tensorflow/python/keras/_impl/keras/engine/network.py
index 453cc8f8b7..e47bba9267 100644
--- a/tensorflow/python/keras/_impl/keras/engine/network.py
+++ b/tensorflow/python/keras/_impl/keras/engine/network.py
@@ -38,6 +38,7 @@ from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary
 from tensorflow.python.layers import base as tf_base_layers
 from tensorflow.python.layers import utils as tf_layers_util
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import checkpointable
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_inspect
 
@@ -302,6 +303,13 @@ class Network(base_layer.Layer):
       if not is_graph_network:
         if value not in self._layers:
           self._layers.append(value)
+    if isinstance(value, checkpointable.CheckpointableBase):
+      # Layer (and therefore Network/Model) inherit from CheckpointableBase
+      # rather than Checkpointable, which means there is no Checkpointable
+      # __setattr__ override (it would be a performance issue for functional
+      # layers). Therefore Model tracks Checkpointable objects itself.
+      self._track_checkpointable(
+          checkpointable=value, name=name, overwrite=True)
     super(Network, self).__setattr__(name, value)
 
   def add_variable(self, name, shape, dtype=None, initializer=None,
diff --git a/tensorflow/python/keras/_impl/keras/engine/training.py b/tensorflow/python/keras/_impl/keras/engine/training.py
index 2d040e7c0f..81ab77094e 100644
--- a/tensorflow/python/keras/_impl/keras/engine/training.py
+++ b/tensorflow/python/keras/_impl/keras/engine/training.py
@@ -879,6 +879,15 @@ class Model(Network):
     else:
       self._symbolic_set_inputs(inputs, training=training)
 
+  def _set_scope(self, scope=None):
+    """Modify the Layer scope creation logic to create ResourceVariables."""
+    super(Model, self)._set_scope(scope=scope)
+    # Subclassed Models create ResourceVariables by default. This makes it
+    # easier to use Models in an eager/graph agnostic way (since eager execution
+    # always uses ResourceVariables).
+    if not self._is_graph_network:
+      self._scope.set_use_resource(True)
+
   def _eager_set_inputs(self, inputs):
     """Set model's input and output specs based on the input data received.
 
-- 
GitLab


From 45daab910a3c730380594317749d911db5e933e6 Mon Sep 17 00:00:00 2001
From: Xiaoqiang Zheng <zhengxq@google.com>
Date: Thu, 1 Mar 2018 15:41:11 -0800
Subject: [PATCH 516/575] A fp16 implemention for ReluGrad. On V100 with Cuda
 9, it reduces the average ReluGrad kernel time in Resnet50 from 249.44 us to
 175.60 us, a 42% speedup. On Titan-X Pascal with Cuda 9, it reduces the
 average ReluGrad kernel time in Resnet50 from 747.98 us to 509.37 us, a 46.8%
 improvement.

PiperOrigin-RevId: 187545504
---
 tensorflow/core/kernels/relu_op_gpu.cu.cc     | 93 ++++++++++++++++++-
 tensorflow/core/util/cuda_kernel_helper.h     |  5 +
 .../python/kernel_tests/relu_op_test.py       | 31 +++++++
 3 files changed, 127 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/relu_op_gpu.cu.cc b/tensorflow/core/kernels/relu_op_gpu.cu.cc
index ec09d8dfea..6e46c979f3 100644
--- a/tensorflow/core/kernels/relu_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/relu_op_gpu.cu.cc
@@ -19,15 +19,104 @@ limitations under the License.
 
 #include <stdio.h>
 
-#include "tensorflow/core/kernels/relu_op_functor.h"
-
+#include "third_party/eigen3/Eigen/Core"
 #include "tensorflow/core/framework/register_types.h"
 #include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/kernels/relu_op_functor.h"
+#include "tensorflow/core/util/cuda_kernel_helper.h"
+#include "tensorflow/core/util/cuda_launch_config.h"
 
 namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
+namespace functor {
+#ifdef TF_HAS_CUDA_FP16
+
+// This kernel computes ReluGrad by processing one half2, two fp16, at a time.
+// It effectively does: backdrops = (feature > 0) ? gradient : 0
+// It also tries to use native half2 primitives as much as possible.
+__global__ void ReluGradHalfKernel(const Eigen::half* gradient,
+                                   const Eigen::half* feature,
+                                   Eigen::half* backprop, int32 count) {
+  int32 half2_count = count >> 1;
+  int32 index = blockIdx.x * blockDim.x + threadIdx.x;
+  const int32 total_device_threads = gridDim.x * blockDim.x;
+
+  while (index < half2_count) {
+    // The fast branch.
+    // One half2, two fp16, is fetched and processed at a time.
+    half2 gradient_h2 = reinterpret_cast<const half2*>(gradient)[index];
+    half2 feature_h2 = reinterpret_cast<const half2*>(feature)[index];
+    half2* p_backprop_h2 = reinterpret_cast<half2*>(backprop) + index;
+
+#if __CUDA_ARCH__ >= 530
+    // Fast path, when half2 primitives are available.
+    const half2 kZeroH2 = __float2half2_rn(0.f);
+    // mask = (feature > 0)
+    half2 mask_h2 = __hgt2(feature_h2, kZeroH2);
+    // backprop = mask * gradient
+    half2 backprop_h2 = __hmul2(mask_h2, gradient_h2);
+#else
+    // Fall back: convert half2 to float2 for processing.
+    float2 feature_f2 = __half22float2(feature_h2);
+    float2 gradient_f2 = __half22float2(gradient_h2);
+    float2 backprop_f2 = make_float2((feature_f2.x > 0) ? gradient_f2.x : 0,
+                                     (feature_f2.y > 0) ? gradient_f2.y : 0);
+    // Convert back to half2.
+    half2 backprop_h2 = __float22half2_rn(backprop_f2);
+#endif
+
+    // Write back the result.
+    *p_backprop_h2 = backprop_h2;
+
+    index += total_device_threads;
+  }
+
+  if ((count & 0x1) == 1 && index == half2_count) {
+    // If the total number of the elements is odd, process the last element.
+    Eigen::half grad_h = gradient[count - 1];
+    Eigen::half feature_h = feature[count - 1];
+
+    float grad_f = static_cast<float>(grad_h);
+    float feature_f = static_cast<float>(feature_h);
+    float backprop_f = (feature_f > 0) ? grad_f : 0;
+
+    Eigen::half backprop_h(backprop_f);
+    backprop[count - 1] = backprop_h;
+  }
+}
+
+template <typename Device>
+struct ReluGrad<Device, Eigen::half> {
+  // Computes ReluGrad backprop.
+  //
+  // gradient: gradient backpropagated to the Relu op.
+  // feature: either the inputs that were passed to the Relu, or its outputs
+  //           (using either one yields the same result here).
+  // backprop: gradient to backpropagate to the Relu inputs.
+  void operator()(const Device& d,
+                  typename TTypes<Eigen::half>::ConstTensor gradient,
+                  typename TTypes<Eigen::half>::ConstTensor feature,
+                  typename TTypes<Eigen::half>::Tensor backprop) {
+    // NOTE: When the activation is exactly zero, we do not propagate the
+    // associated gradient value. This allows the output of the Relu to be used,
+    // as well as its input.
+    int32 count = gradient.size();
+    if (count == 0) return;
+    int32 half2_count = Eigen::divup(count, 2);
+    const int32 kThreadInBlock = 512;
+    CudaLaunchConfig config = GetCudaLaunchConfigFixedBlockSize(
+        half2_count, d, ReluGradHalfKernel, 0, kThreadInBlock);
+    ReluGradHalfKernel<<<config.block_count, config.thread_per_block, 0,
+                         d.stream()>>>(gradient.data(), feature.data(),
+                                       backprop.data(), count);
+  }
+};
+
+#endif  // TF_HAS_CUDA_FP16
+}  // namespace functor
+
 // Definition of the GPU implementations declared in relu_op.cc.
 #define DEFINE_GPU_KERNELS(T)                       \
   template struct functor::Relu<GPUDevice, T>;      \
diff --git a/tensorflow/core/util/cuda_kernel_helper.h b/tensorflow/core/util/cuda_kernel_helper.h
index 18a4c008f1..01a5b6828a 100644
--- a/tensorflow/core/util/cuda_kernel_helper.h
+++ b/tensorflow/core/util/cuda_kernel_helper.h
@@ -21,6 +21,11 @@ limitations under the License.
 #include "tensorflow/core/util/cuda_device_functions.h"
 #include "tensorflow/core/util/cuda_launch_config.h"
 
+#if CUDA_VERSION >= 7050
+#include "cuda/include/cuda_fp16.h"
+#define TF_HAS_CUDA_FP16
+#endif
+
 // Deprecated, use 'for(int i : CudaGridRangeX(n))' instead.
 #define CUDA_1D_KERNEL_LOOP(i, n) \
   for (int i : ::tensorflow::CudaGridRangeX<int>(n))
diff --git a/tensorflow/python/kernel_tests/relu_op_test.py b/tensorflow/python/kernel_tests/relu_op_test.py
index 6b4091ae5d..25e947f09e 100644
--- a/tensorflow/python/kernel_tests/relu_op_test.py
+++ b/tensorflow/python/kernel_tests/relu_op_test.py
@@ -19,12 +19,14 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
+from six.moves import xrange  # pylint: disable=redefined-builtin
 
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gradient_checker
 from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
@@ -87,6 +89,35 @@ class ReluTest(test.TestCase):
     print("relu (float32) gradient err = ", err)
     self.assertLess(err, 1e-4)
 
+  # The gradient for fp16 is inaccurate due to the low-precision.
+  # Instead of relying on compute_gradient_error, we compare the fp16 analytical
+  # gradient against their fp32 counterpart.
+  def testGradientFloat16(self):
+    with self.test_session(use_gpu=True) as sess:
+      # Randomly construct a 1D shape from [1, 40)
+      shape = random_ops.random_uniform(
+          [1], minval=1, maxval=40, dtype=dtypes.int32)
+
+      # Construct the fp32 graph and its gradient.
+      x = random_ops.random_uniform(shape, minval=-1, maxval=1, name="x")
+      y1 = nn_ops.relu(x, name="relu_fp32")
+      l1 = nn_ops.l2_loss(y1)
+      dx_f32 = gradients_impl.gradients(l1, x)
+
+      # Construct the fp16 graph and its gradient.
+      # It starts with the same x, in fp32. But before it reaches Relu, it is
+      # cast into fp16. So during backprop, the gradient computation is in fp16.
+      x2 = math_ops.cast(x, dtype=dtypes.float16, name="cast")
+      y2 = nn_ops.relu(x2, name="relu_fp16")
+      l2 = nn_ops.l2_loss(y2)
+      dx_f16 = gradients_impl.gradients(l2, x)
+
+      # Repeat the experiment for 100 times. All tensor shapes and its tensor
+      # values are randomly generated for each run.
+      for _ in xrange(100):
+        dx_f32_v, dx_f16_v = sess.run([dx_f32, dx_f16])
+        self.assertAllClose(dx_f32_v, dx_f16_v, atol=3e-4)
+
   def testGradientFloat64(self):
     with self.test_session():
       x = constant_op.constant(
-- 
GitLab


From 80ebc380ec8dacdf900cc66c6590054e26b6dade Mon Sep 17 00:00:00 2001
From: Anna R <annarev@google.com>
Date: Thu, 1 Mar 2018 15:47:28 -0800
Subject: [PATCH 517/575] Fix batch_norm_benchmark.

PiperOrigin-RevId: 187546384
---
 tensorflow/python/ops/batch_norm_benchmark.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/batch_norm_benchmark.py b/tensorflow/python/ops/batch_norm_benchmark.py
index 4f65e3771c..5d68b47aea 100644
--- a/tensorflow/python/ops/batch_norm_benchmark.py
+++ b/tensorflow/python/ops/batch_norm_benchmark.py
@@ -41,7 +41,7 @@ def batch_norm_op(tensor, mean, variance, beta, gamma, scale):
   # _batch_norm_with_global_normalization is deprecated in v9
   ops.get_default_graph().graph_def_versions.producer = 8
   # pylint: disable=protected-access
-  return gen_nn_ops.batch_norm_with_global_normalization(
+  return gen_nn_ops._batch_norm_with_global_normalization(
       tensor, mean, variance, beta, gamma, 0.001, scale)
   # pylint: enable=protected-access
 
-- 
GitLab


From 6db78cd5266dc761c4f90a80d7555c6c33fc453a Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 1 Mar 2018 16:00:17 -0800
Subject: [PATCH 518/575] [ClusterFLR] Prolong the lifetime of the
 RunGraphRequest until the call has completed.

Some WorkerService implementations rely on the request object remaining live until the callback is called.

PiperOrigin-RevId: 187548140
---
 .../cluster_function_library_runtime.cc       | 28 +++++++++++--------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/cluster_function_library_runtime.cc b/tensorflow/core/distributed_runtime/cluster_function_library_runtime.cc
index 3a8d591236..0c5c4d59ed 100644
--- a/tensorflow/core/distributed_runtime/cluster_function_library_runtime.cc
+++ b/tensorflow/core/distributed_runtime/cluster_function_library_runtime.cc
@@ -175,32 +175,33 @@ void ClusterFunctionLibraryRuntime::Run(
     return;
   }
 
-  RunGraphRequest req;
-  req.set_session_handle(worker_session_->session_name);
-  req.set_graph_handle(function_data->graph_handle);
+  RunGraphRequest* req = new RunGraphRequest;
+  req->set_session_handle(worker_session_->session_name);
+  req->set_graph_handle(function_data->graph_handle);
   // Borrowed from master_session.cc
   const uint64 step_id = (random::New64() & ((1uLL << 56) - 1)) | (1uLL << 56);
-  req.set_step_id(step_id);
+  req->set_step_id(step_id);
   int i = 0;
   for (const auto& send_key : function_data->send_keys) {
-    NamedTensorProto* send = req.add_send();
+    NamedTensorProto* send = req->add_send();
     send->set_name(send_key);
     args[i].AsProtoTensorContent(send->mutable_tensor());
     i++;
   }
   const std::vector<string>& recv_keys = function_data->recv_keys;
   for (const auto& recv_key : recv_keys) {
-    req.add_recv_key(recv_key);
+    req->add_recv_key(recv_key);
   }
 
   RunGraphResponse* resp = new RunGraphResponse();
   CallOptions* call_options = new CallOptions();
   wi->RunGraphAsync(
-      call_options, &req, resp,
-      [call_options, resp, rets, recv_keys, done](const Status& status) {
+      call_options, req, resp,
+      [call_options, req, resp, rets, recv_keys, done](const Status& status) {
         if (!status.ok()) {
           done(status);
           delete call_options;
+          delete req;
           delete resp;
           return;
         }
@@ -212,25 +213,28 @@ void ClusterFunctionLibraryRuntime::Run(
         for (const auto& recv_key : recv_keys) {
           TensorProto* tp = mapped_recvs[recv_key];
           if (tp == nullptr) {
+            done(errors::Internal("Could not find key: ", recv_key));
             delete call_options;
+            delete req;
             delete resp;
-            done(errors::Internal("Could not find key: ", recv_key));
             return;
           }
           Tensor t;
           if (t.FromProto(*tp)) {
             rets->push_back(t);
           } else {
-            delete call_options;
-            delete resp;
             done(errors::Internal("Could not convert tensor proto: ",
                                   tp->DebugString()));
+            delete call_options;
+            delete req;
+            delete resp;
             return;
           }
         }
+        done(status);
         delete call_options;
+        delete req;
         delete resp;
-        done(status);
       });
 }
 
-- 
GitLab


From c4a50c5897170edf3055afcce25c981ee331de07 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 16:06:22 -0800
Subject: [PATCH 519/575] Do not crash if we failed to get the field name.

PiperOrigin-RevId: 187549153
---
 tensorflow/contrib/lite/java/proguard.flags                   | 3 +++
 .../lite/java/src/main/native/nativeinterpreterwrapper_jni.cc | 4 +++-
 2 files changed, 6 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/contrib/lite/java/proguard.flags

diff --git a/tensorflow/contrib/lite/java/proguard.flags b/tensorflow/contrib/lite/java/proguard.flags
new file mode 100644
index 0000000000..8ee3d7e7ae
--- /dev/null
+++ b/tensorflow/contrib/lite/java/proguard.flags
@@ -0,0 +1,3 @@
+-keepclassmembers class org.tensorflow.lite.NativeInterpreterWrapper {
+    private long inferenceDurationNanoseconds;
+}
\ No newline at end of file
diff --git a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
index 47bf4c9c9d..475b467fac 100644
--- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
+++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc
@@ -447,7 +447,9 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_run(
   jclass wrapper_clazz = env->GetObjectClass(wrapper);
   jfieldID fid =
       env->GetFieldID(wrapper_clazz, "inferenceDurationNanoseconds", "J");
-  if (fid != 0) {
+  if (env->ExceptionCheck()) {
+    env->ExceptionClear();
+  } else if (fid != nullptr) {
     env->SetLongField(
         wrapper, fid,
         ::tflite::timespec_diff_nanoseconds(&beforeInference, &afterInference));
-- 
GitLab


From 980028f59f96c7e60688fef9106df2d043e02629 Mon Sep 17 00:00:00 2001
From: Karmel Allison <karmel@google.com>
Date: Thu, 1 Mar 2018 16:33:26 -0800
Subject: [PATCH 520/575] Adds a TensorServingInputReceiver that allows
 export_savedmodel to pass raw tensors to model functions. Addresses #11674.

PiperOrigin-RevId: 187552824
---
 tensorflow/python/estimator/estimator.py      |  2 +-
 tensorflow/python/estimator/estimator_test.py | 55 ++++++++++++++++
 tensorflow/python/estimator/export/export.py  | 56 +++++++++++++++++
 .../python/estimator/export/export_lib.py     |  2 +
 .../python/estimator/export/export_test.py    | 62 +++++++++++++++++++
 ...xport.-tensor-serving-input-receiver.pbtxt | 27 ++++++++
 .../golden/tensorflow.estimator.export.pbtxt  |  4 ++
 7 files changed, 207 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 1167b3834e..1a2b33721a 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -570,7 +570,7 @@ class Estimator(object):
       export_dir_base: A string containing a directory in which to create
         timestamped subdirectories containing exported SavedModels.
       serving_input_receiver_fn: A function that takes no argument and
-        returns a `ServingInputReceiver`.
+        returns a `ServingInputReceiver` or `TensorServingInputReceiver`.
       assets_extra: A dict specifying how to populate the assets.extra directory
         within the exported SavedModel, or `None` if no extra assets are needed.
       as_text: whether to write the SavedModel proto in text format.
diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py
index 7a0745b1d0..ac0ff41dd2 100644
--- a/tensorflow/python/estimator/estimator_test.py
+++ b/tensorflow/python/estimator/estimator_test.py
@@ -48,6 +48,7 @@ from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import lookup_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import metrics as metrics_lib
 from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import state_ops
@@ -1936,6 +1937,60 @@ class EstimatorExportTest(test.TestCase):
     # cleanup
     gfile.DeleteRecursively(tmpdir)
 
+  def test_export_savedmodel_tensor_features(self):
+    """Test that models accepting a single raw Tensor can be exported.
+
+    See https://github.com/tensorflow/tensorflow/issues/11674
+
+    If the model_fn and receiver_fn accept raw tensors rather than dictionaries
+    as input, export_savedmodel should be okay with that, too.
+
+    """
+
+    tmpdir = tempfile.mkdtemp()
+
+    def _input_fn_tensor_features():
+      t = array_ops.constant([1, 2, 3], dtype=dtypes.float32, shape=[1, 3])
+      return (t, None)
+
+    def _model_fn_tensor_features(features, labels, mode):
+      _ = labels
+      prediction = math_ops.matmul(features, features, transpose_b=True)
+
+      return model_fn_lib.EstimatorSpec(
+          mode,
+          predictions=prediction,
+          loss=constant_op.constant(1.),
+          train_op=state_ops.assign_add(training.get_global_step(), 1),
+          export_outputs={
+              'test': export_output.PredictOutput({'prediction': prediction})
+          })
+
+    def _serving_input_receiver_fn():
+      feat = array_ops.placeholder(dtype=dtypes.float32)
+      return export.TensorServingInputReceiver(
+          features=feat, receiver_tensors=feat)
+
+    est = estimator.Estimator(model_fn=_model_fn_tensor_features)
+    est.train(input_fn=_input_fn_tensor_features, steps=1)
+
+    # Perform the export.
+    export_dir_base = os.path.join(
+        compat.as_bytes(tmpdir), compat.as_bytes('export'))
+    export_dir = est.export_savedmodel(
+        export_dir_base, _serving_input_receiver_fn)
+
+    # Restore, to validate that the export was well-formed.
+    with ops.Graph().as_default() as graph:
+      with session.Session(graph=graph) as sess:
+        loader.load(sess, [tag_constants.SERVING], export_dir)
+        graph_ops = [x.name.lower() for x in graph.get_operations()]
+        self.assertTrue('const' in graph_ops)
+        self.assertTrue('matmul' in graph_ops)
+
+    # Clean up.
+    gfile.DeleteRecursively(tmpdir)
+
   def test_scaffold_is_used_for_saver(self):
     tmpdir = tempfile.mkdtemp()
 
diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py
index 83251c79fc..f240e11478 100644
--- a/tensorflow/python/estimator/export/export.py
+++ b/tensorflow/python/estimator/export/export.py
@@ -120,6 +120,62 @@ class ServingInputReceiver(collections.namedtuple(
         receiver_tensors_alternatives=receiver_tensors_alternatives)
 
 
+@tf_export('estimator.export.TensorServingInputReceiver')
+class TensorServingInputReceiver(collections.namedtuple(
+    'TensorServingInputReceiver',
+    ['features', 'receiver_tensors', 'receiver_tensors_alternatives'])):
+  """A return type for a serving_input_receiver_fn.
+
+  This is for use with models that expect a single `Tensor` or `SparseTensor`
+  as an input feature, as opposed to a dict of features.
+
+  The normal `ServingInputReceiver` always returns a feature dict, even if it
+  contains only one entry, and so can be used only with models that accept such
+  a dict.  For models that accept only a single raw feature, the
+  `serving_input_receiver_fn` provided to `Estimator.export_savedmodel()` should
+  return this `TensorServingInputReceiver` instead.  See:
+  https://github.com/tensorflow/tensorflow/issues/11674
+
+  Note that the receiver_tensors and receiver_tensor_alternatives arguments
+  will be automatically converted to the dict representation in either case,
+  because the SavedModel format requires each input `Tensor` to have a name
+  (provided by the dict key).
+
+  The expected return values are:
+    features: A single `Tensor` or `SparseTensor`, representing the feature
+      to be passed to the model.
+    receiver_tensors: a `Tensor`, or a dict of string to `Tensor`, specifying
+      input nodes where this receiver expects to be fed by default.  Typically,
+      this is a single placeholder expecting serialized `tf.Example` protos.
+    receiver_tensors_alternatives: a dict of string to additional
+      groups of receiver tensors, each of which may be a `Tensor` or a dict of
+      string to `Tensor`.  These named receiver tensor alternatives generate
+      additional serving signatures, which may be used to feed inputs at
+      different points within the input receiver subgraph.  A typical usage is
+      to allow feeding raw feature `Tensor`s *downstream* of the
+      tf.parse_example() op.  Defaults to None.
+  """
+
+  def __new__(cls, features, receiver_tensors,
+              receiver_tensors_alternatives=None):
+    if features is None:
+      raise ValueError('features must be defined.')
+    if not (isinstance(features, ops.Tensor)
+            or isinstance(features, sparse_tensor.SparseTensor)):
+      raise ValueError('feature must be a Tensor or SparseTensor.')
+
+    receiver = ServingInputReceiver(
+        features=features,
+        receiver_tensors=receiver_tensors,
+        receiver_tensors_alternatives=receiver_tensors_alternatives)
+
+    return super(TensorServingInputReceiver, cls).__new__(
+        cls,
+        features=receiver.features[_SINGLE_FEATURE_DEFAULT_NAME],
+        receiver_tensors=receiver.receiver_tensors,
+        receiver_tensors_alternatives=receiver.receiver_tensors_alternatives)
+
+
 @tf_export('estimator.export.build_parsing_serving_input_receiver_fn')
 def build_parsing_serving_input_receiver_fn(feature_spec,
                                             default_batch_size=None):
diff --git a/tensorflow/python/estimator/export/export_lib.py b/tensorflow/python/estimator/export/export_lib.py
index 99cd81d678..226fc97fd3 100644
--- a/tensorflow/python/estimator/export/export_lib.py
+++ b/tensorflow/python/estimator/export/export_lib.py
@@ -22,6 +22,7 @@ from __future__ import print_function
 from tensorflow.python.estimator.export.export import build_parsing_serving_input_receiver_fn
 from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn
 from tensorflow.python.estimator.export.export import ServingInputReceiver
+from tensorflow.python.estimator.export.export import TensorServingInputReceiver
 from tensorflow.python.estimator.export.export_output import ClassificationOutput
 from tensorflow.python.estimator.export.export_output import ExportOutput
 from tensorflow.python.estimator.export.export_output import PredictOutput
@@ -34,6 +35,7 @@ _allowed_symbols = [
     'build_parsing_serving_input_receiver_fn',
     'build_raw_serving_input_receiver_fn',
     'ServingInputReceiver',
+    'TensorServingInputReceiver',
     'ClassificationOutput',
     'ExportOutput',
     'PredictOutput',
diff --git a/tensorflow/python/estimator/export/export_test.py b/tensorflow/python/estimator/export/export_test.py
index 8442bf04ac..eb9688bc97 100644
--- a/tensorflow/python/estimator/export/export_test.py
+++ b/tensorflow/python/estimator/export/export_test.py
@@ -385,5 +385,67 @@ class ExportTest(test_util.TensorFlowTestCase):
     self.assertTrue(int(time_2) < int(time_3))
 
 
+class TensorServingReceiverTest(test_util.TensorFlowTestCase):
+
+  def test_tensor_serving_input_receiver_constructor(self):
+    features = constant_op.constant([0])
+    receiver_tensors = {
+        "example0": array_ops.placeholder(dtypes.string, name="example0"),
+        u"example1": array_ops.placeholder(dtypes.string, name="example1"),
+    }
+    r = export.TensorServingInputReceiver(features, receiver_tensors)
+    self.assertTrue(isinstance(r.features, ops.Tensor))
+    self.assertTrue(isinstance(r.receiver_tensors, dict))
+
+  def test_tensor_serving_input_receiver_sparse(self):
+    features = sparse_tensor.SparseTensor(
+        indices=[[0, 0]], values=[1], dense_shape=[1, 1])
+    receiver_tensors = {
+        "example0": array_ops.placeholder(dtypes.string, name="example0"),
+        u"example1": array_ops.placeholder(dtypes.string, name="example1"),
+    }
+    r = export.TensorServingInputReceiver(features, receiver_tensors)
+    self.assertTrue(isinstance(r.features, sparse_tensor.SparseTensor))
+    self.assertTrue(isinstance(r.receiver_tensors, dict))
+
+  def test_serving_input_receiver_features_invalid(self):
+    receiver_tensors = {
+        "example0": array_ops.placeholder(dtypes.string, name="example0"),
+        u"example1": array_ops.placeholder(dtypes.string, name="example1"),
+    }
+
+    with self.assertRaisesRegexp(ValueError, "features must be defined"):
+      export.TensorServingInputReceiver(
+          features=None,
+          receiver_tensors=receiver_tensors)
+
+    with self.assertRaisesRegexp(ValueError, "feature must be a Tensor"):
+      export.TensorServingInputReceiver(
+          features={"1": constant_op.constant([1])},
+          receiver_tensors=receiver_tensors)
+
+  def test_serving_input_receiver_receiver_tensors_invalid(self):
+    features = constant_op.constant([0])
+
+    with self.assertRaisesRegexp(
+        ValueError, "receiver_tensors must be defined"):
+      export.TensorServingInputReceiver(
+          features=features,
+          receiver_tensors=None)
+
+    with self.assertRaisesRegexp(
+        ValueError, "receiver_tensors keys must be strings"):
+      export.TensorServingInputReceiver(
+          features=features,
+          receiver_tensors={
+              1: array_ops.placeholder(dtypes.string, name="example0")})
+
+    with self.assertRaisesRegexp(
+        ValueError, "receiver_tensor example1 must be a Tensor"):
+      export.TensorServingInputReceiver(
+          features=features,
+          receiver_tensors={"example1": [1]})
+
+
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt
new file mode 100644
index 0000000000..4fe92643bf
--- /dev/null
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt
@@ -0,0 +1,27 @@
+path: "tensorflow.estimator.export.TensorServingInputReceiver"
+tf_class {
+  is_instance: "<class \'tensorflow.python.estimator.export.export.TensorServingInputReceiver\'>"
+  is_instance: "<class \'tensorflow.python.estimator.export.export.TensorServingInputReceiver\'>"
+  is_instance: "<type \'tuple\'>"
+  member {
+    name: "features"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "receiver_tensors"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "receiver_tensors_alternatives"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+  }
+  member_method {
+    name: "count"
+  }
+  member_method {
+    name: "index"
+  }
+}
diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt
index 4d0dddb3bc..bd72f6cd79 100644
--- a/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt
+++ b/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt
@@ -20,6 +20,10 @@ tf_module {
     name: "ServingInputReceiver"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "TensorServingInputReceiver"
+    mtype: "<type \'type\'>"
+  }
   member_method {
     name: "build_parsing_serving_input_receiver_fn"
     argspec: "args=[\'feature_spec\', \'default_batch_size\'], varargs=None, keywords=None, defaults=[\'None\'], "
-- 
GitLab


From 72b3a5cd8d787bcdab40a94de4788e7e555c76da Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Thu, 1 Mar 2018 16:52:07 -0800
Subject: [PATCH 521/575] Expose Checkpointable symbols in tf.contrib.eager/tfe

- tfe.Checkpoint
    Utility for grouping Checkpointable objects into training checkpoints, has
    save/restore methods which call CheckpointableSaver.
- tfe.Checkpointable
    For user-defined Checkpointable objects.
- tfe.CheckpointableSaver
    More control over saving/restoring than tfe.Checkpoint.

Only tfe.Checkpoint is required to switch examples over, so I can leave the
others out if there are objections.

PiperOrigin-RevId: 187555472
---
 tensorflow/contrib/cmake/python_modules.txt | 1 +
 tensorflow/contrib/cmake/python_protos.txt  | 1 +
 tensorflow/contrib/eager/python/BUILD       | 1 +
 tensorflow/contrib/eager/python/tfe.py      | 7 +++++++
 4 files changed, 10 insertions(+)

diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt
index bfe53c01b3..0d2a6a23db 100644
--- a/tensorflow/contrib/cmake/python_modules.txt
+++ b/tensorflow/contrib/cmake/python_modules.txt
@@ -165,6 +165,7 @@ tensorflow/contrib/distributions/python
 tensorflow/contrib/distributions/python/ops
 tensorflow/contrib/distributions/python/ops/bijectors
 tensorflow/contrib/eager
+tensorflow/contrib/eager/proto
 tensorflow/contrib/eager/python
 tensorflow/contrib/estimator
 tensorflow/contrib/estimator/python
diff --git a/tensorflow/contrib/cmake/python_protos.txt b/tensorflow/contrib/cmake/python_protos.txt
index 8a9c406d8b..c03c0c80fe 100644
--- a/tensorflow/contrib/cmake/python_protos.txt
+++ b/tensorflow/contrib/cmake/python_protos.txt
@@ -4,6 +4,7 @@ tensorflow/python
 tensorflow/contrib/boosted_trees/proto
 tensorflow/contrib/cloud/kernels
 tensorflow/contrib/decision_trees/proto
+tensorflow/contrib/eager/proto
 tensorflow/contrib/gdr
 tensorflow/contrib/lite/toco
 tensorflow/contrib/mpi
diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD
index 8c4b0827fd..e8c514c114 100644
--- a/tensorflow/contrib/eager/python/BUILD
+++ b/tensorflow/contrib/eager/python/BUILD
@@ -11,6 +11,7 @@ py_library(
     srcs_version = "PY2AND3",
     visibility = ["//visibility:public"],
     deps = [
+        ":checkpointable_utils",
         ":datasets",
         ":metrics",
         ":network",
diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py
index d32bebf90c..fce7a60853 100644
--- a/tensorflow/contrib/eager/python/tfe.py
+++ b/tensorflow/contrib/eager/python/tfe.py
@@ -56,6 +56,10 @@ To use, at program startup, call `tfe.enable_eager_execution()`.
 @@save_network_checkpoint
 @@restore_network_checkpoint
 
+@@Checkpoint
+@@Checkpointable
+@@CheckpointableSaver
+
 @@in_eager_mode
 @@in_graph_mode
 
@@ -74,6 +78,8 @@ from __future__ import print_function
 # pylint:disable=g-bad-import-order,g-import-not-at-top,unused-import
 #
 from tensorflow.contrib.eager.python import metrics
+from tensorflow.contrib.eager.python.checkpointable_utils import CheckpointableSaver
+from tensorflow.contrib.eager.python.checkpointable_utils import Checkpoint
 from tensorflow.contrib.eager.python.datasets import Iterator
 from tensorflow.contrib.eager.python.network import Network
 from tensorflow.contrib.eager.python.network import Sequential
@@ -105,6 +111,7 @@ from tensorflow.python.ops.resource_variable_ops import ResourceVariable as Vari
 from tensorflow.python.ops.variable_scope import EagerVariableStore
 from tensorflow.python.ops import script_ops
 from tensorflow.python.ops import template
+from tensorflow.python.training.checkpointable import Checkpointable
 from tensorflow.python.util.all_util import remove_undocumented
 
 py_func = script_ops.eager_py_func
-- 
GitLab


From 700c406bc5c9182b91cf32873e8ae0d81e084114 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 1 Mar 2018 17:00:46 -0800
Subject: [PATCH 522/575] Include the response upon any error.

PiperOrigin-RevId: 187556563
---
 .../core/platform/cloud/curl_http_request.cc  | 56 ++++++++++++-------
 .../platform/cloud/curl_http_request_test.cc  |  7 ++-
 2 files changed, 40 insertions(+), 23 deletions(-)

diff --git a/tensorflow/core/platform/cloud/curl_http_request.cc b/tensorflow/core/platform/cloud/curl_http_request.cc
index 4b5f6974c1..80ad1cf0b8 100644
--- a/tensorflow/core/platform/cloud/curl_http_request.cc
+++ b/tensorflow/core/platform/cloud/curl_http_request.cc
@@ -399,6 +399,24 @@ size_t CurlHttpRequest::HeaderCallback(const void* ptr, size_t size,
   return size * nmemb;
 }
 
+// This is pulled out as a separate function so that it's only computed when
+// an error occurs.
+string response_to_error_message(uint64 response_code, StringPiece response,
+                                 size_t response_to_error_limit,
+                                 CURLcode curl_result,
+                                 StringPiece error_buffer) {
+  string error_message = strings::StrCat(
+      "Error executing an HTTP request (HTTP response code ", response_code,
+      ", error code ", curl_result, ", error message '", error_buffer, "')");
+  if (!response.empty()) {
+    return strings::StrCat(
+        error_message, ", response '",
+        response.substr(0, std::min(response.size(), response_to_error_limit)),
+        "'");
+  }
+  return error_message;
+}
+
 Status CurlHttpRequest::Send() {
   CheckNotSent();
   CHECK(is_uri_set_) << "URI has not been set.";
@@ -430,13 +448,7 @@ Status CurlHttpRequest::Send() {
 
   libcurl_->curl_easy_getinfo(curl_, CURLINFO_RESPONSE_CODE, &response_code_);
 
-  const auto& error_message = strings::StrCat(
-      "Error executing an HTTP request (HTTP response code ", response_code_,
-      ", error code ", curl_result, ", error message '", error_buffer, "')");
-
   Status result;
-  StringPiece response = GetResponse();
-  string extended_error_message;
   switch (response_code_) {
     // The group of response codes indicating that the request achieved
     // the expected goal.
@@ -447,7 +459,9 @@ Status CurlHttpRequest::Send() {
       if (curl_result != CURLE_OK) {
         // This means the server executed the request successfully, but then
         // something went wrong during the transmission of the response.
-        result = errors::Unavailable(error_message);
+        result = errors::Unavailable(response_to_error_message(
+            response_code_, GetResponse(), response_to_error_limit_,
+            curl_result, error_buffer));
       } else {
         result = Status::OK();
       }
@@ -463,27 +477,25 @@ Status CurlHttpRequest::Send() {
     // INVALID_ARGUMENT indicates a problem with how the request is constructed.
     case 400:  // Bad Request
     case 411:  // Length Required
-      result = errors::InvalidArgument(error_message);
+      result = errors::InvalidArgument(response_to_error_message(
+          response_code_, GetResponse(), response_to_error_limit_, curl_result,
+          error_buffer));
       break;
 
     // PERMISSION_DENIED indicates an authentication or an authorization issue.
     case 401:  // Unauthorized
     case 403:  // Forbidden
-      if (!response.empty()) {
-        extended_error_message = strings::StrCat(
-            error_message, ", response ",
-            response.substr(
-                0, std::min(response.size(), response_to_error_limit_)));
-        result = errors::PermissionDenied(extended_error_message);
-      } else {
-        result = errors::PermissionDenied(error_message);
-      }
+      result = errors::PermissionDenied(response_to_error_message(
+          response_code_, GetResponse(), response_to_error_limit_, curl_result,
+          error_buffer));
       break;
 
     // NOT_FOUND indicates that the requested resource does not exist.
     case 404:  // Not found
     case 410:  // Gone
-      result = errors::NotFound(error_message);
+      result = errors::NotFound(response_to_error_message(
+          response_code_, GetResponse(), response_to_error_limit_, curl_result,
+          error_buffer));
       break;
 
     // FAILED_PRECONDITION indicates that the request failed because some
@@ -495,7 +507,9 @@ Status CurlHttpRequest::Send() {
     case 307:  // Temporary Redirect
     case 412:  // Precondition Failed
     case 413:  // Payload Too Large
-      result = errors::FailedPrecondition(error_message);
+      result = errors::FailedPrecondition(response_to_error_message(
+          response_code_, GetResponse(), response_to_error_limit_, curl_result,
+          error_buffer));
       break;
 
     // UNAVAILABLE indicates a problem that can go away if the request
@@ -511,7 +525,9 @@ Status CurlHttpRequest::Send() {
     case 502:  // Bad Gateway
     case 503:  // Service Unavailable
     default:   // All other HTTP response codes also should be retried.
-      result = errors::Unavailable(error_message);
+      result = errors::Unavailable(response_to_error_message(
+          response_code_, GetResponse(), response_to_error_limit_, curl_result,
+          error_buffer));
       break;
   }
   if (!result.ok()) {
diff --git a/tensorflow/core/platform/cloud/curl_http_request_test.cc b/tensorflow/core/platform/cloud/curl_http_request_test.cc
index 86d26a0287..94af121768 100644
--- a/tensorflow/core/platform/cloud/curl_http_request_test.cc
+++ b/tensorflow/core/platform/cloud/curl_http_request_test.cc
@@ -378,7 +378,7 @@ TEST(CurlHttpRequestTest, GetRequest_503) {
   EXPECT_EQ(error::UNAVAILABLE, status.code());
   EXPECT_EQ(
       "Error executing an HTTP request (HTTP response code 503, "
-      "error code 23, error message '')",
+      "error code 23, error message ''), response 'get response'",
       status.error_message());
   EXPECT_EQ(503, http_request.GetResponseCode());
 }
@@ -397,7 +397,8 @@ TEST(CurlHttpRequestTest, GetRequest_HttpCode0) {
   EXPECT_EQ(error::UNAVAILABLE, status.code());
   EXPECT_EQ(
       "Error executing an HTTP request (HTTP response code 0, "
-      "error code 28, error message 'Operation timed out')",
+      "error code 28, error message 'Operation timed out'), "
+      "response 'get response'",
       status.error_message());
   EXPECT_EQ(0, http_request.GetResponseCode());
 }
@@ -629,7 +630,7 @@ TEST(CurlHttpRequestTest, ProgressIsStuck) {
   EXPECT_EQ(error::UNAVAILABLE, status.code());
   EXPECT_EQ(
       "Error executing an HTTP request (HTTP response code 200, "
-      "error code 42, error message '')",
+      "error code 42, error message ''), response 'test'",
       status.error_message());
 }
 
-- 
GitLab


From 64bd36057449dd01d6944b8d31a53b1301923f2c Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 1 Mar 2018 17:07:20 -0800
Subject: [PATCH 523/575] Improve the error message when failing to write
 events.

The current error message looks like:
"Failed to sync 10 to <path>"

PiperOrigin-RevId: 187557623
---
 tensorflow/core/util/events_writer.cc | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/util/events_writer.cc b/tensorflow/core/util/events_writer.cc
index 49507616ed..c50e329bda 100644
--- a/tensorflow/core/util/events_writer.cc
+++ b/tensorflow/core/util/events_writer.cc
@@ -122,9 +122,11 @@ Status EventsWriter::Flush() {
   CHECK(recordio_file_ != nullptr) << "Unexpected NULL file";
 
   TF_RETURN_WITH_CONTEXT_IF_ERROR(recordio_writer_->Flush(), "Failed to flush ",
-                                  num_outstanding_events_, " to ", filename_);
+                                  num_outstanding_events_, " events to ",
+                                  filename_);
   TF_RETURN_WITH_CONTEXT_IF_ERROR(recordio_file_->Sync(), "Failed to sync ",
-                                  num_outstanding_events_, " to ", filename_);
+                                  num_outstanding_events_, " events to ",
+                                  filename_);
 
   // The FileStillExists() condition is necessary because
   // recordio_writer_->Sync() can return OK even if the underlying
@@ -135,7 +137,8 @@ Status EventsWriter::Flush() {
   // disappearing file, in case for some file system File::Exists() is
   // false after File::Open() but before File::Sync().
   TF_RETURN_WITH_CONTEXT_IF_ERROR(FileStillExists(), "Failed to flush ",
-                                  num_outstanding_events_, " to ", filename_);
+                                  num_outstanding_events_, " events to ",
+                                  filename_);
   VLOG(1) << "Wrote " << num_outstanding_events_ << " events to disk.";
   num_outstanding_events_ = 0;
   return Status::OK();
-- 
GitLab


From 16f1eea1cdfdb7facdac8ac2ccab3ee80af41409 Mon Sep 17 00:00:00 2001
From: Sami Kama <samikama@users.noreply.github.com>
Date: Thu, 1 Mar 2018 17:20:54 -0800
Subject: [PATCH 524/575] Scaffolding for int8 calibration in TF-TRT (#17309)

* Scaffolding for int8 calibration

* Add ops/trt_calib_op.cc

* Rename files and replace std::string with string

* Line lengths, variable names, conditionals in BUILD

* mode variable renaming

* More fixes for review

* Run clang-format

* Fix the build failue and replace the macro with a function

* Add TODO(aaroey) for future PRs

* Fix namespace for internal build

* Fix mismatched argument name and unused includes to make internal build happy

* Fix order of dependencies in BUILD file

* Remove dangling #undef
---
 tensorflow/contrib/tensorrt/BUILD             |  44 +++++-
 .../contrib/tensorrt/kernels/trt_calib_op.cc  | 129 ++++++++++++++++++
 .../contrib/tensorrt/kernels/trt_calib_op.h   |  52 +++++++
 .../contrib/tensorrt/ops/trt_calib_op.cc      |  37 +++++
 .../tensorrt/resources/trt_int8_calibrator.cc | 119 ++++++++++++++++
 .../tensorrt/resources/trt_int8_calibrator.h  |  65 +++++++++
 .../resources/trt_resource_manager.cc         |  39 ++++++
 .../tensorrt/resources/trt_resource_manager.h |  49 +++++++
 .../tensorrt/resources/trt_resources.h        |  95 +++++++++++++
 9 files changed, 625 insertions(+), 4 deletions(-)
 create mode 100644 tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc
 create mode 100644 tensorflow/contrib/tensorrt/kernels/trt_calib_op.h
 create mode 100644 tensorflow/contrib/tensorrt/ops/trt_calib_op.cc
 create mode 100644 tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc
 create mode 100644 tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h
 create mode 100644 tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc
 create mode 100644 tensorflow/contrib/tensorrt/resources/trt_resource_manager.h
 create mode 100644 tensorflow/contrib/tensorrt/resources/trt_resources.h

diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD
index 65a0e903a7..9909fcaca2 100644
--- a/tensorflow/contrib/tensorrt/BUILD
+++ b/tensorflow/contrib/tensorrt/BUILD
@@ -47,7 +47,10 @@ tf_cuda_cc_test(
 
 tf_custom_op_library(
     name = "python/ops/_trt_engine_op.so",
-    srcs = ["ops/trt_engine_op.cc"],
+    srcs = [
+        "ops/trt_calib_op.cc",
+        "ops/trt_engine_op.cc",
+    ],
     deps = [
         ":trt_engine_op_kernel",
         ":trt_shape_function",
@@ -71,11 +74,18 @@ tf_cuda_library(
 
 cc_library(
     name = "trt_engine_op_kernel",
-    srcs = ["kernels/trt_engine_op.cc"],
-    hdrs = ["kernels/trt_engine_op.h"],
+    srcs = [
+        "kernels/trt_calib_op.cc",
+        "kernels/trt_engine_op.cc",
+    ],
+    hdrs = [
+        "kernels/trt_calib_op.h",
+        "kernels/trt_engine_op.h",
+    ],
     copts = tf_copts(),
     deps = [
         ":trt_logging",
+        ":trt_resources",
         "//tensorflow/core:gpu_headers_lib",
         "//tensorflow/core:lib_proto_parsing",
         "//tensorflow/core:stream_executor_headers_lib",
@@ -87,7 +97,10 @@ cc_library(
 )
 
 tf_gen_op_libs(
-    op_lib_names = ["trt_engine_op"],
+    op_lib_names = [
+        "trt_engine_op",
+        "trt_calib_op",
+    ],
     deps = if_tensorrt([
         "@local_config_tensorrt//:nv_infer",
     ]),
@@ -108,6 +121,7 @@ tf_cuda_library(
 tf_gen_op_wrapper_py(
     name = "trt_engine_op",
     deps = [
+        ":trt_calib_op_op_lib",
         ":trt_engine_op_op_lib",
         ":trt_logging",
         ":trt_shape_function",
@@ -171,6 +185,27 @@ tf_py_wrap_cc(
     ],
 )
 
+tf_cuda_library(
+    name = "trt_resources",
+    srcs = [
+        "resources/trt_int8_calibrator.cc",
+        "resources/trt_resource_manager.cc",
+    ],
+    hdrs = [
+        "resources/trt_int8_calibrator.h",
+        "resources/trt_resource_manager.h",
+        "resources/trt_resources.h",
+    ],
+    deps = [
+        ":trt_logging",
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:framework_lite",
+        "//tensorflow/core:lib_proto_parsing",
+    ] + if_tensorrt([
+        "@local_config_tensorrt//:nv_infer",
+    ]),
+)
+
 # Library for the node-level conversion portion of TensorRT operation creation
 tf_cuda_library(
     name = "trt_conversion",
@@ -185,6 +220,7 @@ tf_cuda_library(
     deps = [
         ":segment",
         ":trt_logging",
+        ":trt_resources",
         "//tensorflow/core/grappler:grappler_item",
         "//tensorflow/core/grappler:utils",
         "//tensorflow/core:framework",
diff --git a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc
new file mode 100644
index 0000000000..1dcb87e768
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.cc
@@ -0,0 +1,129 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/kernels/trt_calib_op.h"
+#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h"
+#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h"
+#include "tensorflow/contrib/tensorrt/resources/trt_resources.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/framework/types.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "cuda_runtime_api.h"
+#include "tensorrt/include/NvInfer.h"
+
+namespace tensorflow {
+namespace tensorrt {
+
+TRTCalibOp::TRTCalibOp(OpKernelConstruction* context) : OpKernel(context) {
+  OP_REQUIRES_OK(context, context->GetAttr("segment_nodes", &segment_nodes_));
+  OP_REQUIRES_OK(context, context->GetAttr("input_names", &input_names_));
+  OP_REQUIRES_OK(context, context->GetAttr("resource_name", &resource_name_));
+};
+
+#define TYPECASE(dt, X, Y)                                                \
+  case dt: {                                                              \
+    return (void*)X->flat<tensorflow::EnumToDataType<dt>::Type>().data(); \
+  }
+
+void* GetTensorAddress(const Tensor* tensor_ptr) {
+  auto tensor_type = tensor_ptr->dtype();
+  switch (tensor_type) {
+    TYPECASE(tensorflow::DT_FLOAT, tensor_ptr, dest_ptr);
+    TYPECASE(tensorflow::DT_HALF, tensor_ptr, dest_ptr);
+    TYPECASE(tensorflow::DT_INT8, tensor_ptr, dest_ptr);
+    default: {
+      LOG(FATAL) << "Unsupported Data type "
+                 << tensorflow::DataTypeString(tensor_type);
+      return nullptr;
+    }
+  }
+}
+
+void TRTCalibOp::Compute(tensorflow::OpKernelContext* ctx) {
+  // TODO(aaroey): make sure ctx->resource_mgr() is used in future PR.
+  auto trt_rm = tensorflow::tensorrt::TRTResourceManager::instance();
+  auto res_mgr = trt_rm->getManager("TRTCalibOps");
+  tensorflow::tensorrt::TRTCalibrationResource* calib_res = nullptr;
+  auto status = res_mgr->Lookup(resource_name_, resource_name_, &calib_res);
+
+  if (!status.ok()) {
+    ctx->SetStatus(status);
+    return;
+  }
+  int num_inputs = ctx->num_inputs();
+  // first run instantiate calibrator
+  if (calib_res->calibrator_ == nullptr) {
+    dev_tensors_.resize(num_inputs);
+    int batch_size = ctx->input(0).dim_size(0);
+    VLOG(1) << " Constructing calibrator";
+    for (int i = 0; i < num_inputs; i++) {
+      // allocate workspace on device for inputs
+      const tensorflow::Tensor& t = ctx->input(i);
+      OP_REQUIRES_OK(ctx,
+                     ctx->allocate_persistent(t.dtype(), t.shape(),
+                                              &dev_tensors_.at(i), nullptr));
+      const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx);
+      CHECK_EQ(t.TotalBytes(), device_tensor->TotalBytes());
+      void* device_address = GetTensorAddress(device_tensor);
+      device_buffers_.emplace(input_names_.at(i),
+                              std::pair<void*, size_t>(
+                                  device_address, device_tensor->TotalBytes()));
+    }
+
+    calib_res->calibrator_ =
+        new TRTInt8Calibrator(device_buffers_, batch_size, resource_name_);
+    string label(resource_name_);
+    calib_res->thr_ = new std::thread([calib_res, label]() {
+      VLOG(1) << "Starting calibration thread, Calibration Resource @ "
+              << calib_res;
+      calib_res->builder_->setInt8Calibrator(calib_res->calibrator_);
+      calib_res->builder_->setInt8Mode(true);
+      calib_res->engine_ = calib_res->builder_->buildCudaEngine(
+          *calib_res->network_);  // will loop until we terminate calibrator
+      VLOG(1) << "Calibration loop terminated " << label;
+    });
+    VLOG(1) << "initialized calibrator resource";
+  }  //  calibrator initialized
+
+  // Pass input data to calibrator
+  std::unordered_map<string, void*> input_data;
+  for (int i = 0; i < num_inputs; i++) {
+    const Tensor& t = ctx->input(i);
+    void* data_address = GetTensorAddress(&t);
+    const auto device_tensor = dev_tensors_.at(i).AccessTensor(ctx);
+    CHECK_EQ(t.TotalBytes(),
+             device_tensor->TotalBytes());  // use the tensor so FW keeps it
+    input_data.emplace(input_names_.at(i), data_address);
+    ctx->set_output(i, t);
+  }
+  VLOG(2) << "Filled map for sending";
+  calib_res->calibrator_->setBatch(input_data);
+  VLOG(2) << "Passed calibration data";
+  // TODO(aaroey): make sure we wait for the completion of calibration on the
+  // last batch in future PR.
+};
+
+#undef TYPECASE
+
+REGISTER_KERNEL_BUILDER(Name("TRTCalibOp").Device(DEVICE_GPU), TRTCalibOp);
+
+}  // namespace tensorrt
+}  // namespace tensorflow
+#endif
+#endif
diff --git a/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h
new file mode 100644
index 0000000000..23df9db32f
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/kernels/trt_calib_op.h
@@ -0,0 +1,52 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H
+#define TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/platform/types.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+namespace tensorflow {
+namespace tensorrt {
+// TODO(sami): Convert this to async kernel!
+class TRTCalibOp : public OpKernel {
+ public:
+  explicit TRTCalibOp(OpKernelConstruction* context);
+
+  void Compute(OpKernelContext* context) override;
+
+ private:
+  string resource_name_;
+  std::vector<string> segment_nodes_;
+  std::vector<string> input_names_;
+  std::vector<tensorflow::TensorShape> shapes_;
+  std::unordered_map<string, std::pair<void*, size_t>> device_buffers_;
+  std::vector<tensorflow::PersistentTensor> dev_tensors_;
+};
+}  // namespace tensorrt
+}  // namespace tensorflow
+#endif
+#endif
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_KERNELS_TRT_CALIB_OP_H
diff --git a/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc b/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc
new file mode 100644
index 0000000000..4835e50650
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/ops/trt_calib_op.cc
@@ -0,0 +1,37 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+namespace tensorflow {
+
+REGISTER_OP("TRTCalibOp")
+    .Attr("segment_nodes: list(string)")         // names of the ops in segment
+    .Attr("segment_output_names: list(string)")  // names of the output ops in
+                                                 // segment
+    .Attr("input_names: list(string)")           // names of the inputs for
+                                                 // passing into tensorrt
+    .Attr("resource_name: string")
+    .Attr("InT: list({int8, float16, float32})")
+    .Input("in_tensor: InT")
+    .Output("out_tensor: InT")
+    .SetShapeFn([](tensorflow::shape_inference::InferenceContext* c) {
+      for (int i = 0; i < c->num_inputs(); i++) {
+        c->set_output(i, c->input(i));
+      }
+      return Status::OK();
+    });
+
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc
new file mode 100644
index 0000000000..3d5cc76c42
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.cc
@@ -0,0 +1,119 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h"
+
+#include <atomic>
+#include <chrono>
+#include <unordered_map>
+
+#include "tensorflow/core/platform/logging.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "cuda_runtime_api.h"
+
+namespace tensorflow {
+namespace tensorrt {
+
+// set the batch size before constructing the thread to execute engine
+int TRTInt8Calibrator::getBatchSize() const { return batch_size_; }
+
+TRTInt8Calibrator::TRTInt8Calibrator(
+    const std::unordered_map<string, std::pair<void*, size_t>>& dev_buffers,
+    int batch_size, string engine_name)
+    : batch_size_(batch_size),
+      done_(false),
+      dev_buffers_(dev_buffers),
+      calib_running_(false),
+      engine_name_(engine_name) {}
+
+bool TRTInt8Calibrator::setBatch(
+    const std::unordered_map<string, void*>& data) {
+  // TODO(aaroey): make sure that in future PR:
+  // 1. the mutex_lock is outside of the loop
+  // 2. wait() is used instead of wait_for()
+  // 3. done_ is to be protected by the mutex
+  // 4. the first batch is not missed
+  if (done_) return false;
+  while (calib_running_.load(
+      std::memory_order_acquire)) {  // wait while calibration is running
+    tensorflow::mutex_lock l(cond_mtx_);
+    cond_.wait_for(l, std::chrono::milliseconds(50));
+    if (done_) return false;
+  }
+  VLOG(1) << "Set Batch Waiting finished";
+  for (const auto it : data) {
+    auto devptr = dev_buffers_.find(it.first);
+    if (devptr == dev_buffers_.end()) {
+      LOG(FATAL) << "FATAL " << engine_name_ << " input name '" << it.first
+                 << "' does not match with the buffer names";
+    }
+    const auto& d = devptr->second;
+
+    // TODO(aaroey): we should not use sync copy on default stream. Make sure
+    // stream->ThenMemcpy() is used in future PRs.
+    auto status =
+        cudaMemcpy(d.first, it.second, d.second, cudaMemcpyDeviceToDevice);
+    if (status != cudaSuccess) {
+      LOG(FATAL) << "cudaMemcpy " << engine_name_ << " for '" << it.first
+                 << "' failed with " << status;
+    }
+  }
+  calib_running_.store(true, std::memory_order_release);  // release builder
+  cond_.notify_all();
+  return true;
+}
+
+bool TRTInt8Calibrator::getBatch(void** bindings, const char** names,
+                                 int num_bindings) {
+  calib_running_.store(false, std::memory_order_release);  // wait for new batch
+  cond_.notify_all();
+  while (!calib_running_.load(
+      std::memory_order_acquire)) {  // wait until new batch arrives
+    tensorflow::mutex_lock l(cond_mtx_);
+    cond_.wait_for(l, std::chrono::milliseconds(50));
+    if (done_) return false;
+  }
+  if (done_) {
+    return false;
+  }
+
+  for (int i = 0; i < num_bindings; i++) {
+    auto it = dev_buffers_.find(names[i]);
+    if (it == dev_buffers_.end()) {
+      LOG(FATAL) << "Calibration engine asked for unknown tensor name '"
+                 << names[i] << "' at position " << i;
+    }
+
+    bindings[i] = it->second.first;
+  }
+  return true;
+}
+
+const void* TRTInt8Calibrator::readCalibrationCache(std::size_t& length) {
+  return nullptr;
+}
+
+void TRTInt8Calibrator::writeCalibrationCache(const void* ptr,
+                                              std::size_t length) {}
+TRTInt8Calibrator::~TRTInt8Calibrator() {
+  VLOG(1) << "Destroying calibrator for " << engine_name_;
+}
+
+}  // namespace tensorrt
+}  // namespace tensorflow
+#endif
+#endif
diff --git a/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h
new file mode 100644
index 0000000000..8830f7efe7
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h
@@ -0,0 +1,65 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_
+
+#include <atomic>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include "tensorflow/core/platform/mutex.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "tensorrt/include/NvInfer.h"
+namespace tensorflow {
+namespace tensorrt {
+// This class provides a 1 element queue to match TFs push model to
+// TRTs pull model for calibration. When TRT implements a means for
+// a push calibration This class should be updated accordingly
+
+struct TRTInt8Calibrator : public nvinfer1::IInt8EntropyCalibrator {
+ public:
+  TRTInt8Calibrator(
+      const std::unordered_map<string, std::pair<void*, size_t>>& dev_buffers,
+      int batch_size, string engine_name);
+  int getBatchSize() const override;
+  bool getBatch(void* bindings[], const char* names[],
+                int num_bindings) override;
+  bool setBatch(const std::unordered_map<string, void*>& data);
+  void setDone() { done_ = true; }
+  const void* readCalibrationCache(std::size_t& length) override;
+  void writeCalibrationCache(const void* ptr, std::size_t length) override;
+  ~TRTInt8Calibrator();
+
+ private:
+  const int batch_size_;
+  tensorflow::mutex cond_mtx_;           // mutex for condition_variable
+  tensorflow::condition_variable cond_;  // condition variable to implement
+                                         // producer-consumer queue for
+                                         // calibration
+  bool done_;
+  const std::unordered_map<string, std::pair<void*, size_t>>
+      dev_buffers_;  // map to keep tensorrt input buffers and sizes keyed with
+                     // buffer names
+  std::atomic_bool calib_running_;
+  string engine_name_;
+};
+}  // namespace tensorrt
+}  // namespace tensorflow
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_INT8_CALIBRATOR_H_
+#endif
+#endif
diff --git a/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc
new file mode 100644
index 0000000000..e663eed4dd
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.cc
@@ -0,0 +1,39 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace tensorflow {
+namespace tensorrt {
+
+std::shared_ptr<tensorflow::ResourceMgr>
+tensorflow::tensorrt::TRTResourceManager::getManager(const string& op_name) {
+  // mutex is held for lookup only. Most instantiations where mutex will be held
+  // longer will be during op creation and should be ok.
+  tensorflow::mutex_lock lock(map_mutex_);
+  auto s = managers_.find(op_name);
+  if (s == managers_.end()) {
+    auto it = managers_.emplace(
+        op_name, std::make_shared<tensorflow::ResourceMgr>(op_name));
+    VLOG(1) << "Returning a new manager " << op_name;
+    return it.first->second;
+  }
+  VLOG(1) << "Returning old manager " << op_name;
+  return s->second;
+}
+
+}  // namespace tensorrt
+}  // namespace tensorflow
diff --git a/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h
new file mode 100644
index 0000000000..5f8ad491d3
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/resources/trt_resource_manager.h
@@ -0,0 +1,49 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCE_MANAGER_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_RESOURCE_MANAGER_H_
+#include <memory>
+
+#include <string>
+#include <unordered_map>
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/platform/mutex.h"
+
+namespace tensorflow {
+namespace tensorrt {
+
+class TRTResourceManager {
+  TRTResourceManager() = default;
+
+ public:
+  static std::shared_ptr<TRTResourceManager> instance() {
+    static std::shared_ptr<TRTResourceManager> instance_(
+        new TRTResourceManager);
+    return instance_;
+  }
+  // returns a manager for given op, if it doesn't exists it creates one
+  std::shared_ptr<tensorflow::ResourceMgr> getManager(const string& op_name);
+
+ private:
+  std::unordered_map<string, std::shared_ptr<tensorflow::ResourceMgr>>
+      managers_;
+  tensorflow::mutex map_mutex_;
+};
+
+}  // namespace tensorrt
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_RESOURCE_TRT_RESOURCE_MANAGER_H_
diff --git a/tensorflow/contrib/tensorrt/resources/trt_resources.h b/tensorflow/contrib/tensorrt/resources/trt_resources.h
new file mode 100644
index 0000000000..3c85968ae7
--- /dev/null
+++ b/tensorflow/contrib/tensorrt/resources/trt_resources.h
@@ -0,0 +1,95 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRTRESOURCES_H_
+#define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRTRESOURCES_H_
+
+#include <list>
+#include <sstream>
+#include <string>
+#include <thread>
+#include <vector>
+#include "tensorflow/contrib/tensorrt/log/trt_logger.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+
+#if GOOGLE_CUDA
+#if GOOGLE_TENSORRT
+#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h"
+#include "tensorrt/include/NvInfer.h"
+
+namespace tensorflow {
+namespace tensorrt {
+class TRTCalibrationResource : public tensorflow::ResourceBase {
+ public:
+  TRTCalibrationResource()
+      : calibrator_(nullptr),
+        builder_(nullptr),
+        network_(nullptr),
+        engine_(nullptr),
+        logger_(nullptr),
+        thr_(nullptr) {}
+  string DebugString() override {
+    std::stringstream oss;
+    oss << " Calibrator = " << std::hex << calibrator_ << std::dec << std::endl
+        << " Builder    = " << std::hex << builder_ << std::dec << std::endl
+        << " Network    = " << std::hex << network_ << std::dec << std::endl
+        << " Engine     = " << std::hex << engine_ << std::dec << std::endl
+        << " Logger     = " << std::hex << logger_ << std::dec << std::endl
+        << " Thread     = " << std::hex << thr_ << std::dec << std::endl;
+    return oss.str();
+  }
+  ~TRTCalibrationResource() {
+    VLOG(0) << "Destroying Calibration Resource " << std::endl << DebugString();
+  }
+  TRTInt8Calibrator* calibrator_;
+  nvinfer1::IBuilder* builder_;
+  nvinfer1::INetworkDefinition* network_;
+  nvinfer1::ICudaEngine* engine_;
+  tensorflow::tensorrt::Logger* logger_;
+  // TODO(sami): Use threadpool threads!
+  std::thread* thr_;
+};
+
+class TRTWeightStore : public tensorflow::ResourceBase {
+ public:
+  TRTWeightStore() {}
+  std::list<std::vector<uint8_t>> store_;
+  string DebugString() override {
+    std::stringstream oss;
+    size_t lenBytes = 0;
+    for (const auto& v : store_) {
+      lenBytes += v.size() * sizeof(uint8_t);
+    }
+    oss << " Number of entries     = " << store_.size() << std::endl
+        << " Total number of bytes = "
+        << store_.size() * sizeof(std::vector<uint8_t>) + lenBytes << std::endl;
+    return oss.str();
+  }
+  virtual ~TRTWeightStore() { VLOG(1) << "Destroying store" << DebugString(); }
+};
+
+class TRTEngineResource : public tensorflow::ResourceBase {
+ public:
+  TRTEngineResource() : runtime_(nullptr), ctx_(nullptr){};
+  string DebugString() override { return string(""); }
+  nvinfer1::IRuntime* runtime_;
+  nvinfer1::IExecutionContext* ctx_;
+};
+
+}  // namespace tensorrt
+}  // namespace tensorflow
+#endif  // TENSORFLOW_CONTRIB_TENSORRT_RESOURCEMGR_TRTRESOURCES_H_
+#endif
+#endif
-- 
GitLab


From 0770b3f963405974692bf0908fcb7db8df81d3f6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 17:28:48 -0800
Subject: [PATCH 525/575] Implement partial constant propagation through
 IdentityN.

PiperOrigin-RevId: 187560028
---
 .../grappler/optimizers/constant_folding.cc   | 51 ++++++++++++++++++
 .../optimizers/constant_folding_test.cc       | 53 +++++++++++++++++++
 2 files changed, 104 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc
index 32c8a9b2f5..77804142e6 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding.cc
@@ -1843,6 +1843,57 @@ Status ConstantFolding::SimplifyGraph(GraphDef* output,
       continue;
     }
 
+    // Partial constant propagation through IdentityN.
+    if (IsIdentityN(*node) && NumNonControlInputs(*node) > 0) {
+      const std::set<NodeDef*>& tmp = node_map_->GetOutputs(node->name());
+      const std::vector<NodeDef*> consumers(tmp.begin(), tmp.end());
+      for (int input_idx = 0; input_idx < node->input_size(); ++input_idx) {
+        const string& input = node->input(input_idx);
+        if (IsControlInput(input)) {
+          break;
+        }
+        const NodeDef* input_node = node_map_->GetNode(NodeName(input));
+        if (input_node == nullptr) {
+          LOG(ERROR) << "Bad input: " << input;
+          break;
+        }
+        // Forward constant inputs to outputs and add a control dependency on
+        // the IdentityN node.
+        if (IsReallyConstant(*input_node)) {
+          // Update each consumer.
+          for (NodeDef* consumer : consumers) {
+            bool add_dep = false;
+            for (int consumer_input_idx = 0;
+                 consumer_input_idx < consumer->input_size();
+                 ++consumer_input_idx) {
+              const string& consumer_input =
+                  consumer->input(consumer_input_idx);
+              if (IsControlInput(consumer_input)) {
+                break;
+              }
+              int output_idx;
+              const string input_node_name =
+                  ParseNodeName(consumer_input, &output_idx);
+              if (input_node_name == node->name() && output_idx == input_idx) {
+                consumer->set_input(consumer_input_idx, input);
+                // We will keep the input from IdentityN through a control
+                // dependendy, so we only need to add the consumer as an output
+                // for the constant input node.
+                node_map_->AddOutput(NodeName(input), consumer->name());
+                add_dep = true;
+              }
+            }
+            if (add_dep) {
+              consumer->add_input(AsControlDependency(node->name()));
+            }
+          }
+        }
+      }
+      for (NodeDef* consumer : consumers) {
+        DedupControlInputs(consumer);
+      }
+    }
+
     // Partial constant folding for associative operators:
     // Split AddN/AccumulateNV2 to enable partial
     // folding of ops when more than one but not all inputs are constant.
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 3149e1d53e..29dc93c257 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -1646,6 +1646,59 @@ TEST_F(ConstantFoldingTest, PartialFolding_AssociativeAndCommutative) {
   }
 }
 
+TEST_F(ConstantFoldingTest, IdenticalN) {
+  tensorflow::Scope scope = tensorflow::Scope::NewRootScope();
+  Output x = ops::Placeholder(scope.WithOpName("x"), DT_FLOAT,
+                              ops::Placeholder::Shape(TensorShape({})));
+  Output c1 = ops::Const(scope.WithOpName("c1"), 1.0f, {2, 2});
+  Output c2 = ops::Const(scope.WithOpName("c2"), 2.0f, {2, 2});
+  auto id_n = ops::IdentityN(scope.WithOpName("id_n"), {c1, x, c2});
+  auto id0 = ops::Identity(scope.WithOpName("id0"), id_n[1]);
+  auto id1 = ops::Identity(scope.WithOpName("id1"), id_n[0]);
+  auto add0 = ops::Add(scope.WithOpName("add0"), id_n[0], id_n[1]);
+  auto add1 = ops::Add(scope.WithOpName("add1"), id_n[0], id_n[2]);
+
+  GrapplerItem item;
+  TF_CHECK_OK(scope.ToGraphDef(&item.graph));
+  item.fetch.push_back("id0");
+  item.fetch.push_back("id1");
+  item.fetch.push_back("add0");
+  item.fetch.push_back("add1");
+
+  ConstantFolding fold(nullptr /* cpu_device */);
+  GraphDef output;
+  Status status = fold.Optimize(nullptr, item, &output);
+
+  TF_EXPECT_OK(status);
+  EXPECT_EQ(8, output.node_size());
+  // id_n should remain unchanged.
+  EXPECT_EQ("id_n", output.node(3).name());
+  EXPECT_EQ(3, output.node(3).input_size());
+  EXPECT_EQ("c1", output.node(3).input(0));
+  EXPECT_EQ("x", output.node(3).input(1));
+  EXPECT_EQ("c2", output.node(3).input(2));
+  // id0 is unchanged.
+  EXPECT_EQ("id0", output.node(4).name());
+  EXPECT_EQ(1, output.node(4).input_size());
+  // id1 should have the constant input forwarded to it,
+  // and a control dependency from id_n.
+  EXPECT_EQ("id1", output.node(5).name());
+  EXPECT_EQ(2, output.node(5).input_size());
+  EXPECT_EQ("c1", output.node(5).input(0));
+  EXPECT_EQ("^id_n", output.node(5).input(1));
+
+  EXPECT_EQ("add0", output.node(6).name());
+  EXPECT_EQ(2, output.node(6).input_size());
+  EXPECT_EQ("c1", output.node(6).input(0));
+  EXPECT_EQ("id_n:1", output.node(6).input(1));
+
+  EXPECT_EQ("add1", output.node(7).name());
+  EXPECT_EQ(3, output.node(7).input_size());
+  EXPECT_EQ("c1", output.node(7).input(0));
+  EXPECT_EQ("c2", output.node(7).input(1));
+  EXPECT_EQ("^id_n", output.node(7).input(2));
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From 8a591af6854ee1b010d82d262072b5d3b2cdf7cc Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Thu, 1 Mar 2018 17:37:49 -0800
Subject: [PATCH 526/575] Checkpointable: Make Templates Checkpointable

Uses a variable_creator_scope to hook all variable creation within the
Template. For variables without a more deeply nested Template parent, it adds a
dependency directly. For variables with a Template parent, it adds a dependency
on the sub-Template instead.

The variable scope prefix for the Template itself is stripped. However, any
variable_scopes inside the Template (such as those for Layers) will be included
in the dependency names. So within Templates we essentially have name-based
saving (with the exception of dependencies between Templates themselves, which
use the object-based dependency mechanism). This isn't ideal, but will hopefully
allow migration toward object oriented dependencies more smoothly.

Throws an error on object-based save() if the dependencies don't match
between Checkpointable and .variables.

Includes a semi-related usability fix for the Checkpoint utility; mostly in
unit tests, restore() is not called before save(), which when graph building
was leading to the save counter not being initialized. Fixes that.

PiperOrigin-RevId: 187560911
---
 .../eager/python/checkpointable_utils.py      |  11 +-
 .../eager/python/checkpointable_utils_test.py |  80 ++++++++++++
 .../python/kernel_tests/template_test.py      |   4 +
 tensorflow/python/ops/template.py             | 117 +++++++++++++++++-
 4 files changed, 207 insertions(+), 5 deletions(-)

diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py
index ed431e02ea..89cd543f77 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils.py
@@ -843,10 +843,17 @@ class Checkpoint(core_checkpointable.Checkpointable):
 
   def save(self, file_prefix, session=None):
     """Save a checkpoint. Wraps `tfe.CheckpointableSaver.save`."""
-    assign_op = self.save_counter.assign_add(1)
-    if context.in_graph_mode():
+    in_graph_mode = context.in_graph_mode()
+    if in_graph_mode:
       if session is None:
         session = ops.get_default_session()
+      if self._save_counter is None:
+        # When graph building, if this is a new save counter variable then it
+        # needs to be initialized before assign_add. This is only an issue if
+        # restore() has not been called first.
+        session.run(self.save_counter.initializer)
+    assign_op = self.save_counter.assign_add(1)
+    if in_graph_mode:
       session.run(assign_op)
     return self._saver.save(
         file_prefix=file_prefix,
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index 9424de0835..c9db2bcafc 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -34,6 +34,7 @@ from tensorflow.python.layers import core
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
+from tensorflow.python.ops import template
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.training import adam
 from tensorflow.python.training import checkpointable
@@ -855,6 +856,85 @@ class CheckpointingTests(test.TestCase):
         self.assertAllEqual(3., self.evaluate(beta1_power))
 
 
+class TemplateTests(test.TestCase):
+
+  @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
+  def test_checkpointable_save_restore(self):
+
+    def _templated():
+      v = variable_scope.get_variable(
+          "v", shape=[1], initializer=init_ops.zeros_initializer())
+      v2 = variable_scope.get_variable(
+          "v2", shape=[1], initializer=init_ops.zeros_initializer())
+      return v, v + 1., v2
+
+    save_template = template.make_template("s1", _templated)
+    save_root = checkpointable_utils.Checkpoint(my_template=save_template)
+    v1_save, _, v2_save = save_template()
+    self.evaluate(v1_save.assign([12.]))
+    self.evaluate(v2_save.assign([14.]))
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    save_path = save_root.save(checkpoint_prefix)
+
+    load_template = template.make_template("s2", _templated)
+    load_root = checkpointable_utils.Checkpoint(my_template=load_template)
+    status = load_root.restore(save_path)
+    var, var_plus_one, var2 = load_template()
+    self.assertEqual(2, len(load_template._checkpoint_dependencies))
+    self.assertEqual("v", load_template._checkpoint_dependencies[0].name)
+    self.assertEqual("v2", load_template._checkpoint_dependencies[1].name)
+    status.assert_consumed().run_restore_ops()
+    self.assertAllEqual([12.], self.evaluate(var))
+    self.assertAllEqual([13.], self.evaluate(var_plus_one))
+    self.assertAllEqual([14.], self.evaluate(var2))
+
+  @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
+  def test_checkpointable_save_restore_nested(self):
+
+    def _inner_template():
+      v = variable_scope.get_variable(
+          "v", shape=[1], initializer=init_ops.zeros_initializer())
+      return v
+
+    def _outer_template():
+      first_inner = template.make_template("i1", _inner_template)
+      second_inner = template.make_template("i2", _inner_template)
+      v1 = first_inner()
+      v2 = second_inner()
+      v3 = second_inner()
+      return (first_inner, second_inner), (v1, v2, v3)
+
+    with variable_scope.variable_scope("ignored"):
+      save_template = template.make_template("s1", _outer_template)
+      save_root = checkpointable_utils.Checkpoint(my_template=save_template)
+      (inner_template_one, inner_template_two), _ = save_template()
+    self.evaluate(inner_template_one.variables[0].assign([20.]))
+    self.evaluate(inner_template_two.variables[0].assign([25.]))
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    save_path = save_root.save(checkpoint_prefix)
+
+    load_template = template.make_template("s2", _outer_template)
+    load_root = checkpointable_utils.Checkpoint(my_template=load_template)
+    status = load_root.restore(save_path)
+    (inner_template_one, inner_template_two), (v1, v2, v3) = load_template()
+    outer_template_dependencies = load_root.my_template._checkpoint_dependencies
+    self.assertEqual(2, len(outer_template_dependencies))
+    self.assertEqual("i1", outer_template_dependencies[0].name)
+    self.assertIs(inner_template_one, outer_template_dependencies[0].ref)
+    self.assertEqual("i2", outer_template_dependencies[1].name)
+    self.assertIs(inner_template_two, outer_template_dependencies[1].ref)
+    self.assertEqual(1, len(inner_template_one._checkpoint_dependencies))
+    self.assertEqual("v", inner_template_one._checkpoint_dependencies[0].name)
+    self.assertEqual(1, len(inner_template_two._checkpoint_dependencies))
+    self.assertEqual("v", inner_template_two._checkpoint_dependencies[0].name)
+    status.assert_consumed().run_restore_ops()
+    self.assertAllEqual([20.], self.evaluate(v1))
+    self.assertAllEqual([25.], self.evaluate(v2))
+    self.assertAllEqual([25.], self.evaluate(v3))
+
+
 class CheckpointCompatibilityTests(test.TestCase):
 
   def _initialized_model(self):
diff --git a/tensorflow/python/kernel_tests/template_test.py b/tensorflow/python/kernel_tests/template_test.py
index a519b69b22..c42ae5a77d 100644
--- a/tensorflow/python/kernel_tests/template_test.py
+++ b/tensorflow/python/kernel_tests/template_test.py
@@ -356,6 +356,10 @@ class TemplateTest(test.TestCase):
     self.assertEqual("s1_1/nested/dummy:0", v5.name)
     self.assertEqual("s1_1/nested_1/dummy:0", v6.name)
 
+    self.assertEqual(2, len(tmpl1._checkpoint_dependencies))
+    self.assertEqual("nested", tmpl1._checkpoint_dependencies[0].name)
+    self.assertEqual("nested_1", tmpl1._checkpoint_dependencies[1].name)
+
   @test_util.run_in_graph_and_eager_modes()
   def test_nested_templates_with_defun(self):
 
diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py
index 424582b348..70e8040512 100644
--- a/tensorflow/python/ops/template.py
+++ b/tensorflow/python/ops/template.py
@@ -26,6 +26,7 @@ from tensorflow.python.eager import function
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import checkpointable
 from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util import tf_decorator
 from tensorflow.python.util.deprecation import deprecated
@@ -230,7 +231,7 @@ def _skip_common_stack_elements(stacktrace, base_case):
   return stacktrace[-1:]
 
 
-class Template(object):
+class Template(checkpointable.CheckpointableBase):
   """Wrap a function to aid in variable sharing.
 
   Templates are functions that create variables the first time they are called
@@ -294,12 +295,115 @@ class Template(object):
     # which is not the same as whether the scope has been created.
     self._variables_created = False
 
+  @property
+  def _checkpoint_dependencies(self):
+    """Sanity checking for object-based saving.
+
+    Does not override Checkpointable dependency tracking, but checks that
+    variables accessible through Checkpointable dependencies on other `Template`
+    objects include all of the variable_scope-filtered `Template.variables`.
+
+    Returns:
+      A list of checkpointable.CheckpointableReference objects.
+    Raises:
+      ValueError: If this object is not compatible with object-based saving.
+    """
+    dependencies = super(Template, self)._checkpoint_dependencies
+    dependency_variables = []
+    for _, dependency in dependencies:
+      if isinstance(dependency, Template):
+        dependency_variables.extend(dependency.variables)
+      else:
+        dependency_variables.append(dependency)
+    dependency_variables = set(dependency_variables)
+    not_included_variables = []
+    for expected_variable in sorted(self.variables, key=lambda v: v.name):
+      if expected_variable not in dependency_variables:
+        not_included_variables.append(expected_variable)
+    if not_included_variables:
+      # Trying to save a Template which improperly tracks its variables.
+      raise ValueError(
+          ("The Template '%s' references variables which are not included via "
+           "object-based dependency tracking. Most likely a custom "
+           "getter/creator was registered which does not call Template's "
+           "custom variable creator (which is responsible for tracking "
+           "dependencies).\n\nExpected these variables to be dependencies: %s")
+          % (self, not_included_variables))
+    return dependencies
+
+  def _checkpointable_custom_creator(self, next_creator, name, initial_value,
+                                     checkpointable_parent=None, **kwargs):
+    """A variable creation hook which adds Checkpointable dependencies.
+
+    Set during the `Template`'s first wrapped function execution. Ensures that
+    (a) `Template` objects depend on `Template`s created inside them which
+    create variables, and (b) that any variables not in a more deeply nested
+    `Template` are added as dependencies directly.
+
+    The `checkpointable_parent` argument is passed between `Template` custom
+    creators but ignored when the variable object itself is created. This
+    argument indicates (if not `None`) that a more deeply nested `Template` has
+    already added the variable as a dependency, and that parent `Template`s
+    should add a dependency on that `Template` rather than on the variable
+    directly.
+
+    Args:
+      next_creator: See `variable_scope.variable_creator_scope`; the next
+        creator in the chain.
+      name: The (full, scope-influenced) name of the variable. The scope name
+        for the Template itself is stripped for the purposes of object-based
+        dependency tracking, but scopes within Templates are respected.
+      initial_value: See `variable_scope.variable_creator_scope`. Taken
+        explicitly so the argument can be re-named and used with
+        `Checkpointable._add_variable_with_custom_getter`.
+      checkpointable_parent: If not None, a more deeply nested Template object
+        to add a dependency on (rather than depending on the variable directly).
+      **kwargs: Passed through to the next creator.
+    Returns:
+      The output of `next_creator`: the fetched/created variable object.
+    """
+    def _call_next_creator_renaming_initializer(initializer, **inner_kwargs):
+      inner_kwargs.pop("name")  # Ignored; this is the scope-stripped name which
+                                # we don't want to propagate.
+      return next_creator(
+          initial_value=initializer,
+          name=name,
+          **inner_kwargs)
+    if name.startswith(self._variable_scope.name):
+      scope_stripped_name = name[len(self._variable_scope.name) + 1:]
+      if not checkpointable_parent:
+        return self._add_variable_with_custom_getter(
+            initializer=initial_value,
+            name=scope_stripped_name,
+            getter=_call_next_creator_renaming_initializer,
+            # Disable error checking for Checkpointable. Exceptions are instead
+            # raised if necessary when the object-based saver tries to
+            # save/restore the object.
+            overwrite=True,
+            checkpointable_parent=self,
+            **kwargs)
+      else:
+        self._track_checkpointable(
+            checkpointable_parent,
+            name=checkpointable_parent._variable_scope.name[  # pylint: disable=protected-access
+                len(self._variable_scope.name) + 1:],
+            overwrite=True)
+    return next_creator(name=name, initial_value=initial_value,
+                        checkpointable_parent=self, **kwargs)
+
   def _call_func(self, args, kwargs):
     try:
       vars_at_start = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
       trainable_at_start = len(
           ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES))
-      result = self._func(*args, **kwargs)
+      if self._variables_created:
+        result = self._func(*args, **kwargs)
+      else:
+        # The first time we run, restore variables if necessary (via
+        # Checkpointable).
+        with variable_scope.variable_creator_scope(
+            self._checkpointable_custom_creator):
+          result = self._func(*args, **kwargs)
 
       if self._variables_created:
         # Variables were previously created, implying this is not the first
@@ -563,7 +667,14 @@ class EagerTemplate(Template):
     try:
       vars_at_start = self._template_store.variables()
       trainable_at_start = self._template_store.trainable_variables()
-      result = self._func(*args, **kwargs)
+      if self._variables_created:
+        result = self._func(*args, **kwargs)
+      else:
+        # The first time we run, restore variables if necessary (via
+        # Checkpointable).
+        with variable_scope.variable_creator_scope(
+            self._checkpointable_custom_creator):
+          result = self._func(*args, **kwargs)
 
       if self._variables_created:
         # Variables were previously created, implying this is not the first
-- 
GitLab


From 4669767c4c6d830c2234c3ba15944a362b08fa14 Mon Sep 17 00:00:00 2001
From: "Joshua V. Dillon" <jvdillon@google.com>
Date: Thu, 1 Mar 2018 17:41:41 -0800
Subject: [PATCH 527/575] Add util which creates Python callable with
 tf.Variables explicitly as arguments.

PiperOrigin-RevId: 187561302
---
 tensorflow/contrib/bayesflow/BUILD            |  17 --
 tensorflow/contrib/bayesflow/__init__.py      |   2 -
 .../kernel_tests/variable_utils_test.py       | 135 ---------------
 .../bayesflow/python/ops/variable_utils.py    |  29 ----
 .../python/ops/variable_utils_impl.py         | 157 ------------------
 5 files changed, 340 deletions(-)
 delete mode 100644 tensorflow/contrib/bayesflow/python/kernel_tests/variable_utils_test.py
 delete mode 100644 tensorflow/contrib/bayesflow/python/ops/variable_utils.py
 delete mode 100644 tensorflow/contrib/bayesflow/python/ops/variable_utils_impl.py

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 270c309ec3..3592cff90b 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -251,23 +251,6 @@ cuda_py_test(
     tags = ["notsan"],
 )
 
-cuda_py_test(
-    name = "variable_utils_test",
-    size = "small",
-    srcs = ["python/kernel_tests/variable_utils_test.py"],
-    additional_deps = [
-        ":bayesflow_py",
-        "//third_party/py/numpy",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:gradients",
-        "//tensorflow/python:math_ops",
-        "//tensorflow/python:platform_test",
-    ],
-)
-
 cuda_py_test(
     name = "variational_sgd_optimizer_test",
     size = "small",
diff --git a/tensorflow/contrib/bayesflow/__init__.py b/tensorflow/contrib/bayesflow/__init__.py
index 528c4fbacd..c411026346 100644
--- a/tensorflow/contrib/bayesflow/__init__.py
+++ b/tensorflow/contrib/bayesflow/__init__.py
@@ -30,7 +30,6 @@ from tensorflow.contrib.bayesflow.python.ops import mcmc_diagnostics
 from tensorflow.contrib.bayesflow.python.ops import metropolis_hastings
 from tensorflow.contrib.bayesflow.python.ops import monte_carlo
 from tensorflow.contrib.bayesflow.python.ops import optimizers
-from tensorflow.contrib.bayesflow.python.ops import variable_utils
 # pylint: enable=unused-import,line-too-long
 
 from tensorflow.python.util.all_util import remove_undocumented
@@ -49,7 +48,6 @@ _allowed_symbols = [
     'optimizers',
     'special_math',
     'stochastic_variables',
-    'variable_utils',
     'variational_inference',
 ]
 
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/variable_utils_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/variable_utils_test.py
deleted file mode 100644
index f978cf8641..0000000000
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/variable_utils_test.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for utility functions related to managing `tf.Variable`s."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import warnings
-
-import numpy as np
-
-from tensorflow.contrib.bayesflow.python.ops import variable_utils
-
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import variable_scope as varscope_ops
-from tensorflow.python.ops import variables as variables_ops
-from tensorflow.python.platform import test
-
-
-def test_fn(x):
-  x = ops.convert_to_tensor(x, name="x")
-  dtype = x.dtype.as_numpy_dtype
-  s = x.shape.as_list()
-  z = varscope_ops.get_variable(
-      name="z",
-      dtype=dtype,
-      initializer=np.arange(np.prod(s)).reshape(s).astype(dtype))
-  y = varscope_ops.get_variable(
-      name="y",
-      dtype=dtype,
-      initializer=np.arange(np.prod(s)).reshape(s).astype(dtype)**2)
-  return x + y + z
-
-
-class _WrapCallableTest(object):
-
-  def testDefaultArgsWorkCorrectly(self):
-    with self.test_session():
-      x = constant_op.constant(self.dtype([0.1, 0.2]))
-      wrapped_fn, vars_args = variable_utils.externalize_variables_as_args(
-          test_fn, [x])
-
-      varscope_ops.get_variable_scope().reuse_variables()
-
-      result = wrapped_fn(self.dtype(2), [3, 4, 5], 0.5)
-
-      y_actual = varscope_ops.get_variable("y", dtype=self.dtype)
-      z_actual = varscope_ops.get_variable("z", dtype=self.dtype)
-
-      variables_ops.global_variables_initializer().run()
-      result_ = result.eval()
-
-      self.assertEqual(self.dtype, result_.dtype)
-      self.assertAllEqual([5.5, 6.5, 7.5], result_)
-      self.assertAllEqual([y_actual, z_actual], vars_args)
-
-  def testNonDefaultArgsWorkCorrectly(self):
-    with self.test_session():
-      x = constant_op.constant(self.dtype([0.1, 0.2]))
-
-      _ = test_fn(self.dtype([0., 0.]))   # Needed to create vars.
-      varscope_ops.get_variable_scope().reuse_variables()
-
-      y_actual = varscope_ops.get_variable("y", dtype=self.dtype)
-
-      wrapped_fn, vars_args = variable_utils.externalize_variables_as_args(
-          test_fn, [x], possible_ancestor_vars=[y_actual])
-
-      result = wrapped_fn(self.dtype([2, 3]), 0.5)  # x, y
-
-      variables_ops.global_variables_initializer().run()
-      result_ = result.eval()
-
-      self.assertEqual(self.dtype, result_.dtype)
-      self.assertAllEqual([2.5, 4.5], result_)
-      self.assertAllEqual([y_actual], vars_args)
-
-  def testWarnings(self):
-    with self.test_session():
-      x = constant_op.constant(self.dtype([0.1, 0.2]))
-      wrapped_fn, _ = variable_utils.externalize_variables_as_args(
-          test_fn, [x], possible_ancestor_vars=[])
-      varscope_ops.get_variable_scope().reuse_variables()
-      with warnings.catch_warnings(record=True) as w:
-        wrapped_fn(self.dtype(2))
-      w = sorted(w, key=lambda w: str(w.message))
-      self.assertEqual(2, len(w))
-      self.assertRegexpMatches(
-          str(w[0].message),
-          r"Variable .* 'y:0' .* not found in bypass dict.")
-      self.assertRegexpMatches(
-          str(w[1].message),
-          r"Variable .* 'z:0' .* not found in bypass dict.")
-
-  def testExceptions(self):
-    with self.test_session():
-      x = constant_op.constant(self.dtype([0.1, 0.2]))
-      wrapped_fn, _ = variable_utils.externalize_variables_as_args(
-          test_fn,
-          [x],
-          possible_ancestor_vars=[],
-          assert_variable_override=True)
-      varscope_ops.get_variable_scope().reuse_variables()
-      with self.assertRaisesRegexp(ValueError, r"not found"):
-        wrapped_fn(self.dtype(2))
-
-
-class WrapCallableTest16(test.TestCase, _WrapCallableTest):
-  dtype = np.float16
-
-
-class WrapCallableTest32(test.TestCase, _WrapCallableTest):
-  dtype = np.float32
-
-
-class WrapCallableTest64(test.TestCase, _WrapCallableTest):
-  dtype = np.float64
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/variable_utils.py b/tensorflow/contrib/bayesflow/python/ops/variable_utils.py
deleted file mode 100644
index eadf6f4d5f..0000000000
--- a/tensorflow/contrib/bayesflow/python/ops/variable_utils.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Utility functions related to managing `tf.Variable`s."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-# go/tf-wildcard-import
-from tensorflow.contrib.bayesflow.python.ops.variable_utils_impl import *  # pylint: disable=wildcard-import,unused-wildcard-import,g-importing-member
-from tensorflow.python.util import all_util
-
-_allowed_symbols = [
-    "externalize_variables_as_args",
-]
-
-all_util.remove_undocumented(__name__, _allowed_symbols)
diff --git a/tensorflow/contrib/bayesflow/python/ops/variable_utils_impl.py b/tensorflow/contrib/bayesflow/python/ops/variable_utils_impl.py
deleted file mode 100644
index ca3d75b5bf..0000000000
--- a/tensorflow/contrib/bayesflow/python/ops/variable_utils_impl.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Utility functions related to managing `tf.Variable`s.
-
-@@externalize_variables_as_args
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import warnings
-
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import gradients_impl as gradients_ops
-from tensorflow.python.ops import variable_scope as varscope_ops
-from tensorflow.python.ops import variables as variables_ops
-
-__all__ = [
-    "externalize_variables_as_args",
-]
-
-
-# Cause all warnings to always be triggered.
-# Not having this means subsequent calls wont trigger the warning.
-warnings.simplefilter("always")
-
-
-def externalize_variables_as_args(fn,
-                                  fn_args=(),
-                                  ancestor_variables=None,
-                                  possible_ancestor_vars=None,
-                                  assert_variable_override=False,
-                                  name=None):
-  """"Converts variables within a callable into explicit args.
-
-  Makes a new callable from `fn` which has arguments `list(fn_args) +
-  list(ancestor_variables)`. If `ancestor_variables` is not specified, it is
-  inferred by checking which of `possible_ancestor_vars` actually influences the
-  return value of `fn` (concretely, gradient of `fn(*fn_args)` is not `None`).
-  By default `possible_ancestor_vars` is `tf.trainable_variables() +
-  tf.get_collection(tf.GraphKeys.TRAINABLE_RESOURCE_VARIABLES)`.
-
-  #### Examples:
-
-  ```python
-  num_samples = 2
-  num_dims = 1
-  dtype = np.float32
-
-  def foo(x):
-    x = tf.convert_to_tensor(x, dtype=dtype, name="x")
-    s = x.shape.as_list()
-    y = tf.get_variable(
-        name="y",
-        dtype=dtype,
-        initializer=np.arange(np.prod(s)).reshape(s).astype(dtype))
-    return x + y
-
-  x = tf.constant(dtype([0.1, 0.2]))
-
-  wrapped_foo, discovered_ancestor_variables = (
-      externalize_variables_as_args(foo, [x]))
-
-  new_x = dtype([[1.], [2.]])
-  new_y = dtype([[3.], [4.]])
-  new_result = wrapped_foo(new_x, new_y)
-  # ==> [[4.], [6.]]
-
-  discovered_ancestor_variables == [tf.get_variable("y", dtype)]
-  # ==> [True]
-  ```
-
-  Args:
-    fn: Python callable which returns a `Tensor` and accepts `*fn_args`.
-    fn_args: Python list of args to `fn`. Represents dummy arguments passed to
-      `fn` to trace its execution; actual values are unimportant. These args are
-      only used to construct the output of `fn` and to resolve the ancestor
-      `tf.Variable`s.
-      Default value: `()` (i.e., `fn` takes no args).
-    ancestor_variables: Python list of `tf.Variable`s. When `None` the list is
-      expanded to non-`None` gradients of `fn(*fn_args)`. By directly providing
-      the `ancestor_variables` the internal call to `fn` is avoided.
-      Default value: `None` (i.e., `tf.Variable` dependencies are discovered).
-    possible_ancestor_vars: Python list of possible `tf.Variable`s which might
-      be a dependency of computing `fn(*fn_args)`.
-      Default value: `None` (i.e., expanded as described above).
-    assert_variable_override: Python `bool` indicating that not finding a
-      `tf.Variable` in the override list is an exception.
-      Default value: `False` (i.e., missing a `Variable` triggers a `warning`).
-    name: Python `str` name prefixed to Ops created by this function.
-      Default value: `None` (i.e., "externalize_variables_as_args").
-
-  Returns:
-    wrapped_fn: Python callable taking arguments like
-      `*(list(fn_args) + discovered_ancestor_variables)`.
-    discovered_ancestor_variables: Python list of `tf.Variable`s known to be a
-      dependency of `fn(*fn_args)`.
-
-  Raises:
-    ValueError: if `assert_variable_override` is `True` and `Variable` is
-      requested but not overridden.
-  """
-  def _make_bypassing_custom_getter_fn(new_var_dict):
-    """Return dict value rather than what would otherwise be dict key."""
-    def _custom_getter(getter, *args, **kwargs):
-      v = getter(*args, **kwargs)
-      new_v = new_var_dict.get(v, None)
-      if new_v is None:
-        msg = "Variable \"{}\" not found in bypass dict.".format(v)
-        if assert_variable_override:
-          raise ValueError(msg)
-        warnings.warn(msg)
-        return v
-      return new_v
-    return _custom_getter
-
-  with ops.name_scope(name, "externalize_variables_as_args"):
-    if ancestor_variables is not None and not ancestor_variables:
-      return fn, ()
-    if ancestor_variables is None:
-      y = fn(*fn_args)  # Side-effect: adds trainable vars.
-      if possible_ancestor_vars is None:
-        possible_ancestor_vars = (
-            variables_ops.trainable_variables() +
-            ops.get_collection(ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES))
-      # TODO(b/72873296): Add a dedicated op for identifying ancestors.
-      ancestors = [v for g, v
-                   in zip(gradients_ops.gradients(y, possible_ancestor_vars),
-                          possible_ancestor_vars)
-                   if g is not None]
-      ancestor_variables = sorted(ancestors, key=lambda v: v.name)
-  n = len(fn_args)
-  def _fn(*args):
-    with ops.name_scope("wrapped_fn"):
-      vars_dict = dict(
-          (k, ops.convert_to_tensor(
-              v, dtype=k.dtype.base_dtype, name=k.op.name))
-          for k, v in zip(ancestor_variables, args[n:]))
-      with varscope_ops.variable_scope(
-          varscope_ops.get_variable_scope(),
-          reuse=True,
-          custom_getter=_make_bypassing_custom_getter_fn(vars_dict)):
-        return fn(*args[:n])
-  return _fn, ancestor_variables
-- 
GitLab


From e927be3872e00c9b0e5e9aa64e6aae90c4ae1315 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 1 Mar 2018 17:53:49 -0800
Subject: [PATCH 528/575] Improve CURL error reporting and handling in the GCS
 filesystem.

- The main semantics change is that we return immediately if curl_easy_perform doesn't return CURLE_OK. The CURL documentation indicates that it's not ok to fetch info if the curl call failed:
https://curl.haxx.se/libcurl/c/curl_easy_getinfo.html
- LOG errors where we can't return a status. Otherwise return with a status immediately.

PiperOrigin-RevId: 187562481
---
 .../core/platform/cloud/curl_http_request.cc  | 190 ++++++++++++------
 .../core/platform/cloud/curl_http_request.h   |  44 +++-
 .../platform/cloud/curl_http_request_test.cc  |  18 +-
 3 files changed, 176 insertions(+), 76 deletions(-)

diff --git a/tensorflow/core/platform/cloud/curl_http_request.cc b/tensorflow/core/platform/cloud/curl_http_request.cc
index 80ad1cf0b8..9bc06d56ae 100644
--- a/tensorflow/core/platform/cloud/curl_http_request.cc
+++ b/tensorflow/core/platform/cloud/curl_http_request.cc
@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/core/lib/gtl/map_util.h"
 #include "tensorflow/core/lib/strings/scanner.h"
 #include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/public/version.h"
 
@@ -129,20 +130,34 @@ CurlHttpRequest::CurlHttpRequest(LibCurl* libcurl, Env* env)
   //       default in //third_party:curl.BUILD and can be customized via an
   //       environment variable.
 
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_VERBOSE, kVerboseOutput);
-  libcurl_->curl_easy_setopt(
-      curl_, CURLOPT_USERAGENT,
-      strings::StrCat("TensorFlow/", TF_VERSION_STRING).c_str());
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_VERBOSE, kVerboseOutput),
+      "Setting verbose output");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(
+          curl_, CURLOPT_USERAGENT,
+          strings::StrCat("TensorFlow/", TF_VERSION_STRING).c_str()),
+      "Setting user agent");
   // Do not use signals for timeouts - does not work in multi-threaded programs.
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_NOSIGNAL, 1L);
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_HTTP_VERSION,
-                             CURL_HTTP_VERSION_2_0);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_NOSIGNAL, 1L),
+      "Disabling signals");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_HTTP_VERSION,
+                                 CURL_HTTP_VERSION_2_0),
+      "Setting HTTP version");
 
   // Set up the progress meter.
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_NOPROGRESS, 0ULL);
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_XFERINFODATA, this);
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_XFERINFOFUNCTION,
-                             &CurlHttpRequest::ProgressCallback);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_NOPROGRESS, 0ULL),
+      "Disabling progress meter");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_XFERINFODATA, this),
+      "Setting custom pointer to the progress callback");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_XFERINFOFUNCTION,
+                                 &CurlHttpRequest::ProgressCallback),
+      "Setting the progress callback");
 
   // If response buffer is not set, libcurl will print results to stdout,
   // so we always set it.
@@ -175,13 +190,17 @@ void CurlHttpRequest::SetUri(const string& uri) {
   CheckNotSent();
   is_uri_set_ = true;
   uri_ = uri;
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_URL, uri.c_str());
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_URL, uri.c_str()),
+      "Setting URL");
 }
 
 void CurlHttpRequest::SetRange(uint64 start, uint64 end) {
   CheckNotSent();
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_RANGE,
-                             strings::StrCat(start, "-", end).c_str());
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_RANGE,
+                                 strings::StrCat(start, "-", end).c_str()),
+      "Setting range");
 }
 
 void CurlHttpRequest::AddHeader(const string& name, const string& value) {
@@ -210,7 +229,9 @@ void CurlHttpRequest::SetDeleteRequest() {
   CheckNotSent();
   CheckMethodNotSet();
   is_method_set_ = true;
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_CUSTOMREQUEST, "DELETE");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_CUSTOMREQUEST, "DELETE"),
+      "Setting delete request");
 }
 
 Status CurlHttpRequest::SetPutFromFile(const string& body_filepath,
@@ -232,9 +253,12 @@ Status CurlHttpRequest::SetPutFromFile(const string& body_filepath,
 
   curl_headers_ = libcurl_->curl_slist_append(
       curl_headers_, strings::StrCat("Content-Length: ", size).c_str());
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_PUT, 1);
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
-                             reinterpret_cast<void*>(put_body_));
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_PUT, 1), "Setting PUT request");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
+                                 reinterpret_cast<void*>(put_body_)),
+      "Setting read data");
   // Using the default CURLOPT_READFUNCTION, which is doing an fread() on the
   // FILE * userdata set with CURLOPT_READDATA.
   return Status::OK();
@@ -244,13 +268,18 @@ void CurlHttpRequest::SetPutEmptyBody() {
   CheckNotSent();
   CheckMethodNotSet();
   is_method_set_ = true;
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_PUT, 1);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_PUT, 1), "Setting put request");
   curl_headers_ =
       libcurl_->curl_slist_append(curl_headers_, "Content-Length: 0");
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
-                             reinterpret_cast<void*>(this));
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_READFUNCTION,
-                             &CurlHttpRequest::ReadCallback);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
+                                 reinterpret_cast<void*>(this)),
+      "Setting read data");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_READFUNCTION,
+                                 &CurlHttpRequest::ReadCallback),
+      "Setting read callback");
 }
 
 void CurlHttpRequest::SetPostFromBuffer(const char* buffer, size_t size) {
@@ -259,11 +288,17 @@ void CurlHttpRequest::SetPostFromBuffer(const char* buffer, size_t size) {
   is_method_set_ = true;
   curl_headers_ = libcurl_->curl_slist_append(
       curl_headers_, strings::StrCat("Content-Length: ", size).c_str());
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_POST, 1);
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
-                             reinterpret_cast<void*>(this));
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_READFUNCTION,
-                             &CurlHttpRequest::ReadCallback);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_POST, 1),
+      "Setting POST request");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
+                                 reinterpret_cast<void*>(this)),
+      "Setting read data");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_READFUNCTION,
+                                 &CurlHttpRequest::ReadCallback),
+      "Setting read callback");
   post_body_buffer_ = StringPiece(buffer, size);
 }
 
@@ -271,13 +306,19 @@ void CurlHttpRequest::SetPostEmptyBody() {
   CheckNotSent();
   CheckMethodNotSet();
   is_method_set_ = true;
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_POST, 1);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_POST, 1),
+      "Setting POST request");
   curl_headers_ =
       libcurl_->curl_slist_append(curl_headers_, "Content-Length: 0");
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
-                             reinterpret_cast<void*>(this));
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_READFUNCTION,
-                             &CurlHttpRequest::ReadCallback);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_READDATA,
+                                 reinterpret_cast<void*>(this)),
+      "Setting read data");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_READFUNCTION,
+                                 &CurlHttpRequest::ReadCallback),
+      "Setting read callback");
 }
 
 void CurlHttpRequest::SetResultBuffer(std::vector<char>* out_buffer) {
@@ -287,10 +328,14 @@ void CurlHttpRequest::SetResultBuffer(std::vector<char>* out_buffer) {
   out_buffer->clear();
   response_buffer_ = out_buffer;
 
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_WRITEDATA,
-                             reinterpret_cast<void*>(this));
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_WRITEFUNCTION,
-                             &CurlHttpRequest::WriteCallback);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_WRITEDATA,
+                                 reinterpret_cast<void*>(this)),
+      "Setting write data");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_WRITEFUNCTION,
+                                 &CurlHttpRequest::WriteCallback),
+      "Setting write callback");
 }
 
 void CurlHttpRequest::SetResultBufferDirect(char* buffer, size_t size) {
@@ -299,10 +344,14 @@ void CurlHttpRequest::SetResultBufferDirect(char* buffer, size_t size) {
 
   direct_response_ = DirectResponseState{buffer, size, 0};
 
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_WRITEDATA,
-                             reinterpret_cast<void*>(this));
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_WRITEFUNCTION,
-                             &CurlHttpRequest::WriteCallbackDirect);
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_WRITEDATA,
+                                 reinterpret_cast<void*>(this)),
+      "Setting write data");
+  TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_WRITEFUNCTION,
+                                 &CurlHttpRequest::WriteCallbackDirect),
+      "Setting write callback");
 }
 
 bool CurlHttpRequest::IsDirectResponse() const {
@@ -424,29 +473,50 @@ Status CurlHttpRequest::Send() {
   is_sent_ = true;
 
   if (curl_headers_) {
-    libcurl_->curl_easy_setopt(curl_, CURLOPT_HTTPHEADER, curl_headers_);
+    TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+        libcurl_->curl_easy_setopt(curl_, CURLOPT_HTTPHEADER, curl_headers_),
+        "Setting HTTP header");
   }
   if (resolve_list_) {
-    libcurl_->curl_easy_setopt(curl_, CURLOPT_RESOLVE, resolve_list_);
-  }
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_HEADERDATA,
-                             reinterpret_cast<void*>(this));
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_HEADERFUNCTION,
-                             &CurlHttpRequest::HeaderCallback);
-
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_TIMEOUT, request_timeout_secs_);
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_CONNECTTIMEOUT,
-                             connect_timeout_secs_);
+    TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+        libcurl_->curl_easy_setopt(curl_, CURLOPT_RESOLVE, resolve_list_),
+        "Setting custom resolves");
+  }
+  TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_HEADERDATA,
+                                 reinterpret_cast<void*>(this)),
+      "Setting header data");
+  TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_HEADERFUNCTION,
+                                 &CurlHttpRequest::HeaderCallback),
+      "Setting header function");
+
+  TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_TIMEOUT, request_timeout_secs_),
+      "Setting request timeout");
+  TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_CONNECTTIMEOUT,
+                                 connect_timeout_secs_),
+      "Setting connection timeout");
 
   char error_buffer[CURL_ERROR_SIZE] = {0};
-  libcurl_->curl_easy_setopt(curl_, CURLOPT_ERRORBUFFER, error_buffer);
+  TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_setopt(curl_, CURLOPT_ERRORBUFFER, error_buffer),
+      "Setting error buffer");
 
-  const auto curl_result = libcurl_->curl_easy_perform(curl_);
+  const CURLcode curl_result = libcurl_->curl_easy_perform(curl_);
+  TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+      curl_result, "Performing request. Detailed error: ", error_buffer);
 
   double written_size = 0;
-  libcurl_->curl_easy_getinfo(curl_, CURLINFO_SIZE_DOWNLOAD, &written_size);
+  TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_getinfo(curl_, CURLINFO_SIZE_DOWNLOAD, &written_size),
+      "Fetching written size");
 
-  libcurl_->curl_easy_getinfo(curl_, CURLINFO_RESPONSE_CODE, &response_code_);
+  TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(
+      libcurl_->curl_easy_getinfo(curl_, CURLINFO_RESPONSE_CODE,
+                                  &response_code_),
+      "Fetching response code");
 
   Status result;
   switch (response_code_) {
@@ -616,4 +686,12 @@ int CurlHttpRequest::ProgressCallback(void* this_object, curl_off_t dltotal,
   return 0;
 }
 
+Status CURLcodeToStatus(CURLcode code) {
+  // Return Unavailable to retry by default. We probably should distinguish
+  // between permanent or temporary failures.
+  return errors::Unavailable("Error executing an HTTP request (error code ",
+                             code, ", error message '",
+                             curl_easy_strerror(code), "')");
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/curl_http_request.h b/tensorflow/core/platform/cloud/curl_http_request.h
index cfa26f2b79..c9f60cb5fc 100644
--- a/tensorflow/core/platform/cloud/curl_http_request.h
+++ b/tensorflow/core/platform/cloud/curl_http_request.h
@@ -229,26 +229,28 @@ class LibCurl {
 
   virtual CURL* curl_easy_init() = 0;
   virtual CURLcode curl_easy_setopt(CURL* curl, CURLoption option,
-                                    uint64 param) = 0;
+                                    uint64 param) TF_MUST_USE_RESULT = 0;
   virtual CURLcode curl_easy_setopt(CURL* curl, CURLoption option,
-                                    const char* param) = 0;
+                                    const char* param) TF_MUST_USE_RESULT = 0;
   virtual CURLcode curl_easy_setopt(CURL* curl, CURLoption option,
-                                    void* param) = 0;
-  virtual CURLcode curl_easy_setopt(CURL* curl, CURLoption option,
-                                    size_t (*param)(void*, size_t, size_t,
-                                                    FILE*)) = 0;
+                                    void* param) TF_MUST_USE_RESULT = 0;
+  virtual CURLcode curl_easy_setopt(
+      CURL* curl, CURLoption option,
+      size_t (*param)(void*, size_t, size_t, FILE*)) TF_MUST_USE_RESULT = 0;
   virtual CURLcode curl_easy_setopt(CURL* curl, CURLoption option,
                                     size_t (*param)(const void*, size_t, size_t,
-                                                    void*)) = 0;
+                                                    void*))
+      TF_MUST_USE_RESULT = 0;
   virtual CURLcode curl_easy_setopt(
       CURL* curl, CURLoption option,
       int (*param)(void* clientp, curl_off_t dltotal, curl_off_t dlnow,
-                   curl_off_t ultotal, curl_off_t ulnow)) = 0;
-  virtual CURLcode curl_easy_perform(CURL* curl) = 0;
+                   curl_off_t ultotal,
+                   curl_off_t ulnow)) TF_MUST_USE_RESULT = 0;
+  virtual CURLcode curl_easy_perform(CURL* curl) TF_MUST_USE_RESULT = 0;
   virtual CURLcode curl_easy_getinfo(CURL* curl, CURLINFO info,
-                                     uint64* value) = 0;
+                                     uint64* value) TF_MUST_USE_RESULT = 0;
   virtual CURLcode curl_easy_getinfo(CURL* curl, CURLINFO info,
-                                     double* value) = 0;
+                                     double* value) TF_MUST_USE_RESULT = 0;
   virtual void curl_easy_cleanup(CURL* curl) = 0;
   virtual curl_slist* curl_slist_append(curl_slist* list, const char* str) = 0;
   virtual void curl_slist_free_all(curl_slist* list) = 0;
@@ -258,6 +260,26 @@ class LibCurl {
   virtual const char* curl_easy_strerror(CURLcode errornum) = 0;
 };
 
+Status CURLcodeToStatus(CURLcode code);
+
+#define TF_CURL_RETURN_WITH_CONTEXT_IF_ERROR(_code, ...)                    \
+  do {                                                                      \
+    if (_code != CURLE_OK) {                                                \
+      ::tensorflow::Status _status = ::tensorflow::CURLcodeToStatus(_code); \
+      ::tensorflow::errors::AppendToMessage(&_status, __VA_ARGS__);         \
+      return _status;                                                       \
+    }                                                                       \
+  } while (0)
+
+#define TF_CURL_LOG_WITH_CONTEXT_IF_ERROR(_code, ...)                       \
+  do {                                                                      \
+    if (_code != CURLE_OK) {                                                \
+      ::tensorflow::Status _status = ::tensorflow::CURLcodeToStatus(_code); \
+      ::tensorflow::errors::AppendToMessage(&_status, __VA_ARGS__);         \
+      LOG(ERROR) << "curl error: " << _status.error_message();              \
+    }                                                                       \
+  } while (0)
+
 }  // namespace tensorflow
 
 #endif  // TENSORFLOW_CORE_PLATFORM_CLOUD_CURL_HTTP_REQUEST_H_
diff --git a/tensorflow/core/platform/cloud/curl_http_request_test.cc b/tensorflow/core/platform/cloud/curl_http_request_test.cc
index 94af121768..4cded9b81b 100644
--- a/tensorflow/core/platform/cloud/curl_http_request_test.cc
+++ b/tensorflow/core/platform/cloud/curl_http_request_test.cc
@@ -346,7 +346,6 @@ TEST(CurlHttpRequestTest, GetRequest_Empty) {
 
 TEST(CurlHttpRequestTest, GetRequest_RangeOutOfBound) {
   FakeLibCurl libcurl("get response", 416);
-  libcurl.curl_easy_perform_result_ = CURLE_WRITE_ERROR;
   CurlHttpRequest http_request(&libcurl);
 
   std::vector<char> scratch;
@@ -377,10 +376,10 @@ TEST(CurlHttpRequestTest, GetRequest_503) {
   const auto& status = http_request.Send();
   EXPECT_EQ(error::UNAVAILABLE, status.code());
   EXPECT_EQ(
-      "Error executing an HTTP request (HTTP response code 503, "
-      "error code 23, error message ''), response 'get response'",
+      "Error executing an HTTP request (error code 23, error message 'Failed "
+      "writing received data to disk/application')\n\tPerforming request. "
+      "Detailed error: ",
       status.error_message());
-  EXPECT_EQ(503, http_request.GetResponseCode());
 }
 
 TEST(CurlHttpRequestTest, GetRequest_HttpCode0) {
@@ -396,9 +395,9 @@ TEST(CurlHttpRequestTest, GetRequest_HttpCode0) {
   const auto& status = http_request.Send();
   EXPECT_EQ(error::UNAVAILABLE, status.code());
   EXPECT_EQ(
-      "Error executing an HTTP request (HTTP response code 0, "
-      "error code 28, error message 'Operation timed out'), "
-      "response 'get response'",
+      "Error executing an HTTP request (error code 28, error message 'Timeout "
+      "was reached')\n\tPerforming request. Detailed error: Operation timed "
+      "out",
       status.error_message());
   EXPECT_EQ(0, http_request.GetResponseCode());
 }
@@ -629,8 +628,9 @@ TEST(CurlHttpRequestTest, ProgressIsStuck) {
   auto status = http_request.Send();
   EXPECT_EQ(error::UNAVAILABLE, status.code());
   EXPECT_EQ(
-      "Error executing an HTTP request (HTTP response code 200, "
-      "error code 42, error message ''), response 'test'",
+      "Error executing an HTTP request (error code 42, error message "
+      "'Operation was aborted by an application callback')\n\tPerforming "
+      "request. Detailed error: ",
       status.error_message());
 }
 
-- 
GitLab


From 80a647612e2cc0b98f763ffca1f7f35df7d27805 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 17:58:07 -0800
Subject: [PATCH 529/575] Allow replacing attributes in templates.

PiperOrigin-RevId: 187562864
---
 tensorflow/contrib/py2tf/pyct/templates.py    | 11 +++++++++++
 .../contrib/py2tf/pyct/templates_test.py      | 19 ++++++++++++++++++-
 2 files changed, 29 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/py2tf/pyct/templates.py b/tensorflow/contrib/py2tf/pyct/templates.py
index 6ee6c0c5ce..7021e2ba93 100644
--- a/tensorflow/contrib/py2tf/pyct/templates.py
+++ b/tensorflow/contrib/py2tf/pyct/templates.py
@@ -79,6 +79,17 @@ class ReplaceTransformer(gast.NodeTransformer):
     else:
       raise ValueError('unexpected node type "%s"' % node)
 
+  def visit_Attribute(self, node):
+    node = self.generic_visit(node)
+    if node.attr not in self.replacements:
+      return node
+    repl = self.replacements[node.attr]
+    if not isinstance(repl, gast.Name):
+      raise ValueError(
+          'An attribute can only be replaced by a Name node. Found: %s' % repl)
+    node.attr = repl.id
+    return node
+
   def visit_Name(self, node):
     if node.id not in self.replacements:
       return node
diff --git a/tensorflow/contrib/py2tf/pyct/templates_test.py b/tensorflow/contrib/py2tf/pyct/templates_test.py
index 8ccfde8573..0d1c1c5d9e 100644
--- a/tensorflow/contrib/py2tf/pyct/templates_test.py
+++ b/tensorflow/contrib/py2tf/pyct/templates_test.py
@@ -18,6 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import imp
+
 import gast
 
 from tensorflow.contrib.py2tf.pyct import compiler
@@ -62,7 +64,7 @@ class TemplatesTest(test.TestCase):
     result, _ = compiler.ast_to_object(node)
     self.assertEquals(7, result.test_fn(2))
 
-  def test_code_block(self):
+  def test_replace_code_block(self):
     template = """
       def test_fn(a):
         block
@@ -79,6 +81,21 @@ class TemplatesTest(test.TestCase):
     result, _ = compiler.ast_to_object(node)
     self.assertEquals(3, result.test_fn(1))
 
+  def test_replace_attribute(self):
+    template = """
+      def test_fn(a):
+        return a.foo
+    """
+
+    node = templates.replace(template, foo='b')[0]
+    result, _ = compiler.ast_to_object(node)
+    mod = imp.new_module('test')
+    mod.b = 3
+    self.assertEquals(3, result.test_fn(mod))
+
+    with self.assertRaises(ValueError):
+      templates.replace(template, foo=1)
+
 
 if __name__ == '__main__':
   test.main()
-- 
GitLab


From 6d1309419497d52ef9a28df927a0b214cde9507c Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 1 Mar 2018 18:03:19 -0800
Subject: [PATCH 530/575] Grappler: Change memory optimizer recomputation name
 prefix into a regexp. This allows us to match any node names, especially
 those under different scopes. This still performs a prefix regexp match, so
 it is basically backwards compatible.

PiperOrigin-RevId: 187563544
---
 tensorflow/core/BUILD                         |  1 +
 tensorflow/core/grappler/optimizers/BUILD     |  1 +
 .../grappler/optimizers/memory_optimizer.cc   | 20 ++++++++-----
 .../grappler/optimizers/memory_optimizer.h    | 10 +++----
 .../grappler/optimizers/meta_optimizer.cc     |  4 +--
 .../core/protobuf/rewriter_config.proto       | 16 +++++-----
 .../python/grappler/memory_optimizer_test.py  | 29 ++++++++++++++++++-
 7 files changed, 56 insertions(+), 25 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 3271825251..96e30ca3c0 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -2231,6 +2231,7 @@ cc_library(
     ],
     visibility = [
         "//tensorflow/compiler:__subpackages__",
+        "//tensorflow/core/grappler:__subpackages__",
         "//tensorflow/core/profiler:__subpackages__",
     ],
     deps = [":lib_internal"],
diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 037438ee75..0a4330b524 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -363,6 +363,7 @@ cc_library(
         ":graph_rewriter",
         ":static_schedule",
         "//tensorflow/core:framework",
+        "//tensorflow/core:regexp_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:graph_view",
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 694139fa50..d73050ac4d 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -36,6 +36,7 @@ limitations under the License.
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/grappler/utils/topological_sort.h"
 #include "tensorflow/core/grappler/utils/traversal.h"
+#include "tensorflow/core/platform/regexp.h"
 #include "tensorflow/core/protobuf/rewriter_config.pb.h"
 
 namespace tensorflow {
@@ -413,7 +414,7 @@ void RecomputeSubgraph(
 }
 
 void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
-                                const string& recomputation_targets_name_prefix,
+                                const string& recomputation_targets_name_regexp,
                                 GraphDef* graph, const GrapplerItem& item) {
   if (optimization_level != RewriterConfig::RECOMPUTATION_HEURISTICS &&
       optimization_level != RewriterConfig::HEURISTICS &&
@@ -437,16 +438,19 @@ void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
   for (const auto& feed : item.feed) {
     feeds.insert(NodeName(feed.first));
   }
+  RE2 recomputation_targets_re(recomputation_targets_name_regexp);
   std::function<bool(const NodeDef&)> is_target =
-      [&recomputation_targets_name_prefix](const NodeDef& node) {
-        // Nodes whose inputs we may want to recompute. Typically targets will
-        // be gradients (recomputation_targets_name_prefix="gradients/"),
-        // although the prefix is configurable since gradients may be created
-        // in a name scope.
+      [&recomputation_targets_re](const NodeDef& node) {
+        // Nodes whose inputs we may want to recompute. This does a prefix
+        // regexp match, and typically one sets regexp="gradients/" meaning
+        // it will match all node names with scope beginning with "gradients/".
+        // If used within scopes, one may want to set regexp="(.+/)?gradients/".
         // TODO(allenl): Use a static schedule
         // (grappler::EstimateEarliestExecutionTimes) to recompute only nodes
         // whose outputs will sit around for a while.
-        return node.name().find(recomputation_targets_name_prefix) == 0;
+        bool match = recomputation_targets_re.Match(
+            node.name(), 0, node.name().size(), RE2::ANCHOR_START, nullptr, 0);
+        return match;
       };
 
   if (optimization_level == RewriterConfig::RECOMPUTATION_HEURISTICS ||
@@ -1225,7 +1229,7 @@ Status MemoryOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
   *optimized_graph = item.graph;
 
   RecomputationRewritingPass(optimization_level_,
-                             recomputation_targets_name_prefix_,
+                             recomputation_targets_name_regexp_,
                              optimized_graph, item);
 
   GrapplerItem optimized_item(item, std::move(*optimized_graph));
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.h b/tensorflow/core/grappler/optimizers/memory_optimizer.h
index c3dd0c45c6..62ab969848 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.h
@@ -27,14 +27,14 @@ class MemoryOptimizer : public GraphOptimizer {
  public:
   // optimization_level: Controls the level of autonomy for the memory
   //   optimizer. See RewriterConfig::memory_optimization.
-  // recomputation_targets_name_prefix: Name prefix for potential outputs of
+  // recomputation_targets_name_regxp: Name regxp for potential outputs of
   //   recomputations. See
-  //   RewriterConfig::memory_optimizer_target_node_name_prefix.
+  //   RewriterConfig::memory_optimizer_target_node_name_regxp.
   explicit MemoryOptimizer(
       RewriterConfig::MemOptType optimization_level,
-      const string& recomputation_targets_name_prefix = "gradients/")
+      const string& recomputation_targets_name_regexp = "gradients/")
       : optimization_level_(optimization_level),
-        recomputation_targets_name_prefix_(recomputation_targets_name_prefix) {}
+        recomputation_targets_name_regexp_(recomputation_targets_name_regexp) {}
   ~MemoryOptimizer() override {}
 
   string name() const override { return "memory_optimizer"; };
@@ -47,7 +47,7 @@ class MemoryOptimizer : public GraphOptimizer {
 
  private:
   RewriterConfig::MemOptType optimization_level_;
-  string recomputation_targets_name_prefix_;
+  string recomputation_targets_name_regexp_;
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 72d7b94dc8..979f3e7161 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -119,7 +119,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
           std::unique_ptr<GraphOptimizer>(new LayoutOptimizer()));
     }
     if (cfg_.memory_optimization() != RewriterConfig::NO_MEM_OPT) {
-      if (cfg_.memory_optimizer_target_node_name_prefix().empty()) {
+      if (cfg_.memory_optimizer_target_node_name_regexp().empty()) {
         optimizers.push_back(std::unique_ptr<GraphOptimizer>(
             // Use the default target node name prefix "gradients/"
             new MemoryOptimizer(cfg_.memory_optimization())));
@@ -127,7 +127,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
         optimizers.push_back(
             std::unique_ptr<GraphOptimizer>(new MemoryOptimizer(
                 cfg_.memory_optimization(),
-                cfg_.memory_optimizer_target_node_name_prefix())));
+                cfg_.memory_optimizer_target_node_name_regexp())));
       }
     }
     if (cfg_.auto_parallel().enable()) {
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 9ebf217811..63303fa968 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -78,16 +78,14 @@ message RewriterConfig {
   // effect on manually requested memory optimization passes in the optimizers
   // field.
   MemOptType memory_optimization = 4;
-  // The prefix for nodes which are valid outputs of recomputations. Inputs to
-  // nodes with this name prefix may be recomputed (subject either to manual
+  // A regexp for node names which are valid outputs of recomputations. Inputs
+  // to nodes that match this regexp may be recomputed (subject either to manual
   // annotation of those input nodes or to manual annotation and heuristics
-  // depending on memory_optimization), but the prefixed nodes themselves will
-  // not be recomputed. Typically this will be "gradients/", indicating that
-  // activations from the forward pass of a graph may be recomputed as inputs to
-  // gradients, but may be adjusted if gradients are inside a name scope or if
-  // inputs to non-gradients should be recomputed. Defaults to "gradients/" if
-  // empty or not set.
-  string memory_optimizer_target_node_name_prefix = 6;
+  // depending on memory_optimization), but the nodes themselves will not be
+  // recomputed. This is a prefix match, meaning it matches any node name that
+  // contains a prefix that matches this regexp. Defaults to "gradients/" if
+  // not provided, but can be changed if used within scopes.
+  string memory_optimizer_target_node_name_regexp = 6;
 
   // Configures AutoParallel optimization passes either through the
   // meta-optimizer or when manually specified through the optimizers field.
diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py
index 948911f099..58d3c1e85f 100644
--- a/tensorflow/python/grappler/memory_optimizer_test.py
+++ b/tensorflow/python/grappler/memory_optimizer_test.py
@@ -162,7 +162,34 @@ class MemoryOptimizerRecomputeTest(test.TestCase):
             arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
             memory_optimization=rewriter_config_pb2.RewriterConfig.
             RECOMPUTATION_HEURISTICS,
-            memory_optimizer_target_node_name_prefix='optimizer/gradients/'),
+            memory_optimizer_target_node_name_regexp='optimizer/gradients/'),
+        original_metagraph)
+    self.assertGreater(
+        len(rewritten_graph_def.node),
+        len(original_metagraph.graph_def.node))
+    self.assertEqual(
+        0,
+        len([node for node in original_metagraph.graph_def.node
+             if 'Recomputed/' in node.name]))
+    self.assertEqual(
+        20,  # Two per layer
+        len([node for node in rewritten_graph_def.node
+             if 'Recomputed/' in node.name]))
+
+  def testRewritingNameScopedGradientNamesRegexp(self):
+    """Tests that rewriting occurs with non-standard gradient names."""
+    (original_metagraph, _, _, _) = self._GetMetaGraph(
+        optimizer_scope_name='foo/bar')
+    rewritten_graph_def = tf_optimizer.OptimizeGraph(
+        rewriter_config_pb2.RewriterConfig(
+            disable_model_pruning=True,
+            constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
+            dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF,
+            layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,
+            arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
+            memory_optimization=rewriter_config_pb2.RewriterConfig.
+            RECOMPUTATION_HEURISTICS,
+            memory_optimizer_target_node_name_regexp='(.+/)gradients/'),
         original_metagraph)
     self.assertGreater(
         len(rewritten_graph_def.node),
-- 
GitLab


From bf1abe945330dffe3f93b81344185f629bef023f Mon Sep 17 00:00:00 2001
From: Peter Hawkins <phawkins@google.com>
Date: Thu, 1 Mar 2018 18:49:05 -0800
Subject: [PATCH 531/575] [XLA] For graphviz graph dumps that are colored by
 sharding, choose the fill color for fusion nodes according to the sharding
 color rather than always choosing grey.

PiperOrigin-RevId: 187567679
---
 .../compiler/xla/service/hlo_graph_dumper.cc  | 104 ++++++++++--------
 1 file changed, 61 insertions(+), 43 deletions(-)

diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
index 99c4932a38..1dc72355cf 100644
--- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
+++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc
@@ -157,52 +157,60 @@ enum ColorScheme {
   kDashedBorder,
 };
 
+// Graphviz attributes/colors that make up a color scheme.
+struct NodeColors {
+  const char* style;
+  const char* fill_color;
+  const char* stroke_color;
+  const char* font_color;
+};
+
+NodeColors NodeColorsForScheme(ColorScheme color) {
+  switch (color) {
+    case kBlue:
+      return NodeColors{"filled", "#bbdefb", "#8aacc8", "black"};
+    case kBrown:
+      return NodeColors{"filled", "#bcaaa4", "#8c7b75", "black"};
+    case kDarkBlue:
+      return NodeColors{"filled", "#1565c0", "#003c8f", "white"};
+    case kDarkGreen:
+      return NodeColors{"filled", "#2e7d32", "#005005", "white"};
+    case kDarkRed:
+      return NodeColors{"filled", "#b71c1c", "#7f0000", "white"};
+    case kGray:
+      return NodeColors{"filled", "#cfd8dc", "#9ea7aa", "black"};
+    case kGreen:
+      return NodeColors{"filled", "#c8e6c9", "#97b498", "black"};
+    case kOrange:
+      return NodeColors{"filled", "#ffe0b2", "#cbae82", "black"};
+    case kPurple:
+      return NodeColors{"filled", "#e1bee7", "#af8eb5", "black"};
+    case kRed:
+      return NodeColors{"filled", "#ffcdd2", "#cb9ca1", "black"};
+    case kWhite:
+      return NodeColors{"filled", "white", "black", "black"};
+    case kYellow:
+      return NodeColors{"filled", "#fff9c4", "#cbc693", "black"};
+    case kDashedBorder:
+      // "filled,dashed" looks the same as "dashed", since we have a white
+      // background.  But we use "filled,dashed" so that when you hover over
+      // any part of the node (not just the text inside the node), our css
+      // :hover rule is triggered.
+      return NodeColors{"filled,dashed", "white", "#757575", "#757575"};
+  }
+}
+
 // Given a ColorScheme, returns an attribute string for a node of that color.
 // Sets the node's style and fill/stroke/text colors.
 //
 // Colors are from https://material.io/color.
 string NodeColorAttributes(ColorScheme color) {
-  using std::make_tuple;
-
-  const char *style, *fill_color, *stroke_color, *font_color;
-  std::tie(style, fill_color, stroke_color, font_color) = [color] {
-    switch (color) {
-      case kBlue:
-        return make_tuple("filled", "#bbdefb", "#8aacc8", "black");
-      case kBrown:
-        return make_tuple("filled", "#bcaaa4", "#8c7b75", "black");
-      case kDarkBlue:
-        return make_tuple("filled", "#1565c0", "#003c8f", "white");
-      case kDarkGreen:
-        return make_tuple("filled", "#2e7d32", "#005005", "white");
-      case kDarkRed:
-        return make_tuple("filled", "#b71c1c", "#7f0000", "white");
-      case kGray:
-        return make_tuple("filled", "#cfd8dc", "#9ea7aa", "black");
-      case kGreen:
-        return make_tuple("filled", "#c8e6c9", "#97b498", "black");
-      case kOrange:
-        return make_tuple("filled", "#ffe0b2", "#cbae82", "black");
-      case kPurple:
-        return make_tuple("filled", "#e1bee7", "#af8eb5", "black");
-      case kRed:
-        return make_tuple("filled", "#ffcdd2", "#cb9ca1", "black");
-      case kWhite:
-        return make_tuple("filled", "white", "black", "black");
-      case kYellow:
-        return make_tuple("filled", "#fff9c4", "#cbc693", "black");
-      case kDashedBorder:
-        // "filled,dashed" looks the same as "dashed", since we have a white
-        // background.  But we use "filled,dashed" so that when you hover over
-        // any part of the node (not just the text inside the node), our css
-        // :hover rule is triggered.
-        return make_tuple("filled,dashed", "white", "#757575", "#757575");
-    }
-  }();
+  NodeColors node_colors = NodeColorsForScheme(color);
 
   return Printf(
-      R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")", style,
-      font_color, stroke_color, fill_color);
+      R"(style="%s", fontcolor="%s", color="%s", fillcolor="%s")",
+      node_colors.style, node_colors.font_color, node_colors.stroke_color,
+      node_colors.fill_color);
 }
 
 // Replaces <> with &lt;&gt;, so that this string is safe(er) for use in a
@@ -604,11 +612,21 @@ tooltip = " ";
       StrAppend(&subcomp_label, "<br/>", extra_info);
     }
 
-    // Subcomputation's fill/stroke color is light/dark red/gray, depending on
-    // whether or not the subcomputation's fusion node is highlighted.
     bool highlight = filter_.Highlight(parent_instr);
-    const char* fillcolor = highlight ? "#ffcdd2" : "#f5f5f5";
-    const char* strokecolor = highlight ? "#b71c1c" : "#c2c2c2";
+    const char* fillcolor;
+    const char* strokecolor;
+    if (debug_options_.xla_hlo_graph_sharding_color() && !highlight) {
+      // Use the sharding color, if the node isn't highlighted.
+      NodeColors node_colors =
+          NodeColorsForScheme(GetInstructionColor(parent_instr));
+      fillcolor = node_colors.fill_color;
+      strokecolor = node_colors.stroke_color;
+    } else {
+      // Subcomputation's fill/stroke color is light/dark red/gray, depending on
+      // whether or not the subcomputation's fusion node is highlighted.
+      fillcolor = highlight ? "#ffcdd2" : "#f5f5f5";
+      strokecolor = highlight ? "#b71c1c" : "#c2c2c2";
+    }
     style =
         Printf(R"(style="rounded,filled,bold"; fillcolor="%s"; color="%s;")",
                fillcolor, strokecolor);
-- 
GitLab


From 4735af25c0edfdc012d16a09377161b48839d858 Mon Sep 17 00:00:00 2001
From: Brett Koonce <koonce@hello.com>
Date: Thu, 1 Mar 2018 21:00:45 -0800
Subject: [PATCH 532/575] minor spelling tweaks for contrib/verbs docs

---
 tensorflow/contrib/verbs/README.md                          | 2 +-
 tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/verbs/README.md b/tensorflow/contrib/verbs/README.md
index 58fed4e5cb..4b6104a8b4 100644
--- a/tensorflow/contrib/verbs/README.md
+++ b/tensorflow/contrib/verbs/README.md
@@ -93,7 +93,7 @@ When the receiver receives the RDMA write, it will locate the relevant **RdmaTen
 
 1. When the sender receives a tensor request, the source tensor may or may not be ready yet. The situation is handled through a process of tag matching:
 	* If the request arrives before the tensor is ready, then a callback is put in a local table, and will be invoked once the tensor arrives.
-	* If the tensor is ready before the request arives, than the tensor is put in a local table. When the request arrives, it will invoke the callback immediately.
+	* If the tensor is ready before the request arrives, than the tensor is put in a local table. When the request arrives, it will invoke the callback immediately.
    In code it is done by calling **RecvLocalAsync()**, which receives the tensor's key, step-id, and the callback.
 2. When the callback is invoked, the relevant tensor is removed from the tag matching table. In the case where we need to send the tensor's meta-data, the **RdmaTensorResponse** will store a copy of the tensor until the re-request arrives.
 3. The sending of protocol messages (**RDMA_MESSAGE_TENSOR_REQUEST**, **RDMA_MESSAGE_META_DATA_RESPONSE** and **RDMA_MESSAGE_TENSOR_RE_REQUEST**) is done by the class **RdmaMessageBuffer**. All messages are sent using RDMA writes from/to fixed messages buffers. This implies that we cannot send on a specific channel more than one message at a time. In order to synchronize the messages, the **RdmaMessageBuffer** holds the a local and remote buffer statuses which can be either busy or idle. When a write is issued, both statuses will be changed to busy. When the write-complete event is received, the local status is changed to idle. When the write is received on the remote side, the remote side will parse the message, and return an ACK back to the sending side on which the sending side will update the remote status to idle. When both the local and remote statuses are idle, the next message can be sent.
diff --git a/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md b/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md
index 956b8f2147..da6fdd48e1 100644
--- a/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md
+++ b/tensorflow/contrib/verbs/patch_notes_verbs_with_0_copies.md
@@ -64,7 +64,7 @@ The protocol messages themselves will remain mostly unchanged at the first stage
 	* type - The message type.
 	* request_index - Request index.
 	* is_dead/data_type/tensor_shape/tensor_bytes - The up-to-date meta-data.
-* **RDMA_MESSAGE_BUFFER_RESPONSE** - (receiver ==> sender) Tensor re-requset after meta-data update and reallocation of result/proxy tensors.
+* **RDMA_MESSAGE_BUFFER_RESPONSE** - (receiver ==> sender) Tensor re-request after meta-data update and reallocation of result/proxy tensors.
 	* type - The message type.
 	* name (name_size) - Name of the requested tensor.
 	* step_id - Step ID.
-- 
GitLab


From 1401b731cc2df2ca48117216b5f91c9f2070ae3c Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Thu, 1 Mar 2018 22:25:41 -0800
Subject: [PATCH 533/575] Automated g4 rollback of changelist 187563544

PiperOrigin-RevId: 187582263
---
 tensorflow/core/BUILD                         |  1 -
 tensorflow/core/grappler/optimizers/BUILD     |  1 -
 .../grappler/optimizers/memory_optimizer.cc   | 20 +++++--------
 .../grappler/optimizers/memory_optimizer.h    | 10 +++----
 .../grappler/optimizers/meta_optimizer.cc     |  4 +--
 .../core/protobuf/rewriter_config.proto       | 16 +++++-----
 .../python/grappler/memory_optimizer_test.py  | 29 +------------------
 7 files changed, 25 insertions(+), 56 deletions(-)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 96e30ca3c0..3271825251 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -2231,7 +2231,6 @@ cc_library(
     ],
     visibility = [
         "//tensorflow/compiler:__subpackages__",
-        "//tensorflow/core/grappler:__subpackages__",
         "//tensorflow/core/profiler:__subpackages__",
     ],
     deps = [":lib_internal"],
diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 0a4330b524..037438ee75 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -363,7 +363,6 @@ cc_library(
         ":graph_rewriter",
         ":static_schedule",
         "//tensorflow/core:framework",
-        "//tensorflow/core:regexp_internal",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:graph_view",
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index d73050ac4d..694139fa50 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -36,7 +36,6 @@ limitations under the License.
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/grappler/utils/topological_sort.h"
 #include "tensorflow/core/grappler/utils/traversal.h"
-#include "tensorflow/core/platform/regexp.h"
 #include "tensorflow/core/protobuf/rewriter_config.pb.h"
 
 namespace tensorflow {
@@ -414,7 +413,7 @@ void RecomputeSubgraph(
 }
 
 void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
-                                const string& recomputation_targets_name_regexp,
+                                const string& recomputation_targets_name_prefix,
                                 GraphDef* graph, const GrapplerItem& item) {
   if (optimization_level != RewriterConfig::RECOMPUTATION_HEURISTICS &&
       optimization_level != RewriterConfig::HEURISTICS &&
@@ -438,19 +437,16 @@ void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
   for (const auto& feed : item.feed) {
     feeds.insert(NodeName(feed.first));
   }
-  RE2 recomputation_targets_re(recomputation_targets_name_regexp);
   std::function<bool(const NodeDef&)> is_target =
-      [&recomputation_targets_re](const NodeDef& node) {
-        // Nodes whose inputs we may want to recompute. This does a prefix
-        // regexp match, and typically one sets regexp="gradients/" meaning
-        // it will match all node names with scope beginning with "gradients/".
-        // If used within scopes, one may want to set regexp="(.+/)?gradients/".
+      [&recomputation_targets_name_prefix](const NodeDef& node) {
+        // Nodes whose inputs we may want to recompute. Typically targets will
+        // be gradients (recomputation_targets_name_prefix="gradients/"),
+        // although the prefix is configurable since gradients may be created
+        // in a name scope.
         // TODO(allenl): Use a static schedule
         // (grappler::EstimateEarliestExecutionTimes) to recompute only nodes
         // whose outputs will sit around for a while.
-        bool match = recomputation_targets_re.Match(
-            node.name(), 0, node.name().size(), RE2::ANCHOR_START, nullptr, 0);
-        return match;
+        return node.name().find(recomputation_targets_name_prefix) == 0;
       };
 
   if (optimization_level == RewriterConfig::RECOMPUTATION_HEURISTICS ||
@@ -1229,7 +1225,7 @@ Status MemoryOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
   *optimized_graph = item.graph;
 
   RecomputationRewritingPass(optimization_level_,
-                             recomputation_targets_name_regexp_,
+                             recomputation_targets_name_prefix_,
                              optimized_graph, item);
 
   GrapplerItem optimized_item(item, std::move(*optimized_graph));
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.h b/tensorflow/core/grappler/optimizers/memory_optimizer.h
index 62ab969848..c3dd0c45c6 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.h
@@ -27,14 +27,14 @@ class MemoryOptimizer : public GraphOptimizer {
  public:
   // optimization_level: Controls the level of autonomy for the memory
   //   optimizer. See RewriterConfig::memory_optimization.
-  // recomputation_targets_name_regxp: Name regxp for potential outputs of
+  // recomputation_targets_name_prefix: Name prefix for potential outputs of
   //   recomputations. See
-  //   RewriterConfig::memory_optimizer_target_node_name_regxp.
+  //   RewriterConfig::memory_optimizer_target_node_name_prefix.
   explicit MemoryOptimizer(
       RewriterConfig::MemOptType optimization_level,
-      const string& recomputation_targets_name_regexp = "gradients/")
+      const string& recomputation_targets_name_prefix = "gradients/")
       : optimization_level_(optimization_level),
-        recomputation_targets_name_regexp_(recomputation_targets_name_regexp) {}
+        recomputation_targets_name_prefix_(recomputation_targets_name_prefix) {}
   ~MemoryOptimizer() override {}
 
   string name() const override { return "memory_optimizer"; };
@@ -47,7 +47,7 @@ class MemoryOptimizer : public GraphOptimizer {
 
  private:
   RewriterConfig::MemOptType optimization_level_;
-  string recomputation_targets_name_regexp_;
+  string recomputation_targets_name_prefix_;
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 979f3e7161..72d7b94dc8 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -119,7 +119,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
           std::unique_ptr<GraphOptimizer>(new LayoutOptimizer()));
     }
     if (cfg_.memory_optimization() != RewriterConfig::NO_MEM_OPT) {
-      if (cfg_.memory_optimizer_target_node_name_regexp().empty()) {
+      if (cfg_.memory_optimizer_target_node_name_prefix().empty()) {
         optimizers.push_back(std::unique_ptr<GraphOptimizer>(
             // Use the default target node name prefix "gradients/"
             new MemoryOptimizer(cfg_.memory_optimization())));
@@ -127,7 +127,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
         optimizers.push_back(
             std::unique_ptr<GraphOptimizer>(new MemoryOptimizer(
                 cfg_.memory_optimization(),
-                cfg_.memory_optimizer_target_node_name_regexp())));
+                cfg_.memory_optimizer_target_node_name_prefix())));
       }
     }
     if (cfg_.auto_parallel().enable()) {
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 63303fa968..9ebf217811 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -78,14 +78,16 @@ message RewriterConfig {
   // effect on manually requested memory optimization passes in the optimizers
   // field.
   MemOptType memory_optimization = 4;
-  // A regexp for node names which are valid outputs of recomputations. Inputs
-  // to nodes that match this regexp may be recomputed (subject either to manual
+  // The prefix for nodes which are valid outputs of recomputations. Inputs to
+  // nodes with this name prefix may be recomputed (subject either to manual
   // annotation of those input nodes or to manual annotation and heuristics
-  // depending on memory_optimization), but the nodes themselves will not be
-  // recomputed. This is a prefix match, meaning it matches any node name that
-  // contains a prefix that matches this regexp. Defaults to "gradients/" if
-  // not provided, but can be changed if used within scopes.
-  string memory_optimizer_target_node_name_regexp = 6;
+  // depending on memory_optimization), but the prefixed nodes themselves will
+  // not be recomputed. Typically this will be "gradients/", indicating that
+  // activations from the forward pass of a graph may be recomputed as inputs to
+  // gradients, but may be adjusted if gradients are inside a name scope or if
+  // inputs to non-gradients should be recomputed. Defaults to "gradients/" if
+  // empty or not set.
+  string memory_optimizer_target_node_name_prefix = 6;
 
   // Configures AutoParallel optimization passes either through the
   // meta-optimizer or when manually specified through the optimizers field.
diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py
index 58d3c1e85f..948911f099 100644
--- a/tensorflow/python/grappler/memory_optimizer_test.py
+++ b/tensorflow/python/grappler/memory_optimizer_test.py
@@ -162,34 +162,7 @@ class MemoryOptimizerRecomputeTest(test.TestCase):
             arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
             memory_optimization=rewriter_config_pb2.RewriterConfig.
             RECOMPUTATION_HEURISTICS,
-            memory_optimizer_target_node_name_regexp='optimizer/gradients/'),
-        original_metagraph)
-    self.assertGreater(
-        len(rewritten_graph_def.node),
-        len(original_metagraph.graph_def.node))
-    self.assertEqual(
-        0,
-        len([node for node in original_metagraph.graph_def.node
-             if 'Recomputed/' in node.name]))
-    self.assertEqual(
-        20,  # Two per layer
-        len([node for node in rewritten_graph_def.node
-             if 'Recomputed/' in node.name]))
-
-  def testRewritingNameScopedGradientNamesRegexp(self):
-    """Tests that rewriting occurs with non-standard gradient names."""
-    (original_metagraph, _, _, _) = self._GetMetaGraph(
-        optimizer_scope_name='foo/bar')
-    rewritten_graph_def = tf_optimizer.OptimizeGraph(
-        rewriter_config_pb2.RewriterConfig(
-            disable_model_pruning=True,
-            constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
-            dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF,
-            layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,
-            arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
-            memory_optimization=rewriter_config_pb2.RewriterConfig.
-            RECOMPUTATION_HEURISTICS,
-            memory_optimizer_target_node_name_regexp='(.+/)gradients/'),
+            memory_optimizer_target_node_name_prefix='optimizer/gradients/'),
         original_metagraph)
     self.assertGreater(
         len(rewritten_graph_def.node),
-- 
GitLab


From 974822bcde764eb6a0b1498a575fdde7001aae15 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 01:17:19 -0800
Subject: [PATCH 534/575] [XLA:GPU] Extract multiplication of complex numbers
 into a helper function.

Also add helper functions for getting the real and the imaginary part of a
complex number.

PiperOrigin-RevId: 187593341
---
 .../compiler/xla/service/gpu/ir_emitter.cc    | 65 +++++++++++--------
 1 file changed, 37 insertions(+), 28 deletions(-)

diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc
index a3df67a873..1e0db2821a 100644
--- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc
+++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <string>
 #include <unordered_map>
+#include <utility>
 
 #include "tensorflow/core/platform/logging.h"
 // IWYU pragma: no_include "llvm/IR/Intrinsics.gen.inc"
@@ -438,6 +439,32 @@ Status IrEmitter::HandleSelect(HloInstruction* select) {
   return IrEmitter::DefaultAction(select);
 }
 
+namespace {
+llvm::Value* Real(llvm::Value* x, llvm::IRBuilder<>* ir_builder) {
+  return ir_builder->CreateExtractValue(x, {0});
+}
+
+llvm::Value* Imag(llvm::Value* x, llvm::IRBuilder<>* ir_builder) {
+  return ir_builder->CreateExtractValue(x, {1});
+}
+
+std::pair<llvm::Value*, llvm::Value*> MultiplyComplex(
+    llvm::Value* lhs_value, llvm::Value* rhs_value,
+    llvm::IRBuilder<>* ir_builder) {
+  llvm::Value* lhs_real = Real(lhs_value, ir_builder);
+  llvm::Value* lhs_imag = Imag(lhs_value, ir_builder);
+  llvm::Value* rhs_real = Real(rhs_value, ir_builder);
+  llvm::Value* rhs_imag = Imag(rhs_value, ir_builder);
+  llvm::Value* real_result1 = ir_builder->CreateFMul(lhs_real, rhs_real);
+  llvm::Value* real_result2 = ir_builder->CreateFMul(lhs_imag, rhs_imag);
+  llvm::Value* real_result = ir_builder->CreateFSub(real_result1, real_result2);
+  llvm::Value* imag_result1 = ir_builder->CreateFMul(lhs_real, rhs_imag);
+  llvm::Value* imag_result2 = ir_builder->CreateFMul(lhs_imag, rhs_real);
+  llvm::Value* imag_result = ir_builder->CreateFAdd(imag_result1, imag_result2);
+  return {real_result, imag_result};
+}
+}  // namespace
+
 Status IrEmitter::HandleDot(HloInstruction* dot) {
   auto lhs_instruction = dot->operand(0);
   auto rhs_instruction = dot->operand(1);
@@ -456,21 +483,10 @@ Status IrEmitter::HandleDot(HloInstruction* dot) {
         rhs_array.EmitReadArrayElement(/*index=*/{}, &ir_builder_);
     llvm::Value* result;
     if (ShapeUtil::ElementIsComplex(lhs_shape)) {
-      auto real = [&](llvm::Value* x) {
-        return ir_builder_.CreateExtractValue(x, {0});
-      };
-      auto imag = [&](llvm::Value* x) {
-        return ir_builder_.CreateExtractValue(x, {1});
-      };
-      llvm::Value* real_result = ir_builder_.CreateFSub(
-          ir_builder_.CreateFMul(real(lhs_value), real(rhs_value)),
-          ir_builder_.CreateFMul(imag(lhs_value), imag(rhs_value)));
-      llvm::Value* imag_result = ir_builder_.CreateFAdd(
-          ir_builder_.CreateFMul(real(lhs_value), imag(rhs_value)),
-          ir_builder_.CreateFMul(imag(lhs_value), real(rhs_value)));
+      auto value = MultiplyComplex(lhs_value, rhs_value, &ir_builder_);
       result = llvm::ConstantAggregateZero::get(lhs_array.GetElementLlvmType());
-      result = ir_builder_.CreateInsertValue(result, real_result, {0});
-      result = ir_builder_.CreateInsertValue(result, imag_result, {1});
+      result = ir_builder_.CreateInsertValue(result, value.first, {0});
+      result = ir_builder_.CreateInsertValue(result, value.second, {1});
     } else {
       result = ir_builder_.CreateFMul(lhs_value, rhs_value);
     }
@@ -548,20 +564,13 @@ Status IrEmitter::HandleDot(HloInstruction* dot) {
   llvm::Value* accum = ir_builder_.CreateLoad(accum_address);
   llvm::Value* updated_accum;
   if (ShapeUtil::ElementIsComplex(lhs_shape)) {
-#define REAL(x) ir_builder_.CreateExtractValue(x, {0})
-#define IMAG(x) ir_builder_.CreateExtractValue(x, {1})
-    llvm::Value* product_real = ir_builder_.CreateFSub(
-        ir_builder_.CreateFMul(REAL(lhs_element), REAL(rhs_element)),
-        ir_builder_.CreateFMul(IMAG(lhs_element), IMAG(rhs_element)));
-    llvm::Value* product_imag = ir_builder_.CreateFAdd(
-        ir_builder_.CreateFMul(REAL(lhs_element), IMAG(rhs_element)),
-        ir_builder_.CreateFMul(IMAG(lhs_element), REAL(rhs_element)));
-    updated_accum = ir_builder_.CreateInsertValue(
-        accum, ir_builder_.CreateFAdd(REAL(accum), product_real), {0});
-    updated_accum = ir_builder_.CreateInsertValue(
-        updated_accum, ir_builder_.CreateFAdd(IMAG(accum), product_imag), {1});
-#undef IMAG
-#undef REAL
+    auto value = MultiplyComplex(lhs_element, rhs_element, &ir_builder_);
+    llvm::Value* accum_real = Real(accum, &ir_builder_);
+    llvm::Value* real_sum = ir_builder_.CreateFAdd(accum_real, value.first);
+    updated_accum = ir_builder_.CreateInsertValue(accum, real_sum, {0});
+    llvm::Value* accum_imag = Imag(accum, &ir_builder_);
+    llvm::Value* imag_sum = ir_builder_.CreateFAdd(accum_imag, value.second);
+    updated_accum = ir_builder_.CreateInsertValue(updated_accum, imag_sum, {1});
   } else {
     llvm::Value* product = ir_builder_.CreateFMul(lhs_element, rhs_element);
     updated_accum = ir_builder_.CreateFAdd(accum, product);
-- 
GitLab


From 353dbff0cbabe8d8b38530b13669271b4d047c9b Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Fri, 2 Mar 2018 01:48:59 -0800
Subject: [PATCH 535/575] Java: Update to 1.6.0

PiperOrigin-RevId: 187595636
---
 tensorflow/java/maven/libtensorflow/pom.xml         | 2 +-
 tensorflow/java/maven/libtensorflow_jni/pom.xml     | 2 +-
 tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml | 2 +-
 tensorflow/java/maven/pom.xml                       | 2 +-
 tensorflow/java/maven/proto/pom.xml                 | 2 +-
 tensorflow/java/maven/tensorflow/pom.xml            | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml
index d35bb41112..1c84eae540 100644
--- a/tensorflow/java/maven/libtensorflow/pom.xml
+++ b/tensorflow/java/maven/libtensorflow/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.6.0-rc1</version>
+    <version>1.6.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>libtensorflow</artifactId>
diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml
index d9ba1bbbfb..cf1a7b6c9c 100644
--- a/tensorflow/java/maven/libtensorflow_jni/pom.xml
+++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.6.0-rc1</version>
+    <version>1.6.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>libtensorflow_jni</artifactId>
diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml
index f6f532c2c1..b202dcd5c7 100644
--- a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml
+++ b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.6.0-rc1</version>
+    <version>1.6.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>libtensorflow_jni_gpu</artifactId>
diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml
index 0a6b3d23d7..606805ff33 100644
--- a/tensorflow/java/maven/pom.xml
+++ b/tensorflow/java/maven/pom.xml
@@ -6,7 +6,7 @@
   <modelVersion>4.0.0</modelVersion>
   <groupId>org.tensorflow</groupId>
   <artifactId>parentpom</artifactId>
-  <version>1.6.0-rc1</version>
+  <version>1.6.0</version>
   <packaging>pom</packaging>
 
   <url>https://www.tensorflow.org</url>
diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml
index 1d8e872373..c6bba4e536 100644
--- a/tensorflow/java/maven/proto/pom.xml
+++ b/tensorflow/java/maven/proto/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.6.0-rc1</version>
+    <version>1.6.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>proto</artifactId>
diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml
index 5c1b55085c..a22663f9f3 100644
--- a/tensorflow/java/maven/tensorflow/pom.xml
+++ b/tensorflow/java/maven/tensorflow/pom.xml
@@ -6,7 +6,7 @@
   <parent>
     <groupId>org.tensorflow</groupId>
     <artifactId>parentpom</artifactId>
-    <version>1.6.0-rc1</version>
+    <version>1.6.0</version>
     <relativePath>../</relativePath>
   </parent>
   <artifactId>tensorflow</artifactId>
-- 
GitLab


From 2d3e25245ec4dc2b791212b65b17a7ff4051dfe3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 05:50:55 -0800
Subject: [PATCH 536/575] Add support to convert ResourceVariables of graphs
 into constants.

This involves a change to the implementation of convert_variables_to_constants.

PiperOrigin-RevId: 187610062
---
 tensorflow/python/BUILD                       |   1 +
 .../python/framework/graph_util_impl.py       |  18 ++-
 .../python/framework/graph_util_test.py       | 106 ++++++++++--------
 3 files changed, 76 insertions(+), 49 deletions(-)

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index b0cb48c80c..fbdf15a69f 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3654,6 +3654,7 @@ py_test(
         ":framework_for_generated_wrappers",
         ":math_ops",
         ":state_ops_gen",
+        ":variable_scope",
         ":variables",
         "//tensorflow/core:protos_all_py",
     ],
diff --git a/tensorflow/python/framework/graph_util_impl.py b/tensorflow/python/framework/graph_util_impl.py
index 5a543317e6..910364364c 100644
--- a/tensorflow/python/framework/graph_util_impl.py
+++ b/tensorflow/python/framework/graph_util_impl.py
@@ -235,7 +235,7 @@ def convert_variables_to_constants(sess,
   variable_names = []
   variable_dict_names = []
   for node in inference_graph.node:
-    if node.op in ["Variable", "VariableV2"]:
+    if node.op in ["Variable", "VariableV2", "VarHandleOp"]:
       variable_name = node.name
       if ((variable_names_whitelist is not None and
            variable_name not in variable_names_whitelist) or
@@ -243,7 +243,10 @@ def convert_variables_to_constants(sess,
            variable_name in variable_names_blacklist)):
         continue
       variable_dict_names.append(variable_name)
-      variable_names.append(variable_name + ":0")
+      if node.op == "VarHandleOp":
+        variable_names.append(variable_name + "/Read/ReadVariableOp:0")
+      else:
+        variable_names.append(variable_name + ":0")
   if variable_names:
     returned_variables = sess.run(variable_names)
   else:
@@ -266,6 +269,17 @@ def convert_variables_to_constants(sess,
               tensor=tensor_util.make_tensor_proto(
                   data, dtype=dtype.type, shape=data.shape)))
       how_many_converted += 1
+    elif input_node.op == "ReadVariableOp" and (
+        input_node.input[0] in found_variables):
+      # The preceding branch converts all VarHandleOps of ResourceVariables to
+      # constants, so we need to convert the associated ReadVariableOps to
+      # Identity ops.
+      output_node.op = "Identity"
+      output_node.name = input_node.name
+      output_node.input.extend([input_node.input[0]])
+      output_node.attr["T"].CopyFrom(input_node.attr["dtype"])
+      if "_class" in input_node.attr:
+        output_node.attr["_class"].CopyFrom(input_node.attr["_class"])
     else:
       output_node.CopyFrom(input_node)
     output_graph_def.node.extend([output_node])
diff --git a/tensorflow/python/framework/graph_util_test.py b/tensorflow/python/framework/graph_util_test.py
index 1cdd738198..b618152b02 100644
--- a/tensorflow/python/framework/graph_util_test.py
+++ b/tensorflow/python/framework/graph_util_test.py
@@ -32,6 +32,7 @@ from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import gen_state_ops
 from tensorflow.python.ops import math_ops  # pylint: disable=unused-import
 from tensorflow.python.ops import math_ops as math_ops_lib
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
@@ -226,52 +227,62 @@ class DeviceFunctionsTest(test.TestCase):
                          constant_graph_def.library)
 
   def testConvertVariablesToConsts(self):
-    with ops.Graph().as_default():
-      variable_node = variables.Variable(1.0, name="variable_node")
-      _ = variables.Variable(1.0, name="unused_variable_node")
-      output_node = math_ops_lib.multiply(
-          variable_node, 2.0, name="output_node")
-      with session.Session() as sess:
-        init = variables.initialize_variables([variable_node])
-        sess.run(init)
-        output = sess.run(output_node)
-        self.assertNear(2.0, output, 0.00001)
-        variable_graph_def = sess.graph.as_graph_def()
-        # First get the constant_graph_def when variable_names_whitelist is set,
-        # note that if variable_names_whitelist is not set an error will be
-        # thrown because unused_variable_node is not initialized.
-        constant_graph_def = graph_util.convert_variables_to_constants(
-            sess,
-            variable_graph_def, ["output_node"],
-            variable_names_whitelist=set(["variable_node"]))
+    self._test_variable_to_const_conversion(use_resource=False)
 
-        # Then initialize the unused variable, and get another
-        # constant_graph_def when variable_names_whitelist is not set.
-        sess.run(variables.global_variables_initializer())
-        constant_graph_def_without_variable_whitelist = (
-            graph_util.convert_variables_to_constants(sess, variable_graph_def,
-                                                      ["output_node"]))
-
-        # The unused variable should be cleared so the two graphs should be
-        # equivalent.
-        self.assertEqual(
-            str(constant_graph_def),
-            str(constant_graph_def_without_variable_whitelist))
-
-        # Test variable name black list. This should result in the variable not
-        # being a const.
-        sess.run(variables.global_variables_initializer())
-        constant_graph_def_with_blacklist = (
-            graph_util.convert_variables_to_constants(
-                sess,
-                variable_graph_def, ["output_node"],
-                variable_names_blacklist=set(["variable_node"])))
-        variable_node = None
-        for node in constant_graph_def_with_blacklist.node:
-          if node.name == "variable_node":
-            variable_node = node
-        self.assertIsNotNone(variable_node)
-        self.assertEqual(variable_node.op, "VariableV2")
+  def testConvertResourceVariablesToConsts(self):
+    self._test_variable_to_const_conversion(use_resource=True)
+
+  def _test_variable_to_const_conversion(self, use_resource):
+    with ops.Graph().as_default():
+      with variable_scope.variable_scope("", use_resource=use_resource):
+        variable_node = variable_scope.get_variable(
+            "variable_node", initializer=1.0)
+        another_variable = variable_scope.get_variable(
+            "unused_variable_node", initializer=1.0)
+        output_node = math_ops_lib.multiply(
+            variable_node, 2.0, name="output_node")
+        with session.Session() as sess:
+          sess.run(variable_node.initializer)
+          output = sess.run(output_node)
+          self.assertNear(2.0, output, 0.00001)
+          variable_graph_def = sess.graph.as_graph_def()
+          # First get the constant_graph_def when variable_names_whitelist is
+          # set, note that if variable_names_whitelist is not set an error will
+          # be thrown because unused_variable_node is not initialized.
+          constant_graph_def = graph_util.convert_variables_to_constants(
+              sess,
+              variable_graph_def, ["output_node"],
+              variable_names_whitelist=set(["variable_node"]))
+
+          # Then initialize the unused variable, and get another
+          # constant_graph_def when variable_names_whitelist is not set.
+          sess.run(another_variable.initializer)
+          constant_graph_def_without_variable_whitelist = (
+              graph_util.convert_variables_to_constants(
+                  sess, variable_graph_def, ["output_node"]))
+
+          # The unused variable should be cleared so the two graphs should be
+          # equivalent.
+          self.assertEqual(
+              str(constant_graph_def),
+              str(constant_graph_def_without_variable_whitelist))
+
+          # Test variable name black list. This should result in the variable
+          # not being a const.
+          constant_graph_def_with_blacklist = (
+              graph_util.convert_variables_to_constants(
+                  sess,
+                  variable_graph_def, ["output_node"],
+                  variable_names_blacklist=set(["variable_node"])))
+          variable_node = None
+          for node in constant_graph_def_with_blacklist.node:
+            if node.name == "variable_node":
+              variable_node = node
+          self.assertIsNotNone(variable_node)
+          if use_resource:
+            self.assertEqual(variable_node.op, "VarHandleOp")
+          else:
+            self.assertEqual(variable_node.op, "VariableV2")
 
     # Now we make sure the variable is now a constant, and that the graph still
     # produces the expected result.
@@ -279,8 +290,9 @@ class DeviceFunctionsTest(test.TestCase):
       _ = importer.import_graph_def(constant_graph_def, name="")
       self.assertEqual(4, len(constant_graph_def.node))
       for node in constant_graph_def.node:
-        self.assertNotEqual("Variable", node.op)
-        self.assertNotEqual("VariableV2", node.op)
+        self.assertNotIn(
+            node.op,
+            ["Variable", "VariableV2", "VarHandleOp", "ReadVariableOp"])
       with session.Session() as sess:
         output_node = sess.graph.get_tensor_by_name("output_node:0")
         output = sess.run(output_node)
-- 
GitLab


From 95be42c41c77aed8dd811398332687f45105c926 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 2 Mar 2018 10:18:40 -0500
Subject: [PATCH 537/575] Remove underscore prefix from
 gen_array_ops._unique_with_counts

---
 tensorflow/python/ops/array_ops.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index e537787398..e0bcac0641 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1326,10 +1326,10 @@ def unique_with_counts(x, out_idx=dtypes.int32, name=None):
   # period (3 weeks) pass.
   # TODO(yongtang): The documentation should also
   # be updated when switch  to v2.
-  return gen_array_ops._unique_with_counts(x, out_idx, name)
+  return gen_array_ops.unique_with_counts(x, out_idx, name)
 
 
-unique_with_counts.__doc__ = gen_array_ops._unique_with_counts.__doc__
+unique_with_counts.__doc__ = gen_array_ops.unique_with_counts.__doc__
 
 
 @tf_export("split")
-- 
GitLab


From 7b7ce88a073530dd3ea6ec5ee329fb45dd64b06b Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 2 Mar 2018 10:32:27 -0500
Subject: [PATCH 538/575] Remove underscore prefix from
 gen_array_ops._unique_with_counts_v2

---
 tensorflow/python/kernel_tests/unique_op_test.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/kernel_tests/unique_op_test.py b/tensorflow/python/kernel_tests/unique_op_test.py
index 3c9650ef6e..bbc040dc13 100644
--- a/tensorflow/python/kernel_tests/unique_op_test.py
+++ b/tensorflow/python/kernel_tests/unique_op_test.py
@@ -137,10 +137,10 @@ class UniqueWithCountsTest(test.TestCase):
     for dtype in [np.int32, np.int64]:
       x = np.array([[1, 0, 0], [1, 0, 0], [2, 0, 0]])
       with self.test_session() as sess:
-        y0, idx0, count0 = gen_array_ops._unique_with_counts_v2(
+        y0, idx0, count0 = gen_array_ops.unique_with_counts_v2(
             x, axis=np.array([0], dtype))
         tf_y0, tf_idx0, tf_count0 = sess.run([y0, idx0, count0])
-        y1, idx1, count1 = gen_array_ops._unique_with_counts_v2(
+        y1, idx1, count1 = gen_array_ops.unique_with_counts_v2(
             x, axis=np.array([1], dtype))
         tf_y1, tf_idx1, tf_count1 = sess.run([y1, idx1, count1])
       self.assertAllEqual(tf_y0, np.array([[1, 0, 0], [2, 0, 0]]))
@@ -155,7 +155,7 @@ class UniqueWithCountsTest(test.TestCase):
     # by default, the axis will be wrapped to allow `axis=None`.
     x = np.random.randint(2, high=10, size=7000)
     with self.test_session() as sess:
-      y, idx, count = gen_array_ops._unique_with_counts_v2(
+      y, idx, count = gen_array_ops.unique_with_counts_v2(
           x, axis=np.array([], np.int32))
       tf_y, tf_idx, tf_count = sess.run([y, idx, count])
 
-- 
GitLab


From 60740a489475365815c50d5b0d3c352d420454ab Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 08:20:27 -0800
Subject: [PATCH 539/575] Eliminate the creation of unnecessary read ops when
 working with ResourceVariables.

In particular:
1. Don't create additional read ops when creating a ResourceVariable from a VariableDef proto.
2. Expose the ability to assign a ResourceVariable without reading & returning the new value.
3. Colocating with a ResourceVariable's ".op" property eliminates the creation of additional read ops.
4. Savers can read a variable's value using the _graph_element property, since these reads don't need control dependencies.

This makes the visualization of graphs on TensorBoard much nicer.

PiperOrigin-RevId: 187622122
---
 tensorflow/contrib/framework/BUILD            |  1 +
 tensorflow/python/BUILD                       |  1 +
 .../python/framework/meta_graph_test.py       | 14 ----
 .../resource_variable_ops_test.py             | 45 ++++++++++
 .../python/ops/resource_variable_ops.py       | 82 +++++++++++++++----
 .../python/training/checkpoint_utils.py       |  9 +-
 .../python/training/checkpoint_utils_test.py  | 26 ++++++
 tensorflow/python/training/saver.py           | 10 ++-
 tensorflow/python/training/saver_test.py      | 18 ++++
 9 files changed, 171 insertions(+), 35 deletions(-)

diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD
index 50868c6d6c..ac043fda06 100644
--- a/tensorflow/contrib/framework/BUILD
+++ b/tensorflow/contrib/framework/BUILD
@@ -62,6 +62,7 @@ tf_custom_op_py_library(
         "//tensorflow/python:math_ops",
         "//tensorflow/python:platform",
         "//tensorflow/python:pywrap_tensorflow",
+        "//tensorflow/python:resource_variable_ops",
         "//tensorflow/python:script_ops",
         "//tensorflow/python:smart_cond",
         "//tensorflow/python:sparse_tensor",
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index fbdf15a69f..cb54cebf0f 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3954,6 +3954,7 @@ py_test(
         ":partitioned_variables",
         ":platform",
         ":pywrap_tensorflow",
+        ":resource_variable_ops",
         ":state_ops",
         ":training",
         ":variable_scope",
diff --git a/tensorflow/python/framework/meta_graph_test.py b/tensorflow/python/framework/meta_graph_test.py
index 19dcd6a1b3..21963d0bee 100644
--- a/tensorflow/python/framework/meta_graph_test.py
+++ b/tensorflow/python/framework/meta_graph_test.py
@@ -905,20 +905,6 @@ class ExportImportAcrossScopesTest(test.TestCase):
       with variable_scope.variable_scope("importA/keepA"):
         graph_fn(use_resource=use_resource)
 
-      if use_resource:
-        # Bringing in collections that contain ResourceVariables will adds ops
-        # to the graph the first time a variable is encountered, so mimic the
-        # same behavior.
-        seen_variables = set()
-        for collection_key in sorted([
-            ops.GraphKeys.GLOBAL_VARIABLES,
-            ops.GraphKeys.TRAINABLE_VARIABLES,
-        ]):
-          for var in expected_graph.get_collection(collection_key):
-            if var not in seen_variables:
-              var._read_variable_op()
-              seen_variables.add(var)
-
     result = meta_graph.export_scoped_meta_graph(graph=imported_graph)[0]
     expected = meta_graph.export_scoped_meta_graph(graph=expected_graph)[0]
 
diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
index 8503f3e031..71699fe0ad 100644
--- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py
+++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
@@ -277,6 +277,20 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
     self.evaluate(v.assign(2.0))
     self.assertEqual(2.0, self.evaluate(v.value()))
 
+    # Tests for the 'read_value' argument:
+    assign_with_read = v.assign(3.0, read_value=True)
+    if context.in_graph_mode():
+      self.assertEqual(3.0, assign_with_read.eval())
+    else:
+      self.assertEqual(3.0, self.evaluate(assign_with_read))
+    assign_without_read = v.assign(4.0, read_value=False)
+    if context.in_graph_mode():
+      self.assertIsInstance(assign_without_read, ops.Operation)
+    else:
+      self.assertIsNone(assign_without_read)
+    self.evaluate(assign_without_read)
+    self.assertEqual(4.0, self.evaluate(v.value()))
+
   @test_util.run_in_graph_and_eager_modes()
   def testLoad(self):
     v = resource_variable_ops.ResourceVariable(1.0, name="var0")
@@ -329,6 +343,9 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
       w = resource_variable_ops.ResourceVariable.from_proto(v.to_proto())
       self.assertEquals(2, math_ops.add(w, 1).eval())
 
+      self.assertEquals(v._handle, w._handle)
+      self.assertEquals(v._graph_element, w._graph_element)
+
   @test_util.run_in_graph_and_eager_modes()
   def testAssignAddMethod(self):
     v = resource_variable_ops.ResourceVariable(1.0, name="var0")
@@ -336,6 +353,20 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
     self.evaluate(v.assign_add(1.0))
     self.assertEqual(2.0, self.evaluate(v.value()))
 
+    # Tests for the 'read_value' argument:
+    assign_with_read = v.assign_add(1.0, read_value=True)
+    if context.in_graph_mode():
+      self.assertEqual(3.0, assign_with_read.eval())
+    else:
+      self.assertEqual(3.0, self.evaluate(assign_with_read))
+    assign_without_read = v.assign_add(1.0, read_value=False)
+    if context.in_graph_mode():
+      self.assertIsInstance(assign_without_read, ops.Operation)
+    else:
+      self.assertIsNone(assign_without_read)
+    self.evaluate(assign_without_read)
+    self.assertEqual(4.0, self.evaluate(v.value()))
+
   @test_util.run_in_graph_and_eager_modes()
   def testAssignSubMethod(self):
     v = resource_variable_ops.ResourceVariable(3.0, name="var0")
@@ -343,6 +374,20 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
     self.evaluate(v.assign_sub(1.0))
     self.assertEqual(2.0, self.evaluate(v.value()))
 
+    # Tests for the 'read_value' argument:
+    assign_with_read = v.assign_sub(1.0, read_value=True)
+    if context.in_graph_mode():
+      self.assertEqual(1.0, assign_with_read.eval())
+    else:
+      self.assertEqual(1.0, self.evaluate(assign_with_read))
+    assign_without_read = v.assign_sub(1.0, read_value=False)
+    if context.in_graph_mode():
+      self.assertIsInstance(assign_without_read, ops.Operation)
+    else:
+      self.assertIsNone(assign_without_read)
+    self.evaluate(assign_without_read)
+    self.assertEqual(0.0, self.evaluate(v.value()))
+
   @test_util.run_in_graph_and_eager_modes()
   def testDestroyResource(self):
     v = resource_variable_ops.ResourceVariable(3.0, name="var0")
diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index 2d6d0672e0..bf186f1734 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -534,7 +534,8 @@ class ResourceVariable(variables.Variable):
       self._save_slice_info = None
     self._caching_device = None
     self._dtype = dtypes.as_dtype(self._handle.op.get_attr("dtype"))
-    self._graph_element = self.value()
+    self._graph_element = g.get_tensor_by_name(
+        self._handle.op.name + "/Read/ReadVariableOp:0")
     self._constraint = None
 
   def __nonzero__(self):
@@ -788,20 +789,52 @@ class ResourceVariable(variables.Variable):
 
   __array_priority__ = 100
 
-  def assign_sub(self, delta, use_locking=None, name=None):
+  def assign_sub(self, delta, use_locking=None, name=None, read_value=True):
+    """Subtracts a value from this variable.
+
+    Args:
+      delta: A `Tensor`. The value to subtract from this variable.
+      use_locking: If `True`, use locking during the operation.
+      name: The name to use for the operation.
+      read_value: A `bool`. Whether to read and return the new value of the
+          variable or not.
+
+    Returns:
+      If `read_value` is `True`, this method will return the new value of the
+      variable after the assignment has completed. Otherwise, when in graph mode
+      it will return the `Operation` that does the assignment, and when in eager
+      mode it will return `None`.
+    """
     # TODO(apassos): this here and below is not atomic. Consider making it
     # atomic if there's a way to do so without a performance cost for those who
     # don't need it.
-    return self._lazy_read(gen_resource_variable_ops.assign_sub_variable_op(
-        self.handle,
-        ops.convert_to_tensor(delta, dtype=self.dtype),
-        name=name))
+    assign_sub_op = gen_resource_variable_ops.assign_sub_variable_op(
+        self.handle, ops.convert_to_tensor(delta, dtype=self.dtype), name=name)
+    if read_value:
+      return self._lazy_read(assign_sub_op)
+    return assign_sub_op
+
+  def assign_add(self, delta, use_locking=None, name=None, read_value=True):
+    """Adds a value to this variable.
+
+    Args:
+      delta: A `Tensor`. The value to add to this variable.
+      use_locking: If `True`, use locking during the operation.
+      name: The name to use for the operation.
+      read_value: A `bool`. Whether to read and return the new value of the
+          variable or not.
 
-  def assign_add(self, delta, use_locking=None, name=None):
-    return self._lazy_read(gen_resource_variable_ops.assign_add_variable_op(
-        self.handle,
-        ops.convert_to_tensor(delta, dtype=self.dtype),
-        name=name))
+    Returns:
+      If `read_value` is `True`, this method will return the new value of the
+      variable after the assignment has completed. Otherwise, when in graph mode
+      it will return the `Operation` that does the assignment, and when in eager
+      mode it will return `None`.
+    """
+    assign_add_op = gen_resource_variable_ops.assign_add_variable_op(
+        self.handle, ops.convert_to_tensor(delta, dtype=self.dtype), name=name)
+    if read_value:
+      return self._lazy_read(assign_add_op)
+    return assign_add_op
 
   def _lazy_read(self, op):
     if hasattr(self, "_trainable") and self._trainable:
@@ -811,14 +844,29 @@ class ResourceVariable(variables.Variable):
         self._in_graph_mode,
         self._handle_deleter if not self._in_graph_mode else None, op)
 
-  def assign(self, value, use_locking=None, name=None):
+  def assign(self, value, use_locking=None, name=None, read_value=True):
+    """Assigns a new value to this variable.
+
+    Args:
+      value: A `Tensor`. The new value for this variable.
+      use_locking: If `True`, use locking during the assignment.
+      name: The name to use for the assignment.
+      read_value: A `bool`. Whether to read and return the new value of the
+          variable or not.
+
+    Returns:
+      If `read_value` is `True`, this method will return the new value of the
+      variable after the assignment has completed. Otherwise, when in graph mode
+      it will return the `Operation` that does the assignment, and when in eager
+      mode it will return `None`.
+    """
     value_tensor = ops.convert_to_tensor(value, dtype=self.dtype)
     self._shape.assert_is_compatible_with(value_tensor.shape)
-    return self._lazy_read(
-        gen_resource_variable_ops.assign_variable_op(
-            self.handle,
-            value_tensor,
-            name=name))
+    assign_op = gen_resource_variable_ops.assign_variable_op(
+        self.handle, value_tensor, name=name)
+    if read_value:
+      return self._lazy_read(assign_op)
+    return assign_op
 
   def _strided_slice_assign(self, begin, end, strides, value, name, begin_mask,
                             end_mask, ellipsis_mask, new_axis_mask,
diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py
index 0af1cdecfa..52d092bc22 100644
--- a/tensorflow/python/training/checkpoint_utils.py
+++ b/tensorflow/python/training/checkpoint_utils.py
@@ -23,6 +23,7 @@ import six
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import io_ops
+from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables
@@ -289,10 +290,14 @@ def _set_checkpoint_initializer(variable,
     name: Name of the operation.
   """
   base_type = variable.dtype.base_dtype
-  with ops.colocate_with(variable):
+  with ops.colocate_with(variable.op):
     restore_op = io_ops.restore_v2(
         ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
-    variable._initializer_op = state_ops.assign(variable, restore_op)  # pylint:disable=protected-access
+    if isinstance(variable, resource_variable_ops.ResourceVariable):
+      init_op = variable.assign(restore_op, read_value=False)
+    else:
+      init_op = state_ops.assign(variable, restore_op)
+    variable._initializer_op = init_op  # pylint:disable=protected-access
     restore_op.set_shape(variable.shape)
     variable._initial_value = restore_op  # pylint:disable=protected-access
 
diff --git a/tensorflow/python/training/checkpoint_utils_test.py b/tensorflow/python/training/checkpoint_utils_test.py
index a461b24cbb..640bd665cb 100644
--- a/tensorflow/python/training/checkpoint_utils_test.py
+++ b/tensorflow/python/training/checkpoint_utils_test.py
@@ -26,6 +26,7 @@ from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import partitioned_variables
+from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -362,6 +363,31 @@ class CheckpointsTest(test.TestCase):
           checkpoint_utils.init_from_checkpoint(checkpoint_dir,
                                                 {"useful_scope": "some_scope/"})
 
+  def testNoAdditionalReadOpsForResourceVariables(self):
+    checkpoint_dir = self.get_temp_dir()
+    with self.test_session() as session:
+      v1, _, _, _ = _create_checkpoints(session, checkpoint_dir)
+
+    # New graph and session.
+    with ops.Graph().as_default() as g:
+      with self.test_session(graph=g) as session:
+        my1 = resource_variable_ops.ResourceVariable([[0.0] * 10], name="my1")
+
+        with ops.name_scope("init_from_checkpoint"):
+          checkpoint_utils.init_from_checkpoint(checkpoint_dir, {"var1": my1})
+
+        # Basic sanity checks:
+        session.run(variables.global_variables_initializer())
+        self.assertAllEqual(session.run(my1), v1)
+
+    ops_in_init_from_checkpoint_scope = [
+        op for op in g.get_operations()
+        if (op.name.startswith("init_from_checkpoint/") and
+            not op.name.startswith("init_from_checkpoint/checkpoint_initializer"
+                                  ) and op.type != "AssignVariableOp")
+    ]
+    self.assertEqual(ops_in_init_from_checkpoint_scope, [])
+
 
 if __name__ == "__main__":
   test.main()
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index e8ea5abfbd..6c80562968 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -584,7 +584,10 @@ class BaseSaverBuilder(object):
       else:
         if context.in_graph_mode():
           if convert_variable_to_tensor:
-            var = ops.internal_convert_to_tensor(var, as_ref=True)
+            if isinstance(var, resource_variable_ops.ResourceVariable):
+              var = var._graph_element  # pylint: disable=protected-access
+            else:
+              var = ops.internal_convert_to_tensor(var, as_ref=True)
             if not BaseSaverBuilder._IsVariable(var):
               raise TypeError("Variable to save is not a Variable: %s" % var)
           if var.op.type == "ReadVariableOp":
@@ -674,7 +677,10 @@ class BaseSaverBuilder(object):
                              "mode is enabled, type: %s." % type(op))
           saveable = BaseSaverBuilder.ResourceVariableSaveable(op, "", name)
         else:
-          variable = ops.internal_convert_to_tensor(op, as_ref=True)
+          if isinstance(op, resource_variable_ops.ResourceVariable):
+            variable = op._graph_element  # pylint: disable=protected-access
+          else:
+            variable = ops.internal_convert_to_tensor(op, as_ref=True)
           if not BaseSaverBuilder._IsVariable(variable):
             raise TypeError("names_to_saveables must be a dict mapping string "
                             "names to Tensors/Variables. Not a variable: %s" %
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index b758ceaab0..7947765449 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -262,6 +262,24 @@ class SaverTest(test.TestCase):
       save2.restore(sess, save_path)
       self.assertEquals(self.evaluate(v), [1])
 
+  def testNoAdditionalOpsAddedBySaverForResourceVariablesOutsideSaveScope(self):
+    with ops_lib.Graph().as_default() as g:
+      v = resource_variable_ops.ResourceVariable(1.0, name="v")
+      with ops_lib.name_scope("saver1"):
+        saver_module.Saver()
+      with ops_lib.name_scope("saver2"):
+        saver_module.Saver({"name": v})
+    ops_in_saver1_scope_but_not_save_scope = [
+        op for op in g.get_operations()
+        if (op.name.startswith("saver1/") and
+            not op.name.startswith("saver1/save/"))]
+    self.assertEqual(ops_in_saver1_scope_but_not_save_scope, [])
+    ops_in_saver2_scope_but_not_save_scope = [
+        op for op in g.get_operations()
+        if (op.name.startswith("saver2/") and
+            not op.name.startswith("saver2/save/"))]
+    self.assertEqual(ops_in_saver2_scope_but_not_save_scope, [])
+
   def testSaveCopyRestoreWithSaveRelativePaths(self):
     """Save, copy checkpoint dir and restore from copied dir.
 
-- 
GitLab


From 84fe908258550e1ce27e8725de1e2af279479c9d Mon Sep 17 00:00:00 2001
From: Minmin Sun <mmsun@foxmail.com>
Date: Sat, 3 Mar 2018 00:26:31 +0800
Subject: [PATCH 540/575] =?UTF-8?q?Add=20LINM=20(Loop=20Invariant=20Node?=
 =?UTF-8?q?=20Motion)=20optimization=20pass=20in=20GraphOptim=E2=80=A6=20(?=
 =?UTF-8?q?#16306)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add Loop Invariant Node Motion optimization in grappler

* linm: disable loop optimizations by default, remove includes not needed from loop_optimizer_test.cc

* remove redundant lines after merging with master

* LINM: a minor change in BUILD to fix gen_ci_sanity_out failure, and remove 'No newline at end of file' warning
---
 tensorflow/core/grappler/optimizers/BUILD     |   2 +
 .../grappler/optimizers/loop_optimizer.cc     | 382 +++++++++++++-
 .../core/grappler/optimizers/loop_optimizer.h |  26 +
 .../optimizers/loop_optimizer_test.cc         | 489 +++++++++++++++++-
 .../grappler/optimizers/meta_optimizer.cc     |   8 +-
 5 files changed, 901 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index a52d1c8df2..0a72a68a66 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -480,6 +480,7 @@ cc_library(
     ],
     visibility = ["//visibility:public"],
     deps = [
+        ":constant_folding",
         ":graph_optimizer",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
@@ -489,6 +490,7 @@ cc_library(
         "//tensorflow/core/grappler:op_types",
         "//tensorflow/core/grappler:utils",
         "//tensorflow/core/grappler/costs:graph_properties",
+        "//tensorflow/core/grappler/utils:frame",
     ],
 )
 
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.cc b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
index 102526e22f..0223930d74 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
@@ -15,23 +15,403 @@ limitations under the License.
 
 #include "tensorflow/core/grappler/optimizers/loop_optimizer.h"
 
+#include <algorithm>
+#include <limits>
 #include <unordered_map>
 #include <unordered_set>
+#include <vector>
+#include <deque>
 
+#include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/tensor_shape.pb.h"
+#include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/grappler/optimizers/constant_folding.h"
+#include "tensorflow/core/grappler/utils.h"
+#include "tensorflow/core/grappler/utils/frame.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/tensor_coding.h"
+#include "tensorflow/core/util/device_name_utils.h"
+#include "tensorflow/core/util/saved_tensor_slice_util.h"
+
+using tensorflow::strings::StrCat;
 
 namespace tensorflow {
 namespace grappler {
 
+Status LoopOptimizer::LINMHandleInvariantEnter(NodeDef* node,
+                                               const int num_outputs) {
+  auto consumers = node_map_->GetOutputs(node->name());
+  std::vector<string> enter_control_inputs;
+  string enter_input;
+  for (auto& input : node->input()) {
+    if (IsControlInput(input)) {
+      enter_control_inputs.push_back(input);
+    } else {
+      enter_input = input;
+    }
+  }
+  for (auto* consumer : consumers) {
+    if (invariant_nodes_.count(consumer)) {
+      for (int i = 0; i < consumer->input_size(); ++i) {
+        if (NodeName(consumer->input(i)) == node->name()) {
+          consumer->set_input(i, enter_input);
+          node_map_->AddOutput(NodeName(enter_input), consumer->name());
+          node_map_->RemoveOutput(node->name(), consumer->name());
+        }
+      }
+      for (auto& control_input : enter_control_inputs) {
+        consumer->add_input(control_input);
+        node_map_->AddOutput(NodeName(control_input), consumer->name());
+      }
+    }
+  }
+  return Status::OK();
+}
+
+Status LoopOptimizer::LINMHandleConst(NodeDef* node,
+    const int num_outputs, const int frame_id) {
+  NodeDef* const_node;
+  if (num_outputs == 0) {
+    // all successor nodes are invariant
+    // Remove the control inputs from this frame to the const node,
+    // when moving it out of the frame (in parent frame)
+    const_node = node;
+    node_map_->RemoveInputs(node->name());
+    node->clear_input();
+  } else {
+    // some successor nodes are variant
+    // Have to keep the const node in the frame,
+    // so create a new one outside the frame (in parent frame)
+    const_node = optimized_graph_->add_node();
+    const_node->set_name(AddPrefixToNodeName(node->name(), kLoopOptimizer));
+    const_node->set_op("Const");
+    const_node->set_device(node->device());
+    *const_node->mutable_attr() = node->attr();
+    node_map_->AddNode(const_node->name(), const_node);
+    auto consumers = node_map_->GetOutputs(node->name());
+    for (auto* consumer : consumers) {
+      if (invariant_nodes_.count(consumer)) {
+        for (int i = 0; i < consumer->input_size(); ++i) {
+          if (NodeName(consumer->input(i)) == node->name()) {
+            if (IsControlInput(consumer->input(i))) {
+              *consumer->mutable_input(i) = AsControlDependency(*const_node);
+            } else {
+              *consumer->mutable_input(i) = const_node->name();
+            }
+            node_map_->AddOutput(const_node->name(), consumer->name());
+            node_map_->RemoveOutput(node->name(), consumer->name());
+          }
+        }
+      }
+    }
+  }
+  // add a control input from the parent frame
+  auto parent_it = frame_parent_.find(frame_id);
+  if (parent_it != frame_parent_.end()) {
+    int parent_id = parent_it->second;
+    auto loop_cond_it = loop_cond_.find(parent_id);
+    if (loop_cond_it == loop_cond_.end()) {
+      return errors::InvalidArgument(
+          "Frame ", frame_id, " doesn't have a LoopCond node");
+    }
+    auto& loop_cond_name = loop_cond_it->second->name();
+    NodeDef* switch_node = nullptr;
+    for (auto* node : node_map_->GetOutputs(loop_cond_name)) {
+      if (node->op() == "Switch") {
+        switch_node = node;
+        break;
+      }
+    }
+    if (!switch_node) {
+      return errors::InvalidArgument(
+          "LoopCond node of Frame ", frame_id,
+          " doesn't connect to any Switch node");
+    }
+    string switch_output = StrCat(switch_node->name(), ":1");
+    const string ctrl_dep = ConstantFolding::AddControlDependency(
+        switch_output, optimized_graph_, node_map_.get());
+    const_node->add_input(ctrl_dep);
+    node_map_->AddOutput(NodeName(ctrl_dep), const_node->name());
+  }
+  return Status::OK();
+}
+
+Status LoopOptimizer::LINMHandleInvariantNode(NodeDef* node,
+    const int num_outputs, const int frame_id) {
+  // have to remove control inputs to the invariant node from the same frame
+  // when moving this node out of this frame
+  for (int i = 0; i < node->input_size(); ++i) {
+    if (IsControlInput(node->input(i))) {
+      node->mutable_input()->SwapElements(i, node->input_size() - 1);
+      node->mutable_input()->RemoveLast();
+    }
+  }
+  if (num_outputs == 0) {
+    return Status::OK();
+  }
+
+  DataTypeVector input_types;
+  DataTypeVector output_types;
+  OpRegistryInterface* op_registry = OpRegistry::Global();
+  const OpRegistrationData* op_reg_data = nullptr;
+  TF_RETURN_IF_ERROR(
+      op_registry->LookUp(node->op(), &op_reg_data));
+  TF_RETURN_IF_ERROR(
+      InOutTypesForNode(*node, op_reg_data->op_def,
+                        &input_types, &output_types));
+
+  auto consumers = node_map_->GetOutputs(node->name());
+  string fname = invariant_enters_[frame_id][0]->attr().at("frame_name").s();
+  int piterations = invariant_enters_[frame_id][0]
+                    ->attr().at("parallel_iterations").i();
+  for (auto* consumer : consumers) {
+    if (!invariant_nodes_.count(consumer)) {
+      for (int i = 0; i < consumer->input_size(); ++i) {
+        int port;
+        string node_name = ParseNodeName(consumer->input(i), &port);
+        if (node_name != node->name()) {
+          continue;
+        }
+        if (port < 0) {
+          return errors::InvalidArgument(
+              "Invariant node should not have control outputs "
+              "to variant node");
+        }
+        DataType output_type = output_types[port];
+        NodeDef* new_enter = optimized_graph_->add_node();
+        new_enter->set_op("Enter");
+        new_enter->set_device(node->device());
+        new_enter->set_name(AddPrefixToNodeName(
+            StrCat(fname, "_enter_", new_enter_id_++), kLoopOptimizer));
+        AttrValue data_type;
+        data_type.set_type(output_type);
+        new_enter->mutable_attr()->insert({"T", data_type});
+        AttrValue frame_name;
+        frame_name.set_s(fname);
+        new_enter->mutable_attr()->insert({"frame_name", frame_name});
+        AttrValue is_const;
+        is_const.set_b(true);
+        new_enter->mutable_attr()->insert({"is_constant", is_const});
+        AttrValue parallel_iterations;
+        parallel_iterations.set_i(piterations);
+        new_enter->mutable_attr()->insert(
+            {"parallel_iterations", parallel_iterations});
+        new_enter->add_input(consumer->input(i));
+        *consumer->mutable_input(i) = new_enter->name();
+        node_map_->AddNode(new_enter->name(), new_enter);
+        node_map_->AddOutput(node->name(), new_enter->name());
+        node_map_->AddOutput(new_enter->name(), consumer->name());
+      }
+    }
+  }
+  return Status::OK();
+}
+
+Status LoopOptimizer::MoveInvariantNodes(const int frame_id) {
+  for (auto iter = invariant_nodes_.begin();
+       iter != invariant_nodes_.end(); ++iter) {
+    auto* invariant_node = iter->first;
+    const int num_outputs = iter->second;
+    if (IsEnter(*invariant_node)) {
+      TF_RETURN_IF_ERROR(
+          LINMHandleInvariantEnter(invariant_node, num_outputs));
+    } else if (IsConstant(*invariant_node)) {
+      TF_RETURN_IF_ERROR(
+          LINMHandleConst(invariant_node, num_outputs, frame_id));
+    } else {
+      TF_RETURN_IF_ERROR(
+          LINMHandleInvariantNode(invariant_node, num_outputs, frame_id));
+    }
+  }
+  return Status::OK();
+}
+
+Status LoopOptimizer::RevertInvariantNodes() {
+  std::deque<const NodeDef*> reverted_nodes;
+  for (auto iter=invariant_nodes_.begin(); iter != invariant_nodes_.end();) {
+    bool erased = false;
+    const auto* node = iter->first;
+    if (!IsConstant(*node) && !IsEnter(*node) && iter->second > 0) {
+      auto& consumers = node_map_->GetOutputs(node->name());
+      for (auto* consumer : consumers) {
+        if (!invariant_nodes_.count(consumer)) {
+          for (const auto& input : consumer->input()) {
+            if (IsControlInput(input) && NodeName(input) == node->name()) {
+              reverted_nodes.push_back(node);
+              invariant_nodes_.erase(iter++);
+              erased = true;
+              break;
+            }
+          }
+          if (erased) break;
+        }
+      }
+    }
+    if (!erased) ++iter;
+  }
+  while (!reverted_nodes.empty()) {
+    const auto* node = reverted_nodes.front();
+    reverted_nodes.pop_front();
+    std::set<NodeDef*> producers;
+    for (const auto& input : node->input()) {
+      auto* producer = node_map_->GetNode(input);
+      auto iter = invariant_nodes_.find(producer);
+      if (iter != invariant_nodes_.end()) {
+        if (IsControlInput(input) &&
+            !IsConstant(*producer) && !IsEnter(*producer)) {
+          reverted_nodes.push_back(producer);
+          invariant_nodes_.erase(iter);
+        } else {
+          producers.insert(producer);
+        }
+      }
+    }
+    for (auto* producer : producers) {
+      auto iter = invariant_nodes_.find(producer);
+      if (iter != invariant_nodes_.end()) {
+        ++iter->second;
+      }
+    }
+    for (auto* consumer : node_map_->GetOutputs(node->name())) {
+      auto iter = invariant_nodes_.find(consumer);
+      if (iter != invariant_nodes_.end()) {
+        reverted_nodes.push_back(consumer);
+        invariant_nodes_.erase(iter);
+      }
+    }
+  }
+  return Status::OK();
+}
+
+Status LoopOptimizer::FindInvariantNodes(NodeDef* node) {
+  auto consumers = node_map_->GetOutputs(node->name());
+  invariant_nodes_.insert(std::make_pair(node, consumers.size()));
+  for (auto* consumer : consumers) {
+    if (invariant_nodes_.count(consumer) ||
+        ModifiesFrameInfo(*consumer)) {
+      continue;
+    }
+    bool is_invariant = true;
+    for (const auto& input : consumer->input()) {
+      if (!IsControlInput(input)) {
+        const auto& name = NodeName(input);
+        auto* producer = node_map_->GetNode(name);
+        if (!invariant_nodes_.count(producer)) {
+          if (IsConstant(*producer)) {
+            invariant_nodes_.insert(
+                std::make_pair(producer, node_map_->GetOutputs(name).size()));
+          } else {
+            is_invariant = false;
+            break;
+          }
+        }
+      }
+    }
+    if (is_invariant) {
+      std::set<NodeDef*> producers;
+      for (const auto& input : consumer->input()) {
+        auto* producer = node_map_->GetNode(input);
+        producers.insert(producer);
+      }
+      for (auto* producer : producers) {
+        auto iter = invariant_nodes_.find(producer);
+        if (iter != invariant_nodes_.end()) {
+          --iter->second;
+        }
+      }
+      TF_RETURN_IF_ERROR(FindInvariantNodes(consumer));
+    }
+  }
+  return Status::OK();
+}
+
+Status LoopOptimizer::LoopInvariantNodeMotion() {
+  std::deque<int> worklist;
+  for (auto iter = frame_map_.begin(); iter != frame_map_.end(); ++iter) {
+    auto* node = iter->first;
+    auto& frame_ids = iter->second;
+    if (frame_ids.size() >= 3) {
+      for (unsigned int i = 1; i < frame_ids.size() - 1; ++i) {
+        frame_parent_[frame_ids[i]] = frame_ids[i - 1];
+        frame_children_[frame_ids[i]].insert(frame_ids[i + 1]);
+      }
+    }
+    if (frame_ids.size() >= 2) {
+      frame_children_[frame_ids[0]].insert(frame_ids[1]);
+      frame_parent_[frame_ids.back()] = frame_ids[frame_ids.size() - 2];
+    }
+    if (frame_ids.size() >= 1) {
+      frame_children_.insert(std::make_pair(frame_ids.back(), empty_set_));
+      if (node->op() == "LoopCond") {
+        if (loop_cond_.count(frame_ids.back())) {
+          return errors::InvalidArgument(
+              "Loop ", frame_ids.back(),
+              " has more than one LoopCond node: ", node->name(), " and ",
+              loop_cond_[frame_ids.back()]->name());
+        }
+        loop_cond_[frame_ids.back()] = node;
+      }
+      if (IsEnter(*node) && node->attr().at("is_constant").b()) {
+        invariant_enters_[frame_ids.back()].push_back(
+            const_cast<NodeDef*>(node));
+      }
+    }
+  }
+
+  for (auto it = frame_children_.begin(); it != frame_children_.end(); ++it) {
+    if (it->second.size() == 0) {
+      worklist.push_back(it->first);
+    }
+  }
+
+  while (!worklist.empty()) {
+    int frame_id = worklist.front();
+    new_enter_id_ = 0;
+    worklist.pop_front();
+    auto parent_it = frame_parent_.find(frame_id);
+    if (parent_it != frame_parent_.end()) {
+      int parent_id = parent_it->second;
+      frame_children_[parent_id].erase(frame_id);
+      if (frame_children_[parent_id].size() == 0) {
+        worklist.push_back(parent_id);
+      }
+    }
+
+    if (invariant_enters_[frame_id].empty()) {
+      continue;
+    }
+    invariant_nodes_.clear();
+    for (auto* enter : invariant_enters_[frame_id]) {
+      TF_RETURN_IF_ERROR(FindInvariantNodes(enter));
+    }
+
+    // revert invariant nodes that have control outputs to variant nodes
+    TF_RETURN_IF_ERROR(RevertInvariantNodes());
+
+    TF_RETURN_IF_ERROR(MoveInvariantNodes(frame_id));
+  }
+  return Status::OK();
+}
+
 Status LoopOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
                                GraphDef* optimized_graph) {
-  *optimized_graph = item.graph;
+  optimized_graph_ = optimized_graph;
+  *optimized_graph_ = item.graph;
+
+  // Set up helper data structures.
+  node_map_.reset(new NodeMap(optimized_graph_));
+  int num_frames;
+  TF_RETURN_IF_ERROR(IdentifyFramesWithNodeMap(*optimized_graph_, *node_map_,
+                                               &frame_map_, &num_frames));
+
+  TF_RETURN_IF_ERROR(LoopInvariantNodeMotion());
 
   return Status::OK();
 }
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.h b/tensorflow/core/grappler/optimizers/loop_optimizer.h
index 106d4628ae..b5944cd30b 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer.h
@@ -17,13 +17,17 @@ limitations under the License.
 #define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_LOOP_OPTIMIZER_H_
 
 #include <unordered_set>
+#include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/optimizers/graph_optimizer.h"
 #include "tensorflow/core/grappler/utils.h"
+#include "tensorflow/core/grappler/utils/frame.h"
 #include "tensorflow/core/protobuf/rewriter_config.pb.h"
 
 namespace tensorflow {
 namespace grappler {
 
+constexpr char kLoopOptimizer[] = "LoopOptimizer";
+
 class LoopOptimizer : public GraphOptimizer {
  public:
   LoopOptimizer() : opt_level_(RewriterConfig::ON) {}
@@ -40,7 +44,29 @@ class LoopOptimizer : public GraphOptimizer {
                 const GraphDef& optimized_graph, double result) override;
 
  private:
+  Status LoopInvariantNodeMotion();
+  Status FindInvariantNodes(NodeDef* node);
+  Status RevertInvariantNodes();
+  Status MoveInvariantNodes(const int fname);
+  Status LINMHandleInvariantNode(NodeDef* node, const int num_outputs,
+      const int frame_id);
+  Status LINMHandleConst(NodeDef* node, const int num_outputs,
+      const int frame_id);
+  Status LINMHandleInvariantEnter(NodeDef* node, const int num_outputs);
+
+  std::map<NodeDef*, int> invariant_nodes_;
+  std::set<int> empty_set_;
+  std::map<int, std::set<int>> frame_children_;
+  std::map<int, int> frame_parent_;
+  std::map<int, const NodeDef*> loop_cond_;
+  std::map<int, std::vector<NodeDef*>> invariant_enters_;
+  int new_enter_id_;
   RewriterConfig::Toggle opt_level_;
+
+  std::unique_ptr<NodeMap> node_map_;
+  FrameMap frame_map_;
+  std::unique_ptr<GraphProperties> graph_properties_;
+  GraphDef* optimized_graph_;  // Not owned.
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc b/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
index c09434f609..cc0432c3ed 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
@@ -26,7 +26,494 @@ namespace tensorflow {
 namespace grappler {
 namespace {
 
-class LoopOptimizerTest : public ::testing::Test {};
+class LoopOptimizerTest : public ::testing::Test {
+ protected:
+  static NodeDef CreateNode(const string& name,
+                            const std::vector<string>& inputs) {
+    return CreateNode(name, "Identity", "", false, 0, inputs);
+  }
+  static NodeDef CreateNode(const string& name, const string& op,
+                            const std::vector<string>& inputs) {
+    return CreateNode(name, op, "", false, 0, inputs);
+  }
+  static NodeDef CreateNode(const string& name, const string& op,
+                            const string& frame,
+                            const bool is_constant,
+                            const int piterations,
+                            const std::vector<string>& inputs) {
+    NodeDef node;
+    node.set_name(name);
+    if (!op.empty()) {
+      node.set_op(op);
+    }
+    if (!frame.empty()) {
+      AttrValue frame_name;
+      frame_name.set_s(frame);
+      node.mutable_attr()->insert({"frame_name", frame_name});
+    }
+    if (op == "Enter") {
+      AttrValue is_const;
+      is_const.set_b(is_constant);
+      node.mutable_attr()->insert({"is_constant", is_const});
+      AttrValue parallel_iterations;
+      parallel_iterations.set_i(piterations);
+      node.mutable_attr()->insert(
+          {"parallel_iterations", parallel_iterations});
+    }
+    AttrValue type;
+    type.set_type(DT_FLOAT);
+    node.mutable_attr()->insert({"T", type});
+    for (const string& input : inputs) {
+      node.add_input(input);
+    }
+    return node;
+  }
+};
+
+TEST_F(LoopOptimizerTest, Basic) {
+  GraphDef graph;
+  *graph.add_node() = CreateNode("0", {});
+  *graph.add_node() = CreateNode(
+      "InvariantEnter", "Enter", "while/while_context", true, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd", "Add", {"InvariantEnter", "InvariantEnter"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd", "Add", {"InvariantAdd", "Identity"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter", "Enter", "while/while_context", false, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "Merge", "Merge", {"VariantEnter", "NextIteration"});
+  *graph.add_node() = CreateNode("Less/y", "Const", {"^Identity"});
+  *graph.add_node() = CreateNode("Less", "Less", {"VariantAdd", "less/y"});
+  *graph.add_node() = CreateNode("LoopCond", "LoopCond", {"Less"});
+  *graph.add_node() = CreateNode("Switch", "Switch", {"Merge", "LoopCond"});
+  *graph.add_node() = CreateNode("Identity", {"Switch:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration", "NextIteration", {"VariantAdd"});
+  *graph.add_node() = CreateNode("Exit", "Exit", {"Switch"});
+  *graph.add_node() = CreateNode("1", {"Exit"});
+
+  GrapplerItem item;
+  item.graph = graph;
+
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  std::unique_ptr<NodeMap> node_map;
+  std::unordered_map<const NodeDef*, std::vector<int>> frames;
+  int num_frames;
+
+  node_map.reset(new NodeMap(&graph));
+  EXPECT_TRUE(IdentifyFrames(graph, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).back(), 0);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd")).back(), 0);
+
+  node_map.reset(new NodeMap(&output));
+  EXPECT_TRUE(IdentifyFrames(output, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).size(), 0);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd")).back(), 0);
+}
+
+TEST_F(LoopOptimizerTest, Const) {
+  GraphDef graph;
+  *graph.add_node() = CreateNode("0", {});
+  *graph.add_node() = CreateNode(
+      "InvariantEnter", "Enter", "while/while_context", true, 1, {"0"});
+  *graph.add_node() = CreateNode("Const", "Const", {"^Identity"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd", "Add", {"InvariantEnter", "Const"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd", "Add", {"InvariantAdd", "Identity"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter", "Enter", "while/while_context", false, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "Merge", "Merge", {"VariantEnter", "NextIteration"});
+  *graph.add_node() = CreateNode("Less/y", "Const", {"^Identity"});
+  *graph.add_node() = CreateNode("Less", "Less", {"VariantAdd", "less/y"});
+  *graph.add_node() = CreateNode("LoopCond", "LoopCond", {"Less"});
+  *graph.add_node() = CreateNode("Switch", "Switch", {"Merge", "LoopCond"});
+  *graph.add_node() = CreateNode("Identity", {"Switch:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration", "NextIteration", {"VariantAdd"});
+  *graph.add_node() = CreateNode("Exit", "Exit", {"Switch"});
+  *graph.add_node() = CreateNode("1", {"Exit"});
+
+  GrapplerItem item;
+  item.graph = graph;
+
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  std::unique_ptr<NodeMap> node_map;
+  std::unordered_map<const NodeDef*, std::vector<int>> frames;
+  int num_frames;
+
+  node_map.reset(new NodeMap(&graph));
+  EXPECT_TRUE(IdentifyFrames(graph, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).back(), 0);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const")).back(), 0);
+
+  node_map.reset(new NodeMap(&output));
+  EXPECT_TRUE(IdentifyFrames(output, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).size(), 0);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const")).size(), 0);
+}
+
+TEST_F(LoopOptimizerTest, ControlOutput) {
+  GraphDef graph;
+  *graph.add_node() = CreateNode("0", {});
+  *graph.add_node() = CreateNode(
+      "InvariantEnter", "Enter", "while/while_context", true, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd", "Add", {"InvariantEnter", "InvariantEnter"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd", "Add", {"InvariantAdd", "Identity"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter", "Enter", "while/while_context", false, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "Merge", "Merge", {"VariantEnter", "NextIteration"});
+  *graph.add_node() = CreateNode("Less/y", "Const", {"^Identity"});
+  *graph.add_node() = CreateNode(
+      "Less", "Less", {"VariantAdd", "less/y", "^InvariantAdd"});
+  *graph.add_node() = CreateNode("LoopCond", "LoopCond", {"Less"});
+  *graph.add_node() = CreateNode("Switch", "Switch", {"Merge", "LoopCond"});
+  *graph.add_node() = CreateNode("Identity", {"Switch:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration", "NextIteration", {"VariantAdd"});
+  *graph.add_node() = CreateNode("Exit", "Exit", {"Switch"});
+  *graph.add_node() = CreateNode("1", {"Exit"});
+
+  GrapplerItem item;
+  item.graph = graph;
+
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  std::unique_ptr<NodeMap> node_map;
+  std::unordered_map<const NodeDef*, std::vector<int>> frames;
+  int num_frames;
+
+  node_map.reset(new NodeMap(&graph));
+  EXPECT_TRUE(IdentifyFrames(graph, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).back(), 0);
+
+  node_map.reset(new NodeMap(&output));
+  EXPECT_TRUE(IdentifyFrames(output, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).back(), 0);
+}
+
+TEST_F(LoopOptimizerTest, NestedLoop1) {
+  GraphDef graph;
+  *graph.add_node() = CreateNode("0", {});
+  *graph.add_node() = CreateNode(
+      "InvariantEnter", "Enter", "while/while_context", true, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd", "Add", {"InvariantEnter", "InvariantEnter"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd", "Add", {"InvariantAdd", "Identity"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter", "Enter", "while/while_context", false, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "Merge", "Merge", {"VariantEnter", "NextIteration"});
+  *graph.add_node() = CreateNode("Less/y", "Const", {"^Identity"});
+  *graph.add_node() = CreateNode("Less", "Less", {"Exit2", "less/y"});
+  *graph.add_node() = CreateNode("LoopCond", "LoopCond", {"Less"});
+  *graph.add_node() = CreateNode("Switch", "Switch", {"Merge", "LoopCond"});
+  *graph.add_node() = CreateNode("Identity", {"Switch:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration", "NextIteration", {"Exit2"});
+  *graph.add_node() = CreateNode("Exit", "Exit", {"Switch"});
+  *graph.add_node() = CreateNode("1", {"Exit"});
+
+  *graph.add_node() = CreateNode(
+      "InvariantEnter2", "Enter", "while/while/while_context", true, 1,
+      {"VariantAdd"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd2", "Add", {"InvariantEnter2", "InvariantEnter2"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd2", "Add", {"InvariantAdd2", "Identity2"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter2", "Enter", "while/while/while_context", false, 1,
+      {"VariantEnter"});
+  *graph.add_node() = CreateNode(
+      "Merge2", "Merge", {"VariantEnter2", "NextIteration2"});
+  *graph.add_node() = CreateNode("Less2/y", "Const", {"^Identity2"});
+  *graph.add_node() = CreateNode("Less2", "Less", {"VariantAdd2", "less2/y"});
+  *graph.add_node() = CreateNode("LoopCond2", "LoopCond", {"Less2"});
+  *graph.add_node() = CreateNode("Switch2", "Switch", {"Merge2", "LoopCond2"});
+  *graph.add_node() = CreateNode("Identity2", {"Switch2:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration2", "NextIteration", {"VariantAdd2"});
+  *graph.add_node() = CreateNode("Exit2", "Exit", {"Switch2"});
+
+  GrapplerItem item;
+  item.graph = graph;
+
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  std::unique_ptr<NodeMap> node_map;
+  std::unordered_map<const NodeDef*, std::vector<int>> frames;
+  int num_frames;
+
+  node_map.reset(new NodeMap(&graph));
+  EXPECT_TRUE(IdentifyFrames(graph, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).back(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd2")).back(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).back(), 0);
+
+  node_map.reset(new NodeMap(&output));
+  EXPECT_TRUE(IdentifyFrames(output, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).back(), 0);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd2")).back(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd")).size(), 0);
+}
+
+TEST_F(LoopOptimizerTest, NestedLoop2) {
+  GraphDef graph;
+  *graph.add_node() = CreateNode("0", {});
+  *graph.add_node() = CreateNode(
+      "InvariantEnter", "Enter", "while/while_context", true, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd", "Add", {"InvariantEnter", "InvariantEnter"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd", "Add", {"InvariantAdd", "Identity"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter", "Enter", "while/while_context", false, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "Merge", "Merge", {"VariantEnter", "NextIteration"});
+  *graph.add_node() = CreateNode("Less/y", "Const", {"^Identity"});
+  *graph.add_node() = CreateNode("Less", "Less", {"Exit2", "less/y"});
+  *graph.add_node() = CreateNode("LoopCond", "LoopCond", {"Less"});
+  *graph.add_node() = CreateNode("Switch", "Switch", {"Merge", "LoopCond"});
+  *graph.add_node() = CreateNode("Identity", {"Switch:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration", "NextIteration", {"Exit2"});
+  *graph.add_node() = CreateNode("Exit", "Exit", {"Switch"});
+  *graph.add_node() = CreateNode("1", {"Exit"});
+
+  *graph.add_node() = CreateNode(
+      "InvariantEnter2", "Enter", "while/while/while_context", true, 1,
+      {"InvariantAdd"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd2", "Add", {"InvariantEnter2", "InvariantEnter2"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd2", "Add", {"InvariantAdd2", "Identity2"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter2", "Enter", "while/while/while_context", false, 1,
+      {"VariantEnter"});
+  *graph.add_node() = CreateNode(
+      "Merge2", "Merge", {"VariantEnter2", "NextIteration2"});
+  *graph.add_node() = CreateNode("Less2/y", "Const", {"^Identity2"});
+  *graph.add_node() = CreateNode("Less2", "Less", {"VariantAdd2", "less2/y"});
+  *graph.add_node() = CreateNode("LoopCond2", "LoopCond", {"Less2"});
+  *graph.add_node() = CreateNode("Switch2", "Switch", {"Merge2", "LoopCond2"});
+  *graph.add_node() = CreateNode("Identity2", {"Switch2:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration2", "NextIteration", {"VariantAdd2"});
+  *graph.add_node() = CreateNode("Exit2", "Exit", {"Switch2"});
+
+  GrapplerItem item;
+  item.graph = graph;
+
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  std::unique_ptr<NodeMap> node_map;
+  std::unordered_map<const NodeDef*, std::vector<int>> frames;
+  int num_frames;
+
+  node_map.reset(new NodeMap(&graph));
+  EXPECT_TRUE(IdentifyFrames(graph, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).back(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd2")).back(), 1);
+
+  node_map.reset(new NodeMap(&output));
+  EXPECT_TRUE(IdentifyFrames(output, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).size(), 0);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("VariantAdd2")).back(), 1);
+}
+
+TEST_F(LoopOptimizerTest, NestedLoopConst1) {
+  GraphDef graph;
+  *graph.add_node() = CreateNode("0", {});
+  *graph.add_node() = CreateNode(
+      "InvariantEnter", "Enter", "while/while_context", true, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd", "Add", {"InvariantEnter", "InvariantEnter"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd", "Add", {"InvariantAdd", "Identity"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter", "Enter", "while/while_context", false, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "Merge", "Merge", {"VariantEnter", "NextIteration"});
+  *graph.add_node() = CreateNode("Less/y", "Const", {"^Identity"});
+  *graph.add_node() = CreateNode("Less", "Less", {"Exit2", "less/y"});
+  *graph.add_node() = CreateNode("LoopCond", "LoopCond", {"Less"});
+  *graph.add_node() = CreateNode("Switch", "Switch", {"Merge", "LoopCond"});
+  *graph.add_node() = CreateNode("Identity", {"Switch:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration", "NextIteration", {"Exit2"});
+  *graph.add_node() = CreateNode("Exit", "Exit", {"Switch"});
+  *graph.add_node() = CreateNode("1", {"Exit"});
+
+  *graph.add_node() = CreateNode(
+      "InvariantEnter2", "Enter", "while/while/while_context", true, 1,
+      {"VariantAdd"});
+  *graph.add_node() = CreateNode("Const2", "Const", {"^Identity2"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd2", "Add", {"InvariantEnter2", "Const2"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd2", "Add", {"InvariantAdd2", "Identity2"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter2", "Enter", "while/while/while_context", false, 1,
+      {"VariantEnter"});
+  *graph.add_node() = CreateNode(
+      "Merge2", "Merge", {"VariantEnter2", "NextIteration2"});
+  *graph.add_node() = CreateNode("Less2/y", "Const", {"^Identity2"});
+  *graph.add_node() = CreateNode("Less2", "Less", {"VariantAdd2", "less2/y"});
+  *graph.add_node() = CreateNode("LoopCond2", "LoopCond", {"Less2"});
+  *graph.add_node() = CreateNode("Switch2", "Switch", {"Merge2", "LoopCond2"});
+  *graph.add_node() = CreateNode("Identity2", {"Switch2:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration2", "NextIteration", {"VariantAdd2"});
+  *graph.add_node() = CreateNode("Exit2", "Exit", {"Switch2"});
+
+  GrapplerItem item;
+  item.graph = graph;
+
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  std::unique_ptr<NodeMap> node_map;
+  std::unordered_map<const NodeDef*, std::vector<int>> frames;
+  int num_frames;
+
+  node_map.reset(new NodeMap(&graph));
+  EXPECT_TRUE(IdentifyFrames(graph, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).back(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const2")).back(), 1);
+
+  node_map.reset(new NodeMap(&output));
+  EXPECT_TRUE(IdentifyFrames(output, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).back(), 0);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const2")).size(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const2")).back(), 0);
+}
+
+TEST_F(LoopOptimizerTest, NestedLoopConst2) {
+  GraphDef graph;
+  *graph.add_node() = CreateNode("0", {});
+  *graph.add_node() = CreateNode(
+      "InvariantEnter", "Enter", "while/while_context", true, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd", "Add", {"InvariantEnter", "InvariantEnter"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd", "Add", {"InvariantAdd", "Identity"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter", "Enter", "while/while_context", false, 1, {"0"});
+  *graph.add_node() = CreateNode(
+      "Merge", "Merge", {"VariantEnter", "NextIteration"});
+  *graph.add_node() = CreateNode("Less/y", "Const", {"^Identity"});
+  *graph.add_node() = CreateNode("Less", "Less", {"Exit2", "less/y"});
+  *graph.add_node() = CreateNode("LoopCond", "LoopCond", {"Less"});
+  *graph.add_node() = CreateNode("Switch", "Switch", {"Merge", "LoopCond"});
+  *graph.add_node() = CreateNode("Identity", {"Switch:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration", "NextIteration", {"Exit2"});
+  *graph.add_node() = CreateNode("Exit", "Exit", {"Switch"});
+  *graph.add_node() = CreateNode("1", {"Exit"});
+
+  *graph.add_node() = CreateNode(
+      "InvariantEnter2", "Enter", "while/while/while_context", true, 1,
+      {"InvariantAdd"});
+  *graph.add_node() = CreateNode("Const2", "Const", {"^Identity2"});
+  *graph.add_node() = CreateNode(
+      "InvariantAdd2", "Add", {"InvariantEnter2", "Const2"});
+  *graph.add_node() = CreateNode(
+      "VariantAdd2", "Add", {"InvariantAdd2", "Identity2"});
+  *graph.add_node() = CreateNode(
+      "VariantEnter2", "Enter", "while/while/while_context", false, 1,
+      {"VariantEnter"});
+  *graph.add_node() = CreateNode(
+      "Merge2", "Merge", {"VariantEnter2", "NextIteration2"});
+  *graph.add_node() = CreateNode("Less2/y", "Const", {"^Identity2"});
+  *graph.add_node() = CreateNode("Less2", "Less", {"VariantAdd2", "less2/y"});
+  *graph.add_node() = CreateNode("LoopCond2", "LoopCond", {"Less2"});
+  *graph.add_node() = CreateNode("Switch2", "Switch", {"Merge2", "LoopCond2"});
+  *graph.add_node() = CreateNode("Identity2", {"Switch2:1"});
+  *graph.add_node() = CreateNode(
+      "NextIteration2", "NextIteration", {"VariantAdd2"});
+  *graph.add_node() = CreateNode("Exit2", "Exit", {"Switch2"});
+
+  GrapplerItem item;
+  item.graph = graph;
+
+  LoopOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  std::unique_ptr<NodeMap> node_map;
+  std::unordered_map<const NodeDef*, std::vector<int>> frames;
+  int num_frames;
+
+  node_map.reset(new NodeMap(&graph));
+  EXPECT_TRUE(IdentifyFrames(graph, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).back(), 1);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const2")).size(), 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const2")).back(), 1);
+
+  node_map.reset(new NodeMap(&output));
+  EXPECT_TRUE(IdentifyFrames(output, &frames, &num_frames).ok());
+  EXPECT_EQ(num_frames, 2);
+  EXPECT_EQ(frames.at(node_map->GetNode("InvariantAdd2")).size(), 0);
+  EXPECT_EQ(frames.at(node_map->GetNode("Const2")).size(), 0);
+}
 
 void VerifyGraphsEqual(const GraphDef& original_graph,
                        const GraphDef& optimized_graph, const string& func) {
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 7ae77207af..39ecf017db 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -98,13 +98,13 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
           new ArithmeticOptimizer(cfg_.arithmetic_optimization())));
     }
-    if (cfg_.dependency_optimization() != RewriterConfig::OFF) {
+    if (cfg_.loop_optimization() == RewriterConfig::ON) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
-          new DependencyOptimizer(cfg_.dependency_optimization())));
+          new LoopOptimizer(cfg_.loop_optimization())));
     }
-    if (cfg_.loop_optimization() != RewriterConfig::OFF) {
+    if (cfg_.dependency_optimization() != RewriterConfig::OFF) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
-          new LoopOptimizer(cfg_.loop_optimization())));
+          new DependencyOptimizer(cfg_.dependency_optimization())));
     }
     if (cfg_.layout_optimizer() != RewriterConfig::OFF) {
       optimizers.push_back(
-- 
GitLab


From 1534cf92b4710d29dea780b1a17a6f7d2f10fc7b Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Fri, 2 Mar 2018 08:31:21 -0800
Subject: [PATCH 541/575] Internal-only change.

PiperOrigin-RevId: 187623121
---
 tensorflow/contrib/tpu/python/tpu/datasets.py      | 2 +-
 tensorflow/contrib/tpu/python/tpu/datasets_test.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/tpu/python/tpu/datasets.py b/tensorflow/contrib/tpu/python/tpu/datasets.py
index 29aea98542..71a3a92540 100644
--- a/tensorflow/contrib/tpu/python/tpu/datasets.py
+++ b/tensorflow/contrib/tpu/python/tpu/datasets.py
@@ -116,7 +116,7 @@ def StreamingFilesDataset(files,
 
   file_reader_job = file_reader_job or 'coordinator'
 
-  worker_job = worker_job or 'worker'
+  worker_job = worker_job or 'tpu_worker'
 
   if filename_shuffle_buffer_size is None:
     filename_shuffle_buffer_size = 4096
diff --git a/tensorflow/contrib/tpu/python/tpu/datasets_test.py b/tensorflow/contrib/tpu/python/tpu/datasets_test.py
index 2c40797792..0173aac4f7 100644
--- a/tensorflow/contrib/tpu/python/tpu/datasets_test.py
+++ b/tensorflow/contrib/tpu/python/tpu/datasets_test.py
@@ -44,7 +44,7 @@ class DatasetsTest(test.TestCase):
 
     self._cluster_def = cluster_pb2.ClusterDef()
     worker_job = self._cluster_def.job.add()
-    worker_job.name = 'worker'
+    worker_job.name = 'tpu_worker'
     worker_job.tasks[0] = self._worker.target[len('grpc://'):]
     coord_job = self._cluster_def.job.add()
     coord_job.name = 'coordinator'
-- 
GitLab


From 4397f80b34d28144ed523a3f31a0fcbd1f3a9ba1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 08:45:01 -0800
Subject: [PATCH 542/575] Add a testing utility that can be called from
 compiled code, and which can mock a TF module for internal tests. Use it in
 api_test.py

PiperOrigin-RevId: 187624343
---
 tensorflow/contrib/py2tf/impl/api_test.py  | 35 ++++++++++++----------
 tensorflow/contrib/py2tf/utils/BUILD       |  1 +
 tensorflow/contrib/py2tf/utils/__init__.py |  1 +
 tensorflow/contrib/py2tf/utils/testing.py  | 35 ++++++++++++++++++++++
 4 files changed, 56 insertions(+), 16 deletions(-)
 create mode 100644 tensorflow/contrib/py2tf/utils/testing.py

diff --git a/tensorflow/contrib/py2tf/impl/api_test.py b/tensorflow/contrib/py2tf/impl/api_test.py
index 51e99864ad..13f8e66018 100644
--- a/tensorflow/contrib/py2tf/impl/api_test.py
+++ b/tensorflow/contrib/py2tf/impl/api_test.py
@@ -18,23 +18,26 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.py2tf import utils
 from tensorflow.contrib.py2tf.impl import api
 from tensorflow.contrib.py2tf.impl import config
 from tensorflow.contrib.py2tf.pyct import parser
 from tensorflow.python.framework import constant_op
-from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
 
+tf = utils.fake_tf()
+
+
 class ApiTest(test.TestCase):
 
   def setUp(self):
-    config.DEFAULT_UNCOMPILED_MODULES.add((math_ops.__name__,))
     config.COMPILED_IMPORT_STATEMENTS = (
-        'from tensorflow.python.framework '
-        'import ops as tf',
+        'from __future__ import print_function',
         'from tensorflow.contrib.py2tf import utils as '
-        'py2tf_utils')
+        'py2tf_utils',
+        'tf = py2tf_utils.fake_tf()'
+    )
 
   def test_decorator_recurses(self):
 
@@ -47,7 +50,7 @@ class ApiTest(test.TestCase):
 
       @api.convert(recursive=True)
       def test_method(self, x, s, a):
-        while math_ops.reduce_sum(x) > s:
+        while tf.reduce_sum(x) > s:
           x //= self.called_member(a)
         return x
 
@@ -63,11 +66,11 @@ class ApiTest(test.TestCase):
     class TestClass(object):
 
       def called_member(self, a):
-        return math_ops.negative(a)
+        return tf.negative(a)
 
       @api.convert(recursive=False)
       def test_method(self, x, s, a):
-        while math_ops.reduce_sum(x) > s:
+        while tf.reduce_sum(x) > s:
           x //= self.called_member(a)
         return x
 
@@ -84,11 +87,11 @@ class ApiTest(test.TestCase):
 
       @api.graph_ready
       def called_member(self, a):
-        return math_ops.negative(a)
+        return tf.negative(a)
 
       @api.convert(recursive=True)
       def test_method(self, x, s, a):
-        while math_ops.reduce_sum(x) > s:
+        while tf.reduce_sum(x) > s:
           x //= self.called_member(a)
         return x
 
@@ -111,7 +114,7 @@ class ApiTest(test.TestCase):
 
       @api.convert(recursive=True)
       def test_method(self, x, s, a):
-        while math_ops.reduce_sum(x) > s:
+        while tf.reduce_sum(x) > s:
           x //= self.called_member(a)
         return x
 
@@ -133,7 +136,7 @@ class ApiTest(test.TestCase):
 
       @api.convert(recursive=True)
       def test_method(self, x, s, a):
-        while math_ops.reduce_sum(x) > s:
+        while tf.reduce_sum(x) > s:
           x //= api.convert_inline(self.called_member, a)
         return x
 
@@ -149,11 +152,11 @@ class ApiTest(test.TestCase):
     class TestClass(object):
 
       def called_member(self, a):
-        return math_ops.negative(a)
+        return tf.negative(a)
 
       @api.convert(recursive=True)
       def test_method(self, x, s, a):
-        while math_ops.reduce_sum(x) > s:
+        while tf.reduce_sum(x) > s:
           x //= api.graph_ready(self.called_member(a))
         return x
 
@@ -166,7 +169,7 @@ class ApiTest(test.TestCase):
 
   def test_to_graph_basic(self):
     def test_fn(x, s):
-      while math_ops.reduce_sum(x) > s:
+      while tf.reduce_sum(x) > s:
         x //= 2
       return x
 
@@ -178,7 +181,7 @@ class ApiTest(test.TestCase):
 
   def test_to_code_basic(self):
     def test_fn(x, s):
-      while math_ops.reduce_sum(x) > s:
+      while tf.reduce_sum(x) > s:
         x /= 2
       return x
 
diff --git a/tensorflow/contrib/py2tf/utils/BUILD b/tensorflow/contrib/py2tf/utils/BUILD
index 2086a9ef60..63261d5043 100644
--- a/tensorflow/contrib/py2tf/utils/BUILD
+++ b/tensorflow/contrib/py2tf/utils/BUILD
@@ -26,6 +26,7 @@ py_library(
         "multiple_dispatch.py",
         "py_func.py",
         "tensor_list.py",
+        "testing.py",
         "type_check.py",
     ],
     srcs_version = "PY2AND3",
diff --git a/tensorflow/contrib/py2tf/utils/__init__.py b/tensorflow/contrib/py2tf/utils/__init__.py
index 19bf2272bc..313e5c97cc 100644
--- a/tensorflow/contrib/py2tf/utils/__init__.py
+++ b/tensorflow/contrib/py2tf/utils/__init__.py
@@ -25,4 +25,5 @@ from tensorflow.contrib.py2tf.utils.misc import alias_tensors
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_cond
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_while
 from tensorflow.contrib.py2tf.utils.py_func import wrap_py_func
+from tensorflow.contrib.py2tf.utils.testing import fake_tf
 from tensorflow.contrib.py2tf.utils.type_check import is_tensor
diff --git a/tensorflow/contrib/py2tf/utils/testing.py b/tensorflow/contrib/py2tf/utils/testing.py
new file mode 100644
index 0000000000..cb4785d0dc
--- /dev/null
+++ b/tensorflow/contrib/py2tf/utils/testing.py
@@ -0,0 +1,35 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Testing utilities."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import imp
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import math_ops
+
+
+def fake_tf():
+  """Creates a fake module that looks like TensorFlow, for testing."""
+  mod = imp.new_module('tensorflow')
+  mod_contents = dict()
+  mod_contents.update(math_ops.__dict__)
+  mod_contents.update(ops.__dict__)
+  mod_contents.update(mod.__dict__)
+  mod.__dict__.update(mod_contents)
+  return mod
-- 
GitLab


From 7013a5ae241cd0c5375065f549aec27fcee6465d Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 2 Mar 2018 09:24:26 -0800
Subject: [PATCH 543/575] Take into account the return value mapping of
 functions

PiperOrigin-RevId: 187628382
---
 .../grappler/optimizers/function_optimizer.cc |   6 +-
 .../optimizers/function_optimizer_test.cc     | 156 +++++++++++++++++-
 tensorflow/core/grappler/utils/functions.cc   |  17 +-
 .../core/grappler/utils/functions_test.cc     |  85 +++++++++-
 4 files changed, 256 insertions(+), 8 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index a5cf00c155..167e5a153a 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -102,7 +102,8 @@ Status InlineFunction(const NodeDef& node, const FunctionDef& func,
   func_outputs->set_op("IdentityN");
   func_outputs->set_device(node.device());
   type_list = (*func_outputs->mutable_attr())["T"].mutable_list();
-  for (const OpDef::ArgDef& arg : func.signature().output_arg()) {
+  for (int i = 0; i < func.signature().output_arg_size(); ++i) {
+    const OpDef::ArgDef& arg = func.signature().output_arg(i);
     if (arg.type() != DT_INVALID) {
       type_list->add_type(arg.type());
     } else {
@@ -114,7 +115,8 @@ Status InlineFunction(const NodeDef& node, const FunctionDef& func,
       }
       type_list->add_type(it->second.type());
     }
-    func_outputs->add_input(strings::StrCat(node.name(), "/", arg.name()));
+    // Use the fetch names since they take into account the output mapping.
+    func_outputs->add_input(strings::StrCat(node.name(), "/", item->fetch[i]));
   }
 
   return Status::OK();
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
index fd61c067ed..5072abaac7 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
@@ -79,7 +79,7 @@ TEST_F(FunctionOptimizerTest, SimpleFunction) {
       EXPECT_EQ("IdentityN", node.op());
       EXPECT_EQ(device, node.device());
       EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("y/y", node.input(0));
+      EXPECT_EQ("y/y:0", node.input(0));
     } else if (node.name() == "z") {
       count++;
       EXPECT_EQ("Identity", node.op());
@@ -166,7 +166,7 @@ TEST_F(FunctionOptimizerTest, FixedTypeFunction) {
       EXPECT_EQ("IdentityN", node.op());
       EXPECT_EQ(device, node.device());
       EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("y/y", node.input(0));
+      EXPECT_EQ("y/y:0", node.input(0));
     } else if (node.name() == "z") {
       count++;
       EXPECT_EQ("Identity", node.op());
@@ -187,6 +187,158 @@ TEST_F(FunctionOptimizerTest, FixedTypeFunction) {
   test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
 }
 
+TEST_F(FunctionOptimizerTest, FunctionWithOutputMapping) {
+  FunctionDef func = FunctionDefHelper::Create(
+      // Name
+      "Exp_func",
+      // Args
+      {"in: float"},
+      // Return values
+      {"out: float"},
+      // Attr def
+      {},
+      // Nodes
+      {{{"Linear_func"}, "Identity", {"in"}, {{"T", DT_FLOAT}}},
+       {{"Exp"}, "Exp", {"Linear_func:output:0"}, {{"T", DT_FLOAT}}}},
+      // Mapping
+      {{"out", "Exp:y:0"}});
+
+  GrapplerItem item;
+  constexpr char device[] = "/device:CPU:0";
+  item.graph = test::function::GDef(
+      {test::function::NDef("x", "Placeholder", {}, {{"dtype", DT_FLOAT}},
+                            device),
+       test::function::NDef("y", "Exp_func", {"x"}, {}, device),
+       test::function::NDef("z", "Identity", {"y"}, {{"T", DT_FLOAT}}, device)},
+      // FunctionLib
+      {
+          func,
+      });
+
+  FunctionOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  int count = 0;
+  for (const NodeDef& node : output.node()) {
+    if (node.name() == "y/inlined_inputs") {
+      count++;
+      EXPECT_EQ("IdentityN", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("x", node.input(0));
+    } else if (node.name() == "y/in") {
+      count++;
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y/inlined_inputs:0", node.input(0));
+    } else if (node.name() == "y/Linear_func") {
+      count++;
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y/in", node.input(0));
+    } else if (node.name() == "y/Exp") {
+      count++;
+      EXPECT_EQ("Exp", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y/Linear_func:0", node.input(0));
+    } else if (node.name() == "y") {
+      count++;
+      EXPECT_EQ("IdentityN", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y/Exp:0", node.input(0));
+    } else if (node.name() == "z") {
+      count++;
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(device, node.device());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("y", node.input(0));
+    }
+  }
+  EXPECT_EQ(6, count);
+
+  item.fetch = {"z"};
+  Tensor pi(DT_FLOAT, {});
+  pi.flat<float>()(0) = 3.14f;
+  item.feed.emplace_back("x", pi);
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
+}
+
+TEST_F(FunctionOptimizerTest, FunctionWithInputForwarding) {
+  FunctionDef func = FunctionDefHelper::Create(
+      // Name
+      "ForwardInputs",
+      // Args
+      {"in0: float", "in1: float", "arg2: float", "arg3: int32", "arg4: float"},
+      // Return values
+      {"out0: float", "arg2: float", "arg3: int32"},
+      // Attr def
+      {},
+      // Nodes
+      {},
+      // Mapping
+      {{"out0", "in0"}, {"arg2", "arg2"}, {"arg3", "arg3"}});
+
+  GrapplerItem item;
+  constexpr char device[] = "/device:CPU:0";
+  item.graph = test::function::GDef(
+      {test::function::NDef("x0", "Placeholder", {}, {{"dtype", DT_FLOAT}},
+                            device),
+       test::function::NDef("x1", "Placeholder", {}, {{"dtype", DT_FLOAT}},
+                            device),
+       test::function::NDef("x2", "Placeholder", {}, {{"dtype", DT_FLOAT}},
+                            device),
+       test::function::NDef("x3", "Placeholder", {}, {{"dtype", DT_INT32}},
+                            device),
+       test::function::NDef("x4", "Placeholder", {}, {{"dtype", DT_FLOAT}},
+                            device),
+       test::function::NDef("y", "ForwardInputs",
+                            {"x0", "x1", "x2", "x3", "x4"}, {}, device),
+       test::function::NDef("z0", "Identity", {"y:0"}, {{"T", DT_FLOAT}},
+                            device),
+       test::function::NDef("z1", "Identity", {"y:1"}, {{"T", DT_FLOAT}},
+                            device),
+       test::function::NDef("z2", "Identity", {"y:2"}, {{"T", DT_INT32}},
+                            device)},
+      // FunctionLib
+      {
+          func,
+      });
+
+  FunctionOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  item.fetch = {"z0", "z1", "z2"};
+  Tensor in(DT_FLOAT, {});
+  in.flat<float>()(0) = 3.14f;
+  item.feed.emplace_back("x0", in);
+  in.flat<float>()(0) = 2.7f;
+  item.feed.emplace_back("x1", in);
+  in.flat<float>()(0) = 1.0f;
+  item.feed.emplace_back("x2", in);
+  in.flat<float>()(0) = -1.0f;
+  item.feed.emplace_back("x4", in);
+  Tensor in_int(DT_INT32, {});
+  in_int.flat<int>()(0) = 1234;
+  item.feed.emplace_back("x3", in_int);
+  auto tensors_expected = EvaluateFetchNodes(item);
+  GrapplerItem optimized(item, std::move(output));
+  auto tensors = EvaluateFetchNodes(optimized);
+  test::ExpectTensorEqual<float>(tensors_expected[0], tensors[0]);
+  test::ExpectTensorEqual<float>(tensors_expected[1], tensors[1]);
+  test::ExpectTensorEqual<int>(tensors_expected[2], tensors[2]);
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/utils/functions.cc b/tensorflow/core/grappler/utils/functions.cc
index 37b00e0a30..4f286ce1c8 100644
--- a/tensorflow/core/grappler/utils/functions.cc
+++ b/tensorflow/core/grappler/utils/functions.cc
@@ -124,9 +124,22 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromFunctionDef(
     }
   }
 
-  // Add the function outputs to the list of fetch nodes.
+  // Add the function outputs to the list of fetch nodes, taking into account
+  // the output mapping if any.
   for (const auto& out : func.signature().output_arg()) {
-    new_item->fetch.emplace_back(out.name());
+    auto it = func.ret().find(out.name());
+    if (it != func.ret().end()) {
+      auto it2 = port_map.find(it->second);
+      if (it2 == port_map.end()) {
+        LOG(ERROR) << "Unknown output mapping: " << it->first << " to "
+                   << it->second;
+        return nullptr;
+      } else {
+        new_item->fetch.emplace_back(it2->second);
+      }
+    } else {
+      new_item->fetch.emplace_back(out.name());
+    }
   }
   // Add the function inputs to the list of feeds.
   for (const auto& inp : func.signature().input_arg()) {
diff --git a/tensorflow/core/grappler/utils/functions_test.cc b/tensorflow/core/grappler/utils/functions_test.cc
index 25ccb50084..25ec50d478 100644
--- a/tensorflow/core/grappler/utils/functions_test.cc
+++ b/tensorflow/core/grappler/utils/functions_test.cc
@@ -54,7 +54,7 @@ TEST_F(FunctionsTest, FromSimpleFunctionDef) {
   CHECK(item);
   EXPECT_EQ("XTimesTwo", item->id);
   EXPECT_EQ(4, item->graph.node_size());
-  EXPECT_EQ(std::vector<string>({"y"}), item->fetch);
+  EXPECT_EQ(std::vector<string>({"y:0"}), item->fetch);
   EXPECT_EQ(1, item->feed.size());
   EXPECT_EQ("x", item->feed[0].first);
 
@@ -121,7 +121,7 @@ TEST_F(FunctionsTest, FromFunctionDefWithMultiOutputNodes) {
   CHECK(item);
   EXPECT_EQ("SubGrad", item->id);
   EXPECT_EQ(12, item->graph.node_size());
-  EXPECT_EQ(std::vector<string>({"dx", "dy"}), item->fetch);
+  EXPECT_EQ(std::vector<string>({"dx:0", "dy:0"}), item->fetch);
   EXPECT_EQ(3, item->feed.size());
   EXPECT_EQ("x", item->feed[0].first);
   EXPECT_EQ("y", item->feed[1].first);
@@ -184,6 +184,7 @@ TEST_F(FunctionsTest, FromFunctionDefWithNestedFuncs) {
        {{"x2"}, "Mul", {"x", "x"}, {{"T", DT_FLOAT}}},
        {{"y2"}, "Mul", {"y", "y"}, {{"T", DT_FLOAT}}, {"a1"}},
        {{"o"}, "Add", {"x2:z:0", "y2:z:0"}, {{"T", DT_FLOAT}}}},
+      // Output Mapping
       {{"o", "o:z:0"}});
 
   std::unordered_map<string, AttrValue> func_attr;
@@ -227,6 +228,86 @@ TEST_F(FunctionsTest, FromFunctionDefWithNestedFuncs) {
   }
 }
 
+TEST_F(FunctionsTest, FromFunctionDefWithOutputMappings) {
+  FunctionDef func = FunctionDefHelper::Create(
+      // Name
+      "Exp_func",
+      // Args
+      {"in: float"},
+      // Return values
+      {"out: float"},
+      // Attr def
+      {},
+      // Nodes
+      {{{"Linear_func"}, "Identity", {"in"}, {{"T", DT_FLOAT}}},
+       {{"Exp"}, "Exp", {"Linear_func:output:0"}, {{"T", DT_FLOAT}}}},
+      // Mapping
+      {{"out", "Exp:y:0"}});
+
+  std::unordered_map<string, AttrValue> func_attr;
+  FunctionDefLibrary library;
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromFunctionDef(func, func_attr, library);
+
+  EXPECT_EQ(1, item->fetch.size());
+  EXPECT_EQ("Exp:0", item->fetch[0]);
+
+  for (const NodeDef &node : item->graph.node()) {
+    if (node.name() == "in") {
+      EXPECT_EQ("Placeholder", node.op());
+      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
+      EXPECT_EQ(0, node.input_size());
+    } else if (node.name() == "Linear_func") {
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("in", node.input(0));
+    } else if (node.name() == "Exp") {
+      EXPECT_EQ("Exp", node.op());
+      EXPECT_EQ(1, node.input_size());
+      EXPECT_EQ("Linear_func:0", node.input(0));
+    }
+  }
+}
+
+TEST_F(FunctionsTest, FromFunctionDefWithInputForwarding) {
+  FunctionDef func = FunctionDefHelper::Create(
+      // Name
+      "ForwardInputs",
+      // Args
+      {"in0: float", "in1: float", "arg2: float", "arg3: int32", "arg4: float"},
+      // Return values
+      {"out0: float", "arg2: float", "arg3: int32"},
+      // Attr def
+      {},
+      // Nodes
+      {},
+      // Mapping
+      {{"out0", "in0"}});
+
+  std::unordered_map<string, AttrValue> func_attr;
+  FunctionDefLibrary library;
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromFunctionDef(func, func_attr, library);
+
+  EXPECT_EQ(3, item->fetch.size());
+  EXPECT_EQ("in0", item->fetch[0]);
+  EXPECT_EQ("arg2", item->fetch[1]);
+  EXPECT_EQ("arg3", item->fetch[2]);
+
+  EXPECT_EQ(5, item->graph.node_size());
+  for (const NodeDef &node : item->graph.node()) {
+    EXPECT_TRUE(node.name() == "in0" || node.name() == "in1" ||
+                node.name() == "arg2" || node.name() == "arg3" ||
+                node.name() == "arg4");
+    EXPECT_EQ("Placeholder", node.op());
+    if (node.name() == "arg3") {
+      EXPECT_EQ(DT_INT32, node.attr().at("T").type());
+    } else {
+      EXPECT_EQ(DT_FLOAT, node.attr().at("T").type());
+    }
+  }
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab


From 96845a7c31aea72d44b4e16084ab5350896ca5c8 Mon Sep 17 00:00:00 2001
From: Andrew Selle <aselle@google.com>
Date: Fri, 2 Mar 2018 09:32:36 -0800
Subject: [PATCH 544/575] Only use softfp for android builds to make odroid
 builds work.

PiperOrigin-RevId: 187629282
---
 tensorflow/contrib/lite/kernels/internal/BUILD | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD
index f47fb04cba..6ccad3b1ce 100644
--- a/tensorflow/contrib/lite/kernels/internal/BUILD
+++ b/tensorflow/contrib/lite/kernels/internal/BUILD
@@ -10,21 +10,25 @@ tflite_deps_intel = [
     "@arm_neon_2_x86_sse",
 ]
 
+HARD_FP_FLAGS_IF_APPLICABLE = select({
+    "//tensorflow:android_arm": ["-mfloat-abi=softfp"],
+    "//tensorflow:android_arm64": ["-mfloat-abi=softfp"],
+    "//tensorflow:android_armeabi": ["-mfloat-abi=softfp"],
+    "//conditions:default": [],
+})
+
 NEON_FLAGS_IF_APPLICABLE = select({
     ":arm": [
         "-O3",
         "-mfpu=neon",
-        "-mfloat-abi=softfp",
     ],
     ":armeabi-v7a": [
         "-O3",
         "-mfpu=neon",
-        "-mfloat-abi=softfp",
     ],
     ":armv7a": [
         "-O3",
         "-mfpu=neon",
-        "-mfloat-abi=softfp",
     ],
     "//conditions:default": [
         "-O3",
@@ -283,7 +287,7 @@ cc_library(
         "optimized/neon_tensor_utils.h",
         "optimized/tensor_utils_impl.h",
     ],
-    copts = NEON_FLAGS_IF_APPLICABLE,
+    copts = NEON_FLAGS_IF_APPLICABLE + HARD_FP_FLAGS_IF_APPLICABLE,
     deps = [
         ":cpu_check",
         ":portable_tensor_utils",
-- 
GitLab


From cd810e21bdb0a5631836c69e5273135e4b15a441 Mon Sep 17 00:00:00 2001
From: Alexandre Passos <apassos@google.com>
Date: Fri, 2 Mar 2018 09:53:54 -0800
Subject: [PATCH 545/575] No need to override _handle_device for variables
 anymore

PiperOrigin-RevId: 187631915
---
 .../resource_variable_ops_test.py             |  1 -
 .../python/ops/resource_variable_ops.py       | 25 ++++++-------------
 2 files changed, 7 insertions(+), 19 deletions(-)

diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
index 71699fe0ad..10ba9fa674 100644
--- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py
+++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py
@@ -526,7 +526,6 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase):
       self.assertEqual(dtypes.int32, v.dtype)
       self.assertEqual("foo/var7:0", v.name)
       self.assertAllEqual([10, 20, 35], v.shape.as_list())
-      self.assertEqual(context.get_default_context().device_name, v.device)
       self.assertTrue(isinstance(v.handle, ops.EagerTensor))
       self.assertEqual(constraint, v.constraint)
       self.assertAllEqual(init.numpy(), v.read_value().numpy())
diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index bf186f1734..cbac3c686d 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -384,9 +384,6 @@ class ResourceVariable(variables.Variable):
                   shared_name=handle_name,
                   name=name,
                   graph_mode=self._in_graph_mode)
-              self._handle_device = (
-                  self._handle.device if self._in_graph_mode else
-                  context.get_default_context().device_name)
               self._shape = initial_value.get_shape()
           else:
             initial_value = initial_value()
@@ -399,9 +396,6 @@ class ResourceVariable(variables.Variable):
                 shared_name=handle_name,
                 name=name,
                 graph_mode=False)
-            self._handle_device = (
-                self._handle.device if self._in_graph_mode else
-                context.get_default_context().device_name)
             self._shape = initial_value.get_shape()
         # pylint: enable=protected-access
 
@@ -425,8 +419,6 @@ class ResourceVariable(variables.Variable):
               shared_name=handle_name,
               name=name,
               graph_mode=self._in_graph_mode)
-          self._handle_device = (self._handle.device if self._in_graph_mode else
-                                 context.get_default_context().device_name)
           self._shape = initial_value.get_shape()
 
         self._initial_value = initial_value if self._in_graph_mode else None
@@ -449,7 +441,7 @@ class ResourceVariable(variables.Variable):
           with ops.name_scope("Read"), ops.colocate_with(self._handle):
             # Manually assign reads to the handle's device to avoid log
             # messages.
-            with ops.device(self._handle_device):
+            with ops.device(self._handle.device):
               value = self._read_variable_op()
             self._graph_element = value
             if caching_device is not None:
@@ -489,7 +481,7 @@ class ResourceVariable(variables.Variable):
       # cycles being uncollectable, and means that no __del__ will be defined at
       # all in graph mode.
       self._handle_deleter = EagerResourceDeleter(
-          handle=self._handle, handle_device=self._handle_device)
+          handle=self._handle, handle_device=self._handle.device)
 
   def _init_from_proto(self, variable_def, import_scope=None):
     """Initializes from `VariableDef` proto."""
@@ -507,7 +499,6 @@ class ResourceVariable(variables.Variable):
             variable_def.variable_name, import_scope=import_scope))
     self._shape = tensor_shape.TensorShape(
         self._handle.op.get_attr("shape"))
-    self._handle_device = self._handle.device
     self._handle_name = self._handle.name
     self._initializer_op = g.as_graph_element(
         ops.prepend_name_scope(
@@ -552,7 +543,7 @@ class ResourceVariable(variables.Variable):
   @property
   def device(self):
     """The device this variable is on."""
-    return self._handle_device
+    return self._handle.device
 
   @property
   def graph(self):
@@ -586,7 +577,7 @@ class ResourceVariable(variables.Variable):
     if self._cached_value is not None:
       return self._cached_value
     with ops.colocate_with(None, ignore_existing=True):
-      with ops.device(self._handle_device):
+      with ops.device(self._handle.device):
         return self._read_variable_op()
 
   def _as_graph_element(self):
@@ -683,7 +674,7 @@ class ResourceVariable(variables.Variable):
     """
     with ops.name_scope("Read"):
       # Ensure we read the variable in the same device as the handle.
-      with ops.device(self._handle_device):
+      with ops.device(self._handle.device):
         value = self._read_variable_op()
     # Return an identity so it can get placed on whatever device the context
     # specifies instead of the device where the variable is.
@@ -840,8 +831,7 @@ class ResourceVariable(variables.Variable):
     if hasattr(self, "_trainable") and self._trainable:
       tape.watch_variable(self)
     return _UnreadVariable(
-        self._handle, self.dtype, self._handle_device, self._shape,
-        self._in_graph_mode,
+        self._handle, self.dtype, self._shape, self._in_graph_mode,
         self._handle_deleter if not self._in_graph_mode else None, op)
 
   def assign(self, value, use_locking=None, name=None, read_value=True):
@@ -952,7 +942,7 @@ class _UnreadVariable(ResourceVariable):
   Pretends to be the tensor if anyone looks.
   """
 
-  def __init__(self, handle, dtype, handle_device,  # pylint: disable=super-init-not-called
+  def __init__(self, handle, dtype,  # pylint: disable=super-init-not-called
                shape, in_graph_mode, deleter, parent_op):
     # We do not call super init on purpose.
     self._trainable = False
@@ -960,7 +950,6 @@ class _UnreadVariable(ResourceVariable):
     self._graph_key = ops.get_default_graph()._graph_key  # pylint: disable=protected-access
     self._in_graph_mode = in_graph_mode
     self._handle = handle
-    self._handle_device = handle_device
     self._shape = shape
     self._initial_value = None
     if isinstance(self._handle, ops.EagerTensor):
-- 
GitLab


From 929c435bcba105cf558e1942b63389812b62aff3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 10:05:14 -0800
Subject: [PATCH 546/575] Add bfloat16 support for CPU ops.

PiperOrigin-RevId: 187633511
---
 tensorflow/core/kernels/check_numerics_op.cc |  3 ++
 tensorflow/core/kernels/cwise_op_add_1.cc    |  8 +++---
 tensorflow/core/kernels/cwise_op_isnan.cc    |  3 +-
 tensorflow/core/kernels/cwise_op_mul_1.cc    |  4 +--
 tensorflow/core/kernels/cwise_op_square.cc   |  4 +--
 tensorflow/core/kernels/cwise_op_sub.cc      |  4 +--
 tensorflow/core/kernels/cwise_ops_common.h   |  2 ++
 tensorflow/core/kernels/training_ops.cc      | 17 +++++++++++
 tensorflow/core/lib/bfloat16/bfloat16.h      | 30 ++++++++++++++++++++
 9 files changed, 64 insertions(+), 11 deletions(-)

diff --git a/tensorflow/core/kernels/check_numerics_op.cc b/tensorflow/core/kernels/check_numerics_op.cc
index 6040b2b399..d3b67f4614 100644
--- a/tensorflow/core/kernels/check_numerics_op.cc
+++ b/tensorflow/core/kernels/check_numerics_op.cc
@@ -15,6 +15,8 @@ limitations under the License.
 
 // See docs in ../ops/array_ops.cc.
 
+#include "tensorflow/core/lib/bfloat16/bfloat16.h"
+
 #include <math.h>
 #include <algorithm>
 #include <numeric>
@@ -219,6 +221,7 @@ class CheckNumericsOp<GPUDevice, T> : public AsyncOpKernel {
       Name("CheckNumerics").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
       CheckNumericsOp<CPUDevice, T>);
 TF_CALL_half(REGISTER_CPU_KERNEL);
+TF_CALL_bfloat16(REGISTER_CPU_KERNEL);
 TF_CALL_float(REGISTER_CPU_KERNEL);
 TF_CALL_double(REGISTER_CPU_KERNEL);
 
diff --git a/tensorflow/core/kernels/cwise_op_add_1.cc b/tensorflow/core/kernels/cwise_op_add_1.cc
index bf32c8a54b..9e4ffe950c 100644
--- a/tensorflow/core/kernels/cwise_op_add_1.cc
+++ b/tensorflow/core/kernels/cwise_op_add_1.cc
@@ -16,10 +16,10 @@ limitations under the License.
 #include "tensorflow/core/kernels/cwise_ops_common.h"
 
 namespace tensorflow {
-REGISTER5(BinaryOp, CPU, "Add", functor::add, float, Eigen::half, double, int32,
-          int64);
-REGISTER5(BinaryOp, CPU, "AddV2", functor::add, float, Eigen::half, double,
-          int32, int64);
+REGISTER6(BinaryOp, CPU, "Add", functor::add, float, Eigen::half, double, int32,
+          int64, bfloat16);
+REGISTER6(BinaryOp, CPU, "AddV2", functor::add, float, Eigen::half, double,
+          int32, int64, bfloat16);
 
 #if GOOGLE_CUDA
 REGISTER3(BinaryOp, GPU, "Add", functor::add, float, Eigen::half, double);
diff --git a/tensorflow/core/kernels/cwise_op_isnan.cc b/tensorflow/core/kernels/cwise_op_isnan.cc
index aa180c247e..707dc9e49c 100644
--- a/tensorflow/core/kernels/cwise_op_isnan.cc
+++ b/tensorflow/core/kernels/cwise_op_isnan.cc
@@ -16,7 +16,8 @@ limitations under the License.
 #include "tensorflow/core/kernels/cwise_ops_common.h"
 
 namespace tensorflow {
-REGISTER3(UnaryOp, CPU, "IsNan", functor::isnan, float, Eigen::half, double);
+REGISTER4(UnaryOp, CPU, "IsNan", functor::isnan, float, Eigen::half, double,
+          bfloat16);
 
 #if GOOGLE_CUDA
 REGISTER3(UnaryOp, GPU, "IsNan", functor::isnan, float, Eigen::half, double);
diff --git a/tensorflow/core/kernels/cwise_op_mul_1.cc b/tensorflow/core/kernels/cwise_op_mul_1.cc
index 0e8d2e3735..cff0407b83 100644
--- a/tensorflow/core/kernels/cwise_op_mul_1.cc
+++ b/tensorflow/core/kernels/cwise_op_mul_1.cc
@@ -17,8 +17,8 @@ limitations under the License.
 
 namespace tensorflow {
 
-REGISTER5(BinaryOp, CPU, "Mul", functor::mul, float, Eigen::half, double, uint8,
-          int32);
+REGISTER6(BinaryOp, CPU, "Mul", functor::mul, float, Eigen::half, double, uint8,
+          int32, bfloat16);
 #if defined(__ANDROID_TYPES_SLIM__)
 // We only register the first type when we have multi-argument calls in the
 // case where we're trying to reduce executable size, but it turns out that the
diff --git a/tensorflow/core/kernels/cwise_op_square.cc b/tensorflow/core/kernels/cwise_op_square.cc
index 7fc2f6bf08..84f695ddc2 100644
--- a/tensorflow/core/kernels/cwise_op_square.cc
+++ b/tensorflow/core/kernels/cwise_op_square.cc
@@ -16,8 +16,8 @@ limitations under the License.
 #include "tensorflow/core/kernels/cwise_ops_common.h"
 
 namespace tensorflow {
-REGISTER7(UnaryOp, CPU, "Square", functor::square, float, Eigen::half, double,
-          int32, int64, complex64, complex128);
+REGISTER8(UnaryOp, CPU, "Square", functor::square, float, Eigen::half, double,
+          int32, int64, complex64, complex128, bfloat16);
 
 #if GOOGLE_CUDA
 REGISTER4(UnaryOp, GPU, "Square", functor::square, float, Eigen::half, double,
diff --git a/tensorflow/core/kernels/cwise_op_sub.cc b/tensorflow/core/kernels/cwise_op_sub.cc
index 025041946a..eb27bddb78 100644
--- a/tensorflow/core/kernels/cwise_op_sub.cc
+++ b/tensorflow/core/kernels/cwise_op_sub.cc
@@ -16,8 +16,8 @@ limitations under the License.
 #include "tensorflow/core/kernels/cwise_ops_common.h"
 
 namespace tensorflow {
-REGISTER7(BinaryOp, CPU, "Sub", functor::sub, float, Eigen::half, double, int32,
-          int64, complex64, complex128);
+REGISTER8(BinaryOp, CPU, "Sub", functor::sub, float, Eigen::half, double, int32,
+          int64, bfloat16, complex64, complex128);
 #if !defined(__ANDROID_TYPES_SLIM__)
 // Sub op for int8, uint8, int16, uint16
 REGISTER4(BinaryOp, CPU, "Sub", functor::sub, int8, uint8, int16, uint16);
diff --git a/tensorflow/core/kernels/cwise_ops_common.h b/tensorflow/core/kernels/cwise_ops_common.h
index 8295fa939e..e32eccf547 100644
--- a/tensorflow/core/kernels/cwise_ops_common.h
+++ b/tensorflow/core/kernels/cwise_ops_common.h
@@ -20,6 +20,8 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
+#include "tensorflow/core/lib/bfloat16/bfloat16.h"
+
 #ifdef TENSORFLOW_USE_SYCL
 #include "tensorflow/core/kernels/cwise_ops_sycl_common.h"
 #endif
diff --git a/tensorflow/core/kernels/training_ops.cc b/tensorflow/core/kernels/training_ops.cc
index 233aa03c32..f53c567c4d 100644
--- a/tensorflow/core/kernels/training_ops.cc
+++ b/tensorflow/core/kernels/training_ops.cc
@@ -15,6 +15,8 @@ limitations under the License.
 
 #define EIGEN_USE_THREADS
 
+#include "tensorflow/core/lib/bfloat16/bfloat16.h"
+
 #include <algorithm>
 
 #include "tensorflow/core/framework/op_kernel.h"
@@ -494,6 +496,7 @@ class ApplyGradientDescentOp<SYCLDevice, T> : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -647,6 +650,7 @@ class ApplyAdadeltaOp : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -822,6 +826,7 @@ class SparseApplyAdadeltaOp : public OpKernel {
   REGISTER_KERNELS(T, int64);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -1107,6 +1112,7 @@ class ApplyAdagradOp : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -1360,6 +1366,7 @@ class SparseApplyAdagradOp : public OpKernel {
   REGISTER_KERNELS(T, int64);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -1961,6 +1968,7 @@ class ApplyFtrlOp : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -1982,6 +1990,7 @@ TF_CALL_double(REGISTER_CPU_KERNELS);
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -2230,6 +2239,7 @@ class SparseApplyFtrlOp : public OpKernel {
   REGISTER_KERNELS(T, int64);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -2254,6 +2264,7 @@ TF_CALL_double(REGISTER_CPU_KERNELS);
   REGISTER_KERNELS(T, int64);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -2332,6 +2343,7 @@ class ApplyMomentumOp : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -2471,6 +2483,7 @@ class SparseApplyMomentumOp : public OpKernel {
   REGISTER_KERNELS(T, int64);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -2698,6 +2711,7 @@ class ApplyAdamOp<SYCLDevice, T> : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -2937,6 +2951,7 @@ class ApplyCenteredRMSPropOp : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -3352,6 +3367,7 @@ class ApplyAddSignOp : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
@@ -3457,6 +3473,7 @@ class ApplyPowerSignOp : public OpKernel {
 #define REGISTER_CPU_KERNELS(T) REGISTER_KERNELS(CPU, T);
 
 TF_CALL_half(REGISTER_CPU_KERNELS);
+TF_CALL_bfloat16(REGISTER_CPU_KERNELS);
 TF_CALL_float(REGISTER_CPU_KERNELS);
 TF_CALL_double(REGISTER_CPU_KERNELS);
 
diff --git a/tensorflow/core/lib/bfloat16/bfloat16.h b/tensorflow/core/lib/bfloat16/bfloat16.h
index f9cca0ef2a..de8f92d1eb 100644
--- a/tensorflow/core/lib/bfloat16/bfloat16.h
+++ b/tensorflow/core/lib/bfloat16/bfloat16.h
@@ -16,6 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_CORE_LIB_BFLOAT16_BFLOAT16_H_
 #define TENSORFLOW_CORE_LIB_BFLOAT16_BFLOAT16_H_
 
+#include <cmath>
 #include <complex>
 
 #ifdef __CUDACC__
@@ -271,6 +272,35 @@ struct hash<tensorflow::bfloat16> {
     return hash<float>()(static_cast<float>(v));
   }
 };
+
+using tensorflow::bfloat16;
+inline bool isinf(const bfloat16& a) { return std::isinf(float(a)); }
+inline bool isnan(const bfloat16& a) { return std::isnan(float(a)); }
+inline bool isfinite(const bfloat16& a) { return std::isfinite(float(a)); }
+inline bfloat16 abs(const bfloat16& a) { return bfloat16(std::abs(float(a))); }
+inline bfloat16 exp(const bfloat16& a) { return bfloat16(std::exp(float(a))); }
+inline bfloat16 log(const bfloat16& a) { return bfloat16(std::log(float(a))); }
+inline bfloat16 log10(const bfloat16& a) {
+  return bfloat16(std::log10(float(a)));
+}
+inline bfloat16 sqrt(const bfloat16& a) {
+  return bfloat16(std::sqrt(float(a)));
+}
+inline bfloat16 pow(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(std::pow(float(a), float(b)));
+}
+inline bfloat16 sin(const bfloat16& a) { return bfloat16(std::sin(float(a))); }
+inline bfloat16 cos(const bfloat16& a) { return bfloat16(std::cos(float(a))); }
+inline bfloat16 tan(const bfloat16& a) { return bfloat16(std::tan(float(a))); }
+inline bfloat16 tanh(const bfloat16& a) {
+  return bfloat16(std::tanh(float(a)));
+}
+inline bfloat16 floor(const bfloat16& a) {
+  return bfloat16(std::floor(float(a)));
+}
+inline bfloat16 ceil(const bfloat16& a) {
+  return bfloat16(std::ceil(float(a)));
+}
 }  // namespace std
 
 #endif  // TENSORFLOW_CORE_LIB_BFLOAT16_BFLOAT16_H_
-- 
GitLab


From 3942fbfcc3252e2e479e3dde8d996e8e156558c4 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 2 Mar 2018 13:28:17 -0500
Subject: [PATCH 547/575] Disable loop_optimizer_test for now

---
 tensorflow/core/grappler/optimizers/BUILD | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index b0a7587600..4e14f0ba40 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -538,6 +538,10 @@ tf_cc_test(
     name = "loop_optimizer_test",
     size = "small",
     srcs = ["loop_optimizer_test.cc"],
+    tags = [
+        "manual",
+        "no_oss",
+    ],  # b/74111495
     deps = [
         ":loop_optimizer",
         "//tensorflow/cc:cc_ops",
-- 
GitLab


From bce4f52b7201b943d544606dcca51ef4ba2b2c1a Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Fri, 2 Mar 2018 10:30:01 -0800
Subject: [PATCH 548/575] tf.keras: Remove unnecessary "with
 self.test_sesion()" statements in tests.

The test decorator that runs the test twice (once with eager execution enabled,
once without) doesn't require the block, and this makes the code appear more
eager-friendly (as there is no concept of a session when eager execution is
enabled).

PiperOrigin-RevId: 187637008
---
 .../_impl/keras/model_subclassing_test.py     | 245 +++++++++---------
 1 file changed, 117 insertions(+), 128 deletions(-)

diff --git a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
index 3d71a620fc..58b144365b 100644
--- a/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
+++ b/tensorflow/python/keras/_impl/keras/model_subclassing_test.py
@@ -174,19 +174,18 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 100
     input_dim = 50
 
-    with self.test_session():
-      model = SimpleTestModel(num_classes=num_classes,
-                              use_dp=True,
-                              use_bn=True)
-      model.compile(loss='mse',
-                    optimizer=RMSPropOptimizer(learning_rate=0.001),
-                    metrics=['acc'])
+    model = SimpleTestModel(num_classes=num_classes,
+                            use_dp=True,
+                            use_bn=True)
+    model.compile(loss='mse',
+                  optimizer=RMSPropOptimizer(learning_rate=0.001),
+                  metrics=['acc'])
 
-      x = np.ones((num_samples, input_dim))
-      y = np.zeros((num_samples, num_classes))
+    x = np.ones((num_samples, input_dim))
+    y = np.zeros((num_samples, num_classes))
 
-      model.fit(x, y, epochs=2, batch_size=32, verbose=0)
-      _ = model.evaluate(x, y, verbose=0)
+    model.fit(x, y, epochs=2, batch_size=32, verbose=0)
+    _ = model.evaluate(x, y, verbose=0)
 
   @test_util.run_in_graph_and_eager_modes()
   def test_multi_io_workflow_with_np_arrays(self):
@@ -194,21 +193,20 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 1000
     input_dim = 50
 
-    with self.test_session():
-      model = MultiIOTestModel(num_classes=num_classes,
-                               use_dp=True,
-                               use_bn=True)
-      model.compile(loss='mse',
-                    optimizer=RMSPropOptimizer(learning_rate=0.001),
-                    metrics=['acc'])
+    model = MultiIOTestModel(num_classes=num_classes,
+                             use_dp=True,
+                             use_bn=True)
+    model.compile(loss='mse',
+                  optimizer=RMSPropOptimizer(learning_rate=0.001),
+                  metrics=['acc'])
 
-      x1 = np.ones((num_samples, input_dim))
-      x2 = np.ones((num_samples, input_dim))
-      y1 = np.zeros((num_samples, num_classes[0]))
-      y2 = np.zeros((num_samples, num_classes[1]))
+    x1 = np.ones((num_samples, input_dim))
+    x2 = np.ones((num_samples, input_dim))
+    y1 = np.zeros((num_samples, num_classes[0]))
+    y2 = np.zeros((num_samples, num_classes[1]))
 
-      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
-      _ = model.evaluate([x1, x2], [y1, y2], verbose=0)
+    model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
+    _ = model.evaluate([x1, x2], [y1, y2], verbose=0)
 
   def test_single_io_workflow_with_tensors(self):
 
@@ -321,14 +319,13 @@ class ModelSubclassingTest(test.TestCase):
     x = np.ones((num_samples, input_dim))
     y = np.ones((num_samples, input_dim))
 
-    with self.test_session():
-      model = BNNet()
-      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      y_ref = model.predict(x)
+    model = BNNet()
+    model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+    y_ref = model.predict(x)
 
-      model.train_on_batch(x, y)
-      y_new = model.predict(x)
-      self.assertGreater(np.sum(np.abs(y_ref - y_new)), 0.1)
+    model.train_on_batch(x, y)
+    y_new = model.predict(x)
+    self.assertGreater(np.sum(np.abs(y_ref - y_new)), 0.1)
 
   @test_util.run_in_graph_and_eager_modes()
   def test_training_and_inference_behavior(self):
@@ -350,14 +347,13 @@ class ModelSubclassingTest(test.TestCase):
         x = self.dp(inputs)
         return self.dense(x)
 
-    with self.test_session():
-      model = DPNet()
-      x = np.ones((num_samples, input_dim))
-      y = model.predict(x)
-      self.assertEqual(np.sum(y), np.sum(x))
-      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      loss = model.train_on_batch(x, y)
-      self.assertGreater(loss, 0.1)
+    model = DPNet()
+    x = np.ones((num_samples, input_dim))
+    y = model.predict(x)
+    self.assertEqual(np.sum(y), np.sum(x))
+    model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+    loss = model.train_on_batch(x, y)
+    self.assertGreater(loss, 0.1)
 
   @test_util.run_in_graph_and_eager_modes()
   def test_training_methods(self):
@@ -373,21 +369,20 @@ class ModelSubclassingTest(test.TestCase):
     y1 = np.zeros((num_samples, num_classes[0]))
     y2 = np.zeros((num_samples, num_classes[1]))
 
-    with self.test_session():
-      model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
-      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
-      model.fit({'input_1': x1, 'input_2': x2},
-                {'output_1': y1, 'output_2': y2},
-                epochs=2, batch_size=32)
-      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0,
-                validation_data=([x1, x2], [y1, y2]))
+    model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
+    model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+    model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
+    model.fit({'input_1': x1, 'input_2': x2},
+              {'output_1': y1, 'output_2': y2},
+              epochs=2, batch_size=32)
+    model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0,
+              validation_data=([x1, x2], [y1, y2]))
 
-      model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
-      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      model.train_on_batch([x1, x2], [y1, y2])
-      model.train_on_batch({'input_1': x1, 'input_2': x2},
-                           {'output_1': y1, 'output_2': y2})
+    model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
+    model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+    model.train_on_batch([x1, x2], [y1, y2])
+    model.train_on_batch({'input_1': x1, 'input_2': x2},
+                         {'output_1': y1, 'output_2': y2})
 
   @test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True)
   def test_inference_methods(self):
@@ -402,17 +397,16 @@ class ModelSubclassingTest(test.TestCase):
     y1 = np.zeros((num_samples, num_classes[0]))
     y2 = np.zeros((num_samples, num_classes[1]))
 
-    with self.test_session():
-      model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
-      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      model.evaluate([x1, x2], [y1, y2])
-      model.test_on_batch([x1, x2], [y1, y2])
+    model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
+    model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+    model.evaluate([x1, x2], [y1, y2])
+    model.test_on_batch([x1, x2], [y1, y2])
 
-      model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
-      model.predict([x1, x2])
+    model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
+    model.predict([x1, x2])
 
-      model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
-      model.predict_on_batch([x1, x2])
+    model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
+    model.predict_on_batch([x1, x2])
 
   @test_util.run_in_graph_and_eager_modes()
   def test_trainable_mutation(self):
@@ -435,26 +429,25 @@ class ModelSubclassingTest(test.TestCase):
     y1 = np.zeros((num_samples, num_classes[0]))
     y2 = np.zeros((num_samples, num_classes[1]))
 
-    with self.test_session():
-      model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
-      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
-      y_ref_1, y_ref_2 = model.predict([x1, x2])
+    model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
+    model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+    model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
+    y_ref_1, y_ref_2 = model.predict([x1, x2])
 
-      fd, fname = tempfile.mkstemp('.h5')
-      model.save_weights(fname)
+    fd, fname = tempfile.mkstemp('.h5')
+    model.save_weights(fname)
 
-      model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
-      # need to build the model before loading weights
-      # (otherwise no weights to load)
-      model._set_inputs([x1, x2])
-      model.load_weights(fname)
+    model = MultiIOTestModel(num_classes=num_classes, use_bn=True)
+    # need to build the model before loading weights
+    # (otherwise no weights to load)
+    model._set_inputs([x1, x2])
+    model.load_weights(fname)
 
-      y1, y2 = model.predict([x1, x2])
-      self.assertAllClose(y_ref_1, y1, atol=1e-5)
-      self.assertAllClose(y_ref_2, y2, atol=1e-5)
-      os.close(fd)
-      os.remove(fname)
+    y1, y2 = model.predict([x1, x2])
+    self.assertAllClose(y_ref_1, y1, atol=1e-5)
+    self.assertAllClose(y_ref_2, y2, atol=1e-5)
+    os.close(fd)
+    os.remove(fname)
 
   @test_util.run_in_graph_and_eager_modes()
   def test_summary(self):
@@ -488,23 +481,22 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 100
     input_dim = 50
 
-    with self.test_session():
-      model = NestedTestModel1(num_classes=num_classes)
-      model.compile(loss='mse',
-                    optimizer=RMSPropOptimizer(learning_rate=0.001),
-                    metrics=['acc'])
+    model = NestedTestModel1(num_classes=num_classes)
+    model.compile(loss='mse',
+                  optimizer=RMSPropOptimizer(learning_rate=0.001),
+                  metrics=['acc'])
 
-      x = np.ones((num_samples, input_dim))
-      y = np.zeros((num_samples, num_classes))
+    x = np.ones((num_samples, input_dim))
+    y = np.zeros((num_samples, num_classes))
 
-      model.fit(x, y, epochs=2, batch_size=32, verbose=0)
-      _ = model.evaluate(x, y, verbose=0)
+    model.fit(x, y, epochs=2, batch_size=32, verbose=0)
+    _ = model.evaluate(x, y, verbose=0)
 
-      self.assertEqual(len(model.weights), 8 + len(model.test_net.weights))
-      self.assertEqual(len(model.non_trainable_weights),
-                       2 + len(model.test_net.non_trainable_weights))
-      self.assertEqual(len(model.trainable_weights),
-                       6 + len(model.test_net.trainable_weights))
+    self.assertEqual(len(model.weights), 8 + len(model.test_net.weights))
+    self.assertEqual(len(model.non_trainable_weights),
+                     2 + len(model.test_net.non_trainable_weights))
+    self.assertEqual(len(model.trainable_weights),
+                     6 + len(model.test_net.trainable_weights))
 
   @test_util.run_in_graph_and_eager_modes()
   def test_graph_nested_in_subclass(self):
@@ -512,23 +504,22 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 100
     input_dim = 50
 
-    with self.test_session():
-      model = NestedTestModel2(num_classes=num_classes)
-      model.compile(loss='mse',
-                    optimizer=RMSPropOptimizer(learning_rate=0.001),
-                    metrics=['acc'])
+    model = NestedTestModel2(num_classes=num_classes)
+    model.compile(loss='mse',
+                  optimizer=RMSPropOptimizer(learning_rate=0.001),
+                  metrics=['acc'])
 
-      x = np.ones((num_samples, input_dim))
-      y = np.zeros((num_samples, num_classes))
+    x = np.ones((num_samples, input_dim))
+    y = np.zeros((num_samples, num_classes))
 
-      model.fit(x, y, epochs=2, batch_size=32, verbose=0)
-      _ = model.evaluate(x, y, verbose=0)
+    model.fit(x, y, epochs=2, batch_size=32, verbose=0)
+    _ = model.evaluate(x, y, verbose=0)
 
-      self.assertEqual(len(model.weights), 8 + len(model.test_net.weights))
-      self.assertEqual(len(model.non_trainable_weights),
-                       2 + len(model.test_net.non_trainable_weights))
-      self.assertEqual(len(model.trainable_weights),
-                       6 + len(model.test_net.trainable_weights))
+    self.assertEqual(len(model.weights), 8 + len(model.test_net.weights))
+    self.assertEqual(len(model.non_trainable_weights),
+                     2 + len(model.test_net.non_trainable_weights))
+    self.assertEqual(len(model.trainable_weights),
+                     6 + len(model.test_net.trainable_weights))
 
   @test_util.run_in_graph_and_eager_modes()
   def test_subclass_nested_in_graph(self):
@@ -536,22 +527,21 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 100
     input_dim = 50
 
-    with self.test_session():
-      model = get_nested_model_3(input_dim=input_dim, num_classes=num_classes)
-      model.compile(loss='mse',
-                    optimizer=RMSPropOptimizer(learning_rate=0.001),
-                    metrics=['acc'])
+    model = get_nested_model_3(input_dim=input_dim, num_classes=num_classes)
+    model.compile(loss='mse',
+                  optimizer=RMSPropOptimizer(learning_rate=0.001),
+                  metrics=['acc'])
 
-      x = np.ones((num_samples, input_dim))
-      y = np.zeros((num_samples, num_classes))
+    x = np.ones((num_samples, input_dim))
+    y = np.zeros((num_samples, num_classes))
 
-      model.fit(x, y, epochs=2, batch_size=32, verbose=0)
-      _ = model.evaluate(x, y, verbose=0)
+    model.fit(x, y, epochs=2, batch_size=32, verbose=0)
+    _ = model.evaluate(x, y, verbose=0)
 
-      self.assertEqual(len(model.weights), 16)
-      self.assertEqual(
-          len(model.non_trainable_weights), 4)
-      self.assertEqual(len(model.trainable_weights), 12)
+    self.assertEqual(len(model.weights), 16)
+    self.assertEqual(
+        len(model.non_trainable_weights), 4)
+    self.assertEqual(len(model.trainable_weights), 12)
 
   @test_util.run_in_graph_and_eager_modes()
   def test_support_for_manual_training_arg(self):
@@ -575,14 +565,13 @@ class ModelSubclassingTest(test.TestCase):
         x = self.dp(inputs, training=training)
         return self.dense(x)
 
-    with self.test_session():
-      model = DPNet()
-      x = np.ones((10, 10))
-      y = model.predict(x)
-      self.assertEqual(np.sum(y), np.sum(x))
-      model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
-      loss = model.train_on_batch(x, y)
-      self.assertGreater(loss, 0.1)
+    model = DPNet()
+    x = np.ones((10, 10))
+    y = model.predict(x)
+    self.assertEqual(np.sum(y), np.sum(x))
+    model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
+    loss = model.train_on_batch(x, y)
+    self.assertGreater(loss, 0.1)
 
 
 if __name__ == '__main__':
-- 
GitLab


From b253460fd13dcfcf27eca610c5d397ef6ac980d2 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 2 Mar 2018 13:37:29 -0500
Subject: [PATCH 549/575] Fix formatting in grappler/optimizers/BUILD

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

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 4e14f0ba40..1381bfd18b 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -540,8 +540,8 @@ tf_cc_test(
     srcs = ["loop_optimizer_test.cc"],
     tags = [
         "manual",
-        "no_oss",
-    ],  # b/74111495
+        "no_oss",  # b/74111495
+    ],
     deps = [
         ":loop_optimizer",
         "//tensorflow/cc:cc_ops",
-- 
GitLab


From b5fa6af52198570a758d88b4bd64495353d8e7c6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 11:11:15 -0800
Subject: [PATCH 550/575] Updating toolchain configs for GPU builds

PiperOrigin-RevId: 187643585
---
 tensorflow/tools/ci_build/Dockerfile.rbe.gpu  |   26 +
 third_party/gpus/cuda/remote.BUILD.tpl        |   26 +-
 third_party/toolchains/gpus/crosstool/BUILD   |    5 +
 .../toolchains/gpus/crosstool/CROSSTOOL       |    6 +-
 third_party/toolchains/gpus/cuda/BUILD        | 2016 ++++++++---------
 third_party/toolchains/gpus/py/BUILD          |  171 ++
 6 files changed, 1186 insertions(+), 1064 deletions(-)
 create mode 100644 tensorflow/tools/ci_build/Dockerfile.rbe.gpu
 create mode 100644 third_party/toolchains/gpus/py/BUILD

diff --git a/tensorflow/tools/ci_build/Dockerfile.rbe.gpu b/tensorflow/tools/ci_build/Dockerfile.rbe.gpu
new file mode 100644
index 0000000000..24ff4765a6
--- /dev/null
+++ b/tensorflow/tools/ci_build/Dockerfile.rbe.gpu
@@ -0,0 +1,26 @@
+FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
+
+LABEL maintainer="Nick Lopez <ngiraldo@google.com>"
+
+# In the Ubuntu 16.04 images, cudnn is placed in system paths. Move them to
+# /usr/local/cuda
+RUN cp -P /usr/include/cudnn.h /usr/local/cuda/include
+RUN cp -P /usr/lib/x86_64-linux-gnu/libcudnn* /usr/local/cuda/lib64
+
+# Copy and run the install scripts.
+COPY install/*.sh /install/
+ARG DEBIAN_FRONTEND=noninteractive
+RUN /install/install_bootstrap_deb_packages.sh
+RUN add-apt-repository -y ppa:openjdk-r/ppa && \
+    add-apt-repository -y ppa:george-edison55/cmake-3.x
+RUN /install/install_deb_packages.sh
+RUN /install/install_pip_packages.sh
+RUN /install/install_golang.sh
+
+# Install clang from pre-built package
+RUN cd /tmp && \
+    wget https://storage.googleapis.com/clang-builds-stable/clang-ubuntu16_04/clang_r323528.tar.gz && \
+    echo "26752d9f5785df07193fac8316ba5d5ba3bec36d970c29a1577360848818ac74  clang_r323528.tar.gz" | sha256sum -c && \
+    tar -C /usr/local -xf clang_r323528.tar.gz && \
+    rm clang_r323528.tar.gz
+
diff --git a/third_party/gpus/cuda/remote.BUILD.tpl b/third_party/gpus/cuda/remote.BUILD.tpl
index d88d512b90..f774def5e6 100644
--- a/third_party/gpus/cuda/remote.BUILD.tpl
+++ b/third_party/gpus/cuda/remote.BUILD.tpl
@@ -41,65 +41,65 @@ config_setting(
 
 alias(
     name = "cuda_headers",
-    actual = "%{remote_cuda_repo}cuda:cuda_headers",
+    actual = "%{remote_cuda_repo}/cuda:cuda_headers",
 )
 
 alias(
     name = "cudart_static",
-    actual = "%{remote_cuda_repo}cuda:cudart_static",
+    actual = "%{remote_cuda_repo}/cuda:cudart_static",
 )
 
 alias(
     name = "cuda_driver",
-    actual = "%{remote_cuda_repo}cuda:cuda_driver",
+    actual = "%{remote_cuda_repo}/cuda:cuda_driver",
 )
 
 alias(
     name = "cudart",
-    actual = "%{remote_cuda_repo}cuda:cudart",
+    actual = "%{remote_cuda_repo}/cuda:cudart",
 )
 
 alias(
     name = "cublas",
-    actual = "%{remote_cuda_repo}cuda:cublas",
+    actual = "%{remote_cuda_repo}/cuda:cublas",
 )
 
 alias(
     name = "cusolver",
-    actual = "%{remote_cuda_repo}cuda:cusolver",
+    actual = "%{remote_cuda_repo}/cuda:cusolver",
 )
 
 alias(
     name = "cudnn",
-    actual = "%{remote_cuda_repo}cuda:cudnn",
+    actual = "%{remote_cuda_repo}/cuda:cudnn",
 )
 
 alias(
     name = "cufft",
-    actual = "%{remote_cuda_repo}cuda:cufft",
+    actual = "%{remote_cuda_repo}/cuda:cufft",
 )
 
 alias(
     name = "curand",
-    actual = "%{remote_cuda_repo}cuda:curand",
+    actual = "%{remote_cuda_repo}/cuda:curand",
 )
 
 alias(
     name = "cuda",
-    actual = "%{remote_cuda_repo}cuda:cuda",
+    actual = "%{remote_cuda_repo}/cuda:cuda",
 )
 
 alias(
     name = "cupti_headers",
-    actual = "%{remote_cuda_repo}cuda:cupti_headers",
+    actual = "%{remote_cuda_repo}/cuda:cupti_headers",
 )
 
 alias(
     name = "cupti_dsos",
-    actual = "%{remote_cuda_repo}cuda:cupti_dsos",
+    actual = "%{remote_cuda_repo}/cuda:cupti_dsos",
 )
 
 alias(
     name = "libdevice_root",
-    actual = "%{remote_cuda_repo}cuda:libdevice_root",
+    actual = "%{remote_cuda_repo}/cuda:libdevice_root",
 )
diff --git a/third_party/toolchains/gpus/crosstool/BUILD b/third_party/toolchains/gpus/crosstool/BUILD
index a8c6b0f029..1f9065007c 100644
--- a/third_party/toolchains/gpus/crosstool/BUILD
+++ b/third_party/toolchains/gpus/crosstool/BUILD
@@ -50,3 +50,8 @@ filegroup(
     name = "empty",
     srcs = [],
 )
+
+filegroup(
+    name = "crosstool_wrapper_driver_is_not_gcc",
+    srcs = ["clang/bin/crosstool_wrapper_driver_is_not_gcc"],
+)
diff --git a/third_party/toolchains/gpus/crosstool/CROSSTOOL b/third_party/toolchains/gpus/crosstool/CROSSTOOL
index 16ee2f82c6..d6ee7e38c4 100644
--- a/third_party/toolchains/gpus/crosstool/CROSSTOOL
+++ b/third_party/toolchains/gpus/crosstool/CROSSTOOL
@@ -144,8 +144,8 @@ toolchain {
       flag_group {
         # All warnings are enabled. Maybe enable -Werror as well?
         flag: "-Wall"
-        # TODO(ngiraldo): Some parts of the codebase set -Werror and hit this 
-        # warning, so switch it off for now.
+        # Some parts of the codebase set -Werror and hit this warning, so
+        # switch it off for now.
         flag: "-Wno-invalid-partial-specialization"
       }
     }
@@ -303,7 +303,7 @@ toolchain {
   cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu/c++/5.4.0"
   cxx_builtin_include_directory: "/usr/include/c++/5.4.0/backward"
   cxx_builtin_include_directory: "/usr/local/include"
-  cxx_builtin_include_directory: "/usr/local/lib/clang/6.0.0/include"
+  cxx_builtin_include_directory: "/usr/local/lib/clang/7.0.0/include"
   cxx_builtin_include_directory: "/usr/include/x86_64-linux-gnu"
   cxx_builtin_include_directory: "/usr/include"
 }
diff --git a/third_party/toolchains/gpus/cuda/BUILD b/third_party/toolchains/gpus/cuda/BUILD
index 39136de99c..cfc6930851 100644
--- a/third_party/toolchains/gpus/cuda/BUILD
+++ b/third_party/toolchains/gpus/cuda/BUILD
@@ -51,6 +51,7 @@ cc_library(
     includes = [
         ".",
         "cuda/include",
+        "cuda/include/crt",
     ],
     visibility = ["//visibility:public"],
 )
@@ -84,8 +85,8 @@ cc_library(
 
 cc_library(
     name = "cudart",
-    srcs = ["cuda/lib/libcudart.so.8.0"],
-    data = ["cuda/lib/libcudart.so.8.0"],
+    srcs = ["cuda/lib/libcudart.so.9.0"],
+    data = ["cuda/lib/libcudart.so.9.0"],
     includes = [
         ".",
         "cuda/include",
@@ -96,8 +97,8 @@ cc_library(
 
 cc_library(
     name = "cublas",
-    srcs = ["cuda/lib/libcublas.so.8.0"],
-    data = ["cuda/lib/libcublas.so.8.0"],
+    srcs = ["cuda/lib/libcublas.so.9.0"],
+    data = ["cuda/lib/libcublas.so.9.0"],
     includes = [
         ".",
         "cuda/include",
@@ -108,8 +109,8 @@ cc_library(
 
 cc_library(
     name = "cusolver",
-    srcs = ["cuda/lib/libcusolver.so.8.0"],
-    data = ["cuda/lib/libcusolver.so.8.0"],
+    srcs = ["cuda/lib/libcusolver.so.9.0"],
+    data = ["cuda/lib/libcusolver.so.9.0"],
     includes = [
         ".",
         "cuda/include",
@@ -121,8 +122,8 @@ cc_library(
 
 cc_library(
     name = "cudnn",
-    srcs = ["cuda/lib/libcudnn.so.6"],
-    data = ["cuda/lib/libcudnn.so.6"],
+    srcs = ["cuda/lib/libcudnn.so.7"],
+    data = ["cuda/lib/libcudnn.so.7"],
     includes = [
         ".",
         "cuda/include",
@@ -133,8 +134,8 @@ cc_library(
 
 cc_library(
     name = "cufft",
-    srcs = ["cuda/lib/libcufft.so.8.0"],
-    data = ["cuda/lib/libcufft.so.8.0"],
+    srcs = ["cuda/lib/libcufft.so.9.0"],
+    data = ["cuda/lib/libcufft.so.9.0"],
     includes = [
         ".",
         "cuda/include",
@@ -145,8 +146,8 @@ cc_library(
 
 cc_library(
     name = "curand",
-    srcs = ["cuda/lib/libcurand.so.8.0"],
-    data = ["cuda/lib/libcurand.so.8.0"],
+    srcs = ["cuda/lib/libcurand.so.9.0"],
+    data = ["cuda/lib/libcurand.so.9.0"],
     includes = [
         ".",
         "cuda/include",
@@ -183,7 +184,7 @@ cc_library(
 
 cc_library(
     name = "cupti_dsos",
-    data = ["cuda/lib/libcupti.so.8.0"],
+    data = ["cuda/lib/libcupti.so.9.0"],
     includes = [
         ".",
         "cuda/include",
@@ -200,1063 +201,990 @@ cc_library(
 genrule(
     name = "cuda-include",
     outs = [
-        "cuda/include/math_functions.hpp",
-        "cuda/include/cufft.h",
-        "cuda/include/nvgraph.h",
-        "cuda/include/curand_normal.h",
-        "cuda/include/curand_uniform.h",
-        "cuda/include/nppi_data_exchange_and_initialization.h",
-        "cuda/include/cuda_gl_interop.h",
-        "cuda/include/nppi_compression_functions.h",
-        "cuda/include/npp.h",
+        "cuda/include/CL/cl.h",
+        "cuda/include/CL/cl.hpp",
+        "cuda/include/CL/cl_egl.h",
+        "cuda/include/CL/cl_ext.h",
+        "cuda/include/CL/cl_gl.h",
+        "cuda/include/CL/cl_gl_ext.h",
+        "cuda/include/CL/cl_platform.h",
+        "cuda/include/CL/opencl.h",
+        "cuda/include/builtin_types.h",
+        "cuda/include/channel_descriptor.h",
+        "cuda/include/common_functions.h",
+        "cuda/include/cooperative_groups.h",
+        "cuda/include/cooperative_groups_helpers.h",
+        "cuda/include/crt/common_functions.h",
+        "cuda/include/crt/device_double_functions.h",
+        "cuda/include/crt/device_double_functions.hpp",
+        "cuda/include/crt/device_functions.h",
+        "cuda/include/crt/device_functions.hpp",
+        "cuda/include/crt/func_macro.h",
+        "cuda/include/crt/host_config.h",
+        "cuda/include/crt/host_defines.h",
+        "cuda/include/crt/host_runtime.h",
+        "cuda/include/crt/math_functions.h",
+        "cuda/include/crt/math_functions.hpp",
+        "cuda/include/crt/mma.h",
+        "cuda/include/crt/mma.hpp",
+        "cuda/include/crt/nvfunctional",
+        "cuda/include/crt/sm_70_rt.h",
+        "cuda/include/crt/sm_70_rt.hpp",
+        "cuda/include/crt/storage_class.h",
+        "cuda/include/cuComplex.h",
+        "cuda/include/cublas.h",
+        "cuda/include/cublasXt.h",
+        "cuda/include/cublas_api.h",
+        "cuda/include/cublas_v2.h",
         "cuda/include/cuda.h",
-        "cuda/include/nppi_statistics_functions.h",
-        "cuda/include/vector_functions.hpp",
-        "cuda/include/sm_32_intrinsics.hpp",
-        "cuda/include/sm_32_intrinsics.h",
-        "cuda/include/curand_discrete.h",
+        "cuda/include/cudaEGL.h",
+        "cuda/include/cudaGL.h",
+        "cuda/include/cudaProfiler.h",
+        "cuda/include/cudaVDPAU.h",
+        "cuda/include/cuda_device_runtime_api.h",
+        "cuda/include/cuda_fp16.h",
+        "cuda/include/cuda_fp16.hpp",
+        "cuda/include/cuda_gl_interop.h",
+        "cuda/include/cuda_occupancy.h",
+        "cuda/include/cuda_profiler_api.h",
         "cuda/include/cuda_runtime.h",
+        "cuda/include/cuda_runtime_api.h",
+        "cuda/include/cuda_surface_types.h",
+        "cuda/include/cuda_texture_types.h",
+        "cuda/include/cuda_vdpau_interop.h",
+        "cuda/include/cudalibxt.h",
+        "cuda/include/cudnn.h",
+        "cuda/include/cufft.h",
         "cuda/include/cufftXt.h",
-        "cuda/include/sm_61_intrinsics.h",
-        "cuda/include/texture_fetch_functions.h",
+        "cuda/include/cufftw.h",
+        "cuda/include/curand.h",
+        "cuda/include/curand_discrete.h",
+        "cuda/include/curand_discrete2.h",
+        "cuda/include/curand_globals.h",
+        "cuda/include/curand_kernel.h",
+        "cuda/include/curand_lognormal.h",
         "cuda/include/curand_mrg32k3a.h",
-        "cuda/include/host_defines.h",
-        "cuda/include/common_functions.h",
-        "cuda/include/nppi_support_functions.h",
-        "cuda/include/nppi_linear_transforms.h",
-        "cuda/include/device_double_functions.hpp",
-        "cuda/include/math_constants.h",
-        "cuda/include/nvToolsExtSync.h",
-        "cuda/include/npps_initialization.h",
+        "cuda/include/curand_mtgp32.h",
+        "cuda/include/curand_mtgp32_host.h",
+        "cuda/include/curand_mtgp32_kernel.h",
+        "cuda/include/curand_mtgp32dc_p_11213.h",
+        "cuda/include/curand_normal.h",
+        "cuda/include/curand_normal_static.h",
+        "cuda/include/curand_philox4x32_x.h",
+        "cuda/include/curand_poisson.h",
+        "cuda/include/curand_precalc.h",
+        "cuda/include/curand_uniform.h",
+        "cuda/include/cusolverDn.h",
+        "cuda/include/cusolverRf.h",
+        "cuda/include/cusolverSp.h",
         "cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h",
-        "cuda/include/texture_indirect_functions.hpp",
-        "cuda/include/cudaProfiler.h",
-        "cuda/include/npps_filtering_functions.h",
+        "cuda/include/cusolver_common.h",
+        "cuda/include/cusparse.h",
         "cuda/include/cusparse_v2.h",
-        "cuda/include/nppi.h",
-        "cuda/include/surface_indirect_functions.h",
-        "cuda/include/sm_30_intrinsics.h",
+        "cuda/include/device_atomic_functions.h",
+        "cuda/include/device_atomic_functions.hpp",
         "cuda/include/device_double_functions.h",
-        "cuda/include/sm_35_intrinsics.h",
-        "cuda/include/cusolverSp.h",
-        "cuda/include/library_types.h",
-        "cuda/include/surface_indirect_functions.hpp",
-        "cuda/include/cudalibxt.h",
-        "cuda/include/channel_descriptor.h",
+        "cuda/include/device_double_functions.hpp",
+        "cuda/include/device_functions.h",
+        "cuda/include/device_functions.hpp",
         "cuda/include/device_functions_decls.h",
-        "cuda/include/curand_kernel.h",
-        "cuda/include/curand_mtgp32_host.h",
-        "cuda/include/nvToolsExtCuda.h",
-        "cuda/include/nvToolsExt.h",
-        "cuda/include/cuComplex.h",
-        "cuda/include/sm_32_atomic_functions.h",
-        "cuda/include/texture_indirect_functions.h",
-        "cuda/include/sm_32_atomic_functions.hpp",
-        "cuda/include/sm_20_intrinsics.hpp",
         "cuda/include/device_launch_parameters.h",
-        "cuda/include/curand_mtgp32.h",
-        "cuda/include/texture_fetch_functions.hpp",
-        "cuda/include/cuda_occupancy.h",
-        "cuda/include/CL/opencl.h",
-        "cuda/include/CL/cl_platform.h",
-        "cuda/include/CL/cl_egl.h",
-        "cuda/include/CL/cl_gl.h",
-        "cuda/include/CL/cl.h",
-        "cuda/include/CL/cl_gl_ext.h",
-        "cuda/include/CL/cl_ext.h",
-        "cuda/include/CL/cl.hpp",
+        "cuda/include/device_types.h",
+        "cuda/include/driver_functions.h",
+        "cuda/include/driver_types.h",
+        "cuda/include/dynlink_cuda.h",
+        "cuda/include/dynlink_cuda_cuda.h",
+        "cuda/include/dynlink_cuviddec.h",
+        "cuda/include/dynlink_nvcuvid.h",
+        "cuda/include/fatBinaryCtl.h",
+        "cuda/include/fatbinary.h",
         "cuda/include/host_config.h",
-        "cuda/include/cuda_surface_types.h",
+        "cuda/include/host_defines.h",
+        "cuda/include/library_types.h",
+        "cuda/include/math_constants.h",
         "cuda/include/math_functions.h",
+        "cuda/include/math_functions.hpp",
+        "cuda/include/math_functions_dbl_ptx3.h",
+        "cuda/include/math_functions_dbl_ptx3.hpp",
+        "cuda/include/mma.h",
+        "cuda/include/npp.h",
+        "cuda/include/nppcore.h",
+        "cuda/include/nppdefs.h",
+        "cuda/include/nppi.h",
+        "cuda/include/nppi_arithmetic_and_logical_operations.h",
+        "cuda/include/nppi_color_conversion.h",
+        "cuda/include/nppi_compression_functions.h",
+        "cuda/include/nppi_computer_vision.h",
+        "cuda/include/nppi_data_exchange_and_initialization.h",
+        "cuda/include/nppi_filtering_functions.h",
+        "cuda/include/nppi_geometry_transforms.h",
+        "cuda/include/nppi_linear_transforms.h",
+        "cuda/include/nppi_morphological_operations.h",
+        "cuda/include/nppi_statistics_functions.h",
+        "cuda/include/nppi_support_functions.h",
+        "cuda/include/nppi_threshold_and_compare_operations.h",
+        "cuda/include/npps.h",
+        "cuda/include/npps_arithmetic_and_logical_operations.h",
+        "cuda/include/npps_conversion_functions.h",
+        "cuda/include/npps_filtering_functions.h",
+        "cuda/include/npps_initialization.h",
+        "cuda/include/npps_statistics_functions.h",
+        "cuda/include/npps_support_functions.h",
+        "cuda/include/nppversion.h",
+        "cuda/include/nvToolsExt.h",
+        "cuda/include/nvToolsExtCuda.h",
+        "cuda/include/nvToolsExtCudaRt.h",
         "cuda/include/nvToolsExtMeta.h",
+        "cuda/include/nvToolsExtSync.h",
+        "cuda/include/nvblas.h",
+        "cuda/include/nvfunctional",
+        "cuda/include/nvgraph.h",
+        "cuda/include/nvml.h",
+        "cuda/include/nvrtc.h",
+        "cuda/include/sm_20_atomic_functions.h",
         "cuda/include/sm_20_atomic_functions.hpp",
-        "cuda/include/device_functions.h",
-        "cuda/include/device_types.h",
-        "cuda/include/npps_conversion_functions.h",
-        "cuda/include/curand_precalc.h",
-        "cuda/include/cusolverRf.h",
+        "cuda/include/sm_20_intrinsics.h",
+        "cuda/include/sm_20_intrinsics.hpp",
+        "cuda/include/sm_30_intrinsics.h",
+        "cuda/include/sm_30_intrinsics.hpp",
+        "cuda/include/sm_32_atomic_functions.h",
+        "cuda/include/sm_32_atomic_functions.hpp",
+        "cuda/include/sm_32_intrinsics.h",
+        "cuda/include/sm_32_intrinsics.hpp",
+        "cuda/include/sm_35_atomic_functions.h",
+        "cuda/include/sm_35_intrinsics.h",
+        "cuda/include/sm_60_atomic_functions.h",
         "cuda/include/sm_60_atomic_functions.hpp",
-        "cuda/include/cuviddec.h",
-        "cuda/include/curand_discrete2.h",
-        "cuda/include/device_functions.hpp",
-        "cuda/include/thrust/transform_scan.h",
-        "cuda/include/thrust/system_error.h",
-        "cuda/include/thrust/device_malloc.h",
-        "cuda/include/thrust/partition.h",
-        "cuda/include/thrust/unique.h",
-        "cuda/include/thrust/device_delete.h",
-        "cuda/include/thrust/execution_policy.h",
+        "cuda/include/sm_61_intrinsics.h",
+        "cuda/include/sm_61_intrinsics.hpp",
+        "cuda/include/sobol_direction_vectors.h",
+        "cuda/include/surface_functions.h",
+        "cuda/include/surface_functions.hpp",
+        "cuda/include/surface_indirect_functions.h",
+        "cuda/include/surface_indirect_functions.hpp",
+        "cuda/include/surface_types.h",
+        "cuda/include/texture_fetch_functions.h",
+        "cuda/include/texture_fetch_functions.hpp",
+        "cuda/include/texture_indirect_functions.h",
+        "cuda/include/texture_indirect_functions.hpp",
+        "cuda/include/texture_types.h",
         "cuda/include/thrust/adjacent_difference.h",
-        "cuda/include/thrust/sequence.h",
-        "cuda/include/thrust/merge.h",
-        "cuda/include/thrust/device_new.h",
-        "cuda/include/thrust/transform_reduce.h",
-        "cuda/include/thrust/device_vector.h",
-        "cuda/include/thrust/gather.h",
-        "cuda/include/thrust/sort.h",
-        "cuda/include/thrust/scan.h",
-        "cuda/include/thrust/detail/temporary_array.h",
-        "cuda/include/thrust/detail/util/align.h",
-        "cuda/include/thrust/detail/util/blocking.h",
-        "cuda/include/thrust/detail/transform.inl",
-        "cuda/include/thrust/detail/device_vector.inl",
+        "cuda/include/thrust/advance.h",
+        "cuda/include/thrust/binary_search.h",
+        "cuda/include/thrust/complex.h",
+        "cuda/include/thrust/copy.h",
+        "cuda/include/thrust/count.h",
+        "cuda/include/thrust/detail/adjacent_difference.inl",
+        "cuda/include/thrust/detail/advance.inl",
+        "cuda/include/thrust/detail/allocator/allocator_traits.h",
+        "cuda/include/thrust/detail/allocator/allocator_traits.inl",
+        "cuda/include/thrust/detail/allocator/copy_construct_range.h",
+        "cuda/include/thrust/detail/allocator/copy_construct_range.inl",
+        "cuda/include/thrust/detail/allocator/default_construct_range.h",
+        "cuda/include/thrust/detail/allocator/default_construct_range.inl",
+        "cuda/include/thrust/detail/allocator/destroy_range.h",
+        "cuda/include/thrust/detail/allocator/destroy_range.inl",
+        "cuda/include/thrust/detail/allocator/fill_construct_range.h",
+        "cuda/include/thrust/detail/allocator/fill_construct_range.inl",
+        "cuda/include/thrust/detail/allocator/malloc_allocator.h",
+        "cuda/include/thrust/detail/allocator/malloc_allocator.inl",
+        "cuda/include/thrust/detail/allocator/no_throw_allocator.h",
+        "cuda/include/thrust/detail/allocator/tagged_allocator.h",
+        "cuda/include/thrust/detail/allocator/tagged_allocator.inl",
+        "cuda/include/thrust/detail/allocator/temporary_allocator.h",
+        "cuda/include/thrust/detail/allocator/temporary_allocator.inl",
         "cuda/include/thrust/detail/binary_search.inl",
-        "cuda/include/thrust/detail/overlapped_copy.h",
-        "cuda/include/thrust/detail/vector_base.inl",
-        "cuda/include/thrust/detail/device_reference.inl",
-        "cuda/include/thrust/detail/functional/actor.h",
-        "cuda/include/thrust/detail/functional/value.h",
-        "cuda/include/thrust/detail/functional/operators.h",
-        "cuda/include/thrust/detail/functional/operators/logical_operators.h",
-        "cuda/include/thrust/detail/functional/operators/relational_operators.h",
-        "cuda/include/thrust/detail/functional/operators/assignment_operator.h",
-        "cuda/include/thrust/detail/functional/operators/bitwise_operators.h",
-        "cuda/include/thrust/detail/functional/operators/operator_adaptors.h",
-        "cuda/include/thrust/detail/functional/operators/arithmetic_operators.h",
-        "cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h",
-        "cuda/include/thrust/detail/functional/argument.h",
-        "cuda/include/thrust/detail/functional/placeholder.h",
-        "cuda/include/thrust/detail/functional/actor.inl",
-        "cuda/include/thrust/detail/functional/composite.h",
-        "cuda/include/thrust/detail/static_map.h",
-        "cuda/include/thrust/detail/type_traits/has_nested_type.h",
-        "cuda/include/thrust/detail/type_traits/is_call_possible.h",
-        "cuda/include/thrust/detail/type_traits/function_traits.h",
-        "cuda/include/thrust/detail/type_traits/pointer_traits.h",
-        "cuda/include/thrust/detail/type_traits/has_member_function.h",
-        "cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h",
-        "cuda/include/thrust/detail/type_traits/minimum_type.h",
-        "cuda/include/thrust/detail/type_traits/has_trivial_assign.h",
-        "cuda/include/thrust/detail/type_traits/is_metafunction_defined.h",
-        "cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h",
-        "cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h",
-        "cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h",
-        "cuda/include/thrust/detail/reference.h",
-        "cuda/include/thrust/detail/inner_product.inl",
-        "cuda/include/thrust/detail/use_default.h",
-        "cuda/include/thrust/detail/sequence.inl",
-        "cuda/include/thrust/detail/sort.inl",
-        "cuda/include/thrust/detail/equal.inl",
-        "cuda/include/thrust/detail/execution_policy.h",
-        "cuda/include/thrust/detail/integer_traits.h",
-        "cuda/include/thrust/detail/type_traits.h",
-        "cuda/include/thrust/detail/reverse.inl",
-        "cuda/include/thrust/detail/tabulate.inl",
-        "cuda/include/thrust/detail/unique.inl",
-        "cuda/include/thrust/detail/scatter.inl",
-        "cuda/include/thrust/detail/set_operations.inl",
-        "cuda/include/thrust/detail/device_malloc.inl",
-        "cuda/include/thrust/detail/copy_if.inl",
-        "cuda/include/thrust/detail/fill.inl",
-        "cuda/include/thrust/detail/temporary_array.inl",
-        "cuda/include/thrust/detail/transform_scan.inl",
-        "cuda/include/thrust/detail/minmax.h",
-        "cuda/include/thrust/detail/swap.inl",
-        "cuda/include/thrust/detail/pointer.inl",
-        "cuda/include/thrust/detail/transform_reduce.inl",
-        "cuda/include/thrust/detail/config.h",
-        "cuda/include/thrust/detail/distance.inl",
-        "cuda/include/thrust/detail/pair.inl",
-        "cuda/include/thrust/detail/allocator/temporary_allocator.h",
-        "cuda/include/thrust/detail/allocator/tagged_allocator.h",
-        "cuda/include/thrust/detail/allocator/destroy_range.inl",
-        "cuda/include/thrust/detail/allocator/destroy_range.h",
-        "cuda/include/thrust/detail/allocator/no_throw_allocator.h",
-        "cuda/include/thrust/detail/allocator/default_construct_range.inl",
-        "cuda/include/thrust/detail/allocator/fill_construct_range.inl",
-        "cuda/include/thrust/detail/allocator/tagged_allocator.inl",
-        "cuda/include/thrust/detail/allocator/malloc_allocator.h",
-        "cuda/include/thrust/detail/allocator/allocator_traits.h",
-        "cuda/include/thrust/detail/allocator/copy_construct_range.h",
-        "cuda/include/thrust/detail/allocator/allocator_traits.inl",
-        "cuda/include/thrust/detail/allocator/default_construct_range.h",
-        "cuda/include/thrust/detail/allocator/copy_construct_range.inl",
-        "cuda/include/thrust/detail/allocator/malloc_allocator.inl",
-        "cuda/include/thrust/detail/allocator/temporary_allocator.inl",
-        "cuda/include/thrust/detail/allocator/fill_construct_range.h",
-        "cuda/include/thrust/detail/temporary_buffer.h",
-        "cuda/include/thrust/detail/reduce.inl",
-        "cuda/include/thrust/detail/device_new.inl",
-        "cuda/include/thrust/detail/pointer.h",
-        "cuda/include/thrust/detail/for_each.inl",
-        "cuda/include/thrust/detail/generate.inl",
-        "cuda/include/thrust/detail/dispatch/is_trivial_copy.h",
-        "cuda/include/thrust/detail/adjacent_difference.inl",
-        "cuda/include/thrust/detail/tuple_meta_transform.h",
-        "cuda/include/thrust/detail/functional.inl",
-        "cuda/include/thrust/detail/remove.inl",
-        "cuda/include/thrust/detail/tuple_transform.h",
-        "cuda/include/thrust/detail/merge.inl",
-        "cuda/include/thrust/detail/extrema.inl",
-        "cuda/include/thrust/detail/trivial_sequence.h",
-        "cuda/include/thrust/detail/vector_base.h",
-        "cuda/include/thrust/detail/count.inl",
-        "cuda/include/thrust/detail/uninitialized_copy.inl",
-        "cuda/include/thrust/detail/function.h",
-        "cuda/include/thrust/detail/swap_ranges.inl",
-        "cuda/include/thrust/detail/device_delete.inl",
-        "cuda/include/thrust/detail/static_assert.h",
-        "cuda/include/thrust/detail/logical.inl",
-        "cuda/include/thrust/detail/seq.h",
-        "cuda/include/thrust/detail/mpl/math.h",
-        "cuda/include/thrust/detail/mismatch.inl",
-        "cuda/include/thrust/detail/internal_functional.h",
-        "cuda/include/thrust/detail/get_iterator_value.h",
-        "cuda/include/thrust/detail/copy.inl",
-        "cuda/include/thrust/detail/copy.h",
+        "cuda/include/thrust/detail/complex/arithmetic.h",
+        "cuda/include/thrust/detail/complex/c99math.h",
+        "cuda/include/thrust/detail/complex/catrig.h",
         "cuda/include/thrust/detail/complex/catrigf.h",
-        "cuda/include/thrust/detail/complex/cpowf.h",
-        "cuda/include/thrust/detail/complex/csqrtf.h",
+        "cuda/include/thrust/detail/complex/ccosh.h",
         "cuda/include/thrust/detail/complex/ccoshf.h",
-        "cuda/include/thrust/detail/complex/csinhf.h",
+        "cuda/include/thrust/detail/complex/cexp.h",
+        "cuda/include/thrust/detail/complex/cexpf.h",
+        "cuda/include/thrust/detail/complex/clog.h",
         "cuda/include/thrust/detail/complex/clogf.h",
-        "cuda/include/thrust/detail/complex/ccosh.h",
-        "cuda/include/thrust/detail/complex/arithmetic.h",
-        "cuda/include/thrust/detail/complex/csqrt.h",
-        "cuda/include/thrust/detail/complex/cpow.h",
         "cuda/include/thrust/detail/complex/complex.inl",
-        "cuda/include/thrust/detail/complex/math_private.h",
-        "cuda/include/thrust/detail/complex/c99math.h",
+        "cuda/include/thrust/detail/complex/cpow.h",
+        "cuda/include/thrust/detail/complex/cpowf.h",
         "cuda/include/thrust/detail/complex/cproj.h",
-        "cuda/include/thrust/detail/complex/catrig.h",
-        "cuda/include/thrust/detail/complex/ctanhf.h",
-        "cuda/include/thrust/detail/complex/cexpf.h",
         "cuda/include/thrust/detail/complex/csinh.h",
-        "cuda/include/thrust/detail/complex/stream.h",
+        "cuda/include/thrust/detail/complex/csinhf.h",
+        "cuda/include/thrust/detail/complex/csqrt.h",
+        "cuda/include/thrust/detail/complex/csqrtf.h",
         "cuda/include/thrust/detail/complex/ctanh.h",
-        "cuda/include/thrust/detail/complex/cexp.h",
-        "cuda/include/thrust/detail/complex/clog.h",
-        "cuda/include/thrust/detail/range/head_flags.h",
-        "cuda/include/thrust/detail/range/tail_flags.h",
-        "cuda/include/thrust/detail/execute_with_allocator.h",
-        "cuda/include/thrust/detail/integer_math.h",
-        "cuda/include/thrust/detail/swap.h",
-        "cuda/include/thrust/detail/uninitialized_fill.inl",
-        "cuda/include/thrust/detail/scan.inl",
-        "cuda/include/thrust/detail/gather.inl",
-        "cuda/include/thrust/detail/reference_forward_declaration.h",
-        "cuda/include/thrust/detail/numeric_traits.h",
-        "cuda/include/thrust/detail/reference.inl",
-        "cuda/include/thrust/detail/cstdint.h",
-        "cuda/include/thrust/detail/device_free.inl",
-        "cuda/include/thrust/detail/copy_if.h",
-        "cuda/include/thrust/detail/partition.inl",
-        "cuda/include/thrust/detail/find.inl",
-        "cuda/include/thrust/detail/config/forceinline.h",
-        "cuda/include/thrust/detail/config/debug.h",
-        "cuda/include/thrust/detail/config/config.h",
-        "cuda/include/thrust/detail/config/host_device.h",
-        "cuda/include/thrust/detail/config/host_system.h",
+        "cuda/include/thrust/detail/complex/ctanhf.h",
+        "cuda/include/thrust/detail/complex/math_private.h",
+        "cuda/include/thrust/detail/complex/stream.h",
+        "cuda/include/thrust/detail/config.h",
         "cuda/include/thrust/detail/config/compiler.h",
-        "cuda/include/thrust/detail/config/device_system.h",
         "cuda/include/thrust/detail/config/compiler_fence.h",
+        "cuda/include/thrust/detail/config/config.h",
+        "cuda/include/thrust/detail/config/debug.h",
+        "cuda/include/thrust/detail/config/device_system.h",
         "cuda/include/thrust/detail/config/exec_check_disable.h",
-        "cuda/include/thrust/detail/config/simple_defines.h",
+        "cuda/include/thrust/detail/config/forceinline.h",
         "cuda/include/thrust/detail/config/global_workarounds.h",
-        "cuda/include/thrust/detail/replace.inl",
+        "cuda/include/thrust/detail/config/host_device.h",
+        "cuda/include/thrust/detail/config/host_system.h",
+        "cuda/include/thrust/detail/config/simple_defines.h",
+        "cuda/include/thrust/detail/contiguous_storage.h",
+        "cuda/include/thrust/detail/contiguous_storage.inl",
+        "cuda/include/thrust/detail/copy.h",
+        "cuda/include/thrust/detail/copy.inl",
+        "cuda/include/thrust/detail/copy_if.h",
+        "cuda/include/thrust/detail/copy_if.inl",
+        "cuda/include/thrust/detail/count.inl",
+        "cuda/include/thrust/detail/cstdint.h",
+        "cuda/include/thrust/detail/device_delete.inl",
+        "cuda/include/thrust/detail/device_free.inl",
+        "cuda/include/thrust/detail/device_malloc.inl",
+        "cuda/include/thrust/detail/device_new.inl",
         "cuda/include/thrust/detail/device_ptr.inl",
-        "cuda/include/thrust/detail/tuple.inl",
-        "cuda/include/thrust/detail/malloc_and_free.h",
+        "cuda/include/thrust/detail/device_reference.inl",
+        "cuda/include/thrust/detail/device_vector.inl",
+        "cuda/include/thrust/detail/dispatch/is_trivial_copy.h",
+        "cuda/include/thrust/detail/distance.inl",
+        "cuda/include/thrust/detail/equal.inl",
+        "cuda/include/thrust/detail/execute_with_allocator.h",
+        "cuda/include/thrust/detail/execution_policy.h",
+        "cuda/include/thrust/detail/extrema.inl",
+        "cuda/include/thrust/detail/fill.inl",
+        "cuda/include/thrust/detail/find.inl",
+        "cuda/include/thrust/detail/for_each.inl",
+        "cuda/include/thrust/detail/function.h",
+        "cuda/include/thrust/detail/functional.inl",
+        "cuda/include/thrust/detail/functional/actor.h",
+        "cuda/include/thrust/detail/functional/actor.inl",
+        "cuda/include/thrust/detail/functional/argument.h",
+        "cuda/include/thrust/detail/functional/composite.h",
+        "cuda/include/thrust/detail/functional/operators.h",
+        "cuda/include/thrust/detail/functional/operators/arithmetic_operators.h",
+        "cuda/include/thrust/detail/functional/operators/assignment_operator.h",
+        "cuda/include/thrust/detail/functional/operators/bitwise_operators.h",
+        "cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h",
+        "cuda/include/thrust/detail/functional/operators/logical_operators.h",
+        "cuda/include/thrust/detail/functional/operators/operator_adaptors.h",
+        "cuda/include/thrust/detail/functional/operators/relational_operators.h",
+        "cuda/include/thrust/detail/functional/placeholder.h",
+        "cuda/include/thrust/detail/functional/value.h",
+        "cuda/include/thrust/detail/gather.inl",
+        "cuda/include/thrust/detail/generate.inl",
+        "cuda/include/thrust/detail/get_iterator_value.h",
         "cuda/include/thrust/detail/host_vector.inl",
+        "cuda/include/thrust/detail/inner_product.inl",
+        "cuda/include/thrust/detail/integer_math.h",
+        "cuda/include/thrust/detail/integer_traits.h",
+        "cuda/include/thrust/detail/internal_functional.h",
+        "cuda/include/thrust/detail/logical.inl",
+        "cuda/include/thrust/detail/malloc_and_free.h",
+        "cuda/include/thrust/detail/merge.inl",
+        "cuda/include/thrust/detail/minmax.h",
+        "cuda/include/thrust/detail/mismatch.inl",
+        "cuda/include/thrust/detail/mpl/math.h",
+        "cuda/include/thrust/detail/numeric_traits.h",
+        "cuda/include/thrust/detail/overlapped_copy.h",
+        "cuda/include/thrust/detail/pair.inl",
+        "cuda/include/thrust/detail/partition.inl",
+        "cuda/include/thrust/detail/pointer.h",
+        "cuda/include/thrust/detail/pointer.inl",
+        "cuda/include/thrust/detail/range/head_flags.h",
+        "cuda/include/thrust/detail/range/tail_flags.h",
         "cuda/include/thrust/detail/raw_pointer_cast.h",
-        "cuda/include/thrust/detail/advance.inl",
-        "cuda/include/thrust/detail/contiguous_storage.h",
         "cuda/include/thrust/detail/raw_reference_cast.h",
-        "cuda/include/thrust/detail/contiguous_storage.inl",
-        "cuda/include/thrust/reverse.h",
-        "cuda/include/thrust/device_malloc_allocator.h",
-        "cuda/include/thrust/scatter.h",
-        "cuda/include/thrust/pair.h",
-        "cuda/include/thrust/advance.h",
-        "cuda/include/thrust/find.h",
-        "cuda/include/thrust/device_ptr.h",
-        "cuda/include/thrust/generate.h",
-        "cuda/include/thrust/uninitialized_fill.h",
-        "cuda/include/thrust/system/system_error.h",
-        "cuda/include/thrust/system/detail/bad_alloc.h",
-        "cuda/include/thrust/system/detail/adl/transform_scan.h",
-        "cuda/include/thrust/system/detail/adl/unique_by_key.h",
-        "cuda/include/thrust/system/detail/adl/partition.h",
-        "cuda/include/thrust/system/detail/adl/unique.h",
-        "cuda/include/thrust/system/detail/adl/adjacent_difference.h",
-        "cuda/include/thrust/system/detail/adl/sequence.h",
-        "cuda/include/thrust/system/detail/adl/merge.h",
-        "cuda/include/thrust/system/detail/adl/transform_reduce.h",
-        "cuda/include/thrust/system/detail/adl/gather.h",
-        "cuda/include/thrust/system/detail/adl/sort.h",
-        "cuda/include/thrust/system/detail/adl/scan.h",
-        "cuda/include/thrust/system/detail/adl/temporary_buffer.h",
-        "cuda/include/thrust/system/detail/adl/scan_by_key.h",
-        "cuda/include/thrust/system/detail/adl/reverse.h",
-        "cuda/include/thrust/system/detail/adl/assign_value.h",
-        "cuda/include/thrust/system/detail/adl/scatter.h",
-        "cuda/include/thrust/system/detail/adl/find.h",
-        "cuda/include/thrust/system/detail/adl/generate.h",
-        "cuda/include/thrust/system/detail/adl/uninitialized_fill.h",
-        "cuda/include/thrust/system/detail/adl/remove.h",
-        "cuda/include/thrust/system/detail/adl/tabulate.h",
-        "cuda/include/thrust/system/detail/adl/for_each.h",
-        "cuda/include/thrust/system/detail/adl/reduce_by_key.h",
-        "cuda/include/thrust/system/detail/adl/reduce.h",
-        "cuda/include/thrust/system/detail/adl/equal.h",
-        "cuda/include/thrust/system/detail/adl/copy.h",
-        "cuda/include/thrust/system/detail/adl/swap_ranges.h",
-        "cuda/include/thrust/system/detail/adl/uninitialized_copy.h",
-        "cuda/include/thrust/system/detail/adl/binary_search.h",
-        "cuda/include/thrust/system/detail/adl/set_operations.h",
-        "cuda/include/thrust/system/detail/adl/mismatch.h",
-        "cuda/include/thrust/system/detail/adl/extrema.h",
-        "cuda/include/thrust/system/detail/adl/count.h",
-        "cuda/include/thrust/system/detail/adl/replace.h",
+        "cuda/include/thrust/detail/reduce.inl",
+        "cuda/include/thrust/detail/reference.h",
+        "cuda/include/thrust/detail/reference.inl",
+        "cuda/include/thrust/detail/reference_forward_declaration.h",
+        "cuda/include/thrust/detail/remove.inl",
+        "cuda/include/thrust/detail/replace.inl",
+        "cuda/include/thrust/detail/reverse.inl",
+        "cuda/include/thrust/detail/scan.inl",
+        "cuda/include/thrust/detail/scatter.inl",
+        "cuda/include/thrust/detail/seq.h",
+        "cuda/include/thrust/detail/sequence.inl",
+        "cuda/include/thrust/detail/set_operations.inl",
+        "cuda/include/thrust/detail/sort.inl",
+        "cuda/include/thrust/detail/static_assert.h",
+        "cuda/include/thrust/detail/static_map.h",
+        "cuda/include/thrust/detail/swap.h",
+        "cuda/include/thrust/detail/swap.inl",
+        "cuda/include/thrust/detail/swap_ranges.inl",
+        "cuda/include/thrust/detail/tabulate.inl",
+        "cuda/include/thrust/detail/temporary_array.h",
+        "cuda/include/thrust/detail/temporary_array.inl",
+        "cuda/include/thrust/detail/temporary_buffer.h",
+        "cuda/include/thrust/detail/transform.inl",
+        "cuda/include/thrust/detail/transform_reduce.inl",
+        "cuda/include/thrust/detail/transform_scan.inl",
+        "cuda/include/thrust/detail/trivial_sequence.h",
+        "cuda/include/thrust/detail/tuple.inl",
+        "cuda/include/thrust/detail/tuple_meta_transform.h",
+        "cuda/include/thrust/detail/tuple_transform.h",
+        "cuda/include/thrust/detail/type_traits.h",
+        "cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h",
+        "cuda/include/thrust/detail/type_traits/function_traits.h",
+        "cuda/include/thrust/detail/type_traits/has_member_function.h",
+        "cuda/include/thrust/detail/type_traits/has_nested_type.h",
+        "cuda/include/thrust/detail/type_traits/has_trivial_assign.h",
+        "cuda/include/thrust/detail/type_traits/is_call_possible.h",
+        "cuda/include/thrust/detail/type_traits/is_metafunction_defined.h",
+        "cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h",
+        "cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h",
+        "cuda/include/thrust/detail/type_traits/minimum_type.h",
+        "cuda/include/thrust/detail/type_traits/pointer_traits.h",
+        "cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h",
+        "cuda/include/thrust/detail/uninitialized_copy.inl",
+        "cuda/include/thrust/detail/uninitialized_fill.inl",
+        "cuda/include/thrust/detail/unique.inl",
+        "cuda/include/thrust/detail/use_default.h",
+        "cuda/include/thrust/detail/util/align.h",
+        "cuda/include/thrust/detail/util/blocking.h",
+        "cuda/include/thrust/detail/vector_base.h",
+        "cuda/include/thrust/detail/vector_base.inl",
+        "cuda/include/thrust/device_allocator.h",
+        "cuda/include/thrust/device_delete.h",
+        "cuda/include/thrust/device_free.h",
+        "cuda/include/thrust/device_malloc.h",
+        "cuda/include/thrust/device_malloc_allocator.h",
+        "cuda/include/thrust/device_new.h",
+        "cuda/include/thrust/device_new_allocator.h",
+        "cuda/include/thrust/device_ptr.h",
+        "cuda/include/thrust/device_reference.h",
+        "cuda/include/thrust/device_vector.h",
+        "cuda/include/thrust/distance.h",
+        "cuda/include/thrust/equal.h",
+        "cuda/include/thrust/execution_policy.h",
+        "cuda/include/thrust/extrema.h",
+        "cuda/include/thrust/fill.h",
+        "cuda/include/thrust/find.h",
+        "cuda/include/thrust/for_each.h",
+        "cuda/include/thrust/functional.h",
+        "cuda/include/thrust/gather.h",
+        "cuda/include/thrust/generate.h",
+        "cuda/include/thrust/host_vector.h",
+        "cuda/include/thrust/inner_product.h",
+        "cuda/include/thrust/iterator/constant_iterator.h",
+        "cuda/include/thrust/iterator/counting_iterator.h",
+        "cuda/include/thrust/iterator/detail/any_assign.h",
+        "cuda/include/thrust/iterator/detail/any_system_tag.h",
+        "cuda/include/thrust/iterator/detail/constant_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/counting_iterator.inl",
+        "cuda/include/thrust/iterator/detail/device_system_tag.h",
+        "cuda/include/thrust/iterator/detail/discard_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/distance_from_result.h",
+        "cuda/include/thrust/iterator/detail/host_system_tag.h",
+        "cuda/include/thrust/iterator/detail/is_iterator_category.h",
+        "cuda/include/thrust/iterator/detail/is_trivial_iterator.h",
+        "cuda/include/thrust/iterator/detail/iterator_adaptor_base.h",
+        "cuda/include/thrust/iterator/detail/iterator_category_to_system.h",
+        "cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h",
+        "cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h",
+        "cuda/include/thrust/iterator/detail/iterator_facade_category.h",
+        "cuda/include/thrust/iterator/detail/iterator_traits.inl",
+        "cuda/include/thrust/iterator/detail/iterator_traversal_tags.h",
+        "cuda/include/thrust/iterator/detail/join_iterator.h",
+        "cuda/include/thrust/iterator/detail/minimum_category.h",
+        "cuda/include/thrust/iterator/detail/minimum_system.h",
+        "cuda/include/thrust/iterator/detail/normal_iterator.h",
+        "cuda/include/thrust/iterator/detail/permutation_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/retag.h",
+        "cuda/include/thrust/iterator/detail/reverse_iterator.inl",
+        "cuda/include/thrust/iterator/detail/reverse_iterator_base.h",
+        "cuda/include/thrust/iterator/detail/tagged_iterator.h",
+        "cuda/include/thrust/iterator/detail/transform_iterator.inl",
+        "cuda/include/thrust/iterator/detail/transform_output_iterator.inl",
+        "cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h",
+        "cuda/include/thrust/iterator/detail/universal_categories.h",
+        "cuda/include/thrust/iterator/detail/zip_iterator.inl",
+        "cuda/include/thrust/iterator/detail/zip_iterator_base.h",
+        "cuda/include/thrust/iterator/discard_iterator.h",
+        "cuda/include/thrust/iterator/iterator_adaptor.h",
+        "cuda/include/thrust/iterator/iterator_categories.h",
+        "cuda/include/thrust/iterator/iterator_facade.h",
+        "cuda/include/thrust/iterator/iterator_traits.h",
+        "cuda/include/thrust/iterator/permutation_iterator.h",
+        "cuda/include/thrust/iterator/retag.h",
+        "cuda/include/thrust/iterator/reverse_iterator.h",
+        "cuda/include/thrust/iterator/transform_iterator.h",
+        "cuda/include/thrust/iterator/transform_output_iterator.h",
+        "cuda/include/thrust/iterator/zip_iterator.h",
+        "cuda/include/thrust/logical.h",
+        "cuda/include/thrust/memory.h",
+        "cuda/include/thrust/merge.h",
+        "cuda/include/thrust/mismatch.h",
+        "cuda/include/thrust/pair.h",
+        "cuda/include/thrust/partition.h",
+        "cuda/include/thrust/random.h",
+        "cuda/include/thrust/random/detail/discard_block_engine.inl",
+        "cuda/include/thrust/random/detail/linear_congruential_engine.inl",
+        "cuda/include/thrust/random/detail/linear_congruential_engine_discard.h",
+        "cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl",
+        "cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h",
+        "cuda/include/thrust/random/detail/mod.h",
+        "cuda/include/thrust/random/detail/normal_distribution.inl",
+        "cuda/include/thrust/random/detail/normal_distribution_base.h",
+        "cuda/include/thrust/random/detail/random_core_access.h",
+        "cuda/include/thrust/random/detail/subtract_with_carry_engine.inl",
+        "cuda/include/thrust/random/detail/uniform_int_distribution.inl",
+        "cuda/include/thrust/random/detail/uniform_real_distribution.inl",
+        "cuda/include/thrust/random/detail/xor_combine_engine.inl",
+        "cuda/include/thrust/random/detail/xor_combine_engine_max.h",
+        "cuda/include/thrust/random/discard_block_engine.h",
+        "cuda/include/thrust/random/linear_congruential_engine.h",
+        "cuda/include/thrust/random/linear_feedback_shift_engine.h",
+        "cuda/include/thrust/random/normal_distribution.h",
+        "cuda/include/thrust/random/subtract_with_carry_engine.h",
+        "cuda/include/thrust/random/uniform_int_distribution.h",
+        "cuda/include/thrust/random/uniform_real_distribution.h",
+        "cuda/include/thrust/random/xor_combine_engine.h",
+        "cuda/include/thrust/reduce.h",
+        "cuda/include/thrust/remove.h",
+        "cuda/include/thrust/replace.h",
+        "cuda/include/thrust/reverse.h",
+        "cuda/include/thrust/scan.h",
+        "cuda/include/thrust/scatter.h",
+        "cuda/include/thrust/sequence.h",
+        "cuda/include/thrust/set_operations.h",
+        "cuda/include/thrust/sort.h",
+        "cuda/include/thrust/swap.h",
+        "cuda/include/thrust/system/cpp/detail/adjacent_difference.h",
+        "cuda/include/thrust/system/cpp/detail/assign_value.h",
+        "cuda/include/thrust/system/cpp/detail/binary_search.h",
+        "cuda/include/thrust/system/cpp/detail/copy.h",
+        "cuda/include/thrust/system/cpp/detail/copy_if.h",
+        "cuda/include/thrust/system/cpp/detail/count.h",
+        "cuda/include/thrust/system/cpp/detail/equal.h",
+        "cuda/include/thrust/system/cpp/detail/execution_policy.h",
+        "cuda/include/thrust/system/cpp/detail/extrema.h",
+        "cuda/include/thrust/system/cpp/detail/fill.h",
+        "cuda/include/thrust/system/cpp/detail/find.h",
+        "cuda/include/thrust/system/cpp/detail/for_each.h",
+        "cuda/include/thrust/system/cpp/detail/gather.h",
+        "cuda/include/thrust/system/cpp/detail/generate.h",
+        "cuda/include/thrust/system/cpp/detail/get_value.h",
+        "cuda/include/thrust/system/cpp/detail/inner_product.h",
+        "cuda/include/thrust/system/cpp/detail/iter_swap.h",
+        "cuda/include/thrust/system/cpp/detail/logical.h",
+        "cuda/include/thrust/system/cpp/detail/malloc_and_free.h",
+        "cuda/include/thrust/system/cpp/detail/memory.inl",
+        "cuda/include/thrust/system/cpp/detail/merge.h",
+        "cuda/include/thrust/system/cpp/detail/mismatch.h",
+        "cuda/include/thrust/system/cpp/detail/par.h",
+        "cuda/include/thrust/system/cpp/detail/partition.h",
+        "cuda/include/thrust/system/cpp/detail/reduce.h",
+        "cuda/include/thrust/system/cpp/detail/reduce_by_key.h",
+        "cuda/include/thrust/system/cpp/detail/remove.h",
+        "cuda/include/thrust/system/cpp/detail/replace.h",
+        "cuda/include/thrust/system/cpp/detail/reverse.h",
+        "cuda/include/thrust/system/cpp/detail/scan.h",
+        "cuda/include/thrust/system/cpp/detail/scan_by_key.h",
+        "cuda/include/thrust/system/cpp/detail/scatter.h",
+        "cuda/include/thrust/system/cpp/detail/sequence.h",
+        "cuda/include/thrust/system/cpp/detail/set_operations.h",
+        "cuda/include/thrust/system/cpp/detail/sort.h",
+        "cuda/include/thrust/system/cpp/detail/swap_ranges.h",
+        "cuda/include/thrust/system/cpp/detail/tabulate.h",
+        "cuda/include/thrust/system/cpp/detail/temporary_buffer.h",
+        "cuda/include/thrust/system/cpp/detail/transform.h",
+        "cuda/include/thrust/system/cpp/detail/transform_reduce.h",
+        "cuda/include/thrust/system/cpp/detail/transform_scan.h",
+        "cuda/include/thrust/system/cpp/detail/uninitialized_copy.h",
+        "cuda/include/thrust/system/cpp/detail/uninitialized_fill.h",
+        "cuda/include/thrust/system/cpp/detail/unique.h",
+        "cuda/include/thrust/system/cpp/detail/unique_by_key.h",
+        "cuda/include/thrust/system/cpp/detail/vector.inl",
+        "cuda/include/thrust/system/cpp/execution_policy.h",
+        "cuda/include/thrust/system/cpp/memory.h",
+        "cuda/include/thrust/system/cpp/vector.h",
+        "cuda/include/thrust/system/cuda/config.h",
+        "cuda/include/thrust/system/cuda/detail/adjacent_difference.h",
+        "cuda/include/thrust/system/cuda/detail/assign_value.h",
+        "cuda/include/thrust/system/cuda/detail/binary_search.h",
+        "cuda/include/thrust/system/cuda/detail/copy.h",
+        "cuda/include/thrust/system/cuda/detail/copy_if.h",
+        "cuda/include/thrust/system/cuda/detail/core/agent_launcher.h",
+        "cuda/include/thrust/system/cuda/detail/core/alignment.h",
+        "cuda/include/thrust/system/cuda/detail/core/triple_chevron_launch.h",
+        "cuda/include/thrust/system/cuda/detail/core/util.h",
+        "cuda/include/thrust/system/cuda/detail/count.h",
+        "cuda/include/thrust/system/cuda/detail/cross_system.h",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/cub.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/host/mutex.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_device.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/util_type.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh",
+        "cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh",
+        "cuda/include/thrust/system/cuda/detail/equal.h",
+        "cuda/include/thrust/system/cuda/detail/error.inl",
+        "cuda/include/thrust/system/cuda/detail/execution_policy.h",
+        "cuda/include/thrust/system/cuda/detail/extrema.h",
+        "cuda/include/thrust/system/cuda/detail/fill.h",
+        "cuda/include/thrust/system/cuda/detail/find.h",
+        "cuda/include/thrust/system/cuda/detail/for_each.h",
+        "cuda/include/thrust/system/cuda/detail/gather.h",
+        "cuda/include/thrust/system/cuda/detail/generate.h",
+        "cuda/include/thrust/system/cuda/detail/get_value.h",
+        "cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h",
+        "cuda/include/thrust/system/cuda/detail/guarded_driver_types.h",
+        "cuda/include/thrust/system/cuda/detail/inner_product.h",
+        "cuda/include/thrust/system/cuda/detail/internal/copy_cross_system.h",
+        "cuda/include/thrust/system/cuda/detail/internal/copy_device_to_device.h",
+        "cuda/include/thrust/system/cuda/detail/iter_swap.h",
+        "cuda/include/thrust/system/cuda/detail/logical.h",
+        "cuda/include/thrust/system/cuda/detail/malloc_and_free.h",
+        "cuda/include/thrust/system/cuda/detail/memory.inl",
+        "cuda/include/thrust/system/cuda/detail/memory_buffer.h",
+        "cuda/include/thrust/system/cuda/detail/merge.h",
+        "cuda/include/thrust/system/cuda/detail/mismatch.h",
+        "cuda/include/thrust/system/cuda/detail/par.h",
+        "cuda/include/thrust/system/cuda/detail/par_to_seq.h",
+        "cuda/include/thrust/system/cuda/detail/parallel_for.h",
+        "cuda/include/thrust/system/cuda/detail/partition.h",
+        "cuda/include/thrust/system/cuda/detail/reduce.h",
+        "cuda/include/thrust/system/cuda/detail/reduce_by_key.h",
+        "cuda/include/thrust/system/cuda/detail/remove.h",
+        "cuda/include/thrust/system/cuda/detail/replace.h",
+        "cuda/include/thrust/system/cuda/detail/reverse.h",
+        "cuda/include/thrust/system/cuda/detail/scan.h",
+        "cuda/include/thrust/system/cuda/detail/scan_by_key.h",
+        "cuda/include/thrust/system/cuda/detail/scatter.h",
+        "cuda/include/thrust/system/cuda/detail/sequence.h",
+        "cuda/include/thrust/system/cuda/detail/set_operations.h",
+        "cuda/include/thrust/system/cuda/detail/sort.h",
+        "cuda/include/thrust/system/cuda/detail/swap_ranges.h",
+        "cuda/include/thrust/system/cuda/detail/tabulate.h",
+        "cuda/include/thrust/system/cuda/detail/temporary_buffer.h",
+        "cuda/include/thrust/system/cuda/detail/terminate.h",
+        "cuda/include/thrust/system/cuda/detail/transform.h",
+        "cuda/include/thrust/system/cuda/detail/transform_reduce.h",
+        "cuda/include/thrust/system/cuda/detail/transform_scan.h",
+        "cuda/include/thrust/system/cuda/detail/uninitialized_copy.h",
+        "cuda/include/thrust/system/cuda/detail/uninitialized_fill.h",
+        "cuda/include/thrust/system/cuda/detail/unique.h",
+        "cuda/include/thrust/system/cuda/detail/unique_by_key.h",
+        "cuda/include/thrust/system/cuda/detail/util.h",
+        "cuda/include/thrust/system/cuda/detail/vector.inl",
+        "cuda/include/thrust/system/cuda/error.h",
+        "cuda/include/thrust/system/cuda/execution_policy.h",
+        "cuda/include/thrust/system/cuda/experimental/pinned_allocator.h",
+        "cuda/include/thrust/system/cuda/memory.h",
+        "cuda/include/thrust/system/cuda/vector.h",
+        "cuda/include/thrust/system/detail/adl/adjacent_difference.h",
+        "cuda/include/thrust/system/detail/adl/assign_value.h",
+        "cuda/include/thrust/system/detail/adl/binary_search.h",
+        "cuda/include/thrust/system/detail/adl/copy.h",
+        "cuda/include/thrust/system/detail/adl/copy_if.h",
+        "cuda/include/thrust/system/detail/adl/count.h",
+        "cuda/include/thrust/system/detail/adl/equal.h",
+        "cuda/include/thrust/system/detail/adl/extrema.h",
+        "cuda/include/thrust/system/detail/adl/fill.h",
+        "cuda/include/thrust/system/detail/adl/find.h",
+        "cuda/include/thrust/system/detail/adl/for_each.h",
+        "cuda/include/thrust/system/detail/adl/gather.h",
+        "cuda/include/thrust/system/detail/adl/generate.h",
         "cuda/include/thrust/system/detail/adl/get_value.h",
         "cuda/include/thrust/system/detail/adl/inner_product.h",
-        "cuda/include/thrust/system/detail/adl/copy_if.h",
-        "cuda/include/thrust/system/detail/adl/logical.h",
         "cuda/include/thrust/system/detail/adl/iter_swap.h",
+        "cuda/include/thrust/system/detail/adl/logical.h",
         "cuda/include/thrust/system/detail/adl/malloc_and_free.h",
-        "cuda/include/thrust/system/detail/adl/fill.h",
+        "cuda/include/thrust/system/detail/adl/merge.h",
+        "cuda/include/thrust/system/detail/adl/mismatch.h",
+        "cuda/include/thrust/system/detail/adl/partition.h",
+        "cuda/include/thrust/system/detail/adl/reduce.h",
+        "cuda/include/thrust/system/detail/adl/reduce_by_key.h",
+        "cuda/include/thrust/system/detail/adl/remove.h",
+        "cuda/include/thrust/system/detail/adl/replace.h",
+        "cuda/include/thrust/system/detail/adl/reverse.h",
+        "cuda/include/thrust/system/detail/adl/scan.h",
+        "cuda/include/thrust/system/detail/adl/scan_by_key.h",
+        "cuda/include/thrust/system/detail/adl/scatter.h",
+        "cuda/include/thrust/system/detail/adl/sequence.h",
+        "cuda/include/thrust/system/detail/adl/set_operations.h",
+        "cuda/include/thrust/system/detail/adl/sort.h",
+        "cuda/include/thrust/system/detail/adl/swap_ranges.h",
+        "cuda/include/thrust/system/detail/adl/tabulate.h",
+        "cuda/include/thrust/system/detail/adl/temporary_buffer.h",
         "cuda/include/thrust/system/detail/adl/transform.h",
+        "cuda/include/thrust/system/detail/adl/transform_reduce.h",
+        "cuda/include/thrust/system/detail/adl/transform_scan.h",
+        "cuda/include/thrust/system/detail/adl/uninitialized_copy.h",
+        "cuda/include/thrust/system/detail/adl/uninitialized_fill.h",
+        "cuda/include/thrust/system/detail/adl/unique.h",
+        "cuda/include/thrust/system/detail/adl/unique_by_key.h",
+        "cuda/include/thrust/system/detail/bad_alloc.h",
         "cuda/include/thrust/system/detail/errno.h",
         "cuda/include/thrust/system/detail/error_category.inl",
-        "cuda/include/thrust/system/detail/sequential/transform_scan.h",
-        "cuda/include/thrust/system/detail/sequential/unique_by_key.h",
-        "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h",
-        "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl",
-        "cuda/include/thrust/system/detail/sequential/stable_merge_sort.h",
-        "cuda/include/thrust/system/detail/sequential/sort.inl",
-        "cuda/include/thrust/system/detail/sequential/partition.h",
-        "cuda/include/thrust/system/detail/sequential/unique.h",
-        "cuda/include/thrust/system/detail/sequential/execution_policy.h",
-        "cuda/include/thrust/system/detail/sequential/adjacent_difference.h",
-        "cuda/include/thrust/system/detail/sequential/sequence.h",
-        "cuda/include/thrust/system/detail/sequential/merge.h",
-        "cuda/include/thrust/system/detail/sequential/transform_reduce.h",
-        "cuda/include/thrust/system/detail/sequential/gather.h",
-        "cuda/include/thrust/system/detail/sequential/sort.h",
-        "cuda/include/thrust/system/detail/sequential/copy_backward.h",
-        "cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl",
-        "cuda/include/thrust/system/detail/sequential/scan.h",
-        "cuda/include/thrust/system/detail/sequential/temporary_buffer.h",
-        "cuda/include/thrust/system/detail/sequential/scan_by_key.h",
-        "cuda/include/thrust/system/detail/sequential/reverse.h",
-        "cuda/include/thrust/system/detail/sequential/assign_value.h",
-        "cuda/include/thrust/system/detail/sequential/scatter.h",
-        "cuda/include/thrust/system/detail/sequential/find.h",
-        "cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl",
-        "cuda/include/thrust/system/detail/sequential/merge.inl",
-        "cuda/include/thrust/system/detail/sequential/generate.h",
-        "cuda/include/thrust/system/detail/sequential/uninitialized_fill.h",
-        "cuda/include/thrust/system/detail/sequential/general_copy.h",
-        "cuda/include/thrust/system/detail/sequential/insertion_sort.h",
-        "cuda/include/thrust/system/detail/sequential/remove.h",
-        "cuda/include/thrust/system/detail/sequential/tabulate.h",
-        "cuda/include/thrust/system/detail/sequential/for_each.h",
-        "cuda/include/thrust/system/detail/sequential/reduce_by_key.h",
-        "cuda/include/thrust/system/detail/sequential/reduce.h",
-        "cuda/include/thrust/system/detail/sequential/equal.h",
-        "cuda/include/thrust/system/detail/sequential/stable_radix_sort.h",
-        "cuda/include/thrust/system/detail/sequential/copy.inl",
-        "cuda/include/thrust/system/detail/sequential/copy.h",
-        "cuda/include/thrust/system/detail/sequential/swap_ranges.h",
-        "cuda/include/thrust/system/detail/sequential/uninitialized_copy.h",
-        "cuda/include/thrust/system/detail/sequential/binary_search.h",
-        "cuda/include/thrust/system/detail/sequential/set_operations.h",
-        "cuda/include/thrust/system/detail/sequential/mismatch.h",
-        "cuda/include/thrust/system/detail/sequential/extrema.h",
-        "cuda/include/thrust/system/detail/sequential/count.h",
-        "cuda/include/thrust/system/detail/sequential/trivial_copy.h",
-        "cuda/include/thrust/system/detail/sequential/replace.h",
-        "cuda/include/thrust/system/detail/sequential/get_value.h",
-        "cuda/include/thrust/system/detail/sequential/inner_product.h",
-        "cuda/include/thrust/system/detail/sequential/copy_if.h",
-        "cuda/include/thrust/system/detail/sequential/logical.h",
-        "cuda/include/thrust/system/detail/sequential/iter_swap.h",
-        "cuda/include/thrust/system/detail/sequential/malloc_and_free.h",
-        "cuda/include/thrust/system/detail/sequential/fill.h",
-        "cuda/include/thrust/system/detail/sequential/transform.h",
-        "cuda/include/thrust/system/detail/error_condition.inl",
-        "cuda/include/thrust/system/detail/internal/decompose.h",
         "cuda/include/thrust/system/detail/error_code.inl",
-        "cuda/include/thrust/system/detail/generic/transform_scan.h",
-        "cuda/include/thrust/system/detail/generic/memory.inl",
-        "cuda/include/thrust/system/detail/generic/transform.inl",
-        "cuda/include/thrust/system/detail/generic/binary_search.inl",
-        "cuda/include/thrust/system/detail/generic/scan_by_key.inl",
-        "cuda/include/thrust/system/detail/generic/unique_by_key.h",
-        "cuda/include/thrust/system/detail/generic/inner_product.inl",
-        "cuda/include/thrust/system/detail/generic/select_system.h",
-        "cuda/include/thrust/system/detail/generic/sequence.inl",
-        "cuda/include/thrust/system/detail/generic/sort.inl",
-        "cuda/include/thrust/system/detail/generic/equal.inl",
-        "cuda/include/thrust/system/detail/generic/partition.h",
-        "cuda/include/thrust/system/detail/generic/unique.h",
+        "cuda/include/thrust/system/detail/error_condition.inl",
         "cuda/include/thrust/system/detail/generic/adjacent_difference.h",
-        "cuda/include/thrust/system/detail/generic/tag.h",
-        "cuda/include/thrust/system/detail/generic/unique_by_key.inl",
-        "cuda/include/thrust/system/detail/generic/sequence.h",
-        "cuda/include/thrust/system/detail/generic/type_traits.h",
-        "cuda/include/thrust/system/detail/generic/merge.h",
-        "cuda/include/thrust/system/detail/generic/reverse.inl",
-        "cuda/include/thrust/system/detail/generic/tabulate.inl",
-        "cuda/include/thrust/system/detail/generic/unique.inl",
-        "cuda/include/thrust/system/detail/generic/scatter.inl",
-        "cuda/include/thrust/system/detail/generic/set_operations.inl",
-        "cuda/include/thrust/system/detail/generic/copy_if.inl",
-        "cuda/include/thrust/system/detail/generic/transform_reduce.h",
-        "cuda/include/thrust/system/detail/generic/transform_scan.inl",
-        "cuda/include/thrust/system/detail/generic/gather.h",
-        "cuda/include/thrust/system/detail/generic/reduce_by_key.inl",
-        "cuda/include/thrust/system/detail/generic/transform_reduce.inl",
-        "cuda/include/thrust/system/detail/generic/sort.h",
-        "cuda/include/thrust/system/detail/generic/distance.inl",
-        "cuda/include/thrust/system/detail/generic/scan.h",
-        "cuda/include/thrust/system/detail/generic/temporary_buffer.h",
-        "cuda/include/thrust/system/detail/generic/reduce.inl",
-        "cuda/include/thrust/system/detail/generic/scan_by_key.h",
-        "cuda/include/thrust/system/detail/generic/reverse.h",
-        "cuda/include/thrust/system/detail/generic/temporary_buffer.inl",
-        "cuda/include/thrust/system/detail/generic/scatter.h",
-        "cuda/include/thrust/system/detail/generic/generate.inl",
         "cuda/include/thrust/system/detail/generic/adjacent_difference.inl",
-        "cuda/include/thrust/system/detail/generic/remove.inl",
         "cuda/include/thrust/system/detail/generic/advance.h",
-        "cuda/include/thrust/system/detail/generic/find.h",
-        "cuda/include/thrust/system/detail/generic/merge.inl",
-        "cuda/include/thrust/system/detail/generic/scalar/binary_search.inl",
-        "cuda/include/thrust/system/detail/generic/scalar/binary_search.h",
-        "cuda/include/thrust/system/detail/generic/extrema.inl",
-        "cuda/include/thrust/system/detail/generic/generate.h",
-        "cuda/include/thrust/system/detail/generic/uninitialized_fill.h",
+        "cuda/include/thrust/system/detail/generic/advance.inl",
+        "cuda/include/thrust/system/detail/generic/binary_search.h",
+        "cuda/include/thrust/system/detail/generic/binary_search.inl",
+        "cuda/include/thrust/system/detail/generic/copy.h",
+        "cuda/include/thrust/system/detail/generic/copy.inl",
+        "cuda/include/thrust/system/detail/generic/copy_if.h",
+        "cuda/include/thrust/system/detail/generic/copy_if.inl",
+        "cuda/include/thrust/system/detail/generic/count.h",
         "cuda/include/thrust/system/detail/generic/count.inl",
-        "cuda/include/thrust/system/detail/generic/remove.h",
-        "cuda/include/thrust/system/detail/generic/uninitialized_copy.inl",
-        "cuda/include/thrust/system/detail/generic/tabulate.h",
-        "cuda/include/thrust/system/detail/generic/for_each.h",
         "cuda/include/thrust/system/detail/generic/distance.h",
-        "cuda/include/thrust/system/detail/generic/swap_ranges.inl",
-        "cuda/include/thrust/system/detail/generic/reduce_by_key.h",
-        "cuda/include/thrust/system/detail/generic/reduce.h",
+        "cuda/include/thrust/system/detail/generic/distance.inl",
         "cuda/include/thrust/system/detail/generic/equal.h",
-        "cuda/include/thrust/system/detail/generic/mismatch.inl",
-        "cuda/include/thrust/system/detail/generic/copy.inl",
-        "cuda/include/thrust/system/detail/generic/copy.h",
-        "cuda/include/thrust/system/detail/generic/swap_ranges.h",
-        "cuda/include/thrust/system/detail/generic/uninitialized_copy.h",
-        "cuda/include/thrust/system/detail/generic/binary_search.h",
-        "cuda/include/thrust/system/detail/generic/set_operations.h",
-        "cuda/include/thrust/system/detail/generic/uninitialized_fill.inl",
-        "cuda/include/thrust/system/detail/generic/mismatch.h",
-        "cuda/include/thrust/system/detail/generic/scan.inl",
-        "cuda/include/thrust/system/detail/generic/gather.inl",
+        "cuda/include/thrust/system/detail/generic/equal.inl",
         "cuda/include/thrust/system/detail/generic/extrema.h",
-        "cuda/include/thrust/system/detail/generic/count.h",
-        "cuda/include/thrust/system/detail/generic/replace.h",
+        "cuda/include/thrust/system/detail/generic/extrema.inl",
+        "cuda/include/thrust/system/detail/generic/fill.h",
+        "cuda/include/thrust/system/detail/generic/find.h",
+        "cuda/include/thrust/system/detail/generic/find.inl",
+        "cuda/include/thrust/system/detail/generic/for_each.h",
+        "cuda/include/thrust/system/detail/generic/gather.h",
+        "cuda/include/thrust/system/detail/generic/gather.inl",
+        "cuda/include/thrust/system/detail/generic/generate.h",
+        "cuda/include/thrust/system/detail/generic/generate.inl",
         "cuda/include/thrust/system/detail/generic/inner_product.h",
-        "cuda/include/thrust/system/detail/generic/copy_if.h",
+        "cuda/include/thrust/system/detail/generic/inner_product.inl",
         "cuda/include/thrust/system/detail/generic/logical.h",
-        "cuda/include/thrust/system/detail/generic/partition.inl",
         "cuda/include/thrust/system/detail/generic/memory.h",
-        "cuda/include/thrust/system/detail/generic/find.inl",
+        "cuda/include/thrust/system/detail/generic/memory.inl",
+        "cuda/include/thrust/system/detail/generic/merge.h",
+        "cuda/include/thrust/system/detail/generic/merge.inl",
+        "cuda/include/thrust/system/detail/generic/mismatch.h",
+        "cuda/include/thrust/system/detail/generic/mismatch.inl",
+        "cuda/include/thrust/system/detail/generic/partition.h",
+        "cuda/include/thrust/system/detail/generic/partition.inl",
+        "cuda/include/thrust/system/detail/generic/reduce.h",
+        "cuda/include/thrust/system/detail/generic/reduce.inl",
+        "cuda/include/thrust/system/detail/generic/reduce_by_key.h",
+        "cuda/include/thrust/system/detail/generic/reduce_by_key.inl",
+        "cuda/include/thrust/system/detail/generic/remove.h",
+        "cuda/include/thrust/system/detail/generic/remove.inl",
+        "cuda/include/thrust/system/detail/generic/replace.h",
         "cuda/include/thrust/system/detail/generic/replace.inl",
-        "cuda/include/thrust/system/detail/generic/advance.inl",
-        "cuda/include/thrust/system/detail/generic/fill.h",
+        "cuda/include/thrust/system/detail/generic/reverse.h",
+        "cuda/include/thrust/system/detail/generic/reverse.inl",
+        "cuda/include/thrust/system/detail/generic/scalar/binary_search.h",
+        "cuda/include/thrust/system/detail/generic/scalar/binary_search.inl",
+        "cuda/include/thrust/system/detail/generic/scan.h",
+        "cuda/include/thrust/system/detail/generic/scan.inl",
+        "cuda/include/thrust/system/detail/generic/scan_by_key.h",
+        "cuda/include/thrust/system/detail/generic/scan_by_key.inl",
+        "cuda/include/thrust/system/detail/generic/scatter.h",
+        "cuda/include/thrust/system/detail/generic/scatter.inl",
+        "cuda/include/thrust/system/detail/generic/select_system.h",
+        "cuda/include/thrust/system/detail/generic/sequence.h",
+        "cuda/include/thrust/system/detail/generic/sequence.inl",
+        "cuda/include/thrust/system/detail/generic/set_operations.h",
+        "cuda/include/thrust/system/detail/generic/set_operations.inl",
+        "cuda/include/thrust/system/detail/generic/sort.h",
+        "cuda/include/thrust/system/detail/generic/sort.inl",
+        "cuda/include/thrust/system/detail/generic/swap_ranges.h",
+        "cuda/include/thrust/system/detail/generic/swap_ranges.inl",
+        "cuda/include/thrust/system/detail/generic/tabulate.h",
+        "cuda/include/thrust/system/detail/generic/tabulate.inl",
+        "cuda/include/thrust/system/detail/generic/tag.h",
+        "cuda/include/thrust/system/detail/generic/temporary_buffer.h",
+        "cuda/include/thrust/system/detail/generic/temporary_buffer.inl",
         "cuda/include/thrust/system/detail/generic/transform.h",
+        "cuda/include/thrust/system/detail/generic/transform.inl",
+        "cuda/include/thrust/system/detail/generic/transform_reduce.h",
+        "cuda/include/thrust/system/detail/generic/transform_reduce.inl",
+        "cuda/include/thrust/system/detail/generic/transform_scan.h",
+        "cuda/include/thrust/system/detail/generic/transform_scan.inl",
+        "cuda/include/thrust/system/detail/generic/type_traits.h",
+        "cuda/include/thrust/system/detail/generic/uninitialized_copy.h",
+        "cuda/include/thrust/system/detail/generic/uninitialized_copy.inl",
+        "cuda/include/thrust/system/detail/generic/uninitialized_fill.h",
+        "cuda/include/thrust/system/detail/generic/uninitialized_fill.inl",
+        "cuda/include/thrust/system/detail/generic/unique.h",
+        "cuda/include/thrust/system/detail/generic/unique.inl",
+        "cuda/include/thrust/system/detail/generic/unique_by_key.h",
+        "cuda/include/thrust/system/detail/generic/unique_by_key.inl",
+        "cuda/include/thrust/system/detail/internal/decompose.h",
+        "cuda/include/thrust/system/detail/sequential/adjacent_difference.h",
+        "cuda/include/thrust/system/detail/sequential/assign_value.h",
+        "cuda/include/thrust/system/detail/sequential/binary_search.h",
+        "cuda/include/thrust/system/detail/sequential/copy.h",
+        "cuda/include/thrust/system/detail/sequential/copy.inl",
+        "cuda/include/thrust/system/detail/sequential/copy_backward.h",
+        "cuda/include/thrust/system/detail/sequential/copy_if.h",
+        "cuda/include/thrust/system/detail/sequential/count.h",
+        "cuda/include/thrust/system/detail/sequential/equal.h",
+        "cuda/include/thrust/system/detail/sequential/execution_policy.h",
+        "cuda/include/thrust/system/detail/sequential/extrema.h",
+        "cuda/include/thrust/system/detail/sequential/fill.h",
+        "cuda/include/thrust/system/detail/sequential/find.h",
+        "cuda/include/thrust/system/detail/sequential/for_each.h",
+        "cuda/include/thrust/system/detail/sequential/gather.h",
+        "cuda/include/thrust/system/detail/sequential/general_copy.h",
+        "cuda/include/thrust/system/detail/sequential/generate.h",
+        "cuda/include/thrust/system/detail/sequential/get_value.h",
+        "cuda/include/thrust/system/detail/sequential/inner_product.h",
+        "cuda/include/thrust/system/detail/sequential/insertion_sort.h",
+        "cuda/include/thrust/system/detail/sequential/iter_swap.h",
+        "cuda/include/thrust/system/detail/sequential/logical.h",
+        "cuda/include/thrust/system/detail/sequential/malloc_and_free.h",
+        "cuda/include/thrust/system/detail/sequential/merge.h",
+        "cuda/include/thrust/system/detail/sequential/merge.inl",
+        "cuda/include/thrust/system/detail/sequential/mismatch.h",
+        "cuda/include/thrust/system/detail/sequential/partition.h",
+        "cuda/include/thrust/system/detail/sequential/reduce.h",
+        "cuda/include/thrust/system/detail/sequential/reduce_by_key.h",
+        "cuda/include/thrust/system/detail/sequential/remove.h",
+        "cuda/include/thrust/system/detail/sequential/replace.h",
+        "cuda/include/thrust/system/detail/sequential/reverse.h",
+        "cuda/include/thrust/system/detail/sequential/scan.h",
+        "cuda/include/thrust/system/detail/sequential/scan_by_key.h",
+        "cuda/include/thrust/system/detail/sequential/scatter.h",
+        "cuda/include/thrust/system/detail/sequential/sequence.h",
+        "cuda/include/thrust/system/detail/sequential/set_operations.h",
+        "cuda/include/thrust/system/detail/sequential/sort.h",
+        "cuda/include/thrust/system/detail/sequential/sort.inl",
+        "cuda/include/thrust/system/detail/sequential/stable_merge_sort.h",
+        "cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl",
+        "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h",
+        "cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl",
+        "cuda/include/thrust/system/detail/sequential/stable_radix_sort.h",
+        "cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl",
+        "cuda/include/thrust/system/detail/sequential/swap_ranges.h",
+        "cuda/include/thrust/system/detail/sequential/tabulate.h",
+        "cuda/include/thrust/system/detail/sequential/temporary_buffer.h",
+        "cuda/include/thrust/system/detail/sequential/transform.h",
+        "cuda/include/thrust/system/detail/sequential/transform_reduce.h",
+        "cuda/include/thrust/system/detail/sequential/transform_scan.h",
+        "cuda/include/thrust/system/detail/sequential/trivial_copy.h",
+        "cuda/include/thrust/system/detail/sequential/uninitialized_copy.h",
+        "cuda/include/thrust/system/detail/sequential/uninitialized_fill.h",
+        "cuda/include/thrust/system/detail/sequential/unique.h",
+        "cuda/include/thrust/system/detail/sequential/unique_by_key.h",
         "cuda/include/thrust/system/detail/system_error.inl",
-        "cuda/include/thrust/system/omp/execution_policy.h",
-        "cuda/include/thrust/system/omp/vector.h",
-        "cuda/include/thrust/system/omp/detail/transform_scan.h",
-        "cuda/include/thrust/system/omp/detail/memory.inl",
-        "cuda/include/thrust/system/omp/detail/reduce_intervals.inl",
-        "cuda/include/thrust/system/omp/detail/unique_by_key.h",
-        "cuda/include/thrust/system/omp/detail/sort.inl",
-        "cuda/include/thrust/system/omp/detail/partition.h",
-        "cuda/include/thrust/system/omp/detail/unique.h",
-        "cuda/include/thrust/system/omp/detail/execution_policy.h",
+        "cuda/include/thrust/system/error_code.h",
         "cuda/include/thrust/system/omp/detail/adjacent_difference.h",
-        "cuda/include/thrust/system/omp/detail/unique_by_key.inl",
-        "cuda/include/thrust/system/omp/detail/sequence.h",
-        "cuda/include/thrust/system/omp/detail/merge.h",
-        "cuda/include/thrust/system/omp/detail/unique.inl",
+        "cuda/include/thrust/system/omp/detail/assign_value.h",
+        "cuda/include/thrust/system/omp/detail/binary_search.h",
+        "cuda/include/thrust/system/omp/detail/copy.h",
+        "cuda/include/thrust/system/omp/detail/copy.inl",
+        "cuda/include/thrust/system/omp/detail/copy_if.h",
         "cuda/include/thrust/system/omp/detail/copy_if.inl",
-        "cuda/include/thrust/system/omp/detail/transform_reduce.h",
-        "cuda/include/thrust/system/omp/detail/gather.h",
-        "cuda/include/thrust/system/omp/detail/reduce_by_key.inl",
-        "cuda/include/thrust/system/omp/detail/sort.h",
-        "cuda/include/thrust/system/omp/detail/scan.h",
-        "cuda/include/thrust/system/omp/detail/temporary_buffer.h",
+        "cuda/include/thrust/system/omp/detail/count.h",
         "cuda/include/thrust/system/omp/detail/default_decomposition.h",
-        "cuda/include/thrust/system/omp/detail/reduce.inl",
-        "cuda/include/thrust/system/omp/detail/scan_by_key.h",
-        "cuda/include/thrust/system/omp/detail/reverse.h",
-        "cuda/include/thrust/system/omp/detail/assign_value.h",
-        "cuda/include/thrust/system/omp/detail/scatter.h",
-        "cuda/include/thrust/system/omp/detail/for_each.inl",
         "cuda/include/thrust/system/omp/detail/default_decomposition.inl",
-        "cuda/include/thrust/system/omp/detail/remove.inl",
-        "cuda/include/thrust/system/omp/detail/vector.inl",
-        "cuda/include/thrust/system/omp/detail/find.h",
-        "cuda/include/thrust/system/omp/detail/generate.h",
-        "cuda/include/thrust/system/omp/detail/uninitialized_fill.h",
-        "cuda/include/thrust/system/omp/detail/remove.h",
-        "cuda/include/thrust/system/omp/detail/tabulate.h",
-        "cuda/include/thrust/system/omp/detail/for_each.h",
-        "cuda/include/thrust/system/omp/detail/reduce_by_key.h",
-        "cuda/include/thrust/system/omp/detail/reduce.h",
         "cuda/include/thrust/system/omp/detail/equal.h",
-        "cuda/include/thrust/system/omp/detail/copy.inl",
-        "cuda/include/thrust/system/omp/detail/copy.h",
-        "cuda/include/thrust/system/omp/detail/swap_ranges.h",
-        "cuda/include/thrust/system/omp/detail/uninitialized_copy.h",
-        "cuda/include/thrust/system/omp/detail/binary_search.h",
-        "cuda/include/thrust/system/omp/detail/set_operations.h",
-        "cuda/include/thrust/system/omp/detail/mismatch.h",
+        "cuda/include/thrust/system/omp/detail/execution_policy.h",
         "cuda/include/thrust/system/omp/detail/extrema.h",
-        "cuda/include/thrust/system/omp/detail/count.h",
-        "cuda/include/thrust/system/omp/detail/replace.h",
+        "cuda/include/thrust/system/omp/detail/fill.h",
+        "cuda/include/thrust/system/omp/detail/find.h",
+        "cuda/include/thrust/system/omp/detail/for_each.h",
+        "cuda/include/thrust/system/omp/detail/for_each.inl",
+        "cuda/include/thrust/system/omp/detail/gather.h",
+        "cuda/include/thrust/system/omp/detail/generate.h",
         "cuda/include/thrust/system/omp/detail/get_value.h",
         "cuda/include/thrust/system/omp/detail/inner_product.h",
-        "cuda/include/thrust/system/omp/detail/copy_if.h",
-        "cuda/include/thrust/system/omp/detail/logical.h",
-        "cuda/include/thrust/system/omp/detail/partition.inl",
         "cuda/include/thrust/system/omp/detail/iter_swap.h",
+        "cuda/include/thrust/system/omp/detail/logical.h",
+        "cuda/include/thrust/system/omp/detail/malloc_and_free.h",
+        "cuda/include/thrust/system/omp/detail/memory.inl",
+        "cuda/include/thrust/system/omp/detail/merge.h",
+        "cuda/include/thrust/system/omp/detail/mismatch.h",
         "cuda/include/thrust/system/omp/detail/par.h",
+        "cuda/include/thrust/system/omp/detail/partition.h",
+        "cuda/include/thrust/system/omp/detail/partition.inl",
+        "cuda/include/thrust/system/omp/detail/reduce.h",
+        "cuda/include/thrust/system/omp/detail/reduce.inl",
+        "cuda/include/thrust/system/omp/detail/reduce_by_key.h",
+        "cuda/include/thrust/system/omp/detail/reduce_by_key.inl",
         "cuda/include/thrust/system/omp/detail/reduce_intervals.h",
-        "cuda/include/thrust/system/omp/detail/malloc_and_free.h",
-        "cuda/include/thrust/system/omp/detail/fill.h",
+        "cuda/include/thrust/system/omp/detail/reduce_intervals.inl",
+        "cuda/include/thrust/system/omp/detail/remove.h",
+        "cuda/include/thrust/system/omp/detail/remove.inl",
+        "cuda/include/thrust/system/omp/detail/replace.h",
+        "cuda/include/thrust/system/omp/detail/reverse.h",
+        "cuda/include/thrust/system/omp/detail/scan.h",
+        "cuda/include/thrust/system/omp/detail/scan_by_key.h",
+        "cuda/include/thrust/system/omp/detail/scatter.h",
+        "cuda/include/thrust/system/omp/detail/sequence.h",
+        "cuda/include/thrust/system/omp/detail/set_operations.h",
+        "cuda/include/thrust/system/omp/detail/sort.h",
+        "cuda/include/thrust/system/omp/detail/sort.inl",
+        "cuda/include/thrust/system/omp/detail/swap_ranges.h",
+        "cuda/include/thrust/system/omp/detail/tabulate.h",
+        "cuda/include/thrust/system/omp/detail/temporary_buffer.h",
         "cuda/include/thrust/system/omp/detail/transform.h",
-        "cuda/include/thrust/system/omp/memory.h",
-        "cuda/include/thrust/system/tbb/execution_policy.h",
-        "cuda/include/thrust/system/tbb/vector.h",
-        "cuda/include/thrust/system/tbb/detail/transform_scan.h",
-        "cuda/include/thrust/system/tbb/detail/memory.inl",
-        "cuda/include/thrust/system/tbb/detail/unique_by_key.h",
-        "cuda/include/thrust/system/tbb/detail/sort.inl",
-        "cuda/include/thrust/system/tbb/detail/partition.h",
-        "cuda/include/thrust/system/tbb/detail/unique.h",
-        "cuda/include/thrust/system/tbb/detail/execution_policy.h",
+        "cuda/include/thrust/system/omp/detail/transform_reduce.h",
+        "cuda/include/thrust/system/omp/detail/transform_scan.h",
+        "cuda/include/thrust/system/omp/detail/uninitialized_copy.h",
+        "cuda/include/thrust/system/omp/detail/uninitialized_fill.h",
+        "cuda/include/thrust/system/omp/detail/unique.h",
+        "cuda/include/thrust/system/omp/detail/unique.inl",
+        "cuda/include/thrust/system/omp/detail/unique_by_key.h",
+        "cuda/include/thrust/system/omp/detail/unique_by_key.inl",
+        "cuda/include/thrust/system/omp/detail/vector.inl",
+        "cuda/include/thrust/system/omp/execution_policy.h",
+        "cuda/include/thrust/system/omp/memory.h",
+        "cuda/include/thrust/system/omp/vector.h",
+        "cuda/include/thrust/system/system_error.h",
         "cuda/include/thrust/system/tbb/detail/adjacent_difference.h",
-        "cuda/include/thrust/system/tbb/detail/unique_by_key.inl",
-        "cuda/include/thrust/system/tbb/detail/sequence.h",
-        "cuda/include/thrust/system/tbb/detail/merge.h",
-        "cuda/include/thrust/system/tbb/detail/unique.inl",
-        "cuda/include/thrust/system/tbb/detail/copy_if.inl",
-        "cuda/include/thrust/system/tbb/detail/transform_reduce.h",
-        "cuda/include/thrust/system/tbb/detail/gather.h",
-        "cuda/include/thrust/system/tbb/detail/reduce_by_key.inl",
-        "cuda/include/thrust/system/tbb/detail/sort.h",
-        "cuda/include/thrust/system/tbb/detail/scan.h",
-        "cuda/include/thrust/system/tbb/detail/temporary_buffer.h",
-        "cuda/include/thrust/system/tbb/detail/reduce.inl",
-        "cuda/include/thrust/system/tbb/detail/scan_by_key.h",
-        "cuda/include/thrust/system/tbb/detail/reverse.h",
         "cuda/include/thrust/system/tbb/detail/assign_value.h",
-        "cuda/include/thrust/system/tbb/detail/scatter.h",
-        "cuda/include/thrust/system/tbb/detail/for_each.inl",
-        "cuda/include/thrust/system/tbb/detail/remove.inl",
-        "cuda/include/thrust/system/tbb/detail/vector.inl",
-        "cuda/include/thrust/system/tbb/detail/find.h",
-        "cuda/include/thrust/system/tbb/detail/merge.inl",
-        "cuda/include/thrust/system/tbb/detail/generate.h",
-        "cuda/include/thrust/system/tbb/detail/uninitialized_fill.h",
-        "cuda/include/thrust/system/tbb/detail/remove.h",
-        "cuda/include/thrust/system/tbb/detail/tabulate.h",
-        "cuda/include/thrust/system/tbb/detail/for_each.h",
-        "cuda/include/thrust/system/tbb/detail/reduce_by_key.h",
-        "cuda/include/thrust/system/tbb/detail/reduce.h",
-        "cuda/include/thrust/system/tbb/detail/equal.h",
-        "cuda/include/thrust/system/tbb/detail/copy.inl",
-        "cuda/include/thrust/system/tbb/detail/copy.h",
-        "cuda/include/thrust/system/tbb/detail/swap_ranges.h",
-        "cuda/include/thrust/system/tbb/detail/uninitialized_copy.h",
         "cuda/include/thrust/system/tbb/detail/binary_search.h",
-        "cuda/include/thrust/system/tbb/detail/set_operations.h",
-        "cuda/include/thrust/system/tbb/detail/mismatch.h",
-        "cuda/include/thrust/system/tbb/detail/scan.inl",
-        "cuda/include/thrust/system/tbb/detail/extrema.h",
+        "cuda/include/thrust/system/tbb/detail/copy.h",
+        "cuda/include/thrust/system/tbb/detail/copy.inl",
+        "cuda/include/thrust/system/tbb/detail/copy_if.h",
+        "cuda/include/thrust/system/tbb/detail/copy_if.inl",
         "cuda/include/thrust/system/tbb/detail/count.h",
-        "cuda/include/thrust/system/tbb/detail/replace.h",
+        "cuda/include/thrust/system/tbb/detail/equal.h",
+        "cuda/include/thrust/system/tbb/detail/execution_policy.h",
+        "cuda/include/thrust/system/tbb/detail/extrema.h",
+        "cuda/include/thrust/system/tbb/detail/fill.h",
+        "cuda/include/thrust/system/tbb/detail/find.h",
+        "cuda/include/thrust/system/tbb/detail/for_each.h",
+        "cuda/include/thrust/system/tbb/detail/for_each.inl",
+        "cuda/include/thrust/system/tbb/detail/gather.h",
+        "cuda/include/thrust/system/tbb/detail/generate.h",
         "cuda/include/thrust/system/tbb/detail/get_value.h",
         "cuda/include/thrust/system/tbb/detail/inner_product.h",
-        "cuda/include/thrust/system/tbb/detail/copy_if.h",
-        "cuda/include/thrust/system/tbb/detail/logical.h",
-        "cuda/include/thrust/system/tbb/detail/partition.inl",
         "cuda/include/thrust/system/tbb/detail/iter_swap.h",
+        "cuda/include/thrust/system/tbb/detail/logical.h",
+        "cuda/include/thrust/system/tbb/detail/malloc_and_free.h",
+        "cuda/include/thrust/system/tbb/detail/memory.inl",
+        "cuda/include/thrust/system/tbb/detail/merge.h",
+        "cuda/include/thrust/system/tbb/detail/merge.inl",
+        "cuda/include/thrust/system/tbb/detail/mismatch.h",
         "cuda/include/thrust/system/tbb/detail/par.h",
+        "cuda/include/thrust/system/tbb/detail/partition.h",
+        "cuda/include/thrust/system/tbb/detail/partition.inl",
+        "cuda/include/thrust/system/tbb/detail/reduce.h",
+        "cuda/include/thrust/system/tbb/detail/reduce.inl",
+        "cuda/include/thrust/system/tbb/detail/reduce_by_key.h",
+        "cuda/include/thrust/system/tbb/detail/reduce_by_key.inl",
         "cuda/include/thrust/system/tbb/detail/reduce_intervals.h",
-        "cuda/include/thrust/system/tbb/detail/malloc_and_free.h",
-        "cuda/include/thrust/system/tbb/detail/fill.h",
+        "cuda/include/thrust/system/tbb/detail/remove.h",
+        "cuda/include/thrust/system/tbb/detail/remove.inl",
+        "cuda/include/thrust/system/tbb/detail/replace.h",
+        "cuda/include/thrust/system/tbb/detail/reverse.h",
+        "cuda/include/thrust/system/tbb/detail/scan.h",
+        "cuda/include/thrust/system/tbb/detail/scan.inl",
+        "cuda/include/thrust/system/tbb/detail/scan_by_key.h",
+        "cuda/include/thrust/system/tbb/detail/scatter.h",
+        "cuda/include/thrust/system/tbb/detail/sequence.h",
+        "cuda/include/thrust/system/tbb/detail/set_operations.h",
+        "cuda/include/thrust/system/tbb/detail/sort.h",
+        "cuda/include/thrust/system/tbb/detail/sort.inl",
+        "cuda/include/thrust/system/tbb/detail/swap_ranges.h",
+        "cuda/include/thrust/system/tbb/detail/tabulate.h",
+        "cuda/include/thrust/system/tbb/detail/temporary_buffer.h",
         "cuda/include/thrust/system/tbb/detail/transform.h",
-        "cuda/include/thrust/system/tbb/memory.h",
-        "cuda/include/thrust/system/error_code.h",
-        "cuda/include/thrust/system/cpp/execution_policy.h",
-        "cuda/include/thrust/system/cpp/vector.h",
-        "cuda/include/thrust/system/cpp/detail/transform_scan.h",
-        "cuda/include/thrust/system/cpp/detail/memory.inl",
-        "cuda/include/thrust/system/cpp/detail/unique_by_key.h",
-        "cuda/include/thrust/system/cpp/detail/partition.h",
-        "cuda/include/thrust/system/cpp/detail/unique.h",
-        "cuda/include/thrust/system/cpp/detail/execution_policy.h",
-        "cuda/include/thrust/system/cpp/detail/adjacent_difference.h",
-        "cuda/include/thrust/system/cpp/detail/sequence.h",
-        "cuda/include/thrust/system/cpp/detail/merge.h",
-        "cuda/include/thrust/system/cpp/detail/transform_reduce.h",
-        "cuda/include/thrust/system/cpp/detail/gather.h",
-        "cuda/include/thrust/system/cpp/detail/sort.h",
-        "cuda/include/thrust/system/cpp/detail/scan.h",
-        "cuda/include/thrust/system/cpp/detail/temporary_buffer.h",
-        "cuda/include/thrust/system/cpp/detail/scan_by_key.h",
-        "cuda/include/thrust/system/cpp/detail/reverse.h",
-        "cuda/include/thrust/system/cpp/detail/assign_value.h",
-        "cuda/include/thrust/system/cpp/detail/scatter.h",
-        "cuda/include/thrust/system/cpp/detail/vector.inl",
-        "cuda/include/thrust/system/cpp/detail/find.h",
-        "cuda/include/thrust/system/cpp/detail/generate.h",
-        "cuda/include/thrust/system/cpp/detail/uninitialized_fill.h",
-        "cuda/include/thrust/system/cpp/detail/remove.h",
-        "cuda/include/thrust/system/cpp/detail/tabulate.h",
-        "cuda/include/thrust/system/cpp/detail/for_each.h",
-        "cuda/include/thrust/system/cpp/detail/reduce_by_key.h",
-        "cuda/include/thrust/system/cpp/detail/reduce.h",
-        "cuda/include/thrust/system/cpp/detail/equal.h",
-        "cuda/include/thrust/system/cpp/detail/copy.h",
-        "cuda/include/thrust/system/cpp/detail/swap_ranges.h",
-        "cuda/include/thrust/system/cpp/detail/uninitialized_copy.h",
-        "cuda/include/thrust/system/cpp/detail/binary_search.h",
-        "cuda/include/thrust/system/cpp/detail/set_operations.h",
-        "cuda/include/thrust/system/cpp/detail/mismatch.h",
-        "cuda/include/thrust/system/cpp/detail/extrema.h",
-        "cuda/include/thrust/system/cpp/detail/count.h",
-        "cuda/include/thrust/system/cpp/detail/replace.h",
-        "cuda/include/thrust/system/cpp/detail/get_value.h",
-        "cuda/include/thrust/system/cpp/detail/inner_product.h",
-        "cuda/include/thrust/system/cpp/detail/copy_if.h",
-        "cuda/include/thrust/system/cpp/detail/logical.h",
-        "cuda/include/thrust/system/cpp/detail/iter_swap.h",
-        "cuda/include/thrust/system/cpp/detail/par.h",
-        "cuda/include/thrust/system/cpp/detail/malloc_and_free.h",
-        "cuda/include/thrust/system/cpp/detail/fill.h",
-        "cuda/include/thrust/system/cpp/detail/transform.h",
-        "cuda/include/thrust/system/cpp/memory.h",
-        "cuda/include/thrust/system/cuda/execution_policy.h",
-        "cuda/include/thrust/system/cuda/vector.h",
-        "cuda/include/thrust/system/cuda/error.h",
-        "cuda/include/thrust/system/cuda/detail/copy_device_to_device.h",
-        "cuda/include/thrust/system/cuda/detail/transform_scan.h",
-        "cuda/include/thrust/system/cuda/detail/memory.inl",
-        "cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/util_device.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/util_type.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/cub.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh",
-        "cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh",
-        "cuda/include/thrust/system/cuda/detail/reduce_intervals.inl",
-        "cuda/include/thrust/system/cuda/detail/copy_cross_system.inl",
-        "cuda/include/thrust/system/cuda/detail/unique_by_key.h",
-        "cuda/include/thrust/system/cuda/detail/bulk.h",
-        "cuda/include/thrust/system/cuda/detail/sort.inl",
-        "cuda/include/thrust/system/cuda/detail/partition.h",
-        "cuda/include/thrust/system/cuda/detail/unique.h",
-        "cuda/include/thrust/system/cuda/detail/execution_policy.h",
-        "cuda/include/thrust/system/cuda/detail/cuda_launch_config.h",
-        "cuda/include/thrust/system/cuda/detail/cub.h",
-        "cuda/include/thrust/system/cuda/detail/adjacent_difference.h",
-        "cuda/include/thrust/system/cuda/detail/sequence.h",
-        "cuda/include/thrust/system/cuda/detail/merge.h",
-        "cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl",
-        "cuda/include/thrust/system/cuda/detail/copy_if.inl",
-        "cuda/include/thrust/system/cuda/detail/transform_reduce.h",
-        "cuda/include/thrust/system/cuda/detail/error.inl",
-        "cuda/include/thrust/system/cuda/detail/gather.h",
-        "cuda/include/thrust/system/cuda/detail/reduce_by_key.inl",
-        "cuda/include/thrust/system/cuda/detail/sort.h",
-        "cuda/include/thrust/system/cuda/detail/synchronize.h",
-        "cuda/include/thrust/system/cuda/detail/scan.h",
-        "cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h",
-        "cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h",
-        "cuda/include/thrust/system/cuda/detail/detail/set_operation.inl",
-        "cuda/include/thrust/system/cuda/detail/detail/balanced_path.h",
-        "cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h",
-        "cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h",
-        "cuda/include/thrust/system/cuda/detail/detail/set_operation.h",
-        "cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl",
-        "cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h",
-        "cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl",
-        "cuda/include/thrust/system/cuda/detail/detail/merge.h",
-        "cuda/include/thrust/system/cuda/detail/detail/alignment.h",
-        "cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl",
-        "cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h",
-        "cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl",
-        "cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl",
-        "cuda/include/thrust/system/cuda/detail/detail/launch_closure.h",
-        "cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h",
-        "cuda/include/thrust/system/cuda/detail/detail/uninitialized.h",
-        "cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h",
-        "cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h",
-        "cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl",
-        "cuda/include/thrust/system/cuda/detail/temporary_buffer.h",
-        "cuda/include/thrust/system/cuda/detail/default_decomposition.h",
-        "cuda/include/thrust/system/cuda/detail/reduce.inl",
-        "cuda/include/thrust/system/cuda/detail/scan_by_key.h",
-        "cuda/include/thrust/system/cuda/detail/reverse.h",
-        "cuda/include/thrust/system/cuda/detail/assign_value.h",
-        "cuda/include/thrust/system/cuda/detail/scatter.h",
-        "cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp",
-        "cuda/include/thrust/system/cuda/detail/for_each.inl",
-        "cuda/include/thrust/system/cuda/detail/default_decomposition.inl",
-        "cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h",
-        "cuda/include/thrust/system/cuda/detail/adjacent_difference.inl",
-        "cuda/include/thrust/system/cuda/detail/vector.inl",
-        "cuda/include/thrust/system/cuda/detail/throw_on_error.h",
-        "cuda/include/thrust/system/cuda/detail/find.h",
-        "cuda/include/thrust/system/cuda/detail/terminate.h",
-        "cuda/include/thrust/system/cuda/detail/merge.inl",
-        "cuda/include/thrust/system/cuda/detail/trivial_copy.inl",
-        "cuda/include/thrust/system/cuda/detail/generate.h",
-        "cuda/include/thrust/system/cuda/detail/execute_on_stream.h",
-        "cuda/include/thrust/system/cuda/detail/uninitialized_fill.h",
-        "cuda/include/thrust/system/cuda/detail/remove.h",
-        "cuda/include/thrust/system/cuda/detail/tabulate.h",
-        "cuda/include/thrust/system/cuda/detail/for_each.h",
-        "cuda/include/thrust/system/cuda/detail/reduce_by_key.h",
-        "cuda/include/thrust/system/cuda/detail/decomposition.h",
-        "cuda/include/thrust/system/cuda/detail/reduce.h",
-        "cuda/include/thrust/system/cuda/detail/equal.h",
-        "cuda/include/thrust/system/cuda/detail/runtime_introspection.h",
-        "cuda/include/thrust/system/cuda/detail/copy.inl",
-        "cuda/include/thrust/system/cuda/detail/copy.h",
-        "cuda/include/thrust/system/cuda/detail/swap_ranges.h",
-        "cuda/include/thrust/system/cuda/detail/uninitialized_copy.h",
-        "cuda/include/thrust/system/cuda/detail/binary_search.h",
-        "cuda/include/thrust/system/cuda/detail/runtime_introspection.inl",
-        "cuda/include/thrust/system/cuda/detail/set_operations.h",
-        "cuda/include/thrust/system/cuda/detail/mismatch.h",
-        "cuda/include/thrust/system/cuda/detail/scan.inl",
-        "cuda/include/thrust/system/cuda/detail/synchronize.inl",
-        "cuda/include/thrust/system/cuda/detail/extrema.h",
-        "cuda/include/thrust/system/cuda/detail/set_union.inl",
-        "cuda/include/thrust/system/cuda/detail/set_intersection.inl",
-        "cuda/include/thrust/system/cuda/detail/count.h",
-        "cuda/include/thrust/system/cuda/detail/trivial_copy.h",
-        "cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl",
-        "cuda/include/thrust/system/cuda/detail/replace.h",
-        "cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/async.hpp",
-        "cuda/include/thrust/system/cuda/detail/bulk/future.hpp",
-        "cuda/include/thrust/system/cuda/detail/guarded_driver_types.h",
-        "cuda/include/thrust/system/cuda/detail/get_value.h",
-        "cuda/include/thrust/system/cuda/detail/inner_product.h",
-        "cuda/include/thrust/system/cuda/detail/copy_if.h",
-        "cuda/include/thrust/system/cuda/detail/logical.h",
-        "cuda/include/thrust/system/cuda/detail/iter_swap.h",
-        "cuda/include/thrust/system/cuda/detail/block/merge.h",
-        "cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h",
-        "cuda/include/thrust/system/cuda/detail/block/merge.inl",
-        "cuda/include/thrust/system/cuda/detail/block/merging_sort.h",
-        "cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h",
-        "cuda/include/thrust/system/cuda/detail/block/reduce.h",
-        "cuda/include/thrust/system/cuda/detail/block/copy.h",
-        "cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h",
-        "cuda/include/thrust/system/cuda/detail/par.h",
-        "cuda/include/thrust/system/cuda/detail/copy_cross_system.h",
-        "cuda/include/thrust/system/cuda/detail/reduce_intervals.h",
-        "cuda/include/thrust/system/cuda/detail/malloc_and_free.h",
-        "cuda/include/thrust/system/cuda/detail/fill.h",
-        "cuda/include/thrust/system/cuda/detail/set_difference.inl",
-        "cuda/include/thrust/system/cuda/detail/transform.h",
-        "cuda/include/thrust/system/cuda/experimental/pinned_allocator.h",
-        "cuda/include/thrust/system/cuda/memory.h",
-        "cuda/include/thrust/remove.h",
+        "cuda/include/thrust/system/tbb/detail/transform_reduce.h",
+        "cuda/include/thrust/system/tbb/detail/transform_scan.h",
+        "cuda/include/thrust/system/tbb/detail/uninitialized_copy.h",
+        "cuda/include/thrust/system/tbb/detail/uninitialized_fill.h",
+        "cuda/include/thrust/system/tbb/detail/unique.h",
+        "cuda/include/thrust/system/tbb/detail/unique.inl",
+        "cuda/include/thrust/system/tbb/detail/unique_by_key.h",
+        "cuda/include/thrust/system/tbb/detail/unique_by_key.inl",
+        "cuda/include/thrust/system/tbb/detail/vector.inl",
+        "cuda/include/thrust/system/tbb/execution_policy.h",
+        "cuda/include/thrust/system/tbb/memory.h",
+        "cuda/include/thrust/system/tbb/vector.h",
+        "cuda/include/thrust/system_error.h",
         "cuda/include/thrust/tabulate.h",
-        "cuda/include/thrust/for_each.h",
-        "cuda/include/thrust/distance.h",
-        "cuda/include/thrust/reduce.h",
-        "cuda/include/thrust/equal.h",
-        "cuda/include/thrust/complex.h",
-        "cuda/include/thrust/device_allocator.h",
-        "cuda/include/thrust/copy.h",
+        "cuda/include/thrust/transform.h",
+        "cuda/include/thrust/transform_reduce.h",
+        "cuda/include/thrust/transform_scan.h",
+        "cuda/include/thrust/tuple.h",
         "cuda/include/thrust/uninitialized_copy.h",
-        "cuda/include/thrust/device_reference.h",
-        "cuda/include/thrust/binary_search.h",
-        "cuda/include/thrust/set_operations.h",
-        "cuda/include/thrust/swap.h",
-        "cuda/include/thrust/mismatch.h",
-        "cuda/include/thrust/extrema.h",
-        "cuda/include/thrust/count.h",
-        "cuda/include/thrust/device_free.h",
-        "cuda/include/thrust/random/discard_block_engine.h",
-        "cuda/include/thrust/random/normal_distribution.h",
-        "cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h",
-        "cuda/include/thrust/random/detail/subtract_with_carry_engine.inl",
-        "cuda/include/thrust/random/detail/xor_combine_engine_max.h",
-        "cuda/include/thrust/random/detail/linear_congruential_engine_discard.h",
-        "cuda/include/thrust/random/detail/uniform_int_distribution.inl",
-        "cuda/include/thrust/random/detail/discard_block_engine.inl",
-        "cuda/include/thrust/random/detail/uniform_real_distribution.inl",
-        "cuda/include/thrust/random/detail/random_core_access.h",
-        "cuda/include/thrust/random/detail/mod.h",
-        "cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl",
-        "cuda/include/thrust/random/detail/linear_congruential_engine.inl",
-        "cuda/include/thrust/random/detail/xor_combine_engine.inl",
-        "cuda/include/thrust/random/detail/normal_distribution.inl",
-        "cuda/include/thrust/random/detail/normal_distribution_base.h",
-        "cuda/include/thrust/random/uniform_int_distribution.h",
-        "cuda/include/thrust/random/linear_feedback_shift_engine.h",
-        "cuda/include/thrust/random/xor_combine_engine.h",
-        "cuda/include/thrust/random/subtract_with_carry_engine.h",
-        "cuda/include/thrust/random/linear_congruential_engine.h",
-        "cuda/include/thrust/random/uniform_real_distribution.h",
-        "cuda/include/thrust/functional.h",
-        "cuda/include/thrust/replace.h",
-        "cuda/include/thrust/device_new_allocator.h",
-        "cuda/include/thrust/host_vector.h",
+        "cuda/include/thrust/uninitialized_fill.h",
+        "cuda/include/thrust/unique.h",
         "cuda/include/thrust/version.h",
-        "cuda/include/thrust/inner_product.h",
-        "cuda/include/thrust/iterator/iterator_traits.h",
-        "cuda/include/thrust/iterator/discard_iterator.h",
-        "cuda/include/thrust/iterator/retag.h",
-        "cuda/include/thrust/iterator/permutation_iterator.h",
-        "cuda/include/thrust/iterator/transform_iterator.h",
-        "cuda/include/thrust/iterator/detail/reverse_iterator.inl",
-        "cuda/include/thrust/iterator/detail/zip_iterator.inl",
-        "cuda/include/thrust/iterator/detail/counting_iterator.inl",
-        "cuda/include/thrust/iterator/detail/distance_from_result.h",
-        "cuda/include/thrust/iterator/detail/host_system_tag.h",
-        "cuda/include/thrust/iterator/detail/iterator_traversal_tags.h",
-        "cuda/include/thrust/iterator/detail/retag.h",
-        "cuda/include/thrust/iterator/detail/tagged_iterator.h",
-        "cuda/include/thrust/iterator/detail/iterator_traits.inl",
-        "cuda/include/thrust/iterator/detail/minimum_category.h",
-        "cuda/include/thrust/iterator/detail/discard_iterator_base.h",
-        "cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h",
-        "cuda/include/thrust/iterator/detail/zip_iterator_base.h",
-        "cuda/include/thrust/iterator/detail/normal_iterator.h",
-        "cuda/include/thrust/iterator/detail/join_iterator.h",
-        "cuda/include/thrust/iterator/detail/device_system_tag.h",
-        "cuda/include/thrust/iterator/detail/universal_categories.h",
-        "cuda/include/thrust/iterator/detail/reverse_iterator_base.h",
-        "cuda/include/thrust/iterator/detail/minimum_system.h",
-        "cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h",
-        "cuda/include/thrust/iterator/detail/is_iterator_category.h",
-        "cuda/include/thrust/iterator/detail/permutation_iterator_base.h",
-        "cuda/include/thrust/iterator/detail/any_assign.h",
-        "cuda/include/thrust/iterator/detail/any_system_tag.h",
-        "cuda/include/thrust/iterator/detail/is_trivial_iterator.h",
-        "cuda/include/thrust/iterator/detail/iterator_category_to_system.h",
-        "cuda/include/thrust/iterator/detail/iterator_adaptor_base.h",
-        "cuda/include/thrust/iterator/detail/constant_iterator_base.h",
-        "cuda/include/thrust/iterator/detail/transform_iterator.inl",
-        "cuda/include/thrust/iterator/detail/iterator_facade_category.h",
-        "cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h",
-        "cuda/include/thrust/iterator/constant_iterator.h",
-        "cuda/include/thrust/iterator/counting_iterator.h",
-        "cuda/include/thrust/iterator/iterator_adaptor.h",
-        "cuda/include/thrust/iterator/iterator_facade.h",
-        "cuda/include/thrust/iterator/iterator_categories.h",
-        "cuda/include/thrust/iterator/reverse_iterator.h",
-        "cuda/include/thrust/iterator/zip_iterator.h",
-        "cuda/include/thrust/logical.h",
-        "cuda/include/thrust/tuple.h",
-        "cuda/include/thrust/memory.h",
-        "cuda/include/thrust/random.h",
-        "cuda/include/thrust/fill.h",
-        "cuda/include/thrust/transform.h",
-        "cuda/include/texture_types.h",
-        "cuda/include/nppversion.h",
-        "cuda/include/cuda_texture_types.h",
-        "cuda/include/fatbinary.h",
-        "cuda/include/cublasXt.h",
-        "cuda/include/cuda_fp16.h",
         "cuda/include/vector_functions.h",
-        "cuda/include/cusparse.h",
-        "cuda/include/nppi_filtering_functions.h",
-        "cuda/include/nppi_morphological_operations.h",
-        "cuda/include/sobol_direction_vectors.h",
-        "cuda/include/nvblas.h",
-        "cuda/include/curand_mtgp32dc_p_11213.h",
-        "cuda/include/nvcuvid.h",
-        "cuda/include/cuda_runtime_api.h",
-        "cuda/include/curand_mtgp32_kernel.h",
-        "cuda/include/cublas_v2.h",
-        "cuda/include/builtin_types.h",
-        "cuda/include/nppi_geometry_transforms.h",
-        "cuda/include/npps_support_functions.h",
-        "cuda/include/cufftw.h",
-        "cuda/include/cuda_device_runtime_api.h",
-        "cuda/include/sm_30_intrinsics.hpp",
+        "cuda/include/vector_functions.hpp",
         "cuda/include/vector_types.h",
-        "cuda/include/sm_35_atomic_functions.h",
-        "cuda/include/sm_20_intrinsics.h",
-        "cuda/include/driver_types.h",
-        "cuda/include/nvToolsExtCudaRt.h",
-        "cuda/include/curand_globals.h",
-        "cuda/include/device_atomic_functions.h",
-        "cuda/include/surface_types.h",
-        "cuda/include/nvrtc.h",
-        "cuda/include/nppdefs.h",
-        "cuda/include/sm_60_atomic_functions.h",
-        "cuda/include/driver_functions.h",
-        "cuda/include/cusolver_common.h",
-        "cuda/include/cublas.h",
-        "cuda/include/curand_lognormal.h",
-        "cuda/include/device_atomic_functions.hpp",
-        "cuda/include/crt/device_runtime.h",
-        "cuda/include/crt/storage_class.h",
-        "cuda/include/crt/func_macro.h",
-        "cuda/include/crt/host_runtime.h",
-        "cuda/include/nppi_arithmetic_and_logical_operations.h",
-        "cuda/include/npps_arithmetic_and_logical_operations.h",
-        "cuda/include/nppi_computer_vision.h",
-        "cuda/include/surface_functions.hpp",
-        "cuda/include/surface_functions.h",
-        "cuda/include/curand_normal_static.h",
-        "cuda/include/curand.h",
-        "cuda/include/math_functions_dbl_ptx3.h",
-        "cuda/include/curand_philox4x32_x.h",
-        "cuda/include/nppi_threshold_and_compare_operations.h",
-        "cuda/include/nvml.h",
-        "cuda/include/npps.h",
-        "cuda/include/cuda_vdpau_interop.h",
-        "cuda/include/sm_61_intrinsics.hpp",
-        "cuda/include/cublas_api.h",
-        "cuda/include/nppi_color_conversion.h",
-        "cuda/include/math_functions_dbl_ptx3.hpp",
-        "cuda/include/nppcore.h",
-        "cuda/include/cudaGL.h",
-        "cuda/include/fatBinaryCtl.h",
-        "cuda/include/npps_statistics_functions.h",
-        "cuda/include/cudaVDPAU.h",
-        "cuda/include/curand_poisson.h",
-        "cuda/include/cusolverDn.h",
-        "cuda/include/cuda_profiler_api.h",
-        "cuda/include/sm_20_atomic_functions.h",
-        "cuda/include/nvfunctional",
     ],
     cmd = """
-if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-8.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-8.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-8.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-8.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-8.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-8.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-8.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-8.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-8.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-8.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-8.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-8.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-8.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-8.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-8.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-8.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-8.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-8.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-8.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-8.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-8.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-8.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-8.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-8.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-8.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-8.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-8.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-8.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-8.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-8.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-8.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-8.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-8.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-8.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-8.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-8.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-8.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-8.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-8.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-8.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-8.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-8.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-8.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-8.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-8.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/cuviddec.h" "$(@D)/cuda/include/cuviddec.h" && cp "/usr/local/cuda-8.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-8.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-8.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-8.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-8.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-8.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.h" "$(@D)/cuda/include/thrust/detail/reference.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reverse.inl" "$(@D)/cuda/include/thrust/detail/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reduce.inl" "$(@D)/cuda/include/thrust/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/remove.inl" "$(@D)/cuda/include/thrust/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference_forward_declaration.h" "$(@D)/cuda/include/thrust/detail/reference_forward_declaration.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/reference.inl" "$(@D)/cuda/include/thrust/detail/reference.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/replace.inl" "$(@D)/cuda/include/thrust/detail/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_pointer_cast.h" "$(@D)/cuda/include/thrust/detail/raw_pointer_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/raw_reference_cast.h" "$(@D)/cuda/include/thrust/detail/raw_reference_cast.h" && cp "/usr/local/cuda-8.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-8.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-8.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-8.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_rle_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_histogram_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_by_key_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_scan_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_select_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_reduce_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/device_radix_sort_dispatch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_histo.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_satomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/specializations/block_range_histo_gatomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_select.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_range/block_range_reduce_by_key.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_histogram_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_rle_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_select_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_satomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_sort_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/specializations/block_histogram_gatomic_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_reduce_by_key_sweep.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block_sweep/block_scan_prefix_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/spinlock.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shift.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk.h" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cuda_launch_config.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cuda_launch_config.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/cub.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cub.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_symmetric_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_symmetric_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.h" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_indirect_permutation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extern_shared_ptr.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extern_shared_ptr.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/balanced_path.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/balanced_path.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/virtualized_smem_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/set_operation.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/set_operation.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_primitive_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/alignment.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_merge_sort.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_closure.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_closure.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_radix_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/uninitialized.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/uninitialized.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/cached_temporary_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/launch_calculator.h" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/launch_calculator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/detail/stable_sort_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/default_decomposition.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/throw_on_error.h" "$(@D)/cuda/include/thrust/system/cuda/detail/throw_on_error.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/execute_on_stream.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execute_on_stream.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/decomposition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/decomposition.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.h" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/scan.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/synchronize.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/synchronize.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_union.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_union.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_intersection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_intersection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/trivial_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/trivial_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_device_to_device.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_device_to_device.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/malloc.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/malloc.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/closure.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tail_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/terminate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/alignment.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/guarded_cuda_runtime_api.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/choose_sizes.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_meta_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_task.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/head_flags.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/synchronize.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/throw_on_error.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/parameter_ptr.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/triple_chevron_launcher.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/cuda_launch_config.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/cuda_launcher/runtime_introspection.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/async.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/async.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/tuple_transform.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/pointer_traits.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/apply_from_tuple.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/detail/is_contiguous_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/choose_sizes.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/copy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/merge.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/accumulate.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scan.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/detail/stable_merge_sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/gather.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/sort.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/scatter.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/adjacent_difference.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/reduce_by_key.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/algorithm/for_each.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/bulk.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/bulk.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/execution_policy.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/iterator/strided_iterator.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/uninitialized.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/async.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/async.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/bulk/future.hpp" "$(@D)/cuda/include/thrust/system/cuda/detail/bulk/future.hpp" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/inclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/inclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merge.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merge.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/merging_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/merging_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/exclusive_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/exclusive_scan.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/block/odd_even_sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/block/odd_even_sort.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_cross_system.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_intervals.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/set_difference.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/set_difference.inl" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-8.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-8.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-8.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-8.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-8.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-8.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-8.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-8.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-8.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-8.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-8.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-8.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-8.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-8.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-8.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-8.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-8.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-8.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-8.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-8.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-8.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-8.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-8.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-8.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-8.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-8.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-8.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-8.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-8.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-8.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-8.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-8.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-8.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-8.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-8.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-8.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-8.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-8.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-8.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-8.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-8.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-8.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-8.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-8.0/include/nvcuvid.h" "$(@D)/cuda/include/nvcuvid.h" && cp "/usr/local/cuda-8.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-8.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-8.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-8.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-8.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-8.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-8.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-8.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-8.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h" && cp "/usr/local/cuda-8.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-8.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-8.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-8.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-8.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-8.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-8.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-8.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-8.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-8.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-8.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-8.0/include/crt/device_runtime.h" "$(@D)/cuda/include/crt/device_runtime.h" && cp "/usr/local/cuda-8.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-8.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-8.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-8.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-8.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-8.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-8.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-8.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-8.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-8.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-8.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-8.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-8.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-8.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-8.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-8.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-8.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-8.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-8.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-8.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-8.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-8.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-8.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-8.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-8.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-8.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-8.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-8.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional"
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/include/CL/cl.h" "$(@D)/cuda/include/CL/cl.h" && cp "/usr/local/cuda-9.0/include/CL/cl.hpp" "$(@D)/cuda/include/CL/cl.hpp" && cp "/usr/local/cuda-9.0/include/CL/cl_egl.h" "$(@D)/cuda/include/CL/cl_egl.h" && cp "/usr/local/cuda-9.0/include/CL/cl_ext.h" "$(@D)/cuda/include/CL/cl_ext.h" && cp "/usr/local/cuda-9.0/include/CL/cl_gl.h" "$(@D)/cuda/include/CL/cl_gl.h" && cp "/usr/local/cuda-9.0/include/CL/cl_gl_ext.h" "$(@D)/cuda/include/CL/cl_gl_ext.h" && cp "/usr/local/cuda-9.0/include/CL/cl_platform.h" "$(@D)/cuda/include/CL/cl_platform.h" && cp "/usr/local/cuda-9.0/include/CL/opencl.h" "$(@D)/cuda/include/CL/opencl.h" && cp "/usr/local/cuda-9.0/include/builtin_types.h" "$(@D)/cuda/include/builtin_types.h" && cp "/usr/local/cuda-9.0/include/channel_descriptor.h" "$(@D)/cuda/include/channel_descriptor.h" && cp "/usr/local/cuda-9.0/include/common_functions.h" "$(@D)/cuda/include/common_functions.h" && cp "/usr/local/cuda-9.0/include/cooperative_groups.h" "$(@D)/cuda/include/cooperative_groups.h" && cp "/usr/local/cuda-9.0/include/cooperative_groups_helpers.h" "$(@D)/cuda/include/cooperative_groups_helpers.h" && cp "/usr/local/cuda-9.0/include/crt/common_functions.h" "$(@D)/cuda/include/crt/common_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_double_functions.h" "$(@D)/cuda/include/crt/device_double_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_double_functions.hpp" "$(@D)/cuda/include/crt/device_double_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/device_functions.h" "$(@D)/cuda/include/crt/device_functions.h" && cp "/usr/local/cuda-9.0/include/crt/device_functions.hpp" "$(@D)/cuda/include/crt/device_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/func_macro.h" "$(@D)/cuda/include/crt/func_macro.h" && cp "/usr/local/cuda-9.0/include/crt/host_config.h" "$(@D)/cuda/include/crt/host_config.h" && cp "/usr/local/cuda-9.0/include/crt/host_defines.h" "$(@D)/cuda/include/crt/host_defines.h" && cp "/usr/local/cuda-9.0/include/crt/host_runtime.h" "$(@D)/cuda/include/crt/host_runtime.h" && cp "/usr/local/cuda-9.0/include/crt/math_functions.h" "$(@D)/cuda/include/crt/math_functions.h" && cp "/usr/local/cuda-9.0/include/crt/math_functions.hpp" "$(@D)/cuda/include/crt/math_functions.hpp" && cp "/usr/local/cuda-9.0/include/crt/mma.h" "$(@D)/cuda/include/crt/mma.h" && cp "/usr/local/cuda-9.0/include/crt/mma.hpp" "$(@D)/cuda/include/crt/mma.hpp" && cp "/usr/local/cuda-9.0/include/crt/nvfunctional" "$(@D)/cuda/include/crt/nvfunctional" && cp "/usr/local/cuda-9.0/include/crt/sm_70_rt.h" "$(@D)/cuda/include/crt/sm_70_rt.h" && cp "/usr/local/cuda-9.0/include/crt/sm_70_rt.hpp" "$(@D)/cuda/include/crt/sm_70_rt.hpp" && cp "/usr/local/cuda-9.0/include/crt/storage_class.h" "$(@D)/cuda/include/crt/storage_class.h" && cp "/usr/local/cuda-9.0/include/cuComplex.h" "$(@D)/cuda/include/cuComplex.h" && cp "/usr/local/cuda-9.0/include/cublas.h" "$(@D)/cuda/include/cublas.h" && cp "/usr/local/cuda-9.0/include/cublasXt.h" "$(@D)/cuda/include/cublasXt.h" && cp "/usr/local/cuda-9.0/include/cublas_api.h" "$(@D)/cuda/include/cublas_api.h" && cp "/usr/local/cuda-9.0/include/cublas_v2.h" "$(@D)/cuda/include/cublas_v2.h" && cp "/usr/local/cuda-9.0/include/cuda.h" "$(@D)/cuda/include/cuda.h" && cp "/usr/local/cuda-9.0/include/cudaEGL.h" "$(@D)/cuda/include/cudaEGL.h" && cp "/usr/local/cuda-9.0/include/cudaGL.h" "$(@D)/cuda/include/cudaGL.h" && cp "/usr/local/cuda-9.0/include/cudaProfiler.h" "$(@D)/cuda/include/cudaProfiler.h" && cp "/usr/local/cuda-9.0/include/cudaVDPAU.h" "$(@D)/cuda/include/cudaVDPAU.h" && cp "/usr/local/cuda-9.0/include/cuda_device_runtime_api.h" "$(@D)/cuda/include/cuda_device_runtime_api.h" && cp "/usr/local/cuda-9.0/include/cuda_fp16.h" "$(@D)/cuda/include/cuda_fp16.h" && cp "/usr/local/cuda-9.0/include/cuda_fp16.hpp" "$(@D)/cuda/include/cuda_fp16.hpp" && cp "/usr/local/cuda-9.0/include/cuda_gl_interop.h" "$(@D)/cuda/include/cuda_gl_interop.h" && cp "/usr/local/cuda-9.0/include/cuda_occupancy.h" "$(@D)/cuda/include/cuda_occupancy.h" && cp "/usr/local/cuda-9.0/include/cuda_profiler_api.h" "$(@D)/cuda/include/cuda_profiler_api.h" && cp "/usr/local/cuda-9.0/include/cuda_runtime.h" "$(@D)/cuda/include/cuda_runtime.h" && cp "/usr/local/cuda-9.0/include/cuda_runtime_api.h" "$(@D)/cuda/include/cuda_runtime_api.h" && cp "/usr/local/cuda-9.0/include/cuda_surface_types.h" "$(@D)/cuda/include/cuda_surface_types.h" && cp "/usr/local/cuda-9.0/include/cuda_texture_types.h" "$(@D)/cuda/include/cuda_texture_types.h" && cp "/usr/local/cuda-9.0/include/cuda_vdpau_interop.h" "$(@D)/cuda/include/cuda_vdpau_interop.h" && cp "/usr/local/cuda-9.0/include/cudalibxt.h" "$(@D)/cuda/include/cudalibxt.h" && cp "/usr/local/cuda-9.0/include/cudnn.h" "$(@D)/cuda/include/cudnn.h" && cp "/usr/local/cuda-9.0/include/cufft.h" "$(@D)/cuda/include/cufft.h" && cp "/usr/local/cuda-9.0/include/cufftXt.h" "$(@D)/cuda/include/cufftXt.h" && cp "/usr/local/cuda-9.0/include/cufftw.h" "$(@D)/cuda/include/cufftw.h" && cp "/usr/local/cuda-9.0/include/curand.h" "$(@D)/cuda/include/curand.h" && cp "/usr/local/cuda-9.0/include/curand_discrete.h" "$(@D)/cuda/include/curand_discrete.h" && cp "/usr/local/cuda-9.0/include/curand_discrete2.h" "$(@D)/cuda/include/curand_discrete2.h" && cp "/usr/local/cuda-9.0/include/curand_globals.h" "$(@D)/cuda/include/curand_globals.h" && cp "/usr/local/cuda-9.0/include/curand_kernel.h" "$(@D)/cuda/include/curand_kernel.h" && cp "/usr/local/cuda-9.0/include/curand_lognormal.h" "$(@D)/cuda/include/curand_lognormal.h" && cp "/usr/local/cuda-9.0/include/curand_mrg32k3a.h" "$(@D)/cuda/include/curand_mrg32k3a.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32.h" "$(@D)/cuda/include/curand_mtgp32.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32_host.h" "$(@D)/cuda/include/curand_mtgp32_host.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32_kernel.h" "$(@D)/cuda/include/curand_mtgp32_kernel.h" && cp "/usr/local/cuda-9.0/include/curand_mtgp32dc_p_11213.h" "$(@D)/cuda/include/curand_mtgp32dc_p_11213.h" && cp "/usr/local/cuda-9.0/include/curand_normal.h" "$(@D)/cuda/include/curand_normal.h" && cp "/usr/local/cuda-9.0/include/curand_normal_static.h" "$(@D)/cuda/include/curand_normal_static.h" && cp "/usr/local/cuda-9.0/include/curand_philox4x32_x.h" "$(@D)/cuda/include/curand_philox4x32_x.h" && cp "/usr/local/cuda-9.0/include/curand_poisson.h" "$(@D)/cuda/include/curand_poisson.h" && cp "/usr/local/cuda-9.0/include/curand_precalc.h" "$(@D)/cuda/include/curand_precalc.h" && cp "/usr/local/cuda-9.0/include/curand_uniform.h" "$(@D)/cuda/include/curand_uniform.h" && cp "/usr/local/cuda-9.0/include/cusolverDn.h" "$(@D)/cuda/include/cusolverDn.h" && cp "/usr/local/cuda-9.0/include/cusolverRf.h" "$(@D)/cuda/include/cusolverRf.h" && cp "/usr/local/cuda-9.0/include/cusolverSp.h" "$(@D)/cuda/include/cusolverSp.h" && cp "/usr/local/cuda-9.0/include/cusolverSp_LOWLEVEL_PREVIEW.h" "$(@D)/cuda/include/cusolverSp_LOWLEVEL_PREVIEW.h" && cp "/usr/local/cuda-9.0/include/cusolver_common.h" "$(@D)/cuda/include/cusolver_common.h" && cp "/usr/local/cuda-9.0/include/cusparse.h" "$(@D)/cuda/include/cusparse.h" && cp "/usr/local/cuda-9.0/include/cusparse_v2.h" "$(@D)/cuda/include/cusparse_v2.h" && cp "/usr/local/cuda-9.0/include/device_atomic_functions.h" "$(@D)/cuda/include/device_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/device_atomic_functions.hpp" "$(@D)/cuda/include/device_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_double_functions.h" "$(@D)/cuda/include/device_double_functions.h" && cp "/usr/local/cuda-9.0/include/device_double_functions.hpp" "$(@D)/cuda/include/device_double_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_functions.h" "$(@D)/cuda/include/device_functions.h" && cp "/usr/local/cuda-9.0/include/device_functions.hpp" "$(@D)/cuda/include/device_functions.hpp" && cp "/usr/local/cuda-9.0/include/device_functions_decls.h" "$(@D)/cuda/include/device_functions_decls.h" && cp "/usr/local/cuda-9.0/include/device_launch_parameters.h" "$(@D)/cuda/include/device_launch_parameters.h" && cp "/usr/local/cuda-9.0/include/device_types.h" "$(@D)/cuda/include/device_types.h" && cp "/usr/local/cuda-9.0/include/driver_functions.h" "$(@D)/cuda/include/driver_functions.h" && cp "/usr/local/cuda-9.0/include/driver_types.h" "$(@D)/cuda/include/driver_types.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuda.h" "$(@D)/cuda/include/dynlink_cuda.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuda_cuda.h" "$(@D)/cuda/include/dynlink_cuda_cuda.h" && cp "/usr/local/cuda-9.0/include/dynlink_cuviddec.h" "$(@D)/cuda/include/dynlink_cuviddec.h" && cp "/usr/local/cuda-9.0/include/dynlink_nvcuvid.h" "$(@D)/cuda/include/dynlink_nvcuvid.h" && cp "/usr/local/cuda-9.0/include/fatBinaryCtl.h" "$(@D)/cuda/include/fatBinaryCtl.h" && cp "/usr/local/cuda-9.0/include/fatbinary.h" "$(@D)/cuda/include/fatbinary.h" && cp "/usr/local/cuda-9.0/include/host_config.h" "$(@D)/cuda/include/host_config.h" && cp "/usr/local/cuda-9.0/include/host_defines.h" "$(@D)/cuda/include/host_defines.h" && cp "/usr/local/cuda-9.0/include/library_types.h" "$(@D)/cuda/include/library_types.h" && cp "/usr/local/cuda-9.0/include/math_constants.h" "$(@D)/cuda/include/math_constants.h" && cp "/usr/local/cuda-9.0/include/math_functions.h" "$(@D)/cuda/include/math_functions.h" && cp "/usr/local/cuda-9.0/include/math_functions.hpp" "$(@D)/cuda/include/math_functions.hpp" && cp "/usr/local/cuda-9.0/include/math_functions_dbl_ptx3.h" "$(@D)/cuda/include/math_functions_dbl_ptx3.h" && cp "/usr/local/cuda-9.0/include/math_functions_dbl_ptx3.hpp" "$(@D)/cuda/include/math_functions_dbl_ptx3.hpp" && cp "/usr/local/cuda-9.0/include/mma.h" "$(@D)/cuda/include/mma.h" && cp "/usr/local/cuda-9.0/include/npp.h" "$(@D)/cuda/include/npp.h" && cp "/usr/local/cuda-9.0/include/nppcore.h" "$(@D)/cuda/include/nppcore.h" && cp "/usr/local/cuda-9.0/include/nppdefs.h" "$(@D)/cuda/include/nppdefs.h" && cp "/usr/local/cuda-9.0/include/nppi.h" "$(@D)/cuda/include/nppi.h" && cp "/usr/local/cuda-9.0/include/nppi_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/nppi_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-9.0/include/nppi_color_conversion.h" "$(@D)/cuda/include/nppi_color_conversion.h" && cp "/usr/local/cuda-9.0/include/nppi_compression_functions.h" "$(@D)/cuda/include/nppi_compression_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_computer_vision.h" "$(@D)/cuda/include/nppi_computer_vision.h" && cp "/usr/local/cuda-9.0/include/nppi_data_exchange_and_initialization.h" "$(@D)/cuda/include/nppi_data_exchange_and_initialization.h" && cp "/usr/local/cuda-9.0/include/nppi_filtering_functions.h" "$(@D)/cuda/include/nppi_filtering_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_geometry_transforms.h" "$(@D)/cuda/include/nppi_geometry_transforms.h" && cp "/usr/local/cuda-9.0/include/nppi_linear_transforms.h" "$(@D)/cuda/include/nppi_linear_transforms.h" && cp "/usr/local/cuda-9.0/include/nppi_morphological_operations.h" "$(@D)/cuda/include/nppi_morphological_operations.h" && cp "/usr/local/cuda-9.0/include/nppi_statistics_functions.h" "$(@D)/cuda/include/nppi_statistics_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_support_functions.h" "$(@D)/cuda/include/nppi_support_functions.h" && cp "/usr/local/cuda-9.0/include/nppi_threshold_and_compare_operations.h" "$(@D)/cuda/include/nppi_threshold_and_compare_operations.h" && cp "/usr/local/cuda-9.0/include/npps.h" "$(@D)/cuda/include/npps.h" && cp "/usr/local/cuda-9.0/include/npps_arithmetic_and_logical_operations.h" "$(@D)/cuda/include/npps_arithmetic_and_logical_operations.h" && cp "/usr/local/cuda-9.0/include/npps_conversion_functions.h" "$(@D)/cuda/include/npps_conversion_functions.h" && cp "/usr/local/cuda-9.0/include/npps_filtering_functions.h" "$(@D)/cuda/include/npps_filtering_functions.h" && cp "/usr/local/cuda-9.0/include/npps_initialization.h" "$(@D)/cuda/include/npps_initialization.h" && cp "/usr/local/cuda-9.0/include/npps_statistics_functions.h" "$(@D)/cuda/include/npps_statistics_functions.h" && cp "/usr/local/cuda-9.0/include/npps_support_functions.h" "$(@D)/cuda/include/npps_support_functions.h" && cp "/usr/local/cuda-9.0/include/nppversion.h" "$(@D)/cuda/include/nppversion.h" && cp "/usr/local/cuda-9.0/include/nvToolsExt.h" "$(@D)/cuda/include/nvToolsExt.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtCuda.h" "$(@D)/cuda/include/nvToolsExtCuda.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtCudaRt.h" "$(@D)/cuda/include/nvToolsExtCudaRt.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtMeta.h" "$(@D)/cuda/include/nvToolsExtMeta.h" && cp "/usr/local/cuda-9.0/include/nvToolsExtSync.h" "$(@D)/cuda/include/nvToolsExtSync.h" && cp "/usr/local/cuda-9.0/include/nvblas.h" "$(@D)/cuda/include/nvblas.h" && cp "/usr/local/cuda-9.0/include/nvfunctional" "$(@D)/cuda/include/nvfunctional" && cp "/usr/local/cuda-9.0/include/nvgraph.h" "$(@D)/cuda/include/nvgraph.h" && cp "/usr/local/cuda-9.0/include/nvml.h" "$(@D)/cuda/include/nvml.h" && cp "/usr/local/cuda-9.0/include/nvrtc.h" "$(@D)/cuda/include/nvrtc.h" && cp "/usr/local/cuda-9.0/include/sm_20_atomic_functions.h" "$(@D)/cuda/include/sm_20_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_20_atomic_functions.hpp" "$(@D)/cuda/include/sm_20_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_20_intrinsics.h" "$(@D)/cuda/include/sm_20_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_20_intrinsics.hpp" "$(@D)/cuda/include/sm_20_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_30_intrinsics.h" "$(@D)/cuda/include/sm_30_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_30_intrinsics.hpp" "$(@D)/cuda/include/sm_30_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_32_atomic_functions.h" "$(@D)/cuda/include/sm_32_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_32_atomic_functions.hpp" "$(@D)/cuda/include/sm_32_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_32_intrinsics.h" "$(@D)/cuda/include/sm_32_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_32_intrinsics.hpp" "$(@D)/cuda/include/sm_32_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sm_35_atomic_functions.h" "$(@D)/cuda/include/sm_35_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_35_intrinsics.h" "$(@D)/cuda/include/sm_35_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_60_atomic_functions.h" "$(@D)/cuda/include/sm_60_atomic_functions.h" && cp "/usr/local/cuda-9.0/include/sm_60_atomic_functions.hpp" "$(@D)/cuda/include/sm_60_atomic_functions.hpp" && cp "/usr/local/cuda-9.0/include/sm_61_intrinsics.h" "$(@D)/cuda/include/sm_61_intrinsics.h" && cp "/usr/local/cuda-9.0/include/sm_61_intrinsics.hpp" "$(@D)/cuda/include/sm_61_intrinsics.hpp" && cp "/usr/local/cuda-9.0/include/sobol_direction_vectors.h" "$(@D)/cuda/include/sobol_direction_vectors.h" && cp "/usr/local/cuda-9.0/include/surface_functions.h" "$(@D)/cuda/include/surface_functions.h" && cp "/usr/local/cuda-9.0/include/surface_functions.hpp" "$(@D)/cuda/include/surface_functions.hpp" && cp "/usr/local/cuda-9.0/include/surface_indirect_functions.h" "$(@D)/cuda/include/surface_indirect_functions.h" && cp "/usr/local/cuda-9.0/include/surface_indirect_functions.hpp" "$(@D)/cuda/include/surface_indirect_functions.hpp" && cp "/usr/local/cuda-9.0/include/surface_types.h" "$(@D)/cuda/include/surface_types.h" && cp "/usr/local/cuda-9.0/include/texture_fetch_functions.h" "$(@D)/cuda/include/texture_fetch_functions.h" && cp "/usr/local/cuda-9.0/include/texture_fetch_functions.hpp" "$(@D)/cuda/include/texture_fetch_functions.hpp" && cp "/usr/local/cuda-9.0/include/texture_indirect_functions.h" "$(@D)/cuda/include/texture_indirect_functions.h" && cp "/usr/local/cuda-9.0/include/texture_indirect_functions.hpp" "$(@D)/cuda/include/texture_indirect_functions.hpp" && cp "/usr/local/cuda-9.0/include/texture_types.h" "$(@D)/cuda/include/texture_types.h" && cp "/usr/local/cuda-9.0/include/thrust/adjacent_difference.h" "$(@D)/cuda/include/thrust/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/advance.h" "$(@D)/cuda/include/thrust/advance.h" && cp "/usr/local/cuda-9.0/include/thrust/binary_search.h" "$(@D)/cuda/include/thrust/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/complex.h" "$(@D)/cuda/include/thrust/complex.h" && cp "/usr/local/cuda-9.0/include/thrust/copy.h" "$(@D)/cuda/include/thrust/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/count.h" "$(@D)/cuda/include/thrust/count.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/adjacent_difference.inl" "$(@D)/cuda/include/thrust/detail/adjacent_difference.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/advance.inl" "$(@D)/cuda/include/thrust/detail/advance.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/allocator_traits.h" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/allocator_traits.inl" "$(@D)/cuda/include/thrust/detail/allocator/allocator_traits.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/copy_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/copy_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/copy_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/default_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/default_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/default_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/destroy_range.h" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/destroy_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/destroy_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/fill_construct_range.h" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/fill_construct_range.inl" "$(@D)/cuda/include/thrust/detail/allocator/fill_construct_range.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/malloc_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/malloc_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/malloc_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/no_throw_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/no_throw_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/tagged_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/tagged_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/tagged_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/temporary_allocator.h" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/allocator/temporary_allocator.inl" "$(@D)/cuda/include/thrust/detail/allocator/temporary_allocator.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/binary_search.inl" "$(@D)/cuda/include/thrust/detail/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/arithmetic.h" "$(@D)/cuda/include/thrust/detail/complex/arithmetic.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/c99math.h" "$(@D)/cuda/include/thrust/detail/complex/c99math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/catrig.h" "$(@D)/cuda/include/thrust/detail/complex/catrig.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/catrigf.h" "$(@D)/cuda/include/thrust/detail/complex/catrigf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ccosh.h" "$(@D)/cuda/include/thrust/detail/complex/ccosh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ccoshf.h" "$(@D)/cuda/include/thrust/detail/complex/ccoshf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cexp.h" "$(@D)/cuda/include/thrust/detail/complex/cexp.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cexpf.h" "$(@D)/cuda/include/thrust/detail/complex/cexpf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/clog.h" "$(@D)/cuda/include/thrust/detail/complex/clog.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/clogf.h" "$(@D)/cuda/include/thrust/detail/complex/clogf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/complex.inl" "$(@D)/cuda/include/thrust/detail/complex/complex.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cpow.h" "$(@D)/cuda/include/thrust/detail/complex/cpow.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cpowf.h" "$(@D)/cuda/include/thrust/detail/complex/cpowf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/cproj.h" "$(@D)/cuda/include/thrust/detail/complex/cproj.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csinh.h" "$(@D)/cuda/include/thrust/detail/complex/csinh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csinhf.h" "$(@D)/cuda/include/thrust/detail/complex/csinhf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csqrt.h" "$(@D)/cuda/include/thrust/detail/complex/csqrt.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/csqrtf.h" "$(@D)/cuda/include/thrust/detail/complex/csqrtf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ctanh.h" "$(@D)/cuda/include/thrust/detail/complex/ctanh.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/ctanhf.h" "$(@D)/cuda/include/thrust/detail/complex/ctanhf.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/math_private.h" "$(@D)/cuda/include/thrust/detail/complex/math_private.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/complex/stream.h" "$(@D)/cuda/include/thrust/detail/complex/stream.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config.h" "$(@D)/cuda/include/thrust/detail/config.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/compiler.h" "$(@D)/cuda/include/thrust/detail/config/compiler.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/compiler_fence.h" "$(@D)/cuda/include/thrust/detail/config/compiler_fence.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/config.h" "$(@D)/cuda/include/thrust/detail/config/config.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/debug.h" "$(@D)/cuda/include/thrust/detail/config/debug.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/device_system.h" "$(@D)/cuda/include/thrust/detail/config/device_system.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/exec_check_disable.h" "$(@D)/cuda/include/thrust/detail/config/exec_check_disable.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/forceinline.h" "$(@D)/cuda/include/thrust/detail/config/forceinline.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/global_workarounds.h" "$(@D)/cuda/include/thrust/detail/config/global_workarounds.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/host_device.h" "$(@D)/cuda/include/thrust/detail/config/host_device.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/host_system.h" "$(@D)/cuda/include/thrust/detail/config/host_system.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/config/simple_defines.h" "$(@D)/cuda/include/thrust/detail/config/simple_defines.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/contiguous_storage.h" "$(@D)/cuda/include/thrust/detail/contiguous_storage.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/contiguous_storage.inl" "$(@D)/cuda/include/thrust/detail/contiguous_storage.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy.h" "$(@D)/cuda/include/thrust/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy.inl" "$(@D)/cuda/include/thrust/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy_if.h" "$(@D)/cuda/include/thrust/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/copy_if.inl" "$(@D)/cuda/include/thrust/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/count.inl" "$(@D)/cuda/include/thrust/detail/count.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/cstdint.h" "$(@D)/cuda/include/thrust/detail/cstdint.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_delete.inl" "$(@D)/cuda/include/thrust/detail/device_delete.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_free.inl" "$(@D)/cuda/include/thrust/detail/device_free.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_malloc.inl" "$(@D)/cuda/include/thrust/detail/device_malloc.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_new.inl" "$(@D)/cuda/include/thrust/detail/device_new.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_ptr.inl" "$(@D)/cuda/include/thrust/detail/device_ptr.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_reference.inl" "$(@D)/cuda/include/thrust/detail/device_reference.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/device_vector.inl" "$(@D)/cuda/include/thrust/detail/device_vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/dispatch/is_trivial_copy.h" "$(@D)/cuda/include/thrust/detail/dispatch/is_trivial_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/distance.inl" "$(@D)/cuda/include/thrust/detail/distance.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/equal.inl" "$(@D)/cuda/include/thrust/detail/equal.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/execute_with_allocator.h" "$(@D)/cuda/include/thrust/detail/execute_with_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/execution_policy.h" "$(@D)/cuda/include/thrust/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/extrema.inl" "$(@D)/cuda/include/thrust/detail/extrema.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/fill.inl" "$(@D)/cuda/include/thrust/detail/fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/find.inl" "$(@D)/cuda/include/thrust/detail/find.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/for_each.inl" "$(@D)/cuda/include/thrust/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/function.h" "$(@D)/cuda/include/thrust/detail/function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional.inl" "$(@D)/cuda/include/thrust/detail/functional.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/actor.h" "$(@D)/cuda/include/thrust/detail/functional/actor.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/actor.inl" "$(@D)/cuda/include/thrust/detail/functional/actor.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/argument.h" "$(@D)/cuda/include/thrust/detail/functional/argument.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/composite.h" "$(@D)/cuda/include/thrust/detail/functional/composite.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/arithmetic_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/arithmetic_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/assignment_operator.h" "$(@D)/cuda/include/thrust/detail/functional/operators/assignment_operator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/bitwise_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/bitwise_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/compound_assignment_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/compound_assignment_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/logical_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/logical_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/operator_adaptors.h" "$(@D)/cuda/include/thrust/detail/functional/operators/operator_adaptors.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/operators/relational_operators.h" "$(@D)/cuda/include/thrust/detail/functional/operators/relational_operators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/placeholder.h" "$(@D)/cuda/include/thrust/detail/functional/placeholder.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/functional/value.h" "$(@D)/cuda/include/thrust/detail/functional/value.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/gather.inl" "$(@D)/cuda/include/thrust/detail/gather.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/generate.inl" "$(@D)/cuda/include/thrust/detail/generate.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/get_iterator_value.h" "$(@D)/cuda/include/thrust/detail/get_iterator_value.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/host_vector.inl" "$(@D)/cuda/include/thrust/detail/host_vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/inner_product.inl" "$(@D)/cuda/include/thrust/detail/inner_product.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/integer_math.h" "$(@D)/cuda/include/thrust/detail/integer_math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/integer_traits.h" "$(@D)/cuda/include/thrust/detail/integer_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/internal_functional.h" "$(@D)/cuda/include/thrust/detail/internal_functional.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/logical.inl" "$(@D)/cuda/include/thrust/detail/logical.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/merge.inl" "$(@D)/cuda/include/thrust/detail/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/minmax.h" "$(@D)/cuda/include/thrust/detail/minmax.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/mismatch.inl" "$(@D)/cuda/include/thrust/detail/mismatch.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/mpl/math.h" "$(@D)/cuda/include/thrust/detail/mpl/math.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/numeric_traits.h" "$(@D)/cuda/include/thrust/detail/numeric_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/overlapped_copy.h" "$(@D)/cuda/include/thrust/detail/overlapped_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/pair.inl" "$(@D)/cuda/include/thrust/detail/pair.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/partition.inl" "$(@D)/cuda/include/thrust/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/pointer.h" "$(@D)/cuda/include/thrust/detail/pointer.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/pointer.inl" "$(@D)/cuda/include/thrust/detail/pointer.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/range/head_flags.h" "$(@D)/cuda/include/thrust/detail/range/head_flags.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/range/tail_flags.h" "$(@D)/cuda/include/thrust/detail/range/tail_flags.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/raw_pointer_cast.h" "$(@D)/cuda/include/thrust/detail/raw_pointer_cast.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/raw_reference_cast.h" "$(@D)/cuda/include/thrust/detail/raw_reference_cast.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/reduce.inl" "$(@D)/cuda/include/thrust/detail/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/reference.h" "$(@D)/cuda/include/thrust/detail/reference.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/reference.inl" "$(@D)/cuda/include/thrust/detail/reference.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/reference_forward_declaration.h" "$(@D)/cuda/include/thrust/detail/reference_forward_declaration.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/remove.inl" "$(@D)/cuda/include/thrust/detail/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/replace.inl" "$(@D)/cuda/include/thrust/detail/replace.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/reverse.inl" "$(@D)/cuda/include/thrust/detail/reverse.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/scan.inl" "$(@D)/cuda/include/thrust/detail/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/scatter.inl" "$(@D)/cuda/include/thrust/detail/scatter.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/seq.h" "$(@D)/cuda/include/thrust/detail/seq.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/sequence.inl" "$(@D)/cuda/include/thrust/detail/sequence.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/set_operations.inl" "$(@D)/cuda/include/thrust/detail/set_operations.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/sort.inl" "$(@D)/cuda/include/thrust/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/static_assert.h" "$(@D)/cuda/include/thrust/detail/static_assert.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/static_map.h" "$(@D)/cuda/include/thrust/detail/static_map.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap.h" "$(@D)/cuda/include/thrust/detail/swap.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap.inl" "$(@D)/cuda/include/thrust/detail/swap.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/swap_ranges.inl" "$(@D)/cuda/include/thrust/detail/swap_ranges.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/tabulate.inl" "$(@D)/cuda/include/thrust/detail/tabulate.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_array.h" "$(@D)/cuda/include/thrust/detail/temporary_array.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_array.inl" "$(@D)/cuda/include/thrust/detail/temporary_array.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform.inl" "$(@D)/cuda/include/thrust/detail/transform.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform_reduce.inl" "$(@D)/cuda/include/thrust/detail/transform_reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/transform_scan.inl" "$(@D)/cuda/include/thrust/detail/transform_scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/trivial_sequence.h" "$(@D)/cuda/include/thrust/detail/trivial_sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple.inl" "$(@D)/cuda/include/thrust/detail/tuple.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple_meta_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_meta_transform.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/tuple_transform.h" "$(@D)/cuda/include/thrust/detail/tuple_transform.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" "$(@D)/cuda/include/thrust/detail/type_traits/algorithm/intermediate_type_from_function_and_iterators.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/function_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/function_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_member_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_member_function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_nested_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_nested_type.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/has_trivial_assign.h" "$(@D)/cuda/include/thrust/detail/type_traits/has_trivial_assign.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/is_call_possible.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_call_possible.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/is_metafunction_defined.h" "$(@D)/cuda/include/thrust/detail/type_traits/is_metafunction_defined.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_discard_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/iterator/is_output_iterator.h" "$(@D)/cuda/include/thrust/detail/type_traits/iterator/is_output_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/minimum_type.h" "$(@D)/cuda/include/thrust/detail/type_traits/minimum_type.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/pointer_traits.h" "$(@D)/cuda/include/thrust/detail/type_traits/pointer_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/type_traits/result_of_adaptable_function.h" "$(@D)/cuda/include/thrust/detail/type_traits/result_of_adaptable_function.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/detail/uninitialized_fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/unique.inl" "$(@D)/cuda/include/thrust/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/detail/use_default.h" "$(@D)/cuda/include/thrust/detail/use_default.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/util/align.h" "$(@D)/cuda/include/thrust/detail/util/align.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/util/blocking.h" "$(@D)/cuda/include/thrust/detail/util/blocking.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/vector_base.h" "$(@D)/cuda/include/thrust/detail/vector_base.h" && cp "/usr/local/cuda-9.0/include/thrust/detail/vector_base.inl" "$(@D)/cuda/include/thrust/detail/vector_base.inl" && cp "/usr/local/cuda-9.0/include/thrust/device_allocator.h" "$(@D)/cuda/include/thrust/device_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_delete.h" "$(@D)/cuda/include/thrust/device_delete.h" && cp "/usr/local/cuda-9.0/include/thrust/device_free.h" "$(@D)/cuda/include/thrust/device_free.h" && cp "/usr/local/cuda-9.0/include/thrust/device_malloc.h" "$(@D)/cuda/include/thrust/device_malloc.h" && cp "/usr/local/cuda-9.0/include/thrust/device_malloc_allocator.h" "$(@D)/cuda/include/thrust/device_malloc_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_new.h" "$(@D)/cuda/include/thrust/device_new.h" && cp "/usr/local/cuda-9.0/include/thrust/device_new_allocator.h" "$(@D)/cuda/include/thrust/device_new_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/device_ptr.h" "$(@D)/cuda/include/thrust/device_ptr.h" && cp "/usr/local/cuda-9.0/include/thrust/device_reference.h" "$(@D)/cuda/include/thrust/device_reference.h" && cp "/usr/local/cuda-9.0/include/thrust/device_vector.h" "$(@D)/cuda/include/thrust/device_vector.h" && cp "/usr/local/cuda-9.0/include/thrust/distance.h" "$(@D)/cuda/include/thrust/distance.h" && cp "/usr/local/cuda-9.0/include/thrust/equal.h" "$(@D)/cuda/include/thrust/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/execution_policy.h" "$(@D)/cuda/include/thrust/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/extrema.h" "$(@D)/cuda/include/thrust/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/fill.h" "$(@D)/cuda/include/thrust/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/find.h" "$(@D)/cuda/include/thrust/find.h" && cp "/usr/local/cuda-9.0/include/thrust/for_each.h" "$(@D)/cuda/include/thrust/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/functional.h" "$(@D)/cuda/include/thrust/functional.h" && cp "/usr/local/cuda-9.0/include/thrust/gather.h" "$(@D)/cuda/include/thrust/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/generate.h" "$(@D)/cuda/include/thrust/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/host_vector.h" "$(@D)/cuda/include/thrust/host_vector.h" && cp "/usr/local/cuda-9.0/include/thrust/inner_product.h" "$(@D)/cuda/include/thrust/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/constant_iterator.h" "$(@D)/cuda/include/thrust/iterator/constant_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/counting_iterator.h" "$(@D)/cuda/include/thrust/iterator/counting_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/any_assign.h" "$(@D)/cuda/include/thrust/iterator/detail/any_assign.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/any_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/any_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/constant_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/constant_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/counting_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/counting_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/device_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/device_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/discard_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/discard_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/distance_from_result.h" "$(@D)/cuda/include/thrust/iterator/detail/distance_from_result.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/host_system_tag.h" "$(@D)/cuda/include/thrust/iterator/detail/host_system_tag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/is_iterator_category.h" "$(@D)/cuda/include/thrust/iterator/detail/is_iterator_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/is_trivial_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/is_trivial_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_adaptor_base.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_adaptor_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_to_system.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_system.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_to_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_to_traversal.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_category_with_system_and_traversal.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_facade_category.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_facade_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_traits.inl" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traits.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/iterator_traversal_tags.h" "$(@D)/cuda/include/thrust/iterator/detail/iterator_traversal_tags.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/join_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/join_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/minimum_category.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_category.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/minimum_system.h" "$(@D)/cuda/include/thrust/iterator/detail/minimum_system.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/normal_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/normal_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/permutation_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/permutation_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/retag.h" "$(@D)/cuda/include/thrust/iterator/detail/retag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/reverse_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/reverse_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/reverse_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/tagged_iterator.h" "$(@D)/cuda/include/thrust/iterator/detail/tagged_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/transform_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/transform_output_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/transform_output_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/tuple_of_iterator_references.h" "$(@D)/cuda/include/thrust/iterator/detail/tuple_of_iterator_references.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/universal_categories.h" "$(@D)/cuda/include/thrust/iterator/detail/universal_categories.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/zip_iterator.inl" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator.inl" && cp "/usr/local/cuda-9.0/include/thrust/iterator/detail/zip_iterator_base.h" "$(@D)/cuda/include/thrust/iterator/detail/zip_iterator_base.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/discard_iterator.h" "$(@D)/cuda/include/thrust/iterator/discard_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_adaptor.h" "$(@D)/cuda/include/thrust/iterator/iterator_adaptor.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_categories.h" "$(@D)/cuda/include/thrust/iterator/iterator_categories.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_facade.h" "$(@D)/cuda/include/thrust/iterator/iterator_facade.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/iterator_traits.h" "$(@D)/cuda/include/thrust/iterator/iterator_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/permutation_iterator.h" "$(@D)/cuda/include/thrust/iterator/permutation_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/retag.h" "$(@D)/cuda/include/thrust/iterator/retag.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/reverse_iterator.h" "$(@D)/cuda/include/thrust/iterator/reverse_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/transform_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/transform_output_iterator.h" "$(@D)/cuda/include/thrust/iterator/transform_output_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/iterator/zip_iterator.h" "$(@D)/cuda/include/thrust/iterator/zip_iterator.h" && cp "/usr/local/cuda-9.0/include/thrust/logical.h" "$(@D)/cuda/include/thrust/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/memory.h" "$(@D)/cuda/include/thrust/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/merge.h" "$(@D)/cuda/include/thrust/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/mismatch.h" "$(@D)/cuda/include/thrust/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/pair.h" "$(@D)/cuda/include/thrust/pair.h" && cp "/usr/local/cuda-9.0/include/thrust/partition.h" "$(@D)/cuda/include/thrust/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/random.h" "$(@D)/cuda/include/thrust/random.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/discard_block_engine.inl" "$(@D)/cuda/include/thrust/random/detail/discard_block_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_congruential_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_congruential_engine_discard.h" "$(@D)/cuda/include/thrust/random/detail/linear_congruential_engine_discard.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_feedback_shift_engine.inl" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" "$(@D)/cuda/include/thrust/random/detail/linear_feedback_shift_engine_wordmask.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/mod.h" "$(@D)/cuda/include/thrust/random/detail/mod.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/normal_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/normal_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/normal_distribution_base.h" "$(@D)/cuda/include/thrust/random/detail/normal_distribution_base.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/random_core_access.h" "$(@D)/cuda/include/thrust/random/detail/random_core_access.h" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/subtract_with_carry_engine.inl" "$(@D)/cuda/include/thrust/random/detail/subtract_with_carry_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/uniform_int_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_int_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/uniform_real_distribution.inl" "$(@D)/cuda/include/thrust/random/detail/uniform_real_distribution.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/xor_combine_engine.inl" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine.inl" && cp "/usr/local/cuda-9.0/include/thrust/random/detail/xor_combine_engine_max.h" "$(@D)/cuda/include/thrust/random/detail/xor_combine_engine_max.h" && cp "/usr/local/cuda-9.0/include/thrust/random/discard_block_engine.h" "$(@D)/cuda/include/thrust/random/discard_block_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/linear_congruential_engine.h" "$(@D)/cuda/include/thrust/random/linear_congruential_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/linear_feedback_shift_engine.h" "$(@D)/cuda/include/thrust/random/linear_feedback_shift_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/normal_distribution.h" "$(@D)/cuda/include/thrust/random/normal_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/subtract_with_carry_engine.h" "$(@D)/cuda/include/thrust/random/subtract_with_carry_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/random/uniform_int_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_int_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/uniform_real_distribution.h" "$(@D)/cuda/include/thrust/random/uniform_real_distribution.h" && cp "/usr/local/cuda-9.0/include/thrust/random/xor_combine_engine.h" "$(@D)/cuda/include/thrust/random/xor_combine_engine.h" && cp "/usr/local/cuda-9.0/include/thrust/reduce.h" "$(@D)/cuda/include/thrust/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/remove.h" "$(@D)/cuda/include/thrust/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/replace.h" "$(@D)/cuda/include/thrust/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/reverse.h" "$(@D)/cuda/include/thrust/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/scan.h" "$(@D)/cuda/include/thrust/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/scatter.h" "$(@D)/cuda/include/thrust/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/sequence.h" "$(@D)/cuda/include/thrust/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/set_operations.h" "$(@D)/cuda/include/thrust/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/sort.h" "$(@D)/cuda/include/thrust/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/swap.h" "$(@D)/cuda/include/thrust/swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cpp/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cpp/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cpp/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/count.h" "$(@D)/cuda/include/thrust/system/cpp/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/equal.h" "$(@D)/cuda/include/thrust/system/cpp/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cpp/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/find.h" "$(@D)/cuda/include/thrust/system/cpp/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cpp/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/gather.h" "$(@D)/cuda/include/thrust/system/cpp/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/generate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cpp/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cpp/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cpp/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/logical.h" "$(@D)/cuda/include/thrust/system/cpp/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cpp/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/merge.h" "$(@D)/cuda/include/thrust/system/cpp/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cpp/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/par.h" "$(@D)/cuda/include/thrust/system/cpp/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/partition.h" "$(@D)/cuda/include/thrust/system/cpp/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/remove.h" "$(@D)/cuda/include/thrust/system/cpp/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/replace.h" "$(@D)/cuda/include/thrust/system/cpp/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cpp/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cpp/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cpp/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/sort.h" "$(@D)/cuda/include/thrust/system/cpp/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cpp/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cpp/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cpp/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cpp/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cpp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/unique.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cpp/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cpp/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/execution_policy.h" "$(@D)/cuda/include/thrust/system/cpp/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/memory.h" "$(@D)/cuda/include/thrust/system/cpp/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cpp/vector.h" "$(@D)/cuda/include/thrust/system/cpp/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/config.h" "$(@D)/cuda/include/thrust/system/cuda/config.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/cuda/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/cuda/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/cuda/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/agent_launcher.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/agent_launcher.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/alignment.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/alignment.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/triple_chevron_launch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/triple_chevron_launch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/core/util.h" "$(@D)/cuda/include/thrust/system/cuda/detail/core/util.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/count.h" "$(@D)/cuda/include/thrust/system/cuda/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/cross_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_downsweep.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_radix_sort_upsweep.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_reduce_by_key.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_rle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_segment_fixup.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_select_if.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_csrt.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_orig.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/agent_spmv_row_based.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/agent/single_pass_scan_operators.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_adjacent_difference.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_discontinuity.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_exchange.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_load.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_rank.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_raking_layout.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_shuffle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/block_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/block_store.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_atomic.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_histogram_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_raking_commutative_only.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_reduce_warp_reductions.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_raking.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans2.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/block/specializations/block_scan_warp_scans3.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/cub.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/cub.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_partition.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_run_length_encode.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_segmented_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_select.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_select.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/device_spmv.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_histogram.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_radix_sort.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_reduce_by_key.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_rle.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_select_if.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_csrt.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_orig.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/device/dispatch/dispatch_spmv_row_based.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_barrier.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_even_share.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_mapping.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/grid/grid_queue.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/host/mutex.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/host/mutex.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/arg_index_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/cache_modified_output_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/constant_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/counting_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/discard_output_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_obj_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/tex_ref_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/iterator/transform_input_iterator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_load.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_operators.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_search.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/thread/thread_store.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_allocator.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_allocator.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_arch.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_arch.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_debug.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_debug.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_device.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_device.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_macro.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_macro.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_namespace.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_namespace.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_ptx.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_ptx.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/util_type.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/util_type.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_shfl.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_reduce_smem.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_shfl.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/specializations/warp_scan_smem.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_reduce.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" "$(@D)/cuda/include/thrust/system/cuda/detail/cub/warp/warp_scan.cuh" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/equal.h" "$(@D)/cuda/include/thrust/system/cuda/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/error.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/error.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/extrema.h" "$(@D)/cuda/include/thrust/system/cuda/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/find.h" "$(@D)/cuda/include/thrust/system/cuda/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/for_each.h" "$(@D)/cuda/include/thrust/system/cuda/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/gather.h" "$(@D)/cuda/include/thrust/system/cuda/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/generate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/get_value.h" "$(@D)/cuda/include/thrust/system/cuda/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_cuda_runtime_api.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/guarded_driver_types.h" "$(@D)/cuda/include/thrust/system/cuda/detail/guarded_driver_types.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/cuda/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/internal/copy_cross_system.h" "$(@D)/cuda/include/thrust/system/cuda/detail/internal/copy_cross_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/internal/copy_device_to_device.h" "$(@D)/cuda/include/thrust/system/cuda/detail/internal/copy_device_to_device.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/cuda/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/logical.h" "$(@D)/cuda/include/thrust/system/cuda/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/cuda/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/memory.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/memory_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/memory_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/merge.h" "$(@D)/cuda/include/thrust/system/cuda/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/cuda/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/par.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/par_to_seq.h" "$(@D)/cuda/include/thrust/system/cuda/detail/par_to_seq.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/parallel_for.h" "$(@D)/cuda/include/thrust/system/cuda/detail/parallel_for.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/partition.h" "$(@D)/cuda/include/thrust/system/cuda/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/remove.h" "$(@D)/cuda/include/thrust/system/cuda/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/replace.h" "$(@D)/cuda/include/thrust/system/cuda/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/reverse.h" "$(@D)/cuda/include/thrust/system/cuda/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/scatter.h" "$(@D)/cuda/include/thrust/system/cuda/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/sequence.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/cuda/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/sort.h" "$(@D)/cuda/include/thrust/system/cuda/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/cuda/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/cuda/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/terminate.h" "$(@D)/cuda/include/thrust/system/cuda/detail/terminate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/cuda/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/cuda/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/unique.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/cuda/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/util.h" "$(@D)/cuda/include/thrust/system/cuda/detail/util.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/detail/vector.inl" "$(@D)/cuda/include/thrust/system/cuda/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/error.h" "$(@D)/cuda/include/thrust/system/cuda/error.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/execution_policy.h" "$(@D)/cuda/include/thrust/system/cuda/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/experimental/pinned_allocator.h" "$(@D)/cuda/include/thrust/system/cuda/experimental/pinned_allocator.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/memory.h" "$(@D)/cuda/include/thrust/system/cuda/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/cuda/vector.h" "$(@D)/cuda/include/thrust/system/cuda/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/adl/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/adl/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/adl/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/count.h" "$(@D)/cuda/include/thrust/system/detail/adl/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/equal.h" "$(@D)/cuda/include/thrust/system/detail/adl/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/extrema.h" "$(@D)/cuda/include/thrust/system/detail/adl/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/find.h" "$(@D)/cuda/include/thrust/system/detail/adl/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/for_each.h" "$(@D)/cuda/include/thrust/system/detail/adl/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/gather.h" "$(@D)/cuda/include/thrust/system/detail/adl/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/generate.h" "$(@D)/cuda/include/thrust/system/detail/adl/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/get_value.h" "$(@D)/cuda/include/thrust/system/detail/adl/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/adl/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/adl/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/logical.h" "$(@D)/cuda/include/thrust/system/detail/adl/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/adl/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/merge.h" "$(@D)/cuda/include/thrust/system/detail/adl/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/adl/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/partition.h" "$(@D)/cuda/include/thrust/system/detail/adl/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/remove.h" "$(@D)/cuda/include/thrust/system/detail/adl/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/replace.h" "$(@D)/cuda/include/thrust/system/detail/adl/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/reverse.h" "$(@D)/cuda/include/thrust/system/detail/adl/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/scatter.h" "$(@D)/cuda/include/thrust/system/detail/adl/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/sequence.h" "$(@D)/cuda/include/thrust/system/detail/adl/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/adl/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/sort.h" "$(@D)/cuda/include/thrust/system/detail/adl/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/adl/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/adl/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/adl/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/adl/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/adl/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/unique.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/adl/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/adl/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/bad_alloc.h" "$(@D)/cuda/include/thrust/system/detail/bad_alloc.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/errno.h" "$(@D)/cuda/include/thrust/system/detail/errno.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_category.inl" "$(@D)/cuda/include/thrust/system/detail/error_category.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_code.inl" "$(@D)/cuda/include/thrust/system/detail/error_code.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/error_condition.inl" "$(@D)/cuda/include/thrust/system/detail/error_condition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/adjacent_difference.inl" "$(@D)/cuda/include/thrust/system/detail/generic/adjacent_difference.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/advance.h" "$(@D)/cuda/include/thrust/system/detail/generic/advance.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/advance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/advance.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/copy_if.inl" "$(@D)/cuda/include/thrust/system/detail/generic/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/count.h" "$(@D)/cuda/include/thrust/system/detail/generic/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/count.inl" "$(@D)/cuda/include/thrust/system/detail/generic/count.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/distance.h" "$(@D)/cuda/include/thrust/system/detail/generic/distance.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/distance.inl" "$(@D)/cuda/include/thrust/system/detail/generic/distance.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/equal.h" "$(@D)/cuda/include/thrust/system/detail/generic/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/equal.inl" "$(@D)/cuda/include/thrust/system/detail/generic/equal.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/extrema.h" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/extrema.inl" "$(@D)/cuda/include/thrust/system/detail/generic/extrema.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/find.h" "$(@D)/cuda/include/thrust/system/detail/generic/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/find.inl" "$(@D)/cuda/include/thrust/system/detail/generic/find.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/for_each.h" "$(@D)/cuda/include/thrust/system/detail/generic/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/gather.h" "$(@D)/cuda/include/thrust/system/detail/generic/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/gather.inl" "$(@D)/cuda/include/thrust/system/detail/generic/gather.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/generate.h" "$(@D)/cuda/include/thrust/system/detail/generic/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/generate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/generate.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/inner_product.inl" "$(@D)/cuda/include/thrust/system/detail/generic/inner_product.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/logical.h" "$(@D)/cuda/include/thrust/system/detail/generic/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/memory.h" "$(@D)/cuda/include/thrust/system/detail/generic/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/memory.inl" "$(@D)/cuda/include/thrust/system/detail/generic/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/merge.h" "$(@D)/cuda/include/thrust/system/detail/generic/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/merge.inl" "$(@D)/cuda/include/thrust/system/detail/generic/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/mismatch.inl" "$(@D)/cuda/include/thrust/system/detail/generic/mismatch.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/partition.h" "$(@D)/cuda/include/thrust/system/detail/generic/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/partition.inl" "$(@D)/cuda/include/thrust/system/detail/generic/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/remove.h" "$(@D)/cuda/include/thrust/system/detail/generic/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/remove.inl" "$(@D)/cuda/include/thrust/system/detail/generic/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/replace.h" "$(@D)/cuda/include/thrust/system/detail/generic/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/replace.inl" "$(@D)/cuda/include/thrust/system/detail/generic/replace.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reverse.h" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/reverse.inl" "$(@D)/cuda/include/thrust/system/detail/generic/reverse.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scalar/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scalar/binary_search.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scalar/binary_search.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scan_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scan_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scatter.h" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/scatter.inl" "$(@D)/cuda/include/thrust/system/detail/generic/scatter.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/select_system.h" "$(@D)/cuda/include/thrust/system/detail/generic/select_system.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sequence.h" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sequence.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sequence.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/set_operations.inl" "$(@D)/cuda/include/thrust/system/detail/generic/set_operations.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sort.h" "$(@D)/cuda/include/thrust/system/detail/generic/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/sort.inl" "$(@D)/cuda/include/thrust/system/detail/generic/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/swap_ranges.inl" "$(@D)/cuda/include/thrust/system/detail/generic/swap_ranges.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tabulate.inl" "$(@D)/cuda/include/thrust/system/detail/generic/tabulate.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/tag.h" "$(@D)/cuda/include/thrust/system/detail/generic/tag.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/temporary_buffer.inl" "$(@D)/cuda/include/thrust/system/detail/generic/temporary_buffer.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_reduce.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/transform_scan.inl" "$(@D)/cuda/include/thrust/system/detail/generic/transform_scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/type_traits.h" "$(@D)/cuda/include/thrust/system/detail/generic/type_traits.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_copy.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/uninitialized_fill.inl" "$(@D)/cuda/include/thrust/system/detail/generic/uninitialized_fill.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/generic/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/detail/generic/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/internal/decompose.h" "$(@D)/cuda/include/thrust/system/detail/internal/decompose.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/detail/sequential/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/assign_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/binary_search.h" "$(@D)/cuda/include/thrust/system/detail/sequential/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy_backward.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_backward.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/copy_if.h" "$(@D)/cuda/include/thrust/system/detail/sequential/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/count.h" "$(@D)/cuda/include/thrust/system/detail/sequential/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/equal.h" "$(@D)/cuda/include/thrust/system/detail/sequential/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/execution_policy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/extrema.h" "$(@D)/cuda/include/thrust/system/detail/sequential/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/find.h" "$(@D)/cuda/include/thrust/system/detail/sequential/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/for_each.h" "$(@D)/cuda/include/thrust/system/detail/sequential/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/gather.h" "$(@D)/cuda/include/thrust/system/detail/sequential/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/general_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/general_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/generate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/get_value.h" "$(@D)/cuda/include/thrust/system/detail/sequential/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/inner_product.h" "$(@D)/cuda/include/thrust/system/detail/sequential/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/insertion_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/insertion_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/iter_swap.h" "$(@D)/cuda/include/thrust/system/detail/sequential/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/logical.h" "$(@D)/cuda/include/thrust/system/detail/sequential/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/detail/sequential/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/merge.h" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/merge.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/mismatch.h" "$(@D)/cuda/include/thrust/system/detail/sequential/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/partition.h" "$(@D)/cuda/include/thrust/system/detail/sequential/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/remove.h" "$(@D)/cuda/include/thrust/system/detail/sequential/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/replace.h" "$(@D)/cuda/include/thrust/system/detail/sequential/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/reverse.h" "$(@D)/cuda/include/thrust/system/detail/sequential/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scan_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/scatter.h" "$(@D)/cuda/include/thrust/system/detail/sequential/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sequence.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/set_operations.h" "$(@D)/cuda/include/thrust/system/detail/sequential/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_merge_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_merge_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_merge_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_primitive_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_primitive_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_primitive_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_radix_sort.h" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/stable_radix_sort.inl" "$(@D)/cuda/include/thrust/system/detail/sequential/stable_radix_sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/swap_ranges.h" "$(@D)/cuda/include/thrust/system/detail/sequential/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/tabulate.h" "$(@D)/cuda/include/thrust/system/detail/sequential/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/detail/sequential/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform_reduce.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/transform_scan.h" "$(@D)/cuda/include/thrust/system/detail/sequential/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/trivial_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/trivial_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/detail/sequential/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/unique.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/sequential/unique_by_key.h" "$(@D)/cuda/include/thrust/system/detail/sequential/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/detail/system_error.inl" "$(@D)/cuda/include/thrust/system/detail/system_error.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/error_code.h" "$(@D)/cuda/include/thrust/system/error_code.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/omp/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/omp/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/omp/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/count.h" "$(@D)/cuda/include/thrust/system/omp/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/default_decomposition.h" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/default_decomposition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/default_decomposition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/equal.h" "$(@D)/cuda/include/thrust/system/omp/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/extrema.h" "$(@D)/cuda/include/thrust/system/omp/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/find.h" "$(@D)/cuda/include/thrust/system/omp/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/for_each.h" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/omp/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/gather.h" "$(@D)/cuda/include/thrust/system/omp/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/generate.h" "$(@D)/cuda/include/thrust/system/omp/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/get_value.h" "$(@D)/cuda/include/thrust/system/omp/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/omp/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/omp/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/logical.h" "$(@D)/cuda/include/thrust/system/omp/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/omp/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/memory.inl" "$(@D)/cuda/include/thrust/system/omp/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/merge.h" "$(@D)/cuda/include/thrust/system/omp/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/omp/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/par.h" "$(@D)/cuda/include/thrust/system/omp/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/partition.h" "$(@D)/cuda/include/thrust/system/omp/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/partition.inl" "$(@D)/cuda/include/thrust/system/omp/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reduce_intervals.inl" "$(@D)/cuda/include/thrust/system/omp/detail/reduce_intervals.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/remove.h" "$(@D)/cuda/include/thrust/system/omp/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/remove.inl" "$(@D)/cuda/include/thrust/system/omp/detail/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/replace.h" "$(@D)/cuda/include/thrust/system/omp/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/reverse.h" "$(@D)/cuda/include/thrust/system/omp/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/scatter.h" "$(@D)/cuda/include/thrust/system/omp/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sequence.h" "$(@D)/cuda/include/thrust/system/omp/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/omp/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sort.h" "$(@D)/cuda/include/thrust/system/omp/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/sort.inl" "$(@D)/cuda/include/thrust/system/omp/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/omp/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/omp/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/omp/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/omp/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/omp/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/omp/detail/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/detail/vector.inl" "$(@D)/cuda/include/thrust/system/omp/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/execution_policy.h" "$(@D)/cuda/include/thrust/system/omp/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/memory.h" "$(@D)/cuda/include/thrust/system/omp/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/omp/vector.h" "$(@D)/cuda/include/thrust/system/omp/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system/system_error.h" "$(@D)/cuda/include/thrust/system/system_error.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/adjacent_difference.h" "$(@D)/cuda/include/thrust/system/tbb/detail/adjacent_difference.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/assign_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/assign_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/binary_search.h" "$(@D)/cuda/include/thrust/system/tbb/detail/binary_search.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy_if.h" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/copy_if.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/copy_if.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/count.h" "$(@D)/cuda/include/thrust/system/tbb/detail/count.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/equal.h" "$(@D)/cuda/include/thrust/system/tbb/detail/equal.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/extrema.h" "$(@D)/cuda/include/thrust/system/tbb/detail/extrema.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/find.h" "$(@D)/cuda/include/thrust/system/tbb/detail/find.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/for_each.h" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/for_each.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/for_each.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/gather.h" "$(@D)/cuda/include/thrust/system/tbb/detail/gather.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/generate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/generate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/get_value.h" "$(@D)/cuda/include/thrust/system/tbb/detail/get_value.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/inner_product.h" "$(@D)/cuda/include/thrust/system/tbb/detail/inner_product.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/iter_swap.h" "$(@D)/cuda/include/thrust/system/tbb/detail/iter_swap.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/logical.h" "$(@D)/cuda/include/thrust/system/tbb/detail/logical.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/malloc_and_free.h" "$(@D)/cuda/include/thrust/system/tbb/detail/malloc_and_free.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/memory.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/memory.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/merge.h" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/merge.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/merge.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/mismatch.h" "$(@D)/cuda/include/thrust/system/tbb/detail/mismatch.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/par.h" "$(@D)/cuda/include/thrust/system/tbb/detail/par.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/partition.h" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/partition.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/partition.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reduce_intervals.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reduce_intervals.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/remove.h" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/remove.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/remove.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/replace.h" "$(@D)/cuda/include/thrust/system/tbb/detail/replace.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/reverse.h" "$(@D)/cuda/include/thrust/system/tbb/detail/reverse.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/scan.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scan_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scan_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/scatter.h" "$(@D)/cuda/include/thrust/system/tbb/detail/scatter.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sequence.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sequence.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/set_operations.h" "$(@D)/cuda/include/thrust/system/tbb/detail/set_operations.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sort.h" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/sort.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/sort.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/swap_ranges.h" "$(@D)/cuda/include/thrust/system/tbb/detail/swap_ranges.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/tabulate.h" "$(@D)/cuda/include/thrust/system/tbb/detail/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/temporary_buffer.h" "$(@D)/cuda/include/thrust/system/tbb/detail/temporary_buffer.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform_reduce.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/transform_scan.h" "$(@D)/cuda/include/thrust/system/tbb/detail/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/uninitialized_copy.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/uninitialized_fill.h" "$(@D)/cuda/include/thrust/system/tbb/detail/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique_by_key.h" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/unique_by_key.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/unique_by_key.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/detail/vector.inl" "$(@D)/cuda/include/thrust/system/tbb/detail/vector.inl" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/execution_policy.h" "$(@D)/cuda/include/thrust/system/tbb/execution_policy.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/memory.h" "$(@D)/cuda/include/thrust/system/tbb/memory.h" && cp "/usr/local/cuda-9.0/include/thrust/system/tbb/vector.h" "$(@D)/cuda/include/thrust/system/tbb/vector.h" && cp "/usr/local/cuda-9.0/include/thrust/system_error.h" "$(@D)/cuda/include/thrust/system_error.h" && cp "/usr/local/cuda-9.0/include/thrust/tabulate.h" "$(@D)/cuda/include/thrust/tabulate.h" && cp "/usr/local/cuda-9.0/include/thrust/transform.h" "$(@D)/cuda/include/thrust/transform.h" && cp "/usr/local/cuda-9.0/include/thrust/transform_reduce.h" "$(@D)/cuda/include/thrust/transform_reduce.h" && cp "/usr/local/cuda-9.0/include/thrust/transform_scan.h" "$(@D)/cuda/include/thrust/transform_scan.h" && cp "/usr/local/cuda-9.0/include/thrust/tuple.h" "$(@D)/cuda/include/thrust/tuple.h" && cp "/usr/local/cuda-9.0/include/thrust/uninitialized_copy.h" "$(@D)/cuda/include/thrust/uninitialized_copy.h" && cp "/usr/local/cuda-9.0/include/thrust/uninitialized_fill.h" "$(@D)/cuda/include/thrust/uninitialized_fill.h" && cp "/usr/local/cuda-9.0/include/thrust/unique.h" "$(@D)/cuda/include/thrust/unique.h" && cp "/usr/local/cuda-9.0/include/thrust/version.h" "$(@D)/cuda/include/thrust/version.h" && cp "/usr/local/cuda-9.0/include/vector_functions.h" "$(@D)/cuda/include/vector_functions.h" && cp "/usr/local/cuda-9.0/include/vector_functions.hpp" "$(@D)/cuda/include/vector_functions.hpp" && cp "/usr/local/cuda-9.0/include/vector_types.h" "$(@D)/cuda/include/vector_types.h"
    """,
 )
 
@@ -1264,72 +1192,69 @@ genrule(
     name = "cuda-nvvm",
     outs = [
         "cuda/nvvm/bin/cicc",
-        "cuda/nvvm/libdevice/libdevice.compute_50.10.bc",
-        "cuda/nvvm/libdevice/libdevice.compute_30.10.bc",
-        "cuda/nvvm/libdevice/libdevice.compute_20.10.bc",
-        "cuda/nvvm/libdevice/libdevice.compute_35.10.bc",
-        "cuda/nvvm/lib64/libnvvm.so.3",
-        "cuda/nvvm/lib64/libnvvm.so",
-        "cuda/nvvm/lib64/libnvvm.so.3.1.0",
         "cuda/nvvm/include/nvvm.h",
-        "cuda/nvvm/libnvvm-samples/ptxgen/README.txt",
-        "cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c",
-        "cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt",
+        "cuda/nvvm/lib64/libnvvm.so",
+        "cuda/nvvm/lib64/libnvvm.so.3",
+        "cuda/nvvm/lib64/libnvvm.so.3.2.0",
+        "cuda/nvvm/libdevice/libdevice.10.bc",
+        "cuda/nvvm/libnvvm-samples/CMakeLists.txt",
+        "cuda/nvvm/libnvvm-samples/README.txt",
         "cuda/nvvm/libnvvm-samples/build.bat",
-        "cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt",
-        "cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu",
+        "cuda/nvvm/libnvvm-samples/build.sh",
+        "cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h",
+        "cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h",
         "cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt",
+        "cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt",
         "cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp",
-        "cuda/nvvm/libnvvm-samples/README.txt",
-        "cuda/nvvm/libnvvm-samples/simple/simple.c",
-        "cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll",
+        "cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu",
+        "cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt",
+        "cuda/nvvm/libnvvm-samples/ptxgen/README.txt",
+        "cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c",
+        "cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt",
         "cuda/nvvm/libnvvm-samples/simple/README.txt",
+        "cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll",
         "cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll",
-        "cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt",
-        "cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h",
-        "cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h",
-        "cuda/nvvm/libnvvm-samples/build.sh",
-        "cuda/nvvm/libnvvm-samples/CMakeLists.txt",
+        "cuda/nvvm/libnvvm-samples/simple/simple.c",
     ],
     cmd = """
-if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_50.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_50.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_30.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_30.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_20.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_20.10.bc" && cp "/usr/local/cuda-8.0/nvvm/libdevice/libdevice.compute_35.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.compute_35.10.bc" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-8.0/nvvm/lib64/libnvvm.so.3.1.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.1.0" && cp "/usr/local/cuda-8.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-8.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt"
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/nvvm/bin/cicc" "$(@D)/cuda/nvvm/bin/cicc" && cp "/usr/local/cuda-9.0/nvvm/include/nvvm.h" "$(@D)/cuda/nvvm/include/nvvm.h" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so" "$(@D)/cuda/nvvm/lib64/libnvvm.so" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so.3" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3" && cp "/usr/local/cuda-9.0/nvvm/lib64/libnvvm.so.3.2.0" "$(@D)/cuda/nvvm/lib64/libnvvm.so.3.2.0" && cp "/usr/local/cuda-9.0/nvvm/libdevice/libdevice.10.bc" "$(@D)/cuda/nvvm/libdevice/libdevice.10.bc" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/build.bat" "$(@D)/cuda/nvvm/libnvvm-samples/build.bat" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/build.sh" "$(@D)/cuda/nvvm/libnvvm-samples/build.sh" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/common/include/DDSWriter.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/DDSWriter.h" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" "$(@D)/cuda/nvvm/libnvvm-samples/common/include/drvapi_error_string.h" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/cuda-c-linking.cpp" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" "$(@D)/cuda/nvvm/libnvvm-samples/cuda-c-linking/math-funcs.cu" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/ptxgen/ptxgen.c" "$(@D)/cuda/nvvm/libnvvm-samples/ptxgen/ptxgen.c" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/CMakeLists.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/CMakeLists.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/README.txt" "$(@D)/cuda/nvvm/libnvvm-samples/simple/README.txt" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple-gpu.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu.ll" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple-gpu64.ll" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple-gpu64.ll" && cp "/usr/local/cuda-9.0/nvvm/libnvvm-samples/simple/simple.c" "$(@D)/cuda/nvvm/libnvvm-samples/simple/simple.c"
    """,
 )
 
 genrule(
     name = "cuda-extras",
     outs = [
-        "cuda/extras/CUPTI/include/cupti_result.h",
+        "cuda/extras/CUPTI/include/GL/gl.h",
+        "cuda/extras/CUPTI/include/GL/glew.h",
+        "cuda/extras/CUPTI/include/GL/glext.h",
+        "cuda/extras/CUPTI/include/GL/glu.h",
+        "cuda/extras/CUPTI/include/GL/glut.h",
+        "cuda/extras/CUPTI/include/GL/glx.h",
+        "cuda/extras/CUPTI/include/GL/glxext.h",
+        "cuda/extras/CUPTI/include/GL/wglew.h",
+        "cuda/extras/CUPTI/include/GL/wglext.h",
+        "cuda/extras/CUPTI/include/cuda_stdint.h",
+        "cuda/extras/CUPTI/include/cupti.h",
+        "cuda/extras/CUPTI/include/cupti_activity.h",
+        "cuda/extras/CUPTI/include/cupti_callbacks.h",
+        "cuda/extras/CUPTI/include/cupti_driver_cbid.h",
         "cuda/extras/CUPTI/include/cupti_events.h",
-        "cuda/extras/CUPTI/include/openacc/cupti_openacc.h",
+        "cuda/extras/CUPTI/include/cupti_metrics.h",
+        "cuda/extras/CUPTI/include/cupti_nvtx_cbid.h",
+        "cuda/extras/CUPTI/include/cupti_result.h",
+        "cuda/extras/CUPTI/include/cupti_runtime_cbid.h",
         "cuda/extras/CUPTI/include/cupti_version.h",
-        "cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h",
+        "cuda/extras/CUPTI/include/generated_cudaGL_meta.h",
         "cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h",
-        "cuda/extras/CUPTI/include/cupti_activity.h",
-        "cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h",
+        "cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h",
         "cuda/extras/CUPTI/include/generated_cuda_meta.h",
-        "cuda/extras/CUPTI/include/cupti_nvtx_cbid.h",
-        "cuda/extras/CUPTI/include/cuda_stdint.h",
-        "cuda/extras/CUPTI/include/generated_cudaGL_meta.h",
+        "cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h",
         "cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h",
-        "cuda/extras/CUPTI/include/cupti_metrics.h",
-        "cuda/extras/CUPTI/include/cupti_callbacks.h",
-        "cuda/extras/CUPTI/include/cupti_runtime_cbid.h",
-        "cuda/extras/CUPTI/include/cupti.h",
-        "cuda/extras/CUPTI/include/GL/glut.h",
-        "cuda/extras/CUPTI/include/GL/glu.h",
-        "cuda/extras/CUPTI/include/GL/glxext.h",
-        "cuda/extras/CUPTI/include/GL/wglext.h",
-        "cuda/extras/CUPTI/include/GL/glx.h",
-        "cuda/extras/CUPTI/include/GL/glext.h",
-        "cuda/extras/CUPTI/include/GL/wglew.h",
-        "cuda/extras/CUPTI/include/GL/gl.h",
-        "cuda/extras/CUPTI/include/GL/glew.h",
-        "cuda/extras/CUPTI/include/cupti_driver_cbid.h",
         "cuda/extras/CUPTI/include/generated_nvtx_meta.h",
+        "cuda/extras/CUPTI/include/openacc/cupti_openacc.h",
     ],
     cmd = """
-if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-8.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h"
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/gl.h" "$(@D)/cuda/extras/CUPTI/include/GL/gl.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glew.h" "$(@D)/cuda/extras/CUPTI/include/GL/glew.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glu.h" "$(@D)/cuda/extras/CUPTI/include/GL/glu.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glut.h" "$(@D)/cuda/extras/CUPTI/include/GL/glut.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glx.h" "$(@D)/cuda/extras/CUPTI/include/GL/glx.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/glxext.h" "$(@D)/cuda/extras/CUPTI/include/GL/glxext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/wglew.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglew.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/GL/wglext.h" "$(@D)/cuda/extras/CUPTI/include/GL/wglext.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cuda_stdint.h" "$(@D)/cuda/extras/CUPTI/include/cuda_stdint.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti.h" "$(@D)/cuda/extras/CUPTI/include/cupti.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_activity.h" "$(@D)/cuda/extras/CUPTI/include/cupti_activity.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_callbacks.h" "$(@D)/cuda/extras/CUPTI/include/cupti_callbacks.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_driver_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_driver_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_events.h" "$(@D)/cuda/extras/CUPTI/include/cupti_events.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_metrics.h" "$(@D)/cuda/extras/CUPTI/include/cupti_metrics.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_nvtx_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_nvtx_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_result.h" "$(@D)/cuda/extras/CUPTI/include/cupti_result.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_runtime_cbid.h" "$(@D)/cuda/extras/CUPTI/include/cupti_runtime_cbid.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/cupti_version.h" "$(@D)/cuda/extras/CUPTI/include/cupti_version.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cudaGL_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaGL_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cudaVDPAU_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cudaVDPAU_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_gl_interop_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_runtime_api_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_cuda_vdpau_interop_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/generated_nvtx_meta.h" "$(@D)/cuda/extras/CUPTI/include/generated_nvtx_meta.h" && cp "/usr/local/cuda-9.0/extras/CUPTI/include/openacc/cupti_openacc.h" "$(@D)/cuda/extras/CUPTI/include/openacc/cupti_openacc.h"
    """,
 )
 
@@ -1337,26 +1262,21 @@ genrule(
     name = "cuda-lib",
     outs = [
         "cuda/lib/libcuda.so",
-        "cuda/lib/libcudart.so.8.0",
+        "cuda/lib/libcudart.so.9.0",
         "cuda/lib/libcudart_static.a",
-        "cuda/lib/libcublas.so.8.0",
-        "cuda/lib/libcusolver.so.8.0",
-        "cuda/lib/libcurand.so.8.0",
-        "cuda/lib/libcufft.so.8.0",
-        "cuda/lib/libcudnn.so.6",
-        "cuda/lib/libcupti.so.8.0",
+        "cuda/lib/libcublas.so.9.0",
+        "cuda/lib/libcusolver.so.9.0",
+        "cuda/lib/libcurand.so.9.0",
+        "cuda/lib/libcufft.so.9.0",
+        "cuda/lib/libcudnn.so.7",
+        "cuda/lib/libcupti.so.9.0",
     ],
     cmd = """
-if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart.so.8.0.61" "$(@D)/cuda/lib/libcudart.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcublas.so.8.0.88" "$(@D)/cuda/lib/libcublas.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcusolver.so.8.0.61" "$(@D)/cuda/lib/libcusolver.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcurand.so.8.0.61" "$(@D)/cuda/lib/libcurand.so.8.0" && cp "/usr/local/cuda-8.0/targets/x86_64-linux/lib/libcufft.so.8.0.61" "$(@D)/cuda/lib/libcufft.so.8.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.6.0.21" "$(@D)/cuda/lib/libcudnn.so.6" && cp "/usr/local/cuda-8.0/extras/CUPTI/lib64/libcupti.so.8.0.61" "$(@D)/cuda/lib/libcupti.so.8.0"
+if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/stubs/libcuda.so" "$(@D)/cuda/lib/libcuda.so" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudart.so.9.0.176" "$(@D)/cuda/lib/libcudart.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcudart_static.a" "$(@D)/cuda/lib/libcudart_static.a" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcublas.so.9.0.282" "$(@D)/cuda/lib/libcublas.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcusolver.so.9.0.176" "$(@D)/cuda/lib/libcusolver.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcurand.so.9.0.176" "$(@D)/cuda/lib/libcurand.so.9.0" && cp "/usr/local/cuda-9.0/targets/x86_64-linux/lib/libcufft.so.9.0.176" "$(@D)/cuda/lib/libcufft.so.9.0" && cp "/usr/lib/x86_64-linux-gnu/libcudnn.so.7.0.5" "$(@D)/cuda/lib/libcudnn.so.7" && cp "/usr/local/cuda-9.0/extras/CUPTI/lib64/libcupti.so.9.0.176" "$(@D)/cuda/lib/libcupti.so.9.0"
    """,
 )
 
-genrule(
+filegroup(
     name = "cudnn-include",
-    outs = [
-        "cuda/include/cudnn.h",
-    ],
-    cmd = """
-if [ -d "$(@D)/extras" ]; then rm $(@D)/extras -drf; fi && if [ -d "$(@D)/include" ]; then rm $(@D)/include -drf; fi && if [ -d "$(@D)/lib" ]; then rm $(@D)/lib -drf; fi && if [ -d "$(@D)/nvvm" ]; then rm $(@D)/nvvm -drf; fi && cp "/usr/include/cudnn.h" "$(@D)/cudnn.h"
-   """,
+    srcs = [],
 )
diff --git a/third_party/toolchains/gpus/py/BUILD b/third_party/toolchains/gpus/py/BUILD
new file mode 100644
index 0000000000..2d5ace93ff
--- /dev/null
+++ b/third_party/toolchains/gpus/py/BUILD
@@ -0,0 +1,171 @@
+# A build file to configure python remote repository used with Bazel remote
+# execution service
+# DO NOT EDIT: automatically generated BUILD file
+
+licenses(["restricted"])
+
+package(default_visibility = ["//visibility:public"])
+
+cc_library(
+    name = "python_headers",
+    hdrs = [":python_include"],
+    data = select({
+        ":windows": [":python_import_lib"],
+        "//conditions:default": [],
+    }),
+    includes = ["python_include"],
+    linkopts = select({
+        # TODO(pcloudy): Ideally, this should just go into deps after resolving
+        # https://github.com/bazelbuild/bazel/issues/3237,
+        ":windows": ["$(locations :python_import_lib)"],
+        "//conditions:default": [],
+    }),
+)
+
+cc_library(
+    name = "numpy_headers",
+    hdrs = [":numpy_include"],
+    includes = ["numpy_include"],
+)
+
+config_setting(
+    name = "windows",
+    values = {"cpu": "x64_windows"},
+    visibility = ["//visibility:public"],
+)
+
+genrule(
+    name = "python_include",
+    outs = [
+        "python_include/Python-ast.h",
+        "python_include/Python.h",
+        "python_include/abstract.h",
+        "python_include/asdl.h",
+        "python_include/ast.h",
+        "python_include/bitset.h",
+        "python_include/boolobject.h",
+        "python_include/bufferobject.h",
+        "python_include/bytearrayobject.h",
+        "python_include/bytes_methods.h",
+        "python_include/bytesobject.h",
+        "python_include/cStringIO.h",
+        "python_include/cellobject.h",
+        "python_include/ceval.h",
+        "python_include/classobject.h",
+        "python_include/cobject.h",
+        "python_include/code.h",
+        "python_include/codecs.h",
+        "python_include/compile.h",
+        "python_include/complexobject.h",
+        "python_include/datetime.h",
+        "python_include/descrobject.h",
+        "python_include/dictobject.h",
+        "python_include/dtoa.h",
+        "python_include/enumobject.h",
+        "python_include/errcode.h",
+        "python_include/eval.h",
+        "python_include/fileobject.h",
+        "python_include/floatobject.h",
+        "python_include/frameobject.h",
+        "python_include/funcobject.h",
+        "python_include/genobject.h",
+        "python_include/graminit.h",
+        "python_include/grammar.h",
+        "python_include/import.h",
+        "python_include/intobject.h",
+        "python_include/intrcheck.h",
+        "python_include/iterobject.h",
+        "python_include/listobject.h",
+        "python_include/longintrepr.h",
+        "python_include/longobject.h",
+        "python_include/marshal.h",
+        "python_include/memoryobject.h",
+        "python_include/metagrammar.h",
+        "python_include/methodobject.h",
+        "python_include/modsupport.h",
+        "python_include/moduleobject.h",
+        "python_include/node.h",
+        "python_include/object.h",
+        "python_include/objimpl.h",
+        "python_include/opcode.h",
+        "python_include/osdefs.h",
+        "python_include/parsetok.h",
+        "python_include/patchlevel.h",
+        "python_include/pgen.h",
+        "python_include/pgenheaders.h",
+        "python_include/py_curses.h",
+        "python_include/pyarena.h",
+        "python_include/pycapsule.h",
+        "python_include/pyconfig.h",
+        "python_include/pyctype.h",
+        "python_include/pydebug.h",
+        "python_include/pyerrors.h",
+        "python_include/pyexpat.h",
+        "python_include/pyfpe.h",
+        "python_include/pygetopt.h",
+        "python_include/pymacconfig.h",
+        "python_include/pymactoolbox.h",
+        "python_include/pymath.h",
+        "python_include/pymem.h",
+        "python_include/pyport.h",
+        "python_include/pystate.h",
+        "python_include/pystrcmp.h",
+        "python_include/pystrtod.h",
+        "python_include/pythonrun.h",
+        "python_include/pythread.h",
+        "python_include/rangeobject.h",
+        "python_include/setobject.h",
+        "python_include/sliceobject.h",
+        "python_include/stringobject.h",
+        "python_include/structmember.h",
+        "python_include/structseq.h",
+        "python_include/symtable.h",
+        "python_include/sysmodule.h",
+        "python_include/timefuncs.h",
+        "python_include/token.h",
+        "python_include/traceback.h",
+        "python_include/tupleobject.h",
+        "python_include/ucnhash.h",
+        "python_include/unicodeobject.h",
+        "python_include/warnings.h",
+        "python_include/weakrefobject.h",
+    ],
+    cmd = """
+cp "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h" && cp "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp "/usr/include/python2.7/code.h" "$(@D)/python_include/code.h" && cp "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/usr/include/python2.7/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/usr/include/python2.7/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp "/usr/include/python2.7/weakrefobject.h" "$(@D)/python_include/weakrefobject.h"
+   """,
+)
+
+genrule(
+    name = "numpy_include",
+    outs = [
+        "numpy_include/numpy/__multiarray_api.h",
+        "numpy_include/numpy/__ufunc_api.h",
+        "numpy_include/numpy/_neighborhood_iterator_imp.h",
+        "numpy_include/numpy/_numpyconfig.h",
+        "numpy_include/numpy/arrayobject.h",
+        "numpy_include/numpy/arrayscalars.h",
+        "numpy_include/numpy/halffloat.h",
+        "numpy_include/numpy/multiarray_api.txt",
+        "numpy_include/numpy/ndarrayobject.h",
+        "numpy_include/numpy/ndarraytypes.h",
+        "numpy_include/numpy/noprefix.h",
+        "numpy_include/numpy/npy_1_7_deprecated_api.h",
+        "numpy_include/numpy/npy_3kcompat.h",
+        "numpy_include/numpy/npy_common.h",
+        "numpy_include/numpy/npy_cpu.h",
+        "numpy_include/numpy/npy_endian.h",
+        "numpy_include/numpy/npy_interrupt.h",
+        "numpy_include/numpy/npy_math.h",
+        "numpy_include/numpy/npy_no_deprecated_api.h",
+        "numpy_include/numpy/npy_os.h",
+        "numpy_include/numpy/numpyconfig.h",
+        "numpy_include/numpy/old_defines.h",
+        "numpy_include/numpy/oldnumeric.h",
+        "numpy_include/numpy/ufunc_api.txt",
+        "numpy_include/numpy/ufuncobject.h",
+        "numpy_include/numpy/utils.h",
+    ],
+    cmd = """
+cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h"
+   """,
+)
-- 
GitLab


From cf11a4cb47cb550cc6a1de5e5eb4394a9d949e09 Mon Sep 17 00:00:00 2001
From: Yuanzhong Xu <yuanzx@google.com>
Date: Fri, 2 Mar 2018 11:15:14 -0800
Subject: [PATCH 551/575] [XLA] Support while loops and constant in HLO BF16
 propagation.

PiperOrigin-RevId: 187644155
---
 tensorflow/compiler/xla/literal_util.cc       |  18 +
 tensorflow/compiler/xla/literal_util.h        |   5 +
 tensorflow/compiler/xla/service/BUILD         |   2 +
 .../xla/service/bfloat16_propagation.cc       | 390 ++++++++++++++----
 .../xla/service/bfloat16_propagation.h        |  41 +-
 .../xla/service/bfloat16_propagation_test.cc  | 227 ++++++++++
 6 files changed, 598 insertions(+), 85 deletions(-)

diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc
index a345e95a8b..1d1418fc2f 100644
--- a/tensorflow/compiler/xla/literal_util.cc
+++ b/tensorflow/compiler/xla/literal_util.cc
@@ -1434,6 +1434,24 @@ StatusOr<std::unique_ptr<Literal>> Literal::Convert(
   }
 }
 
+StatusOr<std::unique_ptr<Literal>> Literal::ConvertToShape(
+    const Shape& dest_shape) const {
+  if (!ShapeUtil::IsTuple(dest_shape)) {
+    return Convert(dest_shape.element_type());
+  }
+  std::vector<Literal> elements;
+  for (int i = 0; i < ShapeUtil::TupleElementCount(shape()); ++i) {
+    auto element = LiteralView::Create(*this, {i});
+    TF_ASSIGN_OR_RETURN(
+        auto new_element,
+        element.ConvertToShape(ShapeUtil::GetSubshape(dest_shape, {i})));
+    elements.push_back(std::move(*new_element));
+  }
+  auto converted = MakeUnique<Literal>();
+  *converted = Literal::MoveIntoTuple(&elements);
+  return std::move(converted);
+}
+
 template <typename NativeT>
 bool Literal::Piece::EqualElementsInternal(
     const Literal::Piece& other, std::vector<int64>* multi_index) const {
diff --git a/tensorflow/compiler/xla/literal_util.h b/tensorflow/compiler/xla/literal_util.h
index 1d58f0cbc7..cdc5d807e0 100644
--- a/tensorflow/compiler/xla/literal_util.h
+++ b/tensorflow/compiler/xla/literal_util.h
@@ -333,6 +333,11 @@ class Literal {
   StatusOr<std::unique_ptr<Literal>> Convert(
       PrimitiveType primitive_dest_type) const;
 
+  // Converts this literal to the given shape. Returns an error is the
+  // conversion is not possible.
+  StatusOr<std::unique_ptr<Literal>> ConvertToShape(
+      const Shape& dest_shape) const;
+
   // Creates a scalar literal value zero of the given primitive type.
   static Literal Zero(PrimitiveType primitive_type);
 
diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD
index e4ae812532..d71790fb2d 100644
--- a/tensorflow/compiler/xla/service/BUILD
+++ b/tensorflow/compiler/xla/service/BUILD
@@ -129,6 +129,7 @@ cc_library(
         ":hlo_dce",
         ":hlo_pass",
         ":tuple_simplifier",
+        "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_tree",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:util",
@@ -148,6 +149,7 @@ tf_cc_test(
         "//tensorflow/compiler/xla:test_helpers",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/compiler/xla/tests:literal_test_util",
         "//tensorflow/compiler/xla/tests:xla_internal_test_main",  # fixdeps: keep
     ],
 )
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc
index 6145c690b9..7708504dc9 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/service/bfloat16_propagation.h"
 
+#include "tensorflow/compiler/xla/literal_util.h"
 #include "tensorflow/compiler/xla/map_util.h"
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_dce.h"
@@ -68,33 +69,53 @@ void BFloat16Propagation::DetermineAndMutateFusionComputationPrecision(
   for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
     DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false);
   }
+  computations_visited_in_mutation_pass_.insert(
+      fusion->fused_instructions_computation());
 }
 
-void BFloat16Propagation::AdjustFusionParameters(HloInstruction* fusion) {
-  CHECK_EQ(fusion->fused_parameters().size(), fusion->operand_count());
-  for (int64 i = 0; i < fusion->operand_count(); ++i) {
-    auto parameter = fusion->fused_parameter(i);
-    ShapeUtil::ForEachMutableSubshape(
-        parameter->mutable_shape(),
-        [&](Shape* subshape, const ShapeIndex& index) {
-          if (!ShapeUtil::IsLeafIndex(parameter->shape(), index)) {
-            return;
-          }
-          PrimitiveType operand_type =
-              ShapeUtil::GetSubshape(fusion->operand(i)->shape(), index)
-                  .element_type();
-          if (subshape->element_type() == operand_type) {
-            return;
-          }
-          CHECK(operand_type == F32 || operand_type == BF16);
-          subshape->set_element_type(operand_type);
+void BFloat16Propagation::DetermineAndMutateWhileComputationsPrecision(
+    HloInstruction* while_hlo) {
+  CHECK_EQ(while_hlo->opcode(), HloOpcode::kWhile);
+
+  // We are depending on the while node itself having already been analyzed for
+  // whether it can output BF16 and this has been adjusted in the output shape,
+  // and now we're looking to update the body and condition computations to
+  // match the new output shape, as well as recursively process the whole while
+  // node even if the output shape was not modified.
+  HloComputation* body = while_hlo->while_body();
+  auto body_root = body->root_instruction();
+  HloComputation* condition = while_hlo->while_condition();
+
+  ShapeUtil::ForEachMutableSubshape(
+      body_root->mutable_shape(),
+      [this, while_hlo, body_root](Shape* subshape, const ShapeIndex& index) {
+        if (subshape->element_type() != F32) {
+          return;
+        }
+        if (ShapeUtil::GetSubshape(while_hlo->shape(), index).element_type() ==
+            BF16) {
+          subshape->set_element_type(BF16);
           changed_ = true;
-          VLOG(2) << "Fused parameter " << parameter->ToString()
+          VLOG(2) << "While body root " << body_root->ToString()
                   << " at shape index " << index
-                  << " adjusted to match operand in fusion "
-                  << fusion->ToString();
-        });
+                  << " changed to BF16 precision for while "
+                  << while_hlo->ToString();
+        }
+      });
+
+  auto body_insts = body->MakeInstructionPostOrder();
+  for (auto inst_it = body_insts.rbegin(); inst_it != body_insts.rend();
+       ++inst_it) {
+    DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false);
   }
+  computations_visited_in_mutation_pass_.insert(body);
+
+  auto condition_insts = condition->MakeInstructionPostOrder();
+  for (auto inst_it = condition_insts.rbegin();
+       inst_it != condition_insts.rend(); ++inst_it) {
+    DetermineAndMutateInstructionPrecision(*inst_it, /*skip_parameters=*/false);
+  }
+  computations_visited_in_mutation_pass_.insert(condition);
 }
 
 bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo,
@@ -108,14 +129,45 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo,
       continue;
     }
     for (const HloUse& use : value->uses()) {
+      if (!ContainsKey(instructions_visited_in_mutation_pass_,
+                       use.instruction)) {
+        // We don't know yet whether use.instruction will consume BF16 since it
+        // hasn't been visited. Although we visit instructions in reverse
+        // topological order, this is still possible because there may be
+        // unvisited instruction that alias the same buffer. In this case, we
+        // aggressively skip this use, and if this causes inconsistency (e.g.,
+        // one use is in BF16 but another use is in F32), it will be resolved at
+        // the end of the BFloat16Propagation pass.
+        continue;
+      }
+      // Any visited user that can accept BF16 has already been updated if
+      // necessary, e.g., the output has been changed to BF16 if it propagates
+      // precision, or a called computation's parameters have been changed to
+      // BF16 for fusions or whiles.
       if (use.instruction->opcode() == HloOpcode::kFusion) {
-        auto fused_parameter =
+        const auto* fused_parameter =
             use.instruction->fused_parameter(use.operand_number);
         if (ShapeUtil::GetSubshape(fused_parameter->shape(), use.operand_index)
                 .element_type() != BF16) {
           return false;
         }
         continue;
+      } else if (use.instruction->opcode() == HloOpcode::kWhile) {
+        const auto* cond_parameter =
+            use.instruction->while_condition()->parameter_instruction(
+                use.operand_number);
+        if (ShapeUtil::GetSubshape(cond_parameter->shape(), use.operand_index)
+                .element_type() != BF16) {
+          return false;
+        }
+        const auto* body_parameter =
+            use.instruction->while_body()->parameter_instruction(
+                use.operand_number);
+        if (ShapeUtil::GetSubshape(body_parameter->shape(), use.operand_index)
+                .element_type() != BF16) {
+          return false;
+        }
+        continue;
       }
       if (bfloat16_support_->EffectiveOperandPrecisionIsBF16(
               *use.instruction, use.operand_number)) {
@@ -149,24 +201,36 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo,
 
 void BFloat16Propagation::DetermineAndMutateInstructionPrecision(
     HloInstruction* hlo, bool skip_parameters) {
-  // We handle any fusion computation after the instruction is handled, because
-  // we need to know a fusion's output shape before propagating inside its fused
-  // computation.
-  auto cleaner = tensorflow::gtl::MakeCleanup([this, hlo] {
-    if (hlo->opcode() == HloOpcode::kFusion) {
-      DetermineAndMutateFusionComputationPrecision(hlo);
-    }
-  });
+  // We handle any fusion computation or while body/condition after the
+  // instruction is handled, because we need to know the output shape of a
+  // fusion or while before propagating inside its  computations.
+  bool postpone_processing_called_computations = false;
+  auto cleaner = tensorflow::gtl::MakeCleanup(
+      [this, hlo, &postpone_processing_called_computations] {
+        if (!postpone_processing_called_computations) {
+          if (hlo->opcode() == HloOpcode::kFusion) {
+            DetermineAndMutateFusionComputationPrecision(hlo);
+          } else if (hlo->opcode() == HloOpcode::kWhile) {
+            DetermineAndMutateWhileComputationsPrecision(hlo);
+          }
+        }
+        instructions_visited_in_mutation_pass_.insert(hlo);
+      });
+
+  if (hlo->opcode() == HloOpcode::kWhile &&
+      (caller_counts_[hlo->while_condition()] > 1 ||
+       caller_counts_[hlo->while_body()] > 1)) {
+    postpone_processing_called_computations = true;
+    return;
+  }
 
   // Do not change precision for instructions related to entry and exit of a
   // computation, and control flow, because this pass might break the interfaces
   // or assumptions for them.
   if (hlo->opcode() == HloOpcode::kInfeed ||       //
       hlo->opcode() == HloOpcode::kOutfeed ||      //
-      hlo->opcode() == HloOpcode::kConstant ||     //
       hlo->opcode() == HloOpcode::kCustomCall ||   //
       hlo->opcode() == HloOpcode::kCall ||         //
-      hlo->opcode() == HloOpcode::kWhile ||        //
       hlo->opcode() == HloOpcode::kConditional ||  //
       (hlo->opcode() == HloOpcode::kParameter && skip_parameters)) {
     return;
@@ -231,60 +295,198 @@ bool BFloat16Propagation::InstructionIsCandidateForBF16Output(
   return true;
 }
 
-Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
-    HloModule* module) {
-  std::list<HloComputation*> computations_topological_order =
-      module->MakeComputationPostOrder();
-  for (auto comp_it = computations_topological_order.rbegin();
-       comp_it != computations_topological_order.rend(); ++comp_it) {
-    auto insts = (*comp_it)->MakeInstructionPostOrder();
-    // Do the adjustment on each instruction in the computation in reverse
-    // topological order.
-    for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
-      auto hlo = *inst_it;
-      auto adjust_buffer = [this, hlo](Shape* subshape,
-                                       const ShapeIndex& index) {
-        if (subshape->element_type() != F32 &&
-            subshape->element_type() != BF16) {
-          return;
+void BFloat16Propagation::AdjustCalledComputationParameters(
+    HloInstruction* hlo) {
+  auto adjust_computation =
+      [this, hlo](HloComputation* computation,
+                  tensorflow::gtl::ArraySlice<HloInstruction*> operands) {
+        // Adjust parameters.
+        CHECK_EQ(operands.size(), computation->num_parameters());
+        for (int64 i = 0; i < operands.size(); ++i) {
+          auto parameter = computation->parameter_instruction(i);
+          ShapeUtil::ForEachMutableSubshape(
+              parameter->mutable_shape(),
+              [this, i, hlo, &operands, parameter](Shape* subshape,
+                                                   const ShapeIndex& index) {
+                if (!ShapeUtil::IsLeafIndex(parameter->shape(), index)) {
+                  return;
+                }
+                PrimitiveType operand_type =
+                    ShapeUtil::GetSubshape(operands[i]->shape(), index)
+                        .element_type();
+                if (subshape->element_type() == operand_type) {
+                  return;
+                }
+                CHECK(operand_type == F32 || operand_type == BF16);
+                subshape->set_element_type(operand_type);
+                changed_ = true;
+                VLOG(2) << "Called computation parameter "
+                        << parameter->ToString() << " at shape index " << index
+                        << " adjusted to match operand in HLO "
+                        << hlo->ToString();
+              });
         }
-        PrimitiveType type = BF16;
-        for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) {
-          if (value->shape().element_type() == BF16) {
-            continue;
+      };
+
+  switch (hlo->opcode()) {
+    case HloOpcode::kFusion:
+      adjust_computation(hlo->fused_instructions_computation(),
+                         hlo->operands());
+      break;
+    case HloOpcode::kWhile:
+      adjust_computation(hlo->while_condition(), hlo->operands());
+      adjust_computation(hlo->while_body(), hlo->operands());
+      break;
+    default:
+      break;
+  }
+}
+
+void BFloat16Propagation::AdjustCalledComputationRoot(HloInstruction* hlo) {
+  auto adjust_computation = [this, hlo](HloComputation* computation,
+                                        const Shape& output_shape) {
+    // Adjust root.
+    HloInstruction* root = computation->root_instruction();
+    ShapeUtil::ForEachMutableSubshape(
+        root->mutable_shape(), [this, hlo, root, &output_shape](
+                                   Shape* subshape, const ShapeIndex& index) {
+          if (!ShapeUtil::IsLeafIndex(hlo->shape(), index)) {
+            return;
           }
-          CHECK_EQ(value->shape().element_type(), F32);
-          type = F32;
-          break;
-        }
-        // It's possible that a user has been changed from BF16 to F32
-        // during this final adjustment pass, so we need to check
-        // AllUsersConsumeBF16() again.
-        if (type == BF16 && !AllUsersConsumeBF16(*hlo, index)) {
-          type = F32;
-        }
-        if (type == F32) {
-          for (const auto* value :
-               dataflow_->GetValueSet(hlo, index).values()) {
-            // We rely on the fact that this adjustment works in reverse
-            // topological order. Adding the value to
-            // values_that_must_be_kept_as_f32_ will ensure the correctness
-            // of the adjustment for HLOs that will be processed later.
-            values_that_must_be_kept_as_f32_.insert(value);
+          const PrimitiveType output_type =
+              ShapeUtil::GetSubshape(output_shape, index).element_type();
+          if (subshape->element_type() == output_type) {
+            return;
+          }
+          CHECK(output_type == F32 || output_type == BF16);
+          subshape->set_element_type(output_type);
+          // It's possible that output_type is F32, but the root instruction's
+          // type is BF16; e.g., a fusion node's output was changed to BF16
+          // initially but then adjusted back to F32, and the fusion computation
+          // is now being adjusted after the fusion node.
+          if (output_type == F32) {
+            for (const auto* value :
+                 dataflow_->GetValueSet(root, index).values()) {
+              // We rely on the fact that this adjustment works in reverse
+              // topological order so that called computation will be
+              // processed later. Adding the value to
+              // values_that_must_be_kept_as_f32_ will ensure the
+              // correctness of the adjustment for HLOs that will be
+              // processed later.
+              values_that_must_be_kept_as_f32_.insert(value);
+            }
           }
+          changed_ = true;
+          VLOG(2) << "Called computation root " << root->ToString()
+                  << " at shape index " << index
+                  << " adjusted to match output shape of " << hlo->ToString();
+        });
+  };
+
+  switch (hlo->opcode()) {
+    case HloOpcode::kFusion:
+      adjust_computation(hlo->fused_instructions_computation(), hlo->shape());
+      break;
+    case HloOpcode::kWhile:
+      adjust_computation(hlo->while_condition(), hlo->shape());
+      adjust_computation(hlo->while_body(), hlo->shape());
+      break;
+    default:
+      break;
+  }
+}
+
+bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper(
+    HloComputation* computation,
+    tensorflow::gtl::FlatSet<const HloComputation*>* visited_computations) {
+  bool parameter_changed = false;
+  auto insts = computation->MakeInstructionPostOrder();
+  // Do the adjustment on each instruction in the computation in reverse
+  // topological order.
+  for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
+    auto hlo = *inst_it;
+    auto adjust_hlo_output = [this, hlo, &parameter_changed](
+                                 Shape* subshape, const ShapeIndex& index) {
+      if (subshape->element_type() != F32 && subshape->element_type() != BF16) {
+        return;
+      }
+      PrimitiveType type = BF16;
+      for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) {
+        if (value->shape().element_type() == BF16) {
+          continue;
         }
+        CHECK_EQ(value->shape().element_type(), F32);
+        type = F32;
+        break;
+      }
+      // It's possible that a user has been changed from BF16 to F32
+      // during this final adjustment pass, so we need to check
+      // AllUsersConsumeBF16() again.
+      if (type == BF16 && !AllUsersConsumeBF16(*hlo, index)) {
+        type = F32;
+      }
+      if (type == F32) {
+        for (const auto* value : dataflow_->GetValueSet(hlo, index).values()) {
+          // We rely on the fact that this adjustment works in reverse
+          // topological order. Adding the value to
+          // values_that_must_be_kept_as_f32_ will ensure the correctness
+          // of the adjustment for HLOs that will be processed later.
+          values_that_must_be_kept_as_f32_.insert(value);
+        }
+      }
+      if (type != subshape->element_type()) {
         subshape->set_element_type(type);
-      };
-      ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), adjust_buffer);
-    }
-    // Now adjust parameters of fusions inside this computation.
-    for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
-      auto hlo = *inst_it;
-      if (hlo->opcode() == HloOpcode::kFusion) {
-        AdjustFusionParameters(hlo);
+        VLOG(2) << "HloInstruction output at shape index " << index
+                << " adjusted to " << *subshape << ": " << hlo->ToString();
+        if (hlo->opcode() == HloOpcode::kParameter) {
+          parameter_changed = true;
+        }
+      }
+    };
+    ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(), adjust_hlo_output);
+    AdjustCalledComputationRoot(hlo);
+    if (hlo->opcode() == HloOpcode::kWhile) {
+      // We need to run on the while body and condition repeatedly until a fixed
+      // point is reached, i.e., the parameters do not change any more. We may
+      // need more than one iteration because the while input and output alias
+      // each other, so changing one input parameter requires changing the
+      // corresponding output element and thus may transitively require changing
+      // another input parameter. A fixed point will be reached because the
+      // parameters can only be changed from BF16 to F32, not the other way
+      // around.
+      tensorflow::gtl::FlatSet<const HloComputation*> visited_in_while;
+      while (ResolveInconsistencyOfAliasingBuffersHelper(hlo->while_condition(),
+                                                         &visited_in_while) ||
+             ResolveInconsistencyOfAliasingBuffersHelper(hlo->while_body(),
+                                                         &visited_in_while)) {
+        visited_in_while.clear();
+        ShapeUtil::ForEachMutableSubshape(hlo->mutable_shape(),
+                                          adjust_hlo_output);
+        AdjustCalledComputationRoot(hlo);
       }
+      visited_computations->insert(visited_in_while.begin(),
+                                   visited_in_while.end());
     }
   }
+  // Now adjust parameters of called computations.
+  for (auto inst_it = insts.rbegin(); inst_it != insts.rend(); ++inst_it) {
+    AdjustCalledComputationParameters(*inst_it);
+  }
+  return parameter_changed;
+}
+
+Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
+    HloModule* module) {
+  std::list<HloComputation*> computations_topological_order =
+      module->MakeComputationPostOrder();
+  tensorflow::gtl::FlatSet<const HloComputation*> resolved;
+  for (auto comp_it = computations_topological_order.rbegin();
+       comp_it != computations_topological_order.rend(); ++comp_it) {
+    if (ContainsKey(resolved, *comp_it)) {
+      continue;
+    }
+    ResolveInconsistencyOfAliasingBuffersHelper(*comp_it, &resolved);
+  }
 
   // We could have changed a fusion computation's root shape to have a different
   // precision than the fusion node's output, if the fusion root does not
@@ -382,9 +584,39 @@ Status BFloat16Propagation::ResolveInconsistencyOfAliasingBuffers(
       needs_tuple_simplifier |= ShapeUtil::IsTuple(hlo->shape());
     }
   }
+
+  // We may have converted some constants from F32 to BF16, so adjust the
+  // constant literals in such cases. We do this here instead of when the
+  // constant node's is changed because 1) the HloInstruction interface does not
+  // allow resetting the literal so we have to create a new kConstant
+  // instruction to replace the old one, which invalidates dataflow analysis,
+  // and 2) it's possible that a kConstant's output gets changed to BF16 at the
+  // beginning but later on adjusted back to F32, so converting literals here
+  // can avoid repeated conversions.
+  //
+  // TODO(b/73833576): Consider resetting literal in HloInstruction.
+  bool needs_dce = needs_tuple_simplifier;
+  for (auto computation : computations_topological_order) {
+    for (auto hlo : computation->MakeInstructionPostOrder()) {
+      if (hlo->opcode() != HloOpcode::kConstant) {
+        continue;
+      }
+      if (!ShapeUtil::Equal(hlo->literal().shape(), hlo->shape())) {
+        TF_ASSIGN_OR_RETURN(auto converted_literal,
+                            hlo->literal().ConvertToShape(hlo->shape()));
+        auto new_constant = computation->AddInstruction(
+            HloInstruction::CreateConstant(std::move(converted_literal)));
+        TF_RETURN_IF_ERROR(hlo->ReplaceAllUsesWith(new_constant));
+        needs_dce = true;
+      }
+    }
+  }
+
   if (needs_tuple_simplifier) {
     TupleSimplifier tuple_simplifier;
     TF_RETURN_IF_ERROR(tuple_simplifier.Run(module).status());
+  }
+  if (needs_dce) {
     HloDCE dce;
     TF_RETURN_IF_ERROR(dce.Run(module).status());
   }
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.h b/tensorflow/compiler/xla/service/bfloat16_propagation.h
index ccf77d7b4e..89a5ac5db1 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation.h
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation.h
@@ -38,7 +38,8 @@ namespace xla {
 // be bitwise identical to that without this pass; this is possible if the
 // backend already reduces precision to BF16 on some HLO instructions.
 //
-// This pass will not modify the signature of any non-fusion computation.
+// This pass will not modify the signature of a computation, unless it is a
+// fusion computation or its only caller is a while.
 //
 // !!! WARNING !!! This pass can introduce mixed precision in individual HLOs,
 // which has two issues:
@@ -92,8 +93,23 @@ class BFloat16Propagation : public HloPassInterface {
                                               bool skip_parameters);
 
   // Special handling in the mutation pass for fusion computations.
+  //
+  // Precondition: hlo->opcode() == kFusion
   void DetermineAndMutateFusionComputationPrecision(HloInstruction* fusion);
 
+  // Special handling in the mutation pass for while computations.
+  //
+  // Precondition: hlo->opcode() == kWhile
+  void DetermineAndMutateWhileComputationsPrecision(HloInstruction* while_hlo);
+
+  // The set of HloInstructions that have been visited in the mutation pass.
+  tensorflow::gtl::FlatSet<const HloInstruction*>
+      instructions_visited_in_mutation_pass_;
+
+  // The set of HloComputations that have been visited in the mutation pass.
+  tensorflow::gtl::FlatSet<const HloComputation*>
+      computations_visited_in_mutation_pass_;
+
   // ***************************
   // Functions called by the final inconsistency resolving pass.
 
@@ -102,9 +118,20 @@ class BFloat16Propagation : public HloPassInterface {
   // same precision.
   Status ResolveInconsistencyOfAliasingBuffers(HloModule* module);
 
-  // Makes the fusion parameters match the precision of the actual parameters
-  // passed to the fusion node.
-  void AdjustFusionParameters(HloInstruction* fusion);
+  // Resolves inconsistency of aliasing buffers for the given computation, and
+  // recursively runs on a while instruction's condition and body until a fixed
+  // point is reached.
+  bool ResolveInconsistencyOfAliasingBuffersHelper(
+      HloComputation* computation,
+      tensorflow::gtl::FlatSet<const HloComputation*>* visited_computations);
+
+  // Makes the parameters of called computations match how they are called by
+  // the given HLO.
+  void AdjustCalledComputationParameters(HloInstruction* hlo);
+
+  // Makes the root instructions of called computations match how they are used
+  // by the given HLO.
+  void AdjustCalledComputationRoot(HloInstruction* hlo);
 
   // ***************************
   // Functions called and state used by two or more passes.
@@ -117,8 +144,10 @@ class BFloat16Propagation : public HloPassInterface {
   // The set of F32 HLO values that must be kept in F32.
   tensorflow::gtl::FlatSet<const HloValue*> values_that_must_be_kept_as_f32_;
 
-  // ***************************
-  // State used by both passes.
+  // Mapping from each HloComputation to the number of callers to it in the
+  // module. Populated at the beginning of this pass.
+  tensorflow::gtl::FlatMap<const HloComputation*, int64> caller_counts_;
+
   const BFloat16Support* bfloat16_support_;
   std::unique_ptr<HloDataflowAnalysis> dataflow_;
 
diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
index 2047e2053a..5950b004b3 100644
--- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
+++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/test.h"
 #include "tensorflow/compiler/xla/test_helpers.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/compiler/xla/tests/literal_test_util.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
 
 namespace xla {
@@ -121,6 +122,41 @@ TEST_F(BFloat16PropagationTest, PropagateThroughSelectButNotAdd) {
   EXPECT_FALSE(OutputsBF16(c));
 }
 
+// Tests that if a constant is converted to BF16 then its literal must also be
+// converted.
+TEST_F(BFloat16PropagationTest, ConvertConstantLiteral) {
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {4, 4});
+  Array2D<float> array_a(4, 4);
+  array_a.FillUnique(1.0f);
+  Array2D<float> array_b(4, 4);
+  array_b.FillUnique(10.0f);
+
+  HloInstruction* a = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateFromArray(array_a)));
+  HloInstruction* b = builder.AddInstruction(
+      HloInstruction::CreateConstant(Literal::CreateFromArray(array_b)));
+  HloInstruction* dot = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kDot, a, b));
+
+  auto module = CreateNewModule();
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), dot);
+  EXPECT_TRUE(OutputsBF16(dot->operand(0)));
+  EXPECT_TRUE(OutputsBF16(dot->operand(1)));
+  EXPECT_EQ(dot->operand(0)->opcode(), HloOpcode::kConstant);
+  EXPECT_EQ(dot->operand(1)->opcode(), HloOpcode::kConstant);
+  LiteralTestUtil::ExpectEqual(
+      dot->operand(0)->literal(),
+      *LiteralTestUtil::ConvertF32ToBF16(*Literal::CreateFromArray(array_a)));
+  LiteralTestUtil::ExpectEqual(
+      dot->operand(1)->literal(),
+      *LiteralTestUtil::ConvertF32ToBF16(*Literal::CreateFromArray(array_b)));
+}
+
 // Tests that BF16 can be propagated through nested tuples.
 TEST_F(BFloat16PropagationTest, PropagateThroughTuples) {
   auto builder = HloComputation::Builder(TestName());
@@ -390,4 +426,195 @@ TEST_F(BFloat16PropagationTest, SelectOverTuples) {
   EXPECT_TRUE(OutputsBF16(xpose));
 }
 
+// Tests that BF16 is propagated properly through while computations.
+TEST_F(BFloat16PropagationTest, PropagateThroughWhile) {
+  auto module = CreateNewModule();
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {4, 4});
+
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, shape, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, shape, "param1"));
+  HloInstruction* add0 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1));
+  HloInstruction* add1 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1));
+  HloInstruction* tuple =
+      builder.AddInstruction(HloInstruction::CreateTuple({add0, add1}));
+
+  auto builder_cond = HloComputation::Builder("cond");
+  auto cond_param = builder_cond.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple->shape(), "cond_param"));
+  auto cond_lhs = builder_cond.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, cond_param, 0));
+  auto cond_rhs = builder_cond.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, cond_param, 1));
+  // This add should prevent RHS from using BF16
+  auto cond_add_rhs = builder_cond.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, cond_rhs, cond_rhs));
+  auto cond_dot = builder_cond.AddInstruction(HloInstruction::CreateBinary(
+      shape, HloOpcode::kDot, cond_lhs, cond_add_rhs));
+  builder_cond.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt,
+      builder_cond.AddInstruction(HloInstruction::CreateSlice(
+          ShapeUtil::MakeShape(F32, {}), cond_dot, {0, 0}, {1, 1}, {1, 1})),
+      builder_cond.AddInstruction(HloInstruction::CreateSlice(
+          ShapeUtil::MakeShape(F32, {}), cond_dot, {1, 1}, {2, 2}, {1, 1}))));
+  auto cond = module->AddEmbeddedComputation(builder_cond.Build());
+
+  auto builder_body = HloComputation::Builder("body");
+  auto body_param = builder_body.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple->shape(), "body_param"));
+  auto body_lhs = builder_body.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, body_param, 0));
+  auto body_rhs = builder_body.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, body_param, 1));
+  auto body_dot = builder_body.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_lhs, body_rhs));
+  builder_body.AddInstruction(
+      HloInstruction::CreateTuple({body_dot, body_rhs}));
+  auto body = module->AddEmbeddedComputation(builder_body.Build());
+
+  auto while_hlo = builder.AddInstruction(
+      HloInstruction::CreateWhile(tuple->shape(), cond, body, tuple));
+
+  auto lhs = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, while_hlo, 0));
+  auto rhs = builder.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, while_hlo, 1));
+  auto dot = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kDot, lhs, rhs));
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+
+  EXPECT_EQ(computation->root_instruction(), dot);
+  EXPECT_TRUE(OutputsBF16(lhs));
+  EXPECT_FALSE(OutputsBF16(rhs));
+  EXPECT_TRUE(OutputsBF16(body_dot));
+  EXPECT_TRUE(OutputsBF16(body_lhs));
+  EXPECT_FALSE(OutputsBF16(body_rhs));
+  EXPECT_TRUE(OutputsBF16(cond_lhs));
+  EXPECT_FALSE(OutputsBF16(cond_rhs));
+  EXPECT_TRUE(OutputsBF16(add0));
+  EXPECT_FALSE(OutputsBF16(add1));
+}
+
+// Tests that BF16 is not propagated through multiple whiles that invoke the
+// same computation as long as one while prevents the propagation.
+TEST_F(BFloat16PropagationTest, DoNotPropagateWhilesCallingSameComputation) {
+  auto module = CreateNewModule();
+  auto builder = HloComputation::Builder(TestName());
+  Shape shape = ShapeUtil::MakeShape(F32, {4, 4});
+
+  HloInstruction* param0 = builder.AddInstruction(
+      HloInstruction::CreateParameter(0, shape, "param0"));
+  HloInstruction* param1 = builder.AddInstruction(
+      HloInstruction::CreateParameter(1, shape, "param1"));
+  HloInstruction* add0 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1));
+  HloInstruction* add1 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1));
+  HloInstruction* add2 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1));
+  HloInstruction* add3 = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1));
+  HloInstruction* tuple0 =
+      builder.AddInstruction(HloInstruction::CreateTuple({add0, add1}));
+  HloInstruction* tuple1 =
+      builder.AddInstruction(HloInstruction::CreateTuple({add2, add3}));
+
+  // Condition computation for the first while.
+  auto builder_cond0 = HloComputation::Builder("cond0");
+  auto cond0_param = builder_cond0.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple0->shape(), "cond0_param"));
+  auto cond0_lhs = builder_cond0.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, cond0_param, 0));
+  auto cond0_rhs = builder_cond0.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, cond0_param, 1));
+  // This add should prevent RHS from using BF16
+  auto cond0_add_rhs =
+      builder_cond0.AddInstruction(HloInstruction::CreateBinary(
+          shape, HloOpcode::kAdd, cond0_rhs, cond0_rhs));
+  auto cond0_dot = builder_cond0.AddInstruction(HloInstruction::CreateBinary(
+      shape, HloOpcode::kDot, cond0_lhs, cond0_add_rhs));
+  builder_cond0.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt,
+      builder_cond0.AddInstruction(HloInstruction::CreateSlice(
+          ShapeUtil::MakeShape(F32, {}), cond0_dot, {0, 0}, {1, 1}, {1, 1})),
+      builder_cond0.AddInstruction(HloInstruction::CreateSlice(
+          ShapeUtil::MakeShape(F32, {}), cond0_dot, {1, 1}, {2, 2}, {1, 1}))));
+  auto cond0 = module->AddEmbeddedComputation(builder_cond0.Build());
+
+  // Condition computation for the second while.
+  auto builder_cond1 = HloComputation::Builder("cond1");
+  auto cond1_param = builder_cond1.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple1->shape(), "cond1_param"));
+  auto cond1_lhs = builder_cond1.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, cond1_param, 0));
+  auto cond1_rhs = builder_cond1.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, cond1_param, 1));
+  // This add should prevent LHS from using BF16
+  auto cond1_add_lhs =
+      builder_cond1.AddInstruction(HloInstruction::CreateBinary(
+          shape, HloOpcode::kAdd, cond1_lhs, cond1_lhs));
+  auto cond1_dot = builder_cond1.AddInstruction(HloInstruction::CreateBinary(
+      shape, HloOpcode::kDot, cond1_add_lhs, cond1_rhs));
+  builder_cond1.AddInstruction(HloInstruction::CreateBinary(
+      ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt,
+      builder_cond1.AddInstruction(HloInstruction::CreateSlice(
+          ShapeUtil::MakeShape(F32, {}), cond1_dot, {0, 0}, {1, 1}, {1, 1})),
+      builder_cond1.AddInstruction(HloInstruction::CreateSlice(
+          ShapeUtil::MakeShape(F32, {}), cond1_dot, {1, 1}, {2, 2}, {1, 1}))));
+  auto cond1 = module->AddEmbeddedComputation(builder_cond1.Build());
+
+  // Body computation shared by both whiles.
+  auto builder_body = HloComputation::Builder("body");
+  auto body_param = builder_body.AddInstruction(
+      HloInstruction::CreateParameter(0, tuple0->shape(), "body_param"));
+  auto body_lhs = builder_body.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, body_param, 0));
+  auto body_rhs = builder_body.AddInstruction(
+      HloInstruction::CreateGetTupleElement(shape, body_param, 1));
+  auto body_dot = builder_body.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_lhs, body_rhs));
+  builder_body.AddInstruction(
+      HloInstruction::CreateTuple({body_dot, body_rhs}));
+  auto body = module->AddEmbeddedComputation(builder_body.Build());
+
+  auto while0 = builder.AddInstruction(
+      HloInstruction::CreateWhile(tuple0->shape(), cond0, body, tuple0));
+  auto while1 = builder.AddInstruction(
+      HloInstruction::CreateWhile(tuple1->shape(), cond1, body, tuple1));
+
+  auto lhs = builder.AddInstruction(HloInstruction::CreateBinary(
+      shape, HloOpcode::kDot,
+      builder.AddInstruction(
+          HloInstruction::CreateGetTupleElement(shape, while0, 0)),
+      builder.AddInstruction(
+          HloInstruction::CreateGetTupleElement(shape, while0, 1))));
+  auto rhs = builder.AddInstruction(HloInstruction::CreateBinary(
+      shape, HloOpcode::kDot,
+      builder.AddInstruction(
+          HloInstruction::CreateGetTupleElement(shape, while1, 0)),
+      builder.AddInstruction(
+          HloInstruction::CreateGetTupleElement(shape, while1, 1))));
+  auto dot = builder.AddInstruction(
+      HloInstruction::CreateBinary(shape, HloOpcode::kDot, lhs, rhs));
+  auto computation = module->AddEntryComputation(builder.Build());
+
+  EXPECT_TRUE(PropagatePrecision(module.get()));
+  EXPECT_FALSE(OutputsBF16(body_dot));
+  EXPECT_FALSE(OutputsBF16(body_rhs));
+  EXPECT_FALSE(OutputsBF16(body_lhs));
+  EXPECT_FALSE(OutputsBF16(cond0_lhs));
+  EXPECT_FALSE(OutputsBF16(cond0_rhs));
+  EXPECT_FALSE(OutputsBF16(cond1_lhs));
+  EXPECT_FALSE(OutputsBF16(cond1_rhs));
+  EXPECT_TRUE(OutputsBF16(cond0_add_rhs));
+  EXPECT_TRUE(OutputsBF16(cond1_add_lhs));
+  EXPECT_EQ(computation->root_instruction(), dot);
+}
+
 }  // namespace xla
-- 
GitLab


From 3fb65ed8667df659ea8634a7e142e989cecea9f8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 11:18:41 -0800
Subject: [PATCH 552/575] Add a configurable preference for scheduling fuller
 batches sooner to the adaptive shared batcher.  A full batch will now be
 scheduled before an older, nearly empty batch as long as the age gap is less
 than full_batch_scheduling_boost_micros.

This parameter improves latency under heavy load, but too large a value will harm tail latency.

PiperOrigin-RevId: 187644796
---
 .../adaptive_shared_batch_scheduler.h         | 61 +++++++++-------
 .../adaptive_shared_batch_scheduler_test.cc   | 71 +++++++++++++++++++
 2 files changed, 107 insertions(+), 25 deletions(-)

diff --git a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
index 661ed239d3..339d792302 100644
--- a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
+++ b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h
@@ -19,7 +19,6 @@ limitations under the License.
 #include <algorithm>
 #include <functional>
 #include <memory>
-#include <queue>
 #include <random>
 #include <unordered_map>
 #include <vector>
@@ -44,15 +43,14 @@ template <typename TaskType>
 class ASBSQueue;
 }  // namespace internal
 
-// EXPERIMENTAL: API MAY BE SUBJECTED TO SUDDEN CHANGES.
-//
 // Shared batch scheduler designed to minimize latency. The scheduler keeps
 // track of a number of queues (one per model or model version) which are
 // continuously enqueuing requests. The scheduler groups the requests into
 // batches which it periodically sends off for processing (see
 // shared_batch_scheduler.h for more details). AdaptiveSharedBatchScheduler
-// (ASBS) prioritizes batches by age (i.e. the batch's oldest request)
-// irrespective of queue or batch size.
+// (ASBS) prioritizes batches primarily by age (i.e. the batch's oldest request)
+// along with a configurable preference for scheduling larger batches first.
+//
 //
 // ASBS tries to keep the system busy by maintaining an adjustable number of
 // concurrently processed batches.  If a new batch is created, and the number of
@@ -93,6 +91,13 @@ class AdaptiveSharedBatchScheduler
     // for num_batch_threads allows for large in_flight_batches_limit_, which
     // will harm latency for some time once load increases again.
     int64 num_batch_threads = port::NumSchedulableCPUs();
+    // Although batch selection is primarily based on age, this parameter
+    // specifies a preference for larger batches.  A full batch will be
+    // scheduled before an older, nearly empty batch as long as the age gap is
+    // less than full_batch_scheduling_boost_micros.  The optimal value for this
+    // parameter should be of order the batch processing latency, but must be
+    // chosen carefully, as too large a value will harm tail latency.
+    int64 full_batch_scheduling_boost_micros = 0;
     // The environment to use (typically only overridden by test code).
     Env* env = Env::Default();
     // Initial limit for number of batches being concurrently processed.
@@ -153,17 +158,9 @@ class AdaptiveSharedBatchScheduler
 
   const Options options_;
 
-  struct BatchCompare {
-    bool operator()(const internal::ASBSBatch<TaskType>* a,
-                    const internal::ASBSBatch<TaskType>* b);
-  };
-
   // Collection of batches added by AddBatch, ordered by age. Owned by scheduler
   // until they are released for processing.
-  std::priority_queue<const internal::ASBSBatch<TaskType>*,
-                      std::vector<const internal::ASBSBatch<TaskType>*>,
-                      BatchCompare>
-      batches_ GUARDED_BY(mu_);
+  std::vector<const internal::ASBSBatch<TaskType>*> batches_ GUARDED_BY(mu_);
 
   // Unowned queues and callbacks added by AddQueue.
   std::unordered_map<const internal::ASBSQueue<TaskType>*, BatchProcessor>
@@ -288,6 +285,11 @@ Status AdaptiveSharedBatchScheduler<TaskType>::Create(
     return errors::InvalidArgument("num_batch_threads must be positive; was ",
                                    options.num_batch_threads);
   }
+  if (options.full_batch_scheduling_boost_micros < 0) {
+    return errors::InvalidArgument(
+        "full_batch_scheduling_boost_micros can't be negative; was ",
+        options.full_batch_scheduling_boost_micros);
+  }
   if (options.initial_in_flight_batches_limit > options.num_batch_threads) {
     return errors::InvalidArgument(
         "initial_in_flight_batches_limit (",
@@ -348,7 +350,7 @@ template <typename TaskType>
 void AdaptiveSharedBatchScheduler<TaskType>::AddBatch(
     const internal::ASBSBatch<TaskType>* batch) {
   mutex_lock l(mu_);
-  batches_.push(batch);
+  batches_.push_back(batch);
   MaybeScheduleNextBatch();
 }
 
@@ -366,10 +368,26 @@ void AdaptiveSharedBatchScheduler<TaskType>::MaybeScheduleNextBatch() {
   // Non-integer limit handled probabilistially.
   if (in_flight_batches_limit_ - in_flight_batches_ < 1 &&
       rand_double_(rand_engine_) >
-          (in_flight_batches_limit_ - in_flight_batches_))
+          in_flight_batches_limit_ - in_flight_batches_) {
     return;
-  const internal::ASBSBatch<TaskType>* batch = batches_.top();
-  batches_.pop();
+  }
+  auto best_it = batches_.begin();
+  double best_score =
+      (*best_it)->creation_time_micros() -
+      options_.full_batch_scheduling_boost_micros * (*best_it)->size() /
+          static_cast<double>((*best_it)->queue()->max_task_size());
+  for (auto it = batches_.begin() + 1; it != batches_.end(); it++) {
+    const double score =
+        (*it)->creation_time_micros() -
+        options_.full_batch_scheduling_boost_micros * (*it)->size() /
+            static_cast<double>((*it)->queue()->max_task_size());
+    if (score < best_score) {
+      best_score = score;
+      best_it = it;
+    }
+  }
+  const internal::ASBSBatch<TaskType>* batch = *best_it;
+  batches_.erase(best_it);
   // Queue may destroy itself after ReleaseBatch is called.
   batch->queue()->ReleaseBatch(batch);
   batch_thread_pool_->Schedule(
@@ -427,13 +445,6 @@ void AdaptiveSharedBatchScheduler<TaskType>::CallbackWrapper(
   MaybeScheduleNextBatch();
 }
 
-template <typename TaskType>
-bool AdaptiveSharedBatchScheduler<TaskType>::BatchCompare::operator()(
-    const internal::ASBSBatch<TaskType>* a,
-    const internal::ASBSBatch<TaskType>* b) {
-  return a->creation_time_micros() > b->creation_time_micros();
-}
-
 // ---------------- ASBSQueue ----------------
 
 namespace internal {
diff --git a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
index 109234287e..1be0c1f5c6 100644
--- a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
+++ b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler_test.cc
@@ -180,6 +180,77 @@ TEST(AdaptiveSharedBatchSchedulerTest, InFlightBatchesLimitTuning) {
   stop_teardown.Notify();
 }
 
+TEST(AdaptiveSharedBatchSchedulerTest, FullBatchSchedulingBoostMicros) {
+  test_util::FakeClockEnv env(Env::Default());
+  Notification start_teardown, stop_teardown;
+  std::unique_ptr<Thread> teardown_thread =
+      CreateFakeClockAdvancerThread(&env, &start_teardown, &stop_teardown);
+  {
+    AdaptiveSharedBatchScheduler<FakeTask>::Options options;
+    options.env = &env;
+    options.initial_in_flight_batches_limit = 1;
+    options.batches_to_average_over = 1000;
+    options.full_batch_scheduling_boost_micros = 100;
+    mutex mu;
+    int processed_batches = 0;
+    Notification finish_processing;
+    auto queue_callback = [&mu, &processed_batches, &finish_processing](
+                              std::unique_ptr<Batch<FakeTask>> batch) {
+      ASSERT_TRUE(batch->IsClosed());
+      finish_processing.WaitForNotification();
+      mutex_lock l(mu);
+      processed_batches++;
+      switch (processed_batches) {
+        case 1:
+          EXPECT_EQ(100, batch->size());
+          break;
+        case 2:
+          EXPECT_EQ(50, batch->size());
+          break;
+        case 3:
+          EXPECT_EQ(900, batch->size());
+          break;
+        case 4:
+          EXPECT_EQ(200, batch->size());
+          break;
+        default:
+          EXPECT_TRUE(false) << "Should only have 4 batches";
+      }
+    };
+    std::shared_ptr<AdaptiveSharedBatchScheduler<FakeTask>> scheduler;
+    TF_ASSERT_OK(
+        AdaptiveSharedBatchScheduler<FakeTask>::Create(options, &scheduler));
+    AdaptiveSharedBatchScheduler<FakeTask>::QueueOptions queue_options;
+    std::unique_ptr<BatchScheduler<FakeTask>> queue1;
+    std::unique_ptr<BatchScheduler<FakeTask>> queue2;
+    queue_options.max_batch_size = 1000;
+    TF_ASSERT_OK(scheduler->AddQueue(queue_options, queue_callback, &queue1));
+    queue_options.max_batch_size = 100;
+    TF_ASSERT_OK(scheduler->AddQueue(queue_options, queue_callback, &queue2));
+
+    // First batch immediately processed.
+    TF_ASSERT_OK(ScheduleTask(100, queue1.get()));
+
+    TF_ASSERT_OK(ScheduleTask(100, queue1.get()));
+    env.AdvanceByMicroseconds(10);
+    TF_ASSERT_OK(ScheduleTask(100, queue1.get()));
+    env.AdvanceByMicroseconds(10);
+
+    TF_ASSERT_OK(ScheduleTask(50, queue2.get()));
+    env.AdvanceByMicroseconds(45);
+
+    TF_ASSERT_OK(ScheduleTask(900, queue1.get()));
+
+    // Second batch - creation time: 0, fullness: 0.2, sched score: -20
+    // Third batch - creation time: 20, fullness: 0.5, sched score: -30
+    // Fourth batch - creation time: 65, fullness: 0.9, sched score: -25
+
+    finish_processing.Notify();
+    start_teardown.Notify();
+  }
+  stop_teardown.Notify();
+}
+
 TEST(AdaptiveSharedBatchSchedulerTest, DeleteQueue) {
   AdaptiveSharedBatchScheduler<FakeTask>::Options options;
   options.initial_in_flight_batches_limit = 1;
-- 
GitLab


From 1ded0ecca819e8569f120a3eb35cc477636f3340 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Fri, 2 Mar 2018 11:35:53 -0800
Subject: [PATCH 553/575] GCS: Update throttle state even if disabled.

PiperOrigin-RevId: 187647263
---
 tensorflow/core/platform/cloud/gcs_throttle.cc |  4 +---
 tensorflow/core/platform/cloud/gcs_throttle.h  | 13 +++++++++++--
 .../core/platform/cloud/gcs_throttle_test.cc   | 18 ++++++++++++++++++
 3 files changed, 30 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/platform/cloud/gcs_throttle.cc b/tensorflow/core/platform/cloud/gcs_throttle.cc
index eb5f8958a3..27dd06a625 100644
--- a/tensorflow/core/platform/cloud/gcs_throttle.cc
+++ b/tensorflow/core/platform/cloud/gcs_throttle.cc
@@ -26,10 +26,9 @@ GcsThrottle::GcsThrottle(EnvTime* env_time)
 
 bool GcsThrottle::AdmitRequest() {
   mutex_lock l(mu_);
-  if (!config_.enabled) return true;
   UpdateState();
   if (available_tokens_ < config_.tokens_per_request) {
-    return false;
+    return false || !config_.enabled;
   }
   available_tokens_ -= config_.tokens_per_request;
   return true;
@@ -37,7 +36,6 @@ bool GcsThrottle::AdmitRequest() {
 
 void GcsThrottle::RecordResponse(size_t num_bytes) {
   mutex_lock l(mu_);
-  if (!config_.enabled) return;
   UpdateState();
   available_tokens_ -= request_bytes_to_tokens(num_bytes);
 }
diff --git a/tensorflow/core/platform/cloud/gcs_throttle.h b/tensorflow/core/platform/cloud/gcs_throttle.h
index 1a89daef08..6d5eed7338 100644
--- a/tensorflow/core/platform/cloud/gcs_throttle.h
+++ b/tensorflow/core/platform/cloud/gcs_throttle.h
@@ -109,13 +109,22 @@ class GcsThrottle {
    * purpose of this function is to make available to monitoring or other
    * instrumentation the number of available tokens in the pool.
    */
-  inline int64 available_tokens() {
+  inline int64 available_tokens() LOCKS_EXCLUDED(mu_) {
     mutex_lock l(mu_);
-    if (!config_.enabled) return 0;
     UpdateState();
     return available_tokens_;
   }
 
+  /**
+   * is_enabled determines if the throttle is enabled.
+   *
+   * If !is_enabled(), AdmitRequest() will always return true.
+   */
+  bool is_enabled() LOCKS_EXCLUDED(mu_) {
+    mutex_lock l(mu_);
+    return config_.enabled;
+  }
+
  private:
   /**
    * UpdateState updates the available_tokens_ and last_updated_secs_ variables.
diff --git a/tensorflow/core/platform/cloud/gcs_throttle_test.cc b/tensorflow/core/platform/cloud/gcs_throttle_test.cc
index 694756022e..57193ac405 100644
--- a/tensorflow/core/platform/cloud/gcs_throttle_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_throttle_test.cc
@@ -96,6 +96,24 @@ TEST_F(GcsThrottleTest, ReverseTime) {
   EXPECT_EQ(200000, throttle_.available_tokens());
 }
 
+TEST(GcsThrottleDisabledTest, Disabled) {
+  TestTime time;
+  GcsThrottle throttle(&time);
+  ASSERT_FALSE(throttle.is_enabled());  // Verify throttle is disabled.
+
+  EXPECT_EQ(0, throttle.available_tokens());
+  time.AdvanceSeconds(1);
+  EXPECT_EQ(100000, throttle.available_tokens());
+  EXPECT_TRUE(throttle.AdmitRequest());
+  EXPECT_EQ(99900, throttle.available_tokens());
+  time.AdvanceSeconds(1);
+  EXPECT_EQ(199900, throttle.available_tokens());
+  throttle.RecordResponse(128000000);  // 128 MB response.
+  EXPECT_LT(0, throttle.available_tokens());
+  // Admit request even without available tokens
+  EXPECT_TRUE(throttle.AdmitRequest());
+}
+
 }  // namespace
 
 }  // namespace tensorflow
-- 
GitLab


From 2abc47106624e0102c917535dd6df45561550ade Mon Sep 17 00:00:00 2001
From: Jianwei Xie <xiejw@google.com>
Date: Fri, 2 Mar 2018 11:59:02 -0800
Subject: [PATCH 554/575] Move the PS_OPS from Estimator to device_setter to
 benefit more users.

PiperOrigin-RevId: 187650283
---
 tensorflow/python/estimator/estimator.py    | 10 ++--------
 tensorflow/python/training/device_setter.py |  9 +++++++++
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py
index 1a2b33721a..60351471f1 100644
--- a/tensorflow/python/estimator/estimator.py
+++ b/tensorflow/python/estimator/estimator.py
@@ -49,6 +49,7 @@ from tensorflow.python.saved_model import builder as saved_model_builder
 from tensorflow.python.saved_model import tag_constants
 from tensorflow.python.summary import summary
 from tensorflow.python.summary.writer import writer_cache
+from tensorflow.python.training import device_setter
 from tensorflow.python.training import evaluation
 from tensorflow.python.training import monitored_session
 from tensorflow.python.training import saver
@@ -1007,13 +1008,6 @@ def _get_replica_device_setter(config):
   Returns:
     A replica device setter, or None.
   """
-  ps_ops = [
-      'Variable', 'VariableV2', 'AutoReloadVariable', 'MutableHashTable',
-      'MutableHashTableV2', 'MutableHashTableOfTensors',
-      'MutableHashTableOfTensorsV2', 'MutableDenseHashTable',
-      'MutableDenseHashTableV2', 'VarHandleOp'
-  ]
-
   if config.task_type:
     worker_device = '/job:%s/task:%d' % (config.task_type, config.task_id)
   else:
@@ -1024,7 +1018,7 @@ def _get_replica_device_setter(config):
         ps_tasks=config.num_ps_replicas,
         worker_device=worker_device,
         merge_devices=True,
-        ps_ops=ps_ops,
+        ps_ops=list(device_setter.STANDARD_PS_OPS),
         cluster=config.cluster_spec)
   else:
     return None
diff --git a/tensorflow/python/training/device_setter.py b/tensorflow/python/training/device_setter.py
index 689088bb41..0e824d89e9 100644
--- a/tensorflow/python/training/device_setter.py
+++ b/tensorflow/python/training/device_setter.py
@@ -25,6 +25,15 @@ from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import server_lib
 from tensorflow.python.util.tf_export import tf_export
 
+# This is a tuple of PS ops used by tf.estimator.Esitmator which should work in
+# almost all of cases.
+STANDARD_PS_OPS = (
+    "Variable", "VariableV2", "AutoReloadVariable", "MutableHashTable",
+    "MutableHashTableV2", "MutableHashTableOfTensors",
+    "MutableHashTableOfTensorsV2", "MutableDenseHashTable",
+    "MutableDenseHashTableV2", "VarHandleOp"
+)
+
 
 class _RoundRobinStrategy(object):
   """Returns the next ps task index for placement in round-robin order.
-- 
GitLab


From 41aa3e75ca35c763c23aeedf2409589b7814c7f1 Mon Sep 17 00:00:00 2001
From: Brennan Saeta <saeta@google.com>
Date: Fri, 2 Mar 2018 12:19:23 -0800
Subject: [PATCH 555/575] GCS: Extract block cache interface from
 implementation.

PiperOrigin-RevId: 187652953
---
 tensorflow/core/platform/cloud/BUILD          |  20 +-
 .../core/platform/cloud/file_block_cache.h    | 161 +-----------
 .../core/platform/cloud/gcs_file_system.cc    |  15 +-
 ...block_cache.cc => ram_file_block_cache.cc} |  35 +--
 .../platform/cloud/ram_file_block_cache.h     | 229 ++++++++++++++++++
 ...e_test.cc => ram_file_block_cache_test.cc} |  60 ++---
 6 files changed, 311 insertions(+), 209 deletions(-)
 rename tensorflow/core/platform/cloud/{file_block_cache.cc => ram_file_block_cache.cc} (89%)
 create mode 100644 tensorflow/core/platform/cloud/ram_file_block_cache.h
 rename tensorflow/core/platform/cloud/{file_block_cache_test.cc => ram_file_block_cache_test.cc} (92%)

diff --git a/tensorflow/core/platform/cloud/BUILD b/tensorflow/core/platform/cloud/BUILD
index 9ba25dea4f..0a17a419d3 100644
--- a/tensorflow/core/platform/cloud/BUILD
+++ b/tensorflow/core/platform/cloud/BUILD
@@ -38,13 +38,24 @@ cc_library(
 
 cc_library(
     name = "file_block_cache",
-    srcs = ["file_block_cache.cc"],
     hdrs = ["file_block_cache.h"],
     copts = tf_copts(),
     visibility = ["//tensorflow:__subpackages__"],
     deps = ["//tensorflow/core:lib"],
 )
 
+cc_library(
+    name = "ram_file_block_cache",
+    srcs = ["ram_file_block_cache.cc"],
+    hdrs = ["ram_file_block_cache.h"],
+    copts = tf_copts(),
+    visibility = ["//tensorflow:__subpackages__"],
+    deps = [
+        ":file_block_cache",
+        "//tensorflow/core:lib",
+    ],
+)
+
 cc_library(
     name = "gcs_dns_cache",
     srcs = ["gcs_dns_cache.cc"],
@@ -83,6 +94,7 @@ cc_library(
         ":gcs_throttle",
         ":google_auth_provider",
         ":http_request",
+        ":ram_file_block_cache",
         ":retrying_file_system",
         ":retrying_utils",
         ":time_util",
@@ -245,12 +257,12 @@ tf_cc_test(
 )
 
 tf_cc_test(
-    name = "file_block_cache_test",
+    name = "ram_file_block_cache_test",
     size = "small",
-    srcs = ["file_block_cache_test.cc"],
+    srcs = ["ram_file_block_cache_test.cc"],
     deps = [
-        ":file_block_cache",
         ":now_seconds_env",
+        ":ram_file_block_cache",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:test",
diff --git a/tensorflow/core/platform/cloud/file_block_cache.h b/tensorflow/core/platform/cloud/file_block_cache.h
index 5c180e2332..da16788247 100644
--- a/tensorflow/core/platform/cloud/file_block_cache.h
+++ b/tensorflow/core/platform/cloud/file_block_cache.h
@@ -32,7 +32,7 @@ limitations under the License.
 
 namespace tensorflow {
 
-/// \brief An LRU block cache of file contents, keyed by {filename, offset}.
+/// \brief A block cache of file contents, keyed by {filename, offset}.
 ///
 /// This class should be shared by read-only random access files on a remote
 /// filesystem (e.g. GCS).
@@ -48,27 +48,7 @@ class FileBlockCache {
                                size_t* bytes_transferred)>
       BlockFetcher;
 
-  FileBlockCache(size_t block_size, size_t max_bytes, uint64 max_staleness,
-                 BlockFetcher block_fetcher, Env* env = Env::Default())
-      : block_size_(block_size),
-        max_bytes_(max_bytes),
-        max_staleness_(max_staleness),
-        block_fetcher_(block_fetcher),
-        env_(env) {
-    if (max_staleness_ > 0) {
-      pruning_thread_.reset(env_->StartThread(ThreadOptions(), "TF_prune_FBC",
-                                              [this] { Prune(); }));
-    }
-  }
-
-  ~FileBlockCache() {
-    if (pruning_thread_) {
-      stop_pruning_thread_.Notify();
-      // Destroying pruning_thread_ will block until Prune() receives the above
-      // notification and returns.
-      pruning_thread_.reset();
-    }
-  }
+  virtual ~FileBlockCache() {}
 
   /// Read `n` bytes from `filename` starting at `offset` into `out`. This
   /// method will return:
@@ -84,143 +64,22 @@ class FileBlockCache {
   ///    placed in `out`.
   /// 4) OK otherwise (i.e. the read succeeded, and at least one byte was placed
   ///    in `out`).
-  Status Read(const string& filename, size_t offset, size_t n, char* buffer,
-              size_t* bytes_transferred);
+  virtual Status Read(const string& filename, size_t offset, size_t n,
+                      char* buffer, size_t* bytes_transferred) = 0;
 
   /// Remove all cached blocks for `filename`.
-  void RemoveFile(const string& filename) LOCKS_EXCLUDED(mu_);
+  virtual void RemoveFile(const string& filename) = 0;
 
   /// Remove all cached data.
-  void Flush() LOCKS_EXCLUDED(mu_);
+  virtual void Flush() = 0;
 
   /// Accessors for cache parameters.
-  size_t block_size() const { return block_size_; }
-  size_t max_bytes() const { return max_bytes_; }
-  uint64 max_staleness() const { return max_staleness_; }
+  virtual size_t block_size() const = 0;
+  virtual size_t max_bytes() const = 0;
+  virtual uint64 max_staleness() const = 0;
 
   /// The current size (in bytes) of the cache.
-  size_t CacheSize() const LOCKS_EXCLUDED(mu_);
-
- private:
-  /// The size of the blocks stored in the LRU cache, as well as the size of the
-  /// reads from the underlying filesystem.
-  const size_t block_size_;
-  /// The maximum number of bytes (sum of block sizes) allowed in the LRU cache.
-  const size_t max_bytes_;
-  /// The maximum staleness of any block in the LRU cache, in seconds.
-  const uint64 max_staleness_;
-  /// The callback to read a block from the underlying filesystem.
-  const BlockFetcher block_fetcher_;
-  /// The Env from which we read timestamps.
-  Env* const env_;  // not owned
-
-  /// \brief The key type for the file block cache.
-  ///
-  /// The file block cache key is a {filename, offset} pair.
-  typedef std::pair<string, size_t> Key;
-
-  /// \brief The state of a block.
-  ///
-  /// A block begins in the CREATED stage. The first thread will attempt to read
-  /// the block from the filesystem, transitioning the state of the block to
-  /// FETCHING. After completing, if the read was successful the state should
-  /// be FINISHED. Otherwise the state should be ERROR. A subsequent read can
-  /// re-fetch the block if the state is ERROR.
-  enum class FetchState {
-    CREATED,
-    FETCHING,
-    FINISHED,
-    ERROR,
-  };
-
-  /// \brief A block of a file.
-  ///
-  /// A file block consists of the block data, the block's current position in
-  /// the LRU cache, the timestamp (seconds since epoch) at which the block
-  /// was cached, a coordination lock, and state & condition variables.
-  ///
-  /// Thread safety:
-  /// The iterator and timestamp fields should only be accessed while holding
-  /// the block-cache-wide mu_ instance variable. The state variable should only
-  /// be accessed while holding the Block's mu lock. The data vector should only
-  /// be accessed after state == FINISHED, and it should never be modified.
-  ///
-  /// In order to prevent deadlocks, never grab the block-cache-wide mu_ lock
-  /// AFTER grabbing any block's mu lock. It is safe to grab mu without locking
-  /// mu_.
-  struct Block {
-    /// The block data.
-    std::vector<char> data;
-    /// A list iterator pointing to the block's position in the LRU list.
-    std::list<Key>::iterator lru_iterator;
-    /// A list iterator pointing to the block's position in the LRA list.
-    std::list<Key>::iterator lra_iterator;
-    /// The timestamp (seconds since epoch) at which the block was cached.
-    uint64 timestamp;
-    /// Mutex to guard state variable
-    mutex mu;
-    /// The state of the block.
-    FetchState state GUARDED_BY(mu) = FetchState::CREATED;
-    /// Wait on cond_var if state is FETCHING.
-    condition_variable cond_var;
-  };
-
-  /// \brief The block map type for the file block cache.
-  ///
-  /// The block map is an ordered map from Key to Block.
-  typedef std::map<Key, std::shared_ptr<Block>> BlockMap;
-
-  /// Prune the cache by removing files with expired blocks.
-  void Prune() LOCKS_EXCLUDED(mu_);
-
-  bool BlockNotStale(const std::shared_ptr<Block>& block)
-      EXCLUSIVE_LOCKS_REQUIRED(mu_);
-
-  /// Look up a Key in the block cache.
-  std::shared_ptr<Block> Lookup(const Key& key) LOCKS_EXCLUDED(mu_);
-
-  Status MaybeFetch(const Key& key, const std::shared_ptr<Block>& block)
-      LOCKS_EXCLUDED(mu_);
-
-  /// Trim the block cache to make room for another entry.
-  void Trim() EXCLUSIVE_LOCKS_REQUIRED(mu_);
-
-  /// Update the LRU iterator for the block at `key`.
-  Status UpdateLRU(const Key& key, const std::shared_ptr<Block>& block)
-      LOCKS_EXCLUDED(mu_);
-
-  /// Remove all blocks of a file, with mu_ already held.
-  void RemoveFile_Locked(const string& filename) EXCLUSIVE_LOCKS_REQUIRED(mu_);
-
-  /// Remove the block `entry` from the block map and LRU list, and update the
-  /// cache size accordingly.
-  void RemoveBlock(BlockMap::iterator entry) EXCLUSIVE_LOCKS_REQUIRED(mu_);
-
-  /// The cache pruning thread that removes files with expired blocks.
-  std::unique_ptr<Thread> pruning_thread_;
-
-  /// Notification for stopping the cache pruning thread.
-  Notification stop_pruning_thread_;
-
-  /// Guards access to the block map, LRU list, and cached byte count.
-  mutable mutex mu_;
-
-  /// The block map (map from Key to Block).
-  BlockMap block_map_ GUARDED_BY(mu_);
-
-  /// The LRU list of block keys. The front of the list identifies the most
-  /// recently accessed block.
-  std::list<Key> lru_list_ GUARDED_BY(mu_);
-
-  /// The LRA (least recently added) list of block keys. The front of the list
-  /// identifies the most recently added block.
-  ///
-  /// Note: blocks are added to lra_list_ only after they have successfully been
-  /// fetched from the underlying block store.
-  std::list<Key> lra_list_ GUARDED_BY(mu_);
-
-  /// The combined number of bytes in all of the cached blocks.
-  size_t cache_size_ GUARDED_BY(mu_) = 0;
+  virtual size_t CacheSize() const = 0;
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 01ca0d76ba..84b65cec4f 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -36,6 +36,7 @@ limitations under the License.
 #include "tensorflow/core/platform/cloud/curl_http_request.h"
 #include "tensorflow/core/platform/cloud/file_block_cache.h"
 #include "tensorflow/core/platform/cloud/google_auth_provider.h"
+#include "tensorflow/core/platform/cloud/ram_file_block_cache.h"
 #include "tensorflow/core/platform/cloud/retrying_utils.h"
 #include "tensorflow/core/platform/cloud/time_util.h"
 #include "tensorflow/core/platform/env.h"
@@ -783,13 +784,13 @@ Status GcsFileSystem::NewRandomAccessFile(
 // A helper function to build a FileBlockCache for GcsFileSystem.
 std::unique_ptr<FileBlockCache> GcsFileSystem::MakeFileBlockCache(
     size_t block_size, size_t max_bytes, uint64 max_staleness) {
-  std::unique_ptr<FileBlockCache> file_block_cache(
-      new FileBlockCache(block_size, max_bytes, max_staleness,
-                         [this](const string& filename, size_t offset, size_t n,
-                                char* buffer, size_t* bytes_transferred) {
-                           return LoadBufferFromGCS(filename, offset, n, buffer,
-                                                    bytes_transferred);
-                         }));
+  std::unique_ptr<FileBlockCache> file_block_cache(new RamFileBlockCache(
+      block_size, max_bytes, max_staleness,
+      [this](const string& filename, size_t offset, size_t n, char* buffer,
+             size_t* bytes_transferred) {
+        return LoadBufferFromGCS(filename, offset, n, buffer,
+                                 bytes_transferred);
+      }));
   return file_block_cache;
 }
 
diff --git a/tensorflow/core/platform/cloud/file_block_cache.cc b/tensorflow/core/platform/cloud/ram_file_block_cache.cc
similarity index 89%
rename from tensorflow/core/platform/cloud/file_block_cache.cc
rename to tensorflow/core/platform/cloud/ram_file_block_cache.cc
index 6add1142a1..55a5657a50 100644
--- a/tensorflow/core/platform/cloud/file_block_cache.cc
+++ b/tensorflow/core/platform/cloud/ram_file_block_cache.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/core/platform/cloud/file_block_cache.h"
+#include "tensorflow/core/platform/cloud/ram_file_block_cache.h"
 #include <cstring>
 #include <memory>
 #include "tensorflow/core/lib/gtl/cleanup.h"
@@ -21,7 +21,7 @@ limitations under the License.
 
 namespace tensorflow {
 
-bool FileBlockCache::BlockNotStale(const std::shared_ptr<Block>& block) {
+bool RamFileBlockCache::BlockNotStale(const std::shared_ptr<Block>& block) {
   mutex_lock l(block->mu);
   if (block->state != FetchState::FINISHED) {
     return true;  // No need to check for staleness.
@@ -30,7 +30,8 @@ bool FileBlockCache::BlockNotStale(const std::shared_ptr<Block>& block) {
   return env_->NowSeconds() - block->timestamp <= max_staleness_;
 }
 
-std::shared_ptr<FileBlockCache::Block> FileBlockCache::Lookup(const Key& key) {
+std::shared_ptr<RamFileBlockCache::Block> RamFileBlockCache::Lookup(
+    const Key& key) {
   mutex_lock lock(mu_);
   auto entry = block_map_.find(key);
   if (entry != block_map_.end()) {
@@ -55,15 +56,15 @@ std::shared_ptr<FileBlockCache::Block> FileBlockCache::Lookup(const Key& key) {
 }
 
 // Remove blocks from the cache until we do not exceed our maximum size.
-void FileBlockCache::Trim() {
+void RamFileBlockCache::Trim() {
   while (!lru_list_.empty() && cache_size_ > max_bytes_) {
     RemoveBlock(block_map_.find(lru_list_.back()));
   }
 }
 
 /// Move the block to the front of the LRU list if it isn't already there.
-Status FileBlockCache::UpdateLRU(const Key& key,
-                                 const std::shared_ptr<Block>& block) {
+Status RamFileBlockCache::UpdateLRU(const Key& key,
+                                    const std::shared_ptr<Block>& block) {
   mutex_lock lock(mu_);
   if (block->timestamp == 0) {
     // The block was evicted from another thread. Allow it to remain evicted.
@@ -92,8 +93,8 @@ Status FileBlockCache::UpdateLRU(const Key& key,
   return Status::OK();
 }
 
-Status FileBlockCache::MaybeFetch(const Key& key,
-                                  const std::shared_ptr<Block>& block) {
+Status RamFileBlockCache::MaybeFetch(const Key& key,
+                                     const std::shared_ptr<Block>& block) {
   bool downloaded_block = false;
   auto reconcile_state =
       gtl::MakeCleanup([this, &downloaded_block, &key, &block] {
@@ -151,11 +152,11 @@ Status FileBlockCache::MaybeFetch(const Key& key,
     }
   }
   return errors::Internal(
-      "Control flow should never reach the end of FileBlockCache::Fetch.");
+      "Control flow should never reach the end of RamFileBlockCache::Fetch.");
 }
 
-Status FileBlockCache::Read(const string& filename, size_t offset, size_t n,
-                            char* buffer, size_t* bytes_transferred) {
+Status RamFileBlockCache::Read(const string& filename, size_t offset, size_t n,
+                               char* buffer, size_t* bytes_transferred) {
   *bytes_transferred = 0;
   if (n == 0) {
     return Status::OK();
@@ -216,12 +217,12 @@ Status FileBlockCache::Read(const string& filename, size_t offset, size_t n,
   return Status::OK();
 }
 
-size_t FileBlockCache::CacheSize() const {
+size_t RamFileBlockCache::CacheSize() const {
   mutex_lock lock(mu_);
   return cache_size_;
 }
 
-void FileBlockCache::Prune() {
+void RamFileBlockCache::Prune() {
   while (!WaitForNotificationWithTimeout(&stop_pruning_thread_, 1000000)) {
     mutex_lock lock(mu_);
     uint64 now = env_->NowSeconds();
@@ -238,7 +239,7 @@ void FileBlockCache::Prune() {
   }
 }
 
-void FileBlockCache::Flush() {
+void RamFileBlockCache::Flush() {
   mutex_lock lock(mu_);
   block_map_.clear();
   lru_list_.clear();
@@ -246,12 +247,12 @@ void FileBlockCache::Flush() {
   cache_size_ = 0;
 }
 
-void FileBlockCache::RemoveFile(const string& filename) {
+void RamFileBlockCache::RemoveFile(const string& filename) {
   mutex_lock lock(mu_);
   RemoveFile_Locked(filename);
 }
 
-void FileBlockCache::RemoveFile_Locked(const string& filename) {
+void RamFileBlockCache::RemoveFile_Locked(const string& filename) {
   Key begin = std::make_pair(filename, 0);
   auto it = block_map_.lower_bound(begin);
   while (it != block_map_.end() && it->first.first == filename) {
@@ -261,7 +262,7 @@ void FileBlockCache::RemoveFile_Locked(const string& filename) {
   }
 }
 
-void FileBlockCache::RemoveBlock(BlockMap::iterator entry) {
+void RamFileBlockCache::RemoveBlock(BlockMap::iterator entry) {
   // This signals that the block is removed, and should not be inadvertently
   // reinserted into the cache in UpdateLRU.
   entry->second->timestamp = 0;
diff --git a/tensorflow/core/platform/cloud/ram_file_block_cache.h b/tensorflow/core/platform/cloud/ram_file_block_cache.h
new file mode 100644
index 0000000000..7fdd7b2e02
--- /dev/null
+++ b/tensorflow/core/platform/cloud/ram_file_block_cache.h
@@ -0,0 +1,229 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_PLATFORM_CLOUD_RAM_FILE_BLOCK_CACHE_H_
+#define TENSORFLOW_CORE_PLATFORM_CLOUD_RAM_FILE_BLOCK_CACHE_H_
+
+#include <functional>
+#include <list>
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/platform/cloud/file_block_cache.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/notification.h"
+#include "tensorflow/core/platform/thread_annotations.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+/// \brief An LRU block cache of file contents, keyed by {filename, offset}.
+///
+/// This class should be shared by read-only random access files on a remote
+/// filesystem (e.g. GCS).
+class RamFileBlockCache : public FileBlockCache {
+ public:
+  /// The callback executed when a block is not found in the cache, and needs to
+  /// be fetched from the backing filesystem. This callback is provided when the
+  /// cache is constructed. The returned Status should be OK as long as the
+  /// read from the remote filesystem succeeded (similar to the semantics of the
+  /// read(2) system call).
+  typedef std::function<Status(const string& filename, size_t offset,
+                               size_t buffer_size, char* buffer,
+                               size_t* bytes_transferred)>
+      BlockFetcher;
+
+  RamFileBlockCache(size_t block_size, size_t max_bytes, uint64 max_staleness,
+                    BlockFetcher block_fetcher, Env* env = Env::Default())
+      : block_size_(block_size),
+        max_bytes_(max_bytes),
+        max_staleness_(max_staleness),
+        block_fetcher_(block_fetcher),
+        env_(env) {
+    if (max_staleness_ > 0) {
+      pruning_thread_.reset(env_->StartThread(ThreadOptions(), "TF_prune_FBC",
+                                              [this] { Prune(); }));
+    }
+  }
+
+  ~RamFileBlockCache() override {
+    if (pruning_thread_) {
+      stop_pruning_thread_.Notify();
+      // Destroying pruning_thread_ will block until Prune() receives the above
+      // notification and returns.
+      pruning_thread_.reset();
+    }
+  }
+
+  /// Read `n` bytes from `filename` starting at `offset` into `out`. This
+  /// method will return:
+  ///
+  /// 1) The error from the remote filesystem, if the read from the remote
+  ///    filesystem failed.
+  /// 2) PRECONDITION_FAILED if the read from the remote filesystem succeeded,
+  ///    but the read returned a partial block, and the LRU cache contained a
+  ///    block at a higher offset (indicating that the partial block should have
+  ///    been a full block).
+  /// 3) OUT_OF_RANGE if the read from the remote filesystem succeeded, but
+  ///    the file contents do not extend past `offset` and thus nothing was
+  ///    placed in `out`.
+  /// 4) OK otherwise (i.e. the read succeeded, and at least one byte was placed
+  ///    in `out`).
+  Status Read(const string& filename, size_t offset, size_t n, char* buffer,
+              size_t* bytes_transferred) override;
+
+  /// Remove all cached blocks for `filename`.
+  void RemoveFile(const string& filename) override LOCKS_EXCLUDED(mu_);
+
+  /// Remove all cached data.
+  void Flush() LOCKS_EXCLUDED(mu_) override;
+
+  /// Accessors for cache parameters.
+  size_t block_size() const override { return block_size_; }
+  size_t max_bytes() const override { return max_bytes_; }
+  uint64 max_staleness() const override { return max_staleness_; }
+
+  /// The current size (in bytes) of the cache.
+  size_t CacheSize() const override LOCKS_EXCLUDED(mu_);
+
+ private:
+  /// The size of the blocks stored in the LRU cache, as well as the size of the
+  /// reads from the underlying filesystem.
+  const size_t block_size_;
+  /// The maximum number of bytes (sum of block sizes) allowed in the LRU cache.
+  const size_t max_bytes_;
+  /// The maximum staleness of any block in the LRU cache, in seconds.
+  const uint64 max_staleness_;
+  /// The callback to read a block from the underlying filesystem.
+  const BlockFetcher block_fetcher_;
+  /// The Env from which we read timestamps.
+  Env* const env_;  // not owned
+
+  /// \brief The key type for the file block cache.
+  ///
+  /// The file block cache key is a {filename, offset} pair.
+  typedef std::pair<string, size_t> Key;
+
+  /// \brief The state of a block.
+  ///
+  /// A block begins in the CREATED stage. The first thread will attempt to read
+  /// the block from the filesystem, transitioning the state of the block to
+  /// FETCHING. After completing, if the read was successful the state should
+  /// be FINISHED. Otherwise the state should be ERROR. A subsequent read can
+  /// re-fetch the block if the state is ERROR.
+  enum class FetchState {
+    CREATED,
+    FETCHING,
+    FINISHED,
+    ERROR,
+  };
+
+  /// \brief A block of a file.
+  ///
+  /// A file block consists of the block data, the block's current position in
+  /// the LRU cache, the timestamp (seconds since epoch) at which the block
+  /// was cached, a coordination lock, and state & condition variables.
+  ///
+  /// Thread safety:
+  /// The iterator and timestamp fields should only be accessed while holding
+  /// the block-cache-wide mu_ instance variable. The state variable should only
+  /// be accessed while holding the Block's mu lock. The data vector should only
+  /// be accessed after state == FINISHED, and it should never be modified.
+  ///
+  /// In order to prevent deadlocks, never grab the block-cache-wide mu_ lock
+  /// AFTER grabbing any block's mu lock. It is safe to grab mu without locking
+  /// mu_.
+  struct Block {
+    /// The block data.
+    std::vector<char> data;
+    /// A list iterator pointing to the block's position in the LRU list.
+    std::list<Key>::iterator lru_iterator;
+    /// A list iterator pointing to the block's position in the LRA list.
+    std::list<Key>::iterator lra_iterator;
+    /// The timestamp (seconds since epoch) at which the block was cached.
+    uint64 timestamp;
+    /// Mutex to guard state variable
+    mutex mu;
+    /// The state of the block.
+    FetchState state GUARDED_BY(mu) = FetchState::CREATED;
+    /// Wait on cond_var if state is FETCHING.
+    condition_variable cond_var;
+  };
+
+  /// \brief The block map type for the file block cache.
+  ///
+  /// The block map is an ordered map from Key to Block.
+  typedef std::map<Key, std::shared_ptr<Block>> BlockMap;
+
+  /// Prune the cache by removing files with expired blocks.
+  void Prune() LOCKS_EXCLUDED(mu_);
+
+  bool BlockNotStale(const std::shared_ptr<Block>& block)
+      EXCLUSIVE_LOCKS_REQUIRED(mu_);
+
+  /// Look up a Key in the block cache.
+  std::shared_ptr<Block> Lookup(const Key& key) LOCKS_EXCLUDED(mu_);
+
+  Status MaybeFetch(const Key& key, const std::shared_ptr<Block>& block)
+      LOCKS_EXCLUDED(mu_);
+
+  /// Trim the block cache to make room for another entry.
+  void Trim() EXCLUSIVE_LOCKS_REQUIRED(mu_);
+
+  /// Update the LRU iterator for the block at `key`.
+  Status UpdateLRU(const Key& key, const std::shared_ptr<Block>& block)
+      LOCKS_EXCLUDED(mu_);
+
+  /// Remove all blocks of a file, with mu_ already held.
+  void RemoveFile_Locked(const string& filename) EXCLUSIVE_LOCKS_REQUIRED(mu_);
+
+  /// Remove the block `entry` from the block map and LRU list, and update the
+  /// cache size accordingly.
+  void RemoveBlock(BlockMap::iterator entry) EXCLUSIVE_LOCKS_REQUIRED(mu_);
+
+  /// The cache pruning thread that removes files with expired blocks.
+  std::unique_ptr<Thread> pruning_thread_;
+
+  /// Notification for stopping the cache pruning thread.
+  Notification stop_pruning_thread_;
+
+  /// Guards access to the block map, LRU list, and cached byte count.
+  mutable mutex mu_;
+
+  /// The block map (map from Key to Block).
+  BlockMap block_map_ GUARDED_BY(mu_);
+
+  /// The LRU list of block keys. The front of the list identifies the most
+  /// recently accessed block.
+  std::list<Key> lru_list_ GUARDED_BY(mu_);
+
+  /// The LRA (least recently added) list of block keys. The front of the list
+  /// identifies the most recently added block.
+  ///
+  /// Note: blocks are added to lra_list_ only after they have successfully been
+  /// fetched from the underlying block store.
+  std::list<Key> lra_list_ GUARDED_BY(mu_);
+
+  /// The combined number of bytes in all of the cached blocks.
+  size_t cache_size_ GUARDED_BY(mu_) = 0;
+};
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_CORE_PLATFORM_CLOUD_RAM_FILE_BLOCK_CACHE_H_
diff --git a/tensorflow/core/platform/cloud/file_block_cache_test.cc b/tensorflow/core/platform/cloud/ram_file_block_cache_test.cc
similarity index 92%
rename from tensorflow/core/platform/cloud/file_block_cache_test.cc
rename to tensorflow/core/platform/cloud/ram_file_block_cache_test.cc
index 596fdbf19e..d555b682a6 100644
--- a/tensorflow/core/platform/cloud/file_block_cache_test.cc
+++ b/tensorflow/core/platform/cloud/ram_file_block_cache_test.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/core/platform/cloud/file_block_cache.h"
+#include "tensorflow/core/platform/cloud/ram_file_block_cache.h"
 #include <cstring>
 #include "tensorflow/core/lib/core/blocking_counter.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -25,8 +25,8 @@ limitations under the License.
 namespace tensorflow {
 namespace {
 
-Status ReadCache(FileBlockCache* cache, const string& filename, size_t offset,
-                 size_t n, std::vector<char>* out) {
+Status ReadCache(RamFileBlockCache* cache, const string& filename,
+                 size_t offset, size_t n, std::vector<char>* out) {
   out->clear();
   out->resize(n, 0);
   size_t bytes_transferred = 0;
@@ -37,7 +37,7 @@ Status ReadCache(FileBlockCache* cache, const string& filename, size_t offset,
   return status;
 }
 
-TEST(FileBlockCacheTest, PassThrough) {
+TEST(RamFileBlockCacheTest, PassThrough) {
   const string want_filename = "foo/bar";
   const size_t want_offset = 42;
   const size_t want_n = 1024;
@@ -54,9 +54,9 @@ TEST(FileBlockCacheTest, PassThrough) {
     return Status::OK();
   };
   // If block_size, max_bytes, or both are zero, the cache is a pass-through.
-  FileBlockCache cache1(1, 0, 0, fetcher);
-  FileBlockCache cache2(0, 1, 0, fetcher);
-  FileBlockCache cache3(0, 0, 0, fetcher);
+  RamFileBlockCache cache1(1, 0, 0, fetcher);
+  RamFileBlockCache cache2(0, 1, 0, fetcher);
+  RamFileBlockCache cache3(0, 0, 0, fetcher);
   std::vector<char> out;
   TF_EXPECT_OK(ReadCache(&cache1, want_filename, want_offset, want_n, &out));
   EXPECT_EQ(calls, 1);
@@ -66,7 +66,7 @@ TEST(FileBlockCacheTest, PassThrough) {
   EXPECT_EQ(calls, 3);
 }
 
-TEST(FileBlockCacheTest, BlockAlignment) {
+TEST(RamFileBlockCacheTest, BlockAlignment) {
   // Initialize a 256-byte buffer.  This is the file underlying the reads we'll
   // do in this test.
   const size_t size = 256;
@@ -89,7 +89,7 @@ TEST(FileBlockCacheTest, BlockAlignment) {
   for (size_t block_size = 2; block_size <= 4; block_size++) {
     // Make a cache of N-byte block size (1 block) and verify that reads of
     // varying offsets and lengths return correct data.
-    FileBlockCache cache(block_size, block_size, 0, fetcher);
+    RamFileBlockCache cache(block_size, block_size, 0, fetcher);
     for (size_t offset = 0; offset < 10; offset++) {
       for (size_t n = block_size - 2; n <= block_size + 2; n++) {
         std::vector<char> got;
@@ -117,7 +117,7 @@ TEST(FileBlockCacheTest, BlockAlignment) {
   }
 }
 
-TEST(FileBlockCacheTest, CacheHits) {
+TEST(RamFileBlockCacheTest, CacheHits) {
   const size_t block_size = 16;
   std::set<size_t> calls;
   auto fetcher = [&calls, block_size](const string& filename, size_t offset,
@@ -132,7 +132,7 @@ TEST(FileBlockCacheTest, CacheHits) {
     return Status::OK();
   };
   const uint32 block_count = 256;
-  FileBlockCache cache(block_size, block_count * block_size, 0, fetcher);
+  RamFileBlockCache cache(block_size, block_count * block_size, 0, fetcher);
   std::vector<char> out;
   out.resize(block_count, 0);
   // The cache has space for `block_count` blocks. The loop with i = 0 should
@@ -146,7 +146,7 @@ TEST(FileBlockCacheTest, CacheHits) {
   }
 }
 
-TEST(FileBlockCacheTest, OutOfRange) {
+TEST(RamFileBlockCacheTest, OutOfRange) {
   // Tests reads of a 24-byte file with block size 16.
   const size_t block_size = 16;
   const size_t file_size = 24;
@@ -172,7 +172,7 @@ TEST(FileBlockCacheTest, OutOfRange) {
     *bytes_transferred = bytes_to_copy;
     return Status::OK();
   };
-  FileBlockCache cache(block_size, block_size, 0, fetcher);
+  RamFileBlockCache cache(block_size, block_size, 0, fetcher);
   std::vector<char> out;
   // Reading the first 16 bytes should be fine.
   TF_EXPECT_OK(ReadCache(&cache, "", 0, block_size, &out));
@@ -191,7 +191,7 @@ TEST(FileBlockCacheTest, OutOfRange) {
   EXPECT_EQ(out.size(), file_size - block_size);
 }
 
-TEST(FileBlockCacheTest, Inconsistent) {
+TEST(RamFileBlockCacheTest, Inconsistent) {
   // Tests the detection of interrupted reads leading to partially filled blocks
   // where we expected complete blocks.
   const size_t block_size = 16;
@@ -205,7 +205,7 @@ TEST(FileBlockCacheTest, Inconsistent) {
     *bytes_transferred = 1;
     return Status::OK();
   };
-  FileBlockCache cache(block_size, 2 * block_size, 0, fetcher);
+  RamFileBlockCache cache(block_size, 2 * block_size, 0, fetcher);
   std::vector<char> out;
   // Read the second block; this should yield an OK status and a single byte.
   TF_EXPECT_OK(ReadCache(&cache, "", block_size, block_size, &out));
@@ -216,7 +216,7 @@ TEST(FileBlockCacheTest, Inconsistent) {
   EXPECT_EQ(status.code(), error::INTERNAL);
 }
 
-TEST(FileBlockCacheTest, LRU) {
+TEST(RamFileBlockCacheTest, LRU) {
   const size_t block_size = 16;
   std::list<size_t> calls;
   auto fetcher = [&calls, block_size](const string& filename, size_t offset,
@@ -233,7 +233,7 @@ TEST(FileBlockCacheTest, LRU) {
     return Status::OK();
   };
   const uint32 block_count = 2;
-  FileBlockCache cache(block_size, block_count * block_size, 0, fetcher);
+  RamFileBlockCache cache(block_size, block_count * block_size, 0, fetcher);
   std::vector<char> out;
   // Read blocks from the cache, and verify the LRU behavior based on the
   // fetcher calls that the cache makes.
@@ -265,7 +265,7 @@ TEST(FileBlockCacheTest, LRU) {
   TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
 }
 
-TEST(FileBlockCacheTest, MaxStaleness) {
+TEST(RamFileBlockCacheTest, MaxStaleness) {
   int calls = 0;
   auto fetcher = [&calls](const string& filename, size_t offset, size_t n,
                           char* buffer, size_t* bytes_transferred) {
@@ -278,7 +278,7 @@ TEST(FileBlockCacheTest, MaxStaleness) {
   std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
   // Create a cache with max staleness of 2 seconds, and verify that it works as
   // expected.
-  FileBlockCache cache1(8, 16, 2 /* max staleness */, fetcher, env.get());
+  RamFileBlockCache cache1(8, 16, 2 /* max staleness */, fetcher, env.get());
   // Execute the first read to load the block.
   TF_EXPECT_OK(ReadCache(&cache1, "", 0, 1, &out));
   EXPECT_EQ(calls, 1);
@@ -294,7 +294,7 @@ TEST(FileBlockCacheTest, MaxStaleness) {
   // as expected.
   calls = 0;
   env->SetNowSeconds(0);
-  FileBlockCache cache2(8, 16, 0 /* max staleness */, fetcher, env.get());
+  RamFileBlockCache cache2(8, 16, 0 /* max staleness */, fetcher, env.get());
   // Execute the first read to load the block.
   TF_EXPECT_OK(ReadCache(&cache2, "", 0, 1, &out));
   EXPECT_EQ(calls, 1);
@@ -305,7 +305,7 @@ TEST(FileBlockCacheTest, MaxStaleness) {
   EXPECT_EQ(calls, 1);
 }
 
-TEST(FileBlockCacheTest, RemoveFile) {
+TEST(RamFileBlockCacheTest, RemoveFile) {
   int calls = 0;
   auto fetcher = [&calls](const string& filename, size_t offset, size_t n,
                           char* buffer, size_t* bytes_transferred) {
@@ -321,7 +321,7 @@ TEST(FileBlockCacheTest, RemoveFile) {
   };
   // This cache has space for 4 blocks; we'll read from two files.
   const size_t n = 3;
-  FileBlockCache cache(8, 32, 0, fetcher);
+  RamFileBlockCache cache(8, 32, 0, fetcher);
   std::vector<char> out;
   std::vector<char> a(n, 'a');
   std::vector<char> b(n, 'b');
@@ -367,7 +367,7 @@ TEST(FileBlockCacheTest, RemoveFile) {
   EXPECT_EQ(calls, 6);
 }
 
-TEST(FileBlockCacheTest, Prune) {
+TEST(RamFileBlockCacheTest, Prune) {
   int calls = 0;
   auto fetcher = [&calls](const string& filename, size_t offset, size_t n,
                           char* buffer, size_t* bytes_transferred) {
@@ -381,7 +381,7 @@ TEST(FileBlockCacheTest, Prune) {
   std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
   uint64 now = Env::Default()->NowSeconds();
   env->SetNowSeconds(now);
-  FileBlockCache cache(8, 32, 1 /* max staleness */, fetcher, env.get());
+  RamFileBlockCache cache(8, 32, 1 /* max staleness */, fetcher, env.get());
   // Read three blocks into the cache, and advance the timestamp by one second
   // with each read. Start with a block of "a" at the current timestamp `now`.
   TF_EXPECT_OK(ReadCache(&cache, "a", 0, 1, &out));
@@ -426,7 +426,7 @@ TEST(FileBlockCacheTest, Prune) {
   EXPECT_EQ(cache.CacheSize(), 0);
 }
 
-TEST(FileBlockCacheTest, ParallelReads) {
+TEST(RamFileBlockCacheTest, ParallelReads) {
   // This fetcher won't respond until either `callers` threads are calling it
   // concurrently (at which point it will respond with success to all callers),
   // or 10 seconds have elapsed (at which point it will respond with an error).
@@ -444,7 +444,7 @@ TEST(FileBlockCacheTest, ParallelReads) {
     return Status::OK();
   };
   const int block_size = 8;
-  FileBlockCache cache(block_size, 2 * callers * block_size, 0, fetcher);
+  RamFileBlockCache cache(block_size, 2 * callers * block_size, 0, fetcher);
   std::vector<std::unique_ptr<Thread>> threads;
   for (int i = 0; i < callers; i++) {
     threads.emplace_back(
@@ -461,7 +461,7 @@ TEST(FileBlockCacheTest, ParallelReads) {
   // executed, or 10 seconds have passed).
 }
 
-TEST(FileBlockCacheTest, CoalesceConcurrentReads) {
+TEST(RamFileBlockCacheTest, CoalesceConcurrentReads) {
   // Concurrent reads to the same file blocks should be de-duplicated.
   const size_t block_size = 16;
   int num_requests = 0;
@@ -479,7 +479,7 @@ TEST(FileBlockCacheTest, CoalesceConcurrentReads) {
     Env::Default()->SleepForMicroseconds(100000);  // 0.1 secs
     return Status::OK();
   };
-  FileBlockCache cache(block_size, block_size, 0, fetcher);
+  RamFileBlockCache cache(block_size, block_size, 0, fetcher);
   // Fork off thread for parallel read.
   std::unique_ptr<Thread> concurrent(
       Env::Default()->StartThread({}, "concurrent", [&cache, block_size] {
@@ -496,7 +496,7 @@ TEST(FileBlockCacheTest, CoalesceConcurrentReads) {
   EXPECT_EQ(1, num_requests);
 }
 
-TEST(FileBlockCacheTest, Flush) {
+TEST(RamFileBlockCacheTest, Flush) {
   int calls = 0;
   auto fetcher = [&calls](const string& filename, size_t offset, size_t n,
                           char* buffer, size_t* bytes_transferred) {
@@ -505,7 +505,7 @@ TEST(FileBlockCacheTest, Flush) {
     *bytes_transferred = n;
     return Status::OK();
   };
-  FileBlockCache cache(16, 32, 0, fetcher);
+  RamFileBlockCache cache(16, 32, 0, fetcher);
   std::vector<char> out;
   TF_EXPECT_OK(ReadCache(&cache, "", 0, 16, &out));
   TF_EXPECT_OK(ReadCache(&cache, "", 0, 16, &out));
-- 
GitLab


From 45f56944c862a8c67c34efedcee501f365a08aee Mon Sep 17 00:00:00 2001
From: Suharsh Sivakumar <suharshs@google.com>
Date: Fri, 2 Mar 2018 12:25:13 -0800
Subject: [PATCH 556/575] FreezeSavedModel support for ResourceVariables.

PiperOrigin-RevId: 187653676
---
 tensorflow/cc/tools/BUILD                     |   1 +
 tensorflow/cc/tools/freeze_saved_model.cc     |  55 +++-
 .../cc/tools/freeze_saved_model_test.cc       | 268 +++++++++++-------
 3 files changed, 211 insertions(+), 113 deletions(-)

diff --git a/tensorflow/cc/tools/BUILD b/tensorflow/cc/tools/BUILD
index 97f66e79b8..f413a5cc52 100644
--- a/tensorflow/cc/tools/BUILD
+++ b/tensorflow/cc/tools/BUILD
@@ -32,6 +32,7 @@ tf_cc_test(
     deps = [
         ":freeze_saved_model",
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:resource_variable_ops",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:protos_all_cc",
diff --git a/tensorflow/cc/tools/freeze_saved_model.cc b/tensorflow/cc/tools/freeze_saved_model.cc
index ddf372cdef..4ddddcb586 100644
--- a/tensorflow/cc/tools/freeze_saved_model.cc
+++ b/tensorflow/cc/tools/freeze_saved_model.cc
@@ -75,16 +75,13 @@ void GetNodeNameToNodeDefMap(
 // variable nodes to convert.
 void GetReachableNodesAndVariables(
     GraphDef* graph_def, const std::unordered_set<string>& outputs,
+    const std::unordered_map<string, NodeDef*>& name_to_node_map,
     std::unordered_set<string>* reachable_node_names,
     std::unordered_set<string>* variable_node_names) {
   // TODO(suharshs): Add support for ResourceVariables.
   static const std::unordered_set<string>* kVariableTypes =
-      new std::unordered_set<string>({"Variable", "VariableV2"});
-  // name_to_node_map is needed to get the inputs from the NodeDef corresponding
-  // the a string node name. These inputs are used when doing our backwards
-  // traversal.
-  std::unordered_map<string, NodeDef*> name_to_node_map;
-  GetNodeNameToNodeDefMap(graph_def, &name_to_node_map);
+      new std::unordered_set<string>({"Variable", "VariableV2", "VarHandleOp"});
+
   std::queue<string> nodes_to_visit;
   for (const string& tensor_name : outputs) {
     // We need to strip off the tensor part to get the node name.
@@ -99,7 +96,7 @@ void GetReachableNodesAndVariables(
       continue;
     }
     reachable_node_names->insert(node_name);
-    NodeDef* node = name_to_node_map[node_name];
+    NodeDef* node = name_to_node_map.at(node_name);
     if (kVariableTypes->find(node->op()) != kVariableTypes->end()) {
       variable_node_names->insert(node->name());
     }
@@ -111,7 +108,9 @@ void GetReachableNodesAndVariables(
 
 // Gets a map from variable name to variable value.
 Status GetVariableNameToTensorMap(
-    Session* session, std::unordered_set<string> variable_names_set,
+    Session* session,
+    const std::unordered_map<string, NodeDef*>& name_to_node_map,
+    std::unordered_set<string> variable_names_set,
     std::unordered_map<string, Tensor>* variable_name_to_value_map) {
   if (variable_names_set.empty()) {
     return Status::OK();
@@ -120,8 +119,14 @@ Status GetVariableNameToTensorMap(
   std::vector<string> tensor_names;
   for (const string& node_name : variable_names_set) {
     variable_names.push_back(node_name);
-    // We need to run tensors, so append ":0".
-    tensor_names.push_back(node_name + ":0");
+    NodeDef* node_def = name_to_node_map.at(node_name);
+    if (node_def->op() == "VarHandleOp") {
+      // If this is a resource variable, we have to run the corresponding
+      // ReadVariableOp.
+      tensor_names.push_back(node_name + "/Read/ReadVariableOp:0");
+    } else {
+      tensor_names.push_back(node_name + ":0");
+    }
   }
   std::vector<Tensor> outputs;
   TF_RETURN_IF_ERROR(
@@ -143,6 +148,15 @@ void ConvertVariableToConstant(const NodeDef& variable_node,
       (*const_node->mutable_attr())["value"].mutable_tensor());
 }
 
+// Converts a ReadVariableOp NodeDef to an Identity NodeDef.
+void ConvertReadVariableOpToIdentity(const NodeDef& node,
+                                     NodeDef* identity_node) {
+  identity_node->set_name(node.name());
+  identity_node->set_op("Identity");
+  (*identity_node->mutable_attr())["T"] = node.attr().at("dtype");
+  identity_node->add_input(node.input(0));
+}
+
 // Freezes the subgraph of all nodes needed by `outputs`.
 Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle,
                       const std::unordered_set<string>& outputs,
@@ -155,14 +169,19 @@ Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle,
   if (graph_def.node_size() == 0) {
     return Status::OK();
   }
+  // name_to_node_map is needed to get the inputs from the NodeDef corresponding
+  // the a string node name. These inputs are used when doing our backwards
+  // traversal.
+  std::unordered_map<string, NodeDef*> name_to_node_map;
+  GetNodeNameToNodeDefMap(&graph_def, &name_to_node_map);
   std::unordered_set<string> reachable_node_names;
   std::unordered_set<string> variable_node_names;
-  GetReachableNodesAndVariables(&graph_def, outputs, &reachable_node_names,
-                                &variable_node_names);
+  GetReachableNodesAndVariables(&graph_def, outputs, name_to_node_map,
+                                &reachable_node_names, &variable_node_names);
   std::unordered_map<string, Tensor> variable_to_value_map;
-  TF_RETURN_IF_ERROR(
-      GetVariableNameToTensorMap(saved_model_bundle.session.get(),
-                                 variable_node_names, &variable_to_value_map));
+  TF_RETURN_IF_ERROR(GetVariableNameToTensorMap(
+      saved_model_bundle.session.get(), name_to_node_map, variable_node_names,
+      &variable_to_value_map));
   // We copy the nodes in the same order they were in the original graph_def.
   for (const NodeDef& node : graph_def.node()) {
     if (reachable_node_names.find(node.name()) == reachable_node_names.end()) {
@@ -171,6 +190,12 @@ Status FreezeGraphDef(const SavedModelBundle& saved_model_bundle,
     if (variable_node_names.find(node.name()) != variable_node_names.end()) {
       ConvertVariableToConstant(node, variable_to_value_map[node.name()],
                                 frozen_graph_def->add_node());
+    } else if (node.op() == "ReadVariableOp" &&
+               variable_node_names.find(node.input(0)) !=
+                   variable_node_names.end()) {
+      // If the node is a ReadVariableOp, its input VarHandleOp will be
+      // converted to a Constant, so we will need to convert it to an Identity.
+      ConvertReadVariableOpToIdentity(node, frozen_graph_def->add_node());
     } else {
       // If the node isn't a variable, just copy the node as-is.
       *frozen_graph_def->add_node() = node;
diff --git a/tensorflow/cc/tools/freeze_saved_model_test.cc b/tensorflow/cc/tools/freeze_saved_model_test.cc
index 52a81a5028..cd35fd3b95 100644
--- a/tensorflow/cc/tools/freeze_saved_model_test.cc
+++ b/tensorflow/cc/tools/freeze_saved_model_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/cc/tools/freeze_saved_model.h"
 
+#include "tensorflow/cc/ops/resource_variable_ops.h"
 #include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/core/framework/function_testlib.h"
 #include "tensorflow/core/framework/graph.pb.h"
@@ -113,6 +114,160 @@ class FreezeTest : public ::testing::Test {
 
     test::ExpectTensorEqual<float>(unfrozen_outputs[0], frozen_outputs[0]);
   }
+
+  void TestFreezeGraphWithoutDependentVariables(bool use_resource) {
+    // Test freezing a graph with variables that are not needed by the outputs
+    // in the SignatureDef. The resulting graph shouldn't be frozen, but
+    // non-dependent nodes should be pruned.
+    SavedModelBundle saved_model_bundle;
+    GraphDef graph_def;
+    Scope scope = Scope::NewRootScope();
+    Output a = ops::Const(scope.WithOpName("a"), 10.0f, {});
+    Output b = ops::Const(scope.WithOpName("b"), 10.0f, {});
+    Output c = ops::Mul(scope.WithOpName("c"), a, b);
+    if (use_resource) {
+      Output var =
+          ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {});
+      Output read_var = ops::ReadVariableOp(
+          scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT);
+      auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a);
+    } else {
+      Output var =
+          ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT);
+      Output assign = ops::Assign(scope.WithOpName("assign"), var, a);
+    }
+
+    TF_ASSERT_OK(scope.ToGraphDef(&graph_def));
+    // "c" isnt dependent on the variable, so nothing should be frozen.
+    TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle(
+        graph_def, {"c:0"}, "assign", &saved_model_bundle));
+
+    GraphDef frozen_graph_def;
+    std::unordered_set<string> inputs;
+    std::unordered_set<string> outputs;
+    TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def,
+                                  &inputs, &outputs));
+
+    GraphDef expected_graph_def;
+    Scope expected_scope = Scope::NewRootScope();
+    Output expected_a = ops::Const(expected_scope.WithOpName("a"), 10.0f, {});
+    Output expected_b = ops::Const(expected_scope.WithOpName("b"), 10.0f, {});
+    Output expected_c =
+        ops::Mul(expected_scope.WithOpName("c"), expected_a, expected_b);
+    TF_ASSERT_OK(expected_scope.ToGraphDef(&expected_graph_def));
+
+    GraphDefEqual(frozen_graph_def, expected_graph_def);
+
+    RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(),
+                                         frozen_graph_def, "c:0");
+  }
+
+  void TestFreezeGraphWithDependentVariables(bool use_resource) {
+    // Test freezing a graph with variables that are needed by outputs in the
+    // SignatureDef. The variables should be frozen.
+    SavedModelBundle saved_model_bundle;
+    GraphDef graph_def;
+    Scope scope = Scope::NewRootScope();
+    Output a = ops::Const(scope.WithOpName("a"), 10.0f, {});
+    Output read_var;
+    if (use_resource) {
+      Output var =
+          ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {});
+      read_var = ops::ReadVariableOp(
+          scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT);
+      auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a);
+    } else {
+      Output read_var =
+          ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT);
+      Output assign = ops::Assign(scope.WithOpName("assign"), read_var, a);
+    }
+    Output c = ops::Mul(scope.WithOpName("c"), a, read_var);
+    TF_ASSERT_OK(scope.ToGraphDef(&graph_def));
+    // "c" isnt dependent on the variable, so nothing should be frozen.
+    TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle(
+        graph_def, {"c:0"}, "assign", &saved_model_bundle));
+
+    GraphDef frozen_graph_def;
+    std::unordered_set<string> inputs;
+    std::unordered_set<string> outputs;
+    TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def,
+                                  &inputs, &outputs));
+
+    // If using normal variables there should be 3 nodes in the resulting
+    // graph_def. If using resource variables there should be 4 nodes in the
+    // resulting graph_def.
+    // In both cases, none should be variables.
+    size_t expected_nodes = use_resource ? 4 : 3;
+    EXPECT_EQ(frozen_graph_def.node_size(), expected_nodes);
+    for (const NodeDef& node : frozen_graph_def.node()) {
+      EXPECT_NE(node.op(), "Variable") << node.name();
+      EXPECT_NE(node.op(), "VariableV2") << node.name();
+      EXPECT_NE(node.op(), "VarHandleOp") << node.name();
+      EXPECT_NE(node.op(), "ReadVariableOp") << node.name();
+    }
+
+    RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(),
+                                         frozen_graph_def, "c:0");
+  }
+
+  void TestFreezeGraphWithAndWithoutDependentVariables(bool use_resource) {
+    // Test freezing a graph with some variables that are needed and not needed
+    // by
+    // the outputs in the SignatureDef. The resulting graph should only freeze
+    // dependent variables.
+    SavedModelBundle saved_model_bundle;
+    GraphDef graph_def;
+    Scope scope = Scope::NewRootScope();
+    Output a = ops::Const(scope.WithOpName("a"), 10.0f, {});
+    Output read_var;
+
+    if (use_resource) {
+      Output var =
+          ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {});
+      read_var = ops::ReadVariableOp(
+          scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT);
+      auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a);
+      Output var_1 =
+          ops::VarHandleOp(scope.WithOpName("var_1"), DataType::DT_FLOAT, {});
+      Output read_var_1 =
+          ops::ReadVariableOp(scope.WithOpName("var_1/Read/ReadVariableOp"),
+                              var, DataType::DT_FLOAT);
+      auto assign_1 =
+          ops::AssignVariableOp(scope.WithOpName("assign_1"), var_1, a);
+    } else {
+      read_var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT);
+      Output assign = ops::Assign(scope.WithOpName("assign"), read_var, a);
+      Output var_1 =
+          ops::Variable(scope.WithOpName("var_1"), {}, DataType::DT_FLOAT);
+      Output assign_1 = ops::Assign(scope.WithOpName("assign_1"), var_1, a);
+    }
+
+    Output c = ops::Mul(scope.WithOpName("c"), a, read_var);
+    TF_ASSERT_OK(scope.ToGraphDef(&graph_def));
+    // "c" isnt dependent on the variable, so nothing should be frozen.
+    TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle(
+        graph_def, {"c:0"}, "assign", &saved_model_bundle));
+
+    GraphDef frozen_graph_def;
+    std::unordered_set<string> inputs;
+    std::unordered_set<string> outputs;
+    TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def,
+                                  &inputs, &outputs));
+
+    // There should be 3 nodes in the resulting graph_def, and none should be
+    // variables.
+    size_t expected_nodes = use_resource ? 4 : 3;
+    EXPECT_EQ(frozen_graph_def.node_size(), expected_nodes);
+    for (const NodeDef& node : frozen_graph_def.node()) {
+      EXPECT_NE(node.op(), "Variable") << node.name();
+      EXPECT_NE(node.op(), "VariableV2") << node.name();
+      EXPECT_NE(node.op(), "VarHandleOp") << node.name();
+      EXPECT_NE(node.op(), "ReadVariableOp") << node.name();
+    }
+
+    RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(),
+                                         frozen_graph_def, "c:0");
+  }
 };
 
 TEST_F(FreezeTest, InputsAndOutputsSingleSignatureDef) {
@@ -196,111 +351,28 @@ TEST_F(FreezeTest, GraphDefWithNoVariables) {
   GraphDefEqual(frozen_graph_def, graph_def);
 }
 
-TEST_F(FreezeTest, GraphDefWithVariablesNotNeededByOutputs) {
-  // Test freezing a graph with variables that are not needed by the outputs in
-  // the SignatureDef. The resulting graph shouldn't be frozen, but
-  // non-dependent nodes should be pruned.
-  SavedModelBundle saved_model_bundle;
-  GraphDef graph_def;
-  Scope scope = Scope::NewRootScope();
-  Output a = ops::Const(scope.WithOpName("a"), 10.0f, {});
-  Output b = ops::Const(scope.WithOpName("b"), 10.0f, {});
-  Output c = ops::Mul(scope.WithOpName("c"), a, b);
-  Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT);
-  Output assign = ops::Assign(scope.WithOpName("assign"), var, a);
-  TF_ASSERT_OK(scope.ToGraphDef(&graph_def));
-  // "c" isnt dependent on the variable, so nothing should be frozen.
-  TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle(
-      graph_def, {"c:0"}, assign.name(), &saved_model_bundle));
-
-  GraphDef frozen_graph_def;
-  std::unordered_set<string> inputs;
-  std::unordered_set<string> outputs;
-  TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs,
-                                &outputs));
-
-  GraphDef expected_graph_def;
-  Scope expected_scope = Scope::NewRootScope();
-  Output expected_a = ops::Const(expected_scope.WithOpName("a"), 10.0f, {});
-  Output expected_b = ops::Const(expected_scope.WithOpName("b"), 10.0f, {});
-  Output expected_c =
-      ops::Mul(expected_scope.WithOpName("c"), expected_a, expected_b);
-  TF_ASSERT_OK(expected_scope.ToGraphDef(&expected_graph_def));
-
-  GraphDefEqual(frozen_graph_def, expected_graph_def);
-
-  RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(),
-                                       frozen_graph_def, "c:0");
+TEST_F(FreezeTest, GraphDefWithoutDependentVariables) {
+  TestFreezeGraphWithoutDependentVariables(false);
 }
 
-TEST_F(FreezeTest, GraphDefWithVariablesNeededByOutputs) {
-  // Test freezing a graph with variables that are needed by outputs in the
-  // SignatureDef. The variables should be frozen.
-  SavedModelBundle saved_model_bundle;
-  GraphDef graph_def;
-  Scope scope = Scope::NewRootScope();
-  Output a = ops::Const(scope.WithOpName("a"), 10.0f, {});
-  Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT);
-  Output c = ops::Mul(scope.WithOpName("c"), a, var);
-  Output assign = ops::Assign(scope.WithOpName("assign"), var, a);
-  TF_ASSERT_OK(scope.ToGraphDef(&graph_def));
-  // "c" isnt dependent on the variable, so nothing should be frozen.
-  TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle(
-      graph_def, {"c:0"}, assign.name(), &saved_model_bundle));
-
-  GraphDef frozen_graph_def;
-  std::unordered_set<string> inputs;
-  std::unordered_set<string> outputs;
-  TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs,
-                                &outputs));
-
-  // There should be 3 nodes in the resulting graph_def, and none should be
-  // variables.
-  EXPECT_EQ(frozen_graph_def.node_size(), 3);
-  for (const NodeDef& node : frozen_graph_def.node()) {
-    EXPECT_NE(node.op(), "Variable") << node.name();
-    EXPECT_NE(node.op(), "VariableV2") << node.name();
-  }
-
-  RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(),
-                                       frozen_graph_def, "c:0");
+TEST_F(FreezeTest, GraphDefWithoutDependentResourceVariables) {
+  TestFreezeGraphWithoutDependentVariables(true);
 }
 
-TEST_F(FreezeTest, GraphDefWithVariablesNeededAndNotNeededByOutputs) {
-  // Test freezing a graph with some variables that are needed and not needed by
-  // the outputs in the SignatureDef. The resulting graph should only freeze
-  // dependent variables.
-  SavedModelBundle saved_model_bundle;
-  GraphDef graph_def;
-  Scope scope = Scope::NewRootScope();
-  Output a = ops::Const(scope.WithOpName("a"), 10.0f, {});
-  Output var = ops::Variable(scope.WithOpName("var"), {}, DataType::DT_FLOAT);
-  Output c = ops::Mul(scope.WithOpName("c"), a, var);
-  Output assign = ops::Assign(scope.WithOpName("assign"), var, a);
-  Output var_1 =
-      ops::Variable(scope.WithOpName("var_1"), {}, DataType::DT_FLOAT);
-  Output assign_1 = ops::Assign(scope.WithOpName("assign_1"), var, a);
-  TF_ASSERT_OK(scope.ToGraphDef(&graph_def));
-  // "c" isnt dependent on the variable, so nothing should be frozen.
-  TF_ASSERT_OK(AddGraphDefWithOutputsToSavedModelBundle(
-      graph_def, {"c:0"}, assign.name(), &saved_model_bundle));
+TEST_F(FreezeTest, GraphDefWithDependentVariables) {
+  TestFreezeGraphWithDependentVariables(false);
+}
 
-  GraphDef frozen_graph_def;
-  std::unordered_set<string> inputs;
-  std::unordered_set<string> outputs;
-  TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs,
-                                &outputs));
+TEST_F(FreezeTest, GraphDefWithDependentResourceVariables) {
+  TestFreezeGraphWithDependentVariables(true);
+}
 
-  // There should be 3 nodes in the resulting graph_def, and none should be
-  // variables.
-  EXPECT_EQ(frozen_graph_def.node_size(), 3);
-  for (const NodeDef& node : frozen_graph_def.node()) {
-    EXPECT_NE(node.op(), "Variable") << node.name();
-    EXPECT_NE(node.op(), "VariableV2") << node.name();
-  }
+TEST_F(FreezeTest, GraphDefWithAndWithoutDependentVariables) {
+  TestFreezeGraphWithAndWithoutDependentVariables(false);
+}
 
-  RunAndCompareFrozenAndUnfrozenGraphs(saved_model_bundle.session.get(),
-                                       frozen_graph_def, "c:0");
+TEST_F(FreezeTest, GraphDefWithAndWithoutDependentResourceVariables) {
+  TestFreezeGraphWithAndWithoutDependentVariables(true);
 }
 
 }  // namespace
-- 
GitLab


From faab0cf5407dcf11967371b51b97f8eef6964a35 Mon Sep 17 00:00:00 2001
From: Guangda Lai <laigd@google.com>
Date: Fri, 2 Mar 2018 12:33:40 -0800
Subject: [PATCH 557/575] Exclude flaky tests for cuda_on_cpu.

PiperOrigin-RevId: 187654568
---
 tensorflow/contrib/data/python/kernel_tests/BUILD    | 1 +
 tensorflow/contrib/eager/python/examples/spinn/BUILD | 5 ++++-
 tensorflow/python/BUILD                              | 6 +++++-
 tensorflow/python/feature_column/BUILD               | 5 ++++-
 4 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD
index 82cd276ce8..10cb05ece1 100644
--- a/tensorflow/contrib/data/python/kernel_tests/BUILD
+++ b/tensorflow/contrib/data/python/kernel_tests/BUILD
@@ -168,6 +168,7 @@ py_test(
     srcs = ["interleave_dataset_op_test.py"],
     srcs_version = "PY2AND3",
     tags = [
+        "no_cuda_on_cpu_tap",
         "no_oss",
         "no_pip",
     ],
diff --git a/tensorflow/contrib/eager/python/examples/spinn/BUILD b/tensorflow/contrib/eager/python/examples/spinn/BUILD
index a1f8a759e2..98d01ad1d5 100644
--- a/tensorflow/contrib/eager/python/examples/spinn/BUILD
+++ b/tensorflow/contrib/eager/python/examples/spinn/BUILD
@@ -38,5 +38,8 @@ cuda_py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_test_lib",
     ],
-    tags = ["no_pip"],  # because spinn.py is under third_party/.
+    tags = [
+        "no_cuda_on_cpu_tap",
+        "no_pip",  # because spinn.py is under third_party/.
+    ],
 )
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index cb54cebf0f..f282abb0a5 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -3945,7 +3945,10 @@ py_test(
     size = "small",
     srcs = ["training/checkpoint_utils_test.py"],
     srcs_version = "PY2AND3",
-    tags = ["no_windows"],
+    tags = [
+        "no_cuda_on_cpu_tap",
+        "no_windows",
+    ],
     deps = [
         ":client",
         ":client_testlib",
@@ -4739,6 +4742,7 @@ py_test(
     srcs_version = "PY2AND3",
     tags = [
         "grappler",
+        "no_cuda_on_cpu_tap",
         "no_pip",
     ],
     deps = [
diff --git a/tensorflow/python/feature_column/BUILD b/tensorflow/python/feature_column/BUILD
index a758f8a4fc..238a90b67d 100644
--- a/tensorflow/python/feature_column/BUILD
+++ b/tensorflow/python/feature_column/BUILD
@@ -74,7 +74,10 @@ py_test(
     srcs = ["feature_column_test.py"],
     data = [":vocabulary_testdata"],
     srcs_version = "PY2AND3",
-    tags = ["no_pip"],
+    tags = [
+        "no_cuda_on_cpu_tap",
+        "no_pip",
+    ],
     deps = [
         ":feature_column",
         ":feature_column_py",
-- 
GitLab


From 85daa2e4553e49ca6ab2fbb412b18c23b5399524 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 2 Mar 2018 12:43:22 -0800
Subject: [PATCH 558/575] TFTS: Switch more variables to ResourceVariables to
 avoid race conditions

The LSTM example test was a bit flaky.

PiperOrigin-RevId: 187655714
---
 .../contrib/timeseries/python/timeseries/head.py      | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py
index f0330bfbbd..8731b10923 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/head.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/head.py
@@ -73,7 +73,10 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
 
   def _train_ops(self, features):
     """Add training ops to the graph."""
-    with variable_scope.variable_scope("model"):
+    with variable_scope.variable_scope(
+        "model",
+        # Use ResourceVariables to avoid race conditions.
+        use_resource=True):
       model_outputs = self.state_manager.define_loss(
           self.model, features, estimator_lib.ModeKeys.TRAIN)
 
@@ -107,7 +110,7 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
 
   def _evaluate_ops(self, features):
     """Add ops for evaluation (aka filtering) to the graph."""
-    with variable_scope.variable_scope("model"):
+    with variable_scope.variable_scope("model", use_resource=True):
       model_outputs = self.state_manager.define_loss(
           self.model, features, estimator_lib.ModeKeys.EVAL)
     metrics = {}
@@ -128,7 +131,7 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
 
   def _predict_ops(self, features):
     """Add ops for prediction to the graph."""
-    with variable_scope.variable_scope("model"):
+    with variable_scope.variable_scope("model", use_resource=True):
       prediction = self.model.predict(features=features)
     prediction[feature_keys.PredictionResults.TIMES] = features[
         feature_keys.PredictionFeatures.TIMES]
@@ -137,7 +140,7 @@ class _TimeSeriesRegressionHead(head_lib._Head):  # pylint:disable=protected-acc
 
   def _serving_ops(self, features):
     """Add ops for serving to the graph."""
-    with variable_scope.variable_scope("model"):
+    with variable_scope.variable_scope("model", use_resource=True):
       prediction_outputs = self.model.predict(features=features)
     with variable_scope.variable_scope("model", reuse=True):
       filtering_outputs = self.state_manager.define_loss(
-- 
GitLab


From e0fac18b63e80963d42cb1e39243d84ae86ae01a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 12:58:08 -0800
Subject: [PATCH 559/575] Automated g4 rollback of changelist 187582263

PiperOrigin-RevId: 187657654
---
 .../grappler/optimizers/memory_optimizer.cc   | 23 +++++++------
 .../grappler/optimizers/memory_optimizer.h    | 10 +++---
 .../grappler/optimizers/meta_optimizer.cc     |  4 +--
 .../core/protobuf/rewriter_config.proto       | 19 ++++++-----
 .../python/grappler/memory_optimizer_test.py  | 32 ++++++++++++++++++-
 5 files changed, 58 insertions(+), 30 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.cc b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
index 694139fa50..27e9d2c78d 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.cc
@@ -413,7 +413,7 @@ void RecomputeSubgraph(
 }
 
 void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
-                                const string& recomputation_targets_name_prefix,
+                                const string& recomputation_targets_name_scope,
                                 GraphDef* graph, const GrapplerItem& item) {
   if (optimization_level != RewriterConfig::RECOMPUTATION_HEURISTICS &&
       optimization_level != RewriterConfig::HEURISTICS &&
@@ -438,15 +438,14 @@ void RecomputationRewritingPass(RewriterConfig::MemOptType optimization_level,
     feeds.insert(NodeName(feed.first));
   }
   std::function<bool(const NodeDef&)> is_target =
-      [&recomputation_targets_name_prefix](const NodeDef& node) {
-        // Nodes whose inputs we may want to recompute. Typically targets will
-        // be gradients (recomputation_targets_name_prefix="gradients/"),
-        // although the prefix is configurable since gradients may be created
-        // in a name scope.
-        // TODO(allenl): Use a static schedule
-        // (grappler::EstimateEarliestExecutionTimes) to recompute only nodes
-        // whose outputs will sit around for a while.
-        return node.name().find(recomputation_targets_name_prefix) == 0;
+      [&recomputation_targets_name_scope](const NodeDef& node) {
+        // Nodes whose inputs we may want to recompute. This matches node names
+        // that contain recomputation_targets_name_scope as a name scope,
+        // meaning it either begins with or contains the name scope.
+        // Defaults to "gradients/" which will match any node names that begins
+        // with "gradients/" or contains "/gradients/".
+        return node.name().find(recomputation_targets_name_scope) == 0 ||
+               node.name().find("/" + recomputation_targets_name_scope) != -1;
       };
 
   if (optimization_level == RewriterConfig::RECOMPUTATION_HEURISTICS ||
@@ -1225,8 +1224,8 @@ Status MemoryOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
   *optimized_graph = item.graph;
 
   RecomputationRewritingPass(optimization_level_,
-                             recomputation_targets_name_prefix_,
-                             optimized_graph, item);
+                             recomputation_targets_name_scope_, optimized_graph,
+                             item);
 
   GrapplerItem optimized_item(item, std::move(*optimized_graph));
   std::unordered_set<string> skip_list;
diff --git a/tensorflow/core/grappler/optimizers/memory_optimizer.h b/tensorflow/core/grappler/optimizers/memory_optimizer.h
index c3dd0c45c6..5c555a2674 100644
--- a/tensorflow/core/grappler/optimizers/memory_optimizer.h
+++ b/tensorflow/core/grappler/optimizers/memory_optimizer.h
@@ -27,14 +27,14 @@ class MemoryOptimizer : public GraphOptimizer {
  public:
   // optimization_level: Controls the level of autonomy for the memory
   //   optimizer. See RewriterConfig::memory_optimization.
-  // recomputation_targets_name_prefix: Name prefix for potential outputs of
+  // recomputation_targets_name_scope: Name scope for potential outputs of
   //   recomputations. See
-  //   RewriterConfig::memory_optimizer_target_node_name_prefix.
+  //   RewriterConfig::memory_optimizer_target_node_name_scope.
   explicit MemoryOptimizer(
       RewriterConfig::MemOptType optimization_level,
-      const string& recomputation_targets_name_prefix = "gradients/")
+      const string& recomputation_targets_name_scope = "gradients/")
       : optimization_level_(optimization_level),
-        recomputation_targets_name_prefix_(recomputation_targets_name_prefix) {}
+        recomputation_targets_name_scope_(recomputation_targets_name_scope) {}
   ~MemoryOptimizer() override {}
 
   string name() const override { return "memory_optimizer"; };
@@ -47,7 +47,7 @@ class MemoryOptimizer : public GraphOptimizer {
 
  private:
   RewriterConfig::MemOptType optimization_level_;
-  string recomputation_targets_name_prefix_;
+  string recomputation_targets_name_scope_;
 };
 
 }  // end namespace grappler
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index 72d7b94dc8..fff1e354f4 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -119,7 +119,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
           std::unique_ptr<GraphOptimizer>(new LayoutOptimizer()));
     }
     if (cfg_.memory_optimization() != RewriterConfig::NO_MEM_OPT) {
-      if (cfg_.memory_optimizer_target_node_name_prefix().empty()) {
+      if (cfg_.memory_optimizer_target_node_name_scope().empty()) {
         optimizers.push_back(std::unique_ptr<GraphOptimizer>(
             // Use the default target node name prefix "gradients/"
             new MemoryOptimizer(cfg_.memory_optimization())));
@@ -127,7 +127,7 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
         optimizers.push_back(
             std::unique_ptr<GraphOptimizer>(new MemoryOptimizer(
                 cfg_.memory_optimization(),
-                cfg_.memory_optimizer_target_node_name_prefix())));
+                cfg_.memory_optimizer_target_node_name_scope())));
       }
     }
     if (cfg_.auto_parallel().enable()) {
diff --git a/tensorflow/core/protobuf/rewriter_config.proto b/tensorflow/core/protobuf/rewriter_config.proto
index 9ebf217811..0ccf2149f2 100644
--- a/tensorflow/core/protobuf/rewriter_config.proto
+++ b/tensorflow/core/protobuf/rewriter_config.proto
@@ -78,16 +78,15 @@ message RewriterConfig {
   // effect on manually requested memory optimization passes in the optimizers
   // field.
   MemOptType memory_optimization = 4;
-  // The prefix for nodes which are valid outputs of recomputations. Inputs to
-  // nodes with this name prefix may be recomputed (subject either to manual
-  // annotation of those input nodes or to manual annotation and heuristics
-  // depending on memory_optimization), but the prefixed nodes themselves will
-  // not be recomputed. Typically this will be "gradients/", indicating that
-  // activations from the forward pass of a graph may be recomputed as inputs to
-  // gradients, but may be adjusted if gradients are inside a name scope or if
-  // inputs to non-gradients should be recomputed. Defaults to "gradients/" if
-  // empty or not set.
-  string memory_optimizer_target_node_name_prefix = 6;
+  // A node name scope for node names which are valid outputs of recompuations.
+  // Inputs to nodes that match this scope may be recomputed (subject either to
+  // manual annotation of those input nodes or to manual annotation and
+  // heuristics depending on memory_optimization), but the nodes themselves will
+  // not be recomputed. This matches any sub-scopes as well, meaning the scope
+  // can appear not just as a top-level scope. For example, if the value is
+  // "gradients/", the default, it will match node name "gradients/foo",
+  // "foo/gradients/bar", but not "foo_gradients/"
+  string memory_optimizer_target_node_name_scope = 6;
 
   // Configures AutoParallel optimization passes either through the
   // meta-optimizer or when manually specified through the optimizers field.
diff --git a/tensorflow/python/grappler/memory_optimizer_test.py b/tensorflow/python/grappler/memory_optimizer_test.py
index 948911f099..4df959ce04 100644
--- a/tensorflow/python/grappler/memory_optimizer_test.py
+++ b/tensorflow/python/grappler/memory_optimizer_test.py
@@ -162,7 +162,8 @@ class MemoryOptimizerRecomputeTest(test.TestCase):
             arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
             memory_optimization=rewriter_config_pb2.RewriterConfig.
             RECOMPUTATION_HEURISTICS,
-            memory_optimizer_target_node_name_prefix='optimizer/gradients/'),
+            # Checks that name scope "gradients/" also match sub-scope.
+            memory_optimizer_target_node_name_scope='gradients/'),
         original_metagraph)
     self.assertGreater(
         len(rewritten_graph_def.node),
@@ -176,6 +177,35 @@ class MemoryOptimizerRecomputeTest(test.TestCase):
         len([node for node in rewritten_graph_def.node
              if 'Recomputed/' in node.name]))
 
+  def testRewritingNameScopedGradientNamesScope(self):
+    """Tests that rewriting occurs with non-standard gradient names."""
+    (original_metagraph, _, _,
+     _) = self._GetMetaGraph(optimizer_scope_name='foo/bar')
+    rewritten_graph_def = tf_optimizer.OptimizeGraph(
+        rewriter_config_pb2.RewriterConfig(
+            disable_model_pruning=True,
+            constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
+            dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF,
+            layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,
+            arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
+            memory_optimization=rewriter_config_pb2.RewriterConfig.
+            RECOMPUTATION_HEURISTICS,
+            # This should not match anything.
+            memory_optimizer_target_node_name_scope='r/gradients/'),
+        original_metagraph)
+    self.assertEqual(
+        len(rewritten_graph_def.node), len(original_metagraph.graph_def.node))
+    self.assertEqual(0,
+                     len([
+                         node for node in original_metagraph.graph_def.node
+                         if 'Recomputed/' in node.name
+                     ]))
+    self.assertEqual(0,
+                     len([
+                         node for node in rewritten_graph_def.node
+                         if 'Recomputed/' in node.name
+                     ]))
+
   def _GetMemoryOptimizerSessionConfig(self):
     rewrite_options = rewriter_config_pb2.RewriterConfig(
         disable_model_pruning=True,
-- 
GitLab


From 6da9a6a739ac9a49dcf85617ed7bccfe4bccff4c Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 2 Mar 2018 13:03:42 -0800
Subject: [PATCH 560/575] Make tfe.Metrics Checkpointable

Same principle as Layers: use add_variable to add a dependency on any variables
created. I've ignored the global/local distinction, since it makes more sense
for users to control saving by either adding a dependency on the Metric or not.

PiperOrigin-RevId: 187658433
---
 tensorflow/contrib/eager/python/BUILD         |  1 +
 .../contrib/eager/python/metrics_impl.py      | 23 ++++++++++------
 .../contrib/eager/python/metrics_test.py      | 27 +++++++++++++++++++
 3 files changed, 43 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD
index e8c514c114..6fb8287030 100644
--- a/tensorflow/contrib/eager/python/BUILD
+++ b/tensorflow/contrib/eager/python/BUILD
@@ -117,6 +117,7 @@ py_library(
     srcs_version = "PY2AND3",
     visibility = ["//tensorflow:internal"],
     deps = [
+        "//tensorflow/contrib/eager/python:checkpointable_utils",
         "//tensorflow/contrib/summary:summary_ops",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py
index 5571e77c70..a34c4f758a 100644
--- a/tensorflow/contrib/eager/python/metrics_impl.py
+++ b/tensorflow/contrib/eager/python/metrics_impl.py
@@ -30,12 +30,12 @@ from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variable_scope
-
+from tensorflow.python.training import checkpointable
 
 _to_replace = re.compile("[^A-Za-z0-9.]")
 
 
-class Metric(object):
+class Metric(checkpointable.CheckpointableBase):
   """A metric holds state for aggregating statistics over an evaluation run.
 
   Example use with eager execution:
@@ -254,14 +254,21 @@ class Metric(object):
       else:
         collections = [ops.GraphKeys.LOCAL_VARIABLES]
       collections += [ops.GraphKeys.METRIC_VARIABLES]
-    v = variable_scope.get_variable(
-        name,
-        shape,
-        dtype,
-        initializer,
+    # Variables are Checkpointable dependencies of Metrics regardless of the
+    # global/local distinction. Users can avoid saving variables by not adding a
+    # dependency on the Metric.
+    v = self._add_variable_with_custom_getter(
+        name=name,
+        shape=shape,
+        dtype=dtype,
+        initializer=initializer,
         trainable=False,
         collections=collections,
-        use_resource=True)
+        use_resource=True,
+        getter=variable_scope.get_variable,
+        # Raise duplicate variable exceptions from get_variable rather than
+        # Checkpointable.
+        overwrite=True)
     self._vars.append(v)
     if context.in_eager_mode():
       self._initial_values[v] = v.value()
diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py
index c9106294dc..6b5450ba89 100644
--- a/tensorflow/contrib/eager/python/metrics_test.py
+++ b/tensorflow/contrib/eager/python/metrics_test.py
@@ -18,8 +18,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import tempfile
 
+from tensorflow.contrib.eager.python import checkpointable_utils
 from tensorflow.contrib.eager.python import metrics
 from tensorflow.contrib.summary import summary_ops
 from tensorflow.contrib.summary import summary_test_util
@@ -206,6 +208,31 @@ class MetricsTest(test.TestCase):
       self.assertAllEqual(m2.result().eval(), 2.0)
       self.assertAllEqual(m1.result().eval(), 1.0)
 
+  @test_util.run_in_graph_and_eager_modes()
+  def testSaveRestore(self):
+    checkpoint_directory = self.get_temp_dir()
+    checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
+    mean = metrics.Mean()
+    checkpoint = checkpointable_utils.Checkpoint(mean=mean)
+    mean.build()
+    mean._built = True
+    self.evaluate(mean.init_variables())
+    self.evaluate(mean(100.))
+    self.evaluate(mean(200.))
+    save_path = checkpoint.save(checkpoint_prefix)
+    self.evaluate(mean(1000.))
+    checkpoint.restore(save_path).assert_consumed().run_restore_ops()
+    self.evaluate(mean(300.))
+    self.assertAllEqual(200., self.evaluate(mean.value()))
+
+    restore_mean = metrics.Mean()
+    restore_checkpoint = checkpointable_utils.Checkpoint(mean=restore_mean)
+    status = restore_checkpoint.restore(save_path)
+    restore_update = restore_mean(300.)
+    status.assert_consumed().run_restore_ops()
+    self.evaluate(restore_update)
+    self.assertAllEqual(200., self.evaluate(restore_mean.value()))
+    self.assertEqual(3, self.evaluate(restore_mean.denom))
 
 if __name__ == "__main__":
   test.main()
-- 
GitLab


From 1e2c2f1cddd52ed86f8d5d7f10faa6498f13dded Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 13:32:35 -0800
Subject: [PATCH 561/575] Add /learning/tfx/ to the visibility group of
 tensorflow/compiler/jit.

PiperOrigin-RevId: 187661883
---
 tensorflow/compiler/jit/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD
index c7c9e9bd7a..955d12dc20 100644
--- a/tensorflow/compiler/jit/BUILD
+++ b/tensorflow/compiler/jit/BUILD
@@ -29,7 +29,10 @@ load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured")
 # Target that bundles up the XLA CPU and GPU JIT devices.
 cc_library(
     name = "jit",
-    visibility = [":friends"],
+    visibility = [
+        ":friends",
+        "//learning/tfx:__subpackages__",
+    ],
     deps = [
         ":xla_cpu_device",
         ":xla_cpu_jit",
-- 
GitLab


From 4b038da7006c81e3e6cd542a7015d4a84d5c2385 Mon Sep 17 00:00:00 2001
From: Eli Bendersky <eliben@google.com>
Date: Fri, 2 Mar 2018 13:37:41 -0800
Subject: [PATCH 562/575] Make shape inference error messages more consistent.

PiperOrigin-RevId: 187662562
---
 .../compiler/xla/service/shape_inference.cc   | 410 +++++++++---------
 .../xla/service/shape_inference_test.cc       |  59 ++-
 .../xla/tests/broadcast_simple_test.cc        |   6 +-
 tensorflow/compiler/xla/tests/concat_test.cc  |   2 +-
 tensorflow/compiler/xla/tests/map_test.cc     |   2 +-
 5 files changed, 236 insertions(+), 243 deletions(-)

diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc
index 607a672025..c54cb3b48d 100644
--- a/tensorflow/compiler/xla/service/shape_inference.cc
+++ b/tensorflow/compiler/xla/service/shape_inference.cc
@@ -169,11 +169,11 @@ bool AllUnique(tensorflow::gtl::ArraySlice<int64> slice) {
 tensorflow::Status ExpectNotTupleOrOpaque(const Shape& shape,
                                           tensorflow::StringPiece op_type) {
   if (ShapeUtil::IsTuple(shape)) {
-    return InvalidArgument("Expected non-tuple argument for %s. Got: %s",
+    return InvalidArgument("Expected non-tuple argument for %s, but got %s.",
                            op_type.ToString().c_str(),
                            ShapeUtil::HumanString(shape).c_str());
   } else if (ShapeUtil::IsOpaque(shape)) {
-    return InvalidArgument("Expected non-opaque argument for %s. Got: %s",
+    return InvalidArgument("Expected non-opaque argument for %s, but got %s.",
                            op_type.ToString().c_str(),
                            ShapeUtil::HumanString(shape).c_str());
   } else {
@@ -193,8 +193,7 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape,
 
   const Shape& accumulator_shape = reducer_shape.result();
   if (ShapeUtil::Rank(accumulator_shape) != 0) {
-    return Unimplemented(
-        "Reduction function currently must have rank-0 result.");
+    return InvalidArgument("Reduction function must have rank 0.");
   }
 
   // Check that the accumulator can be passed in as the first argument.
@@ -235,8 +234,8 @@ tensorflow::Status VerifyReducerShape(const ProgramShape& reducer_shape,
   if (!ShapeUtil::CompatibleIgnoringFpPrecision(accumulator_shape,
                                                 reducer_shape.parameters(1))) {
     return InvalidArgument(
-        "Reduction function's second parameter shape currently must "
-        "match the result shape. Got %s vs %s",
+        "Reduction function's second parameter shape must "
+        "match the result shape, but got %s vs %s.",
         ShapeUtil::HumanString(reducer_shape.parameters(1)).c_str(),
         ShapeUtil::HumanString(accumulator_shape).c_str());
   }
@@ -258,29 +257,29 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
   for (int64 i = 0; i < window.dimensions_size(); ++i) {
     const auto& dim = window.dimensions(i);
     if (dim.size() <= 0) {
-      return InvalidArgument("Window has a non-positive dimension. Window: %s",
+      return InvalidArgument("Window %s has a non-positive dimension.",
                              window.DebugString().c_str());
     }
     if (dim.stride() <= 0) {
-      return InvalidArgument("Window has a non-positive stride. Window: %s",
+      return InvalidArgument("Window %s has a non-positive stride.",
                              window.DebugString().c_str());
     }
     if (!allow_negative_padding && dim.padding_low() < 0) {
-      return InvalidArgument("Window has a negative low padding. Window: %s",
+      return InvalidArgument("Window %s has a negative low padding.",
                              window.DebugString().c_str());
     }
     if (!allow_negative_padding && dim.padding_high() < 0) {
-      return InvalidArgument("Window has a negative high padding. Window: %s",
+      return InvalidArgument("Window %s has a negative high padding.",
                              window.DebugString().c_str());
     }
     if (dim.base_dilation() < 1) {
       return InvalidArgument(
-          "Window has a non-positive base area dilation factor. Window: %s",
+          "Window %s has a non-positive base area dilation factor.",
           window.DebugString().c_str());
     }
     if (dim.window_dilation() < 1) {
       return InvalidArgument(
-          "Window has a non-positive window dilation factor. Window: %s",
+          "Window %s has a non-positive window dilation factor.",
           window.DebugString().c_str());
     }
 
@@ -320,8 +319,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     case UNOP_CEIL:
       if (!ShapeUtil::ElementIsFloating(arg)) {
         return InvalidArgument(
-            "expected element type in shape to be floating for floor/ceil "
-            "operation; got %s",
+            "Expected element type in shape to be floating for floor/ceil "
+            "operation; got %s.",
             PrimitiveType_Name(arg.element_type()).c_str());
       }
       return arg;
@@ -333,8 +332,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
       if (!ShapeUtil::ElementIsFloating(arg) &&
           !ShapeUtil::ElementIsComplex(arg)) {
         return InvalidArgument(
-            "expected element type in shape to be floating or complex for "
-            "sin/cos/exp/log/tanh operation; got %s",
+            "Expected element type in shape to be floating or complex for "
+            "sin/cos/exp/log/tanh operation; got %s.",
             PrimitiveType_Name(arg.element_type()).c_str());
       }
       return arg;
@@ -342,8 +341,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     case UNOP_IMAG:
       if (!ShapeUtil::ElementIsComplex(arg)) {
         return InvalidArgument(
-            "expected element type in shape to be complex for real/imag "
-            "operation; got %s",
+            "Expected element type in shape to be complex for real/imag "
+            "operation; got %s.",
             PrimitiveType_Name(arg.element_type()).c_str());
       }
       return ShapeUtil::ChangeElementType(arg, F32);
@@ -363,8 +362,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
       if (arg.element_type() != PRED &&
           !primitive_util::IsIntegralType(arg.element_type())) {
         return InvalidArgument(
-            "expected pred or an integral element type in argument to not "
-            "operation; got %s",
+            "Expected pred or an integral element type in argument to Not "
+            "operation; got %s.",
             PrimitiveType_Name(arg.element_type()).c_str());
       }
       return arg;
@@ -372,8 +371,8 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     case UNOP_IS_FINITE:
       if (!ShapeUtil::ElementIsFloating(arg)) {
         return InvalidArgument(
-            "expected element type in shape to be floating point for IsFinite "
-            "operation; got %s",
+            "Expected element type in shape to be floating point for IsFinite "
+            "operation; got %s.",
             PrimitiveType_Name(arg.element_type()).c_str());
       }
       return ShapeUtil::ChangeElementType(arg, PRED);
@@ -389,10 +388,10 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     tensorflow::gtl::ArraySlice<const Shape*> arg_shapes,
     const int64 dimension) {
   if (arg_shapes.empty()) {
-    return InvalidArgument("Concatenate expects at least one argument");
+    return InvalidArgument("Concatenate expects at least one argument.");
   }
   if (dimension < 0 || dimension >= ShapeUtil::Rank(*arg_shapes[0])) {
-    return InvalidArgument("dimension to concatenate along out of bounds: %lld",
+    return InvalidArgument("Concatenate dimension out of bounds: %lld.",
                            dimension);
   }
   const Shape* arg_shape = nullptr;
@@ -408,14 +407,14 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     if (ShapeUtil::Rank(*arg_shape) != ShapeUtil::Rank(*shape)) {
       return InvalidArgument(
           "Cannot concatenate arrays with different ranks: %lld (%s) vs %lld "
-          "(%s)",
+          "(%s).",
           ShapeUtil::Rank(*arg_shape),
           ShapeUtil::HumanString(*arg_shape).c_str(), ShapeUtil::Rank(*shape),
           ShapeUtil::HumanString(*shape).c_str());
     }
     if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(*arg_shape, *shape)) {
       return InvalidArgument(
-          "cannot concatenate arrays with different element types: %s vs %s",
+          "Cannot concatenate arrays with different element types: %s vs %s.",
           PrimitiveType_Name(arg_shape->element_type()).c_str(),
           PrimitiveType_Name(shape->element_type()).c_str());
     }
@@ -428,9 +427,9 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
                      // concatenating.
         }
         return InvalidArgument(
-            "cannot concatenate arrays that differ in dimensions other than "
+            "Cannot concatenate arrays that differ in dimensions other than "
             "the one being concatenated (the other array dimensions must be "
-            "the same): %s vs %s in dimension %lld",
+            "the same): %s vs %s in dimension %lld.",
             ShapeUtil::HumanString(*arg_shape).c_str(),
             ShapeUtil::HumanString(*shape).c_str(), dimension);
       }
@@ -452,7 +451,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
   if (primitive_util::IsComplexType(old_element_type) &&
       !primitive_util::IsComplexType(new_element_type)) {
     return Unimplemented(
-        "Unsupported conversion from complex to real type: %s => %s",
+        "Conversion from complex to real type %s => %s is not implemented.",
         ShapeUtil::HumanString(operand_shape).c_str(),
         PrimitiveType_Name(new_element_type).c_str());
   }
@@ -461,7 +460,7 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     // future, by recursing into the tuple elements to check all sub-conversions
     // are valid. For now we just reject them, though.
     return InvalidArgument(
-        "cannot convert from or to tuple type; requested conversion: %s => %s",
+        "Convert does not allow tuples, so cannot convert from %s to %s.",
         ShapeUtil::HumanString(operand_shape).c_str(),
         PrimitiveType_Name(new_element_type).c_str());
   }
@@ -474,24 +473,23 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
   auto old_element_type = operand_shape.element_type();
   if (primitive_util::IsComplexType(old_element_type) !=
       primitive_util::IsComplexType(new_element_type)) {
-    return Unimplemented(
-        "Unsupported conversion between real and complex types: %s => %s",
-        ShapeUtil::HumanString(operand_shape).c_str(),
-        PrimitiveType_Name(new_element_type).c_str());
+    return InvalidArgument("Conversion from complex to real type %s => %s.",
+                           ShapeUtil::HumanString(operand_shape).c_str(),
+                           PrimitiveType_Name(new_element_type).c_str());
   }
   if (ShapeUtil::IsTuple(operand_shape) || new_element_type == TUPLE) {
     // Note: we may want to support tuple conversions via this operation in the
     // future, by recursing into the tuple elements to check all sub-conversions
     // are valid. For now we just reject them, though.
     return InvalidArgument(
-        "cannot convert from or to tuple type; requested conversion: %s => %s",
+        "Cannot convert from or to tuple type; requested conversion: %s => %s.",
         ShapeUtil::HumanString(operand_shape).c_str(),
         PrimitiveType_Name(new_element_type).c_str());
   }
   if (primitive_util::BitWidth(old_element_type) !=
       primitive_util::BitWidth(new_element_type)) {
     return InvalidArgument(
-        "cannot bitcast types with different bit-widths: %s => %s",
+        "Cannot bitcast types with different bit-widths: %s => %s.",
         PrimitiveType_Name(old_element_type).c_str(),
         PrimitiveType_Name(new_element_type).c_str());
   }
@@ -504,20 +502,20 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     const int mantissa_bits) {
   if (!ShapeUtil::ElementIsFloating(operand_shape)) {
     return InvalidArgument(
-        "expected element type in shape to be floating point for "
-        "ReducePrecision operation; got %s",
+        "Expected element type in shape to be floating point for "
+        "ReducePrecision operation; got %s.",
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
   if (exponent_bits < 1) {
     // One exponent bit is necessary to distinguish 0 from infinity.  Having
     // no exponent bits doesn't produce a sensible number, so we require at
     // least one.
-    return InvalidArgument("expected exponent_bits >= 1; got %d",
+    return InvalidArgument("Expected exponent_bits >= 1; got %d.",
                            exponent_bits);
   }
   if (mantissa_bits < 0) {
     // A number with no mantissa bits is still meaningful, however.
-    return InvalidArgument("expected non-negative mantissa_bits; got %d",
+    return InvalidArgument("Expected non-negative mantissa_bits; got %d.",
                            mantissa_bits);
   }
   return operand_shape;
@@ -528,23 +526,23 @@ StatusOr<Shape> InferWindowOutputShape(const Shape& base_shape,
     const PaddingConfig& padding_config) {
   if (ShapeUtil::IsTuple(operand_shape)) {
     return InvalidArgument(
-        "pad operation does not support tuple-shape operands");
+        "Pad operation does not support tuple-shape operands.");
   }
   if (!ShapeUtil::IsScalar(padding_value_shape)) {
     return InvalidArgument(
-        "pad operation does not support non-scalar padding values");
+        "Pad operation does not support non-scalar padding values.");
   }
   if (ShapeUtil::Rank(operand_shape) != padding_config.dimensions_size()) {
     return InvalidArgument(
         "The rank of the operand and the padding configuration do not match: "
-        "%s vs %s",
+        "%s vs %s.",
         ShapeUtil::HumanString(operand_shape).c_str(),
         padding_config.ShortDebugString().c_str());
   }
   if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(operand_shape,
                                                      padding_value_shape)) {
     return InvalidArgument(
-        "the element types of the operands to pad do not match");
+        "The element types of the operands to Pad do not match.");
   }
   std::vector<int64> dimensions(ShapeUtil::Rank(operand_shape));
   for (int64 i = 0; i < operand_shape.dimensions_size(); ++i) {
@@ -605,7 +603,7 @@ Status ValidateDotDimensionNumbers(
                      lhs_batch_dimensions) ||
       !dims_in_range(ShapeUtil::Rank(rhs), rhs_contracting_dimensions,
                      rhs_batch_dimensions)) {
-    return InvalidArgument("A dimension number is out of range in dot: %s",
+    return InvalidArgument("A dimension number is out of range in Dot: %s.",
                            dimension_numbers.DebugString().c_str());
   }
 
@@ -623,7 +621,7 @@ Status ValidateDotDimensionNumbers(
 
   if (!dims_unique(lhs_contracting_dimensions, lhs_batch_dimensions) ||
       !dims_unique(rhs_contracting_dimensions, rhs_batch_dimensions)) {
-    return InvalidArgument("A dimension number is not unique in dot: %s",
+    return InvalidArgument("A dimension number is not unique in Dot: %s.",
                            dimension_numbers.DebugString().c_str());
   }
 
@@ -641,8 +639,7 @@ Status ValidateDotDimensionNumbers(
       rhs_non_contracting_non_batch_dims < 0 ||
       rhs_non_contracting_non_batch_dims > 1) {
     return InvalidArgument(
-        "batch and contracting dimension number mismatch "
-        "with rank ");
+        "Batch and contracting dimension number mismatch with rank.");
   }
 
   // Check that batch dimension numbers are ordered before all others, and
@@ -654,7 +651,7 @@ Status ValidateDotDimensionNumbers(
       !std::equal(batch_dim_numbers.begin(), batch_dim_numbers.end(),
                   rhs_batch_dimensions.begin())) {
     return InvalidArgument(
-        "batch dimension numbers must precede non-batch dimensions and be"
+        "Batch dimension numbers must precede non-batch dimensions and be"
         "monotonically increasing.");
   }
 
@@ -671,22 +668,22 @@ Status ValidateDotDimensionNumbers(
 
   auto fail = [lhs, rhs](const string& addendum) -> Status {
     string message = tensorflow::strings::Printf(
-        "cannot infer shape for dot operation: %s <dot> %s",
+        "Cannot infer shape for dot operation: %s <dot> %s.",
         ShapeUtil::HumanString(lhs).c_str(),
         ShapeUtil::HumanString(rhs).c_str());
     if (!addendum.empty()) {
-      message += ": " + addendum;
+      message += " " + addendum;
     }
     return InvalidArgument("%s", message.c_str());
   };
 
   // Check if both element types are the same.
   if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) {
-    return fail("element types do not match");
+    return fail("Element types do not match.");
   }
 
   if ((ShapeUtil::Rank(lhs) < 1) || (ShapeUtil::Rank(rhs) < 1)) {
-    return fail("dot only supports rank 1 or above.");
+    return fail("Dot only supports rank 1 or above.");
   }
 
   // Validate basic properties of dot dimension numbers.
@@ -696,7 +693,7 @@ Status ValidateDotDimensionNumbers(
   if (dimension_numbers.lhs_contracting_dimensions_size() !=
           dimension_numbers.rhs_contracting_dimensions_size() ||
       dimension_numbers.lhs_contracting_dimensions_size() != 1) {
-    return fail("must specify one contracting dimension for both lhs and rhs.");
+    return fail("Must specify one contracting dimension for both lhs and rhs.");
   }
 
   // Check that contracting dimension sizes match.
@@ -706,13 +703,13 @@ Status ValidateDotDimensionNumbers(
       dimension_numbers.rhs_contracting_dimensions(0);
   if (lhs.dimensions(lhs_contracting_dimension) !=
       rhs.dimensions(rhs_contracting_dimension)) {
-    return fail("contracting dimension sizes do not match.");
+    return fail("Contracting dimension sizes do not match.");
   }
 
   // Check that number of batch dimensions match.
   if (dimension_numbers.lhs_batch_dimensions_size() !=
       dimension_numbers.rhs_batch_dimensions_size()) {
-    return fail("must the same number of batch dimensions for lhs and rhs.");
+    return fail("Must the same number of batch dimensions for lhs and rhs.");
   }
 
   // Check that batch dimension numbers and sizes match.
@@ -721,7 +718,7 @@ Status ValidateDotDimensionNumbers(
             dimension_numbers.rhs_batch_dimensions(i) ||
         lhs.dimensions(dimension_numbers.lhs_batch_dimensions(i)) !=
             rhs.dimensions(dimension_numbers.rhs_batch_dimensions(i))) {
-      return fail("batch dimension numbers and sizes must match for lhs/rhs.");
+      return fail("Batch dimension numbers and sizes must match for lhs/rhs.");
     }
   }
 
@@ -770,10 +767,11 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     } else if (rhs.dimensions(i) == 1) {
       output_dimensions[i] = lhs.dimensions(i);
     } else {
-      return InvalidArgument("binary op %s with incompatible shapes: %s and %s",
-                             BinaryOperation_Name(operation).c_str(),
-                             ShapeUtil::HumanString(lhs).c_str(),
-                             ShapeUtil::HumanString(rhs).c_str());
+      return InvalidArgument(
+          "Binary op %s with incompatible shapes: %s and %s.",
+          BinaryOperation_Name(operation).c_str(),
+          ShapeUtil::HumanString(lhs).c_str(),
+          ShapeUtil::HumanString(rhs).c_str());
     }
   }
   return ShapeUtil::MakeShape(ShapeUtil::HigherPrecisionElementType(lhs, rhs),
@@ -788,15 +786,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     // Reject "magic" inference for binops on different shapes, requiring
     // the user to provide an explicit broadcast dimension in this case.
     // See b/25177275 for more details.
-    return InvalidArgument("automatic shape inference not supported: %s and %s",
+    return InvalidArgument("Automatic shape inference not supported: %s and %s",
                            ShapeUtil::HumanString(smaller_shape).c_str(),
                            ShapeUtil::HumanString(larger_shape).c_str());
   } else if (broadcast_dimensions.size() != ShapeUtil::Rank(smaller_shape)) {
     return InvalidArgument(
-        "size of broadcast_dimensions has to match lower-rank operand's "
+        "Size of broadcast_dimensions has to match lower-rank operand's "
         "rank; "
         " lower-rank operand's rank is %lld, size of broadcast_dimensions is "
-        "%zu",
+        "%zu.",
         ShapeUtil::Rank(smaller_shape), broadcast_dimensions.size());
   }
 
@@ -846,13 +844,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     int64 dimension_to_match = broadcast_dimensions.at(i);
     if (dimension_to_match < 0) {
       return InvalidArgument(
-          "broadcast dimension number (%lld) cannot be negative",
+          "Broadcast dimension number (%lld) cannot be negative.",
           dimension_to_match);
     }
     if (dimension_to_match >= larger_shape.dimensions_size()) {
       return InvalidArgument(
-          "broadcast dimension number (%lld) too large; higher-rank "
-          "operand has rank %d",
+          "Broadcast dimension number (%lld) too large; higher-rank "
+          "operand has rank %d.",
           dimension_to_match, larger_shape.dimensions_size());
     }
     int64 small_dimension_size = smaller_shape.dimensions(i);
@@ -863,7 +861,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     if (small_dimension_size != large_dimension_size &&
         small_dimension_size != 1 && large_dimension_size != 1) {
       return InvalidArgument(
-          "broadcast dimension %d mismatch: %lld != %lld; %s and %s", i,
+          "Broadcast dimension %d mismatch: %lld != %lld; %s and %s.", i,
           small_dimension_size, large_dimension_size,
           ShapeUtil::HumanString(smaller_shape).c_str(),
           ShapeUtil::HumanString(larger_shape).c_str());
@@ -872,7 +870,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     // order.
     if (i > 0 && broadcast_dimensions.at(i - 1) >= dimension_to_match) {
       return InvalidArgument(
-          "broadcast dimensions order is wrong: %lld comes after %lld",
+          "Broadcast dimensions order is wrong: %lld comes after %lld.",
           dimension_to_match, broadcast_dimensions.at(i - 1));
     }
 
@@ -892,7 +890,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) {
     return InvalidArgument(
-        "binary op %s with different element types: %s and %s",
+        "Binary op %s with different element types: %s and %s.",
         BinaryOperation_Name(operation).c_str(),
         ShapeUtil::HumanString(lhs).c_str(),
         ShapeUtil::HumanString(rhs).c_str());
@@ -904,8 +902,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     if (!broadcast_dimensions.empty() &&
         broadcast_dimensions != identity_dims) {
       return InvalidArgument(
-          "broadcast dimensions field must either be not set or be the "
-          "identity on binary operations with operands of the same rank");
+          "Broadcast dimensions field must either be not set or be the "
+          "identity on binary operations with operands of the same rank.");
     }
   }
 
@@ -979,8 +977,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     case BINOP_COMPLEX: {
       if (!ShapeUtil::ElementIsFloating(lhs)) {
         return InvalidArgument(
-            "expected element type in shape to be floating for complex compose "
-            "operation; got %s",
+            "Expected element type in shape to be floating for complex compose "
+            "operation; got %s.",
             PrimitiveType_Name(lhs.element_type()).c_str());
       }
       TF_ASSIGN_OR_RETURN(const Shape& shape,
@@ -989,7 +987,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       if (lhs.element_type() == F32 && rhs.element_type() == F32) {
         return ShapeUtil::ChangeElementType(shape, C64);
       } else {
-        return Unimplemented("complex component type not supported");
+        return Unimplemented("Complex component type is not implemented.");
       }
     }
     case BINOP_AND:
@@ -997,8 +995,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       if (lhs.element_type() != PRED &&
           !primitive_util::IsIntegralType(lhs.element_type())) {
         return InvalidArgument(
-            "expected pred or integral type in argument to and/or operation; "
-            "got %s",
+            "Expected pred or integral type in argument to and/or operation; "
+            "got %s.",
             PrimitiveType_Name(lhs.element_type()).c_str());
       }
       return InferElementwiseBinaryOpShape(operation, lhs, rhs,
@@ -1016,7 +1014,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     }
     default:
       return Unimplemented(
-          "not yet implemented; infer binary op shape: %s; lhs: %s; rhs: %s",
+          "Binary op shape inference: %s; lhs: %s; rhs: %s is not implemented.",
           BinaryOperation_Name(operation).c_str(),
           lhs.ShortDebugString().c_str(), rhs.ShortDebugString().c_str());
   }
@@ -1041,7 +1039,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     case TRIOP_SELECT:
       return InferSelectShape(lhs, rhs, ehs);
     default:
-      return InvalidArgument("unknown operation %s",
+      return InvalidArgument("Unknown operation %s.",
                              TernaryOperation_Name(operation).c_str());
   }
 }
@@ -1072,7 +1070,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       return result;
     }
     default:
-      return InvalidArgument("unknown operation %s",
+      return InvalidArgument("Unknown operation %s.",
                              VariadicOperation_Name(operation).c_str());
   }
 }
@@ -1082,7 +1080,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const ProgramShape& to_apply,
     tensorflow::gtl::ArraySlice<int64> dimensions) {
   if (arg_shapes.empty()) {
-    return InvalidArgument("Map expects at least one argument");
+    return InvalidArgument("Map expects at least one argument.");
   }
 
   // All arguments must have the same shape.
@@ -1113,7 +1111,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     }
     return InvalidArgument(
         "Map operation requires all operands to have the same shape; got: "
-        "%s",
+        "%s.",
         Join(pieces, ", ").c_str());
   }
 
@@ -1122,7 +1120,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   if (dimensions.size() != arg_shape->dimensions_size()) {
     return InvalidArgument(
         "Map applied to a subset of dimensions currently not supported: "
-        "arg_dimension_size: %d, requested_map_dimensions_size: %zu",
+        "arg_dimension_size: %d, requested_map_dimensions_size: %zu.",
         arg_shape->dimensions_size(), dimensions.size());
   }
 
@@ -1130,7 +1128,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   for (int i = 0; i < dimensions.size(); ++i) {
     if (dimensions[i] != i) {
       return InvalidArgument(
-          "Map requires monotonically increasing dimension numbers, found: %s ",
+          "Map requires monotonically increasing dimension numbers; got: %s.",
           Join(dimensions, ", ").c_str());
     }
   }
@@ -1139,7 +1137,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   if (arg_shapes.size() != to_apply.parameters_size()) {
     return InvalidArgument(
         "Map applied function arity must match number of arguments; got: "
-        "arity: %d, arguments: %zu",
+        "arity: %d, arguments: %zu.",
         to_apply.parameters_size(), arg_shapes.size());
   }
 
@@ -1147,8 +1145,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   const Shape& output_shape = to_apply.result();
   if (!ShapeUtil::IsScalar(output_shape)) {
     return InvalidArgument(
-        "mapped computation's result has to be a scalar; "
-        "got: %s",
+        "Mapped computation's result has to be a scalar; got: %s.",
         ShapeUtil::HumanString(output_shape).c_str());
   }
 
@@ -1157,16 +1154,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
     if (!ShapeUtil::IsScalar(parameter_shape)) {
       return InvalidArgument(
-          "mapped computation's parameter has to be a scalar; "
-          "got parameter %d shape: %s",
+          "Mapped computation's parameter has to be a scalar; "
+          "got parameter %d shape: %s.",
           i, ShapeUtil::HumanString(parameter_shape).c_str());
     }
 
     if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(parameter_shape,
                                                        *arg_shape)) {
       return InvalidArgument(
-          "mapped computation's parameter type has to match argument element "
-          "type; got parameter %d shape: %s, argument shape: %s",
+          "Mapped computation's parameter type has to match argument element "
+          "type; got parameter %d shape: %s, argument shape: %s.",
           i, ShapeUtil::HumanString(parameter_shape).c_str(),
           ShapeUtil::HumanString(*arg_shape).c_str());
     }
@@ -1197,21 +1194,21 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "Expected feature_index of batch-norm-training to be "
         "smaller than the rank of operand_shape; "
-        "got feature_index %lld, and rank %lld",
+        "got feature_index %lld, and rank %lld.",
         feature_index, ShapeUtil::Rank(operand_shape));
   }
 
   if (feature_index < 0) {
     return InvalidArgument(
         "Expected feature_index of batch-norm-training to "
-        "be a non-negative number, got %lld",
+        "be a non-negative number, got %lld.",
         feature_index);
   }
 
   if (ShapeUtil::Rank(operand_shape) < 1) {
     return InvalidArgument(
         "Expected the rank of operand to "
-        "batch-norm-training to be at least 1; got %lld",
+        "batch-norm-training to be at least 1; got %lld.",
         ShapeUtil::Rank(operand_shape));
   }
 
@@ -1232,7 +1229,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   if (!ShapeUtil::ElementIsFloating(operand_shape)) {
     return InvalidArgument(
         "The operand to batch-norm-training must have a floating point "
-        "element type, but the shape is %s",
+        "element type, but the shape is %s.",
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
 
@@ -1241,7 +1238,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The inputs should have the same element type for batch-norm-training, "
         "but the shape of offset factor is %s "
-        "and the shape of operand is %s",
+        "and the shape of operand is %s.",
         PrimitiveType_Name(offset_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1251,7 +1248,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The inputs should have the same element type for batch-norm-training, "
         "but the shape of scale factor is %s "
-        "and the shape of operand is %s",
+        "and the shape of operand is %s.",
         PrimitiveType_Name(scale_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1264,7 +1261,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of offset factor should be the same as feature count,"
         "but the size of offset factor is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(offset_shape, 0), feature_count);
   }
 
@@ -1272,7 +1269,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of scale factor should be the same as feature count,"
         "but the size of scale factor is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(scale_shape, 0), feature_count);
   }
 
@@ -1307,21 +1304,21 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "Expected feature_index of batch-norm-inference to be "
         "smaller than the rank of operand_shape; "
-        "got feature_index %lld, and rank %lld",
+        "got feature_index %lld, and rank %lld.",
         feature_index, ShapeUtil::Rank(operand_shape));
   }
 
   if (feature_index < 0) {
     return InvalidArgument(
         "Expected feature_index of batch-norm-inference to "
-        "be a non-negative number, got %lld",
+        "be a non-negative number, got %lld.",
         feature_index);
   }
 
   if (ShapeUtil::Rank(operand_shape) < 1) {
     return InvalidArgument(
         "Expected the rank of operand to "
-        "batch-norm-inference to be at least 1; got %lld",
+        "batch-norm-inference to be at least 1; got %lld.",
         ShapeUtil::Rank(operand_shape));
   }
 
@@ -1342,7 +1339,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   if (!ShapeUtil::ElementIsFloating(operand_shape)) {
     return InvalidArgument(
         "The operand to batch-norm-inference must have a floating point "
-        "element type, but the shape is %s",
+        "element type, but the shape is %s.",
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
 
@@ -1352,7 +1349,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
         "The inputs should have the same element type for "
         "batch-norm-inference, "
         "but the shape of offset factor is %s "
-        "and the shape of operand is %s",
+        "and the shape of operand is %s.",
         PrimitiveType_Name(offset_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1363,7 +1360,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
         "The inputs should have the same element type for "
         "batch-norm-inference, "
         "but the shape of scale factor is %s "
-        "and the shape of operand is %s",
+        "and the shape of operand is %s.",
         PrimitiveType_Name(scale_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1374,7 +1371,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
         "The inputs should have the same element type for "
         "batch-norm-inference, "
         "but the shape of mean is %s "
-        "and the shape of operand is %s",
+        "and the shape of operand is %s.",
         PrimitiveType_Name(mean_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1385,7 +1382,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
         "The inputs should have the same element type for "
         "batch-norm-inference, "
         "but the shape of variance is %s "
-        "and the shape of operand is %s",
+        "and the shape of operand is %s.",
         PrimitiveType_Name(mean_shape.element_type()).c_str(),
         PrimitiveType_Name(variance_shape.element_type()).c_str());
   }
@@ -1398,7 +1395,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of offset factor should be the same as feature count,"
         "but the size of offset factor is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(offset_shape, 0), feature_count);
   }
 
@@ -1406,7 +1403,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of scale factor should be the same as feature count,"
         "but the size of scale factor is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(scale_shape, 0), feature_count);
   }
 
@@ -1414,7 +1411,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of mean should be the same as feature count,"
         "but the size of mean is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(mean_shape, 0), feature_count);
   }
 
@@ -1422,7 +1419,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of variance should be the same as feature count,"
         "but the size of variance is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(variance_shape, 0), feature_count);
   }
 
@@ -1455,7 +1452,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "Expected feature_index of batch-norm-grad to be "
         "smaller than the rank of operand_shape; "
-        "got feature_index %lld, and rank %lld",
+        "got feature_index %lld, and rank %lld.",
         feature_index, ShapeUtil::Rank(operand_shape));
   }
 
@@ -1463,7 +1460,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "Expected operand_shape of batch-norm-grad to have the same rank as"
         " output_grad_shape; got rank(oprand_shape) %lld, and"
-        " rank(output_grad_shape) %lld",
+        " rank(output_grad_shape) %lld.",
         ShapeUtil::Rank(operand_shape), ShapeUtil::Rank(output_grad_shape));
   }
 
@@ -1491,14 +1488,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   if (!ShapeUtil::ElementIsFloating(operand_shape)) {
     return InvalidArgument(
         "The operand to batch-norm-grad must have a floating point "
-        "element type, but the shape is %s",
+        "element type, but the shape is %s.",
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
 
   if (!ShapeUtil::ElementIsFloating(output_grad_shape)) {
     return InvalidArgument(
         "The output_grad to batch-norm-grad must have a floating point "
-        "element type, but the shape is %s",
+        "element type, but the shape is %s.",
         PrimitiveType_Name(output_grad_shape.element_type()).c_str());
   }
 
@@ -1507,7 +1504,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The inputs should have the same element type for batch-norm-grad, "
         "but the element type of output_grad is %s "
-        "and the element type of operand is %s",
+        "and the element type of operand is %s.",
         PrimitiveType_Name(output_grad_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1517,7 +1514,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The inputs should have the same element type for batch-norm-grad, "
         "but the element type of scale factor is %s "
-        "and the element type of operand is %s",
+        "and the element type of operand is %s.",
         PrimitiveType_Name(scale_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1527,7 +1524,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The inputs should have the same element type for batch-norm-grad, "
         "but the element type of mean is %s "
-        "and the element type of operand is %s",
+        "and the element type of operand is %s.",
         PrimitiveType_Name(mean_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1537,7 +1534,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The inputs should have the same element type for batch-norm-grad, "
         "but the element type of mean is %s "
-        "and the element type of operand is %s",
+        "and the element type of operand is %s.",
         PrimitiveType_Name(mean_shape.element_type()).c_str(),
         PrimitiveType_Name(operand_shape.element_type()).c_str());
   }
@@ -1551,7 +1548,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of mean should be the same as feature count,"
         "but the size of offset factor is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(mean_shape, 0), feature_count);
   }
 
@@ -1559,7 +1556,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of scale factor should be the same as feature count,"
         "but the size of scale factor is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(scale_shape, 0), feature_count);
   }
 
@@ -1567,7 +1564,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "The size of variance should be the same as feature count,"
         "but the size of variance is %lld "
-        "and the feature count is %lld",
+        "and the feature count is %lld.",
         ShapeUtil::GetDimension(var_shape, 0), feature_count);
   }
 
@@ -1578,7 +1575,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       return InvalidArgument(
           "The bounds of operand shape should be the same as output_grad's,"
           "but the bound of operand_shape at dimension %lld is %lld "
-          "and the bound of output_grad_shape is %lld",
+          "and the bound of output_grad_shape is %lld.",
           i, ShapeUtil::GetDimension(operand_shape, i),
           ShapeUtil::GetDimension(output_grad_shape, i));
     }
@@ -1596,7 +1593,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(lhs, rhs)) {
     return InvalidArgument(
-        "Convolution with different element types: %s and %s",
+        "Convolution with different element types: %s and %s.",
         ShapeUtil::HumanString(lhs).c_str(),
         ShapeUtil::HumanString(rhs).c_str());
   }
@@ -1612,21 +1609,19 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   if (window.dimensions_size() != num_spatial_dims) {
     return InvalidArgument(
         "Window must have same number of dimensions as dimension numbers.\n"
-        "Window: %s\nDimension numbers: %s",
+        "Window: %s\nDimension numbers: %s.",
         window.DebugString().c_str(), dnums.DebugString().c_str());
   }
 
   const int num_dims = num_spatial_dims + 2;
   if (ShapeUtil::Rank(lhs) != num_dims) {
     return InvalidArgument(
-        "The LHS argument to a convolution should have rank %d.\n"
-        "lhs: %s",
+        "The LHS argument to a convolution should have rank %d; lhs: %s.",
         num_dims, ShapeUtil::HumanString(lhs).c_str());
   }
   if (ShapeUtil::Rank(rhs) != num_dims) {
     return InvalidArgument(
-        "The RHS argument to a convolution should have rank %d.\n"
-        "lhs: %s",
+        "The RHS argument to a convolution should have rank %d; lhs: %s.",
         num_dims, ShapeUtil::HumanString(lhs).c_str());
   }
   TF_DCHECK_OK(ShapeUtil::ValidateShapeWithOptionalLayout(lhs));
@@ -1663,26 +1658,26 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       !std::all_of(window_dnums.begin(), window_dnums.end(), in_range) ||
       !std::all_of(output_dnums.begin(), output_dnums.end(), in_range)) {
     return InvalidArgument(
-        "A dimension number is out of range in convolution: %s",
+        "A dimension number is out of range in convolution: %s.",
         dnums.DebugString().c_str());
   }
 
   if (input_dnums != expected_dnums) {
     return InvalidArgument(
         "Input dimensions of convolution must contain each dimension exactly "
-        "once: %s",
+        "once: %s.",
         dnums.DebugString().c_str());
   }
   if (window_dnums != expected_dnums) {
     return InvalidArgument(
         "Window dimensions of convolution must contain each dimension exactly "
-        "once: %s",
+        "once: %s.",
         dnums.DebugString().c_str());
   }
   if (output_dnums != expected_dnums) {
     return InvalidArgument(
         "Output dimensions of convolution must contain each dimension exactly "
-        "once: %s",
+        "once: %s.",
         dnums.DebugString().c_str());
   }
 
@@ -1706,7 +1701,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "Expected LHS feature dimension (value %lld) to match RHS "
         "input feature dimension (value %lld); got <conv>(%s, %s)\n"
-        "Dimension numbers: {%s}",
+        "Dimension numbers: {%s}.",
         input_features, kernel_input_features,
         ShapeUtil::HumanString(lhs).c_str(),
         ShapeUtil::HumanString(rhs).c_str(), dnums.DebugString().c_str());
@@ -1720,7 +1715,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
         "Window dimensions do not match RHS shape:\n\t"
         "RHS shape: %s\n\t"
         "Window: {%s}\n\t"
-        "Dimension numbers: {%s}",
+        "Dimension numbers: {%s}.",
         ShapeUtil::HumanString(rhs).c_str(), window.ShortDebugString().c_str(),
         dnums.ShortDebugString().c_str());
   }
@@ -1748,8 +1743,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const tensorflow::gtl::ArraySlice<int64> fft_length) {
   const int64 fft_rank = fft_length.size();
   if (fft_rank < 1 || fft_rank > 3) {
-    return InvalidArgument("FFT only supports ranks 1-3, but got %lld",
-                           fft_rank);
+    return InvalidArgument("FFT only supports ranks 1-3; got %lld.", fft_rank);
   }
 #define RET_CHECK_RANK(x)                              \
   if (x.dimensions_size() < fft_rank) {                \
@@ -1762,7 +1756,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     case FFT:
     case IFFT:
       if (in.element_type() != C64) {
-        return InvalidArgument("%s requires C64 input type, found %s",
+        return InvalidArgument("%s requires C64 input type, found %s.",
                                FftType_Name(fft_type).c_str(),
                                PrimitiveType_Name(in.element_type()).c_str());
       }
@@ -1770,7 +1764,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
       return in;
     case RFFT: {
       if (in.element_type() != F32) {
-        return InvalidArgument("RFFT requires F32 input type, found %s",
+        return InvalidArgument("RFFT requires F32 input type, found %s.",
                                PrimitiveType_Name(in.element_type()).c_str());
       }
       RET_CHECK_RANK(in);
@@ -1779,7 +1773,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
             fft_length[i]) {
           return InvalidArgument(
               "RFFT requires innermost dimensions match fft_length but "
-              "dimension %lld is %lld and should be %lld",
+              "dimension %lld is %lld and should be %lld.",
               in.dimensions_size() - fft_rank + i,
               in.dimensions(in.dimensions_size() - fft_rank + i),
               fft_length[i]);
@@ -1792,7 +1786,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     }
     case IRFFT: {
       if (in.element_type() != C64) {
-        return InvalidArgument("IRFFT requires C64 input type, found %s",
+        return InvalidArgument("IRFFT requires C64 input type, found %s.",
                                PrimitiveType_Name(in.element_type()).c_str());
       }
       RET_CHECK_RANK(in);
@@ -1802,7 +1796,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
             fft_length[i]) {
           return InvalidArgument(
               "IRFFT requires all but one innermost dimensions match "
-              "fft_length, but dimension %lld is %lld and should be %lld",
+              "fft_length, but dimension %lld is %lld and should be %lld.",
               in.dimensions_size() - fft_rank + i,
               in.dimensions(in.dimensions_size() - fft_rank + i),
               fft_length[i]);
@@ -1812,7 +1806,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
           fft_length[fft_rank - 1] / 2 + 1) {
         return InvalidArgument(
             "IRFFT requires innermost dimension matches fft_length/2+1, but "
-            "dimension %d is %lld and should be %lld",
+            "dimension %d is %lld and should be %lld.",
             in.dimensions_size() - 1, in.dimensions(in.dimensions_size() - 1),
             fft_length[fft_rank - 1] / 2 + 1);
       }
@@ -1850,8 +1844,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   for (int64 dimension : dimensions_to_reduce) {
     if (dimension >= ShapeUtil::Rank(arg) || dimension < 0) {
       return InvalidArgument(
-          "attempting to reduce out-of-bounds dimension %lld in shape %s",
-          dimension, ShapeUtil::HumanString(arg).c_str());
+          "Reducing out-of-bounds dimension %lld in shape %s.", dimension,
+          ShapeUtil::HumanString(arg).c_str());
     }
   }
   TF_RETURN_IF_ERROR(
@@ -1891,30 +1885,30 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   // Check if the select function has a proper shape of (T,T) -> PRED.
   if (select_shape.parameters_size() != 2) {
     return InvalidArgument(
-        "select function must take 2 parameters, but "
+        "Select function must take 2 parameters, but "
         "takes %d parameter(s).",
         select_shape.parameters_size());
   }
   const Shape& select_result_shape = select_shape.result();
   if (!ShapeUtil::Compatible(select_result_shape,
                              ShapeUtil::MakeShape(PRED, {}))) {
-    return Unimplemented("select function must have rank-0 PRED result.");
+    return InvalidArgument("Select function must have rank-0 PRED result.");
   }
   const Shape& operand_element_shape =
       ShapeUtil::MakeShape(operand_shape.element_type(), {});
   if (!ShapeUtil::CompatibleIgnoringFpPrecision(operand_element_shape,
                                                 select_shape.parameters(0))) {
     return InvalidArgument(
-        "select function's first parameter shape currently must "
-        "match the operand element shape. Got %s vs %s",
+        "Select function's first parameter shape currently must "
+        "match the operand element shape, but got %s vs %s.",
         ShapeUtil::HumanString(select_shape.parameters(0)).c_str(),
         ShapeUtil::HumanString(operand_element_shape).c_str());
   }
   if (!ShapeUtil::CompatibleIgnoringFpPrecision(operand_element_shape,
                                                 select_shape.parameters(1))) {
     return InvalidArgument(
-        "select function's second parameter shape currently must "
-        "match the operand element shape. Got %s vs %s",
+        "Select function's second parameter shape currently must "
+        "match the operand element shape, but got %s vs %s.",
         ShapeUtil::HumanString(select_shape.parameters(1)).c_str(),
         ShapeUtil::HumanString(operand_element_shape).c_str());
   }
@@ -1931,8 +1925,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   if (!ShapeUtil::CompatibleIgnoringFpPrecision(source_shape,
                                                 window_result_shape)) {
     return InvalidArgument(
-        "source shape does not match the shape of window-reduced operand: "
-        "source(%s), window-reduced operand(%s)",
+        "Source shape does not match the shape of window-reduced operand: "
+        "source(%s), window-reduced operand(%s).",
         ShapeUtil::HumanString(source_shape).c_str(),
         ShapeUtil::HumanString(window_result_shape).c_str());
   }
@@ -1946,7 +1940,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   auto error = [&](const string& message) {
     return InvalidArgument(
         "%s in slice operation; argument shape: %s; starts: {%s}; limits: "
-        "{%s}; strides: {%s}",
+        "{%s}; strides: {%s}.",
         message.c_str(), ShapeUtil::HumanString(arg).c_str(),
         Join(starts, ",").c_str(), Join(limits, ",").c_str(),
         Join(strides, ",").c_str());
@@ -1969,7 +1963,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   if (starts.size() != ShapeUtil::Rank(arg)) {
     return InvalidArgument(
-        "slice index count does not match argument rank: %zu vs %lld",
+        "Slice index count does not match argument rank: %zu vs %lld.",
         starts.size(), ShapeUtil::Rank(arg));
   }
 
@@ -1979,7 +1973,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     int64 limit_index = limits[dimension];
     int64 stride = strides[dimension];
     if (start_index < 0) {
-      return InvalidArgument("negative start index to slice: %lld",
+      return InvalidArgument("Negative start index to slice: %lld.",
                              start_index);
     }
     if (limit_index > arg.dimensions(dimension)) {
@@ -1999,7 +1993,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
                  limit_index, start_index));
     }
     if (stride <= 0) {
-      return InvalidArgument("stride (%lld) must be positive", stride);
+      return InvalidArgument("Stride (%lld) must be positive.", stride);
     }
     sizes.push_back((limit_index - start_index + stride - 1) / stride);
   }
@@ -2023,20 +2017,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   if (ShapeUtil::Rank(start_indices_shape) != 1) {
     return InvalidArgument(
-        "dynamic slice start indices of rank %lld must be rank1.",
+        "Dynamic slice start indices of rank %lld must be rank1.",
         ShapeUtil::Rank(start_indices_shape));
   }
 
   if (!ShapeUtil::ElementIsIntegral(start_indices_shape)) {
     return InvalidArgument(
-        "dynamic slice start indices must be of integral type.");
+        "Dynamic slice start indices must be of integral type.");
   }
 
   const int64 start_num_dims = start_indices_shape.dimensions(0);
   if (ShapeUtil::Rank(operand_shape) != start_num_dims) {
     return InvalidArgument(
-        "dynamic slice start number of dimensions %lld (%s) must match rank "
-        "%lld of slice input (%s)",
+        "Dynamic slice start number of dimensions %lld (%s) must match rank "
+        "%lld of slice input (%s).",
         start_num_dims, ShapeUtil::HumanString(start_indices_shape).c_str(),
         ShapeUtil::Rank(operand_shape),
         ShapeUtil::HumanString(operand_shape).c_str());
@@ -2044,7 +2038,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   if (slice_sizes.size() != ShapeUtil::Rank(operand_shape)) {
     return InvalidArgument(
-        "dynamic slice index count does not match argument rank: %zu vs %lld",
+        "Dynamic slice index count does not match argument rank: %zu vs %lld.",
         slice_sizes.size(), ShapeUtil::Rank(operand_shape));
   }
 
@@ -2052,12 +2046,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const int64 input_dim_size = operand_shape.dimensions(dim);
     const int64 slice_dim_size = slice_sizes[dim];
     if (slice_dim_size < 0) {
-      return InvalidArgument("negative size index to dynamic slice: %lld",
+      return InvalidArgument("Negative size index to dynamic slice: %lld.",
                              slice_dim_size);
     }
     if (slice_dim_size > input_dim_size) {
       return InvalidArgument(
-          "slice dim size %lld greater than dynamic slice dimension: %lld",
+          "Slice dim size %lld greater than dynamic slice dimension: %lld.",
           slice_dim_size, input_dim_size);
     }
     VLOG(2) << tensorflow::strings::Printf("slice_sizes[%lld] = %lld", dim,
@@ -2086,20 +2080,20 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   if (ShapeUtil::Rank(start_indices_shape) != 1) {
     return InvalidArgument(
-        "dynamic update slice start indices of rank %lld must be rank1.",
+        "Dynamic update slice start indices of rank %lld must be rank1.",
         ShapeUtil::Rank(start_indices_shape));
   }
 
   if (!ShapeUtil::ElementIsIntegral(start_indices_shape)) {
     return InvalidArgument(
-        "dynamic update slice start indices must be of integral type.");
+        "Dynamic update slice start indices must be of integral type.");
   }
 
   const int64 start_num_dims = start_indices_shape.dimensions(0);
   if (ShapeUtil::Rank(operand_shape) != start_num_dims) {
     return InvalidArgument(
-        "dynamic slice start number of dimensions %lld (%s) must match rank "
-        "%lld of slice input (%s)",
+        "Dynamic slice start number of dimensions %lld (%s) must match rank "
+        "%lld of slice input (%s).",
         start_num_dims, ShapeUtil::HumanString(start_indices_shape).c_str(),
         ShapeUtil::Rank(operand_shape),
         ShapeUtil::HumanString(operand_shape).c_str());
@@ -2107,16 +2101,16 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   if (ShapeUtil::Rank(update_shape) != ShapeUtil::Rank(operand_shape)) {
     return InvalidArgument(
-        "dynamic update slice update rank does not match argument rank: "
-        "%lld vs %lld",
+        "Dynamic update slice update rank does not match argument rank: "
+        "%lld vs %lld.",
         ShapeUtil::Rank(update_shape), ShapeUtil::Rank(operand_shape));
   }
 
   if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(operand_shape,
                                                      update_shape)) {
     return InvalidArgument(
-        "dynamic update slice update element type does not match argument. "
-        "operand.element_type: %s vs update.element_type: %s",
+        "Dynamic update slice update element type does not match argument. "
+        "operand.element_type: %s vs update.element_type: %s.",
         PrimitiveType_Name(operand_shape.element_type()).c_str(),
         PrimitiveType_Name(update_shape.element_type()).c_str());
   }
@@ -2126,12 +2120,12 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const int64 update_dim_size = update_shape.dimensions(dim);
     if (update_dim_size < 0) {
       return InvalidArgument(
-          "size index %lld to dynamic update slice must be >= 0",
+          "Size index %lld to dynamic update slice must be >= 0.",
           update_dim_size);
     }
     if (update_dim_size > input_dim_size) {
       return InvalidArgument(
-          "update dim size %lld greater than dynamic slice dimension: %lld",
+          "Update dim size %lld greater than dynamic slice dimension: %lld.",
           update_dim_size, input_dim_size);
     }
     VLOG(2) << tensorflow::strings::Printf("update_sizes[%lld] = %lld", dim,
@@ -2151,7 +2145,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   for (int64 dimension : dimensions) {
     if (dimension >= ShapeUtil::Rank(operand_shape) || dimension < 0) {
       return InvalidArgument(
-          "one of the reverse dimensions (%lld) is out-of-bounds in shape %s",
+          "One of the reverse dimensions (%lld) is out-of-bounds in shape %s.",
           dimension, ShapeUtil::HumanString(operand_shape).c_str());
     }
   }
@@ -2162,14 +2156,14 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const Shape& arg, int64 index) {
   if (!ShapeUtil::IsTuple(arg)) {
     return InvalidArgument(
-        "cannot infer shape: attempting to index into non-tuple: %s",
+        "Cannot infer shape: attempting to index into non-tuple: %s.",
         ShapeUtil::HumanString(arg).c_str());
   }
 
   if (index >= arg.tuple_shapes_size()) {
     return InvalidArgument(
-        "cannot infer shape: attempt to index out of tuple bounds: %lld "
-        ">= %d in shape %s",
+        "Cannot infer shape: attempt to index out of tuple bounds: %lld "
+        ">= %d in shape %s.",
         index, arg.tuple_shapes_size(), ShapeUtil::HumanString(arg).c_str());
   }
 
@@ -2181,17 +2175,17 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const Shape& init) {
   // Check the number of parameters for given computations.
   if (condition.parameters_size() != 1) {
-    return InvalidArgument("condition must take 1 arguments; got %d",
+    return InvalidArgument("Condition must take 1 arguments; got %d.",
                            condition.parameters_size());
   }
   if (body.parameters_size() != 1) {
-    return InvalidArgument("body must take 1 arguments; got %d",
+    return InvalidArgument("Body must take 1 arguments; got %d.",
                            body.parameters_size());
   }
 
   auto shape_string = [&]() {
     return tensorflow::strings::Printf(
-        "condition: %s; body: %s; init: %s",
+        "Condition: %s; body: %s; init: %s.",
         ShapeUtil::HumanString(condition).c_str(),
         ShapeUtil::HumanString(body).c_str(),
         ShapeUtil::HumanString(init).c_str());
@@ -2199,15 +2193,15 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   // Check the shapes of computation parameters and return types.
   if (!ShapeUtil::ShapeIs(condition.result(), PRED, {})) {
-    return InvalidArgument("condition must return a boolean; got %s",
+    return InvalidArgument("Condition must return a boolean; got %s.",
                            shape_string().c_str());
   }
   if (!ShapeUtil::Compatible(body.result(), condition.parameters(0)) ||
       !ShapeUtil::Compatible(body.result(), body.parameters(0)) ||
       !ShapeUtil::Compatible(body.result(), init)) {
     return InvalidArgument(
-        "the parameter of condition and body, the result of the body, and init "
-        "must all have the same shape; got %s",
+        "The parameter of condition and body, the result of the body, and init "
+        "must all have the same shape; got %s.",
         shape_string().c_str());
   }
 
@@ -2219,7 +2213,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     const Shape& false_operand, const ProgramShape& true_computation,
     const ProgramShape& false_computation) {
   if (!ShapeUtil::ShapeIs(predicate, PRED, {})) {
-    return InvalidArgument("predicate must be a boolean; got %s.",
+    return InvalidArgument("Predicate must be a boolean; got %s.",
                            ShapeUtil::HumanString(predicate).c_str());
   }
 
@@ -2302,8 +2296,8 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
 
   if (ShapeUtil::ElementsIn(operand) != ShapeUtil::ElementsIn(inferred_shape)) {
     return InvalidArgument(
-        "reshape operation has mismatched element counts: from=%lld (%s) "
-        "to=%lld (%s)",
+        "Reshape operation has mismatched element counts: from=%lld (%s) "
+        "to=%lld (%s).",
         ShapeUtil::ElementsIn(operand), ShapeUtil::HumanString(operand).c_str(),
         ShapeUtil::ElementsIn(inferred_shape),
         ShapeUtil::HumanString(inferred_shape).c_str());
@@ -2351,7 +2345,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   TF_RETURN_IF_ERROR(ExpectNotTupleOrOpaque(max, "clamp max"));
   if (!ShapeUtil::SameElementTypeIgnoringFpPrecision(min, operand) ||
       !ShapeUtil::SameElementTypeIgnoringFpPrecision(max, operand)) {
-    return InvalidArgument("clamp op with different operand types: %s, %s, %s",
+    return InvalidArgument("Clamp with different operand types: %s, %s, %s.",
                            ShapeUtil::HumanString(min).c_str(),
                            ShapeUtil::HumanString(operand).c_str(),
                            ShapeUtil::HumanString(max).c_str());
@@ -2372,7 +2366,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     }
   }
   return Unimplemented(
-      "not yet implemented: %s, %s <clamp> %s", min.ShortDebugString().c_str(),
+      "%s, %s <clamp> %s is not implemented.", min.ShortDebugString().c_str(),
       max.ShortDebugString().c_str(), operand.ShortDebugString().c_str());
 }
 
@@ -2391,13 +2385,13 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
   }
   if (!compatible) {
     return InvalidArgument(
-        "operands to select must be the same shape; got %s and %s",
+        "Operands to select must be the same shape; got %s and %s.",
         ShapeUtil::HumanString(on_true).c_str(),
         ShapeUtil::HumanString(on_false).c_str());
   }
   if (pred.element_type() != PRED) {
     return InvalidArgument(
-        "select's pred operand must have PRED element type; got %s",
+        "Select's pred operand must have PRED element type; got %s.",
         ShapeUtil::HumanString(pred).c_str());
   }
   if (ShapeUtil::SameDimensions(pred, on_true) || ShapeUtil::Rank(pred) == 0) {
@@ -2407,9 +2401,9 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return ShapeUtil::ChangeElementType(
         on_true, ShapeUtil::HigherPrecisionElementType(on_true, on_false));
   } else {
-    return Unimplemented(
-        "select operation with non-scalar predicate with dimensionality "
-        " different from the other operands: %s",
+    return InvalidArgument(
+        "Select operation with non-scalar predicate with dimensionality "
+        " different from the other operands: %s.",
         ShapeUtil::HumanString(pred).c_str());
   }
 }
@@ -2427,7 +2421,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     return InvalidArgument(
         "Call applied function arity must match number of arguments; got: "
         "arity: %d, arguments: %zu; computation signature: %s; argument "
-        "shapes: [%s]",
+        "shapes: [%s].",
         to_apply.parameters_size(), arg_shapes.size(),
         computation_signature.c_str(), argument_shapes.c_str());
   }
@@ -2439,7 +2433,7 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(
     if (!ShapeUtil::Compatible(arg_shape, param_shape)) {
       return InvalidArgument(
           "Call parameter must match argument; got parameter %d shape: %s, "
-          "argument shape: %s",
+          "argument shape: %s.",
           i, ShapeUtil::HumanString(param_shape).c_str(),
           ShapeUtil::HumanString(arg_shape).c_str());
     }
@@ -2454,14 +2448,14 @@ static Status ValidateGatherDimensionNumbers(
     const GatherDimensionNumbers& dim_numbers) {
   if (!c_is_sorted(dim_numbers.output_window_dims())) {
     return InvalidArgument(
-        "Output window dimensions in gather op must be ascending; got: %s",
+        "Output window dimensions in gather op must be ascending; got: %s.",
         Join(dim_numbers.output_window_dims(), ", ").c_str());
   }
 
   if (c_adjacent_find(dim_numbers.output_window_dims()) !=
       dim_numbers.output_window_dims().end()) {
     return InvalidArgument(
-        "Output window dimensions in gather op must not repeat; got: %s",
+        "Output window dimensions in gather op must not repeat; got: %s.",
         Join(dim_numbers.output_window_dims(), ", ").c_str());
   }
 
@@ -2474,7 +2468,7 @@ static Status ValidateGatherDimensionNumbers(
     if (window_index < 0 || window_index >= output_shape_rank) {
       return InvalidArgument(
           "Window index %d in gather op is out of bounds; got %lld, but should "
-          "have been in [0,%lld)",
+          "have been in [0,%lld).",
           i, window_index, output_shape_rank);
     }
   }
@@ -2496,7 +2490,7 @@ static Status ValidateGatherDimensionNumbers(
         gather_dim_to_input_dim >= input_shape.dimensions_size()) {
       return InvalidArgument(
           "Invalid gather_dims_to_operand_dims mapping; domain is [0, %d), "
-          "got: %d->%lld",
+          "got: %d->%lld.",
           input_shape.dimensions_size(), i, gather_dim_to_input_dim);
     }
   }
@@ -2511,7 +2505,7 @@ static Status ValidateGatherDimensionNumbers(
       sorted_gather_dims_to_operand_dims.end()) {
     return InvalidArgument(
         "Repeated dimensions are not allowed in gather_dims_to_operand_dims; "
-        "got: %s",
+        "got: %s.",
         Join(dim_numbers.gather_dims_to_operand_dims(), ", ").c_str());
   }
 
@@ -2519,7 +2513,7 @@ static Status ValidateGatherDimensionNumbers(
     if (elided_dim < 0 || elided_dim >= input_shape.dimensions_size()) {
       return InvalidArgument(
           "Invalid elided_window_dims set in gather op; valid range is [0, "
-          "%d), got: %lld",
+          "%d), got: %lld.",
           input_shape.dimensions_size(), elided_dim);
     }
   }
@@ -2534,7 +2528,7 @@ static Status ValidateGatherDimensionNumbers(
       dim_numbers.elided_window_dims().end()) {
     return InvalidArgument(
         "Repeated dimensions not allowed in elided_window_dims in gather op; "
-        "got: %s",
+        "got: %s.",
         Join(dim_numbers.elided_window_dims(), ", ").c_str());
   }
 
@@ -2552,7 +2546,7 @@ static Status ValidateGatherDimensionNumbers(
 
   if (!ShapeUtil::ElementIsIntegral(gather_indices_shape)) {
     return InvalidArgument(
-        "Gather indices parameter must be an integral tensor; got %s",
+        "Gather indices parameter must be an integral tensor; got %s.",
         ShapeUtil::HumanString(gather_indices_shape).c_str());
   }
 
@@ -2586,7 +2580,7 @@ static Status ValidateGatherDimensionNumbers(
   if (window_bounds.size() != input_shape.dimensions_size()) {
     return InvalidArgument(
         "Gather op must have one window bound for every input dimension; got: "
-        "len(window_bounds)=%lu, input_shape.rank=%d",
+        "len(window_bounds)=%lu, input_shape.rank=%d.",
         window_bounds.size(), input_shape.dimensions_size());
   }
 
@@ -2596,7 +2590,7 @@ static Status ValidateGatherDimensionNumbers(
     return InvalidArgument(
         "All components of the window index in a gather op must either be a "
         "output window index or explicitly elided; got len(window_bounds)=%lu, "
-        "output_window_bounds=%s, elided_window_bounds=%s",
+        "output_window_bounds=%s, elided_window_bounds=%s.",
         window_bounds.size(),
         Join(gather_dim_numbers.output_window_dims(), ",").c_str(),
         Join(gather_dim_numbers.elided_window_dims(), ",").c_str());
@@ -2609,7 +2603,7 @@ static Status ValidateGatherDimensionNumbers(
       return InvalidArgument(
           "Window bound at index %d in gather op is out of range, must be "
           "within "
-          "[0, %lld), got %lld",
+          "[0, %lld), got %lld.",
           i, corresponding_input_bound + 1, window_bound);
     }
   }
@@ -2618,7 +2612,7 @@ static Status ValidateGatherDimensionNumbers(
     if (window_bounds[gather_dim_numbers.elided_window_dims(i)] != 1) {
       return InvalidArgument(
           "Gather op can only elide window indices with bound 1, but bound is "
-          "%lld for index %lld at position %d",
+          "%lld for index %lld at position %d.",
           window_bounds[gather_dim_numbers.elided_window_dims(i)],
           gather_dim_numbers.elided_window_dims(i), i);
     }
diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc
index 029d2b3b86..0e61994a78 100644
--- a/tensorflow/compiler/xla/service/shape_inference_test.cc
+++ b/tensorflow/compiler/xla/service/shape_inference_test.cc
@@ -135,7 +135,7 @@ TEST_F(ShapeInferenceTest, SelectBadShapes) {
       TernaryOperation::TRIOP_SELECT, pred_, matrix_64_48_, matrix_32_64_);
   ASSERT_FALSE(inferred_status_error1.ok());
   ASSERT_THAT(inferred_status_error1.status().error_message(),
-              HasSubstr("operands to select must be the same shape"));
+              HasSubstr("Operands to select must be the same shape"));
 
   auto inferred_status_error2 = ShapeInference::InferTernaryOpShape(
       TernaryOperation::TRIOP_SELECT, s32_, matrix_64_48_, matrix_64_48_);
@@ -340,7 +340,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSourceShape) {
       init_value_shape_, scatter_program_shape_);
   ASSERT_FALSE(inferred_status_fail.ok());
   ASSERT_THAT(inferred_status_fail.status().error_message(),
-              HasSubstr("source shape does not match"));
+              HasSubstr("Source shape does not match"));
 }
 
 TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape1) {
@@ -351,7 +351,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape1) {
       init_value_shape_, scatter_program_shape_);
   ASSERT_FALSE(inferred_status_fail.ok());
   ASSERT_THAT(inferred_status_fail.status().error_message(),
-              HasSubstr("select function must take 2 parameters"));
+              HasSubstr("Select function must take 2 parameters"));
 }
 
 TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape2) {
@@ -362,7 +362,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape2) {
       init_value_shape_, scatter_program_shape_);
   ASSERT_FALSE(inferred_status_fail.ok());
   ASSERT_THAT(inferred_status_fail.status().error_message(),
-              HasSubstr("select function must have rank-0 PRED"));
+              HasSubstr("Select function must have rank-0 PRED"));
 }
 
 TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape3) {
@@ -373,7 +373,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape3) {
       init_value_shape_, scatter_program_shape_);
   ASSERT_FALSE(inferred_status_fail.ok());
   ASSERT_THAT(inferred_status_fail.status().error_message(),
-              HasSubstr("select function's first parameter"));
+              HasSubstr("Select function's first parameter"));
 }
 
 TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape4) {
@@ -384,7 +384,7 @@ TEST_F(SelectAndScatterShapeInferenceTest, SelectAndScatterWrongSelectShape4) {
       init_value_shape_, scatter_program_shape_);
   ASSERT_FALSE(inferred_status_fail.ok());
   ASSERT_THAT(inferred_status_fail.status().error_message(),
-              HasSubstr("select function's second parameter"));
+              HasSubstr("Select function's second parameter"));
 }
 
 TEST_F(ShapeInferenceTest, Convolve) {
@@ -906,7 +906,7 @@ TEST_F(ShapeInferenceTest, ScalarDotVector) {
       ShapeInference::InferDotOpShape(f32_, vector_32_, dot_dnums);
   ASSERT_FALSE(inferred_status.ok());
   ASSERT_THAT(inferred_status.status().error_message(),
-              HasSubstr("dot only supports rank"));
+              HasSubstr("Dot only supports rank"));
 }
 
 // 3D <dot> 2D: error
@@ -918,7 +918,7 @@ TEST_F(ShapeInferenceTest, DotWithRankHigherThanTwo) {
       ShapeUtil::MakeShape(F32, {32, 32, 32}), matrix_32_64_, dot_dnums);
   ASSERT_FALSE(inferred_status.ok());
   ASSERT_THAT(inferred_status.status().error_message(),
-              HasSubstr("batch and contracting dimension number mismatch"));
+              HasSubstr("Batch and contracting dimension number mismatch"));
 }
 
 // vector <dot> vector -> scalar
@@ -1024,7 +1024,7 @@ TEST_F(ShapeInferenceTest, DotWithTwoContractingDimsFails) {
       ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums);
   ASSERT_FALSE(inferred_status.ok());
   ASSERT_THAT(inferred_status.status().error_message(),
-              HasSubstr("must specify one contracting dimension for both "
+              HasSubstr("Must specify one contracting dimension for both "
                         "lhs and rhs"));
 }
 
@@ -1044,7 +1044,7 @@ TEST_F(ShapeInferenceTest, DotWithMisatchedBatchDimSizesFails) {
       ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums);
   ASSERT_FALSE(inferred_status.ok());
   ASSERT_THAT(inferred_status.status().error_message(),
-              HasSubstr("batch dimension numbers and sizes must match"));
+              HasSubstr("Batch dimension numbers and sizes must match"));
 }
 
 // BatchMatMul with different batch dimension numbers fails.
@@ -1063,7 +1063,7 @@ TEST_F(ShapeInferenceTest, DotWithMisatchedBatchDimNumbersFails) {
       ShapeInference::InferDotOpShape(lhs_shape, rhs_shape, dot_dnums);
   ASSERT_FALSE(inferred_status.ok());
   ASSERT_THAT(inferred_status.status().error_message(),
-              HasSubstr("batch dimension numbers must precede non-batch"));
+              HasSubstr("Batch dimension numbers must precede non-batch"));
 }
 
 // BatchMatMul with out-of-range dimension numbers fails.
@@ -1166,42 +1166,42 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastBadDimension) {
       BinaryOperation::BINOP_ADD, tensor, vec8, {});
   ASSERT_FALSE(inferred_status_error1.ok());
   ASSERT_THAT(inferred_status_error1.status().error_message(),
-              HasSubstr("automatic"));
+              HasSubstr("Automatic"));
 
   // broadcast_dimension out of bounds for tensor's rank
   auto inferred_status_error2 = ShapeInference::InferBinaryOpShape(
       BinaryOperation::BINOP_ADD, tensor, vec8, {3});
   ASSERT_FALSE(inferred_status_error2.ok());
   ASSERT_THAT(inferred_status_error2.status().error_message(),
-              ContainsRegex("broadcast dimension number .* too large"));
+              ContainsRegex("Broadcast dimension number .* too large"));
 
   // broadcast_dimension doesn't match corresponding dimension
   auto inferred_status_error3 = ShapeInference::InferBinaryOpShape(
       BinaryOperation::BINOP_ADD, tensor, vec8, {0});
   ASSERT_FALSE(inferred_status_error3.ok());
   ASSERT_THAT(inferred_status_error3.status().error_message(),
-              HasSubstr("broadcast dimension 0 mismatch"));
+              HasSubstr("Broadcast dimension 0 mismatch"));
 
   // broadcast_dimensions list too long
   auto inferred_status_error4 = ShapeInference::InferBinaryOpShape(
       BinaryOperation::BINOP_ADD, tensor, matrix8_4, {0, 1, 2});
   ASSERT_FALSE(inferred_status_error4.ok());
   ASSERT_THAT(inferred_status_error4.status().error_message(),
-              HasSubstr("size of broadcast_dimensions has to match"));
+              HasSubstr("broadcast_dimensions has to match"));
 
   // there's a dimension above the rank of the tensor
   auto inferred_status_error5 = ShapeInference::InferBinaryOpShape(
       BinaryOperation::BINOP_ADD, tensor, matrix8_4, {3, 0});
   ASSERT_FALSE(inferred_status_error5.ok());
   ASSERT_THAT(inferred_status_error5.status().error_message(),
-              ContainsRegex("broadcast dimension number .* too large"));
+              ContainsRegex("dimension number .* too large"));
 
   // broadcasting dimensions don't match in this order
   auto inferred_status_error6 = ShapeInference::InferBinaryOpShape(
       BinaryOperation::BINOP_ADD, tensor, matrix8_4, {2, 1});
   ASSERT_FALSE(inferred_status_error6.ok());
   ASSERT_THAT(inferred_status_error6.status().error_message(),
-              HasSubstr("broadcast dimension 0 mismatch"));
+              HasSubstr("dimension 0 mismatch"));
 
   // The following two tests make sure that broadcasting dimensions are listed
   // in a proper (strictly increasing) order, even if the lower-rank array
@@ -1210,13 +1210,13 @@ TEST_F(ShapeInferenceTest, BinOpBroadcastBadDimension) {
       BinaryOperation::BINOP_ADD, tensor8_8_8, matrix8_8, {0, 0});
   ASSERT_FALSE(inferred_status_error7.ok());
   ASSERT_THAT(inferred_status_error7.status().error_message(),
-              HasSubstr("broadcast dimensions order is wrong"));
+              HasSubstr("dimensions order is wrong"));
 
   auto inferred_status_error8 = ShapeInference::InferBinaryOpShape(
       BinaryOperation::BINOP_ADD, tensor8_8_8, matrix8_8, {1, 0});
   ASSERT_FALSE(inferred_status_error8.ok());
   ASSERT_THAT(inferred_status_error8.status().error_message(),
-              HasSubstr("broadcast dimensions order is wrong"));
+              HasSubstr("dimensions order is wrong"));
 }
 
 // Tests for the while instruction with proper shapes.
@@ -1242,7 +1242,7 @@ TEST_F(ShapeInferenceTest, WhileWithBadShapes) {
       ShapeInference::InferWhileShape(bad_shape_1, body, result_shape);
   ASSERT_FALSE(inferred_status_error1.ok());
   ASSERT_THAT(inferred_status_error1.status().error_message(),
-              HasSubstr("condition must take 1 arguments"));
+              HasSubstr("Condition must take 1 arguments"));
 
   auto bad_shape_2 =
       ShapeUtil::MakeProgramShape({s32_, result_shape}, result_shape);
@@ -1250,14 +1250,14 @@ TEST_F(ShapeInferenceTest, WhileWithBadShapes) {
       ShapeInference::InferWhileShape(cond, bad_shape_2, result_shape);
   ASSERT_FALSE(inferred_status_error2.ok());
   ASSERT_THAT(inferred_status_error2.status().error_message(),
-              HasSubstr("body must take 1 arguments"));
+              HasSubstr("Body must take 1 arguments"));
 
   auto bad_shape_3 = ShapeUtil::MakeProgramShape({result_shape}, s32_);
   auto inferred_status_error3 =
       ShapeInference::InferWhileShape(bad_shape_3, body, result_shape);
   ASSERT_FALSE(inferred_status_error3.ok());
   ASSERT_THAT(inferred_status_error3.status().error_message(),
-              HasSubstr("condition must return a boolean"));
+              HasSubstr("Condition must return a boolean"));
 
   auto bad_shape_4 = ShapeUtil::MakeProgramShape({result_shape}, vector_32_);
   auto inferred_status_error4 =
@@ -1301,13 +1301,13 @@ TEST_F(ShapeInferenceTest, ConcatenateWithBadShapes) {
       ShapeInference::InferConcatOpShape({&vector_32_}, /*dimension=*/-1);
   ASSERT_FALSE(inferred_status_error2.ok());
   ASSERT_THAT(inferred_status_error2.status().error_message(),
-              HasSubstr("dimension to concatenate along out of bounds: -1"));
+              HasSubstr("dimension out of bounds: -1"));
 
   auto inferred_status_error3 =
       ShapeInference::InferConcatOpShape({&vector_32_}, /*dimension=*/1);
   ASSERT_FALSE(inferred_status_error3.ok());
   ASSERT_THAT(inferred_status_error3.status().error_message(),
-              HasSubstr("dimension to concatenate along out of bounds: 1"));
+              HasSubstr("dimension out of bounds: 1"));
 
   Shape tuple = ShapeUtil::MakeTupleShape({vector_32_});
   auto inferred_status_error4 = ShapeInference::InferConcatOpShape(
@@ -1315,21 +1315,20 @@ TEST_F(ShapeInferenceTest, ConcatenateWithBadShapes) {
   ASSERT_FALSE(inferred_status_error4.ok());
   ASSERT_THAT(
       inferred_status_error4.status().error_message(),
-      HasSubstr("Expected non-tuple argument for operand of concatenation."));
+      HasSubstr("Expected non-tuple argument for operand of concatenation"));
 
   const Shape vector_s32 = ShapeUtil::MakeShape(S32, {32});
   auto inferred_status_error5 = ShapeInference::InferConcatOpShape(
       {&vector_32_, &vector_s32}, /*dimension=*/0);
   ASSERT_FALSE(inferred_status_error5.ok());
-  ASSERT_THAT(
-      inferred_status_error5.status().error_message(),
-      HasSubstr("cannot concatenate arrays with different element types"));
+  ASSERT_THAT(inferred_status_error5.status().error_message(),
+              HasSubstr("concatenate arrays with different element types"));
 
   auto inferred_status_error6 = ShapeInference::InferConcatOpShape(
       {&matrix_32_48_, &matrix_32_64_}, /*dimension=*/0);
   ASSERT_FALSE(inferred_status_error6.ok());
   ASSERT_THAT(inferred_status_error6.status().error_message(),
-              HasSubstr("cannot concatenate arrays that differ in "
+              HasSubstr("concatenate arrays that differ in "
                         "dimensions other than the one being "
                         "concatenated"));
 }
@@ -1467,7 +1466,7 @@ TEST_F(ShapeInferenceTest, Conditional) {
       ShapeUtil::MakeProgramShape({vector_64_}, f32_));
   EXPECT_FALSE(inferred_status_error0.ok());
   EXPECT_THAT(inferred_status_error0.status().error_message(),
-              HasSubstr("predicate must be a boolean"));
+              HasSubstr("Predicate must be a boolean"));
 
   auto inferred_status_error1 = ShapeInference::InferConditionalShape(
       pred_, ShapeUtil::MakeTupleShape({f32_, vector_32_}), matrix_32_48_,
diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
index 03f5e08315..97095f1cc4 100644
--- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
+++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc
@@ -662,7 +662,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidBinaryAndDegenerateBroadcasting) {
   auto result_status = Execute(&b, {});
   EXPECT_FALSE(result_status.ok());
   EXPECT_THAT(result_status.status().error_message(),
-              HasSubstr("broadcast dimension 0 mismatch"));
+              HasSubstr("dimension 0 mismatch"));
 }
 
 XLA_TEST_F(BroadcastSimpleTest, InvalidInDimensionBroadcasting) {
@@ -675,7 +675,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidInDimensionBroadcasting) {
   auto result_status = Execute(&b, {});
   EXPECT_FALSE(result_status.ok());
   EXPECT_THAT(result_status.status().error_message(),
-              HasSubstr("binary op BINOP_ADD with incompatible shapes"));
+              HasSubstr("op BINOP_ADD with incompatible shapes"));
 }
 
 XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) {
@@ -688,7 +688,7 @@ XLA_TEST_F(BroadcastSimpleTest, InvalidDegenerateBroadcasting) {
   auto result_status = Execute(&b, {});
   EXPECT_FALSE(result_status.ok());
   EXPECT_THAT(result_status.status().error_message(),
-              HasSubstr("binary op BINOP_ADD with incompatible shapes"));
+              HasSubstr("op BINOP_ADD with incompatible shapes"));
 }
 
 }  // namespace
diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc
index 1bcad5a3f3..fb0e9c724a 100644
--- a/tensorflow/compiler/xla/tests/concat_test.cc
+++ b/tensorflow/compiler/xla/tests/concat_test.cc
@@ -75,7 +75,7 @@ XLA_TEST_F(ConcatTest, CannotConcatR0WithR0) {
   StatusOr<Computation> computation_status = builder.Build();
   ASSERT_FALSE(computation_status.ok());
   EXPECT_THAT(computation_status.status().ToString(),
-              HasSubstr("dimension to concatenate along out of bounds: 0"));
+              HasSubstr("out of bounds: 0"));
 }
 
 XLA_TEST_F(ConcatTest, Concat_R1_L0_With_R1_L0) {
diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc
index 2b0f7e6e80..0cd812fd1b 100644
--- a/tensorflow/compiler/xla/tests/map_test.cc
+++ b/tensorflow/compiler/xla/tests/map_test.cc
@@ -531,7 +531,7 @@ TEST_F(MapTest, MapOperantionWithBuildError) {
   ASSERT_TRUE(!computation_status.ok());
   EXPECT_THAT(
       computation_status.status().ToString(),
-      ::testing::HasSubstr("error from: ErrorAdd: binary op BINOP_ADD with "
+      ::testing::HasSubstr("error from: ErrorAdd: Binary op BINOP_ADD with "
                            "different element types: f32[] and u16[]"));
 }
 
-- 
GitLab


From 1a15d58c8204b145c545b27efdd0a1ca069cacdc Mon Sep 17 00:00:00 2001
From: Sanjoy Das <sanjoy@google.com>
Date: Fri, 2 Mar 2018 14:00:07 -0800
Subject: [PATCH 563/575] [TF:XLA] Bump open source llvm revision to r326571

PiperOrigin-RevId: 187665541
---
 tensorflow/workspace.bzl | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index ea8f42ab8d..1af246f9dc 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -475,11 +475,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   tf_http_archive(
       name = "llvm",
       urls = [
-          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/9a6e78e4adc959d2825f7af35b4ed0e09394d840.tar.gz",
-          "https://github.com/llvm-mirror/llvm/archive/9a6e78e4adc959d2825f7af35b4ed0e09394d840.tar.gz",
+          "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/193aea3782308c66a7a12f1c37520a1b4ff1dbd8.tar.gz",
+          "https://github.com/llvm-mirror/llvm/archive/193aea3782308c66a7a12f1c37520a1b4ff1dbd8.tar.gz",
       ],
-      sha256 = "7990b4d446de971e0acc481942920452a182d2f87a8164bdc117fd9b9ace591d",
-      strip_prefix = "llvm-9a6e78e4adc959d2825f7af35b4ed0e09394d840",
+      sha256 = "2eda56deafb8da85bc23aa52fa1fb8c39da6a58c865e5216d0a0787bd09a09ed",
+      strip_prefix = "llvm-193aea3782308c66a7a12f1c37520a1b4ff1dbd8",
       build_file = str(Label("//third_party/llvm:llvm.BUILD")),
   )
 
-- 
GitLab


From d3ece65e340ca7cd00874c460cf9f3e631346921 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 2 Mar 2018 14:33:39 -0800
Subject: [PATCH 564/575] Checkpointable: Have MultiRNNCell add its dependent
 cells as dependencies

PiperOrigin-RevId: 187670464
---
 .../contrib/rnn/python/kernel_tests/core_rnn_cell_test.py     | 2 ++
 tensorflow/python/ops/rnn_cell_impl.py                        | 4 ++++
 2 files changed, 6 insertions(+)

diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
index 0e62b315b6..d41fc0b3ac 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
@@ -187,6 +187,8 @@ class RNNCellTest(test.TestCase):
               ],
               state_is_tuple=False)
           self.assertEqual(cell.dtype, None)
+          self.assertEqual("cell-0", cell._checkpoint_dependencies[0].name)
+          self.assertEqual("cell-1", cell._checkpoint_dependencies[1].name)
           g, out_m = cell(x, m)
           # Layer infers the input type.
           self.assertEqual(cell.dtype, dtype.name)
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index 923348ea44..bd7c731210 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -1187,6 +1187,10 @@ class MultiRNNCell(RNNCell):
           "cells must be a list or tuple, but saw: %s." % cells)
 
     self._cells = cells
+    for cell_number, cell in enumerate(self._cells):
+      # Add Checkpointable dependencies on these cells so their variables get
+      # saved with this object when using object-based saving.
+      self._track_checkpointable(cell, name="cell-%d" % (cell_number,))
     self._state_is_tuple = state_is_tuple
     if not state_is_tuple:
       if any(nest.is_sequence(c.state_size) for c in self._cells):
-- 
GitLab


From 16f74956eb75511f1bf47a62a998ed9a434a8249 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 14:55:26 -0800
Subject: [PATCH 565/575] Add a small helper which is useful for quicker
 debugging.

PiperOrigin-RevId: 187673654
---
 tensorflow/contrib/py2tf/pyct/transformer.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tensorflow/contrib/py2tf/pyct/transformer.py b/tensorflow/contrib/py2tf/pyct/transformer.py
index 877d52af01..57016bb4ce 100644
--- a/tensorflow/contrib/py2tf/pyct/transformer.py
+++ b/tensorflow/contrib/py2tf/pyct/transformer.py
@@ -44,6 +44,12 @@ class Base(gast.NodeTransformer):
     self._col_offset = 0
     self.context = context
 
+  def debug_print(self, node):
+    """Helper method useful for debugging."""
+    if __debug__:
+      print(pretty_printer.fmt(node))
+    return node
+
   def visit(self, node):
     source_code = self.context.source_code
     source_file = self.context.source_file
-- 
GitLab


From 809c84dc3a6252efab2b366f167135ed7826dee7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 15:06:13 -0800
Subject: [PATCH 566/575] Begin a library for statistical testing of samplers.

So far, it consists of one-sample and two-sample equality-of-means
assertions, and power analysis and experimental design for those,
because that's what was needed for testing the LKJ distribution.
If this API shape proves viable, more to come.

PiperOrigin-RevId: 187675337
---
 tensorflow/contrib/distributions/BUILD        |  13 +
 .../kernel_tests/statistical_testing_test.py  | 166 ++++
 .../python/ops/statistical_testing.py         | 728 ++++++++++++++++++
 3 files changed, 907 insertions(+)
 create mode 100644 tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py
 create mode 100644 tensorflow/contrib/distributions/python/ops/statistical_testing.py

diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD
index ed79ef70f8..1b4877c57f 100644
--- a/tensorflow/contrib/distributions/BUILD
+++ b/tensorflow/contrib/distributions/BUILD
@@ -474,6 +474,19 @@ cuda_py_test(
     tags = ["nomsan"],  # disable to avoid false positives from scipy.
 )
 
+cuda_py_test(
+    name = "statistical_testing_test",
+    size = "medium",
+    srcs = [
+        "python/kernel_tests/statistical_testing_test.py",
+    ],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:client_testlib",
+    ],
+)
+
 cuda_py_test(
     name = "vector_sinh_arcsinh_diag_test",
     size = "medium",
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py b/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py
new file mode 100644
index 0000000000..3548ac1807
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/kernel_tests/statistical_testing_test.py
@@ -0,0 +1,166 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the statistical testing library."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.distributions.python.ops import statistical_testing as st
+from tensorflow.python.framework import errors
+from tensorflow.python.ops import check_ops
+from tensorflow.python.platform import test
+
+
+class StatisticalTestingTest(test.TestCase):
+
+  def test_dkwm_design_mean_one_sample_soundness(self):
+    numbers = [1e-5, 1e-2, 1.1e-1, 0.9, 1., 1.02, 2., 10., 1e2, 1e5, 1e10]
+    rates = [1e-6, 1e-3, 1e-2, 1.1e-1, 0.2, 0.5, 0.7, 1.]
+    with self.test_session() as sess:
+      for ff in rates:
+        for fp in rates:
+          sufficient_n = st.min_num_samples_for_dkwm_mean_test(
+              numbers, 0., 1., false_fail_rate=ff, false_pass_rate=fp)
+          detectable_d = st.min_discrepancy_of_true_means_detectable_by_dkwm(
+              sufficient_n, 0., 1., false_fail_rate=ff, false_pass_rate=fp)
+          sess.run(check_ops.assert_less_equal(detectable_d, numbers))
+
+  def test_dkwm_design_mean_two_sample_soundness(self):
+    numbers = [1e-5, 1e-2, 1.1e-1, 0.9, 1., 1.02, 2., 10., 1e2, 1e5, 1e10]
+    rates = [1e-6, 1e-3, 1e-2, 1.1e-1, 0.2, 0.5, 0.7, 1.]
+    with self.test_session() as sess:
+      for ff in rates:
+        for fp in rates:
+          (sufficient_n1,
+           sufficient_n2) = st.min_num_samples_for_dkwm_mean_two_sample_test(
+               numbers, 0., 1., 0., 1.,
+               false_fail_rate=ff, false_pass_rate=fp)
+          d_fn = st.min_discrepancy_of_true_means_detectable_by_dkwm_two_sample
+          detectable_d = d_fn(
+              sufficient_n1, 0., 1., sufficient_n2, 0., 1.,
+              false_fail_rate=ff, false_pass_rate=fp)
+          sess.run(check_ops.assert_less_equal(detectable_d, numbers))
+
+  def test_true_mean_confidence_interval_by_dkwm_one_sample(self):
+    rng = np.random.RandomState(seed=0)
+
+    num_samples = 5000
+    # 5000 samples is chosen to be enough to find discrepancies of
+    # size 0.1 or more with assurance 1e-6, as confirmed here:
+    with self.test_session() as sess:
+      d = st.min_discrepancy_of_true_means_detectable_by_dkwm(
+          num_samples, 0., 1., false_fail_rate=1e-6, false_pass_rate=1e-6)
+      d = sess.run(d)
+      self.assertLess(d, 0.1)
+
+    # Test that the confidence interval computed for the mean includes
+    # 0.5 and excludes 0.4 and 0.6.
+    with self.test_session() as sess:
+      samples = rng.uniform(size=num_samples).astype(np.float32)
+      (low, high) = st.true_mean_confidence_interval_by_dkwm(
+          samples, 0., 1., error_rate=1e-6)
+      low, high = sess.run([low, high])
+      self.assertGreater(low, 0.4)
+      self.assertLess(low, 0.5)
+      self.assertGreater(high, 0.5)
+      self.assertLess(high, 0.6)
+
+  def test_dkwm_mean_one_sample_assertion(self):
+    rng = np.random.RandomState(seed=0)
+    num_samples = 5000
+
+    # Test that the test assertion agrees that the mean of the standard
+    # uniform distribution is 0.5.
+    samples = rng.uniform(size=num_samples).astype(np.float32)
+    with self.test_session() as sess:
+      sess.run(st.assert_true_mean_equal_by_dkwm(
+          samples, 0., 1., 0.5, false_fail_rate=1e-6))
+
+      # Test that the test assertion confirms that the mean of the
+      # standard uniform distribution is not 0.4.
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(st.assert_true_mean_equal_by_dkwm(
+            samples, 0., 1., 0.4, false_fail_rate=1e-6))
+
+      # Test that the test assertion confirms that the mean of the
+      # standard uniform distribution is not 0.6.
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(st.assert_true_mean_equal_by_dkwm(
+            samples, 0., 1., 0.6, false_fail_rate=1e-6))
+
+  def test_dkwm_mean_two_sample_assertion(self):
+    rng = np.random.RandomState(seed=0)
+    num_samples = 15000
+
+    # 15000 samples is chosen to be enough to find discrepancies of
+    # size 0.1 or more with assurance 1e-6, as confirmed here:
+    with self.test_session() as sess:
+      d = st.min_discrepancy_of_true_means_detectable_by_dkwm_two_sample(
+          num_samples, 0., 1., num_samples, 0., 1.,
+          false_fail_rate=1e-6, false_pass_rate=1e-6)
+      d = sess.run(d)
+      self.assertLess(d, 0.1)
+
+    # Test that the test assertion agrees that the standard
+    # uniform distribution has the same mean as itself.
+    samples1 = rng.uniform(size=num_samples).astype(np.float32)
+    samples2 = rng.uniform(size=num_samples).astype(np.float32)
+    with self.test_session() as sess:
+      sess.run(st.assert_true_mean_equal_by_dkwm_two_sample(
+          samples1, 0., 1., samples2, 0., 1., false_fail_rate=1e-6))
+
+      # Test that the test assertion confirms that the mean of the
+      # standard uniform distribution is different from the mean of beta(2, 1).
+      beta_high_samples = rng.beta(2, 1, size=num_samples).astype(np.float32)
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(st.assert_true_mean_equal_by_dkwm_two_sample(
+            samples1, 0., 1.,
+            beta_high_samples, 0., 1.,
+            false_fail_rate=1e-6))
+
+      # Test that the test assertion confirms that the mean of the
+      # standard uniform distribution is different from the mean of beta(1, 2).
+      beta_low_samples = rng.beta(1, 2, size=num_samples).astype(np.float32)
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(st.assert_true_mean_equal_by_dkwm_two_sample(
+            samples1, 0., 1.,
+            beta_low_samples, 0., 1.,
+            false_fail_rate=1e-6))
+
+  def test_dkwm_argument_validity_checking(self):
+    rng = np.random.RandomState(seed=0)
+    samples = rng.uniform(size=5000).astype(np.float32)
+
+    # Test that the test library complains if the given samples fall
+    # outside the purported bounds.
+    with self.test_session() as sess:
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(st.true_mean_confidence_interval_by_dkwm(
+            samples, 0., 0.5, error_rate=0.5))
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(st.true_mean_confidence_interval_by_dkwm(
+            samples, 0.5, 1., error_rate=0.5))
+
+      # But doesn't complain if they don't.
+      op = st.true_mean_confidence_interval_by_dkwm(
+          samples, 0., 1., error_rate=0.5)
+      _ = sess.run(op)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/statistical_testing.py b/tensorflow/contrib/distributions/python/ops/statistical_testing.py
new file mode 100644
index 0000000000..d66c34cc1a
--- /dev/null
+++ b/tensorflow/contrib/distributions/python/ops/statistical_testing.py
@@ -0,0 +1,728 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Statistical test assertions calibrated for their error rates."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+
+__all__ = [
+    "true_mean_confidence_interval_by_dkwm",
+    "assert_true_mean_equal_by_dkwm",
+    "min_discrepancy_of_true_means_detectable_by_dkwm",
+    "min_num_samples_for_dkwm_mean_test",
+    "assert_true_mean_equal_by_dkwm_two_sample",
+    "min_discrepancy_of_true_means_detectable_by_dkwm_two_sample",
+    "min_num_samples_for_dkwm_mean_two_sample_test",
+]
+
+
+def _batch_sort_vector(x, ascending=True, name=None):
+  with ops.name_scope(name, "sort_each_row", [x]):
+    x = ops.convert_to_tensor(x, name="x")
+    n = array_ops.shape(x)[-1]
+    if ascending:
+      y, _ = nn_ops.top_k(-x, k=n, sorted=True)
+      y = -y
+    else:
+      y, _ = nn_ops.top_k(x, k=n, sorted=True)
+    y.set_shape(x.shape)
+    return y
+
+
+def _do_maximum_mean(samples, envelope, high, name=None):
+  """Common code between maximum_mean and minimum_mean."""
+  with ops.name_scope(name, "do_maximum_mean", [samples, envelope, high]):
+    n = array_ops.rank(samples)
+    # Move the batch dimension of `samples` to the rightmost position,
+    # where the _batch_sort_vector function wants it.
+    perm = array_ops.concat([math_ops.range(1, n), [0]], axis=0)
+    samples = array_ops.transpose(samples, perm)
+
+    samples = _batch_sort_vector(samples)
+    batch_shape = array_ops.shape(samples)[:-1]
+    n = array_ops.shape(samples)[-1]
+    step = 1. / math_ops.cast(n, dtype=samples.dtype.base_dtype)
+
+    def _loop_body(iter_, total, to_skip):
+      total = array_ops.where(
+          step <= to_skip,
+          total,
+          array_ops.where(
+              to_skip > 0.,
+              total + (step - to_skip) * samples[..., iter_],
+              total + step * samples[..., iter_]))
+      to_skip = array_ops.where(step <= to_skip, to_skip - step, 0.)
+      return [iter_ + 1, total, to_skip]
+
+    _, total, _ = control_flow_ops.while_loop(
+        cond=lambda iter_, *args: iter_ < n,
+        body=_loop_body,
+        loop_vars=[
+            0,
+            array_ops.zeros(batch_shape, dtype=samples.dtype.base_dtype),
+            envelope,  # to_skip
+        ])
+
+  return total + envelope * high
+
+
+def _maximum_mean(samples, envelope, high, name=None):
+  """Returns a stochastic upper bound on the mean of a scalar distribution.
+
+  The idea is that if the true CDF is within an `eps`-envelope of the
+  empirical CDF of the samples, and the support is bounded above, then
+  the mean is bounded above as well.  In symbols,
+
+  ```none
+  sup_x(|F_n(x) - F(x)|) < eps
+  ```
+
+  The 0th dimension of `samples` is interpreted as independent and
+  identically distributed samples.  The remaining dimensions are
+  broadcast together with `envelope` and `high`, and operated on
+  separately.
+
+  Args:
+    samples: Floating-point tensor of samples from the distribution(s)
+      of interest.  Entries are assumed IID across the 0th dimension.
+      The other dimensions must broadcast with `envelope` and `high`.
+    envelope: Floating-point tensor of sizes of admissible CDF
+      envelopes (i.e., the `eps` above).
+    high: Floating-point tensor of upper bounds on the distributions'
+      supports.
+    name: A name for this operation (optional).
+
+  Returns:
+    bound: Floating-point tensor of upper bounds on the true means.
+
+  Raises:
+    InvalidArgumentError: If some `sample` is found to be larger than
+      the corresponding `high`.
+  """
+  with ops.name_scope(name, "maximum_mean", [samples, envelope, high]):
+    samples = ops.convert_to_tensor(samples, name="samples")
+    envelope = ops.convert_to_tensor(envelope, name="envelope")
+    high = ops.convert_to_tensor(high, name="high")
+
+    xmax = math_ops.reduce_max(samples, axis=[-1])
+    msg = "Given sample maximum value exceeds expectations"
+    check_op = check_ops.assert_less_equal(xmax, high, message=msg)
+    with ops.control_dependencies([check_op]):
+      return array_ops.identity(_do_maximum_mean(samples, envelope, high))
+
+
+def _minimum_mean(samples, envelope, low, name=None):
+  """Returns a stochastic lower bound on the mean of a scalar distribution.
+
+  The idea is that if the true CDF is within an `eps`-envelope of the
+  empirical CDF of the samples, and the support is bounded below, then
+  the mean is bounded below as well.  In symbols,
+
+  ```none
+  sup_x(|F_n(x) - F(x)|) < eps
+  ```
+
+  The 0th dimension of `samples` is interpreted as independent and
+  identically distributed samples.  The remaining dimensions are
+  broadcast together with `envelope` and `low`, and operated on
+  separately.
+
+  Args:
+    samples: Floating-point tensor of samples from the distribution(s)
+      of interest.  Entries are assumed IID across the 0th dimension.
+      The other dimensions must broadcast with `envelope` and `low`.
+    envelope: Floating-point tensor of sizes of admissible CDF
+      envelopes (i.e., the `eps` above).
+    low: Floating-point tensor of lower bounds on the distributions'
+      supports.
+    name: A name for this operation (optional).
+
+  Returns:
+    bound: Floating-point tensor of lower bounds on the true means.
+
+  Raises:
+    InvalidArgumentError: If some `sample` is found to be smaller than
+      the corresponding `low`.
+  """
+  with ops.name_scope(name, "minimum_mean", [samples, envelope, low]):
+    samples = ops.convert_to_tensor(samples, name="samples")
+    envelope = ops.convert_to_tensor(envelope, name="envelope")
+    low = ops.convert_to_tensor(low, name="low")
+
+    xmin = math_ops.reduce_min(samples, axis=[-1])
+    msg = "Given sample minimum value falls below expectations"
+    check_op = check_ops.assert_greater_equal(xmin, low, message=msg)
+    with ops.control_dependencies([check_op]):
+      return - _do_maximum_mean(-samples, envelope, -low)
+
+
+def _dkwm_cdf_envelope(n, error_rate, name=None):
+  """Computes the CDF envelope that the DKWM inequality licenses.
+
+  The [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
+  (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval)
+  gives a stochastic bound on the distance between the true cumulative
+  distribution function (CDF) of any distribution and its empirical
+  CDF.  To wit, for `n` iid samples from any distribution with CDF F,
+
+  ```none
+  P(sup_x |F_n(x) - F(x)| > eps) < 2exp(-2n eps^2)
+  ```
+
+  This function computes the envelope size `eps` as a function of the
+  number of samples `n` and the desired limit on the left-hand
+  probability above.
+
+  Args:
+    n: Tensor of numbers of samples drawn.
+    error_rate: Floating-point tensor of admissible rates of mistakes.
+    name: A name for this operation (optional).
+
+  Returns:
+    eps: Tensor of maximum distances the true CDF can be from the
+      empirical CDF.  This scales as `O(sqrt(-log(error_rate)))` and
+      as `O(1 / sqrt(n))`.  The shape is the broadcast of `n` and
+      `error_rate`.
+  """
+  with ops.name_scope(name, "dkwm_cdf_envelope", [n, error_rate]):
+    n = math_ops.cast(n, dtype=error_rate.dtype)
+    return math_ops.sqrt(-gen_math_ops.log(error_rate / 2.) / (2. * n))
+
+
+def _check_shape_dominates(tensor, tensors):
+  """Check that broadcasting `tensor` against `tensors` does not expand it.
+
+  Why?  Because I want to be very sure that the samples tensor is not
+  accidentally enlarged by broadcasting against tensors that are
+  supposed to be describing the distribution(s) sampled from, lest the
+  sample counts end up inflated.
+
+  Args:
+    tensor: A Tensor whose shape is to be protected against broadcasting.
+    tensors: A list of Tensors to check
+
+  Returns:
+    tensor: `tf.identity(tensor)` with control dependencies attached;
+      be sure to use that downstream.
+  """
+  def check(t):
+    target = array_ops.shape(tensor)[1:]
+    result = array_ops.broadcast_dynamic_shape(target, array_ops.shape(t))
+    # This rank check ensures that I don't get a wrong answer from the
+    # _shapes_ broadcasting against each other.
+    gt = check_ops.assert_greater(array_ops.rank(target), array_ops.rank(t))
+    eq = check_ops.assert_equal(target, result)
+    return gt, eq
+  checks = list(itertools.chain(*[check(t) for t in tensors]))
+  with ops.control_dependencies(checks):
+    return array_ops.identity(array_ops.identity(tensor))
+
+
+def true_mean_confidence_interval_by_dkwm(
+    samples, low, high, error_rate=1e-6, name=None):
+  """Computes a confidence interval for the mean of a scalar distribution.
+
+  In batch mode, computes confidence intervals for all distributions
+  in the batch (which need not be identically distributed).
+
+  Relies on the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
+  (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval).
+
+  The probability (over the randomness of drawing the given samples)
+  that any true mean is outside the corresponding returned interval is
+  no more than the given `error_rate`.  The size of the intervals
+  scale as
+  `O(1 / sqrt(#samples))`, as `O(high - low)`, and as `O(-log(error_rate))`.
+
+  Note that `error_rate` is a total error rate for all the confidence
+  intervals in the batch.  As such, if the batch is nontrivial, the
+  error rate is not broadcast but divided (evenly) among the batch
+  members.
+
+  Args:
+    samples: Floating-point tensor of samples from the distribution(s)
+      of interest.  Entries are assumed IID across the 0th dimension.
+      The other dimensions must broadcast with `low` and `high`.
+    low: Floating-point tensor of lower bounds on the distributions'
+      supports.
+    high: Floating-point tensor of upper bounds on the distributions'
+      supports.
+    error_rate: *Scalar* admissible total rate of mistakes.
+    name: A name for this operation (optional).
+
+  Returns:
+    low: A floating-point tensor of stochastic lower bounds on the true means.
+    high: A floating-point tensor of stochastic upper bounds on the true means.
+  """
+  with ops.name_scope(
+      name, "true_mean_confidence_interval_by_dkwm",
+      [samples, low, high, error_rate]):
+    samples = ops.convert_to_tensor(samples, name="samples")
+    low = ops.convert_to_tensor(low, name="low")
+    high = ops.convert_to_tensor(high, name="high")
+    error_rate = ops.convert_to_tensor(error_rate, name="error_rate")
+    samples = _check_shape_dominates(samples, [low, high])
+    check_ops.assert_scalar(error_rate)  # Static shape
+    error_rate = _itemwise_error_rate(error_rate, [low, high], samples)
+    n = array_ops.shape(samples)[0]
+    envelope = _dkwm_cdf_envelope(n, error_rate)
+    min_mean = _minimum_mean(samples, envelope, low)
+    max_mean = _maximum_mean(samples, envelope, high)
+    return min_mean, max_mean
+
+
+def _itemwise_error_rate(
+    total_error_rate, param_tensors, sample_tensor=None, name=None):
+  with ops.name_scope(
+      name, "itemwise_error_rate",
+      [total_error_rate, param_tensors, sample_tensor]):
+    result_shape = [1]
+    for p_tensor in param_tensors:
+      result_shape = array_ops.broadcast_dynamic_shape(
+          array_ops.shape(p_tensor), result_shape)
+    if sample_tensor is not None:
+      result_shape = array_ops.broadcast_dynamic_shape(
+          array_ops.shape(sample_tensor)[1:], result_shape)
+    num_items = math_ops.reduce_prod(result_shape)
+    return total_error_rate / math_ops.cast(
+        num_items, dtype=total_error_rate.dtype)
+
+
+def assert_true_mean_equal_by_dkwm(
+    samples, low, high, expected, false_fail_rate=1e-6, name=None):
+  """Asserts the mean of the given distribution is as expected.
+
+  More precisely, fails if there is enough evidence (using the
+  [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
+  (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval))
+  that the true mean of some distribution from which the given samples are
+  drawn is _not_ the given expected mean with statistical significance
+  `false_fail_rate` or stronger, otherwise passes.  If you also want to
+  check that you are gathering enough evidence that a pass is not
+  spurious, see `min_num_samples_for_dkwm_mean_test` and
+  `min_discrepancy_of_true_means_detectable_by_dkwm`.
+
+  Note that `false_fail_rate` is a total false failure rate for all
+  the assertions in the batch.  As such, if the batch is nontrivial,
+  the assertion will insist on stronger evidence to fail any one member.
+
+  Args:
+    samples: Floating-point tensor of samples from the distribution(s)
+      of interest.  Entries are assumed IID across the 0th dimension.
+      The other dimensions must broadcast with `low` and `high`.
+    low: Floating-point tensor of lower bounds on the distributions'
+      supports.
+    high: Floating-point tensor of upper bounds on the distributions'
+      supports.
+    expected: Floating-point tensor of expected true means.
+    false_fail_rate: *Scalar* admissible total rate of mistakes.
+    name: A name for this operation (optional).
+
+  Returns:
+    check: Op that raises `InvalidArgumentError` if any expected mean is
+      outside the corresponding confidence interval.
+  """
+  with ops.name_scope(
+      name, "assert_true_mean_equal_by_dkwm",
+      [samples, low, high, expected, false_fail_rate]):
+    samples = ops.convert_to_tensor(samples, name="samples")
+    low = ops.convert_to_tensor(low, name="low")
+    high = ops.convert_to_tensor(high, name="high")
+    expected = ops.convert_to_tensor(expected, name="expected")
+    false_fail_rate = ops.convert_to_tensor(
+        false_fail_rate, name="false_fail_rate")
+    samples = _check_shape_dominates(samples, [low, high, expected])
+    min_mean, max_mean = true_mean_confidence_interval_by_dkwm(
+        samples, low, high, error_rate=false_fail_rate)
+    less_op = check_ops.assert_less(
+        min_mean, expected, message="Mean confidence interval too high")
+    with ops.control_dependencies([less_op]):
+      return check_ops.assert_greater(
+          max_mean, expected, message="Mean confidence interval too low")
+
+
+def min_discrepancy_of_true_means_detectable_by_dkwm(
+    n, low, high, false_fail_rate, false_pass_rate, name=None):
+  """Returns the minimum mean discrepancy that a DKWM-based test can detect.
+
+  DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
+  (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval).
+
+  Note that `false_fail_rate` is a total false failure rate for all
+  the tests in the batch.  As such, if the batch is nontrivial, each
+  member will demand more samples.  The `false_pass_rate` is also
+  interpreted as a total, but is treated asymmetrically: If each test
+  in the batch detects its corresponding discrepancy with probability
+  at least `1 - false_pass_rate`, then running all those tests and
+  failing if any one fails will jointly detect all those discrepancies
+  with the same `false_pass_rate`.
+
+  Args:
+    n: Tensor of numbers of samples to be drawn from the distributions
+      of interest.
+    low: Floating-point tensor of lower bounds on the distributions'
+      supports.
+    high: Floating-point tensor of upper bounds on the distributions'
+      supports.
+    false_fail_rate: *Scalar* admissible total rate of false failures.
+    false_pass_rate: *Scalar* admissible rate of false passes.
+    name: A name for this operation (optional).
+
+  Returns:
+    discr: Tensor of lower bounds on the distances between true
+       means detectable by a DKWM-based test.
+
+  For each batch member `i`, of `K` total, drawing `n[i]` samples from
+  some scalar distribution supported on `[low[i], high[i]]` is enough
+  to detect a difference in means of size `discr[i]` or more.
+  Specifically, we guarantee that (a) if the true mean is the expected
+  mean, `assert_true_mean_equal_by_dkwm` will fail with probability at
+  most `false_fail_rate / K` (which amounts to `false_fail_rate` if
+  applied to the whole batch at once), and (b) if the true mean
+  differs from the expected mean by at least `discr[i]`,
+  `assert_true_mean_equal_by_dkwm` will pass with probability at most
+  `false_pass_rate`.
+
+  The detectable discrepancy scales as
+
+  - `O(high[i] - low[i])`,
+  - `O(1 / sqrt(n[i]))`,
+  - `O(-log(false_fail_rate/K))`, and
+  - `O(-log(false_pass_rate))`.
+  """
+  with ops.name_scope(
+      name, "min_discrepancy_of_true_means_detectable_by_dkwm",
+      [n, low, high, false_fail_rate, false_pass_rate]):
+    n = ops.convert_to_tensor(n, name="n")
+    low = ops.convert_to_tensor(low, name="low")
+    high = ops.convert_to_tensor(high, name="high")
+    false_fail_rate = ops.convert_to_tensor(
+        false_fail_rate, name="false_fail_rate")
+    false_pass_rate = ops.convert_to_tensor(
+        false_pass_rate, name="false_pass_rate")
+    # Algorithm: Assume a true CDF F.  The DKWM inequality gives a
+    # stochastic bound on how far the observed empirical CDF F_n can be.
+    # Then, using the DKWM inequality again gives a stochastic bound on
+    # the farthest candidate true CDF F' that
+    # true_mean_confidence_interval_by_dkwm might consider.  At worst, these
+    # errors may go in the same direction, so the distance between F and
+    # F' is bounded by the sum.
+    # On batching: false fail rates sum, so I need to reduce
+    # the input to account for the batching.  False pass rates
+    # max, so I don't.
+    sampling_envelope = _dkwm_cdf_envelope(n, false_pass_rate)
+    false_fail_rate = _itemwise_error_rate(false_fail_rate, [n, low, high])
+    analysis_envelope = _dkwm_cdf_envelope(n, false_fail_rate)
+    return (high - low) * (sampling_envelope + analysis_envelope)
+
+
+def min_num_samples_for_dkwm_mean_test(
+    discrepancy, low, high,
+    false_fail_rate=1e-6, false_pass_rate=1e-6, name=None):
+  """Returns how many samples suffice for a one-sample DKWM mean test.
+
+  To wit, returns an upper bound on the number of samples necessary to
+  guarantee detecting a mean difference of at least the given
+  `discrepancy`, with the given `false_fail_rate` and `false_pass_rate`,
+  using the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
+  (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval)
+  on a scalar distribution supported on `[low, high]`.
+
+  Args:
+    discrepancy: Floating-point tensor of desired upper limits on mean
+      differences that may go undetected with probability higher than
+      `1 - false_pass_rate`.
+    low: Tensor of lower bounds on the distributions' support.
+    high: Tensor of upper bounds on the distributions' support.
+    false_fail_rate: *Scalar* admissible total rate of false failures.
+    false_pass_rate: *Scalar* admissible rate of false passes.
+    name: A name for this operation (optional).
+
+  Returns:
+    n: Tensor of numbers of samples to be drawn from the distributions
+      of interest.
+
+  The `discrepancy`, `low`, and `high` tensors must have
+  broadcast-compatible shapes.
+
+  For each batch member `i`, of `K` total, drawing `n[i]` samples from
+  some scalar distribution supported on `[low[i], high[i]]` is enough
+  to detect a difference in means of size `discrepancy[i]` or more.
+  Specifically, we guarantee that (a) if the true mean is the expected
+  mean, `assert_true_mean_equal_by_dkwm` will fail with probability at
+  most `false_fail_rate / K` (which amounts to `false_fail_rate` if
+  applied to the whole batch at once), and (b) if the true mean
+  differs from the expected mean by at least `discrepancy[i]`,
+  `assert_true_mean_equal_by_dkwm` will pass with probability at most
+  `false_pass_rate`.
+
+  The required number of samples scales
+  as `O((high[i] - low[i])**2)`, `O(-log(false_fail_rate/K))`,
+  `O(-log(false_pass_rate))`, and `O(1 / discrepancy[i]**2)`.
+  """
+  with ops.name_scope(
+      name, "min_num_samples_for_dkwm_mean_test",
+      [low, high, false_fail_rate, false_pass_rate, discrepancy]):
+    discrepancy = ops.convert_to_tensor(
+        discrepancy, name="discrepancy")
+    low = ops.convert_to_tensor(low, name="low")
+    high = ops.convert_to_tensor(high, name="high")
+    false_fail_rate = ops.convert_to_tensor(
+        false_fail_rate, name="false_fail_rate")
+    false_pass_rate = ops.convert_to_tensor(
+        false_pass_rate, name="false_pass_rate")
+    # Could choose to cleverly allocate envelopes, but this is sound.
+    envelope1 = discrepancy / (2. * (high - low))
+    envelope2 = envelope1
+    false_fail_rate = _itemwise_error_rate(
+        false_fail_rate, [low, high, discrepancy])
+    n1 = -math_ops.log(false_fail_rate / 2.) / (2. * envelope1**2)
+    n2 = -math_ops.log(false_pass_rate / 2.) / (2. * envelope2**2)
+    return math_ops.maximum(n1, n2)
+
+
+def assert_true_mean_equal_by_dkwm_two_sample(
+    samples1, low1, high1, samples2, low2, high2,
+    false_fail_rate=1e-6, name=None):
+  """Asserts the means of the given distributions are equal.
+
+  More precisely, fails if there is enough evidence (using the
+  [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
+  (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval))
+  that the means of the distributions from which the given samples are
+  drawn are _not_ equal with statistical significance `false_fail_rate`
+  or stronger, otherwise passes.  If you also want to check that you
+  are gathering enough evidence that a pass is not spurious, see
+  `min_num_samples_for_dkwm_mean_two_sample_test` and
+  `min_discrepancy_of_true_means_detectable_by_dkwm_two_sample`.
+
+  Note that `false_fail_rate` is a total false failure rate for all
+  the assertions in the batch.  As such, if the batch is nontrivial,
+  the assertion will insist on stronger evidence to fail any one member.
+
+  Args:
+    samples1: Floating-point tensor of samples from the
+      distribution(s) A.  Entries are assumed IID across the 0th
+      dimension.  The other dimensions must broadcast with `low1`,
+      `high1`, `low2`, and `high2`.
+    low1: Floating-point tensor of lower bounds on the supports of the
+      distributions A.
+    high1: Floating-point tensor of upper bounds on the supports of
+      the distributions A.
+    samples2: Floating-point tensor of samples from the
+      distribution(s) B.  Entries are assumed IID across the 0th
+      dimension.  The other dimensions must broadcast with `low1`,
+      `high1`, `low2`, and `high2`.
+    low2: Floating-point tensor of lower bounds on the supports of the
+      distributions B.
+    high2: Floating-point tensor of upper bounds on the supports of
+      the distributions B.
+    false_fail_rate: *Scalar* admissible total rate of mistakes.
+    name: A name for this operation (optional).
+
+  Returns:
+    check: Op that raises `InvalidArgumentError` if any pair of confidence
+      intervals true for corresponding true means do not overlap.
+  """
+  with ops.name_scope(
+      name, "assert_true_mean_equal_by_dkwm_two_sample",
+      [samples1, low1, high1, samples2, low2, high2, false_fail_rate]):
+    samples1 = ops.convert_to_tensor(samples1, name="samples1")
+    low1 = ops.convert_to_tensor(low1, name="low1")
+    high1 = ops.convert_to_tensor(high1, name="high1")
+    samples2 = ops.convert_to_tensor(samples2, name="samples2")
+    low2 = ops.convert_to_tensor(low2, name="low2")
+    high2 = ops.convert_to_tensor(high2, name="high2")
+    false_fail_rate = ops.convert_to_tensor(
+        false_fail_rate, name="false_fail_rate")
+    samples1 = _check_shape_dominates(samples1, [low1, high1])
+    samples2 = _check_shape_dominates(samples2, [low2, high2])
+    compatible_samples = check_ops.assert_equal(
+        array_ops.shape(samples1)[1:], array_ops.shape(samples2)[1:])
+    with ops.control_dependencies([compatible_samples]):
+      # Could in principle play games with cleverly allocating
+      # significance instead of the even split below.  It may be possible
+      # to get tighter intervals, in order to obtain a higher power test.
+      # Any allocation strategy that depends only on the support bounds
+      # and sample counts should be valid; however, because the intervals
+      # scale as O(-log(false_fail_rate)), there doesn't seem to be much
+      # room to win.
+      min_mean_1, max_mean_1 = true_mean_confidence_interval_by_dkwm(
+          samples1, low1, high1, false_fail_rate / 2.)
+      min_mean_2, max_mean_2 = true_mean_confidence_interval_by_dkwm(
+          samples2, low2, high2, false_fail_rate / 2.)
+      # I want to assert
+      #   not (max_mean_1 < min_mean_2 or min_mean_1 > max_mean_2),
+      # but I think I only have and-combination of asserts, so use DeMorgan.
+      clause1_op = check_ops.assert_greater_equal(max_mean_1, min_mean_2)
+      with ops.control_dependencies([clause1_op]):
+        return check_ops.assert_less_equal(min_mean_1, max_mean_2)
+
+
+def min_discrepancy_of_true_means_detectable_by_dkwm_two_sample(
+    n1, low1, high1, n2, low2, high2,
+    false_fail_rate, false_pass_rate, name=None):
+  """Returns the minimum mean discrepancy for a two-sample DKWM-based test.
+
+  DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
+  (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval).
+
+  Note that `false_fail_rate` is a total false failure rate for all
+  the tests in the batch.  As such, if the batch is nontrivial, each
+  member will demand more samples.  The `false_pass_rate` is also
+  interpreted as a total, but is treated asymmetrically: If each test
+  in the batch detects its corresponding discrepancy with probability
+  at least `1 - false_pass_rate`, then running all those tests and
+  failing if any one fails will jointly detect all those discrepancies
+  with the same `false_pass_rate`.
+
+  Args:
+    n1: Tensor of numbers of samples to be drawn from the distributions A.
+    low1: Floating-point tensor of lower bounds on the supports of the
+      distributions A.
+    high1: Floating-point tensor of upper bounds on the supports of
+      the distributions A.
+    n2: Tensor of numbers of samples to be drawn from the distributions B.
+    low2: Floating-point tensor of lower bounds on the supports of the
+      distributions B.
+    high2: Floating-point tensor of upper bounds on the supports of
+      the distributions B.
+    false_fail_rate: *Scalar* admissible total rate of false failures.
+    false_pass_rate: *Scalar* admissible rate of false passes.
+    name: A name for this operation (optional).
+
+  Returns:
+    discr: Tensor of lower bounds on the distances between true means
+       detectable by a two-sample DKWM-based test.
+
+  For each batch member `i`, of `K` total, drawing `n1[i]` samples
+  from scalar distribution A supported on `[low1[i], high1[i]]` and `n2[i]`
+  samples from scalar distribution B supported on `[low2[i], high2[i]]`
+  is enough to detect a difference in their true means of size
+  `discr[i]` or more.  Specifically, we guarantee that (a) if their
+  true means are equal, `assert_true_mean_equal_by_dkwm_two_sample`
+  will fail with probability at most `false_fail_rate/K` (which
+  amounts to `false_fail_rate` if applied to the whole batch at once),
+  and (b) if their true means differ by at least `discr[i]`,
+  `assert_true_mean_equal_by_dkwm_two_sample` will pass with
+  probability at most `false_pass_rate`.
+
+  The detectable distribution scales as
+
+  - `O(high1[i] - low1[i])`, `O(high2[i] - low2[i])`,
+  - `O(1 / sqrt(n1[i]))`, `O(1 / sqrt(n2[i]))`,
+  - `O(-log(false_fail_rate/K))`, and
+  - `O(-log(false_pass_rate))`.
+  """
+  with ops.name_scope(
+      name, "min_discrepancy_of_true_means_detectable_by_dkwm_two_sample",
+      [n1, low1, high1, n2, low2, high2, false_fail_rate, false_pass_rate]):
+    n1 = ops.convert_to_tensor(n1, name="n1")
+    low1 = ops.convert_to_tensor(low1, name="low1")
+    high1 = ops.convert_to_tensor(high1, name="high1")
+    n2 = ops.convert_to_tensor(n2, name="n2")
+    low2 = ops.convert_to_tensor(low2, name="low2")
+    high2 = ops.convert_to_tensor(high2, name="high2")
+    false_fail_rate = ops.convert_to_tensor(
+        false_fail_rate, name="false_fail_rate")
+    false_pass_rate = ops.convert_to_tensor(
+        false_pass_rate, name="false_pass_rate")
+    det_disc1 = min_discrepancy_of_true_means_detectable_by_dkwm(
+        n1, low1, high1, false_fail_rate / 2., false_pass_rate / 2.)
+    det_disc2 = min_discrepancy_of_true_means_detectable_by_dkwm(
+        n2, low2, high2, false_fail_rate / 2., false_pass_rate / 2.)
+    return det_disc1 + det_disc2
+
+
+def min_num_samples_for_dkwm_mean_two_sample_test(
+    discrepancy, low1, high1, low2, high2,
+    false_fail_rate=1e-6, false_pass_rate=1e-6, name=None):
+  """Returns how many samples suffice for a two-sample DKWM mean test.
+
+  DKWM is the [Dvoretzky-Kiefer-Wolfowitz-Massart inequality]
+  (https://en.wikipedia.org/wiki/CDF-based_nonparametric_confidence_interval).
+
+  Args:
+    discrepancy: Floating-point tensor of desired upper limits on mean
+      differences that may go undetected with probability higher than
+      `1 - false_pass_rate`.
+    low1: Floating-point tensor of lower bounds on the supports of the
+      distributions A.
+    high1: Floating-point tensor of upper bounds on the supports of
+      the distributions A.
+    low2: Floating-point tensor of lower bounds on the supports of the
+      distributions B.
+    high2: Floating-point tensor of upper bounds on the supports of
+      the distributions B.
+    false_fail_rate: *Scalar* admissible total rate of false failures.
+    false_pass_rate: *Scalar* admissible rate of false passes.
+    name: A name for this operation (optional).
+
+  Returns:
+    n1: Tensor of numbers of samples to be drawn from the distributions A.
+    n2: Tensor of numbers of samples to be drawn from the distributions B.
+
+  For each batch member `i`, of `K` total, drawing `n1[i]` samples
+  from scalar distribution A supported on `[low1[i], high1[i]]` and `n2[i]`
+  samples from scalar distribution B supported on `[low2[i], high2[i]]`
+  is enough to detect a difference in their true means of size
+  `discr[i]` or more.  Specifically, we guarantee that (a) if their
+  true means are equal, `assert_true_mean_equal_by_dkwm_two_sample`
+  will fail with probability at most `false_fail_rate/K` (which
+  amounts to `false_fail_rate` if applied to the whole batch at once),
+  and (b) if their true means differ by at least `discr[i]`,
+  `assert_true_mean_equal_by_dkwm_two_sample` will pass with
+  probability at most `false_pass_rate`.
+
+  The required number of samples scales as
+
+  - `O((high1[i] - low1[i])**2)`, `O((high2[i] - low2[i])**2)`,
+  - `O(-log(false_fail_rate/K))`,
+  - `O(-log(false_pass_rate))`, and
+  - `O(1 / discrepancy[i]**2)`.
+  """
+  with ops.name_scope(
+      name, "min_num_samples_for_dkwm_mean_two_sample_test",
+      [low1, high1, low2, high2,
+       false_fail_rate, false_pass_rate, discrepancy]):
+    discrepancy = ops.convert_to_tensor(discrepancy, name="discrepancy")
+    low1 = ops.convert_to_tensor(low1, name="low1")
+    high1 = ops.convert_to_tensor(high1, name="high1")
+    low2 = ops.convert_to_tensor(low2, name="low2")
+    high2 = ops.convert_to_tensor(high2, name="high2")
+    false_fail_rate = ops.convert_to_tensor(
+        false_fail_rate, name="false_fail_rate")
+    false_pass_rate = ops.convert_to_tensor(
+        false_pass_rate, name="false_pass_rate")
+    # Could choose to cleverly allocate discrepancy tolerances and
+    # failure probabilities, but this is sound.
+    n1 = min_num_samples_for_dkwm_mean_test(
+        discrepancy / 2., low1, high1,
+        false_fail_rate / 2., false_pass_rate / 2.)
+    n2 = min_num_samples_for_dkwm_mean_test(
+        discrepancy / 2., low2, high2,
+        false_fail_rate / 2., false_pass_rate / 2.)
+    return n1, n2
-- 
GitLab


From 6d014ecbd63fec208742b327b94c39afd4953fb8 Mon Sep 17 00:00:00 2001
From: Akshay Modi <nareshmodi@google.com>
Date: Fri, 2 Mar 2018 15:11:13 -0800
Subject: [PATCH 567/575] ReadVariableOp in C for eager (only for the fastpath)

PiperOrigin-RevId: 187676012
---
 tensorflow/python/eager/benchmarks_test.py    |  21 +
 tensorflow/python/eager/pywrap_tfe.h          |   7 +
 tensorflow/python/eager/pywrap_tfe_src.cc     | 460 ++++++++++++------
 tensorflow/python/eager/pywrap_tfe_test.py    |  31 ++
 .../python/ops/resource_variable_ops.py       |   4 +
 tensorflow/python/pywrap_tfe.i                |   1 +
 6 files changed, 377 insertions(+), 147 deletions(-)

diff --git a/tensorflow/python/eager/benchmarks_test.py b/tensorflow/python/eager/benchmarks_test.py
index 527a919ab0..551d5647dd 100644
--- a/tensorflow/python/eager/benchmarks_test.py
+++ b/tensorflow/python/eager/benchmarks_test.py
@@ -275,6 +275,16 @@ class MicroBenchmarks(test.Benchmark):
   def _benchmark_read_variable(self, m, num_iters):
     self._run(m.value, num_iters)
 
+  def _benchmark_matmul_read_variable(self, m, num_iters):
+    self._benchmark_gen_math_ops_matmul(
+        m, transpose_b=False, num_iters=num_iters)
+
+  def _benchmark_matmul_read_variable_with_tape(self, m, num_iters):
+    with backprop.GradientTape() as tape:
+      tape.watch(m)
+      self._benchmark_gen_math_ops_matmul(
+          m, transpose_b=False, num_iters=num_iters)
+
   def _benchmark_read_variable_with_tape(self, m, num_iters):
     with backprop.GradientTape() as tape:
       tape.watch(m)
@@ -416,6 +426,17 @@ class MicroBenchmarks(test.Benchmark):
       self._benchmark_defun_matmul(
           m, transpose_b=True, num_iters=self._num_iters_100_by_784)
 
+  def benchmark_matmul_read_variable_op_2_by_2_CPU(self):
+    with context.device(CPU):
+      m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
+      self._benchmark_matmul_read_variable(m, num_iters=self._num_iters_2_by_2)
+
+  def benchmark_matmul_read_variable_op_with_tape_2_by_2_CPU(self):
+    with context.device(CPU):
+      m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
+      self._benchmark_matmul_read_variable_with_tape(
+          m, num_iters=self._num_iters_2_by_2)
+
   def benchmark_read_variable_op_2_by_2_CPU(self):
     with context.device(CPU):
       m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
diff --git a/tensorflow/python/eager/pywrap_tfe.h b/tensorflow/python/eager/pywrap_tfe.h
index b1b4a6b214..32d731d0f6 100644
--- a/tensorflow/python/eager/pywrap_tfe.h
+++ b/tensorflow/python/eager/pywrap_tfe.h
@@ -51,6 +51,13 @@ void TFE_Py_Execute(TFE_Context* ctx, const char* device_name,
 // This function is not thread-safe.
 PyObject* TFE_Py_RegisterExceptionClass(PyObject* e);
 
+// Registers e as the type of the ResourceVariable class.
+// Returns Py_None if registration succeeds, else throws a TypeError and returns
+// NULL.
+//
+// This function is not thread-safe.
+PyObject* TFE_Py_RegisterResourceVariableType(PyObject* e);
+
 // Registers e as the Exception to be raised when the conditions of
 // TFE_Py_FastPathExecute_C have not been met. When this exception is set, it
 // is a signal to the calling code that it should fall back to the safer (and
diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc
index 42d97dfe3f..27c9d05081 100644
--- a/tensorflow/python/eager/pywrap_tfe_src.cc
+++ b/tensorflow/python/eager/pywrap_tfe_src.cc
@@ -38,6 +38,23 @@ using tensorflow::strings::Printf;
 
 namespace {
 
+struct FastPathOpExecInfo {
+  TFE_Context* ctx;
+  const char* device_name;
+  // The op def of the main op being executed.
+  const tensorflow::OpDef* op_def;
+
+  bool run_callbacks;
+  bool run_post_exec_callbacks;
+  bool run_gradient_callback;
+
+  // The op name of the main op being executed.
+  PyObject* name;
+  // The op type name of the main op being executed.
+  PyObject* op_name;
+  PyObject* callbacks;
+};
+
 #define PARSE_VALUE(fn_name, type, check_fn, parse_fn)                       \
   bool fn_name(const string& key, PyObject* py_value, TF_Status* status,     \
                type* value) {                                                \
@@ -120,6 +137,11 @@ bool ParseTypeValue(const string& key, PyObject* py_value, TF_Status* status,
 
   PyObject* py_type_enum = PyObject_GetAttrString(py_value, "_type_enum");
   if (py_type_enum == nullptr) {
+    TF_SetStatus(
+        status, TF_INVALID_ARGUMENT,
+        tensorflow::strings::StrCat("Expecting a DType.dtype for attr ", key,
+                                    ", got ", py_value->ob_type->tp_name)
+            .c_str());
     return false;
   }
 
@@ -580,6 +602,8 @@ PyObject* fallback_exception_class = nullptr;
 // Python function that returns a backward_function.
 PyObject* backward_function_getter = nullptr;
 
+PyTypeObject* resource_variable_type = nullptr;
+
 tensorflow::mutex _uid_mutex(tensorflow::LINKER_INITIALIZED);
 tensorflow::int64 _uid GUARDED_BY(_uid_mutex) = 0;
 
@@ -628,11 +652,28 @@ PyObject* TFE_Py_RegisterExceptionClass(PyObject* e) {
                     "TFE_Py_RegisterExceptionClass: "
                     "Registered class should be subclass of Exception.");
     return nullptr;
-  } else {
-    Py_INCREF(e);
-    exception_class = e;
-    Py_RETURN_NONE;
   }
+
+  Py_INCREF(e);
+  exception_class = e;
+  Py_RETURN_NONE;
+}
+
+PyObject* TFE_Py_RegisterResourceVariableType(PyObject* e) {
+  if (!PyType_Check(e)) {
+    PyErr_SetString(
+        PyExc_TypeError,
+        "TFE_Py_RegisterResourceVariableType: Need to register a type.");
+    return nullptr;
+  }
+
+  if (resource_variable_type != nullptr) {
+    Py_DECREF(resource_variable_type);
+  }
+
+  Py_INCREF(e);
+  resource_variable_type = reinterpret_cast<PyTypeObject*>(e);
+  Py_RETURN_NONE;
 }
 
 PyObject* TFE_Py_RegisterFallbackExceptionClass(PyObject* e) {
@@ -1375,8 +1416,12 @@ PyObject* GetPythonObjectFromString(const char* s) {
 #endif
 }
 
-bool CheckEagerTensors(PyObject* seq, int start_index,
-                       const tensorflow::OpDef& op_def) {
+bool CheckResourceVariable(PyObject* item) {
+  return PyObject_TypeCheck(item, resource_variable_type);
+}
+
+bool CheckInputsOk(PyObject* seq, int start_index,
+                   const tensorflow::OpDef& op_def) {
   for (int i = 0; i < op_def.input_arg_size(); i++) {
     PyObject* item = PyTuple_GET_ITEM(seq, i + start_index);
     if (!op_def.input_arg(i).number_attr().empty() ||
@@ -1384,9 +1429,13 @@ bool CheckEagerTensors(PyObject* seq, int start_index,
       // This item should be a list input.
       if (!PyList_Check(item)) return false;
       for (Py_ssize_t j = 0; j < PyList_Size(item); j++) {
-        if (!EagerTensor_CheckExact(PyList_GET_ITEM(item, j))) return false;
+        PyObject* inner_item = PyList_GET_ITEM(item, j);
+        if (!EagerTensor_CheckExact(inner_item) &&
+            !CheckResourceVariable(inner_item)) {
+          return false;
+        }
       }
-    } else if (!EagerTensor_CheckExact(item)) {
+    } else if (!EagerTensor_CheckExact(item) && !CheckResourceVariable(item)) {
       return false;
     }
   }
@@ -1394,71 +1443,6 @@ bool CheckEagerTensors(PyObject* seq, int start_index,
   return true;
 }
 
-// Adds input and type attr to the op, and to the list of flattened
-// inputs/attrs.
-bool AddInputToOp(PyObject* input, const tensorflow::OpDef::ArgDef* input_arg,
-                  std::vector<PyObject*>* flattened_attrs,
-                  std::vector<PyObject*>* flattened_inputs, TFE_Op* op,
-                  TF_Status* status) {
-  TFE_TensorHandle* input_handle = EagerTensor_Handle(input);
-  if (input_arg != nullptr && !input_arg->type_attr().empty()) {
-    auto dtype = TFE_TensorHandleDataType(input_handle);
-    TFE_OpSetAttrType(op, input_arg->type_attr().data(), dtype);
-    if (flattened_attrs != nullptr) {
-      flattened_attrs->push_back(
-          GetPythonObjectFromString(input_arg->type_attr().data()));
-      flattened_attrs->push_back(PyLong_FromLong(dtype));
-    }
-  }
-
-  if (flattened_inputs != nullptr) {
-    flattened_inputs->push_back(input);
-  }
-  TFE_OpAddInput(op, input_handle, status);
-  if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) {
-    return false;
-  }
-  return true;
-}
-
-const tensorflow::OpDef* GetOpDef(PyObject* py_op_name) {
-  const char* op_name = TFE_GetPythonString(py_op_name);
-  if (op_name == nullptr) {
-    PyErr_SetString(PyExc_TypeError,
-                    Printf("expected a string for op_name, got %s instead",
-                           py_op_name->ob_type->tp_name)
-                        .c_str());
-    return nullptr;
-  }
-
-  const tensorflow::OpRegistrationData* op_reg_data = nullptr;
-  const tensorflow::Status lookup_status =
-      tensorflow::OpRegistry::Global()->LookUp(op_name, &op_reg_data);
-  if (MaybeRaiseExceptionFromStatus(lookup_status, nullptr)) {
-    return nullptr;
-  }
-  return &op_reg_data->op_def;
-}
-
-const char* GetDeviceName(PyObject* py_device_name) {
-  if (py_device_name != Py_None) {
-    return TFE_GetPythonString(py_device_name);
-  }
-  return nullptr;
-}
-
-bool RaiseIfNotPyList(PyObject* list, const string& attr_name) {
-  if (!PyList_Check(list)) {
-    PyErr_SetString(PyExc_TypeError,
-                    Printf("expected a list for attr %s, got %s instead",
-                           attr_name.data(), list->ob_type->tp_name)
-                        .data());
-
-    return false;
-  }
-  return true;
-}
-
 bool OpDoesntRequireOutput(const string& op_name) {
   static tensorflow::gtl::FlatSet<string>* ops_that_dont_require_outputs =
       new tensorflow::gtl::FlatSet<string>({
@@ -1583,7 +1567,6 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
       break;
     }
   }
-
   if (!should_record) Py_RETURN_NONE;
 
   string c_op_name = TFE_GetPythonString(op_name);
@@ -1617,50 +1600,212 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs,
   Py_RETURN_NONE;
 }
 
-bool RunCallbacks(bool run_gradient_callback, bool run_post_exec_callbacks,
-                  const tensorflow::OpDef* op_def, PyObject* args,
-                  const std::vector<PyObject*>& flattened_inputs,
-                  const std::vector<PyObject*>& flattened_attrs,
-                  PyObject* flattened_result, PyObject* op_name, PyObject* name,
-                  PyObject* callbacks) {
-  tensorflow::Safe_PyObjectPtr inputs =
-      tensorflow::make_safe(PyTuple_New(flattened_inputs.size()));
+void MaybeWatchVariable(PyObject* input) {
+  DCHECK(CheckResourceVariable(input));
+  DCHECK(PyObject_HasAttrString(input, "_trainable"));
+
+  tensorflow::Safe_PyObjectPtr trainable(
+      PyObject_GetAttrString(input, "_trainable"));
+  if (trainable.get() == Py_False) return;
+  TFE_Py_TapeSetWatchVariable(input);
+}
+
+bool ReadVariableOp(const FastPathOpExecInfo& parent_op_exec_info,
+                    PyObject* input, tensorflow::Safe_PyObjectPtr* output,
+                    TF_Status* status) {
+  MaybeWatchVariable(input);
+
+  TFE_Op* op = TFE_NewOp(parent_op_exec_info.ctx, "ReadVariableOp", status);
+  auto cleaner = tensorflow::gtl::MakeCleanup([op] { TFE_DeleteOp(op); });
+  if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false;
+
+  // Set dtype
+  DCHECK(PyObject_HasAttrString(input, "_dtype"));
+  tensorflow::Safe_PyObjectPtr dtype(PyObject_GetAttrString(input, "_dtype"));
+  int value;
+  if (!ParseTypeValue("_dtype", dtype.get(), status, &value)) {
+    return false;
+  }
+  TFE_OpSetAttrType(op, "dtype", static_cast<TF_DataType>(value));
+
+  TFE_OpSetDevice(op, parent_op_exec_info.device_name, status);
+  if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false;
+
+  // Get handle
+  tensorflow::Safe_PyObjectPtr handle(PyObject_GetAttrString(input, "_handle"));
+  if (!EagerTensor_CheckExact(handle.get())) return false;
+  TFE_OpAddInput(op, EagerTensor_Handle(handle.get()), status);
+  if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false;
+
+  int num_retvals = 1;
+  TFE_TensorHandle* output_handle;
+  TFE_Execute(op, &output_handle, &num_retvals, status);
+  if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) return false;
+
+  // Always create the py object (and correctly DECREF it) from the returned
+  // value, else the data will leak.
+  output->reset(EagerTensorFromHandle(output_handle));
+
+  // TODO(nareshmodi): Should we run post exec callbacks here?
+  if (parent_op_exec_info.run_gradient_callback) {
+    tensorflow::Safe_PyObjectPtr inputs(PyTuple_New(1));
+    PyTuple_SET_ITEM(inputs.get(), 0, handle.release());
+
+    tensorflow::Safe_PyObjectPtr outputs(PyTuple_New(1));
+    Py_INCREF(output->get());  // stay alive after since tuple steals.
+    PyTuple_SET_ITEM(outputs.get(), 0, output->get());
+
+    if (!RecordGradient(GetPythonObjectFromString("ReadVariableOp"),
+                        inputs.get(), Py_None, outputs.get(), Py_None)) {
+      return false;
+    }
+  }
+
+  return true;
+}
+
+// Supports only 2 cases at the moment:
+//  i) input is an EagerTensor
+//  ii) input is a ResourceVariable - in this case, the is_variable param is set
+//  to true.
+bool ConvertToTensor(const FastPathOpExecInfo& op_exec_info, PyObject* input,
+                     tensorflow::Safe_PyObjectPtr* output_handle,
+                     TF_Status* status) {
+  if (CheckResourceVariable(input)) {
+    return ReadVariableOp(op_exec_info, input, output_handle, status);
+  }
+
+  Py_INCREF(input);
+  output_handle->reset(input);
+
+  return true;
+}
+
+// Adds input and type attr to the op, and to the list of flattened
+// inputs/attrs.
+bool AddInputToOp(const FastPathOpExecInfo& op_exec_info, PyObject* input,
+                  const tensorflow::OpDef::ArgDef* input_arg,
+                  std::vector<tensorflow::Safe_PyObjectPtr>* flattened_attrs,
+                  std::vector<tensorflow::Safe_PyObjectPtr>* flattened_inputs,
+                  TFE_Op* op, TF_Status* status) {
+  // py_eager_tensor's ownership is transferred to flattened_inputs if it is
+  // required, else the object is destroyed and DECREF'd when the object goes
+  // out of scope in this function.
+  tensorflow::Safe_PyObjectPtr py_eager_tensor = nullptr;
+
+  if (!ConvertToTensor(op_exec_info, input, &py_eager_tensor, status)) {
+    return false;
+  }
+
+  TFE_TensorHandle* input_handle = EagerTensor_Handle(py_eager_tensor.get());
+
+  if (input_arg != nullptr && !input_arg->type_attr().empty()) {
+    auto dtype = TFE_TensorHandleDataType(input_handle);
+    TFE_OpSetAttrType(op, input_arg->type_attr().data(), dtype);
+    if (flattened_attrs != nullptr) {
+      flattened_attrs->emplace_back(
+          GetPythonObjectFromString(input_arg->type_attr().data()));
+      flattened_attrs->emplace_back(PyLong_FromLong(dtype));
+    }
+  }
+
+  if (flattened_inputs != nullptr) {
+    flattened_inputs->emplace_back(std::move(py_eager_tensor));
+  }
+
+  TFE_OpAddInput(op, input_handle, status);
+  if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) {
+    return false;
+  }
+
+  return true;
+}
+
+const tensorflow::OpDef* GetOpDef(PyObject* py_op_name) {
+  const char* op_name = TFE_GetPythonString(py_op_name);
+  if (op_name == nullptr) {
+    PyErr_SetString(PyExc_TypeError,
+                    Printf("expected a string for op_name, got %s instead",
+                           py_op_name->ob_type->tp_name)
+                        .c_str());
+    return nullptr;
+  }
+
+  const tensorflow::OpRegistrationData* op_reg_data = nullptr;
+  const tensorflow::Status lookup_status =
+      tensorflow::OpRegistry::Global()->LookUp(op_name, &op_reg_data);
+  if (MaybeRaiseExceptionFromStatus(lookup_status, nullptr)) {
+    return nullptr;
+  }
+  return &op_reg_data->op_def;
+}
+
+const char* GetDeviceName(PyObject* py_device_name) {
+  if (py_device_name != Py_None) {
+    return TFE_GetPythonString(py_device_name);
+  }
+  return nullptr;
+}
+
+bool RaiseIfNotPyList(PyObject* list, const string& attr_name) {
+  if (!PyList_Check(list)) {
+    PyErr_SetString(PyExc_TypeError,
+                    Printf("expected a list for attr %s, got %s instead",
+                           attr_name.data(), list->ob_type->tp_name)
+                        .data());
+
+    return false;
+  }
+  return true;
+}
+
+bool RunCallbacks(
+    const FastPathOpExecInfo& op_exec_info, PyObject* args,
+    const std::vector<tensorflow::Safe_PyObjectPtr>& flattened_inputs,
+    const std::vector<tensorflow::Safe_PyObjectPtr>& flattened_attrs,
+    PyObject* flattened_result) {
+  if (!op_exec_info.run_callbacks) return true;
+
+  tensorflow::Safe_PyObjectPtr inputs(PyTuple_New(flattened_inputs.size()));
   for (int i = 0; i < flattened_inputs.size(); i++) {
-    PyObject* input = flattened_inputs[i];
+    PyObject* input = flattened_inputs[i].get();
     Py_INCREF(input);
     PyTuple_SET_ITEM(inputs.get(), i, input);
   }
 
   int num_non_inferred_attrs = PyTuple_GET_SIZE(args) -
-                               op_def->input_arg_size() -
+                               op_exec_info.op_def->input_arg_size() -
                                kFastPathExecuteInputStartIndex;
   int num_attrs = flattened_attrs.size() + num_non_inferred_attrs;
-  tensorflow::Safe_PyObjectPtr attrs =
-      tensorflow::make_safe(PyTuple_New(num_attrs));
+  tensorflow::Safe_PyObjectPtr attrs(PyTuple_New(num_attrs));
 
   for (int i = 0; i < num_non_inferred_attrs; i++) {
-    auto* attr = PyTuple_GET_ITEM(
-        args, kFastPathExecuteInputStartIndex + op_def->input_arg_size() + i);
+    auto* attr =
+        PyTuple_GET_ITEM(args, kFastPathExecuteInputStartIndex +
+                                   op_exec_info.op_def->input_arg_size() + i);
     Py_INCREF(attr);
     PyTuple_SET_ITEM(attrs.get(), i, attr);
   }
   for (int i = num_non_inferred_attrs; i < num_attrs; i++) {
-    // Not INCREFing anything in flattened_attrs as each of those is a new
-    // reference, so allow the attrs tuple to steal the reference.
-    PyTuple_SET_ITEM(attrs.get(), i,
-                     flattened_attrs.at(i - num_non_inferred_attrs));
+    PyObject* attr_or_name =
+        flattened_attrs.at(i - num_non_inferred_attrs).get();
+    Py_INCREF(attr_or_name);
+    PyTuple_SET_ITEM(attrs.get(), i, attr_or_name);
   }
 
-  if (run_gradient_callback) {
-    RecordGradient(op_name, inputs.get(), attrs.get(), flattened_result, name);
+  if (op_exec_info.run_gradient_callback) {
+    if (!RecordGradient(op_exec_info.op_name, inputs.get(), attrs.get(),
+                        flattened_result, op_exec_info.name)) {
+      return false;
+    }
   }
 
-  if (run_post_exec_callbacks) {
-    tensorflow::Safe_PyObjectPtr callback_args = tensorflow::make_safe(
-        Py_BuildValue("OOOOO", op_name, inputs.get(), attrs.get(),
-                      flattened_result, name));
-    for (Py_ssize_t i = 0; i < PyList_Size(callbacks); i++) {
-      PyObject* callback_fn = PyList_GET_ITEM(callbacks, i);
+  if (op_exec_info.run_post_exec_callbacks) {
+    tensorflow::Safe_PyObjectPtr callback_args(
+        Py_BuildValue("OOOOO", op_exec_info.op_name, inputs.get(), attrs.get(),
+                      flattened_result, op_exec_info.name));
+    for (Py_ssize_t i = 0; i < PyList_Size(op_exec_info.callbacks); i++) {
+      PyObject* callback_fn = PyList_GET_ITEM(op_exec_info.callbacks, i);
       if (!PyCallable_Check(callback_fn)) {
         PyErr_SetString(
             PyExc_TypeError,
@@ -1695,14 +1840,30 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
     return nullptr;
   }
 
-  TFE_Context* ctx = reinterpret_cast<TFE_Context*>(
+  FastPathOpExecInfo op_exec_info;
+
+  op_exec_info.ctx = reinterpret_cast<TFE_Context*>(
       PyCapsule_GetPointer(PyTuple_GET_ITEM(args, 0), nullptr));
-  const char* device_name = GetDeviceName(PyTuple_GET_ITEM(args, 1));
-  PyObject* op_name = PyTuple_GET_ITEM(args, 2);
-  const tensorflow::OpDef* op_def = GetOpDef(op_name);
-  if (op_def == nullptr) return nullptr;
-  PyObject* name = PyTuple_GET_ITEM(args, 3);
-  PyObject* callbacks = PyTuple_GET_ITEM(args, 4);
+  op_exec_info.device_name = GetDeviceName(PyTuple_GET_ITEM(args, 1));
+  op_exec_info.op_name = PyTuple_GET_ITEM(args, 2);
+  op_exec_info.op_def = GetOpDef(op_exec_info.op_name);
+  if (op_exec_info.op_def == nullptr) return nullptr;
+  op_exec_info.name = PyTuple_GET_ITEM(args, 3);
+  op_exec_info.callbacks = PyTuple_GET_ITEM(args, 4);
+
+  const tensorflow::OpDef* op_def = op_exec_info.op_def;
+
+  // TODO(nareshmodi): Add a benchmark for the fast-path with gradient callbacks
+  // (similar to benchmark_tf_gradient_function_*). Also consider using an
+  // InlinedVector for flattened_attrs and flattened_inputs if the benchmarks
+  // point out problems with heap allocs.
+  op_exec_info.run_gradient_callback =
+      !*ThreadTapeIsStopped() && !GetTapeSet()->empty();
+  op_exec_info.run_post_exec_callbacks =
+      op_exec_info.callbacks != Py_None &&
+      PyList_Size(op_exec_info.callbacks) > 0;
+  op_exec_info.run_callbacks = op_exec_info.run_gradient_callback ||
+                               op_exec_info.run_post_exec_callbacks;
 
   if (args_size < kFastPathExecuteInputStartIndex + op_def->input_arg_size()) {
     PyErr_SetString(
@@ -1715,7 +1876,7 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
     return nullptr;
   }
 
-  if (!CheckEagerTensors(args, kFastPathExecuteInputStartIndex, *op_def)) {
+  if (!CheckInputsOk(args, kFastPathExecuteInputStartIndex, *op_def)) {
     RaiseFallbackException(
         "This function does not handle the case of the path where "
         "all inputs are not already EagerTensors.");
@@ -1723,7 +1884,7 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
   }
 
   TF_Status* status = TF_NewStatus();
-  TFE_Op* op = TFE_NewOp(ctx, op_def->name().c_str(), status);
+  TFE_Op* op = TFE_NewOp(op_exec_info.ctx, op_def->name().c_str(), status);
   auto cleaner = tensorflow::gtl::MakeCleanup([status, op] {
     TF_DeleteStatus(status);
     TFE_DeleteOp(op);
@@ -1750,8 +1911,8 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
     // OpRegistrationData.
     for (const auto& attr : op_def->attr()) {
       if (attr_name == attr.name()) {
-        SetOpAttrWithDefaults(ctx, op, attr, attr_name.data(), py_attr_value,
-                              &attr_list_sizes, status);
+        SetOpAttrWithDefaults(op_exec_info.ctx, op, attr, attr_name.data(),
+                              py_attr_value, &attr_list_sizes, status);
 
         if (TF_GetCode(status) != TF_OK) {
           RaiseFallbackException(TF_Message(status));
@@ -1763,33 +1924,28 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
     }
   }
 
-  TFE_OpSetDevice(op, device_name, status);
+  TFE_OpSetDevice(op, op_exec_info.device_name, status);
   if (MaybeRaiseExceptionFromTFStatus(status, nullptr)) {
     return nullptr;
   }
 
-  // TODO(nareshmodi): Add a benchmark for the fast-path with gradient callbacks
-  // (similar to benchmark_tf_gradient_function_*). Also consider using an
-  // InlinedVector for flattened_attrs and flattened_inputs if the benchmarks
-  // point out problems with heap allocs.
-  bool run_gradient_callback =
-      !*ThreadTapeIsStopped() && !GetTapeSet()->empty();
-  bool run_post_exec_callbacks =
-      callbacks != Py_None && PyList_Size(callbacks) > 0;
-  bool run_callbacks = run_gradient_callback || run_post_exec_callbacks;
   // Flat attrs and inputs as required by the record_gradient call. The attrs
   // here only contain inferred attrs (non-inferred attrs are added directly
   // from the input args).
-  // All items in flattened_attrs contain new references.
-  // All items in flattened_inputs contain borrowed references.
+  // All items in flattened_attrs and flattened_inputs contain
+  // Safe_PyObjectPtr - any time something steals a reference to this, it must
+  // INCREF.
   // TODO(nareshmodi): figure out why PyList_New/PyList_Append don't work
   // directly.
-  std::unique_ptr<std::vector<PyObject*>> flattened_attrs = nullptr;
-  std::unique_ptr<std::vector<PyObject*>> flattened_inputs = nullptr;
+  std::unique_ptr<std::vector<tensorflow::Safe_PyObjectPtr>> flattened_attrs =
+      nullptr;
+  std::unique_ptr<std::vector<tensorflow::Safe_PyObjectPtr>> flattened_inputs =
+      nullptr;
 
-  if (run_callbacks) {
-    flattened_attrs.reset(new std::vector<PyObject*>);
-    flattened_inputs.reset(new std::vector<PyObject*>);
+  // TODO(nareshmodi): Encapsulate callbacks information into a struct.
+  if (op_exec_info.run_callbacks) {
+    flattened_attrs.reset(new std::vector<tensorflow::Safe_PyObjectPtr>);
+    flattened_inputs.reset(new std::vector<tensorflow::Safe_PyObjectPtr>);
   }
 
   // Add inferred attrs and inputs.
@@ -1809,16 +1965,16 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
       Py_ssize_t len = PyList_Size(input);
 
       TFE_OpSetAttrInt(op, input_arg.number_attr().data(), len);
-      if (run_callbacks) {
-        flattened_attrs->push_back(
+      if (op_exec_info.run_callbacks) {
+        flattened_attrs->emplace_back(
             GetPythonObjectFromString(input_arg.number_attr().data()));
-        flattened_attrs->push_back(PyLong_FromLong(len));
+        flattened_attrs->emplace_back(PyLong_FromLong(len));
       }
       attr_list_sizes[input_arg.number_attr()] = len;
 
       if (len > 0) {
         // First item adds the type attr.
-        if (!AddInputToOp(PyList_GET_ITEM(input, 0), &input_arg,
+        if (!AddInputToOp(op_exec_info, PyList_GET_ITEM(input, 0), &input_arg,
                           flattened_attrs.get(), flattened_inputs.get(), op,
                           status)) {
           return nullptr;
@@ -1826,7 +1982,8 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
 
         for (Py_ssize_t j = 1; j < len; j++) {
           // Since the list is homogeneous, we don't need to re-add the attr.
-          if (!AddInputToOp(PyList_GET_ITEM(input, j), nullptr /* input_arg */,
+          if (!AddInputToOp(op_exec_info, PyList_GET_ITEM(input, j),
+                            nullptr /* input_arg */,
                             nullptr /* flattened_attrs */,
                             flattened_inputs.get(), op, status)) {
             return nullptr;
@@ -1840,12 +1997,20 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
       Py_ssize_t len = PyList_Size(input);
       tensorflow::gtl::InlinedVector<TF_DataType, 4> attr_value(len);
       PyObject* py_attr_value = nullptr;
-      if (run_callbacks) {
+      if (op_exec_info.run_callbacks) {
         py_attr_value = PyTuple_New(len);
       }
       for (Py_ssize_t j = 0; j < len; j++) {
         PyObject* py_input = PyList_GET_ITEM(input, j);
-        TFE_TensorHandle* input_handle = EagerTensor_Handle(py_input);
+        tensorflow::Safe_PyObjectPtr py_eager_tensor;
+        if (!ConvertToTensor(op_exec_info, py_input, &py_eager_tensor,
+                             status)) {
+          return nullptr;
+        }
+
+        TFE_TensorHandle* input_handle =
+            EagerTensor_Handle(py_eager_tensor.get());
+
         attr_value[j] = TFE_TensorHandleDataType(input_handle);
 
         TFE_OpAddInput(op, input_handle, status);
@@ -1853,22 +2018,23 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
           return nullptr;
         }
 
-        if (run_callbacks) {
-          flattened_inputs->push_back(py_input);
+        if (op_exec_info.run_callbacks) {
+          flattened_inputs->emplace_back(std::move(py_eager_tensor));
 
           PyTuple_SET_ITEM(py_attr_value, j, PyLong_FromLong(attr_value[j]));
         }
       }
-      if (run_callbacks) {
-        flattened_attrs->push_back(GetPythonObjectFromString(attr_name.data()));
-        flattened_attrs->push_back(py_attr_value);
+      if (op_exec_info.run_callbacks) {
+        flattened_attrs->emplace_back(
+            GetPythonObjectFromString(attr_name.data()));
+        flattened_attrs->emplace_back(py_attr_value);
       }
       TFE_OpSetAttrTypeList(op, attr_name.data(), attr_value.data(),
                             attr_value.size());
       attr_list_sizes[attr_name] = len;
     } else {
       // The item is a single item.
-      if (!AddInputToOp(input, &input_arg, flattened_attrs.get(),
+      if (!AddInputToOp(op_exec_info, input, &input_arg, flattened_attrs.get(),
                         flattened_inputs.get(), op, status)) {
         return nullptr;
       }
@@ -1892,12 +2058,14 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
   Py_BEGIN_ALLOW_THREADS;
   TFE_Execute(op, retvals.data(), &num_retvals, status);
   Py_END_ALLOW_THREADS;
+
   if (TF_GetCode(status) != TF_OK) {
     // Augment the status with the op_name for easier debugging similar to
     // TFE_Py_Execute.
     TF_SetStatus(status, TF_GetCode(status),
-                 tensorflow::strings::StrCat(TF_Message(status), " [Op:",
-                                             TFE_GetPythonString(op_name), "]")
+                 tensorflow::strings::StrCat(
+                     TF_Message(status),
+                     " [Op:", TFE_GetPythonString(op_exec_info.op_name), "]")
                      .c_str());
 
     MaybeRaiseExceptionFromTFStatus(status, nullptr);
@@ -1909,10 +2077,8 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject*, PyObject* args) {
     PyList_SET_ITEM(flat_result, i, EagerTensorFromHandle(retvals[i]));
   }
 
-  if (run_callbacks &&
-      !RunCallbacks(run_gradient_callback, run_post_exec_callbacks, op_def,
-                    args, *flattened_inputs, *flattened_attrs, flat_result,
-                    op_name, name, callbacks)) {
+  if (!RunCallbacks(op_exec_info, args, *flattened_inputs, *flattened_attrs,
+                    flat_result)) {
     return nullptr;
   }
 
diff --git a/tensorflow/python/eager/pywrap_tfe_test.py b/tensorflow/python/eager/pywrap_tfe_test.py
index 418ed75178..46c5601f47 100644
--- a/tensorflow/python/eager/pywrap_tfe_test.py
+++ b/tensorflow/python/eager/pywrap_tfe_test.py
@@ -27,6 +27,7 @@ from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import resource_variable_ops
 
 
 class Tests(test.TestCase):
@@ -53,6 +54,21 @@ class Tests(test.TestCase):
             ctx._handle, ctx.device_name, "MatMul", None, None, a_100_by_784,
             b_100_by_784, "transpose_a", False, "transpose_b", True))
 
+  @test_util.assert_no_new_tensors
+  @test_util.assert_no_garbage_created
+  def testFastpathExecute_ResourceVariableMatMulCorrectResponse(self):
+    ctx = context.context()
+    a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
+    m = resource_variable_ops.ResourceVariable(a_2_by_2)
+    x = pywrap_tensorflow.TFE_Py_FastPathExecute(
+        ctx._handle, ctx.device_name, "MatMul", None, None, m, m, "transpose_a",
+        False, "transpose_b", False)
+    y = pywrap_tensorflow.TFE_Py_FastPathExecute(
+        ctx._handle, ctx.device_name, "MatMul", None, None, a_2_by_2, a_2_by_2,
+        "transpose_a", False, "transpose_b", False)
+
+    self.assertAllEqual(x, y)
+
   @test_util.assert_no_new_tensors
   @test_util.assert_no_garbage_created
   def testFastpathExecute_TapeWrite(self):
@@ -67,6 +83,21 @@ class Tests(test.TestCase):
     self.assertAllEqual(dz_dy.numpy(),
                         constant_op.constant(4.0, shape=[2, 2]).numpy())
 
+  @test_util.assert_no_new_tensors
+  @test_util.assert_no_garbage_created
+  def testFastpathExecute_ResourceVariableTapeWrite(self):
+    ctx = context.context()
+    with backprop.GradientTape(persistent=True) as tape:
+      a_2_by_2 = constant_op.constant(1.0, shape=[2, 2])
+      m = resource_variable_ops.ResourceVariable(a_2_by_2)
+      tape.watch(m)
+      z = pywrap_tensorflow.TFE_Py_FastPathExecute(
+          ctx._handle, ctx.device_name, "MatMul", None, None, m, m,
+          "transpose_a", False, "transpose_b", False)
+    dz_dy = tape.gradient(z, [m])[0]
+    self.assertAllEqual(dz_dy.numpy(),
+                        constant_op.constant(4.0, shape=[2, 2]).numpy())
+
   # Tests homogeneous list op
   @test_util.assert_no_new_tensors
   @test_util.assert_no_garbage_created
diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index cbac3c686d..6c5d692e82 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -21,6 +21,7 @@ from __future__ import print_function
 
 from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import variable_pb2
+from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import context
 from tensorflow.python.eager import tape
 from tensorflow.python.framework import dtypes
@@ -932,6 +933,9 @@ class ResourceVariable(variables.Variable):
                        "Tensor object.")
 
 
+pywrap_tensorflow.TFE_Py_RegisterResourceVariableType(ResourceVariable)
+
+
 def _dense_var_to_tensor(var, dtype=None, name=None, as_ref=False):
   return var._dense_var_to_tensor(dtype=dtype, name=name, as_ref=as_ref)  # pylint: disable=protected-access
 
diff --git a/tensorflow/python/pywrap_tfe.i b/tensorflow/python/pywrap_tfe.i
index 7ab0db5268..b481ddf5d4 100644
--- a/tensorflow/python/pywrap_tfe.i
+++ b/tensorflow/python/pywrap_tfe.i
@@ -31,6 +31,7 @@ limitations under the License.
 %rename("%s") TFE_Py_RegisterExceptionClass;
 %rename("%s") TFE_Py_RegisterBackwardFunctionGetter;
 %rename("%s") TFE_Py_RegisterFallbackExceptionClass;
+%rename("%s") TFE_Py_RegisterResourceVariableType;
 %rename("%s") TFE_Py_Execute;
 %rename("%s") TFE_Py_FastPathExecute;
 %rename("%s") TFE_Py_RecordGradient;
-- 
GitLab


From 9a45b6bdf0246477754f50fab357e568051bed4f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 15:24:33 -0800
Subject: [PATCH 568/575] Adds setUseNNAPI to Interpreter.java, to enable
 develoeprs turn on & off NNAPI.

PiperOrigin-RevId: 187677765
---
 .../java/org/tensorflow/lite/Interpreter.java | 10 +++-
 .../org/tensorflow/lite/InterpreterTest.java  | 48 +++++++++++++++++++
 2 files changed, 57 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
index b071cda5df..9e47e921a6 100644
--- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
+++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java
@@ -167,7 +167,6 @@ public final class Interpreter implements AutoCloseable {
     return wrapper.getOutputIndex(opName);
   }
 
-
   /**
    * Returns native inference timing.
    * <p>IllegalArgumentException will be thrown if the model is not initialized by the
@@ -180,6 +179,15 @@ public final class Interpreter implements AutoCloseable {
     return wrapper.getLastNativeInferenceDurationNanoseconds();
   }
 
+  /** Turns on/off Android NNAPI for hardware acceleration when it is available. */
+  public void setUseNNAPI(boolean useNNAPI) {
+    if (wrapper != null) {
+      wrapper.setUseNNAPI(useNNAPI);
+    } else {
+      throw new IllegalStateException("NativeInterpreterWrapper has already been closed.");
+    }
+  }
+
   /** Release resources associated with the {@code Interpreter}. */
   @Override
   public void close() {
diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java
index 424b3de6c9..61d6c35ec8 100644
--- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java
+++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java
@@ -218,4 +218,52 @@ public final class InterpreterTest {
     int index = interpreter.getOutputIndex("MobilenetV1/Predictions/Softmax");
     assertThat(index).isEqualTo(0);
   }
+
+  @Test
+  public void testTurnOffNNAPI() throws Exception {
+    Path path = MODEL_FILE.toPath();
+    FileChannel fileChannel =
+        (FileChannel) Files.newByteChannel(path, EnumSet.of(StandardOpenOption.READ));
+    MappedByteBuffer mappedByteBuffer =
+        fileChannel.map(FileChannel.MapMode.READ_ONLY, 0, fileChannel.size());
+    Interpreter interpreter = new Interpreter(mappedByteBuffer);
+    interpreter.setUseNNAPI(true);
+    float[] oneD = {1.23f, 6.54f, 7.81f};
+    float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD};
+    float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD};
+    float[][][][] fourD = {threeD, threeD};
+    float[][][][] parsedOutputs = new float[2][8][8][3];
+    interpreter.run(fourD, parsedOutputs);
+    float[] outputOneD = parsedOutputs[0][0][0];
+    float[] expected = {3.69f, 19.62f, 23.43f};
+    assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder();
+    interpreter.setUseNNAPI(false);
+    interpreter.run(fourD, parsedOutputs);
+    outputOneD = parsedOutputs[0][0][0];
+    assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder();
+    interpreter.close();
+    fileChannel.close();
+  }
+
+  @Test
+  public void testTurnOnNNAPI() throws Exception {
+    Path path = MODEL_FILE.toPath();
+    FileChannel fileChannel =
+        (FileChannel) Files.newByteChannel(path, EnumSet.of(StandardOpenOption.READ));
+    MappedByteBuffer mappedByteBuffer =
+        fileChannel.map(FileChannel.MapMode.READ_ONLY, 0, fileChannel.size());
+    Interpreter interpreter = new Interpreter(mappedByteBuffer);
+    interpreter.setUseNNAPI(true);
+    float[] oneD = {1.23f, 6.54f, 7.81f};
+    float[][] twoD = {oneD, oneD, oneD, oneD, oneD, oneD, oneD, oneD};
+    float[][][] threeD = {twoD, twoD, twoD, twoD, twoD, twoD, twoD, twoD};
+    float[][][][] fourD = {threeD, threeD};
+    float[][][][] parsedOutputs = new float[2][8][8][3];
+    interpreter.run(fourD, parsedOutputs);
+    float[] outputOneD = parsedOutputs[0][0][0];
+    float[] expected = {3.69f, 19.62f, 23.43f};
+    assertThat(outputOneD).usingTolerance(0.1f).containsExactly(expected).inOrder();
+    interpreter.close();
+    fileChannel.close();
+  }
 }
-- 
GitLab


From e8e4e5b99b721dcd79e0d1a9a7fe6bfb990744ba Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 15:25:41 -0800
Subject: [PATCH 569/575] Fix some compiler warnings in MKL build.

PiperOrigin-RevId: 187677893
---
 tensorflow/core/BUILD                         |  6 ++-
 .../core/common_runtime/mkl_cpu_allocator.cc  | 27 ++++++++++
 .../core/common_runtime/mkl_cpu_allocator.h   |  4 +-
 tensorflow/core/graph/mkl_graph_util.h        |  4 +-
 tensorflow/core/graph/mkl_layout_pass.cc      | 13 ++---
 tensorflow/core/kernels/BUILD                 | 50 +++++++++----------
 tensorflow/core/kernels/mkl_concat_op.cc      | 12 +++--
 .../core/kernels/mkl_conv_grad_bias_ops.cc    |  5 +-
 tensorflow/core/kernels/mkl_conv_ops.cc       |  6 ++-
 .../core/kernels/mkl_fused_batch_norm_op.cc   |  1 -
 tensorflow/core/kernels/mkl_lrn_op.cc         |  9 ++--
 tensorflow/core/kernels/mkl_maxpooling_op.cc  |  7 +--
 tensorflow/core/kernels/mkl_relu_op.cc        |  1 -
 tensorflow/core/ops/nn_ops.cc                 |  8 +--
 14 files changed, 93 insertions(+), 60 deletions(-)
 create mode 100644 tensorflow/core/common_runtime/mkl_cpu_allocator.cc

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 3271825251..3a436ff680 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -1920,7 +1920,7 @@ tf_cuda_library(
     ) + if_mkl(
         [
             "//third_party/mkl:intel_binary_blob",
-            "@mkl_dnn//:mkl_dnn",
+            "@mkl_dnn",
         ],
     ),
     alwayslink = 1,
@@ -2135,6 +2135,7 @@ tf_cuda_library(
         "common_runtime/graph_runner.cc",
         "common_runtime/local_device.cc",
         "common_runtime/memory_types.cc",
+        "common_runtime/mkl_cpu_allocator.cc",
         "common_runtime/optimization_registry.cc",
         "common_runtime/parallel_concat_optimizer.cc",
         "common_runtime/placer.cc",
@@ -2174,6 +2175,7 @@ tf_cuda_library(
     ] + if_mkl(
         [
             "//third_party/mkl:intel_binary_blob",
+            "@mkl_dnn",
         ],
     ),
     alwayslink = 1,
@@ -2218,7 +2220,7 @@ tf_cuda_library(
     ] + if_mkl(
         [
             "//third_party/mkl:intel_binary_blob",
-            "@mkl_dnn//:mkl_dnn",
+            "@mkl_dnn",
         ],
     ) + tf_additional_core_deps() + if_static([":core_cpu_impl"]),
     alwayslink = 1,
diff --git a/tensorflow/core/common_runtime/mkl_cpu_allocator.cc b/tensorflow/core/common_runtime/mkl_cpu_allocator.cc
new file mode 100644
index 0000000000..43a909466e
--- /dev/null
+++ b/tensorflow/core/common_runtime/mkl_cpu_allocator.cc
@@ -0,0 +1,27 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifdef INTEL_MKL
+
+#include "tensorflow/core/common_runtime/mkl_cpu_allocator.h"
+
+namespace tensorflow {
+
+constexpr const char* MklCPUAllocator::kMaxLimitStr;
+constexpr const size_t MklCPUAllocator::kDefaultMaxLimit;
+
+}  // namespace tensorflow
+
+#endif  // INTEL_MKL
diff --git a/tensorflow/core/common_runtime/mkl_cpu_allocator.h b/tensorflow/core/common_runtime/mkl_cpu_allocator.h
index fb092424bf..55c8411ad0 100644
--- a/tensorflow/core/common_runtime/mkl_cpu_allocator.h
+++ b/tensorflow/core/common_runtime/mkl_cpu_allocator.h
@@ -53,7 +53,7 @@ class MklCPUAllocator : public VisitableAllocator {
   static constexpr const char* kMaxLimitStr = "TF_MKL_ALLOC_MAX_BYTES";
 
   /// Default upper limit on allocator size - 64GB
-  static const size_t kDefaultMaxLimit = 64LL << 30;
+  static constexpr size_t kDefaultMaxLimit = 64LL << 30;
 
   MklCPUAllocator() { TF_CHECK_OK(Initialize()); }
 
@@ -158,7 +158,7 @@ class MklCPUAllocator : public VisitableAllocator {
   static constexpr const char* kName = "mklcpu";
 
   /// The alignment that we need for the allocations
-  static const size_t kAlignment = 64;
+  static constexpr const size_t kAlignment = 64;
 
   VisitableAllocator* allocator_;  // owned by this class
 };
diff --git a/tensorflow/core/graph/mkl_graph_util.h b/tensorflow/core/graph/mkl_graph_util.h
index 1b99d54e8e..5f51d6083b 100644
--- a/tensorflow/core/graph/mkl_graph_util.h
+++ b/tensorflow/core/graph/mkl_graph_util.h
@@ -90,7 +90,7 @@ inline string GetMklOpName(const string& name) {
 // @input: name of the op
 // @input: T datatype to be used for checking op
 // @return: true if opname is registered as Mkl op; false otherwise
-static inline bool IsMklOp(const std::string& op_name, DataType T) {
+static inline bool IsMklOp(const string& op_name, DataType T) {
   string kernel = KernelsRegisteredForOp(op_name);
   bool result =
       kernel.find(kMklOpLabelPattern) != string::npos && (T == DT_FLOAT);
@@ -104,7 +104,7 @@ static inline bool IsMklOp(const std::string& op_name, DataType T) {
 // @input: T datatype to be used for checking op
 // @return: true if opname is registered as element-wise Mkl op;
 // false otherwise
-static inline bool IsMklElementWiseOp(const std::string& op_name, DataType T) {
+static inline bool IsMklElementWiseOp(const string& op_name, DataType T) {
   if (!IsMklOp(op_name, T)) {
     return false;
   }
diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc
index 7d3be15299..02038c5d77 100644
--- a/tensorflow/core/graph/mkl_layout_pass.cc
+++ b/tensorflow/core/graph/mkl_layout_pass.cc
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+// TODO(intel): Improve error handling in this file; instead of CHECK failing
+// all over the place, we should log an error and execute the original graph.
 #ifdef INTEL_MKL
 
 #include <algorithm>
@@ -1030,8 +1032,7 @@ void MklLayoutRewritePass::GetDummyMklTensorNode(std::unique_ptr<Graph>* g,
   TensorProto proto;
   proto.set_dtype(dt);
   uint8 zero[8] = {0, 0, 0, 0, 0, 0, 0, 0};
-  proto.set_tensor_content(const_cast<const void*>(static_cast<void*>(&zero)),
-                           8);
+  proto.set_tensor_content(string(reinterpret_cast<const char*>(zero), 8));
   TensorShape dummy_shape({8});
   dummy_shape.AsProto(proto.mutable_tensor_shape());
   TF_CHECK_OK(NodeBuilder((*g)->NewName("DMT"), "Const")
@@ -1144,7 +1145,8 @@ int MklLayoutRewritePass::SetUpContiguousInputs(
     // For that let's first find filter node that is 2nd input (slot 1)
     // of BackpropInput.
     Node* filter_node = nullptr;
-    old_node->input_node(kConv2DBackpropInputFilterInputSlotIdx, &filter_node);
+    TF_CHECK_OK(old_node->input_node(kConv2DBackpropInputFilterInputSlotIdx,
+                                     &filter_node));
     CHECK_NOTNULL(filter_node);
 
     // Now check which nodes receive from filter_node. Filter feeds as
@@ -1323,8 +1325,7 @@ void MklLayoutRewritePass::GetDummyWorkspaceTensorNode(
   TensorProto proto;
   proto.set_dtype(dt);
   float zero[1] = {0};
-  proto.set_tensor_content(const_cast<const void*>(static_cast<void*>(&zero)),
-                           4);
+  proto.set_tensor_content(string(reinterpret_cast<char*>(&zero), 4));
   TensorShape dummy_shape({1});
   dummy_shape.AsProto(proto.mutable_tensor_shape());
   TF_CHECK_OK(NodeBuilder((*g)->NewName("DMT"), "Const")
@@ -1829,7 +1830,7 @@ Status MklLayoutRewritePass::MergeNode(std::unique_ptr<Graph>* g, Node* succ,
 
     // Create node.
     Node* new_node;
-    nb.Finalize(&**g, &new_node);
+    TF_CHECK_OK(nb.Finalize(&**g, &new_node));
     CHECK_NOTNULL(new_node);
 
     // Set the Mkl layer label for this op.
diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index feacee5d63..52be90ea1f 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -879,7 +879,7 @@ tf_kernel_library(
     hdrs = ["transpose_op.h"],
     deps = ARRAY_DEPS + if_mkl([
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
+        "@mkl_dnn",
     ]),
 )
 
@@ -2810,7 +2810,7 @@ tf_kernel_library(
         "//conditions:default": [],
     }) + if_mkl([
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
+        "@mkl_dnn",
     ]) + if_cuda([
         "//tensorflow/core/platform/default/build_config:cublas_plugin",
     ]),
@@ -5850,10 +5850,9 @@ tf_mkl_kernel_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:nn_ops_op_lib",
-    ] + if_mkl([
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
-    ]),
+        "@mkl_dnn",
+    ],
 )
 
 tf_mkl_kernel_library(
@@ -5867,10 +5866,9 @@ tf_mkl_kernel_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:nn_ops_op_lib",
-    ] + if_mkl([
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
-    ]),
+        "@mkl_dnn",
+    ],
 )
 
 tf_mkl_kernel_library(
@@ -5898,6 +5896,7 @@ tf_mkl_kernel_library(
     ],
     hdrs = ["mkl_pooling_ops_common.h"],
     deps = [
+        ":bounds_check",
         ":ops_util",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
@@ -5919,10 +5918,10 @@ tf_mkl_kernel_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:nn_ops_op_lib",
-    ] + if_mkl([
+        "//third_party/eigen3",
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
-    ]),
+        "@mkl_dnn",
+    ],
 )
 
 tf_mkl_kernel_library(
@@ -5936,19 +5935,18 @@ tf_mkl_kernel_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:nn_ops_op_lib",
-    ] + if_mkl([
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
-    ]),
+        "@mkl_dnn",
+    ],
 )
 
 tf_mkl_kernel_library(
     name = "mkl_fused_batch_norm_op",
     srcs = ["mkl_fused_batch_norm_op.cc"],
-    deps = NN_DEPS + if_mkl([
+    deps = NN_DEPS + [
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
-    ]),
+        "@mkl_dnn",
+    ],
 )
 
 tf_mkl_kernel_library(
@@ -5962,10 +5960,10 @@ tf_mkl_kernel_library(
 tf_mkl_kernel_library(
     name = "mkl_concat_op",
     prefix = "mkl_concat_op",
-    deps = ARRAY_DEPS + if_mkl([
+    deps = ARRAY_DEPS + [
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
-    ]),
+        "@mkl_dnn",
+    ],
 )
 
 tf_mkl_kernel_library(
@@ -5979,19 +5977,19 @@ tf_mkl_kernel_library(
 tf_mkl_kernel_library(
     name = "mkl_identity_op",
     prefix = "mkl_identity_op",
-    deps = ARRAY_DEPS + if_mkl([
+    deps = ARRAY_DEPS + [
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
-    ]),
+        "@mkl_dnn",
+    ],
 )
 
 tf_mkl_kernel_library(
     name = "mkl_lrn_op",
     prefix = "mkl_lrn_op",
-    deps = NN_DEPS + if_mkl([
+    deps = NN_DEPS + [
         "//third_party/mkl:intel_binary_blob",
-        "@mkl_dnn//:mkl_dnn",
-    ]),
+        "@mkl_dnn",
+    ],
 )
 
 tf_mkl_kernel_library(
diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc
index f1f267e849..aa3ea890b0 100644
--- a/tensorflow/core/kernels/mkl_concat_op.cc
+++ b/tensorflow/core/kernels/mkl_concat_op.cc
@@ -519,9 +519,11 @@ class MklConcatOp : public OpKernel {
     mkl_tensor_tf_shape.AddDim(
         SIZE_OF_MKL_SERIAL_DATA(mkl_tensor_mkl_shape.GetDimension()));
     int tf_output_index = 0;
-    context->allocate_output(
+    // TODO(jktomer): replace this with OP_REQUIRES_OK and clean up this file
+    // to propagate the status up the call stack.
+    TF_CHECK_OK(context->allocate_output(
         GetTensorMetaDataIndex(tf_output_index, context->num_outputs()),
-        mkl_tensor_tf_shape, &mkl_tensor);
+        mkl_tensor_tf_shape, &mkl_tensor));
     mkl_tensor_mkl_shape.SerializeMklShape(
         mkl_tensor->flat<uint8>().data(),
         mkl_tensor->flat<uint8>().size() * sizeof(uint8));
@@ -549,9 +551,11 @@ class MklConcatOp : public OpKernel {
     mkl_tensor_tf_shape.AddDim(
         SIZE_OF_MKL_SERIAL_DATA(mkl_tensor_mkl_shape.GetDimension()));
     int tf_output_index = 0;
-    context->allocate_output(
+    // TODO(jktomer): replace this with OP_REQUIRES_OK and clean up this file
+    // to propagate the status up the call stack.
+    TF_CHECK_OK(context->allocate_output(
         GetTensorMetaDataIndex(tf_output_index, context->num_outputs()),
-        mkl_tensor_tf_shape, &mkl_tensor);
+        mkl_tensor_tf_shape, &mkl_tensor));
     mkl_tensor_mkl_shape.SerializeMklShape(
         mkl_tensor->flat<uint8>().data(),
         mkl_tensor->flat<uint8>().size() * sizeof(uint8));
diff --git a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc
index 25c2573741..d23027a54d 100644
--- a/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_grad_bias_ops.cc
@@ -79,8 +79,9 @@ class MklConv2DCustomBackpropBiasOp : public OpKernel {
     } else if (data_format_ == FORMAT_NHWC || data_format_ == FORMAT_NCHW) {
       mkl_context.c_size = GetTensorDim(input, data_format_, 'C');
     } else {
-      errors::InvalidArgument("Unknown format ",
-                              " Format must be either NCHW or NHWC. ");
+      context->CtxFailure(errors::InvalidArgument(
+          "Unknown format ", " Format must be either NCHW or NHWC. "));
+      return;
     }
     TensorShape output_shape{mkl_context.c_size};
 
diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc
index 2953426d58..1440da8f82 100644
--- a/tensorflow/core/kernels/mkl_conv_ops.cc
+++ b/tensorflow/core/kernels/mkl_conv_ops.cc
@@ -294,8 +294,10 @@ class MklConv2DOp : public OpKernel {
     mkl_filter_output_mkl_shape.SetMklLayout(mkl_context.prim_fwd,
                                              dnnResourceFilter);
 
-    size_t filter_sizes[4] = {filter.dim_size(0), filter.dim_size(1),
-                              filter.dim_size(2), filter.dim_size(3)};
+    size_t filter_sizes[4] = {static_cast<size_t>(filter.dim_size(0)),
+                              static_cast<size_t>(filter.dim_size(1)),
+                              static_cast<size_t>(filter.dim_size(2)),
+                              static_cast<size_t>(filter.dim_size(3))};
     mkl_filter_output_mkl_shape.SetTfLayout(filter.dims(), filter_sizes,
                                             mkl_context.filter_strides);
 
diff --git a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
index 8313224d7f..9b2146aca3 100644
--- a/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
+++ b/tensorflow/core/kernels/mkl_fused_batch_norm_op.cc
@@ -262,7 +262,6 @@ class MklFusedBatchNormOp : public OpKernel {
     }
 
     void MklCreateInputLayout(OpKernelContext* context) {
-      const Tensor& input = MklGetInput(context, 0);
       bool input_in_mkl_format = mkl_shape_input_shape.IsMklTensor();
       if (input_in_mkl_format) {
         mkl_lt_input =
diff --git a/tensorflow/core/kernels/mkl_lrn_op.cc b/tensorflow/core/kernels/mkl_lrn_op.cc
index 5f0a12a1fb..282012c719 100644
--- a/tensorflow/core/kernels/mkl_lrn_op.cc
+++ b/tensorflow/core/kernels/mkl_lrn_op.cc
@@ -88,7 +88,8 @@ class MklLRNOp : public OpKernel {
     OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_));
     OP_REQUIRES_OK(context, context->GetAttr("beta", &beta_));
     workspace_enabled_ = false;
-    context->GetAttr("workspace_enabled", &workspace_enabled_);
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("workspace_enabled", &workspace_enabled_));
   }
 
   void Compute(OpKernelContext* context) override {
@@ -357,7 +358,8 @@ class MklLRNGradOp : public OpKernel {
     OP_REQUIRES_OK(context, context->GetAttr("alpha", &alpha_));
     OP_REQUIRES_OK(context, context->GetAttr("beta", &beta_));
     workspace_enabled_ = false;
-    context->GetAttr("workspace_enabled", &workspace_enabled_);
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("workspace_enabled", &workspace_enabled_));
   }
 
   void Compute(OpKernelContext* context) override {
@@ -535,7 +537,6 @@ class MklLRNGradOp : public OpKernel {
                                 Tensor* mkl_tmp_outimage_buf_tensor) {
       const Tensor& in_grads = MklGetInput(context, 0);
       const Tensor& in_image = MklGetInput(context, 1);
-      const Tensor& out_image = MklGetInput(context, 2);
       const Tensor& workspace = MklGetInput(
           context,
           3); /*Worskpsace is enabled, get the buffer to the workspace */
@@ -544,8 +545,6 @@ class MklLRNGradOp : public OpKernel {
           static_cast<const void*>(in_grads.flat<T>().data()));
       void* user_fwd_input = const_cast<void*>(
           static_cast<const void*>(in_image.flat<T>().data()));
-      void* user_fwd_output = const_cast<void*>(
-          static_cast<const void*>(out_image.flat<T>().data()));
       void* workspace_buffer = const_cast<void*>(
           static_cast<const void*>(workspace.flat<T>().data()));
 
diff --git a/tensorflow/core/kernels/mkl_maxpooling_op.cc b/tensorflow/core/kernels/mkl_maxpooling_op.cc
index 14607f26e0..ea537524b1 100644
--- a/tensorflow/core/kernels/mkl_maxpooling_op.cc
+++ b/tensorflow/core/kernels/mkl_maxpooling_op.cc
@@ -69,7 +69,8 @@ class MklMaxPoolingOp : public OpKernel {
     // We may not get this attribute for this node if it does not go through
     // graph rewrite pass. So we do not check for error while retrieving this
     // attribute value.
-    context->GetAttr("workspace_enabled", &workspace_enabled_);
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("workspace_enabled", &workspace_enabled_));
   }
 
   void Compute(OpKernelContext* context) override {
@@ -118,7 +119,6 @@ class MklMaxPoolingOp : public OpKernel {
                               mkl_out_shape);
 
     Tensor* workspace_tensor;
-    void* workspace_buf = nullptr;
 
     TensorShape workspace_shape;
     mkl_workspace_shape.SetMklTensor(false);
@@ -226,7 +226,8 @@ class MklMaxPoolingGradOp : public OpKernel {
     // We may not get this attribute for this node if it does not go through
     // graph rewrite pass. So we do not check for error while retrieving this
     // attribute value.
-    context->GetAttr("workspace_enabled", &workspace_enabled_);
+    OP_REQUIRES_OK(context,
+                   context->GetAttr("workspace_enabled", &workspace_enabled_));
   }
 
   void Compute(OpKernelContext* context) override {
diff --git a/tensorflow/core/kernels/mkl_relu_op.cc b/tensorflow/core/kernels/mkl_relu_op.cc
index 51db3991e2..f006954c03 100644
--- a/tensorflow/core/kernels/mkl_relu_op.cc
+++ b/tensorflow/core/kernels/mkl_relu_op.cc
@@ -25,7 +25,6 @@ limitations under the License.
 
 #include "mkl_dnn.h"
 #include "mkl_dnn_types.h"
-#include "tensorflow/core/platform/default/logging.h"
 #include "tensorflow/core/util/mkl_util.h"
 
 #ifndef INTEL_MKL_ML
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index 67481fd202..910fbaca9e 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -2007,10 +2007,10 @@ REGISTER_OP("_MklFusedBatchNorm")
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &x));
 
       bool is_training;
-      c->GetAttr("is_training", &is_training);
+      TF_RETURN_IF_ERROR(c->GetAttr("is_training", &is_training));
       int number_inputs = (is_training) ? 3 : 5;
       string data_format;
-      c->GetAttr("data_format", &data_format);
+      TF_RETURN_IF_ERROR(c->GetAttr("data_format", &data_format));
       DimensionHandle channel_dim =
           (data_format == "NHWC") ? c->Dim(x, 3) : c->Dim(x, 1);
 
@@ -2076,8 +2076,8 @@ REGISTER_OP("_MklFusedBatchNormGrad")
 
       bool is_training;
       string data_format;
-      c->GetAttr("is_training", &is_training);
-      c->GetAttr("data_format", &data_format);
+      TF_RETURN_IF_ERROR(c->GetAttr("is_training", &is_training));
+      TF_RETURN_IF_ERROR(c->GetAttr("data_format", &data_format));
       DimensionHandle channel_dim = (data_format == "NHWC")
                                         ? c->Dim(y_backprop, 3)
                                         : c->Dim(y_backprop, 1);
-- 
GitLab


From 737d2e73c82abe35ae76bd7d17793243f3dc9dd5 Mon Sep 17 00:00:00 2001
From: Michael Case <mikecase@google.com>
Date: Fri, 2 Mar 2018 15:52:32 -0800
Subject: [PATCH 570/575] Exit with failure if a free gpu is not found by
 parallel_gpu_execute.

If TF_GPU_COUNT was a value greater than the actual number of GPUs, it was
possible for tests to just pass without running when running under
parallel_gpu_execute.sh.

PiperOrigin-RevId: 187681032
---
 tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh b/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh
index cfeaebdbf5..d0816c92b7 100755
--- a/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh
+++ b/tensorflow/tools/ci_build/gpu_build/parallel_gpu_execute.sh
@@ -54,3 +54,6 @@ for i in `seq 0 $((TF_GPU_COUNT-1))`; do
   fi
 done
 
+echo "Cannot find a free GPU to run the test $* on, exiting with failure..."
+exit 1
+
-- 
GitLab


From c12f0c5f84699835f9b8111299febf9fc7aba343 Mon Sep 17 00:00:00 2001
From: Asim Shankar <ashankar@google.com>
Date: Fri, 2 Mar 2018 16:05:05 -0800
Subject: [PATCH 571/575] eager/examples: Use tf.keras.Model in RNN examples.

Some notable differences between tf.keras.Model and tfe.Network:

- tf.keras.Model doesn't have a track_layer() method. It tracks Layer and Checkpointable valued attributes automatically. For list and other complex structures, __setattr__ performs the role of tfe.Network.track_layer()

- tf.keras.Model accepts a single positional argument. Thus either all arguments must be packaged into a single list/tuple (as in rnn_ptb.py) or be provided as keyword arguments (as in rnn_colorbot.py).

PiperOrigin-RevId: 187682716
---
 .../examples/rnn_colorbot/rnn_colorbot.py     | 40 ++++++++++--------
 .../eager/python/examples/rnn_ptb/rnn_ptb.py  | 42 ++++++++++++-------
 2 files changed, 50 insertions(+), 32 deletions(-)

diff --git a/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py b/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py
index aa87b94e7b..29f0232454 100644
--- a/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py
+++ b/tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py
@@ -109,7 +109,7 @@ def load_dataset(data_dir, url, batch_size):
 
 
 # pylint: disable=not-callable
-class RNNColorbot(tfe.Network):
+class RNNColorbot(tf.keras.Model):
   """Multi-layer (LSTM) RNN that regresses on real-valued vector labels.
   """
 
@@ -127,23 +127,20 @@ class RNNColorbot(tfe.Network):
     self.label_dimension = label_dimension
     self.keep_prob = keep_prob
 
-    # Note the calls to `track_layer` below; these calls register the layers as
-    # network components that house trainable variables.
-    self.cells = [
-        self.track_layer(tf.nn.rnn_cell.BasicLSTMCell(size))
-        for size in rnn_cell_sizes
-    ]
-    self.relu = self.track_layer(
-        tf.layers.Dense(label_dimension, activation=tf.nn.relu, name="relu"))
+    self.cells = self._add_cells(
+        [tf.nn.rnn_cell.BasicLSTMCell(size) for size in rnn_cell_sizes])
+    self.relu = tf.layers.Dense(
+        label_dimension, activation=tf.nn.relu, name="relu")
 
-  def call(self, chars, sequence_length, training=False):
+  def call(self, inputs, training=False):
     """Implements the RNN logic and prediction generation.
 
     Args:
-      chars: a Tensor of dimension [batch_size, time_steps, 256] holding a
-        batch of one-hot encoded color names
-      sequence_length: a Tensor of dimension [batch_size] holding the length
-        of each character sequence (i.e., color name)
+      inputs: A tuple (chars, sequence_length), where chars is a batch of
+        one-hot encoded color names represented as a Tensor with dimensions
+        [batch_size, time_steps, 256] and sequence_length holds the length
+        of each character sequence (color name) as a Tensor with dimension
+        [batch_size].
       training: whether the invocation is happening during training
 
     Returns:
@@ -151,6 +148,7 @@ class RNNColorbot(tfe.Network):
       passing chars through a multi-layer RNN and applying a ReLU to the final
       hidden state.
     """
+    (chars, sequence_length) = inputs
     # Transpose the first and second dimensions so that chars is of shape
     # [time_steps, batch_size, dimension].
     chars = tf.transpose(chars, [1, 0, 2])
@@ -181,6 +179,14 @@ class RNNColorbot(tfe.Network):
     hidden_states = tf.gather_nd(chars, indices)
     return self.relu(hidden_states)
 
+  def _add_cells(self, cells):
+    # "Magic" required for keras.Model classes to track all the variables in
+    # a list of tf.layers.Layer objects.
+    # TODO(ashankar): Figure out API so user code doesn't have to do this.
+    for i, c in enumerate(cells):
+      setattr(self, "cell-%d" % i, c)
+    return cells
+
 
 def loss(labels, predictions):
   """Computes mean squared loss."""
@@ -191,7 +197,7 @@ def test(model, eval_data):
   """Computes the average loss on eval_data, which should be a Dataset."""
   avg_loss = tfe.metrics.Mean("loss")
   for (labels, chars, sequence_length) in tfe.Iterator(eval_data):
-    predictions = model(chars, sequence_length, training=False)
+    predictions = model((chars, sequence_length), training=False)
     avg_loss(loss(labels, predictions))
   print("eval/loss: %.6f\n" % avg_loss.result())
   with tf.contrib.summary.always_record_summaries():
@@ -204,7 +210,7 @@ def train_one_epoch(model, optimizer, train_data, log_interval=10):
   tf.train.get_or_create_global_step()
 
   def model_loss(labels, chars, sequence_length):
-    predictions = model(chars, sequence_length, training=True)
+    predictions = model((chars, sequence_length), training=True)
     loss_value = loss(labels, predictions)
     tf.contrib.summary.scalar("loss", loss_value)
     return loss_value
@@ -277,7 +283,7 @@ def main(_):
       (chars, length) = (tf.identity(chars), tf.identity(length))
       chars = tf.expand_dims(chars, 0)
       length = tf.expand_dims(length, 0)
-      preds = tf.unstack(model(chars, length, training=False)[0])
+      preds = tf.unstack(model((chars, length), training=False)[0])
 
     # Predictions cannot be negative, as they are generated by a ReLU layer;
     # they may, however, be greater than 1.
diff --git a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py
index 5c5c59c877..69cd16d12c 100644
--- a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py
+++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py
@@ -39,21 +39,23 @@ from tensorflow.contrib.cudnn_rnn.python.layers import cudnn_rnn
 from tensorflow.contrib.eager.python import tfe
 
 
-class RNN(tfe.Network):
+class RNN(tf.keras.Model):
   """A static RNN.
 
-  Similar to tf.nn.static_rnn, implemented as a tf.layer.Layer.
+  Similar to tf.nn.static_rnn, implemented as a class.
   """
 
   def __init__(self, hidden_dim, num_layers, keep_ratio):
     super(RNN, self).__init__()
     self.keep_ratio = keep_ratio
-    for _ in range(num_layers):
-      self.track_layer(tf.nn.rnn_cell.BasicLSTMCell(num_units=hidden_dim))
+    self.cells = self._add_cells([
+        tf.nn.rnn_cell.BasicLSTMCell(num_units=hidden_dim)
+        for _ in range(num_layers)
+    ])
 
   def call(self, input_seq, training):
     batch_size = int(input_seq.shape[1])
-    for c in self.layers:
+    for c in self.cells:
       state = c.zero_state(batch_size, tf.float32)
       outputs = []
       input_seq = tf.unstack(input_seq, num=int(input_seq.shape[0]), axis=0)
@@ -64,7 +66,19 @@ class RNN(tfe.Network):
       input_seq = tf.stack(outputs, axis=0)
       if training:
         input_seq = tf.nn.dropout(input_seq, self.keep_ratio)
-    return input_seq, None
+    # Returning a list instead of a single tensor so that the line:
+    # y = self.rnn(y, ...)[0]
+    # in PTBModel.call works for both this RNN and CudnnLSTM (which returns a
+    # tuple (output, output_states).
+    return [input_seq]
+
+  def _add_cells(self, cells):
+    # "Magic" required for keras.Model classes to track all the variables in
+    # a list of tf.layers.Layer objects.
+    # TODO(ashankar): Figure out API so user code doesn't have to do this.
+    for i, c in enumerate(cells):
+      setattr(self, "cell-%d" % i, c)
+    return cells
 
 
 class Embedding(tf.layers.Layer):
@@ -87,7 +101,8 @@ class Embedding(tf.layers.Layer):
     return tf.nn.embedding_lookup(self.embedding, x)
 
 
-class PTBModel(tfe.Network):
+# pylint: disable=not-callable
+class PTBModel(tf.keras.Model):
   """LSTM for word language modeling.
 
   Model described in:
@@ -109,19 +124,16 @@ class PTBModel(tfe.Network):
 
     self.keep_ratio = 1 - dropout_ratio
     self.use_cudnn_rnn = use_cudnn_rnn
-    self.embedding = self.track_layer(Embedding(vocab_size, embedding_dim))
+    self.embedding = Embedding(vocab_size, embedding_dim)
 
     if self.use_cudnn_rnn:
       self.rnn = cudnn_rnn.CudnnLSTM(
           num_layers, hidden_dim, dropout=dropout_ratio)
     else:
       self.rnn = RNN(hidden_dim, num_layers, self.keep_ratio)
-    self.track_layer(self.rnn)
 
-    self.linear = self.track_layer(
-        tf.layers.Dense(
-            vocab_size,
-            kernel_initializer=tf.random_uniform_initializer(-0.1, 0.1)))
+    self.linear = tf.layers.Dense(
+        vocab_size, kernel_initializer=tf.random_uniform_initializer(-0.1, 0.1))
     self._output_shape = [-1, embedding_dim]
 
   def call(self, input_seq, training):
@@ -136,7 +148,7 @@ class PTBModel(tfe.Network):
     y = self.embedding(input_seq)
     if training:
       y = tf.nn.dropout(y, self.keep_ratio)
-    y, _ = self.rnn(y, training=training)
+    y = self.rnn(y, training=training)[0]
     return self.linear(tf.reshape(y, self._output_shape))
 
 
@@ -148,7 +160,7 @@ def clip_gradients(grads_and_vars, clip_ratio):
 
 def loss_fn(model, inputs, targets, training):
   labels = tf.reshape(targets, [-1])
-  outputs = model(inputs, training)
+  outputs = model(inputs, training=training)
   return tf.reduce_mean(
       tf.nn.sparse_softmax_cross_entropy_with_logits(
           labels=labels, logits=outputs))
-- 
GitLab


From 284dac189dcae46c77f1ec70055b13e69c31e4c0 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 2 Mar 2018 16:06:24 -0800
Subject: [PATCH 572/575] Checkpointable: Fix CPU/GPU device placement issues

Restore ops go on the CPU, then the value gets copied to whichever device it needs to be on. This I need to do manually for restores passed as initial_values; for regular save/restore it's done by the SaveableObjects for variables.

Also explicitly places some counters on the CPU.

Adds a GPU-using test for Checkpointable usage.

PiperOrigin-RevId: 187683050
---
 tensorflow/contrib/eager/python/BUILD         |  7 ++-
 .../eager/python/checkpointable_utils.py      | 50 +++++++++++--------
 .../eager/python/checkpointable_utils_test.py |  2 +-
 tensorflow/python/BUILD                       |  1 +
 tensorflow/python/training/checkpointable.py  | 18 ++++---
 5 files changed, 45 insertions(+), 33 deletions(-)

diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD
index 6fb8287030..7fde53476d 100644
--- a/tensorflow/contrib/eager/python/BUILD
+++ b/tensorflow/contrib/eager/python/BUILD
@@ -243,13 +243,13 @@ py_library(
     ],
 )
 
-py_test(
+cuda_py_test(
     name = "checkpointable_utils_test",
     srcs = ["checkpointable_utils_test.py"],
-    srcs_version = "PY2AND3",
-    deps = [
+    additional_deps = [
         ":checkpointable_utils",
         ":network",
+        "@six_archive//:six",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_ops",
@@ -265,7 +265,6 @@ py_test(
         "//tensorflow/python/eager:context",
         "//tensorflow/python/eager:test",
         "//tensorflow/python/keras",
-        "@six_archive//:six",
     ],
 )
 
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils.py b/tensorflow/contrib/eager/python/checkpointable_utils.py
index 89cd543f77..cd742991af 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils.py
@@ -549,7 +549,8 @@ class CheckpointableSaver(object):
     # `Checkpointable` objects save themselves.
     self._root_checkpointable_ref = root_checkpointable
     if context.in_graph_mode():
-      self._file_prefix_placeholder = constant_op.constant("model")
+      with ops.device("/cpu:0"):
+        self._file_prefix_placeholder = constant_op.constant("model")
     else:
       self._file_prefix_placeholder = None
 
@@ -601,14 +602,16 @@ class CheckpointableSaver(object):
       if session is None:
         session = ops.get_default_session()
       if self._object_graph_feed_tensor is None:
-        self._object_graph_feed_tensor = constant_op.constant(
-            "", dtype=dtypes.string)
+        with ops.device("/cpu:0"):
+          self._object_graph_feed_tensor = constant_op.constant(
+              "", dtype=dtypes.string)
       object_graph_tensor = self._object_graph_feed_tensor
       feed_additions = {object_graph_tensor: graph_proto.SerializeToString()}
     else:
       session = None
-      object_graph_tensor = constant_op.constant(
-          graph_proto.SerializeToString(), dtype=dtypes.string)
+      with ops.device("/cpu:0"):
+        object_graph_tensor = constant_op.constant(
+            graph_proto.SerializeToString(), dtype=dtypes.string)
       feed_additions = None
     assert _OBJECT_GRAPH_PROTO_KEY not in named_variables
     named_variables[_OBJECT_GRAPH_PROTO_KEY] = _NoRestoreSaveable(
@@ -627,12 +630,13 @@ class CheckpointableSaver(object):
         self._last_save_object_graph = graph_proto
     else:
       saver = self._last_save_saver
-    save_path = saver.save(
-        sess=_SessionWithFeedDictAdditions(
-            session=session, feed_additions=feed_additions),
-        save_path=file_prefix,
-        write_meta_graph=False,
-        global_step=checkpoint_number)
+    with ops.device("/cpu:0"):
+      save_path = saver.save(
+          sess=_SessionWithFeedDictAdditions(
+              session=session, feed_additions=feed_additions),
+          save_path=file_prefix,
+          write_meta_graph=False,
+          global_step=checkpoint_number)
     return save_path
 
   def _global_variable_names(self):
@@ -718,16 +722,18 @@ class CheckpointableSaver(object):
       file_prefix_feed_dict = {self._file_prefix_placeholder: save_path}
     else:
       session = None
-      file_prefix_tensor = constant_op.constant(save_path)
+      with ops.device("/cpu:0"):
+        file_prefix_tensor = constant_op.constant(save_path)
       file_prefix_feed_dict = None
     try:
       if not in_graph_mode or self._object_graph_restore_tensor is None:
-        object_graph_string, = io_ops.restore_v2(
-            prefix=file_prefix_tensor,
-            tensor_names=[_OBJECT_GRAPH_PROTO_KEY],
-            shape_and_slices=[""],
-            dtypes=[dtypes.string],
-            name="object_graph_proto_read")
+        with ops.device("/cpu:0"):
+          object_graph_string, = io_ops.restore_v2(
+              prefix=file_prefix_tensor,
+              tensor_names=[_OBJECT_GRAPH_PROTO_KEY],
+              shape_and_slices=[""],
+              dtypes=[dtypes.string],
+              name="object_graph_proto_read")
         if in_graph_mode:
           self._object_graph_restore_tensor = object_graph_string
       if in_graph_mode:
@@ -826,8 +832,9 @@ class Checkpoint(core_checkpointable.Checkpointable):
     """Create a save counter if it does not yet exist."""
     if self._save_counter is None:
       # Initialized to 0 and incremented before saving.
-      self._save_counter = add_variable(
-          self, name="save_counter", initializer=0, dtype=dtypes.int64)
+      with ops.device("/cpu:0"):
+        self._save_counter = add_variable(
+            self, name="save_counter", initializer=0, dtype=dtypes.int64)
 
   @property
   def save_counter(self):
@@ -852,7 +859,8 @@ class Checkpoint(core_checkpointable.Checkpointable):
         # needs to be initialized before assign_add. This is only an issue if
         # restore() has not been called first.
         session.run(self.save_counter.initializer)
-    assign_op = self.save_counter.assign_add(1)
+    with ops.colocate_with(self.save_counter):
+      assign_op = self.save_counter.assign_add(1)
     if in_graph_mode:
       session.run(assign_op)
     return self._saver.save(
diff --git a/tensorflow/contrib/eager/python/checkpointable_utils_test.py b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
index c9db2bcafc..9ec89edce8 100644
--- a/tensorflow/contrib/eager/python/checkpointable_utils_test.py
+++ b/tensorflow/contrib/eager/python/checkpointable_utils_test.py
@@ -387,7 +387,7 @@ class CheckpointingTests(test.TestCase):
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
     for training_continuation in range(3):
       with ops.Graph().as_default(), self.test_session(
-          graph=ops.get_default_graph()):
+          graph=ops.get_default_graph()), test_util.device(use_gpu=True):
         model = MyModel()
         optimizer = adam.AdamOptimizer(0.001)
         root = checkpointable_utils.Checkpoint(
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index f282abb0a5..db17a3fe02 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -2882,6 +2882,7 @@ py_library(
     srcs = ["training/checkpointable.py"],
     srcs_version = "PY2AND3",
     deps = [
+        ":array_ops",
         ":dtypes",
         ":io_ops_gen",
         ":ops",
diff --git a/tensorflow/python/training/checkpointable.py b/tensorflow/python/training/checkpointable.py
index 02c3aebda8..92e8ff3308 100644
--- a/tensorflow/python/training/checkpointable.py
+++ b/tensorflow/python/training/checkpointable.py
@@ -22,6 +22,7 @@ import collections
 from tensorflow.python.eager import context
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_io_ops as io_ops
 from tensorflow.python.util import nest
 
@@ -181,13 +182,16 @@ class _CheckpointPosition(object):
       dtype = self._checkpoint.dtype_map[checkpoint_key]
       base_type = dtype.base_dtype
       with ops.init_scope():
-        value, = io_ops.restore_v2(
-            prefix=self._checkpoint.save_path,
-            tensor_names=[checkpoint_key],
-            shape_and_slices=[""],
-            dtypes=[base_type],
-            name="%s_checkpoint_read" % (serialized_tensor.name,))
-        value_tensors[serialized_tensor.name] = value
+        with ops.device("/cpu:0"):
+          # Run the restore itself on the CPU.
+          value, = io_ops.restore_v2(
+              prefix=self._checkpoint.save_path,
+              tensor_names=[checkpoint_key],
+              shape_and_slices=[""],
+              dtypes=[base_type],
+              name="%s_checkpoint_read" % (serialized_tensor.name,))
+        # Copy the value to the current device if necessary.
+        value_tensors[serialized_tensor.name] = array_ops.identity(value)
       return value_tensors
 
   def restore_ops(self):
-- 
GitLab


From 4df167ac55346357afd612d15674c7556e21ab00 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 2 Mar 2018 16:25:21 -0800
Subject: [PATCH 573/575] Loop optimizer: Convert StackPush nodes to Identity
 instead of eliminating them completely. Move loop optimizer to run before
 dependency optimizer so identity nodes will be pruned.

PiperOrigin-RevId: 187685669
---
 tensorflow/core/grappler/optimizers/BUILD     |  1 +
 .../grappler/optimizers/loop_optimizer.cc     | 91 ++++++++++---------
 .../optimizers/loop_optimizer_test.cc         | 74 ++++++++++-----
 .../grappler/optimizers/meta_optimizer.cc     | 22 ++---
 tensorflow/core/grappler/utils.cc             |  8 +-
 tensorflow/core/grappler/utils.h              |  1 +
 6 files changed, 117 insertions(+), 80 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/BUILD b/tensorflow/core/grappler/optimizers/BUILD
index 037438ee75..7ec137373b 100644
--- a/tensorflow/core/grappler/optimizers/BUILD
+++ b/tensorflow/core/grappler/optimizers/BUILD
@@ -521,6 +521,7 @@ cc_library(
     ],
     visibility = ["//visibility:public"],
     deps = [
+        ":constant_folding",
         ":graph_optimizer",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.cc b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
index cc226c01db..9e427001d5 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer.cc
@@ -18,10 +18,12 @@ limitations under the License.
 #include <unordered_map>
 #include <unordered_set>
 
+#include "tensorflow/core/framework/attr_value.pb.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/grappler/costs/graph_properties.h"
 #include "tensorflow/core/grappler/grappler_item.h"
 #include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/grappler/optimizers/constant_folding.h"
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
@@ -31,55 +33,60 @@ namespace tensorflow {
 namespace grappler {
 namespace {
 
+std::vector<int> GetStackPushNodesToConvert(const SimpleGraphView& graph_view,
+                                            int stack_node_idx) {
+  VLOG(1) << "Stack node: " << graph_view.graph()->node(stack_node_idx).name();
+  const std::unordered_set<string> op_types_to_traverse(
+      {"Stack", "StackV2", "Enter", "RefEnter", "Switch", "RefSwitch",
+       "Identity", "RefIdentity"});
+  std::vector<int> nodes_to_convert;
+  std::set<int> fanout;
+  graph_view.DepthFirstSearch(op_types_to_traverse, stack_node_idx, &fanout);
+  for (int fanout_idx : fanout) {
+    const NodeDef& fanout_node = graph_view.graph()->node(fanout_idx);
+    VLOG(1) << "Fanout " << fanout_idx << " : " << fanout_node.name();
+    if (IsStackPushOp(fanout_node)) {
+      nodes_to_convert.push_back(fanout_idx);
+    } else if (IsStackOp(fanout_node) || IsStackCloseOp(fanout_node) ||
+               op_types_to_traverse.find(fanout_node.op()) !=
+                   op_types_to_traverse.end()) {
+      continue;
+    } else {
+      // The node is either a StackPop node or something unexpected behind which
+      // may hide a StackPop node, so we leave the graph alone.
+      nodes_to_convert.clear();
+      break;
+    }
+  }
+  return nodes_to_convert;
+}
+
 Status RemoveStackOps(const GraphDef& graph, GraphDef* optimized_graph) {
+  *optimized_graph = graph;
+  NodeMap node_map(optimized_graph);
   SimpleGraphView graph_view;
   TF_RETURN_IF_ERROR(graph_view.Initialize(graph));
-  const std::unordered_set<string> op_types_to_traverse(
-      {"Stack", "StackV2", "Enter", "Switch", "RefSwitch", "Identity"});
-  std::set<int> nodes_to_delete;
   for (int node_idx = 0; node_idx < graph.node_size(); ++node_idx) {
-    const NodeDef& node = graph.node(node_idx);
-    if (IsStackOp(node)) {
-      std::set<int> nodes_found;
-      graph_view.DepthFirstSearch(op_types_to_traverse, node_idx, &nodes_found);
-      bool found_pop = false;
-      bool found_unexpected = false;
-      for (int found_idx : nodes_found) {
-        const NodeDef& node = graph.node(found_idx);
-        if (IsStackPushOp(node) || IsStackOp(node) || IsStackCloseOp(node)) {
-          continue;
-        } else if (IsStackPopOp(node)) {
-          found_pop = true;
-        } else {
-          // Don't modify the graph if we found an unexpected op. There may be
-          // a pop hiding behind it.
-          found_unexpected = true;
+    if (IsStackOp(graph.node(node_idx))) {
+      for (int push_node_idx :
+           GetStackPushNodesToConvert(graph_view, node_idx)) {
+        // We found push nodes without corresponding pops. Convert them to
+        // Identity passing the data through and add a control dependency from
+        // the op supplying the handle.
+        NodeDef* push_node = optimized_graph->mutable_node(push_node_idx);
+        VLOG(1) << "Converting " << push_node_idx << " : "
+                << push_node->DebugString();
+        if (push_node->attr().count("swap_memory") != 0) {
+          push_node->mutable_attr()->erase("swap_memory");
         }
+        push_node->set_op("Identity");
+        push_node->mutable_input()->SwapElements(0, 1);
+        const string ctrl_dep = ConstantFolding::AddControlDependency(
+            push_node->input(1), optimized_graph, &node_map);
+        push_node->set_input(1, ctrl_dep);
+        VLOG(1) << "After converting: " << push_node->DebugString();
       }
-      if (!found_unexpected && !found_pop) {
-        VLOG(1) << "Found stack node with no pop: " << node.DebugString();
-        // Remove all pushes.
-        for (int found_idx : nodes_found) {
-          const NodeDef& node = graph.node(found_idx);
-          if (IsStackPushOp(node)) {
-            nodes_to_delete.insert(found_idx);
-          }
-        }
-      }
-    }
-  }
-
-  *optimized_graph = graph;
-  if (!nodes_to_delete.empty()) {
-    int last = optimized_graph->node_size() - 1;
-    for (auto it = nodes_to_delete.rbegin(); it != nodes_to_delete.rend();
-         ++it) {
-      const int node_to_delete = *it;
-      optimized_graph->mutable_node()->SwapElements(node_to_delete, last);
-      --last;
     }
-    optimized_graph->mutable_node()->DeleteSubrange(last + 1,
-                                                    nodes_to_delete.size());
   }
   return Status::OK();
 }
diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc b/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
index bb2ee6b02b..cc9dd22b9e 100644
--- a/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/loop_optimizer_test.cc
@@ -59,34 +59,46 @@ TEST_F(LoopOptimizerTest, NoOp) {
 
 namespace {
 NodeDef* AddNode(const string& name, const string& op,
-                 const std::vector<string>& inputs, GraphDef* graph) {
+                 const std::vector<string>& inputs,
+                 const std::vector<std::pair<string, AttrValue>>& attributes,
+                 GraphDef* graph) {
   NodeDef* node = graph->add_node();
   node->set_name(name);
   node->set_op(op);
   for (const string& input : inputs) {
     node->add_input(input);
   }
+  for (auto attr : attributes) {
+    (*node->mutable_attr())[attr.first] = attr.second;
+  }
   return node;
 }
 }  // namespace
 
 TEST_F(LoopOptimizerTest, RemovePush_NoOp) {
   GrapplerItem item;
+  AttrValue frame_name;
+  frame_name.set_s("foo");
+  AttrValue type;
+  type.set_type(DT_RESOURCE);
   GraphDef& graph = item.graph;
+  AddNode("c", "Const", {}, {}, &graph);
   // Stack with corresponding push/pop.
-  AddNode("stack1", "StackV2", {}, &graph);
-  AddNode("push1", "StackPushV2", {"stack1"}, &graph);
-  AddNode("pop1", "StackPopV2", {"stack1"}, &graph);
+  AddNode("stack1", "StackV2", {}, {}, &graph);
+  AddNode("push1", "StackPushV2", {"stack1", "c"}, {}, &graph);
+  AddNode("pop1", "StackPopV2", {"stack1"}, {}, &graph);
   // Stack with corresponding push/pop behind Enter.
-  AddNode("stack2", "StackV2", {}, &graph);
-  AddNode("push_enter", "Enter", {"stack1"}, &graph);
-  AddNode("push2", "StackPushV2", {"push_enter"}, &graph);
-  AddNode("pop_enter", "Enter", {"stack1"}, &graph);
-  AddNode("pop2", "StackPopV2", {"pop_enter"}, &graph);
+  AddNode("stack2", "StackV2", {}, {}, &graph);
+  AddNode("push_enter", "Enter", {"stack2"},
+          {{"T", type}, {"frame_name", frame_name}}, &graph);
+  AddNode("push2", "StackPushV2", {"push_enter", "c"}, {}, &graph);
+  AddNode("pop_enter", "Enter", {"stack2"},
+          {{"T", type}, {"frame_name", frame_name}}, &graph);
+  AddNode("pop2", "StackPopV2", {"pop_enter"}, {}, &graph);
   // Stack with unexpected op type in fanout of Stack.
-  AddNode("stack3", "StackV2", {}, &graph);
-  AddNode("push3", "StackPushV2", {"stack3"}, &graph);
-  AddNode("stop", "StopGradient", {"stack3"}, &graph);
+  AddNode("stack3", "StackV2", {}, {}, &graph);
+  AddNode("push3", "StackPushV2", {"stack3", "c"}, {}, &graph);
+  AddNode("stop", "StopGradient", {"stack3"}, {}, &graph);
   LoopOptimizer optimizer;
   GraphDef output;
   Status status = optimizer.Optimize(nullptr, item, &output);
@@ -97,23 +109,39 @@ TEST_F(LoopOptimizerTest, RemovePush_NoOp) {
 TEST_F(LoopOptimizerTest, RemovePushWithoutMatchingPop) {
   GrapplerItem item;
   GraphDef& graph = item.graph;
-  AddNode("stack1", "StackV2", {}, &graph);
-  AddNode("push1", "StackPushV2", {"stack1"}, &graph);
-  AddNode("stack2", "StackV2", {}, &graph);
-  AddNode("push_enter", "Enter", {"stack2"}, &graph);
-  AddNode("push2", "StackPushV2", {"push_enter"}, &graph);
+  AttrValue frame_name;
+  frame_name.set_s("foo");
+  AttrValue type;
+  type.set_type(DT_RESOURCE);
+  AddNode("c", "Const", {}, {}, &graph);
+  AddNode("stack1", "StackV2", {}, {}, &graph);
+  AddNode("push1", "StackPushV2", {"stack1", "c"}, {}, &graph);
+  AddNode("stack2", "StackV2", {}, {}, &graph);
+  AddNode("push_enter", "Enter", {"stack2"},
+          {{"T", type}, {"frame_name", frame_name}}, &graph);
+  AddNode("push2", "StackPushV2", {"push_enter", "c"}, {}, &graph);
   LoopOptimizer optimizer;
   GraphDef output;
   Status status = optimizer.Optimize(nullptr, item, &output);
   TF_EXPECT_OK(status);
-  EXPECT_EQ(3, output.node_size());
-  int found = 0;
+  EXPECT_EQ(6, output.node_size());
   for (int i = 0; i < output.node_size(); ++i) {
-    if (output.node(i).name() == "stack1") ++found;
-    if (output.node(i).name() == "push_enter") ++found;
-    if (output.node(i).name() == "stack2") ++found;
+    const NodeDef& node = output.node(i);
+    if (node.name() == "push1") {
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("c", node.input(0));
+      EXPECT_EQ("^stack1", node.input(1));
+    } else if (node.name() == "push2") {
+      EXPECT_EQ("Identity", node.op());
+      EXPECT_EQ(2, node.input_size());
+      EXPECT_EQ("c", node.input(0));
+      EXPECT_EQ("^push_enter", node.input(1));
+    } else {
+      const NodeDef& orig_node = item.graph.node(i);
+      EXPECT_EQ(orig_node.ShortDebugString(), node.ShortDebugString());
+    }
   }
-  EXPECT_EQ(3, found);
 }
 
 }  // namespace
diff --git a/tensorflow/core/grappler/optimizers/meta_optimizer.cc b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
index fff1e354f4..6fa8c03548 100644
--- a/tensorflow/core/grappler/optimizers/meta_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/meta_optimizer.cc
@@ -77,13 +77,13 @@ std::unique_ptr<GraphOptimizer> MetaOptimizer::NewOptimizer(
     graph_optimizer.reset(
         new AutoParallel(cfg_.auto_parallel().num_replicas()));
   }
+  if (optimizer == "loop") {
+    graph_optimizer.reset(new LoopOptimizer(cfg_.loop_optimization()));
+  }
   if (optimizer == "dependency") {
     graph_optimizer.reset(
         new DependencyOptimizer(cfg_.dependency_optimization()));
   }
-  if (optimizer == "loop") {
-    graph_optimizer.reset(new LoopOptimizer(cfg_.loop_optimization()));
-  }
   return graph_optimizer;
 }
 
@@ -106,14 +106,14 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
           new ArithmeticOptimizer(cfg_.arithmetic_optimization())));
     }
-    if (cfg_.dependency_optimization() != RewriterConfig::OFF) {
-      optimizers.push_back(std::unique_ptr<GraphOptimizer>(
-          new DependencyOptimizer(cfg_.dependency_optimization())));
-    }
     if (cfg_.loop_optimization() == RewriterConfig::ON) {
       optimizers.push_back(std::unique_ptr<GraphOptimizer>(
           new LoopOptimizer(cfg_.loop_optimization())));
     }
+    if (cfg_.dependency_optimization() != RewriterConfig::OFF) {
+      optimizers.push_back(std::unique_ptr<GraphOptimizer>(
+          new DependencyOptimizer(cfg_.dependency_optimization())));
+    }
     if (cfg_.layout_optimizer() != RewriterConfig::OFF) {
       optimizers.push_back(
           std::unique_ptr<GraphOptimizer>(new LayoutOptimizer()));
@@ -136,8 +136,8 @@ Status MetaOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
     }
   } else {
     const std::set<string> available_optimizers = {
-        "pruning",      "function",   "constfold",  "layout", "memory",
-        "autoparallel", "arithmetic", "dependency", "loop"};
+        "pruning",      "function",   "constfold", "layout",    "memory",
+        "autoparallel", "arithmetic", "loop",      "dependency"};
     std::vector<string> custom_optimizer_names;
     for (const auto& optimizer_name : cfg_.optimizers()) {
       if (available_optimizers.find(optimizer_name) !=
@@ -233,9 +233,9 @@ bool MetaOptimizerEnabled(const RewriterConfig& cfg) {
          cfg.layout_optimizer() != RewriterConfig::OFF ||
          cfg.function_optimization() == RewriterConfig::ON ||
          cfg.constant_folding() != RewriterConfig::OFF ||
-         cfg.dependency_optimization() != RewriterConfig::OFF ||
-         cfg.loop_optimization() == RewriterConfig::ON ||
          cfg.arithmetic_optimization() != RewriterConfig::OFF ||
+         cfg.loop_optimization() == RewriterConfig::ON ||
+         cfg.dependency_optimization() != RewriterConfig::OFF ||
          cfg.auto_parallel().enable() ||
          cfg.memory_optimization() != RewriterConfig::NO_MEM_OPT ||
          !cfg.optimizers().empty();
diff --git a/tensorflow/core/grappler/utils.cc b/tensorflow/core/grappler/utils.cc
index a611a93086..eb1f882ff1 100644
--- a/tensorflow/core/grappler/utils.cc
+++ b/tensorflow/core/grappler/utils.cc
@@ -398,12 +398,12 @@ Status SimpleGraphView::Initialize(const GraphDef& graph, bool dedup_inputs,
 void SimpleGraphView::DepthFirstSearch(
     const std::unordered_set<string>& op_types_to_traverse, int node_idx,
     std::set<int>* nodes_found) const {
-  const NodeDef& node = graph_->node(node_idx);
-  if (op_types_to_traverse.find(node.op()) == op_types_to_traverse.end()) {
-    nodes_found->insert(node_idx);
+  if (nodes_found->find(node_idx) != nodes_found->end()) {
     return;
   }
-  if (nodes_found->find(node_idx) != nodes_found->end()) {
+  nodes_found->insert(node_idx);
+  const string& op_type = graph_->node(node_idx).op();
+  if (op_types_to_traverse.find(op_type) == op_types_to_traverse.end()) {
     return;
   }
   for (auto output_idx : this->outputs(node_idx)) {
diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h
index 1b91a57154..fbd38c1531 100644
--- a/tensorflow/core/grappler/utils.h
+++ b/tensorflow/core/grappler/utils.h
@@ -178,6 +178,7 @@ class SimpleGraphView {
   Status Initialize(const GraphDef& graph, bool dedup_inputs,
                     bool dedup_outputs);
 
+  const GraphDef* graph() const { return graph_; }
   inline int num_nodes() const { return index_to_name_.size(); }
   inline const int index(const string& node_name) const {
     const auto& it = name_to_index_.find(node_name);
-- 
GitLab


From 1bbb03eb59fcb3a4b52c45d0063dcc9875206910 Mon Sep 17 00:00:00 2001
From: Allen Lavoie <allenl@google.com>
Date: Fri, 2 Mar 2018 16:58:11 -0800
Subject: [PATCH 574/575] Don't throw errors if non-Checkpointable objects are
 passed to MultiRNNCell

PiperOrigin-RevId: 187689371
---
 tensorflow/python/ops/rnn_cell_impl.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index bd7c731210..3ae1d1184d 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -46,6 +46,7 @@ from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import checkpointable
 from tensorflow.python.util import nest
 from tensorflow.python.util.tf_export import tf_export
 
@@ -1190,7 +1191,9 @@ class MultiRNNCell(RNNCell):
     for cell_number, cell in enumerate(self._cells):
       # Add Checkpointable dependencies on these cells so their variables get
       # saved with this object when using object-based saving.
-      self._track_checkpointable(cell, name="cell-%d" % (cell_number,))
+      if isinstance(cell, checkpointable.CheckpointableBase):
+        # TODO(allenl): Track down non-Checkpointable callers.
+        self._track_checkpointable(cell, name="cell-%d" % (cell_number,))
     self._state_is_tuple = state_is_tuple
     if not state_is_tuple:
       if any(nest.is_sequence(c.state_size) for c in self._cells):
-- 
GitLab


From 0c92f574d18cd01134bb9f7a5a679866a0f92f7e Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 2 Mar 2018 17:18:00 -0800
Subject: [PATCH 575/575] Properly handle the case of functions with no inputs

PiperOrigin-RevId: 187691555
---
 .../grappler/optimizers/function_optimizer.cc | 12 +++++-
 .../optimizers/function_optimizer_test.cc     | 34 +++++++++++++++++
 .../core/grappler/utils/functions_test.cc     | 37 +++++++++++++++++++
 3 files changed, 81 insertions(+), 2 deletions(-)

diff --git a/tensorflow/core/grappler/optimizers/function_optimizer.cc b/tensorflow/core/grappler/optimizers/function_optimizer.cc
index 167e5a153a..4b830bcc6e 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer.cc
@@ -126,9 +126,17 @@ Status FunctionOptimizer::Optimize(Cluster* cluster, const GrapplerItem& item,
                                    GraphDef* optimized_graph) {
   std::unordered_map<string, const FunctionDef*> functions;
   for (const FunctionDef& func : item.graph.library().function()) {
-    if (func.attr().count("_noinline") == 0) {
-      functions[func.signature().name()] = &func;
+    // Don't inline functions marked as noinline
+    if (func.attr().count("_noinline") != 0) {
+      continue;
     }
+    // Can't create IdentityN nodes with no input or output: skip these
+    // functions for now.
+    if (func.signature().input_arg_size() == 0 ||
+        func.signature().output_arg_size() == 0) {
+      continue;
+    }
+    functions[func.signature().name()] = &func;
   }
 
   // Nothing to do.
diff --git a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
index 5072abaac7..8db9b7f77a 100644
--- a/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
+++ b/tensorflow/core/grappler/optimizers/function_optimizer_test.cc
@@ -339,6 +339,40 @@ TEST_F(FunctionOptimizerTest, FunctionWithInputForwarding) {
   test::ExpectTensorEqual<int>(tensors_expected[2], tensors[2]);
 }
 
+TEST_F(FunctionOptimizerTest, FunctionWithoutInput) {
+  const Tensor kTwo = test::AsScalar<int64>(2);
+  FunctionDef func = FunctionDefHelper::Define(
+      // Name
+      "GenerateTwo",
+      // Args
+      {},
+      // Return value
+      {"o: T"},
+      // Attr def
+      {"T: {float, double}"},
+      // Nodes
+      {{{"two"}, "Const", {}, {{"value", kTwo}, {"dtype", DT_INT64}}},
+       {{"o"}, "Cast", {"two"}, {{"SrcT", DT_INT64}, {"DstT", "$T"}}}});
+
+  GrapplerItem item;
+  constexpr char device[] = "/device:CPU:0";
+  item.graph = test::function::GDef(
+      {test::function::NDef("y", "GenerateTwo", {}, {}, device),
+       test::function::NDef("z", "Identity", {"y"}, {{"T", DT_FLOAT}}, device)},
+      // FunctionLib
+      {
+          func,
+      });
+
+  FunctionOptimizer optimizer;
+  GraphDef output;
+  Status status = optimizer.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+
+  // For now we won't inline the function.
+  EXPECT_EQ(item.graph.DebugString(), output.DebugString());
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
diff --git a/tensorflow/core/grappler/utils/functions_test.cc b/tensorflow/core/grappler/utils/functions_test.cc
index 25ec50d478..6a7d766b1c 100644
--- a/tensorflow/core/grappler/utils/functions_test.cc
+++ b/tensorflow/core/grappler/utils/functions_test.cc
@@ -308,6 +308,43 @@ TEST_F(FunctionsTest, FromFunctionDefWithInputForwarding) {
   }
 }
 
+TEST_F(FunctionsTest, FromFunctionDefWithoutInput) {
+  const Tensor kTwo = test::AsScalar<int64>(2);
+  FunctionDef func = FunctionDefHelper::Define(
+      // Name
+      "GenerateTwo",
+      // Args
+      {},
+      // Return value
+      {"o: T"},
+      // Attr def
+      {"T: {float, double}"},
+      // Nodes
+      {{{"two"}, "Const", {}, {{"value", kTwo}, {"dtype", DT_INT64}}},
+       {{"o"}, "Cast", {"two"}, {{"SrcT", DT_INT64}, {"DstT", "$T"}}}});
+
+  std::unordered_map<string, AttrValue> func_attr;
+  func_attr["T"].set_type(DT_FLOAT);
+  FunctionDefLibrary library;
+  std::unique_ptr<GrapplerItem> item =
+      GrapplerItemFromFunctionDef(func, func_attr, library);
+
+  EXPECT_EQ(0, item->feed.size());
+  EXPECT_EQ(1, item->fetch.size());
+  EXPECT_EQ("o:0", item->fetch[0]);
+
+  EXPECT_EQ(2, item->graph.node_size());
+  const NodeDef &two = item->graph.node(0);
+  EXPECT_EQ("two", two.name());
+  EXPECT_EQ(0, two.input_size());
+  const NodeDef &cast = item->graph.node(1);
+  EXPECT_EQ("o", cast.name());
+  EXPECT_EQ(1, cast.input_size());
+  EXPECT_EQ("two:0", cast.input(0));
+
+  std::cout << item->graph.DebugString() << std::endl;
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow
-- 
GitLab